Severity of COVID-19 in Patients with Diarrhoea: A Systematic Review and Meta-Analysis

COVID-19 patients occasionally present with diarrhoea. Our objective was to estimate the risk of developing the severe disease in COVID-19 patients with and without diarrhoea and to provide a more precise estimate of the prevalence of COVID-19-associated digestive symptoms. A total of 88 studies (n = 67,794) on patients with a COVID-19 infection published between 1 January 2020 and 20 October 2022 were included in this meta-analysis. The overall prevalence of digestive symptoms was 27% (95% confidence interval (CI): 21-34%; I² = 99%). According to our data, the pooled prevalence of diarrhoea symptoms in the 88 studies analysed was 17% (95% CI: 14-20%; I² = 98%). The pooled estimate of nausea or vomiting in a total of 60 studies was 12% (95% CI: 8-15%; I² = 98%). We also analysed 23 studies with eligible individuals (n = 3800) to assess the association between the disease severity and diarrhoea. Individuals who had diarrhoea were more likely to have experienced severe COVID-19 (odds ratio: 1.71; 95% CI: 1.31-2.24; p < 0.0001; I² = 10%). Gastrointestinal symptoms and diarrhoea are frequently presenting COVID-19 manifestations that physicians should be aware of.

Congruence of radiological scoring systems used in COVID-19 pneumonia and effect of comorbid diseases on radiological features

Background/Aim: Several scoring systems have been developed to standardize radiological findings in patients with COVID-19 pneumonia. The most commonly used scoring systems in the radiological examination of COVID-19 are those of the North American Radiology Association (RSNA), British Thoracic Society (BTS), and COVID-19 Reporting and Data System (CO-RADS). However, the compatibility between these radiological scoring systems has not been evaluated before. Therefore, this study evaluated the radiological features of COVID-19 pneumonia and congruence between radiological scoring systems and determined the effect of comorbidities and demographic characteristics on radiological features and thoracic computed tomography (TCT) findings in the context of COVID-19. Methods: A retrospective cohort study was performed on patients attending our unit with a suspicion of COVID-19 who also had a positive real-time transcriptase-polymerase chain reaction (RT-PCR) test. All TCT images were subjected to the RSNA, BTS, and CO-RADS scoring systems. Demographic data such as age and gender, and comorbid conditions were recorded. Results: TCT showed peripheral, posterior, and bilateral involvement in 97.7%, 97.7%, and 87.6% of the patients, respectively. The most common TCT finding was ground glass appearance, which was found in 95.5% of the patients. The Charlson Comorbidity Index (CCI) score was found to have an impact on RSNA and BTS criteria (P=0.011 and P=0.014), while age, gender, and the presence of comorbidities such as cardiovascular disease (CVD), diabetes mellitus (DM), and chronic pulmonary disease (CPD) did not have such an effect (P>0.05 for all). On the other hand, CCI scores and the presence of CPD had an association with CO-RADS, but there was no effect of age, gender, DM, and CVD (P=0.915 and P=0.730). Conclusion: TCT plays an important role in early management, isolation, and follow-up of patients with COVID-19 pneumonia. The radiological scoring systems were found to exhibit good compatibility, but comorbid conditions could have an impact on the assessment. Therefore, we conclude that these radiological assessment criteria are useful in the management and monitoring of such patients.

BPCN: bilateral progressive compensation network for lung infection image segmentation

Lung infection image segmentation is a key technology for autonomous understanding of the potential illness. However, current approaches usually lose the low-level details, which leads to a considerable accuracy decrease for lung infection areas with varied shapes and sizes. In this paper, we propose bilateral progressive compensation network (BPCN), a bilateral progressive compensation network to improve the accuracy of lung lesion segmentation through complementary learning of spatial and semantic features. The proposed BPCN are mainly composed of two deep branches. One branch is the multi-scale progressive fusion for main region features. The other branch is a flow-field based adaptive body-edge aggregation operations to explicitly learn detail features of lung infection areas which is supplement to region features. In addition, we propose a bilateral spatial-channel down-sampling to generate a hierarchical complementary feature which avoids losing discriminative features caused by pooling operations. Experimental results show that our proposed network outperforms state-of-the-art segmentation methods in lung infection segmentation on two public image datasets with or without a pseudo-label training strategy.

The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review

Coronavirus disease 2019 (COVID-19) is caused by a coronavirus family member known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The main laboratory test to confirm the quick diagnosis of COVID-19 infection is reverse transcription-polymerase chain reaction (RT-PCR) based on nasal or throat swab sampling. A small percentage of false-negative RT-PCR results have been reported. The RT-PCR test has a sensitivity of 50-72%, which could be attributed to a low viral load in test specimens or laboratory errors. In contrast, chest CT has shown 56-98% of sensitivity in diagnosing COVID-19 at initial presentation and has been suggested to be useful in correcting false negatives from RT-PCR. Chest X-rays and CT scans have been proposed to predict COVID-19 disease severity by displaying the score of lung involvement and thus providing information about the diagnosis and prognosis of COVID-19 infection. As a result, the current study provides a comprehensive overview of the utility of the severity score index using X-rays and CT scans in diagnosing patients with COVID-19 when compared to RT-PCR.

Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification

COVID-19 is the disease that has spread over the world since December 2019. This disease has a negative impact on individuals, governments, and even the global economy, which has caused the WHO to declare COVID-19 as a PHEIC (Public Health Emergency of International Concern). Until now, there has been no medicine that can completely cure COVID-19. Therefore, to prevent the spread and reduce the negative impact of COVID-19, an accurate and fast test is needed. The use of chest radiography imaging technology, such as CXR and CT-scan, plays a significant role in the diagnosis of COVID-19. In this study, CT-scan segmentation will be carried out using the 3D version of the most recommended segmentation algorithm for bio-medical images, namely 3D UNet, and three other architectures from the 3D UNet modifications, namely 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. These four architectures will be used in two cases of segmentation: binary-class segmentation, where each architecture will segment the lung area from a CT scan; and multi-class segmentation, where each architecture will segment the lung and infection area from a CT scan. Before entering the model, the dataset is preprocessed first by applying a minmax scaler to scale the pixel value to a range of zero to one, and the CLAHE method is also applied to eliminate intensity in homogeneity and noise from the data. Of the four models tested in this study, surprisingly, the original 3D UNet produced the most satisfactory results compared to the other three architectures, although it requires more iterations to obtain the maximum results. For the binary-class segmentation case, 3D UNet produced IoU scores, Dice scores, and accuracy of 94.32%, 97.05%, and 99.37%, respectively. For the case of multi-class segmentation, 3D UNet produced IoU scores, Dice scores, and accuracy of 81.58%, 88.61%, and 98.78%, respectively. The use of 3D segmentation architecture will be very helpful for medical personnel because, apart from helping the process of diagnosing someone with COVID-19, they can also find out the severity of the disease through 3D infection projections.

