A Review of Research on Privacy Protection of Internet of Vehicles Based on Blockchain

Numerous academic and industrial fields, such as healthcare, banking, and supply chain management, are rapidly adopting and relying on blockchain technology. It has also been suggested for application in the internet of vehicles (IoV) ecosystem as a way to improve service availability and reliability. Blockchain offers decentralized, distributed and tamper-proof solutions that bring innovation to data sharing and management, but do not themselves protect privacy and data confidentiality. Therefore, solutions using blockchain technology must take user privacy concerns into account. This article reviews the proposed solutions that use blockchain technology to provide different vehicle services while overcoming the privacy leakage problem which inherently exists in blockchain and vehicle services. We analyze the key features and attributes of prior schemes and identify their contributions to provide a comprehensive and critical overview. In addition, we highlight prospective future research topics and present research problems

Molecular epidemiological characteristics of Mycobacterium leprae in highly endemic areas of China during the COVID-19 epidemic

ObjectivesThe present study analyzed the impact of the COVID-19 pandemic on the prevalence and incidence of new leprosy cases, as well as the diversity, distribution, and temporal transmission of Mycobacterium leprae strains at the county level in leprae-endemic provinces in Southwest China.MethodsA total of 219 new leprosy cases during two periods, 2018–2019 and 2020–2021, were compared. We genetically characterized 83 clinical isolates of M. leprae in Guizhou using variable number tandem repeats (VNTRs) and single nucleotide polymorphisms (SNPs). The obtained genetic profiles and cluster consequences of M. leprae were compared between the two periods.ResultsThere was an 18.97% decrease in the number of counties and districts reporting cases. Considering the initial months (January–March) of virus emergence, the number of new cases in 2021 increased by 167% compared to 2020. The number of patients with a delay of >12 months before COVID-19 (63.56%) was significantly higher than that during COVID-19 (48.51%). Eighty-one clinical isolates (97.60%) were positive for all 17 VNTR types, whereas two (2.40%) clinical isolates were positive for 16 VNTR types. The (GTA)9, (TA)18, (TTC)21 and (TA)10 loci showed higher polymorphism than the other loci. The VNTR profile of these clinical isolates generated five clusters, among which the counties where the patients were located were adjacent or relatively close to each other. SNP typing revealed that all clinical isolates possessed the single SNP3K.ConclusionCOVID-19 may have a negative/imbalanced impact on the prevention and control measures of leprosy, which could be a considerable fact for official health departments. Isolates formed clusters among counties in Guizhou, indicating that the transmission chain remained during the epidemic and was less influenced by COVID-19 preventative policies

Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society