Search

BackgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Early diagnosis of AF is crucial for preventing AF-related morbidity, mortality, and economic burden, yet the detection of the disease remains challenging. The 12-lead electrocardiogram (ECG) is the gold standard for the diagnosis of AF. Because of technological advances, ambulatory devices may serve as convenient screening tools for AF. ObjectiveThe objective of this review was to investigate the diagnostic accuracy of 2 relatively new technologies used in ambulatory devices, non-12-lead ECG and photoplethysmography (PPG), in detecting AF. We performed a meta-analysis to evaluate the diagnostic accuracy of non-12-lead ECG and PPG compared to the reference standard, 12-lead ECG. We also conducted a subgroup analysis to assess the impact of study design and participant recruitment on diagnostic accuracy. MethodsThis systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were systematically searched for articles published from January 1, 2015 to January 23, 2021. A bivariate model was used to pool estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating curve (SROC) as the main diagnostic measures. Study quality was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. ResultsOur search resulted in 16 studies using either non-12-lead ECG or PPG for detecting AF, comprising 3217 participants and 7623 assessments. The pooled estimates of sensitivity, specificity, PLR, NLR, and diagnostic odds ratio for the detection of AF were 89.7% (95% CI 83.2%-93.9%), 95.7% (95% CI 92.0%-97.7%), 20.64 (95% CI 10.10-42.15), 0.11 (95% CI 0.06-0.19), and 224.75 (95% CI 70.10-720.56), respectively, for the automatic interpretation of non-12-lead ECG measurements and 94.7% (95% CI 93.3%-95.8%), 97.6% (95% CI 94.5%-99.0%), 35.51 (95% CI 18.19-69.31), 0.05 (95% CI 0.04-0.07), and 730.79 (95% CI 309.33-1726.49), respectively, for the automatic interpretation of PPG measurements. ConclusionsBoth non-12-lead ECG and PPG offered high diagnostic accuracies for AF. Detection employing automatic analysis techniques may serve as a useful preliminary screening tool before administering a gold standard test, which generally requires competent physician analyses. Subgroup analysis indicated variations of sensitivity and specificity between studies that recruited low-risk and high-risk populations, warranting future validity tests in the general population. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020179937; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=179937

BackgroundCOVID-19 has affected more than 180 countries and is the first known pandemic to be caused by a new virus. COVID-19’s emergence and rapid spread is a global public health and economic crisis. However, investigations into the disease, patient-tracking mechanisms, and case report transmissions are both labor-intensive and slow. ObjectiveThe pandemic has overwhelmed health care systems, forcing hospitals and medical facilities to find effective ways to share data. This study aims to design a global infectious disease surveillance and case tracking system that can facilitate the detection and control of COVID-19. MethodsThe International Patient Summary (IPS; an electronic health record that contains essential health care information about a patient) was used. The IPS was designed to support the used case scenario for unplanned cross-border care. The design, scope, utility, and potential for reuse of the IPS for unplanned cross-border care make it suitable for situations like COVID-19. The Fast Healthcare Interoperability Resources confirmed that IPS data, which includes symptoms, therapies, medications, and laboratory data, can be efficiently transferred and exchanged on the system for easy access by physicians. To protect privacy, patient data are deidentified. All systems are protected by blockchain architecture, including data encryption, validation, and exchange of records. ResultsTo achieve worldwide COVID-19 surveillance, a global infectious disease information exchange must be enacted. The COVID-19 surveillance system was designed based on blockchain architecture. The IPS was used to exchange case study information among physicians. After being verified, physicians can upload IPS files and receive IPS data from other global cases. The system includes a daily IPS uploading and enhancement plan, which covers real-time uploading through the interoperation of the clinic system, with the module based on the Open Application Programming Interface architecture. Through the treatment of different cases, drug treatments, and the exchange of treatment results, the disease spread can be controlled, and treatment methods can be funded. In the Infectious Disease Case Tracking module, we can track the moving paths of infectious disease cases. The location information recorded in the blockchain is used to check the locations of different cases. The Case Tracking module was established for the Centers for Disease Control and Prevention to track cases and prevent disease spread. ConclusionsWe created the IPS of infectious diseases for physicians treating patients with COVID-19. Our system can help health authorities respond quickly to the transmission and spread of unknown diseases, and provides a system for information retrieval on disease transmission. In addition, this system can help researchers form trials and analyze data from different countries. A common forum to facilitate the mutual sharing of experiences, best practices, therapies, useful medications, and clinical intervention outcomes from research in various countries could help control an unknown virus. This system could be an effective tool for global collaboration in evidence-based efforts to fight COVID-19.

