Efficacy and safety of laparoscopic Heller's myotomy versus pneumatic dilatation for achalasia: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND AND OBJECTIVES: Achalasia has several treatment modalities. We aim to compare the efficacy and safety of laparoscopic Heller myotomy (LHM) with those of pneumatic dilatation (PD) in adult patients suffering from achalasia.

METHODS

We searched Cochrane CENTRAL, PubMed, Web of Science, SCOPUS and Embase for related clinical trials about patients suffering from achalasia. The quality appraisal and assessment of risk of bias were conducted with GRADE and Cochrane's risk of bias tool, respectively. Homogeneous and heterogeneous data was analyzed under fixed and random-effects models, respectively.

RESULTS

The pooled analysis of 10 studies showed that PD was associated with a higher rate of remission at three months, one year, three years and five years (RR = 1.25 [1.09, 1.42] (p = 0.001); RR = 1.13 [1.05, 1.20] (p = 0.0004); RR = 1.48 [1.19, 1.82] (p = 0.0003); RR = 1.49 [1.18, 1.89] (p = 0.001)), respectively. LHM was associated with lower number of cases suffering from adverse events, dysphagia and relapses (RR = 0.50 [0.25, 0.98] (p = 0.04); RR = 0.33 [0.16, 0.71] (p = 0.004); RR = 0.38 [0.15, 0.97] (p = 0.04)), respectively. There is no significant difference between both groups regarding the lower esophageal pressure, perforations, remission rate at two years, Eckardt score after one year and reflux.

CONCLUSION

PD had higher remission rates than LHM at three months, one year and three years, but not at two years or five years. More research is needed to determine whether PD has a significant advantage over LHM in terms of long-term remission rates.

Efficacy and safety of terlipressin and albumin vs. noradrenaline and albumin in adult patients with hepatorenal syndrome: A systematic review and meta-analysis

INTRODUCTION AND OBJECTIVES

Hepatorenal syndrome (HRS) is a serious complication of cirrhosis treated with several medications. We want to evaluate terlipressin and albumin's effectiveness and safety compared to albumin and noradrenaline in adult hepatorenal disease patients.

MATERIALS AND METHODS

Clinical trials from four databases were included. Cochrane's approach for calculating bias risk was utilized. We rated the quality evaluation by Grading of Recommendations Assessment, Development, and Evaluation (GRADE). We included the following outcomes: Serum creatinine (mg/dl), Urine output (ml/24 h), Mean arterial pressure (mmHg), Reversal rate of HRS, Mortality rate, Serum sodium (mEq/l), Blood plasma renin activity (ng/ml/h), plasma aldosterone concentration (pg/ml), urine sodium (mEq/l), and creatinine clearance (ml/min).

RESULTS

Our analysis of nine clinical studies revealed that the noradrenaline group was linked to a considerable decrease in serum sodium (MD = 1.38 [0.16, 2.60]), (P = 0.03) and increased creatinine clearance that is noticeable (MD = 4.22 [0.40, 8.05]), (P = 0.03). There were no significant differences in serum creatinine levels (MD = 0.03 [-0.07, 0.13]), urinary sodium (MD = -1.02 [-5.15, 3.11]), urine output (MD = 32.75 [-93.94, 159.44]), mean arterial pressure (MD = 1.40 [-1.17, 3.96]), plasma renin activity (MD = 1.35 [-0.17, 2.87]), plasma aldosterone concentration (MD = 55.35 [-24.59, 135.29]), reversal rate of HRS (RR = 1.15 [0.96, 1.37]), or mortality rate (RR = 0.87 [0.74, 1.01]) between the two groups (p-values > 0.05).

CONCLUSIONS

Noradrenaline is a safe and comparable therapy to terlipressin for HRS.

Development of low-cost and high-efficiency solar modules based on perovskite solar cells for large-scale applications

Solar energy has emerged as a viable and competitive renewable resource due to its abundance and cost-effectiveness. To meet the global energy demands, there is a growing need for efficient devices with unique compositions. In this study, we designed and analyzed a perovskite solar cell (PSC) incorporating methylammonium tin iodide (CH3NH3SnI3) as the active optical absorber material, tin iodide (SnO2) as the electron transport layer (ETL), and copper thiocyanate (CuSCN) as the hole transport layer (HTL) using SCAPS-1D software for numerical investigations. Subsequently, the optimized outcomes were implemented in the PVSyst software package to derive the characteristics of a solar module based on the proposed novel solar cell composition. The objective of our research was to enhance the stability of solar cell for use in solar module. This was achieved by optimizing the thicknesses of the compositional layers which resulted in the enhancement of excess electron and hole mobilities and a reduction in defect densities, thereby leading to an improvement in the device performance. The optimization of excess electron and hole mobilities, as well as defect densities, was conducted to improve the device performance. SCAPS calculations indicated that the perovskite absorber layer (CH3NH3SnI3) may achieve the best possible performance with a maximum optimized thickness of 3.2 μm. The optimized thickness value for CuSCN-HTL and SnO2-ETL were found to be 0.07 μm and 0.05 μm respectively resulting in a maximum power conversion efficiency (PCE) of 23.57%. Variations in open circuit voltage (Voc), short circuit current (Jsc), fill factor (FF %), and quantum efficiency (QE) associated with the optimized thickness values of all layers in the ITO/SnO2/CH3NH3SnI3/CuSCN/Mo composition were critically analyzed. The use of these input parameters resulted in power creation of 557.4 W for a module consisting of 72 cells with an annual performance ratio of 80.3%. These recent investigations are expected to be effective in the design and fabrication of eco-friendly and high-performance solar cells in terms of efficiency.

