Palmitic acid methyl ester induces cardiac hypertrophy through activating the GPR receptor-mediated changes of intracellular calcium concentrations and mitochondrial functions.

Myocardial hypertrophy is associated with a significant increase in intracellular Ca2+ , which can be induced by long-chain fatty acid. Palmitic acid methyl ester (PAME), a fatty acid ester released from adipose tissue, superior cervical ganglion, and retina, has been found to have anti-inflammation, antifibrosis, and peripheral vasodilation effects. However, the effects of PAME on cardiomyocytes are still unclear. The aim of this study was to determine whether PAME could disrupt the intracellular Ca2+ balance, leading to cardiomyocyte hypertrophy. Neonatal rat cardiomyocytes were treated with various concentrations (10-100 µM) of PAME for 1-4 days. Cytosolic Ca2+ and mitochondrial Ca2+ concentrations were examined using Fura-2 AM and Rhod-2, respectively. After treatment with PAME for 4 days, mitochondrial Ca2+ , an indicator of the state of mitochondrial permeability transition pore (MPTP), and cell death were monitored by flow cytometric analysis. ATP levels were detected using the ATP assay kit. Cardiomyocyte hypertrophy was analyzed by measuring the cardiac hypertrophy biomarker and cell area using quantitative real time-polymerase chain reaction, Western Blot analysis and immunofluorescence analysis. Our results show that PAME concentration- and time-dependently increased cytosolic and mitochondria Ca2+ through the mitochondrial calcium uniporter. Moreover, treatment with PAME for 4 days caused MPTP opening, thereby reducing ATP production and enhancing reactive oxygen species (ROS) generation, and finally led to cardiomyocyte hypertrophy. These effects caused by PAME treatment were attenuated by the G-protein coupled receptor 40 (GPR40) inhibitor. In conclusion, PAME impaired mitochondrial function, which in turn led to cardiomyocyte hypertrophy through increasing the mitochondrial Ca2+ levels mediated by activating the GPR40 signaling pathway.

Application of machine learning-based models to boost the predictive power of the SPAN index.

BACKGROUND: This study re-explored the predictive validity of Stroke Prognostication using Age and National Institutes of Health Stroke Scale (SPAN) index in patients who received different treatments for acute ischemic stroke (AIS) and developed machine learning-boosted outcome prediction models. METHODS: We evaluated the prognostic relevance of SPAN index in patients with AIS who received intravenous tissue-type plasminogen activator (IV-tPA), intra-arterial thrombolysis (IAT) or non-thrombolytic treatments (non-tPA), and applied machine learning algorithms to develop SPAN-based outcome prediction models in a cohort of 2145 hospitalized AIS patients. The performance of the models was assessed and compared using the area under the receiver operating characteristic curves (AUCs). RESULTS: SPAN index ≥100 was associated with higher mortality rate and higher modified Rankin Scale at discharge in AIS patients who received the different treatments. Compared to the lower AUCs for the SPAN-alone model across all groups, the AUCs of the logistic regression-boosted model were 0.838, 0.857, 0.766 and 0.875 for the whole cohort, non-tPA, IV-tPA and IAT groups, respectively. Similarly, the AUCs of the generated artificial neural network were 0.846, 0.858, 0.785 and 0.859 for the whole cohort, non-tPA, IV-tPA and IAT groups, respectively, while for gradient boosting decision tree model, we computed 0.850, 0.863, 0.779 and 0.815. CONCLUSIONS: SPAN index has prognostic relevance in patients with AIS who received different treatments. The generated machine learning-based models exhibit good performance for predicting the functional recovery of AIS; thus, their proposed clinical application to aid outcome prediction and decision-making for the patients with AIS.

Plasma extracellular vesicles tau and ß-amyloid as biomarkers of cognitive dysfunction of Parkinson's disease.

