Reporting and literature analysis of misdiagnosis and therapy of one case with tuberculous meningoencephalitis under the coronavirus disease 2019 epidemic

Objective To improve the diagnosis and treatment level for tuberculous meningitis (TBM) under the Coronavirus disease 2019 (COVID-19) epidemic. Methods The diagnosis and treatment course of a female patient under the COVID-19 epidemic was analyzed for high fever, vomiting for 23 days, headache, talk nonsense for 10 days, inability to stand, and double vision lasting 5 days. The patient was successively misdiagnosed to suffer from viral pneumonia and acute disseminated encephalomyelitis (ADEM) in other hospitals. The patient had a history of transit at Hankou railway station (wearing a mask without departing the station throughout the process) under the COVID-19 epidemic. The patient had a history of leukopenia and long-term medical therapy. The patient was diagnosed as TBM by applying the diagnostic scheme for the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines after physical examination, cerebrospinal fluid test, magnetic resonance imaging (MRI) plain scan and enhanced examination. The analysis on reasons for extramural hospital misdiagnosis showed it was related to the lack of careful physical examination and lack of scientific analysis of laboratory test results. Results The intracranial pressure reduction, anti-tuberculosis treatment, adrenal cortex hormone treatment and symptomatic treatment were immediately administered according to the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines. Intensive anti-tuberculosis treatment (4 months) was implemented firstly and followed by the anti-tuberculosis treatment (12 months) during the consolidation phase, clinically enabling the patient to be cured. Conclusion Careful inquiry of medical history, careful physical examination, timely cerebrospinal fluid examination and MRI examination and scientific analysis on clinical data are critical to confirmation of TBM. Standard anti-tuberculosis treatment, rational use of adrenal cortex hormones and lowering intracranial pressure are critical factors for curing. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

Reporting and literature analysis of misdiagnosis and therapy of one case with tuberculous meningoencephalitis under the coronavirus disease 2019 epidemic

Objective To improve the diagnosis and treatment level for tuberculous meningitis (TBM) under the Coronavirus disease 2019 (COVID-19) epidemic. Methods The diagnosis and treatment course of a female patient under the COVID-19 epidemic was analyzed for high fever, vomiting for 23 days, headache, talk nonsense for 10 days, inability to stand, and double vision lasting 5 days. The patient was successively misdiagnosed to suffer from viral pneumonia and acute disseminated encephalomyelitis (ADEM) in other hospitals. The patient had a history of transit at Hankou railway station (wearing a mask without departing the station throughout the process) under the COVID-19 epidemic. The patient had a history of leukopenia and long-term medical therapy. The patient was diagnosed as TBM by applying the diagnostic scheme for the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines after physical examination, cerebrospinal fluid test, magnetic resonance imaging (MRI) plain scan and enhanced examination. The analysis on reasons for extramural hospital misdiagnosis showed it was related to the lack of careful physical examination and lack of scientific analysis of laboratory test results. Results The intracranial pressure reduction, anti-tuberculosis treatment, adrenal cortex hormone treatment and symptomatic treatment were immediately administered according to the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines. Intensive anti-tuberculosis treatment (4 months) was implemented firstly and followed by the anti-tuberculosis treatment (12 months) during the consolidation phase, clinically enabling the patient to be cured. Conclusion Careful inquiry of medical history, careful physical examination, timely cerebrospinal fluid examination and MRI examination and scientific analysis on clinical data are critical to confirmation of TBM. Standard anti-tuberculosis treatment, rational use of adrenal cortex hormones and lowering intracranial pressure are critical factors for curing. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

Examining the job burnout of Chinese hospitality management students in internships via the transactional model

