ABSTRACT
OBJECTIVE This study investigated the characteristics of newly diagnosed type 1 diabetes mellitus (T1DM) related to autoimmunity and the frequency of diabetic ketoacidosis (DKA) in children and adolescents from 2017–2022 in China.RESEARCH DESIGN AND METHODS Single-center regional data from the Department of Pediatric Endocrinology, Tongji Hospital, were used to compare 88 children and adolescents newly diagnosed with T1DM from 2020 to 2022 (i.e. during the COVID-19 pandemic in China) and 76 children and adolescents diagnosed with T1DM from 2017 to 2019. Auto-antibodies, including glutamic acid decarboxylase-65 and insulin auto-antibodies, were detected by enzyme-linked immunoassays. DKA was defined as a pH < 7.3 and/or a bicarbonate level < 15 mmol/L.RESULTS The median age of the 164 children and adolescents newly diagnosed with T1DM from 2017 to 2022 was 7.0 years (interquartile range [IQR]: 3.8–10.0 years; 51.83% male). The mean annual incidence of T1DM was 4.25/100,000. The estimated frequency of auto-antibody positivity was 51.22% (n = 84), and there was no difference between the 2020–2022 group and 2017–2019 group (55.68% [n = 49] vs. 46.5% [n = 35]; p = 0.219). The incidence of DKA among the entire cohort was 57.93% (n = 95). The frequency of DKA was not significantly higher in the 2020–2022 group compared with the 2017–2019 group (60.23% [n = 53] vs. 55.26% [n = 42]; p = 0.521). We found no significant difference in the frequency of DKA between patients who were negative vs. positive for auto-antibodies in the 2020–2022 group (64.10% [n = 25] vs. 57.14% [n = 28], p > 0.05). The C-peptide level and HbA1c (%) were positively correlated with onset age (R1 = 0.389, p < 0.01; R2 = 0.371, p < 0.01), and the estimated mean C-peptide level was 0.26 ng/ml (IQR: 0.2–0.4 ng/ml) in patients with DKA and 0.370 ng/ml (IQR: 0.2–0.6 ng/ml) in patients without DKA (p = 0.044).CONCLUSIONS This study showed the annual incidence of T1DM was 4.25/100,000, gradually increased over the study period, and there was no significant increase in T1DM with auto-antibody positivity in children and adolescents newly diagnosed from 2020–2022 in China compared with the previous 3 years. Furthermore, the frequencies of DKA were not significantly different between patients who were negative vs. positive for auto-antibodies.
Subject(s)
Diabetic Ketoacidosis , Diabetes Mellitus , COVID-19ABSTRACT
In this study we examined inclusive leadership as an important factor in promoting the emotional labor strategies of frontline medical staff, and investigated the role of work regulatory focus as a mediator in this relationship. Data were collected from 52 supervisors and 231 frontline medical staff employed at 15 hospitals in China. We found that inclusive leadership inspired frontline medical staff to engage more in deep acting than in surface acting. Further, work regulatory focus played a mediating role in the relationship between inclusive leadership and subordinates' emotional labor strategy. Promotion focus positively affected deep acting and negatively affected surface acting, and prevention focus positively affected both surface acting and deep acting. Theoretical and practical management implications are discussed.
ABSTRACT
The COVID-19 pandemic has once again brought the significance of biopharmaceutical and medical technology sectors to the spotlight. Seeing that some of the most critical medical breakthroughs such as the speedy mRNA vaccine development were results of cross-border patenting collaboration, we have proposed in a previous work a new method to identify the cross-border collaborative regional centres in the patent networks, using on a clustering comparison approach based on adjusted mutual information (AMI). In this paper, we focus on the UK industrial landscape. We use the UK bioscience and health technology sector statistics from 2015 to 2020 and look into the regional growth of each postcode area. We compare the top growth regions with the cross-border collaborative centers identified using AMI comparison at the postcode area level, and find that both long-term and short-term AMI gains show an increase in the correlation with regional annual growth rates of firm numbers in the studies sectors from 2016 to 2020, and the increase is more consistent with the short-term AMI gain. Areas more central in the long-term cross-regional R&D collaboration tend to have more developed industrial settings with higher business numbers and, potentially more employment and turnover in the field.
