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.

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.

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.

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.

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.

Employing feature engineering strategies to improve the performance of machine learning algorithms on echocardiogram dataset.

Objectives: This study mainly uses machine learning (ML) to make predictions by inputting features during training and inference. The method of feature selection is an important factor affecting the accuracy of ML models, and the process includes data extraction, which is the collection of all data required for ML. It also needs to import the concept of feature engineering, namely, this study needs to label the raw data of the cardiac ultrasound dataset with one or more meaningful and informative labels so that the ML model can learn from it and predict more accurate target values. Therefore, this study will enhance the strategies of feature selection methods from the raw dataset, as well as the issue of data scrubbing. Methods: In this study, the ultrasound dataset was cleaned and critical features were selected through data standardization, normalization, and missing features imputation in the field of feature engineering. The aim of data scrubbing was to retain and select critical features of the echocardiogram dataset while making the prediction of the ML algorithm more accurate. Results: This paper mainly utilizes commonly used methods in feature engineering and finally selects four important feature values. With the ML algorithms available on the Azure platform, namely, Random Forest and CatBoost, a Voting Ensemble method is used as the training algorithm, and this study also uses visual tools to gain a clearer understanding of the raw data and to improve the accuracy of the predictive model. Conclusion: This paper emphasizes feature engineering, specifically on the cleaning and analysis of missing values in the raw dataset of echocardiography and the identification of important critical features in the raw dataset. The Azure platform is used to predict patients with a history of heart disease (individuals who have been under surveillance in the past three years and those who haven't). Through data scrubbing and preprocessing methods in feature engineering, the model can more accurately predict the future occurrence of heart disease in patients.

Increased intraoperative vein ligation in microsurgical varicocelectomy is associated with pain improvement.

Varicocele is a major cause of male infertility. However, few studies have discussed the potential associations between the pain caused by varicocele and preoperative and intraoperative factors. The aim of this study was to evaluate factors potentially associated with changes in pain score after microsurgical varicocelectomy. This retrospective study was conducted between August 2020 and August 2022 at China Medical University Hospital in Taichung, Taiwan. Patient characteristics including age, body mass index, semen analysis, testicular volume, and the number of veins ligated were collected. Preoperative and intraoperative factors were analyzed to determine if they were correlated with changes in numeric rating scale (NRS) after microsurgical varicocelectomy. A total of 44 patients with clinical varicocele underwent subinguinal microsurgical varicocelectomy and were analyzed. The overall pain resolution rate was 91%, and the average satisfaction score after surgery was 9.2 according to their subjective feelings. Multivariate analysis revealed that severe varicocele grade (odds ratio [OR] 16.5, 95% confidence interval [CI] 3.01-90.47; P = .018) and the number of veins ligated (OR 6, 95% CI 1.6-22.48; P = .013), were significantly associated with changes in NRS after surgery. In addition, the area under the receiver operating characteristic curve for changes in NRS and the total number of veins ligated was 0.869. Microsurgical varicocelectomy had a high success rate for scrotal pain and satisfaction. Severe varicocele grade and the number of veins ligated in microsurgical varicocelectomy were associated with postoperative pain improvement.

A model for predicting physical function upon discharge of hospitalized older adults in Taiwan-a machine learning approach based on both electronic health records and comprehensive geriatric assessment.

Background: Predicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan. Methods: Data was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression. Results: In total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission. Conclusion: The results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs.

Indoor CO2 monitoring in a surgical intensive care unit under visitation restrictions during the COVID-19 pandemic.

