The present study performed laser-Doppler flowmetry (LDF) measurements on the skin surface around the ankle with the aim of verifying if beat-to-beat analysis of the LDF waveform can help to discriminate the microcirculatory-blood-flow (MBF) characteristics between diabetic, prediabetic, and healthy subjects. 84 subjects were assigned to three groups (diabetic, prediabetic, and normal) according to the results of oral glucose tolerance tests. Beat-to-beat analysis was performed on the pulsatile LDF waveform to obtain foot delay time (FDT), flow rise time (FRT), and the corresponding MBF-variability parameters (FDTCV and FRTCV). Relative to the control group, FDT and FRT were significantly shorter in prediabetic subjects, FDT was significantly shorter in diabetic subjects, and FRTCV and FDTCV were significantly larger in prediabetic and diabetic subjects. There were no significant associations for FRT after adjustment for age and gender. The present results indicate that FRT may help to discriminate differences in the elastic properties of local vascular beds during diabetes or even during prediabetic stages. The proposed blood-filling-volume model can help to explain the underlying mechanism. The present findings may aid the noninvasive early detection of diabetes-associated vascular damage, and could be used in the development of home-care and telemedicine applications.

We aimed to elucidate the frequency-specific microcirculatory blood-flow (MBF) effect induced by weak vibration stimulation (VS) in healthy human subjects. VS was implemented by a rod (connected to a DC motor) hitting a water-filled bag. VS was applied to the left palm at frequencies near to the heart rate (HR) (n=35; Group A) and 50% higher than the HR (n=20; Group B), and laser-Doppler-flowmetry measurements were made on the back of the left hand (Site 1). A control group without applying VS was also used (n=21). The mean MBF (MMBF), pulsatile MBF, and pulse width at Site 1 only increased significantly in Group A, and the coefficient of variance of the MMBF sequence increased significantly at Site 1 in Groups A and B. These results illustrate the VS-induced changes in the regulatory activities of arteriolar openings, and improvement of the MBF near the VS application site in Group A. The improvement in MBF depended on whether the VS frequency was near to, or higher than the HR. The present findings may be pertinent to amelioration of disease induced by an abnormal MBF.

Background/Objectives: Artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis may assist in objective and reproducible risk stratification. However, the prognostic utility of serial ECGs, particularly the follow-up ECG prior to discharge, has not been extensively studied. This study aimed to evaluate whether dynamic changes in AI-predicted ECG risk scores could enhance prediction of post-discharge outcomes. Methods: This retrospective cohort study included 11,508 ED visits from a single medical center where patients underwent two ECGs and were directly discharged. We stratified the mortality risk of patients as low risk, medium risk, and high risk based on the first and follow-up ECG prior to discharge using AI-enabled ECG models. The Area Under the Curve (AUC) was calculated for the predictive performance of the two ECGs. Kaplan–Meier (KM) curves were used for 90-day mortality analysis, and the Cox proportional hazards model was utilized to compare the risk of death across categories. Results: The AI-enabled ECG risk prediction model, based on the initial and follow-up ECGs prior to discharge, indicated risk transitions among different groups. The AUC for mortality risk was 78.6% for the first ECG and 83.3% for the follow-up ECG. KM curves revealed a significant increase in 90-day mortality for patients transitioning from low to medium/high risk upon discharge (Hazard Ratio: 6.01; Confidence Interval: 1.70–21.27). Conclusions: AI-enabled ECGs obtained prior to discharge provide superior mortality risk stratification for ED patients compared to initial ECGs. Patients classified as medium- or high-risk at discharge require careful consideration, whereas those at low risk can generally be discharged safely. Although AI-ECG alone does not replace comprehensive risk assessment, it offers a practical tool to support clinical judgment, particularly in the dynamic ED environment, by aiding safer discharge decisions.