The ongoing COVID-19 pandemic has increased the psychological burden on employees in hotels, which is not conducive to the development of the hospitality industry. Based on a survey of 379 hotel interns from higher vocational colleges in China, this study empirically analyzed the status quo of job burnout in future hotel employees and its influencing factors. The results showed that interns’ job burnout and reduced personal accomplishment were at a medium level. Secondly, according to the transaction model, this study classified the antecedents of job burnout into two categories: personal factors and contextual factors. The results showed that personal factors such as attitude and self-efficacy, and contextual factors such as perceived co-worker support and job satisfaction all had a negative effect on job burnout. However, the influence of ability and perceived supervisor support on job burnout was not significant. This study also investigated the influencing factors of each sub-dimension of job burnout. Self-efficacy, attitude and job satisfaction all had a negative influence on the three sub-dimensions. Ability and perceived co-worker support only had a negative impact on reduced personal accomplishment. There was no statistical correlation between perceived supervisor support and the three sub-dimensions. The results of this study will lay a theoretical foundation so that higher vocational colleges can better organize and implement internships, and hotels can recruit energetic future employees.

Inorganic-based Surface Materials with Anti-SARS-CoV-2 Properties and Their Mechanisms of Action

The COVID-19 outbreak caused by SARS-CoV-2 has posed a serious threat to human health. The wide⁃ spread of the virus has increased the demand for anti-virus surface materials，especially in public places. This article reviews a series of inorganic surface materials with antiviral properties，including metals and its derivatives，graphene and its derivatives，and zeolites，and their antiviral mechanisms. The challenges and development prospects are summarized and prospected. © 2022 Higher Education Press. All rights reserved.

CO-INTEGRATION AMONG COVID-19, INVESTOR SENTIMENT, AND THE STOCK MARKET

This study focuses on stock market performance during the COVID-19 pandemic, aiming to research the co-integration among COVID-19 cases, investor sentiment, and the stock market. The data for the study comprised index returns, trading volume, turnover rate, and volatility from CSI300 index from January 2020 to December 2021. The paper planned to introduce methods that used the autoregressive distributed lag model, cointegration test, and error correction model to achieve the aims. The results show that there is a long-term co-integration relationship among these variables. However, when we consider the long-term association between COVID-19 and investor sentiment and individual stock market variables separately, we find no association among COVID-19 and market volatility, trading volume, or turnover rates. From the perspective of investor sentiment, there is no long-term relationship between investor sentiment and market volatility. Therefore, the model results show that there is a long-term relationship among the variables only when the data are integrated, but this relationship does not always persist when considering individual variables. As the COVID19 still continues, the study results would have the implications for the investment decision-making and risk avoidance to face the pandemic. © 2022 Conscientia Beam. All rights reserved.

DEA-UNet: a dense-edge-attention UNet architecture for medical image segmentation

Classical UNet with an encoder and decoder structure and its variants perform very well in the field of medical image segmentation. They have a key similarity of a skip-connection, which combines deep, semantic, and coarse-grained feature maps from the decoder subnetwork with shallow, low-level, and fine-grained feature maps from the encoder subnetwork. We noted that, in many cases in medical image segmentation, the boundary of the segmentation target is fuzzy and complex. Traditional UNet cannot accurately segment these details. The main purpose is to solve the fuzzy boundary problem in medical image segmentation. To solve this problem, we combine the advantages of previous models and improve them and propose a new dense edge attention U-type network (DEA-UNet) for medical image segmentation. Starting from the traditional UNet, we modified the concat and skip-connection operations in the latter part. We designed an edge guidance module that fused the features of all layers. Starting from the upsample at the deepest layer, the reverse attention module was used step by step to extract features from high to low, and the edge guidance module was combined with it, so each layer could fully extract boundary details that were difficult to be noticed by previous models, thus solving the problem of the fuzzy boundary of the lesion region. We conducted experiments on two kinds of medical datasets (chest CT and colonoscopic polyp) and compared them with the traditional network. The experimental results showed that our DEA-UNet performed better in multiple indicators. In the segmentation of coronavirus disease-19 images, the results indicate that DEA-UNet has a Dice of 74.6%, sensitivity (Sen) of 70.8%, specificity (Spe) of 96.7%, structural measure (S-alpha) of 0.766%, enhanced-alignment measure (E-phi) of 0.910%, and mean absolute error (MAE) of 0.062%. Our DEA-UNET is 31%, 16%, 3%, and 0.7 and higher than the traditional medical segmentation model UNet, UNet++, the last model Few-shot UNet, and Inf-Net in Dice. In the segmentation of colonoscopic polyp dataset Kvasir, the results indicate that DEA-UNet has a Dice of 95%, structural measure (S-alpha) of 0.953%, enhanced-alignment measure (E-phi) of 0.974%, and MAE of 0.015%. Our DEA-UNet is 13%, 13%, 23%, and 5% higher than the traditional medical segmentation model UNet, UNet++, the last model SFA, and PraNet in Dice. In other evaluation metrics, our DEA-UNet also performed better. When designing DEA-UNet, we also consider the balance between model size and prediction accuracy. Experiments show that, by proper pruning, we can greatly reduce the number of model parameters while maintaining the accuracy of prediction results with little change. This proves that our DEA-UNET has great potential in the field of medical image segmentation.