Pediatric COVID-19: Correlations between Clinical and Imaging Perspectives

Background

Although SARS-CoV-2 infection primarily affects adults, the increasing emergence of infected pediatric patients has been recently reported. However, there is a paucity of data regarding the value of imaging in relation to the clinical severity of this pandemic emergency.

Objectives

To demonstrate the relationships between clinical and radiological COVID-19 findings and to determine the most effective standardized pediatric clinical and imaging strategies predicting the disease severity. Patients and Methods. This observational study enrolled eighty pediatric patients with confirmed COVID-19 infection. The studied patients were categorized according to the disease severity and the presence of comorbidities. Patients' clinical findings, chest X-ray, and CT imaging results were analyzed. Patients' evaluations using several clinical and radiological severity scores were recorded. The relations between clinical and radiological severities were examined.

Results

Significant associations were found between severe-to-critical illness and abnormal radiological findings (p = 0.009). In addition, chest X-ray score, chest CT severity score, and rapid evaluation of anamnesis, PO2, imaging disease, and dyspnea-COVID (RAPID-COVID) score were significantly higher among patients with severe infection (p < 0.001, <0.001, and 0.001) and those with comorbidities (p = 0.005, 0.002, and <0.001).

Conclusions

Chest imaging of pediatric patients with COVID-19 infection may be of value during the evaluation of severe cases of infected pediatric patients and in those with underlying comorbid conditions, especially during the early stage of infection. Moreover, the combined use of specific clinical and radiological COVID-19 scores are likely to be a successful measure of the extent of disease severity.

Relation between Cytokine Levels and Pulmonary Dysfunction in COVID-19 Patients: A Case-Control Study

Aim: The study aimed to assess the relationships between serum cytokine levels and pulmonary dysfunctions in individuals with COVID-19. These correlations may help to suggest strategies for prevention and therapies of coronavirus disease. Patients and methods: Fifty healthy participants and one hundred COVID-19 patients participated in this study. COVID-19 participants were subdivided into moderate and severe groups based on the severity of their symptoms. In both patients and healthy controls, white blood cells (WBCs) and lymphocytes counts and serum C-reactive protein (CRP), interleukin (IL)-1, IL-4, IL-6, IL-18, and IL-35 levels were estimated. All the patients were examined by chest computed tomography (CT) and the COVID-19 Reporting and Data System (CO-RADS) score was assessed. Results: All COVID-19 patients had increased WBCs count and CRP, IL-1β, IL-4, IL-6, IL-18, and IL-35 serum levels than healthy controls. Whereas WBCs, CRP, and cytokines like IL-6 showed significantly higher levels in the severe group as compared to moderate patients, IL-4, IL-35, and IL-18 showed comparable levels in both disease groups. Lymphocytes count in all patient groups exhibited a significant decrease as compared to the heathy controls and it was significantly lower in severe COVID-19 than moderate. Furthermore, CO-RADS score was positively connected with WBCs count as well as CRP and cytokine (IL-35, IL-18, IL-6, IL-4 and IL-1β) levels in both groups. CO-RADS score, also demonstrated a positive correlation with lymphocytes count in the moderate COVID-19 patients, whereas it demonstrated a negative correlation in the severe patients. The receiver operator characteristic (ROC) curve analysis indicated that IL-1β, IL-4, IL-18, and IL-35 were fair (acceptable) predictors for COVID-19 in moderate cases. Whereas IL-6 was good predictor of COVID-19 in severe cases (AUC > 0.800), IL-18 and IL-35 were fair. Conclusion: Severe COVID-19 patients, compared to individuals with moderate illness and healthy controls, had lower lymphocyte counts and increased CRP with greater WBCs counts. In contrast to moderate COVID-19 patients, severe COVID-19 patients had higher levels of IL-6, but IL-4, IL-18, and IL-35 between both illness categories were at close levels. IL-6 level was the most potent predictor of COVID-19 progress and severity. CO-RADS 5 was the most frequent category in both moderate and severe cases. Patients with a typical CO-RADS involvement had a higher CRP and cytokine (IL-1β, IL-6, IL-4, IL-18, and IL-35) levels and WBCs count with a lower lymphocyte number than the others. Cytokine and CRP levels as well as WBCs and lymphocyte counts were considered surrogate markers of severe lung affection and pneumonia in COVID 19 patients.

Significance of Catecholamine Biosynthetic/Metabolic Pathway in SARS-CoV-2 Infection and COVID-19 Severity

The SARS-CoV-2 infection was previously associated with the expression of the dopamine biosynthetic enzyme L-Dopa decarboxylase (DDC). Specifically, a negative correlation was detected between DDC mRNA and SARS-CoV-2 RNA levels in in vitro infected epithelial cells and the nasopharyngeal tissue of COVID-19 patients with mild/no symptoms. However, DDC, among other genes related to both DDC expression and SARS-CoV-2-infection (ACE2, dACE2, EPO), was upregulated in these patients, possibly attributed to an orchestrated host antiviral response. Herein, by comparing DDC expression in the nasopharyngeal swab samples of severe/critical to mild COVID-19 cases, we showed a 20 mean-fold reduction, highlighting the importance of the expression of this gene as a potential marker of COVID-19 severity. Moreover, we identified an association of SARS-CoV-2 infection with the expression of key catecholamine biosynthesis/metabolism-related genes, in whole blood samples from hospitalized patients and in cultured cells. Specifically, viral infection downregulated the biosynthetic part of the dopamine pathway (reduction in DDC expression up to 7.5 mean-fold), while enhanced the catabolizing part (increase in monoamine oxidases A and B expression up to 15 and 10 mean-fold, respectively) in vivo, irrespectively of the presence of comorbidities. In accordance, dopamine levels in the sera of severe cases were reduced (up to 3.8 mean-fold). Additionally, a moderate positive correlation between DDC and MAOA mRNA levels (r = 0.527, p < 00001) in the blood was identified upon SARS-CoV-2-infection. These observations were consistent to the gene expression data from SARS-CoV-2-infected Vero E6 and A549 epithelial cells. Furthermore, L-Dopa or dopamine treatment of infected cells attenuated the virus-derived cytopathic effect by 55% and 59%, respectively. The SARS-CoV-2 mediated suppression of dopamine biosynthesis in cell culture was, at least in part, attributed to hypoxia-like conditions triggered by viral infection. These findings suggest that L-Dopa/dopamine intake may have a preventive or therapeutic value for COVID-19 patients.

Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia

Background

The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients.

Results

All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001).

Conclusion

Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.

Submillisievert CT chest for COVID-19 patients in a rural hospital with limited resources

Background

This is a secondary analysis of prospectively acquired data approved by the hospital institutional board committee. We performed a retrospective chart review of 463 patients who underwent a CT Chest for suspected COVID-19 infection between April 1st, 2020, and March 31st, 2021. Patients were grouped based on the CT chest obtained protocol: ultra-low dose or full dose. The likelihood of suspicion of COVID-19 infection was classified on a Likert scale based on the probability of pulmonary involvement. For each group, the sensitivity and specificity of CT were compared to nasopharyngeal swab as standard of reference. The median dose length product and duration of apnea were compared between both groups using two-tailed Mann–Whitney U test. The aim of this study is to share our experience of reducing radiation dose in COVID-19 patients by using an ultra-low dose CT chest protocol on a 16 row multidetector CT scan in a hospital with limited resources.

Results

Two hundred sixty-nine patients underwent a full dose CT and 194 patients an ultra-low dose CT. In the former group, the median dose length product was 341.11 mGy*cm [Interquartile range (IQR), 239.1–443.2] and the median duration of apnea was 13.29 s [IQR, 10.85–15.73]. In the latter group, the median dose length product was 30.8 mGy*cm [IQR, 28.9–32.7] and median duration of apnea was 8.27 s [IQR, 7.69–8.85]. The sensitivity of the ultra-low dose CT was 91.2% and that of the full dose was 94%.

Conclusion

A 90% reduction in estimated dose and 38% reduction in apnea duration could be achieved using an ultra-low dose CT chest protocol on a 16-row MDCT without significant loss in the sensitivity of CT to detect COVID-related parenchymal involvement.

Multi-modal approach for COVID-19 detection using coughs and self-reported symptoms

 COVID-19 (Coronavirus Disease of 2019) is one of the most challenging healthcare crises of the twenty-first century. The pandemic causes many negative impacts on all aspects of life and livelihoods. Although recent developments of relevant vaccines, such as Pfizer/BioNTech mRNA, AstraZeneca, or Moderna, the emergence of new virus mutations and their fast infection rate yet pose significant threats to public health. In this context, early detection of the disease is an important factor to reduce its effect and quickly control the spread of pandemic. Nevertheless, many countries still rely on methods that are either expensive and time-consuming (i.e., Reverse-transcription polymerase chain reaction) or uncomfortable and difficult for self-testing (i.e., Rapid Antigen Test Nasal). Recently, deep learning methods have been proposed as a potential solution for COVID-19 analysis. However, previous works usually focus on a single symptom, which can omit critical information for disease diagnosis. Therefore, in this study, we propose a multi-modal method to detect COVID-19 using cough sounds and self-reported symptoms. The proposed method consists of five neural networks to deal with different input features, including CNN-biLSTM for MFCC features, EfficientNetV2 for Mel spectrogram images, MLP for self-reported symptoms, C-YAMNet for cough detection, and RNNoise for noise-canceling. Experimental results demonstrated that our method outperformed the other state-of-the-art methods with a high AUC, accuracy, and F1-score of 98.6%, 96.9%, and 96.9% on the testing set.

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

A Review of the Rational and Current Evidence on Colchicine for COVID-19

The current coronavirus disease (COVID-19) pandemic has affected millions of individuals worldwide. Despite extensive research efforts, few therapeutic options currently offer direct clinical benefits for COVID-19 patients. Despite the advances in our understanding of COVID-19, the mortality rates remain significantly high owing to the high viral transmission rates in several countries and the rise of various mutations in the SARS-CoV-2. One currently available and widely used drug that combines both anti-inflammatory and immunomodulatory actions is colchicine, which has been proposed as a possible treatment option for COVID-19. Colchicine still did not get much attention from the medical and scientific communities despite its antiinflammatory and immunomodulatory mechanisms of action and positive preliminary data from early trials. This literature review article provides the scientific rationale for repurposing colchicine as a potential therapy for COVID-19. Further, we summarize colchicine's mechanisms of action and possible roles in COVID-19 patients. Finally, we supplement this review with a summary of the doses, side effects, and early efficacy data from clinical trials to date. Despite the promising early findings from multiple observational and clinical trials about the potential of colchicine in COVID-19, the data from the RECOVERY trial, the largest COVID-19 randomized controlled trial (RCT) in the world, showed no evidence of clinical benefits in mortality, hospital stays, or disease progression (n = 11340 patients). However, multiple other smaller clinical trials showed significant clinical benefits. We conclude that while current evidence does not support the use of colchicine for treating COVID-19, the present body of evidence is heterogeneous and inconclusive. The drug cannot be used in clinical practice or abandoned from clinical research without additional large RCTs providing more robust evidence. At present, the drug should not be used except for investigational purposes.

Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation

Segmentation of COVID-19 infection is a challenging task due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, especially for small infection regions. COV-TransNet is presented to achieve high-precision segmentation of COVID-19 infection regions in this paper. The proposed segmentation network is composed of the auxiliary branch and the backbone branch. The auxiliary branch network adopts transformer to provide global information, helping the convolution layers in backbone branch to learn specific local features better. A multi-scale feature attention module is introduced to capture contextual information and adaptively enhance feature representations. Specially, a high internal resolution is maintained during the attention calculation process. Moreover, feature activation module can effectively reduce the loss of valid information during sampling. The proposed network can take full advantage of different depth and multi-scale features to achieve high sensitivity for identifying lesions of varied sizes and locations. We experiment on several datasets of the COVID-19 lesion segmentation task, including COVID-19-CT-Seg, UESTC-COVID-19, MosMedData and COVID-19-MedSeg. Comprehensive results demonstrate that COV-TransNet outperforms the existing state-of-the-art segmentation methods and achieves better segmentation performance for multi-scale lesions.

Jaya-tunicate swarm algorithm based generative adversarial network for COVID-19 prediction with chest computed tomography images

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.

