Receiver Operating Curve Area Research Articles

Peptide-Bound Glycative, AGE and Oxidative Modifications as Biomarkers for the Diagnosis of Alzheimer's Disease-A Feasibility Study.

Background: The diagnosis of Alzheimer's disease (AD) relies on core cerebrospinal fluid (CSF) biomarkers, amyloid beta (Aβ) and tau. As the brain is then already damaged, researchers still strive to discover earlier biomarkers of disease onset and the progression of AD. Glycation, advanced glycation end products (AGEs) and oxidative modifications on proteins in CSF mirror the underlying biological mechanisms that contribute to early AD pathology. However, analyzing free AGEs in the body fluids of AD patients has led to controversial results. Thus, this pilot study aimed to test the feasibility of detecting, identifying and quantifying differentially glycated, AGE or oxidatively modified peptides in CSF proteins of AD patients (n = 5) compared to a control group (n = 5). Methods: To this end, we utilized a data-dependent (DDA) nano liquid chromatography (LC) linear ion trap-Orbitrap tandem mass spectrometry (MS/MS) ) approach and database search that included over 30 glycative and oxidative modifications in four search nodes to analyze endogenous modifications on individual peptides. Furthermore, we quantified candidate peptide abundance using LC Quan. Results: We identified 299 sites of early and advanced glycation and 53 sites of oxidatively modified tryptophan. From those, we identified 17 promising candidates as putative biomarkers (receiver operating curve-area under the curve (ROC-AUC) > 0.8), albeit without statistical significance. Conclusions: The potential candidates with higher discrimination power showed correlations with established diagnostic markers, thus hinting toward the potential of those peptides as biomarkers.

Clinical Utility of Protein Language Models in Resolution of Variants of Uncertain Significance in KCNQ1, KCNH2, and SCN5A Compared With Patch-Clamp Functional Characterization.

Genetic testing for cardiac channelopathies is the standard of care. However, many rare genetic variants remain classified as variants of uncertain significance (VUS) due to lack of epidemiological and functional data. Whether deep protein language models may aid in VUS resolution remains unknown. Here, we set out to compare how 2 deep protein language models perform at VUS resolution in the 3 most common long-QT syndrome-causative genes compared with the gold-standard patch clamp. A total of 72 rare nonsynonymous VUS (9 KCNQ1, 19 KCNH2, and 50 SCN5A) were engineered by site-directed mutagenesis and expressed in either HEK293 cells or TSA201 cells. Whole-cell patch-clamp technique was used to functionally characterize these variants. The protein language models, ESM1b and AlphaMissense, were used to predict the variant effect of missense variants and compared with patch clamp. Considering variants in all 3 genes, the ESM1b model had a receiver operator curve-area under the curve of 0.75 (P=0.0003). It had a sensitivity of 88% and a specificity of 50%. AlphaMissense performed well compared with patch-clamp with an receiver operator curve-area under the curve of 0.85 (P<0.0001), sensitivity of 80%, and specificity of 76%. Deep protein language models aid in VUS resolution with high sensitivity but lower specificity. Thus, these tools cannot fully replace functional characterization but can aid in reducing the number of variants that may require functional analysis.

A prediction model for secondary invasive fungal infection among severe SARS-CoV-2 positive patients in ICU.