Gold nanoparticle-based selective and efficient spectrophotometric assay for the insecticide methamidophos

A reliable, rapid, and inexpensive nano-sized chemosensor is presented for methamidophos (MET) - an insecticide. Poly(lactic acid) (PLA)-stabilized gold nanoparticles (AuNPs) were synthesized by a simple one-pot, two-phase chemical reduction method. The synthesized PLA-AuNPs were subsequently employed for selective, efficient, and quantitative detection of MET. MET is one of the highly toxic pesticides used for eradication of agricultural and urban insects. Upon the addition of MET, the wine-red color of PLA-AuNPs swiftly transformed into greyish-blue, further corroborated by a significant bathochromic and hyperchromic shift in the SPR band. The presence of other interfering insecticides, metal salts, and drugs did not have any pronounced effect on quantitative MET detection. The detection limit, the quantification limit, and linear dynamic range of MET utilizing PLA-AuNPs were  0.0027 µM, 0.005 µM, and 0.005-1000 µM, respectively. The PLA-AuNP-based assay renders an efficient, rapid, accurate, and selective quantification of MET in food, biological, and environmental samples. The proposed sensor provides an appropriate platform for fast and on-the-spot determination of MET without requiring a well-equipped lab setup.

Technostress, Quality of Work Life, and Job Performance: A Moderated Mediation Model

This study examines the effect of technostress on teachers' quality of work life and job performance. A moderated mediation model is proposed and tested based on the transactional model of stress and coping. This study proposes organizational flexibility as the boundary condition-a first-level moderator-and quality of work life as the explanatory variable. A sample of 199 university teachers who worked from home or used the hybrid teaching mode was selected. Data were collected through closed-ended questionnaires. Structural equation modeling (SEM) and the Hayes PROCESS Macro (extension in SPSS) were used for hypothesis testing. The results found that the three dimensions of technostress (Techno complexity, Techno invasion, and Techno overload) negatively and significantly affect teachers' quality of work life. However, there are significant positive direct effects of these three dimensions of technostress on employee performance and significant negative indirect effects on performance through quality of work life. Organizational flexibility acts as a significant moderator, where a low value of organizational flexibility enhances the negative relationship between technostress and quality of work life. In contrast, high values of organizational flexibility convert the significant negative relationship into an insignificant impact. The university management must take measures to overcome technostress among teachers by showing flexibility.

Enhancement in photocatalytic selectivity of TiO2-based nano-catalyst through molecular imprinting technology

Improvement in the photocatalytic selectivity is imperative for the effective and efficient utilization of catalysts. In this study, a molecularly imprinted polymer-coated iron-doped titanium dioxide (Fe-TiO2@MIP) nanocomposite was successfully synthesized by precipitation polymerization while using RB-19 as a template. The synthesized nanocomposites (Fe-TiO2@MIP and Fe-TiO2@NIP) were characterized by Fourier transform infrared spectroscopy (FT-IR), atomic force microscopy (AFM), field emission scanning electron microscopy (FESEM) with energy dispersive X-ray (EDX), X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), Brunauer-EMMETT-Teller (BET), and UV-visible spectrophotometry. The optimized binding experiments revealed a high imprinting factor of 5.0 for RB-19. The catalytic degradation efficiency and selectivity of Fe-TiO2@MIP enhanced to almost complete degradation of RB-19 from 70% for the parent Fe-TiO2 and 76% for Fe-TiO2@NIP. An outstanding degradation selectivity of RB-19 was achieved compared to other competitive dyes. Finally, the analysis of the non-degraded and degraded RB-19 by ESI-MS revealed the presence of different intermediates that fits well with the proposed degradation mechanism. The study opens new possibilities of selective photo-degradation of targeted contaminants that may ultimately lead to efficient use of photocatalysts.

Novel epoxy-terminated macromonomers and their polymerization for synthesis of bottle-brush type amphiphilic block copolymers

Architecture of polymers has vital implications for their physical properties and applications. In this study, synthesis of a series of novel epoxy-terminated macromonomers namely Ep-DEGMME, Ep-TEGMME, Ep-EGMEE, Ep-EGMBE, and Ep-EGMHE is reported. The synthesized macromonomers vary in number of ethylene oxide units and length of the alkyl group. These macromonomers are first homopolymerized by anionic ring-opening polymerization for synthesis of homopolymers of a molar mass range. Subsequently, these macromonomers with different lengths of two segments (alkyl group and ethylene oxide units) are copolymerized with other monomers for synthesis of bottle-brush type architectures. In the first case, di- and tri-block copolymers of Ep-EGMBE are synthesized while using MeO-PEG or PEG as a macroinitiator; the resulting block copolymers have hydrophilic handle and hydrophobic brush. On the same lines, block copolymers of Ep-TEGMME with ε-caprolactone have hydrophobic handle and hydrophilic brush. The synthesized block copolymers are comprehensively characterized by SEC and liquid chromatography at critical conditions. The analysis reveals the successful synthesis of block copolymers while providing information on relative total molar mass, and individual block lengths of the block copolymers, along with amount of unwanted homopolymers in the sample.

Fecal Microbiota Transplantation in Human Immunodeficiency Virus-Infected Patient Population: A Systematic Review and Meta-Analysis

Background

Patients with human immunodeficiency virus (HIV) infection suffer from alterations in gut microbiota due to recurrent gastrointestinal infections and systemic inflammation. Fecal microbiota transplantation (FMT) appears to be a potential therapy; however, there are concerns about its safety. Likewise, no previous meta-analysis evaluated FMT efficacy in HIV-infected patients.

