Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.

BACKGROUND: Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data. METHODS: We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models. RESULTS: Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6). CONCLUSIONS: We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes. SIGNIFICANCE: Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

Incidence trends of gastric cancer in the United States over 2000-2020: A population-based analysis.

BACKGROUND: Gastric cancer ranks among the top cancers in terms of both occurrence and death rates in the United States (US). Our objective was to provide the incidence trends of gastric cancer in the US from 2000 to 2020 by age, sex, histology, and race/ethnicity, and to evaluate the effects of the COVID-19 pandemic. METHODS: We obtained data from the Surveillance, Epidemiology, and End Results 22 program. The morphologies of gastric cancer were classified as adenocarcinoma, gastrointestinal stromal tumor, signet ring cell carcinoma, and carcinoid tumor. We used average annual percent change (AAPC) and compared pairs using parallelism and coincidence. The numbers were displayed as both counts and age-standardized incidence rates (ASIRs) per 100000 individuals, along with their corresponding 95% confidence intervals (CIs). RESULTS: Over 2000-2019, most gastric cancers were among those aged ≥55 years (81.82%), men (60.37%), and Non-Hispanic Whites (62.60%). By histology, adenocarcinoma had the highest incident cases. During the COVID-19 pandemic, there was a remarkable decline in ASIRs of gastric cancer in both sexes and all races (AAPC: -8.92; 95% CI: -11.18 to -6.67). The overall incidence trends of gastric cancer were not parallel, nor identical. CONCLUSIONS: The incidence of gastric cancer shows notable variations by age, race, and sex, with a rising trend across ethnicities. While the overall incidence has declined, a noteworthy increase has been observed among younger adults, particularly young Hispanic women; however, rates decreased significantly in 2020.

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the showcases XAI's effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.

Prediction of acute methanol poisoning prognosis using machine learning techniques.

Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and prognosis assessment. The study, conducted at Loghman Hakim Hospital in Tehran, Iran. The data pertaining to individuals afflicted with methanol poisoning was retrieved retrospectively and divided into training and test groups at a ratio of 70:30. The selected features were then inputted into various machine learning methods. The models were implemented using the Scikit-learn library in the Python programming language. Ultimately, the efficacy of the developed models was assessed through ten-fold cross-validation techniques and specific evaluation criteria, with a confidence level of 95%. A total number of 897 patients were included and divided in three groups including without sequel (n = 573), with sequel (n = 234), and patients who died (n = 90). The two-step feature selection was yielded 43 features in first step and 23 features in second step. In best model (Gradient Boosting Classifier) test dataset metric by 32 features younger age, higher methanol ingestion, respiratory symptoms, lower GCS scores, type of visual symptom, duration of therapeutic intervention, ICU admission, and elevated CPK levels were among the most important features predicting the prognosis of methanol poisoning. The Gradient Boosting Classifier demonstrated the highest predictive capability, achieving AUC values of 0.947 and 0.943 in the test dataset with 43 and 23 features, respectively. This research introduces a machine learning-driven prognostic model for methanol poisoning, demonstrating superior predictive capabilities compared to traditional statistical methods. The identified features provide valuable insights for early intervention and personalized treatment strategies.

Relapse of nephrotic syndrome with unusual thromboembolic event: A case report.

Key Clinical Message: Most children with nephrotic syndrome heal without any sequelae. However, rare life-threatening complications such as thromboembolism may occur in pediatric nephrotic syndrome and should be considered in those with a new-onset neurologic deficit. Abstract: The thromboembolism (TE) as a complication of nephrotic syndrome (NS) is rare and serious, and may involve renal, cerebral, pulmonary, or peripheral venous and/or arterial thrombosis. Here, we describe a 4.5-year-old male with a history of nephrotic syndrome, who developed hemorrhagic stroke in the territory of middle cerebral artery (MCA).

Co-occurrence of celiac disease and glycogen storage disease in a five-year-old patient with diabetes mellitus; a case report.

A patient presented with edema, ascites and jaundice. Histologic report was consistent with Celiac Disease. Liver biopsy commensurate with Glycogen storage disease III, which was confirmed by genetic testing. A gluten-free diet was initiated. After 2 months, ascites was relieved, hepatic function was improved, and hepatic size reduced.

Role of anti-tumor necrosis factor-alpha agents in treatment of sarcoidosis: A meta-analysis.

IMPORTANCE: Anti-tumor necrosis factor-alpha agent (anti-TNF-α) is considered an effective third-line therapy for refractory sarcoidosis,studies observing the efficacy of anti-TNF-α agents show conflicting results. OBJECTIVE: We performed an up-to-date systemic meta-analysis to determine effectiveness and further elucidate the role of anti-TNF-α in the treatment of sarcoidosis. DATA SOURCES: A systematic search was carried out in PubMed/Medline, EMBASE, and Cochrane Library for studies reporting the therapeutic effects of anti-TNF drugs on patients with pulmonary and extra-pulmonary sarcoidosis, published up to April 10, 2022. The study was registered in the international prospective register of systematic reviews (PROSPERO) under ID: CRD42022364614. STUDY SELECTION: Clinical trials written reporting the therapeutic effects of anti-TNF drugs on patients with pulmonary and extra-pulmonary sarcoidosis were included. DATA EXTRACTION AND SYNTHESIS: Statistical analyses were performed with Comprehensive Meta-Analysis software, and the random-effects model was used. The combined overall treatment success was determined for patients with pulmonary and extrapulmonary sarcoidosis. MAIN OUTCOMES AND MEASURES: Overall treatment success rate wasdefined as no disease progression or improvement in symptoms. RESULTS: Eight clinical trial articles were included in the meta-analysis; four used Infliximab, two Etanercept, one Adalimumab, and one Ustekinumab and Golimumab. The mean age of participants was 48.5 years. The duration of drug therapy ranged from 14 to 45 weeks. We found a combined overall treatment success rate, defined as no disease progression or improvement in symptoms, of 69.9% (95% CI 35.0-90.9, I2: 70%) in the pulmonary sarcoidosis group and 74.5% (95% CI 36.3-93.7, I2: 90%) in the extrapulmonary sarcoidosis group. There was no evidence of publication bias in either group. CONCLUSION AND RELEVANCE: Treatment of refractory sarcoidosis with anti-TNF-α agents was effective in both pulmonary and extrapulmonary sarcoidosis, with a slightly higher efficacy seen in extrapulmonary sarcoidosis. Further randomized controlled trials should be conducted to determine the effects of anti-TNF-α agents as a part of the management strategy of sarcoidosis. Patients with pulmonary sarcoidosis should be studied separately from patients with extrapulmonary sarcoidosis to adjust for confounding results.

Transmission electron microscopy study of suspected primary ciliary dyskinesia patients.

Primary ciliary dyskinesia (PCD) is a rare autosomal recessive condition often presenting with chronic respiratory infections in early life. Transmission electron microscopy (TEM) is used to detect ciliary ultrastructural defects. In this study, we aimed to assess ciliary ultrastructural defects using quantitative methods on TEM to identify its diagnostic role in confirming PCD. Nasal samples of 67 patients, including 37 females and 30 males (20.3 ± 10.7 years old), with suspected PCD symptoms were examined by TEM. The most common presentations were bronchiectasis: 26 (38.8%), chronic sinusitis: 23 (34.3%), and recurrent lower respiratory infections: 21 (31.3%). Secondary ciliary dyskinesia, including compound cilia (41.4%) and extra-tubules (44.3%), were the most prevalent TEM finding. Twelve patients (17.9%) had hallmark diagnostic criteria for PCD (class 1) consisting of 11 (16.4%) outer and inner dynein arm (ODA and IDA) defects and only one concurrent IDA defect and microtubular disorganization. Also, 11 patients (16.4%) had probable criteria for PCD (class 2), 26 (38.8%) had other defects, and 18 (26.9%) had normal ciliary ultrastructure. Among our suspected PCD patients, the most common ultrastructural ciliary defects were extra-tubules and compound cilia. However, the most prevalent hallmark diagnostic defect confirming PCD was simultaneous defects of IDA and ODA.

Epidemiology of COVID-19 in Tehran, Iran: A Cohort Study of Clinical Profile, Risk Factors, and Outcomes.

Background: The outbreak of coronavirus disease 2019 (COVID-19) dates back to December 2019 in China. Iran has been among the most prone countries to the virus. The aim of this study was to report demographics, clinical data, and their association with death and CFR. Methods: This observational cohort study was performed from 20th March 2020 to 18th March 2021 in three tertiary educational hospitals in Tehran, Iran. All patients were admitted based on the WHO, CDC, and Iran's National Guidelines. Their information was recorded in their medical files. Multivariable analysis was performed to assess demographics, clinical profile, outcomes of disease, and finding the predictors of death due to COVID-19. Results: Of all 5318 participants, the median age was 60.0 years, and 57.2% of patients were male. The most significant comorbidities were hypertension and diabetes mellitus. Cough, dyspnea, and fever were the most dominant symptoms. Results showed that ICU admission, elderly age, decreased consciousness, low BMI, HTN, IHD, CVA, dialysis, intubation, Alzheimer disease, blood injection, injection of platelets or FFP, and high number of comorbidities were associated with a higher risk of death related to COVID-19. The trend of CFR was increasing (WPC: 1.86) during weeks 25 to 51. Conclusions: Accurate detection of predictors of poor outcomes helps healthcare providers in stratifying patients, based on their risk factors and healthcare requirements to improve their survival chance.

Comparison of Antifungal Properties of Gold, Silver, and Selenium Nanoparticles Against Amphotericin B-Resistant Candida glabrata Clinical Isolates.

BACKGROUND: The present study aimed to investigate the antifungal activity of Nanoparticles (NPs) against amphotericin B-resistant Candida glabrata (C. glabrata) strains. METHODS: Twelve resistant (C. glabrata) strains were isolated from archived clinical isolates. Antifungal activity was conducted according to Clinical and Laboratory Standards Institute's (CLSI) guidelines, document M27-A3/S4. The Scanning Electron Microscope (SEM) was used to observe the morphological changes of strains exposed to each nanoparticle. RESULTS: Minimum Inhibitory Concentration (MIC) of nanoparticles of all strains was in the concentration range of 0.125 to 0.5 µg/Ml. The synthesized Ag-NPs showed superior antifungal activity against (C. glabrata) compared to Se-NPs and Au-NPs. The scanning electron microscope images revealed the difference in the fungal morphology between the untreated and treated fungi with nanoparticles. CONCLUSION: The Ag-NPs, followed by Se-NPs synthesized, revealed significant anti-fungal activity against resistance regardless of their antifungal-resistant mechanisms.

Descriptive Analysis of COVID-19 among Health Care Workers in a Tertiary Center in Iran.

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spread widely all around the world and has infected too many healthcare workers (HCWs) as the pioneers combating coronavirus disease 2019 (COVID-19). This study aims to evaluate the symptoms and outcome of medical staff from a tertiary hospital in Tehran, Iran. Materials and Methods: The diagnoses of 29 HCWs presenting COVID-19 symptoms were confirmed by molecular and imaging studies. Epidemiologic and disease-related data were collected via phone calls and filling a questionnaire and then analyzed descriptively. Results: Eighteen (62.1%) of the affected HCWs were males. The mean age of them was 41.86 years with a lower average (38.27) for females than males. Nurses comprised 41.4% of our population. Only 2 (6.9%) patients were admitted to the respiratory care unit (RCU) (), marked as critical patients. The most presented symptoms were fever (79.3%) and dyspnea (79.3%). Overall, 55.2% of them had a longer exposure time (more than a week), which was more frequent in men than women. Conclusion: Fever was the most prevalent symptom among the study group. Even though the clinical features of COVID-19 among HCWs cannot be copiously determined by this study, it highlights the requirement for comparative studies to illustrate differences among HCWs and the general population. There might be an association between the duration of the exposure and the risk of the infection in men.

A Review on Molecular Mechanisms of Antifungal Resistance in Candida glabrata: Update and Recent Advances.

Candida glabrata is the second frequent etiologic agent of mucosal and invasive candidiasis. Based on the recent developments in molecular methods, C. glabrata has been introduced as a complex composed of C. glabrata, Candida nivariensis, and Candida bracarensis. The four main classes of antifungal drugs effective against C. glabrata are pyrimidine analogs (flucytosine), azoles, echinocandins, and polyenes. Although the use of antifungal drugs is related to the predictable development of drug resistance, it is not clear why C. glabrata is able to rapidly resist against multiple antifungals in clinics. The enhanced incidence and antifungal resistance of C. glabrata and the high mortality and morbidity need more investigation regarding the resistance mechanisms and virulence associated with C. glabrata; additional progress concerning the drug resistance of C. glabrata has to be further prevented. The present review highlights the mechanism of resistance to antifungal drugs in C. glabrata.

Neurological Manifestations and their Correlated Factors in COVID-19 Patients; a Cross-Sectional Study.

INTRODUCTION: COVID-19 might present with other seemingly unrelated manifestations; for instance, neurological symptoms. This study aimed to evaluate the neurologic manifestations and their correlated factors in COVID-19 patients. METHODS: This retrospective observational study was conducted from March 17, 2020 to June 20, 2020 in a tertiary hospital in Iran. The study population consisted of adult patients with a positive result for COVID-19 real-time reverse transcriptase polymerase chain reaction (RT-PCR) using nasopharyngeal swabs. Both written and electronic data regarding baseline characteristic, laboratory findings, and neurological manifestations were evaluated and reported. RESULTS: 727 COVID-19 patients with the mean age of 49.94 ± 17.49 years were studied (56.9% male). At least one neurological symptom was observed in 403 (55.4%) cases. Headache (29.0%), and smell (22.3%) and taste (22.0%) impairment were the most prevalent neurological symptoms, while seizure (1.1%) and stroke (2.3%) were the least common ones. Patients with neurological manifestations were significantly older (p = 0.04), had greater body mass index (BMI) (p = 0.02), longer first symptom to admission duration (p < 0.001) and were more frequently opium users (p = 0.03) compared to COVID-19 patients without neurological symptoms. O2 saturation was significantly lower in patients with neurological manifestations (p = 0.04). In addition, medians of neutrophil count (p = 0.006), neutrophil-lymphocyte ratio (NLR) (p = 0.02) and c-reactive protein (CRP) (p = 0.001) were significantly higher and the median of lymphocyte count (p = 0.03) was significantly lower in patients with neurological manifestations. CONCLUSION: The prevalence of neurological manifestations in the studied cases was high (55.4%). This prevalence was significantly higher in older age, grated BMI, longer lasting disease, and opium usage.

COVID-19 and ICU admission associated predictive factors in Iranian patients.

BACKGROUND: The pandemic situation created an overwhelmed needs for ICU facilities, according to this problem, the need of accurate management of facilities represents boldness. In this study, prognostic risk factors for ICU admission among COVID-19 hospitalized patients were evaluated. METHODS: From 22 February to April 20, 2020. A total of 214 COVID-19 patients participated in this study. The included patients were between 18- 80 years old, and the patients who previously admitted for COVID-19 were excluded. The comorbid medical conditions, admission laboratory, demographic data, and first manifestations were analyzed between two groups, including ICU and non-ICU admitted patients. The statistical analysis, univariate and multivariate analysis were afforded. The value of the predictors in the risk assessment of ICU admission was estimated. RESULTS: 55(25.7%) patients were admitted in ICU. The ICU admitted patient's mortality rate was about 68%. The age was significantly higher among ICU admission group (P=0.03). Admission O2 saturation was significantly lower among ICU admitted patients (P=0.00). The kidney disease and malignancy history were more frequent in ICU-admitted patients (P=0.04, P=0.00). Myalgia was the clinical manifestation that significantly presented more frequent in ICU-admitted patients. INR, CRP, ESR, HB, and lymphocyte were significantly different between two groups. After multivariable analysis, admission O2 saturation, hematocrit, CRP and myalgia could significantly predict the risk of ICU admission. Furthermore, the value of predictors was estimated in our study. CONCLUSION: Based on our results, the admission O2 saturation, HCT, CRP levels at first admission and myalgia presentation could be considered as the valuable predictors of ICU admission.