Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion.

The metabolic challenges present in tumors attenuate the metabolic fitness and antitumor activity of tumor-infiltrating T lymphocytes (TILs). However, it remains unclear whether persistent metabolic insufficiency can imprint permanent T cell dysfunction. We found that TILs accumulated depolarized mitochondria as a result of decreased mitophagy activity and displayed functional, transcriptomic and epigenetic characteristics of terminally exhausted T cells. Mechanistically, reduced mitochondrial fitness in TILs was induced by the coordination of T cell receptor stimulation, microenvironmental stressors and PD-1 signaling. Enforced accumulation of depolarized mitochondria with pharmacological inhibitors induced epigenetic reprogramming toward terminal exhaustion, indicating that mitochondrial deregulation caused T cell exhaustion. Furthermore, supplementation with nicotinamide riboside enhanced T cell mitochondrial fitness and improved responsiveness to anti-PD-1 treatment. Together, our results reveal insights into how mitochondrial dynamics and quality orchestrate T cell antitumor responses and commitment to the exhaustion program.

α-ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming.

Glutamine metabolism provides synergistic support for macrophage activation and elicitation of desirable immune responses; however, the underlying mechanisms regulated by glutamine metabolism to orchestrate macrophage activation remain unclear. Here we show that the production of α-ketoglutarate (αKG) via glutaminolysis is important for alternative (M2) activation of macrophages, including engagement of fatty acid oxidation (FAO) and Jmjd3-dependent epigenetic reprogramming of M2 genes. This M2-promoting mechanism is further modulated by a high αKG/succinate ratio, whereas a low ratio strengthens the proinflammatory phenotype in classically activated (M1) macrophages. As such, αKG contributes to endotoxin tolerance after M1 activation. This study reveals new mechanistic regulations by which glutamine metabolism tailors the immune responses of macrophages through metabolic and epigenetic reprogramming.

An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools.

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

Liver-specific metastases as an independent prognostic factor in cancer patients receiving hospice care in hospital.

BACKGROUND: Survival prediction is important in cancer patients receiving hospice care. Palliative prognostic index (PPI) and palliative prognostic (PaP) scores have been used to predict survival in cancer patients. However, cancer primary site with metastatic status, enteral feeding tubes, Foley catheter, tracheostomy, and treatment interventions are not considered in aforementioned tools. The study aimed to investigate the cancer features and potential clinical factors other than PPI and PaP to predict patient survival. METHODS: We conducted a retrospective study for cancer patients admitted to a hospice ward between January 2021 and December 2021. We examined the correlation of PPI and PaP scores with survival time since hospice ward admission. Multiple linear regression was used to test the potential clinical factors other than PPI and PaP for predicting survival. RESULTS: A total of 160 patients were enrolled. The correlation coefficients for PPI and PaP scores with survival time were -0.305 and -0.352 (both p < 0.001), but the predictabilities were only marginal at 0.087 and 0.118, respectively. In multiple regression, liver metastasis was an independent poor prognostic factor as adjusted by PPI (ß = -8.495, p = 0.013) or PaP score (ß = -7.139, p = 0.034), while feeding gastrostomy or jejunostomy were found to prolong survival as adjusted by PPI (ß = 24.461, p < 0.001) or PaP score (ß = 27.419, p < 0.001). CONCLUSIONS: Association between PPI and PaP with patient survival in cancer patients at their terminal stages is low. The presence of liver metastases is a poor survival factor independent of PPI and PaP score.

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications.

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater's power consumption and the hot-pressing furnace's fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.

Automatic Assessment of 3-Dimensional Facial Soft Tissue Symmetry Before and After Orthognathic Surgery Using a Machine Learning Model: A Preliminary Experience.

