The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.Human immunodeficiency virus (HIV) causes acquired immune deficiency syndrome (AIDS) and enters the host cell via CD4 and either CC-chemokine receptor 5 (CCR) or CXC-chemokine receptor 4 (CXCR4). HIV is directly recognized by toll-like receptor 4 (TLR4) and affects downstream immune-related signal pathways. In addition, stimulated TLR4 inhibits HIV-1 invasion, and the rs4986790 single nucleotide polymorphism (SNP) (D299G) of the TLR4 gene contributes to the risk of HIV-1 infection in an Indian population. To evaluate whether the rs4986790 SNP of the TLR4 gene is related to vulnerability to HIV-1 infection, we collected genetic information from HIV-1 patients in previous studies and performed an association analysis with a matched control population obtained from the 1000 Genomes Project. Semaxanib inhibitor In addition, to strengthen the results of association analysis, we performed a meta-analysis. We identified a strong association between the rs4986791 SNP and susceptibility to HIV infection in HIV-infected patients in previous studies and a matched control population obtained from the 1000 Genomes Project. In addition, we found that the G allele of the rs4986791 SNP in the TLR4 gene is strongly related to susceptibility to HIV infection in three Caucasian populations (odd ratio = 2.29, 95% confidence interval 1.72-3.07, p = 1.438 × 10-7) and all four populations (odd ratio = 2.22, 95% confidence interval 1.74-2.84, p = 2 × 10-10) in a meta-analysis. To the best our knowledge, this was the first meta-analysis on the association between the rs4986791 SNP of the TLR4 gene and susceptibility to HIV infection.A suitable HPLC method has been selected and validated for rapid simultaneous separation and determination of four imidazole anti-infective drugs, secnidazole, omeprazole, albendazole, and fenbendazole, in their final dosage forms, in addition to human plasma within 5 min. The method suitability was derived from the superiority of using the environmentally benign solvent, methanol over acetonitrile as a mobile phase component in respect of safety issues and migration times. Separation of the four anti-infective drugs was performed on a Thermo Scientific® BDS Hypersil C8 column (5 µm, 2.50 × 4.60 mm) using a mobile phase consist of MeOH 0.025 M KH2PO4 (7030, v/v) adjusted to pH 3.20 with ortho-phosphoric acid at room temperature. The flow rate was 1.00 mL/min and maximum absorption was measured with UV detector set at 300 nm. Limits of detection were reported to be 0.41, 0.13, 0.18, and 0.15 µg/mL for secnidazole, omeprazole, albendazole, and fenbendazole, respectively, showing a high degree of the method sensitivity. The method of analysis was validated according to Food and Drug Administration (FDA)guidelines for the determination of the drugs, either in their dosage forms with highly precise recoveries, or clinically in human plasma, especially regarding pharmacokinetic and bioequivalence studies.In December 2019, the latest member of the coronavirus family, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, leading to the outbreak of an unusual viral pneumonia known as coronavirus disease 2019 (COVID-19). COVID-19 was then declared as a pandemic in March 2020 by the World Health Organization (WHO). The initial mortality rate of COVID-19 declared by WHO was 2%; however, this rate has increased to 3.4% as of 3 March 2020. People of all ages can be infected with SARS-CoV-2, but those aged 60 or above and those with underlying medical conditions are more prone to develop severe symptoms that may lead to death. Patients with severe infection usually experience a hyper pro-inflammatory immune reaction (i.e., cytokine storm) causing acute respiratory distress syndrome (ARDS), which has been shown to be the leading cause of death in COVID-19 patients. However, the factors associated with COVID-19 susceptibility, resistance and severity remain poorly understood. In this review, we thoroughly explore the correlation between various host, viral and environmental markers, and SARS-CoV-2 in terms of susceptibility and severity.In the present study, magnetic oil palm empty fruits bunch cellulose nanofiber (M-OPEFB-CNF) composite was isolated by sol-gel method using cellulose nanofiber (CNF) obtained from oil palm empty fruits bunch (OPEFB) and Fe3O4 as magnetite. Several analytical methods were utilized to characterize the mechanical, chemical, thermal, and morphological properties of the isolated CNF and M-OPEFB-CNF. Subsequently, the isolated M-OPEFB-CNF composite was utilized for the adsorption of Cr(VI) and Cu(II) from aqueous solution with varying parameters, such as pH, adsorbent doses, treatment time, and temperature. Results showed that the M-OPEFB-CNF as an effective bio-sorbent for the removal of Cu(II) and Cr(VI) from aqueous solution. The adsorption isotherm modeling revealed that the Freundlich equation better describes the adsorption of Cu(II) and Cr(VI) on M-OPEFB-CNF composite. The kinetics studies revealed the pseudo-second-order kinetics model was a better-described kinetics model for the removal of Cu(II) and Cr(VI) using M-OPEFB-CNF composite as bio-sorbent.