New Insights on the Mechanisms of Myocardial Injury in Hypertensive Patients With COVID-19

PURPOSE

Myocardial injury is common in hypertensive patients with 2019 coronavirus disease (COVID-19). Immune dysregulation could be associated to cardiac injury in these patients, but the underlying mechanism has not been fully elucidated.

METHODS

All patients were selected prospectively from a multicenter registry of adults hospitalized with confirmed COVID-19. Cases had hypertension and myocardial injury, defined by troponin levels above the 99th percentile upper reference limit, and controls were hypertensive patients with no myocardial injury. Biomarkers and immune cell subsets were quantified and compared between the two groups. A multiple logistic regression model was used to analyze the associations of clinical and immune variables with myocardial injury.

RESULTS

The sample comprised 193 patients divided into two groups: 47 cases and 146 controls. Relative to controls, cases had lower total lymphocyte count, percentage of T lymphocytes, CD8+CD38+ mean fluorescence intensity (MFI), and percentage of CD8+ human leukocyte antigen DR isotope (HLA-DR)+ CD38-cells and higher percentage of natural killer lymphocytes, natural killer group 2A (NKG2A)+ MFI, percentage of CD8+CD38+cells, CD8+HLA-DR+MFI, CD8+NKG2A+MFI, and percentage of CD8+HLA-DR-CD38+cells. On multivariate regression, the CD8+HLA-DR+MFI, CD8+CD38+MFI, and total lymphocyte count were associated significantly with myocardial injury.

CONCLUSION

Our findings suggest that lymphopenia, CD8+CD38+MFI, and CD8+HLA-DR+MFI are immune biomarkers of myocardial injury in hypertensive patients with COVID-19. The immune signature described here may aid in understanding the mechanisms underlying myocardial injury in these patients. The study data might open a new window for improvement in the treatment of hypertensive patients with COVID-19 and myocardial injury.

Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram

RATIONALE AND OBJECTIVES

Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC.

MATERIALS AND METHODS

Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup.

RESULTS

The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001).

CONCLUSION

Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.

Elevated neutrophil-lymphocyte ratio is associated with high rates of ICU mortality, length of stay, and invasive mechanical ventilation in critically ill patients with COVID-19 : NRL and severe COVID-19

Neutrophil and lymphocyte ratio (NLR) has emerged as a complementary marker in intensive care. This study aimed to associate high NLR values with mortality as the primary outcome, and length of stay and need for invasive mechanical ventilation as secondary outcomes, in critically ill patients with COVID-19. A cross-sectional study encompassing 189 critically ill patients with COVID-19 was performed. The receiver operating characteristic curve was used to identify the best NLR cutoff value for ICU mortality (≥ 10.6). An NLR ≥ 10.6, compared with an NLR < 10.6, was associated with higher odds of ICU mortality (odds ratio [OR], 2.77; 95% confidence interval [CI], 1.24-6.18), ICU length of stay ≥ 14 days (OR, 3.56; 95% CI, 1.01-12.5), and need for invasive mechanical ventilation (OR, 5.39; 95% CI, 1.96-14.81) in the fully adjusted model (age, sex, kidney dysfunction, diabetes, obesity, hypertension, deep vein thrombosis, antibiotics, anticoagulants, antivirals, corticoids, neuromuscular blockers, and vasoactive drugs). In conclusion, elevated NLR is associated with high rates of mortality, length of stay, and need for invasive mechanical ventilation in critically ill patients with COVID-19.

Dipeptidyl-peptidase 3 and IL-6: potential biomarkers for diagnostics in COVID-19 and association with pulmonary infiltrates

