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1.
Radiol Case Rep ; 17(10): 3659-3662, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1967028

ABSTRACT

Myositis and myonecrosis are rare sequela of coronavirus disease 2019 (COVID-19). Until now, it has not been seen in muscles of the head and neck. We present a 22-year-old male with 4 months of retroauricular headaches following COVID-19 infection. Magnetic resonance imaging revealed rim-enhancing fluid collections in the bilateral masticator spaces which were sampled by fine-needle aspiration. We also discuss this case in the context of the current understanding of COVID-19-related myositis.

2.
Appl Soft Comput ; : 109401, 2022 Jul 29.
Article in English | MEDLINE | ID: covidwho-1966354

ABSTRACT

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

3.
Concurr Comput ; : e7211, 2022 Jul 30.
Article in English | MEDLINE | ID: covidwho-1966037

ABSTRACT

A novel corona virus (COVID-19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID-19. To predict the patients of COVID-19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya-tunicate swarm algorithm driven generative adversarial network (Jaya-TSA with GAN) is proposed in this research to find patients of COVID-19 infections. The developed Jaya-TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID-19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya-TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya-TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

4.
Radiotherapy and Oncology ; 170:S682-S683, 2022.
Article in English | EMBASE | ID: covidwho-1967462

ABSTRACT

Purpose or Objective To assess the pattern of response on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) of presumed local lesions in the setting of salvage radiotherapy (sRT) after radical prostatectomy (RP). Materials and Methods The present prospective study (NCT04703543) was conducted at a single Institution between August 2017 and June 2020. Eligibility criteria were: undetectable prostate specific antigen (PSA) after RP;biochemical recurrence (2 consecutive PSA rises to 0.2 ng/ml or greater);a presumed local failure at DCE-MRI (early/fast enhancing discrete lesion on DCE sequences);no distant metastases at choline-PET/CT;no previous history of androgen deprivation therapy and/or RT. Accrued patients underwent sRT as it follows: 66-69 Gy/30 fractions to the prostatic bed, 73.5 Gy/30 fractions to the local failure at DCE-MRI, 54 Gy/30 fractions to the pelvic nodes (when treated). All patients were offered DCE-MRI 3 months after sRT, and repeated at 3-month intervals until complete disappearance or a maximum of 4 scans. The endpoint of the study, complete response (CR), was defined as the complete disappearance of the target lesion at DCE-MRI. In case of misses before CR, the observation was considered as a persisting partial response (PR). Results 62 patients with 72 nodules at DCE-MRI were accrued. All patients underwent the 1st DCE-MRI at a median of 3.3 months (IQR: 3.1-4.1) after sRT, 33 patients (53.2%) presented a CR, 27 (43,5%) a PR, 2 (3.2%) no response. One patient, implanted with a cardiac device, did not undergo further MRI. Three more patients declined further testing after the 1st (N=2) or the 2nd (N=1) re-evaluation due to the COVID-19 pandemic. Twenty-eight patients underwent a 2nd DCE-MRI after a median of 6.8 months (IQR: 6.5-7.6) from sRT, 20 had a CR, 8 had a PR. After a median time of 10.7 months (IQR: 10.6-12.6), 6 patients were scanned for a 3nd DCE-MRI: 4 CR, 2 PR. The last patient reported a CR after 16.7 months. The majority (94.3%, 95%CI: 88.0-100.0%) of lesions had completely disappeared by the 3rd re-evaluation or a median time of 10.7 months from the end of sRT (Figure).(Figure Presented) Independent predictors of CR at 1st re-evaluation on multivariable analysis were: the volume of the lesion at pre-sRT DCEMRI (OR 0.076, 95%CI 0.009-0.667;p=0.02), the time of re-evaluation from treatment (OR 3.39, 95%CI 1.156-9.993;p=0.026) and the PSA percent decrease at the 5th week of sRT (OR 1.02, 95%CI 0.999-1.050;p= 0.058) (Table). (Table Presented) Receiver-operating characteristic curve (ROC) analysis identified the best cut-off on CR for baseline volume at 0.545 cc, AUC 0.683 (95%CI: 0.548-0.818, p=0.014). The probability of a CR for lesions larger than the cut-off identified at ROC analysis was only around 75% at 10.7 months. Conclusion The vast majority of local lesions disappears at DCE-MRI after sRT, though larger lesions may require more than 10 months from treatment end.