The global COVID-19 pandemic has resulted in over seven million deaths, and IFI can further complicate the clinical course of COVID-19. Coinfection of COVID-19 and IFI (secondary IFI) pose significant threats not only to healthcare systems but also to patient lives. After the control measures for COVID-19 were lifted in China, we observed a substantial number of ICU patients developing COVID-19-associated IFI. This creates an urgent need for predictive assessment of COVID-19 patients in the ICU environment for early detection of suspected fungal infection cases. This study is a single-center, retrospective research endeavor. We conducted a case-control study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. The cases consisted of patients who developed any secondary IFI during their ICU stay at Jilin University China-Japan Union Hospital in Changchun, Jilin Province, China, from December 1st, 2022, to August 31st, 2023. The control group consisted of SARS-CoV-2 positive patients without secondary IFI. Descriptive and comparative analyses were performed, and a logistic regression prediction model for secondary IFI in COVID-19 patients was established. Additionally, we observed an increased incidence of COVID-19-associated pulmonary aspergillosis (CAPA) during this pandemic. Therefore, we conducted a univariate subgroup analysis on top of IFI, using non-CAPA patients as the control subgroup. From multivariate analysis, the prediction model identified 6 factors that are significantly associated with IFI, including the use of broad-spectrum antibiotics for more than 2 weeks (aOR=4.14, 95% CI 2.03-8.67), fever (aOR=2.3, 95%CI 1.16-4.55), elevated log IL-6 levels (aOR=1.22, 95% CI 1.04-1.43) and prone position ventilation (aOR=2.38, 95%CI 1.15-4.97) as independent risk factors for COVID-19 secondary IFI. High BMI (BMI ≥ 28 kg/m2) (aOR=0.85, 95% CI 0.75-0.94) and the use of COVID-19 immunoglobulin (aOR=0.45, 95% CI 0.2-0.97) were identified as independent protective factors against COVID-19 secondary IFI. The Receiver Operating Curve (ROC) area under the curve (AUC) of this model was 0.81, indicating good classification. We recommend paying special attention for the occurrence of secondary IFI in COVID-19 patients with low BMI (BMI < 28 kg/m2), elevated log IL-6 levels and fever. Additionally, during the treatment of COVID-19 patients, we emphasize the importance of minimizing the duration of broad-spectrum antibiotic use and highlight the potential of immunoglobulin application in reducing the incidence of IFI.

How useful is the tuberculin skin test for tuberculosis detection: Assessing diagnostic accuracy metrics through a large Tunisian case-control study

Background and aim During the past decade, the frequency of extrapulmonary forms of tuberculosis (TB) has increased. These forms are often miss-diagnosed. This statement of the TB epidemiological profile modification, conduct us to reflect about the utility of the Tuberculin Skin Test (TST) in active TB detection. This study aimed to evaluate the diagnostic accuracy performance of the TST for active tuberculosis detection. Methods This was a case-control, multicenter study conducted in 11 anti-TB centers in Tunisia (June-November2014). The cases were adults aged between 18 and 55 years with newly diagnosed and confirmed tuberculosis. Controls were free from tuberculosis. A data collection sheet was filled out and a TST was performed for each participant. Diagnostic accuracy measures of TST were estimated using Receiver Operating Curve (ROC) curve and Area Under Curve (AUC) to estimate sensitivity and specificity of a determined cut-off point. Results Overall, 1050 patients were enrolled, composed of 336 cases and 714 controls. The mean age was 38.3±11.8 years for cases and 33.6±11 years for controls. The mean diameter of the TST induration was significantly higher among cases than controls (13.7mm vs.6.2mm; p=10-6). AUC was 0.789 [95% CI: 0.758-0.819; p=0.01], corresponding to a moderate discriminating performance for this test. The most discriminative cut-off value of the TST, which was associated with the best sensitivity (73.7%) and specificity (76.6%) couple was ≥ 11 mm with a Youden index of 0.503. Positive and Negative predictive values were 3.11% and 99.52%, respectively. Conclusions TST could be a useful tool used for active tuberculosis detection, with a moderate global performance and accepted sensitivity and specificity at the cut-off point of 11 mm. However, it cannot be considered as a gold standard test due to its multiple disadvantages.

Prediction of Short-Term Postoperative Complications Following Open Reduction Internal Fixation of Acute Ankle Fractures