The contribution of circulatory tau and ß-amyloid in Parkinson's disease (PD), especially the cognitive function, remains inconclusive. Extracellular vesicles (EVs) cargo these proteins throughout the bloodstream after they are directly secreted from many cells, including neurons. The present study aims to investigate the role of the plasma EV-borne tau and ß-amyloid as biomarkers for cognitive dysfunction in PD by investigating subjects with mild to moderate stage of PD (n = 116) and non-PD controls (n = 46). Plasma EVs were isolated, and immunomagnetic reduction-based immunoassay was used to assess the levels of α-synuclein, tau, and ß-amyloid 1-42 (Aß1-42) within the EVs. Artificial neural network (ANN) models were then applied to predict cognitive dysfunction. We observed no significant difference in plasma EV tau and Aß1-42 between PD patients and controls. Plasma EV tau was significantly associated with cognitive function. Moreover, plasma EV tau and Aß1-42 were significantly elevated in PD patients with cognitive impairment when compared to PD patients with optimal cognition. The ANN model used the plasma EV α-synuclein, tau, and Aß1-42, as well as the patient's age and gender, as predicting factors. The model achieved an accuracy of 91.3% in identifying cognitive dysfunction in PD patients, and plasma EV tau and Aß1-42 are the most valuable factors. In conclusion, plasma EV tau and Aß1-42 are significant markers of cognitive function in PD patients. Combining with the plasma EV α-synuclein, age, and sex, plasma EV tau and Aß1-42 can identify cognitive dysfunction in PD patients. This study corroborates the prognostic roles of plasma EV tau and Aß1-42 in PD.

Exploring the COVID-19 Pandemic as a Catalyst for Behavior Change Among Patient Health Record App Users in Taiwan: Development and Usability Study.

BACKGROUND: During the COVID-19 pandemic, personal health records (PHRs) have enabled patients to monitor and manage their medical data without visiting hospitals and, consequently, minimize their infection risk. Taiwan's National Health Insurance Administration (NHIA) launched the My Health Bank (MHB) service, a national PHR system through which insured individuals to access their cross-hospital medical data. Furthermore, in 2019, the NHIA released the MHB software development kit (SDK), which enables development of mobile apps with which insured individuals can retrieve their MHB data. However, the NHIA MHB service has its limitations, and the participation rate among insured individuals is low. OBJECTIVE: We aimed to integrate the MHB SDK with our developed blockchain-enabled PHR mobile app, which enables patients to access, store, and manage their cross-hospital PHR data. We also collected and analyzed the app's log data to examine patients' MHB use during the COVID-19 pandemic. METHODS: We integrated our existing blockchain-enabled mobile app with the MHB SDK to enable NHIA MHB data retrieval. The app utilizes blockchain technology to encrypt the downloaded NHIA MHB data. Existing and new indexes can be synchronized between the app and blockchain nodes, and high security can be achieved for PHR management. Finally, we analyzed the app's access logs to compare patients' activities during high and low COVID-19 infection periods. RESULTS: We successfully integrated the MHB SDK into our mobile app, thereby enabling patients to retrieve their cross-hospital medical data, particularly those related to COVID-19 rapid and polymerase chain reaction testing and vaccination information and progress. We retrospectively collected the app's log data for the period of July 2019 to June 2021. From January 2020, the preliminary results revealed a steady increase in the number of people who applied to create a blockchain account for access to their medical data and the number of app subscribers among patients who visited the outpatient department (OPD) and emergency department (ED). Notably, for patients who visited the OPD and ED, the peak proportions with respect to the use of the app for OPD and ED notes and laboratory test results also increased year by year. The highest proportions were 52.40% for ED notes in June 2021, 88.10% for ED laboratory test reports in May 2021, 34.61% for OPD notes in June 2021, and 41.87% for OPD laboratory test reports in June 2021. These peaks coincided with Taiwan's local COVID-19 outbreak lasting from May to June 2021. CONCLUSIONS: This study developed a blockchain-enabled mobile app, which can periodically retrieve and integrate PHRs from the NHIA MHB's cross-hospital data and the investigated hospital's self-pay medical data. Analysis of users' access logs revealed that the COVID-19 pandemic substantially increased individuals' use of PHRs and their health awareness with respect to COVID-19 prevention.

Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks.

BACKGROUND: To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN). METHODS: The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively. CONCLUSION: The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making.

Extracorporeal Shockwave Therapy Modulates the Expressions of Proinflammatory Cytokines IL33 and IL17A, and Their Receptors ST2 and IL17RA, within the Articular Cartilage in Early Avascular Necrosis of the Femoral Head in a Rat Model.

Avascular necrosis (AVN) of the femoral head (AVNFH) is a disease caused by injury to the blood supply of the femoral head, resulting in a collapse with osteonecrosis and damage to the articular cartilage. Extracorporeal shockwave therapy (ESWT) has been demonstrated to improve AVNFH owing to its anti-inflammation activity, angiogenesis effect, and tissue regeneration in clinical treatment. However, there are still so many pieces of the jigsaw that need to be fit into place in order to ascertain the mechanism of ESWT for the treatment of AVNFH. The study demonstrated that ESWT significantly protected the trabecular bone volume fraction BV/TV (P < 0.01) and the trabecular thickness (P < 0.001), while in contrast, the trabecular number and trabecular separation were not significantly different after treatment as compared with AVNFH. ESWT protected the articular cartilage in animal model of AVNFH. The levels of IL1-ß and IL33 were significantly induced in the AVNFH group (P < 0.001) as compared with Sham and ESWT groups and reduced in ESWT group (P < 0.001) as compared with AVNFH group. In addition, the expression of the receptor of IL33, ST2, was reduced in AVNFH and induced after ESWT (P < 0.001). The expression of IL17A was induced in the AVNFH group (P < 0.001) and reduced in the ESWT group (P < 0.001). Further, the expression of the receptor of IL17A, IL17RA, was reduced in the AVNFH group (P < 0.001) and improved to a normal level in the ESWT group as compared with Sham group (P < 0.001). Taken together, the results of the study indicated that ESWT modulated the expression of IL1-ß, pro-inflammatory cytokines IL33 and IL17A, and their receptors ST2 and IL17RA, to protect against loss of the extracellular matrix in the articular cartilage of early AVNFH.

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.

PURPOSE: Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation. MATERIALS AND METHODS: The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation. RESULTS: The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided. CONCLUSIONS: Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

Correction to: Risk reduction of long-term major adverse cardiovascular events after endoscopic thoracic sympathectomy in palmar hyperhidrosis.

There is a typographical error in the International Classification of Disease Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic code used for palmar hyperhidrosis. The published manuscript wrongly reports that the ICD-9-CM code used for palmar hyperhidrosis was 708.8 (which indicates a diagnosis of "Other specified urticaria"), when, in actuality, the correct code 780.8 ("Hyperhidrosis") was applied. The authors regret this typographical error.

Risk reduction of long-term major adverse cardiovascular events after endoscopic thoracic sympathectomy in palmar hyperhidrosis.

PURPOSE: Palmar hyperhidrosis (PH) is excessive sweating of the palms resulting from sympathetic overactivity, and patients who undergo endoscopic thoracic sympathectomy (ETS) show reduced cardiac demand after 1 year and improved cerebral perfusion within 2-4 weeks. However, the long-term risks of major adverse cardiovascular events (MACE) following ETS remain unclear. METHODS: We searched the Longitudinal National Health Insurance Database in Taiwan and identified PH patients (International Classification of Disease, Ninth Revision, Clinical Modification diagnostic code 708.8) from the outpatient database and patients who underwent ETS (procedure code 05.29) from the inpatient database between 2000 and 2010; furthermore, we excluded patients younger than 18 years of age or older than 65 years of age. We defined MACE as stroke (diagnostic codes 430-437), myocardial infarction (diagnostic code 410), or death. Patients followed until the first cardiac event or December 31, 2010. Risk factors were identified using a multivariable Cox proportional hazards regression. RESULTS: The incidence of MACE was significantly lower in patients with ETS (0.76%) than without (1.67%). In PH patients, ETS significantly reduced the risk of MACE (adjusted hazard ratio 0.473; 95% confidence interval 0.277-0.808). CONCLUSION: PH patients who underwent ETS showed a reduced risk of MACE over a long-term follow-up period. This result could provide support for patients with PH who are considering undergoing ETS because of its additional cardiovascular benefits.

Importance-Performance Analysis of Personal Health Records in Taiwan: A Web-Based Survey.

BACKGROUND: Empowering personal health records (PHRs) provides basic human right, awareness, and intention for health promotion. As health care delivery changes toward patient-centered services, PHRs become an indispensable platform for consumers and providers. Recently, the government introduced "My health bank," a Web-based electronic medical records (EMRs) repository for consumers. However, it is not yet a PHR. To date, we do not have a platform that can let patients manage their own PHR. OBJECTIVE: This study creates a vision of a value-added platform for personal health data analysis and manages their health record based on the contents of the "My health bank." This study aimed to examine consumer expectation regarding PHR, using the importance-performance analysis. The purpose of this study was to explore consumer perception regarding this type of a platform: it would try to identify the key success factors and important aspects by using the importance-performance analysis, and give some suggestions for future development based on it. METHODS: This is a cross-sectional study conducted in Taiwan. Web-based invitation to participate in this study was distributed through Facebook. Respondents were asked to watch an introductory movie regarding PHR before filling in the questionnaire. The questionnaire was focused on 2 aspects, including (1) system functions, and (2) system design and security and privacy. The questionnaire would employ 12 and 7 questions respectively. The questionnaire was designed following 5-points Likert scale ranging from 1 ("disagree strongly") to 5 ("Agree strongly"). Afterwards, the questionnaire data was sorted using IBM SPSS Statistics 21 for descriptive statistics and the importance-performance analysis. RESULTS: This research received 350 valid questionnaires. Most respondents were female (219 of 350 participants, 62.6%), 21-30 years old (238 of 350 participants, 68.0%), with a university degree (228 of 350 participants, 65.1%). They were still students (195 out of 350 participants, 56.6%), with a monthly income of less than NT $30,000 (230 of 350 participants, 65.7%), and living in the North Taiwan (236 of 350 participants, 67.4%), with a good self-identified health status (171 of 350 participants, 48.9%). After performing the importance-performance analysis, we found the following: (1) instead of complex functions, people just want to have a platform that can let them integrate and manage their medical visit, health examination, and life behavior records; (2) they do not care whether their PHR is shared with others; and (3) most of the participants think the system security design is not important, but they also do not feel satisfied with the current security design. CONCLUSIONS: Overall, the issues receiving the most user attention were the system functions, circulation, integrity, ease of use, and continuity of the PHRs, data security, and privacy protection.

Metadata Correction: Importance-Performance Analysis of Personal Health Records in Taiwan: A Web-Based Survey.

[This corrects the article DOI: .].

Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation.

BACKGROUND/PURPOSE: Radiofrequency ablation (RFA) provides an effective treatment for patients who exhibit early hepatocellular carcinoma (HCC) stages or are waiting for liver transplantation. It is important to assess patients after RFA. The goal of this study was to build artificial neural network models with HCC-related variables to predict the 1-year and 2-year disease-free survival (DFS) of HCC patients receiving RFA treatments. METHODS: This study was a retrospective study that tracked HCC patients who received computer tomography-guided percutaneous RFA between January 2009 and April 2012. The numbers of total patients with 1-year and 2-year DFS were 252 and 179, respectively. A total of 15 HCC clinical variables were collected for the construction of artificial neural network models for DFS prediction. Internal validation and validation conducted using simulated prospective data were performed. RESULTS: The results showed that the model with 15 inputs showed better performance compared with the models including only significant features. Parameters for performance assessment of 1-year DFS prediction were as follows: accuracy 85.0% (70.0%), sensitivity 75.0% (63.3%), specificity 87.5% (71.8%), and area under the curve 0.84 (0.77) for internal validation (simulated prospective validation). For 2-year DFS prediction, the values of accuracy, sensitivity, specificity, and area under the curve were 67.9% (63.9%), 50.0% (56.3%), 85.7% (70.0%), and 0.75 (0.72), respectively, for internal validation (simulated prospective validation). CONCLUSION: This study revealed that the proposed artificial neural network models constructed with 15 clinical HCC relevant features could achieve an acceptable prediction performance for DFS. Such models can support clinical physicians to deal with clinical decision-making processes on the prognosis of HCC patients receiving RFA treatments.