5.
Tomography ; 8(3): 1618-1630, 2022 Jun 20.
Article in English | MEDLINE | ID: covidwho-1964058

ABSTRACT

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass opacity (GGO) and pulmonary infiltrates (PIs) in CT image sequences of COVID-19 cases. Convolutional neural networks (CNNs) were used to classify the disease progression of pneumonia. Each GGO and PI pattern was highlighted with saliency map fusion, and the resulting map was used to train and test a CNN classification scheme with three classes. In addition to patients, this information was shared between the respiratory triage/radiologist and the COVID-19 multidisciplinary teams with the application so that the severity of the disease could be understood through CT and medical diagnosis. The three-class, disease-level COVID-19 classification results exhibited a macro-precision of more than 94.89% in a two-fold cross-validation. Both the segmentation and classification results were comparable to those made by a medical specialist.


Subject(s)
COVID-19 , Deep Learning , Mobile Applications , COVID-19/diagnostic imaging , COVID-19 Testing , Humans , Lung/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed/methods
6.
Tomography ; 8(3): 1578-1585, 2022 Jun 17.
Article in English | MEDLINE | ID: covidwho-1964057

ABSTRACT

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.


Subject(s)
COVID-19 , Aged , COVID-19/diagnostic imaging , Female , Humans , Infant , Lung/diagnostic imaging , Middle Aged , Pandemics , Prospective Studies , Tomography, X-Ray Computed/methods
7.
Egyptian Journal of Hospital Medicine ; 88(1):3567-3575, 2022.
Article in English | Scopus | ID: covidwho-1964932

ABSTRACT

Background: There is a wide variety of CT radiological findings of COVID-19 infection, this study aimed to analyze retrospectively the similarities and differences of CT radiological findings between first and second waves in the confirmed coronavirus patients. Materials and methods: comparative retrospective study between two COVID-19 pandemic waves was conducted on 1000 patients who were diagnosed as COVID-19 patients, at Assiut University hospital, 500 patients in the period from May 2020 to August 2020, while the other 500 patients were in the period from October 2020 to January 2021, all underwent MSCT chest and a comparison between similarities and differences of CT radiological findings was done. Results: Both waves showed nearly the same mean and percentage of total CT severity score with no significant difference between them as p-value > 0.05. There is also a positive moderate correlation between age and total MSCT severity score of the lung in the first wave (r=0.51, p-value<0.001), while a significant positive mild correlation in the second wave (r=0.31 and p-value <0.001), atypical findings were encountered in the second wave more than in the first wave with the most common one was pulmonary fibrosis by (7.2%). Conclusion: Great similarity in CT radiological findings between the two COVID-19 pandemic waves was detected. However, the main difference between them was in the severity of lung involvement in different age groups and demonstration of atypical findings which was more common in the second wave. © 2022, Ain Shams University Faculty of Medicine. All rights reserved.