Admission Predictors of Mortality in Hospitalized COVID-19 Patients-A Serbian Cohort Study

Background: Early prediction of COVID-19 patients’ mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission. Methods: An observational, retrospective−prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients’ medical records. Results: The overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001). Conclusions: Parameters available on hospital admission can be beneficial in predicting COVID-19 mortality.

The importance of using routine laboratory tests in the diagnosis and prognosis of patients with coronavirus disease 2019: Shedding light on clinical laboratory data in COVID-19

BACKGROUND

Novel coronavirus (COVID-19) pandemic has become a global concern and requires early detection, isolation, and treatment. Our purpose is to find some beneficial information by analyzing the COVID-19 laboratory data to provide guidance for clinical practice.

MATERIAL AND METHODS

In this study, 174 patients with confirmed COVID-19 infection were admitted. We evaluated the hematological and biochemical parameters in these patients and in 80 healthy individuals.

RESULTS

We found that there was significant difference between WBC, LYM, RBC, HB, and HCT parameters of patients and healthy counterparts (p < .001), though there was no remarkable change between NEU, MONO, PLT, and other characteristics of RBC values of patients and the control group (p ≥ .09). Among the evaluated biochemical parameters, the values of CK-MB and LDH in the patient group were significantly different from the control group (p < .01), while other biochemical indicators were in the normal range.

CONCLUSION

Several hematological and biochemistry parameters, in particular WBC, LYM, RBC, HB, HCT, CK-MB, and LDH, could be beneficial supplementary approach for COVID-19 infection evaluation to confirm risk stratification and effective management.

Comparative pharmacokinetics of 24 major bioactive components in normal and ARDS rats after oral administration of Xuanfei Baidu granules

ETHNOPHARMACOLOGICAL RELEVANCE

Xuanfei Baidu prescription, consisting of 13 Chinese medicines, was formulated by academicians Boli Zhang and Professor Qingquan Liu based on their experience in first-line clinical treatment of COVID-19. Xuanfei Baidu granules (XFBD granules) are a proprietary Chinese medicine preparation developed based on Xuanfei Baidu prescription. It is recommended for the treatment of patients with the common wet toxin and lung stagnation syndrome of COVID-19. However, the pharmacokinetic characteristics of its major bioactive components in rats under different physiological and pathological conditions are unclear.

MATERIALS AND METHODS

A rapid and sensitive analytical method, ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS/MS), was developed and applied to 24 major bioactive components in normal and ARDS rats after oral administration of XFBD granules. We studied the metabolic process of XFBD granules in vivo to compare the differences in pharmacokinetic parameters between normal and model metabolic processes.

RESULTS

This method was successfully applied to the pharmacokinetic investigation of 24 major components of XFBD granules following oral administration in normal and ARDS rats. Eight components, including ephedrine and amygdalin, were more highly absorbed and had shorter T_max values than the model group; the absorption of six components, such as rhein, decreased in ARDS rats, and there was no significant difference in the absorption of ten components, such as verbenalin and naringin, between the normal and ARDS rats. The results showed that the peak times of other analytes were very short, and 80% of these target constituents were eliminated in both normal and ARDS rats within 6 h except for liquiritigenin and 18β-glycyrrhetinic acid.

CONCLUSIONS

In this study, a rapid and sensitive UPLC-MS/MS analytical method was developed and applied to 24 major bioactive components in normal and ARDS rats after the oral administration of XFBD granules. This will serve to form the basis for further studies on the pharmacokinetic-pharmacodynamic correlation of XFBD granules.

Molnupiravir as an Early Treatment for COVID-19: A Real Life Study

Objectives: Below we report our experience in the use of molnupiravir, the first antiviral drug against SARS-CoV-2 available to us, in the treatment of patients with COVID-19. Materials and Methods: We enrolled patients diagnosed with COVID-19 and comorbidities who were candidates for antiviral drug therapy. All patients received molnupiravir (800 mg twice daily). Blood chemistry checks were carried out at T0 and after 7/10 days after starting therapy (T1). Results: There were enrolled within the cohort 100 patients. There was 100.0% compliance with the antiviral treatment. No patient required hospitalization due to worsening of respiratory function or the appearance of serious side effects. The median downtime of viral load was ten days (IQR 8.0–13.0), regardless of the type of vaccination received. The patients who had a shorter distance from vaccination more frequently presented vomiting/diarrhea. During baseline and T1 we found significant differences in the median serum concentrations of the main parameters, in particular of platelets, RDW CV, neutrophils and lymphocytes, the eGFR, liver enzymes, as well as of the main inflammatory markers, CRP and Ferritin. Conclusion: Participants treated with molnupiravir, albeit in risk categories, demonstrated early clinical improvement, no need for hospitalization, and a low rate of adverse events.

Prospective clinical research of radiomics and deep learning in oncology: A translational review

Radiomics and deep learning (DL) hold transformative promise and substantial and significant advances in oncology; however, most methods have been tested in retrospective or simulated settings. There is considerable interest in the biomarker validation, clinical utility, and methodological robustness of these studies and their deployment in real-world settings. This review summarizes the characteristics of studies, the level of prospective validation, and the overview of research on different clinical endpoints. The discussion of methodological robustness shows the potential for independent external replication of prospectively reported results. These in-depth analyses further describe the barriers limiting the translation of radiomics and DL into primary care options and provide specific recommendations regarding clinical deployment. Finally, we propose solutions for integrating novel approaches into the treatment environment to unravel the critical process of translating AI models into the clinical routine and explore strategies to improve personalized medicine.

DBF-Net: a semi-supervised dual-task balanced fusion network for segmenting infected regions from lung CT images

Accurate segmentation of infected regions in lung computed tomography (CT) images is essential to improve the timeliness and effectiveness of treatment for coronavirus disease 2019 (COVID-19). However, the main difficulties in developing of lung lesion segmentation in COVID-19 are still the fuzzy boundary of the lung-infected region, the low contrast between the infected region and the normal trend region, and the difficulty in obtaining labeled data. To this end, we propose a novel dual-task consistent network framework that uses multiple inputs to continuously learn and extract lung infection region features, which is used to generate reliable label images (pseudo-labels) and expand the dataset. Specifically, we periodically feed multiple sets of raw and data-enhanced images into two trunk branches of the network; the characteristics of the lung infection region are extracted by a lightweight double convolution (LDC) module and fusiform equilibrium fusion pyramid (FEFP) convolution in the backbone. According to the learned features, the infected regions are segmented, and pseudo-labels are made based on the semi-supervised learning strategy, which effectively alleviates the semi-supervised problem of unlabeled data. Our proposed semi-supervised dual-task balanced fusion network (DBF-Net) creates pseudo-labels on the COVID-SemiSeg dataset and the COVID-19 CT segmentation dataset. Furthermore, we perform lung infection segmentation on the DBF-Net model, with a segmentation sensitivity of 70.6% and specificity of 92.8%. The results of the investigation indicate that the proposed network greatly enhances the segmentation ability of COVID-19 infection.

Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms

The objectives of our study were to (a) evaluate the feasibility of using 3D printed phantoms in magnetic resonance imaging (MR) in assessing the robustness and repeatability of radiomic parameters and (b) to compare the results obtained from the 3D printed phantoms to metrics obtained in biological phantoms. To this end, three different 3D phantoms were printed: a Hilbert cube (5 × 5 × 5 cm³) and two cubic quick response (QR) code phantoms (a large phantom (large QR) (5 × 5 × 4 cm³) and a small phantom (small QR) (4 × 4 × 3 cm³)). All 3D printed and biological phantoms (kiwis, tomatoes, and onions) were scanned thrice on clinical 1.5 T and 3 T MR with 1 mm and 2 mm isotropic resolution. Subsequent analyses included analyses of several radiomics indices (RI), their repeatability and reliability were calculated using the coefficient of variation (CV), the relative percentage difference (RPD), and the interclass coefficient (ICC) parameters. Additionally, the readability of QR codes obtained from the MR images was examined with several mobile phones and algorithms. The best repeatability (CV ≤ 10%) is reported for the acquisition protocols with the highest spatial resolution. In general, the repeatability and reliability of RI were better in data obtained at 1.5 T (CV = 1.9) than at 3 T (CV = 2.11). Furthermore, we report good agreements between results obtained for the 3D phantoms and biological phantoms. Finally, analyses of the read-out rate of the QR code revealed better texture analyses for images with a spatial resolution of 1 mm than 2 mm. In conclusion, 3D printing techniques offer a unique solution to create textures for analyzing the reliability of radiomic data from MR scans.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge — 2020.

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

BACKGROUND AND AIMS

Severe manifestations of coronavirus disease 2019 (COVID-19) are associated with alterations in blood cells that regulate immunity, inflammation, and hemostasis. We conducted an updated systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), and COVID-19 progression and mortality.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 2020 and June 2022.

RESULTS

In 71 studies reporting the investigated parameters within 48 hours of admission, higher NLR (HR 1.21, 95% CI 1.16 to 1.27, p < 0.0001), relative neutrophilia (HR 1.62, 95% CI 1.46 to 1.80, p < 0.0001), relative lymphopenia (HR 1.62, 95% CI 1.27 to 2.08, p < 0.001), and relative thrombocytopenia (HR 1.74, 95% CI 1.36 to 2.22, p < 0.001), but not PLR (p = 0.11), were significantly associated with disease progression and mortality. Between-study heterogeneity was large-to-extreme. The magnitude and direction of the effect size were not modified in sensitivity analysis.

CONCLUSIONS

NLR and neutrophil, lymphocyte, and platelet count significantly discriminate COVID-19 patients with different progression and survival outcomes. (PROSPERO registration number: CRD42021267875).

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN

BACKGROUND

Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data.

METHODS

We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists.

RESULTS

The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations.

CONCLUSION

Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.

Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study

COVID-19 has been spreading continuously since its outbreak, and the detection of its manifestations in the lung via chest computed tomography (CT) imaging is essential to investigate the diagnosis and prognosis of COVID-19 as an indispensable step. Automatic and accurate segmentation of infected lesions is highly required for fast and accurate diagnosis and further assessment of COVID-19 pneumonia. However, the two-dimensional methods generally neglect the intraslice context, while the three-dimensional methods usually have high GPU memory consumption and calculation cost. To address these limitations, we propose a two-stage hybrid UNet to automatically segment infected regions, which is evaluated on the multicenter data obtained from seven hospitals. Moreover, we train a 3D-ResNet for COVID-19 pneumonia screening. In segmentation tasks, the Dice coefficient reaches 97.23% for lung segmentation and 84.58% for lesion segmentation. In classification tasks, our model can identify COVID-19 pneumonia with an area under the receiver-operating characteristic curve value of 0.92, an accuracy of 92.44%, a sensitivity of 93.94%, and a specificity of 92.45%. In comparison with other state-of-the-art methods, the proposed approach could be implemented as an efficient assisting tool for radiologists in COVID-19 diagnosis from CT images.

Follow‐up the severity of abnormalities diagnosed in chest CT imaging of COVID‐19 patients: A cross‐sectional study

Background and Aims

Methods

Results

Conclusion

Impact of high-dose vitamin C on the mortality, severity, and duration of hospital stay in COVID-19 patients: A meta-analysis

Background and Aims

Vitamin C has been predicted to be effective as an antioxidant in treating various ailments, including viral infections such as pervasive coronavirus disease (COVID-19). With this meta-analysis, we looked to ascertain the relationship between high-dose vitamin C administration and mortality, severity, and length of hospitalization of COVID-19 patients.

Methods

We collected articles from PubMed, Google Scholar, ScienceDirect, SAGE, and Cochrane databases between January 1, 2020, and May 30, 2022. Odds ratio (ORs) with corresponding 95% confidence interval (CI) and p value were calculated to assess the connection of high-dose vitamin C in COVID-19 patients' mortality and severity. The length of hospitalization was calculated and pooled with the mean difference (MD), 95% CI, and p value. Review manager 5.3 was used to carry out this meta-analysis.

Results

This meta-analysis included 15 complete studies involving 2125 COVID-19 patients. Our study demonstrated a significant correlation between vitamin C consumption and death. Vitamin C consumption significantly reduces mortality risk with COVID-19 patients (OR = 0.54, 95% CI = 0.42-0.69, p < 0.00001). Furthermore, there was a link between the severity of COVID-19 and the intake of vitamin C. Patients who consumed vitamin C showed 0.63 times less severity than those who did not take vitamin C (OR = 0.63, 95% CI = 0.43-0.94, p = 0.02). Patients taking vitamin C spent slightly more time in hospital than those who did not take vitamin C (MD = 0.19, 95% CI = -1.57 to 1.96, p = 0.83).

