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1.
Rheumatology (Oxford) ; 2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1662131

ABSTRACT

OBJECTIVES: Infections including Tuberculosis (TB) are a leading cause of morbidity and mortality in Idiopathic Inflammatory Myopathies (IIM). We systematically reviewed the prevalence of Mycobacterial infections in patients with IIM. METHODS: We screened PUBMED, EMBASE and SCOPUS databases and conference abstracts (2015-20) for original articles using Covidence. Pooled estimates of prevalence were calculated. RESULTS: Of 83 studies (28 cohort-studies, 2 case-control and 53 case reports), 19 were analysed. Of 14043 IIM patients, Dermatomyositis (54.41%) was the most common subset among TB. Most studies were from Asia with high prevalence [5.86%,2.33%-10.60%].Pooled prevalence of Mycobacterial infections among IIM was 3.58% (95% CI = 2.17% - 5.85%, p< 0.01). Disseminated and extrapulmonary forms (46.58%; 95% CI 39.02%-54.31%, p= 1.00) were as common as pulmonary TB (49.07%; 95% CI = 41.43%-56.75%, p= 0.99) both for I2=0. Muscle involvement, an otherwise rare site, was frequently seen in case reports (24.14%). M. Tuberculosis (28.84%) was the most common pathogen followed by Mycobacterium Avium Complex (3.25%). Non-tuberculous Mycobacteria were less common overall (6.25; 95% CI = 3.49%-10.93%) I2=0, p= 0.94.Subgroup analysis & meta-regression based on high vs low TB regions found prevalence 6.61% (2.96%-11.33%) in high TB regions vs 2.05% (0.90%-3.56%) in low TB regions. While death due to TB was occasionally reported [p= 0.82], successful anti-tubercular treatment was common (13.95%). CONCLUSION: TB is common in IIM, particularly in endemic regions though current data is largely heterogeneous. Extra-pulmonary forms &atypical sites including the muscle are frequent. Limited data suggests fair outcomes, although larger prospective studies may offer better understanding.

2.
J Korean Med Sci ; 36(50): e338, 2021 Dec 27.
Article in English | MEDLINE | ID: covidwho-1596045

ABSTRACT

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).


Subject(s)
COVID-19/drug therapy , Research Design , SARS-CoV-2 , COVID-19/epidemiology , Ethics, Research , Humans , Peer Review , Pilot Projects , Publishing
3.
Diagnostics (Basel) ; 11(12)2021 Dec 15.
Article in English | MEDLINE | ID: covidwho-1572404

ABSTRACT

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

4.
Diagnostics (Basel) ; 11(11)2021 Nov 01.
Article in English | MEDLINE | ID: covidwho-1488513

ABSTRACT

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

5.
Mediterr J Rheumatol ; 31(Suppl 2): 243-246, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-1410320

ABSTRACT

The flow of information on Coronavirus Disease 2019 (COVID-19) is intensifying, requiring concerted efforts of all scholars. Peer-reviewed journals as established channels of scientific communications are struggling to keep up with unprecedented high submission rates. Preprint servers are becoming increasingly popular among researchers and authors who set priority over their ideas and research data by pre-publication archiving of their manuscripts on these professional platforms. Most published articles on COVID-19 are now archived by the PubMed Central repository and available for searches on LitCovid, which is a newly designed hub for specialist searches on the subject. Social media platforms are also gaining momentum as channels for rapid dissemination of COVID-19 information. Monitoring, evaluating and filtering information flow through the established and emerging scholarly platforms may improve the situation with the pandemic and save lives.

6.
Rheumatol Int ; 41(11): 1941-1947, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1370384

ABSTRACT

Management of ANCA-associated vasculitis (AAV) during the COVID-19 pandemic poses unique therapeutic challenges. An online survey was conducted to understand physician's choices for treating AAV during the COVID-19 pandemic. Web-based survey featuring nineteen questions was circulated amongst physicians across various specialties. The responses regarding immunosuppressive therapy for remission induction and maintenance, COVID-19 testing, and preventive measures were recorded. A total of 304 responses were recorded. Most of the respondents were from India (83.9%) and comprised rheumatologists (66%) in practice for ≥ 5 years (71%). Though a majority preferred Rituximab or intravenous cyclophosphamide (CYC) as a remission induction agent, a significant proportion opted for oral CYC and mycophenolate mofetil (MMF) also. Only one-third wanted to test for COVID-19 before initiating immunosuppressive therapy in patients with organ/life-threatening manifestations. Rituximab was the most favored maintenance therapy (47%), followed by azathioprine, MMF, and methotrexate. The results of this focused survey of managing AAV patients depict the real-world dilemmas and physicians' choices in this setting.


