QSAR and DFT Studies of Some Tacrine-Hydroxamate Derivatives as Inhibitors of Cholinesterase (AChEs) in the Treatment of Alzheimer's Disease

Introduction: This work was devoted to an in silico investigation conducted on twenty-eight Tacrine-hydroxamate derivatives as a potential treatment for Alzheimer's disease using DFT and QSAR modeling techniques. Method(s): The data set was randomly partitioned into a training set (22 compounds) and a test set (6 compounds). Then, fourteen models were built and were used to compute the predicted pIC50 of compounds belonging to the test set. Result(s): Al built models were individualy validated using both internal and external validation methods, including the Y-Randomization test and Golbraikh and Tropsha's model acceptance criteria. Then, one model was selected for its higher R2, R2test, and Q2cv values (R2 = 0.768, R2adj = 0.713, MSE = 0.304, R2test=0.973, Q2cv = 0.615). From these outcomes, the activity of the studied compounds toward the main protease of Cholinesterase (AChEs) seems to be influenced by 4 descriptors, i.e., the total dipole moment of the molecule (mu), number of rotatable bonds (RB), molecular topology radius (MTR) and molecular topology polar surface area (MTPSA). The effect of these descriptors on the activity was studied, in particular, the increase in the total dipole moment and the topological radius of the molecule and the reduction of the rotatable bond and topology polar surface area increase the activity. Conclusion(s): Some newly designed compounds with higher AChEs inhibitory activity have been designed based on the best-proposed QSAR model. In addition, ADMET pharmacokinetic properties were carried out for the proposed compounds, the toxicity results indicate that 7 molecules are nontoxic.Copyright © 2023 Bentham Science Publishers.

Hematopoietic Stem/Progenitor Cells and Engineering: HEMATOPOIETIC STEM/PROGENITOR CELLS (HPC-A) CRYOPRESERVATION PROCESS VALIDATION USING CRYOSTOR CS10 FREEZE MEDIA

Background & Aim: During the COVID-19 pandemic, we performed HPC-A cryopreservation process validation using the CryoStor CS10 freeze media to replace the current 10% DMSO cryoprotectant (Control), which encountered severe backorder. Methods, Results & Conclusion(s): This process validation included phase I, phase II, and follow-up studies. Ten HPC-A collection cell product samples were cryopreserved in the phase I study using CS10 and Control (1:1) post-plasma depletion. Post-thaw viability tests using the 7-AAD method were performed on the cryopreserved samples for parallel comparison. In phase II, each of three patient HPC-A cell products was split evenly into CS10 and Control cryopreservation. The CS10 cryopreserved HPC-A cell products only were used for infusion. The recipients' engraftment outcomes of white blood cells (WBC), granulocytes (ANC), and platelets (Plts) were monitored. Post-thaw viability test was performed on the quality control samples from both groups. In the follow-up study, engraftment outcomes of WBC, ANC, and Plts were evaluated from ten recipients who received the CS10 cryopreserved HPC-A. In the phase I study, the post-thaw viability of the CS10 group was significantly higher than the Control group (p=0.002). All post-thaw viability results were above 60%, the current lab release criteria. In the phase II study, all cryopreserved cell products met cell product release criteria (> 60%). All engraftment results were within our center-established ranges except for the Pt b's platelet engraftment. Three recipients had not had any cell product infusion-related adverse events post infusion. Both CD34 and CD45 post-thaw viability results in the CS10 group were remarkably higher than the Control group, except for the patient c's CD34 viability. In the follow-up study, the total infused cell product volume ranged from 60 ml to 118 ml, and the WBC concentration in the cryopreserved cell products ranged from 134 to 440 (x10

Enhancer deregulation inTET2-mutant clonal hematopoiesis is associated with increased COVID-19 severity and mortality

