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.

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.

The COVID-19 pandemic profoundly impacted university students, presenting multifaceted challenges including the abrupt transition to virtual learning and significant disruptions to emotional well-being and dietary habits. This study aimed to investigate the dietary and nutritional characteristics associated with academic stress among Mexican university students during the COVID-19 lockdown.

This cross-sectional study was conducted with a sample of 114 university students in Mexico. Participants completed a self-reported questionnaire assessing dietary patterns, nutritional intake, and academic stress levels. Informed consent was obtained from all participants prior to data collection.

Among study participants (n = 114), 57.8% experienced moderate academic stress, while 25.7% reported high academic stress during the COVID-19 lockdown. Notably, 13.5% of students demonstrated food cravings that were significantly associated with increased consumption of red and fatty meats (P = 0.030) and sausages (P = 0.017). A negative virtual education experience was associated with food cravings towards high-calorie and saturated-fat foods (P = 0.014), as well as elevated academic stress levels (P = 0.009). Furthermore, high academic stress levels were positively associated with food cravings (P = 0.020), particularly towards carbohydrate-rich foods (P = 0.037).

The COVID-19 lockdown substantially disrupted the dietary habits and nutritional status of university students, with academic stress serving as a significant mediating factor.

Metaplastic breast carcinoma, a rare entity (<1% of breast neoplasms), lacks comprehensive spectroscopic characterization. This study aimed to address this gap by providing a qualitative and quantitative spectroscopic profile of metaplastic carcinoma in comparison to ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC).

A retrospective analysis was conducted on archival tissue blocks of metaplastic carcinoma (n = 10), DCIS (n = 12), and IDC (n = 31). Sections were stained with hematoxylin and eosin for histological confirmation. Attenuated total reflectance Fourier-transform infrared spectroscopy was performed on adjacent unstained sections, with normal breast tissue (n = 10) serving as the control. Spectral data were analyzed using t-tests to identify significant differences in peak intensities and ratios. Hierarchical clustering analysis and receiver operating characteristic curves were generated to assess the diagnostic potential of selected spectral features.

Spectral analysis revealed that mean peak intensities were generally lower in all carcinoma subtypes compared to normal breast tissue. Specific ratios, including A1237/A1080 (phosphate; p < 0.01), A1043/1543 (glycogen; p < 0.01), and A1080/A1632 (nucleocytoplasmic index; p < 0.03), were significantly elevated in carcinomatous tissues. Receiver operating characteristic analysis identified peak 3,280 (area under the curve (AUC) = 0.93–0.96) as highly effective in differentiating normal from carcinomatous tissues. Peak 2,922 showed specificity for distinguishing normal tissue from IDC (AUC ≈ 0.7). Peak 1,744 effectively discriminated between DCIS and metaplastic carcinoma (AUC = 0.7). The ratio 1,080/1,632 (nucleocytoplasmic ratio) demonstrated exceptional diagnostic accuracy, distinguishing normal from carcinomatous tissues (AUC ≈ 1.0), DCIS from IDC (AUC ≈ 0.86), and DCIS from metaplastic carcinoma (AUC ≈ 0.8).

Attenuated total reflectance Fourier-transform infrared spectroscopy, particularly using peak 3,280 (Amide A) and the 1,080/1,632 ratio (nucleocytoplasmic index), offers a promising approach for discriminating between normal breast tissue and carcinoma, as well as differentiating pre-IDC from metaplastic carcinoma. These spectral markers demonstrate both statistical significance and diagnostic potential.

Fiberoptic bronchoscopy involves various topical airway anesthesia protocols, which can impact patient comfort, procedural ease, and overall outcomes. This study aimed to compare pre-procedure lignocaine spray (PPL) and spray-as-you-go (SAYG) airway anesthesia in terms of patient discomfort and operator comfort during fiberoptic bronchoscopy.

A single-blind randomized controlled trial was conducted at the Pulmonology Department of Shaikh Zayed Hospital, Lahore, Pakistan, from March 2021 to March 2022. Fifty participants were randomly assigned to two groups (n = 25 each). Standard procedural sedation with midazolam and 2 mL of 4% lignocaine spray in the oropharynx was used to suppress the gag reflex. Additionally, 2% lignocaine spray was administered during the procedure according to body weight (3 mg/kg) via oral scope insertion. Cough severity, pain perception, and operator comfort were assessed using the Visual Analogue Scale, Faces Pain Rating Scale, and a 4-point Likert scale, respectively.

Demographic characteristics were comparable between the groups, with a minor age difference (PPL: 53.25 years vs. SAYG: 50.88 years, p = 0.017). No significant differences were observed in pain perception, cough scores, or procedure duration between the PPL and SAYG groups. Operator comfort scores showed a trend favoring PPL (60% rated as “comfortable” or “very comfortable” vs. 28% in SAYG), though the difference was not statistically significant (p = 0.108).

Both PPL and SAYG topical airway anesthesia methods demonstrated similar effectiveness in pain control, cough suppression, operator comfort, and procedure duration. There was a slight, non-significant preference for PPL in operator comfort. These findings suggest that either technique may be effectively used, with potential implications for procedural efficiency and patient outcomes.