Subject(s)
COVID-19ABSTRACT
The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.
Subject(s)
COVID-19ABSTRACT
BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic continues to escalate intensively worldwide. Massive studies on general populations with SARS-CoV-2 infection have revealed that pre-existing comorbidities were a major risk factor for the poor prognosis of COVID-19. Notably, 49-75% of COVID-19 patients had no comorbidities, but this cohort would also progress to severe COVID-19 or even death. However, risk factors contributing to disease progression and death in patients without chronic comorbidities are largely unknown; thus, specific clinical interventions for those patients are challenging. METHODS: A multicenter, retrospective study based on 4806 COVID-19 patients without chronic comorbidities was performed to identify potential risk factors contributing to COVID-19 progression and death using LASSO and a stepwise logistic regression model. RESULTS: Among 4806 patients without pre-existing comorbidities, the proportions with severe progression and mortality were 34.29% and 2.10%, respectively. The median age was 47.00 years [interquartile range, 36.00-56.00], and 2162 (44.99%) were men. Among 51 clinical parameters on admission, age ≥ 47, oxygen saturation < 95%, increased lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen levels, dyspnea, increased blood glucose and prothrombin time levels were associated with COVID-19 mortality in the entire cohort. Of the 3647 patients diagnosed with non-severe COVID-19 on admission, 489(13.41%) progressed to severe disease. The risk factors associated with COVID-19 progression from non-severe to severe illness were increased procalcitonin levels, SpO2 < 95%, age ≥ 47, increased LDH, activated partial thromboplastin time levels, decreased high-density lipoprotein cholesterol levels, dyspnea and increased D-dimer levels. CONCLUSIONS: COVID-19 patients without pre-existing chronic comorbidities have specific traits and disease patterns. COVID-19 accompanied by severe bacterial infections, as indicated by increased procalcitonin levels, was highly associated with disease progression from non-severe to severe. Aging, impaired respiratory function, coagulation dysfunction, tissue injury, and lipid metabolism dysregulation were also associated with disease progression. Once factors for multi-organ damage were elevated and glucose increased at admission, these findings indicated a higher risk for mortality. This study provides information that helps to predict COVID-19 prognosis specifically in patients without chronic comorbidities.
Subject(s)
COVID-19 , Humans , Male , Middle Aged , Oxygen Saturation , Retrospective Studies , Risk Factors , SARS-CoV-2ABSTRACT
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.
Subject(s)
COVID-19ABSTRACT
Background: The novel coronavirus (COVID-19)–infected pneumonia is an international concern as it spreads through human populations and across national and international borders.Methods: In this retrospective study, we consecutively included all cancer cases who had been identified as having a nucleic acid-confirmed COVID-19 infection from two designated hospitals in Wuhan, China. Non-cancer patients were also enrolled for comparison. The clinical data were gathered from the medical recordsfrom Jan 14 to March 12.Results: Among the 117 cancer patients infected with COVID19, the median age was 63 years and 48.7% were male. Male, hematologic cancer, dyspnea on admission, and anti-cancer therapy significantly increased the risk of death. The amounts of cytokines and immune cells were correlated with the outcomeofcancer patients infected with COVIP-19. However, high level of TNF-a, IL-2R, IL-6, IL-8 did not increase the risk of death in non-cancer patients. Moreover, IL-2R and IL-6 markedly decreased in cancer patients recovered from COVID-19.Conclusions: Cancer patients with COVID-19 were associated with high mortality (23.9%).The amounts of cytokines and lymphocytes could be utilized as the reference index in predicting the survival outcome of cancer patients with COVID-19.