National claims data analysis of outcomes of hospitalized cancer patients without COVID-19 infection during versus prior to the COVID-19 pandemic

Background: There has been growing concern regarding the impact of the COVID-19 pandemic on health care delivery and disruption of care to cancer patients. Reductions in cancer surgeries, delays in administration of life saving chemo and radiation therapies, and lower rates of cancer-related hospitalizations have been reported. While cancer patients with COVID-19 infection have poor hospitalization outcomes, less is known about the outcomes of hospitalized cancer patients without the infection. This study aimed to describe the impact of the COVID-19 pandemic on outcomes of the most common cancer-related hospital admissions for patients without COVID-19 infection at a national level using insurance claims. Given the concern for disruptions in their care, we hypothesized that hospitalized cancer patients may have worse outcomes. Methods: We used the Optum Clinformatics Data Mart, consisting of claims records linked to electronic health records, including an average of 8 million adult Americans per year enrolled for at least 6 months. We identified cancer-related hospitalizations from 02/2018-05/2021 and included patients with at least of one of these cancer types: breast, prostate, bladder, ovarian, cervical, lung, colorectal, esophageal, liver, small intestine, gastric, or gallbladder cancer. Patients with cancer-related hospitalization who had COVID-19 infection were excluded. The main outcome was “severe adverse outcome” and included at least one of the following: mortality during or within 30 days of hospitalization, mechanical ventilation during hospitalization, intensive care unit admission, or discharge to hospice. We used Poisson regression to compare the number of hospitalizations before (2/1/2018-1/30/2020) and during (2/1/2020-5/30/2021) the pandemic and a Chisquared test to compare the proportion of cancer-related hospitalizations with severe adverse outcomes over that time period in 4-month intervals and across cancer types, gender, (male vs female) and geography (the 9 Census Bureau regions). Results: There were 82,796 cancer-related hospitalizations in the period 2/2018-05/2021. A slightly higher proportion of cancer-related hospitalizations resulted in a severe adverse outcome during the pandemic as compared to prior to the pandemic (41.8% vs 40.9%;p = 0.012). There were no differences by cancer site, gender, or geography. The number of hospitalizations was lower during vs prior to the pandemic (p < 0.0001). Conclusions: The number of cancer-related hospitalizations during the pandemic was lower compared to before the, and a slightly higher proportion of those hospitalized experienced severe adverse outcomes among insured U.S. cancer patients without COVID-19 infection. The lower number of cancer-related hospitalizations during the pandemic warrants further investigation.

