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BackgroundMiddle-aged and older adults frequently experience hearing loss and a decline in cognitive function. Although an association between hearing difficulty and cognitive function has been demonstrated, its temporal sequence remains unclear. Therefore, we investigated whether there are bidirectional relationships between hearing difficulty and cognitive function and explored the mediating role of depressive symptoms in this relationship.MethodWe used the cross-lagged panel model and the random-intercept cross-lagged panel model to look for any possible two-way link between self-reported hearing difficulty and cognitive function. To investigate depressive symptoms’ role in this association, a mediation analysis was conducted. The sample was made up of 4,363 adults aged 45 and above from the China Health and Retirement Longitudinal Study (CHARLS; 2011–2018; 44.83% were women; mean age was 56.16 years). One question was used to determine whether someone had a hearing impairment. The tests of cognitive function included episodic memory and intelligence. The Center for Epidemiologic Studies Depression Scale, which consists of 10 items, was used to measure depressive symptoms.ResultsA bidirectional association between hearing and cognition was observed, with cognition predominating (Wald χ2 (1) = 7.241, p

Although many carriers for the delivery of chemotherapeutic drugs have been investigated, the disadvantages of passive targeting and uncontrolled drug release limit their utility. Herein, hyaluronic acid (HA) was hydrophobically modified to serve as a carrier for binding to cluster determinant 44 (CD44) overexpressed on tumor cell surfaces. Specifically, after deacetylation, HA was grafted to dodecylamine or tetradecylamine to afford amphiphilic zwitterionic polymer micelles, designated dHAD and dHAT, respectively, for the delivery of paclitaxel (PTX). The micelles were negatively charged at pH 7.4 and positively charged at pH 5.6, and this pH sensitivity facilitated PTX release under acidic conditions. The cell uptake efficiencies of the dHAD-PTX and dHAT-PTX micelles by MCF-7 cells after 4 h of incubation were 96.9% and 95.4%, respectively, and their affinities for CD44 were twice that of HA. Furthermore, the micelles markedly inhibited tumor growth both in vitro and in vivo, with IC50 values of 1.943 μg/mL for dHAD-PTX and 1.874 μg/mL for dHAT-PTX for MCF-7 cells; the tumor inhibition rate of dHAD-PTX (92.96%) was higher than that of dHAT-PTX (78.65%). Importantly, dHAD and dHAT micelles showed negligible systemic toxicity. Our findings suggest that these micelles are promising delivery vehicles for antitumor drugs.

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.Methods: In this study, we identified two CAFs gene expression patterns and performed the single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM).Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration.

BackgroundWe developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer’s disease (AD).MethodsWe thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.ResultsThe progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively,ConclusionsIn conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.

Background: Immune-checkpoint blockade (ICB) has been routinely implemented to treat head and neck squamous cell carcinoma (HNSCC) patients. However, only a few patients benefit from immune checkpoint inhibitor (ICI) therapies.Methods: In this study, we used a combined cohort (including the GSE41613, GSE65858, TCGA, and CELL cohorts) to identify hub genes significantly associated with ICB and activated CD8+ T-cell gene signatures. We performed single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression of hub genes; we then constructed a novel immune signature named “the IMS” that can predict immunotherapy responsiveness, prognosis, immune infiltration, and clinical characteristics. Data from the GSE102349 external cohort and the pembrolizumab cohort obtained from a clinical trial were used to validate the efficiency of the IMS. In addition, we revealed potential mechanisms of the antitumor response by analyzing the HNSCC single-cell database. Finally, we used the LASSO algorithm to build an IMS-related risk model.Results: The high IMS group was associated with significant immune activation, better prognosis, and increased immunotherapy responsiveness; thus, the IMS potentially represents a candidate biomarker for ICB. Moreover, a tumor microenvironment with a higher IMS underwent remarkable metabolic reprogramming characterized by enrichment in the glycolysis/gluconeogenesis, oxidative phosphorylation, and citrate cycle (TCA cycle) pathways. We also revealed key information on cellular crosstalk between the IMS and other immune lineages, which may mechanistically explain immune escape. In addition, we constructed and validated a risk prediction model (CD2, TBC1D10C, and CD3E) that could stratify HNSCC patients based on survival and response to ICB treatment.Conclusion: IMS is a signature closely correlated with the tumor immune microenvironment. The findings of this study contribute to the understanding of the immune landscape in HNSCC patients. IMS may aid in the clinical management of HNSCC patients through the identification of effective immunotherapies for specific patients.