Methods

We conducted a thorough electronic search on PubMed, Scopus, OVID, Web of Science, and Cochrane CENTRAL for clinical studies assessing the safety and efficacy of FMT in patients with HIV and gastrointestinal dysbiosis, where FMT was indicated to restore the disrupted microbiota.

Results

FMT significantly restored the typical microbiome in patients with Clostridium difficile (C. difficile) and non-C. difficile and reduced the risk of gastrointestinal infections in HIV patients receiving antiretroviral therapy (odds ratio (OR) = 0.774, 95% confidence interval (CI): (0.62, 0.966)). Furthermore, adverse events, such as distention and bloating, associated with FMT were comparable between HIV and health controls (OR = 0.60, 95% CI: (0.07, 4.6)), with no statistical difference.

Conclusions

Current evidence demonstrated that FMT is safe and effective in HIV patients suffering from alterations in gut microbiota. We recommend further multi-centric clinical studies to address the optimal transplant amount and source for FMT. To the best of our knowledge, this is the first meta-analysis to assess the safety and efficacy of FMT in patients with HIV.

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.

Exposure to Terrorism, Post-Traumatic Stress Disorder and University Teachers' Performance: Underpinning the Role of Social Support

This study aims to investigate the impact of exposure to terrorism on post-traumatic stress disorder and employee performance, and to determine whether social support acts as a boundary condition that can attenuate the adverse effects of PTSD on employee performance. The study used a cross-sectional sample of 178 university teachers who had experienced a terrorist attack. Data was collected using closed-ended questionnaires and analyzed using PROCESS Macro). The results found a negative and significant relationship between exposure to terrorism, post-traumatic disorder, and employees' performance. Moreover, it was also found that social support helps attenuate the negative impact of PTSD on performance levels. This study adds to the existing body of knowledge by exploring the relationship between terrorism exposure, PTSD, employee performance, and the potential mitigating effects of social support.

Experimental and numerical investigations on feasibility of inorganic KSnCl3 perovskite absorber and SWCNT-HTL for solar cells

Inorganic metal halide perovskite materials have attracted remarkable attention as light harvesters because of their promising optoelectronic merits and photovoltaic features like tunable band gaps, high charge carrier mobilities and greater absorption coefficients. In order to explore new inorganic perovskite materials for use in optoelectronic devices Potassium Tin Chloride (KSnCl₃) has been experimentally synthesized using a supersaturated recrystallization technique at ambient conditions. The resultant nanoparticle (NP) specimens were analyzed for optical and structural properties by characteristic available techniques including scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and UV visible spectroscopy. Experimental investigations about structure reveals that KSnCl₃ crystallizes in orthorhombic phase with particle size of 400-500 nm. SEM showed better crystallization and EDX confirmed the accurate structural composition. UV-Visible analysis indicated a prominent absorption peak at 504 nm, and the band gap is 2.70 eV. Theoretical investigations of KSnCl₃ were also carried out via AB-initio calculations in Wein2k simulation program using modified Becke Johnson (mBJ) and generalized gradient approximations (GGA). Optical properties like extinction coefficient k (ω), complex parts of dielectric constant (ε ₁, ε ₂), reflectivity R (ω), refractive index n (ω), optical conductivity L (ω) and absorption coefficient α (ω) were examined and it was observed that _. theoretical investigations were consistent with experimental findings. Incorporation of KSnCl₃ as an absorber material along with single walled carbon nanotubes as p-type materials in (AZO/IGZO/KSnCl₃/CIGS/SWCNT/Au) solar cell configuration have been investigated by SCAPS-1D simulation package. Open circuit voltage (V_oc) of 0.9914 V, short circuit current density (J_sc) of 47.32067 mA/cm² and a remarkable efficiency of 36.823% with has been predicted. Thermally stable KSnCl₃ may become potential source in manufacturing of photovoltaic and optoelectronic applications on large scale.

Efficacy of endoscopic trans-papillary gallbladder stenting and drainage in acute calculous cholecystitis in high-risk patients: a systematic review and meta-analysis

Background and aims

Acute calculous cholecystitis (ACC) represents about one-third of all surgical emergencies. The gold standard management of ACC is laparoscopic cholecystectomy. Although cholecystectomy is a safe procedure, it may be dangerous and contraindicated in patients with complex comorbidities. Endoscopic transpapillary gallbladder stenting (ETGBS) and drainage had been widely used to manage patients suffering from ACC with comorbidities.

Methods

We searched PubMed, SCOPUS, Web of Science, and Cochrane Library for relevant studies assessing the use of ETGBS in patients suffering from ACC with various comorbidities. Risk of bias assessment was performed using the National Institues of Health (NIH) tool. We included the following outcomes: clinical success, technical success, late complications, and pancreatitis.

Results

We included seven studies that met our inclusion criteria. We found that the pooled proportion of clinical success, technical success, late complications, and pancreatitis was [91.3%, 95% confidence interval (CI) (86.8%, 95.9%)], [92.8%, 95% CI (89%, 96.5%)], [5.4%, 95% CI (2.9%, 7.9%)], and [3.5%, 95% CI (1.2%, 5.8%)], respectively.

Conclusion

We found that an ETGBS was an effective and well-tolerated method for the treatment of cholecystitis, especially in high-risk individuals.

