This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Metabolic dysfunction-associated fatty liver disease （MAFLD） is currently the most prevalent chronic liver disease worldwide， and its disease spectrum encompasses simple steatosis， metabolic-associated steatohepatitis， hepatic fibrosis， liver cirrhosis， and hepatocellular carcinoma. Recent studies have shown that hepatic macrophages play an important regulatory role in the development and progression of MAFLD. Hepatic macrophages mainly consist of resident Kupffer cells and monocyte-derived macrophages， which participate in the pathophysiological processes of the disease by regulating inflammatory response， lipid metabolism， fibrogenic process， and cell death pathways. This article summarizes the diversity and functional plasticity of hepatic macrophages in MAFLD， reviews their interactions with hepatocytes and gut microbiota， elaborates on the core mechanism of hepatic macrophages and related signaling pathways in the progression of MAFLD， and further discusses novel therapeutic strategies targeting hepatic macrophages. An in-depth understanding of the mechanism of action of hepatic macrophages in MAFLD may provide a crucial theoretical basis for developing precise therapeutic approaches for this disease.

ObjectiveThis study explored the risk factors for malignant risks of pulmonary nodules based on clinical data，constructed an integrated traditional Chinese and Western medicine model for predicting malignant risks of pulmonary nodules， and visualized the prediction results by using a nomogram. MethodsBased on a cross-sectional survey study design，patients with pulmonary nodules who were hospitalized in the Department of Respiratory and Cardiothoracic Surgery of Guangdong Provincial Hospital of Traditional Chinese Medicine from April 2023 to January 2024 were included. The dataset was randomly divided into a training set and a validation set according to 7∶3. In the training set，predictive factors were selected through univariate Logistic regression analysis and Least Absolute Shrinkage and Selection Operator （LASSO） regression analysis，and Logistic regression models were built. The discriminative ability，calibration，and clinical decision-making curves of the Western medicine model and the integrated traditional Chinese and Western medicine prediction model were compared to select the optimal model，which was then visualized in a nomogram. ResultsThis study included a total of 366 patients，and they were divided into a training set （258 cases） and a validation set （108 cases）. Seven predictive factors were considered including age，preference for fatty and greasy foods，history of environmental or occupational exposure，Qi deficiency，Yang deficiency，nodule density，and nodule diameter. A Logistic regression model was constructed. A Western medicine model，defined as model1，was created using only age，history of environmental or occupational exposure，nodule density，and nodule diameter as predictive factors. In addition，an integrated traditional Chinese and Western medicine model，defined as model2，was created by adding preference for fatty and greasy foods， Qi deficiency，and Yang deficiency as predictive factors. Model2 demonstrated better predictive performance in both the training and validation sets. Its accuracy in training set was 0.740，with precision of 0.825， recall of 0.829， F1 score of 0.827， the area under the curve （AUC）of 0.865 （95% confidence interval （CI）：0.815-0.915）， and a Brier score of 0.122. The accuracy in validation set was 0.731， with precision of 0.776， recall of 0.831， F1 score of 0.803， AUC of 0.852 （95%CI：0.776-0.927）， and a Brier score of 0.149. The calibration curve and decision-making curve analysis showed that this model exhibited good consistency and clinical utility in prediction. The equation for the malignant probability of pulmonary nodules was defined as p=eY/(1+eY)，where Y=-4.187+0.022×（age）+1.079×（history of environmental or occupational exposure）+0.718×（preference for fatty and greasy foods）+ 1.403×（Qi deficiency）+0.732×（Yang deficiency）+0.349×（pure ground glass density nodules）+1.304×（partially solid density nodules）+0.148×（nodule diameter）. A nomogram was used for intuitive representation. ConclusionThis study develops a comprehensive assessment model for the malignant risk of pulmonary nodules， which integrates factors from traditional Chinese medicine and Western medicine. Embedding traditional Chinese medicine elements in the prediction model allows for a multidimensional assessment of relevant risk factors for nodule malignancy. This demonstrates good diagnostic performance and provides a reference for early identification of lung cancer.

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.

