Endometrial polyp (EMP) is one of the most common diagnoses in the evaluation of women with abnormal uterine bleeding. Understanding the malignancy risk associated with EMPs and related risk factors is essential for guiding both pathology practice and clinical management. This study aimed to explore risk factors for malignancy in EMPs.

The pathology database was searched for women diagnosed with EMP between 2021 and 2022. Patient age, polyp size, background endometrium, recurrence, and (if applicable) cancer types were recorded. Immunohistochemistry (IHC) for p53 and p16 was performed on selected cases. Risk factors for malignancy were analyzed using Chi-square and analysis of variance tests.

Among the 740 EMP cases analyzed, 94% were benign, 2% were premalignant, and 4% were malignant. The median patient age was 54 years (range: 19–92). Minimal serous carcinoma (n = 14, 2%) was the most prevalent cancer. Among the 52 cases with p53 IHC, 38 were diagnosed as benign, supported by a wild-type p53 pattern, while 14 were diagnosed as serous carcinoma, supported by a mutant p53 pattern. Malignant polyps were found to be significantly associated with advanced age and malignant background endometrium (p < 0.001). Large size and recurrence were not identified as significant risk factors.

EMPs carry a low risk of malignancy, which is not significantly influenced by the polyp’s size or its recurrence. Our findings highlight the significantly elevated risk of malignancy in elderly patients and the importance of p53 IHC in improving diagnostic accuracy.

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.

Accumulating evidence indicates that fecal syndecan-2 (SDC2) methylation is a promising biomarker for early detection of colorectal cancer. This study aimed to investigate the diagnostic efficacy of fecal SDC2 methylation testing for adenomas and evaluate the risk stratification efficacy of the Asia-Pacific Colorectal Screening Scoring (APCS) combined with SDC2 methylation status.

This was a prospective, multicenter diagnostic study. Adult participants with no history of colonoscopy within the past three years were enrolled. Demographic data were collected, and APCS scores were evaluated. All participants underwent fecal SDC2 methylation testing and colonoscopy. Colonoscopy outcomes and pathological results of any polyps served as reference standards. The fecal SDC2 methylation test and reference standard assessments were conducted in a blinded manner. The APCS-SDC2 scoring system was developed by integrating fecal SDC2 methylation results with APCS scores, and its efficacy was assessed.

In total, 985 participants were enrolled, among whom 62 (6.3%) tested positive for fecal SDC2 methylation. The sensitivity and specificity of fecal SDC2 methylation in detecting advanced adenomas were 31.3% (95% confidence interval (CI): 21.6–42.7%) and 96.1% (95% CI: 94.6–97.2%), respectively. The APCS-SDC2 scoring system demonstrated superior discriminatory performance for advanced adenomas (area under the curve: 0.7032; 95% CI: 0.5869–0.8195). For advanced adenoma screening, the specificity of the APCS-SDC2 score was higher than that of the APCS score (86.7% vs. 66.7%; P < 0.001).

A positive fecal SDC2 methylation test indicated a higher risk of advanced adenoma, and colonoscopy should be prioritized. The APCS-SDC2 scoring system demonstrated superior risk stratification performance for advanced adenomas.

ATP-binding cassette (ABC) transporters are transmembrane proteins involved in the translocation of bilirubin, bile acids, phospholipids, and cholesterol into bile canaliculi. Mutations in particular genes encoding these transporters—including BSEP (ABCB11 gene), MDR3 (ABCB4 gene), sterolin-1 and sterolin-2 (ABCG5/8 genes), and MRP2 (ABCC2 gene)—result in a wide spectrum of liver diseases, ranging from benign conditions such as Dubin-Johnson syndrome to more severe presentations like progressive familial intrahepatic cholestasis. The severity of disease is influenced by many factors, including zygosity, mutation type, and environmental modifiers such as hormones, consanguinity, and founder effects. Homozygous and compound heterozygous mutations typically result in severe and early-onset diseases, while heterozygous single-allelic mutants generally result in milder diseases. Next-generation genetic testing has proven to have high diagnostic value and is important for prognostication. With knowledge of the underlying specific mutations, there is also potential for future targeted therapy for many severe diseases. The aim of this review is to update and discuss the hepatic diseases associated with ABC transporter mutations, the genetic and environmental effects that influence the severity of disease, typical presentations of these cholestatic hepatic diseases, diagnostic considerations, and treatment options.

