COVID-19 has resulted in significant long-term sequelae in convalescent patients, impacting overall quality of life. Traditional Chinese medicine (TCM) has shown promise in managing post-COVID-19 symptoms through syndrome differentiation. This study aimed to evaluate the efficacy and safety of TCM in COVID-19 convalescent patients in a real-world setting.

This prospective, real-world study will be conducted at Guangdong Provincial Hospital of Traditional Chinese Medicine. A total of 528 COVID-19 convalescent patients will be recruited and divided into two groups: a control group receiving routine Western medical treatment and an intervention group receiving additional TCM treatment based on syndrome differentiation. Patients will be assessed for three major TCM syndromes: Lung-Spleen Qi Deficiency, Qi-Yin Deficiency, and Cold Phlegm Obstructing the Lung, with corresponding TCM prescriptions administered accordingly. The primary outcome measure will be the improvement in clinical symptom scores based on a TCM symptom scoring system. Secondary outcomes will include changes in laboratory tests, imaging studies, heart function classification, and quality of life scores. Safety will be assessed through liver and kidney function tests and adverse event monitoring.

The study is expected to demonstrate that TCM treatment, based on syndrome differentiation, can significantly improve clinical symptoms and overall health in COVID-19 convalescent patients compared to routine Western medical treatment. These findings will provide evidence for integrating TCM into post-acute COVID-19 care.

This study will contribute to the evidence supporting TCM as an effective treatment for post-COVID-19 syndrome, enhancing patient outcomes and informing comprehensive recovery strategies.

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.

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.

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.

Micro- and nanoplastics (MNPs) are plastic particles smaller than 5 mm and 1 µm, respectively, and are emerging environmental pollutants with growing implications for human health. These particles stem from either ‘primary sources’, such as intentionally manufactured microbeads and industrial abrasives, or ‘secondary sources’, where larger plastic items break down into smaller fragments over time. Human exposure primarily occurs through ingestion and inhalation, with contaminated seafood and plastic-laden food packaging representing key routes of entry. Once ingested, MNPs can cross the intestinal barrier, accumulate in gastrointestinal (GI) tissues, and trigger biological responses. Mechanistic studies reveal that MNPs induce oxidative stress, DNA damage, chronic inflammation, and endocrine disruption, all of which are hallmarks of carcinogenic pathways. They also alter gut microbiota, potentially promoting dysbiosis and immune dysregulation. The GI tract is particularly vulnerable to these effects due to direct luminal mucosal contact and high epithelial turnover. Epidemiological data remain limited, but early evidence supports a plausible link between MNPs exposure and GI malignancies. Such findings are particularly concerning given the increasing global incidence and early age presentation of colorectal and esophageal cancers. Given that MNPs may represent a modifiable environmental risk factor in GI cancer prevention, public health strategies must prioritize reducing plastic exposure, promoting antioxidant-rich diets, and improving environmental monitoring. This review explores the potential carcinogenic effects of microplastics while also examining their emerging roles in cancer therapeutics. It highlights critical avenues for future investigation and underscores the importance of cross-disciplinary efforts to tackle this growing global health concern.

Chronic diabetes mellitus is marked by hyperglycemia and metabolic dysfunction, increasing the risk of complications such as nephropathy. This study aimed to evaluate key biochemical parameters among participants with diabetic nephropathy (DNp), diabetes control (DC), nephropathy control (NC), and healthy control groups.

A prospective case-control study was conducted with 200 participants categorized into four groups: DNp, NC, DC, and healthy controls. Biochemical parameters, including glucose, glycated hemoglobin, waste metabolites, proteins, enzymes, electrolytes, and lipids, were analyzed using an Advia 1800 chemical system analyzer (Siemens, Germany) with standard kits.

