Journal of Clinical and Biomedical Research
The Journal of Clinical and Biomedical Research (J Clin Biomed Res) is an international, peer-reviewed, open-access journal that publishes high-quality research in clinical medicine, biomedical sciences, and medical education. The journal provides a scholarly platform for medical doctors, medical educators, master’s students, and PhD researchers to share original research, reviews, clinical studies, and educational innovations. J Clin Biomed Res emphasizes evidence-based medicine, translational research, and studies that bridge basic science with clinical practice. With a commitment to ethical publishing, rigorous peer review, and global accessibility, the journal aims to support academic development and contribute to the advancement of modern healthcare and biomedical research.
Journal of Clinical and Biomedical Research (JCBR)
Teaching Medical Students in English in Uzbekistan: A Comprehensive Review
English-medium instruction (EMI) and English for Specific Purposes (ESP) have become increasingly significant in Uzbekistan’s medical education. Driven by globalization, the need to access international literature, and national reforms, Uzbek medical institutions are integrating English into curricula. This review examines current research on pedagogical strategies, student outcomes, challenges, and future directions in teaching English to medical students in Uzbekistan. Findings underscore improved professional communication and academic competencies, alongside persistent challenges in language proficiency and resource allocation.
Copyright (c) 2026 Journal of Clinical and Biomedical Research
Modern Therapeutic Approaches in Clinical Medicine: From Conventional Treatment to Personalized Therapy
Therapy remains the cornerstone of clinical medicine, evolving continuously with advances in biomedical science, pharmacology, and digital health technologies. Traditional therapeutic strategies, largely centered on standardized pharmacological interventions, are increasingly complemented by non-pharmacological modalities, biologic agents, and personalized treatment approaches. This review synthesizes current concepts in modern therapy, focusing on pharmacological treatment, physical and behavioral interventions, targeted and biologic therapies, and emerging personalized and precision medicine strategies. Emphasis is placed on therapeutic mechanisms, clinical effectiveness, safety considerations, and patient-centered outcomes. The article highlights how integrative and individualized therapies are reshaping disease management across chronic, metabolic, inflammatory, and oncological conditions, while also outlining future directions for research and clinical practice.
Bridging Local Practice and Global Standards: The Rise of English Medium Medical Education in Uzbekistan
English has become a central component of medical education reform in Uzbekistan, driven by policies aimed at aligning curricula with international standards, expanding access to global medical literature, and increasing the country’s attractiveness to foreign students. Medical universities have introduced English‑medium instruction (EMI) programs, strengthened English for Specific Purposes (ESP) and medical English courses, and established institutional language centers to support both students and faculty. Recent initiatives, including partnerships with international organizations, have focused on developing pedagogically sound approaches that integrate language learning with clinical content, promote student‑centred methods, and enhance assessment practices. Empirical reports highlight substantial benefits of English‑medium and English‑supported training for Uzbek and international medical students, particularly in terms of access to evidence‑based resources, preparation for international examinations, and opportunities for academic mobility, while also documenting persistent challenges related to uneven English proficiency, limited teaching materials, and faculty capacity.
Impact of Integrated Medical English Instruction on Uzbek Medical Students’ Reading Proficiency: A Quasi‑Experimental Study
English proficiency has become essential for Uzbek medical students to access international literature and participate in global medical practice, yet many enter university with limited skills in reading and understanding specialized texts. This study evaluated the impact of an integrated English for Medical Purposes (EMP) module, combining explicit grammar teaching, medical vocabulary instruction, extensive reading, and communicative tasks, on first‑ and second‑year medical students’ reading proficiency at a Tashkent medical university. A quasi‑experimental pre‑test/post‑test design compared an intervention group receiving the integrated EMP module with a control group following the standard general English syllabus over one semester. Reading comprehension tests based on authentic medical articles and a self‑report questionnaire on reading strategies and confidence were administered at baseline and after 16 weeks
Advancing Medical Training Through English: Integrating Medical English Instruction for Uzbek Medical Students
English proficiency has become essential for Uzbek medical students to access international literature and participate in global medical practice, yet many enter university with limited skills in reading and understanding specialized texts. This study evaluated the impact of an integrated English for Medical Purposes (EMP) module, combining explicit grammar teaching, medical vocabulary instruction, extensive reading, and communicative tasks, on first‑ and second‑year medical students' reading proficiency at a Tashkent medical university. A quasi‑experimental pre‑test/post‑test design compared an intervention group (n=64) receiving the integrated EMP module with a control group (n=62) following the standard general English syllabus over one semester. Reading comprehension tests based on authentic medical articles and a self‑report questionnaire on reading strategies and confidence were administered at baseline and after 16 weeks.
