Search

Medical procedure entity normalization is an important task to realize medical information sharing at the semantic level; it faces main challenges such as variety and similarity in real-world practice. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend on traditional context-independent word embeddings, and there is minimal research on medical entity recognition in Chinese Regarding the entity normalization task as a sentence pair classification task, we applied a three-step framework to normalize Chinese medical procedure terms, and it consists of dataset construction, candidate concept generation and candidate concept ranking. For dataset construction, external knowledge base and easy data augmentation skills were used to increase the diversity of training samples. For candidate concept generation, we implemented the BM25 retrieval method based on integrating synonym knowledge of SNOMED CT and train data. For candidate concept ranking, we designed a stacking-BERT model, including the original BERT-based and Siamese-BERT ranking models, to capture the semantic information and choose the optimal mapping pairs by the stacking mechanism. In the training process, we also added the tricks of adversarial training to improve the learning ability of the model on small-scale training data. Based on the clinical entity normalization task dataset of the 5th China Health Information Processing Conference, our stacking-BERT model achieved an accuracy of 93.1%, which outperformed the single BERT models and other traditional deep learning models. In conclusion, this paper presents an effective method for Chinese medical procedure entity normalization and validation of different BERT-based models. In addition, we found that the tricks of adversarial training and data augmentation can effectively improve the effect of the deep learning model for small samples, which might provide some useful ideas for future research.

The fifth generation (5G) aims to connect massive amounts of devices with higher reliability, lower latency, and faster transmission speed, which are vital for implementing e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from the physical layer, upper layer, and cross-layer perspectives on designing 5G e-health systems. We then elaborate two use cases according to our implementations (i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment). We finally envision the future research trends and challenges of 5G e-health systems.

The key features of 5G network (i.e., high bandwidth, low latency, and high concurrency) along with the capability of supporting big data platforms with high mobility make it valuable in coping with emerging medical needs, such as COVID-19 and future healthcare challenges. However, enforcing the security aspect of a 5G-based smart healthcare system that hosts critical data and services is becoming more urgent and critical. Passive security mechanisms (e.g., data encryption and isolation) used in legacy medical platforms cannot provide sufficient protection for a healthcare system that is deployed in a distributed manner and fail to meet the need for data/service sharing across "cloud-edge-terminal" in the 5G era. In this article, we propose a security awareness and protection system that leverages zero-trust architecture for a 5G-based smart medical platform. Driven by the four key dimensions of 5G smart healthcare including "subject" (i.e., users, terminals, and applications), "object" (i.e., data, platforms, and services), "behavior," and "environment," our system constructs trustable dynamic access control models and achieves real-time network security situational awareness, continuous identity authentication, analysis of access behavior, and fine-grained access control. The proposed security system is implemented and tested thoroughly at industrial-grade, which proves that it satisfies the needs of active defense and end-to-end security enforcement of data, users, and services involved in a 5G-based smart medical system.

Background Hepatocyte growth factor (HGF) binds to the c-mesenchymal-epithelial transition (C-MET) receptor and activates downstream signaling pathways, playing an essential role in the development of various cancers. Given the role of this signaling pathway, the primary therapeutic direction focuses on identifying and designing HGF inhibitors, antagonists and other molecules to block the binding of HGF to C-MET, thereby limiting the abnormal state of other downstream genes. Methods This study focuses on the analysis of immune-related genes and corresponding immune functions that are significantly associated with the HGF/c-MET pathway using transcriptome data from 11 solid tumors. Results We systematically analyzed 11 different cancers, including expression correlation, immune infiltration, tumor diagnosis and survival prognosis from HGF/c-MET pathway and immune regulation, two biological mechanisms having received extensive attention in cancer analysis. Conclusion We found that the HGF/c-MET pathway affected the tumor microenvironment mainly by interfering with expression levels of other genes. Immune infiltration is another critical factor involved in changes to the tumor microenvironment. The downstream immune-related genes activated by the HGF/c-MET pathway regulate immune-related pathways, which in turn affect the degree of infiltration of immune cells. Immune infiltration is significantly associated with cancer development and prognosis.

Esophageal cancer (EC) is one of the most common malignant tumors of the digestive system with high incidence and mortality rate worldwide. Therefore, exploring the pathogenesis of EC and searching for new targeted therapies are the current research hotspot for EC treatment. Long non‐coding RNAs (lncRNAs) are endogenous RNAs with more than 200 nucleotides, but without protein‐coding function. In recent years, lncRNAs have gradually become the focuses in the field of non‐coding RNA. Some lncRNAs have been proved to be closely related to the pathogenesis of EC. Many lncRNAs are abnormally expressed in EC and participate in many biological processes including cell proliferation, apoptosis, and metastasis by inhibiting or promoting target gene expression. LncRNAs can also regulate the progression of EC through epithelial‐mesenchymal transformation (EMT), which is closely related to the occurrence, development, and prognosis of EC. In this article, we review and discuss the involvement of lncRNAs in the progression of EC., LncRNAs play complex and diverse roles in EC by mediating proliferation, cell cycle, apoptosis, invasion, migration, EMT, drug resistance, etc., and are closely related to EC clinicopathology and prognosis.

In-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA’s ICI suppression ability and the system capacity performance.

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients’ ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients’ EMRs and specialists’ long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists’ activity and patients’ perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists’ profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists’ activity and patients’ perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients’ demand and service providers’ capabilities, and providing beneficial insights for data-driven telemedicine services.

Mobile medicine plays a significant role in optimizing medical resource allocation, improving medical efficiency, etc. Identifying and analyzing user concern elements from active online reviews can help to improve service quality and enhance product competitiveness in a targeted manner. Based on the latent Dirichlet allocation (LDA) topic model, this study carries out a topic-clustering analysis of users’ online comments and builds an evaluation index system of mobile medical users’ satisfaction by using grounded theory. After that, the evaluation information of users is obtained through an emotional analysis of online comments. Then, in order to fully consider the uncertainty of decision makers’ evaluations, rough number theory and the fuzzy comprehensive evaluation method are used to confirm the conclusions of experts and indicators and to evaluate the satisfaction of mobile medical users. The empirical results show that users are satisfied with the service quality and content quality of mobile medical apps, and less satisfied with the management and technology qualities. Therefore, this paper puts forward corresponding countermeasures from the aspects of strengthening safety supervision, strengthening scientific research, strengthening information audit, attaching importance to service quality management and strengthening doctors’ sense of gain. This study uses text mining for index extraction and satisfaction analysis of online reviews to quantitatively evaluate user satisfaction with mobile medical apps, providing a reference for the improvement of mobile medical apps. However, there are still certain shortcomings in the current study, and subsequent studies can screen false reviews for a deeper study of online review information.

Background: Most secondary transmission of COVID-19 is occurring in a hospital setting. To decrease person-to-person contact, health care providers have built many isolation wards. However, out-of-hospital professionals cannot access patient information, which has greatly reduced the efficiency of treatment; it is inconvenient for health care professionals to issue a case discussion with professionals from other wards. This article mainly introduces a mobile telehealth system (MTS) applied to facilitate patient information presentation and case discussion. Materials and Methods: The MTS searches patient information, which is stored in hospital intranet, and uses five modules to display patient information. By a request/response module and a real-time interaction module, we successfully conducted case discussions. In addition, we took measures in three areas to prevent patient information leakage. Results: The system uses mobile collaboration technology to present patient information and support case discussion. MTS was officially launched for 37 days, during which it has been used 3,061 times. Conclusions: The building of the MTS not only provides convenience and benefit for health care professionals, but also reduces person-to-person contact.

