[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms. METHODS: The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-IV v2.0 (MIMIC-IV v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA). RESULTS: A total of 3 295 patients with septic shock were included, with 2 164 surviving and 1 131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2 307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95%CI) was 0.795-0.849], 0.823 (95%CI was 0.796-0.849), and 0.810 (95%CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95%CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95%CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score III (SAPS III), the highest WBC, acute physiology score III (APS III), the lowest Na+, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95%CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APS III score, SAPS III score, and sequential organ failure assessment (SOFA) score] with AUC (95%CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. CONCLUSIONS: The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca2+, Hb, WBC, SAPS III score, APS III score, Na+, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

SINE-Associated LncRNA SAWPA Regulates Porcine Zygotic Genome Activation.

In mice, retrotransposon-associated long noncoding RNAs (lncRNA) play important regulatory roles in pre-implantation development; however, it is largely unknown whether they function in the pre-implantation development in pigs. The current study aims to screen for retrotransposon-associated lncRNA in porcine early embryos and identifies a porcine 8-cell embryo-specific SINE-associated nuclear long noncoding RNA named SAWPA. SAWPA is essential for porcine embryonic development as depletion of SAWPA results in a developmental arrest at the 8-cell stage, accompanied by the inhibition of the JNK-MAPK signaling pathway. Mechanistically, SAWPA works in trans as a transcription factor for JNK through the formation of an RNA-protein complex with HNRNPA1 and MED8 binding the SINE elements upstream of JNK. Therefore, as the first functional SINE-associated long noncoding RNAs in pigs, SAWPA provides novel insights for the mechanism research on retrotransposons in mammalian pre-implantation development.

Sexual dimorphism in the gut microbiota and sexual dimorphism in chronic diseases: Association or causation?

Understanding the sexual dimorphism in diseases is essential to investigate the pathogenesis of some chronic diseases (e.g., autoimmune diseases, etc). The gut microbiota has been found to show a notable impact on the pathology of several chronic diseases in recent years. Intriguingly, the composition of the gut microbiota varies between sexes. Here, we reviewed 'facts and fiction' regarding sexual dimorphism in chronic diseases and sexual dimorphism in the gut microbiota respectively. The association and causative relationship between them aiming to elucidate the pathological mechanisms of sexual dimorphism in chronic diseases were further explored. The development of gender-special food products based on the sexual dimorphism in the gut microbiota were recommended, which would be beneficial to facilitating the personalized treatment.

Exploration of s new biomarker in osteosarcoma and association with clinical outcomes: TOP2A+ cancer associated fibroblasts.

BACKGROUND: Osteosarcoma (OS) is the leading malignant primary bone tumor in young adults and children and has a high mortality rate. Cancer-associated fibroblasts (CAFs) are major components of the tumor microenvironment, influencing cancer progression and metastasis. However, there is no systematic study on the role of CAF in OS. METHODS: We collected six OS patients' single-cell RNA sequencing data from the TISCH database, which was processed using the Seurat package. We selected gene sets from the well-known MSigDB database and resorted to the clusterprofiler package for gene set enrichment analysis (GSEA). The least absolute shrinkage and selection operator (LASSO) regression model was used for identification of the variables. Receiver operating characteristic and decision curve analyses were utilized for determining the efficacy of the monogram model. RESULTS: TOP2A+ CAFs was recognized as the carcinogenic CAFs subset, given its intense interaction with OS malignant cells and association with the critical cancer driver pathway. We intersected the differentially expressed genes of TOP2A+ CAFs with the prognostic genes selected from 88 OS samples. The acquired gene set was selected using the LASSO regression model and integrated with clinical factors to obtain a monogram model of high prognosis predicting power (area under the curve of 5 year survival at 0.883). Functional enrichment analysis revealed the detailed difference between two risk groups. CONCLUSION: We identified TOP2A+ CAFs as a subset of oncogenic CAFs in OS. Based on differentially expressed genes derived from TOP2A+ CAFs, combined with bulk transcriptome prognostic genes, we constructed a risk model that can efficiently predict OS prognosis. Collectively, our study may provide new insights for future studies to elucidate the role of CAF in OS.

An empowerment-based, healthy dietary behavioral intervention to ameliorate functional constipation.