BackgroundCommunity-acquired acute kidney injury (CA-AKI)-associated hospitalizations impose significant health care needs and contribute to in-hospital mortality. However, most risk prediction models developed to date have focused on AKI in a specific group of patients during hospitalization, and there is limited knowledge on the baseline risk in the general population for preventing CA-AKI-associated hospitalization. ObjectiveTo gain further insight into risk exploration, the aim of this study was to develop, validate, and establish a scoring system to facilitate health professionals in enabling early recognition and intervention of CA-AKI to prevent permanent kidney damage using different machine-learning techniques. MethodsA nested case-control study design was employed using electronic health records derived from a group of Chang Gung Memorial Hospitals in Taiwan from 2010 to 2017 to identify 234,867 adults with at least two measures of serum creatinine at hospital admission. Patients were classified into a derivation cohort (2010-2016) and a temporal validation cohort (2017). Patients with the first episode of CA-AKI at hospital admission were classified into the case group and those without CA-AKI were classified in the control group. A total of 47 potential candidate variables, including age, gender, prior use of nephrotoxic medications, Charlson comorbid conditions, commonly measured laboratory results, and recent use of health services, were tested to develop a CA-AKI hospitalization risk model. Permutation-based selection with both the extreme gradient boost (XGBoost) and least absolute shrinkage and selection operator (LASSO) algorithms was performed to determine the top 10 important features for scoring function development. ResultsThe discriminative ability of the risk model was assessed by the area under the receiver operating characteristic curve (AUC), and the predictive CA-AKI risk model derived by the logistic regression algorithm achieved an AUC of 0.767 (95% CI 0.764-0.770) on derivation and 0.761 on validation for any stage of AKI, with positive and negative predictive values of 19.2% and 96.1%, respectively. The risk model for prediction of CA-AKI stages 2 and 3 had an AUC value of 0.818 for the validation cohort with positive and negative predictive values of 13.3% and 98.4%, respectively. These metrics were evaluated at a cut-off value of 7.993, which was determined as the threshold to discriminate the risk of AKI. ConclusionsA machine learning–generated risk score model can identify patients at risk of developing CA-AKI-related hospitalization through a routine care data-driven approach. The validated multivariate risk assessment tool could help clinicians to stratify patients in primary care, and to provide monitoring and early intervention for preventing AKI while improving the quality of AKI care in the general population.

BackgroundPersonal health record (PHR) security, correctness, and protection are essential for health and medical services. Blockchain architecture can provide efficient data retrieval and security requirements. Exchangeable PHRs and the self-management of patient health can offer many benefits to traditional medical services by allowing people to manage their own health records for disease prevention, prediction, and control while reducing resource burdens on the health care infrastructure and improving population health and quality of life. ObjectiveThis study aimed to build a blockchain-based architecture for an international health record exchange platform to ensure health record confidentiality, integrity, and availability for health management and used Health Level 7 Fast Healthcare Interoperability Resource international standards as the data format that could allow international, cross-institutional, and patient/doctor exchanges of PHRs. MethodsThe PHR architecture in this study comprised 2 main components. The first component was the PHR management platform, on which users could upload PHRs, view their record content, authorize PHR exchanges with doctors or other medical health care providers, and check their block information. When a PHR was uploaded, the hash value of the PHR would be calculated by the SHA-256 algorithm and the PHR would be encrypted by the Rivest-Shamir-Adleman encryption mechanism before being transferred to a secure database. The second component was the blockchain exchange architecture, which was based on Ethereum to create a private chain. Proof of authority, which delivers transactions through a consensus mechanism based on identity, was used for consensus. The hash value was calculated based on the previous hash value, block content, and timestamp by a hash function. ResultsThe PHR blockchain architecture constructed in this study is an effective method for the management and utilization of PHRs. The platform has been deployed in Southeast Asian countries via the Asia eHealth Information Network (AeHIN) and has become the first PHR management platform for cross-region medical data exchange. ConclusionsSome systems have shown that blockchain technology has great potential for electronic health record applications. This study combined different types of data storage modes to effectively solve the problems of PHR data security, storage, and transmission and proposed a hybrid blockchain and data security approach to enable effective international PHR exchange. By partnering with the AeHIN and making use of the network’s regional reach and expert pool, the platform could be deployed and promoted successfully. In the future, the PHR platform could be utilized for the purpose of precision and individual medicine in a cross-country manner because of the platform’s provision of a secure and efficient PHR sharing and management architecture, making it a reasonable base for future data collection sources and the data analytics needed for precision medicine.