Service is now considered as the basis of exchange and defined as “the use and application of competencies and knowledge for mutual benefits”. In the current stream of development, service systems emerge as widely accepted, interesting and logical area to study the concepts related to service interactions. Service systems are dynamic entities which interact with each other and integrate their resources to co-create value. This paper characterizes Akhuwat Foundation Microfinance project as a Service System with the application of service system framework developed by Lyon and Tracy. The purpose of the study is to evaluate Akhuwat microfinance project to understand its service activities and to define business exchanges from the perspective of reciprocal benefits. Assuming the interprevitist assumptions, a qualitative approach was adopted and data was gathered through in-depth interviews with the employees of Akhuwat microfinance project. Data analysis revealed that Akhuwat microfinance is in fact, a true representation of a service system. Their objectives, processes, control and performances are service oriented. The main focus of transactions with donors, borrowers and other network partners is poverty alleviation, financial empowerment and a sustainable society. This paper motivates future researchers to apply the service system framework in other sectors, and characterize organizations that are, in its true essence service oriented.

To explore health care professionals' usage and perceptions of telemedicine, including their experience, evaluation and attitude towards telemedicine, and the factors affecting their satisfaction, using intention, and willingness to recommend telemedicine.A nationwide and web-based survey was conducted. Statistical charts were used to describe the usage and perceptions of telemedicine by health care professionals. And the ordinal logistic regression was applied to analyze the influencing factors.A total of 1349 valid questionnaires were collected. In the survey, 74.0 % of Chinese health care professionals used telemedicine once a week. The average duration of participating in telemedicine services lasted mainly 11-30 min (64.0 %). More than half (52.5 %) of participants' hospitals adopted fee subsidy measures, followed by the award for excellent evaluation (36.4 %). The majority (92.5 %) believed that telemedicine could reduce patient referral rates. More than 95 % were satisfied with telemedicine, were willing to continue, and would recommend telemedicine for patients, respectively. The main problems existing were: incomplete system equipment, insufficient timeliness, high costs, inability to reimburse medical insurance, non-standardized medical records, inadequate publicity, cumbersome processes and long waiting time. Ordinal logistic regression showed that too short service duration (≤10 min) could significantly reduce medical professionals' overall satisfaction, using intention and willingness to recommend (P = 0.026, P = 0.017 and P = 0.040 respectively), while the convenience of the operating system had a significant positive impact (P = 0.005, P = 0.003 and P = 0.001 respectively). And cost subsidy incentive and the promotion of professional titles could significantly enhance their overall satisfaction(P = 0.006, P = 0.030), using intention (P = 0.011, P = 0.001), and willingness to recommend (P = 0.040, P = 0.004).The usage of telemedicine is relatively insufficient. Most health care professionals have fairly positive opinions toward telemedicine. The most mentioned issues and expectations for telemedicine can be roughly divided into four categories: infrastructure, service process, charge and cost, and popularity. It is possible to improve their evaluation by improving system convenience, enhancing service punctuality, avoiding excessively short service duration and adopting incentive measures.

Skeletal bone age assessment is widely applied in growth prediction and auxiliary diagnosis of medical problems. X-ray images of hands are observed in the evaluation of bone age, where the ossification centers of epiphysis and carpal bones are the key regions. Traditional skeletal bone age assessment methods extract these areas to predict the bone age but few of them can achieve satisfactory efficiency or accuracy. While automatic bone age assessment methods with deep learning techniques have achieved the leading performance, most of them can only accept fixed-size small images and ignore these key regions. In this paper, we take full consideration of the significant regions and propose a novel deep automated skeletal bone age assessment model via region-based convolutional neural network (R-CNN). We transfer Faster Region-based Convolutional Neural Network (Faster R-CNN) model from object detection to bone age regression in order to detect the ossification centers of epiphysis and carpal bones and evaluate bone age. The proposed model has overcome the limitation of other CNN based models, taking large-scale original X-ray images as inputs. It can automatically extract the features, detect the key regions and further predict the bone age. To validate the effectiveness of the proposed model, we realized different prior methods and conducted a series of experiments on two data sets using 10-fold cross-validation to compute the Mean absolute errors (MAEs). The results show that the MAEs of the proposed model are 0.51 and 0.48 years old respectively, better than other bone age assessment methods including state of the art.