Workplace Incivility and Employee Performance: Does Trust in Supervisors Matter? (A Dual Theory Perspective)

Employee performance is the backbone of achieving competitiveness and sustainability. This study aims to examine the impact of workplace incivility on employee performance. In addition, trust in supervisors is examined as a mediator. The conservation of resources (COR) theory and Maslow's hierarchy of needs theory provided the grounds for developing the framework. The data were collected through closed-ended questionnaires and were analyzed using structural equation modeling with SmartPLS. The results affirmed that incivility is harmful to the performance of employees, and that trust in supervisors helps employees to perform well. The trust in the supervisor significantly mediates the incivility-performance relationship. The examination of the proposed model through the lens of two theories as well as the study of low-intensity deviant workplace behavior in a collectivist and developing economy are the contributions of this study to the growing body of literature. However, the use of a single sector was one of the limitations of this study.

Fluorene intercalated graphene oxide based CoQ10 imprinted polymer composite as a selective platform for electrochemical sensing of CoQ10

The new objective of sustainable analytical chemistry is to develop validated robust, swift, simple and highly sensitive analytical methods that are based on cost effective sensing technology. Therefore, in this study the electro-chemical detection of coenzyme Q10 (CoQ10) was achieved using a fluorene intercalated graphene oxide based CoQ10 imprinted polymer composite modified glassy carbon electrode (CoQ10-IGOPC/GCE). The synthesized sensing material was characterized using SEM, XRD and FT-IR to determine the morphology and functional properties. The CoQ10-IGOPC/GCE was characterized by EIS for its electrochemical properties. CoQ10 was detected selectively using Differential Pulse Voltammetry (DPV). Under ideal circumstances, a linear calibration curve with a correlation coefficient (R ²) of 0.991 was produced in the concentration range of 0.0967 to 28.7 μM. The limit of detection (LOD) and limit of quantification (LOQ) were found to be 0.029 and 0.0967 μM, respectively. Furthermore, the proposed electrochemical sensor was extremely selective, accurate and thoroughly validated with RSD values less than 5%. The developed CoQ10-IGOPC/GCE based electrochemical sensor was successfully used for the detection of CoQ10 in samples of fruits, vegetables, nuts, human blood serum and pharmaceuticals. The CoQ10-IGOPC/GCE based electrochemical method showed good percent recoveries ranging from 94 to 103 percent.

Safety Management and Safety Performance Nexus: Role of Safety Consciousness, Safety Climate, and Responsible Leadership

Drawing from social system theory, social identity theory, and social exchange theory, this study examines how safety management practices are linked with employee safety performance through safety consciousness and safety climate. Furthermore, responsible leadership is introduced as a boundary condition in the safety consciousness-safety performance and safety climate-safety performance relationships. Data were collected from employees belonging to pharmaceutical firms located in different industrial zones of Lahore, Pakistan. The support is found for full mediation of safety consciousness and safety climate for the safety management and safety performance relationships. Responsible leadership moderates the safety consciousness-safety performance and safety climate-safety performance relationships so that when the safety climate is weak or the safety consciousness is low, a high level of responsible leadership enhances safety performance.

ADH-PPI: An Attention based Deep Hybrid Model for Protein Protein Interaction Prediction

Protein-protein interaction (PPI) prediction is essential to understand the functions of proteins in various biological processes and their roles in the development, progression, and treatment of different diseases. To perform economical large-scale PPI analysis, several artificial intelligence-based approaches have been proposed. However, these approaches have limited predictive performance due to the use of in-effective statistical representation learning methods and predictors that lack the ability to extract comprehensive discriminative features. The paper in hand generates statistical representation of protein sequences by applying transfer learning in an unsupervised manner using FastText embedding generation approach. Furthermore, it presents “ADH-PPI” classifier which reaps the benefits of three different neural layers, long short-term memory, convolutional, and self-attention layers. Over two different species benchmark datasets, proposed ADH-PPI predictor outperforms existing approaches by an overall accuracy of 4%, and matthews correlation coefficient of 6%. In addition, it achieves an overall accuracy increment of 7% on four independent test sets. Availability: ADH-PPI web server is publicly available at https://sds_genetic_analysis.opendfki.de/PPI/

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Protein sequences representation generation through unsupervised transfer learning

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A unique paradigm for sequence fixed length generation

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Development of a robust, precise, and interpretable classifier

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Development of a public web server to predict protein-protein interactions on the go

Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.

Evaluation of the effect of probiotics on septic complications in patients with severe acute pancreatitis. A systematic review and meta-analysis

Introduction

Severe acute pancreatitis (SAP) with major complications such as necrosis and multiple organ dysfunction syndrome (MODS) often leads to high mortality rates despite intensive treatment.

Aim

To evaluate the effect of symbiotics (probiotics) on septic complications in patients with SAP.

Material and methods

We searched the PubMed, Cochrane CENTRAL, SCOPUS, and Web of Science databases for relevant clinical trials and excluded observational studies. Quality appraisal was evaluated according to GRADE, and we assessed the risk of bias using Cochrane's risk of bias tool. We included the following outcomes: C-reactive protein (CRP), APACHE II score, hospital stay, multiorgan failure (MOF), systemic inflammatory response syndrome, infected pancreatic necrosis, septicaemia, need for operation, and death. We performed the analysis of homogeneous data under a fixed-effects model, while analysis of heterogeneous data were analysed under a random-effects model. We performed the analysis of dichotomous outcomes using the risk ratio (RR) and relative 95% confidence interval (CI).

