Medical sciences (Basel, Switzerland)最新文献

Background: This study aimed to investigate longitudinal changes in muscle mass, quality, and composition (sarcopenia and myosteatosis) in adult people with cystic fibrosis (pwCF) using artificial intelligence (AI)-assisted body composition analysis (BCA) with chest computed tomography (CT) at the T12 level and to examine the influence of CFTR modulator therapy with elexacaftor/tezacaftor/ivacaftor (ETI).

Methods: A retrospective observational study was conducted on 102 adult pwCF (42 females (41%), mean age 33.9 ± 11.1 years) who underwent routine chest CT scans with a minimum of six months between scans. PwCF were categorized into ETI and no ETI groups. AI-assisted BCA was performed on chest CT images at the T12 level to measure skeletal muscle area (SMA), inter- and intramuscular adipose tissue (IMAT), and low-attenuation muscle area (LAMA). IMAT/SMA ratio and height- and weight-related skeletal muscle indices (SMI) were calculated.

Results: The ETI group showed a significant increase in SMA over time (p < 0.001), whereas the IMAT, LAMA, and IMAT/SMA ratio increased in both groups (all p < 0.05). SMI showed alterations only in the ETI group, with an increase in SMA/m² (p < 0.001) and a decrease in SMA/kg (p = 0.003) and SMA/BMI (p = 0.006). Sex-specific analysis showed that SMA and myosteatosis increased regardless of sex (all p < 0.05). Weight-adjusted SMI decreased only in females receiving ETI therapy (p < 0.05).

Conclusions: Adult pwCF, particularly those undergoing ETI therapy, experience significant changes in body composition, including increased muscle mass and myosteatosis. Trends in the development of sarcopenic obesity have been observed, particularly in female pwCF. These findings emphasize the importance of comprehensive body composition assessments and targeted interventions in pwCF treated with ETI to optimize muscle mass and quality while managing adipose tissue accumulation.

Background/objectives: Accurate histopathological classification of lung and colon tissues remains difficult due to subtle morphological overlap between benign and malignant regions. Deep learning approaches have advanced diagnostic precision, yet models often lack interpretability or require complex multi-stage pipelines. This study aimed to develop an end-to-end dual-branch attention network capable of achieving high accuracy while preserving computational efficiency and transparency.

Methods: The architecture integrates EfficientNetV2-B0 and MobileNetV3-Small backbones through a cross-gated fusion mechanism that adaptively balances global context and fine structural details. Efficient channel attention and generalized mean pooling enhance discriminative learning without external feature extraction or optimization stages.

Results: The network achieved 99.84% accuracy, precision, recall, and F1-score, with an MCC of 0.998. Grad-CAM maps showed strong spatial correspondence with diagnostically relevant histological structures.

Conclusions: The end-to-end framework enables the reliable, interpretable, and computationally efficient classification of lung and colon histopathology and has potential applicability to computer-assisted diagnostic workflows.

Background/Objectives: Breast cancer is a leading cause of female mortality worldwide. Immune system dynamics, reflected in hematological ratios derived from leukocytes, have been increasingly recognized for their prognostic value in cancer patients. This study aimed to evaluate the clinical significance of leukocyte-based hematological ratios in breast cancer patients undergoing cytotoxic polychemotherapy, focusing on their association with prognosis, chemoresistance, recurrence, metastasis, and mortality. Methods: A mixed observational study was conducted with 185 breast cancer patients undergoing AC-T chemotherapy. Hematologic ratios, including neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), and platelet-to-lymphocyte (PLR), were calculated at multiple treatment points (D0-D168) and correlated with clinical outcomes. Statistical analyses included ANOVA and ROC curve evaluations to determine the prognostic accuracy of these markers. Results: Significant alterations in hematological ratios were observed during chemotherapy. An increase in MLR correlated with intermediate risk for death and metastasis, while elevated PLR and platelet-to-neutrophil ratio (PNR) were strongly associated with metastasis, recurrence, and mortality. Decreases in lymphocyte-to-platelet ratio (LPR) were linked to chemoresistance and adverse outcomes. ROC curve analysis identified PLR at D84 (sensitivity: 83.33%) and PNR at D126 (specificity: 87.01%) as robust prognostic markers. Conclusions: Leukocyte-based hematological ratios provide valuable insights into immune dynamics and prognosis in breast cancer patients undergoing chemotherapy. Their integration into routine clinical evaluations could enhance risk stratification and personalized treatment approaches.