Background: Sepsis is a systemic inflammatory response syndrome (SIRS) with heterogeneity of clinical symptoms. Studies further exploring the molecular subtypes of sepsis and elucidating its probable mechanisms are urgently needed.Methods: Microarray datasets of peripheral blood in sepsis were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) analysis was conducted to screen key module genes. Consensus clustering analysis was carried out to identify distinct sepsis molecular subtypes. Subtype-specific pathways were explored using gene set variation analysis (GSVA). Afterward, we intersected subtype-related, dramatically expressed and module-specific genes to screen consensus DEGs (co-DEGs). Enrichment analysis was carried out to identify key pathways. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for screen potential diagnostic biomarkers.Results: Patients with sepsis were classified into three clusters. GSVA showed these DEGs among different clusters in sepsis were assigned to metabolism, oxidative phosphorylation, autophagy regulation, and VEGF pathways, etc. In addition, we identified 40 co-DEGs and several dysregulated pathways. A diagnostic model with 25-gene signature was proven to be of high value for the diagnosis of sepsis. Genes in the diagnostic model with AUC values more than 0.95 in external datasets were screened as key genes for the diagnosis of sepsis. Finally, ANKRD22, GPR84, GYG1, BLOC1S1, CARD11, NOG, and LRG1 were recognized as critical genes associated with sepsis molecular subtypes.Conclusion: There are remarkable differences in and enriched pathways among different molecular subgroups of sepsis, which may be the key factors leading to heterogeneity of clinical symptoms and prognosis in patients with sepsis. Our current study provides novel diagnostic and therapeutic biomarkers for sepsis molecular subtypes.

ObjectiveMaintaining and delaying a decline in physical function in older adults is critical for healthy aging. This study aimed to explore trajectories, critical points of the trajectory changes, and predictors among older people in the Chinese community.DesignThis study was one with a longitudinal design performed in China.Setting and participantsThe target population was community-dwelling older adults aged over 65 years. A total of 2,503 older adults from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included in this study.MethodsPhysical functioning was measured by instrumental activities of daily living (IADL). Population-based trajectory models were used to identify potential heterogeneity in longitudinal changes over 16 years and to investigate associations between baseline predictors and different trajectories for different cohort members using LASSO regression and logistic regression.ResultsFour trajectories of physical function were identified: slow decline (33.0%), poor function and moderate decline (8.1%), rapid decline (23.5%), and stable function (35.4%). Older age, male sex, worse self-reported health status, worse vision status, more chronic diseases, worse cognitive function, and a decreased frequency of leisure activity influenced changes in the trajectory of physical function. Having fewer teeth, stronger depressive symptoms, a lack of exercise, and reduced hearing may increase the rate of decline.Conclusion and implicationsFour trajectories of physical function were identified in the Chinese elderly population. Early prevention or intervention of the determinants of these trajectories can maintain or delay the rate of decline in physical function and improve healthy aging.

IntroductionAlzheimer's disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer's disease and construct a prediction model.MethodsBased on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer's disease. Using 310 Alzheimer's disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.ResultsThe dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer's disease and non-Alzheimer's disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer's disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer's disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.ConclusionOur study systematically illustrated the complicated relationship between cuproptosis and Alzheimer's disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer's disease patients.

The latest research suggesting that necroptosis plays a vital role in immune response. However, the influence of necroptosis on tumor microenvironment (TME) remodeling and immunotherapy is still unclear. We analyzed the variations in the expression of 26 necroptosis-related molecules in HNSCC and the influence of genome changes. We investigated HNSCC samples and determined that there are two necroptosis phenotypes in HNSCC cancer, and there are significant differences in cell infiltration characteristics and survival in different necroptosis phenotypes. We used the single‐sample gene set enrichment analysis to measure the level of necroptosis in patients with NecroticScore, we confirmed that the NecroticScore can predict the prognosis of HNSCC patients and the response to immunotherapy. Patients with a high NecroticScore are more sensitive to immunotherapy and have a better prognosis. Our study suggests a significant correlation between the expression imbalance of necroptosis-related molecules and suggests necroptosis plays an important role in modeling the TME. In addition, we construct a risk prediction model which could stratify patients with HNSCC and predict patient prognosis according to this necroptosis-related molecules. In conclusion, evaluating necroptosis modification patterns contributes to enhancing our understanding of TME and can guide more effective immunotherapy strategies.

Spermatocyte meiosis is the cornerstone of mammalian production. Thousands of long noncoding RNAs (lncRNAs) have been reported to be functional in various cellular processes, but the function of lncRNAs in meiosis remains largely unknown. Here, we profiled lncRNAs in spermatocytes at stage I of meiosis and identified a testis-specific lncRNA, Rbakdn, as a vital regulator of meiosis. Rbakdn is dynamically expressed during meiosis I, and Rbakdn knockdown inhibits meiosis in vitro. Furthermore, Rbakdn knockdown in testes in mice by intratesticular injection disturbs meiosis, reduces testicular volume, and increases apoptosis of spermatocytes, resulting in vacuolation of the seminiferous tubules. Rbakdn can bind to Ptbp2, an RNA-binding protein that is important in the regulation of the alternative splicing of many genes in spermatogenesis. Rbakdn knockdown leads to a decrease in Ptbp2 through the ubiquitination degradation pathway, indicating that Rbakdn maintains the stability of Ptbp2. In conclusion, our study identified an lncRNA, Rbakdn, with a crucial role in meiosis.

Casearlucin A, a diterpenoid obtained from Casearia graveolens, has been reported to possess strong cytotoxic activity. However, the in vivo anti-tumor effects and the action mechanism of casearlucin A remain poorly understood. Our study revealed that casearlucin A arrested cell cycle at G0/G1 stage and induced cell apoptosis in cell level. Additionally, casearlucin A inhibited HepG2 cell migration via regulating a few of metastasis-related proteins. Furthermore, it inhibited tumor angiogenesis in zebrafish in vivo. More importantly, casearlucin A significantly inhibited cell proliferation and migration in an in vivo zebrafish xenograft model. Collectively, these results are valuable for the further development and application of casearlucin A as an anticancer agent.

The latest research suggesting that necroptosis plays a vital role in immune response. However, the influence of necroptosis on tumor microenvironment (TME) remodeling and immunotherapy is still unclear. We analyzed the variations in the expression of 26 necroptosis-related molecules in HNSCC and the influence of genome changes. We investigated HNSCC samples and determined that there are two necroptosis phenotypes in HNSCC cancer, and there are significant differences in cell infiltration characteristics and survival in different necroptosis phenotypes. We used the single‐sample gene set enrichment analysis to measure the level of necroptosis in patients with NecroticScore, we confirmed that the NecroticScore can predict the prognosis of HNSCC patients and the response to immunotherapy. Patients with a high NecroticScore are more sensitive to immunotherapy and have a better prognosis. Our study suggests a significant correlation between the expression imbalance of necroptosis-related molecules and suggests necroptosis plays an important role in modeling the TME. In addition, we construct a risk prediction model which could stratify patients with HNSCC and predict patient prognosis according to this necroptosis-related molecules. In conclusion, evaluating necroptosis modification patterns contributes to enhancing our understanding of TME and can guide more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]

Casearlucin A, a diterpenoid obtained from Casearia graveolens , has been reported to possess strong cytotoxic activity. However, the in vivo anti-tumor effects and the action mechanism of casearlucin A remain poorly understood. Our study revealed that casearlucin A arrested cell cycle at G0/G1 stage and induced cell apoptosis in cell level. Additionally, casearlucin A inhibited HepG2 cell migration via regulating a few of metastasis-related proteins. Furthermore, it inhibited tumor angiogenesis in zebrafish in vivo. More importantly, casearlucin A significantly inhibited cell proliferation and migration in an in vivo zebrafish xenograft model. Collectively, these results are valuable for the further development and application of casearlucin A as an anticancer agent. [ABSTRACT FROM AUTHOR]