Sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection. Although fish oil has been used as an immunonutritional preparations for the treatment of sepsis patients, there is still controversy as to whether it is beneficial to them. We systematically reviewed published clinical trial data to evaluate the effectiveness of fish oil-containing nutrition supplementation in sepsis patients. A systematic search was undertaken in PubMed, Embase, Chinese Biomedicine Database, the Cochrane Library and the China Knowledge Resource Integrated Database to obtain clinical controlled trails. RCTs on nutrition therapy containing fish oil among adult sepsis patients were selected for analysis in comparison with routine therapy. Twenty-five published trials were included in the meta-analysis. Fish oil-containing nutrition supplementation reduced the mortality compared with the control group (relative risk (RR) 0.74, I² = 0%). Fish oil also shortened the ICU stay (MD −3.57 days; 95% CI −4.54, −2.59; p0.05). Parenteral nutrition containing fish oil could significantly decrease mortality in sepsis patients while enteral administration could not. Fish oil-containing nutrition supplementation.

Severe burn injury causes a hypermetabolic response, resulting in muscle protein catabolism and multiple organ damage syndrome. However, this response has not yet been continuously characterized by metabolomics in patients. This study aims to quantify temporal changes in the metabolic processes of patients with severe burns. We employed ¹H-nuclear magnetic resonance (NMR) spectroscopy to scrutinize metabolic alterations during the initial 35 days following burn injury in a cohort of 17 adult patients with severe burns, with 10 healthy individuals included as controls. Plasma specimens were collected from patients on postburn days 1, 3, 7, 14, 21, 28 and 35. After performing multivariate statistical analysis, repeated-measures analysis of variance and time-series analysis, we quantified changes in metabolite concentrations. Among the 36 metabolites quantified across 119 samples from burn patients, branched-chain amino acids, glutamate, glycine, glucose, pyruvate, lactate, trimethylamine N-oxide and others exhibited obvious temporal variations in concentration. Notably, these metabolites could be categorized into three clusters based on their temporal characteristics. The initial response to injury was characterized by changes in lactate and amino acids, while later changes were driven by an increase in fatty acid catabolism and microbial metabolism, leading to the accumulation of ketone bodies and microbial metabolites. Metabolomics techniques utilizing NMR have the potential to monitor the intricate processes of metabolism in patients with severe burns. This study confirmed that the third day after burn injury serves as the boundary between the ebb phase and the flow phase. Furthermore, identification of three distinct temporal patterns of metabolites revealed the intrinsic temporal relationships between these metabolites, providing clinical data for optimizing therapeutic strategies.

Ossifying Fibromyxoid Tumor (OFMT) is a rare mesenchymal neoplasm of intermediate biological potential that typically presents as a slow-growing mass. We report a diagnostic challenge involving a 54-year-old male with a 20-year history of a thigh mass that recently exhibited rapid enlargement and ulceration. Initial superficial biopsy led to a high suspicion of cutaneous tuberculosis due to false-positive acid-fast bacilli (AFB) staining and a positive T-SPOT, despite negative tuberculosis serology and normal inflammatory markers. This diagnostic pitfall was attributed to the “shell” of superficial ossification, which caused sampling error and nonspecific dye retention mimicking mycobacteria. Computed tomography (CT) was pivotal in resolving this discrepancy by revealing a characteristic “core–shell” pattern—a well-defined soft tissue tumor core beneath a dense ossified layer. Following a multimodal diagnostic integration, the patient underwent wide local excision with a 2.0 cm margin and reconstruction via split-thickness skin grafting. Histopathology and immunohistochemistry, showing partial loss of INI-1 expression, confirmed the diagnosis of OFMT. No recurrence or metastasis was observed during a 2-year follow-up. This case underscores the necessity of imaging to bypass biopsy limitations imposed by superficial ossification and highlights the risk of inappropriate anti-tuberculosis therapy when microbiological morphology conflicts with the broader clinical and radiological picture.

Background: Wound infection is a common and clinically significant complication that can delay healing, increase healthcare costs, and contribute to inappropriate antimicrobial use. Rapid, objective, and scalable screening tools are urgently needed, particularly in resource-limited or non-specialist clinical settings. This study aimed to develop and evaluate a deep learning–based framework for automated wound infection detection using clinical wound images, with a focus on improving diagnostic consistency and supporting public health–oriented wound management. Methods: A dataset of 4,000 diverse clinical wound images was used to train and evaluate multiple deep learning models. The Swin Transformer architecture was compared with conventional convolutional neural networks. Model performance was assessed using accuracy, area under the receiver operating characteristic curve, and F1-score. To evaluate real-world applicability, model predictions were further compared with assessments made by non-specialist clinicians. Results: The Swin Transformer outperformed conventional convolutional neural networks, achieving an accuracy of 0.9025 (95% CI: 0.8695–0.9279), an area under the receiver operating characteristic curve of 0.9546, and an F1-score of 0.9042. Compared with non-specialist clinicians, the model reduced diagnostic variability and enabled earlier and more consistent recognition of wound infections. Conclusion: Deep learning applied to clinical wound images provides a scalable and objective approach for wound infection screening. Such tools have the potential to support earlier detection, reduce diagnostic variability, and improve wound management and antimicrobial stewardship, particularly in public health and resource-limited settings.