8.
Messenger of Anesthesiology and Resuscitation ; 19(3):7-14, 2022.
Article in Russian | Scopus | ID: covidwho-1964918

ABSTRACT

The coronavirus infection (COVID-19) is characterized by a high incidence of pneumonia. Extensive damage, high mortality associated with COVID-19 make the rapid bedside diagnosis and dynamic monitoring of the volume and nature of lung tissue damage a challenge. Lung ultrasound examination can be used as a tool to answer it. The objective: to compare the signs detected by lung computed tomography and ultrasound and to assess the sensitivity and specificity of ultrasound in the diagnosis of pneumonia induced by COVID-19. Subjects and Methods. The observational prospective clinical study included 388 patients aged 18–75 years old;they had a confirmed diagnosis of pneumonia caused by COVID-19 or suspected COVID-19. Lung ultrasound was performed within 24 hours after computed tomography (CT) of the chest organs. During CT, pathological signs, infiltration and consolidation of the lungs were visualized which were documented by lung segments. Lung ultrasound was performed according to the Russian Protocol, ultrasound signs of B-lines and consolidation were also documented based on the projection of lung segments on the chest wall. The distributions of variables was analyzed, described and summarized. The sensitivity and specificity of ultrasound methods were evaluated on the basis of ROC analysis according to CT gold standard. Results. Bilateral involvement was found in 100% of cases. Typical CT signs of pneumonia caused by coronavirus infection were ground-glass opacity of the pulmonary parenchyma, thickened pleura, consolidation, interstitium, reticular induration, and cobblestone appearance. With ultrasound examination of the lungs and pleura, the detected signs corresponded to CT signs. B lines (multifocal, discrete or merging) and consolidation of various volumes of lung tissue were most common during ultrasound. The sign of consolidation was detected less frequently versus infiltration (p < 0.001). The sensitivity of lung ultrasound in the diagnosis of lung lesions was 95.3%, and the specificity was 85.4%, the area under the curve was 0.976 with a confidence interval of 0.961–0.991 (p < 0.001). Conclusion. The use of lung ultrasound during the COVID-19 pandemic makes it possible to identify, assess the volume and nature of lung damage. Lung ultrasound demonstrated accuracy comparable to CT of the chest organs in detecting pneumonia in patients with COVID-19. © Chinese Journal of Microsurgery.All right reserved.

9.
International Journal of Advanced Technology and Engineering Exploration ; 9(90):623-643, 2022.
Article in English | ProQuest Central | ID: covidwho-1964885

ABSTRACT

A rapid diagnostic system is a primary role in the healthcare system exclusively during a pandemic situation to control contagious diseases like coronavirus disease-2019 (COVID-19). Many countries remain lacking to spot COVID cases by the reverse transcription-polymerase chain reaction (RT-PCR) test. On this stretch, deep learning algorithms have been strengthened the medical image processing system to analyze the infection, categorization, and further diagnosis. It is motivated to discover the alternate way to identify the disease using existing medical implications. Hence, this review narrated the character and attainment of deep learning algorithms at each juncture from origin to COVID-19. This literature highlights the importance of deep learning and further focused the medical image processing research on handling the data of magnetic resonance imaging (MRI), computed tomography (CT) scan, and electromagnetic radiation (X-ray) images. Additionally, this systematic review tabulates the popular deep learning networks with operational parameters, peer-reviewed research with their outcomes, popular nets, and prevalent datasets, and highlighted the facts to stimulate future research. The consequence of this literature ascertains convolutional neural network-based deep learning approaches work better in the medical image processing system, and especially it is very supportive of sorting out the COVID-19 complications.

10.
Polish Journal of Medical Physics and Engineering ; 28(3):117-126, 2022.
Article in English | ProQuest Central | ID: covidwho-1963314

ABSTRACT

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT.Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists.Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy.Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

11.
IEEE Intelligent Systems ; 37(3):54-64, 2022.
Article in English | ProQuest Central | ID: covidwho-1961415

ABSTRACT

In this article, we present a model that fuses lesion segmentation with attention mechanism to predict coronavirus (COVID-19) from chest CT scans. The model segments lesions, extracts regions of interest from scans and applies attention to them to determine the most relevant ones for image classification. Additionally, we augment the model with long-short term memory network layers that learn features from a sequence of regions of interest before computing attention. The model is trained in one shot for both problems, using two different sets of data. We achieve 0.4683 mean average precision for lesion segmentation, 95.74% COVID-19 sensitivity and 98.15% class-adjusted F1 score for image classification on a large CNCB-NCOV dataset.

12.
8th International Conference on Control, Decision and Information Technologies, CoDIT 2022 ; : 407-412, 2022.
Article in English | Scopus | ID: covidwho-1961367

ABSTRACT

With the start of 2020, the world witnessed the spread of Coronavirus disease (COVID-19). We aim in this work to employ artificial intelligence (AI) to develop a computer-aided diagnosis system (CAD) in order to automatically detect COVID-19 cases and differentiate them from normal and community-acquired pneumonia (CAP) cases through the use of lung Computed Tomography (CT) images and then evaluate its performance. Deep residual learning offers a wide variety of algorithms that helps in classification problems. We apply in this work a ResNet50 based model to recognize Covid-19 cases. Extensive analysis based on an international dataset (24256 images of 304 patients) proved that the ResNet50-optimized model can recognize COVID-19 through the use of CT images with 82% accuracy, 90% recall, 65% precision, and 76% of F1.Score. © 2022 IEEE.

13.
Applied Radiology ; 51(4):27-28,30, 2022.
Article in English | ProQuest Central | ID: covidwho-1958327

ABSTRACT

Both the ACR, through its workforce survey, and the American Society of Radiation Oncology (ASTRO), through its Workforce Task Force, are performing "deep dives into data analysis to evaluate the staffing and hiring landscape;final results are not yet available. Statistics for average budgeted FTEs for five other modalities for 2021 vs 2003 are: * Computed tomography, 6.2, up from 3.4;* Magnetic resonance imaging, 4.7, up from 1.7;* Mammography, 4.9, up from 2.1;* Nuclear medicine, 3.6, up from 1.8;and, * Sonography, 5.0, up from 2.6 The U.S. Bureau of Labor Statistics (BLS) projects that radiologic and MRI technologist employment will grow 9% by 2030, about as fast as the average for all occupations, estimating 20,800 openings for these positions each year, on average.2 The BLS statistics point to a trend that more technologists will be needed to meet growing demand for imaging services. In radiation oncology, a lack of programs is contributing to shortages of physicists and dosimetrists and, to a lesser extent, radiation therapists, says Bruce G Haffty, MD, FACR, FASTRO, FASCO, chair of ASTRO's workforce subcommittee, associate vice chancellor of Cancer Programs at Rutgers Biomedical and Health Sciences, and professor and chair of the department of radiation oncology at Robert Wood Johnson and NJ Medical School, Rutgers Cancer Institute of NJ. "People have taken a step back to ask themselves, 'Is this really what I want to continue to do?'" She says the trend may impact her institution's ability to bring new talent into the profession and may even reduce the number of applicants to radiologic technology programs, "because people don't want to work in a hospital anymore."

14.
Indian Journal of Forensic Medicine and Toxicology ; 16(2):326-333, 2022.
Article in English | EMBASE | ID: covidwho-1957671

ABSTRACT

Coronavirus disease 2019 discovered in December 2019, Wuhan, China. It was transmitted globally producing the present COVID-19 pandemic. Concerns have been raised about the potential impact of COVID-19 on male reproductive organs and male fertility as the number of infections in the male community has increased. The objectives of current study are studying the relationship between the plasma levels of testosterone and the markers of immune reaction with the severity and mortality in a sample of COVID-19 patients. A cross section study included NO= 103 male patients affected by SARS-CoV-2 pneumonia, diagnosed by PCR and chest CT scan, (≥ 18 years old), and recovered in the respiratory intensive care unit (RICU). Several biochemical risk factors were determined Free Testosterone, sex hormone binding globulin (SHBG) were measured by Enzyme-Linked Immunosorbent Assay(ELISA), D-dimer, Ferritin, CRP, Urea, Creatinine were measured by automated method by using Abbott Architect c4000 and Complete Blood Count(CBC). The results show that the serum free testosterone and SHBG levels a significant lower in non-survivor patients than survivor patients with COVID-19. While the other biomarkers (D-dimer, Ferritin, Urea, Creatinine) were significant higher in non-survivor patients than survivor patients. The CRP, WBC and lymphocyte showed that no significant between the both group of patients. In conclusion the study showed that lower free testosterone and SHBG levels enable significant role in increasing risk of COVID-19 mortality amongst adult male patients.

