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Objective:To explore the differences in laboratory indicators test results of coronavirus disease 2019 (COVID-19) and influenza A and to establish a differential diagnosis model for the two diseases, and to clarify the clinical significance of the model for distinguishing the two diseases. Methods :A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled , and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated,and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection,in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds,the elastic net models were fit for 200 times to select the high-frequency indexes ( frequency>90% ). A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses;a nomogram was used to represent the final model;an independent data were used as an external validation set,and the area under the curve (AUC) of the validation set were calculate to evaluate the predictive the performance of the model. Results:After the first round of screening, 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening,albumin/ globulin (A/G),total bilirubin (TBIL) and erythrocyte volume (HCT) were identified as the final indicators. The model had good predictive performance , and the AUC of the verification set was 0. 844 (95% CI:0. 747-0. 941). Conclusion:A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established,and it will help clinical and timely diagnosis of both diseases.Copyright © 2022 Jilin University Press. All rights reserved.
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BACKGROUND: Hepatopancreatobiliary (HPB) cancer incidence and mortality are increasing worldwide. An initial diagnostic predictor is needed for recommending further diagnostic modalities, referral, and curative or palliative decisions. There were no studies conducted in area with limited accessibility setting of the COVID-19 pandemic, coupled with limited human resources and facilities. AIM: We aimed to investigate the advantages of total bilirubin for predicting malignant obstructive jaundice, a combination of the pandemic era and limited resources settings. METHOD(S): Data from all cholestasis jaundice patients at M. Djamil Hospital in Pandemic COVID-19 period from July 2020 to May 2022 were retrospectively collected. The data included demographics, bilirubin fraction results, and final diagnosis. Bivariate analysis for obtain demographic risk factor, and Receiver Operating Characteristics (ROC) analysis for getting bilirubin value. RESULT(S): Of a total 132 patients included, 35.6% were malignant obstructive jaundice, and Pancreatic adeno ca was the most malignant etiology (34.4%). Bivariate analysis showed a significant correlation between age and malignant etiology (p = 0,024). Direct and total Bilirubin reach the same level of Area Under Curve (AUC). Total bilirubin at the cutoff point level of 10.7 mg/dl had the most optimal results on all elements of ROC output, AUC 0.88, sensitivity 76.6%, specificity 90.1%, +LR 8.14, and-LR 0.26. CONCLUSION(S): The bilirubin fraction is a good initial indicator for differentiating benign and malignant etiology (AUC 0.8-0.9) in pandemic era and resource-limited areas to improve diagnostic effectiveness and reduce referral duration.Copyright © 2023 Avit Suchitra, M. Iqbal Rivai, Juni Mitra, Irwan Abdul Rachman, Rini Suswita, Rizqy Tansa.
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The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Mortality attributable to COVID-19 remains considerably high, with case fatality rates as high as 8-11%. Early medical intervention in patients who are seriously and critically ill with COVID-19 reduces fatal outcomes. Thus, there is an urgent need to identify biomarkers that could help clinicians determine which patients with SARS-CoV-2 infection are at a higher risk of developing the most adverse outcomes, which include intensive care unit (ICU) admission, invasive ventilation, and death. In COVID-19 patients experiencing the most severe form of the disease, tests of liver function are frequently abnormal and liver enzymes are found to be elevated. For this reason, we examine the most promising liver biomarkers for COVID-19 prognosis in an effort to help clinicians predict the risk of ARDS, ICU admission, and death at hospital admission. In patients meeting hospitalization criteria for COVID-19, serum albumin < 36 g/L is an independent risk factor for ICU admission, with an AUC of 0.989, whereas lactate dehydrogenase (LDH) values > 365 U/L accurately predict death with an AUC of 0.943.The clinical scores COVID-GRAM and SOFA that include measures of liver function such as albumin, LDH, and total bilirubin are also good predictors of pneumonia development, ICU admission, and death, with AUC values ranging from 0.88 to 0.978.Thus, serum albumin and LDH, together with clinical risk scores such as COVID-GRAM and SOFA, are the most accurate biomarkers in the prognosis of COVID-19.Copyright © 2021 Sociedad Medica del Hospital General de Mexico. Published by Permanyer.
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Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective(s): This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases "promising" for the anti-SARS-CoV-2 target. Method(s): In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Result(s): Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only beta-sitosterol-3-O-beta-D-glucopyranoside with a lower docking score of -9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 A, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 A and 0.773 A, respectively. Conclusion(s): This result suggests that beta-sitosterol-3-O-beta-Dglucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.Copyright © 2023, Page Press Publications. All rights reserved.
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Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.
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Background: Recently, many biomarkers have been studied to determine severe cases of COVID-19. C-reactive protein (CRP) has shown high sensitivity in identifying patients with severe disease and utility comparable to computed tomography. Aim(s): To determine the usefulness of CRP to predict the severity of SARS-CoV-2 infection in patients hospitalized at the Naval Medical Center of Peru during the period January-September in the year 2021. Method(s): A quantita-tive, observational, analytical, retrospective, and diagnostic test type design was used. A sample size of 503 patients was calculated, which were divided into two groups according to their severity. Result(s): An optimal cut-off point of 10.92 mg/L for CRP levels was determined for the diagnosis of severe COVID-19. An area under the curve (AUC) of 0.762 was calculated and sensitivity, specificity, positive and negative predictive values and diagnostic accuracy values of 78.88%, 66.4%;41.42%;87.01%;and 67.27%;respectively. Fagan's normogram showed a post-test probability of 41%. In the adjusted model, CRP (aOR = 4.853;95% CI 2.987-7.886;p = 0.001), ferritin (aOR = 1.001;95% CI: 1.001-1.002;p = 0.001) and hypothyroidism (adjusted OR = 4899;95% CI: 1272-18872;p = 0.021) showed significance. Conclusion(s): The present study showed an association between CRP and the severity of SARS-CoV-2 infection in an adjusted model, showing its potential utility and contributing to determine the cut-off point of CRP in the Peruvian population and its international comparison.Copyright © 2023, Sociedad Chilena de Infectologia. All rights reserved.
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Objective To investigate the clinical significance of serum interleukin 6 (IL-6) in elderly patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant and its correlation with underlying diseases. Methods A total of 22 elderly patients (>80 years old) infected with omicron variant, who were admitted to Department of Infectious Diseases, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. to Jun. 2022 and tested positive for SARS-CoV-2 RNA, were included. The level of serum IL-6 was measured by flow cytometry, and the level of serum C reactive protein (CRP) was measured by immunonephelometry. Patients were divided into pneumonia group (16 cases) and non-pneumonia group (6 cases) according to the imaging examination results, and were divided into severe group (severe and critical type, 5 cases) and non-severe group (mild and normal type, 17 cases) according to the condition. Binary logistic regression model and receiver operating characteristic (ROC) curve were used to analyze the correlation between serum IL-6 and CRP levels and the severity of the disease and whether it would progress to pneumonia. Meanwhile, the relationships between underlying diseases and serum IL-6 level were explored. Results Among the 22 patients, 6 were mild, 11 were normal, 3 were severe, and 2 were critical. The baseline serum IL-6 level in the pneumonia group was significantly higher than that in the non-pneumonia group ([20.16+/-12.36]pg/mL vs [5.42+/-1.57] pg/mL, P=0.009), and there was no significant difference in baseline serum CRP level between the 2 groups (P>0.05). There were no significant differences in baseline serum IL-6 or CRP levels between the severe group and the non-severe group (both P>0.05). Logistic regression analysis showed that the baseline serum IL-6 and CRP might be related to pneumonia after infection with omicron variant (odds ratio [OR]=2.407, 95% confidence interval [CI]0.915-6.328;OR=1.030, 95% CI 0.952-1.114). ROC curve analysis showed that the area under curve values of serum IL-6 and CRP in predicting the progression to pneumonia were 0.969 (95% CI 0.900-1.000) and 0.656 (95% CI 0.380-0.932), respectively, with statistical significance (Z=2.154, P=0.030). There were no significant differences in the baseline serum IL-6 level or proportions of severe patients or pneumonia patients among patients with or without hypertension, diabetes mellitus, coronary heart disease, chronic kidney disease or chronic obstructive pulmonary disease (all P>0.05). The baseline serum IL-6 levels of the omicron variant infected elderly patients with 1, 2, and 3 or more underlying diseases were 12.50 (9.15, 21.75), 23.55 (9.63, 50.10), and 10.90 (5.20, 18.88) pg/mL, respectively, with no statistical significance (P>0.05). Conclusion For omicron variant infected patients, serum IL-6 level is significantly increased in patients with pneumonia manifestations and is correlated with disease progression. Serum IL-6 level is of great guiding significance to judge disease progression and evaluate efficacy and prognosis of elderly coronavirus disease 2019 patients.Copyright © 2022, Second Military Medical University Press. All rights reserved.