PURPOSE: An objective and quantitative assessment of facial symmetry is essential for the surgical planning and evaluation of treatment outcomes in orthognathic surgery (OGS). This study applied the transfer learning model with a convolutional neural network based on 3-dimensional (3D) contour line features to evaluate the facial symmetry before and after OGS. METHODS: A total of 158 patients were recruited in a retrospective cohort study for the assessment and comparison of facial symmetry before and after OGS from January 2018 to March 2020. Three-dimensional facial photographs were captured by the 3dMD face system in a natural head position, with eyes looking forward, relaxed facial muscles, and habitual dental occlusion before and at least 6 months after surgery. Three-dimensional contour images were extracted from 3D facial images for the subsequent Web-based automatic assessment of facial symmetry by using the transfer learning with a convolutional neural network model. RESULTS: The mean score of postoperative facial symmetry showed significant improvements from 2.74 to 3.52, and the improvement degree of facial symmetry (in percentage) after surgery was 21% using the constructed machine learning model. A Web-based system provided a user-friendly interface and quick assessment results for clinicians and was an effective doctor-patient communication tool. CONCLUSIONS: This work was the first attempt to automatically assess the facial symmetry before and after surgery in an objective and quantitative value by using a machine learning model based on the 3D contour feature map.

Synthesis of Dibenzosuberones Bearing an Isoxazole Group via Palladium-Catalyzed Intramolecular C-H/C-Br Bond Cross-Coupling of Ortho-Aroylated 3,5-Diarylisoxazoles.

A facile and efficient synthetic methodology for preparing dibenzosuberones via a C-H bond activation strategy is presented. The ortho-aroylated 3,5-diarylisoxazole was employed as the starting substrate to undergo palladium-catalyzed intramolecular C-H/C-Br bond cross-coupling to produce a variety of dibenzosuberones bearing an isoxazole group in 24 to >99% 1H NMR yields. The dibenzosuberone structure was further confirmed by X-ray crystallography. The developed methodology exhibits very good functional group tolerance. In addition, a rational mechanism was presented for describing the reaction process. For the prepared dibenzosuberone, the use of Mo(CO)6 as the catalyst can easily transform the isoxazole ring into the ß-aminoenone group. Finally, the structure of the anticipated ring-opening product, dibenzosuberenone, bearing a ß-amino-α-ketone group was secured by X-ray crystallography.

MicroRNA-23a-3p Down-Regulation in Active Pulmonary Tuberculosis Patients with High Bacterial Burden Inhibits Mononuclear Cell Function and Phagocytosis through TLR4/TNF-α/TGF-ß1/IL-10 Signaling via Targeting IRF1/SP1.

The aim of this study is to explore the role of microRNAs (miR)-21/23a/146a/150/155 targeting the toll-like receptor pathway in active tuberculosis (TB) disease and latent TB infection (LTBI). Gene expression levels of the five miRs and predicted target genes were assessed in peripheral blood mononuclear cells from 46 patients with active pulmonary TB, 15 subjects with LTBI, and 17 non-infected healthy subjects (NIHS). THP-1 cell lines were transfected with miR-23a-3p mimics under stimuli with Mycobacterium TB-specific antigens. Both miR-155-5p and miR-150-5p gene expressions were decreased in the active TB group versus the NIHS group. Both miR-23a-3p and miR-146a-5p gene expressions were decreased in active TB patients with high bacterial burden versus those with low bacterial burden or control group (LTBI + NIHS). TLR2, TLR4, and interleukin (IL)10 gene expressions were all increased in active TB versus NIHS group. MiR-23a-3p mimic transfection reversed ESAT6-induced reduction of reactive oxygen species generation, and augmented ESAT6-induced late apoptosis and phagocytosis, in association with down-regulations of the predicted target genes, including tumor necrosis factor (TNF)-α, TLR4, TLR2, IL6, IL10, Notch1, IL6R, BCL2, TGF-ß1, SP1, and IRF1. In conclusion, the down-regulation of miR-23a-3p in active TB patients with high bacterial burden inhibited mononuclear cell function and phagocytosis through TLR4/TNF-α/TGF-ß1/IL-10 signaling via targeting IRF1/SP1.

Whole Genome DNA Methylation Analysis of Active Pulmonary Tuberculosis Disease Identifies Novel Epigenotypes: PARP9/miR-505/RASGRP4/GNG12 Gene Methylation and Clinical Phenotypes.