15.
Journal of Clinical Periodontology ; 49:84, 2022.
Article in English | EMBASE | ID: covidwho-1956753

ABSTRACT

The aim is to determine oral manifestations in patients with COVID-19 disease and in the postcovid period. Methods: A special survey (questionnaire) was made in 424 people who had COVID-19 confirmed by RT-PCR, ELISA for specific IgM and IgG antibodies and Chest CT scan (168 people). 123 people had complaints and clinical symptoms in the oral cavity 2-6 months after the illness and they came to the University dental clinic. Laboratory tests have been performed (clinical blood test, blood immunogram, virus and fungal identification). Results: Survey results showed that 16,0% participants had asymptomatic COVID-19, 23,6% - mild and 48,1% moderate disease. 12,3% with severe COVID-19 were treated in a hospital with oxygen support. In the first 2 weeks 44,3% indicated xerostomia, dysgeusia (21,7%), muscle pain during chewing (11,3%), pain during swallowing (30,2%), burning and painful tongue (1,9%), tongue swelling (30,2%), catharal stomatitis (16,0%), gingival bleeding (22,6%), painful ulcers (aphthae) (8,5%) and signs of candidiasis - white plaque in the tongue (12,3%). After illness (3-6 months), patients indicated dry mouth (12,3%), progressing of gingivitis (20,7%) and periodontitis (11,3%). In patients who applied to the clinic we identified such diagnoses: desquamative glossitis - 16 cases, glossodynia (11), herpes labialis and recurrent herpetic gingivostomatitis (27), hairy leukoplakia (1), recurrent aphthous stomatitis (22), aphthosis Sutton (4), necrotising ulcerative gingivitis (13), oral candidiasis (14), erythema multiforme (8), Stevens-Johnson syndrome (2), oral squamous cell papillomas on the gingiva (4) and the lower lip (1). According to laboratory studies, virus reactivation (HSV, VZV, EBV, CMV, Papilloma viruces) was noted in 52 patients (42,3%), immunodeficiency in 96 people (78,0%), immunoregulation disorders (allergic and autoimmune reactions) in 24 people (19,5%). Conclusions: Lack of oral hygiene, hyposalivation, vascular compromise, stress, immunodeficiency and reactivation of persistent viral and fungal infections in patients with COVID-19 disease are risk factors for progression of periodontal and oral mucosal diseases.

16.
IHJ Cardiovascular Case Reports (CVCR) ; 6(2):67-72, 2022.
Article in English | EMBASE | ID: covidwho-1956163

ABSTRACT

Transcatheter aortic valve replacement (TAVR) is now the standard of therapy for elderly population with severe aortic stenosis. Several studies have established that the outcomes of TAVR are superior when compared with Surgical aortic valve replacement (SAVR), especially when the access route is transfemoral arterial approach. In the elderly population with advanced age and numerous comorbidities, iliofemoral arterial disease (IAD) is not uncommon and it precludes the use of this route for TAVR. Peripheral Intravascular lithotripsy (IVL) has been previously established as an excellent safe and efficient modality to treat symptomatic occlusive calcific iliofemoral artery disease. The same principle of IVL has been recently used successfully to modify the vascular compliance of heavily calcified iliofemoral arteries thereby enabling large bore sheath advancement and safe passage of TAVR delivery catheter systems. We report the first case of Intravascular lithotripsy facilitated Transfemoral TAVR (TF-TAVR) in India. This case was done in December 2020 by the “femoral route” in order to keep the TAVR procedure simple straightforward and discharge the patient back home quickly in Covid times. The use of Intravascular Lithotripsy (IVL)was based on evidence of good outcomes in trials of peripheral vascular disease of lower limbs as well as from the good outcomes of few registries on IVL facilitated TAVR.1,2,3,4,5,6,8 The second case was done in August 2021 by us for another patient successfully.

17.
IHJ Cardiovascular Case Reports (CVCR) ; 6(2):83-85, 2022.
Article in English | EMBASE | ID: covidwho-1956162
18.
American Journal of Stem Cells ; 11(3):37-55, 2022.
Article in English | EMBASE | ID: covidwho-1955743

ABSTRACT

Objective: Mesenchymal stem cells can serve as a therapeutic option for COVID-19. Their immunomodula-tory and anti-inflammatory properties can regulate the exaggerated inflammatory response and promote recovery of lung damage. Method: Phase-1, single-centre open-label, prospective clinical trial was conducted to evaluate the safety and efficacy of intravenous administration of mesenchymal stem cells derived from umbilical cord and placenta in moderate COVID-19. The study was done in 2 stages with total 20 patients. Herein, the results of stage 1 including first 10 patients receiving 100 million cells on day 1 and 4 with a follow up of 6 months have been discussed. Results: No adverse events were recorded immediately after the administration of MSCs or on follow up. There was no deterioration observed in clinical, laboratory and radiological parameters. All symptoms of the study group resolved within 10 days. Levels of inflammatory biomarkers such as NLR, CRP, IL6, ferritin and D-dimer improved in all patients after intervention along with improved oxygenation demonstrated by improvement in the SpO2/FiO2 ratio and PaO2/FiO2 ratio. None of the patients progressed to severe stage. 9 out of 10 patients were discharged within 9 days of their admission. Improvements were noted in chest x-ray and chest CT scan scores at day 7 in most patients. No post-covid fibrosis was observed on chest CT 28 days after intervention and Chest X ray after 6 months of the intervention. Conclusion: Administration of 100 million mesenchymal stem cells in combina-tion with standard treatment was found to be safe and resulted in prevention of the cytokine storm, halting of the disease progression and acceleration of recovery in moderate COVID-19. This clinical trial has been registered with the Clinical Trial Registry-India (CTRI) as CTRI/2020/08/027043. http://www.ctri.nic.in/Clinicaltrials/pmaindet2. php?trialid=43175.

19.
J Clin Med ; 11(15)2022 Jul 25.
Article in English | MEDLINE | ID: covidwho-1957363

ABSTRACT

(1) Background: This study aims to evaluate the association of CRP, NLR, IL-6, and Procalcitonin with lung damage observed on CT scans; (2) Methods: A cross-sectional study was performed among 106 COVID-19 patients hospitalized in Timisoara Municipal Emergency Hospital. Chest CT and laboratory analysis were performed in all patients. The rank Spearmen correlation was used to assess the association between inflammatory markers and lung involvement. In addition, ROC curve analysis was used to determine the accuracy of inflammatory markers in the diagnosis of severe lung damage; (3) Results: CRP, NLR, and IL-6 were significantly positively correlated with lung damage. All inflammatory markers had good accuracy for diagnosis of severe lung involvement. Moreover, IL-6 has the highest AUC- ROC curve; (4) Conclusions: The inflammatory markers are associated with lung damage and can be used to evaluate COVID-19 severity.

20.
Inform Med Unlocked ; 32: 101025, 2022.
Article in English | MEDLINE | ID: covidwho-1956179

ABSTRACT

A new artificial intelligence (AI) supported T-Ray imaging system designed and implemented for non-invasive and non-ionizing screening for coronavirus-affected patients. The new system has the potential to replace the standard conventional X-Ray based imaging modality of virus detection. This research article reports the development of solid state room temperature terahertz source for thermograph study. Exposure time and radiation energy are optimized through several real-time experiments. During its incubation period, Coronavirus stays within the cell of the upper respiratory tract and its presence often causes an increased level of blood supply to the virus-affected cells/inter-cellular region that results in a localized increase of water content in those cells & tissues in comparison to its neighbouring normal cells. Under THz-radiation exposure, the incident energy gets absorbed more in virus-affected cells/inter-cellular region and gets heated; thus, the sharp temperature gradient is observed in the corresponding thermograph study. Additionally, structural changes in virus-affected zones make a significant contribution in getting better contrast in thermographs. Considering the effectiveness of the Artificial Intelligence (AI) analysis tool in various medical diagnoses, the authors have employed an explainable AI-assisted methodology to correctly identify and mark the affected pulmonary region for the developed imaging technique and thus validate the model. This AI-enabled non-ionizing THz-thermography method is expected to address the voids in early COVID diagnosis, at the onset of infection.

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