Subject(s)
Dyspnea , Pneumonia , Neoplasms , Death , COVID-19ABSTRACT
The COVID-19 pandemic has spawned a rare opportunity to study some latent social structures using data science. The Chinese government and its people have been blamed for the outbreak of the virus. Face mask wearing can signal an embodied stigma and Chinese people living outside China have been subject to discrimination, assault, and other hate crimes, particularly at the early stages of the crisis. However, as we accumulate more evidence surrounding mask use, the stigma is shifting. As more scientific data become available and people leave even more information on social media during the lockdown, data science can help better understand the trajectories of the stigma. The insights generated have implications for anti-stigma interventions for future undesirable conditions and diseases.
Subject(s)
COVID-19ABSTRACT
We propose an equilibrium-driven deformation algorithm (EDDA) to simulate the inbetweening transformations starting from an initial image to an equilibrium image, which covers images varying from a greyscale type to a colorful type on plane or manifold. The algorithm is based on Fokker-Planck dynamics on manifold, which automatically cooperates positivity, unconditional stability, mass conservation law, exponentially convergence and also the manifold structure suggested by dataset. The thresholding scheme is adapted for the sharp interface dynamics and is used to achieve the finite time convergence. Using EDDA, three challenging examples, (I) facial aging process, (II) coronavirus disease 2019 (COVID-19) invading/treatment process, and (III) continental evolution process are conducted efficiently.
Subject(s)
COVID-19 , Musculoskeletal DiseasesABSTRACT
An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.
Subject(s)
Coronavirus Infections , Adenocarcinoma, Bronchiolo-Alveolar , Death , COVID-19 , Respiratory InsufficiencyABSTRACT
Objective: To describe and evaluate the impact of diseases control and prevention on epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai. Design: A retrospective descriptive study Setting: China Participants: Epidemiology information was collected from publicly accessible database. 265 patients admitted to Shanghai Public Health Center with confirmed COVID-19 were enrolled for clinical features analysis. Main outcome measure: Prevention and control measures taken by Shanghai government, epidemiological, demographic, clinical, laboratory and radiology data were collected. Weibull distribution, Chi-square test, Fisher's exact test, t test or Mann-Whitney U test were used in statistical analysis. Results: COVID-19 transmission rate within Shanghai had reduced over 99% than previous speculated, and the exponential growth has been stopped so far. Epidemic was characterized by the first stage mainly composed of imported cases and the second stage where >50% of cases were local. The incubation period was 6.4 (95% CI 5.3 to 7.6) days and the mean onset-admission interval was 5.5 days (95% CI, 5.1 to 5.9). Median time for COVID-19 progressed to severe diseases were 8.5 days (IQR: 4.8-11.0 days). By February 11th, proportion of patients being mild, moderate, severe and critically ill were 1.9%(5/265), 89.8%(238/265), 3.8%(10/265), 4.5%(12/265), respectively; 47 people in our cohort were discharged, and 1 patient died. Conclusion: Strict controlling of the transmission rate at the early stage of an epidemic in metropolis can quickly prohibit the spread of the diseases. Controlling local clusters is the key to prevent outbreaks from imported cases. Most COVID-19 severe cases progressed within 14 days of disease onset. Multiple systemic laboratory abnormalities had been observed before significant respiratory dysfunction. Keyword: COVID-19, SARS-CoV-2, epidemics dynamics, diseases control, clinical features
Subject(s)
COVID-19 , Respiratory Insufficiency , DiseaseABSTRACT
The outbreak of pneumonia caused by a novel coronavirus (2019-nCoV) in Wuhan City of China obtained global concern, the population outflow from Wuhan has contributed to spatial expansion in other parts of China. We examined the effects of population outflow from Wuhan on the 2019-nCoV transmission in other provinces and cities of China, as well as the impacts of the city closure in Wuhan. We observed a significantly positive association between population movement and the number of cases. Further analysis revealed that if the city closure policy was implemented two days earlier, 1420 (95% CI: 1059, 1833) cases could be prevented, and if two days later, 1462 (95% CI: 1090, 1886) more cases would be possible. Our findings suggest that population movement might be one important trigger of the 2019-nCoV infection transmission in China, and the policy of city closure is effective to prevent the epidemic.