We hypothesized that DNA methylation patterns may contribute to the development of active pulmonary tuberculosis (TB). Illumina's DNA methylation 450 K assay was used to identify differentially methylated loci (DML) in a discovery cohort of 12 active pulmonary TB patients and 6 healthy subjects (HS). DNA methylation levels were validated in an independent cohort of 64 TB patients and 24 HS. Microarray analysis identified 1028 DMLs in TB patients versus HS, and 3747 DMLs in TB patients after versus before anti-TB treatment, while autophagy was the most enriched signaling pathway. In the validation cohort, PARP9 and miR505 genes were hypomethylated in the TB patients versus HS, while RASGRP4 and GNG12 genes were hypermethylated, with the former two further hypomethylated in those with delayed sputum conversion, systemic symptoms, or far advanced lesions. MRPS18B and RPTOR genes were hypomethylated in TB patients with pleural involvement. RASGRP4 gene hypermethylation and RPTOR gene down-regulation were associated with high mycobacterial burden. TB patients with WIPI2/GNG12 hypermethylation or MRPS18B/FOXO3 hypomethylation had lower one-year survival. In vitro ESAT6 and CFP10 stimuli of THP-1 cells resulted in DNA de-methylation changes of the PARP9, RASGRP4, WIPI2, and FOXO3 genes. In conclusions, aberrant DNA methylation over the PARP9/miR505/RASGRP4/GNG12 genes may contribute to the development of active pulmonary TB disease and its clinical phenotypes, while aberrant DNA methylation over the WIPI2/GNG12/MARPS18B/FOXO3 genes may constitute a determinant of long-term outcomes.

Mode Selection and Spectrum Allocation in Coexisting D2D and Cellular Networks with Cooperative Precoding.

In this paper, we investigate the mode selection strategies for a new device-to-device (D2D) pair becoming active in a network with a number of existing D2D sensors or users coexisting with cellular users in a D2D-enabled heterogeneous network. Specifically, we propose two selection rules, the signal-to-interference-plus-noise-ratio (SINR)-based and the capacity-based, combined with two sets of different precoding schemes and discuss their impacts on the system under a variety of scenarios. While the cooperative block diagonalization (BD) among the cellular users combined with the zero-forcing (ZF) precoding among D2D users can eliminate interference observed at the new D2D receiving sensor, the maximum signal-to-leakage-and-noise-ratio (SLNR) precoding is often a preferred option due to low-complexity implementations and comparable performance. We note that the two selection rules, the SINR-based and the capacity-based, considered in this paper impact on the system differently, with interesting tradeoff from different perspectives. Finally, we provide insights by simulations into the best selection among the three modes depending on a variety of use cases in the network.

Defective formyl peptide receptor 2/3 and annexin A1 expressions associated with M2a polarization of blood immune cells in patients with chronic obstructive pulmonary disease.

BACKGROUND: Controversy exists in previous studies on macrophage M1/M2 polarization in chronic obstructive pulmonary disease (COPD). We hypothesized that formyl peptide receptor (FPR), a marker of efferocytosis and mediator of M1/M2 polarization, may be involved in the development of COPD. METHODS: We examined FPR 1/2/3 expressions of blood M1/M2a monocyte, neutrophil, natural killer (NK) cell, NK T cell, T helper (Th) cell, and T cytotoxic (Tc) cell by flowcytometry method in 40 patients with cigarette smoking-related COPD and 16 healthy non-smokers. Serum levels of five FPR ligands were measured by ELISA method. RESULTS: The COPD patients had lower M2a percentage and higher percentages of NK, NK T, Th, and Tc cells than the healthy non-smokers. FPR2 expressions on Th/Tc cells, FPR3 expressions of M1, M2a, NK, NK T, Th, and Tc cells, and serum annexin A1 (an endogenous FPR2 ligand) levels were all decreased in the COPD patients as compared with that in the healthy non-smokers. FPR1 expression on neutrophil was increased in the COPD patient with a high MMRC dyspnea scale, while FPR2 expression on neutrophil and annexin A1 were both decreased in the COPD patients with a history of frequent moderate exacerbation (≥ 2 events in the past 1 year). In 10 COPD patients whose blood samples were collected again after 1-year treatment, M2a percentage, FPR3 expressions of M1/NK/Th cells, FPR2 expression on Th cell, and FPR1 expression on neutrophil were all reversed to normal, in parallel with partial improvement in small airway dysfunction. CONCLUSIONS: Our findings provide evidence for defective FPR2/3 and annexin A1 expressions that, associated with decreased M2a polarization, might be involved in the development of cigarette smoking induced persistent airflow limitation in COPD.

