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1.
Clinical Nephrology ; 96(4):207-215, 2021.
Article in English | GIM | ID: covidwho-2056047

ABSTRACT

Background: Continuous renal replacement therapy (CRRT) has become an important multiple organ support therapy and it is widely used in the intensive care unit (ICU). The aim of this study was to clarify the association between CRT and 28-day mortality in critically ill coronavirus disease 2019 (COVID-19) patients receiving mechanical ventilation. Materials and methods: 112 respiratory decompensated critically ill adult patients with COVID-19 admitted to a COVID-19-designated ICU were included in this retrospective cohort study. Data on demographic information, comorbidities, laboratory findings upon ICU admission, and clinical outcomes were collected. The Kaplan-Meier method and Cox proportional hazard model were applied to determine the potential risk factors associated with 28-day mortality.

2.
Journal of Telemedicine and Telecare ; : 1357633X221111975, 2022.
Article in English | Sage | ID: covidwho-1968413

ABSTRACT

IntroductionThe popularity of video consultations in healthcare has accelerated during the COVID-19 pandemic. Despite increased availability and obvious benefits, many patients remain hesitant to use video consultations. This study investigates the relative importance of the consultation mode compared to other attributes in patients? appointment choices in Germany.MethodsA discrete choice experiment was conducted to examine the influence of appointment attributes on preferences for video over in-clinic consultations. A total of 350 participants were included in the analysis.ResultsThe level of continuity of care (46%) and the waiting time until the next available appointment (22%) were shown to have higher relative importance than consultation mode (18%) and other attributes. Participants with fewer data privacy concerns, higher technology proficiency, and more fear of COVID-19 tended to prefer video over in-clinic consultations. The predicted choice probability of a video over a typical in-clinic consultation and opting out increased from <1% to 40% when the video consultation was improved from the worst-case to the best-case scenario.ConclusionThis study provides insight into the effect of the consultation mode on appointment choice at a time when telemedicine gains momentum. The results suggest that participants preferred in-clinic over video consultations. Policymakers and service providers should focus on increasing the level of continuity of care and decreasing the time until the next available appointment to prompt the adoption of video consultations. Although participants preferred to talk to their physician in person over consulting via video per se, the demand for video consultations can be increased significantly by improving the other appointment attributes of video consultations such as the level of continuity of care.

3.
Radiology ; 305(2): 454-465, 2022 11.
Article in English | MEDLINE | ID: covidwho-1950321

ABSTRACT

Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes in patient populations and standard of care during the COVID-19 pandemic. A common partial mitigation is transfer learning by pretraining a "generic network" on a large nonmedical data set and then fine-tuning on a task-specific radiology data set. Purpose To reduce data set size requirements for chest radiography deep learning models by using an advanced machine learning approach (supervised contrastive [SupCon] learning) to generate chest radiography networks. Materials and Methods SupCon helped generate chest radiography networks from 821 544 chest radiographs from India and the United States. The chest radiography networks were used as a starting point for further machine learning model development for 10 prediction tasks (eg, airspace opacity, fracture, tuberculosis, and COVID-19 outcomes) by using five data sets comprising 684 955 chest radiographs from India, the United States, and China. Three model development setups were tested (linear classifier, nonlinear classifier, and fine-tuning the full network) with different data set sizes from eight to 85. Results Across a majority of tasks, compared with transfer learning from a nonmedical data set, SupCon reduced label requirements up to 688-fold and improved the area under the receiver operating characteristic curve (AUC) at matching data set sizes. At the extreme low-data regimen, training small nonlinear models by using only 45 chest radiographs yielded an AUC of 0.95 (noninferior to radiologist performance) in classifying microbiology-confirmed tuberculosis in external validation. At a more moderate data regimen, training small nonlinear models by using only 528 chest radiographs yielded an AUC of 0.75 in predicting severe COVID-19 outcomes. Conclusion Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations. © RSNA, 2022 Online supplemental material is available for this article.


