ABSTRACT
Introduction The second wave of the coronavirus disease 2019 (COVID-19) pandemic in India, which started from April 2021, has been more severe and deadly than the first wave. The aim of this prospective study was to determine the possibility of other respiratory pathogens contributing towards the severity and hospitalization in the current second wave. Materials and methods Nasopharyngeal and oropharyngeal swab samples were collected and processed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcription polymerase chain reaction (RT-PCR). These samples were further processed for detection of co-infection in SARS CoV-2 patients by BioFire® Filmarray® 2.0 (bioMérieux, USA). Results We screened 77 COVID-19-positive patients admitted to All India Institute of Medical Sciences (AIIMS), Rishikesh and found cases of co-infections in five (6.49 %) patients. Conclusion Our finding suggests that co-infections had no or minimal role in augmenting the second wave of the COVID-19 pandemic in India, and the emergence of new variants may be the probable cause.
ABSTRACT
India had faced three waves throughout the Coronavirus disease 2019 (COVID-19) pandemic, which had already impacted economic lives and affected the healthcare setting and infrastructure. The widespread impacts have inspired researchers to look for clinical indicators of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prognosis. Cyclic threshold values have been used to correlate the viral load in COVID-19 patients and for viral transmission. In light of this correlation, a retrospective study was conducted to assess the trend of viral load in clinical and demographic profiles across the three waves. Data of a total of 11,125 COVID-19-positive patients were obtained, which had a Ct value of <35. We stratified Ct values as follows: under 25 (high viral load), 25-30 (moderate viral load), and over 30 (low viral load). We found a significantly high proportion of patients with high viral load during the second wave. A significantly high viral load across the symptomatic and vaccinated populations was found in all three waves, whereas a significantly high viral load across age groups was found only in the first wave. With the widespread availability of real-time PCR and the limited use of genomic surveillance, the Ct value and viral load could be a suitable tool for population-level monitoring and forecasting.
ABSTRACT
Objective: The aim of the present study was to design and evaluate two MeVisLab networks, one for co-registration of positron emission tomography/computed tomography (PET/CT) images and second for cropping the co-registered PET/CT images. Materials and Methods: Two MeVisLab networks, one to co-register and export PET/CT DICOM images and second for cropping the co-registered PET/CT images were designed using different modules of registration toolkit MERIT. One hundred and twenty-five PET/CT studies were exported from Siemens and GE scanners in DICOM format. These images were co-registered and cropped with our designed networks. The images co-registered with our network were compared visually with the co-registered images of same PET/CT studies on vendor provided workstations by an experienced nuclear medicine physician (NMP). The perfection of the cropping of co-registered images was also assessed visually. Results: Visually, NMP found all 125 images co-registered using the network designed in our study similar to the co-registered images of vendor provided workstations. Furthermore, the cropping of all co-registered images was perfectly done by our network. Conclusion: Two MeVisLab networks designed and evaluated in the present study can be used for co-registration of PET/CT DICOM images and cropping the co-registered PET/CT images.
ABSTRACT
In this study, we have proposed and validated that histogram of a good-quality bone scan image can enhance a poor-quality bone scan image. The histograms of two good-quality technetium-99m methyl diphosphonate bone scan images IA and IB recommended by nuclear medicine physicians (NMPs) were used to enhance 87 poor-quality bone scan images. Processed images and their corresponding input images were compared visually by two NMPs with scoring and also quantitatively using entropy, Structural similarity index measure, edge-based contrast measure, and absolute brightness mean error. Barnard's unconditional test was applied with a null hypothesis that the histogram of both IA and IB produces similar output image at α =0.05. The mean values of quantitative metrices of the processed images obtained using IA and IB were compared using Kolmogorov-Smirnov test. Histogram of a good-quality bone scan image can enhance a poor-quality bone scan image. Visually, histogram of IB improved statistically significantly higher proportion (P < 0.0001) of images (86/87) as compared to histogram of IA (51/87). Quantitatively, IB performed better than IA, and the Chi-square distance of input and IB was smaller than that of IA. In addition, a statistically significant (P < 0.05) difference in all the quantitative metrics among the outputs obtained using IA and IB was observed. In our study, reference histogram of good-quality bone scan images transformed the majority of poor-quality bone scan images (98.85%) into visually good-quality images acceptable by NMPs.
