Evaluation of the Expression of Infection-Related Long Noncoding RNAs among COVID-19 Patients: A Case-Control Study.

Background and Aims: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a worldwide pandemic, activates signaling cascades and leads to innate immune responses and secretion of multiple chemokines and cytokines. Long noncoding RNAs (lncRNAs) have a crucial role in inflammatory pathways. Through our search on the PubMed database, we discovered that existing research has primarily focused on examining the regulatory impacts of five lncRNAs in the context of viral infections. However, their role in regulating other conditions, including SARS-CoV-2, has not been explored. Therefore, this study aimed to investigate the expression pattern of lncRNAs in the peripheral blood mononuclear cells (PBMC) and their potential roles in SARS-CoV-2 infection. Potentially significant competing endogenous RNA (ceRNA) networks of these five lncRNAs were found using online in-silico techniques. Methods: Ethylenediaminetetraacetic acid (EDTA) blood samples of the control group consisted of 45 healthy people, and a total of 53 COVID-19-infected patients in case group, with a written informed consent, was collected. PBMCs were extracted, and then, the RNA extraction and complementary DNA (cDNA) synthesis was performed. The expression of five lncRNAs (lnc ISR, lnc ATV, lnc PAAN, lnc SG20, and lnc HEAL) was assessed by real-time PCR. In order to evaluate the biomarker roles of genes, receiver operating characteristic (ROC) curve was drawn. Results: Twenty-four (53.3%) and 29 (54.7%) of healthy and COVID-19-infected participants were male, respectively. The most prevalent symptoms were as follows: cough, general weakness, contusion, headache, and sore throat. The results showed that three lncRNAs, including lnc ISR, lnc ATV, and lnc HEAL, were expressed dramatically higher in the case group compared to healthy controls. According to ROC curve analysis, lnc ATV has a higher AUC and is a better biomarker to differentiate COVID-19 patients from the healthy controls. Then, using bioinformatics methods, the ceRNA network of these lncRNAs enabled the identification of mRNAs and miRNAs with crucial functions in COVID-19. Conclusion: The considerable higher expression of ISR, ATV, and HEAL lncRNAs and the significant area under curve (AUC) in ROC curve demonstrate that these RNAs probably have a potential role in controlling the host innate immune responses and regulate the viral replication of SARS-CoV-2. However, these assumptions need further in vitro and in vivo investigations to be confirmed.

Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset.

BACKGROUND: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS: The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION: The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.

Exploring senior managers' experiences preparedness to respond to COVID-19: a qualitative study in Iran.

BACKGROUND: Epidemics caused by emerging respiratory viruses are challenging for the health system of most societies, and preparedness of the health system in responding to such epidemics is important. Therefore, the aim of this study was identifying different fields and key issues of the senior managers' experiences preparedness to respond to the COVID-19 epidemic from the Iranian senior managers' point of view. METHODS: This is a qualitative descriptive study. Eighteen in-depth and semi-structured individual interviews were conducted for data collection. For this purpose, 18 senior managers with work experience in managing the COVID-19 crisis were enrolled in the study using purposive sampling. The collected data were analyzed according to Graneheim and Lundman's approach. RESULTS: Analysis of the data resulted in the emergence of five themes and twelve sub-themes. The main themes and sub-themes included: (1) capacity improvement consisting of performance improvement and logistic improvement; (2) resource and infrastructure management including supply and support of human resources, infrastructure improvement, and supply of equipment; (3) an increase in epidemiology capacity including epidemiology improvement and emerging disease surveillance; (4) application of the principles of disaster and emergency management including intra- and extra-organizational interaction management, disaster risk management, and data management; and (5) society resilience increase including improving adaptation skill and maintaining health and social participation. CONCLUSION: The results of this study present the key issues for the management of future emergency situations. Health system managers and policymakers in Iran and other countries should be aware of these key issues and apply them in practice to prepare the health systems to respond to next outbreaks. Indeed, the study results can help policymakers and health system managers to plan to achieve acceptable preparedness for the management of such outbreaks.

An Improved Optimization Model to Predict the MOR of Glulam Prepared by UF-Oxidized Starch Adhesive: A Hybrid Artificial Neural Network-Modified Genetic Algorithm Optimization Approach.