Results

We included a total of 7 clinical trials. We found that there was no significant difference between both groups regarding MOF (RR = 0.60 (0.25, 1.44), p = 0.26), septicaemia (RR = 0.66 (0.29, 1.50), p = 0.32), death (RR = 0.66 (0.19, 2.26), p = 0.51), infected pancreatic necrosis (RR = 0.50 (0.18, 1.38), p = 0.18), SIRS (RR = 0.81 (0.29, 2.23), p = 0.68), CRP, APACHE II score, and hospital stay.

Conclusions

Contrary to some published trials, our meta-analysis concludes that the use of probiotics in patients with SAP is not effective in reducing the mortality rate, septic complications, and need for operation.

Histone-Net: a multi-paradigm computational framework for histone occupancy and modification prediction

Deep exploration of histone occupancy and covalent post-translational modifications (e.g., acetylation, methylation) is essential to decode gene expression regulation, chromosome packaging, DNA damage, and transcriptional activation. Existing computational approaches are unable to precisely predict histone occupancy and modifications mainly due to the use of sub-optimal statistical representation of histone sequences. For the establishment of an improved histone occupancy and modification landscape for multiple histone markers, the paper in hand presents an end-to-end computational multi-paradigm framework “Histone-Net”. To learn local and global residue context aware sequence representation, Histone-Net generates unsupervised higher order residue embeddings (DNA2Vec) and presents a different application of language modelling, where it encapsulates histone occupancy and modification information while generating higher order residue embeddings (SuperDNA2Vec) in a supervised manner. We perform an intrinsic and extrinsic evaluation of both presented distributed representation learning schemes. A comprehensive empirical evaluation of Histone-Net over ten benchmark histone markers data sets for three different histone sequence analysis tasks indicates that SuperDNA2Vec sequence representation and softmax classifier-based approach outperforms state-of-the-art approach by an average accuracy of 7%. To eliminate the overhead of training separate binary classifiers for all ten histone markers, Histone-Net is evaluated in multi-label classification paradigm, where it produces decent performance for simultaneous prediction of histone occupancy, acetylation, and methylation.

LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework “LGCA-VHPPI” is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets.

Estimating the optimum number of colon biopsies for diagnosing microscopic colitis: a systematic review

Microscopic colitis (MC) is an inflammatory disease of the colon characterized by persistent watery, nonbloody diarrhea. Subtypes of MC include collagenous and lymphocytic MC. Microscopic examination of colon tissue is crucial to confirming the diagnosis because the colonic mucosa often appears normal during flexible sigmoidoscopy or colonoscopy. We aim to determine the optimal sites and minimum number of colon biopsies required to diagnose MC from published studies. We systematically searched PubMed, Web of Science, Scopus, and Cochrane databases from inception until October 2020 using the following keywords: microscopic, lymphocytic, collagenous, colitis, biopsy, and biopsies. We screened the search results for eligibility and extracted data from the included studies. We pooled the numbers of biopsies provided by each study to calculate the mean number of biopsies, SD, and SEM. We included three retrospective cohort studies with 356 patients (148 collagenous, 192 lymphocytic, and 16 mixed), and the total number of biopsies were 1854. The mean number of biopsies that were recommended by the included studies are 4, 4, and 9, respectively. The pooled mean ± SD is 5.67 ± 2.89. The included studies reported that biopsies from the ascending colon (AC) and descending colon (DC) had the highest diagnostic rates. To ensure a high level of certainty in diagnosing MC, a total of six biopsies should be taken from the AC and DC (3 AC and 3 DC). However, special care should be directed toward differentiating MC from other forms of colitis. In addition, detailed and comparative studies are needed to provide stronger recommendations to diagnose MC.

ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions

BACKGROUND AND OBJECTIVES

One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although commonly used eXplainable AI (XAI) methods provide insights into these largely opaque algorithms, such explanations are usually convoluted and not readily comprehensible. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is ambiguous in itself. This work presents ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-to-understand textual explanations and visual maps, to justify the predictions.

METHODS

Our framework relies on Concept Activation Vectors to map human-understandable concepts to those learned by an arbitrary Deep Learning (DL) based algorithm, and Concept Localisation Maps to highlight those concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All decision-related information is presented in a diagnostic interface for use in clinical routines. Moreover, the framework includes an educational mode providing dataset-level explanation statistics as well as tools for data and model exploration to aid medical research and education processes.

RESULTS

Through rigorous quantitative and qualitative evaluation of our framework on a range of publicly available dermoscopic image datasets, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong disease predictions. We demonstrate that concept detectors for the explanation of pre-trained networks reach accuracies of up to 81.46%, which is comparable to supervised networks trained end-to-end.

CONCLUSIONS

We present a new end-to-end framework for the multi-modal explanation of DL-based biomedical image analysis in Melanoma classification and evaluate its utility on an array of datasets. Since perspicuous explanation is one of the cornerstones of any CAD system, we believe that ExAID will accelerate the transition from AI research to practice by providing dermatologists and researchers with an effective tool that they can both understand and trust. ExAID can also serve as the basis for similar applications in other biomedical fields.