Integrating Simulation-Based Anatomy and Operative Skills Training for Medical Students: A Narrative Review
Contemporary surgical practice demands that graduating medical students possess not only sound anatomical knowledge but also foundational operative skills acquired in a safe, structured learning environment. Traditional cadaveric dissection has long been central to anatomy education and early surgical training; however, growing constraints on theatre exposure, work‑hour limitations, and patient safety concerns have accelerated the adoption of simulation‑based approaches. Recent evidence shows that simulation modalities ranging from low‑fidelity bench models and three‑dimensional (3D)‑printed organs to high‑fidelity virtual reality (VR) systems can enhance anatomical understanding, suturing performance, and overall technical proficiency among medical students. Studies comparing cadaveric and simulator‑based training highlight that cadavers remain the gold standard for appreciating realistic tissue handling and complex three‑dimensional relationships, whereas simulators offer repeatability, structured feedback, and risk‑free practice.
Assessment Of Clinical Severity in Children with Seasonal Respiratory Viral Infections: A Vital Sign–Centered Approach in the Emergency Setting
Seasonal respiratory viral infections are among the most frequent causes of pediatric emergency department (ED) visits, yet early recognition of children at risk of deterioration remains challenging when diagnostic resources are limited. This study aimed to evaluate the relationship between admission vital signs and clinical severity among children presenting with seasonal respiratory viral infections in a tertiary pediatric center. We conducted a cross-sectional analysis of 140 children aged 1 month to 15 years admitted from the ED with clinictriageal syndromes consistent with influenza-like illness or acute respiratory infection during peak viral seasons. Demographic characteristics, presenting syndromes, and vital signs (temperature, heart rate, respiratory rate, blood pressure, and oxygen saturation) were recorded at triage and categorized as normal or abnormal using age-adjusted thresholds. Children were stratified into mild, moderate, and severe clinical groups according to need for oxygen therapy, intensive monitoring, or intensive care transfer.
Stratifying Severity in Children with Seasonal Respiratory Viral Infections: A Vital Sign–Derived Risk Gradient in the Emergency Department
Seasonal respiratory viral infections place a recurrent burden on pediatric emergency departments (EDs), yet robust, bedside tools to stratify clinical severity remain limited. This study aimed to evaluate how patterns of admission vital sign abnormalities differ across severity levels in children with seasonal respiratory viral infections and to quantify these differences using simple statistics and bar graph comparisons. We conducted a cross-sectional analysis of 150 children aged 1 month to 15 years admitted from a tertiary pediatric ED during peak viral seasons with clinical syndromes consistent with acute respiratory infection or influenza-like illness. Children were categorized as mild, moderate or severe based on oxygen and ventilatory support requirements and need for intensive care. At triage, temperature, heart rate, respiratory rate, blood pressure and oxygen saturation were recorded and classified as normal or abnormal according to age-adjusted thresholds. Overall, fever was present in 80.0%, tachycardia in 60.7%, tachypnea in 58.0%, hypoxia (SpO₂ < 92%) in 30.0% and hypotension in 10.7% of patients.
