Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

Acute coronary syndrome (ACS) in patients with SARS-CoV-2 infection is primarily driven by inflammation-induced myocardial injury through both direct and indirect mechanisms. Effective clinical management requires a dual approach: addressing cardiovascular lesions while also mitigating virus-induced local and systemic inflammation. This comprehensive approach is essential for improving the diagnosis and treatment of SARS-CoV-2-associated ACS. Emerging evidence highlights the potential of myocardial protective agents, including angiotensin-converting enzyme 2-modulating drugs and traditional Chinese medicine, which not only stabilize plaques and improve endothelial function but also confer cardioprotective effects. Furthermore, advancements in nanotechnology offer promising strategies for targeted therapy—particularly through angiotensin-converting enzyme 2 receptor modulation—by enhancing the precision and efficacy of herbal medicine delivery. This review explores the complex interplay between SARS-CoV-2 infection and ACS pathogenesis, and evaluates the therapeutic potential of pharmacological, herbal, and nanotechnology-based interventions in managing this multifaceted condition.

Inherited metabolic liver diseases (IMLDs) have complex etiologies and vary widely in clinical presentation, with a significant overall incidence. With the advancements in diagnostic and treatment technologies, an increasing number of children with inherited metabolic diseases are surviving into adolescence and adulthood. These advancements have improved our understanding of the IMLD disease spectrum and clinical outcomes. This study aimed to analyze changes in the disease spectrum and epidemiological characteristics of inherited metabolic liver diseases (IMLD) over the past 20 years in two specialized liver disease hospitals in northern China.

A retrospective analysis was conducted on IMLD cases diagnosed between January 1, 2002, and December 31, 2023, at two liver disease specialty hospitals in Beijing. Data were obtained from inpatient and outpatient hospital information systems, with diagnoses based on national and international IMLD diagnosis and treatment guidelines.

A total of 2,103 IMLD patients were analyzed, including 1,213 adults and 890 children. IMLD accounted for 4.58‰ of hospitalized liver disease patients during this period. The most common IMLD was Wilson’s disease, comprising 68% of all IMLD cases. The number of diagnosed IMLD types increased from 15 to 32 across two 11-year periods (2002–2012 and 2013–2023). Among pediatric patients, glycogen storage disease and Alagille syndrome were more prevalent in those under one year of age, while Wilson’s disease was prevalent across all age groups. In adult IMLD patients, Wilson’s disease, polycystic liver disease, and hereditary hyperbilirubinemia were more frequently observed.

Over the past 20 years, both the number of diagnosed IMLD cases and disease diversity have significantly increased, with Wilson’s disease remaining the most prevalent IMLD. These findings provide valuable insights for the long-term management of IMLD patients and the allocation of healthcare resources.

Drug discovery is an exceptionally long and costly process, often taking over 10 years and costing billions of dollars. Despite these efforts, more than 90% of drug candidates fail, with most failures occurring during clinical trials due to issues related to efficacy, safety, or poor pharmacokinetics. A major contributor to these failures is biopharmaceutic barriers, including poor solubility, limited permeability, active efflux by transporters such as P-glycoprotein and breast cancer resistance protein, and extensive first-pass metabolism by CYP450 enzymes. These factors severely limit drug absorption and bioavailability, reducing therapeutic efficacy. Although traditional approaches, such as high-throughput absorption, distribution, metabolism, and excretion screening and improved chemical design, have achieved some progress, a major shift is now occurring through the use of in silico modeling, artificial intelligence (AI), and machine learning. These AI-driven tools enhance the prediction accuracy of absorption, distribution, metabolism, and excretion profiles, identify transporter interactions, and even simulate metabolic pathways. Additionally, modern formulation technologies, such as three-dimensional printing, lipid-based nanocarriers, and biodegradable delivery systems, are increasingly being integrated with AI-powered design platforms to personalize and optimize drug delivery. However, these promising advancements also raise regulatory and ethical concerns that must be addressed before widespread adoption. This review examines the major biopharmaceutic barriers responsible for drug development failures and explores how emerging AI-driven strategies and formulation innovations are being used to overcome these limitations. It also discusses current regulatory challenges and ethical considerations associated with adopting these technologies.