Conclusions

During COVID-19, there was a substantial advantage in taking supplementary vitamin C, at least in terms of severity and mortality.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

BACKGROUND

The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need.

OBJECTIVE

To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity.

METHODS

The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features.

RESULTS

The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one.

CONCLUSIONS

CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

Correlation Between Chest CT Severity Scores and Glycosylated Haemoglobin Levels and its Outcome in Patients With COVID-19: A Retrospective Study in a Tertiary Care Hospital

Background

In this study, we aimed to compare the imaging findings between coronavirus disease (COVID-19) patients with well-controlled, poorly-controlled, and non-diabetic patients and subsequently find any relation between haemoglobin A1c (HbA1c) levels and high-resolution chest computed tomography (HRCT) chest score.

Methodology

A total of 200 individuals with coexisting COVID-19 and type 2 diabetes mellitus were included in this retrospective cohort study. Based on their HbA1c levels, patients were divided into three groups. The imaging data and laboratory values were obtained from the online medical records of the patients. In addition, the chest computed tomography (CT) score was evaluated as the sum of individual scores from five lung lobes: scores of 0, 1, 2, 3, 4, and 5 were assigned to each lobe. Any peripheral opacification pattern was noted. Haemoglobin A1c (HbA1c) levels and HRCT scores were then analysed by multiple linear regression models using R software.

Results

The prevalence of diabetes in the study population was 71.5%. Of this, 56 patients had well-controlled diabetes (28%) and 87 patients had poorly controlled diabetes (43.5%); 126 (63%) patients were male and the median age was 54.45 years (95% CI: 54.45 ± 15.53). We found that diabetes status, co-presence of ground-glass appearance with mixed consolidation, and consolidation and reverse halo sign in the HRCT findings were significant predictors of the HRCT scores in patients with COVID-19.

Conclusions

The presence of any co-morbidities should be viewed as a high-risk case of COVID-19. Diabetes status is significantly associated with the severity of HRCT findings in lab-confirmed COVID-19 infection. Therefore, it is important to prioritise the patients who have COVID-19 along with diabetes.

Comparison of COVID-19 pneumonia during the SARS-CoV-2 Omicron wave and the previous non-Omicron wave in a single facility

BACKGROUND

The characteristics of coronavirus disease 2019 (COVID-19) pneumonia caused by the severe acute respiratory syndrome coronavirus 2 Omicron variant have not been fully described. Unlike other variants, the Omicron variant replicates rapidly in the bronchus. Therefore, we hypothesized that it would have different computed tomography (CT) findings from non-Omicron variants.

METHODS

We enrolled patients with COVID-19 who visited our hospital and underwent chest CT during the first month of the Omicron wave (January 2022; N = 231) and the previous non-Omicron wave (July 2021; N = 87). We retrospectively evaluated the differences in the prevalence rate and CT characteristics of COVID-19 pneumonia between the two waves.

RESULTS

The prevalence of pneumonia was significantly lower in the Omicron wave group (79/231, 34.2%) compared to the previous wave group (67/87, 77.0%) (P < 0.001). For the predominant distribution pattern of pneumonia, the Omicron wave group revealed a significantly lower rate of the peripheral pattern and a higher rate of the random pattern than the previous wave group. In addition, the Omicron wave group had a significantly lower rate of consolidation than the previous wave group. The ground-glass opacities (GGOs) rate was similar between the two wave groups. For GGOs patterns, cluster-like GGOs along the bronchi on chest CT were more frequently observed during the Omicron wave than during the previous wave.

CONCLUSION

The Omicron wave group had a lower COVID-19 pneumonia prevalence than the previous wave group. Cluster-like GGOs should be noted as a characteristic CT finding of pneumonia during the Omicron wave.

Diagnosis Methods for COVID-19: A Systematic Review

At the end of 2019, the coronavirus appeared and spread extremely rapidly, causing millions of infections and deaths worldwide, and becoming a global pandemic. For this reason, it became urgent and essential to find adequate tests for an accurate and fast diagnosis of this disease. In the present study, a systematic review was performed in order to provide an overview of the COVID-19 diagnosis methods and tests already available, as well as their evolution in recent months. For this purpose, the Science Direct, PubMed, and Scopus databases were used to collect the data and three authors independently screened the references, extracted the main information, and assessed the quality of the included studies. After the analysis of the collected data, 34 studies reporting new methods to diagnose COVID-19 were selected. Although RT-PCR is the gold-standard method for COVID-19 diagnosis, it cannot fulfill all the requirements of this pandemic, being limited by the need for highly specialized equipment and personnel to perform the assays, as well as the long time to get the test results. To fulfill the limitations of this method, other alternatives, including biological and imaging analysis methods, also became commonly reported. The comparison of the different diagnosis tests allowed to understand the importance and potential of combining different techniques, not only to improve diagnosis but also for a further understanding of the virus, the disease, and their implications in humans.

Semi-supervised COVID-19 CT image segmentation using deep generative models

Background

A recurring problem in image segmentation is a lack of labelled data. This problem is especially acute in the segmentation of lung computed tomography (CT) of patients with Coronavirus Disease 2019 (COVID-19). The reason for this is simple: the disease has not been prevalent long enough to generate a great number of labels. Semi-supervised learning promises a way to learn from data that is unlabelled and has seen tremendous advancements in recent years. However, due to the complexity of its label space, those advancements cannot be applied to image segmentation. That being said, it is this same complexity that makes it extremely expensive to obtain pixel-level labels, making semi-supervised learning all the more appealing. This study seeks to bridge this gap by proposing a novel model that utilizes the image segmentation abilities of deep convolution networks and the semi-supervised learning abilities of generative models for chest CT images of patients with the COVID-19.

Results

We propose a novel generative model called the shared variational autoencoder (SVAE). The SVAE utilizes a five-layer deep hierarchy of latent variables and deep convolutional mappings between them, resulting in a generative model that is well suited for lung CT images. Then, we add a novel component to the final layer of the SVAE which forces the model to reconstruct the input image using a segmentation that must match the ground truth segmentation whenever it is present. We name this final model StitchNet.

Conclusion

We compare StitchNet to other image segmentation models on a high-quality dataset of CT images from COVID-19 patients. We show that our model has comparable performance to the other segmentation models. We also explore the potential limitations and advantages in our proposed algorithm and propose some potential future research directions for this challenging issue.

Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images

Purpose

To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.

Materials and methods

A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again.

