Yash Srivastav

VEXAS syndrome (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic syndrome) is a more recently described, acquired somatic mutation in the UBA1 gene, and is an autoinflammatory disorder of adults. Its clinical features include systemic inflammation, clonal hematopoiesis, abnormalities of the bone marrow and various hematological, rheumatological and dermatologic changes. This review discusses the molecular pathogenesis of VEXAS syndrome with emphasis on the UBA1 mutations, immune dysfunction and disease pathogenesis. In addition, it emphasizes the importance of artificial intelligence, next generation sequencing (NGS), integration of multi-omics and three-dimensional (3D) genomic fingerprinting in the improvement of diagnosis and molecular characterization. The current therapeutic options such as the use of corticosteroids, JAK inhibitors, azacitidine and biologic agents, as well as novel stem cell-based therapies are reviewed. Special focus is on the most promising potentially curative therapy, namely, allogeneic hematopoietic stem cell transplantation (HSCT). Although genomic medicine and precision diagnostics has come a long way, there are still issues to be addressed, particularly with respect to diagnosis at an early stage and longer-term management. The synergy of genomics, artificial intelligence (AI), and regenerative medicine could have a profound impact on the care of VEXAS syndrome patients in the future.

Lassa fever and septic fever are important infectious diseases which cause high morbidity, mortality and healthcare burden, especially in resource-limited areas. The clinical features of both diseases are so similar, and include fever, hypotension, respiratory failure, and multi-organ failure, that early diagnosis and treatment are challenging. The clinical features, epidemiological features, risk factors, early clinical predictors and biomarkers of disease severity and mortality are analyzed. Prognosis assessment and therapeutic monitoring is an important role of a number of biomarkers, such as lactate, procalcitonin, cystatin-C, and inflammatory cytokines. The review also notes that artificial intelligence, machine learning, 3D imaging technologies and epidemiological surveillance systems are increasingly playing a key role in the prediction, monitoring and clinical decision-making of diseases. The combination of biomarker-based diagnostics, AI-driven predictive modelling, and digital healthcare technologies could greatly enhance the management of infectious diseases and the preparedness of healthcare systems. The multidisciplinary research and international co-operation continue to be critical to decrease the global burden of severe infectious diseases.

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.

Critical Congenital Heart Disease (CHD) is one of the most important causes of neonatal morbidity and mortality and early prenatal diagnosis is essential for effective treatment and better clinical outcomes. In this study, ECHOFOCUS-CHD, an AI-inspired autonomous framework for the diagnosis of Critical Congenital Heart Disease from ultrasound images of fetuses is proposed. A quantitative experimental research design was used and 200 prenatal ultrasound scans acquired from hospitals and fetal echocardiography databases are used. A Convolutional Neural Network (CNN) was created to classify fetal cardiac conditions into normal, mild, moderate and critical CHD categories. The models are enhanced by using image preprocessing methods like noise reduction, normalization, contrast enhancement, and data augmentation. An overall accuracy of 94.5%, sensitivity of 92.8%, specificity of 95.6% with an AUC value of 0.96% was obtained, which shows an excellent diagnostic capability in the proposed system. The reliability of the framework was also verified using confusion matrix and ROC curve analyses. The system was also able to automatically classify the heart defects of foals using images from their prenatal scans, aiding clinical decisions during pregnancy and early treatment planning. The results show that AI-powered prenatal ultrasound analysis has the potential to greatly improve the effectiveness of early detection of CHD and prenatal screening.

Brain-Computer Interfaces (BCIs) have been proposed as assistive technologies for Locked-In Syndrome (LIS) patients that can facilitate communication based on decoding of neural signals. Traditional BCI systems based on recurrent neural network (RNN) models exhibit certain constraints in terms of decoding accuracy, communication speed, and response latency. The current study aims to assess the effectiveness of transformer-based frameworks in optimizing the efficiency of both invasive and non-invasive BCI systems as compared to classical RNN models. A computational-clinical study design was used which involved participation of 48 LIS or severely paralysed participants. Subjects were grouped in accordance with their involvement in invasive or non-invasive BCI groups, and assessments were conducted during a period of eight weeks of intervention. Neural activity data processing was done with the help of two different approaches, including transformer-based model application and RNN application, assessing communication speed, decoding accuracy, latency, and error rates of both systems. Results suggest that transformer-based neural decoding frameworks proved to be superior to RNNs in terms of all evaluated criteria. Invasive transformer-based BCI demonstrated the best results concerning communication speed, decoding accuracy, lowest latency, and lowest error rates. Non-invasive transformer BCIs also yielded better results than RNN-based BCIs.