Implementation of a Big Data Accessing and Processing Platform for Medical Records in Cloud.

Big Data analysis has become a key factor of being innovative and competitive. Along with population growth worldwide and the trend aging of population in developed countries, the rate of the national medical care usage has been increasing. Due to the fact that individual medical data are usually scattered in different institutions and their data formats are varied, to integrate those data that continue increasing is challenging. In order to have scalable load capacity for these data platforms, we must build them in good platform architecture. Some issues must be considered in order to use the cloud computing to quickly integrate big medical data into database for easy analyzing, searching, and filtering big data to obtain valuable information.This work builds a cloud storage system with HBase of Hadoop for storing and analyzing big data of medical records and improves the performance of importing data into database. The data of medical records are stored in HBase database platform for big data analysis. This system performs distributed computing on medical records data processing through Hadoop MapReduce programming, and to provide functions, including keyword search, data filtering, and basic statistics for HBase database. This system uses the Put with the single-threaded method and the CompleteBulkload mechanism to import medical data. From the experimental results, we find that when the file size is less than 300MB, the Put with single-threaded method is used and when the file size is larger than 300MB, the CompleteBulkload mechanism is used to improve the performance of data import into database. This system provides a web interface that allows users to search data, filter out meaningful information through the web, and analyze and convert data in suitable forms that will be helpful for medical staff and institutions.

Impact of clinical parameters and systemic inflammatory status on epidermal growth factor receptor-mutant non-small cell lung cancer patients readministration with epidermal growth factor receptor tyrosine kinase inhibitors.

BACKGROUND: Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) readministration to lung cancer patients is common owing to the few options available. Impact of clinical factors on prognosis of EGFR-mutant non-small cell lung cancer (NSCLC) patients receiving EGFR-TKI readministration after first-line EGFR-TKI failure and a period of TKI holiday remains unclear. Through this retrospective study, we aimed to understand the impact of clinical factors in such patients. METHODS: Of 1386 cases diagnosed between December 2010 and December 2013, 80 EGFR-mutant NSCLC patients who were readministered TKIs after failure of first-line TKIs and intercalated with at least one cycle of cytotoxic agent were included. We evaluated clinical factors that may influence prognosis of TKI readministration as well as systemic inflammatory status in terms of neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR). Baseline NLR and LMR were estimated at the beginning of TKI readministration and trends of NLR and LMR were change amount from patients receiving first-Line TKIs to TKIs readministration. RESULTS: Median survival time since TKI readministration was 7.0 months. In the univariable analysis, progression free survival (PFS) of first-line TKIs, baseline NLR and LMR, and trend of LMR were prognostic factors in patients receiving TKIs readministration. In the multivariate analysis, only PFS of first-line TKIs (p < 0.001), baseline NLR (p = 0.037), and trend of LMR (p = 0.004) were prognostic factors. CONCLUSION: Longer PFS of first-line TKIs, low baseline NLR, and high trend of LMR were good prognostic factors in EGFR-mutant NSCLC patients receiving TKI readministration.

Prognostic significance of DSG3 in rectal adenocarcinoma treated with preoperative chemoradiotherapy.

