Background: In emergency departments (EDs), early diagnosis and timely rescue, which are supported by prediction modes using ED data, can increase patients’ chances of survival. Unfortunately, ED data usually contain missing, imbalanced, and sparse features, which makes it challenging to build early identification models for diseases. Objective: This study aims to propose a systematic approach to deal with the problems of missing, imbalanced, and sparse features for developing sudden-death prediction models using emergency medicine (or ED) data. Methods: We proposed a 3-step approach to deal with data quality issues: a random forest (RF) for missing values, k-means for imbalanced data, and principal component analysis (PCA) for sparse features. For continuous and discrete variables, the decision coefficient R2 and the κ coefficient were used to evaluate performance, respectively. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to estimate the model’s performance. To further evaluate the proposed approach, we carried out a case study using an ED data set obtained from the Hainan Hospital of Chinese PLA General Hospital. A logistic regression (LR) prediction model for patient condition worsening was built. Results: A total of 1085 patients with rescue records and 17,959 patients without rescue records were selected and significantly imbalanced. We extracted 275, 402, and 891 variables from laboratory tests, medications, and diagnosis, respectively. After data preprocessing, the median R2 of the RF continuous variable interpolation was 0.623 (IQR 0.647), and the median of the κ coefficient for discrete variable interpolation was 0.444 (IQR 0.285). The LR model constructed using the initial diagnostic data showed poor performance and variable separation, which was reflected in the abnormally high odds ratio (OR) values of the 2 variables of cardiac arrest and respiratory arrest (201568034532 and 1211118945, respectively) and an abnormal 95% CI. Using processed data, the recall of the model reached 0.746, the F1-score was 0.73, and the AUROC was 0.708. Conclusions: The proposed systematic approach is valid for building a prediction model for emergency patients.

Macrophage polarization is a critical determinant of disease progression and regression. Studies on macrophage plasticity and polarization can provide a theoretical basis for the tactics of diagnosis and treatment for macrophage-related diseases. These include inflammation-related diseases, such as sepsis, tumors, and metabolic disorders. Growth differentiation factor-15 (GDF-15) or macrophage inhibitory cytokine-1, a 25 kDa secreted homodimeric protein, is a member of the transforming growth factor-β (TGF-β) superfamily that is released in response to external stressors. GDF-15 regulates biological effects such as tumor occurrence, inflammatory response, tissue damage, angiogenesis, and bone metabolism. It has been shown to exert anti-inflammatory and pro-inflammatory effects in inflammation-related diseases. Moreover, inflammatory stimuli can induce GDF-15 expression in immune and parenchymal cells. GDF-15 exhibits a feedback inhibitory effect by inhibiting tumor necrosis factor-α secretion during the macrophage activation anaphase, suggesting that there may be a close association between the two. GDF-15 directly induces CD14⁺ monocytes to produce the M2-like macrophage phenotype, inhibits monocyte-derived macrophage for M1-like polarization, and induces monocyte-derived Mφ for M2-like polarization. This review summarizes the macrophage polarization mechanism of GDF-15 under the conditions of sepsis, colon cancer, atherosclerosis, and obesity. An improved understanding of the role and molecular mechanisms of action of GDF-15 could greatly elucidate the mechanism of disease occurrence and development and provide new ideas for targeted disease prevention and treatment. An advanced understanding of the function and molecular mechanisms of action of GDF-15 may be helpful in the assessment of its potential value as a therapeutic and diagnostic target.

Background: Since January 2020, coronavirus disease 2019 (Covid-19) has spread rapidly and developing the pandemic model around the world. Data have been needed on the clinical characteristics of the affected patients in an imported cases as model in island outside Wuhan. Methods: We conducted a retrospective study included all 168 confirmed cases of Covid-19 in Hainan province from 22 January 2020 to 13 March 2020. Cases were confirmed by real-time RT-PCR and were analysed for demographic, clinical, radiological and laboratory data. Results: Of 168 patients, 160 have been discharged, 6 have died and 2 remain hospitalized. The median age was 51.0 years and 51.8% were females. 129 (76.8%) patients were imported cases, and 118 (70.2%), 51 (30.4%) and 52 (31%) of patients lived in Wuhan or traveled to Wuhan, had contact with Covid-19 patients, or had contact with Wuhan residents, respectively. The most common symptoms at onset of illness were fever (65.5%), dry cough (48.8%) and expectoration (32.1%). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (60.2%). The elderly people with diabetes, hypertension and CVD are more likely to develop severe cases. Follow-up of 160 discharged patients found that 20 patients (12.5%) had a positive RT-PCR test results of pharyngeal swabs or anal swabs or fecal. Conclusions: In light of the rapid spread of Covid-19 around the world, early diagnosis and quarantine is important to curb the spread of Covid-19 and intensive treatments in early stage is to prevent patients away from critical condition.