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Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.
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Background: The Brixia Chest X-ray (CXR) score, C-reactive protein (CRP), and the absolute neutrophil count (ANC) have been useful to predict outcomes in Coronavirus disease 2019 (COVID-19 patients). We studied the utility of the Brixia CXR score, CRP, and ANC in predicting the outcomes in terms of the need for invasive mechanical ventilation, length of stay, and mortality in moderate-severe COVID-19 patients. Material(s) and Method(s): This was a single-centre, retrospective, study on 122 COVID-19 patients. Brixia CXR score, CRP, and ANC on admission to the hospital and the fifth day of hospital stay were noted along with the need for invasive mechanical ventilation (IMV), prolonged length of stay (LOS) >= 14 days, and mortality. Result(s): 122 patients were included for analysis. The median and interquartile range (IQR) for baseline CRP was 81.50 (39-151) mg/L and 11.0 (4-30) mg/L (p < 0.001) on the fifth day. The median and IQR for baseline Brixia score was 10.0 (7-13), and on the fifth day was 7 (4-11) (p <0.001). The receiver operating characteristic curve (ROC) showed that the baseline CRP >= 52.5mg/L predicted both the need for IMV, with an area under the curve (AUC) of 0.628, and prolonged LOS with an AUC of 0.608. The ROC curve depicted that the baseline ANC >8500/muL predicted IMV requirement with an AUC of 0.657. The fifth day CRP >= 32 mg/L, ANC >= 11,000/ muL and Brixia CXR score >= 7 predicted a higher mortality in hospitalized patients. Conclusion(s): Baseline CRP (> 52.5mg/L) predicts the need for IMV and a prolonged LOS, but not mortality. Baseline ANC (> 8500/muL) predicted the need for IMV. CRP, Brixia CXR score, and ANC on the fifth day were not useful to predict LOS or mortality, though there was a significant reduction in CRP and Brixia CXR score on the fifth day compared to baseline after treatment. The fifth day CRP >= 32 mg/L, ANC >= 11,000/ muL and Brixia CXR score >= 7 predicted a higher mortality.Copyright © 2023, College of Anaesthesiologists of Sri Lanka. All rights reserved.
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Background: Implementation of vaccination programmes has had a transformational impact on control of the SARS-CoV-2 pandemic, but the need for effective antiviral drugs remains. Molnupiravir (MPV) targets viral RNA polymerase inhibiting replication via lethal mutagenesis and nirmatrelvir (NTV) is a protease inhibitor boosted with ritonavir when given clinically. This study aimed to assess the virological efficacy of NTV and MPV individually and in combination against the SARS-CoV-2 BA.1 Omicron variant in a K18-hACE2 mouse model. Method(s): K18-hACE2 mice were inoculated intranasally with 103 PFU of SARSCoV-2 BA.1 Omicron (B.1.1.529). After 24 hours, mice were orally dosed q12H, as outlined in Figure 1. At 2, 3, and 4-days post infection mice were sacrificed, and lung samples harvested. Animals were weighed and monitored daily throughout. Subsequently, viral replication in the lung was quantified using qRT-PCR to measure total (N-gene) and sub-genomic (E-gene) viral RNA. Data were normalized to 18S for quantitation. Viral exposures expressed as Areas Under viral load Curves (AUCs) were calculated by the trapezoidal method using mean values at each timepoint. Separate studies in Syrian golden hamsters using individual drugs were also conducted, and total serum IgG was measured by ELISA at 4-days post infection. Result(s): Mice gained weight in all groups post-treatment, with no significant difference between groups. A reduction in lung viral exposure was evident in all treatment groups compared to the vehicle control dosed mice (Figure 1). Coadministration of NTV with MPV displayed a trend towards lower lung viral exposure compared to the vehicle control with ~40-and ~45-fold reduction in AUC for N-and SgE-gene assays, respectively. Dosed individually, NTV and MPV reduced viral exposure 5.7-and 7.7-fold for the N-gene assay, respectively. Differences in total serum IgG concentrations were evident between vehicle and NTV-(34-fold reduction, P=0.018), and MPV-(4.2-fold reduction, P=0.053) treated hamsters. Conclusion(s): These data show virological efficacy of NTV and MPV against the SARS-CoV-2 BA.1 Omicron variant. The combination of NTV and MPV demonstrated a lower viral RNA exposure in the lung than either drug alone, albeit not statistically significant. Initial data indicate potential immune alterations in NTV and MPV dosed hamsters. Studies to clarify the utility of NTV/ MPV combinations and further characterize the impact of antiviral therapy on IgG are warranted.
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Background: ASC10 is an oral double prodrug of the active antiviral ribonucleoside analog, ASC10-A (also known as beta-d-N4-hydroxycytidine), which is a potent inhibitor of SARS-CoV-2. ASC10 is rapidly metabolized into ASC10-A in vivo after oral dosing. Here, we report the results of the first-in-human, phase 1 study to determine the safety, tolerability, and pharmacokinetics (PK) of ASC10 in healthy subjects, and to assess the food effect on the pharmacokinetics. Method(s): This study included 2 parts. Part 1 (multiple-ascending-dose) consisted of 6 cohorts (8 or 12 subjects per cohort). Eligible subjects were randomized in a 3:1 ratio to receive either twice-daily (BID) doses of 50 to 800 mg ASC10 or placebo for 5.5 days, and were then followed for 7 days for safety. In Part 2 (food effect), 12 subjects were randomized in a 1:1 ratio to either 800 mg ASC10 in the fed state followed by 800 mg in the fasted state, or vice versa, with a 7-day washout period between doses. PK blood samples were collected and measured for ASC10-A along with ASC10 and molnupiravir. Safety assessments included monitoring of adverse events (AEs), measurement of vital signs, clinical laboratory tests, and physical examinations. Result(s): ASC10-A was the major circulating metabolite ( >99.94%) in subjects after oral dosing of ASC10. ASC10-A appeared rapidly in plasma, with a median Tmax of 1.00 to 2.00 h, and declined with a geometric t1/2 of approximately 1.10 to 3.04 h. After multiple dosing for 5.5 days, both Cmax and AUC of ASC10-A increased in a dose-proportional manner from doses of 50 to 800 mg BID without accumulation. of ASC10-A in the fed state occurred slightly later, with a median of 3.99 h postdose versus 2.00 h (fasted state). However, Cmax and AUC were very similar or the same between fed and fasted states. Thus, administration of ASC10 with food is unlikely to have an effect on exposure. The incidence of AEs was similar between subjects receiving ASC10 or placebo (both 66.7%) and 95.0% of AEs were mild. There were no serious adverse events as well as no clinically significant findings in clinical laboratory, vital signs, or electrocardiography. Conclusion(s): Results of this study showed that ASC10 was well tolerated, and the increase in plasma exposure of ASC10-A was dose proportional across the range of doses tested with no accumulation and no food effect. 800 mg ASC10 BID is selected for further studies in patients infected with SARS-CoV-2.
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The pharmacokinetic (PK) drug-drug interactions (DDIs) of nelfinavir and cepharanthine combination is limited information in human. In addition, the dosage regimen of this combination is not available for COVID-19 treatment. The objective of this study was to perform in silico simulations using GastroPlusTM software to predict physicochemical properties, PK parameters using the physiologically based pharmacokinetic (PBPK) model of healthy adults in different dosage regimens. The DDIs analysis of nelfinavir and cepharanthine combination was carried out to optimize the dosage regimens as a potential against COVID-19. The Spatial Data File (SDF) format of nelfinavir and cepharanthine structures obtained from PubChem database were used to carry out in silico predictions for physicochemical properties and PK parameters using several aspects of modules such as ADMET Predictor, Metabolism and Transporter, PBPK model. Subsequently, all data were utilized in the DDIs simulations. The dynamic simulation feature was selected to calculate and investigate the Cmax, AUC0-120, AUC0-inf, Cmax ratio, AUC0-120 ratio, and AUC0-inf ratio. The victim or nelfinavir dosage regimens were used four oral administration regimens of 500 mg and 750 mg in every 8 and 12 hours for simulations. The perpetrator or cepharanthine oral dosage regimens were used in several regimens from 10 mg to 120 mg in every 8, 12, and 24 hours. From all predicted results, the dosage regimen as a potential combination against COVID-19 was nelfinavir 500 mg every 8 hours and cepharanthine 10 mg every 12 hours.Copyright © 2023 by Faculty of Pharmacy, Mahidol University, Thailand is licensed under CC BY-NC-ND 4.0. To view a copy of this license, visit https://www.creativecommons.org/licenses/by-nc-nd/4.0/.
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Introduction: Early prediction by the use of serum and urinary biomarkers for the detection of acute kidney injury (AKI) may be very valuable to optimize the management and helps in improving the outcomes. This study aims to investigate whether daily measurement of urinary and plasma renal biomarkers have a role in earlier predicting COVID-19 associated AKI. Method(s): The study was conducted as a single-center, prospective, observational cohort study between August 2020 and December 2020 in hospitalized COVID-19 patients. A total of 65 moderate and severe COVID-19 positive adult (>= 18 years) patients were enrolled for this study. We measured serum creatinine, cystatin C, NGAL, KIM-1, Urine-Klotho, TIMP-2, IL-6 level, and urinary microalbumin/urinary creatinine on various days. The receiver operating characteristic curve (ROC) analysis was used to find the sensitivity and specificity of various markers to predict the incidence of AKI. Result(s): A total of 24 moderate and 41 severe COVID-19 patients were included. Out of which 47 patients developed (72.3%) acute kidney injury (AKI) over the course of COVID-19. Among these subjects, 18/47 (38.2%) developed severe AKI (KDIGO 2 + 3), and 5/47 (10.6%) required RRT. NGAL was found to be the best marker to predict the probability of AKI (Area under curve AUC of 0.713-0.786) with a sensitivity of 76-90% and specificity of 56-79% on different days of assessment from Day 1 to Day 7. IL-6 had moderate accuracy of prediction and cystatin C, KIM-1, Urine-Klotho, TIMP-2, IL-6 had poor accuracy for predicting the incidence of AKI. Conclusion(s): Urinary biomarkers like NGAL have good predictability for AKI.
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Introduction: High-flow oxygen therapy (HFOT) is used to provide oxygenation and reduce the need for intubation in severe pneumonia cases caused by SARS-CoV-2 virus. In this study, causes of mortality during hospitalization in the intensive care unit (ICU) in patients receiving HFOT therapy were investigated. Method(s): The data of 215 adult patients, who were admitted to ICU of a university hospital between April 2020 and October 2021, with severe COVID pneumonia and received HFOT were enrolled retrospectively in our study. Result(s): Total mortality among patients was 158 (73.4%). The overall mean age was 72 years, 61 in the survivor group and 73 in the mortality group (p < 0.001). It was determined that mortality rates decreased as the length of stay (LoS) in ICU and HFOT duration of the patients increased (p = 0.008 and < 0.001, respectively). The increase in respiratory rate on arrival was found to be significantly associated with increased mortality. Although the goal of HFOT is to improve oxygenation, no significant mortality-related difference was found in terms of pO2, pCO2 and P/F values calculated at the time of admission. The ROC curve was applied to examine the differential effect of Apache-II, SOFA, ROX and Procalcitonin measurements according to the survival. The area under the curve (AUC) and cut-off values were calculated as follows: APACHE-II (63.9%, 5,) SOFA (62.8%, 2), ROX index (66.8%, 4.72), Procalcitonin (65.7%, 0.23) (Fig. 1). Conclusion(s): Unlike reports in literature on mortality in ICU, LoS was found to have a decreasing effect on mortality rate [1]. In addition to the well-known scoring systems such as APACHE-II and SOFA, the ROX index, which is used to predict the success of HFOT, has emerged as a predictive value for mortality at admission to the intensive care unit [2].
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Background: Allogeneic hematopoietic cell transplantation (HCT) with ex vivo T cell receptor (TCR) alphabeta+ T cell and CD19+ B cell depletion is an effective approach for children with primary immune deficiency disorders (PIDD) as it combines advantages of high CD34+ cell dose facilitating rapid engraftment with low risk of Graft Versus Host Disease (GVHD). The ideal pre-conditioning regimen that facilitates robust donor engraftment without increasing risk of transplant related mortality has not been well defined with this approach. Method(s): We report the outcomes of 4 pediatric subjects: Chronic Granulomatous Disease (CGD) (2), Wiskott Aldrich Syndrome (WAS) (1), and RAC2 deficient Severe Combined Immunodeficiency (1) who underwent haploidentical HCT with TCRalphabeta+ T cell/CD19+ depletion at Johns Hopkins All Children's Hospital/Moffitt Cancer Center from 2020-2022 (NCT04414046). Pre-conditioning regimen consisted of distal thymoglobulin (7.5 mg/kg), fludarabine (175 mg/m2), thiotepa (10 mg/kg) and pharmacokinetic guided busulfan targeting a cumulative area under curve (cAUC) (65-75 mgxhr/L). Rituximab (200 mg/m2) was administered on day +1. Result(s): The median age at HCT was 51 months (range 10-163 months). All patients received mobilized peripheral blood stem cells from HLA- haploidentical donors (paternal=1, maternal=1 sibling=2). Median busulfan cAUC for all patients was 69 mgxhr/L (range 65-76). Median CD34 and TCR alphabeta T cell dose was 9.13x106 cells/kg (range 7.0-18.9x106) and 0.7x105 cells/kg (range 0.09-1.0x105). Median times to neutrophil and platelet engraftment were 11 days (9-12) and 11 days (range 8-15), respectively. All 4 patients are alive with median follow-up of 19.5 months (range 7-24). One patient developed late VOD without organ dysfunction that resolved with defibrotide. At last follow up, peripheral T and myeloid chimerisms exceeded 90% in all 4 patients. Average time to CD4 recovery (> 200x106/L) was 142 days. Pre-existing inflammatory bowel disease in CGD (n=1) and WAS (n=1) patients resolved immediately following transplant. There was no graft failure, and none developed Grade III-IV acute or extensive chronic GVHD. Patient with WAS developed recurrent autoimmune cytopenias requiring corticosteroids, rituximab, sirolimus and daratumumab, and ultimately resolved. Viral reactivations included EBV (n= 1), adeno (n= 1), HHV6 (n= 2), BK (n=1), norovirus (n=1), and late HSV (n=1), all responded to antivirals without disease. All patients acquired SARS-Cov-2 after transplant and recovered without sequelae. Conclusion(s): TCR alphabeta+ and CD19+ depleted haploidentical transplantation using a reduced toxicity conditioning regimen with pharmacokinetic guided busulfan, fludarabine, thiotepa and thymoglobulin is well-tolerated in young children with PIDD that results in rapid, durable engraftment with low likelihood of GVHD and graft rejection.Copyright © 2023 American Society for Transplantation and Cellular Therapy
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The human respiratory system is most affected by COVID-19, a coronavirus illness that has been identified. Infectious disease COVID-19 was brought on by a virus that emerged in Wuhan, China, in December 2019. The key problem for healthcare professionals is early diagnosis. Medical organizations were confused in the early stages because there were no suitable medical tools or medications to detect COVID-19. Reverse Transcription Polymerase Chain Reaction, a novel diagnostic technique, was released. The COVID-19 virus congregates in the patient's nose or throat, thus swab samples from those areas are collected. There are various accuracy and testing time restrictions with this method. Medical professionals advise using a different method called CT (Computerized Tomography), which can rapidly identify the infected lung regions and detect COVID-19 at an earlier stage. With the help of chest CT images, computer scientists created a number of deep learning models to recognize the COVID-19 condition. In this paper, a model for automatic COVID-19 recognition on chest CT images is presented that is based on Convolutional Neural Networks (CNN) and VGG16. A public dataset of 14320 CT scans was used in the experiment, and the findings revealed classification accuracy for CNN and VGG16 of 96.34% and 96.99%, respectively.Copyright © 2022, Anka Publishers. All rights reserved.
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Effective drug delivery to the brain is critical for the treatment of glioblastoma (GBM), an aggressive and invasive primary brain tumor that has a dismal prognosis. Radiation therapy, the mainstay of brain tumor treatment, works by inducing DNA damage. Therefore, inhibiting DNA damage response (DDR) pathways can sensitize tumor cells to radiation and enhance cytotoxicity. AZD1390 is an inhibitor of ataxia-telangiectasia mutated kinase, a critical regulator of DDR. Our in vivo studies in the mouse indicate that delivery of AZD1390 to the central nervous system (CNS) is restricted due to active efflux by P-glycoprotein (P-gp). The free fraction of AZD1390 in brain and spinal cord were found to be low, thereby reducing the partitioning of free drug to these organs. Coadministration of an efflux inhibitor significantly increased CNS exposure of AZD1390. No differences were observed in distribution of AZD1390 within different anatomic regions of CNS, and the functional activity of P-gp and breast cancer resistance protein also remained the same across brain regions. In an intracranial GBM patient-derived xenograft model, AZD1390 accumulation was higher in the tumor core and rim compared with surrounding brain. Despite this heterogenous delivery within tumor-bearing brain, AZD1390 concentrations in normal brain, tumor rim, and tumor core were above in vitro effective radiosensitizing concentrations. These results indicate that despite being a substrate of efflux in the mouse brain, sufficient AZD1390 exposure is anticipated even in regions of normal brain. SIGNIFICANCE STATEMENT Given the invasive nature of glioblastoma (GBM), tumor cells are often protected by an intact blood-brain barrier, requiring the development of brain-penetrant molecules for effective treatment. We show that efflux mediated by P-glycoprotein (P-gp) limits central nervous system (CNS) distribution of AZD1390 and that there are no distributional differences within anatomical regions of CNS. Despite efflux by P-gp, concentrations effective for potent radiosensitization are achieved in GBM tumor-bearing mouse brains, indicating that AZD1390 is an attractive molecule for clinical development of brain tumors.Copyright © 2022 American Society for Pharmacology and Experimental Therapy. All rights reserved.
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Background: Inflammatory acute respiratory syndrome (SARS) is caused by the COV-2 virus. Neutrophils have been implicated in the pathophysiology of the COVID-19 pandemic since its inception, particularly in individuals with advanced disease. This is confirmed by a large number of studies. Method(s): The study included 200 patients, of whom 100 were in the critical group (group 1) and 100 were in the non-critical group (group 2). White blood cell (WBC) percentage, neutrophil count, albumin, and demographic information were recorded. Neutrophil percentage x 100 /Albumin (g/dl) was used to construct the neutrophil count to albumin ratio (NPAR). To determine whether the measure may be utilized as a predictor for mortality, the NPAR is then compared between groups 1 and 2, as well as between discharge and mortality groups. Result(s): Age and gender had no statistically significant differences in either group. There were no significant variations in the median (IQR) values of neutrophil count (89 vs 91.25), WBC (15.65 vs 14.9), and NPAR (41.3 vs 43.7) between the two groups. Significant differences in albumin (3.330.521 vs. 3.10.51) were identified between the critical and non-critical groups. Conclusion(s): According to our research, the COVID-19 patients' NPAR is a poor predictor of mortality. More study is still required to verify and elucidate the clinical implications of these findings.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.
ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) has been associated with significant morbidity and mortality, with cardiovascular involvement being usual. Elevations in cardiac Troponin-I level has proposed as an independent biomarker for mortality among patients with COVID-19. Aim(s): To evaluate the role of high sensivity Troponin-I (hs-TnI) level at hospital admission in predicting 30 day in-hospital mortality and 6-month mortality in patients hospitalized with a COVID-19 diagnosis. Method(s): We performed a retrospective single-center cohort study including consecutive patients aged 18 years and older who were admitted for COVID-19, during a 1-year period (n=818). We excluded patients with acute coronary syndrome (n=23), patients with acute heart failure (n=42), and patients in which hs-TnI level was not dosed at admission (n=163). Patients were divided into two groups according to hs-TnI levels: Hs-TnI <19.8 vs hs-TnI >=19.8 pg/mL. Primary outcomes were 30-day in-hospital mortality and 6-months mortality. According to the data distribution, appropriate statistical tests were conducted to compare independent samples. Multivariable logistic regression was used to analyze mortality risk. Receiver operator characteristics (ROC) curve and area under the curve (AUC) were obtained to determine the discriminative power of hs-TnI as a predictor of mortality. (Figure 1). Result(s): This cohort included 590 patients. Mean age was 71 >=+/-15 years and 52.4% were men. Overall, 209 patients (35.4%) had elevated hs- TnI levels and 381 patients had normal hs-TnI levels. Individuals in the hs-TnI >=19.8 pg/mL group were older (80+/-11 vs 66+/-14 years, p<0.001) and presented higher prevalence of chronic heart failure (24.9% vs 7.1%, p<0.001), hypertension (77.0% vs 57.5%, p<0.001), atrial fibrillation/flutter (19.1% vs 5.5%, p<0.001), prior stroke (12.4% vs 5.2%, p=0.001) and ischemic heart disease (12.4% vs 3.7%, p<0.001). There was no difference in length of hospital stay between the groups (8.0 [IQR 9.6] in hs-TnI 19.8 pg/mL group vs 9.0 [IQR 8.0] normal hs-TnI group, p=0.669). Troponin-I was the only independent predictor of in-hospital mortality (OR 3.80, CI 95%: 2.44-5.93, p<0.001), see Table 1. The troponin levels had the highest area under the receiver operating characteristic curv (AUC) with an AUC of 0.705 (95% CI: 0.667-0.742, p<0.001) for association with the inhospital mortality (figure 1). There was no difference in 6-months mortality between the two groups. Conclusion(s): Acute myocardial injury is common in patients hospitalized with COVID-19. In the present study a TnI level >=19.8 pg/mL was predictor of 30 days in-hospital mortality, suggesting that raised levels of this biomarker is associated with adverse prognosis. This tool might be useful for COVID-19 patient risk stratification. Further studies are needed to provide robust data and reliable recommendations on this theme.
ABSTRACT
Background: The pubertal transition (PT) is characterized by dramatic reproductive hormone fluctuations, a developmental circadian delay, and significant changes in sleep and wake patterns. The PT also marks an abrupt divergence between the sexes in risk for depression and sleep disorders that remains elevated for females across the reproductive lifespan, implicating ovarian hormones (i.e., estradiol (E2)) as a common pathway of risk. Notably, inconsistent schedules during the COVID-19 pandemic have contributed to greater sleep irregularity (especially for adolescents), which is associated with affective impairment and inferior clinical outcomes. The objective of this research is to characterize the pathophysiological impact of E2 on sleep disturbances, endocrine rhythm dysregulation and depressive symptoms in peripubertal females. Method(s): 44 peripubertal females (ages 11-14, within 1-year post-menarche) provided daily hormone (E1G-urinary metabolite of E2) and mood assessments for one menstrual cycle and completed an 8-day sleep assessment (actigraphy, daily sleep diaries), with cortisol and melatonin circadian measurement (over four days) starting at day 7 of the following menstrual cycle. Minute-to-minute consistency in sleep/wake state over 24-hrs was calculated to index sleep regularity (SRI). Result(s): A multiple regression model predicted depressive symptoms (CES-DC) from follicular menstrual cycle phase E1G-AUC, sleep regularity index (SRI), cortisol and melatonin AUCs (F(4,18) = 3.833, p=.020, R2=.46). E1G, cortisol-AUC (p<.05) and SRI (marginally, p=.08) contributed to the prediction. Conclusion(s): Results suggest that greater sleep irregularity, greater follicular estradiol and blunted cortisol may contribute to increased depressive symptoms in peripubertal females, providing mechanistic insight into the estradiol-related sleep and affect disruptions experienced during the pubertal transition. Funding Source: K01MH121575;Foundation of Hope for Research and Treatment of Mental Illness (NC) Keywords: Puberty, Sleep Disturbances, Estradiol, Circadian Rhythms, Depressive SymptomsCopyright © 2023