Background: COVID-19 causes significant morbidity and mortality, albeit with considerable heterogeneity among affected individuals. It remains unclear which host factors determine disease severity and survival. Given the propensity of clonal hematopoiesis (CH) to promote inflammation in healthy individuals, we investigated its effect on COVID-19 outcomes. Method(s): We performed a multi-omics interrogation of the genome, epigenome, transcriptome, and proteome of peripheral blood mononuclear cells from COVID-19 patients (n=227). We obtained clinical data, laboratory studies, and survival outcomes. We determined CH status and TET2-related DNA methylation. We performed single-cell proteogenomics to understand clonal composition in relation to cell phenotype. We interrogated single-cell gene expression in isolation and in conjunction with DNA accessibility. We integrated these multi-omics data to understand the effect of CH on clonal composition, gene expression, methylation of cis-regulatory elements, and lineage commitment in COVID-19 patients. We performed shRNA knockdowns to validate the effect of one candidate transcription factor in myeloid cell lines. Result(s): The presence of CH was strongly associated with COVID-19 severity and all-cause mortality, independent of age (HR 3.48, 95% CI 1.45-8.36, p=0.005). Differential methylation of promoters and enhancers was prevalent in TET2-mutant, but not DNMT3A-mutant CH. TET2- mutant CH was associated with enhanced classical/intermediate monocytosis and single-cell proteogenomics confirmed an enrichment of TET2 mutations in these cell types. We identified celltype specific gene expression changes associated with TET2 mutations in 102,072 single cells (n=34). Single-cell RNA-seq confirmed the skewing of hematopoiesis towards classical and intermediate monocytes and demonstrated the downregulation of EGR1 (a transcription factor important for monocyte differentiation) along with up-regulation of the lncRNA MALAT1 in monocytes. Combined scRNA-/scATAC-seq in 43,160 single cells (n=18) confirmed the skewing of hematopoiesis and up-regulation of MALAT1 in monocytes along with decreased accessibility of EGR1 motifs in known cis-regulatory elements. Using myeloid cell lines for functional validation, shRNA knockdowns of EGR1 confirmed the up-regulation of MALAT1 (in comparison to wildtype controls). Conclusion(s): CH is an independent prognostic factor in COVID-19 and skews hematopoiesis towards monocytosis. TET2-mutant CH is characterized by differential methylation and accessibility of enhancers binding myeloid transcriptions factors including EGR1. The ensuing loss of EGR1 expression in monocytes causes MALAT1 overexpression, a factor known to promote monocyte differentiation and inflammation. These data provide a mechanistic insight to the adverse prognostic impact of CH in COVID-19.

Modeling SARS-CoV-2 True Infections in Catalonia through a Digital Twin

A dynamic view of the evolution of the infections of SARS-CoV-2 in Catalonia using a Digital Twin approach that forecasts the true infection curve is presented. The forecast model incorporates the vaccination process, the confinement, and the detection rate, and virtually allows to consider any nonpharmaceutical intervention, enabling to understand their effects on the disease's containment while forecasting the trend of the pandemic. A continuous validation process of the model is performed using real data and an optimization model that automatically provides information regarding the effects of the containment actions on the population. To simplify this validation process, a formal graphical language that simplifies the interaction with the different specialists and an easy modification of the model parameters are used. The Digital Twin of the pandemic in Catalonia provides a forecast of the future trend of the SARS-CoV-2 spread and information regarding the true cases and effectiveness of the NPIs to control the SARS-CoV-2 spread over the population. This approach can be applied easily to other regions and can become an excellent tool for decision-making. © 2023 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.

American College of Nuclear Medicine (ACNM) 2022 ACNM Annual Meeting Abstracts

The proceedings contain 91 papers. The topics discussed include: the new approach of COVID-19 patients with deteriorating respiratory functions using perfusion SPECT/CT imaging;increasing interest in nuclear medicine: evaluation of an educational workshop;cost-benefit analysis recommends further utilization of cardiac PET/MR for sarcoidosis evaluation;development of a nomogram model for predicting the recurrence of differentiated thyroid carcinoma patients based on a thyroid cancer database from a tertiary hospital in China;multi-center validation of radiomic models in new data using ComBat-based harmonization of features;bone scan with Tc99m-MDP, the missing link in the initial staging of muscle-invasive bladder carcinoma;and comparison of absorbed doses to kidneys calculated employing three time points and employing two time points in neuroendocrine patients undergoing Lu-177 DOTATATE therapy using planar images.