Introduction: Sepsis is the leading cause of death in intensive care units and is characterized by multiple organ failure, including dysfunction of the immune system. In the present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role in the pathological process of sepsis, based on which a prognostic IRG signature for 28-day mortality prediction in patients with sepsis was developed and validated.Methods: Weighted gene co-expression network analysis (WGCNA), Cox regression analysis and least absolute shrinkage and selection operator (LASSO) estimation were used to identify functional IRGs and construct a model for predicting the 28-day mortality. The prognostic value of the model was validated in internal and external sepsis datasets. The correlations of the IRG signature with immunological characteristics, including immune cell infiltration and cytokine expression, were explored. We finally validated the expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls using qPCR.Results: We established a prognostic IRG signature comprising three gene members (LTB4R, HLA-DMB and IL4R). The IRG signature demonstrated good predictive performance for 28-day mortality on the internal and external validation datasets. The immune infiltration and cytokine analyses revealed that the IRG signature was significantly associated with multiple immune cells and cytokines. The molecular pathway analysis uncovered ontology enrichment in myeloid cell differentiation and iron ion homeostasis, providing clues regarding the underlying biological mechanisms of the IRG signature. Finally, qPCR detection verified the differential expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls.Discussion: This study presents an innovative IRG signature for 28-day mortality prediction in sepsis patients, which may be used to facilitate stratification of risky sepsis patients and evaluate patients’ immune state.

Exertional heat stroke (EHS) is a life-threatening condition induced by high-temperature environments, which poses significant health risks. The Qingshu Yiqi Decoction (QSYQD) is a traditional Chinese medicine (TCM) formula that is known to clear summer heat, replenish Qi, and protect intestinal function. However, experimental evidence and mechanistic insights into its role in EHS prevention are limited. The aim of this study is to evaluate the protective effects of QSYQD against EHS-induced hyperthermia and multi-organ injury and investigate the underlying molecular mechanisms. An EHS rat model was established using treadmill exercise in a high-temperature environment. The rats were pretreated with low, medium, or high doses of the QSYQD. The intestinal barrier function was assessed using biomarkers, an ultrastructural analysis, and key protein expressions. Network pharmacology was used to identify potential targets, and molecular mechanisms were validated using western blot. The gut microbiota composition was analyzed using 16S rRNA sequencing. The QSYQD significantly alleviated hyperthermia and multi-organ injury in the EHS rats, and the medium dose had the most pronounced effects. It preserved intestinal barrier integrity by maintaining the tight junction protein levels and reducing biomarkers of epithelial damage. Network pharmacology and experimental validation revealed that the QSYQD inhibited the NF-κB and MLC signaling pathways, key regulators of intestinal barrier function. Additionally, the QSYQD altered the gut microbiota composition and notably increased the abundance of Lactobacillus, that has known heat stress protective effects. QSYQD protects against EHS-induced damage by preserving intestinal barrier integrity, modulating gut microbiota, and inhibiting the NF-κB and MLC signaling pathways. Future studies will focus on identifying active compounds within QSYQD that enhance heat tolerance and provide EHS protection.

Background: Inhibition of indolamine-2,3-dioxygenase 1 (IDO1) has been proposed as a promising strategy for cancer immunotherapy; however, it has failed in clinical trials. Macrophages in the tumor microenvironment (TME) contribute to immune escape and serve as potential therapeutic targets. This study investigated the expression pattern of IDO1 in TME and its impact on prognosis and therapeutic response of patients with esophageal squamous cell carcinoma (ESCC).Methods: RNA sequencing data from 95 patients with ESCC from The Cancer Genome Atlas (TCGA) database were used to explore the prognostic value of IDO1. Bioinformatics tools were used to estimate scores for stromal and immune cells in tumour tissues, abundance of eight immune cell types in TME, and sensitivity of chemotherapeutic drugs and immune checkpoint (IC) blockage. The results were validated using digitalized immunohistochemistry and multiplexed immunofluorescence in ESCC tissue samples obtained from our clinical center.Results: TCGA and validation data suggested that high expression of IDO1 was associated with poor patient survival, and IDO1 was an independent prognostic factor. IDO1 expression positively correlated with macrophages in TME and PDCD1 within diverse IC genes. Single-cell RNA sequencing data analysis and multiplexed immunofluorescence verified the coexpression of IDO1 and PD-1 in tumor-associated macrophages (TAMs). Patients with high IDO1 expression showed increased sensitivity to various chemotherapeutic drugs, while were more likely to resist IC blockage.Conclusion: This study identifies IDO1 as an independent prognostic indicator of OS in patients with ESCC, reveals a compelling connection of IDO1, PD-1, and TAMs, and explores the sensitivity of patients with high IDO1 expression to chemotherapeutic drugs and their resistance to IC blockade. These findings open new avenues for potential targets in ESCC immunotherapy.