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Background We aimed to evaluate the National Health Policies, Strategies and Plans (NHPSPs) of the Organization for Economic Cooperation and Development (OECD) and BRICS before and after the COVID-19 pandemic to explore nations' commitment to strengthen their health systems in defense of health threats and analyze the specific changes.Methods We systematically searched NHPSP documents from the WHO document repository and official governmental websites. Data was then extracted using a standardized extraction template. A coding framework was inductively developed to sort qualitative responses into categories, with frequencies calculated and weighting evaluated, followed by organizing underlying content into subthemes.Results The search yielded 154 documents, with 36 retained after screening, encompassing 14 OECD countries and 3 BRICS countries. The most predominant theme was prevention (88.9% pre-pandemic, 99.4% post-pandemic), which was addressed as a primary theme in 26 included NHPSPs. After the COVID-19 pandemic, 6 out of 14 analyzed themes saw higher occurrences, among which infection prevention and control (22.2–50.0%) and resilience to health crisis (22.2–44.4%) increased most significantly. Themes mainstreamed in post-pandemic NHPSPs included prevention (94.4%), health research and technology (61.1%), and One Health (66.7%). Primary healthcare emerged as the most concerned subtheme under prevention. Notably, OECD countries displayed more increased occurrences of themes (13 out of 14) or increased emphasis on themes with similar occurrences before and after COVID-19, while BRICS countries only differed in infection control. The two sets of countries also varied in subthemes and action plans under the same primary theme.Conclusion Many countries are endeavoring to move towards more robust health systems by optimizing NHPSPs, yet only about half of OECD and BRICS countries have introduced new NHPSPs after COVID-19. We hope our findings attract attention to the necessity of global health system reforms and provide other countries with actionable recommendations for NHPSP formulation.
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COVID-19ABSTRACT
Objectives Vaccination workers play an important role in the acceptance of various vaccines in patients with chronic liver diseases. We mainly investigated the attitude of vaccination workers toward COVID-19 vaccination in patients with chronic liver disease.Methods An anonymous, population-based, cross-sectional online survey were completed by 721 out of 1008 (71.5%) vaccination workers from July 1st to July 14th, 2022, in patients with chronic liver disease in Taizhou, China. The data were uploaded to Wen-Juan-Xing, one of the largest online platforms for collecting survey data.Results We found that only 51.9% of vaccination workers recommended all chronic liver diseases vaccinations. 81% of vaccination workers fully recommended vaccination in patients with fatty liver and chronic hepatitis B, while 53.1% of them fully recommended in patients with cirrhosis and liver cancer. Logistic regression analysis showed that vaccination workers who had undergone systematic training were more likely to recommend that patients with four chronic liver diseases get vaccinated (OR: 1.59; 95% CI: 1.05–2.43, p = 0.030). Vaccination workers that believed it is safe to vaccinate against patients with four chronic liver diseases were likely to recommend (OR: 8.12; 95% CI: 1.84–35.88, p = 0.006).Conclusion Vaccination workers who hold a positive attitude towards recommending vaccination for patients with chronic liver disease needs to be improved. Strengthening the training of vaccination workers could improve vaccine immunization coverage.
Subject(s)
Fatty Liver , End Stage Liver Disease , Carcinoma, Hepatocellular , COVID-19 , Hepatitis B , Liver DiseasesABSTRACT
Objectives The purpose is to analyze existing studies related to the field of demoralization through bibliometrics. Methodology Relevant literature on demoralization was searched from PubMed, Web of Science, the Cochrane Library, and CINAHL Complete. Bibliometric analysis was performed using GraphPad Prisma 8.2.1, VOSviewer 1.6.18 and R software. Research publication trends, author-country collaboration, research hotspots and future trends were explored by generating network relationship maps. Results A total of 1,035 publications related to the field of demoralization were identified. The earliest relevant studies have been published since 1974, and the studies have grown faster since 2000. Psyche-oncology and Psychother Psychosom had the highest number of publications (n = 25). The United States, Italy and Australia have made outstanding contributions to the field and there was an active collaboration among leading scholars. Major research hotspots include the multiple ways of assessing demoralization, the specificity of various demographics and psychological disorders in different disease contexts, and the association and distinction of diverse clinical psychological abnormalities. The impact of COVID-19 on demoralization and subsequent interventions and psychological care may become a future research direction. Conclusion There has been a significant increase in research in the field of demoralization after 2000. The United States provided the most publications. There is overall active collaboration between authors, countries, and institutions. In future research, more attention will be paid to the effects of COVID-19 on demoralization and intervention care for this psychology.
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Background From the beginning of 2020, the world was plunged into a pandemic caused by the novel coronavirus disease-19 (COVID-19). People increasingly searched for information related to COVID-19 on internet websites. The Baidu Index is a data sharing platform. The main data provided is the search index (SI), which represents the frequency that keywords are used in searches. Methods January 9, 2020 is an important date for the outbreak of COVID-19 in China. We compared the changes of SI before and after for 7 keywords, including “fever”, “cough”, “nausea”, “vomiting”, “abdominal pain”, “diarrhea”, “constipation”. The slope and peak values of SI change curves are compared. Ten provinces in China were selected for a separate analysis, including Beijing, Gansu, Guangdong, Guangxi, Heilongjiang, Hubei, Sichuan, Shanghai, Xinjiang, Tibet. The change of SI was analyzed separately, and the correlation between SI and demographic and economic data was analyzed. Results During period I, from January 9 to January 25, 2020, the average daily increase (ADI) of the SI for “diarrhea” was lower than that for “cough” (889.47 vs. 1,799.12, F=11.43, P=0.002). In period II, from January 25 to April 8, 2020, the average daily decrease (ADD) of the SI for “diarrhea” was significantly lower than that for “cough”, with statistical significance (cough, 191.40 vs. 441.44, F=68.66, P<0.001). The mean SI after January 9, 2020 (pre-SI) was lower than that before January 9, 2020 (post-SI) (fever, 2,616.41±116.92 vs. 3,724.51±867.81, P<0.001;cough, 3,260.04±308.43 vs. 5,590.66±874.25, P<0.001;diarrhea, 4,128.80±200.82 vs. 4,423.55±1,058.01, P<0.001). The pre-SI mean was correlated with population (P=0.004, R=0.813) and gross domestic product (GDP) (P<0.001, R=0.966). The post-SI peak was correlated with population (P=0.007, R=0.789), GDP (P=0.005, R=0.804), and previously confirmed cases (PCC) (P=0.03, R=0.670). The growth rate of the SI was correlated with the post-SI peak (P=0.04, R=0.649), PCC (P=0.003, R=0.835). Conclusions Diarrhea was of widespread concern in all provinces before and after the COVID-19 outbreak and may be associated with novel coronavirus infection. Internet big data can reflect the public’s concern about diseases, which is of great significance for the study of the epidemiological characteristics of diseases.
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This work presents an improved self-adaptive power distribution approach for the microgrid in five modes under different pandemic conditions in a typical tourism water village in Northern China. Differently from the other studies, this work concentrates on satisfying the specific power supply requirements under the COVID-19 background, with the maximum value of the composite index as the object function. Composite index includes not only the economic factors, but also some compulsive factors to ensure the requested power supply of the residents/tourists. The improved particle swarm optimization method which employs the modified weighted factor and the elite strategy is utilized to optimize the power dispatching of the microgrid. Moreover, the impact of the pandemic has been fully considered by comparing the power dispatching before and after the pandemic. The case study in Baiyangdian Region confirms the effectiveness of the proposed method. With this method, the optimal power dispatching is determined under different modes.
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BACKGROUND: Previous study suggested that Chinese Herbal Medicine (CHM) Formula Huashibaidu granule might shorten disease course of Corona Virus Disease 2019 (COVID-19) patients. Our research aims to investigate the early treatment effect of Huashibaidu granule in mild COVID-19 patients under well clinical management.METHODS: An unblended cluster-randomized clinical trial was conducted at the Dongxihu FangCang hospital. 2 cabins were randomly allocated to CHM or control group, with 204 randomly sampled mild COVID-19 patients in each cabin. All participants received a 7-day conventional treatment, and CHM group cabin used additional Huashibaidu granule 10g twice daily. Participants were followed up until they met clinical endpoint. The primary outcome was patient become worsening before clinical endpoint occurred. The secondary outcomes was discharge with cure before clinical endpoint occurred and relief of composite symptoms after 7 days treatment.FINDINGS: All 408 participants were followed up to meet clinical endpoint and included in statistical analysis. The baseline characteristics were comparable between 2 groups. The number of worsening patients in the CHM group was 5 (2.5%), and that in the control group was 16 (7.8%). There was a significant difference between groups (P=0.014). 8 foreseeable mild adverse events occurred without statistical difference between groups.INTERPRETATION: 7-day early treatment with Huashibaidu granule reduced worsening conversion of mild COVID-19 patients. Our study supports Huashibaidu Granule as an active option for early treatment of mild COVID-19 in similar medical locations with well management.TRIAL REGISTRATION: The Chinese Clinical Trial Registry: ChiCTR2000029763.FUNDING: This study was supported by “National Key R&D Program of China” (No.2020YFC0841500).DECLARATION OF INTERESTS: The authors guaranteed that there existed no competing interest in this paper.ETHICS APPROVAL STATEMENT: Ethics Review Committee of Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences Approval of Ethical Review Acceptance Number: S2020-001; Approval Number: P20001/PJ01.
Subject(s)
COVID-19 , Virus Diseases , Neurologic ManifestationsABSTRACT
OBJECTIVE To evaluate the efficacy and safety of Chinese medicine (Q-14) plus standard care compared with standard care alone in adult with coronavirus disease 2019 (COVID-19). Study DESIGN Single-center, open label, randomised controlled trial. SETTING Wuhan Jinyintan Hospital, Wuhan, China, February 27 to March 27, 2020. PARTICIPANTS 204 patients with laboratory confirmed COVID-19 were randomised in to treatment group and control group, which was 102 patients each group. INTERVENTIONS In treatment group, Q-14 was administrated at 10g (granules), twice daily for 14 days and plus standard care. In control group, patients were given standard care alone for 14 days. MAIN OUTCOME MEASURE The primary outcome was conversion time of SARS-CoV-2 viral assay. Adverse events were analyzed in the safety population. RESULTS Among 204 patients, 195 were analyzed according to the intention to treat principle. There were 149 patients (71 vs. 78 in treatment group and control group respectively) turning to negative via SARS-CoV-2 viral assay. No statistically significance showed in conversion time between treatment group and control group (FAS: Median (IQR): 10.00 (9.00-11.00) vs. 10.00 (9.00-11.00); Mean rank: 67.92 vs. 81.44; P=0.051.). Time to recovery of fever was shorter in treatment group as compared in control group. The disappearance rate of symptom in cough, fatigue, chest discomfort was significantly higher in treatment group. In chest computed tomography (Chest CT) examinations, overall evaluation of chest CT examination after treatment compared with baseline showed more patients improved in treatment group .There were no significant differences in the other outcomes. CONCLUSION Administration of Q-14 on standard care for COVID-19 was useful for improvement of symptoms (such as fever, cough, fatigue and chest discomfort), while did not result in a significantly higher probability of negative conversion of SARS-CoV-2 viral assay. No serious adverse events were reported. TRIAL REGISTRATION ChiCTR2000030288
Subject(s)
COVID-19 , Fever , FatigueABSTRACT
Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes for the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients’ comorbidity and symptoms (including 26 features), and laboratory testing results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models based on features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and laboratory results as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features’ importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, absolute neutrophil count, IL-6, and LDH, in descending order). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient’s severity and developing personalized treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while laboratory results are applied when accuracy is the priority.Funding Statement: This study was jointly supported by the National Science Foundation for Young Scientists of China (81703201), the Natural Science Foundation for Young Scientists of Jiangsu Province (BK20171076), the Jiangsu Provincial Medical Innovation Team (CXTDA2017029), the Jiangsu Provincial Medical Youth Talent program (QNRC2016548), the Jiangsu Preventive Medicine Association program (Y2018086), the Lifting Program of Jiangsu Provincial Scientific and Technological Association, and the Jiangsu Government Scholarship for Overseas Studies.Declaration of Interests: The authors declare no competing interests in this study.Ethics Approval Statement: Patient-specific identifying information (e.g., name, address of residence) was removed from data collected for this study. This study was evaluated and approved by the IRB committee of Union Hospital, Wuhan, China (approval number: 2020-IEC-J-345).
Subject(s)
COVID-19 , Cardiovascular DiseasesABSTRACT
BACKGROUND: Celastrol has been proven effective in anti-inflammatory but was limited in the clinic due to the poor solubility and side effects induced by low bioavailability. Osteoarthritis has acidic and inflammatory environment. Our aim was to load celastrol into HMSNs and capped with chitosan to construct a pH-responsive nanoparticle medicine (CSL@HMSNs-Cs), which is of high solubility for osteoarthritis intra-articular injection treatment. METHODS: The CSL@HMSNs-Cs were assembled and the characteristics were measured. The CSL@HMSNs-Cs was applied in vitro in the chondrocytes collected from rats cartilage tissue and in vivo in the MIA induced knee osteoarthritis rats via intra-articular injection. Cytotoxicity assay, pH-responsive release, pain behavior, MRI, safranin o fast green staining, ELISA and western blot analysis were applied to evaluate the bioavailability and therapeutic effect of CSL@HMSNs-Cs. RESULTS: CSL@HMSNs-Cs was stable due to the protection of the chitosan layers in alkaline environment (pH = 7.7) but revealed good solubility and therapeutic effect in acidic environment (pH = 6.0). The cytotoxicity assay showed no cytotoxicity at relatively low concentration (200 µg/mL) and the cell viability of chondrocytes stimulated by IL-1ß was increased in CSL@HMSNs-Cs group. Paw withdrawal threshold in CSL@HMSNs-Cs group is increased, and MRI and Safranin O Fast Green staining showed improvements in articular surface erosion and joint effusion. The upregulated expression levels of IL-1ß, TNF-α, IL-6, MMP-3 and MMP-13 and NF-κB signaling pathway of chondrocytes were inhibited in CSL@HMSNs-Cs group. CONCLUSION: Hollow mesoporous silica nanoparticles were an ideal carrier for natural drugs with poor solubility and were of high biocompatibility for intra-articular injection. These intra-articular injectable CSL@HMSNs-Cs with improved solubility, present a pH-responsive therapeutic strategy against osteoarthritis.
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In December, the outbreak of a novel coronavirus (2019-nCoV) in Wuhan, China, has attracted extensive global attention. On January 20, 2020, the Chinese health authorities upgraded the coronavirus to a Class B infectious disease in the Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases, and considered it as Class A infectious diseases in disease control and prevention. On January 18, 2020, the 2019-nCoV nucleic acid detection test was listed as the diagnostic criteria in the "guidelines for diagnosis and treatment of pneumonia due to 2019-nCoV (Trial Version 2)". Therefore, standardizing the operation process of the 2019-nCoV nucleic acid detection in clinical laboratories has become a top priority. It is of paramount importance to establish standard protocols for detection of the 2019-nCoV nucleic acids in clinical laboratories to improve the reliability of the results and ensure the biosafety of laboratory personnel.
Subject(s)
Antigens, CD/metabolism , Pneumonia, Viral/prevention & control , Pneumonia, Viral/virology , Simian Acquired Immunodeficiency Syndrome/virology , Simian Immunodeficiency Virus/physiology , Animals , Lung/pathology , Lung/virology , Macaca mulatta , Pneumonia, Viral/complications , Simian Acquired Immunodeficiency Syndrome/complicationsABSTRACT
Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants' clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.
Subject(s)
COVID-19 , Pneumonia, Viral , Learning DisabilitiesABSTRACT
Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes of the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients' comorbidity and symptoms (26 features), and blood biochemistry (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models using features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and biochemistry as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features' importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, neutrophil, IL-6, and LDH). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient's severity and developing treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority.
Subject(s)
Cardiovascular Diseases , Diabetes Mellitus , Hypertension , Kyasanur Forest Disease , COVID-19ABSTRACT
The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.
Subject(s)
COVID-19 , PneumoniaABSTRACT
Objectives To investigated the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the severity of lung injury in corona virus disease 2019 (COVID-19) patients.Methods The clinical data, laboratory examination, and chest computed tomography (CT) findings of 167 patients with confirmed COVID-19 admitted to 5 hospitals in Chongqing, China from January 2020 to February 2020 were retrospectively reviewed. According to the diagnostic criteria sixth edition of the “Diagnosis and Treatment of New Coronavirus Pneumonitis” published by the China National Health Commission, the patients were stratified by the severity of their illness to 3 groups: mild (n = 17), moderate (n = 119), or severe (n = 31).Results Differences of the NLR among the three groups and between each of the groups were significant (all p < 0.001). The NLR and CT severity score were positively correlated (r = 0.823, p < 0.001). Receiver operating characteristic (ROC) curve analysis found that NLR had diagnostic and prognostic value in COVID-19 patients with either negative or positive CT results. The area under curve (AUC) was 0.819 (95% CI: 0.729-0.910, p < 0.001), the sensitivity was 61.3%, specificity was 94.1%, and the optimal NLR cutoff value was 3.634.Conclusion NLR reflected the degree of lung injury and predicted the progression of COVID-19. NLR is a low-cost, convenient, bedside alternative to chest CT scanning to indicate the severity of lung injury in patients with COVID-19, especially in relatively underdeveloped areas.
Subject(s)
COVID-19 , Lung DiseasesABSTRACT
Background: Affected by a Corona Virus Disease 2019 (COVID-19) outbreak, Since December 2019, there have been more than 76,000 cases of COVID-19 in China, causing more than 3,000 medical staff infections. Due to COVID-19 spreads quickly, is highly contagious, and can be fatal in severe cases, and there are no specific medicines, it poses a huge threat to the life and health of nurses and has a large impact on their emotional responses and coping strategies. Methods: This study conducted an online questionnaire survey from February 1 to 9, 2020 to investigate the current state of emotional responses and coping strategies of nurses and college nursing students in Anhui Province. This study used a modified Brief COPE (Carver, 1997) and a emotional responses scale. Results: The results found that women showed more severe anxiety and fear than men. Participants from cities showed more anxiety and fear than participants from rural, but rural participants showed more sadness than urban participants. The closer COVID-19 is to the participants, the stronger the anxiety and anger. Compared with Nursing college students, nurses have stronger emotional responses and are more willing to use Problem-focused coping. People may have a cycle of "the more fear, the more problem-focused coping". And people may "The more angry, the more emotion-focused coping", "the more problem-focused coping, the more anxious, the more angry, the more sadness". Conclusion: COVID-19 is a pressure source with great influence, both for individuals and for the social public groups. Different individuals and groups may experience different levels of psychological crisis, and those nurses at the core of the incident are affected. Hospitals should focus on providing psychological support to nurses and providing timely psychological assistance and training in coping strategies. Improving nurses' ability to regulate emotions and effective coping strategies, providing a strong guarantee for resolutely winning the battle against epidemic prevention and control.
Subject(s)
COVID-19 , Virus Diseases , Anxiety DisordersABSTRACT
Objective@#To report the first case of a neonatal pneumonia with 2019-nCoV infection, and the experience of successfully diagnosis and treatment in late pregnancy woman with novel coronavirus pneumonia (critical type) in Xinyang city.@*Methods@#The successfully diagnosis and treatment of a woman with 38 weeks singleton pregnancy complicated with novel coronavirus pneumonia (critical type), and a case of neonatal pneumonia with 2019-nCoV infection were retrospectively analyzed.@*Results@#A single male was successfully delivered at 38-week gestation of his mother by cesarean section under third level protection in operation room. The delivery woman was diagnosed with 2019-nCoV infection at day 2 of delivery. Dyspnea and severe hypoxemia soon developed, and invasive mechanical ventilation was given. After active rescue and treatment, the delivery woman had been taken off line successfully and the condition was stable. Pharyngeal swab specimen of the neonate was sent for examination 3 days after birth, and was positive for novel coronavirus nucleic acid by fluorescence reverse transcript polymerase chain reaction.@*Conclusion@#2019-nCoV may be transmitted vertically from mother to child.
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Objective@#To analyze the epidemiological characteristics and clinical features of the patients with 2019-nCoV infection, so as to provide basis for clinical diagnosis.@*Methods@#The epidemiology, clinical symptoms, laboratory and radiologic data of 23 patients with 2019-nCoV infection admitted to the Fifth People's Hospital of Xinyang City from January 22,2020 to January 29, 2020 were retrospectively analyzed.@*Results@#The 23 patients with 2019 nCov infection consisted of 15 men and 8 women, and the median age was 46.0 (40.5, 52.0) years (27-80 years); 9 of them had basic disease (39%), including hypertension (17%), cardiovascular diseases (17%), diabetes (9%), hypothyroidism (4%) and old tuberculosis (4%). All the 23 patients had contact history in Wuhan area or with confirmed infections. Clinical symptoms included: fever (100%), cough (70%), expectoration (43%), myalgia (26%), headache (17%) and dyspnea (17%), and the less common symptoms were diarrhea (4.3%). Blood routine test: white blood cells (WBC) < 4×109/L in 11 cases (48%), (4-10)×109/L in 10 cases (43%), >10 × 109/L in 2 cases (9%); lymphocytopenia in 13 cases (56%). All 23 patients had different degrees of infective lesions in chest CT examination, with 9 cases (39%) on one side and 14 cases (61%) on both sides. Classification: 19 mild cases, 4 severe cases, no critical or death case. Complications included acute respiratory distress syndrome [4 (17%)]. No case was reported with the damage of liver or kidney function and with secondary infection.@*Conclusions@#Epidemic history of contact, fever, pneumonia signs of chest CT, normal or decreased count of WBC and lymphocytopenia are the clinical basis for diagnosis of the disease. However, at present, the treatment of patients has not been completed, the effective treatment strategy and final prognosis are not clear.