Subject(s)
COVID-19 , Deep Learning , Humans , Radiography, Thoracic/methods , Radiographic Image Interpretation, Computer-Assisted/methods , Pandemics , COVID-19/diagnostic imaging , Retrospective Studies , Radiography , Machine Learning
4.
Front Public Health ; 10: 896894, 2022.
Article in English | MEDLINE | ID: covidwho-1903236

ABSTRACT

Tourism is impacted by all types of crises, no matter how big or small. Even though many studies have examined tourism crises, most focus on the number of tourists arriving and departing. As a result of this lack of information, The adaptive differences in tourist behavior caused by various crises are not well understood. When it comes to inbound tourism, the financial and health-related crisis can significantly impact the tourist profile of the country and its visitors' spending habits. The findings show that the health crisis has a significant positive impact on tourism. Moreover, COVID_deaths and COVID_confirm_cases decrease the international tourism in developed and developing countries. According to the study's findings, tourists' sensitivity to crises varies between short- and long-haul markets. The evidence shows that financial inclusion has a significant positive impact on various aspects of tourism development in China. Hence, this article offers numerous policy and practical suggestions for sustainable tourism management.


Subject(s)
COVID-19 , Tourism , China , Humans , Travel
5.
Complement Ther Clin Pract ; 48: 101600, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1821202

ABSTRACT

BACKGROUND: COVID-19 has posed an unprecedented threat to public health and remains a critical challenge for medical staff, especially those who have been fighting against the virus in Wuhan, China. Limited data have been reported regarding the psychological status of these medical staff members. Therefore, we conducted this study to explore the mental health status of medical staff and the efficacy of brief mindfulness meditation (BMM) in improving their mental health. METHODS: A survey was conducted between April 18 and May 3, 2020. Upon completing the pre-test, participants in the treatment group received a 15-min BMM intervention every day at 8 p.m. Post-test questionnaires were completed after 16 days of therapy. The questionnaire comprised demographic data and psychological measurement scales. The levels of pre and post-test depression, anxiety, stress, and insomnia were assessed using the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Perceived Stress Scale, and Athens Insomnia Scale, respectively. RESULTS: A total of 134 completed questionnaires were received. Of the medical staff, 6.7%, 1.5%, and 26.7% reported symptoms of depression, anxiety, and insomnia, respectively. Public officials from military hospitals reported experiencing greater pressure than private officials (t = 2.39, p = 0.018, d = 0.50). Additionally, BMM treatment appeared to effectively alleviate insomnia (t = 2.27, p = 0.027, d = 0.28). CONCLUSIONS: The medical staff suffered negative psychological effects during the COVID-19 pandemic. BMM interventions are advantageous in supporting the mental health of medical staff.


Subject(s)
COVID-19 , Meditation , Mindfulness , Sleep Initiation and Maintenance Disorders , Anxiety/psychology , Anxiety/therapy , COVID-19/epidemiology , COVID-19/prevention & control , Depression/therapy , Humans , Medical Staff , Pandemics
6.
Emerg Microbes Infect ; 11(1): 1115-1125, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1799501

ABSTRACT

Diabetes mellitus (DM) is one of the most common underlying diseases that may aggravates COVID-19. In the present study, we explored islet function, the presence of SARS-CoV-2 and pathological changes in the pancreas of patients with COVID-19. Oral glucose tolerance tests (OGTTs) and the C-peptide release test demonstrated a decrease in glucose-stimulated C-peptide secretory capacity and an increase in HbA1c levels in patients with COVID-19. The prediabetic conditions appeared to be more significant in the severe group than in the moderate group. SARS-CoV-2 receptors (ACE2, CD147, TMPRSS2 and neuropilin-1) were expressed in pancreatic tissue. In addition to SARS-CoV-2 virus spike protein and virus RNA, coronavirus-like particles were present in the autophagolysosomes of pancreatic acinar cells of a patient with COVID-19. Furthermore, the expression and distribution of various proteins in pancreatic islets of patients with COVID-19 were altered. These data suggest that SARS-CoV-2 in the pancreas may directly or indirectly impair islet function.


