Patients with nasopharyngeal carcinoma (NPC) are often diagnosed at advanced stages or with metastases, and they continue to face significant treatment challenges. Consequently, there is an urgent need to develop novel biomarkers to predict outcomes in these patients. Recently, the prognostic importance of pretreatment immunoinflammatory response in cancer patients has received increasing attention. The clinical information of 361 patients with NPC in our clinical center was collected for analysis. High platelet-to-lymphocyte ratio (PLR) and systemic inflammatory response index (SIRI) were associated with poor overall survival (OS) in patients with NPC (P < 0.05). These two indicators collectively formed the optimal combination model for predicting the prognosis of NPC. Notably, a nomogram model that integrated the PLR-SIRI-based risk score, TNM stage, EBV-DNA, gender, and age demonstrated a good ability to predict the 1-, 3-, 5- and 10-year OS rates for NPC patients. Importantly, the novel risk stratification derived from the nomogram model outperformed traditional risk stratification based on TNM stage in predicting the prognosis of NPC patients (P < 0.001). PLR and SIRI collectively form the optimal prognostic model for NPC patients, providing a prognostic risk stratification tool for clinical evaluation.

Intracranial aneurysms (IAs) are clinically categorized as ruptured or unruptured. Size variability complicates precise segmentation and rupture assessment. This study integrates deep learning, machine learning, clinical characteristics, and computed tomography angiography (CTA) radiomics to determine IA rupture status. A dataset of 443 aneurysms (101 unruptured and 342 ruptured) was curated from affiliated hospitals. IAs were segmented via Swin UNETR, with radiomic features extracted via PyRadiomics. Following dimensionality reduction, five classifiers, support vector machine (SVM), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and voting classifier, were evaluated via the area under the receiver operating characteristic curve (AUC-ROC). Among the 1074 radiomic features, 25 were significantly correlated with rupture status. The RF, voting, MLP, LR, and SVM classifiers achieved AUCs of 0.94, 0.93, 0.88, 0.88, and 0.84, respectively. These results demonstrate the discriminative power of the combined t-test, LASSO, and PCA feature selection methods.

DLBCL shows clinical heterogeneity, driven by molecular diversity within the tumor along with diverse components in the tumor microenvironment (TME). Immunogenic cell death (ICD) activates adaptive antitumor immunity by inducing immunogenic signals in cancer cells, positioning it as a key mechanism in tumor immunotherapy. However, the role of ICD in DLBCL pathogenesis remains understood. We analyzed integrated single-cell and bulk RNA-seq data from GEO to identify ICD-related genes via AUCell, ssGSEA, and WGCNA. We constructed an ICD-related signature (ICDRS) via the Mime1 package and validated it in four independent cohorts. Prognostic utility was evaluated via Kaplan‒Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression. The TME is characterized by ESTIMATE, CIBERSORT, and ImmuCC. Finally, a combination therapy was designed on the basis of risk stratification and validated in DLBCL models. Integration of single-cell and bulk RNA-seq data identified 122 ICD-related genes, enabling the construction of an ICDRS. The ICDRS demonstrated prognostic value in DLBCL patients. The ICDRS score was an independent predictor of OS in all cohorts and was correlated with clinical indicators, including the IPI, ECOG, and Ann Arbor stage. Importantly, the ICDRS characterized an immunosuppressive microenvironment in high-risk patients and predicted immunotherapy and chemotherapy responses. In DLBCL models, radiotherapy reduced the risk score, and fludarabine-radiotherapy-anti-PD-1 inhibited tumor progression by reversing radiotherapy-induced immunosuppression. The ICDRS serves as a prognostic biomarker for DLBCL, and the combination therapy overcomes radioresistance, thereby establishing a translatable strategy.

