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Internet of Things (IoT) enabled wireless body area network (WBAN) is a novel technology that combines medical, wireless, and non-medical devices for healthcare management applications. Indoor healthcare institutions, such as hospitals, may employ IoT devices to manage medical equipment, maintain inventory, and monitor patients. In addition to monitoring temperature, humidity, and air quality, IoT sensors may help prevent the spread of sickness. The data generated by small sensor devices need strong security methods for secure transmission over a public network. Security of the data sent from source to destination is handled by transport layer security (TLS) protocol and prevent message loss or reordering. The most difficult component of TLS is determining how to handle reliability. Low-power wireless networks employ the Datagram Transport Layer Security (DTLS) protocol to solve this problem. But if the Server is exposed to a malicious attack (i.e., flooded with ClientHello messages, the DTLS protocol may no longer serve the purpose) as it may lead to Denial-of-Service (DoS). The paper proposes an intelligent gateway-based verification and approval system to protect health data from IoT wearable devices, a key worldwide data security risk. Paper used Contiki Network Simulator for designing an advanced smart gateway-based constrained application Improved DTLS (CoAP I-DTLS) security protocol. To assess the effectiveness of the proposed work, the packet loss ratio is computed for the CoAP, CoAP-DTLS, and CoAP I-DTLS protocols. To assess the effectiveness of the improved CoAP I-DTLS, calculations are also made for data transfer and handshake time. Paper will also look at ways to increase security by integrating the CoAP I-DTLS protocol with the Secure Hash Algorithm (SHA-256) and utilizing Certificate Authority (CA) improvements. Evaluation parameters such as Packet loss ratio, Data transmission time, and Energy consumption average are considered and detailed simulation results have been presented. [ABSTRACT FROM AUTHOR]

For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. While the GRU layer records temporal relationships and sequence information, the CNN layer analyzes the incoming data to extract significant features. Using a publicly accessible type 1 diabetes dataset, the hybrid model's performance is compared to that of the standalone Long Short-Term Memory (LSTM), CNN, and GRU models. The findings show that the hybrid CNN-GRU model performs better than the single models, indicating its potential to significantly improve real-time BGL forecasting in IoT-based diabetes management systems. [ABSTRACT FROM AUTHOR]

Objective: Posterior left atrial wall isolation (PWI) plus traditional pulmonary vein isolation (PVI) has been proposed as a promising intervention to decrease atrial fibrillation (AF) recurrence. We aim to investigate the efficacy and safety of adding PWI to the traditional PVI in patients with AF. Methods: A systematic review and meta‐analysis was conducted using synthesizing randomized controlled trials (RCTs) retrieved by systematically searching PubMed, Web of Science, SCOPUS, EMBASE, and Cochrane through June 14, 2023. We used Stata version 17 to pool dichotomous data using risk ratio (RR) and continuous data using mean difference (MD), with a 95% confidence interval (CI) (PROSPERO ID: CRD42023446227). Results: We included 11 RCTs with a total number of 1534 patients. Combined ablation with PWI + PVI was not associated with any significant difference over PVI only regarding the recurrence of clinical AF (RR: 0.86 with 95% CI [0.70–1.06]), all atrial arrhythmia (RR: 0.93 with 95% CI [0.82–1.07]), nonatrial fibrillation arrhythmia (RR: 1.22 with 95% CI [0.97–1.53]), early AF (RR: 0.89 with 95% CI [0.62–1.27]), and antiarrhythmic drugs at discharge (RR: 0.83 with 95% CI [0.67–1.04]). However, it was associated with increased total ablation duration (minutes) (MD: 12.58 with 95% CI [6.80–18.37]) and total procedure duration (minutes) (MD: 16.77 with 95% CI [9.63–23.91]), without any significant difference regarding adverse events (RR: 1.05 with 95% CI [0.63–1.74]). Conclusion: While the pooled data from PWI + PVI using point‐by‐point radiofrequency did not suggest a benefit in the recurrence of various atrial arrhythmias compared to PVI alone, PWI+PVI using direct posterior wall ablation, especially with cryoballoon, demonstrated a significant reduction in recurrence of AF/atrial arrhythmias. Also, PWI + PVI significantly increased the ablation and total procedure durations. [ABSTRACT FROM AUTHOR]