Subject(s)
COVID-19 , Diabetes Mellitus , C-Peptide/metabolism , Diabetes Mellitus/metabolism , Humans , Pancreas , SARS-CoV-2
7.
Frontiers in medicine ; 9, 2022.
Article in English | EuropePMC | ID: covidwho-1781857

ABSTRACT

Objectives We aimed to investigate how changes in direct bilirubin (DBiL) levels in severely/critically ill the coronavirus disease (COVID-19) patients during their first week of hospital admission affect their subsequent prognoses and mortality. Methods We retrospectively enrolled 337 severely/critically ill COVID-19 patients with two consecutive blood tests at hospital admission and about 7 days after. Based on the trend of the two consecutive tests, we categorized patients into the normal direct bilirubin (DBiL) group (224), declined DBiL group (44) and elevated DBiL group (79). Results The elevated DBiL group had a significantly larger proportion of critically ill patients (χ2-test, p < 0.001), a higher risk of ICU admission, respiratory failure, and shock at hospital admission (χ2-test, all p < 0.001). During hospitalization, the elevated DBiL group had significantly higher risks of shock, acute respiratory distress syndrome (ARDS), and respiratory failure (χ2-test, all p < 0.001). The same findings were observed for heart damage (χ2-test, p = 0.002) and acute renal injury (χ2-test, p = 0.009). Cox regression analysis showed the risk of mortality in the elevated DBiL group was 2.27 (95% CI: 1.50–3.43, p < 0.001) times higher than that in the normal DBiL group after adjusted age, initial symptom, and laboratory markers. The Receiver Operating Characteristic curve (ROC) analysis demonstrated that the second test of DBiL was consistently a better indicator of the occurrence of complications (except shock) and mortality than the first test in severely/critically ill COVID-19 patients. The area under the ROC curve (AUC) combined with two consecutive DBiL levels for respiratory failure and death was the largest. Conclusion Elevated DBiL levels are an independent indicator for complication and mortality in COVID-19 patients. Compared with the DBiL levels at admission, DBiL levels on days 7 days of hospitalization are more advantageous in predicting the prognoses of COVID-19 in severely/critically ill patients.

8.
Ann Palliat Med ; 11(7): 2202-2209, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1743090

ABSTRACT

BACKGROUND: We aimed to identify studies systematically that describe the incidence and outcome of COVID-19-related pulmonary aspergillosis (CAPA). METHODS: We searched ScienceDirect, PubMed, CNKI, and MEDLINE (OVID) from December 31, 2019 to November 20, 2021 for all eligible studies. Random-model was used to reported the incidence, all-cause case fatality rate (CFR) and 95% confidence intervals (CIs). The meta-analysis was registered with PROSPERO (CRD42021242179). RESULTS: In all, thirty-one cohort studies were included in this study. A total of 3,441 patients with severe COVID-19 admitted to an intensive care unit (ICU) were investigated and 442 cases of CAPA were reported (30 studies). The pooled incidence rate of CAPA was 0.14 (95% CI: 0.11-0.17, I2=0.0%). Twenty-eight studies reported 287 deceased patients and 269 surviving patients. The pooled CFR of CAPA was 0.52 (95% CI: 0.47-0.56, I2=3.9%). Interestingly, patients with COVID19 would develop CAPA at 7.28 days after mechanical ventilation (range, 5.48-9.08 days). No significant publication bias was detected in this meta-analysis. DISCUSSION: Patients with COVID-19 admitted to an ICU might develop CAPA and have high all-cause CFR. We recommend conducting prospective screening for CAPA among patients with severe COVID-19, especially for those who receive mechanical ventilation over 7 days.