Burnout among early-career medical oncologists: A single-institution experience

Background: Burnout is a psychological syndrome defined by the Maslach Burnout Inventory (MBI) as emotional exhaustion, depersonalization, and a low sense of personal accomplishment. Risk of job-related burnout for early-career medical oncologists can significantly impact career longevity and health outcomes for providers and patients alike. Because little is known about burnout specific to early-career academic oncologists, we sought to characterize the prevalence of burnout and associated factors among Assistant Professors at MD Anderson Cancer Center (MDACC). Methods: For this IRB-approved retrospective study, an electronic survey was developed for Assistant Professors in medical oncology at MDACC. Participants were all involved directly in patient care with at least some clinical effort. Our survey included nine questions validated in the MBI addressing equally the 3 aforementioned domains of burnout. An additional 31 questions were formulated to assess personal and professional factors that may contribute to burnout at our institution (clinical workload, research expectations, communication, COVID, and home-life). Each question was scored on a scale of 1 to 5, with higher scores correlating to higher levels of burnout. Descriptive statistics were used to describe the prevalence of burnout, and logistic regression analyses were performed to identify characteristics associated with burnout. Results: Among 70 (of 86 total) Assistant Professors who responded, mean duration on faculty was 3.1 years (standard deviation +/-1.8). Mean clinical effort was 67% (range, 19-95). Gender identifications were 44% female, 54% male, and 2% non-binary. 54% of respondents reported symptoms of burnout already, including 21% endorsing severe burnout. Severe burnout was more common for solid tumor providers than liquid tumor providers (55% vs 13%, p =.03). Using the MBI, severe emotional exhaustion (25%) was more prevalent (p <.0001) than depersonalization (6%) or lack of personal accomplishment (17%). Sentiments of being “emotionally drained” (20%), fatigue to face another day on the job” (37%), and “becoming more callous” (30%) were especially concerning among early-career faculty. Emotional exhaustion was associated with a feeling of less autonomy over personal decision making (p =.03) and female gender (p =.04). Conclusions: Burnout exists with high prevalence among early-career medical oncologists in this single-institution analysis. Emotional exhaustion was the specific manifestation of burnout in this population. Further validation of these data nationwide is anticipated. Interventions focusing on reducing emotional exhaustion are under development to reduce medical oncology-specific burnout in an academic setting for faculty retention and for deliverance of optimal care to patients with cancer.

Recipe4Health: Preliminary Analysis of an Integrative Approach to Address Social Isolation and Food Insecurity during COVID-19

Methods: This 16-week intervention conducted at community health centers combines integrative group medical visits with produce prescriptions. Participating patients are adults diagnosed with chronic conditions including diabetes, hypertension and depression. Virtual integrative group medical visits meet weekly in Spanish or English with health coach support between sessions. Participants also receive weekly doorstep delivery of fresh vegetables grown using regenerative agriculture. Ongoing mixed-methods data collection includes: 1) semi-structured interviews with program staff and patients, and 2) pre-and post-program patient surveys including the 8-Item UCLA Loneliness Scale and the 6-item USDA short form for household food insecurity. Preliminary quantitative analysis uses mixed-effects models to assess the effects of participation in the combined intervention (N=185). Qualitative analysis uses reflexive thematic analysis (N=35). Results: Qualitative interview data explored program implementation and stakeholders' experiences with Recipe4Health during COVID-19. Patients and staff reported that virtual group visits provided social connection and supported mental health. Weekly produce delivery increased food security and provided access to new and familiar foods. Preliminary quantitative analysis included 185 patients: 83% female;51% Latin, 27% Black;61% spoke English as primary language, 39% Spanish. Average loneliness scores decreased from 5.2 to 4.7 (p<.04), despite notable national inc1reases in isolation and loneliness during COVID-19. While food insecurity doubled nationwide, the proportion of study participants reporting food insecurity or marginal food security decreased from 79% to 54% (p<.01). Background: Food insecurity has been associated with social isolation;both have risen dramatically in the US during the COVID-19 pandemic. This project, Recipe4Health, is implementing and assessing the impact of integrative group medical visits and produce prescriptions for low-income adults with chronic conditions. Conclusion: Combining integrative group medical visits and produce prescriptions can improve key patient outcomes including loneliness and food security in a pandemic context.