Background: Indoor CO2 concentration is an important metric of indoor air quality (IAQ). The dynamic temporal pattern of CO2 levels in intensive care units (ICUs), where healthcare providers experience high cognitive load and occupant numbers are frequently changing, has not been comprehensively characterized. Objective: We attempted to describe the dynamic change in CO2 levels in the ICU using an Internet of Things-based (IoT-based) monitoring system. Specifically, given that the COVID-19 pandemic makes hospital visitation restrictions necessary worldwide, this study aimed to appraise the impact of visitation restrictions on CO2 levels in the ICU. Methods: Since February 2020, an IoT-based intelligent indoor environment monitoring system has been implemented in a 24-bed university hospital ICU, which is symmetrically divided into areas A and B. One sensor was placed at the workstation of each area for continuous monitoring. The data of CO2 and other pollutants (e.g., PM2.5) measured under standard and restricted visitation policies during the COVID-19 pandemic were retrieved for analysis. Additionally, the CO2 levels were compared between workdays and non-working days and between areas A and B. Results: The median CO2 level (interquartile range [IQR]) was 616 (524-682) ppm, and only 979 (0.34%) data points obtained in area A during standard visitation were ≥ 1,000 ppm. The CO2 concentrations were significantly lower during restricted visitation (median [IQR]: 576 [556-596] ppm) than during standard visitation (628 [602-663] ppm; p < 0.001). The PM2.5 concentrations were significantly lower during restricted visitation (median [IQR]: 1 [0-1] µg/m3) than during standard visitation (2 [1-3] µg/m3; p < 0.001). The daily CO2 and PM2.5 levels were relatively low at night and elevated as the occupant number increased during clinical handover and visitation. The CO2 concentrations were significantly higher in area A (median [IQR]: 681 [653-712] ppm) than in area B (524 [504-547] ppm; p < 0.001). The CO2 concentrations were significantly lower on non-working days (median [IQR]: 606 [587-671] ppm) than on workdays (583 [573-600] ppm; p < 0.001). Conclusion: Our study suggests that visitation restrictions during the COVID-19 pandemic may affect CO2 levels in the ICU. Implantation of the IoT-based IAQ sensing network system may facilitate the monitoring of indoor CO2 levels.

Prediction of risk of ischemic heart disease in first-diagnosed systemic lupus erythematosus patients in taiwan: is air pollution exposure a risk factor?

BACKGROUND: Air pollution is a key public health factor with the capacity to induce diseases. The risk of ischemia heart disease (IHD) in those suffering from systemic lupus erythematosus (SLE) from air pollution exposure is ambiguous. This study aimed to: (1) determine the hazard ratio (HR) of IHD after the first-diagnosed SLE and (2) examine the effects of air pollution exposure on IHD in SLE for 12 years. METHODS: This is a retrospective cohort study. Taiwan's National Health Insurance Research Database and Taiwan Air Quality Monitoring data were used in the study. Cases first diagnosed with SLE in 2006 cases without IHD were recruited as the SLE group. We randomly selected an additional sex-matched non-SLE cohort, four times the size of the SLE cohort, as the control group. Air pollution indices by residence city per period were calculated as the exposure. Life tables and Cox proportional risk models of time-dependent covariance were used in the research. RESULTS: This study identified patients for the SLE group (n = 4,842) and the control group (n = 19,368) in 2006. By the end of 2018, the risk of IHD was significantly higher in the SLE group than in the control group, and risks peaked between the 6th and 9th year. The HR of incidence IHD in the SLE group was 2.42 times that of the control group. Significant correlations with risk of developing IHD were noted for sex, age, CO, NO2, PM10, and PM2.5, of which PM10 exposure had the highest risk of IHD incidence. CONCLUSIONS: Subjects with SLE were at a higher risk of IHD, especially those in the 6th to 9th year after SLE diagnosis. The advanced cardiac health examinations and health education plan should be recommended for SLE patients before the 6th year after SLE diagnosed.

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.

Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study.

Background: Insulin resistance (IR) is associated with diabetes mellitus, cardiovascular disease (CV), and mortality. Few studies have used machine learning to predict IR in the non-diabetic population. Methods: In this prospective cohort study, we trained a predictive model for IR in the non-diabetic populations using the US National Health and Nutrition Examination Survey (NHANES, from JAN 01, 1999 to DEC 31, 2012) database and the Taiwan MAJOR (from JAN 01, 2008 to DEC 31, 2017) database. We analysed participants in the NHANES and MAJOR and participants were excluded if they were aged <18 years old, had incomplete laboratory data, or had DM. To investigate the clinical implications (CV and all-cause mortality) of this trained model, we tested it with the Taiwan biobank (TWB) database from DEC 10, 2008 to NOV 30, 2018. We then used SHapley Additive exPlanation (SHAP) values to explain differences across the machine learning models. Findings: Of all participants (combined NHANES and MJ databases), we randomly selected 14,705 participants for the training group, and 4018 participants for the validation group. In the validation group, their areas under the curve (AUC) were all >0.8 (highest being XGboost, 0.87). In the test group, all AUC were also >0.80 (highest being XGboost, 0.88). Among all 9 features (age, gender, race, body mass index, fasting plasma glucose (FPG), glycohemoglobin, triglyceride, total cholesterol and high-density cholesterol), BMI had the highest value of feature importance on IR (0.43 for XGboost and 0.47 for RF algorithms). All participants from the TWB database were separated into the IR group and the non-IR group according to the XGboost algorithm. The Kaplan-Meier survival curve showed a significant difference between the IR and non-IR groups (p < 0.0001 for CV mortality, and p = 0.0006 for all-cause mortality). Therefore, the XGboost model has clear clinical implications for predicting IR, aside from CV and all-cause mortality. Interpretation: To predict IR in non-diabetic patients with high accuracy, only 9 easily obtained features are needed for prediction accuracy using our machine learning model. Similarly, the model predicts IR patients with significantly higher CV and all-cause mortality. The model can be applied to both Asian and Caucasian populations in clinical practice. Funding: Taichung Veterans General Hospital, Taiwan and Japan Society for the Promotion of Science KAKENHI Grant Number JP21KK0293.

