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.

Liver fibrosis is a key process in the progression of chronic liver diseases. However, there are currently no drugs specifically designed to treat liver fibrosis. Our Phase 2 trial of hydronidone for the treatment of chronic hepatitis B (CHB)-associated liver fibrosis showed that adding hydronidone to entecavir resulted in significant reversal of liver fibrosis. To further evaluate the efficacy of a 270 mg/day dose of hydronidone for treating liver fibrosis associated with CHB, we conducted this Phase 3 trial.

This is a 52-week, randomized (1:1), double-blind, placebo-controlled, multicenter, entecavir-based Phase 3 clinical study conducted at 44 study centers across China. Adult patients aged 18 to 65 years with significant liver fibrosis (defined as an Ishak score ≥ 3 on liver biopsy) associated with CHB were included.

The primary endpoint of the trial is to demonstrate the efficacy of fibrosis reversal, defined as a decrease in the Ishak stage score of liver fibrosis by ≥1 after 52 weeks of treatment, compared to baseline.

The results of this trial are expected to further support the antifibrotic indication for this novel drug.

While metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with obesity, the cause of its rapidly rising prevalence is not well understood. In this study, we aimed to examine the association between arsenic exposure and MASLD in humans.

Urinary inorganic arsenic data from the National Health and Nutrition Examination Survey, 2011–2020, were used. These were combined with death certificate data from the National Death Index of the National Center for Health Statistics to ascertain mortality rates. Weighted linear regression and chi-squared analysis were performed.

The analysis included 6,386 participants after exclusions. The mean urinary arsenic level was 5.92 µg/L in participants with MASLD versus 5.59 µg/L in those without. Alanine aminotransferase levels exhibited a statistically significant increasing trend across both continuous arsenic levels and arsenic quintiles. A statistically significant upward trend was observed for the income-to-poverty ratio and body mass index but not for education status. MASLD prevalence was highest among the white population, while an increasing trend was observed in the Hispanic population over the years (p < 0.001). The proportion of Mexican Americans increased to 12.6% in the MASLD group versus 8.09% in the non-MASLD cohort (p < 0.001). There was a statistically significant increase in the odds of MASLD across arsenic exposure levels, with individuals in the highest quintile having a 32% greater likelihood compared to those in the lowest quintile (p-trend = 0.002). The odds further increased to 55% in the highest quintile (odds ratio 1.55, 95% confidence interval: 1.19–2.03; p-trend < 0.001). MASLD was more prevalent in females than males (57.9% vs. 47.6%; p < 0.001), and the mean age increased from 46.9 years to 49.9 years (p = 0.016).

Our findings reveal a positive association between urinary arsenic exposure and MASLD, with increasing trends particularly observed among Hispanics and those with higher income-to-poverty ratios and body mass index.

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided detection systems have shown limitations, including high false-positive rates and low sensitivity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database.

This retrospective study was conducted on 82 patients (10,496 computed tomography slices) selected from the LIDC-IDRI database. The proposed method consists of five main steps: image preprocessing, lung parenchyma segmentation using Otsu’s thresholding and morphological operations, detection of nodule candidates, feature extraction, and classification using a CNN model. The CNN architecture includes two convolutional layers (20 and 30 filters, 3×3 kernel), ReLU activation, max-pooling layers, and a Softmax output layer. The network was trained with a mini-batch size of 32 for 50 epochs using the Stochastic Gradient Descent with Momentum optimizer (learning rate = 0.001, momentum = 0.9). Model performance was evaluated in terms of sensitivity, specificity, precision, and accuracy.

The proposed CNN model successfully detected pulmonary nodules and achieved accurate classification between benign and malignant nodules. On the LIDC-IDRI dataset, the model achieved a sensitivity of 98.7%, specificity of 97.5%, precision of 97.9%, and accuracy of 98.4%. Comparative analysis with recent studies, including hybrid CNN-long short-term memory and ResNet-based models, demonstrated that the proposed method provides competitive performance while maintaining lower computational complexity. The classification of nodule subtypes (solid, partially frosted, totally frosted) showed satisfactory discrimination results.

The proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Despite strong results, the study acknowledges limitations such as single-database validation and a relatively small training size. Future work will focus on validating the model across other datasets (e.g., ELCAP, NELSON) and optimizing multi-class classification performance to enhance generalizability and clinical applicability.

Epidemiological data on bacterial infections in cirrhosis in China remain limited. Therefore, we aimed to conduct a multicenter study to investigate the characteristics and outcomes of patients with cirrhosis and bacterial infections in China.

We retrospectively enrolled 1,438 hospitalized adult patients with cirrhosis and bacterial or fungal infections from 24 hospitals across China between January 2018 and September 2024. Data on demographics, clinical features, microbiology, treatment, and outcomes were collected.

A total of 1,783 infection episodes were recorded, including 1,668 first infections and 115 second infections. Most infections were community-acquired (86.6%). Pneumonia was the most common infection type (26.7%), followed by spontaneous bacterial peritonitis (19.5%) and spontaneous bacteremia (14.1%). Among 754 pathogens isolated from 620 patients, Klebsiella pneumoniae (20.1%) was nearly as common as Escherichia coli (21.7%). Multidrug-resistant (MDR) organisms accounted for 41.0% of all isolates, with extended-spectrum β-lactamase-producing Escherichia coli being the most prevalent MDR strain (8.9% of patients). Adherence to empirical antibiotic treatment guidelines from the European Association for the Study of the Liver was significantly lower in this cohort compared to the global study (21.5% vs. 61.2%, P < 0.001), accompanied by a lower clinical resolution rate (63.5% vs. 79.8%, P < 0.001).

The clinical and microbiological characteristics of bacterial infections in patients with cirrhosis in China differ substantially from those reported in other regions. These findings highlight the need for region-specific management and prevention strategies, particularly in light of the changing microbiological landscape, high MDR prevalence, and suboptimal antibiotic practices.

Metabolic dysfunction-associated steatotic liver disease (MASLD), is the most common form of chronic liver disease worldwide. This study aimed to explore the role of TM6SF2 in high-fat diet (HFD)-induced MASLD through the gut-liver axis.

The TM6SF2 gut-specific knockout (TM6SF2 GKO) mouse was constructed using CRISPR/Cas9 technology. TM6SF2 GKO and wild-type (CON) mice were fed either a HFD or a control diet for 16 weeks to induce MASLD. Blood, liver, and intestinal lipid content, as well as gut microbiota and serum metabolites, were then analyzed.

TM6SF2 GKO mice fed an HFD showed elevated liver and intestinal lipid deposition compared to CON mice. The gut microbiota of HFD-fed TM6SF2 GKO mice exhibited a decreased Firmicutes/Bacteroidetes ratio compared to HFD-fed CON mice. The HFD also reduced the diversity and abundance of the microbiota and altered its composition.Aspartate aminotransferase, alanineaminotransferase, and total cholesterol levels were higher in HFD-fed TM6SF2 GKO mice compared to CON mice, while triglyceride levels were lower. Serum metabolite analysis revealed that HFD-fed TM6SF2 GKO mice had an increase in the expression of 17 metabolites (e.g., LPC [18:0/0-0]) and a decrease in 22 metabolites (e.g., benzene sulfate). The differential metabolites of LPC (18:0/0-0) may serve as HFD-fed TM6SF2 serum biomarkers, leading to MASLD exacerbation in GKO mice.

TM6SF2 GKO aggravates liver lipid accumulation and liver injury in MASLD mice. TM6SF2 may play an important role in regulating intestinal flora and the progression of MASLD through the gut-liver axis.

The diagnosis of hepatic precancerous lesions (HPC) and early hepatocellular carcinoma (HCC) has significant public health implications and holds the potential to reduce the global burden of HCC. This study aimed to identify molecular features and biomarkers associated with HPC progression and early HCC development.

RNA sequencing was used to identify differentially expressed genes in mouse HPC tissues and normal liver tissues. Cyclin E1 (CCNE1) expression in HPC tissues and HCC cells was assessed using immunohistochemistry, Western blotting, and real-time polymerase chain reaction. The effects of CCNE1 on HCC cell proliferation, migration, invasion, and apoptosis were evaluated using colony formation, wound healing, Transwell assays, and flow cytometry. The mechanism of CCNE1 was explored through Kyoto Encyclopedia of Genes and Genomes pathway analysis and gene set enrichment analysis and further validated through in vitro experiments. The interaction between CCNE1 and tumor-associated macrophages (TAMs) was investigated by co-culturing HCC cells with macrophages.

RNA sequencing and TCGA database analysis showed that CCNE1 expression was significantly elevated in mouse HPC tissues and human HCC samples and was associated with reduced survival rates. In vitro assays demonstrated that CCNE1 promoted HCC cell proliferation, migration, invasion, and survival by activating the PI3K/Akt signaling pathway. Additionally, CCNE1 induced TAM polarization toward the M2 phenotype by promoting the expression of CCL2 and CCL5 in HCC cells.

CCNE1 promotes HPC progression and HCC cell proliferation, migration, invasion, and survival by activating the PI3K/Akt signaling pathway. Furthermore, CCNE1 enhances the secretion of CCL2 and CCL5 by HCC cells, promoting TAM infiltration and M2 polarization, thereby contributing to tumor progression.

Mechanical valve replacement is a primary treatment for rheumatic heart disease, yet prosthesis-related adverse outcomes remain underreported in India. This study aimed to examine the in-hospital mortality rate among patients who underwent prosthetic heart valve replacement surgeries in the past five years.

A retrospective analysis of 221 rheumatic heart disease patients (2019–2023) who underwent aortic valve replacement (AVR), mitral valve replacement (MVR), or double valve replacement (DVR) was conducted. Comorbidities (hypertension, type-2 diabetes mellitus) and valve origin (Indian vs. foreign-made) were also evaluated. Data were analyzed using SPSS (v25.0), with p < 0.05 considered statistically significant.

Among 221 patients, 262 valves were implanted (54 AVR, 126 MVR, 41 DVR). Overall in-hospital mortality was 7.24% (16/221), with rates of 5.55% (AVR), 7.14% (MVR), and 9.75% (DVR). No sex-based differences were observed (p > 0.05). The five-year actuarial survival rate was 92.8±4.8%, with no intergroup disparities (p > 0.05). Mortality was higher in patients >50 years (13/16 deaths) and in females (10/16 deaths), though these differences were not statistically significant. Hypertension was more prevalent in females and type-2 diabetes mellitus in males, but neither condition showed a significant association with outcomes (p > 0.05). Most fatalities were associated with thromboembolism, acute kidney injury, and congestive heart failure, and valve origin did not significantly impact mortality.

Over the past five years, we observed a 7.24% mortality rate at our tertiary care facility following prosthetic heart valve implantation across all age groups. The data suggest that mortality may be more common among females and older individuals; however, these differences did not reach statistical significance.

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.