[Establishment of a rapid risk assessment system for imported COVID-19 cases]

Objective: To develop a rapid risk assessment tool for imported COVID-19 cases and provide reference evidences for prevention and control of COVID-19 at ports. Methods: The information about COVID-19 pandemic and control strategies of 12 concerned countries was collected during July to August 2021, and 12 indexes were selected to assess the importation risk of COVID-19 by risk matrix. Results: The risk for imported COVID-19 cases from 12 countries to China was high or extremely high, and the risk from Russia and the USA was highest. Conclusions: The developed rapid risk assessment tool based on the risk matrix method can be used to determine the risk level of countries for imported COVID-19 cases to China at ports, and the risk of imported COVID-19 was high at Beijing port in August 2021.

CCAT-NET: A Novel Transformer Based Semi-Supervised Framework For Covid-19 Lung Lesion Segmentation

The spread of the novel coronavirus disease 2019 (COVID-19) has claimed millions of lives. Automatic segmentation of lesions from CT images can assist doctors with screening, treatment, and monitoring. However, accurate segmentation of lesions from CT images can be very challenging due to data and model limitations. Recently, Transformer-based networks have attracted a lot of attention in the area of computer vision, as Transformer outperforms CNN at a bunch of tasks. In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions. We further propose an efficient semi-supervised learning framework to address the shortage of labeled data. Extensive experiments showed that our proposed network outperforms most existing networks and the semi-supervised learning framework can outperform the base network by 3.0% and 8.2% in terms of Dice coefficient and sensitivity. © 2022 IEEE.

Detection of SARS-CoV-2 RNA with a Simple Concentration Method in Wastewater in Turkey: A Pilot Study in Çorum

Introduction: Many studies have shown the advantages of monitoring wastewater in the evaluation of microbiological pathogens circulating in the community. It was aimed to detect of SARS-CoV-2 RNA with a simple concentration method in wastewater in this study. Materials and Methods: In our study, 7 wastewater samples were investigated, which were collected from the Urban Wastewater Treatment Plant (WWTP) of Çorum, between October to November 2020. Sorbent bags were left in water for 24 hours. Then they were used to trap and concentrate the virus. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) RNA detected by using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays. Results: As a result, 3 of the 7 samples taken were positive for N and ORF1ab target gene regions. Conclusion: This is the first study reporting the detection of SARS-CoV-2 RNA in wastewater with different concentration and capture method.

A Flexible Clinical Decision Support Model and its Application to COVID-19

The positive role of clinical decision support systems based on clinical guidelines in reducing medical errors and improving patient outcomes has been widely recognized. However, the knowledge in clinical guidelines is usually hard-coded into clinical decision support systems, making it difficult for these systems to adapt to the rapid changes of clinical guidelines. Knowledge being hard-coded into the system also means that the system is a black box, and doctors cannot understand the decision-making logic behind the system. These reasons make it difficult for clinical decision support systems to be applied on a large scale. This paper proposes a flexible clinical decision support model, which contains two key parts, namely the knowledge authoring environment and the knowledge execution environment. The transition of knowledge from hard-coded to flexible editing is illustrated in the COVID-19 case. This flexible method will be applied to more complex clinical problems in the future. © 2021 IEEE.

National claims data analysis of breast cancer diagnosis and treatment before versus during the COVID-19 pandemic