This study focused on early childhood preservice teachers' perceptions of online technology, service learning, and their understanding of their role as community leaders through service learning. A survey was conducted among preservice early childhood teachers in Taiwan. The questionnaire was comprised of three factors: online engagement, civic responsibility, and service learning. These variables were tested for their relationships and interactions with mediating and moderating effects. The findings showed that early childhood preservice teachers had positive perceptions of using online technology in their service learning course, and that they employed this tool to perform their civic responsibilities as teachers and citizens. Most importantly, they responded that service learning experiences were helpful in expanding their civic awareness and sense of social responsibility to help disadvantaged groups achieve better social living situations.

Neurodegeneration arising from aging, injury, or diseases has devastating health consequences. Whereas neuronal survival and axon degeneration have been studied extensively, much less is known about how neurodegeneration affects dendrites, in part due to the limited assay systems available. To develop an assay for dendrite degeneration and repair, we used photo-switchable caspase-3 (caspase-Light-Oxygen-Voltage-sensing [caspase-LOV]) in peripheral class 4 dendrite arborization (c4da) neurons to induce graded neurodegeneration by adjusting illumination duration during development and adulthood in Drosophila melanogaster. We found that both developing and mature c4da neurons were able to survive while sustaining mild neurodegeneration induced by moderate caspase-LOV activation. Further, we observed active dendrite addition and dendrite regeneration in developing and mature c4da neurons, respectively. Using this assay, we found that the mouse Wallerian degeneration slow (WldS) protein can protect c4da neurons from caspase-LOV-induced dendrite degeneration and cell death. Furthermore, our data show that WldS can reduce dendrite elimination without affecting dendrite addition. In summary, we successfully established a photo-switchable assay system in both developing and mature neurons and used WldS as a test case to study the mechanisms underlying dendrite regeneration and repair.

ABSTRACTNeurodegeneration arising from aging, injury or disease has devastating health consequences. Whereas neuronal survival and axon degeneration have been studied extensively, much less is known about how neurodegeneration impacts dendrites. To develop an assay for dendrite degeneration and repair in theDrosophilaperipheral nervous system, we used photo-switchable caspase-3 (caspase-LOV) to induce neuronal damage with tunable severity by adjusting illumination duration, thereby revealing cell type-specific responses to caspase-3 induced dendrite degeneration in dendrite arborization (da) neurons. To ask whether mechanisms underlying axon degeneration also govern dendrite degeneration, we tested the involvement of the Wallerian degeneration pathway by examining the effects of expressing the mouse Wallerian degeneration slow (WldS) protein and knockdown of theDrosophilasterile alpha/Armadillo/Toll-Interleukin receptor homology domain protein (dSarm1) and Axundead (Axed) in class 4 da neurons. Here we report WldSexpression or knockdown of dSarm1 improved dendrite repair following caspase- 3 induced dendrite degeneration. Whereas both dSarm1 and Axed were required for thermal nocifensive behavior in uninjured animals, WldSexpression improved the recovery of thermal nocifensive behavior that was impaired by chronic low-level of caspase-LOV activity. By establishing ways to induce graded dendrite degeneration, we uncover a protective role of WldSin caspase-3 induced dendrite degeneration and repair.