Lung cancer remains the leading cause of cancer-related mortality worldwide, with marked phenotypic differences observed among its major histological subtypes, adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung cancer (SCLC), in both clinical presentation and therapeutic response. In recent years, metabolomics has emerged as a powerful tool for studying cancer metabolic reprogramming, providing new insights into the metabolic distinctions among lung cancer subtypes. This review summarizes recent research advances in the metabolomics of ADC, SCC, and SCLC. Studies have revealed that ADC and SCC display distinct metabolic profiles in lipid metabolism, amino acid metabolism, and cell membrane synthesis, while SCLC demonstrates a unique metabolic pattern. Through metabolomic technologies, particularly mass spectrometry and liquid chromatography, it is possible to effectively differentiate lung cancer subtypes and identify potential biomarkers for early diagnosis and personalized treatment. This review also explores the clinical potential of metabolomics in lung cancer, emphasizing its critical role in early diagnosis and subtype stratification. These methodological advances establish a robust foundation for precision oncology paradigms in thoracic malignancies.

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.

It is established that administration of the COVID-19 vaccine may be associated with an exaggerated immune response leading to enlargement of several lymph nodes. Although most cases are benign and self-limiting, some have been reported in the literature as B-cell or T-cell lymphomas, with no reported cases of chronic lymphocytic leukaemia (CLL). We report two cases of follicular lymphoma and CLL that occurred a few weeks after COVID-19 vaccination. Case 1 is a 48-year-old woman who noticed two significantly palpable masses, one in each axilla, 48 h after receiving the first dose of the Pfizer-BioNTech BNT162b2 vaccine for COVID-19. Seven days later, she noticed another mass on the right side of her neck, which was biopsied within 48 hours. Case 2 is a 75-year-old man who presented with localized swellings in the axilla and on the neck, noted 24 h after the first dose of the Moderna messenger RNA-1273 COVID-19 vaccine. Neither patient reported any constitutional or associated symptoms. Surgical biopsy of the axillary lymph node in case 1 revealed a non-Hodgkin lymphoma, confirmed via immunohistochemistry as CD20-positive B-cell follicular lymphoma. The patient also had multiple pre- and para-aortic lymph nodes. In case 2, complete blood count showed lymphocytosis (total white blood cell – 148 × 109/L; lymphocyte differential – 92%), while peripheral blood film showed lymphocytosis with a predominance of small, mature-looking lymphocytes, both suggesting CLL. Although requested, immunophenotyping and molecular testing were not performed due to patient-related challenges. Although a chance occurrence is possible, lymphoid malignancies should be considered a strong differential. The vaccination history of patients presenting with clinical manifestations suggestive of a lymphoid malignancy should be thoroughly investigated, while ruling out other possible differentials such as a benign, self-limiting inflammatory process.

Bone marrow metastasis (BMM) from non-hematolymphoid malignancies with resultant cytopenia(s) can mimic primary hematolymphoid disorders. This study aimed to investigate the clinical and pathological characteristics of BMM from non-hematopoietic tumors.

We conducted a retrospective cohort study of patients diagnosed with BMM by non-hematolymphoid malignancies at our institution over the past 10 years. Demographic and clinical characteristics, histopathological findings of bone marrow, types of metastatic tumors, and prognosis were analyzed.

A total of 54 cases were included. The four most common malignancies with BMM, regardless of gender, were prostatic adenocarcinoma (29.6%), breast carcinoma (25.9%), colorectal adenocarcinoma (5.5%), and lung carcinoma (5.5%). The main clinical and laboratory manifestations were anemia (90.7%), reticulocytosis (80.5%), thrombocytopenia (73.9%), bone pain (55.5%), disseminated intravascular coagulation (39.6%), leukoerythroblastosis (35.3%), and leukopenia (24%). The vast majority (96.3%) of metastatic tumors were identified by morphology alone; however, in approximately 2.7% of cases, immunohistochemistry was required due to subtle morphologic features. In 29.6% (16/54) of patients, BMM was identified prior to or concurrently with other metastatic sites. The median time interval between the initial diagnosis of non-hematolymphoid malignancies and BMM was 29 months. Although patients who received anti-tumor treatment after BMM diagnosis demonstrated longer overall survival (P < 0.01), no significant differences were observed between those treated with immunotherapy versus chemotherapy and/or radiotherapy (P = 0.145).

Prostate and breast carcinomas are the most common malignancies associated with BMM, with anemia, reticulocytosis, and thrombocytopenia being the most frequent clinical manifestations. While our data show that receipt of anti-neoplastic treatments, regardless of regimen, was associated with improved overall survival after BMM, no significant survival differences were observed when prostate and breast carcinomas were compared with other types of BMM.

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.

Lactate exerts regulatory effects on both cellular homeostasis and disease progression, far beyond being a mere metabolic waste product. As lactate accumulates, the level of lactylation increases significantly. Lactylation, a novel type of post-translational modification, bridges metabolic reprogramming and epigenetic regulation in malignant tumors, including gynecological malignancies. Both lactate and lactylation play critical roles in the tumor microenvironment, ultimately promoting tumor proliferation, metastasis, and drug resistance. Therapies targeting lactate production and transport show considerable anticancer potential, particularly through the inhibition of lactate dehydrogenase and monocarboxylate transporters. These inhibitors can also act as immunotherapy potentiators, producing a synergistic therapeutic effect when combined with immunotherapy. This review emphasizes how lactate and lactylation drive the malignant progression of gynecological cancers and explores promising perspectives on potential therapeutic targets.

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.

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.