Background: The COVID-19 pandemic imposed great burden on the healthcare system and has required patients and their physicians to make unprecedented choices about cancer care. Hospital-based retrospective reviews have suggested changes in breast cancer management during 2020 compared to previous years, including greater use of preoperative therapy. We used insurance claims data to understand the impact of the pandemic on breast cancer diagnosis and treatment at a national level. Methods: We identified new diagnoses of breast cancer from 2017-2020 in the Optum Clinformatics claims data set, consisting of claims records linked to electronic health records. The overall population (enrolled in Optum for at least 6 months with at least one diagnosis of any condition and no prior breast cancer diagnosis) included an average of 8 million adult Americans per year. A new breast cancer diagnosis was defined as a first-ever ICD code for breast cancer with a breast diagnostic biopsy procedure code (considered the cancer diagnosis date) within 6 months before to 3 months after that ICD code. Each year's cohort of breast cancer cases was limited to those diagnosed between February 1 and May 30, with follow-up through June 30 of the diagnosis year. First treatment after diagnosis was classified as either endocrine therapy, chemotherapy, or surgery. Geographic area was defined by the 9 Census Bureau regions. We used a Poisson regression to compare the rate of breast cancer diagnosis in 2020 compared to 2017-2019 and a Chi-squared test to compare the distribution of first treatment in 2020 compared to 2017-2019. To investigate differences in the impact of the pandemic on rate of diagnosis (Poissonregression) or use of preoperative therapy (logistic regression) by race/ethnicity, income, or geographic area, we included each of these covariates as well as its interaction with year (2020 vs 2017-2019) in separate models. Results: There were 2, 841 breast cancer diagnoses February-May 2020 (0.037% of overall population), compared to 3, 880 in 2019 (0.045%), 3, 509 in 2018 (0.043%), and 2, 999 in 2017 (0.041%). In 2020 compared to 2017-2019, new breast cancer diagnoses decreased by 12.3% (95% CI 8.6%-15.9%;p < 0.0001). No significant differences were observed in this reduction in diagnoses by race/ethnicity, income level, or geographic area. Median date of diagnosis was earlier in 2020 (March 11) compared to 2017-2019 (March 29, April 1, and April 1 respectively), a result of a larger drop in diagnoses later in the time interval in 2020. Among patients who received treatment during follow-up (83.1% in 2017-2019 vs 86.2% in 2020, a difference likely reflecting this shift in diagnosis date), there was a marked reduction in surgery as first treatment in 2020 compared to previous years (88.7% in 2017-2019 vs 69.3% in 2020), while both preoperative chemotherapy (6.1% in 2017-2019 vs 10.7% in 2020) and preoperative endocrine therapy (5.2% in 2017-2019 vs 20.1% in 2020) increased (p < 0.0001). There were no differences in the shift toward preoperative therapy by race/ethnicity or income, but there was a significant difference by geographic area (p=0.0003): the Mountain region had least change in use of preoperative therapy (odds ratio 2.46 [95% CI 1.75-3.47] of preoperative therapy during vs before the pandmic) while the Middle Atlantic region had the greatest (odds ratio 5.64 [95% CI 3.79-8.38]). Conclusions: Among insured U.S. patients, new breast cancer diagnoses decreased by 12.3% during February-May 2020 compared to the same period in the previous three years, and use of preoperative therapy, largely endocrine, increased by 2.7-fold. The impact of the pandemic on choice of first treatment differed by geographic area, but not by race/ethnicity or income in this insured population. We will monitor with continued follow-up of claims data to assess the longer-term impact of these pandemic-related changes on treatment patterns, cost, and patient outcomes.

CCR5 Receptor Occupancy Analysis Reveals Increased Peripheral Blood CCR5+CD4+ T Cells Following Treatment With the Anti-CCR5 Antibody Leronlimab.

CCR5 plays a central role in infectious disease, host defense, and cancer progression, thereby making it an ideal target for therapeutic development. Notably, CCR5 is the major HIV entry co-receptor, where its surface density correlates with HIV plasma viremia. The level of CCR5 receptor occupancy (RO) achieved by a CCR5-targeting therapeutic is therefore a critical predictor of its efficacy. However, current methods to measure CCR5 RO lack sensitivity, resulting in high background and overcalculation. Here, we report on two independent, flow cytometric methods of calculating CCR5 RO using the anti-CCR5 antibody, Leronlimab. We show that both methods led to comparable CCR5 RO values, with low background on untreated CCR5+CD4+ T cells and sensitive measurements of occupancy on both blood and tissue-resident CD4+ T cells that correlated longitudinally with plasma concentrations in Leronlimab-treated macaques. Using these assays, we found that Leronlimab stabilized cell surface CCR5, leading to an increase in the levels of circulating and tissue-resident CCR5+CD4+ T cells in vivo in Leronlimab-treated macaques. Weekly Leronlimab treatment in a chronically SIV-infected macaque led to increased CCR5+CD4+ T cells levels and fully suppressed plasma viremia, both concomitant with full CCR5 RO on peripheral blood CD4+ T cells, demonstrating that CCR5+CD4+ T cells were protected from viral replication by Leronlimab binding. Finally, we extended these results to Leronlimab-treated humans and found that weekly 700 mg Leronlimab led to complete CCR5 RO on peripheral blood CD4+ T cells and a statistically significant increase in CCR5+CD4+ T cells in peripheral blood. Collectively, these results establish two RO calculation methods for longitudinal monitoring of anti-CCR5 therapeutic antibody blockade efficacy in both macaques and humans, demonstrate that CCR5+CD4+ T cell levels temporarily increase with Leronlimab treatment, and facilitate future detailed investigations into the immunological impacts of CCR5 inhibition in multiple pathophysiological processes.

Economic burden of public health care and hospitalisation associated with COVID-19 in China.

OBJECTIVES: This study aimed to evaluate the socio-economic burden imposed on the Chinese healthcare system during the coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: A cross-sectional study was used to investigate how COVID-19 impacted health and medical costs in China. Data were derived from a subdivision of the Centers for Disease control and Prevention of China. METHODS: We prospectively collected information from the Centers for Disease Control and Prevention and the designated hospitals to determine the cost of public health care and hospitalisation due to COVID-19. We estimated the resource use and direct medical costs associated with public health. RESULTS: The average costs, per case, for specimen collection and nucleic acid testing (NAT [specifically, polymerase chain reaction {PCR}]) in low-risk populations were $29.49 and $53.44, respectively; however, the average cost of NAT in high-risk populations was $297.94 per capita. The average costs per 1000 population for epidemiological surveys, disinfectant, health education and centralised isolation were $49.54, $247.01, $90.22 and $543.72, respectively. A single hospitalisation for COVID-19 in China cost a median of $2158.06 ($1961.13-$2325.65) in direct medical costs incurred only during hospitalisation, whereas the total costs associated with hospitalisation of patients with COVID-19 were estimated to have reached nearly $373.20 million in China as of 20, May, 2020. The cost of public health care associated with COVID-19 as of 20, May, 2020 ($6.83 billion) was 18.31 times that of hospitalisation. CONCLUSIONS: This study highlights the magnitude of resources needed to treat patients with COVID-19 and control the COVID-19 pandemic. Public health measures implemented by the Chinese government have been valuable in reducing the infection rate and may be cost-effective ways to control emerging infectious diseases.

Classification of COVID-19 in CT Scans Using Image Smoothing and Improved Deep Residual Network

The global spread of coronavirus disease has become a major threat to global public health. There are more than 137 million confirmed cases worldwide at the time of writing. The spread of COVID-19 has resulted in a huge medical load due to the numerous suspected examinations and community screening. Deep learning methods to automatically classify COVID-19 have become an effective assistive technology. However, the current researches on data quality and the use of CT data to diagnose COVID-19 with convolutional neural networks are poor. This study is based on CT scan data of COVID-19 patients, patients with other lung diseases, and healthy people. In this work, we find that data smoothing can improve the quality of CT images of COVID-19 and improve the accuracy of the model. Specifically, an interpolation smoothing method is proposed using the bilinear interpolation algorithm. Besides, we propose an improved ResNet structure to improve the model feature extraction and fusion by optimizing the structure of the input stem and downsampling parts. Compared with the baseline ResNet, the model improves the accuracy of the three-class classification by 3.8% to 93.83%. Our research has particular significance for research on the automatic diagnosis of COVID-19 infectious diseases. © 2021, Springer Nature Switzerland AG.