Results

The median (interquartile range) lesion volume (cm³) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline.

Conclusion

The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.

The efficacy of combined therapy of qingfeiPaidu capsule and lianhuaqingwen capsule nursing interventions for hospitalized patients with COVID-19: A retrospective study of medical records

Coronavirus disease-19 (COVID-19) caused a global pandemic burden, affecting hundreds of thousands of individuals, having life-threatening outcomes. Traditional Chinese Medicine plays a crucial role in the treatment of patients with COVID-19. The purpose of this study was to investigate the efficacy of combined therapy of qingfeiPaidu (QFPD) capsule and lianhuaqingwen (LHQW) capsule nursing interventions in the treatment of patients with COVID-19. A total of 318 patients with COVID-19 were enrolled and randomly received QFPD (n = 106), LHQW (n = 106), and QFPD-LHQW (n = 106). The clinical characteristics of COVID-19, the total lung severity scores, and blood laboratory indices were recorded in each patient in each group before treatment and at the end of treatment. The outcomes demonstrated that QFPD-LHQW group shortened the length of hospitalization, decreased C-reactive protein, creatine kinase, creatine kinase-myocardial band, lactate dehydrogenase, and blood urea nitrogen levels, and improved clinical symptoms, pulmonary inflammation, and prognosis. At the end of treatment, inflammation, immune function, circulating white blood cells, total lymphocyte count, and glutamic-oxaloacetic transaminase levels improved dramatically in 3 groups compared with baseline. All patients met the discharge criteria after 30-day treatment in 3 groups. Combined therapy of QFPD and LHQW demonstrated significant anti-inflammatory effects compared with those of only QFPD or LHQW in patients with mild and moderate COVID-19. The combined therapies may alleviate clinical symptoms of COVID-19 patients by improving inflammation and immune function.

Chest multidetector computed tomography imaging of COVID-19 pneumonia patients with hematologic malignancies

Background

Data on the association between coronavirus disease 2019 (COVID-19) and the epidemiology and outcomes of hematological malignancies are limited. Hence, the present study aimed to assess the imaging findings using chest multidetector computed tomography (MDCT) in patients with hematologic malignancies who developed COVID-19 pneumonia.

Methods

This retrospective study included two groups, the first group consisted of COVID-19 infected patients with hematologic malignancies (100 patients), while the second group consisted of COVID-19 infected patients without hematologic malignancies or other comorbidities (100 patients). The hematological malignancies included in this study were non-Hodgkin’s lymphoma (40 patients), acute myeloid leukemia (25 patients), chronic lymphocytic leukemia (15 patients), multiple myeloma (10 patients), Hodgkin’s lymphoma (8 patients), and myelodysplastic syndrome (2 patients). Chest multidetector CT imaging was performed in all patients to assess for ground-glass opacity, consolidation, pleural effusion, and airway abnormalities.

Results

More than one CT finding was reported in each patient. No significant difference was observed in the ground-glass opacities (P=0.0594), nodule formation (P=0.2278), or airway thickening/dilatation (P=0.0566) between the two groups; meanwhile, a significant difference was observed in the degree of consolidation, the number of lobes affected, and pleural effusion (P=0.0001) as well as in the total lung severity (P=0.0001); minimal, mild, and severe affection rates; and (P=0.0047) moderate affection rates.

Conclusion

Early and reliable diagnosis of lung disease in COVID-19-infected patients may be achieved through multidetector CT imaging. Patients with hematological malignancies are more likely to have severe COVID-19 pneumonia, and radiologists should recognize the CT characteristics of this infection.

Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study

Background

COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality.

Methods

The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method.

Results

While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy.

Conclusion

Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.

FCF: Feature complement fusion network for detecting COVID-19 through CT scan images

COVID-19 spreads and contracts people rapidly, to diagnose this disease accurately and timely is essential for quarantine and medical treatment. RT-PCR plays a crucial role in diagnosing the COVID-19, whereas computed tomography (CT) delivers a faster result when combining artificial assistance. Developing a Deep Learning classification model for detecting the COVID-19 through CT images is conducive to assisting doctors in consultation. We proposed a feature complement fusion network (FCF) for detecting COVID-19 through lung CT scan images. This framework can extract both local features and global features by CNN extractor and ViT extractor severally, which successfully complement the deficiency problem of the receptive field of the other. Due to the attention mechanism in our designed feature complement Transformer (FCT), extracted local and global feature embeddings achieve a better representation. We combined a supervised with a weakly supervised strategy to train our model, which can promote CNN to guide the VIT to converge faster. Finally, we got a 99.34% accuracy on our test set, which surpasses the current state-of-art popular classification model. Moreover, this proposed structure can easily extend to other classification tasks when changing other proper extractors.

The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis

Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

Long-Term Health Consequences of SARS-CoV-2: Assumptions Based on SARS-CoV-1 and MERS-CoV Infections

Coronavirus Disease-2019 (COVID-19) is one of the worst pandemics in the history of the world. It is the third coronavirus disease that has afflicted humans in a short span of time. The world appears to be recovering from the grasp of this deadly pandemic; still, its post-disease health effects are not clearly understood. It is evident that the vast majority of COVID-19 patients usually recovered over time; however, disease manifestation is reported to still exist in some patients even after complete recovery. The disease is known to have left irreversible damage(s) among some patients and these damages are expected to cause mild or severe degrees of health effects. Apart from the apparent damage to the lungs caused by SARS-CoV-1, MERS-CoV, and SARS-CoV-2 infection, COVID-19-surviving patients display a wide spectrum of dysfunctions in different organ systems that is similar to what occurs with SARS-CoV-1 and MERS diseases. The major long COVID-19 manifestations include the following aspects: (1) central nervous system, (2) cardiovascular, (3) pulmonary, (4) gastrointestinal, (5) hematologic, (6) renal and (7) psycho-social systems. COVID-19 has a disease display manifestation in these organs and its related systems amongst a large number of recovered cases. Our study highlights the expected bodily consequences of the pandemic caused by SARS-CoV-2 infection based on the understanding of the long-term effects of SARS-CoV-1 and MERS-CoV.

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.

Quantitative parameters obtained from gadobenate dimeglumine-enhanced MRI at the hepatobiliary phase can predict post-hepatectomy liver failure and overall survival in patients with hepatocellular carcinoma

PURPOSE

To determine the value of the quantitative parameters obtained from gadobenate dimeglumine-enhanced magnetic resonance imaging (MRI) at the hepatobiliary phase for predicting post-hepatectomy liver failure and overall survival in patients with hepatocellular carcinoma.