Implementation of an Attention Mechanism Model for Facial Beauty Assessment Using Transfer Learning.

An important consideration in medical plastic surgery is the evaluation of the patient's facial symmetry. However, because facial attractiveness is a slightly individualized cognitive experience, it is difficult to determine face attractiveness manually. This study aimed to train a model for assessing facial attractiveness using transfer learning while also using the fine-grained image model to separate similar images by first learning features. In this case, the system can make assessments based on the input of facial photos. Thus, doctors can quickly and objectively treat patients' scoring and save time for scoring. The transfer learning was combined with CNN, Xception, and attention mechanism models for training, using the SCUT-FBP5500 dataset for pre-training and freezing the weights as the transfer learning model. Then, we trained the Chang Gung Memorial Hospital Taiwan dataset to train the model based on transfer learning. The evaluation uses the mean absolute error percentage (MAPE) value. The root mean square error (RMSE) value is used as the basis for experimental adjustment and the quantitative standard for the model's predictive. The best model can obtain 0.50 in RMSE and 18.5% average error in MAPE. A web page was developed to infer the deep learning model to visualize the predictive model.

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.

Big data and machine learning driven bioprocessing - Recent trends and critical analysis.

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

QSOFA and SOFA scores are valuable tools for predicting postoperative sepsis resulting from ureteroscopic lithotripsy (URSL).

The sequential organ failure assessment (SOFA) and quick sequential organ failure assessment (qSOFA) scores are new tools which are used to assess sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock Task Force. This study aimed to evaluate the feasibility of using the SOFA and qSOFA to predict post-ureteroscopic lithotripsy (URSL) sepsis. Patients who underwent URSL due to ureteral stone obstruction were retrospectively reviewed using SOFA and qSOFA scores. Patient characteristics including age, gender, comorbidities, American Society of Anesthesiologists Classification, stone burden, stone location, hydronephrosis status, infectious status, preoperative SOFA and qSOFA score were collected. Preoperative factors were analyzed to determine if they were correlated with postoperative sepsis. A total of 830 patients were included in this study, of whom 32 (3.9%) had postoperative sepsis. Multivariate analysis revealed that older age, proximal ureteral stones, severe hydronephrosis, and high preoperative qSOFA or SOFA score were significantly associated with postoperative sepsis. The areas under the curves of a qSOFA score ≥ 1 and SOFA score ≥ 2 for predicting postoperative sepsis were 0.754 and 0.823, respectively. Preoperative qSOFA and SOFA scores are convenient and effective for predicting post-URSL sepsis. Further preventive strategies should be performed in these high-risk patients.

Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments.

Diabetic foot ulcers (DFUs) are considered the most challenging forms of chronic ulcerations to handle their multifactorial nature. It is necessary to establish a comprehensive treatment plan, accurate, and systematic evaluation of a patient with a DFU. This paper proposed an image recognition of diabetic foot wounds to support the effective execution of the treatment plan. In the severity of a diabetic foot ulcer, we refer to the current qualitative evaluation method commonly used in clinical practice, developed by the International Working Group on the Diabetic Foot: PEDIS index, and the evaluation made by physicians. The deep neural network, convolutional neural network, object recognition, and other technologies are applied to analyze the classification, location, and size of wounds by image analysis technology. The image features are labeled with the help of the physician. The Object Detection Fast R-CNN method is applied to these wound images to build and train machine learning modules and evaluate their effectiveness. In the assessment accuracy, it can be indicated that the wound image detection data can be as high as 90%.

Next generation sequencing reveals miR-431-3p/miR-1303 as immune-regulating microRNAs for active tuberculosis.