The purpose of the present article is to study the bending strength of glulam prepared by plane tree (Platanus Orientalis-L) wood layers adhered by UF resin with different formaldehyde to urea molar ratios containing the modified starch adhesive with different NaOCl concentrations. Artificial neural network (ANN) as a modern tool was used to predict this response, too. The multilayer perceptron (MLP) models were used to predict the modulus of rapture (MOR) and the statistics, including the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to validate the prediction. Combining the ANN and the genetic algorithm by using the multiple objective and nonlinear constraint functions, the optimum point was determined based on the experimental and estimated data, respectively. The characterization analysis, performed by FTIR and XRD, was used to describe the effect of the inputs on the output. The results indicated that the statistics obtained show excellent MOR predictions by the feed-forward neural network using Levenberg-Marquardt algorithms. The comparison of the optimal output of the actual values obtained by the genetic algorithm resulting from the multi-objective function and the optimal output of the values estimated by the nonlinear constraint function indicates a minimum difference between both functions.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.

BACKGROUND: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

Impact of miRNA-binding site polymorphisms in STAT3 gene on occurrence and clinical characteristics of systemic lupus erythematosus.

BACKGROUND: Signal transducer and activator of transcription 3 (STAT3) is a major regulator of immune response and chronic inflammatory conditions acting through regulation of B cells, T-helper 17 (Th17) cells, and IL-17 production. Previous studies have demonstrated that dysregulation of STAT3 is crucial for SLE pathogenesis and disease severity. It is believed that single nucleotide polymorphisms (SNPs) located at the 3'-UTR sequence of the target genes could dysregulate their expression by disrupting the binding site of miRNAs. In the present study, we assessed the possible association between rs1053005 and rs1053023 SNPs at miRNA binding sites in the STAT3 gene and the risk of SLE in the Iranian population for the first time. METHODS: 112 SLE cases and 120 healthy controls were genotyped for rs1053005 (A>G) and rs1053023 (A>G) polymorphisms in STAT3 using real-time PCR high resolution melting method (HRM). RESULTS: Our results revealed substantial associations between GG genotype and G allele of rs1053023 with enhanced risk of SLE (OR for GG genotype= 3.13; 95%CI [1.61-6.1], OR for G allele = 2.22; 95%CI [1.51-3.25]). However, no important correlations have been found between rs1053005 polymorphism and SLE susceptibility in this population (p>0.05). Moreover, stratification analysis showed that these polymorphisms are correlated with parameters indicating disease activity and more severe course of the disease. These factors include some laboratory test results and clinical manifestations such as renal involvements. CONCLUSION: The current study suggests a significant association between STAT3 polymorphisms and augmented risk of SLE, clinical symptoms, disease activity, and severity.

Food insecurity arises the likelihood of hospitalization in patients with COVID-19.

The World Health Organization (WHO) has declared the Corona pandemic as a public health emergency. This pandemic affects the main pillars of food security. This study aimed to investigate the relationship between food insecurity and the probability of hospitalization and the length of the recovery period after getting COVID-19. The cross-sectional study was performed through the census on COVID-19 patients diagnosed in Fasa, Iran. Informed consent, demographic, and food security questionnaire were completed over the phone. Then, all patients were followed up until recovery. Data were analyzed using SPSS26 and Chi-square test, t-test, and logistic regression (P < 0.05). In this study, 219 COVID-19 patients [100 (54.7%) male and 119 (54.3%) female] with a mean age of 40.05 ± 15.54 years old were examined. Possibility of hospitalization and the length of the recovery period of more than one month was significantly longer in the food-insecure group (P = 0.001) and (P = 0.37), respectively, but the mean length of hospital stay in the two groups was not significantly different (P = 0.76). After adjusting for all confounding variables, people with food insecurity were 3.9 times more likely to be hospitalized than those with food security. Overall, we observed that food-insecure people were significantly more likely to be hospitalized than the secure group.

Single-pill sofosbuvir and daclatasvir for treating hepatis C in patients co-infected with human immunodeficiency virus.

