text:
Schools turn to slumping tech sector to recruit teachers
By Corey Murray, Assistant Editor, eSchool News
America's schools are falling behind on efforts to assign a high-quality teacher to every classroom by the 2005-06 school year, the U.S. Department of Education (ED) told Congress June 11. But California state officials claim they are making progress in this area by employing a creative solution to the problem: recruiting teacher candidates with solid content knowledge from a slumping technology sector.
California's Technology-to-Teacher program is a recruitment effort that turns currently unemployed technology professionals into fully credentialed, working teachers. The program is designed to place former private-sector employees displaced by the dot-com bust and other high-tech woes into classrooms, where their knowledge, education, and real-world experiences could make up for a lack of well-prepared, properly educated math and science teachers in schools throughout the state.
That premise became especially important yesterday in light of ED's latest report, "Meeting the High-Quality Teacher Challenge." The first in a series of annual reports to Congress on this topic stressed an increased need for highly qualified teachers who demonstrate solid content knowledge—especially in such currently understaffed subjects as math and science—in schools across the country.
"We now have concrete evidence that smart teachers with solid content knowledge have the greatest effect on student achievement," said U.S. Secretary of Education Rod Paige in a statement. "If we are to meet the challenge of having a highly qualified teacher in every classroom by the 2005-06 school year, states and universities must take heed and act now to bring more of these people into our nation's classrooms."
California's Technology-to-Teacher program was conceived by Gov. Gray Davis as a way to help combat the concentration of technology-related layoffs occurring in places such as the once-booming Silicon Valley, said Larry Rios, the program's project director for the Ventura County Business and Employment Services Department.
According to Rios, the program gives state educators $1.6 million to train and qualify former private-sector professionals for more than 200 currently unfilled teaching positions. The money was allocated throughout the state based on those areas having the highest concentration of laid-off technology workers and is being used to help pay for the cost of securing candidates' state-approved teaching credentials, Rios said.
In Ventura County, the project has supplied $167,000 to help fill 18 slots, Rios said; and in Sunnyvale, Calif., recruiters received $536,000 to fill more than 100 vacancies.
"What we are trying to do is take a talent pool that has dried up and redirect it," said Rios. He claims the program already has attracted a number of potential applicants, many of whom had been accustomed to earning at least six-figure annual incomes.
According to Rios, the obvious reduction in salary has done little to curb enthusiasm for the project.
Participants "want to be able to have a positive impact on future generations," said Rios. "They are not in it for the money." Jesse Gonzalez, a 42-year-old electrical engineer and former product manager for a voice-mail services company, said he had long considered teaching as an option—but after losing his job in the tech sector, the opportunity seemed too good to pass up.
"I had a calling that I've done a good job of suppressing most of my life," said Gonzalez, a Sunday school teacher and avid youth leader.
Gonzalez, who hopes to become a math instructor, is eager to have the opportunity to teach. He believes his real-world technology experience will prove to be an added benefit to students, because it enables him to explain how math and science are applied in everyday situations.
"Why do we need to know, that's the question [students ask]," Gonzalez said. "What I feel I could bring is practical application. You need to get people excited that what they are studying is something that they can use."
Practical application isn't the only advantage former technology professionals bring to the classroom, said Leslie Conery, deputy chief executive of the International Society for Technology in Education.
Conery contends that properly trained and teacher-certified recruits from the technology sector would greatly enhance the integration of technology in schools. Unlike many veteran teachers, she said, former technology professionals would be less likely to experience intimidation in the face of new classroom technologies.
"These people are going to know how to demonstrate using the technology," Conery said, adding that former tech professionals also would be able to help train veteran teachers on new equipment.
Despite the potential that exists in this new pool of teaching applicants, Rios cautions that the path to achieving certification can be difficult.
"The program doesn't guarantee anyone will become a teacher," Rios said. "We simply are facilitating the process for potential teachers to get their credentials."
To be eligible for the Technology-to-Teacher program, candidates must be certified dislocated workers and must pass the California Basic Education Skills Test, used to demonstrate essential knowledge of math, reading, and writing. Once they have passed the test, they are required to meet with an academic advisor and construct a path to certification. The final step is enrollment in a university-supported teacher credential program, the cost of which is paid for by the program.
Prospective teachers also must pass background checks, drug screenings, and be fingerprinted to ensure child safety.
"You do have to jump over some hoops and hurdles here," Rios said. "But we help navigate our clients through the process."
In all, said Rios, the program takes an estimated two years to complete. But candidates won't necessarily have to wait until they are wholly certified to begin teaching. In the event that a fully certified teacher cannot be found, the program allows for prospective teachers still involved in the certification process to take over a class as long as they agree to finish out the requirements. In this situation, candidates are deemed intern-qualified and are eligible to receive full benefits.
California is not the only state that has dipped into the technology sector in search of new teachers. The New Teacher Project (TNTP) is a revenue-generating, non-profit consulting group headquartered in New York City. It focuses on recruiting young and mid-career professionals to join the teaching ranks and says it has secured 19 contracts in more than 13 school districts across the nation, placing more than 3,500 new teachers in schools.
"There is a general interest from a certain group of civic-minded people," said Michelle Rhee, TNTP's chief executive officer. "This is a great pool of people to look at, because school districts often have difficulty finding people with direct content knowledge."
Like anything, learning to become a good teacher will take time, Gonzalez pointed out: "Sometimes the fruits of the labor aren't always so noticeable. But they do come."
http://www.ed.gov
http://www.ca.gov
International Society for Technology in Education
http://www.iste.org
The New Teacher Project
http://www.tntp.org