Human-Robot Interaction (HRI) has become an essential area that combines AI, robotics, affective computing, and healthcare technologies. This study considers the application of artificial intelligence (AI) in managing emotional connections, multisensory interaction, and psychological support in robot-led pregnancy systems. This article focuses on emotional recognition systems, multisensory communication, biosensors, adaptive robotics, ethical questions, social acceptance issues, and future advancements in maternal healthcare robotics. There is existing evidence suggesting that the use of emotionally intelligent robot systems may help improve maternal mental state, minimize the negative effects of stress and anxiety, increase engagement in healthcare processes, and give individualized assistance during pregnancy. Machine learning (ML) techniques, natural language processing, affective computing, and physiological sensors play a major role in enhancing the emotional intelligence of robotic healthcare technologies. At the same time, there are some difficulties related to the authenticity of emotions, privacy issues, emotional dependence, biased algorithms, and other ethical concerns that restrict their application. Overall, robot-led pregnancy systems demonstrate great potential as maternal healthcare technology solutions.

Infectious leucorrhoea is one of the most prevalent diseases of gynecologic nature involving infection of the reproductive system by fungi, bacteria, and parasites. Recurrent vaginal infections may lead to microbial cross-infections between male sex partners, adversely affecting sexual relations and intimate connections in the couple. This paper attempted to examine the problem of cross-infection, the state of urogenital health of men involved in the research, and the effect of infectious leucorrhoea on sexual arousal in both parties. A cross-sectional observational clinical study was carried out among 80 couples undergoing gynecology and urology clinics visits due to complaints of infectious vaginal discharge. Clinical evaluation, microbial investigation, laboratory tests, and questionnaire were used in the process of information collection. The results have shown that C. albicans was the most common pathogen among women in the sample group. Dysuria, balanitis, and penile irritation were found among men involved in the research, suggesting possible cross-infection from women. Sexual desire loss and avoidance behavior were noticed as well. Analysis of statistics indicates that there were highly significant relationships between infections with leucorrhoea, urogenital problems among men, and compromised sexual wellbeing (p

Conjoined twinning is a very rare congenital disorder that results from partial separation during the development of the embryos in cases of monozygotic twins. Male-female symmetrical conjoined twins with an opposite phenotype in relation to their biological sex are an extremely rare developmental abnormality due to the complications involved, from an embryological, genetic, hormonal, clinical, and ethical standpoint. In this review, we discuss the embryological causes of conjoined twinning, sexual differentiation processes, and the potential causes of discordant phenotypical sex development through chromosomal mosaicism, epigenetics, asymmetry of hormone distribution, or receptors. Furthermore, Disorders of Sex Development (DSD), prenatal diagnosis and molecular analyses, psychosocial impacts, surgery, and ethical issues related to sexual discordance among conjoined twins are evaluated. Our current scientific knowledge is limited since such cases are extremely rare.

The process of aging is a complicated biological phenomenon that is marked by a loss of functionalities and high susceptibility to illness. Recent developments in geroscience have narrowed down the classical paradigm of aging processes into the so-called Hallmarks of Aging 2.0 where molecular pathways including genomic instability, epigenetic changes, mitochondrial dysfunction, chronic inflammation, and cellular senescence are connected. The buildup of senescent cells also known as the zombie cells has come out as one of the major causes of tissue degeneration during the aging process. These cells are metabolically active and irreversibly differentiate but continue to secrete pro-inflammatory factors referred to as senescence-associated secretory phenotype (SASP). There are two key therapeutic approaches that have been optimized to combat cellular senescence; senolytics, which are specific to eliminate senescent cells, and senomorphics, which inhibit the pathogenic secretory phenotype of senescent cells, but do not kill them. The current review presents an overview of the current results of animal-based experimental research on the molecular hallmark of aging and compares the relative efficacy of senolytic and senomorphic treatment in regulating aging pathways. The article identifies the most important experimental models, mechanisms of action, therapeutic potential and limitations of the existing approaches. The knowledge of these strategies offers an insightful critical view of longevity science and can lead to new possibilities in creating anti-aging interventions to work on basic biological processes.

Three-dimensional (3D) printing has become a transformative technology in pharmaceutical sciences, allowing the creation of customized drug delivery systems with precise control over shape, dosage, and release profiles. This review offers a thorough overview of 3D printing methods used in pharmaceuticals, including their materials, design strategies, characterization techniques, and current clinical and regulatory environments. Applications encompass personalized medicine, polypills, controlled-release implants, and new pediatric formulations. Despite its significant potential, 3D printing faces challenges related to scalability, quality control, and regulatory approval. Recent FDA approvals, notably of the first 3D-printed drug Spritam®, represent important milestones. Future advancements depend on unified guidelines, digital manufacturing integration, and AI-driven formulation improvements.