Introduction/Purpose: Acute ankle fractures are a common injury and represent a significant financial burden to the healthcare system. Reimbursement following ankle fractures is complicated by bundled payments within the 90-day global period which do not account for unpredictable complications. Comorbidity indices, such as the American Society of Anesthesiologists (ASA) classification, are often correlated with complications; however, the positive predictive value of these metrics have not been investigated. As insurers continue to transition towards bundled reimbursements, greater attentiveness towards providing efficient care is paramount. The purpose of this investigation is to develop a machine learning (ML) model that is predictive of complications following operative management of acute ankle fractures. This study will also compare ML algorithms to legacy indices to assess the predictive value in predicting complications. Methods: The American College of Surgeons-National Surgical Quality Improvement Program database was queried via Current Procedural Terminology (CPT) from 2015-2018 for open reduction internal fixation (ORIF) cases for ankle fractures, including ORIF of the medial malleolus, posterior malleolus, lateral malleolus, bimalleolar, trimalleolar, syndesmosis. Training and validation sets were created by randomly assigning 80% and 20% of the data set. Included variables were age, body mass index (BMI), operative time, smoking status, comorbidities, diagnosis, and preoperative hematocrit and albumin. Complications included any adverse event, transfusion, surgical site infection, return to the operating room, deep vein thrombosis or pulmonary embolism, pneumonia, urinary tract infection, cerebrovascular accident, cardiac arrest, myocardial infarction. Each ML algorithm was compared with one another and to a baseline model using American Society of Anesthesiologists (ASA) classification. Model strength was evaluated by calculating the area under the curve (AUC) for receiver operating characteristic (ROC) of any adverse event. Results: We identified a total of 28,736 ORIF cases. Mean age and BMI were 32.2 ± 18.6 years, 30.8 ± 7.6 kg/m^2, respectively. The overall incidence of any medical complication was 3.4% (n=975) with surgical site infection (1.3%, n=386) and return to operating room (1.3%, n=367) being the most common. Percentage hematocrit, BMI, operative time, and age were of highest importance in outcome prediction. Logistic regression (AUC: 72%) and gradient boosting (AUC: 72%) ML algorithms outperformed ASA classification (AUC: 69%) for predicting any adverse event following ORIF of ankle fractures. Conclusion: Legacy comorbidity indices are simple metrics that can be easily constructed from patient demographic variables and past medical history. However, the predictive power is limited as they have shown a low positive predictive value for predicting complications. Machine learning algorithms can calculate patient-specific risk for postoperative complications which may adjust perioperative care and site of surgery. These models have the capability to optimize utilization of healthcare resources and minimize excessive expenditure by providing patient-specific care. Receiver Operating Curve-Area under the Curve Analysis for Adverse Events Following ORIF of Ankle Fractures

Abstract 1056: Development of a B-cell epitope classifier for early detection of renal cell carcinoma

Abstract A blood biomarker for early detection of renal cell carcinoma (RCC) would help decrease the burden of 15,000 deaths annually in the U.S. (1). Antibodies against tumor (neo)antigens are thought to arise early in cancer development and might serve as a more sensitive biomarker for RCC than tumor-derived biomolecules. Serum Epitope Repertoire Analysis (SERA) of human serum/plasma uses a random bacterial peptide display library and next generation sequencing to allow for unbiased highly multiplexed profiling of B cell epitopes. In combination with bioinformatic methods and machine learning, a classifier was trained to predict the presence of RCC using a cohort of 163 treatment-naïve patients (median age 62, stage: I 35%, II 5%, III 20%, IV 40%) with clear cell RCC (ccRCC) and 438 age-matched healthy controls (median age 61). Classifier performance was evaluated by sensitivity, specificity, and receiver operator curve (ROC) area under the curve (AUC) analysis in an independent validation cohort of 43 treatment-naïve patients with ccRCC (age 22-86, median 63, stage: I 33%, II 2%, III 26%, IV 39%) and 1,759 healthy controls (age 18-89, median 40). A total of 1,465 individual B cell epitope features were discovered and enriched in ccRCC vs. controls and included in a random forest model. In the validation cohort, the ROC-AUC was 0.65, and sensitivities of 9%, 16%, 30%, and 36% were achieved at specificities of 99%, 98%, 95%, and 90%, respectively. Performance was better in both early stage ccRCC and low tumor grade (grade 1 or 2). For stage I, the ROC-AUC was 0.88, and sensitivities of 7%, 14%, 36%, and 57% were achieved at specificities of 99%, 98%, 95%, and 90%, respectively. For low tumor grade, the ROC-AUC was 0.86, and sensitivities of 10%, 20%, 50%, and 60% of were achieved at specificities of 99%, 98%, 95%, and 90%, respectively. The classifier did not miscategorize benign renal lesions as ccRCC (Mann-Whitney p-value 0.52, ROC-AUC 0.54) in a separate cohort of 23 patients with benign renal tumors vs. the 1,759 healthy controls. This preliminary performance for a B cell epitope-based classifier is promising: the better performance in early stage ccRCC suggests that B cell epitope biomarkers may complement the lower sensitivity of cell-free DNA for detecting early-stage cancer (2). Future work will further validate these results in larger multi-institutional cohorts to improve classification performance and explore if B cell epitope biomarkers can be combined with existing assays to develop an early detection composite biomarker for RCC and tumor grade. (1)Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023 Jan;73(1):17-48.(2)Francini, E.; Fanelli, G.N.; Pederzoli, F.; Spisak, S.; Minonne, E.; Raffo, M.; Pakula, H.; Tisza, V.; Scatena, C.; Naccarato, A.G.; et al. Circulating Cell-Free DNA in Renal Cell Carcinoma: The New Era of Precision Medicine. Cancers 2022, 14, 4359. Citation Format: Thomas W. Campbell, Christian R. Hoerner, Elizabeth Alli, John T. Leppert, John C. Shon, Alice C. Fan. Development of a B-cell epitope classifier for early detection of renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1056.

