Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Multiple extreme overdose of antipsychotics with minimal side effects: A case report.

Key Clinical Message: Antipsychotic drug treatment is a commonly used therapeutic strategy in the field of psychiatry. Rational and standardized use of antipsychotics is crucial in clinical practice, and excessive use of antipsychotics may lead to severe toxic reactions. Thus, attention should be given to the monitoring of drug concentration and examination of organ function. Abstract: Excessive use of antipsychotics can cause a variety of adverse effects, including dysfunction of the liver and other organs. Liver cytochrome P450 (CYP450) enzymes play an important role in the metabolism of antipsychotics, and metabolizer types of CYP450 enzymes may influence the therapeutic effects. In this case report, we introduced a 52-year-old woman with a 23-year history of schizophrenia who took excessive doses of multiple antipsychotics and other herbal preparations for nearly 2 years, with poor response to treatment and minor side reactions to the antipsychotics. Pharmacogenomic examination showed that this patient was a CYP1A2 ultra-rapid metabolizer. The examination and treatment of this patient may provide a reference for the management of similar cases with poor response to an alarming tolerance for antipsychotics.

Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons.

Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R2 = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.

Prognostic and clinicopathological significance of systemic immune-inflammation index in upper tract urothelial carcinoma: a meta-analysis of 3911 patients.

Background: Systemic immune-inflammation index (SII), a novel prognostic indicator, is being more commonly utilized in different types of cancer. This research project involved combining information from previously published studies to examine how pre-treatment SII can predict outcomes in individuals with upper tract urothelial carcinoma (UTUC). Further examination of the correlation between SII and clinical and pathological features in UTUC. Methods: We thoroughly chose pertinent articles from various databases including PubMed, Embase, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI), WanFang database, and Chinese Scientific Journal Database (VIP) until March 10, 2022.The data collected was analyzed using Stata 17.0 software (Stat Corp, College Station, TX). Subsequently, the impact of SII on the survival outcomes of UTUC patients was evaluated by combining HRs with 95% confidence intervals. Results: Six included studies were finally confirmed, including 3911 UTUC patients in seven cohorts. The results showed that high SII before treatment predicted poor overall survival (HR =1.87, 95%CI 1.20-2.92, p=0.005), cancer specific survival (HR=2.70, 95%CI 1.47-4.96, P=0.001), and recurrence-free survival (HR =1.52, 95%CI 1.12-2.07, P=0.007). And the elevated SII may be related to LVI (present vs. absent) (OR=0.83, 95% CI=0.71-0.97, p=0.018), pT stage (pT ≥3 vs. < 3) (OR=1.82, 95% CI=1.21-2.72, p=0.004), and pN stage (N+ vs. N0) (OR=3.27, 95% CI=1.60-6.71, p=0.001). Conclusion: A comprehensive analysis of all included articles in this study showed that higher pretreatment SII was related to poorer survival outcomes and adverse pathological features independently. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022316333.

Liquid Metal Composites-Enabled Real-Time Hand Gesture Recognizer with Superior Recognition Speed and Accuracy.

Prosthetic hands play a vital role in restoring forearm functionality for patients who have suffered hand loss or deformity. The hand gesture intention recognition system serves as a critical component within the prosthetic hand system. However, accurately and swiftly identifying hand gesture intentions remains a challenge in existing approaches. Here, a real-time motion intention recognition system utilizing liquid metal composite sensor bracelets is proposed. The sensor bracelet detects pressure signals generated by forearm muscle movements to recognize hand gesture intent. Leveraging the remarkable pressure sensitivity of liquid metal composites and the efficient classifier based on the optimized recognition algorithm, this system achieves an average offline and real-time recognition accuracy of 98.2% and 92.04%, respectively, with an average recognition speed of 0.364 s. Thus, this wearable system shows advantages in superior recognition speed and accuracy. Furthermore, this system finds applications in master-slave control of prosthetic hands in unmanned scenarios, such as electrically powered operations, space exploration, and telemedicine. The proposed system promises significant advances in next-generation intent-controlled prosthetic hands and robots.

Identification of Anoikis-Related Genes in Spinal Cord Injury: Bioinformatics and Experimental Validation.

Spinal cord injury (SCI) is a serious disease without effective therapeutic strategies. To identify the potential treatments for SCI, it is extremely important to explore the underlying mechanism. Current studies demonstrate that anoikis might play an important role in SCI. In this study, we aimed to identify the key anoikis-related genes (ARGs) providing therapeutic targets for SCI. The mRNA expression matrix of GSE45006 was downloaded from the Gene Expression Omnibus (GEO) database, and the ARGs were downloaded from the Molecular Signatures Database (MSigDB database). Then, the potential differentially expressed ARGs were identified. Next, correlation analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis were employed for the differentially expressed ARGs. Moreover, miRNA-gene networks were constructed by the hub ARGs. Finally, RNA expression of the top ten hub ARGs was validated in the SCI cell model and rat SCI model. A total of 27 common differentially expressed ARGs were identified at different time points (1, 3, 7, and 14 days) following SCI. The GO and KEGG enrichment analysis of these ARGs indicated several enriched terms related to proliferation, cell cycle, and apoptotic process. The PPI results revealed that most of the ARGs interacted with each other. Ten hub ARGs were further screened, and all the 10 genes were validated in the SCI cell model. In the rat model, only seven genes were validated eventually. We identified 27 differentially expressed ARGs of the SCI through bioinformatic analysis. Seven real hub ARGs (CCND1, FN1, IGF1, MYC, STAT3, TGFB1, and TP53) were identified eventually. These results may expand our understanding of SCI and contribute to the exploration of potential SCI targets.