Early Identification of High-Risk Children with Seasonal Viral Infections: Integrating Vital Signs into Emergency Decision-Making
Seasonal viral infections remain a major driver of pediatric emergency department (ED) visits, yet distinguishing self-limiting illness from incipient severe disease at first contact is often challenging. This study aimed to explore how combinations of routine vital signs can be used to flag children at elevated risk of clinical deterioration during seasonal viral peaks. We conducted a cross-sectional analysis of children aged 1 month to 15 years presenting with fever and respiratory symptoms consistent with seasonal viral infection and subsequently admitted to a tertiary pediatric center. Demographic characteristics, clinical syndromes and admission vital signs (temperature, heart rate, respiratory rate, blood pressure and oxygen saturation) were recorded and classified using age-adjusted thresholds. Children were grouped into a low‑risk and a high‑risk category based on early outcomes: need for oxygen therapy, escalation of respiratory support, or transfer to intensive care within the first 24–48 hours.
Clinical Features and Pain Burden in Women with Endometriosis: A Prospective Study of 56 Patients
Endometriosis is a chronic, estrogen-dependent gynecological disease characterized by ectopic endometrial-like tissue and associated with pelvic pain, dysmenorrhea and subfertility. It substantially impairs quality of life and work productivity, yet symptom patterns and pain burden remain heterogeneous even among women treated in the same center. This prospective study aimed to describe the clinical profile, pain characteristics and functional impact of endometriosis in 56 consecutively managed patients in a gynecology department. Women of reproductive age with laparoscopically or imaging-confirmed endometriosis were enrolled and assessed using standardized questionnaires for pain intensity, pain localizations and daily-life interference, alongside clinical and surgical data. The most frequent symptoms were dysmenorrhea (89%), chronic pelvic pain (71%) and dyspareunia (55%), with 43% reporting pain associated with defecation or urination.
Gut Microbiota–Targeted Therapy as an Emerging Adjunct in Endometriosis Management: A Narrative Review
Endometriosis is now recognized as a chronic, systemic inflammatory disorder affecting an estimated one in ten women of reproductive age worldwide and imposing a substantial burden on pain, fertility and quality of life. Emerging evidence indicates that dysbiosis of the gut microbiota contributes to endometriosis pathogenesis by modulating estrogen metabolism, immune activation and systemic inflammation, creating a new therapeutic frontier focused on microbiome-targeted interventions. This narrative review synthesizes recent data on gut–endometriosis interactions and evaluates the potential of microecological therapies—including probiotics, prebiotics and fecal microbiota transplantation—as adjuncts to established surgical and hormonal treatments. Experimental models and early clinical studies suggest that selected Lactobacillus-based probiotics and broader microecological regimens can reduce inflammatory cytokines, attenuate lesion growth and improve postoperative recovery when combined with laparoscopy.
Precision IVF in 2025: AI, Genomics, and Microfluidic Technologies Reshaping Assisted Reproduction
In vitro fertilization has entered a new phase in 2025, marked by integration of artificial intelligence, advanced genomics and laboratory automation into routine clinical practice. AI‑assisted embryo selection using time‑lapse imaging and deep‑learning algorithms improves objectivity and consistency in ranking embryos, potentially shortening time to pregnancy and reducing multiple embryo transfers. Parallel advances in next‑generation sequencing and emerging non‑invasive preimplantation genetic testing expand the capacity to detect chromosomal and some monogenic abnormalities while minimizing embryo manipulation. Personalized ovarian stimulation protocols now leverage hormonal profiling, pharmacogenomics and data‑driven modeling to balance oocyte yield against safety and patient comfort, while microfluidic platforms optimize sperm selection and gamete handling under more physiological conditions.
Trends and Challenges in Robotic and Minimally Invasive General Surgery: A Narrative Review
Background: Minimally invasive surgery (MIS), including laparoscopic and robotic techniques, has transformed general surgery by reducing postoperative pain, hospital stay, and complications, while maintaining or improving clinical outcomes. Methods: This narrative review synthesizes recent evidence and expert commentary on trends in MIS and robotic general surgery, focusing on upper gastrointestinal, hepatobiliary, and colorectal procedures. Literature from 2019–2025 was identified through targeted searches of PubMed and major surgical society outputs, emphasizing comparative effectiveness studies, cost‑effectiveness analyses, and implementation research. Results: Across multiple procedures, MIS is associated with shorter length of stay, lower wound complication rates, and faster recovery compared with open surgery, without compromising oncologic adequacy in most settings. Robotic platforms enhance dexterity and visualization, facilitating complex reconstruction and difficult pelvic dissection, but increase direct costs and require steep learning curves. Conclusions: MIS and robotic surgery are now central to general surgical practice, offering clear perioperative benefits but posing challenges in cost, training, and resource allocation.