BACKGROUND: The current recommendation for treating hepatitis C virus (HCV) in HIV patients includes the combination of sofosbuvir (SOF) and daclatasvir (DCV). DCV should be used at different doses to compensate for interactions with antiretroviral therapy (ART). Up to three pills a day might be required which will significantly add to the pill burden of these patients. In this study, we have used a single-tablet approach to treating HCV-HIV coinfection. METHODS: Patients coinfected with HIV and HCV were prospectively enrolled from 10 centers throughout the country. Patients received a single once-daily fixed dose combination (FDC) pill containing 400 mg SOF and 30, 60 or 90 mg DCV depending on the type of ART they were receiving for 12 or 24 weeks. (ClinicalTrials.gov ID: NCT03369327). RESULTS: Two hundred thirty-three patients were enrolled from 10 centers. Twenty-three patients were lost to follow-up and two patients died from causes unrelated to treatment. Two hundred eight patients completed the treatment course of which 201 achieved SVR (96.6%). CONCLUSION: Single-tablet combination of DCV and SOF is an effective and safe treatment for patients coinfected with HIV and HCV. The combination works well in patients on ART in which dose adjustment is required. Patients with cirrhosis, previous treatment failure and various genotypes respond identically. The expenses of genotyping can be saved.

Sensitivity and specificity of chest computed tomography scan based on RT-PCR in COVID-19 diagnosis.

PURPOSE: COVID-19 is a novel, severely contagious and progressive infection occurring worldwide. The diagnosis of the disease is based on real-time polymerase chain reaction (RT-PCR) and computed tomography (CT) scan, even though they are still controversial methods. MATERIAL AND METHODS: We studied 54 patients with suspected COVID-19 and the two mentioned methods were compared with each other. RESULTS: Sensitivity and specificity of the abnormal chest CT scan, ground-glass opacity (GGO), consolidation opacity, and both of GGO and consolidation were also surveyed based on RT-PCR. The results showed that RT-PCR assay was negative in 23 (42.6%) patients and positive in 31 (57.4%) cases. Also, the patients with an abnormal chest CT scan comprised 37 (68.5%). The sensitivity and specificity of abnormal CT scan were 78.6% and 42.3%, respectively, based on the RT-PCR method. CONCLUSIONS: Other techniques alongside CT scan and RT-PCR are advocated for accuracy of the COVID-19 diagnosis.

A Forensic Epidemiological Study of Homicide in Isfahan Province and Review of the Literature.

The epidemic of deaths by homicide affects every society around the world and represents a major public health crisis. Detailed information on the characteristics of homicides victims from Islamic countries is limited. This article presents forensic epidemiological data on homicides in Isfahan, Iran, during 2013-2015. Isfahan has a population of more than 5 million and 69,387 deaths with 246 homicides between 2013 and 2015. Most victims were male (73%), between the ages of 15 to 29 years (39.5%), married (54%), and employed (54%). The relationship between the actor and the victims showed that 12% were married, 15% friends, 25% strangers, and 47% others or unknown. The most frequent method of homicides was by stabbing (45%), followed by firearms (23%), strangulation (14%), and blunt force trauma (7%). The top 3 methods among males were stabbing, firearms, and strangulation, whereas among females, it was stabbing, strangulation, and by other methods. There was no significant effect on homicide rates by month, weekday, or temperature. Investigators examining deaths in Muslin countries must understand and adjust for the culture, norms, and religious ideology.

Unnatural Deaths Among Children and Adolescents in Isfahan Province, Iran: A Forensic Epidemiology Study of Postmortem Data.

INTRODUCTION: The issue of child and adolescent injury and violence is often absent from discussions and is largely invisible in public health policies. The purpose of this study was to describe the frequency and pattern of unnatural deaths during childhood and adolescence in Isfahan province in Iran. MATERIALS AND METHODS: This retrospective, descriptive study involved unnatural deaths among individuals under the age of 20 years who died from unnatural causes as determined by a forensic autopsy at the Legal Medicine Center of Isfahan. During the study period, 8,010 unnatural deaths occurred, 1,222 of which were individuals under 20 years old. RESULTS: All 1,222 of these unnatural deaths were identified through autopsy. Among the 1,222 cases, 895 (73.2%) were male, and 327 were female (26.8%). Accidental deaths were found to be the most frequent manner of death comprising 1,029 (83.96%) cases, followed by suicide (120, 9.82%), undetermined cause of death (39, 3.19%), and homicide (9, 2.86%) cases. Road traffic accidents were the number 1 cause of death (597, 49%), followed by burns (122, 10%) and hanging (90, 7.4%). DISCUSSION: Injuries and violence that occur during childhood and adolescence represent a global public health problem, especially in low- and middle-income regions, and require urgent action.