Background: The aim of this study was to evaluate the significance of serum concentrations of the inflammatory marker osteopontin, the blood-brain barrier damage marker occludin, and the neurodegeneration marker neurofilament light chain (NFL) in patients with relapsing-remitting multiple sclerosis (RRMS) for predicting disease activity and progression.

Methods: This prospective cohort study enrolled 150 patients with RRMS. Initial serum levels of NFL, occludin, and osteopontin were compared between patients who met or did not meet the no evidence of disease activity (NEDA) criteria and its components (relapses, magnetic resonance imaging activity, and Expanded Disability Status Scale progression) within 36 months of observation. Independent factors affecting study outcomes at month 36 were identified from baseline data, including age, gender, initial prognostic profile, and serum levels of NFL, occludin, and osteopontin, as well as treatment type.

Results: We found lower osteopontin concentrations in patients receiving highly effective treatment compared with those receiving platform therapies (13.64 ± 5.41 ng/mL, CI 11.75-15.53 vs. 17.33 ± 8.00 ng/mL, CI 15.66-18.61; p = 0.03). There was a significant correlation between NFL levels and patient age (Spearman's rho = 0.3045, p = 0.0002) and between NFL levels and disease duration (Spearman's rho = 0.1945, p = 0.02). NEDA during the 3-year observation period was achieved by 58 (38.67%) patients. Patients with NEDA showed significantly lower serum concentrations of occludin, NFL, and osteopontin than those without NEDA.

Conclusions: Serum levels of NFL, osteopontin, and occludin may serve as biomarkers of disease activity in patients with RRMS. The clinical relevance of these biomarkers should be confirmed through repeated serum marker assessments in MS patients and validation studies involving larger sample sizes.

Background: Coronavirus disease (COVID-19), caused by SARS-CoV-2 infection, presents a broad spectrum of clinical manifestations, ranging from asymptomatic cases to severe and fatal outcomes. Studies have shown that laboratory parameters fluctuate in patients with COVID-19, and these parameters serve as valuable biomarkers for monitoring disease progression. This study examines the relationship between changes in biochemical and hematological markers and patient survival among early COVID-19 cases.

Materials and methods: In this retrospective cohort study, data from adult (≥18 years) hospitalized COVID-19 patients with positive PCR results at HRH Princess Maha Chakri Sirindhorn Medical Center, Srinakharinwirot University, Nakhon Nayok, Thailand, between March and December 2021, were analyzed. Univariate and multivariate logistic regression analyses were conducted on mortality-related laboratory parameters. All measures are reported as adjusted odds ratios (aORs) with 95% confidence intervals (CIs).

Results: The cohort included 397 patients with pneumonia (median age: 52.2 years (IQR: 40.5-64.6); 61.96% female). Among them, 42 patients (10.58%) succumbed during hospitalization, with a median hospital stay of 12.92 days (IQR: 10.03-15.94). Independent mortality predictors were identified as follows: age (aOR = 1.11; 95% CI: 1.04-1.19; p = 0.002), potassium (aOR = 6.27; 95% CI: 1.31-29.93; p = 0.021), creatinine (aOR = 1.62; 95% CI: 1.05-2.50; p = 0.028), hemoglobin A1c (aOR = 1.96; 95% CI: 1.30-2.97; p = 0.001), and red cell distribution width (aOR = 1.45; 95% CI: 1.05-2.02; p = 0.026), respectively. Furthermore, patients with lower platelet counts had a notably higher risk of mortality (aOR = 0.98; 95% CI: 0.97-0.99; p = 0.001).

Conclusions: Our findings suggest that age, potassium, creatinine, hemoglobin A1c, red cell distribution width, and platelet count are significant predictors of mortality risk in patients with COVID-19. Clinicians should consider these biochemical and hematological markers critically before initiating treatment for COVID-19 patients.