Subject(s)
Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis/drug therapy , Practice Patterns, Physicians' , Rheumatology/methods , Adult , COVID-19/epidemiology , COVID-19 Testing , Female , Humans , Immunosuppressive Agents/therapeutic use , Male , Middle Aged , Pandemics , Remission Induction/methods , SARS-CoV-2 , Surveys and Questionnaires
7.
Diagnostics (Basel) ; 11(8)2021 Aug 04.
Article in English | MEDLINE | ID: covidwho-1341653

ABSTRACT

BACKGROUND: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. METHODOLOGY: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. RESULTS: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. CONCLUSIONS: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

8.
Clin Rheumatol ; 40(12): 4807-4815, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1263156

ABSTRACT

Patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) have a two- to threefold greater risk of developing venous as well as arterial thrombotic events. Although such thrombotic events are more commonly seen during phases of active AAV, they are also recognized to occur during AAV in remission. Endothelial injury is a key pathogenic event in AAV. Endothelial injury can be caused by neutrophil activation and release of thrombogenic tissue factor into the circulation. Neutrophil activation further results in the formation of neutrophil extracellular traps (NETs). NETs contribute to thrombosis by expressing tissue factor. NETs have also been detected in cutaneous thrombi from patients with AAV induced by hydralazine. Activated neutrophils in AAV patients release thrombogenic microparticles loaded with tissue factor which further enhances clotting of blood. Antiphospholipid antibodies (APLs) have been detected in up to a third of AAV and might also be induced by drugs such as cocaine adulterated with levamisole and propylthiouracil, which are known to trigger AAV. Such APLs further drive the thrombosis in AAV. Once thrombogenesis occurs, the homeostatic mechanisms resulting in clot dissolution are further impaired in AAV due to anti-plasminogen antibodies. The ongoing pandemic of coronavirus disease 2019 (COVID-19) is associated with endothelial injury and NETosis, mechanisms which are in common with AAV. Reports of new-onset AAV following COVID-19 have been described in the literature, and there could be shared mechanisms driving these processes that require further evaluation.


Subject(s)
Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis , COVID-19 , Extracellular Traps , Thrombosis , Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis/complications , Antibodies, Antineutrophil Cytoplasmic , Humans , Neutrophils , SARS-CoV-2
10.
World J Diabetes ; 12(3): 215-237, 2021 Mar 15.
Article in English | MEDLINE | ID: covidwho-1148329

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".

11.
Comput Biol Med ; 130: 104210, 2021 03.
Article in English | MEDLINE | ID: covidwho-1064978

ABSTRACT

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.


Subject(s)
Artificial Intelligence , COVID-19/diagnostic imaging , Lung/diagnostic imaging , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , Humans
12.
Rev Cardiovasc Med ; 21(4): 541-560, 2020 12 30.
Article in English | MEDLINE | ID: covidwho-1059479

ABSTRACT

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.


Subject(s)
Artificial Intelligence , COVID-19/epidemiology , Cardiovascular Diseases/epidemiology , Delivery of Health Care/methods , Pandemics , Risk Assessment , SARS-CoV-2 , Cardiovascular Diseases/therapy , Comorbidity , Humans , Risk Factors
13.
Disaster Med Public Health Prep ; 14(3): 387-390, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-1030339

ABSTRACT

OBJECTIVE: The coronavirus disease (COVID-19) pandemic is a disaster of unprecedented proportions with global repercussions. Psychological preparedness, the primed cognitive awareness and anticipation of dealing with emotional responses in an adverse situation, has assumed a compelling relevance during a health disaster of this magnitude. METHODS: An anonymized eSurvey was conducted in India to assess psychological preparedness toward the ongoing pandemic with a focus on knowledge, management of own and others' emotional response, and anticipatory coping mechanisms among the survey population. An adapted version of the qualitative Psychological Preparedness for Natural Disaster Scale validated by the World Health Organization was widely circulated over the Internet and various social media platforms for assessment. Results are expressed as median ± standard deviation. Descriptive statistics were used and figures downloaded from surveymonkey.com. RESULTS: Of the 1120 respondents (M:F 1.7:1, age 35 years ±14.1), most expressed a high level of perceived knowledge and confidence of managing COVID-19, such as awareness of the symptoms of the illness (95.1%), actions needed (94.4%), hospital to report to (88.9%), and emergency contact number (89.1%). A majority (95%) monitored regularly the news bulletins and scientific journals regarding COVID-19. However, nearly one-third (29.2%) could not assess their likelihood of developing COVID-19, and 17.5% were unaware of the difference between a mild and severe infection. Twenty-three percent (23.3%) were unfamiliar with the materials needed in an acute illness situation. CONCLUSION: Psychological disaster preparedness is reasonable, although lacking in specific domains. Timely but focused interventions can be a cost-efficient administrative exercise, which federal agencies may prioritize working on.