Immune cells are those involved in or related to immune responses, found throughout immune organs and the body. The discipline of immunometabolism, which merges immunology with metabolism, has gained significant attention in recent years. This emerging field focuses on the metabolic processes and mechanisms of various immune cells, aiming to uncover how these cells’ metabolism influences disease onset and progression. Research in immunometabolism spans several diseases, including chronic inflammatory conditions, infectious diseases, cardiovascular disorders, and cancer, highlighting the critical role of immune cell metabolism in these diseases. Mental illnesses, characterized by brain function abnormalities due to biological, psychological, and environmental factors, lead to impairments in cognitive, emotional, volitional, and behavioral functions. Conditions such as schizophrenia, neurodegenerative diseases (Alzheimer’s disease AD, Parkinson’s disease PD), anxiety, and depression are associated with significant metabolic changes. The intersection of neuroimmunology and immunometabolism has become a focal point for understanding the regulation of mental illnesses by immune cells’ metabolic alterations. This review systematically examines how metabolic reprogramming of central and peripheral immune cells contributes to the pathogenesis of mental disorders, and critically evaluates emerging therapeutic strategies targeting these immunometabolic pathways—including pharmacological modulators (HK2 inhibitors, kynurenine pathway modulators, CD38 checkpoint targeting), lifestyle interventions (ketogenic diet, exercise), and their translational challenges. By integrating mechanistic insights with therapeutic perspectives, this review aims to provide fresh insights into disease mechanisms and inform the development of precision diagnostic and therapeutic approaches for mental disorders.

AIMS:To evaluate the efficacy and safety of neoadjuvant concurrent chemoradiotherapy (CCRT) utilizing nedaplatin and paclitaxel in patients with locally advanced esophageal squamous cell carcinoma (ESCC). PATIENTS & METHODS:Locally advanced ESCC patients received two cycles of nedaplatin plus paclitaxel or albumin-bound paclitaxel with concurrent radiotherapy (41.4-50.4 Gy) as neoadjuvant treatment. Immunohistochemical profiling was performed on biopsies and surgical specimens. The primary endpoint was the pathological complete response (pCR) rate. RESULTS:Among 49 enrolled patients, 33 underwent surgery, yielding a pCR rate of 30.3%. Overall survival was inferior in resected patients with positive postoperative lymph nodes (HR 3.92, 95% CI 1.30-11.80, p = 0.02, log-rank p = 0.009). Higher FOXP3 expression before (HR 0.14, 95% CI 0.02-1.26, p = 0.08, log-rank p = 0.041) and after (HR 0.23, 95% CI 0.07-0.84, p = 0.03, log-rank p = 0.015) treatment nominally contributed to favorable outcomes. Upregulated TIGIT post-treatment trended toward a poorer prognosis in this surgical cohort (HR 7.10, 95% CI 0.79-63.65, p = 0.08, log-rank p = 0.041). CONCLUSION:Neoadjuvant nedaplatin and paclitaxel-based CCRT demonstrates encouraging preliminary antitumor efficacy. Tumor microenvironment profiling highlights FOXP3 and TIGIT dynamics as potential prognostic indicators. These hypothesis-generating insights warrant further validation in large-scale, randomized controlled trials. Clinical trial registration: Chinese Clinical Trial Registry (http://www.chictr.org.cn), identifier is ChiCTR1900024628. Date of registration: 19 July 2019.

Head and neck cancer, the sixth most prevalent malignancy worldwide, presents significant therapeutic challenges for advanced-stage patients due to multidrug resistance and the severe toxicity associated with traditional chemotherapeutic agents, such as platinum-based drugs. In this study, we designed and synthesized three new iridium(III) metal complexes [Ir(piq)2(IPM)]PF6 (Ir1), [Ir(bzq)2(IPM)]PF6 (Ir2), [Ir(ppy)2(IPM)]PF6 (Ir3) and evaluated their capacity to induce immunogenic cell death (ICD) in head and neck cancer cells and to elucidate the underlying molecular mechanisms. In vitro experiments demonstrated that these complexes were efficiently internalized by SCC7/FADU cells, significantly suppressed cell migration and proliferation and induced G0/G1 phase arrest and apoptosis. Mechanistic investigations revealed that Ir1, Ir2 and Ir3 activated endoplasmic reticulum stress (ERS) through the PERK/eIF2α/ATF4/CHOP pathway, causing excessive reactive oxygen species (ROS) production and mitochondrial depolarization, leading to calcium overload. These events collectively triggered the hallmarks of immunogenic cell death (ICD), including surface-exposed calreticulin (CRT), HMGB1 release, and extracellular ATP secretion. In vivo studies showed that Ir1 (5 mg/kg) significantly inhibited tumor growth in Balb/c nude mice bearing FADU xenografts and in C3H/HeNCrl syngeneic SCC7 models. The same dose of Ir1 shows comparable antitumor efficiency with cisplatin on SCC7 and FADU tumors. Immunofluorescence analysis revealed a marked increase in CD3+/CD4+/CD8+ T-cell infiltration and upregulation of apoptotic markers. Acute toxicity testing confirmed a favorable safety profile, with no evidence of organ damage at therapeutic doses. This study provides experimental evidence for the development of novel low-toxicity, immunomodulatory chemotherapeutic agents.