BACKGROUND: Telemedicine is playing an increasingly more important role in disease diagnosis and treatment. The market of telemedicine application is continuously promoted, thus bringing some issues on telemedicine operations management. OBJECTIVE: We aimed to compare the teleconsultation scheduling performance of newly designed proactive strategy and existing static strategy and explore the decision-making under different conditions. METHODS: We developed a discrete-event simulation model based on practical investigation to describe the existing static scheduling strategy of teleconsultation. The static strategy model was verified by comparing it with the historical data. Then a new proactive strategy was proposed, whose average waiting time, variance of waiting time and completed numbers were compared with the static strategy. RESULTS: The analysis indicated that the proactive strategy performed better than static under the current resource allocation. Furthermore, we explored the impact on the system of both strategies varying arrival rate and experts’ shift time. CONCLUSIONS: Under different shift times and arrival rates, the managers of telemedicine center should select different strategy. The experts’ shift time had a significant impact on all system performance indicators. Therefore, if managers wanted to improve the system performance to a greater extent, they needed to reduce the shift time as much as possible.

BACKGROUND Telemedicine that combines information technology and health care augments the operational model of traditional medical services and brings new opportunities to the medical field. China promotes telemedicine with great efforts, and its practices in the deployment of telemedicine platforms and delivery of services have become important references for the research and development in this field. OBJECTIVE Our work described in this paper focuses on a regional telemedicine platform that was built in 2014. We analyzed the system design scheme and remote consultations that were conducted via the system to understand the deployment and service delivery processes of a representative telemedicine platform in China. METHODS We collected information on remote consultations conducted from 2015 to 2020 via the regional telemedicine platform that employs a centralized architectural system model. We used graphs and statistical methods to describe the changing trends of service volume of remote consultation, geographical and demographic distribution of patients, and waiting time and duration of consultations. The factors that affect consultation duration and patient referral were analyzed by multivariable linear regression models and binary logistic regression models, respectively. The attitudes toward telemedicine of 225 medical practitioners and 225 patients were collected using the snowball sampling method. RESULTS The regional telemedicine platform covers all levels of medical institutions and hospitals in all 18 cities of Henan Province as well as some interprovince hospitals. From 2015 to 2020, 103,957 remote medical consultations were conducted via the platform with an annual increasing rate of 0.64%. A total of 86.64% (90,069/103,957) of medical institutions (as clients) that applied for remote consultations were tier 1 or 2 and from less-developed regions; 65.65% (68,243/103,945) of patients who applied for remote consultations were aged over 50 years. The numbers of consultations were high for departments focusing in the treatment of chronic diseases such as neurology, respiratory medicine, and oncology. The invited experts were mainly experienced doctors with senior professional titles. Year of consultation, tier of hospital, consultation department, and necessity of patient referral were the main factors affecting the duration of consultations. In surveys, we found that 60.4% (136/225) of medical practitioners and 53.8% (121/225) of patients had high satisfaction and believed that telemedicine is of vital importance for the treatment of illness. CONCLUSIONS The development of telemedicine in China shows a growing trend and provides great benefits especially to medical institutions located in less developed regions and senior citizens who have less mobility. Cases of remote consultations are mainly for chronic diseases. At present, the importance and necessity of telemedicine are well recognized by both patients and medical practitioners. However, the waiting time needs to be further reduced to improve the efficiency of remote medical services.