Real-world retrospective study of prostate-specific antigen and safety assessment with darolutamide plus androgen deprivation therapy for metastasis hormone-sensitive prostate cancer.

Background: ARASENS has demonstrated the efficacy and safety for darolutamide (DARO) with androgen deprivation therapy (ADT) plus docetaxel in metastasis hormone-sensitive prostate cancer (mHSPC). There is a lack of reports for DARO with ADT in mHSPC though the regimen is used in clinical from time to time. Moreover, recent studies have supported the importance of early and rapid prostate-specific antigen (PSA) reduction, which correlates with reduced disease progression and improved survival in patients with mHSPC. This study aims to evaluate PSA reduction as a primary endpoint for DARO with ADT in the treatment of mHSPC and to evaluate the real-world short-term PSA control of DARO with ADT from two leading medical centers in China. Methods: We retrospectively reviewed the clinical records of patients with mHSPC receiving ADT and DARO (600 mg, b.i.d.). The collection of data spanned from March 1, 2022, to July 31, 2023. The main observation indicators were PSA level and drug-related adverse events (AE) after medication. PSA levels were closely monitored prior to treatment initiation and at 2-week intervals, as well as at 1, 3, and 6 months after the initiation of treatment. We also conducted an analysis to determine the proportion of patients achieving a PSA reduction of 50% or more (PSA50) and 90% or more (PSA90) as well as the percentage of patients with a notable decrease in PSA level to 0.2 ng/mL and PSA nadir of ≤0.02 ng/mL. Results: Fifty-one patients were included in the study, with a median age of 73 years. At diagnosis of HSPC, the majority of patients had a Gleason score ≥8 (n=40, 78.40%) and a median baseline PSA level of 88 ng/mL. Approximately 45.1% (n=23) of patients had a Charlson Comorbidity Index over 1 and were receiving one or more nontumor-related treatments. The median follow-up time was 9.3 months (range, 1.16-15.8 months). The median reductions in PSA levels compared to baseline were 84.37%, 91.48%, 94.67% and 99.81% at 2 weeks, 1 month, 3 months and 6 months after administration of DARO with ADT, respectively. The median time to PSA50, PSA90, significant PSA reduction (PSA <0.2 ng/mL), and PSA nadir (PSA <0.02 ng/mL) was 0.97, 1.27, 1.98, and 2.08 months, respectively. AE mainly included fatigue (two patients) and arm pain (one patient), all of which were grade I or II AE. No grade III or AE were observed. Conclusions: For treating prostate cancer, DARO with ADT has good early efficacy, demonstrating prompt and substantial control of PSA levels, with a favorable safety profile.

Construction of functional neural network tissue combining CBD-NT3-modified linear-ordered collagen scaffold and TrkC-modified iPSC-derived neural stem cells for spinal cord injury repair.

Induced pluripotent stem cells (iPSCs) can be personalized and differentiated into neural stem cells (NSCs), thereby effectively providing a source of transplanted cells for spinal cord injury (SCI). To further improve the repair efficiency of SCI, we designed a functional neural network tissue based on TrkC-modified iPSC-derived NSCs and a CBD-NT3-modified linear-ordered collagen scaffold (LOCS). We confirmed that transplantation of this tissue regenerated neurons and synapses, improved the microenvironment of the injured area, enhanced remodeling of the extracellular matrix, and promoted functional recovery of the hind limbs in a rat SCI model with complete transection. RNA sequencing and metabolomic analyses also confirmed the repair effect of this tissue from multiple perspectives and revealed its potential mechanism for treating SCI. Together, we constructed a functional neural network tissue using human iPSCs-derived NSCs as seed cells based on the interaction of receptors and ligands for the first time. This tissue can effectively improve the therapeutic effect of SCI, thus confirming the feasibility of human iPSCs-derived NSCs and LOCS for SCI repair and providing a valuable direction for SCI research.

Leveraging a gene signature associated with disulfidptosis identified by machine learning to forecast clinical outcomes, immunological heterogeneities, and potential therapeutic targets within lower-grade glioma.

Background: Disulfidptosis, a newly defined type of programmed cell death, has emerged as a significant regulatory process in the development and advancement of malignant tumors, such as lower-grade glioma (LGG). Nevertheless, the precise biological mechanisms behind disulfidptosis in LGG are yet to be revealed, considering the limited research conducted in this field. Methods: We obtained LGG data from the TCGA and CGGA databases and performed comprehensive weighted co-expression network analysis, single-sample gene set enrichment analysis, and transcriptome differential expression analyses. We discovered nine genes associated with disulfidptosis by employing machine learning methods like Cox regression, LASSO regression, and SVM-RFE. These were later used to build a predictive model for patients with LGG. To confirm the expression level, functional role, and impact on disulfidptosis of ABI3, the pivotal gene of the model, validation experiments were carried out in vitro. Results: The developed prognostic model successfully categorized LGG patients into two distinct risk groups: high and low. There was a noticeable difference in the time the groups survived, which was statistically significant. The model's predictive accuracy was substantiated through two independent external validation cohorts. Additional evaluations of the immune microenvironment and the potential for immunotherapy indicated that this risk classification could function as a practical roadmap for LGG treatment using immune-based therapies. Cellular experiments demonstrated that suppressing the crucial ABI3 gene in the predictive model significantly reduced the migratory and invasive abilities of both SHG44 and U251 cell lines while also triggering cytoskeletal retraction and increased cell pseudopodia. Conclusion: The research suggests that the prognostic pattern relying on genes linked to disulfidptosis can provide valuable insights into the clinical outcomes, tumor characteristics, and immune alterations in patients with LGG. This could pave the way for early interventions and suggests that ABI3 might be a potential therapeutic target for disulfidptosis.