Impact of prediabetes on poststroke depression in Chinese patients with acute ischemic stroke.

OBJECTIVE: It is unknown whether prediabetes is a predictor of poststroke depression (PSD). We aimed to explore the relationship between prediabetes and PSD in Chinese patients with acute ischemic stroke. METHODS: This is a prospective cohort study, and a total of 358 patients with acute ischemic stroke were recruited and enrolled. Patients were divided into 3 groups: normal glucose group (NGT, n = 96), prediabetes group (preDM, n = 134, impaired fasting glucose (IFG), and/or impaired glucose tolerance (IGT) and/or HbA1c (A1c) 5.7%-6.4%), and the diabetes mellitus group (DM, n = 128). At 1 month after stroke, patients with a Hamilton Depression Scale score of ≥8 were diagnosed as PSD. RESULTS: In post hoc comparisons, the risk of PSD in patients with diabetes and prediabetes was higher than patients with NGT (37.5% vs 31.3% vs 14.6%, P = .001). Compared with NGT, the incidence rate of PSD in patients with prediabetes with HbA1c 5.7% to 6.4% and patients with prediabetes with IFG/IGT + HbA1c 5.7% to 6.4% was higher (35.3% vs 14.6%, 38.0% vs 14.6%; P = .006; P = .003, respectively). In logistic regression, prediabetes with HbA1c 5.7% to 6.4% and prediabetes with IFG/IGT + HbA1c 5.7% to 6.4% were a significant independent predictor of PSD after adjusting for potential confounding factors, with odd ratios of 1.731 and 1.978, respectively. CONCLUSIONS: Our study showed that prediabetes was associated with PSD and may predict its development at 1 month poststroke. In prediabetes subgroups, patients with HbA1c 5.7% to 6.4% were more likely to develop PSD compared to NGT and IFG/IGT groups.

A Self-Powered, Skin Adhesive, and Flexible Human-Machine Interface Based on Triboelectric Nanogenerator.

Human-machine interactions (HMIs) have penetrated into various academic and industrial fields, such as robotics, virtual reality, and wearable electronics. However, the practical application of most human-machine interfaces faces notable obstacles due to their complex structure and materials, high power consumption, limited effective skin adhesion, and high cost. Herein, we report a self-powered, skin adhesive, and flexible human-machine interface based on a triboelectric nanogenerator (SSFHMI). Characterized by its simple structure and low cost, the SSFHMI can easily convert touch stimuli into a stable electrical signal at the trigger pressure from a finger touch, without requiring an external power supply. A skeleton spacer has been specially designed in order to increase the stability and homogeneity of the output signals of each TENG unit and prevent crosstalk between them. Moreover, we constructed a hydrogel adhesive interface with skin-adhesive properties to adapt to easy wear on complex human body surfaces. By integrating the SSFHMI with a microcontroller, a programmable touch operation platform has been constructed that is capable of multiple interactions. These include medical calling, music media playback, security unlocking, and electronic piano playing. This self-powered, cost-effective SSFHMI holds potential relevance for the next generation of highly integrated and sustainable portable smart electronic products and applications.

The Red Blood Cell Distribution Width-Albumin Ratio: A Promising Predictor of Mortality in Stroke Patients.

OBJECTIVE: Within this study we attempt to express a correlation between the mortality of stroke and stroke related infection to a novel biomarker represented by the red blood cell width-albumin levels ratio within the patient. We hypothesize that this novel biomarker could be utilized as better predictive tool for stroke associated infections. METHODS: Patient data sets were obtained via the Medical Information Mart for Intensive Care Database iii V1.4 (MIMIC-iii). Data from 1480 patients were obtained to serve the testing for the RA biomarker tests. Clinical endpoints of 30-, 60-, and 365-day all-cause mortality in stroke patients were used as subgroups within the analyzed population. Estimation of hazard ratios (HR) were obtained from Cox regression models for stroke-associated infection and all-cause mortality in relation to RA values. RESULTS: A high-RA was associated with increased mortality in ICU patients suffering from a stroke. After adjusting for age and sex, compared to the reference group (the first quartile), the high-RA group had the highest 30-day (HR, 95% CI: 1.88 (1.36, 2.58)), 90-day (HR, 95% CI: 2.12 (1.59, 2.82)), and one-year (HR, 95% CI: 2.15 (1.65, 2.80)) all-cause mortality. The RA values were independently associated with an increased risk of stroke-associated infection when adjusting for confounders. CONCLUSIONS: Our data suggest RA may be an easily accessible, reproducible, and low-cost biomarker for predicting stroke-associated infections and mortality in patients who have suffered from a stroke.

Dual-demodulation large-scope high-sensitivity refractive index sensor based on twin-core PCF.

In this paper, a refractive index (RI) sensor based on the twin-core photonic crystal fiber (TC-PCF) is presented. Introducing the rectangular array in the core area makes the PCF possible to obtain high birefringence and low confinement loss over the wavelength range from 0.6 µm to 1.7 µm. Therefore, the core region can enhance the interaction between the core mode and the filling material. We studied theoretically the evolution characteristics of the birefringence and operating wavelength corresponding to the strongest polarization point under the condition of filling the rectangular array with RI matching fluid range from 1.33 to 1.41. Simulation results reveal that the proposed TC-PCF has opposite evolutions of change rates between the B and wavelength, and the maximum RI sensing sensitivities of 1.809×10-2 B/RIU and 8 700 nm/RIU at low and high RI infill are obtained respectively, which means that the TC-PCF features of dual-parameter demodulation for the RI sensing can maintain a high refractive index sensing sensitivity within a large scope of RI ranging from 1.33 to 1.41. Compared with the results of single-parameter demodulation, it is an optimized method to improve the sensitivity of low refractive index sensors, which has great application potency in the field of biochemical sensing and detection.