Impact of a Simple Preoperative Preventive Care Bundle on Early Postoperative Complications in Gastrointestinal Surgery
Background: Postoperative complications after gastrointestinal surgery worsen outcomes but are influenced by modifiable risk factors; prehabilitation, smoking cessation, and optimization of nutrition and perioperative care may reduce morbidity and improve recovery. Methods: In a prospective cohort of 50 adults undergoing major GI surgery at the Surgery‑2 Department, Fergana Emergency Medical Center, a preventive bundle (preoperative counseling, smoking cessation advice, nutritional screening, early mobilization, early oral intake) was implemented and patients were followed for 30 days; primary outcome was any complication (Clavien–Dindo ≥II), with secondary outcomes including length of stay, postoperative ileus, surgical site infection, and 30‑day readmission. Results: Median age was 56 years; 60% were male, 30% active smokers, and 40% at nutritional risk; overall, 28% developed complications, with severe events in 8%, ileus in 12%, and surgical site infection in 10%, and median postoperative stay was 7 days. Patients adherent to ≥3 bundle components had fewer complications (20% vs 38%) and shorter stays (6 vs 9 days) than less adherent patients. Conclusions: A simple, low‑cost preventive care bundle combining prehabilitation‑like elements and enhanced recovery measures was feasible in this resource‑limited GI surgery unit and was associated with fewer complications and shorter hospitalization among adherent patients, supporting the need for larger controlled studies.
Structural and Clinical Characterization of Gynecomastia: A Cross-Sectional Analysis of Histopathological Patterns, Hormonal Profiles, and Surgical Outcomes
Background: Gynecomastia is the most common male breast condition, yet studies integrating histopathological features with hormonal and clinical parameters remain limited. This study evaluated gynostructural characteristics in relation to clinical grade, hormonal profiles, and surgical outcomes. Methods: A cross-sectional study of 186 males with true gynecomastia who underwent surgical excision was conducted (2020–2024). Specimens were classified as florid, intermediate, or fibrous. Preoperative hormonal profiles and postoperative satisfaction were assessed. Results: Fibrous type predominated (48.9%). Estradiol (p < 0.001) and testosterone (p = 0.003) differed significantly across histopathological types. Clinical grade correlated with histopathological pattern (χ² = 24.87, p < 0.001). Liposuction-assisted mastectomy yielded higher satisfaction than open excision (8.6 vs. 7.5; p = 0.002). Psychological function improved postoperatively (median 42 → 71; p < 0.001). Conclusion: Gynostructural features correlate with disease duration, hormonal milieu, and clinical severity, supporting integrated histopathological-hormonal assessment for individualized management.
Chronic Otitis Media: Current Perspectives on Diagnosis and Management — A Comprehensive Review
Chronic otitis media (COM) is one of the most prevalent conditions in otorhinolaryngology, affecting approximately 297 million people globally, with a disproportionate burden in low- and middle-income countries. The disease is characterized by persistent inflammation of the middle ear mucosa, tympanic membrane perforation, recurrent otorrhea, and progressive hearing loss that significantly impairs quality of life. This review provides a comprehensive synthesis of current evidence on the epidemiology, etiology, pathogenesis, clinical presentation, diagnostic approaches, and therapeutic strategies for COM, with particular emphasis on updates relevant to clinical practitioners. Recent advances in high-resolution computed tomography, diffusion-weighted magnetic resonance imaging, endoscopic ear surgery, biofilm-targeted therapies, and regenerative approaches using platelet-rich plasma are discussed. The role of vaccination in disease prevention and bone conduction hearing implants in auditory rehabilitation is also addressed.
