To establish a TaqMan-based multiplex RT-qPCR method for the identification of Japa-nese encephalitis virus(JEV),Porcine reproductive and respiratory syndrome virus(PRRSV),and Classical swine fever virus(CSFV),this study designed and synthesized three pairs of specific primers and probes based on the conserved sequences of JEV E,PRRSV ORF6,and CSFV E2 a-vailable in the NCBI GenBank.By optimizing the reaction system and protocol,a multiplex RT-qPCR method for detecting these three viruses was developed and applied to the detection of clini-cal samples.The results showed that the established TaqMan multiplex RT-qPCR specifically am-plified the gene fragments of JEV,PRRSV,and CSFV,and did not amplify other non-target genes,indicating good specificity of the method.Intra-assay and inter-assay repeatability tests showed that the coefficient of variation(Cv)values were all below 3％,demonstrating that the method has ex-cellent repeatability.Sensitivity tests revealed that the minimum detectable amount for the recom-binant plasmids of the three viruses was 100 copies/pL.Using the established method,a total of 969 samples,including blood,aborted fetuses,semen,and deceased pigs,from 26 pig farms in Guizhou Province were tested.The detection rates were 34.3％(332/969)for JEV,28.3％(274/969)for PRRSV,and 19.8％(192/969)for CSFV.The co-infection rates were 10.1％(98/969)for JEV and PRRSV,12.1％(117/969)for JEV and CSFV,and 14.6％(141/969)for CSFV and PRRSV.Additionally,the triple co-infection rate of JEV,PRRSV,and CSFV was 7.9％(77/969).These results indicate that the TaqMan multiplex RT-qPCR method developed in this study is ef-fective for detecting these three viruses in pig farms,providing technical support for identifying vi-ral causes of reproductive disorders.

To establish a TaqMan-based multiplex RT-qPCR method for the identification of Japa-nese encephalitis virus(JEV),Porcine reproductive and respiratory syndrome virus(PRRSV),and Classical swine fever virus(CSFV),this study designed and synthesized three pairs of specific primers and probes based on the conserved sequences of JEV E,PRRSV ORF6,and CSFV E2 a-vailable in the NCBI GenBank.By optimizing the reaction system and protocol,a multiplex RT-qPCR method for detecting these three viruses was developed and applied to the detection of clini-cal samples.The results showed that the established TaqMan multiplex RT-qPCR specifically am-plified the gene fragments of JEV,PRRSV,and CSFV,and did not amplify other non-target genes,indicating good specificity of the method.Intra-assay and inter-assay repeatability tests showed that the coefficient of variation(Cv)values were all below 3％,demonstrating that the method has ex-cellent repeatability.Sensitivity tests revealed that the minimum detectable amount for the recom-binant plasmids of the three viruses was 100 copies/pL.Using the established method,a total of 969 samples,including blood,aborted fetuses,semen,and deceased pigs,from 26 pig farms in Guizhou Province were tested.The detection rates were 34.3％(332/969)for JEV,28.3％(274/969)for PRRSV,and 19.8％(192/969)for CSFV.The co-infection rates were 10.1％(98/969)for JEV and PRRSV,12.1％(117/969)for JEV and CSFV,and 14.6％(141/969)for CSFV and PRRSV.Additionally,the triple co-infection rate of JEV,PRRSV,and CSFV was 7.9％(77/969).These results indicate that the TaqMan multiplex RT-qPCR method developed in this study is ef-fective for detecting these three viruses in pig farms,providing technical support for identifying vi-ral causes of reproductive disorders.

OBJECTIVE T o carry out the health economics evaluation and cost-benefit analysis of the type b Hae-mophilus influenzae(Hib)vaccination for the children who were hospitalized due to Hib infection so as to provide evidence for public health policies.METHODS The children who were diagnosed with Hib-related respiratory tract infections or meningitis and were hospitalized in respiratory medicine department,infection management depart-ment,emergency rooms and neurology department of Jiangxi Provincial Children's Hospital from Jan.1,2021 to Dec.31,2023 were recruited as the research subjects.Based on a 1∶1 matching condition,the matching variables included four items such as the same age for the admission to the hospital,same gender,same department and same grade of disease severity.The children for whom the primary immunization of Hib vaccination(including Hib monovalent vaccine and Hib-containing combination vaccine)were completed and the integrity of vaccination infor-mation could be checked out were assigned as the intervention group,while the children for whom the primary im-munization of Hib vaccination was not completed were chosen as the control group.The clinical data,vaccination data and the data such as length of hospital stay and hospitalization cost were collected from the children.The cost-benefit of the Hib vaccination among the children with Hib infection was observed.RESULTS A total of 622 hospi-talized children who were detected with Hib-positive respiratory tract infections or meningitis were enrolled in the study,and 73 children(20 children from infection management department,27 children from respiratory medi-cine department,26 children from emergency rooms)were finally included in the intervention group after matc-hing and multiple rounds of screening,73 children were chosen as the control group based on a 1∶1 matching con-dition.The male children accounted for 57.53％(42 cases)in both groups,and the female children accounted for 42.47％(31 cases)in both groups.With the respect to the length of hospital stay,it was 7.00(5.00,8.00)days in the intervention group,7.00(6.00,8.00)days in the control group(Z=-0.341,P=0.733).In terms of the hospitalization cost,it was 7 756.17(6 617.92,10 617.69)yuan in the intervention group,9 040.65(8 033.76,10 935.84)yuan in the control group(Z=-2.795,P=0.005).The cost of Hib vaccination was 343.03 yuan per capita in the intervention group,and the benefit-cost ratio(BCR)was 1∶3.74(343.03 yuan/1 284.48 yuan).CONCLUSIONS The Hib vaccination can save the hospitalization cost and has high cost effectiveness.It is sugges-ted that the Hib vaccination should be promoted and the coverage rate of Hib vaccination should be raised among the age-eligible children.

