The Kynurenine/Tryptophan Ratio Is a Sensitive Biomarker for the Diagnosis of Pediatric Tuberculosis Among Indian Children

ObjectivesPediatric tuberculosis (TB) remains difficult to diagnose. The plasma kynurenine to tryptophan ratio (K/T ratio) is a potential biomarker for TB diagnosis and treatment response but has not been assessed in children.MethodsWe performed a targeted diagnostic accuracy analysis of four biomarkers: kynurenine abundance, tryptophan abundance, the K/T ratio, and IDO-1 gene expression. Data were obtained from transcriptome and metabolome profiling of children with confirmed tuberculosis and age- and sex-matched uninfected household contacts of pulmonary tuberculosis patients. Each biomarker was assessed as a baseline diagnostic and in response to successful TB treatment.ResultsDespite non-significant between-group differences in unbiased analysis, the K/T ratio achieved an area under the receiver operator characteristic curve (AUC) of 0.667 and 81.5% sensitivity for TB diagnosis. Kynurenine, tryptophan, and IDO-1 demonstrated diagnostic AUCs of 0.667, 0.602, and 0.463, respectively. None of these biomarkers demonstrated high AUCs for treatment response. The AUC of the K/T ratio was lower than biomarkers identified in unbiased analysis, but improved sensitivity over existing commercial assays for pediatric TB diagnosis.ConclusionsPlasma kynurenine and the K/T ratio may be useful biomarkers for pediatric TB. Ongoing studies in geographically diverse populations will determine optimal use of these biomarkers worldwide.

Association Between Preoperative Monocyte to High-Density Lipoprotein Ratio on In-hospital and Long-Term Mortality in Patients Undergoing Endovascular Repair for Acute Type B Aortic Dissection

Aims: The monocyte to high-density lipoprotein ratio (MHR), a novel marker of inflammation and cardiovascular events, has recently been found to facilitate the diagnosis of acute aortic dissection. This study aimed to assess the association of preoperative MHR with in-hospital and long-term mortality after thoracic endovascular aortic repair (TEVAR) for acute type B aortic dissection (TBAD).Methods: We retrospectively evaluated 637 patients with acute TBAD who underwent TEVAR from a prospectively maintained database. Multivariable logistic and cox regression analyses were conducted to assess the relationship between preoperative MHR and in-hospital as well as long-term mortality. For clinical use, MHR was modeled as a continuous variable and a categorical variable with the optimal cutoff evaluated by receiver operator characteristic curve for long-term mortality. Propensity score matching was used to diminish baseline differences and subgroups analyses were conducted to assess the robustness of the results.Results: Twenty-one (3.3%) patients died during hospitalization and 52 deaths (8.4%) were documented after a median follow-up of 48.1 months. The optimal cutoff value was 1.13 selected according to the receiver operator characteristic curve (sensitivity 78.8%; specificity 58.9%). Multivariate analyses showed that MHR was independently associated with either in-hospital death [odds ratio (OR) 2.11, 95% confidence interval (CI) 1.16-3.85, P = 0.015] or long-term mortality [hazard ratio (HR) 1.78, 95% CI 1.31-2.41, P < 0.001). As a categorical variable, MHR > 1.13 remained an independent predictor of in-hospital death (OR 4.53, 95% CI 1.44-14.30, P = 0.010) and long-term mortality (HR 4.16, 95% CI 2.13-8.10, P < 0.001). Propensity score analyses demonstrated similar results for both in-hospital death and long-term mortality. The association was further confirmed by subgroup analyses.Conclusions: MHR might be useful for identifying patients at high risk of in-hospital and long-term mortality, which could be integrated into risk stratification strategies for acute TBAD patients undergoing TEVAR.

A Novel Autophagy-Related Prognostic Risk Model and a Nomogram for Survival Prediction of Oral Cancer Patients

Background. This study is aimed at constructing a risk signature to predict survival outcomes of ORCA patients. Methods. We identified differentially expressed autophagy-related genes (DEARGs) based on the RNA sequencing data in the TCGA database; then, four independent survival-related ARGs were identified to construct an autophagy-associated signature for survival prediction of ORCA patients. The validity and robustness of the prognostic model were validated by clinicopathological data and survival data. Subsequently, four independent prognostic DEARGs that composed the model were evaluated individually. Results. The expressions of 232 autophagy-related genes (ARGs) in 127 ORCA and 13 control tissues were compared, and 36 DEARGs were filtered out. We performed functional enrichment analysis and constructed protein–protein interaction network for 36 DEARGs. Univariate and multivariate Cox regression analyses were adopted for searching prognostic ARGs, and an autophagy-associated signature for ORCA patients was constructed. Eventually, 4 desirable independent survival-related ARGs (WDR45, MAPK9, VEGFA, and ATIC) were confirmed and comprised the prognostic model. We made use of multiple ways to verify the accuracy of the novel autophagy-related signature for survival evaluation, such as receiver-operator characteristic curve, Kaplan–Meier plotter, and clinicopathological correlational analyses. Four independent prognostic DEARGs that formed the model were also associated with the prognosis of ORCA patients. Conclusions. The autophagy-related risk model can evaluate OS for ORCA patients independently since it is accurate and stable. Four prognostic ARGs that composed the model can be studied deeply for target treatment.