The level of urinary albumin is a critical indicator for the early diagnosis and management of chronic kidney disease (CKD). However, existing methods for detecting albumin are not conducive to point-of-care testing due to the complexity of reagent addition and incubation processes. This study presents a smartphone-integrated handheld automated biochemical analyzer (sHABA) designed for point-of-care testing of urinary albumin. The sHABA features a pre-loaded, disposable reagent cassette with reagents for the albumin assay arranged in the order of their addition within a hose. The smartphone-integrated analyzer can drive the reagents following a preset program, to enable automatic sequential addition. The sHABA has a detection limit for albumin of 5.9 mg/L and a linear detection range from 7 to 450 mg/L. The consistency of albumin level detection in 931 urine samples using sHABA with clinical tests indicates good sensitivity (95.78%) and specificity (90.16%). This research advances the field by providing an automated detection method for albumin in a portable device, allowing even untrained individuals to monitor CKD in real time at the patient's bedside. In the context of promoting tiered diagnosis and treatment, the sHABA has the potential to become an essential tool for the early diagnosis and comprehensive management of CKD and other chronic conditions.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

Objective:To investigate the relationship between serum soluble thrombomodulin(sTM),soluble interleukin-2 receptor(sIL-2R)and short-term prognosis in patients with acute exacerbation chronic obstructive pulmonary disease(AECOPD)combine with type Ⅱ respiratory failure(RF).Methods:251 patients with AECOPD combine with type Ⅱ RF who were admitted to our hospital from April 2021 to April 2024 were selected,the serum sTM and sIL-2R levels of all patients were detected,and the prognosis of 10 d after admission of patients was counted,patients were divided into death group(n=40)and survival group(n=211)according to different prognosis.Influencing factors were analyzed by multivariate logistic regression analysis,predictive value was analyzed by receiver operating characteristic(ROC)curve.Results:Serum sTM and sIL-2R levels in death group were higher than those in survival group(P＜0.05).long course of chronic obstructive pulmonary disease(COPD)and severe RF,high sTM and high sIL-2R were risk factors for death of 10 d after admission of patients with AECOPD combine with type Ⅱ RF(P＜0.05).The area under the curve(AUC)of serum sTM and sIL-2R detection in predicting death of 10 d after admission of patients with AECOPD combine with type Ⅱ RF was 0.801 and 0.810,respectively,the AUC of joint detection of serum sTM and sIL-2R was 0.859,which was higher than that of each index alone.Conclusion:Serum sTM and sIL-2R levels in patients with AECOPD combine with type Ⅱ RF are significantly increase,which are relate to the occurrence of poor prognosis of 10 d after admission,serum sTM and sIL-2R are expect to be used as auxiliary predictive markers for short-term prognosis of patients with AECOPD combine with type Ⅱ RF,and long course of COPD and severe RF,APACHE Ⅱ score are the influencing factors of poor prognosis in patients with AECOPD combine with type Ⅱ RF.

Blood from a case of group B epidemic cerebrospinal meningitis identified in February 2024 in Ganzhou City,Jiangxi Province,and throat swabs from close contacts were collected for isolation and culture.The isolates were subjected to serogrouping,drug sensitivity testing,and whole genome sequencing and analysis,to provide a basis for epidemiological inves-tigation and clinical drug use.One strain of Neisseria meningitidis was isolated from the blood of the case and denoted group B.The MLST type was ST-7962,with no clonal group attribution.The phylogenetic tree showed that it was genetically close to the 1977 Shanghai carrier isolate(id-52231).Drug sensitivity results indicated that the strain was sensitive to 8 drugs:azithro-mycin,cefotaxime,minocycline,ceftriaxone,chloramphenicol,meropenem,rifampicin,and benzylpenicillin;resistant to cot-rimoxazole,levofloxacin,and ciprofloxacin;and showed an intermediate response to penicillin.This report describes the first case of ST-7962 group B meningoencephalitis found in Jiangxi Province.Monitoring of Neisseria meningitidis carriage,drug re-sistance,and molecular characteristics of strains in the healthy population in this region should be strengthened,to provide la-boratory support for the clinical use of medications,traceability,and control of the pathogen underlying meningoencephalitis infection.