P75NTR+CD64+ neutrophils promote sepsis-induced acute lung injury.

Patients suffering from sepsis-induced acute lung injury (ALI) exhibit a high mortality rate, and their prognosis is closely associated with infiltration of neutrophils into the lungs. In this study, we found a significant elevation of CD64+ neutrophils, which highly expressed p75 neurotrophin receptor (p75NTR) in peripheral blood of mice and patients with sepsis-induced ALI. p75NTR+CD64+ neutrophils were also abundantly expressed in the lung of ALI mice induced by lipopolysaccharide. Conditional knock-out of the myeloid lineage's p75NTR gene improved the survival rates, attenuated lung tissue inflammation, reduced neutrophil infiltration and enhanced the phagocytic functions of CD64+ neutrophils. In vitro, p75NTR+CD64+ neutrophils exhibited an upregulation and compromised phagocytic activity in blood samples of ALI patients. Blocking p75NTR activity by soluble p75NTR extracellular domain peptide (p75ECD-Fc) boosted CD64+ neutrophils phagocytic activity and reduced inflammatory cytokine production via regulation of the NF-κB activity. The findings strongly indicate that p75NTR+CD64+ neutrophils are a novel pathogenic neutrophil subpopulation promoting sepsis-induced ALI.

The role of cuproptosis-related gene in the classification and prognosis of melanoma.

Background: Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma. Methods: We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma. Results: According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A1, B1, and C1. Among them, C1 subtype had the best survival outcome. Based on the differentially expressed genes shared by A1, B1, and C1 genotypes, we obtained the results of three gene subtypes of A2, B2, and C2. Among them, the B2 group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes. Conclusions: In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning.

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

A Novel Intercellular Communication-Associated Gene Signature for Prognostic Prediction and Clinical Value in Patients With Lung Adenocarcinoma.

BACKGROUND: Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD. METHODS: Eight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication. RESULTS: Eight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset. CONCLUSION: The present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery.

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery. Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation. Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%). Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction. Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850-0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832-0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735-0.755) and 0.709 (95% CI: 0.687-0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837-0.846) and 0.803 (95% CI: 0.798-0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653-0.667) and SIC scores (0.752; 95% CI: 0.747-0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable. Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

Trajectories of Circulating Monocyte Subsets After ST-Elevation Myocardial Infarction During Hospitalization: Latent Class Growth Modeling for High-Risk Patient Identification.

It remains unclear if the developmental trajectories of a specific inflammatory biomarker during the acute phase of ST-elevation myocardial infarction (STEMI) provide outcome prediction. By applying latent class growth modeling (LCGM), we identified three distinctive trajectories of CD14++CD16+ monocytes using serial flow cytometry assays from day 1 to day 7 of symptom onset in 96 de novo STEMI patients underwent primary percutaneous coronary intervention. Membership in the high-hump-shaped trajectory (16.8%) independently predicted adverse cardiovascular outcomes during a median follow-up of 2.5 years. Moreover, inclusion of CD14++CD16+ monocyte trajectories significantly improved area under the curve (AUC) when added to left ventricular ejection fraction-based prediction model (ΔAUC = 0.093, P = 0.013). Therefore, CD14++CD16+ monocyte trajectories during STEMI hospitalization are a novel risk factor for post-STEMI adverse outcomes. These results provide the first proof-of-principle evidence in support of the risk stratification role of LCGM-based longitudinal modeling of specific inflammatory markers during acute STEMI.

Insulin-like growth factor 2 mitigates depressive behavior in a rat model of chronic stress.

Depression is a common psychiatric disorder associated with chronic stress. Insulin-like growth factor 2 (IGF2) is a growth factor that serves important roles in the brain during development and at adulthood. Here, the role of IGF2 expression in the hippocampus was investigated in a rat model of depression. A chronic restraint stress (CRS) model of depression was established in rats, exhibiting depression-like behavior as assessed with the sucrose preference test (SPT) and forced swimming test (FST), and with evaluation of the corticosterone levels. Hippocampal IGF2 levels were significantly lower in rats suffering CRS than in controls, as were levels of pERK1/2 and GluR1. Lentivirus-mediated hippocampal IGF2 overexpression alleviated depressive behavior in restrained rats, elevated the levels of pERK1/2 and GluR1 proteins, but it did not affect the expression of pGSK3ß, GluR2, NMDAR1, and NMDAR2A. These results suggest the chronic restraint stress induces depressive behavior, which may be mediated by ERK-dependent IGF2 signaling, pointing to an antidepressant role for this molecular pathway.

