Yash Srivastav Srivastav

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.

Liquid biopsy using circulating tumor DNA (ctDNA) is gaining momentum as a powerful non-invasive tool for multi-cancer early detection (MCED) and precision medicine. The current paper highlights the role of artificial intelligence (AI), encompassing machine learning and deep learning techniques, in refining the analytical process of ctDNA to facilitate early cancer detection, diagnosis, prediction of the tissue of origin, and individualized disease management. Clinical trials in humans for various cancers, such as lung, colorectal, pancreatic, breast, and ovarian cancer, illustrate the ability of AI-enhanced ctDNA technology to detect even minute molecular changes in terms of mutations, epigenetic patterns, fragmentation features, and chromosome anomalies with higher sensitivity and specificity. In addition, the review highlights the biological relevance of ctDNA, the clinical utility of AI-based MCED systems, and the benefits of non-invasive testing, continuous surveillance, and detection of multiple cancers through a single blood sample. However, significant drawbacks, including low ctDNA concentration in early-stage tumors, false positives and negatives, non-standardization, ethical issues, and expensive technology, are substantial impediments to clinical adoption. Nonetheless, AI-powered ctDNA diagnostics hold immense promise for revolutionizing cancer screening in the future.

The fusion of graphene neural interfaces with adaptive artificial intelligence (AI) systems has become a critical breakthrough in human-centred human-centred neurotechnologies and personalised healthcare. Graphene has an outstanding electrical conductivity, flexibility, transparency, light architecture and biocompatibility, making it an ideal material for wearable and implantable neural devices. At the same time, artificial intelligence systems that adapt their performance benefit the interpretation of the neural signals, learning in real time, signal recognition, and performance of rehabilitation. This review covers the structural and functional characteristics of graphene neural interfaces, adaptive AI in neural signal processing, and the synergy and application of both to brain–computer interfaces (BCIs), neuroprosthetics, assistive communication systems, and personalized neurotherapy. Humans studies show that graphene-AI systems have boosted the stability of neural signals, motor control, speech decoding and rehabilitation efficiency, as well as neural monitoring and remote healthcare. The review also covers critical issues like long-term biocompatibility, privacy of neural data, algorithmic transparency, cybersecurity, and regulatory approval. While small-scale clinical trials and the absence of standardized frameworks pose challenges, the potential applications of graphene-AI combination in neurological rehabilitation and intelligent healthcare systems are promising.

Biomimetic robotics is a cross-disciplinary area combining human biomechanics, robotics, artificial intelligence, and materials science in the development of robotic systems that can mimic human movements and functionality. The current review explores the connection between human musculoskeletal kinematics and advanced high-torque robotic actuation systems through the discussion of biomechanical theory, robotic actuator technologies, biomimetic mapping techniques, and innovative developments in the field of robotic engineering. Modern approaches such as motion capture, electromyography (EMG), inverse dynamics, biomechanical modeling, and artificial intelligence-controlled systems enable enhanced accuracy of movements, sensor fusion, and human-robot interaction in the robotic systems. Research shows that biomimetic robotics enables enhanced adaptability, efficiency, and safety during interactions with humans; nevertheless, it is still difficult to mimic the complex functionality and energy efficiency of the human musculoskeletal system. In addition, this study focuses on the innovative advancements in the field such as brain-computer interfacing and AI-enabled adaptive robotic systems.

Fetus in fetu (FIF) is a rare congenital anomaly that occurs when a malformed parasitic twin grows within the host twin (most frequently in the retroperitoneum). FIF is a rare condition, with a high diagnostic and surgical challenge for neonate and infant patients due to its similarity to retroperitoneal teratoma. The purpose of this study was to review the clinical presentation, radiological findings, surgical management and outcomes of FIF in neonates and infants by analyzing 80 cases of FIF reported between 2000 and 2025. Pediatric surgery journals, radiology reports, medical databases such as PubMed, Scopus, and Google Scholar, were used to collect data. The results indicated that the abdominal distention and palpable abdominal mass were the most common symptoms and male infants were more frequently affected. CT scan and MRI were very helpful for the identification of vertebral columns, limb buds and calcified skeletal structures, which aided in differentiating FIF from retroperitoneal teratoma. Surgical resection led to good postoperative results, with low recurrence and postoperative complications. All cases were diagnosed by histopathological examination. Prompt diagnosis and surgical intervention are still vital for favorable management and neonatal outcomes.

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.

This current study focuses on the impact of Artificial Intelligence (AI) on the rapid identification of new molecules to suppress aging processes, which increases the proportion of healthspan relative to lifespan. The research approach taken involved a quantitative method, where artificial intelligence-based machine learning, bioinformatics, and statistical analysis were used alongside computational molecular docking. Biochemical information from databases such as PubChem, DrugBank, and ChEMBL was leveraged to screen and analyze molecular data. It was observed that AI-assisted predictive models, especially Deep Learning Neural Networks, offered highly accurate predictions concerning the biological activity of anti-aging compounds. The selected molecules showed considerable decreases in oxidative stress, inflammation, and cellular senescence markers, coupled with improved mitochondrial function and cell repair. Moreover, quantitative results showed that the use of AI for predicting the efficacy of anti-aging agents led to more significant healthspan enhancements than lifespan increases.

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.

NTDs persist in animals’ populations because of complicated interactions between livestock and wildlife reservoirs, vectors, and the environment. The present review offers the in-depth analysis of the current developments and the current issues in the control of NTDs with particular attention to the animal-based data and environmental determinants. It outlines the importance of animal reservoirs in perpetuating transmission cycles and explores how ecological factors like climate variability, land-use alterations and the dynamics of vectors affect disease persistence. The review also analyzes the progress of next-generation diagnostics, such as molecular, biosensors, and environmental DNA (eDNA), which have contributed to a considerable enhancement in the accuracy of detection and surveillance. Nevertheless, constraints connected to field applicability, expensive nature, and disjointed surveillance systems remain a barrier to successful implementation. The results highlight the need to consider the incorporation of both animal health surveillance and environmental surveillance to improve early detection and control interventions. Moreover, review indicates important gaps in research such as the underrepresentation of wildlife reservoirs and the lack of scalable and cost-effective diagnostic tools. On the whole, it highlights the need to implement interdisciplinary and combined solutions to ensure sustainable and effective management of NTDs in animals.