Irritable bowel syndrome (IBS) is a common clinical gastrointestinal dysfunction disorders. 5-sertonon (5-hydroxytryptamine, 5-HT) is a very important neurotransmitter, which is involved in gastrointestinal motion and sensation. Solute carrier family 6 member 4 (SLC6A4) gene encode serotonin transporter (SERT) which function is to rapidly reuptake the most of 5-HT. Therefore, it is needed to explore the association between SLC6A4 gene polymorphisms and IBS. 119 patients and 238 healthy controls were administrated to detect the SLC6A4 gene polymorphisms including 5-HT-transporter-gene-linked polymorphic region (5-HTTLPR), variable number of tandem repeats (VNTRs) and three selected tag Single Nucleotide Polymorphisms (SNPs) rs1042173, rs3794808, rs2020936 by using polymerase chain reaction (PCR) and TaqMan® SNP Genotyping. There were significant difference for 5-HTTLPR between IBS and control groups (X2 = 106.168, P<0.0001). In control group, genotypes were mainly L/L (58.4%), however, the genotypes in IBS were S/S (37.8%). The significant difference was shown in D-IBS subjects when compared to the controls (X² = 50.850, P<0.0001) for 5-HTTLPR. For STin2 VNTR, rs1042173, rs3794808, and rs2020936 polymorphisms, there were no any significant differences between IBS and control groups. There were no statistical significantly haplotypes for 5-HTTLPR, VNTRs and the three SNPs between IBS and controls. The S allele in 5-HTTLPR was a susceptible allele with Chinese Han IBS, but other associations of VNTRs, three selected Tag SNPs and positive haplotype with IBS were not found. It is indicated that much research are needed to study the relationship between other polymorphisms in SLC6A4 gene and IBS.

Systemic inflammatory response syndrome (SIRS) and organ dysfunction make it challenging to predict which major trauma patients are at risk of developing sepsis. Additionally, the unclear pathogenesis of sepsis after trauma contributes to its high morbidity and mortality. Identifying early predictive and diagnostic biomarkers, as well as exploring related metabolic pathways, is crucial for improving early prevention, diagnosis, and treatment. This study prospectively analyzed plasma samples from patients with severe trauma collected between March 2022 and November 2023. Trauma patients were divided into two groups based on whether they developed sepsis within two weeks: the TDDS group (trauma patients who did not develop sepsis) and the TDS group (trauma patients who did develop sepsis). Plasma samples from the TDS group were collected at the time of sepsis diagnosis (Sepsis group). Metabolite concentrations were measured using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) through untargeted metabolomics. From the differential metabolites between the TDS and TDDS groups, we identified five significant metabolites (all area under the curve (AUC) ≥ 0.94) as early predictive biomarkers for sepsis after trauma: (1) docosatrienoic acid, (2) 7-alpha-carboxy-17-alpha-carboxyethylandrostan lactone phenyl ester, (3) sphingomyelin (SM) 8:1;2O/26:1, (4) N1-[1-(3-isopropenylphenyl)-1-methylethyl]-3-oxobutanamide, and (5) SM 34:2;2O. Furthermore, five significant metabolites (all AUC ≥ 0.85) were identified as early diagnostic biomarkers from the comparison between the TDS and TDDS groups: (1) lysophosphatidylcholine (LPC) O-22:1, (2) LPC O-22:0, (3) uric acid, (4) LPC O-24:2, and (5) LPC 22:0-SN1. 26 metabolites shared between two comparisons (TDS vs. TDDS and sepsis vs. TDS) were identified. Of which, 19 metabolites belong to lipid metabolism. The top three metabolic pathways related to sepsis after trauma under the impact of severe trauma were: (1) glycerophospholipid metabolism, (2) porphyrin metabolism, and (3) sphingolipid metabolism. The top three metabolic pathways related to sepsis after trauma under the impact of infection were: (1) caffeine metabolism, (2) biosynthesis of unsaturated fatty acids, and (3) steroid hormone biosynthesis. Our study identified early predictive and diagnostic biomarkers and explored metabolic pathways related to sepsis after trauma. These findings provide a foundation for future research on the onset and development of sepsis, facilitating its early prevention, diagnosis, and treatment based on specific metabolites and metabolic pathways.

To construct and validate a simple and easily administered scoring tool for the prediction of short-term prognostic survival in adult patients with sepsis. This study is a retrospective and prospective cohort study. A total of 382 patients with sepsis. 274 sepsis patients from January 2020 to December 2020 were collected as the modeling group and 54 sepsis patients from January 2021 to December 2021 and April to May 2022 in the hospital were randomly collected as the validation group. They were divided into survival and non-survival groups according to the outcome. The receiver operating characteristic (ROC) curves were plotted with subgroup analysis. The resulting models were tested using the Hosmer-Lemeshow test. The prognostic value of the variables on prognosis was expressed using the area under the receiver operating characteristic curve (AUC). A scoring tool was constructed and tested for the prognostic value of this score in the validation group. The model had an AUC of 0.880 [95% CI (0.838-0.922), P < 0.001], model sensitivity of 81.15%, and specificity of 80.26% for predicting short-term prognosis in patients with sepsis. Further simplifying the model scoring rules and adding the lactate variable, the AUCs was 0.876 [95% CI (0.833-0.918)], P < 0.001], sensitivity was 78.69%, specificity was 82.89%, and scoring criteria were established. the AUCs of the internally validated model in 2021 and 2022 were 0.968 [95% CI (0.916 to 1.000), P < 0.001] and 0.943 [95% CI (0.873 to 1.000), P < 0.001], indicating that the constructed scoring tool has a good predictive value for short-term survival outcomes in patients with sepsis. Age, shock, lactate, lactate/albumin ratio (L/A), and interleukin-6 (IL-6) are five risk factors for adult sepsis prognosis in an early emergency. This scoring tool is developed to quickly assess the short-term survival outcome in adult sepsis patients. It is straightforward and easy to administer. It also has a high prognostic predictive value.The Chinese Clinical Trial Registry (ChiCTR2200058375).

