microRNA-874 inhibition targeting STAT3 protects the heart from ischemia-reperfusion injury by attenuating cardiomyocyte apoptosis in a mouse model.

MicroRNAs (miRs) were involved in numerous cardiovascular diseases, especially ischemic heart diseases, but the miR changes during cardiac ischemia-reperfusion (I/R) injury following sevoflurane (SEV) preconditioning are still unknown. This study aims to investigate the effect of miR-874 on cardiac I/R injury in mouse models pretreated with SEV. Following establishment of mouse models with myocardial I/R injury, mice were pretreated with SEV. The functional mechanism of miR-874 in I/R injury was explored when miR-874 and the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathway were inhibited. Terminal deoxynucleotidyl transferase (TdT)-mediated dUTP biotin nick-end labeling (TUNEL) staining was used to detect cardiomyocyte apoptosis and dual luciferase reporter gene assay to identify the targeting relationship between miR-874 and STAT3. Expression of the JAK2/STAT3 signaling pathway and apoptosis-related genes was determined. Initially, upregulated miR-874 was observed in I/R mice. Then, miR-874 inhibition improved cardiac function of I/R mice, inhibited cardiomyocyte apoptosis (also shown as decreased Bcl-2 associated X protein B [Bax] and increased B-cell lymphoma-2 [Bcl-2]), and activated the JAK2/STAT3 signaling pathway. STAT3, a target gene of miR-874, was upregulated following miR-874 inhibition. Finally, we also observed that the effect of miR-874 was lost when the JAK2/STAT3 signaling pathway was blocked. The findings indicate miR-874 as a contributory role in cardiac I/R injury, with miR-874 inhibition alleviating cardiac I/R injury in mice following SEV pretreatment by targeting STAT3 through the JAK2/STAT3 signaling pathway.

Astragaloside suppresses tumor necrosis factor receptor-associated factor 5 signaling pathway and alleviates neurodegenerative changes in retinal pigment epithelial cells induced by isoflurane.

Epidemiological studies showed that isoflurane, a general anesthetic widely used in surgery including those for the children, is associated with impairment of neurodevelopment and neurodegenerative diseases, such as Alzheimer's disease (AD) and age-related macular degeneration (AMD), which are related to the accumulation of reactive oxygen species (ROS). Astragaloside (AS) is an antioxidant derivative from a traditional Chinese herbal medicine Astragalus membraneaceus Bunge. In this study, we used retinal pigment epithelial cells, which share plenty of features with neurodegenerative diseases such as AD and AMD to investigate the effect of AS. Cell cycle re-entry and proapoptosis were seen in retinal pigment epithelium (RPE) cells treated with isoflurane, which was alleviated by pretreatment of AS. Further, tumor necrosis factor receptor-associated factor 5 (TRAF5) and downstream nuclear factor-κB (NF-κB) were investigated to elucidate the molecular mechanism underlying protective effect of AS. RPE cells exposed to isoflurane expressed higher TRAF5 and NF-κB than those pretreated with AS, suggesting a critical role of TRAF5 therein. In Morris water maze (MWM) assay, Sprague-Dawley rats pretreated with AS and then exposed to isoflurane spent less time in swimming to the platform, and their TRAF5 expression was significantly lower than those received anesthesia alone. Further studies on the consequence of forced downregulation or upregulation are warranted that may employ cutting-edge technologies such as optogenetics to overcome the difficulties in manipulating expression of TRAF5. Although the link between TRAF5 and neurodegeneration requires more in-depth investigations, our study provide a novel hint on the pathological mechanism of isoflurane and suggest a potential target for eliminating persistent side effect of anesthesia.

Comprehensive analysis of the clinical significance and molecular mechanism of T-box transcription factor 3 in osteosarcoma.

Background: T-box transcription factor 3 (TBX3) has been implicated in various malignant tumors, while its exact involvement in osteosarcoma (OS) remains unknown. Methods: Utilizing microarray data and bulk and single-cell RNA-seq data and qRT-PCR, we compared TBX3 mRNA expression levels in different stages of OS. Diagnostic ability testing and prognosis analysis were conducted to better understand the clinical importance of TBX3. Enrichment analysis was performed using gene groups with biological functions similar to TBX3 in different stages of OS to investigate the potential role of TBX3 in OS progression. In addition, we predicted medications targeted at TBX3 and identified downstream target genes to gain a comprehensive understanding of its therapeutic direction and regulatory mechanism. Results: TBX3 expression was highly upregulated in OS and was predominantly expressed in osteoblastic OS cells, with higher expression levels in metastatic tissues. TBX3 expression appeared somewhat suitable for discriminating between OS and normal samples, as well as different stages of OS. We found that TBX3 increased the malignant development of OS by altering cell cycle and cell adhesion molecules; exisulind and tacrolimus, which are targeted small-molecule medicines, were anticipated to counteract this dysregulation. The expression of CCNA2 could potentially be regulated by TBX3, contributing to OS advancement. Conclusion: TBX3 emerges as a potential biomarker for OS. In-depth research into its underlying molecular processes may offer new perspectives on treating OS.