Bacterial prediction using internet of things (IoT) and machine learning

Water is a basic and primary resource which is required for sustenance of life on the Earth. The importance of water quality is increasing with the ascending water pollution owing to industrialization and depletion of fresh water sources. The countries having low control on reducing water pollution are likely to retain poor public health. Additionally, the methods being used in most developing countries are not effective and are based more on human intervention than on technological and automated solutions. Typically, most of the water samples and related data are monitored and tested in laboratories, which eventually consumes time and effort at the expense of producing fewer reliable results. In view of the above, there is an imperative need to devise a proper and systematic system to regularly monitor and manage the quality of water resources to arrest the related issues. Towards such ends, Internet of Things (IoT) is a great alternative to such traditional approaches which are complex and ineffective and it allows taking remote measurements in real-time with minimal human involvement. The proposed system consists of various water quality measuring nodes encompassing various sensors including dissolved oxygen, turbidity, pH level, water temperature, and total dissolved solids. These sensors nodes deployed at various sites of the study area transmit data to the server for processing and analysis using GSM modules. The data collected over months is used for water quality classification using water quality indices and for bacterial prediction by employing machine learning algorithms. For data visualization, a Web portal is developed which consists of a dashboard of Web services to display the heat maps and other related info-graphics. The real-time water quality data is collected using IoT nodes and the historic data is acquired from the Rawal Lake Filtration Plant. Several machine learning algorithms including neural networks (NN), convolutional neural networks (CNN), ridge regression (RR), support vector machines (SVM), decision tree regression (DTR), Bayesian regression (BR), and an ensemble of all models are trained for fecal coliform bacterial prediction, where SVM and Bayesian regression models have shown the optimal performance with mean squared error (MSE) of 0.35575 and 0.39566 respectively. The proposed system provides an alternative and more convenient solution for bacterial prediction, which otherwise is done manually in labs and is an expensive and time-consuming approach. In addition to this, it offers several other advantages including remote monitoring, ease of scalability, real-time status of water quality, and a portable hardware.

Fabrication of inverted organic solar cells on stainless steel substrate with electrodeposited and spin coated ZnO buffer layers

Polymer based organic solar cells (OSCs) are of tremendous interest as suitable candidates for producing clean and renewable energy in recent years. In this study, inverted OSCs on stainless steel (SS) substrate with zinc oxide (ZnO) as the electron selective transport layer (ESTL), are investigated, occupying bulk heterojunction blend of regioregular poly(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM) as the active material and poly-(4,3-ethylene dioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) as the hole transport layer (HTL). The device structure is SS/ZnO/P3HT:PCBM/PEDOT:PSS/Au. ZnO films are prepared by spin coating and electrodeposition techniques, followed by annealing under ambient conditions. The insertion of ZnO layer between the SS substrate and active layer has improved short-circuit current (J sc), open-circuit voltage (V oc), fill factor (FF), and power conversion efficiency (PCE) compared to those of the reference cell without ZnO layer, achieving the highest efficiency of 0.66% for the device with spin coated ZnO from sol–gel technique. This enhancement can be attributed to the effective electron extraction and the increased crystallinity of ZnO after annealing treatments at higher temperatures as further confirmed by X-ray diffraction (XRD) and scanning electron microscope (SEM) analyses.

Technostress and Employee Performance Nexus During COVID-19: Training and Creative Self-Efficacy as Moderators

Technostress, a stressor, has implications for employee’s psychological states; however, flexibility like work from home can have positive outcomes, especially for instructors who have to teach and ensure social distance during COVID-19. The present study examined the relationship of technostress and employee performance while taking training and creative self-efficacy as boundary conditions. A sample of 222 university instructors, who worked from home or hybrid (home and workplace) during COVID-19 lockdown, was selected from Pakistan. The responses were recorded using a closed-ended questionnaire. Stepwise linear regression and PROCESS Macro by Hayes (2013) was used to analyze the data. It was revealed that technostress, instead of having adverse effects, had a positive effect on employee’s performance and both training and one’s creative self-efficacy significantly moderated the relationship. As the main finding, it was revealed that the employees continued to perform well despite the prevalence of technostress. The training and one’s creative self-efficacy were useful to control the technostress and maintain the performance of instructors during COVID-19. The university administrators and employees must take technology as a positive tool for performance. The training, along with creative self-efficacy, adds to the working capacity of employees and enhances their performance.

Work Stress Hampering Employee Performance During COVID-19: Is Safety Culture Needed?

During uncertain situations, such as the COVID-19 partial lockdown, maintaining satisfactory levels of employee performance (EP) is an important area of concern for many organizations. The current study examines the relationship of work stress due to COVID-19 (COVID-19 STR) and EP. Using social exchange theory (SET), safety culture (SC) is presented as a moderator for stress and performance relationships. A sample of 213 bank employees was collected using a convenient sampling method. Data were analyzed using stepwise linear regression and PROCESS Macro by Hayes (2013). Results revealed that COVID-19 STR has a negative impact on task and contextual performance (CP) and a positive impact on adaptive performance (AP). Similarly, the prevalence of SC significantly moderates the stress and performance relationships.

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

Colorimetric sensing of cephradine through polypropylene glycol functionalized gold nanoparticles

The development of metal nanoparticle-based facile colorimetric assays for drugs and insecticides is an emerging area of current scientific research. In the present work, polypropylene glycol was used for stabilization of gold nanoparticles (AuNPs) in a simple one-pot two-phase process and subsequently employed it for the specific detection of cephradine (CPH). The characterization of the prepared PPG-AuNPs was conducted through various analytical techniques such as UV-visible spectrophotometry, Fourier transform infrared spectroscopy, atomic force microscopy (AFM), zeta potential and zetasizer techniques. As the major target of the study, the stabilized PPG-AuNPs were employed for colorimetric detection of CPH and other drugs. Typical wine-red colour of PPG-AuNPs disappeared immediately and surface plasmon resonance band quenched by addition of CPH in the presence of several other interferents (drugs and salts) and in real samples. PPG-AuNPs permitted efficient, selective, reliable and rapid determination in a concentration range of 0.01-120 mM with a detection limit (LoD) of 11.0 mM. The developed sensor has the potential to be used for fast scanning of pharmaceutical formulations for quantification of CPH at production facilities.

