Mixed probiotics reduce the severity of stress-induced depressive-like behaviors.

In recent years, the gut microbiome has gained significant attention in the spheres of research and public health. As a result, studies have increasingly explored the potential of probiotic dietary supplements as treatment interventions for conditions such as anxiety and depression. The present study examined the effect of mixed probiotics (Lacticaseibacillus rhamnosus and Enterococcus faecium) on inflammation, microbiome composition, and depressive-like behaviors in a macaque monkey model. The mixed probiotics effectively reduced the severity of depressive-like behaviors in macaque monkeys. Further, treatment with mixed probiotics gradually increased the abundance of beneficial bacteria in the gut, improving the balance of the gut microbiota. Additionally, macaques treated with the mixed probiotics showed decreased serum levels of inflammatory factors (P < 0.05), an increased rate of L-tryptophan metabolism (P < 0.05), and the restoration of 5-HT and 5-HTP levels (P < 0.05). Correlation analysis confirmed that Lacticaseibacillus and other beneficial bacteria exhibited a negative correlation with inflammation in the body (P < 0.05), and a positive correlation with tryptophan metabolism (P < 0.05). In conclusion, the mixed probiotics effectively restored intestinal homeostasis in macaques and enhanced tryptophan metabolism, ultimately alleviating inflammation and depressive-like behaviors.

GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway.

BACKGROUND: Research has indicated that long-term sleep deprivation can lead to immune dysfunction and participate in the occurance and progression of tumors. However, the relationship between sleep deprivation and colon cancer remains unclear. This study explored the specific mechanism through which sleep deprivation promotes the proliferation and migration of colon cancer, with a focus on the neurotransmitter GABA. METHODS: Chronic sleep deprivation mice model were used to investigate the effect of sleep disorder on tumors. We detected neurotransmitter levels in the peripheral blood of mice using ELISA. CCK-8 assay, colony formation assay, wound healing assay, and transwell assay were performed to investigate the effect of GABA on colon cancer cells, while immunofluorescence showed the distribution of macrophages in lung metastatic tissues. We isolated exosomes from a GABA-induced culture medium to explore the effects of GABA-induced colon cancer cells on macrophages. Gain- and loss-of-function experiments, luciferase report analysis, immunohistochemistry, and cytokine detection were performed to reveal the crosstalk between colon cancer cells and macrophages. RESULTS: Sleep deprivation promote peripheral blood GABA level and colon cancer cell proliferation and migration. Immunofluorescence analysis revealed that GABA-induced colon cancer metastasis is associated with enhanced recruitment of macrophages in the lungs. The co-culture results showed that GABA intensified M2 polarization of macrophage induced by colon cancer cells. This effect is due to the activation of the macrophage MAPK pathway by tumor-derived exosomal miR-223-3p. Furthermore, M2-like macrophages promote tumor proliferation and migration by secreting IL-17. We also identified an endogenous miR-223-3p downregulation of the E3 ligase CBLB, which enhances the stability of cMYC protein and augments colon cancer cells proliferation and migration ability. Notably, cMYC acts as a transcription factor and can also regulate the expression of miR-223-3p. CONCLUSION: Our results suggest that sleep deprivation can promote the expression of miR-223-3p in colon cancer cells through GABA, leading to downregulation of the E3 ligase CBLB and inhibition of cMYC ubiquitination. Simultaneously, extracellular miR-223-3p promotes M2-like macrophage polarization, which leads to the secretion of IL-17, further enhancing the proliferation and migration of colon cancer cells.

Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma.

Accurately predicting survival in patients with early hepatocellular carcinoma (HCC) is essential for making informed decisions about treatment and prognosis. Herein, we have developed a machine learning (ML) model that can predict patient survival and guide treatment decisions. We obtained patient demographic information, tumor characteristics, and treatment details from the SEER database. To analyze the data, we employed a Cox proportional hazards (CoxPH) model as well as 3 ML algorithms: neural network multitask logistic regression (N-MLTR), DeepSurv, and random survival forest (RSF). Our evaluation relied on the concordance index (C-index) and Integrated Brier Score (IBS). Additionally, we provided personalized treatment recommendations regarding surgery and chemotherapy choices and validated models' efficacy. A total of 1136 patients with early-stage (I, II) hepatocellular carcinoma (HCC) who underwent liver resection or transplantation were randomly divided into training and validation cohorts at a ratio of 3:7. Feature selection was conducted using Cox regression analyses. The ML models (NMLTR: C-index = 0.6793; DeepSurv: C-index = 0.7028; RSF: C-index = 0.6890) showed better discrimination in predicting survival than the standard CoxPH model (C-index = 0.6696). Patients who received recommended treatments had higher survival rates than those who received unrecommended treatments. ML-based surgery treatment recommendations yielded higher hazard ratios (HRs): NMTLR HR = 0.36 (95% CI: 0.25-0.51, P < .001), DeepSurv HR = 0.34 (95% CI: 0.24-0.49, P < .001), and RSF HR = 0.37 (95% CI: 0.26-0.52, P = <.001). Chemotherapy treatment recommendations were associated with significantly improved survival for DeepSurv (HR: 0.57; 95% CI: 0.4-0.82, P = .002) and RSF (HR: 0.66; 95% CI: 0.46-0.94, P = .020). The ML survival model has the potential to benefit prognostic evaluation and treatment of HCC. This novel analytical approach could provide reliable information on individual survival and treatment recommendations.

Developing a RiskS-core Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction.

AIM: We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskS-core model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment. BACKGROUND: Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD. OBJECTIVE: To identify key angiogenesis-related lncRNAs and build a RiskS-core model to predict the survival probability of COAD patients and help optimize clinical treatment. METHODS: Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskS-core model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package. RESULTS: Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskS-core model. The high RiskS-core group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskS-core signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment. CONCLUSION: We constructed a RiskS-core model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

Temsirolimus is a promising immunomodulatory agent for enhanced transplantation outcomes.

BACKGROUND: Identifying effective immunosuppressive strategies is critical for addressing immunological rejection following organ transplantation. This study explores the potential immunosuppressive effects and mechanisms of temsirolimus, a rapamycin derivative, in organ transplantation. METHODS: A mouse cardiac allograft model was established using a cervical cannula technique with BALB/c donors and C57BL/6 recipients. Mice were administered temsirolimus intragastrically and graft survival was evaluated. Histological staining was used to assess pathological changes. The BrdU assay was used to measure splenic T cell proliferation. Flow cytometry was used to quantify regulatory T cells (Tregs), CD4+ T cells, and CD8+ T cells. ELISA and qPCR assays were used to determine Foxp3, IL-4, IFN-γ, and TGF-ß expression. RESULTS: Temsirolimus displayed potent immunosuppressive effects at 20 mg/kg/day, significantly inhibiting T cell proliferation (84.6%, P < 0.0001) and prolonging graft survival (median 49 days vs. 8.5 days in controls, P < 0.0001). However, median survival decreased to 34.5 days upon withdrawal. Temsirolimus also reduced splenic CD4+ and CD8+ T cells (2.85% and 2.92%, P < 0.001) and antibody levels (IgM, IgG1, IgG2) by 11.85-29.09% (P < 0.0001) and increased Tregs, Foxp3, IL-4 (P < 0.01), and TGF-ß (P < 0.05), while decreasing IFN-γ (P < 0.001). CONCLUSIONS: Temsirolimus exhibited potent immunosuppressive effects, emerging as a strong candidate to mitigate organ transplant rejection.