Characteristics of "do not resuscitate" orders among elderly patients receiving mechanical ventilation in the intensive care unit in Taiwan.

Objective: As patient life expectancy has increased and people are living longer than before, the rate of mechanical ventilation among elderly patients in the intensive care unit has increased. Older patients who receive mechanical ventilation and have multiple comorbidities are more likely to have a do not resuscitate order than are younger patients with fewer comorbidities. The aim of our study was to describe the patient characteristics and predictive factors of do not resuscitate orders during hospitalization among elderly patients who received ventilation in the intensive care unit. Methods: This was a retrospective review of the electronic medical records of patients in the intensive care unit of a teaching hospital in southern Taiwan. We enrolled patients admitted to the general intensive care unit from January 1, 2018, to September 31, 2020, and patients older than 80 years who experienced respiratory failure, were intubated and received mechanical ventilation. We analyzed patient demographics, disease severity during hospitalization and comorbidities. If a patient had multiple admissions to the intensive care unit, only the first admission was recorded. Results: Of the 305 patients over 80 years of age with respiratory failure who were intubated and placed on a ventilator, 66 were excluded because of incomplete data, and 13 were excluded because they had already signed a do not resuscitate order prior to admission to the hospital. Ultimately, 226 patients were included in this study. A higher acute physiology and chronic health evaluation II score (>30) was also associated with an increased likelihood of a do not resuscitate order (odds ratio (OR) = 3.85, 95% CI = 1.09-13.62, p = 0.0362). Patients who had acute kidney injury or cerebrovascular accident were more likely to have a do not resuscitate order (OR = 2.74, 95% CI = 1.03-7.28, p = 0.0428 and OR = 7.32, 95% CI = 2.02-26.49, p = 0.0024, respectively). Conclusion: Our study showed that older age, greater disease severity, and certain critical interventions were associated with a greater propensity for do not resuscitate orders, which is crucial for understanding patient preferences and guiding end-of-life care discussions. These findings highlight the importance of clinical severity and specific health events in predicting end-of-life care preferences in older patient groups.

Research on the application of inertially stabilized platform in the dynamic measurement of cold atomic gravimeter.

Dynamic gravity field measurement based on the cold atom absolute gravity measurement system has important applications in geological exploration, gravity field mapping and other fields. The inertial stabilized platform is the key component of the dynamic cold atom absolute gravity measurement system, which can isolate the interference of carrier angle motion and keep the atomic gravimeter probe in the horizontal attitude during the measurement process. In this paper, according to the dynamic measurement requirements of atomic gravimeter, a high-precision two-axis inertial stabilized platform system is designed. The relationship between attitude angle and gravity measurement error is analyzed, and the stability of the system is enhanced by lead-lag method. Then the static vertical vibration power spectrum of the platform is measured to consider its influence on dynamic gravity measurement. Finally, a dynamic gravity test experiment was conducted in the Yellow Sea to verify the attitude control accuracy of the platform, and the attitude data of the platform under different heading were evaluated. The attitude standard deviation of the platform was better than 4 × 10-5 rad, and the absolute gravity standard deviation of the linear round-trip measurement reached 1.49 mGal. The experimental data show that the inertial stabilized platform can meet the dynamic measurement requirements of the cold atom gravimeter.

The Incidence, Mortality and Medical Expenditure in Patients with Asthma in Taiwan: Ten-year Nationwide Study.

BACKGROUND: This study examines incidence, mortality, medical expenditure and prescription patterns for asthma on a national scale, particularly in Asian countries for asthma is limited. Our aim is to investigate incidence, mortality, prescription patterns and provide a comprehensive overview of healthcare utilization trends for asthma from 2009 to 2018. METHODS: We included patients diagnosed with asthma between 2009 and 2018. We excluded patients with missing demographic data. Our analysis covered comorbidities, including diabetes mellitus, hypertension, allergic rhinitis, eczema, atopic dermatitis, coronary artery disease, congestive heart failure, chronic kidney disease, chronic hepatitis, stroke, and cancer. Investigated medications comprised oral and intravenous steroids, short-acting beta-agonists, inhaled corticosteroids (ICS), combinations of ICS and long-acting beta-agonists, long-acting muscarinic antagonists, and leukotriene receptor antagonists montelukast. We also assessed the number of outpatient visits, emergency visits, and hospitalizations per year, as well as the average length of hospitalization and average medical costs. RESULTS: The study included a final count of 88,244 subjects from 1,998,311 randomly selected samples between 2000 and 2019. Over the past decade, there was a gradual decline in newly diagnosed asthma patients per year, from 10,140 to 6,487. The mean age annually increased from 47.59 in 2009 to 53.41 in 2018. Over 55% of the patients were female. Eczema was diagnosed in over 55% of the patients. Around 90% of the patients used oral steroids, with a peak of 97.29% in 2018, while the usage of ICS varied between 86.20% and 91.75%. Intravenous steroids use rose from 40.94% in 2009 to 54.14% in 2018. The average annual hospital stay ranged from 9 to 12 days, with a maximum of 12.26 days in 2013. Lastly, the average medical expenses per year ranged from New Taiwan dollars 5558 to 7921. CONCLUSIONS: In summary, both asthma incidence and all-cause mortality rates decreased in Taiwan from 2009 to 2018. Further analysis of medical expenses in patients with asthma who required multiple hospitalizations annually revealed an increase in outpatient and emergency visits and hospitalizations, along with longer hospital stays and higher medical costs.