Serum N-Terminal Pro-B-Type Natriuretic Peptide as a Biomarker of Critical Pulmonary Stenosis in Neonates

Objectives: To determine the efficacy of serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in predicting critical pulmonary stenosis (CPS) in neonates.Methods: All neonates with pulmonary stenosis (PS) admitted to the neonatal intensive care unit of Xinhua Hospital from October 2014 to December 2020 were retrospectively reviewed. Infants with serum NT-proBNP levels measured within 48 h after birth were enrolled and divided into CPS and non-CPS groups. Serum NT-proBNP levels and cardiac Doppler indices were compared between the two groups. Correlations were determined using the Spearman's rank correlation coefficient. Receiver operator characteristic curve analysis was used to explore the predictive value of NT-proBNP for identifying neonatal CPS.Results: Among 96 infants diagnosed with PS by echocardiography, 46 were enrolled (21 and 25 in the non-CPS and CPS groups, respectively). Serum NT-proBNP levels were significantly higher in the CPS group than in the non-CPS group [3,600 (2,040–8,251) vs. 1,280 (953–2,386) pg/ml, P = 0.003]. Spearman's analysis suggested a positive correlation between Ln(NT-proBNP) levels and the transvalvular pulmonary gradient (r = 0.311, P = 0.038), as well as between Ln(NT-proBNP) levels and pulmonary artery velocity (r = 0.308, P = 0.040). Receiver operating characteristic curve analysis showed that a cutoff serum NT-proBNP level of 2,395 pg/ml yielded a 66.7 and 78.9% sensitivity and specificity for identifying CPS, respectively. The area under the curve was 0.784 (95% CI, 0.637–0.931). A positive correlation was found between Ln(NT-proBNP) and length of hospital stay (r = 0.312, P < 0.05).Conclusion: Serum NT-proBNP level was positively correlated with PS severity and could be used as a biomarker to identify CPS in neonates.

A preliminary study of commercially available general-purpose chest radiography artificial intelligence-based software for detecting airspace opacity lesions in COVID-19 patients

Purpose To validate commercially available general-purpose artificial intelligence (AI)-based software for detecting airspace opacity in chest radiographs (CXRs) of COVID-19 patients. Materials and Methods We used the ieee8023-covid-chestxray-dataset to validate commercial AI software capable of detecting "Nodule/Mass" and "Airspace opacity" as regions of interest with probability scores. From this dataset, we excluded computed tomography images and CXR images taken using an anteroposterior spine view and analyzed CXR images tagged with "Pneumonia/Viral/COVID-19" and "no findings". A radiologist then reviewed the images and rated them on a 3-point opacity score for the presence of airspace opacity. The maximum probability score of airspace opacity for each image was calculated using this software. The difference in each maximum probability for each opacity score was evaluated using Wilcoxon's rank sum test. The threshold of the probability score was determined by receiver operator characteristic curve analysis for the presence or absence of COVID-19, and the true positive rate (TPR) and false positive rate (FPR) were determined for the individual and overall opacity scores. Results Images from 342 patients with COVID-19 and 15 normal images were included. Opacity scores of 1, 2, and 3 were observed in 44, 70, and 243 images, respectively, of which 33 (75%), 66 (94.2%), and 243 (100%), respectively, were from COVID-19 patients. The overall TPR and FPR were 0.82 and 0.13, respectively, at an area under the curve of 0.88 and a threshold of 0.06, while the FPR for opacity score 1 was 0.18 and the TPR for score 3 was 0.97. Conclusion Using a public database containing CXR images of COVID-19 patients, commercial AI software was shown to be able to detect airspace opacity in severe pneumonia. Summary Commercially available AI software was capable of detecting airspace opacity in CXR images of COVID-19 patients in a public database.

