Antiviral drugs are a class of medicines particularly used for the treatment of viral infections. Drugs that combat viral infections are called antiviral drugs. Viruses are among the major pathogenic agents that cause number of serious diseases in humans, animals and plants. Viruses cause many diseases in humans, from self resolving diseases to acute fatal diseases. Developing strategies for the antiviral drugs are focused on two different approaches: Targeting the viruses themselves or the host cell factors. Antiviral drugs that directly target the viruses include the inhibitors of virus attachment, inhibitors of virus entry, uncoating inhibitors, polymerase inhibitors, protease inhibitors, inhibitors of nucleoside and nucleotide reverse transcriptase and the inhibitors of integrase. The inhibitors of protease (ritonavir, atazanavir and darunavir), viral DNA polymerase (acyclovir, tenofovir, valganciclovir and valacyclovir) and of integrase (raltegravir) are listed among the Top 200 Drugs by sales during 2010s. Still no effective antiviral drugs are available for many viral infections. Though, there are a couple of drugs for herpesviruses, many for influenza and some new antiviral drugs for treating hepatitis C infection and HIV. Action mechanism of antiviral drugs consists of its transformation to triphosphate following the viral DNA synthesis inhibition. An analysis of the action mechanism of known antiviral drugs concluded that they can increase the cell’s resistance to a virus (interferons), suppress the virus adsorption in the cell or its diffusion into the cell and its deproteinisation process in the cell (amantadine) along with antimetabolites that causes the inhibition of nucleic acids synthesis. This review will address currently used antiviral drugs, mechanism of action and antiviral agents reported against COVID-19.

We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).

Tuberculosis (TB) is an important public health issue around the globe which is a chronic infectious disease and is still one of the major challenges for developing countries. The emergence of drug-resistant TB makes the condition worse and there is an urgent need of fast, highly sensitive diagnostic methods. This study was undertaken to evaluate the performance of GeneXpert® MTB/RIF assay and MTB culture for the detection of Mycobacterium tuberculosis (MTB) in sputum smear-negative pulmonary TB/drug-resistant tuberculosis (DR-TB) suspects. A total of 168 sputum smear-negative TB suspects were recruited for the study. Among the suspected TB cases, 52.98% were male and 47.02% were females with the mean age of 42 ± 17.6 years. All the sputum specimens collected from the study population were subjected to Ziehl–Neelsen (ZN) smear microscopy, GeneXpert MTB/RIF assay, and MTB culture. The results revealed that, out of 168 acid-fast bacilli (AFB)/ZN smear microscopy–negative sputum specimens, 48 (28.57%) and 58 (34.52%) were detected MTB positive by GeneXpert MTB/RIF assay and MTB culture, respectively, while 120 (71.43%) and 110 (65.48%) suspected TB cases were confirmed negative by GeneXpert MTB/RIF assay and MTB culture, respectively. The study concluded that GeneXpert assay was found to be a rapid and accurate tool for MTB detection in smear-negative sputum specimens. GeneXpert has advantage over ZN smear microscopy and MTB culture as it detects MTB and rifampicin resistance simultaneously within 2 h with minimal biohazards.

Since the beginning of human civilization, plants have been used in alleviating the human distress and it was recorded for about thousands of years ago that the plants are being used for medicinal purposes. Natural bioactive compounds called phytochemicals are obtained from medicinal plants, vegetables, and fruits, which functions to combat against various ailments. There is dire need to explore the plant biodiversity for its medicinal and pharmacological potentials. Different databases such as Google scholar, Medline, PubMed, and the Directory of Open Access Journals were searched to find the articles describing the cardioprotective function of medicinal plants. Various substances from a variety of plant species are used for the treatment of cardiovascular abnormalities. The cardioprotective plants contain a variety of bioactive compounds, including diosgenin, isoflavones, sulforaphane, carotinized, catechin, and quercetin, have been proved to enhance cardioprotection, hence reducing the risk of cardiac abnormalities. The present review article provides the data on the use of medicinal plants particularly against cardiac diseases and to explore the molecules/phytoconstituents as plant secondary metabolites for their cardioprotective potential.

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance ( p < 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor ( p < 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC.

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N-Acetylneuraminic acid aldolase from Clostridium perfringens was irreversibly inactivated by 1mm-bromopyruvate with a half-life of 4.2min at pH7.2 and 37 degrees C. The rate of inactivation was diminished in the presence of pyruvate but not with N-acetyl-d-mannosamine, indicating that the inhibitor acted at, or close to, the pyruvate-binding site. The apparent K(i) for bromopyruvate, calculated from the variation of half-life with inhibitor concentration, was 0.46mm, compared with a competitive K(i) 3.0mm for pyruvate. Incubation of the enzyme with radioactive bromopyruvate gave a radioactive, enzymically inactive, protein in which the bromopyruvate had alkylated cysteine residues. Incubation of the enzyme with radioactive pyruvate, followed by reduction with sodium borohydride, led to inactivation of the enzyme and binding of the pyruvate to the protein by reduction of a Schiff's base initially formed with the in-amino group of a lysine residue; only one-twentieth as many pyruvyl residues were bound by this method, showing that bromopyruvate is not specific for the active site. After protection of the enzyme active site with pyruvate, treatment with unlabelled bromopyruvate and dialysis, the enzyme retained 72% activity. When this treated enzyme was separately incubated with radioactive bromopyruvate, or radioactive pyruvate followed by sodium borohydride, the ratio of radioactive pyruvyl residues bound by the two methods was 2.3:1. After reduction and hydrolysis of the bromopyruvate-treated enzyme, the only detectable radioactive amino acid derivative was chromatographically and electrophoretically identical with S-(3-lactic acid)-cysteine. The enzyme was fully active in the presence of EDTA and was not stimulated by bivalent metal ions. It was strongly inhibited by silver and mercuric ions. The apparent molecular weight, determined by Sephadex chromatography, was 250000. A mechanism of action is proposed for the enzyme. Bromopyruvate reacts rapidly at pH6.0 with thiol-containing amino acids. Cysteine appears to react anomalously.