Antidepressant exposure and long-term dementia risk in a nationwide retrospective study on US veterans with midlife major depressive disorder.

INTRODUCTION: The use of antidepressants in major depressive disorder (MDD) has been reported to influence long-term risk of Alzheimer's disease (AD) and AD-related dementias (AD/ADRD), but studies are conflicting. METHODS: We used inverse probability weighted (IPW) Cox models with time-varying covariates in a retrospective cohort study among midlife veterans with MDD within the US Veterans Affairs healthcare system from January 1, 2000 to June 1, 2022. RESULTS: A total of 35,200 patients with MDD were identified. No associations were seen regarding the effect of being exposed to any antidepressant versus no exposure on AD/ADRD risk (events = 1,056, hazard ratio = 0.94, 95% confidence interval: 0.81 to 1.09) or the exposure to specific antidepressant classes versus no exposure. A risk reduction was observed for female patients in a stratified analysis; however, the number of cases was small. DISCUSSION: Our study suggests that antidepressant exposure has no effect on AD/ADRD risk. The association in female patients should be interpreted with caution and requires further attention. HIGHLIGHTS: We studied whether antidepressant use was associated with future dementia risk. We specifically focused on patients after their first-ever diagnosis of depression. We used IPW Cox models with time-varying covariates and a large observation window. Our study did not identify an effect of antidepressant use on dementia risk. A risk reduction was observed in female patients, but the number of cases was small.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.

Clinical characteristics and prognosis analysis of postoperative patients with stage I-III colon cancer based on SEER database.

PURPOSE: To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model. METHODS: Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group. RESULTS: Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects. CONCLUSION: Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.

GEMIN4, a potential therapeutic targets for patients with basal-like subtype breast cancer.

BACKGROUND: Basal-like breast cancer (BLBC) takes up about 10-20% of all breast cancer(BC), what's more, BLBC has the lowest survival rate among all BC subtypes because of lacks of efficient treatment methods. We aimed to explore the molecules that can be used as diagnostic maker for BLBC at early stage and provide optimized treatment strategies for BLBC patients in this study. METHODS: Apply weighted gene co-expression network analysis (WGCNA) to identify gene modules related to BLBC;The functional enrichment of candidate genes related to BLBC in the red module of Go data package and KEGG analysis;Overlapping cross analysis of URGs and WGCNA to identify candidate genes in each BC subtype;Divide BCBL patients into high-risk and low-risk groups, and analyze the two groups of overall survival (OS) and relapse free survival (RFS);Screening of GEMIN4 dependent cell lines; QRT PCR was used to verify the expression of GEMIN4 transfected with siRNA; CCK8 was used to determine the effect of GEMIN4 on cell viability; Positive cell count detected by BrdU staining;GO and KEGG enrichment analysis of GEMIN4. RESULTS: The "red module" has the highest correlation with BLBC, with 913 promising candidate genes identified from the red module;913 red module candidate genes related to BLBC participated in multiple GO terms, and KEGG enrichment analysis results mainly enriched in estrogen signaling pathways and pathways in cancer;There are 386 overlapping candidate genes among the 913 "red module" genes identified by 1893 common URG and WGCNA;In BLBC patients, 9 highly expressed genes are associated with OS. Five highly expressed genes are associated with RFS. Kaplan Meier survival analysis suggests that high GEMIN4 expression levels are associated with poor prognosis in BLBC patients;The GEMIN4 gene dependency score in HCC1143 and CAL120 cell lines is negative and low; Si-GEMIN4-1 can significantly reduce the mRNA expression of GEMIN4; Si-GEMIN4 can inhibit cell viability; Si-GEMIN4 can reduce the number of positive cells;GO enrichment analysis showed that GEMIN4 is associated with DNA metabolism processes and adenylate binding; KEGG pathway enrichment analysis shows that GEMIN4 is related to ribosome biogenesis in eukaryotes. CONCLUSION: We hypothesized that GEMIN4 may be the potential target for the treatment of BLBC.