AIM: This study aimed to investigate the prognostic significance of DSG3 and its association with response to neoadjuvant concurrent chemoradiotherapy (CCRT) in rectal cancer. MATERIALS & METHODS: Data mining of a publicly available dataset was performed to find genes associated with CCRT response. Immunohistochemistry was applied to evaluate DSG3 expression. The relationships between DSG3 expression and various clinicopathological parameters and survival were analyzed. RESULTS: The DSG3 gene was significantly associated with CCRT response. The expression of DSG3 negatively correlated with poorer tumor regression (p < 0.001) and had an independent negative impact on disease-specific survival (p = 0.011), local recurrence-free survival (p = 0.031) and metastasis-free survival (p = 0.029). CONCLUSION: DSG3 was a key prognostic factor and predictor for CCRT response in rectal cancer patients.

[Symptom Distress, Depression, and Quality of Life in Colorectal Cancer Patients at Different Disease Stages].

BACKGROUND: Quality of life is increasingly used as a primary outcome measure in studies that are designed to evaluate the effectiveness of treatment in cancer survivors. PURPOSE: Analyze the symptom distress, depression, and quality of life in colorectal cancer patients and explore the relationship of related variables with changes in QoL (quality of life) during and after treatment. METHODS: A cross-sectional study design was used for the present study. Patients (N = 138) with colorectal cancer were recruited from a district hospital in southern Taiwan. Data were collected using a self-report questionnaire. Questionnaire scales included the M.D. Anderson Symptom Inventory-Taiwan Form, the Center for Epidemiologic Studies Depression Scale, and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 Version 3 in Chinese as well as a demographic and disease-related variables datasheet. Descriptive data were presented using percentage, mean, and standard deviation. Chi-square test, independent t-test, one-way ANOVA, and hierarchical multiple regression were used for inferential statistics. RESULTS: The post-treatment group showed a significantly higher average global health QOL score (68.68 vs. 59.54; p < .05). Hierarchical regression showed that the impact factor of quality of life has a depressive effect in many dimensions. The second most significant variable was symptom distress. Symptoms interfered with life activity functions and family income and impacted negatively on patient treatment. In survivorship, depressive tendencies was the variable that was most affected, followed by recurrence, symptoms interference, and surgical treatment, respectively. When controlling for the relevant variables, these predictors accounted for 38.5% and 40.9% of the total variance of global health quality of life. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: This study demonstrates that personal characteristics variables, depressive tendencies, and symptom distress all impact on the quality of life of colorectal cancer patients in terms of receiving treatment and survivorship. These findings imply that healthcare professionals must provide appropriate emotional support in order to decrease depression tendency at different stages. Thus, these patients should receive nursing interventions that effectively decrease depression and symptom distress and enhance quality of life at different disease stages.

Construction and application of an intelligent air quality monitoring system for healthcare environment.

Indoor air quality monitoring in healthcare environment has become a critical part of hospital management and policy. Manual air sampling and analysis are cost-inhibitive and do not provide real-time air quality data and response measures. In this month-long study over 14 sampling locations in a public hospital in Taiwan, we observed a positive correlation between CO(2) concentration and population, total bacteria, and particulate matter concentrations, thus monitoring CO(2) concentration as a general indicator for air quality could be a viable option. Consequently, an intelligent environmental monitoring system consisting of a CO(2)/temperature/humidity sensor, a digital plug, and a ZigBee Router and Coordinator was developed and tested. The system also included a backend server that received and analyzed data, as well as activating ventilation and air purifiers when CO(2) concentration exceeded a pre-set value. Alert messages can also be delivered to offsite users through mobile devices.

Association of Decreased Bone Density and Hyperlipidemia in a Taiwanese Older Adult Population.