Introduction: Double sequential external defibrillation (DSED) in cardiopulmonary resuscitation has shown different results in comparison with standard defibrillation in the treatment of refractory ventricular fibrillation (RVF). This review aims to compare the advantages of DSED with standard defibrillation in the treatment of refractory ventricular fibrillation.Materials and methods: PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to May 1, 2022. Studies included adult patients who developed RVF. The study used random-effects and fixed-effects models for meta-analysis, which was reported by risk ratio (RR) with 95% confidence interval (CI), mean difference (MD), or standardized mean difference (SMD). The risk of bias in individual studies was assessed using the Robins-I tool for observational studies and the Cochrane Risk of Bias 2 (ROB-2) tool for clinical trials. Primary outcomes included the termination of RVF, prehospital return of spontaneous circulation (ROSC), survival to hospital admission, survival to hospital discharge, and good neurological recovery. Secondary outcomes included age, total defibrillation attempts, emergency medical system arrival time, and dose of epinephrine and amiodarone used.Results: In this systematic review and meta-analysis, 10 studies containing 1347 patients with available data on treatment outcomes were included. The pooled estimate was (RR 1.03, 95% CI, 0.89 to 1.19; Z = 0.42, P = 0.678 > 0.05) for Termination of RVF, (RR 0.84, 95% CI, 0.63 to 1.11; Z = 1.23, P = 0.219 > 0.05) for ROSC, (RR 0.86, 95% CI, 0.69 to 1.06; Z = 1.4, P = 0.162 > 0.05) for survival to hospital admission, (RR 0.77, 95%CI, 0.52 to 1.15; Z = 1.26, P = 0.206 > 0.05) for survival to hospital discharge, (RR 0.65, 95%CI, 0.35 to 1.22; Z = 1.33, P = 0.184 > 0.05) for good neurologic recovery, (MD −1.01, 95%CI, −3.07 to 1.06; Z = 0.96, P = 0.34 > 0.05) for age, (MD 2.27, 95%CI, 1.80 to 2.73; Z = 9.50, P = 0.001 < 0.05) for total defibrillation attempts, (MD 1.10, 95%CI, −0.45 to 66; Z = 1.39, P = 0.16 > 0.05) for emergency medical system arrival time, (SMD 0.34, 95%CI, 0.17 to 0.50; Z = 4.04, P = 0.001 < 0.05) for epinephrine, and (SMD −0.30, 95%CI, −0.65 to −0.05; Z = 1.66, P = 0.1 > 0.05) for amiodarone.Conclusion: We discovered no differences between DSED and standard defibrillation in termination of RVF, prehospital return of spontaneous circulation, survival to hospital admission, survival to hospital discharge, good neurological outcome, emergency medical system arrival time, and amiodarone doses in patients with RVF. There were some differences in the number of defibrillations and epinephrine doses utilized during resuscitation.Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329354], identifier [CRD42022329354].

Background: There is still a lack of large-scale clinical studies and evidence-based evidence to prove the relationship between serum amyloid A (SAA) and the severity and prognosis of patients with new coronavirus pneumonia (COVID-19). Methods: We searched PubMed, Cochrane Library, Excerpta Medica Database, and Web of Science for original articles from December 1, 2019 to December 19, 2020. Search criteria include free text search, explosive MESH/EMTREE terms, and all synonyms for SAA and COVID-19. There are no language restrictions on the searched documents. Statistical methods were performed using Stata 14.0 software, and RevMan 5.4 software provided by the Cochrane Collaboration for meta-analysis. The 10 included studies in the literature were classified according to the severity of the novel coronavirus treatment guidelines, with mild/moderate categorized as nonsevere and severe/critical as severe, and the data were meta-analyzed using multiple subgroup standard deviations combined. Severe and nonsevere were finally divided into 2 groups, and the combined data were meta-analyzed according to the standardized mean difference. Results: The results of the meta-analysis given by random effects showed that SAA levels were significantly higher in severe vs nonsevere (standardized mean difference 1.20 [95% confidence interval 0.91–1.48]), which was statistically significant (P < .001). The 3 literatures studied (random effect size 0.11 [95% confidence interval 0.05–0.19]; I² = 56.68%) and were statistically significant, z = 5.46 P < .01, suggesting that the risk of death occurs at higher levels with increasing SAA values, with the risk of death in the severe group being 11% higher than in the nonsevere group. Conclusion: SAA can be considered as a biomarker for predicting the severity and prognosis of COVID-19. SAA can be used for early warning of the poor prognosis of COVID-19 and for monitoring the recovery process, which has important clinical value.