Among the four investigated groups, the DNp group exhibited augmented fasting glucose (178.75 ± 61 mg/dL), glycated hemoglobin (8.13 ± 1.7%), creatinine (5.67 ± 1.8 mg/dL), and blood urea nitrogen (72.02 ± 22.8 mg/dL), indicating poor glycemic control and impaired kidney function. In contrast, the DC group showed elevated random glucose levels (280 ± 3.1 mg/dL). Elevated inflammatory markers (C-reactive protein, 6.35 ± 6.3 mg/L; lactate dehydrogenase, 1,216.43 ± 634 U/L) were observed in the NC group. Compared to the other groups, the DC group demonstrated augmented lipid profiles, including elevated triglycerides (230.67 ± 59 mg/dL), very low-density lipoprotein (48.5 ± 16.5 mg/dL), low-density lipoprotein (107.41 ± 16 mg/dL), and cholesterol (169 ± 19 mg/dL). Statistical analysis was performed using one-way analysis of variance followed by a t-test to investigate differences among groups at P < 0.05.

Altered biochemical variations were noted among groups. The DNp group showed renal dysfunction and poor glycemic control, the DC group had dyslipidemia and hyperglycemia, and the NC group showed elevated inflammatory markers. Early testing is indispensable for the timely diagnosis and management of diabetic complications.

Metabolic dysfunction-associated steatotic liver disease (MASLD) and chronic kidney disease (CKD) have shown a significant increase in comorbidity on a global scale due to the prevalence of metabolic syndrome. In 2023, a number of academic societies formally proposed the concept of MASLD, superseding the previous terminology of “non-alcoholic fatty liver disease” and “metabolic dysfunction-associated fatty liver disease”. The diagnostic criteria have been revised to place greater emphasis on the association between hepatic steatosis and cardiometabolic risk factors. MASLD constitutes an independent risk factor for CKD, with this risk potentially increasing in line with the severity of fatty degeneration and the progression of hepatic fibrosis. CKD may represent a potential risk factor for the progression of fibrosis in patients with MASLD. The interaction between the two conditions may accelerate the occurrence of cardiovascular events and increase the risk of all-cause mortality. MASLD and CKD may share core pathophysiological mechanisms, including genetic variants, insulin resistance, lipid metabolism disorders, chronic inflammation, oxidative stress, and gut microbiota dysbiosis. However, the bidirectional causal relationship between the two conditions and the molecular dialogue between organs remains unclear. Furthermore, there are significant gaps in clinical prediction tools and targeted treatment strategies for comorbidities. This paper reviews common pathophysiological mechanisms in MASLD and CKD, the epidemiological and clinical evidence linking MASLD to the risk of CKD, biomarkers and clinical prediction models for coexisting conditions, and potential therapeutic strategies. Our aim is to provide a theoretical basis for early identification, mechanism exploration, and clinical treatment of comorbidities.

Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The present study investigates the use of electrocardiogram (ECG) non-linear features and different topological medical features (heart rate, anthropometry, blood, glucose, and lipid profile, and heart rate variability) to discriminate between different Framingham Cardiovascular Risk Scale status groups in adult obesity using machine learning.

We conducted a cross-sectional study between November 2023 and May 2024 in Fortaleza, Ceará, Brazil. Based on the Framingham Cardiovascular Risk Scale, patients were categorized into three cardiovascular risk groups: Low (22 participants), Moderate (14 participants), and High (17 participants). From ECG signals at two different positions (ECG_Down and ECG_UP), 27 non-linear features were extracted using multi-band analysis. Additionally, 42 medical features provided by physicians were included. From a pool of 19 machine learning classifiers, models were trained and tested within a nested leave-one-out cross-validation procedure using information solely from ECG, solely from medical features, and combining both (multimodal), respectively, to distinguish between Low vs. Moderate, Low vs. High, Moderate vs. High, and All vs. All.

The multimodal model presented the best results for every comparison group, reaching (1) 88.89% Accuracy and 0.8831 area under the curve (AUC) for Low vs. Moderate; (2) 97.44% Accuracy and 0.9706 AUC for Low vs. High; (3) 93.55% Accuracy and an AUC of 0.9412 for Moderate vs. High; (4) 86.79% Accuracy and 0.9346 AUC for All vs. All.

The multimodal model outperformed single-source models in cardiovascular risk classification. ECG-derived non-linear features, especially from ECG_Down, were key drivers, with medical features adding complementary value. The results support its potential use in clinical triage and diagnosis.