The coronavirus (COVID-19) outbreak has opened an alarming situation for the whole world and has been marked as one of the most severe and acute medical conditions in the last hundred years. Various medical imaging modalities including computer tomography (CT) and chest x-rays are employed for diagnosis. This paper presents an overview of the recently developed COVID-19 detection systems from chest x-ray images using deep learning approaches. This review explores and analyses the data sets, feature engineering techniques, image pre-processing methods, and experimental results of various works carried out in the literature. It also highlights the transfer learning techniques and different performance metrics used by researchers in this field. This information is helpful to point out the future research direction in the domain of automatic diagnosis of COVID-19 using deep learning techniques.
INTRODUCTION: Despite several ongoing efforts in biomedicine and traditional medicine, there are no drugs or vaccines for coronavirus disease 2019 (COVID-19) as of May 2020; Kabasura Kudineer (KSK), a polyherbal formulation from India's Siddha system of medicine, has been traditionally used for clinical presentations similar to that of COVID-19. We explored the efficacy of KSK in reducing viral load and preventing the disease progression in asymptomatic, COVID-19 cases. METHODS: A prospective, single-center, open-labeled, randomized, controlled trial was conducted in a COVID Care Centre in Chennai, India. We recruited reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 of 18 to 55 years of age, without clinical symptoms and co-morbidities. They were randomized (1:1 ratio) to KSK (60 mL twice daily for 7 days) or standard of care (7 days supplementation of vitamin C 60,000 IU morning daily and zinc 100 mg evening daily) groups. The primary outcomes were reduction in the SARS-CoV-2 load [as measured by cyclic threshold (CT) value of RT-PCR], prevention of progression of asymptomatic to symptomatic state, and changes in the immunity markers including interleukins (IL-6, IL-10, IL-2), interferon gamma (IFNÎ³), and tumor necrosis factor (TNF α). Siddha clinical assessment and the occurrence of adverse effects were documented as secondary outcomes. Paired t-test was used in statistical analysis. RESULTS: Viral load in terms of the CT value (RdRp: 95% CI = 1.89 to 5.74) declined significantly on the seventh day in the KSK group and that of the control group, more pronounced in the study group. None progressed to the symptomatic state. There was no significant difference in the biochemical parameters. We did not observe any changes in the Siddha-based clinical examination and adverse events in both groups. CONCLUSION: KSK significantly reduced SARS-CoV-2 viral load among asymptomatic COVID-19 cases and did not record any adverse effect, indicating the use of KSK in the strategy against COVID-19. Larger, multi-centric trials can strengthen the current findings. TRIAL REGISTRATION: Clinical Trial Registry of India CTRI2020/05/025215 . Registered on 16 May 2020.
Subject(s)COVID-19 , SARS-CoV-2 , Ascorbic Acid , Dietary Supplements , Humans , India , Medicine, Ayurvedic , Prospective Studies , Treatment Outcome , Viral Load , Zinc
OBJECTIVES: The primary objectives of this study are to determine efficacy of Siddha medicine, Kabasura kudineer in reduction of SARS-CoV-2 viral load and reducing the onset of symptoms in asymptomatic COVID-19 when compared to Vitamin C and Zinc (CZ) supplementation. In addition, the trial will examine the changes in the immunological markers of the Siddha medicine against control. The secondary objectives of the trial are to evaluate the safety of the Siddha medicine and to document clinical profile of asymptomatic COVID-19 as per principles of Siddha system of Medicine. TRIAL DESIGN: A single centre, open-label, parallel group (1:1 allocation ratio), exploratory randomized controlled trial. PARTICIPANTS: Cases admitted at non-hospital settings designated as COVID Care Centre and managed by the State Government Stanley Medical College, Chennai, Tamil Nadu, India will be recruited. Eligible participants will be those tested positive for COVID-19 by Reverse Transcriptase Polymerase Chain reaction (RT-PCR) aged 18 to 55 years without any symptoms and co-morbidities like diabetes mellitus, hypertension and bronchial asthma. Those pregnant or lactating, with severe respiratory disease, already participating in COVID trials and with severe illness