METHOD

This multicenter retrospective study included 307 patients who underwent gadobenate dimeglumine-enhanced MRI. The quantitative liver-to-portal vein contrast ratio (LPC) and liver-spleen contrast ratio (LSC) at the hepatobiliary phase were measured. Logistic regression analyses were used to evaluate risk factors for post-hepatectomy liver failure. The capacity of the LPC and LSC to predict post-hepatectomy liver failure was evaluated via receiver operating characteristic (ROC) curve. The Cox proportional hazards regression was used to identify prognostic factors for overall survival (OS).

RESULTS

Post-hepatectomy liver failure was observed in 69 patients (22.5%). The LPC and LSC were independent risk factors for the development of post-hepatectomy liver failure, and the areas under the ROC curves of LPC and LSC were 0.882 and 0.782, respectively. The predictive performance of LPC for post-hepatectomy liver failure was superior to LSC. The LPC and LSC were also significant prognostic factors for OS. The cut-off values for the LPC and LSC were 1.07 and 0.89, respectively. The 5-year OS rate was higher in patients with LPC > 1.07 or LSC > 0.89 than in patients with LPC ≤ 1.07 or LSC ≤ 0.89.

CONCLUSIONS

The quantitative parameters obtained from gadobenate dimeglumine-enhanced MRI at the hepatobiliary phase were effective imaging biomarkers for predicting both post-hepatectomy liver failure and overall survival in patients with hepatocellular carcinoma.

Preliminary investigation of gene expression levels of PAPP-A, STC-2, and HIF-1α in SARS-Cov-2 infected patients

Background

Coronavirus-19 is still considered a pandemic that influences the world. Other molecular alterations should be clearer besides the increasing cytokine storm and pro-inflammatory molecules. Hypoxic conditions that induce HIF-1α lead to stimulate gene expression of STC-2 that targets PAPP-A expression. This study aimed to determine gene expression levels of PAPP-A, STC-2, and HIF-1α in COVID-19 infection. We also aimed to reveal the relationship of these genes with laboratory and clinical data of COVID-19 patients.

Materials and Results

We extracted RNA from peripheral blood samples of COVID-19(+) and COVID-19(−) individuals. The real-time PCR method was used to measure mRNA expression of PAPP-A, STC-2, and HIF-1α. Gene expression analysis was evaluated by the 2^−ΔΔCt method. PAPP-A, STC-2, and HIF-1α mRNA expressions of severe patients were higher than healthy individuals (p = 0.0451, p = 0.4466, p < 0.0001, respectively). Correlation analysis of gene expression patterns of severe patients demonstrated a positive correlation between PAPP-A and STC-2 (p < 0.0001, r = 0.8638).

Conclusion

This is the first study that investigates the relation of PAPP-A, STC-2, and HIF-1α gene expression in patients with COVID-19 infection. Besides the routine laboratory findings, PAPP-A, STC-2, and HIF-1α mRNA expressions may be considered to patients’ prognosis as a sign of increased cytokines and pro-inflammatory molecules.

Hypertension as an independent risk factor for severity and mortality in patients with COVID-19: a retrospective study

PURPOSE OF THE STUDY

Hypertension is one of the most common comorbidities in COVID-19 pneumonia. However, whether it is an independent factor on the severity and mortality of COVID-19 has not been studied.

STUDY DESIGN

In this study, 736 patients with a PCR-confirmed diagnosis of COVID-19 were included from 12 January 2020 to 25 March 2020. All patients were divided into two groups according to whether or not they were hypertensive. After propensity score matching (PSM) to remove the interference of mismatches in the baseline data, the clinical characteristics and outcomes of angiotensin II receptor blocker (ARB)/ACE inhibitors application were analysed.

RESULTS

A total of 220 (29.9%) patients were hypertensive, and 516 (70.1%) patients were not hypertensive. PSM eliminated demographic and comorbidity differences between the two groups. Of all participants, 32 patients died (4.3% mortality), including 17 out of 220 in the hypertension group (7.7%) and 15 out of 516 in the non-hypertension group (2.9%). The incidence of intensive care unit (ICU) stay in the hypertension group (12.8%) was higher than in the non-hypertension group (5.3%) (p<0.05). Logistic regression analysis showed that hypertension was an independent risk factor for death, not other comorbidities. Kaplan-Meier analysis showed that mortality was higher in the hypertension group than in the non-hypertension group before and after PSM (p<0.05). There was no statistically significant difference in ICU therapy, mortality and hospitalisation time between hypertensive patients with or without ARBs/ACE inhibitors (p>0.05).

CONCLUSION

Hypertension was an independent risk factor for the severity and mortality of patients with COVID-19. ARBs/ACE inhibitors should not be discontinued in hypertensive patients with COVID-19.

The Impact of Neutrophil-Lymphocyte Count Ratio in COVID-19: A Systematic Review and Meta-Analysis

Background: The neutrophil-lymphocyte count ratio (NLR) has emerged as a potential prognostic tool for different diseases. In the current coronavirus disease (COVID-19) pandemic, the NLR may be a useful tool for risk scarification and the optimal utilization of limited healthcare resources. However, there is no consensus regarding the optimal value of NLR, and the association with disease severity and mortality. Thus, this study aims to systematically analyze the current evidence of the utility of baseline NLR as a predictive tool for mortality, disease severity in COVID-19 patients. Methods: A compendious screening of electronic databases up to June 15, 2021, was done after enlisting the protocol in PROSPERO (CRD42020202659). Studies evaluating the utility of baseline NLR in COVID-19 are included for this review as per the PRISMA statement. Results: We retrieved a total of 13112 and 12986 COVID-19 patients for survivability and severity over 90 studies. The expired and critically sick patients had elevated baseline NLR on admission, in comparison to survivors and noncritical patients. (SMD = 3.82; 95% CI: 2.79-4.85; I2 = 100% and SMD = 1.42; 95% CI: 1.22-1.63; I2 = 95%, respectively). The summary receiver operating curve analysis for mortality (AUC = 0.87; 95% CI: 0.86-0.87; I2 = 94.7%), and severity (AUC = 0.82; 95% CI: 0.80-0.84; I2 = 79.7%) were also suggestive of its significant predictive value. Conclusions: The elevated NLR on admission in COVID-19 patients is associated with poor outcomes.