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BackgroundCOVID-19 became a global pandemic not long after its identification in late 2019. The genomes of SARS-CoV-2 are being rapidly sequenced and shared on public repositories. To keep up with these updates, scientists need to frequently refresh and reclean data sets, which is an ad hoc and labor-intensive process. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. ObjectiveTo address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes. MethodsCoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article. ResultsGiven a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are displayed in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is available for high-throughput processing. In addition, we aggregated all publicly available SARS-CoV-2 sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID), National Center for Biotechnology Information (NCBI), European Nucleotide Archive (ENA), and China National GeneBank (CNGB), and extracted genetic variants from these sequences for download and downstream analysis. The CoV-Seq database is updated weekly. ConclusionsWe have developed CoV-Seq, an integrated web service for fast and easy analysis of custom SARS-CoV-2 sequences. The web server provides an interactive module for the analysis of custom sequences and a weekly updated database of genetic variants of all publicly accessible SARS-CoV-2 sequences. We believe CoV-Seq will help improve our understanding of the genetic underpinnings of COVID-19.

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.

BackgroundNoncommunicable diseases (NCDs) have become the main public health concern worldwide. With rapid economic development and changes in lifestyles, the burden of NCDs in China is increasing dramatically every year. Monitoring is a critical measure for NCDs control and prevention. However, because of the lack of regional representativeness, unsatisfactory data quality, and inefficient data sharing and utilization, the existing surveillance systems and surveys in China cannot track the status and transition of NCDs epidemic. ObjectiveTo efficaciously track NCDs epidemic in China, this pilot program conducted in Ningbo city by the Chinese Center for Disease Control and Prevention (CDC) aimed to develop an innovative model for NCDs surveillance and management: the integrated noncommunicable disease collaborative management system (NCDCMS). MethodsThis Ningbo model was designed and developed through a 3-level (county/district, municipal, and provincial levels) direct reporting system based on the regional health information platform. The uniform data standards and interface specifications were established to connect different platforms and conduct data exchanges. The performance of the system was evaluated based on the 9 attributes of surveillance system evaluation framework recommended by the US CDC. ResultsNCDCMS allows automatic NCDs data exchanging and sharing via a 3-level public health data exchange platform in China. It currently covers 201 medical institutions throughout the city. Compared with previous systems, automatic popping up of the report card, automatic patient information extraction, and real-time data exchange process have highly improved the simplicity and timeliness of the system. The data quality meets the requirements to monitor the incidence trend of NCDs accurately, and the comprehensive data types obtained from the database (ie, directly from the 3-level platform on the data warehouse) also provide a useful information to conduct scientific studies. So far, 98.1% (201/205) of medical institutions across Ningbo having been involved in data exchanges with the model. Evaluations of the system performance showed that NCDCMS has high levels of simplicity, data quality, acceptability, representativeness, and timeliness. ConclusionsNCDCMS completely reshaped the process of NCD surveillance reporting and had unique advantages, which include reducing the work burden of different stakeholders by data sharing and exchange, eliminating unnecessary redundancies, reducing the amount of underreporting, and structuring population-based cohorts. The Ningbo model will be gradually promoted elsewhere following this success of the pilot project, and is expected to be a milestone in NCDs surveillance, control, and prevention in China.

BackgroundComprehensive safety data for vaccines from post-licensure surveillance, especially active surveillance, could guide administrations and individuals to make reasonable decisions on vaccination. Therefore, we designed a pilot study to assess the capability of a regional health care information platform to actively monitor the safety of a newly licensed vaccine. ObjectiveThis study aimed to conduct active surveillance of human papillomavirus (HPV) vaccine safety based on this information platform. MethodsIn 2017, one of China’s most mature information platforms with superior data linkage was selected. A structured questionnaire and open-ended interview guidelines were developed to investigate the feasibility of active surveillance following HPV vaccination using the regional health care information platform in Ningbo. The questionnaire was sent to participants via email, and a face-to-face interview was conducted to confirm details or resolve discrepancies. ResultsFive databases that could be considered essential to active surveillance of vaccine safety were integrated into the platform starting in 2015. Except for residents' health records, which had a coverage rate of 87%, the data sources covered more than 95% of the records that were documented in Ningbo. All the data could be inherently linked using the national identity card. There were 19,328 women who received the HPV vaccine, and 37,988 doses were administered in 2017 and 2018. Women aged 30-40 years accounted for the largest proportion. Quadrivalent vaccination accounted for 73.1% of total vaccination, a much higher proportion than that of bivalent vaccination. Of the first doses, 60 (60/19,328, 0.31%) occurred outside Ningbo. There were no missing data for vaccination-relevant variables, such as identity card, vaccine name, vaccination doses, vaccination date, and manufacturer. ICD-10 coding could be used to identify 9,180 cases using a predefined list of the outcomes of interest, and 1.88% of these cases were missing the identity card. During the 90 days following HPV vaccination, 4 incident cases were found through the linked vaccination history and electronic medical records. The combined incident rate of rheumatoid arthritis, optic neuritis, and Henoch-Schonlein purpura was 8.84/100,000 doses of bivalent HPV, and the incidence rate of rheumatoid arthritis was 3.75/100,000 doses of quadrivalent HPV. ConclusionsThis study presents an available approach to initiate an active surveillance system for adverse events following HPV vaccination, based on a regional health care information platform in China. An extended observation period or the inclusion of additional functional sites is warranted to conduct future hypothesis-generating and hypothesis-confirming studies for vaccine safety concerns.