Subject(s)
COVID-19 , Pulmonary Aspergillosis , Humans , Incidence , Intensive Care Units , Prospective Studies , Pulmonary Aspergillosis/epidemiology
9.
BMC Gastroenterol ; 22(1): 106, 2022 Mar 07.
Article in English | MEDLINE | ID: covidwho-1731517

ABSTRACT

BACKGROUND: Gastrointestinal symptoms have been reported in patients with COVID-19. Several clinical investigations suggested that gastrointestinal symptoms were associated with disease severity of COVID-19. However, the relevance of gastrointestinal symptoms and mortality of COVID-19 remains largely unknown. We aim to investigate the relationship between gastrointestinal symptoms and COVID-19 mortality. METHODS: We searched the PubMed, Embase, Web of science and Cochrane for studies published between Dec 1, 2019 and May 1, 2021, that had data on gastrointestinal symptoms in COVID-19 patients. Additional literatures were obtained by screening the citations of included studies and recent reviews. Only studies that reported the mortality of COVID-19 patients with/without gastrointestinal symptoms were included. Raw data were pooled to calculate OR (Odds Ratio). The mortality was compared between patients with and without gastrointestinal symptoms, as well as between patients with and without individual symptoms (diarrhea, nausea/vomiting, abdominal pain). RESULTS: Fifty-three literatures with 55,245 COVID-19 patients (4955 non-survivors and 50,290 survivors) were included. The presence of GI symptoms was not associated with the mortality of COVID-19 patients (OR=0.88; 95% CI 0.71-1.09; P=0.23). As for individual symptoms, diarrhea (OR=1.01; 95% CI 0.72-1.41; P=0.96), nausea/vomiting (OR=1.16; 95% CI 0.78-1.71; P=0.46) and abdominal pain (OR=1.55; 95% CI 0.68-3.54; P=0.3) also showed non-relevance with the death of COVID-19 patients. CONCLUSIONS: Gastrointestinal symptoms are not associated with higher mortality of COVID-19 patients. The prognostic value of gastrointestinal symptoms in COVID-19 requires further investigation.


Subject(s)
COVID-19 , Gastrointestinal Diseases , COVID-19/complications , Gastrointestinal Diseases/diagnosis , Humans , Nausea/etiology , SARS-CoV-2 , Vomiting/etiology
10.
Journal of inflammation research ; 15:851-864, 2022.
Article in English | EuropePMC | ID: covidwho-1688114

ABSTRACT

Purpose Plant polyphenols possess beneficial functions against various diseases. This study aimed to identify phenolic ingredients in Camellia fascicularis (C. fascicularis) and investigate its possible underlying anti-inflammatory mechanism in lipopolysaccharide (LPS)-induced human monocytes (THP-1) macrophages. Methods C. fascicularis polyphenols (CFP) were characterized by ultra-performance liquid chromatography (UPLC) combined with quadrupole-time-of-flight mass/mass spectrometry (Q-TOF-MS/MS). The THP-1 cells were differentiated into macrophages under the stimulation of phorbol 12-myristate 13-acetate (PMA) and then treated with LPS to build a cellular inflammation model. The cell viability was detected by CCK-8 assay. The levels of reactive oxygen species (ROS) were assessed by flow cytometry. The secretion and expression of inflammatory cytokines were tested by enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR). In addition, the nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways were analyzed by Western blotting. Results Twelve phenolic constituents including (–)-epicatechin, casuariin, agastachoside, etc. in CFP were identified. The CCK-8 assay showed that CFP exhibited no significant cytotoxicity between 100 and 300 μg/mL. After treated with CFP, the release of ROS was significantly suppressed. CFP inhibited inflammation in macrophages by attenuating the polarization of LPS-induced THP-1 macrophages, down-regulating the expression of the pro-inflammatory cytokines IL-6, IL-1β and TNF-α, and up-regulating the expression of the anti-inflammatory cytokine IL-10. Western blotting experiments manifested that CFP could markedly inhibit the phosphorylation of p65, ERK and JNK, thereby suppressing the activation of NF-κB and MAPK signaling pathways. Conclusion These findings indicated that CFP exerted anti-inflammatory activity by inhibiting the activation NF-κB and MAPK pathways which may induce the secretion of pro-inflammatory cytokines. This study offers a reference for C. fascicularis as the source of developing natural, safe anti-inflammatory agents in the future.