As the fifth generation (5G) network comes to the fore, the realization of 5G-enabled service has attracted much attention from both healthcare academics and practitioners. In particular, 5G-enabled emergency ambulance service allows to connect a patient and an ambulance crew at an accident scene or in transit with the awaiting emergency department team at the destination hospital seamlessly so as to improve the rescue rate of patients. However, the application of the 5G network in ambulance service currently lacks a reliable solution and simulation testing of performance in the existing literature. To achieve this end, the primary aim of this study is to propose a 5G-enabled smart ambulance service and then test the quality of service of the proposed solution in experimental settings. We also consider emergency scenarios to investigate the task completion and accuracy of 5G-enabled smart ambulance, and to verify the superiority of our proposed solution. Our study explores the value of a 5G-en-abled smart ambulance and provides practical insights for 5G network construction, business development, and network optimization of smart ambulance service.

Background The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. Objective To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. Methods A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space–time scan statistic were conducted. Results The high incidence stage of China’s COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran’s I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P Conclusions Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

Hospitals are suffering from a critical challenge induced by the rapid spread of Coronavirus disease 2019 (COVID-19). Not only have patients been marginalized, but many clinicians working in the region-al hospitals have limited access to the specialist consultations and treatment guidelines they need from provincial-level hospitals to manage pneumonia cases caused by COVID-19. Telemedicine has been acknowledged as a breakthrough technology in combating epidemics. This study aims to demonstrate the implementation of Emergency Telemedicine Consultation System (ETCS) since COVID-2019 first emerged in Henan Province, beginning in late December 2019. This system was developed for coronavirus care across 126 connected hospitals, serving as the overarching authoritative source for diagnostic decision making and knowledge sharing for treatment. The information shared could rapidly expand to enable open collaborations with key stakeholders such as government authorities, research institutions and laboratories. The experience from building this system during this crisis can provide insights to guide public health institutions as they implement telemedicine to increase resilience to future epidemic outbreaks.

High-quality medical infrastructures and resources are mostly located in big cities. Remote areas are lacking of such medical resources. A rapid siphon effect of big cities makes the medical resources of remote areas even worse. It is widely agreed that technology development should lead to social progress and make human life more convenient; but it should not distinguish people according to their living places or any other characteristics. In order to alleviate the differences in medical conditions, solutions that can provide high-quality medical services to people living in remote areas are thus of great significance. Among them, fifth generation technology-enabled remote health systems (5G remote health) is a promising technology solution.

To analyze the regional epidemic features of coronavirus disease 2019 (COVID-19) in Henan Province, China.According to the data of COVID-19 patients and the resident population at the end of 2018 in Henan Province, statistical description and analysis of epidemiological characteristics of COVID-19 in Henan Province were conducted, including the time distribution, population distribution, and regional distribution.The cumulative incidence of COVID-19 in Henan Province was 1.32/100 000, the cure rate was 98.03%, and the fatality rate was 1.73% by March 9, 2020. The incidence curve showed that the epidemic peak reached from January 24 to January 28. The high-incidence area was Xinyang, with a standardized cumulative incidence rate of 4.36/100 000. There were 580 female COVID-19 patients (45.60%), 688 males (54.09%) in Henan Province. The incidence of males was 1.41/100 000, while the incidence of females was 1.23/100 000. The age with the highest incidence of COVID-19 in Henan Province was 20-69 years old (88.68%). The incidence rate was highest in men aged 30-39 (2.51/ 100 000), while the lowest rate in women aged 0-9 (0.16/100 000). There were 1 225 local patients (96.31%), and the rural patients (45.73%) were slightly higher than the urban patients (44.02%) in Henan Province. A total of 63.60% patients had traveled or lived in Hubei or contacted with people who came from Hubei to Henan. The proportion of patients whose family members suffered from COVID-19 was 32.70%. Global spatial autocorrelation analysis suggested that there was a statistically significant positive correlation in the spatial distribution of COVID-19 patients in Henan Province (Moran'sThere are differences in the morbidity and mortality of COVID-19 patients in different areas of Henan Province, with epidemic peak reaching from January 24 to January 28. Henan is dominated by local patients, male patients, and patients with contact history in Hubei. The space appears to be moderately clustered.