Self-prediction of relations in GO facilitates its quality auditing.

As applications of the gene ontology (GO) increase rapidly in the biomedical field, quality auditing of it is becoming more and more important. Existing auditing methods are mostly based on rules, observed patterns or hypotheses. In this study, we propose a machine-learning-based framework for GO to audit itself: we first predict the IS-A relations among concepts in GO, then use differences between predicted results and existing relations to uncover potential errors. Specifically, we transfer the taxonomy of GO 2020 January release into a dataset with concept pairs as items and relations between them as labels(pairs with no direct IS-A relation are labeled as ndrs). To fully obtain the representation of each pair, we integrate the embeddings for the concept name, concept definition, as well as concept node in a substring-based topological graph. We divide the dataset into 10 parts, and rotate over all the parts by choosing one part as the testing set and the remaining as the training set each time. After 10 rotations, the prediction model predicted 4,640 existing IS-A pairs as ndrs. In the GO 2022 March release, 340 of these predictions were validated, demonstrating significance with a p-value of 1.60e-46 when compared to the results of randomly selected pairs. On the other hand, the model predicted 2,840 out of 17,079 selected ndrs in GO to be IS-A's relations. After deleting those that caused redundancies and circles, 924 predicted IS-A's relations remained. Among 200 pairs randomly selected, 30 were validated as missing IS-A's by domain experts. In conclusion, this study investigates a novel way of auditing biomedical ontologies by predicting the relations in it, which was shown to be useful for discovering potential errors.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

Gamma-Aminobutyric Acid Type A Receptor Subunit Delta (GABRD) Inhibits Breast Cancer Progression by Regulating the Cell Cycle.

Background: The role of γ-aminobutyric acid receptor (GABR) in breast cancer (BC) is unknown. Methods: The expression of different GABR subunits between BC and adjacent normal tissues was compared using transcriptome data set. The clinical and prognostic importance of the various GABR subunit genes in BC was determined using clinical and survival data (Data downloaded from TCGA, May 2022). Only GABRD was discovered to be substantially expressed and strongly related to the prognosis of BC cases. Results: Compared with normal tissues, GABRD expression was increased in all subgroups of breast cancer tissues. Knockdown of GABRD inhibited the growth of BC cells. Mechanistically, the function of GABRD may be attributed to its effect on major pathways such as oxidative phosphorylation, Parkinson disease, and cell cycle. GABRD deletion significantly blocked the G2/M phase in BC cells. Conclusion: Overall, GABRD might be a novel prognostic predictor of BC, providing clues for further studies on GABRD.

Computational ranking-assisted identification of Plexin-B2 in homotypic and heterotypic clustering of circulating tumor cells in breast cancer metastasis.

Metastasis is the cause of over 90% of all deaths associated with breast cancer, yet the strategies to predict cancer spreading based on primary tumor profiles and therefore prevent metastasis are egregiously limited. As rare precursor cells to metastasis, circulating tumor cells (CTCs) in multicellular clusters in the blood are 20-50 times more likely to produce viable metastasis than single CTCs. However, the molecular mechanisms underlying various CTC clusters, such as homotypic tumor cell clusters and heterotypic tumor-immune cell clusters, are yet to be fully elucidated. Combining machine learning-assisted computational ranking with experimental demonstration to assess cell adhesion candidates, we identified a transmembrane protein Plexin- B2 (PB2) as a new therapeutic target that drives the formation of both homotypic and heterotypic CTC clusters. High PB2 expression in human primary tumors predicts an unfavorable distant metastasis-free survival and is enriched in CTC clusters compared to single CTCs in advanced breast cancers. Loss of PB2 reduces formation of homotypic tumor cell clusters as well as heterotypic tumor-myeloid cell clusters in triple-negative breast cancer. Interactions between PB2 and its ligand Sema4C on tumor cells promote homotypic cluster formation, and PB2 binding with Sema4A on myeloid cells (monocytes) drives heterotypic CTC cluster formation, suggesting that metastasizing tumor cells hijack the PB2/Sema family axis to promote lung metastasis in breast cancer. Additionally, using a global proteomic analysis, we identified novel downstream effectors of the PB2 pathway associated with cancer stemness, cell cycling, and tumor cell clustering in breast cancer. Thus, PB2 is a novel therapeutic target for preventing new metastasis.