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text:
Self Archiving Policies
Special Fee Waivers and Discounts
Development of Dry Powder Inhaler Containing Prothionamide-PLGA Nanoparticles Optimized Through Statistical Design: In-vivo Study
Sujit K. Debnath1, *, Saisivam Srinivasan2, Monalisha Debnath1
1 P. G. Institute of Medical Sciences, Chandrakona Town, West Bengal-721201, India
2 N. R. Vekaria Institute of Pharmacy, Junagadh, Gujarat-362001, India
Identifiers and Pagination:
First Page: 30
Last Page: 40
Publisher Id: TONMJ-4-30
DOI: 10.2174/1875933501704010030
Article History:
Received Date: 23/06/2017
Revision Received Date: 28/09/2017
Electronic publication date: 01/11/2017
Collection year: 2017
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© 2017 Debnath et al.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
* Address correspondence to this author at the P. G. Institute of Medical Sciences, Chandrakona Town, West Bengal-721201, India; Tel: +91-9033924827; Fax: 0343-2534972; E-mail: skd.mpharma@rediffmail.com
The objective of the present work was to formulate Prothionamide (PTH) nanoparticles using Poly lactic co-glycolic acid (PLGA), optimized by Box-Behnken Design and further modification to dry powder inhaler followed by in-vivo study.
Poly-lactic co-gycolic acid (PLGA), a biodegradable polymer was used to coat Prothionamide by solvent evaporation technique. Formulation was optimized using Box-Behnken Design. Response surface curve and desirability factors helped in the selection of optimum formulation of PTH nanoparticles. Dry powder inhaler was prepared by adding inhalable grade lactose to optimize PTH nanoparticles. Mass median aerodynamic diameter (MMAD) was carried out using Andersen Cascade Impactor (ACI) to demonstrate its suitability in the pulmonary administration. In-vitro drug release of dry powder inhaler was carried out in simulated lungs fluid. Correlation in-vitro to in-vivo was established after performing animal experiment.
FTIR study reveals no chemical interaction between PTH, lactose and PLGA as the principle peaks was retained with same intensity in the physical mixture. Scanning electron microscope showed the spherical shape and aerodynamic particle size was found to be 1.69µm. Drug release study showed initial burst release followed by zero order release. In-vivo model confirmed the presence of PTH after 24h. Aerodynamic particle size and the release profile revealed the suitability of PTH loaded nanoparticles containing dry powder inhaler for the pulmonary administration.
Prepared DPI containing PTH nanoparticles can improve in the management of tuberculosis by increasing PTH residency in the lungs tissue for prolong period of time.
Keywords: Tuberculosis, Prothionamide, Dry powder inhaler, Pulmonary administration, PLGA, Box-Behnken design, in-vivo study.
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Tuberculosis (TB) is a common and often deadly infectious disease caused mostly due to Mycobacterium tuberculosis. It is an air borne disease, therefore lungs are inflated more recurrently [1]. The main downside of conventional TB treatment is the progress of multiple drug resistance (MDR), resulting in high dose requirements follow by toxicity risk [2]. The thioamide drugs Prothionamide (PTH) is clinically effective in the treatment of some mycobacterial infections. It is bactericidal and recurrently prescribes as second-line drug, particularly in patients with MDR-TB. Due to short half life (2h) and protein binding (15%), it necessitates repeated administration. In addition, less site specificity is demonstrated by PTH and thus the amount of the drug attainment to lungs tissue becomes inadequate [3]. Hence, pulmonary route- an alternate, persuasive and noninvasive route for local delivery of macromolecules was selected for delivering PTH in the form of dry powder inhaler. This route of administration provides large surface area, thin epithelial barrier, avoids first-pass metabolism and high blood flow. In fact, sphere-shaped particles less than 10 μm in diameter can be inhaled. Whereas, particles less than 2.5 μ can accomplish the alveoli [4]. Over the past couple of decades, the field of drug delivery has been revolutionized with the introduction of nanoparticles, wherein these particles act as excellent carriers for drugs to target cells or tissues [5]. Biodegradable polymers such as poly(D,L-lactic acid) (PLA), poly D,L-lactic-co-glycolic acid (PLGA), and poly-ε-caprolactone (PCL) and their copolymers di-blocked or multi blocked with poly ethylene glycol (PEG) have been generally used to form polymeric nanoparticles (NPs) to encapsulate a variety of therapeutic compounds [6]. Safety coupled with commercial availability of PLGA in several monomer ratios and molecular weights have made it a preferred material for wide variety of drugs ranging from small-molecular-weight therapeutic agent to peptide hormones, antibiotics and chemotherapeutic drugs [7]. Solubility of the active ingredient is the foremost pharmaceutical disquiet in developing of novel drug delivery systems [8]. PLGA based nanoparticulate system is one of the most triumphant and fascinating colloidal systems, which successfully delivers the poorly soluble drug. PLGA nanoparticles shield the therapeutic agents, boost stability and used for controlled delivery with improved pharmacokinetic and pharmacodynamic profile [9]. PLGA degrades by hydrolysis, which leads to formation of lactic acid and glycolic acid that are rapidly clear from the body as by-products [3]. The ester linkages react in the presence of water, and rupture away from each other. The rate at which the polymer breaks down is reliant on the ratio of the monomer used in the production [10]. The drug loading is one of the key issues in the process of such an encapsulation. The time of drug intake could be minimized if high drug loading carriers are administered [11]. Both Prothionamide and PLGA are hydrophobic in nature and hence encapsulation is easy. In the present study, PLGA (75:25) was used in the formulation of PTH nanoparticles to decrease the dosing frequency and increase the PTH residency time in lungs. In the optimization of nanoparticles, statistical design employed to meet the industrial requirement. Optimized nanoparticles further modified into dry powder inhaler to make suitable for pulmonary administration and targeting PTH to lungs.
2. MATERIALS AND METHODS
Prothionamide & Poly Vinyl Alcohol (PVA) were procured from Yarrow Chem, Mumbai. PLGA 75:25 (Resomer RG 755 S) was obtained as a gift sample from Evonik Industries, Darmstadt, Germany. Lactose anhydrous for inhalation (40M) was obtained as a gift sample of Kerry ingredients, Rothschild, USA. All the other chemicals and reagents were of analytical grade from Merck Millipore, Mumbai.
2.2. Compatibility Study
FTIR analysis was performed to study the chemical interaction between Prothionamide, PLGA (75:25) and inhalable grade lactose using Perkin Elmer (Massachusetts, USA). FTIR spectra were recorded at room temperature to verify alteration in frequency and intensity of bands of pure drug in the presence of excipients [12]. Two samples were run, one for Prothionamide and another for the physical mixture of Prothionamide, anhydrous lactose and PLGA. The samples were scanned in the IR range from 400 to 4000 cm−1.
2.3. Preparation of PLGA-Prothionamide Nanoparticles
PLGA nanoparticles were prepared by solvent evaporation technique as reported earlier [13]. PLGA and PTH were dissolved in common solvent of Dichloromethane and further transferred to 10ml PVA solution followed by vortexed for 10 min. Micro-emulsion was formed when sonicated for 5min over the ice bath using probe sonicator, Bandelin electronic, Sonoplus. The prepared emulsion kept on magnetic stirrer at room temperature for 2h to evaporate Dichloromethane. The nanoparticles were recovered by ultracentrifugation at 18000 rpm for 25 min, followed by single wash with distilled water. Prepared nanoparticles were freeze-dried using 2% w/v D-mannitol as cryoprotectant [14].
2.3.1. Experimental Design
Particle size with suitable and uniform distribution of particle size is the utmost criteria for controlled drug delivery system. Along with high drug loading, there is also the requirement of delivering less amount of drug in pulmonary delivery. These parameters depend on many factors like polymer amount, surfactant concentration and organic phase volume. Polymer amount increases the particle size as well as entrapment efficiency, whereas surfactant concentration and organic phase volume influences the particle size and charging of particles. From the preliminary trials, a 33 Box-Behnken Design was employed to study the effect of three independent variables, i.e. PLGA amount (X1), PVA concentration in % w/v (X2), and Dichloromethane volume in ml (X3), on three dependent variables like particle size (Y1), percentage drug entrapment (PDE) (Y2), Polydispersibility index (Y3). Effects of independent variables were studied at three different levels (Table 1). Nanoparticles were prepared as per the experiment design matrix generated by the software.
2.3.2. Characterization of Nanoparticles
After centrifugation, un-entrapped PTH in the supernatant was estimated by UV-spectrophotometric method as reported previously [15]. Entrapped drug was calculated by subtracting un-entrapped drug from total drug. Further percentage drug entrapment was calculated from the entrapped drug [3, 4]. Average particle size and Poly Dispersity Index (PDI) of the prepared nanoparticles were determined by laser dynamic light scattering using Zetatrac, Microtac Inc.USA. The PDI value indicates the particle size distribution of nanoparticles in a given sample. Higher value of PDI indicates low homogeneity due to wide distribution of NPs, which results in aggregation of particles. Zeta potential indicates the surface charge on the particles and was measured to determine the stability of nanoparticles in the suspension [16].
2.3.3. Scanning Electron Microscopy (SEM)
The shape and surface morphology of the nanoparticles were examined for the optimized batch by Scanning Electron Microscopy (Karl Zesis with SE detector, EVO-18). The samples were sputter-coated with gold and observed for morphology at an acceleration voltage of 7.0 kV with 10.66KX and highest magnification of 19.99 KX.
2.4. Formulation of Dry Powder Inhaler (DPI) and Characterization
Prothionamide nanoparticles and anhydrous inhalable lactose were mixed manually using geometrical dilution process. Bulk and tapped density were carried out in each stage upon addition of lactose. Optimization of dry power inhaler was carried out based on the excellent flow property. Particle size and zeta potential were carried out to check the stability of nanoparticles in the form of DPI.
2.4.1. Determination of Mean Median Aerodynamic Diameter (MMAD)
Aerodynamic diameter of a particle controls its deposition in pulmonary tract. MMAD represents aerodynamic diameter below which 50% particles remain. MMAD of the optimized dry powder inhaler was determined using an eight stage cascade impactor. Firstly, DPI were passed through the cascade impactor with a flow rate of 28.3 l/min, thereafter calculated the Prothionamide content in each stage by direct measurement of Prothionamide using RP HPLC C18 Column, length-250mm. The mobile phase selected was water: Acetonitrile at 70:30 ratio with a flow rate 1.0ml/min. Absorbance was detected at 290nm. These values were inserted into the MMADCALCULATOR to obtain MMAD and geometric standard deviation (GSD) [17].
2.4.2. In-vitro Release Study