Subject(s)
Adaptation, Psychological , Coronavirus Infections/complications , Health Literacy/standards , Pneumonia, Viral/complications , Stress, Psychological/psychology , Adult , Aged , Aged, 80 and over , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/psychology , Female , Health Literacy/statistics & numerical data , Humans , India/epidemiology , Male , Middle Aged , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/psychology , Surveys and Questionnaires
14.
J Clin Rheumatol ; 27(1): 31-33, 2021 Jan 01.
Article in English | MEDLINE | ID: covidwho-990968

ABSTRACT

BACKGROUND: The coronavirus disease (COVID-19) pandemic and its subsequent effects on health care systems have significantly impacted the management of chronic rheumatic diseases, including systemic sclerosis (SSc). METHODS: In this context, a 25-item anonymized e-survey was posted on the Twitter and Facebook e-groups and pages of various scleroderma organizations and patient communities to assess the problems faced by patients with SSc during the pandemic, with a focus on effects on the disease, drug procurance, continuity of medical care, and prevalent fears among patients. RESULTS: Of the 291 participants (median age of 55 [43.5-63] years, 93.8% females), limited systemic sclerosis was the most common diagnosis (42.3%). Many patients experienced problems attributable to the COVID-19 pandemic (119, 40.9%), of which 46 (38.7%) required an increase in medicines, and 12 (10.1%) of these needed hospitalizations for disease-related complications. More than one-third (36.4%) were on glucocorticoids or had underlying cardiovascular risks (39%) that would predispose them to severe COVID-19.A significant proportion (38.1%) faced hurdles in procuring medicines or experienced disruption in physiotherapy sessions (24.7%). One-quarter (24.1%) felt it was difficult to contact their specialist, whereas another 7.2% were unable to do so. Contracting COVID-19 was the most prevalent fear (71.5%), followed by infection in the family (61.9%), and a flare of the disease (45.4%). Most respondents preferred teleconsultations (55.7%) over hospital visits in the pandemic period. CONCLUSION: The results of the patient survey suggest that the COVID-19 pandemic has affected many patients with SSc and may translate to poorer outcomes in this population in the postpandemic period.


Subject(s)
COVID-19/epidemiology , Health Services Accessibility , Scleroderma, Systemic/complications , Scleroderma, Systemic/therapy , Adult , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Surveys and Questionnaires
15.
Front Public Health ; 8: 571419, 2020.
Article in English | MEDLINE | ID: covidwho-921174

ABSTRACT

Background: The private medical sector is a resource that must be estimated for efficient inclusion into public healthcare during pandemics. Methods: A survey was conducted among private healthcare workers to ascertain their views on the potential resources that can be accessed from the private sector and methods to do the same. Results: There were 213 respondents, 80% of them being doctors. Nearly half (47.4%) felt that the contribution from the private medical sector has been suboptimal. Areas suggested for improved contributions by the private sector related to patient care (71.8%) and provision of equipment (62.4%), with fewer expectations (39.9%) on the research front. Another area of deemed support was maintaining continuity of care for non-COVID patients using virtual consultation services (77.4%), tele-consultation being the preferred option (60%). 58.2% felt that the Government had not involved the private sector adequately; and 45.1% felt they should be part of policy-making. Conclusion: A streamlined pathway to facilitate the private sector to join hands with the public sector for a national cause is the need of the hour. Through our study, we have identified gaps in the current contribution by the private sector and identified areas in which they could contribute, by their own admission.


Subject(s)
COVID-19 , Pandemics , Cross-Sectional Studies , Humans , India/epidemiology , Pandemics/prevention & control , Private Sector , SARS-CoV-2
17.
Rheumatol Int ; 40(12): 2023-2030, 2020 12.
Article in English | MEDLINE | ID: covidwho-848285

ABSTRACT

The evolving research landscape in the time of the Coronavirus disease 2019 (COVID-19) pandemic calls for greater understanding of the perceptions of scholars regarding the current state and future of publishing. An anonymised and validated e-survey featuring 30 questions was circulated among rheumatologists and other specialists over social media to understand preferences while choosing target journals, publishing standards, commercial editing services, preprint archiving, social media and alternative publication activities. Of 108 respondents, a significant proportion were clinicians (68%), researchers (60%) and educators (47%), with median 23 publications and 15 peer-review accomplishments. The respondents were mainly rheumatologists from India, Ukraine and Turkey. While choosing target journals, relevance to their field (69%), PubMed Central archiving (61%) and free publishing (59%) were the major factors. Thirty-nine surveyees (36%) claimed that they often targeted local journals for publishing their research. However, only 18 (17%) perceived their local society journals as trustworthy. Occasional publication in the so-called predatory journals (5, 5%) was reported and obtaining support from commercial editing agencies to improve English and data presentation was not uncommon (23, 21%). The opinion on preprint archiving was disputed; only one-third believed preprints were useful. High-quality peer review (56%), full and immediate open access (46%) and post-publication social media promotion (32%) were identified as key anticipated features of scholarly publishing in the foreseeable future. These perceptions of surveyed scholars call for greater access to free publishing, attention to proper usage of English and editing skills, and a larger role for engagement over social media.


Subject(s)
Coronavirus Infections , Pandemics , Periodicals as Topic/standards , Pneumonia, Viral , Scholarly Communication/standards , Adult , Betacoronavirus , COVID-19 , Humans , Middle Aged , Open Access Publishing/standards , Rheumatology , SARS-CoV-2 , Surveys and Questionnaires
19.
Rheumatol Int ; 40(11): 1741-1751, 2020 11.
Article in English | MEDLINE | ID: covidwho-743717

ABSTRACT

Repurposing of antirheumatic drugs has garnered global attention. The aim of this article is to overview available evidence on the use of widely used antirheumatic drugs hydroxychloroquine, methotrexate and colchicine for additional indications. Hydroxychloroquine has endothelial stabilizing and anti-thrombotic effects. Its use has been explored as an adjunctive therapy in refractory thrombosis in antiphospholipid syndrome. It may also prevent recurrent pregnancy losses in the absence of antiphospholipid antibodies. Hydroxychloroquine favourably modulates atherogenic lipid and glycaemic profiles. Methotrexate has been tried for modulation of cardiovascular events in non-rheumatic clinical conditions, although a large clinical trial failed to demonstrate a benefit. Colchicine has been shown to successfully reduce the risk of recurrent cardiovascular events in a large multicentric trial. Potential antifibrotic effects of colchicine require further exploration. Hydroxychloroquine, methotrexate and colchicine are also being tried at different stages of the ongoing Coronavirus Disease 19 (COVID-19) pandemic for prophylaxis and treatment. While the use of these agents is being diversified, their adverse effects should be timely diagnosed and prevented. Hydroxychloroquine can cause retinopathy and rarely cardiac and auditory toxicity, retinopathy being dose and time dependent. Methotrexate can cause transaminitis, cytopenias and renal failure, particularly in acute overdoses. Colchicine can rarely cause myopathies, cardiomyopathy, cytopenias and transaminitis. Strong evidence is warranted to keep balance between benefits of repurposing these old antirheumatic drugs and risk of their adverse effects.


Subject(s)
Antirheumatic Agents/therapeutic use , Betacoronavirus , Colchicine/therapeutic use , Coronavirus Infections/drug therapy , Drug Repositioning , Hydroxychloroquine/therapeutic use , Methotrexate/therapeutic use , Pneumonia, Viral/drug therapy , COVID-19 , Colchicine/adverse effects , Hydroxychloroquine/adverse effects , Methotrexate/adverse effects , Pandemics , SARS-CoV-2
20.
Comput Biol Med ; 124: 103960, 2020 09.
Article in English | MEDLINE | ID: covidwho-714312

ABSTRACT

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.


Subject(s)
Betacoronavirus , Brain Injuries/epidemiology , Coronavirus Infections/epidemiology , Heart Injuries/epidemiology , Pneumonia, Viral/epidemiology , Artificial Intelligence , Betacoronavirus/pathogenicity , Betacoronavirus/physiology , Brain Injuries/classification , Brain Injuries/diagnostic imaging , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/methods , Comorbidity , Computational Biology , Coronavirus Infections/classification , Coronavirus Infections/diagnosis , Coronavirus Infections/diagnostic imaging , Deep Learning , Heart Injuries/classification , Heart Injuries/diagnostic imaging , Humans , Machine Learning , Pandemics/classification , Pneumonia, Viral/classification , Pneumonia, Viral/diagnostic imaging , Risk Factors , SARS-CoV-2 , Severity of Illness Index
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