A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology.

Wireless sensing-based human-vehicle recognition (WiHVR) methods have become a hot spot for research due to its non-invasiveness and cost-effective advantages. However, existing WiHVR methods shows limited performance and slow execution time on human-vehicle classification task. To address this issue, a lightweight wireless sensing attention-based deep learning model (LW-WADL) is proposed, which consists of a CBAM module and several depthwise separable convolution blocks in series. LW-WADL takes raw channel state information (CSI) as input, and extracts the advanced features of CSI by jointly using depthwise separable convolution and convolutional block attention mechanism (CBAM). Experimental results show that the proposed model achieves 96.26% accuracy on the constructed CSI-based dataset, and the model size is only 5.89% of the state of the art (SOTA) model. The results demonstrate that the proposed model achieves better performance on WiHVR tasks while reducing the model size compared to SOTA model.

lncRNA MALAT1 promotes HCC metastasis through the peripheral vascular infiltration via miRNA-613: a primary study using contrast ultrasound.

BACKGROUND: This study aimed to explore the specific pathogenesis of lncRNA MALAT1 promoting the invasion and metastasis of hepatocellular carcinoma (HCC) through peripheral blood vessels by regulating the expression of miRNA-613 molecule. METHODS: The data of 60 HCC metastatic patients and 60 HCC non-metastatic patients detected by the contrast-enhanced ultrasound (CEUS) in the Second Affiliated Hospital of Qiqihar Medical College from January 2020 to June 2021 were collected, as well as postoperatively retained HCC tissues and paired paracancer tissues (5 cm laterally from the edge of the cancer area), to study the changes of microangiogenesis in HCC tissues with CEUS. The correlation between CEUS grading and lncRNA MALAT1 in patients with HCC was analyzed through Pearson correlation analysis, lncRNA MALAT1 and miRNA-613 in HCC tissues of patients with HCC were detected by qRT-PCR, followed by the bioinformatic analysis for the relationship between lncRNA MALAT1 and miRNA-613. The Log-growing human HCC cell strain, HepG2, was selected for experiments. Adenovirus transfection knocked down lncRNA MALAT1 in HCC cells, which was divided into two groups (inhibitor-NC group and lncR-inhibitor group), followed by knocking down miRNA-613 on the basis of knocking down lncRNA MALAT1, which was divided into three groups (inhibitor-NC group, lncR-inhibitor groups, and lncR/miR613-inhibitor group). The expression of miRNA-613 and lncRNA MALAT1 in each group was detected by qRT-PCR. The migration and invasiveness of cells in each group were detected by Transwell assay. RESULTS: CEUS of HCC and Pearson correlation analysis showed that CEUS grading and lncRNA MALAT1 were positively correlated in patients with HCC. In HCC tissues of patients with HCC, lncRNA MALAT1 expressed high and miRNA-613 expressed low. The results of bioinformatic analysis showed the targeting of lncRNA MALAT1 and miRNA-613. Knocking down lncRNA MALAT1 could increase miRNA-613 expression significantly, and reduce the migration of HCC cells. Inhibiting miRNA-613 based on knocking down lncRNA MALAT1 could increase the survival and migration of HCC cells. CONCLUSIONS: lncRNA MALAT1 can promote HCC metastasis through the peripheral vascular infiltration by inhibiting the level of MiRNA-613, which can, therefore, be used as a potential target for the treatment of HCC.

Platelet Function Is Associated With Dementia Risk in the Framingham Heart Study.