5 mg PTH equivalent DPIs were dispersed in 2ml of Simulated lungs fluid (SLF) of pH 7.4 [18, 19] and subsequently filled in dialysis bag (12,000 molecular cut off). To avoid floating, the drug filled bag was tied over the glass plate and kept at the bottom of the dissolution chamber. In-vitro release study was carried out in 1L SLF of pH 7.4 at 37 ±0.5ºC with a paddle speed of 100 rpm [20]. 10 mL of dissolution fluid was withdrawn each hour up to 24h and replaced with fresh SLF.
2.4.3. Accelerated Stability Study
Accelerated stability study was carried out as per the ICH guideline Q1A (R2). Paraffin tape was used to seal cryoprotectant vials contained freshly prepared DPI of PTH nanoparticles. These vials were kept in stability chamber and maintained at 25±2°C & 60±5% RH. The nanoparticles were analyzed for the period of 6 month [15, 21, 22]. Zeta size, zeta potential, PDI, drug entrapment and drug release were carried out to check the stability of dry powder inhaler of PTH nanoparticles.
2.4.4. In-vivo Study
Animal experiments were performed according to the guidelines of institutional animal ethical committee of Bengal College of Pharmaceutical Sciences and Research, Durgapur, West Bengal with prior approval (Registration No: 1799/PO/ Ere/15/S/CPCSEA under CPCSEA, India). Wistar rats (either sex, 4-6 month old & average weight 200-250g) were used in this study to assess the pharmacokinetic parameters of PTH in the form of DPI. The rats were housed for 12-h light/dark cycle with free availability of food and water. Rats were divided into two groups: one was treated with DPI of Prothionamide nanoparticles; and the other group was treated with pure Prothionamide [23]. Dry powder inhaler delivery device was designed with little modification as described previously [24]. Tongue was gently pulled outside and sprayed the DPI after placing the DPI device in the trachea region. After administration, three separate studies were performed. 1ml blood was withdrawn from the tail vain of rat to check the bio-distribution of PTH at 1, 2, 3, 6, 12 & 24h. After scarification in these specified time interval, lungs were isolated and filled it with 4 ml Phosphate buffer solution (PBS) by injected slowly in fractions to fill the lungs [22]. The fluid was withdrawn very slowly followed by one repetition. To estimate PTH remaining to be released, this fluid was vortexed for 5 min with 1ml of methanol. To check the tissue distribution, lungs and trachea portion were homogenized with 10 ml of PBS solution and 1 ml methanol followed by vortexed for 5min. 30% trichloroacetic acid was added to each sample for deprotenization. After neutralized with sodium bi carbonate, each sample was centrifuged for 10min at 8000rpm. Supernatant containing Prothionamide was estimated by HPLC as described previously. Maximum concentration attained by PTH (Cmax) at specified time (Tmax) and area under curve (AUC) were calculated thereafter.
3. RESULT AND DISCUSSION
Prothionamide-PLGA Nanoparticles were prepared by solvent evaporation method. In this study, a seventeen-run, three factors and three levels Box-Behnken design was employed to construct polynomial models for the optimization process. This design was suitable for investigating the quadratic response surface and constructing a second order polynomial model using Design-Expert software (Trial Version7.1.6, Stat-Ease Inc., MN)(24).
Fig. (1). FTIR of pure Prothionamide and physical mixture of Prothionamide, PLGA and lactose.
Major principle peaks of aromatic stretching, C-N stretching, C=S stretching, =C-H bending were observed for pure Prothionamide at 1592.91, 1211.08, 1024.02 and 900.59 cm-1 respectively. Same peaks were observed in mixture at 1604.48, 1223.13, 1027.50, and 889.0 cm-1 respectively. FTIR spectra (Fig. 1) confirmed the absence of chemical interaction by maintaining the integrity of principle peaks.
3.2. Characterization of Prothionamide Nanoparticles
PLGA nanoparticles containing PTH were prepared by solvent evaporation techniques. Un-entrapment drug was calculated from supernatant after centrifugation using UV-spectrophotometric method. Entrapment efficiency was calculated from it and laid in the range of 55-82%. PDE values were increased with increasing the PLGA amount. The values of particle size, PDE, PDI are shown in Table 2.
3.3. Experimental Design
The design consisted of replicated center points and a set of points lying at the midpoints of each edge of the multidimensional cube, which defined the region of interest used to evaluate the main effects, interaction effects, and quadratic effects of the formulation ingredients, and to optimize the formulation. After filling the data system, suggested quadratic model was followed for further analysis.
3.3.1. Effect of Independent Variables on Particle Size
Polymer amount is known to play an important role in controlling particle size along with release of drug from the matrix. Average particle size was found in the range of 224 to 510nm. The effect of average particle size was explained by the following quadratic equation:
(Y1)= + 299.67 + 20.87X1 - 22.50X2 + 30.13X3 + 13.25X1X2+ 92.50X1X3 - 27.25X2X3 + 20.17X12 - 37.58X22 + 48.17X32.........1
X1, X2 and X3 had the significant effect on the particle size, as the coefficient values were more. Positive effect was seen in X1 and X3 variable due to '+ve' sign. Hence, the average particle size of nanoparticles was increasing with increasing the PLGA amount. Our results are in accordance with those observed by other authors [16, 25]. Mainardes et al. (2005) reported particles size increased with rising the volume of Dichloromethane, but here particle size increased with increasing Dichloromethane volume [13]. On the other side, negative value of factor X2 signifies that the particle size is indirectly proportional with the increasing PVA concentration. With decrease in PVA concentration, the particle size of the NPs increased [16].
3.3.2. Effect of Independent Variables on Entrapment Efficiency
Percentage drug entrapment (PDE) of prepared nanoparticles was found to be in the range of 55 to 82%. The effect of average particle size was explained by the following quadratic equation:
(Y2)= +72.67 + 2.13X1 - 6.63X2- 3.00X3 + 0.75X1X2- 4.00X1X3-6.00X2X3 - 0.71X12- 3.21X22 + 1.04X32 ........................2
Table 1. Adopted variables levels in Box-Behnken design.
Independent variables / Factor
X1 = PLGA amount 25 75 125
X2 = PVA Concentration (%w/v) 0.25 0.75 1.25
X3 = Dichloromethane volume (mL) 2 4 6
Dependent variables / Response Constraints
Y1 = Particle size (nm) Minimum
Y2 = Percentage Drug entrapment (%w/w) Maximum
Y3= Poly Dispersibility Index Minimum
Table 2. Box Behnken experimental design.
Factor X1
Response Y1
1 125 1.25 4 295 64 0.607
3 75 1.25 2 285 74 1.696
10 75 0.75 4 294 71 0.652
Table 3. Optimization of dry powder inhaler.
Formulation code
Nanoparticles: Anhydrous lactose
Angle of repose a
Carr's indexa
Housner ratio a
DPI 1 1.0:0.5 33.68 ± 1.34 11.12 ± 0.41 1.13 ± 0.001
DPI 2 1.0:1.0 27.41 ± 0.73 8.13 ± 1.92 1.09 ± 0.02
DPI 3 1.0:1.5 32.86 ± 2.38 12.08 ± 1.81 1.14 ± 0.02
a Values are mean ± standard deviation
Table 4. Accelerated stability study.
Time point (month)
Particle size (nm)a
PDIa
Zeta potentiala
% Drug Entrapmenta
% drug release in 24ha
0 323.3 ± 10 0.321 ± 0.13 6.11 ± 0.11 80.13 ± 0.11 42.51 ± 1.5
1.5 323.8 ± 09 0.319 ± 0.21 6.19 ± 2.7 80.13 ± 0.09 43.15 ± 0.9
3 326.1 ± 05 0.317 ± 0.32 5.9 ± 3.5 80.12 ± 0.12 43.89 ± 3.2
Table 5. In-vivo analysis.
Percentage drug release
Concentration of PTH in lungs (µg/mL)
AUC of Pulmonary pharmacokinetic (µgh/mL)
Concentration of PTH in blood (µg/mL)
AUC of plasma pharmacokinetic (µgh/mL)
PTH nano.a
Pure PTH a
PTH nano. a
1 19.77 ± 0.89 38.51 ± 0.59 2.31 ± 0.03 4.56 ± 0.31 15.79±0.20 10.48 ± 2.31 0.09 ± 0.005 0.42 ± 0.03 1.16 ± 0.02 0.728 ± 0.01
2 24.65 ± 0.47 67.62 ± 2.95 2.07 ± 0.01 2.05 ± 0.09 0.096 ± 0.0003 0.14 ± 0.01
3 26.38 ± 1.32 91.41 ± 1.22 2.03 ± 0.01 0.72 ± 0.06 0.079 ± 0.005 0.08 ± 0.01
6 28.67± 0.69 99.84 ± 0.08 0.65 ± 0.04 0.18 ± 0.03 0.074 ± 0.003 0.001 ± 0.001
12 33.02 ± 1.04 --- 0.28 ± 0.03 -- 0.028 ± 0.005 ---
24 40.36 ± 1.47 --- 0.22 ± 0.02 --- 0.025 ± 0.001 ---
There was less influence of X1, X2 & X3 on the PDE value due to low coefficient value. Influence of X2 on PDE was more in comparison to X3 and X1. Here, percentage drug entrapment was increased by increasing the PLGA amount (X1) and decreasing the PVA concentration (X2) and organic phase volume (X3).
3.3.3. Effect of Independent Variables on Polydispersibllity Index
Polydispersibility Index or PDI (Y3) was found in the range of 0.607 to 2.248. Correlation tried to establish the relation between PDI with X1, X2 and X3. The value of zeta potential of all the batches was found to be within 5mV. Particle aggregation is less likely to occur for charged particles (i.e. high zeta potential) [26]. In this range, particle has more tendency to aggregate resulting in wide distribution of particle size. Hence, no significant influences were established between independent variables with polydispersibiliy index.
3.3.4. Search for Optimum Nanoparticles of Prothionamide
All the measured responses that may affect the quality of the product were taken into consideration during the optimization [27]. Particle size and PDE were taken under consideration for the selection of optimized batch. Formulation was optimization with set criteria of maximum entrapment efficiency and minimum particle size, followed by higher disability factor. Design suggested that optimized formulation contained 125mg of PLGA, 0.36%w/v solution of PVA and 2ml of DCM. Nanoparticles prepared using this composition and found the particle size of 310 ± 12 nm, PDE 80.08 ± 0.26% and PDI of 0.386 ± 0.13.
3.4. SEM Analysis of System Generated Optimized Nanoparticles
SEM images (Figs. 2a and b) showed that the optimized Prothionamide nanoparticles were spherical in shape. Most of the particle size was of 205.6 nm, which are suitable for the inhalation formulation.
Fig. (2). SEM image of optimized freeze dried nanoparticles at 10.66 KX (2a) and 19.99KX (2b).
3.5. Formulation and Characterization of Dry Powder Inhaler
Different proportion of anhydrous inhalable grade lactose was added to the optimized Prothionamide nanoparticles by physical geometric mixture. In each addition, angle of repose, carr's index, hausner ratio was determined (Table 3). Upon addition of anhydrous lactose, these values were decreased initially but increased afterward. DPI 2 showed excellent flow property in 1:1 proportion of Prothionamide nanoparticles and lactose.
3.5.1. Determination of MMAD and Geometric Standard Deviation
The size and shape of particles are the main parameters that determine their deposition in the different parts of lungs. Aerodynamic particle size in range of 0.5-5 µm can reach to the small airways and alveoli [28]. Prepared DPI of Prothionamide nanoparticles aerodynamic particle size was 1.69 µm and geometric standard deviation was 1.95. Maximum fine particle fraction, extra fine particle fraction and emitted dose were found as 76.88%, 23.88% and 91.46% respectively. This signifies that the prepared DPI can reach deeply to lungs.
The optimized Prothionamide nanoparticles (OP Batch) loaded DPI was subjected for in-vitro drug release behavior. SLF was used as dissolution medium for evaluating the release pattern of Prothionamide from PLGA nanoparticles. The optimized Prothionamide-PLGA nanoparticles showed initial burst release of 20% and then showed sustained drug release up to 43.52% in 24h (Fig. 3). The initial burst release will help to reach the desired plasma concentration in lungs, and the sustained release will help to maintain the PTH dose for prolong period of time. The in-vitro release data was analyzed through zero order, Higuchi, Peppas models and obtained correlation co-efficient (r2) were found to be 0.982, 0.966, and 0.933 respectively. The release best fitted with zero order kinetics.
Fig. (3). In-vitro release of optimized DPI in simulated lungs fluid at pH 7.4. Data represents mean ± SD (n=3).
Various literatures reported the instability of nanoparticles during storage. 6 month accelerated stability study revealed no clamping or aggregation of particles. Zeta size, PDI, and drug release demonstrated that the conservation of dry powder inhaler was stable during stress testing (Table 4).
The minimum inhibitory concentration (MIC) of Prothionamide (0.5µg/ml) was obtained for approximately 16h by a regimen of 250–375 mg twice daily [29]. But this dose is never suitable for the pulmonary administration. So, pulmonary dose was calculated based on the human lungs volume (4.341 l) [21]. The prepared DPI of Prothionamide nanoparticles contained two parts: loading and maintenance dose. As both the dose release at a time, corrected dose was calculated as 78 mg for human administration. Dose calculation for rat was carried out with equivalent surface and weight with no observed adverse effect in 7.7mg/kg body weight [22]. Three different studies were carried out to determine percentage drug release in-vivo, lungs tissue distribution and bio-distribution of Prothionamide (Table 5). In contrast to pure PTH administration, PTH nanoparticles gave sustained release more than 24h, whereas pure PTH gave release 99.84±0.08 in 6h. AUC increased significantly when PTH was given in the form of nanoparticles. As PTH reached very slowly to plasma from PTH nanoparticles, AUC was relatively less 1.16±0.02 µgh/mL in comparison to pure PTH. Cmax was 2.31±0.03 µg/mL for PTH DPI, whereas 4.567 ± 0.31 µg/mL for pure PTH reached at Tmax 1h. DPI of PTH nanoparticles maintained plasma concentration above the MIC for the period of 6h, but pure PTH could be maintained for up to 3h. Concentration attained by PTH nanoparticles 0.22 ± 0.02 µg/mL, which is lower than the MIC. Hence, prepared formulation may be used once every day to maintain the plasma concentration above MIC for 24h. PTH was detected after 24h, when given in the form of nanoparticles, which signified prolong drug residency in lungs.
In this study, Prothionamide-PLGA nanoparticles were optimized by statistical Box-Behnken design. Optimization study revealed that the PLGA amount and surfactant concentration had prominent effect on entrapment efficiency and particle size. Aerodynamic particle size of the DPI containing PTH nanoparticles was favorable for the effective delivery in pulmonary administration. In-vitro zero order release profile of DPI also signifies the residence of Prothionamide for prolong period of time in lungs, which has been confirmed after animal experiment. Animal studies successfully correlate with the in-vitro release profile in simulated lungs fluid. Prepared DPI containing PTH nanoparticles could improve the effectiveness of the treatment by increasing PTH concentration in the lungs tissue with single dose administration daily. Future prospect is to check the level of PTH concentration above MIC for 24h by daily dose administration.
This research did not receive any specific grant from funding agencies in the public, commercial, or profit sectors.
Animal used in this work with permission of institutional animal ethical committee of Bengal College of Pharmaceutical Sciences and Research, Durgapur, West Bengal with prior approval (Registration No: 1799/PO/ Ere/15/S/CPCSEA under CPCSEA, India).
The study was in accordance with the standards set forth in the 8th Edition of Guide for the Care Use of Laboratory Animals (http:// grants.nih.gov/grants/olaw/Guide-for-the-care-and-use-of-laboratory-animals.pdf) published by the National Academy of Sciences, The National Academies Press, Washington DC, United States of America.
The authors declare no conflict of interest, financial or otherwise.
ACKNOWLDEGEMENTS
Authors would like to acknowledge the Department of Seed Science & Technology, Agriculture University, Junagadh for providing free service of Remi centrifugation machine and Bengal College of Pharmaceutical Sciences and Research, Durgapur for providing excellent infrastructure to conduct animal study. Authors are also thankful for the cordial support of Dr. Jayrajsinh Sarvaiya (Assistant Professor) for pursuing the research work.
[1] Bijev A, Georgieva M. The development of new tuberculostatics addressing the return of tuberculosis: Current status and trends. J Univ Chem Technol Metall 2010; 45(2): 111-26.
[2] Kumar N, Kumar P, Kumar M, Kumar R. Nanotechnology: A focus on treatment of tuberculosis. Int J Drug Deliv 2011; 3(1): 25-42.
[3] Jain DS, Athawale RB, Bajaj AN, et al. Unraveling the cytotoxic potential of Temozolomide loaded into PLGA nanoparticles. DARU J Pharm Sci 2014; 22(1): 18.
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[4] Pirooznia N, Hasannia S, Lotfi AS, Ghanei M. Encapsulation of alpha-1 antitrypsin in PLGA nanoparticles: in vitro characterization as an effective aerosol formulation in pulmonary diseases. J Nanobiotechnology 2012; 10(20): 1-15.
[5] Vij N, Min T, Marasigan R, et al. Development of PEGylated PLGA nanoparticle for controlled and sustained drug delivery in cystic fibrosis. J Nanobiotechnol 2010; 8(22): 1-18.
[6] Chan JM, Zhang L, Yuet KP, et al. PLGA-lecithin-PEG core-shell nanoparticles for controlled drug delivery. Biomaterials 2009; 30(8): 1627-34.
[7] Sameni J, Bukhari NI, Azlan NA, Julianto T, Majeed AB. The Effect of Preparation Parameters on the Size and Morphology of PLGA-Based Nanoparticles 2009; 700-4.
[8] Averineni RK, Shavi GV, Gurram AK, et al. PLGA 50:50 nanoparticles of paclitaxel: Development, in vitro anti-tumor activity in BT-549 cells and in vivo evaluation. Bull Mater Sci 2012; 35(3): 319-26.
[9] Manoochehri S, Darvishi B, Kamalinia G, et al. Surface modification of PLGA nanoparticles via human serum albumin conjugation for controlled delivery of docetaxel. Daru J Pharm Sci 2013; 21(58): 1-10.
[10] Hamilla SM, Michael H, Ashley M. Microparticle drug delivery syringe. 2011 IEEE 37th Annual Northeast Bioengineering Conference 2011; 1-2.
[11] Wang Y, Li P, Kong L. Formulation optimization for high drug loading colonic drug delivery carrier 2010; 1686-9.
[12] Kambham V, Kothapalli Bonnoth C. Development of stavudine sustained release tablets: In-vitro studies. Futur J Pharm Sci [Internet] 2016; 2(2): 37-42.
[13] Mainardes RM, Evangelista RC. PLGA nanoparticles containing praziquantel: Effect of formulation variables on size distribution. Int J Pharm 2005; 290(1-2): 137-44.
[14] Abdelwahed W, Degobert G, Stainmesse S, Fessi H. Freeze-drying of nanoparticles: Formulation, process and storage considerations. Adv Drug Deliv Rev 2006; 58(15): 1688-713.
[15] Debnath SK, Saisivam S, Dash DK, Debnath M. Development and validation of UV-spectrophotometric methods for quantitative estimation of Prothionamide in pure and pharmaceutical dosage forms. Int Curr Pharm J 2015; 4(7): 402-4.
[16] Sharma D, Maheshwari D, Philip G, et al. Formulation and optimization of polymeric nanoparticles for intranasal delivery of Lorazepam using Box-Behnken design: In vitro and in vivo evaluation. Biomed Res Int 2014; 1-14.
[17] Sinha B, Mukherjee B. Development of an inhalation chamber and a dry powder inhaler device for administration of pulmonary medication in animal model. Drug Dev Ind Pharm 2012; 38(2): 171-9.
[18] Marques MR, Loebenberg R, Almukainzi M. Simulated biological fluids with possible application in dissolution testing. Dissolut Technol 2011; 18(3): 15-28.
[19] Debnath SK, Saisivam S, Omri A. PLGA ethionamide nanoparticles for pulmonary delivery: Development and in vivo evaluation of dry powder inhaler. J Pharm Biomed Anal 2017; 145: 854-9. [Internet].
[20] May S, Jensen B, Wolkenhauer M, Schneider M, Lehr CM. Dissolution techniques for in vitro testing of dry powders for inhalation. Pharm Res 2012; 29(8): 2157-66.
[21] Fernandes CA, Vanbever R. Preclinical models for pulmonary drug delivery. Expert Opin Drug Deliv 2009; 6(11): 1231-45.
[22] Chougule M, Padhi B, Misra A. Nano-liposomal dry powder inhaler of tacrolimus: preparation, characterization, and pulmonary pharmacokinetics. Int J Nanomedicine 2007; 2(4): 675-88.
[23] Guidance for industry. Estimating the maximun safe starting close in initial clinical trial for therapeutics in adult health volunteers.. 2005. Available at: http://www.fda.gov/downloads/Drugs/Guidance/UCM078932.pdf
[24] Okuda T, Suzuki Y, Kobayashi Y, et al. Development of biodegradable polycation-based inhalable dry gene powders by spray freeze drying. Pharmaceutics 2015; 7(3): 233-54.
[25] Tripathi A, Gupta R, Saraf SA. PLGA nanoparticles of anti tubercular drug: Drug loading and release studies of a water in soluble drug. Int J Pharmatech Res 2010; 2(3): 2116-23.
[26] Kharia AA, Singhai AK, Verma R. Formulation and evaluation of polymeric nanoparticles of an antiviral drug for gastroretention. Int J Pharm Sci Nanotechnol 2012; 4(4): 1557-62.
[27] Pandya VM, Patel JK, Patel DJ. Formulation and optimization of nanosuspensions for enhancing Simvastatin dissolution using central composite design. Dissolut Technol 2011; 18(3): 40-5.
[28] Fernández Tena A, Casan Clarà P. Deposition of inhaled particles in the lungs. Arch Bronconeumol 2012; 48(7): 240-6.
[29] Lee HW, Kim DW, Park JH, et al. Pharmacokinetics of prothionamide in patients with multidrug-resistant tuberculosis. Int J Tuberc Lung Dis 2009; 13(9): 1161-6.
The Open Nanomedicine and Nanotechnology Journal
Volume: 6, 2019
Processing Time: Average publication time of 18 days between final acceptance of revised manuscript and its publication
Victor Manuel Castano Meneses
Center of Applied Physics and Advanced Technology
National Autonomous University of Mexico
Juriquilla Campus
Biography of Victor Manuel Castano Meneses
Victor-Manuel Castano-Meneses holds BSc, MSc and PhD in Physics from the Universidad Nacional Autonoma de Mexico, where he is a Full-Professor at the Centro de Fisica Aplicada y Tecnologia Avanzada. He spent 2 years as a Postdoctoral Fellow at IBM´s Yorktown Heights Research Center and has authored and co-authored over 600 peer-reviewer papers in international journals. Prof. Castano is one of the most cited latinamerican scientists in his field and has advised over 60 PhD thesis and numerous BSc and MSc disertations. He also holds more than 20 patents and has been able to effectively transfer nanotechnology knowledge from the lab to the industry.
Center of Applied Physics and Advanced Technology , National Autonomous University of Mexico, Juriquilla Campus, Queretaro, México
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Zinc Oxide Nanoparticles: Therapeutic Benefits and Toxicological Hazards
Development, Characterization and Transdermal Delivery of Dapsone and an Antibiotic Entrapped in Ethanolic Liposomal Gel for the Treatment of Lapromatous Leprosy
Development, Characterization and Transdermal Delivery of Dapsone and an Antibiotic Entrapped in Ethanolic Liposomal Gel for the Treatment of
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Bug in userauth component cakephp 2.5.5 I'm using Usermgmt plugin. for the login process I make a request with ajax. This works fine on localhost but not on the hosting.
In hosting the function login works except the call " $this->UserAuth->login($user)"
Funtion login in Userscontroller.php
public function login($type=0,$subtype=0) {