Coronavirus SARS-CoV-2 spread worldwide, causing a respiratory disease known as COVID-19. The aim of the present study was to examine whether Dipeptidyl-peptidase 3 (DPP3) and the inflammatory biomarkers IL-6, CRP, and leucocytes are associated with COVID-19 and able to predict the severity of pulmonary infiltrates in COVID-19 patients versus non-COVID-19 patients. 114 COVID-19 patients and 35 patients with respiratory infections other than SARS-CoV-2 were included in our prospective observational study. Blood samples were collected at presentation to the emergency department. 102 COVID-19 patients and 28 non-COVID-19 patients received CT imaging (19 outpatients did not receive CT imaging). If CT imaging was available, artificial intelligence software (CT Pneumonia Analysis) was used to quantify pulmonary infiltrates. According to the median of infiltrate (14.45%), patients who obtained quantitative CT analysis were divided into two groups (> median: 55 COVID-19 and nine non-COVID-19, ≤ median: 47 COVID-19 and 19 non-COVID-19). DPP3 was significantly elevated in COVID-19 patients (median 20.85 ng/ml, 95% CI 18.34-24.40 ng/ml), as opposed to those without SARS-CoV-2 (median 13.80 ng/ml, 95% CI 11.30-17.65 ng/ml; p < 0.001, AUC = 0.72), opposite to IL-6, CRP (each p = n.s.) and leucocytes (p < 0.05, but lower levels in COVID-19 patients). Regarding binary logistic regression analysis, higher DPP3 concentrations (OR = 1.12, p < 0.001) and lower leucocytes counts (OR = 0.76, p < 0.001) were identified as significant and independent predictors of SARS-CoV-2 infection, as opposed to IL-6 and CRP (each p = n.s.). IL-6 was significantly increased in patients with infiltrate above the median compared to infiltrate below the median both in COVID-19 (p < 0.001, AUC = 0.78) and in non-COVID-19 (p < 0.05, AUC = 0.81). CRP, DPP3, and leucocytes were increased in COVID-19 patients with infiltrate above median (each p < 0.05, AUC: CRP 0.82, DPP3 0.70, leucocytes 0.67) compared to infiltrate below median, opposite to non-COVID-19 (each p = n.s.). Regarding multiple linear regression analysis in COVID-19, CRP, IL-6, and leucocytes (each p < 0.05) were associated with the degree of pulmonary infiltrates, as opposed to DPP3 (p = n.s.). DPP3 showed the potential to be a COVID-19-specific biomarker. IL-6 might serve as a prognostic marker to assess the extent of pulmonary infiltrates in respiratory patients.

Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies

Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.

The Impact of Liver Chemistries on Respiratory Failure among Hemodialysis Patients with COVID-19 during the Omicron Wave

Objective Although the coronavirus disease 2019 (COVID-19) Omicron variant causes less severe symptoms than previous variants, early indicators for respiratory failure are needed in hemodialysis patients, who have a higher mortality rate than the general population. Liver chemistries are known to reflect the severity of COVID-19 in the general population. This study explored the early indicators for worsened respiratory failure based on patient characteristics, including liver chemistries. Methods This retrospective study included 117 patients admitted for COVID-19 during the Omicron wave. Respiratory failure was defined as oxygen requirement during treatment. Information on the symptoms and clinical characteristics, including liver chemistries [aspartate aminotransferase (AST) and alanine aminotransferase (ALT)], at admission was collected. Results Thirty-five patients (29.9%) required oxygen supply during treatment. In the multivariate logistic regression analyses, AST [odds ratio (OR) 1.06, 95% confidence interval (CI) 1.00-1.13, p=0.029], ALT (OR 1.09, 95% CI 1.02-1.18, p=0.009), and moderate COVID-19 illness (Model including AST, OR 6.95, 95% CI 2.23-23.17, p<0.001; Model including ALT, OR 7.19, 95% CI 2.21-25.22, p=0.001) were independent predictors for respiratory failure. Based on the cutoff values determined by the receiver operating characteristic curve, higher AST (≥23 IU/L) and ALT levels (≥14 IU/L) were also independently associated with respiratory failure (higher AST: 64.3% vs. 18.8%, OR 3.44, 95% CI 1.08-11.10, p=0.035; higher ALT: 48.8% vs. 19.7%, OR 4.23, 95% CI 1.34-14.52, p=0.013, respectively). Conclusion The measurement of AST and ALT levels at baseline may help predict oxygen requirement in hemodialysis patients with COVID-19.

Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging

BACKGROUND

To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model.

RESULTS

The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively.

CONCLUSION

The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.

Metformin beyond an anti-diabetic agent: A comprehensive and mechanistic review on its effects against natural and chemical toxins

In addition to the anti-diabetic effect of metformin, a growing number of studies have shown that metformin has some exciting properties, such as anti-oxidative capabilities, anticancer, genomic stability, anti-inflammation, and anti-fibrosis, which have potent, that can treat other disorders other than diabetes mellitus. We aimed to describe and review the protective and antidotal efficacy of metformin against biologicals, chemicals, natural, medications, pesticides, and radiation-induced toxicities. A comprehensive search has been performed from Scopus, Web of Science, PubMed, and Google Scholar databases from inception to March 8, 2023. All in vitro, in vivo, and clinical studies were considered. Many studies suggest that metformin affects diseases other than diabetes. It is a radioprotective and chemoprotective drug that also affects viral and bacterial diseases. It can be used against inflammation-related and apoptosis-related abnormalities and against toxins to lower their effects. Besides lowering blood sugar, metformin can attenuate the effects of toxins on body weight, inflammation, apoptosis, necrosis, caspase-3 activation, cell viability and survival rate, reactive oxygen species (ROS), NF-κB, TNF-α, many interleukins, lipid profile, and many enzymes activity such as catalase and superoxide dismutase. It also can reduce the histopathological damages induced by many toxins on the kidneys, liver, and colon. However, clinical trials and human studies are needed before using metformin as a therapeutic agent against other diseases.

A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model

BACKGROUND

In this study, we aimed to identify predictive factors for coronavirus disease 2019 (COVID-19) patients with complicated pneumonia and determine which COVID-19 patients should undergo computed tomography (CT) using classification and regression tree (CART) analysis.

METHODS

This retrospective cross-sectional survey was conducted at a university hospital. We recruited patients diagnosed with COVID-19 between January 1 and December 31, 2020. We extracted clinical information (e.g., vital signs, symptoms, laboratory results, and CT findings) from patient records. Factors potentially predicting COVID-19 pneumonia were analyzed using Student's t-test, the chi-square test, and a CART analysis model.

RESULTS

Among 221 patients (119 men (53.8%); mean age, 54.59±18.61 years), 160 (72.4%) had pneumonia. The CART analysis revealed that patients were at high risk of pneumonia if they had C-reactive protein (CRP) levels of >1.60 mg/dL (incidence of pneumonia: 95.7%); CRP levels of ≤1.60 mg/dL + age >35.5 years + lactate dehydrogenase (LDH)>225.5 IU/L (incidence of pneumonia: 95.5%); and CRP levels of ≤1.60 mg/dL + age >35.5 years + LDH≤225.5 IU/L + hemoglobin ≤14.65 g/dL (incidence of pneumonia: 69.6%). The area of the curve of the receiver operating characteristic of the model was 0.860 (95% CI: 0.804-0.915), indicating sufficient explanatory power.

CONCLUSIONS

The present results are useful for deciding whether to perform CT in COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.

Mining the potential therapeutic targets for COVID-19 infection in patients with severe burn injuries via bioinformatics analysis

The Coronavirus Disease-19 (COVID-19) pandemic is posing a serious challenge to human health. Burn victims are susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leading to delayed recovery and even profound debilitation. Nevertheless, the molecular mechanisms underlying COVID-19 and severe burn are yet to be elucidated. In our work, the differentially expressed genes (DEGs) were identified from GSE157852 and GSE19743, and the common DEGs between COVID-19 and severe burn were extracted. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interactions (PPI), gene coexpression network, and multifactor regulatory network analysis of hub genes were carried out. A total of 44 common DEGs were found between COVID-19 and severe burn. Functional analyses indicated that the pathways of immune regulation and cytokine response participated collectively in the development of severe burn and progression of COVID-19. Ten significant hub genes were identified, including MERTK, SIRPA, TLR3, ITGB1, DPP4, PTPRC, LY75, IFIT1, IL4R, and CD2. In addition, the gene coexpression network and regulatory network were constructed containing 42 microRNAs (miRNAs) and 2 transcription factors (TFs). Our study showed the shared pathogenic link between COVID-19 and severe burn. The identified common genes and pivotal pathways pave a new road for future mechanistic researches in severe burn injuries complicated with COVID-19.

Diagnostic Efficacy of Chest Computed Tomography with a Dual-Reviewer Approach in Patients Diagnosed with Pneumonia Secondary to Severe Acute Respiratory Syndrome Coronavirus 2

To compare the diagnostic effectiveness of chest computed tomography (CT) utilizing a single- versus a dual-reviewer approach in patients with pneumonia secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we conducted a retrospective observational study of data from a cross-section of 4809 patients with probable SARS-CoV-2 from March to November 2020. All patients had a CT radiological report and reverse-transcription polymerase chain reaction (PCR) results. A dual-reviewer approach was applied to two groups while conducting a comparative examination of the data. Reviewer 1 reported 108 patients negative and 374 patients positive for coronavirus disease 2019 (COVID-19) in group A, and 266 negative and 142 positive in group B. Reviewer 2 reported 150 patients negative and 332 patients positive for COVID-19 in group A, and 277 negative and 131 positive in group B. The consensus result reported 87 patients negative and 395 positive for COVID-19 in group A and 274 negative and 134 positive in group B. These findings suggest that a dual-reviewer approach improves chest CT diagnosis compared to a conventional single-reviewer approach.

Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias

The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia.