Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision analytics. In health-related outbreaks, timely and early diagnosis of such complications is conclusive in reducing mortality rates and alleviating the burden on healthcare facilities. Existing methods rely on clinical assessments, medical history reviews, and hospital-based monitoring, which are valuable but have limitations in terms of accessibility, scalability, and timeliness, particularly during pandemics. The proposed scheme commences by deploying wearable ECG sensors on the patient's body. These sensors collect data by continuously monitoring the cardiac activity and respiratory patterns of the patient. The collected raw data is then transmitted securely in a wireless manner to a centralized server and stored in a database. Subsequently, the stored data is assessed using a preprocessing process which extracts relevant and important features like heart rate variability and respiratory rate. The preprocessed data is then used as input into the CNN model for the classification of normal and abnormal cardiorespiratory patterns. To achieve high accuracy in abnormality detection the CNN model is trained on labeled data with optimized parameters. The performance of the proposed scheme is evaluated and gauged using different scenarios, which shows a robust performance in detecting abnormal cardiorespiratory patterns with a sensitivity of 95% and specificity of 92%. Prominent observations, which highlight the potential for early interventions include subtle changes in heart rate variability and preceding respiratory distress. These findings show the significance of wearable ECG technology in improving pandemic management strategies and informing public health policies, which enhances preparedness and resilience in the face of emerging health threats.

Comparative efficacy of concurrent training types on lower limb strength and muscular hypertrophy: A systematic review and network meta-analysis.

Objective: This study aims to compare, through quantitative analysis, the effectiveness of different endurance training types on increasing lower limb strength and muscle cross-sectional area (MCSA) in concurrent training. Methods: This systematic literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [PROSPERO ID: CRD42023396886]. Web of Science, SportDiscuss, Pubmed, Cochrane, and Scopus were systematically searched from their inception date to October 20, 2023. Results: A total of 40 studies (841 participants) were included in this meta-analysis. MCSA analysis showed that, compared to resistance training alone, concurrent high-intensity interval running training and resistance training and concurrent moderate-intensity continuous cycling training and resistance training were more effective (SMD = 0.15, 95% CI = -0.46 to 0.76, and SMD = 0.07, 95% CI = -0.24 to 0.38 respectively), while other modalities of concurrent training not. Lower body maximal strength analysis showed that all modalities of concurrent training were inferior to resistance training alone, but concurrent high-intensity interval training and resistance training showed an advantage in four different concurrent training modalities (SMD = -0.08, 95% CI = -0.25 to 0.08). For explosive strength, only concurrent high-intensity interval training and resistance training was superior to resistance training (SMD = 0.06, 95% CI = -0.21 to 0.33). Conclusion: Different endurance training types have an impact on the effectiveness of concurrent training, particularly on lower limb strength. Adopting high-intensity interval running as the endurance training type in concurrent training can effectively minimize the adverse effects on lower limb strength and MCSA.

Effects of various living-low and training-high modes with distinct training prescriptions on sea-level performance: A network meta-analysis.

This study aimed to separately compare and rank the effect of various living-low and training-high (LLTH) modes on aerobic and anaerobic performances in athletes, focusing on training intensity, modality, and volume, through network meta-analysis. We systematically searched PubMed, Web of Science, Embase, EBSCO, and Cochrane from their inception date to June 30, 2023. Based on the hypoxic training modality and the intensity and duration of work intervals, LLTH was divided into intermittent hypoxic exposure, continuous hypoxic training, repeated sprint training in hypoxia (RSH; work interval: 5-10 s and rest interval: approximately 30 s), interval sprint training in hypoxia (ISH; work interval: 15-30 s), short-duration high-intensity interval training (s-IHT; short work interval: 1-2 min), long-duration high-intensity interval training (l-IHT; long work interval: > 5 min), and continuous and interval training under hypoxia. A meta-analysis was conducted to determine the standardized mean differences (SMDs) among the effects of various hypoxic interventions on aerobic and anaerobic performances. From 2,072 originally identified titles, 56 studies were included in the analysis. The pooled data from 53 studies showed that only l-IHT (SMDs: 0.78 [95% credible interval; CrI, 0.52-1.05]) and RSH (SMDs: 0.30 [95% CrI, 0.10-0.50]) compared with normoxic training effectively improved athletes' aerobic performance. Furthermore, the pooled data from 29 studies revealed that active intermittent hypoxic training compared with normoxic training can effectively improve anaerobic performance, with SMDs ranging from 0.97 (95% CrI, 0.12-1.81) for l-IHT to 0.32 (95% CrI, 0.05-0.59) for RSH. When adopting a program for LLTH, sufficient duration and work intensity intervals are key to achieving optimal improvements in athletes' overall performance, regardless of the potential improvement in aerobic or anaerobic performance. Nevertheless, it is essential to acknowledge that this study incorporated merely one study on the improvement of anaerobic performance by l-IHT, undermining the credibility of the results. Accordingly, more related studies are needed in the future to provide evidence-based support. It seems difficult to achieve beneficial adaptive changes in performance with intermittent passive hypoxic exposure and continuous low-intensity hypoxic training.