    $this->layout = 'plantilla';
    if ($this->request -> isPost()) {
        if($this->User->LoginValidate()) {
            $email  = $this->data['User']['email'];
            $password = $this->data['User']['password'];
            $user = $this->User->findByUsername($email);
            if (empty($user)) {
                $user = $this->User->findByEmail($email);
                if (empty($user)) {
                    //$this->Session->setFlash(__('Usuario y/o contraseña incorrecto.'));
                    echo "Usuario y/o contraseña incorrecto";
                    exit();
                }
            }
            // check for inactive account
            if ($user['User']['id'] != 1 and $user['User']['active']==0) {
                //$this->Session->setFlash(__('Su registro no ha sido confirmado por favor verifique su correo electrónico o contacte al administrador'));
                echo "Su registro no ha sido confirmado por favor verifique su correo electrónico o contacte al administrador";
                exit();
            }

            $hashed = $this->UserAuth->makePassword($password);
            if ($user['User']['password'] === $hashed) {

             $this->UserAuth->login($user);

                $remember = (!empty($this->data['User']['remember']));
                if ($remember) {
                    $this->UserAuth->persist('2 weeks');
                }
                $OriginAfterLogin=$this->Session->read('Usermgmt.OriginAfterLogin');
                $this->Session->delete('Usermgmt.OriginAfterLogin');
                $rol = $this->User->find('all',array('conditions' => array('email' => $email)));
                if($rol['0']['User']['user_group_id']==1){
                    $redirect = "/projects";//(!empty($OriginAfterLogin)) ? $OriginAfterLogin : loginRedirectUrl;
                    //$this->redirect($redirect);
                    echo 1;
                    exit();
                }
                if($this->data['User']['page'] == "projects"){
                    echo 2;
                    exit();
                }

                else {
                    $redirect = "/";
                    //$this->redirect($redirect);
                    echo 0;
                    exit();
                }
            } else {
                //$this->Session->setFlash(__('Usuario y/o contraseña incorrecto.'));
                echo "Usuario y/o contraseña incorrecto.";
                exit();
            }
        }else{
            $this->User->set($this->data);
            //$this->Session->setFlash(__('Formulario Vacío.'));
            echo "Formulario Vacío";
            exit();
        }
    }
    $conditions=$subtype!=0?array('Project.project_subtype_id'=>$subtype):array('Project.project_type_id'=>$type);
    $this->set('parents_type', $this->ProjectType->find('all', array('conditions' => array('ProjectType.parent_id' => 0))));
    $this->set('projects', $this->Project->find('all',array('conditions'=>$conditions)));

}

Peticion con ajax
$(function() {
    //Actions to submit login modal
   $('#user-login').submit(function() {

  var ser = $('#user-login').serialize();

    $.ajax({
      async: true,
     type: 'POST',

     url: '<?php echo $this->webroot?>usermgmt/users/login',
     data: ser,
             success: function(msg){

     if(msg == 1){ window.location = '<?php echo $this->webroot?>projects';}
        else if(msg == 2){ location.reload(true);}
        else if(msg == 0){ window.location ='<?php echo $this->webroot?>';}
        else {$("#error-login").html("
"+msg+"
");}

        //alert(msg);

         },
 error: function(msg){
 alert("Error");
 }
   });
     return false;
 });

redpajama_set_name: RedPajamaStackExchange


text:
package io.crate.lucene;

import com.google.common.collect.Sets;
import io.crate.analyze.WhereClause;
import io.crate.metadata.Functions;
import io.crate.operation.operator.*;
import io.crate.operation.operator.any.*;
import io.crate.planner.symbol.Literal;
import io.crate.planner.symbol.Reference;
import io.crate.test.integration.CrateUnitTest;
import io.crate.types.ArrayType;
import io.crate.types.DataType;
import io.crate.types.DataTypes;
import io.crate.types.SetType;
import org.apache.lucene.queries.BooleanFilter;
import org.apache.lucene.queries.TermsFilter;
import org.apache.lucene.sandbox.queries.regex.RegexQuery;
import org.apache.lucene.search.*;
import org.apache.lucene.util.BytesRef;
import org.elasticsearch.common.inject.ModulesBuilder;
import org.elasticsearch.common.lucene.search.MatchNoDocsQuery;
import org.elasticsearch.common.lucene.search.RegexpFilter;
import org.elasticsearch.common.lucene.search.XConstantScoreQuery;
import org.elasticsearch.index.cache.IndexCache;
import org.elasticsearch.index.cache.filter.FilterCache;
import org.elasticsearch.search.internal.SearchContext;
import org.junit.Before;
import org.junit.Test;
import org.mockito.Answers;
import org.mockito.Matchers;
import org.mockito.invocation.InvocationOnMock;
import org.mockito.stubbing.Answer;

import java.util.Arrays;

import static io.crate.testing.TestingHelpers.*;
import static org.hamcrest.Matchers.*;
import static org.mockito.Mockito.mock;
import static org.mockito.Mockito.when;

public class LuceneQueryBuilderTest extends CrateUnitTest {

    private LuceneQueryBuilder builder;
    private SearchContext searchContext;
    private IndexCache indexCache;

