Traumatic brain injury (TBI)-associated cognitive impairment is highly prevalent, severely impacting patients’ daily life and social functioning, with its mechanisms incompletely understood. Globally, TBI affects over 69 million people annually, and post-TBI cognitive impairment may last for years, or even a lifetime, imposing heavy burdens on patients’ families. The brain-lymphatic axis (glymphatic + peripheral lymphatic systems, especially meningeal vessels) has gained attention: glymphatic dysfunction (dependent on astrocyte endfeet Aquaporin-4 polarization, key for clearing β-amyloid and other wastes) causes metabolic waste accumulation and neuroinflammation, while peripheral lymphatic stasis worsens cognitive decline. This review aims to summarize their roles, dissect mechanisms, and outline therapies. The review found that most current studies explore the glymphatic system and the peripheral lymphatic system in isolation, lacking understanding of their dynamic interplay (e.g., bidirectional inflammatory factor transmission, immune cell migration, synergistic dysfunction); longitudinal studies that track axis changes across TBI stages (acute, subacute, chronic) are scarce; diagnostic tools are insufficient (non-invasive biomarkers lack large-scale clinical validation, and imaging has limited clinical use); and existing therapeutic strategies mostly target single subsystems, with few combined interventions for the whole axis. In conclusion, this review highlights critical gaps in current knowledge and proposes integrated, axis-targeted approaches as a promising direction for future research and therapeutic development.

Artificial intelligence (AI) is transforming the diagnosis, treatment, monitoring, and research of soft tissue disorders, which include muscles, tendons, ligaments, fascia, nerves, and blood vessels. Traditional diagnostic methods often rely on imaging, histopathology, and clinical evaluation, which can be time-consuming and prone to human error. This review aims to explore the impact of AI on enhancing soft tissue care. The review examines the application of deep learning algorithms in medical imaging, pathology, predictive analytics, and treatment planning. It also evaluates AI’s role in monitoring and rehabilitation, as well as its contributions to research in soft tissue disorders. AI significantly improves the accuracy of medical imaging analysis, facilitating the detection of abnormalities such as tumors and tears. AI-powered pathology tools automate slide analysis, enhancing diagnostic consistency and efficiency. Predictive analytics enable early risk assessment and personalized patient management. In surgical contexts, AI supports preoperative simulations and robotic-assisted procedures, leading to improved outcomes. Additionally, AI enhances patient monitoring through wearable devices and telemedicine. The integration of AI into soft tissue diagnostics and therapeutics presents transformative potential for personalized and efficient healthcare. However, challenges related to data security, algorithm bias, interpretability, and ethical considerations must be addressed. Overall, AI holds promise for improving patient outcomes and advancing medical science in the field of soft tissue disorders.

Lactate exerts regulatory effects on both cellular homeostasis and disease progression, far beyond being a mere metabolic waste product. As lactate accumulates, the level of lactylation increases significantly. Lactylation, a novel type of post-translational modification, bridges metabolic reprogramming and epigenetic regulation in malignant tumors, including gynecological malignancies. Both lactate and lactylation play critical roles in the tumor microenvironment, ultimately promoting tumor proliferation, metastasis, and drug resistance. Therapies targeting lactate production and transport show considerable anticancer potential, particularly through the inhibition of lactate dehydrogenase and monocarboxylate transporters. These inhibitors can also act as immunotherapy potentiators, producing a synergistic therapeutic effect when combined with immunotherapy. This review emphasizes how lactate and lactylation drive the malignant progression of gynecological cancers and explores promising perspectives on potential therapeutic targets.

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents an escalating healthcare burden across the Middle East and North Africa (MENA) region; however, system-level preparedness remains largely undefined. This study aimed to assess existing models of care, clinical infrastructure, policy frameworks, and provider perspectives across 17 MENA countries.

A cross-sectional, mixed-methods survey was distributed to clinicians from MASLD-related specialties across the region. A total of 130 experts (87.2% response rate) from academic, public, and private sectors in 17 countries participated. The questionnaire addressed national policies, diagnostic and therapeutic practices, referral pathways, multidisciplinary team (MDT) integration, and patient/public engagement. Quantitative responses were analyzed descriptively, while qualitative inputs underwent thematic analysis.

Only 35.4% of respondents confirmed the presence of national clinical guidelines for MASLD, and 73.1% reported the absence of a national strategy. Structured referral pathways were reported by 39.2% of participants, and only 31.5% believed the current model adequately addresses MASLD. While 60% supported MDT approaches, implementation remained inconsistent. Limited access to transient elastography was reported by 26.2% of providers. Public education efforts were minimal: 22.3% reported no available tools, and 87.7% indicated the absence of patient-reported outcomes data. Nearly half (47.7%) cited poor patient adherence, attributed to low awareness, financial barriers, and lack of follow-up.

Significant policy, structural, and educational gaps persist in MASLD care across the MENA region. To address this rising burden, countries must adopt integrated national strategies, expand access to non-invasive diagnostic tests, institutionalize MDT care, and invest in both public and provider education as essential pillars of system-wide preparedness.

The gut microbiota is crucial in maintaining host health and liver function. Fecal microbiota transplantation (FMT) has shown promising potential in treating chronic liver diseases. To help clinicians quickly master and standardize the clinical application of FMT for chronic liver disease, the Liver Related Digestive Diseases Group of the Chinese Society of Hepatology of the Chinese Medical Association has developed the “Expert Consensus on the Clinical Application of FMT for Chronic Liver Disease.” This consensus addresses the key aspects of FMT, including the indications, contraindications, efficacy, safety, donor selection, transplantation routes, precautions, and the prevention and management of adverse reactions for chronic liver conditions, such as chronic hepatitis, cirrhosis, and liver cancer, thereby offering reference and guidance to clinicians implementing FMT in the treatment of chronic liver disease.

Guaiac fecal occult blood test (gFOBT) is often used to evaluate evidence of food protein-induced allergic proctocolitis (FPIAP) in children in primary care and gastroenterology settings; however, it has not been validated for this diagnosis, and little is known about the positivity rates in early infancy. In this study, we used samples from healthy asymptomatic infants aged two weeks to two months to evaluate the gFOBT positivity rate compared to those diagnosed with FPIAP.

This was a nested case-control study. Frozen stool samples from infants aged two days to five months enrolled in the Gastrointestinal Microbiome and Allergic Proctocolitis study were evaluated using gFOBT (n = 123). The results were interpreted by three blinded staff members, including a trained clinical research coordinator, a pediatric gastroenterologist, and an experienced medical assistant. Additionally, the samples were analyzed using a quantitative fecal immunochemical test (FIT) for hemoglobin to compare with gFOBT results.

Eight percent of samples from the 100 healthy asymptomatic infants were gFOBT positive (11% when including positive and equivocal results). Seventy-four percent of samples from infants diagnosed with FPIAP were gFOBT positive. The interrater reliability of gFOBT interpretation was 81%. Of the healthy samples that yielded a positive gFOBT result, 50% also yielded a positive FIT result. Of the 23 FPIAP samples that yielded a positive gFOBT result, 29% yielded a positive FIT result.

Healthy asymptomatic infants in early infancy were gFOBT positive up to 11% of the time. Caution should be used when interpreting gFOBT results in young infants in a diagnostic setting.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.