BACKGROUND Whole-slide imaging allows the entire slide to be viewed in a manner that simulates microscopy; therefore, it is widely used in telepathology. However, managing the large digital files needed for whole-slide imaging is difficult. To solve this problem, we set up the Chinese National Cloud-Based Telepathology System (CNCTPS). CNCTPS has been running for more than 4 years and has accumulated a large amount of data. OBJECTIVE The main purpose of this study was to comprehensively evaluate the effectiveness of the CNCTPS based on a large sample. The evaluation indicators included service volume, turnaround time, diagnosis accuracy, and economic benefits. METHODS Details of 23,167 cases submitted to the CNCTPS from January 2016 to December 2019 were collected to analyze the service volume, turnaround time, and economic benefits. A total of 564 patients who visited the First Affiliated Hospital of Zhengzhou University and obtained final diagnoses were followed up to analyze the diagnostic accuracy of the CNCTPS. RESULTS From 2016 to 2019, the service volume of the CNCTPS increased from 2335 to 9240, and the number of participating hospitals increased from 60 to 74. Consultation requests from county-level hospitals accounted for 86.57% (20,287/23,167). A total of 17,495 of 23,167 cases (75.52%) were confirmed, including 12,088 benign lesions, 5217 malignant lesions, and 190 borderline lesions. Of the cases, 3.85% (893/23,167) failed to be diagnosed for reasons such as poor slice quality and incomplete sampling. The median turnaround time was 16.93 hours and was shortened yearly (between 2018 and 2019: adjusted P=.01; other groups: adjusted PP=.55). By using this system, a total of US $300,000 was saved for patients every year. CONCLUSIONS The novel cloud-based telepathology system has the potential to relieve the shortage of pathologists in primary hospitals. It can also simultaneously reduce medical costs for patients in China. It should, therefore, be further promoted to enhance the efficiency, quantity, and quality of telepathology diagnoses.

Background Due to the increasing complexity in socioeconomic environments and the ambiguity in human cognition, decision makers prefer to give linguistic cognitive information with different granularities according to their own preferences. Consequently, to consider the uncertainty and preferences in the evaluation process, a method based on Multi-Granularity Linguistic Information (MGLI) for evaluating teleconsultation service quality is proposed, which provides a new research direction for scientific evaluation and improvement of teleconsultation service quality. Methods Firstly, this paper explored a service quality evaluation system from the perspective of regional doctors. And then considering the uncertainty and preferences of decision makers, MGLI was used to optimize the index system according to the similarity degree between the linguistic evaluation information and a given linguistic term set. Finally, the empirical research was conducted using Henan Province Telemedicine Center of China (HTCC) as an example to identify the direction for improving the service quality in teleconsultation. Results This study found that the number of consulting rooms, attitude of operators, consultation duration, charges, and attitude of experts are the key factors affecting the quality of teleconsultation service. Conclusions Suggestions for improving the quality of teleconsultation service are put forward in terms of optimizing the allocation of consulting rooms, improving regional doctors’ experience and standardizing charging standards, which provides a new direction for improving the quality of teleconsultation service.