This study investigates upper gastrointestinal tract (UGIT) involvement in patients with ulcerative colitis (UC), a condition traditionally considered limited to the colon. Although extra-colonic manifestations of UC are well recognized, UGIT issues have received less attention. This research aimed to document the clinical, endoscopic, and histopathological UGIT findings in adults with UC and assess their association with disease severity and extent.

This descriptive cross-sectional study was conducted at Ain Shams University over one year. A total of 78 UC patients underwent comprehensive clinical evaluations, including assessments of gastrointestinal complaints, medication history, disease progression, surgeries, and physical examinations. Endoscopic assessments of both the UGIT and colon were performed, accompanied by biopsies for histopathological analysis.

The study population had a mean age of 35.26 years, with a nearly equal gender distribution. Endoscopic findings revealed significant UGIT involvement: 64% of patients had esophagitis and/or gastroesophageal reflux disease, 93% had gastritis, and 80% had duodenitis. Histopathological findings showed notable inflammation, basal cell hyperplasia, and ulcerations in the esophagus, with 51.3% of patients exhibiting chronic gastritis and 38.5% testing positive for Helicobacter pylori infection. Statistical analysis demonstrated a strong association between colonic disease severity and UGIT endoscopic (p < 0.0001 and p < 0.001 in the esophagus and stomach, respectively) and histopathological (p < 0.004, p < 0.001, and p <0.005 in the esophagus, stomach, and duodenum, respectively) findings, particularly in patients with UGIT symptoms.

This study concludes that UGIT endoscopic and histopathological changes are prevalent among Egyptian UC patients, suggesting a significant link between UC and these UGIT findings.

Histopathology is the gold standard in cancer diagnosis. However, attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy has shown diagnostic potential in other settings. Therefore, this study aimed to investigate the sensitivity and specificity of the ATR-FTIR spectroscopy in evaluating breast lesions.

This study was conducted on formalin-fixed, paraffin-embedded biopsy blocks received at Ladoke Akintola University of Technology Teaching Hospital between 2022 and 2023. The blocks were categorized into 10 normal (from benign breast tissue), 15 benign, and 31 malignant samples. Tissue sections of 15 µm were obtained during block trimming and floated onto FTIR slides. An additional 4 µm tissue sections were stained with hematoxylin and eosin for tumor diagnosis and to identify suitable areas on the FTIR slide. Spectrometer readings were taken within the range of 4,000–600 cm−1, 32 scans, and 16 cm−1 resolution, using the average of 10 preprocessed spectra per slide. Biomarkers were calculated by ratioing peak intensities for A1632/A1543, A1632/A2922, A1632/A1080, A1080/A1543, A1237/A1080, and A1043/A1543, which represent protein, diagnostic marker, cytoplasm-nucleus ratio, carcinogenesis marker, phosphate, and glycogen, respectively. The receiver operating characteristic curve was used to determine sensitivity, specificity, and the area under the curve (AUC).

The AUC analysis showed that cytoplasm-nucleus ratio values of 0.99 and 0.95 effectively distinguished normal from malignant tissue, and benign from malignant tissue, respectively (p < 0.0001). Additionally, protein marker (AUC = 0.73), diagnostic marker (AUC = 0.85), and cytoplasm-nucleus ratio marker (AUC = 0.94) were able to discriminate normal from benign tissue. Overall, the receiver operating characteristic analysis showed 100% sensitivity and specificity ranging from 54% to 87%. Glycogen (AUC = 1.00) exhibited 100% sensitivity in discriminating fibroadenoma from fibrocystic changes.

ATR-FTIR spectroscopy demonstrates high diagnostic accuracy in differentiating normal, benign, and malignant breast tissues using specific spectral biomarkers. Among these, the cytoplasm-nucleus ratio marker showed strong potential as a reliable spectral indicator for distinguishing various types of breast tumors. The cytoplasm-nucleus ratio marker demonstrated strong potential as a reliable spectral indicator for distinguishing various types of breast tumors.

Brain metastases from ovarian cancer (BMFOC) are rare but associated with poor prognosis. This study aimed to evaluate the efficacy and safety of Gamma Knife stereotactic radiosurgery (GKSRS) in managing patients with BMFOC.

