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2.
Nutrients ; 14(5)2022 Mar 05.
Article in English | MEDLINE | ID: covidwho-1732145

ABSTRACT

BACKGROUND: Pulmonary fibrosis (PF) is a chronic, progressive, and, ultimately, terminal interstitial disease caused by a variety of factors, ranging from genetics, bacterial, and viral infections, to drugs and other influences. Varying degrees of PF and its rapid progress have been widely reported in post-COVID-19 patients and there is consequently an urgent need to develop an appropriate, cost-effective approach for the prevention and management of PF. AIM: The potential "therapeutic" effect of the tocotrienol-rich fraction (TRF) and carotene against bleomycin (BLM)-induced lung fibrosis was investigated in rats via the modulation of TGF-ß/Smad, PI3K/Akt/mTOR, and NF-κB signaling pathways. DESIGN/METHODS: Lung fibrosis was induced in Sprague-Dawley rats by a single intratracheal BLM (5 mg/kg) injection. These rats were subsequently treated with TRF (50, 100, and 200 mg/kg body wt/day), carotene (10 mg/kg body wt/day), or a combination of TRF (200 mg/kg body wt/day) and carotene (10 mg/kg body wt/day) for 28 days by gavage administration. A group of normal rats was provided with saline as a substitute for BLM as the control. Lung function and biochemical, histopathological, and molecular alterations were studied in the lung tissues. RESULTS: Both the TRF and carotene treatments were found to significantly restore the BLM-induced alterations in anti-inflammatory and antioxidant functions. The treatments appeared to show pneumoprotective effects through the upregulation of antioxidant status, downregulation of MMP-7 and inflammatory cytokine expressions, and reduction in collagen accumulation (hydroxyproline). We demonstrated that TRF and carotene ameliorate BLM-induced lung injuries through the inhibition of apoptosis, the induction of TGF-ß1/Smad, PI3K/Akt/mTOR, and NF-κB signaling pathways. Furthermore, the increased expression levels were shown to be significantly and dose-dependently downregulated by TRF (50, 100, and 200 mg/kg body wt/day) treatment in high probability. The histopathological findings further confirmed that the TRF and carotene treatments had significantly attenuated the BLM-induced lung injury in rats. CONCLUSION: The results of this study clearly indicate the ability of TRF and carotene to restore the antioxidant system and to inhibit proinflammatory cytokines. These findings, thus, revealed the potential of TRF and carotene as preventive candidates for the treatment of PF in the future.


Subject(s)
COVID-19 , Pulmonary Fibrosis , Tocotrienols , Animals , Bleomycin/toxicity , Carotenoids/adverse effects , Humans , NF-kappa B/metabolism , Phosphatidylinositol 3-Kinases/metabolism , Proto-Oncogene Proteins c-akt/metabolism , Pulmonary Fibrosis/chemically induced , Pulmonary Fibrosis/drug therapy , Pulmonary Fibrosis/prevention & control , Rats , Rats, Sprague-Dawley , SARS-CoV-2 , Signal Transduction , TOR Serine-Threonine Kinases/metabolism , Tocotrienols/adverse effects , Transforming Growth Factor beta/metabolism
3.
Lancet Digit Health ; 2(10): e506-e515, 2020 10.
Article in English | MEDLINE | ID: covidwho-779867

ABSTRACT

Background: Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics. Methods: We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19. Findings: In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940). Interpretation: A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19. Funding: Special Project for Emergency of the Science and Technology Department of Hubei Province, China.


Subject(s)
COVID-19/diagnosis , Deep Learning , Triage/methods , Adult , Aged , Algorithms , COVID-19/diagnostic imaging , COVID-19/pathology , COVID-19/therapy , China , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Reproducibility of Results , Retrospective Studies , Severity of Illness Index , Tomography, X-Ray Computed
4.
Korean J Radiol ; 21(7): 919-924, 2020 07.
Article in English | MEDLINE | ID: covidwho-593300

ABSTRACT

OBJECTIVE: The current study reported a case series to illustrate the early computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) in pediatric patients. MATERIALS AND METHODS: All pediatric patients who were diagnosed with COVID-19 and who underwent CT scan in Zhongnan Hospital of Wuhan University from January 20, 2020 to February 28, 2020 were included in the current study. Data on clinical and CT features were collected and analyzed. RESULTS: Four children were included in the current study. All of them were asymptomatic throughout the disease course (ranging from 7 days to 15 days), and none of them showed abnormalities in blood cell counts. Familial cluster was the main transmission pattern. Thin-section CT revealed abnormalities in three patients, and one patient did not present with any abnormal CT findings. Unilateral lung involvement was observed in two patients, and one patient showed bilateral lung involvement. In total, five small lesions were identified, including ground-glass opacity (n = 4) and consolidation (n = 1). All lesions had ill-defined margins with peripheral distribution and predilection of lower lobe. CONCLUSION: Small patches of ground-glass opacity with subpleural distribution and unilateral lung involvement were common findings on CT scans of pediatric patients in the early stage of the disease.


Subject(s)
Asymptomatic Infections , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Betacoronavirus , COVID-19 , Child , China/epidemiology , Disease Progression , Female , Humans , Image Processing, Computer-Assisted , Lung/diagnostic imaging , Lung/pathology , Male , Pandemics , Retrospective Studies , SARS-CoV-2
5.
JAMA Netw Open ; 3(5): e209666, 2020 05 01.
Article in English | MEDLINE | ID: covidwho-352390

ABSTRACT

Importance: Health care workers (HCWs) have high infection risk owing to treating patients with coronavirus disease 2019 (COVID-19). However, research on their infection risk and clinical characteristics is limited. Objectives: To explore infection risk and clinical characteristics of HCWs with COVID-19 and to discuss possible prevention measures. Design, Setting, and Participants: This single-center case series included 9684 HCWs in Tongji Hospital, Wuhan, China. Data were collected from January 1 to February 9, 2020. Exposures: Confirmed COVID-19. Main Outcomes and Measures: Exposure, epidemiological, and demographic information was collected by a structured questionnaire. Clinical, laboratory, and radiologic information was collected from electronic medical records. A total of 335 medical staff were randomly sampled to estimate the prevalence of subclinical infection among a high-risk, asymptomatic population. Samples from surfaces in health care settings were also collected. Results: Overall, 110 of 9684 HCWs in Tongji Hospital tested positive for COVID-19, with an infection rate of 1.1%. Of them, 70 (71.8%) were women, and they had a median (interquartile range) age of 36.5 (30.0-47.0) years. Seventeen (15.5%) worked in fever clinics or wards, indicating an infection rate of 0.5% (17 of 3110) among first-line HCWs. A total of 93 of 6574 non-first-line HCWs (1.4%) were infected. Non-first-line nurses younger than 45 years were more likely to be infected compared with first-line physicians aged 45 years or older (incident rate ratio, 16.1; 95% CI, 7.1-36.3; P < .001). The prevalence of subclinical infection was 0.74% (1 of 135) among asymptomatic first-line HCWs and 1.0% (2 of 200) among non-first-line HCWs. No environmental surfaces tested positive. Overall, 93 of 110 HCWs (84.5%) with COVID-19 had nonsevere disease, while 1 (0.9%) died. The 5 most common symptoms were fever (67 [60.9%]), myalgia or fatigue (66 [60.0%]), cough (62 [56.4%]), sore throat (55 [50.0%]), and muscle ache (50 [45.5%]). Contact with indexed patients (65 [59.1%]) and colleagues with infection (12 [10.9%]) as well as community-acquired infection (14 [12.7%]) were the main routes of exposure for HCWs. Conclusions and Relevance: In this case series, most infections among HCWs occurred during the early stage of disease outbreak. That non-first-line HCWs had a higher infection rate than first-line HCWs differed from observation of previous viral disease epidemics. Rapid identification of staff with potential infection and routine screening among asymptomatic staff could help protect HCWs.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Cross Infection/epidemiology , Cross Infection/virology , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Adult , COVID-19 , China/epidemiology , Coronavirus Infections/diagnosis , Cross Infection/prevention & control , Female , Health Personnel , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Risk Factors , SARS-CoV-2 , Surveys and Questionnaires , Tertiary Care Centers , Young Adult
6.
Chin. J. Radiol. ; 4(54): 300-304, 20200410.
Article in Chinese | WHO COVID, ELSEVIER | ID: covidwho-142415

ABSTRACT

Objective: To investigate the CT and clinical features of COVID-19. Methods: Chest CT and clinical data of 103 patients who were confirmed as COVID-19 in January 2020 were collected retrospectively. According to diagnosis and treatment of COVID-19 (trial version 5), all the patients were classified into common(n=58), severe (n=36) and critical (n=9) types, and their clinical findings, laboratory examination and CT finding were analyzed. CT features included distribution, location, size, shape, edge, number and density of the lesion, percentage of pneumonia lesions of the whole lung and extra-pulmonary manifestations. The CT features among different clinical types were compared using χ 2test or Fisher's exact probability. Comparisons of age, duration from onset to CT examination, and percentage of pneumonic lesions to total lung volume among different types were performed by using analysis of variance (normal distribution) or Kruskal-Wallis rank sum test (non-normal distribution). Results: In terms of clinical manifestations, the patients with critical COVID-19 were more common in elderly men, with a median age of 65 years. Fever was the first symptom in 49 (84%) of 58 common patients, and also the first symptom in both severe and critical COVID-19 patients. The incidence of coughing in severe (25/36, 69%) and critical (6/9, 67%) COVID-19 patients was higher than that in common patients (20/58, 34%). All critical patients had dyspnea. CT showed the common COVID-19 was located in bilateral lung (40/58, 71%)with multiple (40/58, 69%), ground glass (31/58, 52%) or mixed (25/58, 43%)opacities (56/58, 97%), while all the severe and critical COVID-19 were located in bilateral lung(100%) with multiple (34/36, 96%), patchy (33 /36, 92%), or mixed opacities (26/36, 72%) in severe patients, and with mixed opacities more than 3 cm in critical patients. As for the percentage of pneumonia focus in the whole lung volume, the common type (12.5%±6.1%) was significantly lower than the severe type (25.9%± 10.7%) and the critical type (47.2%±19.2%), with statistically significant differences(P< 0.001 and 0.002 respectively), and the severe type COVID-19 was also significantly lower than the critical type (P= 0.032). Conclusions: CT and clinical features of different clinical types of COVID-19 pneumonia are different. Chest CT findings are characteristic, which can not only help the early diagnosis but also evaluate the clinical course and severity.

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