Specimen Mammography for Intraoperative Margin Assessment in Breast Conserving Surgery: A Meta-Analysis

Specimen mammography is one of the widely used intraoperative methods assessing margin status in breast conserving surgery. We performed a meta-analysis to evaluate the diagnostic accuracy of specimen mammography. Literature databases including Pubmed, Cochrane Library, Web of Science and EMBASE were searched prior to May 2020. 18 studies with a total of 1142 patients were included. Data was extracted to perform pooled analysis, heterogeneity testing, threshold effect testing, sensitivity analysis, publication bias analysis and subgroup analyses. The pooled weighted values were a sensitivity of 0.55 (95% CI, 0.45–0.64), a specificity of 0.85 (95% CI, 0.77–0.90), a DOR of 7 (95% CI, 4–11) and a pooled positive likelihood ratio of 3.6 (95% CI 2.4-5.3). The area under the receiver operator characteristic curve was 0.75 (95% CI 0.71-0.78). In the subgroup analysis, the pooled specificity in the positive margin defined as tumor at margin subgroup was lower than the other positive margin definition subgroup (0.79 [95% CI: 0.66, 0.91] vs. 0.88 [95% CI: 0.81, 0.95], p = 0.01). Our findings indicated specimen mammography to be an accurate and intraoperative imaging technique for margin assessment in breast conserving surgery.

Predictive Modeling Identifies Total Bleeds at 12-Weeks Postswitch to N8-GP Prophylaxis as a Predictor of Treatment Response

Background Predicting annualized bleeding rate (ABR) during factor VIII (FVIII) prophylaxis for severe hemophilia A (SHA) is important for long-term outcomes. This study used supervised machine learning-based predictive modeling to identify predictors of long-term ABR during prophylaxis with an extended half-life FVIII. Methods Data were from 166 SHA patients who received N8-GP prophylaxis (50 IU/kg every 4 days) in the pathfinder 2 study. Predictive models were developed to identify variables associated with an ABR of ≤1 versus >1 during the trial's main phase (median follow-up of 469 days). Model performance was assessed using area under the receiver operator characteristic curve (AUROC). Pre-N8-GP prophylaxis models learned from data collected at baseline; post-N8-GP prophylaxis models learned from data collected up to 12-weeks postswitch to N8-GP, and predicted ABR at the end of the outcome period (final year of treatment in the main phase). Results The predictive model using baseline variables had moderate performance (AUROC = 0.64) for predicting observed ABR. The most performant model used data collected at 12-weeks postswitch (AUROC = 0.79) with cumulative bleed count up to 12 weeks as the most informative variable, followed by baseline von Willebrand factor and mean FVIII at 30 minutes postdose. Univariate cumulative bleed count at 12 weeks performed equally well to the 12-weeks postswitch model (AUROC = 0.75). Pharmacokinetic measures were indicative, but not essential, to predict ABR. Conclusion Cumulative bleed count up to 12-weeks postswitch was as informative as the 12-week post-switch predictive model for predicting long-term ABR, supporting alterations in prophylaxis based on treatment response.

Hypoalbuminemia affects one third of acute pancreatitis patients and is independently associated with severity and mortality

The incidence and medical costs of acute pancreatitis (AP) are on the rise, and severe cases still have a 30% mortality rate. We aimed to evaluate hypoalbuminemia as a risk factor and the prognostic value of human serum albumin in AP. Data from 2461 patients were extracted from the international, prospective, multicentre AP registry operated by the Hungarian Pancreatic Study Group. Data from patients with albumin measurement in the first 48 h (n = 1149) and anytime during hospitalization (n = 1272) were analysed. Multivariate binary logistic regression and Receiver Operator Characteristic curve analysis were used. The prevalence of hypoalbuminemia (< 35 g/L) was 19% on admission and 35.7% during hospitalization. Hypoalbuminemia dose-dependently increased the risk of severity, mortality, local complications and organ failure and is associated with longer hospital stay. The predictive value of hypoalbuminemia on admission was poor for severity and mortality. Severe hypoalbuminemia (< 25 g/L) represented an independent risk factor for severity (OR 48.761; CI 25.276–98.908) and mortality (OR 16.83; CI 8.32–35.13). Albumin loss during AP was strongly associated with severity (p < 0.001) and mortality (p = 0.002). Hypoalbuminemia represents an independent risk factor for severity and mortality in AP, and it shows a dose-dependent relationship with local complications, organ failure and length of stay.

Dynamic forecasting of severe acute graft-versus-host disease after transplantation

To anticipate critical events, clinicians intuitively rely on multidimensional time-series data. It is, however, difficult to model such decision process using machine learning (ML), since real-world medical records often have irregular missing and data sparsity in both feature and longitudinal dimensions. Here we propose a nonparametric approach that updates risk score in real time and can accommodate sampling heterogeneity, using forecasting of severe acute graft-versus-host disease (aGVHD) as the study case. The area under the receiver operator characteristic curve (AUC) rose steadily after transplantation and peaked at >0.7 in both adult and pediatric cohorts. Various numerical experiments provided guidelines for future applications.

Based on improved deep convolutional neural network model pneumonia image classification

Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson’s feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.