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The purpose of this review is to give an up-to-date, thorough, and timely overview of monkeypox (Mpox), a severe infectious viral disease. Furthermore, this review provides an up-to-date treatment option for Mpox. The monkeypox virus (MPXV) has remained the most virulent poxvirus for humans since the elimination of smallpox approximately 41 years ago, with distribution mainly in central and west Africa. Mpox in humans is a zoonotically transferred disease that results in symptoms like those of smallpox. It had spread throughout west and central Africa when it was first diagnosed in the Republic of Congo in 1970. Mpox has become a major threat to global health security, necessitating a quick response by virologists, veterinarians, public health professionals, doctors, and researchers to create high-efficiency diagnostic tests, vaccinations, antivirals, and other infection control techniques. The emergence of epidemics outside of Africa emphasizes the disease's global significance. A better understanding of Mpox's dynamic epidemiology may be attained by increased surveillance and identification of cases.

In recent years, we are facing the challenge of drug resistance emergence in fungi. The availability of limited antifungals and development of multi-drug resistance in fungal pathogens has become a serious concern in the past years in the health sector. Although several cellular, molecular, and genetic mechanisms have been proposed to explain the drug resistance mechanism in fungi, but a complete understanding of the molecular and genetic mechanisms is still lacking. Besides the genetic mechanism, epigenetic mechanisms are pivotal in the fungal lifecycle and disease biology. However, very little is understood about the role of epigenetic mechanisms in the emergence of multi-drug resistance in fungi, especially in Candida auris (C. auris). The current narrative review summaries the clinical characteristics, genomic organization, and molecular/genetic/epigenetic mechanisms underlying the emergence of drug resistance in C. auris. A very few studies have attempted to evaluate the role of epigenetic mechanisms in C. auris. Furthermore, advanced genetic tools such as the CRISP-Cas9 system can be utilized to elucidate the epigenetic mechanisms and their role in the emergence of multi-drug resistance in C. auris., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Leishmaniasis is a zoonotic disease transmitted in humans by the bite of Leishmania -infected phlebotomine sandflies. Each year approximately 58,500 cases of leishmaniasis are diagnosed across the globe, with a mortality rate of nearly seven percent. There are over 20 parasitic strains of Leishmania which are known to cause distinct types of leishmaniasis and pose an endemic threat to humans worldwide. Therefore, it is crucial to develop potential medications and vaccines to combat leishmaniasis. However, the task of developing therapeutic solutions is challenging due to Leishmania 's digenetic lifecycle. The challenge is further intensified by cases of resistance against the available drugs. Owing to these challenges, the conventional drug development regimen is further limited by target discovery and ligand suitability for the targets. On the other hand, as an added advantage, the emergence of omics-based tools, such as high-end proteomics, transcriptomics and genomics, has hastened the pace of target discovery and target-based drug development. It is now becoming apparent that multi-omics convergence and an inter-connected systems approach is less time-consuming and more cost-effective for any drug-development process. This comprehensive review is an attempt to summarize the current knowledge on the muti-omics approach in drug development against leishmaniasis. In particular, it elaborates the potential target identification from secreted proteins in various stages of Leishmania infection and also illustrates the convergence of transcriptomic and genomic data towards the collective goal of drug discovery. This review also provides an understanding of the potential parasite's drug targets and drug resistance characteristics of the parasite, which can be used in designing effective and specific therapeutics.

Conventional anticancer treatments, such as radiotherapy and chemotherapy, have significantly improved cancer therapy. Nevertheless, the existing traditional anticancer treatments have been reported to cause serious side effects and resistance to cancer and even to severely affect the quality of life of cancer survivors, which indicates the utmost urgency to develop effective and safe anticancer treatments. As the primary focus of cancer nanotheranostics, nanomaterials with unique surface chemistry and shape have been investigated for integrating cancer diagnostics with treatment techniques, including guiding a prompt diagnosis, precise imaging, treatment with an effective dose, and real-time supervision of therapeutic efficacy. Several theranostic nanosystems have been explored for cancer diagnosis and treatment in the past decade. However, metal-based nanotheranostics continue to be the most common types of nonentities. Consequently, the present review covers the physical characteristics of effective metallic, functionalized, and hybrid nanotheranostic systems. The scope of coverage also includes the clinical advantages and limitations of cancer nanotheranostics. In light of these viewpoints, future research directions exploring the robustness and clinical viability of cancer nanotheranostics through various strategies to enhance the biocompatibility of theranostic nanoparticles are summarised.

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.