Yash Srivastav

Somatic Cell Nuclear Transfer (SCNT) is an essential process for cloning used in developmental and regenerative medicine. In the current investigation, there is a discussion about the mechanisms involved in genome replication, as well as the factors influencing clone development during SCNT. As a result of the analysis, it was found that cloned embryos have low developmental efficiency due to the insufficient epigenetic reprogramming, improper DNA methylation, problems with mitochondria, high oxidative stress, and insufficient activation of pluripotency genes. Comparing the SCNT embryos with the fertilized ones, there was a difference between the rate of developmental abnormalities and embryo survival. Moreover, the research suggests the possibility to use new techniques, including CRISPR-based epigenetic reprogramming, artificial intelligence-based monitoring of genomes, incorporation of stem cells, and artificial egg activation, to improve the cloning process. Despite some scientific achievements made within SCNT and related areas, many difficulties connected with ethics and biological aspects still do not allow to conduct human reproductive cloning. Therefore, the future research should concentrate only on therapeutic purposes of SCNT.

The current research study explored the physiological and psychological shifts that take place in the bodies of transgender men and transgender women on undergoing hormone therapy. A quantitative comparative study design was employed involving 120 individuals – 60 transgender men on testosterone therapy and 60 transgender women on estrogen and anti-androgen therapy over a period of one year. The study involved data collection using methods such as anthropometry, hormonal profiling, laboratory testing, cardiovascular examination, and psychosocial measures. The results showed marked physiological transformations in the form of increased muscle mass and elevated hemoglobin content among transgender men, and increased body fat accumulation and breast growth and decreased muscle mass among transgender women. In addition, there were certain effects noted on the metabolism and cardiovascular system as a result of hormone therapy. The psychological effects included better emotional well-being, improved body image and self-esteem, and lower levels of anxiety and depression.

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.

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

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.

Eyelid twitching and involuntary facial muscle spasms have become common neuromuscular disorders due to stress, anxiety, sleeplessness, prolonged computer usage, exhaustion, and other external factors. The purpose of this review is to discuss various neurophysiological, psychosomatic, environmental, and medical aspects of eye twitching disorders such as eyelid myokymia, hemifacial spasm, and blepharospasm in highly stressed people. Human research demonstrates that chronic stress along with dysfunction in the autonomic nervous system plays an important role in neuromuscular hyperactivity and ocular muscle spasms. Magnesium is discussed in this review as an important nutrient for nerve signaling, muscle relaxation, and neurotransmitter function. Therefore, magnesium intake in combination with stress management methods like meditation, yoga, and sleep may help alleviate the symptoms of eyelid twitches. Neurological complications like hemifacial spasm and blepharospasm generally require the intervention of drugs, neurological procedures like botulinum toxin injection therapy, anticonvulsants, and microvascular decompression surgery. The review also touches upon the effects of prolonged muscular spasm within the eye muscles on emotions, occupation, and quality of life from a psychosocial perspective. While previous human-based studies have shed light on various clinical aspects of the subject, there remain certain issues like small sample size, variation in therapeutic protocols, and absence of longitudinal studies that underscore the need for further clinical research.

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.