Artificial Intelligence–Assisted Quantitative Histology for Early Detection of Cellular Damage: A Multimodal Morphometric Study
The integration of artificial intelligence (AI) into histopathology represents a rapidly advancing frontier in medical biology. This study evaluates the diagnostic accuracy of AI-assisted quantitative morphometric analysis in detecting early cellular damage in comparison with conventional light microscopy. A total of 180 histological samples subjected to controlled oxidative and inflammatory injury were analyzed using both traditional assessment and deep learning–based digital image segmentation. Quantitative parameters including nuclear-cytoplasmic ratio, chromatin condensation index, mitochondrial density, cytoplasmic vacuolization, and apoptotic body count were measured. AI-based analysis demonstrated significantly higher sensitivity in detecting subcellular alterations (94.6%) compared to conventional microscopy (81.3%, p < 0.01). Morphometric data revealed distinct distribution patterns between reversible and irreversible cellular injury. A double pie chart analysis illustrated the proportional differences in histological features following early and advanced cellular damage. The findings support AI-driven histological quantification as a powerful adjunct tool for improving early detection and reproducibility in diagnostic histology and medical education.
Organoids and 3D Bioprinting in Histology and Medical Biology: Transforming Disease Modeling and Regenerative Medicine
The emergence of organoid technology and three-dimensional (3D) bioprinting has revolutionized experimental histology and translational medical biology. These innovations enable the in vitro reconstruction of tissue architecture that closely mimics in vivo microanatomy and cellular heterogeneity. Organoids derived from stem cells recapitulate organ-specific histological organization, while 3D bioprinting provides spatial control over cellular arrangement and extracellular matrix composition. This review synthesizes recent advances (2020–2025) in organoid-based histological modeling, highlights their application in disease modeling, oncology, drug screening, and regenerative medicine, and discusses the integration of artificial intelligence for morphometric analysis. Key challenges—including vascularization, standardization, and ethical considerations—are critically examined. The convergence of organoid biology, digital histology, and bioengineering marks a paradigm shift in biomedical research and medical education.
Generative AI and LLMs in Clinical Education (2026): A Practical Review for Medical Teachers to Build Students’ Clinical Understanding and Competency
Background: Generative AI and large language models (LLMs) are increasingly used in medical education to support clinical reasoning, feedback, and competency-based training. Their rapid adoption requires structured educational guidance. Objective: To outline a practical framework for integrating AI into clinical education while maintaining validity, ethics, and patient safety. Methods: A focused narrative review (2023–2026) examined literature on AI in medical education, competency-based frameworks, and assessment theory. Results: LLMs enhance case-based learning, simulated clinical reasoning, OSCE preparation, and structured feedback. When aligned with programmatic assessment and faculty oversight, AI can strengthen higher-order cognitive skills. Key risks include hallucination, bias, and overreliance, requiring verification training and clear policy boundaries. Conclusion: AI should act as a cognitive support tool—not a replacement for clinical judgment. Thoughtful integration within competency-based systems can improve clinical understanding while preserving academic rigor.
Digital Revolution in Dentistry and Oral Surgery: A Comprehensive Review of AI, 3D Printing, Robotics and Virtual Surgical Planning
Digital technologies are fundamentally reshaping dentistry and oral–maxillofacial surgery (OMFS), transitioning clinical workflows from analogue, experience-dependent processes toward data-driven, patient-specific paradigms. This comprehensive review synthesizes the most recent evidence (2023–2026) on six converging technological domains: (1) artificial intelligence (AI) in diagnostics and surgical navigation, (2) three-dimensional (3D) printing and computer-aided design/computer-aided manufacturing (CAD/CAM), (3) virtual surgical planning (VSP), (4) robotic-assisted surgery, (5) augmented and mixed reality (AR/MR), and (6) bioprinting and tissue engineering for craniofacial reconstruction. AI-powered implant navigation systems now achieve angular deviations as low as 1.2° and tip errors of 0.4 mm, while robotic-assisted maxillofacial reconstruction has demonstrated mean positional deviations of 0.8 ± 0.3 mm versus 2.1 ± 0.7 mm for conventional surgery.