Background Myocardial Infarction (MI) is a fatal cardiovascular system disease. At present, the diagnosis of MI patients is mainly based on the patient's clinical manifestations, dynamic changes in electrocardiogram (ECG), and changes in myocardial enzymes. ECG is insufficient to diagnose an acute coronary syndrome or acute myocardial infarction, since ST-segment deviation might be also present in other conditions, such as acute pericarditis and early repolarization patterns. Given the low specificity and effectiveness of the current diagnostic strategies, an accurate diagnostic approach based on the level of gene expression is urgently needed in the clinic. Methods and results We compared the gene's expression between MI patients and normal samples. The RNAseq data were downloaded from the GEO database. Differentially expressed genes underwent a feature selection process, and the signatures were selected to train a machine-learning model. In this study, we identified the risk genes associated with MI as signatures and uses the SVM to establish a diagnostic model. The accuracy of the model on discovery data is 0.87, which significantly improves the diagnostic efficiency of early detection of MI patients (MIPs). Two independent datasets were applied to verify the diagnostic model. Our model can effectively distinguish the control group from the disease group. Conclusions We used risk genes to construct a diagnostic model for MI diagnosis, which can effectively distinguish MIPs from normal samples in the both of the discovery data and validation data. In the validation data, we found that percutaneous coronary intervention could indeed reverse MI to a certain extent, and the gene expression level of patients treated with percutaneous coronary intervention (PCI) was closer to the normal state.

BACKGROUND The internet has caused the explosive growth of medical information and has greatly improved the availability of medical knowledge. This makes the internet one of the main ways for residents to obtain medical information and knowledge before seeking medical treatment. However, little has been researched on how the internet affects medical decisions. OBJECTIVE The purpose of this study was to explore the associations between internet behaviors and medical decisions among Chinese adults aged 18 or over, including whether to go to the hospital and which level of medical institution to choose. METHODS With the adult residents (≥18 years old) in 12 regions including urban and rural areas taken as the research objects, the differences in medical choices of adults with various characteristics were analyzed, and generalized linear mixed models were adopted to analyze the longitudinal data of the China Health Nutrition Survey from 2006 to 2015. RESULTS Adult groups with different ages, genders, education levels, regions, places of residence, severities of illness and injury, years of suffering from hypertension, and history of chronic diseases showed diverse medical decisions, and the differences were statistically significant (PP=.03) times that of residents who did not participate in online browsing activities. In terms of medical institution choices, adults who participated in online browsing activities were 1.86 (95% CI 1.35-2.58; PP=.59). Robust analysis verified that accessing the internet had a similar effect on Chinese adults’ medical decisions. CONCLUSIONS Chinese adults who use the internet are a little less likely to go to the hospital than self-care. The internet has broken down the barriers to obtain knowledge of common diseases and thus has a slight substitution effect of self-care on hospital care. Internet use may increase the probability of adults going to municipal hospitals. The rising tendency of visiting high-level medical institutions may be consequently exacerbated due to knowledge monopoly of severe and complicated diseases that is difficult to eliminate, and the increase in inconsistent and incomplete medical information online will blur the residents’ cognitive boundary of common diseases and severe diseases. Exploring the substantive impact of the internet on medical decision making is of great significance for further rational planning and utilization of the internet, in order to guide patients to appropriate medical institution.

BACKGROUND Telemedicine has been used widely in China and has benefited a large number of patients, but little is known about the overall development of telemedicine. OBJECTIVE The aim of this study was to perform a national survey to identify the overall implementation and application of telemedicine in Chinese tertiary hospitals and provide a scientific basis for the successful expansion of telemedicine in the future. METHODS The method of probability proportionate to size sampling was adopted to collect data from 161 tertiary hospitals in 29 provinces, autonomous regions, and municipalities. Charts and statistical tests were applied to compare the development of telemedicine, including management, network, data storage, software and hardware equipment, and application of telemedicine. Ordinal logistic regression was used to analyze the relationship between these factors and telemedicine service effect. RESULTS Approximately 93.8% (151/161) of the tertiary hospitals carried out telemedicine services in business-to-business mode. The most widely used type of telemedicine network was the virtual private network with a usage rate of 55.3% (89/161). Only a few tertiary hospitals did not establish data security and cybersecurity measures. Of the 161 hospitals that took part in the survey, 100 (62.1%) conducted remote videoconferencing supported by hardware instead of software. The top 5 telemedicine services implemented in the hospitals were teleconsultation, remote education, telediagnosis of medical images, tele-electrocardiography, and telepathology, with coverage rates of 86.3% (139/161), 57.1% (92/161), 49.7% (80/161), 37.9% (61/161), and 33.5% (54/161), respectively. The average annual service volume of teleconsultation reached 714 cases per hospital. Teleconsultation and telediagnosis were the core charging services. Multivariate analysis indicated that the adoption of direct-to-consumer mode (P=.003), support from scientific research funds (P=.01), charging for services (PP=.04), network type (P=.02), sharing data with other hospitals (P=.04), and expertise level (P=.03) were related to the effect of teleconsultation. Direct-to-consumer mode (P=.01), research funding (P=.01), charging for services (P=.01), establishment of professional management departments (P=.04), and 15 or more instances of remote education every month (P=.01) were found to significantly influence the effect of remote education. CONCLUSIONS A variety of telemedicine services have been implemented in tertiary hospitals in China with a promising prospect, but the sustainability and further standardization of telemedicine in China are still far from accomplished.