Validation of Commercial Kits Using Standard ICMR Protocol

Background: SARS-CoV-2 highlighted worldwide, the need of enhance testing capacity. Government of India, under Atmanirbhar Bharat provided platform to private/public companies to develop and manufacture diagnostic reagents /kits for SARS CoV 2 testing. Objective(s): * Performance evaluation of commercial kits. * Handholding of private/public companies to improve the kits quality for its diagnostic accuracy to use for Covid 19 diagnosis Material(s) and Method(s): The SOP for the validation of diagnostic kits were prepared and approved by ICMR technical committee. The ICMR NIV single tube assay was used as gold slandered. The panels of known positives and negatives were prepared. Validation of commercially developed RT-PCRs, RNA extraction kits and virus transport medium were undertaken. The sensitivity and specificity of the kit were calculated and reported as per ICMR's acceptance. Result(s): Real time RT-PCR kits evaluation: Total 165 kits were evaluated, which includes 12 LAMP assay. Among domestic kits, 31 kits were satisfactory while 83 were not satisfactory. Among the imported kits, 25 kits were satisfactory while 26 were not satisfactory. RNA extraction kits evaluation:- Total 157 kits were evaluated, Among domestic kits, 57 kits were satisfactory while 53 were not satisfactory. Among the imported kits, 31 kits were satisfactory and 17 were not satisfactory. VTM kits evaluated = Total 89 kits were evaluated among which nine kits were imported while 80 kits were of domestic origin. Performance of 10 kits was not satisfactory. Conclusion(s): Kit validation is important to access the quality of commercial kits and to enhanced the testing capacity exponentially in country.

Covid-19 Vaccination Impacts Functional Immune Responses and Plasma Proteome in Plwh

Background: The impact of COVID-19 infection or COVID-19 vaccination on the immune system of people living with HIV (PLWH) is unclear. We therefore studied the effects of COVID-19 infection or vaccination on functional immune responses and systemic inflammation in PLWH. Method(s): Between 2019 and 2021, 1985 virally suppressed, asymptomatic PLWH were included in the Netherlands in the 2000HIV study (NCT039948350): 1514 participants enrolled after the start of the COVID-19 pandemic were separated into a discovery and validation cohort. PBMCs were incubated with different stimuli for 24 hours: cytokine levels were measured in supernatants. ~3000 targeted plasma proteins were measured with Olink Explore panel. Past COVID-19 infection was proven when a positive PCR was reported or when serology on samples from inclusion proved positive. Compared were unvaccinated PLWH with and without past COVID-19 infection, and PLWH with or without anti-COVID-19 vaccination excluding those with past COVID-19 infection. Result(s): 471 out of 1514 participants were vaccinated (median days since vaccination: 33, IQR 16-66) and 242 had a past COVID-19 infection (median days since +PCR: 137, IQR 56-206). Alcohol, smoking, drug use, BMI, age, latest CD4 count and proportion with viral blips were comparable between groups. Systemic inflammation as assessed by targeted proteomics showed 89 upregulated and 43 downregulated proteins in the vaccinated participants. In contrast, individuals with a past COVID-19 infection display lower levels of 138 plasma proteins compared to the uninfected group (see figure). 'Innate immune system' and 'cell death' were upregulated in pathway analysis in vaccinated PLWH, but downregulated in COVID-19 infected participants. The increased systemic inflammation of the COVID-19 vaccinated group was accompanied by lower TNF-alpha and IL-1beta production capacity upon restimulation with a range of microbial stimuli, while production of IL-1Ra was increased. In COVID-19 infected PLWH only a reduced production of TNF-alpha to S. pneumonia was significant. Vaccinated PLWH also showed upregulation of platelet aggregation pathways. Conclusion(s): COVID-19 vaccination in PLWH leads to an increased systemic inflammation, but less effective cytokine production capacity of its immune cells upon microbial stimulation. This pattern is different from that of COVID-19 infection that leads to a decreased inflammatory profile and only minimal effects on cytokine production capacity. (Figure Presented).