An Analysis of Long-Term Ischemic Stroke Risk in Survivors of Septicemia.

INTRODUCTION: Sepsis increases the long-term incidence of ischemic stroke (IS). The chances for long-term IS in patients who are discharged after sepsis are unclear. Our aim was to demonstrate long-term risk chances of IS after septicemia discharge. We used a nomogram to identify those septicemia survivors with the higher risk of developing IS. METHODS: Inpatient data were used from the Taiwan Longitudinal Health Insurance Database, from 2001 to 2003. The event was IS rehospitalization after discharge of septicemia. We used multivariate Cox proportional regression of the risk factors for IS in septicemia survivors to create a nomogram. RESULTS: There were 642 IS incidents in this study. The risk factors for IS in survivors of septicemia were advanced age (hazard ratio [HR] 1.035 [95% confidence interval (CI) 1.029-1.042]), new-onset atrial fibrillation (HR 1.875 [95% CI 1.327-2.651]), hypertension (HR 2.042 [95% CI 1.687-2.471]), diabetes mellitus (HR 1.735 [95% CI 1.469-2.05]), coronary artery disease (HR 1.661 [95% CI 1.408-1.96]), chronic kidney disease (HR 1.264 [95% CI 1.071-1.49]), chronic obstructive pulmonary disease (HR 1.201 [95% CI 1.016-1.421]), and local hospital admission (HR 1.414 [95% CI 1.155-1.731]). The model showed good calibration and discrimination, with a bootstrap-corrected concordance index of .785. CONCLUSION: With this prognostic nomogram, we found age with the strongest factor for IS. There was increased IS incidence with more comorbid conditions in advance-aged septicemia survivors. Physicians must identify high IS-risk patients and control risk factors to prevent adverse events in the clinical setting.

Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer.

PURPOSE: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is still controversial. This study aimed to develop prediction models to distinguish who is suitable for ACT (ACT-benefit) and who should avoid ACT (ACT-futile) in NSCLC. METHODS: We identified the ACT correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification. Reliability was assessed by cross-data set validation. RESULTS: We obtained 2 probes (2 genes) with T-stage clinical data combination can get good prediction result. These genes included 208893_s_at (DUSP6) and 204891_s_at (LCK). The 10-fold cross validation classification accuracy was 65.71%. The best result of ANN models is MLP14-8-2 with logistic activation function. CONCLUSIONS: Using gene signature profiles to predict ACT benefit in NSCLC is feasible. The key to this analysis was identifying the pertinent genes and classification. This study maybe helps reduce the ineffective medical practices to avoid the waste of medical resources.

Frequency power and coherence of electroencephalography are correlated with the severity of Alzheimer's disease: A multicenter analysis in Taiwan.

BACKGROUND/PURPOSE: Slowing of average electroencephalography (EEG) frequency in Alzheimer's disease (AD) is well established, but whether EEG changes are able to reflect the severity of AD is uncertain. We attempt to establish quantitative EEG parameters that are suitable for evaluating AD in clinical practice. METHODS: Ninety-five patients with newly diagnosed AD at different stages from four neurologic institutes were enrolled for the study. Standard scalp resting EEG data were collected for quantitative analysis. Global band power ratio and interhemispheric alpha band coherence were calculated. RESULTS: Patients with advanced AD had a greater slow-to-fast wave power ratio. Among several power ratio parameters, global theta and delta to alpha and beta band power ratio showed the best correlation with stages of AD (p < 0.05 between any two patient groups). Patients with advanced AD had decreased coherence in multiple brain regions. The phenomenon was most prominent in the centroparietal region (p < 0.05 between any two patient groups). CONCLUSION: Increased global slow-to-fast power ratio and decreased centroparietal interhemispheric alpha band coherence are strongly correlated with disease progress in AD patients. These two quantitative EEG parameters may help evaluate AD patients in daily clinical practice. Global power ratio changes may suggest a shift of dominant frequency, and decreased interhemispheric alpha band coherence may suggest functional disconnection and corpus callosum abnormalities in AD patients.