    @Before
    public void prepare() throws Exception {
        Functions functions = new ModulesBuilder()
                .add(new OperatorModule()).createInjector().getInstance(Functions.class);
        builder = new LuceneQueryBuilder(functions);
        searchContext = mock(SearchContext.class, Answers.RETURNS_MOCKS.get());
        indexCache = mock(IndexCache.class, Answers.RETURNS_MOCKS.get());
        FilterCache filterCache = mock(FilterCache.class);
        when(indexCache.filter()).thenReturn(filterCache);
        when(filterCache.cache(Matchers.any(Filter.class))).thenAnswer(new Answer<Filter>() {
            @Override
            public Filter answer(InvocationOnMock invocation) throws Throwable {
                return (Filter) invocation.getArguments()[0];
            }
        });
    }

    @Test
    public void testNoMatchWhereClause() throws Exception {
        Query query = convert(WhereClause.NO_MATCH);
        assertThat(query, instanceOf(MatchNoDocsQuery.class));
    }

    @Test
    public void testWhereRefEqNullWithDifferentTypes() throws Exception {
        for (DataType type : DataTypes.PRIMITIVE_TYPES) {
            Reference foo = createReference("foo", type);
            Query query = convert(whereClause(EqOperator.NAME, foo, Literal.newLiteral(type, null)));

            // must always become a MatchNoDocsQuery
            // string: term query with null would cause NPE
            // int/numeric: rangeQuery from null to null would match all
            // bool:  term would match false too because of the condition in the eq query builder
            assertThat(query, instanceOf(MatchNoDocsQuery.class));
        }
    }

    @Test
    public void testWhereRefEqRef() throws Exception {
        Reference foo = createReference("foo", DataTypes.STRING);
        Query query = convert(whereClause(EqOperator.NAME, foo, foo));
        assertThat(query, instanceOf(FilteredQuery.class));
    }

    @Test
    public void testLteQuery() throws Exception {
        Query query = convert(new WhereClause(createFunction(LteOperator.NAME,
                DataTypes.BOOLEAN,
                createReference("x", DataTypes.INTEGER),
                Literal.newLiteral(10))));
        assertThat(query, instanceOf(NumericRangeQuery.class));
        assertThat(query.toString(), is("x:{* TO 10]"));
    }

    @Test
    public void testEqOnTwoArraysBecomesGenericFunctionQuery() throws Exception {
        DataType longArray = new ArrayType(DataTypes.LONG);
        Query query = convert(new WhereClause(createFunction(EqOperator.NAME,
                DataTypes.BOOLEAN,
                createReference("x", longArray),
                Literal.newLiteral(longArray, new Object[] { 10L, null, 20L }))));
        assertThat(query, instanceOf(FilteredQuery.class));
        FilteredQuery filteredQuery = (FilteredQuery) query;

        assertThat(filteredQuery.getFilter(), instanceOf(BooleanFilter.class));
        assertThat(filteredQuery.getQuery(), instanceOf(XConstantScoreQuery.class));

        BooleanFilter filter = (BooleanFilter) filteredQuery.getFilter();
        assertThat(filter.clauses().get(0).getFilter(), instanceOf(BooleanFilter.class)); // booleanFilter with terms filter
        assertThat(filter.clauses().get(1).getFilter(), instanceOf(Filter.class)); // generic function filter
    }

    @Test
    public void testEqOnTwoArraysBecomesGenericFunctionQueryAllValuesNull() throws Exception {
        DataType longArray = new ArrayType(DataTypes.LONG);
        Query query = convert(new WhereClause(createFunction(EqOperator.NAME,
                DataTypes.BOOLEAN,
                createReference("x", longArray),
                Literal.newLiteral(longArray, new Object[] { null, null, null }))));
        assertThat(query, instanceOf(FilteredQuery.class));
    }

    @Test
    public void testEqOnArrayWithTooManyClauses() throws Exception {
        Object[] values = new Object[2000]; // should trigger the TooManyClauses exception
        Arrays.fill(values, 10L);
        DataType longArray = new ArrayType(DataTypes.LONG);
        Query query = convert(new WhereClause(createFunction(EqOperator.NAME,
                DataTypes.BOOLEAN,
                createReference("x", longArray),
                Literal.newLiteral(longArray, values))));
        assertThat(query, instanceOf(FilteredQuery.class));
    }

    @Test
    public void testGteQuery() throws Exception {
        Query query = convert(new WhereClause(createFunction(GteOperator.NAME,
                DataTypes.BOOLEAN,
                createReference("x", DataTypes.INTEGER),
                Literal.newLiteral(10))));
        assertThat(query, instanceOf(NumericRangeQuery.class));
        assertThat(query.toString(), is("x:[10 TO *}"));
    }

    @Test
    public void testWhereRefInSetLiteralIsConvertedToBooleanQuery() throws Exception {
        DataType dataType = new SetType(DataTypes.INTEGER);
        Reference foo = createReference("foo", DataTypes.INTEGER);
        WhereClause whereClause = new WhereClause(
                createFunction(InOperator.NAME, DataTypes.BOOLEAN,
                        foo,
                        Literal.newLiteral(dataType, Sets.newHashSet(1, 3))));
        Query query = convert(whereClause);
        assertThat(query, instanceOf(FilteredQuery.class));
        assertThat(((FilteredQuery)query).getFilter(), instanceOf(TermsFilter.class));
    }

    @Test
    public void testWhereStringRefInSetLiteralIsConvertedToBooleanQuery() throws Exception {
        DataType dataType = new SetType(DataTypes.STRING);
        Reference foo = createReference("foo", DataTypes.STRING);
        WhereClause whereClause = new WhereClause(
                createFunction(InOperator.NAME, DataTypes.BOOLEAN,
                        foo,
                        Literal.newLiteral(dataType, Sets.newHashSet(new BytesRef("foo"), new BytesRef("bar")))
                ));
        Query query = convert(whereClause);
        assertThat(query, instanceOf(FilteredQuery.class));
        assertThat(((FilteredQuery)query).getFilter(), instanceOf(TermsFilter.class));
    }

    /**
     * Make sure we still sport the fast Lucene regular
     * expression engine when not using PCRE features.
     */
    @Test
    public void testRegexQueryFast() throws Exception {
        Reference value = createReference("foo", DataTypes.STRING);
        Literal pattern = Literal.newLiteral(new BytesRef("[a-z]"));
        Query query = convert(whereClause(RegexpMatchOperator.NAME, value, pattern));
        assertThat(query, instanceOf(XConstantScoreQuery.class));
        assertThat(((XConstantScoreQuery)query).getFilter(), instanceOf(RegexpFilter.class));
    }

    /**
     * When using PCRE features, switch to different
     * regex implementation on top of java.util.regex.
     */
    @Test
    public void testRegexQueryPcre() throws Exception {
        Reference value = createReference("foo", DataTypes.STRING);
        Literal pattern = Literal.newLiteral(new BytesRef("\\D"));
        Query query = convert(whereClause(RegexpMatchOperator.NAME, value, pattern));
        assertThat(query, instanceOf(RegexQuery.class));
    }

    @Test
    public void testIdQuery() throws Exception {
        Reference ref = createReference("_id", DataTypes.STRING);
        Query query = convert(whereClause(EqOperator.NAME, ref, Literal.newLiteral("i1")));
        assertThat(query, instanceOf(TermQuery.class));
        assertThat(query.toString(), is("_uid:default#i1"));
    }

    @Test
    public void testAnyEqArrayLiteral() throws Exception {
        Reference ref = createReference("d", DataTypes.DOUBLE);
        Literal doubleArrayLiteral = Literal.newLiteral(new Object[]{-1.5d, 0.0d, 1.5d}, new ArrayType(DataTypes.DOUBLE));
        Query query = convert(whereClause(AnyEqOperator.NAME, ref, doubleArrayLiteral));
        assertThat(query, instanceOf(FilteredQuery.class));
        assertThat(((FilteredQuery)query).getFilter(), instanceOf(TermsFilter.class));
    }

    @Test
    public void testAnyEqArrayReference() throws Exception {
        Reference ref = createReference("d_array", new ArrayType(DataTypes.DOUBLE));
        Literal doubleLiteral = Literal.newLiteral(1.5d);
        Query query = convert(whereClause(AnyEqOperator.NAME, doubleLiteral, ref));
        assertThat(query.toString(), is("d_array:[1.5 TO 1.5]"));
    }

    @Test
    public void testAnyGreaterAndSmaller() throws Exception {

        Reference arrayRef = createReference("d_array", new ArrayType(DataTypes.DOUBLE));
        Literal doubleLiteral = Literal.newLiteral(1.5d);

        Reference ref = createReference("d", DataTypes.DOUBLE);
        Literal arrayLiteral = Literal.newLiteral(new Object[]{1.2d, 3.5d}, new ArrayType(DataTypes.DOUBLE));

        // 1.5d < ANY (d_array)
        Query ltQuery = convert(whereClause(AnyLtOperator.NAME, doubleLiteral, arrayRef));
        assertThat(ltQuery.toString(), is("d_array:{1.5 TO *}"));

        // d < ANY ([1.2, 3.5])
        Query ltQuery2 = convert(whereClause(AnyLtOperator.NAME, ref, arrayLiteral));
        assertThat(ltQuery2.toString(), is("(d:{* TO 1.2} d:{* TO 3.5})~1"));