Demand and development trend of china's elderly care service in the post-epidemic period

China has stepped into an aging society. The emergence of 2019- nCoV has increased the risk of the elderly with low immunity and has posed new challenges to China's medical health and elderly care service. Based on the introduction of the epidemic's impact on China's main elderly care modes, this article investigates the demand for elderly care service in Wuhan during the epidemic through questionnaires and proposes the development trend of elderly care service in China during the post-epidemic period, including improving the emergency management system of the elderly care industry, improving the quality of elderly care service, promoting the combination of medical care and health care, and promoting the family based of intelligent pension products. © 2021 Association for Computing Machinery. All rights reserved.

A Medical Guidance Model Driven by Subjective and Objective Knowledge

Because of the impact of Corona Virus Disease 2019 (COVID-19), online medical services have developed rapidly and are widely accepted by people. People can find doctors for diagnosis and treatment by the name of the disease online. However, patients usually lack professional medical knowledge and have their own subjective preferences for health services, which makes it difficult for patients to accurately find a doctor that suits them. To this end, we proposed a medical guidance model driven by subjective and objective knowledge to provide decision support to patients. In the proposed model, the doctor's and disease's own information is regarded as objective knowledge, and the information of doctor feature extracted from patient reviews is regarded as subjective knowledge. They are fused into a knowledge graph. On this basis, a knowledge decision engine is designed to recommend the most suitable doctor based on the patient's objective conditions and subjective preferences. Finally, a prototype system is designed and developed to demonstrate the feasibility of the model as above. The system guides patients to improve their objective conditions and subjective preferences through inquiries, and returns recommended doctors to patients in an interpretable manner. The medical guidance model can effectively meet the personalized and professional needs of patients in online medical services, which has good practical value under the digital healthcare continues to become the trend of the future. © 2021 IEEE.

[The role of large-scale testing platform in the prevention and control of the COVID-19 pandemic: an empirical study based on a novel numerical model].

Objective: China adopted an unprecedented province-scale quarantine since January 23rd 2020, after the novel coronavirus (COVID-19) broke out in Wuhan in December 2019. Responding to the challenge of limited testing capacity, large-scale (>20 000 tests per day) standardized and fully-automated laboratory (Huo-Yan) was built as an ad-hoc measure. There is so far no empirical data or mathematical model to reveal the impact of the testing capacity improvement since quarantine. Methods: Based on the suspected case data released by the Health Commission of Hubei Province and the daily testing data of Huo-Yan Laboratory, the impact of detection capabilities on the realization of "clearing" and "clearing the day" of supected cases was simulated by establishing a novel non-linear and competitive compartments differential model. Results: Without the establishment of Huo-Yan, the suspected cases would increase by 47% to 33 700, the corresponding cost of quarantine would be doubled, the turning point of the increment of suspected cases and the achievement of "daily settlement" (all newly discovered suspected cases are diagnosed according to the nucleic acid testing result) would be delayed for a whole week and 11 days. If the Huo-Yan Laboratory could ran at its full capacity, the number of suspected cases could start to decrease at least a week earlier, the peak of suspected cases would be reduced by at least 44%, and the quarantine cost could be reduced by more than 72%. Ideally, if a daily testing capacity of 10 500 tests was achieved immediately after the Hubei lockdown, "daily settlement" for all suspected cases could be achieved. Conclusions: Large-scale, standardized clinical testing platform, with nucleic acid testing, high-throughput sequencing, and immunoprotein assessment capabilities, need to be implemented simultaneously in order to maximize the effect of quarantine and minimize the duration and cost of the quarantine. Such infrastructure, for both common times and emergencies, is of great significance for the early prevention and control of infectious diseases.