Sonochemical synthesis of Co3O4 nanoparticles deposited on GO sheets and their potential application as a nanofiller in MMMs for O2/N2 separation

In this study we report an environmentally friendly, facile and straightforward sonochemical synthetic strategy for a Co₃O₄/GO nanocomposite using N,N′-bis(salicylidene)ethylenediaminocobalt(ii) as a precursor and graphene oxide sheets as an immobilization support for Co₃O₄ nanoparticles. The synthesis was facilitated by physical and chemical effects of cavitation bubbles. The synthesized nanocomposite was thoroughly characterized for its composition and morphology using Fourier transform infrared spectroscopy (FTIR), Energy dispersive X-ray spectroscopy (EDS), Scanning electron microscopy (SEM), UV-visible, Raman and X-ray diffraction spectroscopy (XRD), etc. The results show Co₃O₄ nanoparticles of 10 nm (SD 3 nm) were prepared on well exfoliated sheets of GO. The applicability of the synthesized Co₃O₄/GO nanocomposite was optimized as a nanofiller for mixed matrix membranes (MMMs) comprised of poly(2-acrylamido-2-methyl-1-propanesulfonic acid) and polyvinyl chloride. The affinity of the prepared MMMs was evaluated for the separation of O₂/N₂ gases by varying the concentration of nanofiller, i.e. 0.03%, 0.04%, 0.05% and 0.075% (w/v). The results display high separation performance for O₂/N₂ gases with excellent permeance (N₂ 167 GPU and O₂ 432 GPU at 1 bar) and O₂/N₂ selectivity of 2.58, when the MMMs were loaded with 0.05% (w/v) of Co₃O₄/GO nanocomposite.

Sonochemical synthesis of Co₃O₄/GO nanocomposite.

Enhancement in the antibacterial activity of cephalexin by its delivery through star-shaped poly(ε-caprolactone)-block-poly(ethylene oxide) coated silver nanoparticles

The antibacterial activity of silver nanoparticles (AgNPs) stabilized with a four-armed star-shaped poly(ε-caprolactone)-block-poly(ethylene oxide) copolymer [St-P(CL-b-EO)] and its application as a drug delivery vehicle for cephalexin (Cp) was evaluated against pathogenic Staphylococcus aureus. The prepared AgNPs were characterized by ultraviolet-visible spectroscopy, Fourier transform infrared spectroscopy, zeta sizer and atomic force microscopy (AFM). The antibacterial efficiency of Cp is enhanced several-fold by its delivery through complexation with St-P(CL-b-EO)-AgNPs, monitored by microplate assay and biofilm destruction studies. Finally, the visual destruction of bacterial cells and its biofilms by employing Cp and its conjugates at their minimum inhibitory concentration (MIC50) and minimum biofilm inhibitory concentration (MBIC50), respectively, is observed by topographic imaging by AFM. Enhanced antibacterial activity of St-P(CL-b-EO)-AgNPs loaded Cp is attributed to penetrative nature of the drug cargo St-P(CL-b-EO)-AgNPs towards the bacterial cell wall.

Olmesartan-Induced Spruelike Enteropathy: An Emerging Cause of Small Bowel Injury

Olmesartan-induced spruelike enteropathy is a rare clinical entity that is characterized by unexplained chronic diarrhea and weight loss. Prompt recognition of this adverse event may be challenging due to clinical and histologic similarities with other small intestinal pathologies. We hereby delineate the case of an elderly female with a 14-month clinical history of non-bloody diarrhea and weight loss. After extensive diagnostic workup and exclusion of probable etiologies, the patient was diagnosed with olmesartan-associated enteropathy. A dramatic clinical and histologic recuperation was achieved after discontinuation of olmesartan. This paper illustrates the overarching need for a detailed clinical history focusing on medication review in patients presenting with chronic diarrhea with no obvious cause. The spruelike enteropathy associated with olmesartan is an emerging cause of small bowel injury. Clinicians should maintain a high index of suspicion for this adverse drug reaction. Early and correct diagnosis carries paramount importance in sparing these patients from unnecessary diagnostic investigations and therapeutic delays.

Critical parameters of liquid chromatography at critical conditions in context of poloxamers: Pore diameter, mobile phase composition, temperature and gradients

At the borderline between size exclusion chromatography (SEC) and interaction chromatography (IC) there is a special mobile phase composition and temperature at which polymer chains become "chromatographically invisible". This point is termed as "chromatographic critical point" and chromatographic separations performed using these conditions are called "liquid chromatography at critical conditions" (LCCC). LCCC is a powerful technique in the analysis of functional polymers and block copolymers. At these so-called critical conditions molar mass discrimination of any specific homopolymer is suppressed rendering elution of whole range of molar mass at same elution volume. These conditions allow enhanced separation with regard to non-critical segment either in exclusion or interaction regime of the polymer chromatography. This article is intended to critically discuss different parameters that can be maneuvered to improve separation and in turn characterization of non-critical segment of block copolymers at LCCC. Different experimental parameters evaluated in this study include pore size of the column, mobile phase composition, temperature and gradients. These parameters can be adeptly adjusted to improve separation of non-critical segment while keeping the other segment close to critical conditions. Current study demonstrates that pore diameter and mobile phase are the only practical variable that can be used for improvement of characterization of non-critical block in the block copolymer while non-critical block is in exclusion regime. On the other hand, pore diameter of the column, temperature, solvent composition and gradients are important parameters that can be skillfully tuned for improvement of separation of non-critical block while non-critical block elutes in interaction regime. The above-mentioned variations are evaluated for di-block as well as tri-block copolymers of A-B-A and B-A-B type. Moreover, LCCC-IC is especially important for analysis of poloxamers.

Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

Background

With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous.

Methods

The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization.

Results

The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset.

Conclusion

Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier’s performance and calls for additional performance metrics to substantiate the results.

Recent Trends in Fast Liquid Chromatography for Pharmaceutical Analysis

Background:

Liquid chromatography is the workhorse of analytical laboratories of pharmaceutical companies for analysis of bulk drug materials, intermediates, drug products, impurities and degradation products. This efficient technique is impeded by its long and tedious analysis procedures. Continuous efforts of scientists to reduce the analysis time resulted in the development of three different approaches namely, HTLC, chromatography using monolithic columns and UHPLC.

Methods:

Modern column technology and advances in chromatographic stationary phase including silica-based monolithic columns and reduction in particle and column size (UHPLC) have not only revolutionized the separation power of chromatographic analysis but also have remarkably reduced the analysis time. Automated ultra high-performance chromatographic systems equipped with state-ofthe- art software and detection systems have now spawned a new field of analysis, termed as Fast Liquid Chromatography (FLC). The chromatographic approaches that can be included in FLC are hightemperature liquid chromatography, chromatography using monolithic column, and ultrahigh performance liquid chromatography.

Results:

This review summarizes the progress of FLC in pharmaceutical analysis during the period from year 2008 to 2017 focusing on detecting pharmaceutical drugs in various matrices, characterizing active compounds of natural products, and drug metabolites. High temperature, change in the mobile phase, use of monolithic columns, new non-porous, semi-porous and fully porous reduced particle size of/less than 3μm packed columns technology with high-pressure pumps have been extensively studied and successively applied to real samples. These factors revolutionized the fast high-performance separations.

Conclusion:

Taking into account the recent development in fast liquid chromatography approaches, future trends can be clearly predicated. UHPLC must be the most popular approach followed by the use of monolithic columns. Use of high temperatures during analysis is not a feasible approach especially for pharmaceutical analysis due to thermosensitive nature of analytes.

Superior Magnetoresistance Performance of Hybrid Graphene Foam/Metal Sulfide Nanocrystal Devices

Interfaces between metals and semiconducting materials can inevitably influence the magnetotransport properties, which are crucial for technological applications ranging from magnetic sensing to storage devices. By taking advantage of this, a metallic graphene foam is integrated with semiconducting copper-based metal sulfide nanocrystals, i.e., Cu₂ZnSnS₄ (copper-zinc-tin-sulfur) without direct chemical bonding and structural damage, which creates numerous nanoboundaries that can be basically used to tune the magnetotransport properties. Herein, the magnetoresistance of a graphene foam is enhanced from nearly 90 to 130% at room temperature and under the application of 5 T magnetic field strength due to the addition of Cu₂ZnSnS₄ nanocrystals in high densities. We believe that the enhancement of magnetoresistance in hybrid graphene foam/Cu₂ZnSnS₄ nanocrystals is due to the evolution of the mobility fluctuation mechanism, triggered by the formation of nanoboundaries. Incorporating Cu₂ZnSnS₄ nanocrystals into a graphene foam not only provides an effective way to further enhance the magnitude of magnetoresistance but also opens a suitable window to achieve efficient and highly functional magnetic sensors with a large, linear, and controllable response.

Synthesis, antimicrobial, antioxidant, cytotoxic, antiurease and molecular docking studies of N-(3-trifluoromethyl)benzoyl-N'-aryl thiourea derivatives

An irrefutable advancement has been noted for the infectious diseases caused due to ureolytic bacteria through the development of various drugs. Keeping in mind the extremely valuable synthetic utility and medicinal significance of thiourea derivatives, synthesis of new 3-trifluoromethyl benzoic acid thiourea derivatives (3a-j) were carried out. The biological potential of all compounds in terms of antimicrobial, antioxidant, cytotoxic and antiurease activities were studied. The compounds 3a, 3c and 3i with dichloro and methoxy groups substitution on the aryl group showed significant activity against all strain of bacteria while moderate to no activity was observed in remaining compounds. Whereas the antifungal evaluation showed that all compounds were active againts C. Albican and no activity was observed against C. Prapsilosis. The cytotoxic findings revealed the non-toxic nature of these compounds as IC₅₀ values of majority of the compounds are above 100 μm except for compounds 3f and 3g. In addition, these compounds exhibited better antioxidant potential as 100 μm concentration inhibited >50% reactive oxygen species (ROS) production except compounds 3e, 3f and 3j. The compound 3a proved to be the most potent urease inhibitor showing the highest enzyme % inhibition (93.1%) with IC₅₀ value of 8.17 ± 0.24 µM and found more active as compare to standard followed by compound 3e (92.6%), 3h (91.6%), 3d (90.8%), 3b (90.6%) and 3f (90.0%) with their respective IC₅₀ values. All the synthesized compounds were docked into the binding cavity of Urease (PDB ID: 4ubp). The most active compound 3a was also ranked as top on the docking score as it was found to show valuable interactions with the target protein along with good docking scores. Hence our results revealed that the synthesized compounds have potential to be used as potent urease inhibitors after further detailed mechanistic studies.