        // 1.5d <= ANY (d_array)
        Query lteQuery = convert(whereClause(AnyLteOperator.NAME, doubleLiteral, arrayRef));
        assertThat(lteQuery.toString(), is("d_array:[1.5 TO *}"));

        // d <= ANY ([1.2, 3.5])
        Query lteQuery2 = convert(whereClause(AnyLteOperator.NAME, ref, arrayLiteral));
        assertThat(lteQuery2.toString(), is("(d:{* TO 1.2] d:{* TO 3.5])~1"));

        // 1.5d > ANY (d_array)
        Query gtQuery = convert(whereClause(AnyGtOperator.NAME, doubleLiteral, arrayRef));
        assertThat(gtQuery.toString(), is("d_array:{* TO 1.5}"));

        // d > ANY ([1.2, 3.5])
        Query gtQuery2 = convert(whereClause(AnyGtOperator.NAME, ref, arrayLiteral));
        assertThat(gtQuery2.toString(), is("(d:{1.2 TO *} d:{3.5 TO *})~1"));

        // 1.5d >= ANY (d_array)
        Query gteQuery = convert(whereClause(AnyGteOperator.NAME, doubleLiteral, arrayRef));
        assertThat(gteQuery.toString(), is("d_array:{* TO 1.5]"));

        // d >= ANY ([1.2, 3.5])
        Query gteQuery2 = convert(whereClause(AnyGteOperator.NAME, ref, arrayLiteral));
        assertThat(gteQuery2.toString(), is("(d:[1.2 TO *} d:[3.5 TO *})~1"));
    }

    @Test
    public void testAnyOnArrayLiteral() throws Exception {
        Reference ref = createReference("d", DataTypes.STRING);
        Literal stringArrayLiteral = Literal.newLiteral(new Object[]{new BytesRef("a"), new BytesRef("b"), new BytesRef("c")}, new ArrayType(DataTypes.STRING));

        // col != ANY (1,2,3)
        Query neqQuery = convert(whereClause(AnyNeqOperator.NAME, ref, stringArrayLiteral));
        assertThat(neqQuery, instanceOf(FilteredQuery.class));
        assertThat(((FilteredQuery)neqQuery).getFilter(), instanceOf(BooleanFilter.class));
        BooleanFilter filter = (BooleanFilter)((FilteredQuery) neqQuery).getFilter();
        assertThat(filter.toString(), is("BooleanFilter(-BooleanFilter(+d:a +d:b +d:c))"));

        // col like any (1,2,3)
        Query likeQuery = convert(whereClause(AnyLikeOperator.NAME, ref, stringArrayLiteral));
        assertThat(likeQuery, instanceOf(BooleanQuery.class));
        BooleanQuery likeBQuery = (BooleanQuery)likeQuery;
        assertThat(likeBQuery.clauses().size(), is(3));
        for (int i = 0; i < 2; i++) {
            // like --> XConstantScoreQuery with regexp-filter
            Query filteredQuery = likeBQuery.clauses().get(i).getQuery();
            assertThat(filteredQuery, instanceOf(XConstantScoreQuery.class));
            assertThat(((XConstantScoreQuery)filteredQuery).getFilter(), instanceOf(RegexpFilter.class));
        }

        // col not like any (1,2,3)
        Query notLikeQuery = convert(whereClause(AnyNotLikeOperator.NAME, ref, stringArrayLiteral));
        assertThat(notLikeQuery, instanceOf(BooleanQuery.class));
        BooleanQuery notLikeBQuery = (BooleanQuery)notLikeQuery;
        assertThat(notLikeBQuery.clauses(), hasSize(1));
        BooleanClause clause = notLikeBQuery.clauses().get(0);
        assertThat(clause.getOccur(), is(BooleanClause.Occur.MUST_NOT));
        assertThat(((BooleanQuery)clause.getQuery()).clauses(), hasSize(3));
        for (BooleanClause innerClause : ((BooleanQuery)clause.getQuery()).clauses()) {
            assertThat(innerClause.getOccur(), is(BooleanClause.Occur.MUST));
            assertThat(innerClause.getQuery(), instanceOf(XConstantScoreQuery.class));
            assertThat(((XConstantScoreQuery)innerClause.getQuery()).getFilter(), instanceOf(RegexpFilter.class));
        }

        // col < any (1,2,3)
        Query ltQuery2 = convert(whereClause(AnyLtOperator.NAME, ref, stringArrayLiteral));
        assertThat(ltQuery2, instanceOf(BooleanQuery.class));
        BooleanQuery ltBQuery = (BooleanQuery)ltQuery2;
        assertThat(ltBQuery.toString(), is("(d:{* TO a} d:{* TO b} d:{* TO c})~1"));
    }

    private Query convert(WhereClause clause) {
        return builder.convert(clause, searchContext.mapperService(), searchContext.fieldData(), indexCache).query;
    }
}


Have you been searching for ways to enhance your organization and do better in the world of business? If so, you have to start working towards being a better leader. There is always room for improvement, even for the best leaders, and the following article will show you how this can be done. Keep reading to find out more about leadership.
Talk about the vision of your team. Use your mission like a compass with company values integrated into daily experiences. Present your vision, but be sure to let your employees know how they can each contribute to achieving it. It is smart and helps to build relationships and give people direction.
A great leader inspires creativity. Creative thinking prompts folks to take chances that lead to amazing outcomes. Make attempts to explore the unknown and take the path less traveled. Remain open to new ideas, even those that do not appear feasible at the moment. Use the ideas of those around you to improve your plans.
Honesty is a critical leadership quality. Effective leadership requires trustworthiness. When you work on your leadership skills, try to set a standard of trustworthiness and honesty. When people know that they can trust you, you will gain respect.
All leaders that are good need to focus on days to come. You should be able to anticipate things before they occur, so you can handle anything that gets thrown your way. While you aren't a mind-reader, you will find that you can achieve some success this way. Set goals for the near future so that you can plan the outcome.
Keep your morals in mind. Consider your decision before you make it. Don't make a decision that goes against your values. Even if others would make the decision anyway, you have to do what feels right to you.
Decisiveness is essential to being a great leader. Many decisions are on your shoulders as the leader. If there are quite a few different ideas floating around, discuss the one that will work for the majority.
TIP! If you're trying to improve your leadership skills, avoid acting like you know everything. While you may truly be a great leader, you can always learn from your peers.
Focus on working well with people and the people will focus more on the work. You must learn what to do to inspire and encourage those around you. Instead of focusing on completing every small task, focus on energizing your team into performing better.
Be sure you are an approachable leader. Some leaders believe that intimidation and bullying is the best way to establish who's in charge. If you try to show that you are boss by striking fear in the heart of your subordinates, it will be next to impossible to earn their respect. Part of your position is to ensure your team performs well, so ensure that they know that your door is open.
Don't do something immoral just to get an edge in business. If your competitors are doing something shady, do something else instead. Remain competitive and use alternative methods for competing. If you find an innovative way to do business, you will be much prouder of your choice.
Never do anything that might be construed as devious or deceitful. Keep your promises if you want others to trust you. If your advertising claims you provide top quality service in your category, ensure that your employees know what must be done to provide that service.
TIP! Do not do things that others may find deceitful. Never fail to live up to your promises.
It is important that you set goals for your team. Use annual goals to motivate your employees to strive for greatness. Do not let your goals disappear after you have established them. Meet on the goals at least monthly, and hold everyone accountable as a team for reaching them.
Don't set unrealistic goals. Don't set the team up to fail. That will just make you a terrible leader.
Listen more than you talk. Good leaders know that listening to their employees and to those around them is vitally important. Make sure you are listening to your employees. Here their issues and learn about the things they love. Use what you are told to learn about your business. You might be amazed at how much you learn by listening.
Give incentives for well done work. It's true that everyone get a salary, but incentives form a large piece of the motivational puzzle. If your employee goes way above their call of duty, make sure they see that you see that and try encouraging that with a type of gift or bonus. The best leaders reward their employees well.
TIP! When someone does good work, provide them with rewards. Everyone may already have a salary, but small incentives make things a lot more fun and productive.
Frequently keep synergy in mind. Have a good grasp of your own personal goals. Always have clarity regarding your business goals. You should be sure that these things are aligned well and perhaps even overlap. You should have the ability to work on either or both whenever possible. If this is not possible, you may lack enthusiasm for your work.
You must be aware of what you are and are not good at to make the most of your leadership skills. You're setting yourself up for failure if you're too overconfident. Focus on your weaknesses so that you can learn how to be more strong in these areas.
Successful leaders know how to listen to workers' feedback on work issues. They might have ideas for improving current products or ideas for new ones. Ask for their opinions and listen well. Understanding any issues and finding a solution helps show your team that they can confide in you.
Synergy is something to think of as often as possible. Have a good grasp of your own personal goals. Always have clarity regarding your business goals. They should be aligned and have similarities. You should want to work on them simultaneously. Otherwise, you may suffer from a noticeable enthusiasm problem.
TIP! You should work on being synergistic. Be sure you're aware of your goals.
Leadership positions often make huge demands of people. It can really eat up your personal life, including time with family. To be a happy, great leader, you'll need to have a well balanced life. Take time to enjoy and develop your non-work life.
An good leader in a company is one who takes the time to evaluate his own effectiveness on a regular basis. That gives you the ability to build the skills which are weaker. A great leader will try to enhance the strengths they possess and work on their weaknesses.
As a leader, you can't be effective unless you are aware of your strengths and weaknesses. If you are showing off thinking that you can do more than you really can, you may end up failing quite a bit. Instead, focus on strengthening any areas you feel weak in.
TIP! What are your weaknesses and strengths as a leader? Being too confident in your lesser abilities sets you up for failure. Stick to building your skills and working towards your goals.