Artificial intelligence (AI) is profoundly transforming the paradigm of solid tumor drug development. By integrating multi-omics data, spatial transcriptomics, and advanced computational models, AI has significantly accelerated the discovery and validation of new targets, compressing the traditional ten-year research and development cycle to two to three years. Generative AI platforms have optimized small molecule inhibitors, biologics, and messenger RNA vaccines, achieving breakthroughs in overcoming tumor heterogeneity, improving efficacy, and predicting drug resistance. However, clinical translation still faces challenges such as data bias, algorithm transparency, and the validation gap between models and real-world human experience. This review aims to systematically elaborate on the transformative role of AI in solid tumor drug development and to promote interdisciplinary cooperation as well as the construction of ethical frameworks to enable the full realization of precision oncology.

Gastric cancer remains a significant global health burden, with limited therapeutic options and poor clinical outcomes. Although conventional treatments such as surgery and chemotherapy are widely used, their effectiveness is often hindered by adverse effects and high recurrence rates, highlighting the urgent need for safer and more effective alternatives. Scleromitrion diffusum (Willd.) (S. diffusum), a well-established anticancer herb in traditional Chinese medicine, has demonstrated promising clinical potential against gastric cancer. This review systematically examines the bioactive components of S. diffusum and their multi-target mechanisms of action against gastric cancer. Key active compounds, including flavonoids, anthraquinones, and terpenoids, have been identified as exerting synergistic anti-gastric cancer effects. These compounds collectively target critical pathways in gastric cancer pathogenesis, including apoptosis induction, suppression of proliferation and angiogenesis, and immune modulation. The mechanistic elucidation presented in this review not only validates the traditional use of S. diffusum in cancer management but also provides a molecular basis for its potential application in precision medicine strategies for gastric cancer. Beyond summarizing existing evidence, this work highlights critical gaps in current knowledge and proposes essential directions for future research, providing important references for integrating traditional medicine with modern oncology approaches.

Glioblastoma (GBM) is the most prevalent and aggressive form of primary brain malignancy in adults. Despite continuous advancements in standard treatment modalities, the prognosis for patients remains extremely poor, with a median survival of less than two years. In recent years, chimeric antigen receptor T-cell (CAR-T) therapy has achieved revolutionary success in hematologic malignancies, marking a significant breakthrough in the field of immunotherapy. However, the successful application of CAR-T therapy to GBM still faces dual challenges: antigen heterogeneity and the immunosuppressive tumor microenvironment. This review systematically summarizes these challenges encountered in CAR-T therapy for GBM and the innovative strategies currently under development to address these challenges, providing insights for the future clinical translation of CAR-T therapy in GBM.

Actively identifying the risk factors and predictive indicators associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) can enable early diagnosis and treatment, which is of great significance for prolonging the survival of patients with LC. Hemodynamic disturbances, advanced LC, vascular endothelial injury, and mutations in thrombophilic genetic factors are established risk factors for PVT-LC. Venous dilatation and decreased blood flow velocity contribute to hemodynamic disturbances. The severity of LC can be assessed by the degree of portal hypertension, liver metabolic function biomarkers, and validated liver scoring systems. Iatrogenic interventions, endotoxemia, and metabolic syndrome may induce vascular endothelial injury and hypercoagulability, the latter of which can be quantified via coagulation-anticoagulation-fibrinolysis biomarkers. Mutations in thrombophilic genetic factors, such as Factor V Leiden, MTHFR C667T, and JAK2 V617F, disrupt coagulation-anticoagulation homeostasis and predispose patients to PVT-LC. This review specifically focuses on comprehensively delineating established risk factors and predictive indicators for PVT-LC, thereby providing a theoretical foundation for the construction of clinically applicable PVT predictive models to guide early interventions and improve the prognosis. Future research should further validate the associations between recently proposed risk factors and PVT-LC, while simultaneously establishing cutoff values for indicators with robust predictive value to construct a clinically applicable PVT prediction framework.

Giant invasive spinal schwannoma (GISS) is a rare benign tumor that extends over two or more vertebral levels with myofascial invasion. No previous case of GISS with vertebral body collapse has been reported. A 44-year-old man presented with a one-year history of progressive limb weakness and difficulty with defecation. He was initially misdiagnosed with a metastatic spinal tumor. Imaging revealed a large extradural mass with C4 vertebral body collapse. Histological examination of tumor tissue from both operations confirmed the diagnosis of schwannoma. The postoperative course was uneventful, and the patient’s limb weakness gradually improved. One year after surgery, the patient was able to walk and write independently. Muscle strength recovered to 4/5 in the upper extremities and 5/5 in the lower extremities, with a modified Japanese Orthopaedic Association score of 15/15. The patient’s neurological function improved significantly, and one-year follow-up showed no recurrence and stable spinal fixation. Currently, the patient’s bowel function has improved; however, the patient still requires defecation in bed. When magnetic resonance imaging reveals giant spinal tumors with imaging features suggestive of malignancy, GISS should be considered. Preoperative biopsy is essential for accurate diagnosis.