Objective: To explore the boost effect on ameliorating functional constipation in elderly patients through empowerment-based, healthy dietary behavioral intervention. Design: In this randomized parallel group study, elderly patients with functional constipation were recruited and assigned to the experimental and control groups at a ratio of 1:1. The control group received routine intervention. The experimental group received 3-month empowerment-based intervention. The results were evaluated based on the Healthy Lifestyle and Personal Control Questionnaire (HLPCQ) and Cleveland Clinic Constipation Score (CCS). GraphPad Prism (Version 9) software was used for the statistical analysis. Setting: As the world's population ages, functional constipation in the elderly has attracted widespread attention. The practical behavioral intervention to ameliorate constipation are worth exploring. Participants: Sixty elderly patients with functional constipation. Results: The study results showed no significant difference in the baseline data between the two groups (P > 0.05). After the intervention, the scores of HLPCQ (77.90 ± 14.57 vs. 61.11 ± 13.64) and CCS (7.48 ± 3.73 vs. 9.70 ± 3.07) in the experimental group were significantly higher than those in the control group (P < 0.05). Conclusion: The results showed that empowerment-based intervention can effectively strengthen the healthy dietary behavior of elderly patients. Through patient empowerment, the subjective initiative and willingness to communicate were boosted in the experimental group. Their symptoms of functional constipation improved considerably better than in the control group.

Combined treatment with glucosamine and chondroitin sulfate improves rheumatoid arthritis in rats by regulating the gut microbiota.

BACKGROUND: To investigate the ameliorative effects of glucosamine (GS), chondroitin sulphate (CS) and glucosamine plus chondroitin sulphate (GC) on rheumatoid arthritis (RA) in rats, and to explore the mechanism of GS, CS and GC in improving RA based on the gut microbiota. METHODS: RA rat models were effectively developed 14 days after CFA injection, and then garaged with GS, CS and GC. Body weight and paw volume of rats were monitored at multiple time points at the beginning of CFA injection. Until D36, serum and ankle tissue specimens were used to measure levels of circulating inflammatory factors (TNF-α, IL-1ß, MMP-3, NO and PGE2) and local inflammatory indicators (TLR-4 and NF-κB). On D18, D25, and D36, intergroup gut microbiota was compared using 16S rRNA gene sequencing and bioinformatics analysis. We also performed the correlation analysis of gut bacteria, joint swelling and inflammatory indicators. RESULTS: GC, rather than GS and CS, could reduce right paw volumes, levels of TLR-4 and NF-κB in synovial tissues. In addition, enriched genera in RA model rats screened out by LEfSe analysis could be inhibited by GC intervention, including potential LPS-producing bacteria (Enterobacter, Bacteroides, Erysipelotrichaceae_unclassified and Erysipelotrichaceae_uncultured) and some other opportunistic pathogens (Esherichia_Shigella, Nosocomiicoccus, NK4A214_group, Odoribacter, Corynebacterium and Candidatus_Saccharimonas.etc.) that positively correlated with pro-inflammatory cytokines, right paw volume, and pathology scores. Furthermore, the gut microbiota dysbiosis was observed to recover before alleviating joint swelling after interventions. CONCLUSIONS: GC could inhibit potential LPS-producing bacteria and the activation of TLR-4/NF-κB pathway in RA rats, thus alleviating RA-induced joint injury.

Bone Homeostasis and Gut Microbial-Dependent Signaling Pathways.

Although research on the osteal signaling pathway has progressed, understanding of gut microbial-dependent signaling pathways for metabolic and immune bone homeostasis remains elusive. In recent years, the study of gut microbiota has shed light on our understanding of bone homeostasis. Here, we review microbiota-mediated gut-bone crosstalk via bone morphogenetic protein/SMADs, Wnt and OPG/receptor activator of nuclear factor-kappa B ligand signaling pathways in direct (translocation) and indirect (metabolite) manners. The mechanisms underlying gut microbiota involvement in these signaling pathways are relevant in immune responses, secretion of hormones, fate of osteoblasts and osteoclasts and absorption of calcium. Collectively, we propose a signaling network for maintaining a dynamic homeostasis between the skeletal system and the gut ecosystem. Additionally, the role of gut microbial improvement by dietary intervention in osteal signaling pathways has also been elucidated. This review provides unique resources from the gut microbial perspective for the discovery of new strategies for further improving treatment of bone diseases by increasing the abundance of targeted gut microbiota.