Full or partial trisomy of human chromosome 21 results in dysregulation of gene expression, leading to the manifestation of specific phenotypes described in individuals with Down syndrome (DS). Defects in brain development, coupled with impairment in neurogenesis, are ultimately expressed as cognitive deficiency, Alzheimer disease (AD), and dementia. Amid the triplication of all human chromosome 21 (HSA21) genes, dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A)-mediated neurogenesis and dendritic development have been attributed to the learning and memory deficits and cognitive impairment in the DS population. Upregulated DYRK1A perturbs the development and function of the brain, collectively affecting neurogenesis, synaptogenesis, synaptic transmission, and cell signaling pathways, which might disproportionately produce inhibitory neurotransmission and contribute to the cognitive phenotype. However, the lack of distinct gene-phenotype associations acts as a potential barrier to therapeutic improvement of cognitive performance and amelioration of AD-related neurodegeneration. The present review aims to summarize the neurogenetic consequences of triplicated DYRK1A in the DS population in relation to sexual dimorphism and expression of the Apolipoprotein Eε4 (APOE ε4) genotype. Notably, normalization of trisomic DYRK1A demonstrated improved synaptic plasticity, glutamatergic/GABAergic (excitatory/inhibitory) balance, and learning and memory in DS mouse models. Therapeutic approaches using inhibitors of DYRK1A, including catechins present in green tea extract and several other natural and synthetic agents, produced variable outcomes in cognitive improvement, depending on age and dose of administration. Mitigation of impairment in neurogenetic differentiation and cognitive performance might help control AD-related dementia and enhance quality of life. This review highlights the consequences of upregulated DYRK1A kinase on impairment of neurogenesis and cognitive deficits, and the therapeutic challenges associated with DYRK1A inhibitors for ameliorating dysregulated gene expression in DS models and human DS.

Microsatellite-stable colorectal cancer, which accounts for roughly 80–85% of cases, remains largely refractory to immune checkpoint inhibitors compared with microsatellite instability-high tumors. This review synthesizes current evidence on tumor-intrinsic and microenvironmental mechanisms underlying immune checkpoint inhibitor resistance in microsatellite-stable colorectal cancer—including low neoantigen burden and impaired antigen presentation, activation of Wnt/β-catenin and MAPK signaling that exclude T cells, an immunosuppressive cellular milieu (regulatory T cells, myeloid-derived suppressor cells, M2-like tumor-associated macrophages, cancer-associated fibroblasts), metabolic reprogramming, and gut microbiome dysbiosis—and evaluates translational strategies aimed at overcoming these barriers. Preclinical and early-phase clinical data indicate that rational, mechanism-guided combinations (vascular normalization, myeloid reprogramming, metabolic inhibitors, antigen-priming approaches, and microbiome modulation) can enhance immune infiltration and produce benefits in biomarker-defined subgroups. Moving the field forward will require biomarker-driven, adaptive clinical trials with embedded translational endpoints to optimize patient selection and manage toxicity.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now considered to be among the most prevalent chronic liver diseases worldwide. Its comprehensive management encompasses multiple stages, including risk assessment, early detection, stratified intervention, and long-term follow-up. Among these, improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice. In recent years, artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field, offering novel tools and pathways for MASLD risk stratification, non-invasive diagnosis, therapeutic evaluation, and patient self-management. This review summarizes the current applications of artificial intelligence and smart devices in MASLD care, highlights their benefits and limitations, and discusses future directions to support precision diagnosis and treatment strategies.