OBJECTIVES: RNA therapeutics is an emerging field that widens the range of treatable targets and would improve disease outcome through bypassing the antibiotic bactericidal targets to kill Mycobacterium tuberculosis (M.tb). METHODS: We screened for microRNA with immune-regulatory functions against M.tb by next generation sequencing of peripheral blood mononuclear cells, followed by validation in an independent cohort. RESULTS: Twenty three differentially expressed microRNAs were identified between 12 active pulmonary TB patients and 4 healthy subjects, and 35 microRNAs before and after 6-month anti-TB therapy. Enriched predicted target pathways included proteoglycan, HIF-1 signaling, longevity-regulating, central carbon metabolism, and autophagy. We validated miR-431-3p down-regulation and miR-1303 up-regulation accompanied with corresponding changes in their predicted target genes in an independent validation cohort of 46 active TB patients, 30 latent TB infection subjects, and 24 non-infected healthy subjects. In vitro experiments of transfections with miR-431-3p mimic/miR-1303 short interfering RNA in THP-1 cells under ESAT-6 stimuli showed that miR-431-3p and miR-1303 were capable to augment and suppress autophagy/apoptosis/phagocytosis of macrophage via targeting MDR1/MMP16/RIPOR2 and ATG5, respectively. CONCLUSIONS: This study provides a proof of concept for microRNA-based host-directed immunotherapy for active TB disease. The combined miR-431-3p over-expression and miR-1303 knock-down revealed new vulnerabilities of treatment-refractory TB disease.

Berberine protects cardiac cells against ferroptosis.

Objectives: Cardiovascular diseases are one of the primary causes of death. Cardiomyocyte loss is a significant feature of cardiac injury. Ferroptosis is iron-dependent cell death, which occurs due to excess iron and reactive oxygen species (ROS) accumulation causing lipid peroxidation, and subsequent cell death. Ferroptosis has been confirmed to mediate ischemia/reperfusion-induced cardiomyopathy and chemotherapy-induced cardiotoxicity. Berberine (BBR) has been proven to protect the heart from cardiomyopathies, including cardiac hypertrophy, heart failure, myocardial infarction, and arrhythmias. It protects cardiomyocytes from apoptosis and autophagy. However, the relation between BBR and ferroptosis is still unknown. This study aimed to confirm if BBR reduces cardiac cell loss via inhibiting ferroptosis. Materials and Methods: We used erastin and Ras-selective lethal small molecule 3 (RSL3) to establish a ferroptosis model in an H9c2 cardiomyoblast cell line and rat neonatal cardiomyocytes to prove that BBR has a protective effect on cardiac cells via inhibiting ferroptosis. Results: In H9c2 cardiomyoblasts, the results showed that BBR reduced erastin and RSL3-induced cell viability loss. Moreover, BBR decreased ROS accumulation and lipid peroxidation in cells induced with ferroptosis. Furthermore, quantitative polymerase chain reaction results showed that Ptgs2 mRNA was reduced in BBR-treated cells. In rat neonatal cardiomyocytes, BBR reduced RSL3-induced loss of cell viability. Conclusion: These results indicated that BBR inhibited ferroptosis via reducing ROS generation and reducing lipid peroxidation in erastin and RSL3-treated cardiac cells.

Effectiveness and response differences of a multidisciplinary workplace health promotion program for healthcare workers.

Background: Workplace health promotion (WHP) in the healthcare industry is an important yet challenging issue to address, given the high workload, heterogeneity of work activities, and long work hours of healthcare workers (HCWs). This study aimed to investigate the effectiveness and response differences of a multidisciplinary WHP program conducted in HCWs. Methods: This retrospective cohort study included HCWs participating in a multidisciplinary WHP program in five healthcare facilities. The 20-week intervention included multiple easy-to-access 90-min exercise classes, one 15-min nutrition consultation, and behavioral education. Pre- and post-interventional anthropometrics, body composition, and physical fitness (PF) were compared with paired sample t-tests. Response differences across sex, age, weight status, and shiftwork status were analyzed with a generalized estimating equation. Results: A total of 302 HCWs were analyzed. The intervention effectively improved all anthropometric (body mass index, waist circumference, waist-hip ratio, and waist-to-height ratio), body composition (body fat percentage, muscle weight, visceral fat area), and PF (grip strength, high jump, sit-up, sit-and-reach, step test) parameters in all participants (all p < 0.05). Subgroup analyses revealed shift workers had a more significant mean reduction in body mass index than non-shift workers (adjusted p = 0.045). However, there was no significant response difference across sex, age, and weight subgroups. Conclusion: This study suggested that a multidisciplinary WHP program can improve anthropometric and PF profiles regardless of sex, age, and weight status for HCWs, and shifter workers might benefit more from the intervention.