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) represents an escalating healthcare burden across the Middle East and North Africa (MENA) region; however, system-level preparedness remains largely undefined. This study aimed to assess existing models of care, clinical infrastructure, policy frameworks, and provider perspectives across 17 MENA countries.

A cross-sectional, mixed-methods survey was distributed to clinicians from MASLD-related specialties across the region. A total of 130 experts (87.2% response rate) from academic, public, and private sectors in 17 countries participated. The questionnaire addressed national policies, diagnostic and therapeutic practices, referral pathways, multidisciplinary team (MDT) integration, and patient/public engagement. Quantitative responses were analyzed descriptively, while qualitative inputs underwent thematic analysis.

Only 35.4% of respondents confirmed the presence of national clinical guidelines for MASLD, and 73.1% reported the absence of a national strategy. Structured referral pathways were reported by 39.2% of participants, and only 31.5% believed the current model adequately addresses MASLD. While 60% supported MDT approaches, implementation remained inconsistent. Limited access to transient elastography was reported by 26.2% of providers. Public education efforts were minimal: 22.3% reported no available tools, and 87.7% indicated the absence of patient-reported outcomes data. Nearly half (47.7%) cited poor patient adherence, attributed to low awareness, financial barriers, and lack of follow-up.

Significant policy, structural, and educational gaps persist in MASLD care across the MENA region. To address this rising burden, countries must adopt integrated national strategies, expand access to non-invasive diagnostic tests, institutionalize MDT care, and invest in both public and provider education as essential pillars of system-wide preparedness.

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.

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.

Potential celiac disease (PCD) is defined as elevated celiac serology with a preserved small intestinal mucosa. This study aimed to identify baseline characteristics and the outcomes of children with PCD consuming a gluten-containing diet.

This was a retrospective cohort study of pediatric PCD patients diagnosed between 12/2018 and 10/2024. Baseline data included demographics, anthropometrics, clinical symptoms and signs, celiac serology, and biopsy results. Follow-up data included repeat serology and biopsy results when performed.

PCD was diagnosed in 75/517 (14.5%) children undergoing upper endoscopy for suspected celiac disease (CeD). Baseline anti-transglutaminase IgA (TTG) was above 10× the upper limit of normal (ULN) in 18 (24%), between 3–10× ULN in 52 (69.3%), and <3× ULN in five (6.6%). Anti-endomysial antibody was positive in 57 (76%). Among 48 children (64%) with at least one year of follow-up, TTG normalized in 26 (54.1%), decreased to <3× ULN in 13 (27.1%), was between 3–10× ULN in six (12.5%), and was above 10× ULN in three (6.3%). Nine children had a repeat endoscopy, and six (66.7%) were diagnosed with CeD, while three remained PCD. Among the 11 children with TTG >10× ULN and at least one year of follow-up, TTG normalized in three, declined in five, and increased or remained above 10× ULN in three.

PCD is common and may be found in children with TTG above 10× ULN; approximately half will normalize TTG. The omission of biopsies may result in an erroneous diagnosis of CeD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of hepatic fibrosis, yet its prevalence in asymptomatic populations remains unclear. This study aimed to assess the prevalence of steatosis and significant fibrosis in asymptomatic individuals without known liver disease in the Greater Vancouver Area.

Interested individuals voluntarily registered online via the Canadian Liver Foundation website or by telephone. Inclusion criteria included age ≥ 19 years, no known liver disease, and low alcohol intake (<30 g/day for men, <20 g/day for women). Demographic and clinical data were collected, and all participants underwent transient elastography after a 3-h fast. The study aimed to collect 4,500 analyzable scans while reflecting the region’s ethnic diversity.

A total of 4,193 participants were analyzed. The median age was 62 years, the median body mass index was 25.4, and 45% were male. Asian individuals comprised 42% of the cohort. Steatosis was present in 59.6% of participants, and 45.7% met diagnostic criteria for MASLD. Significant fibrosis (F2–F4) was found in 8.6%. Age, male sex, ethnicity, cardiac disease, diabetes, hypertension, and obesity were significantly associated with fibrosis. Logistic regression analysis confirmed age, weight, diabetes, dyslipidemia, hypertension, and obesity as independent predictors.

A substantial proportion of asymptomatic individuals in Greater Vancouver have undetected MASLD and significant fibrosis. Early identification of high-risk groups may support broader implementation of transient elastography screening. This study provides one of the first North American population-based estimates of MASLD and fibrosis stratified by ethnicity, offering new insights into liver disease distribution among Caucasian, Chinese, and South Asian populations.

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.

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.