The rapid spread of Coronavirus disease 2019 (COVID-19) presents China with a critical challenge. As normal capacity of the Chinese hospitals is exceeded, healthcare professionals struggling to manage this unprecedented crisis face the difficult question of how best to coordinate the medical resources used in highly separated locations. Responding rapidly to this crisis, the National Telemedicine Center of China (NTCC), located in Zhengzhou, Henan Province, has established the Emergency Telemedicine Consultation System (ETCS), a telemedicine-enabled outbreak alert and response network. ETCS is built upon a doctor-to-doctor (D2D) approach, in which health services can be accessed remotely through terminals across hospitals. The system architecture of ETCS comprises three major architectural layers: (1) telemedicine service platform layer, (2) telemedicine cloud layer, and (3) telemedicine service application layer. Our ETCS has demonstrated substantial benefits in terms of the effectiveness of consultations and remote patient monitoring, multidisciplinary care, and prevention education and training.

The chemokine system is a complex arrangement of molecules that attract leukocytes to the site of injury or inflammation. This chemotactic behavior gives the system the name "Chemokine." The intricate and redundant nature of the chemokine system has made it a subject of ongoing scientific investigation. Obesity is characterized as low-grade systemic or chronic inflammation that is responsible for the release of cytokines, adipokines, and chemokines. Excessive tissue fat expansion triggers the release of chemokines, which in turn attract various leukocytes and activate the resident immune surveillance system, eventually leading to worsening of obesity and other related comorbidities. To date, 50 chemokines and 20 chemokine receptors that belong to the G-protein-coupled receptor family have been discovered, and over the past two decades, the physiological and pathological roles of many of these chemokines and their receptors have been elucidated. The objective of this review is to present an update on the link between chemokines and obesity under the light of recent knowledge.

To date, greenhouse gas (GHG) emissions, mitigation strategies and the accompanying health co-benefits in different economic sectors have not been fully investigated. The purpose of this paper is to review comprehensively the evidence on GHG mitigation measures and the related health co-benefits, identify knowledge gaps, and provide recommendations to promote further development and implementation of climate change response policies. Evidence on GHG emissions, abatement measures and related health co-benefits has been observed at regional, national and global levels, involving both low- and high-income societies. GHG mitigation actions have mainly been taken in five sectors: energy generation, transport, food and agriculture, household and industry, consistent with the main sources of GHG emissions. GHGs and air pollutants to a large extent stem from the same sources and are inseparable in terms of their atmospheric evolution and effects on ecosystem; thus, GHG reductions are usually, although not always, estimated to have cost effective co-benefits for public health. Some integrated mitigation strategies involving multiple sectors, which tend to create greater health benefits. The pros and cons of different mitigation measures, issues with existing knowledge, priorities for research, and potential policy implications were also discussed. Findings from this study can play a role not only in motivating large GHG emitters to make decisive changes in GHG emissions, but also in facilitating cooperation at international, national and regional levels, to promote GHG mitigation policies that protect public health from climate change and air pollution simultaneously.