Optimal type and dose of hypoxic training for improving maximal aerobic capacity in athletes: a systematic review and Bayesian model-based network meta-analysis.

Objective: This study aimed to compare and rank the effect of hypoxic practices on maximum oxygen consumption (VO2max) in athletes and determine the hypoxic dose-response correlation using network meta-analysis. Methods: The Web of Science, PubMed, EMBASE, and EBSCO databases were systematically search for randomized controlled trials on the effect of hypoxc interventions on the VO2max of athletes published from inception until 21 February 2023. Studies that used live-high train-high (LHTH), live-high train-low (LHTL), live-high, train-high/low (HHL), intermittent hypoxic training (IHT), and intermittent hypoxic exposure (IHE) interventions were primarily included. LHTL was further defined according to the type of hypoxic environment (natural and simulated) and the altitude of the training site (low altitude and sea level). A meta-analysis was conducted to determine the standardized mean difference between the effects of various hypoxic interventions on VO2max and dose-response correlation. Furthermore, the hypoxic dosage of the different interventions were coordinated using the "kilometer hour" model. Results: From 2,072 originally identified titles, 59 studies were finally included in this study. After data pooling, LHTL, LHTH, and IHT outperformed normoxic training in improving the VO2max of athletes. According to the P-scores, LHTL combined with low altitude training was the most effective intervention for improving VO2max (natural: 0.92 and simulated: 0.86) and was better than LHTL combined with sea level training (0.56). A reasonable hypoxic dose range for LHTH (470-1,130 kmh) and HL (500-1,415 kmh) was reported with an inverted U-shaped curve relationship. Conclusion: Different types of hypoxic training compared with normoxic training serve as significant approaches for improving aerobic capacity in athletes. Regardless of the type of hypoxic training and the residential condition, LHTL with low altitude training was the most effective intervention. The characteristics of the dose-effect correlation of LHTH and LHTL may be associated with the negative effects of chronic hypoxia.

An "off-on-off" type electrochemical biosensor for detecting multiple biomarkers with DNAzyme-mediated entropy-driven catalytic and DSN enzyme-assisted recycling amplification.

In this work, an intelligent and versatile electrochemical biosensor was constructed to detect two types of biomarkers by utilizing "off-on-off" switching. Firstly, human apurinic/apyrimidinic endonuclease1(APE1) mediated specific cleavage of the AP site, initiating activation DNAzyme and entropy-driven catalytic (EDC) reaction. Subsequently, large amounts of ferrocene labeled single-stranded DNA was released and captured with a remarkable electrochemical signal, achieving "off-on" state. In the presence of microRNA 21(miRNA-21), the DNA/RNA heteroduplexes were formed and cleaved by duplex-specific nuclease (DSN) with recovery the target miRNA-21, causing the current suppression in an "on-off" state. This sensor achieved highly sensitive detection of APE1 and miRNA-21 with a detection limit of 2.5 mU·mL-1 and 1.33 × 10-20 M, respectively, and also exhibited good selectivity, reproducibility and stability. Moreover, this proposed biosensor made it possible to realize analysis of multiple types of biomarkers on a single sensor, which improved utilization and analysis efficiency compared to traditional sensors. This study might open a new avenue to design multifunctional sensing platform for biological research and early disease diagnosis.