like malignancy will be excluded. INTERVENTION AND COMPARATOR: Adopting traditional methods, decoction of Kabasura kudineer will be prepared by boiling 5g of KSK powder in 240 ml water and reduced to one-fourth (60ml) and filtered. The KSK group will receive a dose of 60ml decoction, orally in the morning and evening after food for 14 days. The control group will receive Vitamin C (60000 IU) and Zinc tablets (100mg) orally in the morning and evening respectively for 14 days. MAIN OUTCOMES: The primary outcomes are the reduction in the SARS-CoV-2 load [as measured by cyclic threshold (CT) value of RT-PCR] from the baseline to that of seventh day of the treatment, prevention of progression of asymptomatic to symptomatic state (clinical symptoms like fever, cough and breathlessness) and changes in the immunity markers [Interleukins (IL) 6, IL10, IL2, Interferon gamma (IFNÎ³) and Tumor Necrosis Factor (TNF) alpha]. Clinical assessment of COVID-19 as per standard Siddha system of medicine principles and the occurrence of adverse effects will be documented as secondary outcomes. RANDOMISATION: The assignment to the study or control group will be allocated in equal numbers through randomization using random number generation in Microsoft Excel by a statistician who is not involved in the trial. The allocation scheme will be made by an independent statistician using a sealed envelope. The participants will be allocated immediately after the eligibility assessment and informed consent procedures. BLINDING (MASKING): This study is unblinded. The investigators will be blinded to data analysis, which will be carried out by a statistician who is not involved in the trial. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): Sample size could not be calculated, as there is no prior trial on KSK. This trial will be a pilot trial. Hence, we intend to recruit 60 participants in total using a 1:1 allocation ratio, with 30 participants randomised into each arm. TRIAL STATUS: Protocol version 2.0 dated 16th May 2020. Recruitment is completed. The trial started recruitment on the 25th May 2020. We anticipate study including data analysis will finish on November 2020. We also stated that protocol was submitted before the end of data collection TRIAL REGISTRATION: The study protocol was registered with clinical trial registry of India (CTRI) with CTRI/2020/05/025215 on 16 May 2020. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines (Additional file 2).
Subject(s)Ascorbic Acid , Betacoronavirus , Coronavirus Infections , Medicine, Ayurvedic/methods , Pandemics , Pneumonia, Viral , Zinc , Adult , Ascorbic Acid/administration & dosage , Ascorbic Acid/adverse effects , Asymptomatic Infections/therapy , Betacoronavirus/drug effects , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/drug therapy , Dietary Supplements , Drug Monitoring/methods , Female , Humans , India , Male , Pneumonia, Viral/diagnosis , Pneumonia, Viral/drug therapy , Randomized Controlled Trials as Topic , SARS-CoV-2 , Treatment Outcome , Viral Load/methods , Zinc/administration & dosage , Zinc/adverse effects
India imposed a nationwide lockdown from 25th March 2020 onwards to combat the spread of COVID-19 pandemic. To model the spread of a disease and to predict its future course, epidemiologists make use of compartmental models such as the SIR model. In order to address some of the assumptions of the standard SIR model, a new modified version of SIR model is proposed in this paper that takes into account the percentage of infected individuals who are tested and quarantined. This approach helps overcome the assumption of homogenous mixing of population which is inherent to the conventional SIR model. Using the available data of the number of COVID-19 positive cases reported in the state of Kerala, and in India till 26th April, 2020 and 12th May 2020, respectively, the parameter estimation problem is converted into an optimization problem with the help of a least squared cost function. The optimization problem is then solved using differential evolution optimizer. The impact of lockdown is quantified by comparing the rising trend in infections before and during the lockdown. Using the estimated set of parameters, the model predicts that in the state of Kerala, by using certain interventions the pandemic can be successfully controlled latest by the first week of July, whereas the R 0 value for India is still greater than 1, and hence lifting of lockdown from all regions of the country is not advisable.