Shivani Singh Singh

The mpox virus (MPXV), which is an emergent orthopoxvirus with zoonotic transmission capability, represents a growing public health threat on a global scale due to the recently reported outbreaks in multiple countries after 2022. In view of the rising prevalence of genetically heterogeneous strains, such as Clade I and Clade II, there has been a growing research focus on the molecular aspects of pathogenesis, evolution, transmission, and epidemiology of MPXV clades. The current review focuses on the genome structure, mutations, viral fitness, immune evasiveness, and human-to-human transmission rate associated with MPXV clades. The comparative pathogenicity between the Clade I and Clade II variants is also discussed, with emphasis on the increased virulence and mortality related to Clade I and increased transmissibility of Clade II variants, including Clade IIb. Recent genomic studies have shown that hypermutations caused by the APOBEC3 enzymes, single-nucleotide polymorphism, and adaptive evolution contribute to viral persistence, immune escape, and epidemic spread. Furthermore, the review explains how epidemiologic surveillance efforts, molecular diagnostic tools, genomics techniques, and public health issues related to mpox epidemic response are managed. This information highlights the crucial role of genomics surveillance, timely diagnostics, vaccines, and the One Health approach in future prevention of mpox outbreaks.

Sudden stillbirth still poses as one of the key challenges in maternal and fetus care, especially in developing nations where sophisticated labor ward monitoring systems cannot be afforded. It becomes very challenging to detect a pregnancy at risk early due to the combination of several risk factors related to both mother and the fetus. This paper presents the design of a Human-in-the-Loop Explainable Artificial Intelligence (XA)I-based predictive labor ward model to help detect composite risks related to sudden stillbirth. For this, the research considers clinical records on 90 pregnant mothers and then utilizes machine learning (ML) models such as Logistic Regression, Random Forest, and XGBoost for predictions. XAI algorithms are utilized to enhance transparency, interpretability, and clinician understanding of predictive results. It is found that the highest prediction accuracy can be achieved by usinsg the XGBoost-XAI method, which is superior to traditional approaches. Hypertension in mother, fetal distress, placental inefficiency, gestational diabetes, and prolonged labor are some of the most significant predictors of sudden stillbirth. The Human-in-the-Loop concept makes it more reliable.

Infertility is becoming an increasingly common reproductive health condition globally, leading to a dramatic increase in the use of assisted reproductive technologies, including intracytoplasmic sperm injection (ICSI) and in vitro fertilisation (IVF). Numerous factors, such as the mother, the embryo, and the IVF procedure, contribute to the success rate of in vitro fertilisation (IVF) and live births. Investigated here are in vitro fertilisation (IVF) success rates as a function of oocyte quality, maternal body mass index (BMI), embryo transfer methods, and ICSI. Female infertility patients undergoing in vitro fertilisation procedures at assisted reproduction centres were the subjects of the study, which used a quantitative methodology. Embryonic factors were considered alongside age, BMI, oocyte shape, fertilisation, embryo growth, embryo implantation rate, and pregnancy success rates. A chi-square test, descriptive statistics, regression models, and correlation analyses were all used to analyse the data statistically. The results show that the mother's oocyte quality and body mass index (BMI) significantly affect live birth rates, embryo growth, embryo implantation rate, and fertilisation success. There was a correlation between poor oocyte quality and high maternal BMI, lower rates of IVF success, and lower chances of live births.

Syphilis is a chronic and multi-stage infectious disease caused by Treponema pallidum, which has a rapid spread, resistance to immune responses, and chronic infection. This review is a synthesis of animal evidence to study the pathogenesis, clinical synergies, diagnostic plans, treatment plans, and epidemiological implications of the disease. The use of animal models, especially rabbits, has been critical in understanding the interaction of the host and pathogen, development of lesions, and immunological reactions. This research indicates the relative performance of the traditional and reverse screening algorithm, which shows that reverse screening has a better sensitivity during both early and latent periods, whereas the traditional approach is useful in monitoring active infection. The development of molecular diagnostics, particularly PCR and immunoassays, has improved early diagnosis and evaluation of the disease, whereas penicillin remains the most effective treatment despite the emerging resistance issues in other treatments. Additionally, experimental epidemiological research adds to the knowledge on the dynamics and persistence of transmission. Nevertheless, animal model limitations and issues with vaccine development because of immune evasion remain a major problem. The review highlights the necessity of a better experimental model, combined diagnostic, and novel treatment and vaccine options to improve the management of the disease and future research outcomes.