OBJECTIVE T o carry out the health economics evaluation and cost-benefit analysis of the type b Hae-mophilus influenzae(Hib)vaccination for the children who were hospitalized due to Hib infection so as to provide evidence for public health policies.METHODS The children who were diagnosed with Hib-related respiratory tract infections or meningitis and were hospitalized in respiratory medicine department,infection management depart-ment,emergency rooms and neurology department of Jiangxi Provincial Children's Hospital from Jan.1,2021 to Dec.31,2023 were recruited as the research subjects.Based on a 1∶1 matching condition,the matching variables included four items such as the same age for the admission to the hospital,same gender,same department and same grade of disease severity.The children for whom the primary immunization of Hib vaccination(including Hib monovalent vaccine and Hib-containing combination vaccine)were completed and the integrity of vaccination infor-mation could be checked out were assigned as the intervention group,while the children for whom the primary im-munization of Hib vaccination was not completed were chosen as the control group.The clinical data,vaccination data and the data such as length of hospital stay and hospitalization cost were collected from the children.The cost-benefit of the Hib vaccination among the children with Hib infection was observed.RESULTS A total of 622 hospi-talized children who were detected with Hib-positive respiratory tract infections or meningitis were enrolled in the study,and 73 children(20 children from infection management department,27 children from respiratory medi-cine department,26 children from emergency rooms)were finally included in the intervention group after matc-hing and multiple rounds of screening,73 children were chosen as the control group based on a 1∶1 matching con-dition.The male children accounted for 57.53％(42 cases)in both groups,and the female children accounted for 42.47％(31 cases)in both groups.With the respect to the length of hospital stay,it was 7.00(5.00,8.00)days in the intervention group,7.00(6.00,8.00)days in the control group(Z=-0.341,P=0.733).In terms of the hospitalization cost,it was 7 756.17(6 617.92,10 617.69)yuan in the intervention group,9 040.65(8 033.76,10 935.84)yuan in the control group(Z=-2.795,P=0.005).The cost of Hib vaccination was 343.03 yuan per capita in the intervention group,and the benefit-cost ratio(BCR)was 1∶3.74(343.03 yuan/1 284.48 yuan).CONCLUSIONS The Hib vaccination can save the hospitalization cost and has high cost effectiveness.It is sugges-ted that the Hib vaccination should be promoted and the coverage rate of Hib vaccination should be raised among the age-eligible children.

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Intensive Care Units ; Internet of Things ; Humans ; Internet ; Data Collection

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

Intelligent medical devices are flourishing with the deep integration of modern information and artificial intelligence technologies into healthcare.Testing is an important means of performance evaluation and quality control for intelligent medical devices.Compared with traditional medical devices,the testing methods and technologies of intelligent medical devices are still immature,and need active research to promote the progress in this area.Intelligent medical devices are classified according to their characteristics as artificial intelligence medical devices in the form of software and medical robots based on a general discussion of their development.The medical-device Internet of Things(IoT)system has also been included due to its close relation to the construction of smart hospitals.For each type of intelligent medical device,testing indexes and testing plans are discussed.It is suggested that specific test rules should be further developed for various specific devices.Besides,the evaluation method of complex intelligent systems should be introduced and real-world data should be used for evaluation.This paper aims to accelerate the development of intelligent medical device testing,laying the foundation for quality control and performance evaluation of intelligent medical devices.

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Internet of Things ; Reproducibility of Results ; Internet ; Machine Learning ; Technology

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
Humans ; Artificial Intelligence ; Wearable Electronic Devices ; Monitoring, Physiologic/methods*

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.
Humans ; Artificial Intelligence ; Internet of Things ; Wearable Electronic Devices ; Monitoring, Physiologic/methods* ; Electrocardiography ; Internet