Artificial Intelligence in Orthodontics: From Cephalometric Analysis to Predictive Aligner Therapy — A Focused Review
Artificial intelligence (AI) is rapidly transforming orthodontic practice, moving the specialty from experience-based decision-making toward data-driven, personalized treatment planning. This focused review synthesizes recent evidence (2023–2026) on three core AI applications in orthodontics: automated cephalometric landmark detection, malocclusion classification and treatment decision support, and predictive modeling for clear aligner tooth movement. Deep learning frameworks, notably multimodal architectures that fuse lateral cephalograms, CBCT volumes and digital dental models, now achieve mean radial errors of 1.21 mm for landmark detection—a 13% improvement over single-modality methods—with clinical acceptability rates exceeding 92% at the 2 mm threshold. AI-based malocclusion screening using intraoral photographs demonstrates AUC values ranging from 0.73 to 0.92 depending on the classification task, with deep bite detection showing the highest accuracy. In aligner therapy, AI-assisted treatment planning achieves prediction accuracy of 88–92% for incisor movements, though rotational and intrusive movements remain less reliable. Despite promising performance metrics, barriers including limited training datasets, lack of prospective multicenter validation, regulatory uncertainty and integration challenges restrict routine clinical deployment. This review evaluates the current state of AI in orthodontics, critically appraises the quality and generalizability of existing evidence, and identifies priorities for translating AI from research tool to clinical standard.
Clinical Adoption of Artificial Intelligence in Orthodontic Practice: A Cross‑Sectional Survey of 120 Clinicians
Artificial intelligence (AI) is increasingly integrated into orthodontics, yet real‑world adoption patterns across specific clinical tasks remain unclear. This cross‑sectional survey of 120 practicing orthodontists assessed self‑reported AI use in cephalometric analysis, malocclusion screening, aligner treatment planning, growth prediction and patient education. AI was most frequently used for cephalometric analysis (65%) and aligner treatment planning (55%), with lower uptake for malocclusion screening (40%), patient education (35%) and growth prediction (25%). A detailed table and bar graph illustrate task‑specific usage patterns. These findings highlight a gap between advanced research applications of AI and its routine use in comprehensive diagnostic and prognostic workflows, underscoring the need for better integration, training and validation.
Mitochondrial oxidative stress in pediatric liver disease: Biochemical mechanisms and clinical implications
Mitochondrial oxidative stress is increasingly recognized as a key driver of hepatocellular injury and fibrosis in pediatric liver disease. In children, immature antioxidant defenses and high metabolic demands may enhance vulnerability to reactive oxygen species generated at complexes I and III of the electron transport chain. This article summarizes major biochemical pathways of mitochondrial ROS production in hepatocytes, outlines core mitochondrial antioxidant systems, and links redox imbalance to structural damage, inflammation, and fibrosis in the developing liver. We also highlight emerging diagnostic markers of oxidative stress and discuss potential mitochondria‑targeted therapeutic strategies tailored to pediatric patients. Understanding these mechanisms may support earlier detection and more precise treatment of chronic liver disorders in children.
Quantitative Perspectives on Enzyme Structure, Dynamics, and Catalysis in Modern Biochemistry
Enzymes are central to biochemistry, acting as highly specific biological catalysts that accelerate virtually every chemical reaction underpinning life, from energy metabolism to DNA replication and signal transduction. They lower activation energy by stabilizing transition states and creating optimized microenvironments within defined active sites, enabling reaction rates that can be millions-fold faster than uncatalyzed reactions while remaining tightly regulated in time and space. Modern enzymology integrates structural biology, kinetics, and protein dynamics to explain how conformational motions contribute to catalysis, substrate specificity, and allosteric regulation. This article reviews the classification of enzymes, principles of catalysis and kinetics, mechanisms of regulation, and emerging applications in biotechnology and medicine, highlighting how dynamic protein architectures translate chemical principles into robust biological function. Understanding these molecular machines informs rational drug design, biomarker development, and engineering of tailored biocatalysts for industry and personalized medicine.