text:
Anonymous asked in HealthMental Health · 2 months ago
How do I get over my envy of families that still live life despite the pandemic going on?
My parents will not let us see anyone except those we see in the classroom and in our own home. No friends, no extended family, no outings except walks on our street. It has been this way since March and my mother indicates next summer at the earliest before we can return to civilization and says she is not sure we can then either. I feel it will be 2022 before I can see another soul.
I feel hopeless like this will never end. I have envy, bitter envy, for people who can get together. My cousin Michael for example was with our grandparents since it is just him and his Dad where he lives. We have not been allowed near our grandparents since March. On zoom there he was with them and his Dad. Him and his Dad were headed from their to see our aunt and her family outdoors for a little bit Saturday. They plan to do the same for Christmas. We will be quarantined in our house as we have everyday since March unless in the classroom. My mother says "count your blessings" but there feel like there are no blessings. She says "think of all your cousin Michael has been through" and I agree but am still passionately envy (his mother and sister died in 2009 in a car crash when he was 9 and he has PTSD-anxiety and learning disabilities).
I feel hopeless like this will never end. I don't want to kill myself but the thought it there. I am not in danger but just have the thought. I see no purpose as I feel I will be forever quarantined. I hate the Chinese because of the theory they caused it.
I hate that my mother is being so careful when we need to live life and just use masks and not stand to close to people. Social distancing for her is social isolation. My Dad has no balls so he does whatever she says. When there is a vaccine she says "remember we still have to be careful" so I know that means another summer of quarantine for 2 and a half months. I think by 2022 we will be able to live again but that is too long. I want to die but I don't want to kill myself. Please advise.
Anonymous: I think you're the one spamming with your insults, I suspect you're LAN. Every one of your answers will now be reported.
We have been the same in the U.k but are still alive. In fact most of the world has been following the rules.
Not only you are fed up of it.
All you ever do is spam variations of the same idiocy over and over. So why should anyone bother with you when you are too stupid to do anything other than constantly spam the same crap over and over you cowardly block monkey?
Obviously, you don't. You've been spamming this again and again for months now. Doesn't matter what we say,... you're still going to obsess about it.
RWPossum
Focus on those words "still live." The vaccine is expected to be in distribution in the spring,
How can I move to another state to benefit my mental health when my family doesn't want to move ?
Looks like there's no hope for me to get better?
Should I change my medications?
Can you get brain fog after vaping withdrawal?


text:
Survivor of clergy sex abuse, Phil Saviano, dies at 69
Sacha Pfeiffer
Published November 29, 2021 at 4:01 AM CST
A key figure in exposing the Roman Catholic Church's cover-up of clergy sex abuse has died. Phil Saviano was a survivor of that abuse and, decades later, went public with his experience. He was portrayed in the Oscar-winning movie "Spotlight." And in this scene, he speaks with a group of Boston Globe reporters.
(SOUNDBITE OF FILM, "SPOTLIGHT")
NEAL HUFF: (As Phil Saviano) When you're a poor kid from a poor family and when a priest pays attention to you, it's a big deal. How do you say no to God?
MARTINEZ: NPR's Sacha Pfeiffer was one of the investigative reporters on the Globe Spotlight team. She knew Phil and joins us now.
Sacha, what do we know about Phil Saviano's childhood experience? What did he do about it?
SACHA PFEIFFER, BYLINE: When Phil was 11, he was molested by a priest in Massachusetts. This was the 1960s. Now, we now know clergy sex abuse was tragically common. But back then, it was usually kept secret. The victim told no one, or they told their family and their family told no one, or their family complained to the church and were asked to keep quiet and sign a confidentiality agreement in return for a financial settlement.
When Phil told the church he'd been molested, he refused to sign a confidentiality clause, and that freed him to become a whistleblower. He went public, and that helped lay the groundwork for a gigantic scandal that ultimately brought down a cardinal and got Pope Francis to apologize.
MARTINEZ: Why did Phil decide to go public?
PFEIFFER: In the early 1990s, Phil was extremely sick with AIDS - close to dying - so he felt he had nothing to lose. And you have to remember - the Catholic Church used to be treated much more deferentially in this country. But Phil said if he was going to die, he might as well do some good on his way out. So he made it his mission to research clergy sex abuse. And he gave the Globe key material we needed to show that the church had been covering up this abuse for decades.
MARTINEZ: And, Sacha, you knew Phil. You interviewed him. You spent time with him. What was he like?
PFEIFFER: He was intense. He was relentless. He was determined to get someone to pay attention to what he'd found.
MARTINEZ: How would you describe Saviano's impact?
PFEIFFER: It was huge on an individual and an institutional level. Phil spent his final weeks at his brother's house. And that brother, Jim Saviano, told me he considers Phil a hero - you know, a hero for getting the church to reform how it deals with abusive priests and for encouraging other people who'd been molested to come forward. And Jim told me that Phil received an outpouring of gratitude in recent weeks.
JIM SAVIANO: I've been reading his emails, and he knew that. I would read them to him. And I mean, there were emails that said, you are the reason I'm alive. And you can't have more impact than that, I don't think.
MARTINEZ: And, Sacha, it sounds like Phil really did find a way to turn something really horrible and tragic into something positive.
PFEIFFER: Yes, he really did. He became a true activist and an effective advocate. He founded the New England chapter of the Survivors Network of those Abused by Priests. And by the way, A, he was on stage at the Oscars when the "Spotlight" movie won an Academy Award for Best Picture. He also became good friends with the actor who played him, Neal Huff. I called Neal yesterday, and here's part of what he said.
HUFF: Phil was truly one of the greats to walk the Earth in our time, I believe. So much of what happened in that movie would not have happened if Phil wasn't who he was.
PFEIFFER: Phil was 69 when he died yesterday of gallbladder cancer. And last night, one of his friends sent me a tribute book about him. And it begins with a message from Phil himself that I want to read part of. It says, dear friends, we have accomplished so much together. We have more justice where there was abuse. We have more dignity where there was shame. We have belief where there was denial. And he signs it with much love, Phil.
MARTINEZ: That's NPR's Sacha Pfeiffer. Sacha, thanks.
PFEIFFER: You're welcome.
(SOUNDBITE OF OLAFUR ARNALDS' "PARTIAL") Transcript provided by NPR, Copyright NPR.
Sacha Pfeiffer is a correspondent for NPR's Investigations team and an occasional guest host for some of NPR's national shows.
See stories by Sacha Pfeiffer


text:
Mid-2014 Apple MacBook Pro with 2.5GHz Intel Core i7 (15-inch, 16GB RAM, 512GB SSD Storage) (Renewed)
The MacBook Pro "Core i7" 2.5 15-Inch (Dual Graphics – Mid-2014 Retina Display) features a 22 nm "Haswell/Crystalwell" 2.5 GHz Intel "Core i7" processor (4870HQ), with four independent processor "cores" on a single silicon chip, a 6 MB shared level 3 cache, 16 GB of onboard 1600 MHz DDR3L SDRAM (which cannot be upgraded later), 512 GB of PCIe-based flash storage, and dual graphics processors — a NVIDIA GeForce GT 750M with 2 GB of dedicated GDDR5 memory and an integrated Intel Iris 5200 Pro graphics processor with 128 MB of "Crystalwell" embedded DRAM (and shared system memory). It also has an integrated 720p FaceTime HD webcam and a high-resolution LED-backlit 15.4″ widescreen 2880×1800 (220 ppi) "Retina" display in a case that weighs just less than 4.5 pounds (2.02 kg). It does not have an internal optical drive. Connectivity includes 802.11ac Wi-Fi, Bluetooth 4.0, two USB 3.0 ports, two "Thunderbolt 2" ports, an HDMI port, an audio in/out port, and an SDXC card slot. In addition, this model has a backlit keyboard, a "no button" glass "inertial" multi-touch trackpad, a "MagSafe 2" power adapter, and an internal, sealed battery that provides an Apple estimated 8 hours of battery life.
Renewed products look and work like new. These pre-owned products have been inspected and tested by Amazon-qualified suppliers, which typically perform a full diagnostic test, replacement of any defective parts, and a thorough cleaning process. Packaging and accessories may be generic. All products on Amazon Renewed come with a minimum 90-day supplier-backed warranty.
2.5 GHz Quad-Core Intel Core i7 Processor (Turbo Boost up to 3.7 GHz, 6 MB shared L3 cache)
16 GB 1600 MHz DDR3L RAM / 512 GB Solid State Drive PCIe-based Flash Storage
15.4-inch IPS Retina Display, 2880-by-1800 resolution
Intel Iris Pro and NVIDIA GeForce GT 750M Graphics