Fluoride Exposure from Drinking Water Increases the Risk of Stroke: An Ecological Study in Changwu Town, China.

BACKGROUND: Stroke is a major cause of death globally and the leading cause in China. Excessive fluoride exposure has been linked to cardiovascular conditions related to stroke risk factors such as hypertension, atherosclerosis, dyslipidemia, and cardiomyopathy. However, evidence supporting the association between fluoride exposure and stroke risk is limited. METHODS: We constructed an ecological study in Changwu Town, Heilongjiang Province, China, a typical endemic fluorosis area caused by excessive fluoride exposure from drinking water. We collected demographic data, stroke prevalence, and mortality information from 2017 to 2021. Fluoride exposure data were obtained from the national monitoring project on endemic fluorosis. Water fluoride concentrations were measured using the standardized methods. Trend changes in stroke rates were assessed using annual percentage change (APC). Differences in stroke rates among fluoride exposure groups were analyzed using chi-square tests. RESULTS: From 2017 to 2021, the all-ages and age-standardized stroke prevalence rates of permanent residents in Changwu Town increased year by year, while the all-ages and age-standardized mortality rates did not change significantly. The prevalence rates of stroke were significantly higher in endemic fluorosis areas compared to non-endemic areas (p < 0.001). Stratifying the population into tertile groups based on the water fluoride cumulative exposure index (WFCEI) revealed statistically significant differences in stroke prevalence rates (p < 0.001), showing a dose-response relationship with the WFCEI. However, the all-ages and age-standardized mortality rates of stroke were not found to be related to fluoride exposure. CONCLUSIONS: Long-term excessive fluoride exposure from drinking water may increase the risk of stroke prevalence, indicating fluoride overexposure as a potential risk factor for stroke.

First isolation and characterization of Brucella suis from yak.

Brucella spp., facultative intracellular pathogens that can persistently colonize animal host cells and cause zoonosis, affect public health and safety. A Brucella strain was isolated from yak in Qinghai Province. To detect whether this isolate could cause an outbreak of brucellosis and to reveal its genetic characteristics, several typing and whole-genome sequencing methods were applied to identify its species and genetic characteristics. Phylogenetic analysis based on MLVA and whole-genome sequencing revealed the genetic characteristics of the isolated strain. The results showed that the isolated strain is a B. suis biovar 1 smooth strain, and this isolate was named B. suis QH05. The results of comparative genomics and MLVA showed that B. suis QH05 is not a vaccine strain. Comparison with other B. suis strains isolated from humans and animals indicated that B. suis QH05 may be linked to specific animal and human sources. In conclusion, B. suis QH05 does not belong to the Brucella epidemic species in China, and as the first isolation of B. suis from yak, this strain expands the host range of B. suis.

ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression.

Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular and have found their way into many portable electronics such as smartphones, tablets, and laptops. Furthermore, the use of biometrics enables secure access to private medical data, now collected in wearable devices such as smartwatches. In this work, we present an accurate low-power device authentication system that employs electrocardiogram (ECG) signals as the biometric modality. The proposed ECG processor consists of front-end signal processing of ECG signals and back-end neural networks (NNs) for accurate authentication. The NNs are trained using a cost function that minimizes intra-individual distance over time and maximizes inter-individual distance. Efficient low-power hardware was implemented by using fixed coefficients for ECG signal pre-processing and by using joint optimization of low-precision and structured sparsity for the NNs. We implemented two instances of ECG authentication hardware with 4X and 8X structurally-compressed NNs in 65 nm LP CMOS, which consume low power of 62.37  µW and 75.41  µW for real-time ECG authentication with a low equal error rate of 1.36% and 1.21%, respectively, for a large 741-subject in-house ECG database. The hardware was evaluated at 10 kHz clock frequency and 1.2 V voltage supply.