Development of anxiety-like behavior via hippocampal IGF-2 signaling in the offspring of parental morphine exposure: effect of enriched environment.

Opioid addiction is a major social, economic, and medical problem worldwide. Long-term adverse consequences of chronic opiate exposure not only involve the individuals themselves but also their offspring. Adolescent maternal morphine exposure results in behavior and morphologic changes in the brain of their adult offspring. However, few studies investigate the effect of adult opiate exposure on their offspring. Furthermore, the underlying molecular signals regulating the intergenerational effects of morphine exposure are still elusive. We report here that morphine exposure of adult male and female rats resulted in anxiety-like behavior and dendritic retraction in the dentate gyrus (DG) region of the hippocampus in their adult offspring. The behavior and morphologic changes were concomitant with the downregulation of insulin-like growth factor (IGF)-2 signaling in the granular zone of DG. Overexpression of hippocampal IGF-2 by bilateral intra-DG injection of lentivirus encoding the IGF-2 gene prevented anxiety-like behaviors in the offspring. Furthermore, exposure to an enriched environment during adolescence corrected the reduction of hippocampal IGF-2 expression, normalized anxiety-like behavior and reversed dendritic retraction in the adult offspring. Thus, parental morphine exposure can lead to the downregulation of hippocampal IGF-2, which contributed to the anxiety and hippocampal dendritic retraction in their offspring. An adolescent-enriched environment experience prevented the behavior and morphologic changes in their offspring through hippocampal IGF-2 signaling. IGF-2 and an enriched environment may be a potential intervention to prevention of anxiety and brain atrophy in the offspring of parental opioid exposure.

Activation of ERK1/2 is required for normal response of isosexual social interactions in male rats.

Previous studies have indicated involvement of the mitogen-activated protein kinase (MAPK) pathway in heterosexual interactions among rats. Very few studies, however, have focused its role in isosexual social interactions. We studied the male rat's isosexual social interactional behavior using (i) the three-chambered social interaction box and (ii) phosphorylated extracellular signal-regulated kinase 1 and 2 (pERK1/2) to localize the brain regions that are activated during isosexual behavior. When faced with the social target side of the box versus the inanimate side, all rats preferred the social target side. Within 10min, isosexual social interactions induced a rapid increase in pERK1/2 expression in the brain, especially the main olfactory epithelial (MOE)-related brain regions. After ZnSO4-induced olfactory deprivation, rats showed no preference for either the social target or inanimate side, with a concomitant decrease in pERK1/2 expression in MOE-related brain regions. Additionally, to determine the role of pERK1/2 in isosexual social interactional behavior, rats were injected intraperitoneally with SL327 (30mg/kg, a MAPK kinase inhibitor). Although SL327 dramatically down-regulated expression of brain pERK1/2, experimental animals also spent significantly more time in the social target side. These results indicate that (i) A brief interacting with a male partner induced rapidly phosphorylated ERK1/2 in the rat's brain. (ii) Destroy the function of MOE abolished the rats' isosexual social interactional behavior. (iii) Suppressed the phosphorylated ERK1/2 in the rats' brain disrupt their normal social behaviour.

Activation of Anterior Cingulate Cortex Extracellular Signal-Regulated Kinase-1 and -2 (ERK1/2) Regulates Acetic Acid-Induced, Pain-Related Anxiety in Adult Female Mice.

In visceral pain, anxiety and pain occur simultaneously, but the etiogenesis of this effect is not yet well-described. The anterior cingulate cortex (ACC) is known to be associated with the affective response to noxious stimuli. The aim of the current study is to define the role of ACC extracellular signal-regulated (ERK)-1 and-2 (ERK1/2) activity in the development of pain-related anxiety/depression and the nocifensive response in acetic acid (AA)-elicited visceral pain. The model of visceral pain was created by intraperitoneal (ip) injection of AA to female Kunming mice. We found that AA injection resulted in a dynamic, bilateral ERK1/2 activation pattern in the ACC. Inhibition of ERK1/2 activation 2 hr after AA injection by subcutaneous (sc) injection of the mitogen-activating extracellular kinase (MEK) inhibitor, SL327, had no effect on the nocifensive responses, but did attenuate anxiety-like behavior, as determined by elevated plus-maze and open-field testing results. These data suggest that AA-induced visceral pain activates expression of ACC ERK1/2, which regulates visceral pain-related anxiety, but not the nocifensive response.