Artificial Intelligence

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

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.

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.

Brain-targeted drug delivery represents one of the most challenging frontiers in pharmacology due to the presence of the blood–brain barrier (BBB), enzymatic degradation, and efflux transporters. Integration of biopharmaceutics with neuropharmacology has enabled the design of advanced delivery systems that enhance drug bioavailability, specificity, and therapeutic efficacy in central nervous system (CNS) disorders. This review explores current strategies for brain-targeted delivery, including nanoparticles, liposomes, polymeric carriers, and ligand-mediated systems. Mechanistic insights into BBB penetration, pharmacokinetic considerations, and clinical translation challenges are discussed. Future directions involve stimuli-responsive carriers, nanotheranostics, and precision neuropharmacology for personalized CNS therapy.

Background: Liver, breast, kidney, and brain cancers remain major contributors to global cancer morbidity and mortality. Conventional therapies are limited by systemic toxicity, drug resistance, and tumor heterogeneity. Smart nanoplatforms offer targeted delivery, controlled release, and theranostic capabilities to address these challenges. Objective: This systematic review evaluates the development and clinical translation of smart nanoplatforms between 2019 and 2024, focusing on their design, omics integration, therapy response, and clinical outcomes in liver, breast, kidney, and brain cancers. Methods: Studies published between 2019 and 2024 were systematically analyzed, encompassing preclinical research, clinical trials, and multi-omics-guided nanoparticle strategies. Nanoplatforms were categorized into lipid-based, polymeric, inorganic, and hybrid/bioinspired systems. The review highlights therapy response, biomarker monitoring, and adaptive approaches informed by omics data. Results: Lipid-based and polymeric nanoparticles demonstrated enhanced tumor targeting and reduced systemic toxicity. Inorganic and hybrid/bioinspired platforms enabled imaging-guided therapy and immune evasion. Integration of genomics, transcriptomics, proteomics, and metabolomics with AI-driven analytics facilitated personalized therapy and adaptive treatment strategies. Clinical trials reported improved patient tolerability, quality of life, and preliminary survival benefits, though translational barriers—including tumor heterogeneity, blood–brain barrier penetration, manufacturing, and regulatory hurdles—remain significant. Conclusions: Smart nanoplatforms represent a transformative approach to precision oncology. The combination of targeted delivery, multi-omics guidance, and AI-driven therapy optimization has the potential to enhance treatment efficacy and patient-specific outcomes. Future research should focus on scalable manufacturing, regulatory standardization, and integration of innovative trial designs to accelerate clinical adoption.

Aim and Objectives: This study aims to evaluate the role of physiotherapy in pain modulation and to provide mechanistic insights into non-pharmacological interventions that complement or substitute pharmacologic therapies. The objectives include reviewing underlying neurophysiological mechanisms, therapeutic techniques, and clinical evidence supporting physiotherapy in chronic pain management. Methodology:A literature review was conducted using PubMed, Scopus, and Web of Science, focusing on studies published between 2005 and 2025. Randomized controlled trials, systematic reviews, and mechanistic studies examining physiotherapy modalities—such as exercise therapy, manual therapy, transcutaneous electrical nerve stimulation (TENS), and therapeutic ultrasound—were analyzed. Results: Evidence suggests that physiotherapy alleviates pain through multiple mechanisms including modulation of nociceptive signaling, enhancement of endogenous opioid release, reduction of central sensitization, and improvement of musculoskeletal function. Interventions such as exercise therapy and TENS demonstrated significant reductions in pain intensity, improved mobility, and decreased reliance on pharmacological agents across diverse patient populations. Conclusion: Physiotherapy offers a safe, effective, and mechanistically validated non-pharmacological strategy for chronic pain management. By targeting neural, muscular, and psychosocial components of pain, physiotherapy serves as a vital adjunct or alternative to pharmacological treatments, promoting long-term functional recovery and enhanced quality of life.