Prescribing patterns in patients with chronic obstructive pulmonary disease and atrial fibrillation.

Patients with chronic obstructive pulmonary disease (COPD) had higher risk of atrial fibrillation (AF). The treatment of AF includes medicines to control heart rate and reduce the risk of stroke, and procedures such as cardioversion to restore normal heart rhythm. To reduce the stroke, patients with AF may prescribe some type of antithrombotic medication (such as warfarin, one of the new non-vitamin K antagonist oral anticoagulants [NOACs] - dabigitran, apixaban, rivoraxaban, or edoxaban) or maybe aspirin. The aim of our study was to exam the prescription pattern in patients with COPD and AF. We selected COPD population in Taiwan older than 40 years and less than 90 years old with an COPD diagnosis at least two outpatient claims or at least one inpatient claim coded and also need at least one prescription of bronchodilators. We followed this COPD cohort until they have AF and their prescription pattern. We included 267,740 patients with COPD who meet the inclusion and exclusion criteria and 6,582 patients concomitant with COPD and AF. The mean age was 75 years, and about 77% of the patients were older than 70 years. Three-fourths of patients with COPD were male. The common comorbidities were hypertension (17.58%), diabetes (7.47%), ischemic heart disease (4.66%), and dyslipidemia (3.68%). we found that most patients received aspirin which accounting for 31%, followed by coumadin (8.22%) and clopidogrel. Prescribing NOAC within 30 days after AF diagnosis was low in patients with COPD and the percentage of NOAC usage was also lower than warfarin.

TAF1D Functions as a Novel Biomarker in Osteosarcoma.

Background: The most frequent primary bone cancer in teenagers, osteosarcoma (OS), is particularly aggressive with a high mortality rate. Methods: By combining public databases, OS and non-cancer samples were obtained. The Wilcoxon test and standardized mean difference (SMD) were utilized to evaluate the mRNA expression level of TATA-box binding protein associated factor, RNA polymerase 1 subunit D (TAF1D). The potential of TAF1D to discriminate OS samples from non-cancer samples was revealed by summary receiver operating characteristic curve (sROC). To investigate the prognostic significance, Kaplan‒Meier curve and univariate Cox analysis were performed. Immunohistochemistry (IHC) was used to determine the TAF1D protein expression level. ESTIMATE algorithm and TIMER2.0 database were used to reveal the association between TAF1D expression and the immune microenvironment. Enrichment analysis and potential drug prediction were performed to clarify the underlying molecular mechanisms and possible therapeutic directions of TAF1D. Ultimately, the transcription factors (TFs) and the TAF1D binding site were predicted based on the Cistrome and JASPAR databases. Results: TAF1D was upregulated in OS at the mRNA and protein levels and possessed robust discriminatory power. TAF1D upregulation was suggestive of worse prognosis and enhancement of tumor purity in OS patients. The cell cycle was the most significantly enriched pathway, and NU.1025 was considered to be the potential target agent. Finally, MYC was identified as a TF that regulates the expression of TAF1D. Conclusions: Altogether, TAF1D has the potential to serve as a biological marker and therapeutic target in OS, which could offer new perspectives for OS treatment.

Diagnostic value of the dual-modal imaging radiomics model for subpleural pulmonary lesions.

PURPOSE: To investigate the clinical value of the radiomics model of grayscale ultrasound (GUS) and contrast-enhanced ultrasound (CEUS) to diagnosis subpleural pulmonary tuberculosis and nonpulmonary tuberculosis based on GUS and CEUS images. METHODS: This study included 221 patients with 228 lesions diagnosed using the composite reference standard. The patients were randomly divided into training (n = 183) and test (n = 45) cohorts in an 8:2 ratio. The regions of interest of the GUS and CEUS images were manually segmented to extract the radiomic features. The GUS, CEUS and GUS+CEUS radiomics models were constructed via the multistep selection of highly correlated features. Receiver operating characteristic curves of the different models were plotted, and the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value (NPV) of the different models were compared. RESULTS: Following Least Absolute Shrinkage and Selection Operator dimension reduction we selected 4, 9, and 11 features to construct the GUS, CEUS, and GUS+CEUS radiomics models, respectively. The AUC values of the three groups in the test cohort were 0.689, 0.748 and 0.779, respectively, and they did not differ significantly. In the test cohort, the GUS+CEUS radiomics model exhibited the highest AUC (0.779), accuracy (75.56%), and NPV (68.7%) of the three models. CONCLUSIONS: The GUS+CEUS radiomics model possesses good clinical value in diagnosing pulmonary tuberculosis.

Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records.

OBJECTIVE: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. MATERIALS AND METHODS: The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. RESULTS: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. CONCLUSION: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies.