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.

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.

The bromodomain and extra-terminal domain (BET) protein family, particularly BRD4, is critical for the control of oncogenic transcriptional programs in solid tumors. Although initial-generation BET inhibitors, such as JQ1, molibresib, and birabresib, have demonstrated preclinical efficacy in repressing MYC-dependent pathways, their clinical translation has been hampered by low monotherapy activity, pharmacokinetic heterogeneity, and dose-limiting toxicities. This review aims to update the mechanistic foundations, clinical trial results, and development of therapeutic approaches to BET inhibition in solid tumors, outlining its evolving role in the next generation of cancer treatment strategies. Various clinical trials in different phases have demonstrated heterogeneous responses among solid tumor types, with greater effects in NUT carcinoma and castration-resistant prostate cancer. Resistance mechanisms, including BRD4 isoform switching and compensatory signaling activation, emphasize the need for advanced and innovative BET-targeting modalities. BD2-selective BET inhibitors and proteolysis-targeting chimeras are likely to overcome these limitations by increasing target specificity and reducing systemic side effects. In addition, combination strategies, such as PARP inhibitors, AR antagonists, and immune checkpoint blockade, have synergistic potential to augment anticancer activity. In conclusion, this review provides a comprehensive overview of the advances, challenges, and future directions of BET bromodomain inhibition in solid tumors.

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.

Pediatric low-grade gliomas (pLGGs) exhibit distinct biological and clinical characteristics compared to adult gliomas, and their treatment strategies differ substantially from those used in adults. Since the release of the 2016 World Health Organization Classification of Tumors of the Central Nervous System and its subsequent updates, significant advances have been made in understanding the diagnosis and management of pLGGs. Therefore, updated guidelines tailored to current clinical practice are needed. In this document, we present the consensus guidelines for the diagnosis and management of pLGGs in China. The recommendations were developed through a comprehensive review of relevant domestic and international guidelines and literature, combined with expert consensus meetings and external peer review to ensure rigorous validation. The guideline integrates the levels of evidence from published studies, expert consensus, and practical clinical considerations. All recommendations were reviewed and approved by a multidisciplinary panel of experts from the Pediatric Neurosurgery Group. This guideline is intended to serve as guidance for healthcare professionals involved in pediatric neuro-oncology, as well as for patients, caregivers, and other healthcare providers participating in the management of pLGGs.

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.

Myosteatosis is associated with poor outcomes in various liver diseases. However, standardized methods for assessing, defining, and diagnosing myosteatosis in the context of liver diseases remain unclear. Furthermore, the underlying mechanisms by which myosteatosis leads to pathophysiological progression and adverse health outcomes remain elusive. Therefore, in this review, we elaborate on the currently available measures, definitions, and diagnostic criteria of myosteatosis in the existing literature. We thoroughly clarify the recent evidence and data regarding the possible involvement of myosteatosis in the progression and deterioration of various liver diseases and resulting complications, including liver cirrhosis, chronic viral hepatitis, non-alcoholic/metabolic-associated fatty liver disease, primary sclerosing cholangitis, liver transplantation, and hepatocellular carcinoma. Additionally, it synthesizes insights from basic research on the pathogenesis of myosteatosis, which involves multifactorial mechanisms, including insulin resistance, mitochondrial dysfunction, and chronic inflammation. Finally, from an operational and pragmatic perspective, several regimens, including physical, nutritional, and pharmacological therapies, have been discussed as potential treatments for myosteatosis.

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.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the most common chronic liver disease in the Western world, driven by obesity, insulin resistance, and systemic inflammation. Its progressive form, metabolic dysfunction-associated steatohepatitis (MASH), can culminate in cirrhosis and hepatocellular carcinoma (HCC). While lifestyle modification remains central to MASLD management, there is growing interest in pharmacological interventions, particularly nutrient-stimulated hormone-based therapies (NuSHs), such as GLP-1 receptor agonists. NuSHs exert metabolic and anti-inflammatory effects primarily via weight loss and improved insulin sensitivity. Emerging clinical data support their efficacy in resolving MASH without worsening fibrosis. However, benefits in cirrhotic patients are less evident, suggesting greater utility in early intervention. Observational studies and clinical trials suggest a reduction in liver-related morbidity with GLP-1 receptor agonist use, though fibrosis regression remains inconsistent. Preclinical models indicate that NuSHs may also reduce MASH-related HCC incidence and tumor burden, likely through systemic metabolic improvements rather than direct antineoplastic action. Observational human data following bariatric surgery reinforce this link, suggesting that weight loss itself plays a key preventive role. Herein, we propose that NuSHs are promising candidates for MASH-related HCC prevention. We provide mechanistic suggestions for how this may occur. Furthermore, incorporating NuSHs into the post-locoregional treatment pathway for HCC may delay the need for systemic anti-cancer therapies, improve immunotherapy synergy and transplant eligibility, and even slow disease progression through reversal of carcinogenic drivers. Future studies are needed to target oncological endpoints and clarify immunometabolic mechanisms to guide the integration of NuSHs into MASLD treatment algorithms.