ABSTRACT Background Women with a history of gestational diabetes mellitus (GDM) face an elevated risk of developing diabetes, yet postpartum screening is often This study evaluated the diagnostic utility of a blinded continuous glucose monitoring (CGM) system (Dexcom G7) for assessing glycemic status. Methods Of 40 enrolled participants, 39 completed testing at 6–24 weeks postpartum. Assessments included hemoglobin A1c (HbA1c,), fructosamine, a 75-g oral glucose tolerance test (OGTT), and 8–9 days of CGM wear. CGM metrics analyzed were mean glucose and time in range (TIR; 3.9–7.8 mmol/L). Results Diabetes and prediabetes were identified in 1 and 9 participants by HbA1c, 3 and 10 by fructosamine, and 4 and 10 by OGTT. CGM metrics detected 4 cases of diabetes and 10 of prediabetes. Mean OGTT glucose correlated strongly with mean CGM glucose (r = 0.66, p < 0.001) and inversely with TIR (r = –0.57, p < 0.001). Paired OGTT and CGM values were highly correlated (r = 0.874; p < 0.001). Conclusion Postpartum dysglycemia is prevalent after GDM. HbA1c and fructosamine lacked sensitivity and specificity, while CGM performed comparably to OGTT in detecting abnormal glucose metabolism in this postpartum population. Clinical trial registration http://www.clinicaltrials.gov identifier is NCT06057805

Machine learning-based system for online quantitative monitoring of heavy metals across different aqueous matrices using spectroscopy of plasmas in liquids.

The explosion of Industry 4.0 has augmented the demand for secure, agile, and real-time industrial monitoring and control applications. In this paper, a secure Industrial Internet of Things (IIoT) system for real-time PI control and cloud-based industrial monitoring is proposed. The developed system combines Siemens S-300 PLC (programmable logic controller) and Node-RED platform that provides real-time data acquisition, display, and remote control for industrial parameters like Level/Water temperature. An integrated end-to-end data pipeline is implemented to allow transparent information exchange between the PLC, Node-RED dashboard, MQTT-based services, and cloud infrastructures, providing real-time data synchronization as well as remote access. The PI controller, whose parameters can be adjusted online using a website or mobile app, is implemented by our framework without loss of control loop stability. Safety is improved by authentic access and the regulation of the user’s rights in order to avoid undelegated system operation. The experimental validation in a real-world industrial automation lab reveals the robustness of the real-time monitoring, enabling concurrent visualization over multiple interfaces and satisfactory PI control performance. The results substantiate the efficiency, applicability, and reliability of the developed IIoT system for real-world industrial monitoring and control scenarios.

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