Background: Venous thromboembolism (VTE) is a multifactorial disorder influenced by both genetic and environmental factors, with substantial variability in susceptibility across populations. Data on VTE-associated genetic variants in Asian populations, including Thais, remain limited. To address this, we developed a 39-single-nucleotide polymorphism (SNP) genotyping panel using the MassARRAY platform and evaluated its association with VTE in a Thai cohort. Methods: A total of 209 individuals, comprising 122 patients with objectively confirmed VTE and 87 age- and sex-matched healthy controls, were genotyped. Allele frequencies were compared, and associations with VTE were assessed. Results: Seven SNPs demonstrated significant associations: five risk alleles (PROC rs146922325, ABO rs8176743, FGG rs2066865, F11 rs4253417, and HIVEP1 rs169713) and two protective alleles (F5 rs4524 and TGFB2 rs57615042). To examine cumulative effects, a polygenic risk score (PRS) integrating genetic and clinical factors was constructed. Higher PRS was significantly associated with recurrence, particularly among patients with unprovoked VTE, conferring more than a threefold increase in recurrence risk (HR = 3.53, 95% CI: 1.04-10.2, p = 0.043). These findings provide the first systematic evidence of population-specific genetic risk factors for VTE in Thais and highlight the clinical potential of PRS for recurrence prediction. Conclusions: The MassARRAY-based panel offers a cost-effective, high-throughput strategy for simultaneous SNP detection, supporting scalable genomic studies and personalized risk stratification. Our results contribute to understanding the genetic architecture of VTE and highlight the value of incorporating non-European populations into genetic studies to advance precision medicine.

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous malignancy, with various outcomes despite significant advances in therapeutic options. Current conventional prognostic tools, e.g., the International Prognostic Index (IPI), lack sufficient precision at an individual patient level. However, artificial intelligence (AI), including machine learning (ML) and deep learning (DL), can enable specialists to navigate complex datasets, with the final aim of improving prognostic models for DLBCL. Objectives: This scoping review aims to systematically map the current literature regarding the use of AI/ML techniques in DLBCL outcome prediction and risk stratification. We categorized studies by data modality and computational approach to identify key trends, knowledge gaps, and opportunities for their translation into current practice. Methods: We conducted a structured search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases through July 2025 using terms related to DLBCL, prognosis, and AI/ML. Eligible studies included original papers applying AI/ML to predict survival outcomes, classify risk groups, or identify prognostic subtypes. Studies were categorized based on input modality: clinical, positron emission tomography/computed tomography (PET/CT) imaging, histopathology, transcriptomics, genomics, circulating tumor DNA (ctDNA), and multi-omics data. Narrative synthesis was performed in line with PRISMA-ScR guidelines. Results: From the 215 records screened, 91 studies met the inclusion criteria. Group-wise we report the following categories: clinical risk features (n = 8), PET/CT imaging (n = 30), CT (n = 1), digital pathology (n = 3), conventional histopathology (n = 2), gene expression profiling (n = 19), specific mutational signatures (n = 18), ctDNA (n = 3), microRNA (n = 2), and multi-omics integration (n = 5). The most common techniques reported amongst the papers included ensemble learning, convolutional neural networks (CNNs), and LASSO-based Cox models. Several AI techniques demonstrated superior predictive performance over IPI, with area under the curve (AUC) values frequently exceeding 0.80. Multi-omics models and ctDNA-based predictors showed strong potential for clinical translation, a perspective worth considering in further studies. Conclusions: AI/ML methods are increasingly used in DLBCL to improve prognostic accuracy by leveraging data types with diverse inputs. These approaches allow an enhanced stratification, superior to traditional indices, and support the early identification of high-risk patients, earlier guidance for therapy tailoring, and early trial enrollment for flagged cases. Future investigations should focus on external validation and improvement of model interpretability, with tangible perspectives of integration into real-world workflows and translation from bench to bedside.