Background Vascular function is compromised in Alzheimer disease (AD) years before amyloid and tau pathology are detected and a substantial body of work shows abnormal platelet activation states in patients with AD. The aim of our study was to investigate whether platelet function in middle age is independently associated with future risk of AD. Methods and Results We examined associations of baseline platelet function with incident dementia risk in the community-based FHS (Framingham Heart Study) longitudinal cohorts. The association between platelet function and risk of dementia was evaluated using the cumulative incidence function and inverse probability weighted Cox proportional cause-specific hazards regression models, with adjustment for demographic and clinical covariates. Platelet aggregation response was measured by light transmission aggregometry. The final study sample included 1847 FHS participants (average age, 53.0 years; 57.5% women). During follow-up (median, 20.5 years), we observed 154 cases of incident dementia, of which 121 were AD cases. Results from weighted models indicated that platelet aggregation response to adenosine diphosphate 1.0 µmol/L was independently and positively associated with dementia risk, and it was preceded in importance only by age and hypertension. Sensitivity analyses showed associations with the same directionality for participants defined as adenosine diphosphate hyper-responders, as well as the platelet response to 0.1 µmol/L epinephrine. Conclusions Our study shows individuals free of antiplatelet therapy with a higher platelet response are at higher risk of dementia in late life during a 20-year follow-up, reinforcing the role of platelet function in AD risk. This suggests that platelet phenotypes may be associated with the rate of dementia and potentially have prognostic value.

LMAN1-MCFD2 complex is a cargo receptor for the ER-Golgi transport of α1-antitrypsin.

α1-antitrypsin (AAT) is a serine protease inhibitor synthesized in hepatocytes and protects the lung from damage by neutrophil elastase. AAT gene mutations result in AAT deficiency (AATD), which leads to lung and liver diseases. The AAT Z variant forms polymer within the endoplasmic reticulum (ER) of hepatocytes and results in reduction in AAT secretion and severe disease. Previous studies demonstrated a secretion defect of AAT in LMAN1 deficient cells, and mild decreases in AAT levels in male LMAN1 and MCFD2 deficient mice. LMAN1 is a transmembrane lectin that forms a complex with a small soluble protein MCFD2. The LMAN1-MCFD2 protein complex cycles between the ER and the Golgi. Here, we report that LMAN1 and MCFD2 knockout (KO) HepG2 and HEK293T cells display reduced AAT secretion and elevated intracellular AAT levels due to a delayed ER-to-Golgi transport of AAT. Secretion defects in KO cells were rescued by wild-type LMAN1 or MCFD2, but not by mutant proteins. Elimination of the second glycosylation site of AAT abolished LMAN1 dependent secretion. Co-immunoprecipitation experiment in MCFD2 KO cells suggested that AAT interaction with LMAN1 is independent of MCFD2. Furthermore, our results suggest that secretion of the Z variant, both monomers and polymers, is also LMAN1-dependent. Results provide direct evidence supporting that the LMAN1-MCFD2 complex is a cargo receptor for the ER-to-Golgi transport of AAT and that interactions of LMAN1 with an N-glycan of AAT is critical for this process. These results have implications in production of recombinant AAT and in developing treatments for AATD patients.

Impact of prior SARS-CoV-2 infection on incidence of hospitalization and adverse events following mRNA SARS-CoV-2 vaccination: A nationwide, retrospective cohort study.