Biochemical Roles of Liver Enzymes in Hepatocellular Injury and Cholestasis
Liver enzymes are central biochemical markers that reflect hepatocyte integrity, bile formation, and synthetic capacity, making them indispensable in both clinical practice and hepatology research. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are aminotransferases involved in amino-group transfer during gluconeogenesis and amino acid metabolism, and their leakage into blood primarily indicates hepatocellular injury rather than liver function per se., In contrast, alkaline phosphatase (ALP) and gamma‑glutamyl transferase (GGT) are membrane-associated enzymes enriched in bile canaliculi and biliary epithelium, and their elevation points to cholestasis or biliary tract pathology., This article summarizes the biochemistry, tissue distribution, and clinical interpretation of liver enzymes, highlights patterns that distinguish hepatocellular from cholestatic injury, and discusses their limitations as surrogate markers of disease severity and prognosis.
Recent Advances in Dexmedetomidine for Paediatric Sedation
Dexmedetomidine has transitioned from an off‑label adjunct to a central agent in paediatric sedation, especially for procedural, MRI, and intensive care indications,, –. Recent randomized trials, meta‑analyses, and pharmacologic reviews show that dexmedetomidine provides cooperative, arousable sedation with minimal respiratory depression, reduced emergence agitation, and improved perioperative behaviour compared with conventional sedatives, while bradycardia and hypotension remain the main dose‑related adverse effects,,,. High‑dose monotherapy regimens now achieve MRI completion without rescue propofol in a majority of children, and intranasal formulations broaden use in emergency and diagnostic settings by avoiding intravenous access,,. Ongoing systematic reviews of intranasal dexmedetomidine and evolving pharmacokinetic modelling aim to refine age‑specific dosing and clarify long‑term safety,. This article summarizes recent advances in routes, dosing, indications, and safety considerations that are reshaping dexmedetomidine‑based sedation in paediatric practice.
Evolving Management of Adhesive Small Bowel Obstruction in Paediatric Surgery
Adhesive small bowel obstruction (ASBO) is a frequent late complication of paediatric abdominal surgery and an important cause of morbidity, repeated admissions, and re‑operations in children. Recent paediatric data suggest that most haemodynamically stable children without signs of strangulation can be safely managed non‑operatively, although a significant minority will eventually require timely surgery,,. Adult‑derived protocols using water‑soluble contrast have transformed ASBO decision‑making, but emerging paediatric studies show that enteral contrast may not reduce operative rates or length of stay to the same extent in children,. Laparoscopic adhesiolysis is increasingly used in selected paediatric patients and appears to reduce complications and length of stay compared with laparotomy, but carries a higher risk of bowel injury in difficult cases and remains underused in very young children,,,. This article reviews current evidence on conservative versus operative management, decision‑making tools, and minimally invasive approaches for paediatric ASBO, highlighting areas where adult practice cannot be directly extrapolated.
Emerging Pharmacological Treatments for Major Depressive Disorder: A Narrative Review of Clinical Trials from 2023 to 2025
Major depressive disorder (MDD) remains a leading cause of global disability, affecting approximately 332 million individuals worldwide. Despite decades of monoaminergic antidepressant use, up to 60% of patients fail to achieve adequate remission with first-line therapies. This narrative review examines clinical trial evidence published between 2023 and 2025 for novel and emerging pharmacological treatments targeting non-monoaminergic pathways, including NMDA receptor antagonists (esketamine, dextromethorphan–bupropion, esmethadone), GABA-A receptor modulators (zuranolone), serotonergic psychedelics (psilocybin/COMP360), and kappa-opioid receptor antagonists (aticaprant). Efficacy outcomes are compared across drug classes using MADRS and HAM-D scales. The ESCAPE-TRD trial demonstrated esketamine's superiority over quetiapine in treatment-resistant depression (TRD), while psilocybin showed clinically meaningful sustained effects in phase 2 and phase 3 studies. Zuranolone received FDA approval for postpartum depression. Kappa-opioid antagonists showed promise in phase 2 but failed phase 3 endpoints. A tabular comparison of efficacy by drug class is provided. These findings illustrate a paradigm shift from serotonin-centric models toward rapid-acting, mechanism-driven interventions for depression.
Divergent Injury Pathways in Neonatal Intestine and Kidney: Quantitative Morphology of Necrotizing Enterocolitis and Multicystic Dysplastic Kidney
Necrotizing enterocolitis (NEC) and multicystic dysplastic kidney (MCDK) are leading neonatal entities that exemplify acute inflammatory necrosis versus chronic developmental disorganization on histology. Contemporary grading systems for NEC now quantify depth and extent of intestinal injury, ranging from mucosa‑limited changes to transmural necrosis, and can be complemented by weighted, cumulative point scores capturing subtle mucosal lesions., In parallel, recent series of MCDK describe a histomorphological spectrum defined by primitive ducts with mesenchymal collars, cyst burden, and heterologous elements, with semi‑quantitative assessments of residual parenchyma and later autoinvolution. This article integrates classic pattern‑based descriptions with simplified ordinal scoring to compare tissue injury in NEC and MCDK, illustrated by a bar graph that contrasts histologic severity categories across both diseases. The resulting framework underscores how quantitative morphology can refine diagnosis, prognostication, and study design in neonatal intestinal and renal pathology.
Pathomorphological Changes after Intramuscular Injection: From Acute Needle Myopathy to Chronic Fibrous Contracture
Intramuscular (IM) injection is a ubiquitous procedure in clinical medicine, yet it induces a characteristic sequence of muscle and soft‑tissue pathomorphological changes that range from microscopic “needle myopathy” to disabling fibrous contractures.,,,, Experimental toxin models show that acute IM injury begins with myofiber necrosis, edema, and inflammatory cell influx, followed by activation of satellite cells and regeneration with central nucleation., In routine therapeutic injections, similar but usually milder lesions occur, with local myonecrosis and low‑grade inflammatory infiltrates even when normal saline is administered, and are amplified by myotoxic drugs such as local anesthetics, some NSAIDs, and certain biologics.,, Repeated or improperly sited injections can drive chronic fibrosis, collagen deposition, and muscle shortening, leading to gluteal or deltoid contractures, joint deformity, and occasionally nerve entrapment or sciatic injury.,,,, This review summarizes the temporal and structural spectrum of IM‑induced pathomorphology in skeletal muscle, subcutaneous tissue, and adjacent neurovascular structures, and compares acute necrotic–regenerative lesions with chronic fibrosing myopathy.
AI Enhanced Early Detection of Postoperative Complications: Comparing Traditional, Machine Learning, and Deep Learning Surveillance Models
Artificial intelligence (AI) is increasingly used not only to predict, but also to monitor and detect early complications after surgery, using diverse data streams such as electronic health records, vital signs, wearable sensors, and imaging,,. Machine learning (ML) and deep learning (DL) models can outperform traditional logistic regression and rule‑based systems in discriminating patients at risk of sepsis, pneumonia, surgical site infection (SSI), and mortality, while substantially reducing manual surveillance workload,,. Large, multi‑center neural network models that combine structured and unstructured data (e.g., 3.5 million surgical encounters) demonstrate rapid, multi‑outcome prediction, and specialized SSI‑prediction platforms show AUC values approaching 0.99 in focused cohorts,,. This article reviews AI models for early post‑surgical complication analysis, compares traditional, ML, and DL approaches, and visualizes relative performance and workload impact using a summary table and dual pie charts.
AI Driven Analysis of Postoperative Complications: Transforming Surgical Outcomes Through Predictive, Text Based, and Wound Imaging Tools
Artificial intelligence (AI) has rapidly emerged as a powerful tool to analyze, predict, and detect complications after surgical interventions, leveraging large volumes of perioperative data that are difficult for humans to process alone,,. Machine learning models built on electronic health records, vital signs, intraoperative signals, imaging, clinical notes, and wound photographs can identify patients at high risk for postoperative complications such as sepsis, acute kidney injury, pneumonia, and mortality, with performance that matches or surpasses traditional risk scores and clinicians in several studies,,. Foundation models and large language models that specifically analyze surgical notes further enhance complication prediction and generalizability across settings, while AI‑based wound‑image analysis enables earlier detection of wound problems and prioritization of follow‑up,,. This article reviews key AI tools for postoperative complication analysis, outlines their methodologies, summarizes clinical performance, and discusses integration into perioperative workflows and future directions.