Objective: This study aimed to evaluate the efficacy of empirical antibiotic therapy in treating chronic endometritis (CE) associated with abnormal uterine bleeding (AUB), infertility, or intrauterine lesions. Methods: The prospective cohort study involved 102 women undergoing outpatient hysteroscopy (OH), with immunohistochemical diagnosis of CE based on plasma cell density (PCD). Seventy-six of these women received empirical antibiotic therapy (ofloxacin and metronidazole), while 26 did not. A follow-up OH was conducted in the third cycle following the initial procedure. Results: Hysteroscopic polypectomy significantly reduced PCD regardless of antibiotic use (p = 0.009). In cases without focal lesions but exhibiting CE features, antibiotic therapy notably decreased PCD (p = 0.018). The incidence of certain histopathological features of CE, such as stromal edema and stromal cell compaction, was significantly lower in women treated with antibiotics (p = 0.014). Among intrauterine pathologies, endometrial polyps (p = 0.009) and cesarean scar defects (p = 0.011) significantly increased the risk of CE. Only spindled transformation of stromal cells with edema correlated significantly with elevated PCD (p = 0.022). Antibiotic therapy did not improve obstetric outcomes. Conclusions: Polypectomy alone reduced PCD without antibiotics, while antibiotic treatment significantly decreased PCD and resolved CE features in cases without focal lesions. Therefore, antibiotics may be prioritized for cases without focal lesions, whereas surgical intervention may be sufficient for CE associated with eligible pathologies.

Objectives: This study examined the effectiveness of a physical activity (PA) promotion intervention administered by a sports scientist as part of team-based care in a primary care setting. Methods: A randomised controlled trial was conducted. Physically inactive participants aged 35-70 years with non-communicable diseases (NCDs) were recruited. All participants received PA screening by a nurse and brief PA counselling by a physician. The intervention group also received a tailored PA programme at the first visit and monthly phone calls for 6-8 months (from visit 1 to visit 3). Outcome assessments by a sports scientist were performed for both groups at every visit (visit 1: baseline, visit 2: follow-up, visit 3: end-point, visit 4: continuing). Outcomes included meeting PA recommendations and weekly time spent in aerobic PA. An intention-to-treat analysis was applied. Results: Sixty participants were randomly allocated to each group. At visit 2 (months 3-4), significantly higher proportion of participants in the intervention group were meeting PA recommendations compared with the control group: aerobic PA (23.3% vs. 6.7%, p < 0.05), muscle-strengthening activity (31.7% vs. 0%, p < 0.001), and multicomponent PA (20.0% vs. 0%, p < 0.001). Median time spent in moderate- to vigorous-intensity PA (MVPA) was also higher (90 min/week vs. 60 min/week, p < 0.05). Weekly MVPA time increased significantly from baseline in both groups. Conclusions: Integrating a sports scientist into team-based care effectively improved short-term PA levels when intervention intensity was highest. The team-based care integrating sports scientists into primary care may enhance PA promotion for patients with NCDs.

Background: Chronic pain affects nearly one in five adults worldwide and remains a major healthcare burden due to its persistence, multidimensional impact, and resistance to conventional therapies. The opioid crisis has further highlighted the urgent need for safer and more effective alternatives. Psilocybin, a serotonergic psychedelic compound, has re-emerged as a potential therapeutic option for chronic pain given its effects on neuroplasticity, neuroinflammation, and emotional regulation.

Methods: This narrative review synthesized evidence from published preclinical and clinical studies. The focus was on the mechanisms of action of psilocybin, animal models of neuropathic and inflammatory pain, and early human trials exploring its effects on pain, mood, and quality of life.

Results: Preclinical studies demonstrated that psilocybin promotes synaptogenesis via BDNF-TrkB signalling, modulates 5-HT2A receptor activity, and reduces neuroinflammatory processes, leading to persistent analgesic and anxiolytic effects. Animal models of chemotherapy-induced neuropathy and inflammatory pain showed long-lasting antinociceptive responses. Clinical studies, though limited, reported improvements in depression, anxiety, resilience, and quality of life in patients with advanced cancer and chronic conditions, with preliminary evidence of analgesic benefit.

Conclusions: Psilocybin shows promise as a multidimensional therapy for chronic pain, addressing both sensory and affective components. However, ethical issues, safety concerns, and regulatory barriers necessitate careful management, and robust randomized controlled trials are essential to confirm efficacy and guide clinical translation.