Non-lethal levels of oxidative stress in response to short-term intermittent hypoxia enhance ca²âº handling in neonatal rat cardiomyocytes.

BACKGROUND/AIMS: Intermittent hypoxia (IH) may exert pre-conditioning-like cardioprotective effects and alter Ca(2+) regulation; however, the exact mechanism of these effects remains unclear. Thus, we examined Ca(2+)-handling mechanisms induced by IH in rat neonatal cardiomyocytes. METHODS: Cardiomyocytes were exposed to repetitive hypoxia-re-oxygenation cycles for 1-4 days. Mitochondrial reactive oxygen species (ROS) generation was determined by flow cytometry, and intracellular Ca(2+) concentrations were measured using a live-cell fluorescence imaging system. Protein kinase C (PKC) isoforms and Ca(2+)-handling proteins were analysed using immunofluorescence and western blotting. RESULTS: After IH exposure for 4 days, the rate of Ca(2+) extrusion from the cytosol to the extracellular milieu during 40-mM KCl-induced Ca(2+) mobilization increased significantly, whereas ROS levels increased mildly. IH activated PKC isoforms, which translocated to the membrane from the cytosol, and Na(+)/Ca(2+) exchanger-1, leading to enhanced Ca(2+) efflux capacity. Simultaneously, IH increased sarcoplasmic reticulum (SR) Ca(2+)-ATPase and ryanodine receptor 2 (RyR-2) activities and RyR-2 expression, resulting in improved Ca(2+) uptake and release capacity of SR in cardiomyocytes. CONCLUSIONS: IH-induced mild elevations in ROS generation can enhance Ca(2+) efflux from the cytosol to the extracellular milieu and Ca(2+)-mediated SR regulation in cardiomyocytes, resulting in enhanced Ca(2+)-handling ability.

Intermittent hypoxia-induced protein phosphatase 2A activation reduces PC12 cell proliferation and differentiation.

BACKGROUND: Intermittent hypoxia (IH) plays a critical role in sleep breathing disorder-associated hippocampus impairments, including neurocognitive deficits, irreversible memory and learning impairments. IH-induced neuronal injury in the hippocampus may result from reduced precursor cell proliferation and the relative numbers of postmitotic differentiated neurons. However, the mechanisms underlying IH-induced reactive oxygen species (ROS) generation effects on cell proliferation and neuronal differentiation remain largely unknown. RESULTS: ROS generation significantly increased after 1-4 days of IH without increased pheochromocytoma-12 (PC12) cell death, which resulted in increased protein phosphatase 2A (PP2A) mRNA and protein levels. After 3-4 days of IH, extracellular signal-regulated kinases 1/2 (ERK1/2) protein phosphorylation decreased, which could be reversed by superoxide dismutase (SOD), 1,10-phenanthroline (Phe), the PP2A phosphorylation inhibitors, okadaic acid (OKA) and cantharidin, and the ERK phosphorylation activator nicotine (p < 0.05). In particular, the significantly reduced cell proliferation and increased proportions of cells in the G0/G1 phase after 1-4 days of IH (p < 0.05), which resulted in decreased numbers of PC12 cells, could be reversed by treatment with SOD, Phe, PP2A inhibitors and an ERK activator. In addition, the numbers of nerve growth factor (NGF)-induced PC12 cells with neurite outgrowths after 3-4 days of IH were less than those after 4 days of RA, which was also reversed by SOD, Phe, PP2A inhibitors and an ERK activator. CONCLUSIONS: Our results suggest that IH-induced ROS generation increases PP2A activation and subsequently downregulates ERK1/2 activation, which results in inhibition of PC12 cell proliferation through G0/G1 phase arrest and NGF-induced neuronal differentiation.