Lactylation, an emerging form of post-translational modification derived from lactate, plays a pivotal role in numerous cellular processes such as tumor proliferation, metabolism, inflammation, and embryonic development. However, the precise molecular mechanisms by which lactylation controls these biological functions in both physiological and pathological contexts remain elusive. This review summarizes the latest reported regulatory mechanisms of protein lactylation in various diseases since 2024, introducing the latest research progress regarding the regulatory functions of protein lactylation in pathological processes, with particular attention to the regulatory mechanisms of non-histone lactylation modification in diseases. Finally, it outlines the potential of targeted lactylation therapy, proposes the main directions for future research, and emphasizes its scientific significance for future studies.

AKR1Cs, as a reductase enzyme family, play a pro-carcinogenic role in various types of cancers, including hormone-related malignancies and non-hormonal tumors. However, there exists a notable scarcity of literature concerning AKR1Cs expression in pancreatic cancer and the subsequent impacts on its progression. Analyzing pancreatic cancer database information by employing advanced bioinformatics techniques to unravel AKR1Cs’ intricate involvement in cancer malignancy, their correlation with clinical pathology, prognostic implications, as well as their responsiveness to conventional and immune-based therapies. Furthermore, the role of AKR1C1 in promoting the malignant progression of pancreatic cancer cell lines was validated using cell proliferation assays (EdU labeling and colony formation), and cell migration and invasion experiments including scratch wound healing and Transwell migration/invasion assays. AKR1Cs are not only significantly overexpressed in pancreatic cancer, but also closely associated with poor clinical grading, clinical chemoresistance and poor immune response in pancreatic cancer.Moreover, regulating the expression of AKR1C1 in pancreatic cancer cells will affect its proliferation, migration, invasion and the occurrence of epithelial-mesenchymal transformation (EMT). Our findings are expected to establish AKR1Cs, especially AKR1C1 as a promising therapeutic target for the clinical treatment of pancreatic cancer.

Background:Chronic Coronary Disease (CCD) is a leading global cause of morbidity and mortality. Existing Pre-test Probability (PTP) models mainly rely on in-hospital data and clinician judgment. This study aims to construct machine learning (ML) models for predicting CCD by using easily accessible text data and baseline characteristics, and to evaluate the contribution of text data to the diagnostic model. Methods:The chief complaints, present illness, past medical history and vital signs of the patients from the internal medicine departments of the First Affiliated Hospital and the Second Affiliated Hospital of Wannan Medical College were gathered. The text data of the research subjects were structured by using text mining technology. A customized "stop words" list and "custom dictionary" for cardiovascular medicine were created to optimize the processing of text data. Then, ML algorithms were employed to establish CCD prediction models. Finally, the Shapley additive explanation (SHAP) algorithm was used to interpret the models. Results:We enrolled a total of 21,855 patients in this study, with 7,449 in the CCD group and 14,406 in the non-CCD group. Patients in the CCD group were generally older and had a higher male proportion. After conducting feature engineering, we successfully constructed a Random Forest model. The model achieved an area under the ROC curve (AUC) of 0.93 (95% CI, 0.93-0.94), demonstrating excellent performance in horizontal comparisons. Using the SHAP algorithm, valuable text features like "chest pain", "chest tightness" and structured features such as age, which are crucial for CCD judgment, were identified. Additionally, an illustration of how these features influenced the model's decision-making process was provided. Conclusion:Clinicians can leverage text data to construct a prediction model for CCD and apply the SHAP approach to pinpoint valuable text features and elucidate the model's decision-making mechanism.

In recent years, the abuse of ketamine as a recreational drug has been growing, and has become one of the most widely abused drugs. Continuous using ketamine poses a risk of drug addiction and complications such as attention deficit disorder, memory loss and cognitive decline. Ketamine-induced neurotoxicity is thought to play a key role in the development of these neurological complications. In this paper, we focus on the molecular mechanisms of ketamine-induced neurotoxicity. According to our analyses, drugs in causing neurotoxicity are closely associated with programmed cell death (PCD) such as apoptosis, autophagy, necroptosis, pyroptosis, and Ferroptosis. Therefore, this review will collate the existing mechanisms of programmed death in ketamine-induced neurotoxicity as well as explore the possible mechanisms by outlining the mechanisms of programmed death in other drug-induced neurotoxicity, which may be helpful in identifying potential therapeutic targets for neurotoxicity induced by ketamine abuse.

Background: Ketamine is a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist. It has attracted considerable attention for its rapid antidepressant effects in recent years, but ketamine-induced psychotic-like symptoms limit its clinical application. The molecular mechanisms and key targets underlying ketamine-induced psychiatric disorders remain unclear. Aims and Objectives: In this study, we utilized multi-brain region transcriptome data and bioinformatics methods to identify the key genes and pathways involved. Materials and Methods: First, we obtained transcriptome data of ketamine-treated and control brain tissues (including frontal cortex, hippocampus, striatum, and amygdala) from public databases (GEO). Simultaneously, we retrieved psychiatric disorder-related gene sets from the GeneCards database. For each brain region sample, we performed single-sample gene set enrichment analysis (ssGSEA) to calculate enrichment scores for the psychiatric disorder gene set and assess differences between groups. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with the high-expression phenotype and conducted Gene Ontology (GO) functional annotation. In each brain region, differentially expressed genes (DEGs) between the high-expression and control groups were identified and intersected with WGCNA modules to obtain candidate key genes. Based on these candidates, we used three machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and Random Forest) to obtain 12 sets of candidate feature genes, comparing model performance using receiver operating characteristic (ROC) curves and area under the curve (AUC). Results: The results indicated that the LASSO model for the frontal cortex exhibited the best performance, identifying nine feature genes (Galr1, Cbr3, Crem, Fosl2, Mypn, Maff, Rhbg, Tslp, Klra2). Further GO/KEGG enrichment analysis and protein-protein interaction (PPI) network analysis highlighted the close association of Fosl2 and Maff with ketamine-induced psychiatric disorders. Comparison with our prior proteomic data on the prefrontal cortex of a ketamine model revealed a markedly downregulated protein Cbr3. Subsequent quantitative polymerase chain reaction (qPCR) assays in a ketamine-induced psychiatric disorder mouse model confirmed these findings: Cbr3 was significantly downregulated, while Fosl2 and Maff were significantly upregulated in the prefrontal cortex, consistent with our analysis. Thus, Cbr3, Fosl2, and Maff were identified as core genes in ketamine-induced psychiatric disorders. Finally, we evaluated the correlation between these core genes and immune cell infiltration, and analyzed their functions in humans using Genotype-Tissue Expression (GTEx) data and genome-wide association study (GWAS) loci. Conclusion: This study comprehensively applied gene set enrichment, WGCNA, and machine learning to multi-brain region transcriptomes to systematically screen for potential core genes of ketamine-induced psychiatric disorders, with preliminary qPCR validation. These findings provide new insights into molecular markers and mechanisms in this field.

Intracranial aneurysm (IA) rupture can precipitate severe subarachnoid haemorrhage. Despite the importance of uncovering key disease traits through high-throughput gene expression data, the application of machine learning to identify informative genes linked to IA rupture remains limited. Hence, we present a novel machine-learning model, constructed on the intelligent optimisation algorithms, to forecast IA rupture states and pinpoint efficacious informative genes. The model integrated adaptive boosting (AdaBoost) with particle swarm optimisation (PSO) to eliminate redundant genes, followed by ReliefF for further optimisation. Subsequently, a small set of informative genes fully representing the IA rupture state was obtained and evaluated using various classification models. The experimental results showed the proposed algorithm particle swarm optimisation-adaptive boosting-ReliefF (PSO-AdaBoost-ReliefF) achieved significant improvements in all evaluation metrics. Additionally, Gene ontology (GO) and enrichment analysis were performed to reveal gene-IA association. The PSO-AdaBoost-ReliefF model can effectively mine informative genes, accurately evaluate the rupture state, while potentially identifying new target genes.

Due to drug resistance, a majority of patients with non-small cell lung cancer (NSCLC) experience disease progression following immunotherapy. Therefore, there is an urgent need to develop novel biomarkers to predict the prognosis of NSCLC patients. Clinical data from 544 patients with advanced NSCLC who underwent immune checkpoint blockers (ICBs) at our clinical center were collected in this study. The results indicated that low Albumin-Globulin Ratio (AGR) and Lymphocyte-Monocyte Ratio (LMR) and high Systemic Immune-Inflammation Index (SIRI) were significantly correlated with both poor overall survival (OS) and progression-free survival (PFS) in NSCLC patients (P < 0.01). These three indicators collectively formed the most effective combined model for predicting the prognosis of NSCLC. Importantly, risk stratification based on AGR, LMR and SIRI was better than that based on the TNM stage, and served as an independent predictor of OS and PFS. Notably, the nomogram model developed by risk stratification, sex, age, smoking history, and pathological type demonstrated a good ability to predict the 1 to 5-year OS rates for NSCLC patients. In summary, AGR, LMR, and SIRI represented the optimal combined models for forecasting the prognosis of patients with advanced NSCLC who underwent ICBs, offering promising potential as biomarkers to direct personalized clinical interventions.

Ketamine, a psychoactive substance strictly regulated by international drug conventions, is classified as a "new type drug" due to its excitatory, hallucinogenic, or inhibitory effects. The etiology of ketamine-induced psychiatric symptoms is multifaceted, with the immune regulatory mechanism being the most prominent among several explanatory theories. In recent years, the interaction between the immune system and nervous system have garnered significant attention in neuropsychiatric disorder research. Notably, the infiltration of peripheral lymphocytes into the central nervous system has emerged as an early hallmark of certain neuropsychiatric disorders. However, a notable gap exists in the current literature, regarding the immune regulatory mechanisms, specifically the peripheral immune alterations, associated with ketamine-induced psychiatric symptoms. To address this void, this article endeavors to provide a comprehensive overview of the pathophysiological processes implicated in psychiatric disorders or symptoms, encompassing those elicited by ketamine. This analysis delves into aspects such as nerve damage, alterations within the central immune system, and the regulation of the peripheral immune system. By emphasizing the intricate crosstalk between the peripheral immune system and the central nervous system, this study sheds light on their collaborative role in the onset and progression of psychiatric diseases or symptoms. This insight offers fresh perspectives on the underlying mechanisms, diagnosis and therapeutic strategies for mental disorders stemming from drug abuse.

Intracranial aneurysm (IA) is a focal localized dilation of cerebral arteries and is a life-threatening cerebrovascular disease. Emerging evidences have emphasized the significance of post-transcriptional regulation in diseases, particularly through the two most critical regulatory layers of RNA editing (RE) and alternative splicing (AS). However, the interplay between these mechanisms and their impact on IA pathophysiology remains unclear. This study integrated multi-cohort datasets to establish a comprehensive landscape of RE in IAs. We observed a marked decrease in RNA editing levels during the transition from unruptured to ruptured aneurysms. Further analysis revealed a dual mechanism of AS by RE: direct modulation of AS via edits near splice sites that alter regulatory sequences, and indirect influence through changes in the binding affinity and specificity of RNA-binding proteins (RBPs). By constructing an RES-RBP-AS regulatory network, we identified key nodes potentially involved in IA progression via RE-mediated splicing regulation. These findings not only provide new insights into IA molecular mechanisms, but also lay a theoretical foundation for the developing therapies strategies targeting post-transcriptional regulation.

Ketamine (Ket) is a globally widely used injectable anesthetic and recreational drug that can lead to persistent behavioral deficits and induce psychotic states. Immune pathogenesis is believed to play a pivotal role in psychological symptoms and abnormal behavior. However, the role of the immune system, particularly peripheral immune changes, in ketamine-induced behavioral deficits and even psychotic symptoms remains largely elusive. This study aimed to explore the potential role of the peripheral immune system in ketamine-induced behavioral abnormalities in mice. Continuous administration of high-dose ketamine in C57/B6J mice induced abnormalities representative of anxiety-depressive-like behavior or memory-cognitive behavior, accompanied by morphological changes, elevated levels of inflammatory cytokines, and enhanced expression of markers representing astrocyte activity in the hippocampus and prefrontal cortex. Furthermore, flow cytometry was used to analyze changes in the number and composition of immune cells in the peripheral blood of mice after high-dose ketamine administration. The results showed a significant increase in peripheral T lymphocytes, especially CD4+ lymphocytes, while NK cells and B lymphocytes did not exhibit significant changes. Additionally, there was a significant increase of CD4+ lymphocytes in the hippocampus and prefrontal cortex of the mice. Based on these findings, in vivo neutralization of CD4+ lymphocytes surprisingly reversed the anxiety-depressive-like behavior or memory-cognitive behavior of the mice and partially or fully restored brain tissue morphology and the expression of astrocyte activity molecules. Our results indicate that peripheral CD4+ lymphocytes play a crucial role in ketamine-induced behavioral abnormalities, and the presence of CD4+ lymphocytes may participate in and promote ketamine-induced anxiety, depressive-like behavior, and memory-cognitive dysfunction.

Intracranial aneurysm (IA) is a serious threat to human health and can lead to subarachnoid hemorrhage and other serious consequences. If IAs can be detected in advance and treated before rupture, it will greatly reduce the harm of IAs to patients. Since brain arteries are 3D (three-dimensional) structures, point cloud methods can directly process 3D data, which is crucial for tasks that require spatial understanding, such as object detection. The 3D point cloud object detection methods Point-Voxel Feature Set Abstraction (PV-RCNN), Part-Aware and Part-Aggregation (PartA2), and Sparsely Embedded Convolutional Detection (SECOND) were applied to the detection of IAs. The object detection model was trained and tested on the public dataset IntrA. The results indicate that the trained model can be used for the detection and location of IAs and high recall values have been obtained on the testing set. This work also provides important metrics for evaluating object detection models, including average precisions (APs), recall values, and object detection results on the testing set, that is, predicted bounding boxes and corresponding confidence scores. In terms of detection results, the IA detection results of PV-RCNN are the best of these three methods by leveraging both point cloud and image information. The object detection method of 3D point cloud can be integrated into the medical imaging post-processing system and can be used as a subsequent module of the 3D reconstruction module.

5F-EDMB-PICA is a newly emerged synthetic cannabinoid which has been characterized in relevant literature in recent years. Although phase-I metabolites of 5F-EDMB-PICA have been partly reported, the phase-II metabolism of this synthetic cannabinoid has not been studied yet. In this study, we established a phase-I and phase-II metabolism model in vitro by using pooled human liver microsomes, NADPH regeneration system, and UGT incubation system, with 1 mg/ml 5F-EDMB-PICA added and incubated at 37 °C for 60 min. The metabolites were analyzed by Q Exactive™ Hybrid Quadrupole-Orbitrap™ Mass Spectrometer, via which we discovered and identified 14 phase-I metabolites and 4 phase-II metabolites of 5F-EDMB-PICA, involving pathways such as ester hydrolysis, dehydrogenation, hydrolytic defluorination, hydroxylation, dihydroxylation, glucuronidation, and combinations of the pathways mentioned above. We recommend considering the monohydroxylation metabolites (M9, M10) with higher content and intact ester and 5-fluoropentyl structures as potential biomarkers of 5F-EDMB-PICA.

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