A retrospective analysis was conducted on 22 patients with BMFOC who were treated with GKSRS between January 2015 and May 2019. The median age at the start of treatment was 57.7 years (range, 46–72 years). A total of 70 brain metastases were treated, with each patient having between one and nine metastatic tumors. The mean tumor volume was 3.6 cm3 (range, 0.1–22.7 cm3). The mean peripheral dose was 16 Gy (range, 7–20 Gy), and the mean isodose curve was 54.6% (range, 45–80%).

At 12 months post-GKSRS, 68 metastatic tumors were assessed: 32 (47.1%) showed complete response, 20 (29.4%) had partial response, 14 (20.6%) remained stable, and two (2.9%) progressed, leading to a tumor control rate of 97.1%. No acute or chronic toxicity was observed.

GKSRS appears to be an effective and well-tolerated treatment for BMFOC, offering high tumor control rates and prolonged survival in selected patients.

Multimodal applications combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in cognitive neuroscience and have progressively been applied to clinical applications, such as the joint diagnosis of amyotrophic lateral sclerosis, Alzheimer’s disease, and pediatric epilepsy. This study conducted a bibliometric analysis of EEG-fNIRS synchronization techniques over the past 20 years. The aim was to clarify their diagnostic and therapeutic value in clinical applications, particularly in the neurological system, and to guide future research and development trends.

This study utilized the Web of Science Core Collection database to analyze documents published between January 1, 2005, and May 13, 2024. CiteSpace and VOSviewer were employed for visual analyses of co-author relationships, keywords, citation patterns, and journal distributions. By overlaying dual-map diagrams and analyzing annual publication trends, the study identified research hotspots, development trends, and the evolution of EEG-fNIRS technology.

A total of 645 articles and reviews from 55 countries were analyzed. The USA contributed the most publications. The team led by Michela Balconi at the Catholic University of the Sacred Heart published the highest number of papers. Frontiers in Human Neuroscience had the greatest number of publications, while NeuroImage had the highest citation impact. Recent research has primarily focused on the application of neuroimaging and neurophysiological techniques (e.g., EEG, fNIRS, functional magnetic resonance imaging), brain activation, and brain-computer interface.

This study highlights the applications and developmental trends of dual-modality EEG-fNIRS technology. Specifically, this approach can assist in diagnosing neurological disorders, assessing activation and connectivity within functional brain regions, and evaluating therapeutic neuromodulation in clinical neurology. Overall, multimodal fusion is poised to advance neuroscience research significantly.

Epileptogenesis involves complex mechanisms, including inflammation and apoptosis. Rosiglitazone, a peroxisome proliferator-activated receptor gamma agonist, possesses anti-inflammatory and neuroprotective properties. This study investigated whether rosiglitazone can prevent pentylenetetrazole (PTZ)-induced kindling in mice by modulating inflammatory cytokines and apoptosis pathways.

Male C57BL/6 mice (n = 8 per group) were assigned to sham, control, or rosiglitazone-treated groups. Kindling was induced with intraperitoneal PTZ (40 mg/kg) every 48 h for 17 days. Rosiglitazone (0.1 mg/kg) was administered 30 m before each PTZ injection. Seizure progression was monitored, and hippocampal tissues were analyzed via immunohistochemistry and Western blotting to assess cytokine levels (interleukin (IL)-10, IL-17A, tumor necrosis factor-alpha, interferon-gamma), caspase-3 activity, and glial fibrillary acidic protein expression.

Rosiglitazone significantly delayed seizure progression, reduced seizure scores, and lowered pro-inflammatory cytokine levels (IL-17A, tumor necrosis factor-alpha, interferon-gamma) while increasing IL-10. Immunohistochemical analysis revealed fewer caspase-3-positive cells and reduced glial fibrillary acidic protein expression in the treatment group compared to controls.

Rosiglitazone exerts neuroprotective effects in PTZ-induced kindling, likely through its anti-inflammatory and anti-apoptotic actions. These findings underscore its potential as a therapeutic agent for mitigating epileptogenesis, warranting further investigation in combination therapies and clinical trials.