Potential Strategies to Control the Risk of Antifungal Resistance in Humans: A Comprehensive Review.

Fungal infections are becoming one of the main causes of morbidity and mortality in people with weakened immune systems. Mycoses are becoming more common, despite greater knowledge and better treatment methods, due to the regular emergence of resistance to the antifungal medications used in clinical settings. Antifungal therapy is the mainstay of patient management for acute and chronic mycoses. However, the limited availability of antifungal drug classes limits the range of available treatments. Additionally, several drawbacks to treating mycoses include unfavourable side effects, a limited activity spectrum, a paucity of targets, and fungal resistance, all of which continue to be significant issues in developing antifungal drugs. The emergence of antifungal drug resistance has eliminated accessible drug classes as treatment choices, which significantly compromises the clinical management of fungal illnesses. In some situations, the emergence of strains resistant to many antifungal medications is a major concern. Although new medications have been developed to address this issue, antifungal drug resistance has grown more pronounced, particularly in patients who need long-term care or are undergoing antifungal prophylaxis. Moreover, the mechanisms that cause resistance must be well understood, including modifications in drug target affinities and abundances, along with biofilms and efflux pumps that diminish intracellular drug levels, to find novel antifungal drugs and drug targets. In this review, different classes of antifungal agents, and their resistance mechanisms, have been discussed. The latter part of the review focuses on the strategies by which we can overcome this serious issue of antifungal resistance in humans.

Genetic Variants and Protective Immunity against SARS-CoV-2.

The novel coronavirus-19 (SARS-CoV-2), has infected numerous individuals worldwide, resulting in millions of fatalities. The pandemic spread with high mortality rates in multiple waves, leaving others with moderate to severe symptoms. Co-morbidity variables, including hypertension, diabetes, and immunosuppression, have exacerbated the severity of COVID-19. In addition, numerous efforts have been made to comprehend the pathogenic and host variables that contribute to COVID-19 susceptibility and pathogenesis. One of these endeavours is understanding the host genetic factors predisposing an individual to COVID-19. Genome-Wide Association Studies (GWAS) have demonstrated the host predisposition factors in different populations. These factors are involved in the appropriate immune response, their imbalance influences susceptibility or resistance to viral infection. This review investigated the host genetic components implicated at the various stages of viral pathogenesis, including viral entry, pathophysiological alterations, and immunological responses. In addition, the recent and most updated genetic variations associated with multiple host factors affecting COVID-19 pathogenesis are described in the study.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer.

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.