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.

Patients with acute liver failure (ALF) or acute-on-chronic liver failure (ACLF) are at high risk of bleeding with traditional artificial liver support systems. To address the bleeding risk in liver failure patients, the safety of regional mesylate anticoagulation (RMA) in centrifugation artificial liver support systems (cALSS) is proposed for study.

In this prospective single-arm study, ALF and ACLF patients were treated with cALSS using RMA. Coagulation function was monitored, and the predictors of mesylate dose were analyzed using the area under the curve (AUC). Blood ammonia, model for end-stage liver disease scores, and survival rates at 28 and 90 days were assessed.

All 57 patients showed no new bleeding within 24 h post-cALSS. Most disseminated intravascular coagulation indicators improved at 0.5 h and 24 h post-cALSS. Thromboelastography showed hypocoagulability at 0.5 h post-cALSS. Univariate and multivariate analyses identified pre-R and pre-MA as key factors for R exceeding 10 m at 0.5 h post-cALSS, with odds ratios of 0.91 (95% confidence interval (CI): 0.84–0.98) and 2.03 (95% CI: 1.05–3.90), respectively, P < 0.05. The predictive values were pre-MA ≤ 38 mm (AUC = 0.817, 95% CI [0.690–0.907], P < 0.001) and pre-R > 6.3 m (AUC = 0.790, 95% CI [0.661–0.888], P < 0.001). Patients showed improvements in blood ammonia and model for end-stage liver disease scores after the last session, especially those with high initial levels (>80 µmol/L and >30). The 28-day and 90-day survival rates of ALF patients were similar to those of ACLF patients.

cALSS with RMA is safe for liver failure patients with a high risk of bleeding. Adjusting the mesylate dose based on pre-R and pre-MA enhances safety.

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.

Malignant mixed Müllerian tumor (MMMT) or carcinosarcoma of the female genital tract is a rare but highly aggressive malignancy.

We report a unique case of primary ovarian MMMT with poorly differentiated angiosarcoma as its homologous sarcomatous component in a 53-year-old woman with a known germline BRCA1 mutation who presented with a pelvic mass. She underwent staging cytoreduction surgery including total hysterectomy, bilateral salpingo-oophorectomy, omentectomy, and pelvic and para-aortic lymph node dissections. The removed right ovarian tumor formed a 2.5 cm nodular to cystic mass replacing the entire organ. Microscopic examination revealed two distinct tumor components: high-grade serous carcinoma and poorly differentiated angiosarcoma. The proliferating sarcomatous cells were diffusely positive for CD31 and Factor VIII, but were negative for 100, SOX10 and cytokeratin. Both the serous carcinoma and angiosarcoma components demonstrated aberrant strong and diffuse p53 nuclear positivity. KRAS mutation analysis revealed guanine-adenine-thymine point mutation at codon 12 in both tumor components. Metastatic tumor was found involving the contralateral left ovary with the cellular composition of pure angiosarcomatous component.

This is the first report of an ovarian MMMT with angiosarcoma as its homologous sarcoma component. The presence of aberrant p53 expression and identical KRAS mutation in both the serous carcinoma and angiosarcoma components supports the theory of malignant mesenchymal transition/metaplasia in the development of MMMT.

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.

Fat mass and obesity-associated protein (FTO) has been linked to various cancers, though its role in hepatocellular carcinoma (HCC) remains unclear. This study aimed to investigate FTO expression, its clinical relevance, functional role in HCC progression, and the underlying molecular mechanisms.

Quantitative reverse-transcription polymerase chain reaction and immunohistochemical analysis were used to assess FTO expression in HCC. Functional assays, including proliferation, invasion, and epithelial-mesenchymal transition studies, were conducted using HCC cell lines with FTO knockdown. N6-methyladenosine (m6A) RNA immunoprecipitation and RNA stability assays further elucidated the role of FTO in BUB1 mRNA methylation and stability. Co-immunoprecipitation studies were employed to confirm the interaction between BUB1 and TGF-βR1. In vivo studies in nude mice were conducted to evaluate tumor growth following FTO knockdown.

FTO was significantly upregulated in HCC tissues compared to normal liver tissues, with higher expression observed in advanced tumor-node-metastasis stages and metastatic HCC. Elevated FTO correlated with poor overall survival in patients. Silencing FTO decreased HCC cell proliferation, colony formation, invasion, epithelial-mesenchymal transition, and tumor growth in nude mice. Mechanistically, FTO downregulation led to increased m6A modification of BUB1 mRNA, thereby promoting its degradation via the YTH domain family 2-dependent pathway and reducing BUB1 protein levels. Additionally, BUB1 physically interacted with TGF-βR1, activating downstream TGF-β signaling.

FTO is overexpressed in HCC and is associated with poor clinical outcomes. Mechanistically, FTO promotes HCC progression by stabilizing BUB1 mRNA through an m6A-YTH domain family 2–dependent pathway, which activates TGF-β signaling. Targeting the FTO–BUB1–TGF-βR1 regulatory network may offer a promising therapeutic strategy for HCC.

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.

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.

The development and progression of breast cancer may be influenced by thyroid hormone levels. In this study, we investigated thyroid function in pre- and postmenopausal women with breast cancer, with and without neoadjuvant chemotherapy (NCh).

The study included 198 women diagnosed with infiltrating ductal carcinoma: 83 did not receive NCh (39 premenopausal and 44 postmenopausal), while 115 underwent NCh before surgery (63 premenopausal and 52 postmenopausal). Additionally, 78 healthy volunteers, aged 28 to 69 years, served as the control group. Serum levels of thyroid-stimulating hormone (TSH), free thyroxine (fT4), and free triiodothyronine (fT3) were quantified using chemiluminescent immunoassays.

We observed a significant increase in serum TSH and fT4 levels in both pre- and postmenopausal women with breast cancer, regardless of NCh treatment, compared to control subjects. However, postmenopausal women with breast cancer who received NCh showed lower fT4 levels than their untreated counterparts. Notably, fT3 levels increased only in premenopausal women with breast cancer who underwent NCh, compared to both the premenopausal control group and untreated premenopausal breast cancer patients.

Altered thyroid function was observed in both pre- and postmenopausal women with breast cancer, characterized by increased TSH and fT4 levels. Neoadjuvant chemotherapy appeared to attenuate the rise in fT4 levels in postmenopausal women while elevating fT3 levels in premenopausal women. These findings highlight the importance of monitoring thyroid hormone profiles in women with breast cancer, considering menopausal status, given their potential influence on tumor progression and chemotherapy effectiveness.

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.