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BackgroundParkinson disease (PD) is one of the most common neurological diseases. At present, because the exact cause is still unclear, accurate diagnosis and progression monitoring remain challenging. In recent years, exploring the relationship between PD and speech impairment has attracted widespread attention in the academic world. Most of the studies successfully validated the effectiveness of some vocal features. Moreover, the noninvasive nature of speech signal–based testing has pioneered a new way for telediagnosis and telemonitoring. In particular, there is an increasing demand for artificial intelligence–powered tools in the digital health era. ObjectiveThis study aimed to build a real-time speech signal analysis tool for PD diagnosis and severity assessment. Further, the underlying system should be flexible enough to integrate any machine learning or deep learning algorithm. MethodsAt its core, the system we built consists of two parts: (1) speech signal processing: both traditional and novel speech signal processing technologies have been employed for feature engineering, which can automatically extract a few linear and nonlinear dysphonia features, and (2) application of machine learning algorithms: some classical regression and classification algorithms from the machine learning field have been tested; we then chose the most efficient algorithms and relevant features. ResultsExperimental results showed that our system had an outstanding ability to both diagnose and assess severity of PD. By using both linear and nonlinear dysphonia features, the accuracy reached 88.74% and recall reached 97.03% in the diagnosis task. Meanwhile, mean absolute error was 3.7699 in the assessment task. The system has already been deployed within a mobile app called No Pa. ConclusionsThis study performed diagnosis and severity assessment of PD from the perspective of speech order detection. The efficiency and effectiveness of the algorithms indirectly validated the practicality of the system. In particular, the system reflects the necessity of a publicly accessible PD diagnosis and assessment system that can perform telediagnosis and telemonitoring of PD. This system can also optimize doctors’ decision-making processes regarding treatments.

BackgroundThe number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). ObjectiveThis study proposes methods to diagnose PD through facial expression recognition. MethodsWe collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. ResultsThe experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. ConclusionsThis study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.

Background: Systemic lupus erythematosus (SLE) is associated with adverse pregnancy outcome (APO). However, the genetic causality of this association remains unclear. In this study, Mendelian randomization (MR) was used to explore the potential causal relationship between SLE and APO risk. Methods: We selected 45 single nucleotide polymorphisms (SNPs) associated with SLE from published genome-wide association studies (GWAS). APO's statistics are obtained from the GWAS database. MR estimates were performed using the inverse variance-weighted (IVW) method, the MR-Egger method, and the weighted median (WM) method. Sensitivity analysis was performed using Cochran's Q test, MR-Egger intercept, MR-pleiotropic residual and outlier method, stay-one analysis and funnel plot. Results: The results showed a causal relationship between SLE and pre-eclampsia (OR = 1.036, 95 % confidence interval 1.006 to 1.068, P = 0.019), and no significant causal relationship was found between SLE and other adverse pregnancy outcomes, including postpartum hemorrhage, placental abruption, spontaneous abortion, premature rupture of membranes, fetal distress, gestational diabetes mellitus. These findings were robust in several sensitivity analyses. Conclusion: This MR study demonstrated the causal effect of SLE on preeclampsia. It provides important clues for identifying and early predicting risk factors for preeclampsia.

This paper presents a study on the performance damage of lightweight aggregate high performance concrete (L-HPC) under stress corrosion. While L-HPC was undergoing accelerated corrosion in 15% NaCl solution, it was continuously loaded for 90 d, 180 d and 270 d at stress levels with initial L-HPC peak loads of 0%, 50%, 70%. The chloride ion content change, mechanical property deterioration and microstructural damage of L-HPC were studied in detail. The results show that: The dense structure of L-HPC makes it free from obvious cracks at high stress levels (70% peak load), and the quality does not change significantly. The internal curing effect of the lightweight aggregates makes the interfacial transition zone more dense, which hinders the intrusion of chloride ions. The deterioration of L-HPC was slowed down at the initial stage of stress corrosion, but aggravated at the later stage of stress corrosion due to the corrosion of steel fibers.