Objective: This study aimed to determine if a combination of 2 abnormal lipid profiles revealed a stronger association with low bone mass than a single blood lipid abnormality alone. Methods: This study enrolled 1373 participants who had received a dual-energy x-ray absorptiometry scan from January 2016 to December 2016 in a medical center in southern Taiwan. Logistic regression was used to examine association between lipid profiles and osteopenia or osteoporosis after adjusting for covariates. Results: Compared to people with total cholesterol (TC) < 200 mg/dL, those with TC ≥ 240 mg/dL tended to have osteopenia or osteoporosis (OR 2.61; 95% CI, 1.44-4.71). Compared to people with low-density lipoprotein cholesterol (LDL-C) < 130 mg/dL, those with LDL-C ≥ 160 mg/dL tended to develop osteopenia or osteoporosis (OR 2.13; 95% CI, 1.21-3.74). The association of increased triglyceride and decreased bone mass was similar, although not statistically significant. Those with the combination of TG ≥ 200 mg/dL and TC ≥ 240 mg/dL had a stronger tendency to have osteopenia or osteoporosis (OR 3.51; 95% CI, 1.11-11.13) than people with only one blood lipid abnormality. Similarly, people with TG ≥ 200 mg/dL and LDL-C ≥ 160 mg/dL had a stronger tendency to have osteopenia or osteoporosis (OR 9.31; 95% CI, 1.15-75.42) than people with only one blood lipid abnormality, after adjustment for the same covariates. Conclusion: Blood levels of TC, LDL-C, and TG were associated with osteopenia or osteoporosis. Results indicate that individuals aged older than 50 years with abnormal lipid profiles should be urged to participate in a bone density survey to exclude osteopenia or osteoporosis.

NME3 is a gatekeeper for DRP1-dependent mitophagy in hypoxia.

NME3 is a member of the nucleoside diphosphate kinase (NDPK) family localized on the mitochondrial outer membrane (MOM). Here, we report a role of NME3 in hypoxia-induced mitophagy dependent on its active site phosphohistidine but not the NDPK function. Mice carrying a knock-in mutation in the Nme3 gene disrupting NME3 active site histidine phosphorylation are vulnerable to ischemia/reperfusion-induced infarction and develop abnormalities in cerebellar function. Our mechanistic analysis reveals that hypoxia-induced phosphatidic acid (PA) on mitochondria is essential for mitophagy and the interaction of DRP1 with NME3. The PA binding function of MOM-localized NME3 is required for hypoxia-induced mitophagy. Further investigation demonstrates that the interaction with active NME3 prevents DRP1 susceptibility to MUL1-mediated ubiquitination, thereby allowing a sufficient amount of active DRP1 to mediate mitophagy. Furthermore, MUL1 overexpression suppresses hypoxia-induced mitophagy, which is reversed by co-expression of ubiquitin-resistant DRP1 mutant or histidine phosphorylatable NME3. Thus, the site-specific interaction with active NME3 provides DRP1 a microenvironment for stabilization to proceed the segregation process in mitophagy.

Traumatic brain injury and risk of heart failure and coronary heart disease: A nationwide population-based cohort study.

BACKGROUND: This study examined the long-term risks of heart failure (HF) and coronary heart disease (CHD) following traumatic brain injury (TBI), focusing on gender differences. METHODS: Data from Taiwan's National Health Insurance Research Database included 29,570 TBI patients and 118,280 matched controls based on propensity scores. RESULTS: The TBI cohort had higher incidences of CHD and HF (9.76 vs. 9.07 per 1000 person-years; 4.40 vs. 3.88 per 1000 person-years). Adjusted analyses showed a significantly higher risk of HF in the TBI group (adjusted hazard ratio = 1.08, 95% CI = 1.01-1.17, P = 0.031). The increased CHD risk in the TBI cohort became insignificant after adjustment. Subgroup analysis by gender revealed higher HF risk in men (aHR = 1.14, 95% CI = 1.03-1.25, P = 0.010) and higher CHD risk in women under 50 (aHR = 1.32, 95% CI = 1.15-1.52, P < 0.001). TBI patients without beta-blocker therapy may be at increased risk of HF. CONCLUSION: Our results suggest that TBI increases the risk of HF and CHD in this nationwide cohort of Taiwanese citizens. Gender influences the risks differently, with men at higher HF risk and younger women at higher CHD risk. Beta-blockers have a neutral effect on HF and CHD risk.