BACKGROUND: Previous studies evaluated the SARS-CoV-2 vaccine safety or compared adverse events following vaccination to those from infection. Limited data about the impact of prior infection on post-vaccine adverse events are available. The objective of this study was to evaluate the impact of prior SARS-CoV-2 infection on outcomes shortly after vaccination using a longitudinal design. METHODS: Nationwide, multicenter, retrospective cohort study of hospitalization, death, and pre-specified adverse event rates among Veterans who received mRNA vaccines within the Veterans Health Administration between 12/11/2020 and 8/31/2021. Daily incidence rates were compared before and after vaccine doses, stratified by history of microbiologically-confirmed SARS-CoV-2. RESULTS: 3,118,802 patients received a first dose and 2,979,326 a second, including 102,829 with a history of SARS-CoV-2 infection. Daily incident hospitalization rates were unchanged before and after the second dose among patients without previous infection (28.8/100,000 post-dose versus 28.6/100,000 pre-dose, p = 0.92). In previously-infected patients, the hospitalization rate increased above baseline one day following vaccination (158.2/100,000 after dose 2 versus 57.3/100,000 pre-dose, p < 0.001), then returned to baseline. Chart review indicated vaccine side effects, such as fever, constitutional symptoms, weakness, or falls, as the definite (39%) or possible (18%) cause of hospitalization. Affected patients had mean age 75, and 90% had at least one serious comorbidity. Hospitalizations were brief (median 2 days), with rapid return to baseline health. Worse baseline health among previously-infected patients prevented conclusions about mortality risk. CONCLUSIONS: Two-dose mRNA vaccine regimens are safe in a population with many comorbidities. Transient increased risks of hospitalization were identified among patients with prior SARS-CoV-2, absolute risk ∼1:1000. Findings support additional study regarding the optimal dosing schedule in this population. FUNDING: None.

The COVID-19 hospitalization metric in the pre- and postvaccination eras as a measure of pandemic severity: A retrospective, nationwide cohort study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) hospitalization definitions do not include a disease severity assessment. Thus, we sought to identify a simple and objective mechanism for identifying hospitalized severe cases and to measure the impact of vaccination on trends. METHODS: All admissions to a Veterans' Affairs (VA) hospital, where routine inpatient screening is recommended, between March 1, 2020, and November 22, 2021, with laboratory-confirmed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) were included. Moderate-to-severe COVID-19 was defined as any oxygen supplementation or any oxygen saturation (SpO2) <94% between 1 day before and 2 weeks after the positive SARS-CoV-2 test. Admissions with moderate-to-severe disease were divided by the total number of admissions, and the proportion of admissions with moderate-to-severe COVID-19 was modelled using a penalized spline in a Poisson regression and stratified by vaccination status. Dexamethasone receipt and its correlation with moderate-to-severe cases was also assessed. RESULTS: Among 67,025 admissions with SARS-CoV-2, the proportion with hypoxemia or supplemental oxygen fell from 64% prior to vaccine availability to 56% by November 2021, driven in part by lower rates in vaccinated patients (vaccinated, 52% versus unvaccinated, 58%). The proportion of cases of moderate-to-severe disease identified using SpO2 levels and oxygen supplementation was highly correlated with dexamethasone receipt (correlation coefficient, 0.95), and increased after July 1, 2021, concurrent with δ (delta) variant predominance. CONCLUSIONS: A simple and objective definition of COVID-19 hospitalizations using SpO2 levels and oxygen supplementation can be used to track pandemic severity. This metric could be used to identify risk factors for severe breakthrough infections, to guide clinical treatment algorithms, and to detect trends in changes in vaccine effectiveness over time and against new variants.

Kang-Ai Injection Inhibits Gastric Cancer Cells Proliferation through IL-6/STAT3 Pathway.

OBJECTIVE: To explore the mechanisms underlying the proliferative inhibition of Chinese herbal medicine Kang-Ai injection (KAI) in gastric cancer cells. METHODS: Gastric cancer cell lines MGC803 and BGC823 were treated by 0, 0.3%, 1%, 3% and 10% KAI for 24, 48 and 72 h, respectively. The cell proliferation was evaluated by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. The apoptosis and cell cycle were evaluated by flow cytometry. Interleukin (IL)-6 mRNA and protein expression levels were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immune sorbent assay (ELISA), respectively. The protein expression levels of cyclin A, cyclin E, cyclin B1, cyclin D1, p21, retinoblastoma (RB), protein kinase B (AKT), extracellular regulated protein kinases (ERK), signal transducer and activator of transcription (STAT) 1 and STAT3 were detected by Western blot. RESULTS: KAI inhibited the proliferation of MGC803 and BGC823 gastric cancer cells in dose- and time-dependent manner. After treated with KAI for 48 h, the proportion of G1 phase was increased, expression level of cyclin D1 and phosphorylation-RB were down-regulated, whereas the expression of p21 was up-regulated (all P<0.01). Furthermore, 48-h treatment with KAI decreased the phosphorylation level of STAT3, inhibited the mRNA and protein expressions of IL-6 (all P<0.01). IL-6 at dose of 10 ng/mL significantly attenuated the proliferative effect of both 3% and 10% KAI, and recovered KAI-inhibited STAT3 phosphorylation and cyclin D1 expression level (all P<0.01). CONCLUSION: KAI exerted an anti-proliferative function by inhibiting IL-6/STAT3 signaling pathway followed by the induction of G1 phase arrest in gastric cancer cells.