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.

Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

Obstructive sleep apnea and increased risk of glaucoma: a population-based matched-cohort study.

PURPOSE: Previous studies had reported an increased prevalence of glaucoma in patients with obstructive sleep apnea (OSA). However, the risk of open-angle glaucoma (OAG) among patients with OSA remains unclear. Using a nationwide, population-based dataset in Taiwan, this study aimed to examine the prevalence and risk of OAG among patients with OSA during a 5-year follow-up period after a diagnosis of OSA. DESIGN: A retrospective, matched-cohort study. PARTICIPANTS AND CONTROLS: This study used data sourced from the Longitudinal Health Insurance Database 2000. We included 1012 subjects with OSA in the study cohort and randomly selected 6072 subjects in the comparison group. METHODS: Each subject in this study was individually traced for a 5-year period to identify those subjects who subsequently received a diagnosis of OAG. Cox proportional hazards regression was performed to calculate the 5-year risk of OAG between the study and comparison cohorts. MAIN OUTCOME MEASURES: The incidence and risk of OAG between the study and comparison groups. RESULTS: During the 5-year follow-up period, the incidence rate per 1000 person-years was 11.26 (95% confidence interval [CI], 8.61-14.49) and 6.76 (95% CI, 5.80-7.83) for subjects with and without OSA, respectively. After adjusting for monthly income, geographic region, diabetes, hypertension, coronary heart disease, obesity, hyperlipidemia, renal disease, hypothyroidism, and the number of outpatient visits for ophthalmologic care during the follow-up period, stratified Cox proportional hazards regression revealed that the hazard ratio for OAG within the 5-year period for subjects with OSA was 1.67 (95% CI, 1.30-2.17; P<0.001) that of comparison subjects. CONCLUSIONS: Our results suggest that OSA is associated with an increased risk of subsequent OAG diagnosis during a 5-year follow-up period. FINANCIAL DISCLOSURES(S): The authors have no proprietary or commercial interest in any of the materials discussed in this article.

Deep convolutional neural network for rib fracture recognition on chest radiographs.

Introduction: Rib fractures are a prevalent injury among trauma patients, and accurate and timely diagnosis is crucial to mitigate associated risks. Unfortunately, missed rib fractures are common, leading to heightened morbidity and mortality rates. While more sensitive imaging modalities exist, their practicality is limited due to cost and radiation exposure. Point of care ultrasound offers an alternative but has drawbacks in terms of procedural time and operator expertise. Therefore, this study aims to explore the potential of deep convolutional neural networks (DCNNs) in identifying rib fractures on chest radiographs. Methods: We assembled a comprehensive retrospective dataset of chest radiographs with formal image reports documenting rib fractures from a single medical center over the last five years. The DCNN models were trained using 2000 region-of-interest (ROI) slices for each category, which included fractured ribs, non-fractured ribs, and background regions. To optimize training of the deep learning models (DLMs), the images were segmented into pixel dimensions of 128 × 128. Results: The trained DCNN models demonstrated remarkable validation accuracies. Specifically, AlexNet achieved 92.6%, GoogLeNet achieved 92.2%, EfficientNetb3 achieved 92.3%, DenseNet201 achieved 92.4%, and MobileNetV2 achieved 91.2%. Discussion: By integrating DCNN models capable of rib fracture recognition into clinical decision support systems, the incidence of missed rib fracture diagnoses can be significantly reduced, resulting in tangible decreases in morbidity and mortality rates among trauma patients. This innovative approach holds the potential to revolutionize the diagnosis and treatment of chest trauma, ultimately leading to improved clinical outcomes for individuals affected by these injuries. The utilization of DCNNs in rib fracture detection on chest radiographs addresses the limitations of other imaging modalities, offering a promising and practical solution to improve patient care and management.