Asymmetric Cerebral Perfusion in Comatose Extracorporeal Membrane Oxygenation Patients

Asymmetric cerebral perfusion can occur when extracorporeal membrane oxygenation (ECMO) flow competes with native cardiac circulation. It is unclear whether this phenomenon associates with brain injury. Diffuse correlation spectroscopy (DCS) provides continuous, laser-based, non-invasive, bedside monitoring of relative cerebral blood flow (rCBF). This study measured rCBF in ECMO patients via DCS to determine whether comatose patients experience asymmetric cerebral perfusion. Adults receiving ECMO for any indication were prospectively recruited from 12/2019-3/2021. Patients with prior neurologic injury, scalp/facial lacerations, and SARS-CoV-2 infection were excluded. DCS monitoring was performed daily during ECMO support with sensors placed on bilateral foreheads. Mean arterial pressure (MAP) was continuously recorded from the bedside monitor. The Glasgow Coma Scale (GCS) was assessed by clinical staff multiple times daily with sedation pauses, if possible, per standard of care. rCBF was calculated by comparing continuous cerebral blood flow (CBF) measurements to the daily median CBF, then averaged at each MAP value. Daily rCBF asymmetry was calculated by summing the absolute difference of rCBF between the two hemispheres at each MAP value, normalized for the total MAP range experienced by the patient that day. Twelve subjects were enrolled in this study (ages 21-78, 6 with cardiac arrest, 4 with acute heart failure, 2 with ARDS) and grouped by maximum GCS motor (GCS-M) score during ECMO, with 3 "comatose" subjects (GCS-M <= 4), and 9 "awake" subjects (GCS-M > 4). DCS was performed over 66 sessions with a mean duration of 131.83 +/- 1.13 minutes. Comatose subjects exhibited more rCBF asymmetry than awake subjects (0.28 +/- 0.06 mmHg-1 vs. 0.10 +/- 0.001 mmHg-1, p=0.045). No difference in asymmetry was noted between patients with or without cardiac arrest. We found that comatose ECMO subjects exhibited higher inter-hemispheric rCBF asymmetry over a range of blood pressures than awake subjects. Though our comatose sample is small, further validation of this finding and its causes, such as cerebrovascular dysregulation, is warranted.

Clinical profile and outcomes of patients with COVID-19 respiratory failure requiring renal replacement therapy: a machine learning analysis of 13,576 patients

Introduction: Acute kidney injury (AKI) is a frequent and severe complication of COVID-19 infection in ICU patients. We propose a structured data-driven methodology and develop a model to predict the use of renal replacement therapy for patients on respiratory support with Covid-19 in 126 ICUs from 42 Brazilian hospitals. Method(s): Adult ICU patients (March 2020-December 2021) with confirmed SARS-CoV-2 infection and need of ventilatory support at D1 admission in the ICU. Main outcome was the need of RRT. We estimated 3 prediction models: Logistic Regression (LR), Random Forest (RF) and XGB Boosting. Models were derived in the training set and evaluated in the test set following an 80/20 split ratio, and models' parameters were selected using fivefold cross-validation. We evaluated and selected the best model in terms of discrimination (AUC) and calibration (Brier's Score). Variable importance was estimated for each predictor variable. Result(s): 13,575 ICU patients with need of respiratory support, of which 1828 (14%) needed RRT. ICU and hospital mortality were respectively 15.7%, 20.3% (non-RRT) and 54.3%, 69% (RRT). Mean age was 63.9 and 55.3 years (RRT vs non-RRT). Mean ICU LOS was 27.8 vs. 12 days, in RRT vs non-RRT. RF and XGB models both showed higher discrimination performance compared to LR (95% confidence interval [95% CI]: 0.84 [0.81-0.85] and 0.83 [0.80-0.85] vs 0.78 [0.75-0.80]). RF and XGB models presented similar calibration (Brier's Score: ([95% CI]: 0.09 [0.09- 0.10] and 0.09 [0.09-0.10]), also better than in LR (0.11 [0.10-0.12]). The final model (RF) showed no sign of under or overestimation of predicted probabilities in calibration plots. Conclusion(s): The need of RRT among patients on respiratory support diagnosed with Covid-19 was accurately predicted through machine learning methods. RF and XGB based models using data from general intensive care databases provides an accurate and practical approach for the early prediction of use of RRT in COVID-19 patients.

Derivation and validation of clinical phenotypes and associated treatment responses in critically ill COVID-19 patients

Introduction: Due to variability in the host response, a uniform treatment strategy for severe COVID-19 may be inadequate. We applied unsupervised clustering methods to large cohorts of COVID-19 ICU patients to derive and validate clinical phenotypes, and to explore treatment responses in these phenotypes. Method(s): Phenotypes were derived in 13.279 critically ill COVID-19 patients admitted to 82 Dutch ICUs from September 2020 to February 2022. Twenty-one features were selected from clinical characteristics measured within 24 h after ICU admission. Phenotypes were assigned using consensus k means clustering. External validation was performed in 6225 critically ill COVID-19 patients admitted to 55 Spanish ICUs from February 2020 to December 2021. Individual patient data on corticosteroids therapy enabled us to investigate phenotype-specific responses in this cohort. Result(s): Three distinct clinical phenotypes were derived (Fig. 1A). Patients with phenotype 1 (43%) were younger, had lower APACHE IV scores, higher BMI as well as a lower P/F ratio and 90-day in-hospital mortality (18%, Fig. 1A). Phenotype 2 patients (37%) were older and had slightly higher APACHE IV scores compared with phenotype 1, a lower BMI, and higher mortality compared to phenotype 1 (24%, p = 2.95e-07). Phenotype 3 (20%) included the oldest patients with the most comorbidities and highest APACHE IV scores, severe renal and metabolic impairment, and the worst outcome (47% mortality, p = 6.6e-16 and p = 6.6e-16 versus phenotypes 1 and 2, respectively). Phenotype distribution and outcome were very similar in the validation cohort (Fig. 1B). This cohort also revealed that corticosteroid therapy only benefited phenotype 3 (65% vs. 54% mortality, p = 2.5e-03, Fig. 1C). Conclusion(s): COVID-19 ICU phenotypes based on clinical data are related to outcome and treatment responses. This can inform treatment decisions as well as randomized trials employing precision medicine approaches.

Sotrovimab Pharmacokinetics and Covariate Effects in Hematopoietic Stem Cell Transplant Recipients

Hematopoietic cell transplant (HCT) recipients are at increased risk of morbidity and mortality from COVID-19. They may have lower SARS-CoV-2-directed antibody levels due to protein loss from the gastrointestinal (GI) tract as a result of preparative regimen-related toxicity and graft-vs.-host disease (GVHD). In fact, previous studies suggested that GI GVHD or diarrhea from other etiologies were associated with a reduction in the half-life of monoclonal antibodies (mAbs). Hence, understanding the pharmacokinetic (PK) profile of mAbs targeting SARS-CoV-2 in this vulnerable population is critical for dose-selection and predicting the duration of protection against COVID-19. This analysis aims to use a population pharmacokinetics (popPK) approach to evaluate the PK of sotrovimab and the effect of covariates in HCT recipients. In a Phase I trial (COVIDMAB), all participants received 500 mg sotrovimab IV prophylactically within one week prior to starting transplant conditioning. Sotrovimab serum concentrations were determined weekly for up to 12 weeks in autologous (n=5) and allogeneic (n=15) HCT recipients (129 observations). Sotrovimb PK and the effect of covariates were analyzed using popPK modeling in NONMEM (version 7.4). GVHD and diarrhea severity data were collected weekly via survey and included as time-dependent covariates during the covariate screening process. The final PK model with covariates was validated using simulation-based validation and goodness of fit plots. PK data were compared to non-transplant patients from 1891 patients with COVID-19 in COMET-ICE, COMET-PEAK, BLAZE-4, and COMET-TAIL and 38 healthy individuals enrolled in GlaxoSmithKline Pharma Study 217653. A two-compartment model best described sotrovimab PK in HCT recipients. In comparison to non-transplant patients, sotrovimab clearance (CL) was 14.0% higher in HCT recipients. Weight was a significant covariate on sotrovimab CL and (Figure Presented) volume of distribution in the central compartment (V2). With every 10 kg increase in body weight, sotrovimab CL and V2 were estimated to increase by 9.5% and 5.5%, respectively. Diarrhea severity was also a significant covariate on sotrovimab CL. HCT recipients with grade 3 diarrhea showed an increase in CL by 1.5-fold compared to those without diarrhea. Based on popPK analyses, sotrovimab CL was higher in HCT recipients compared to non-transplant patients. Higher bodyweight as well as diarrhea resulted in increased sotrovimab CL. There were only 3 patients with GI GVHD, and larger studies are needed to determine whether diarrhea due to GI GVHD or conditioning toxicity was responsible for the observed increase in sotrovimab CL. Further validation of these findings in a larger number of HCT recipients is also warranted to help optimize mAb dosing for COVID-19 prophylaxis and determine whether presence of large-volume diarrhea may require intensified dosing strategiesCopyright © 2023 American Society for Transplantation and Cellular Therapy

Predicting COVID-19 Severity Integrating RNA-Seq Data Using Machine Learning Techniques

A fundamental challenge in the fight against COVID-19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear. Method(s): A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID-19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps. Result(s): CD93, RPS24, PSCA, and CD300E were identified as COVID-19 severity gene signatures. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discrimi-nating between control, outpatient, inpatient, and ICU COVID-19 patients was optimized, achieving an accuracy of 97.5%. Conclusion(s): In summary, during this research, a new intelligent pipeline was implemented to develop a specific gene signature that can detect the severity of patients suffering COVID-19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID-19.Copyright © 2023 Bentham Science Publishers.

Class prediction of the prevalent transmission mode of COVID-19 within a geographic area

This research developed a multinomial classification model that predicts the prevalent mode of transmission of the coronavirus from person to person within a geographic area, using data from the World Health Organization (WHO). The WHO defines four transmission modes of the coronavirus disease 2019 (COVID-19);namely, community transmission, pending (unknown), sporadic cases, and clusters of cases. The logistic regression was deployed on the COVID-19 dataset to construct a multinomial model that can predict the prevalent transmission mode of coronavirus within a geographic area. The k-fold cross validation was employed to test predictive accuracy of the model, which yielded 73% accuracy. This model can be adopted by local authorities such as regional, state, local government, and cities, to predict the prevalent transmission mode of the virus within their territories. The outcome of the prediction will determine the appropriate strategies to put in place or re-enforced to curtail further transmission.Copyright © 2023 Inderscience Enterprises Ltd.

Development and Validation of a Novel Stability Indicating Reverse Phase High Performance Liquid Chromatographic Method for Related Substances and Assay Analysis of Molnupiravir Drug Substance and Drug Product

Molnupiravir, a broad-spectrum antiviral is an isopropyl ester prodrug of beta-D-N4-hydroxycytidine. Molnupiravir targets RNA-dependent RNA-polymerase enzyme of the viruses. A new stability-indicating HPLC-method was developed to determine related substances and assay of molnupiravir. Separation was achieved by using Shim-pack GWS C18 column. The method was validated according to current ICH requirements. The calibration plot gave a linear relationship for all known analytes over the concentration range from LOQ to 200%. LOD and LOQ for all known analytes were found in 0.05-0.08 microg mL-1 and 0.12-0.20 microg mL-1, respectively, the mean recovery was found to be 97.79-102.44 %. Study showed that the method, results of robustness, solution stability studies are precise and within the acceptable limits. Molnupiravir was found to degrade in acid, alkali, and oxidative conditions, and was stable in thermal, moisture, and photolytic degradation condition. The method is simple, accurate, precise, and reproducible for routine purity analysis of drug-samples.Copyright © 2022 Indian Drug Manufacturers' Association. All rights reserved.

The Impact of Covid-19 on the Education of Medical Students of Federal Medical College, Islamabad, Pakistan

Objective: The objective of this study was to assess the impact of covid-19 on the learning of medical students of Federal Medical College, Islamabad. Material(s) and Method(s): This descriptive cross-sectional study was conducted on 215 medical students of Federal Medical College, Islamabad from October to December 2021. A pretested validated tool was used to collect primary data from medical students via random sampling. SPSS version 25 was used for data analysis. The chi-square test was used to see the association between various variables. Result(s): This study included 54.9% (118) females and 45.1% (97) males. 188 (87.4%) students reported that they were tak-ing online classes. Most of the students, 181 (84.2%) thought that COVID-19 affected their study durations. The pandemic has caused wastage of time was reported by 155 (72.1%) students and 60 (27.9%) stated pandemic had given them extra time to clear their concepts. More than 2/3rd of the students (78.6%) were concerned about their professional examinations due to the present situation of the pandemic. Among all the respondents, 177 (82.3%) were not satisfied with this method of learning and also 184 (85.6%) students have lost interest in their studies. Most of the students 173 (80.5%) were facing difficulty in establishing the boundary between their work and home and 180 (83.7%) were missing classroom engagement. Conclusion(s): Covid 19 has severely affected medical education. E-learning is not suitable for medical students as most of their learning involves practical performance and interaction with patients.Copyright © 2023, Khyber Medical College. All rights reserved.

Diagnostic Performance of Human Readers and Artificial Intelligence Model in Classification Task of Computed Tomography Images of Different Types of Viral Pneumonia

Purposes: Artificial Intelligence (AI) models are constantly developing to help clinicians in challenging tasks such as classification of images in radiological practice. The aim of this work was to compare the diagnostic performance of an AI classifier model developed in our hospital with the results obtained from the radiologists reading the CT images in discriminating different types of viral pneumonia. Material(s) and Method(s): Chest CT images of 1028 patients with positive swab for SARS-CoV-2 (n=646) and other respiratory viruses (n=382) were segmented automatically for lung extraction and Radiomic Features (RF) of first (n=18) and second (n=120) order were extracted using PyRadiomics tools. RF, together with patient age and sex, were used to develop a Multi-Layer Perceptron classifier to discriminate images of patients with COVID-19 and non-COVID-19 viral pneumonia. The model was trained with 808 CT images performing a LASSO regression (Least Absolute Shrinkage and Selection Operator), a hyper-parameter tuning and a final 4-fold cross validation. The remaining 220 CT images (n=151 COVID-19, n=69 non-COVID-19) were used as independent validation (IV) dataset. Four readers (three radiologists with >10 years of experience and one radiology resident with 3 years of experience) were recruited to blindly evaluate the IV dataset using the 5-points scale CO-RADS score. CT images with CO-RADS >=3 were considered "COVID-19". The same images were classified as "COVID-19" or "non-COVID-19" by applying the AI model with a threshold on the predicted values of 0.5. Diagnostic accuracy, specificity, sensibility and F1 score were calculated for human readers and AI model. Result(s): The AI model was trained using 24 relevant features while the Area under ROC curve values after 4-fold cross validation and its application to the IV dataset were, respectively, 0.89 and 0.85. Interreader agreement in assigning CO-RADS class, analyzed with Fleiss' kappa with ordinal weighting, was good (k=0.68;IC95% 0.63-0.72) and diagnostic performance were then averaged among readers. Diagnostic accuracy, specificity, sensibility and F1 score resulted 78.6%, 78.3%, 78.8% and 78.5% for AI model and 77.7%, 65.6%, 83.3% and 72.0% for human readers. The difference between specificity and sensitivity observed in human readers could be related to the higher rate of false positive due to the higher incidence of COVID-19 patients in comparison with other types of viral pneumonitis during the last 2 years. Conclusion(s): A model based on RF and artificial intelligence provides comparable results with human readers in terms of diagnostic performance in a classification task.Copyright © 2023 Southern Society for Clinical Investigation.

2d Qsar Approach to Develop Newer Generation Small Molecules Active against Sars-Covid

We are in the half past of 2022, but still, we are facing the coronavirus pandemic situation. When a patient is hospitalized, only some FDA-approved drugs were administered to cure the patient. In treating coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors, and various monoclonal antibodies are present. But all the molecules used in the treatment were not so effective in fully curing the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches played an essential role. 2D QSAR studies related to anti-SARS-CoV-2 molecule development, some QSAR models observed with good statistical parameters such as R2: 0.748, cross-validated Q2 (LOO): 0.628, external predicted R2: 0.723 and another model suggested with R2: 0.764, Q2: 0.627 and Rm2: 0.610, Q2 (F1): 0.727, Q2 (F1): 0.652, MAE score: 0.127. We developed a new 2D QSAR model with a higher number of molecules and greater statistical parameters. A dataset of 84 anti-SARS-CoV2 molecules was obtained from literature followed by descriptor calculation PADEL software;the QSAR model was generated using the Modelability index, dataset pretreatment, division, MLR equation, validation, and Y randomization test. The model was pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP with R2 and Q2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298. Atoms E-state and electronic features of the molecules directly related to anti-SARS-CoV-2 drug activity. It can be easily concluded that we want to develop a small molecule effective against SARS-CoV-2 disease in the near future.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.

Enhancing Pharmaceutical Supply Chain Resilience: A Study of Pharmaceutical Companies in Multiple Geographies

Background: Pharmaceutical businesses had enormous difficulties in product distribution during COVID-19, and the solution to this perpetual issue is a resilient supply chain. Aim(s): The study aims to understand the vulnerabilities to which it subjected the pharmaceutical product distribution supply chains during the COVID-19 pandemic and further develop an adaptive model through which the pharmaceutical product supply chain can enhance its resilience capabilities. Material(s) and Method(s): The conceptual model is developed for the supply chain of pharmaceutical companies based on the literature survey, and then the conceptual model is explored through factor analysis. Researchers have developed a validated model after a statistical analysis using Cronbach's alpha. Subjective analysis has concluded that the pharmaceutical supply chain's resilience is driven by factors such as "trade cost," which comprises transport cost, business practices, and raw material sourcing cost;"shock propagation," which comprises country-specific shocks, production shocks, and policy changes;and "technological infrastructure bottleneck," which relates to the availability of cold chain storage warehouses and refrigerated transport vehicle facilities. Result(s): An empirical model pertaining to supply chain resilience may be further studied with different geographies, like Pune, Hyderabad, and Delhi NCR, for the purpose of generalizing the study. Conclusion(s): The identified major factors were trade cost, shock propagation, and technological infrastructure bottlenecks. The sensitivity of the issue under investigation required a personal touch to the survey, as the COVID-19 pandemic had left these respondents emotionally vulnerable. As COVID-19 is the recent catastrophe that has hit humanity, it has made the pharmaceutical product distribution channel vulnerable during the pandemic. This difficult time of pandemic has really tested the pharmaceutical products' supply chain capabilities as well.Copyright © 2023, Association of Pharmaceutical Teachers of India. All rights reserved.

Liquid Chromatographic Approach for Analysis of Favipiravir–A Repurpose Drug for COVID-19

Favipiravir is a potential repurpose moiety to treat COVID-19 by depletion of virus load in infectious patients. To analyze and separate Favipiravir with remarkable efficiency, X-Bridge C8 column (150 x 4.6 mm, 5 µ) and a solvent phase of 0.1% TEA and acetonitrile (40:60 v/v) with 1-mL/min flow rate were used. The eluted favipiravir and possible degradants were detected at 225 nm. Further, the process was validated by using ICH (Q2R1) guidelines to ensure the method's suitability in the pharmaceutical sector. The RT of Favipiravir was observed at 3.7 min with good linearity of 2 to 30 µg/mL. %RSD of both system and method precision was assessed in the series of 0.32 to 0.98. The mean percentage recovery of Favipiravir was in the range of 99.0–100.4%. The limit of detection (LoD) and limit of quantification (LoQ) were assessed to be 0.024 and 0.084 μg/mL for favipiravir. The outcomes confirmed that the projected approach was economical, insightful, simple and precise with better sensitivity. Investigation of Favipiravir in the incidence of a variety of stressed or forced degradation environments ensures stability indicating quality of the developed approach. © 2023, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.