Potential long-term effect of tumor necrosis factor inhibitors on dementia risk: A propensity score matched retrospective cohort study in US veterans.

INTRODUCTION: Tumor necrosis factor (TNF) inhibitors are widely used to treat rheumatoid arthritis (RA) and their potential to retard Alzheimer's disease (AD) progression has been reported. However, their long-term effects on the dementia/AD risk remain unknown. METHODS: A propensity scored matched retrospective cohort study was conducted among 40,207 patients with RA within the US Veterans Affairs health-care system from 2000 to 2020. RESULTS: A total of 2510 patients with RA prescribed TNF inhibitors were 1:2 matched to control patients. TNF inhibitor use was associated with reduced dementia risk (hazard ratio [HR]: 0.64, 95% confidence interval [CI]: 0.52-0.80), which was consistent as the study period increased from 5 to 20 years after RA diagnosis. TNF inhibitor use also showed a long-term effect in reducing the risk of AD (HR: 0.57, 95% CI: 0.39-0.83) during the 20 years of follow-up. CONCLUSION: TNF inhibitor use is associated with lower long-term risk of dementia/AD among US veterans with RA.

Analysis of Long Noncoding RNAs-Related Regulatory Mechanisms in Duchenne Muscular Dystrophy Using a Disease-Related lncRNA-mRNA Pathway Network.

Objective: This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD). Methods: Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log2 fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied. Results: A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1, VEGFA, and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD, RIPK2, and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2, LUM, RPS6, PDCD4, TWIST1, and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1. Conclusion: The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers.

The Neutrophil to Lymphocyte Ratio Is Associated With the Risk of Subsequent Dementia in the Framingham Heart Study.

Objective: Active neutrophils are important contributors to Alzheimer's disease (AD) pathology through the formation of capillary stalls that compromise cerebral blood flow (CBF) and through aberrant neutrophil signaling that advances disease progression. The neutrophil to lymphocyte ratio (NLR) is a proxy of neutrophil-mediated inflammation, and higher NLR is found in persons diagnosed with clinical AD. The objective of this study was to investigate whether increased NLR in older adults is independently associated with the risk of subsequent dementia. Methods: We examined associations of baseline NLR with incident dementia risk in the community-based Framingham Heart Study (FHS) longitudinal cohorts. The association between NLR and risk of dementia was evaluated using the cumulative incidence function (CIF) and inverse probability-weighted Cox proportional cause-specific hazards regression models, with adjustment for age, sex, body mass index (BMI), systolic and diastolic blood pressure, diabetes, current smoking status, low-density lipoprotein (LDH), high-density lipoprotein (LDL), total cholesterol, triglycerides, and history of cardiovascular disease (CVD). Random forest survival models were used to evaluate the relative predictive value of the model covariates on dementia risk. Results: The final study sample included 1,648 participants with FHS (average age, 69 years; 56% women). During follow-up (median, 5.9 years), we observed 51 cases of incident dementia, of which 41 were AD cases. Results from weighted models suggested that the NLR was independently associated with incident dementia, and it was preceded in predictive value only by age, history of CVD, and blood pressure at baseline. Conclusion: Our study shows that individuals with higher NLR are at a greater risk of subsequent dementia during a 5.9-year follow-up period. Further evaluating the role of neutrophil-mediated inflammation in AD progression may be warranted.