11.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-319518

ABSTRACT

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit;ii) it has high computational efficiency and is thus convenient for practical deployment;iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317678

ABSTRACT

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist.

13.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-316031

ABSTRACT

Background: We assessed patient by automated survey method in understanding and satisfaction with the use of fever clinic, and observed the effectiveness of this method. Methods: : Total 873 patients in fever clinc at Jiangsu Province Hospital (JSPH) from 20 January 2019 to 18 June 2020 were investigated by an antomated survey method conbined by Wechat, Short Message Service (SMS) and AI voice call. Responses were assessed for overall positivity or negativity and further compared according to patients types (isolated patients and non-isolated patients). Responses were also described and compared for each type of survey. Results: : A total of 379 patient surveys were returned, for a total response rate of 43.4%. Isolated and non-isolated patients responses were similar and all with more than 90% satisfaction. Most isolated patient represent that the medical staff had explained to them the reason for the isolation and know that can helps prevent COVID-19. AI voice calls had the highest percentage of all response types, followed by WeChat and SMS. Conclusion: The patient has a positive response to the use of fever clinic. The automated survey method combine by different survey types can bring great convenience to the investigation while ensuring good investigation efficiency.

14.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315705

ABSTRACT

Background: A new type of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared in Wuhan, China. However, the risk factors and characteristics related to the severity of the disease and its outcomes need to be further explored. Methods: : In this retrospective study, we evaluated COVID-19 patients with severe disease and those who were critically ill, as diagnosed at Jinyintan Hospital (Wuhan, China). The demographic information, clinical characteristics, complications, and laboratory results for the patients were evaluated. Multivariate logistic regression methods were used to analyze risk factors related to hospital deaths. Results: : The 235 COVID-19 patients included were divided into a severe group of 183 (78%) and a critical group of 52 (22%). Of these patients, 185 (79%) were discharged, and 50 (21%) died during hospitalization. In multivariate logistic analyses, age (OR=1.07, 95% CI 1.02-1.14, P=0.009), critical disease (OR=48.23, 95% CI 10.91-323.13, P<0.001), low lymphocyte counts (OR=15.48, 95% CI 1.98-176.49, P=0.015), elevated interleukin 6 (IL-6) (OR=9.11, 95% CI 1.69-67.75, P=0.017), and elevated aspartate aminotransferase (AST) (OR=8.46, 95% CI 2.16-42.60, P=0.004) were independent risk factors for adverse outcomes. Conclusions: : The results show that advanced age (> 64 years), critical illness, low lymphocyte levels, and elevated IL-6 and AST were factors for the risk of death for COVID-19 patients who had severe disease and those who were critically ill.

15.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308269

ABSTRACT

The COVID-19 pandemic has imposed serious challenges in multiple perspectives of human life. To diagnose COVID-19, oropharyngeal swab (OP SWAB) sampling is generally applied for viral nucleic acid (VNA) specimen collection. However, manual sampling exposes medical staff to a high risk of infection. Robotic sampling is promising to mitigate this risk to the minimum level, but traditional robot suffers from safety, cost, and control complexity issues for wide-scale deployment. In this work, we present soft robotic technology is promising to achieve robotic OP swab sampling with excellent swab manipulability in a confined oral space and works as dexterous as existing manual approach. This is enabled by a novel Tstone soft (TSS) hand, consisting of a soft wrist and a soft gripper, designed from human sampling observation and bio-inspiration. TSS hand is in a compact size, exerts larger workspace, and achieves comparable dexterity compared to human hand. The soft wrist is capable of agile omnidirectional bending with adjustable stiffness. The terminal soft gripper is effective for disposable swab pinch and replacement. The OP sampling force is easy to be maintained in a safe and comfortable range (throat sampling comfortable region) under a hybrid motion and stiffness virtual fixture-based controller. A dedicated 3 DOFs RCM platform is used for TSS hand global positioning. Design, modeling, and control of the TSS hand are discussed in detail with dedicated experimental validations. A sampling test based on human tele-operation is processed on the oral cavity model with excellent success rate. The proposed TOOS robot demonstrates a highly promising solution for tele-operated, safe, cost-effective, and quick deployable COVID-19 OP swab sampling.

16.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308267

ABSTRACT

Background: Patients with Coronavirus disease 2019 (COVID-19) admitted to an intensive care unit (ICU) might develop COVID-19-related pulmonary Aspergillosis (CAPA). We aimed to identify studies systematically that describe the incidence and risks factors of CAPA, and to assess its outcome. Methods: Two authors independently searched ScienceDirect, PubMed, CNKI, MEDLINE (OVID), and MedRXIV from December 31, 2019 to Feb 27, 2021. We included observational cohort studies that investigated patients with CAPA admitted to an ICU. We assessed the quality of all included studies using the Newcastle–Ottawa Scale). The meta-analysis was registered with PROSPERO (CRD42021242179). Results: Twenty-nine cohort studies with 2095 patients with COVID-19 admitted to an ICU and 264 patients who developed to CAPA were included (Pooled incidence: 0.14, 95% confidence interval [CI] = 0.11–0.17). The overall mortality and case fatality rate of CAPA were 0.07 (0.05–0.09) and 0.51 (0.44–0.58), respectively. Patients with COVID‑19 would develop CAPA at 7.28 days after mechanical ventilation (range, 5.48–9.08). Compared with patients without CAPA, those with CAPA had a significantly lower median body mass index (27.32 vs . 28.97 kg/m 2 , P = 0.034), higher median creatinine level (127.94 vs . 88.23 µmol/L, P = 014), and were more likely to receive corticosteroids therapy (41.0% vs. 38.0%, risk ratio [RR] = 1.98, 95% CI=1.08–3.63) and renal replacement therapy (42.0% vs . 28.2%, RR = 1.61, 95% CI=1.04–2.50) during admission. Remarkably, patients with CAPA were associated significantly with a 1.66‑fold higher mortality (RR = 1.66, 95% CI=1.31–2.12) without significant heterogeneity and publication bias. Conclusions: Patients with COVID-19 admitted to an ICU might develop CAPA and have higher all‑cause mortality. We recommend conducting prospective screening for CAPA among patients with severe COVID-19, especially for those who receive mechanical ventilation over 7 days.

17.
BMC Pregnancy Childbirth ; 22(1): 54, 2022 Jan 21.
Article in English | MEDLINE | ID: covidwho-1643118

ABSTRACT

BACKGROUND: A hospital-based retrospective study was conducted to examine the effect of initial COVID-19 outbreak during first trimester on pregnancy outcome in Wuxi, China. METHODS: Women who delivered children at our hospital during June 2020 to July 2020 (control group), and October 2020 to December 2020 (exposure group) were recruited in the present study. All of the participants were not infected with COVID-19. The last menstrual period (LMP) of the exposure group was between January 24th, 2020 and March 12th, 2020, whilst in the control group, the LMP was between May 12th and October 31st, 2019. RESULTS: There were 1,456 women in the exposure group and 1,816 women in the control group. Women in the exposure group were more susceptible to hypertension during pregnancy (HDP, P = 0.004, OR[95%CI] = 1.90[1.22-2.95]) and gestational diabetes mellitus (GDM, P = 0.008, OR[95%CI] = 1.31[1.08-1.60]) compared to those in the control group. Mothers diagnosed with HDP were more likely to deliver premature infants, leading to a higher rate of low birth weight (all P < 0.05). The other common outcomes of pregnancy showed no statistical differences between the two groups. CONCLUSIONS: The initial COVID-19 outbreak might increase the incidence rates of HDP and GDM among pregnant women whose first trimesters were during that period, resulting in higher percentages of premature delivery and low birth weight. These results should be confirmed by studies from other hospitals or cities.


Subject(s)
COVID-19/epidemiology , Maternal Exposure , Pregnancy Complications/epidemiology , Pregnancy Trimester, First , SARS-CoV-2 , Adult , China/epidemiology , Diabetes, Gestational/epidemiology , Female , Humans , Hypertension, Pregnancy-Induced/epidemiology , Incidence , Infant, Low Birth Weight , Pregnancy , Premature Birth , Retrospective Studies
18.
Security and Communication Networks ; 2021, 2021.
Article in English | ProQuest Central | ID: covidwho-1556792

ABSTRACT

Novel coronavirus spreads fast and has a huge impact on the whole world. In light of the spread of novel coronaviruses, we develop one big data prediction model of novel coronavirus epidemic in the context of intelligent medical treatment, taking into account all factors of infection and death and implementing emerging technologies, such as the Internet of Things (IoT) and machine learning. Based on the different application characteristics of various machine learning algorithms in the medical field, we propose one artificial intelligence prediction model based on random forest. Considering the loose coupling between the data preparation stage and the model training stage, such as data collection and data cleaning in the early stage, we adopt the IoT platform technology to integrate the data collection, data cleaning, machine learning training model, and front- and back-end frameworks to ensure the tight coupling of each module. To validate the proposed prediction model, we perform the evaluation work. In addition, the performance of the prediction model is analyzed to ensure the information accuracy of the prediction platform.

19.
Pacific-Basin Finance Journal ; : 101692, 2021.
Article in English | ScienceDirect | ID: covidwho-1540888

ABSTRACT

This paper examines the COVID-19 impact on Chinese farmers’ peer-to-peer (P2P) borrowings using transaction-level data. Our difference-in-differences estimation results suggest that farmers from the most pandemic-affected region, Hubei province, substantially reduced their P2P loans by 13% compared to other areas. Besides, we find a significantly lower equilibrium interest rate change, indicating a more dominant force on the demand side. Finally, we evaluate the lockdown policy, showing that provinces with larger logistics capacities exhibit more considerable credit declines. Overall, our study suggests that Fintech lending functions as an alternative financing channel during the pandemic, though the demand shrinkage dominates the supply.

20.
Ann Transl Med ; 9(23): 1712, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1538973

ABSTRACT

BACKGROUND: Little is known about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC)-contaminated environmental surfaces and air in hospital wards admitting COVID-19 cases. Our study was designed to identify high-risk areas of Delta VOC contamination in the hospital and provide suggestions to in-hospital infection control. We analyzed the SARS-CoV-2 Delta VOC contamination in the air and environmental surface samples collected from a hospital in Nanjing, China. METHODS: We collected data on clinical features, laboratory tests, swab tests, and hospital wards, identified the factors associated with environmental contamination, and analyzed patients' hygiene behaviors during hospitalization. RESULTS: A total of 283 environmental surface and air samples were collected from a hospital admitting 36 COVID-19 patients. Twelve swab samples from ten patients were positive. Toilet seats had the highest contamination rate (11.8%), followed by bedside tables (8.2%), garbage bins (5.9%), and bedrails (1.6%). The median time of symptom onset to surface sampling was shorter in the positive environment group than in the negative environment group (11 vs. 18 days; P=0.001). The results indicated that environmental surface contamination was associated with positive anal swabs [odds ratio (OR) 27.183; 95% CI: 2.359-226.063; P=0.003] and the time from symptom onset to surface sampling (OR 0.801; 95% CI: 0.501-0.990; P=0.046). The survey revealed that 33.3% of the patients never cleaned or disinfected their bedside tables or toilets, and 8.3% of them only cleaned their bedside tables or toilets. More than half of the patients often (25%) or always (30.6%) put the used masks on their bedside tables. Only 16.7% of the patients threw the masks into the specific garbage bin for used masks. CONCLUSIONS: The SARS-CoV-2 Delta VOC was detected on environmental surfaces, especially toilet seats and bedside tables, within a median time of 11 days after symptom onset. Our study provided potential predictors for environmental surface contamination, including positive anal swabs and the time from symptom onset to sampling. Disinfecting high-risk environmental surfaces should be emphasized in hospital wards, especially for patients in the early stage of COVID-19.

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