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy.

In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.

Interpretable prediction of acute respiratory infection disease among under-five children in Ethiopia using ensemble machine learning and Shapley additive explanations (SHAP).

Although the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques. Secondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction. The XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia. The XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.

How useful is the tuberculin skin test for the detection of active tuberculosis: Assessing diagnostic accuracy metrics and selection of best cut-off value through a large Tunisian multicenter case-control study

Background and aim: During the past decade, the frequency of extrapulmonary forms of tuberculosis (TB) has increased. These forms are often miss-diagnosed. This statement of the TB epidemiological profile modification, conduct us to reflect about the utility of the Tuberculin Skin Test (TST) in active TB detection. This study aimed to evaluate the diagnostic accuracy performance of the TST for active tuberculosis detection. Methods: This was a case-control, multicenter study conducted in 11 anti-TB centers in Tunisia (June-November2014). The cases were adults aged between 18 and 55 years with newly diagnosed and confirmed tuberculosis. Controls were free from tuberculosis. A data collection sheet was filled out and a TST was performed for each participant. Diagnostic accuracy measures of TST were estimated using Receiver Operating Curve (ROC) curve and Area Under Curve (AUC) to estimate sensitivity and specificity of a determined cut-off point. Results: Overall, 1050 patients were enrolled, composed of 336 cases and 714 controls. The mean age was 38.3±11.8 years for cases and 33.6±11 years for controls. The mean diameter of the TST induration was significantly higher among cases than controls (13.7mm vs.6.2mm; p=10-6). AUC was 0.789 [95% CI: 0.758-0.819; p=0.01], corresponding to a moderate discriminating performance for this test. The most discriminative cut-off value of the TST, which was associated with the best sensitivity (73.7%) and specificity (76.6%) couple was ≥ 11 mm with a Youden index of 0.503. Positive and Negative predictive values were 3.11% and 99.52%, respectively. Conclusions: TST could be a useful tool used for active tuberculosis detection, with a moderate global performance and accepted sensitivity and specificity at the cut-off point of 11 mm. However, it cannot be considered as a gold standard test due to its multiple disadvantages.

Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study

ObjectivesPatients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes. MethodsA prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm. ResultsOur cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927). ConclusionsOur algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day.

Improving Long-Term Guided Wave Damage Detection With Measurement Resampling

This paper studies strategies for improving long-term guided wave damage detection in an outdoor, uncontrolled environment. We leverage the reconstruction difference between a short-term and long-term principal component analysis to distinguish damage from other variations. While principal component analysis damage detection methods are not new, few are capable of detecting long-term damage in a dynamic environment and in a computationally feasible manner. In this paper, we study the factors that reduce damage detection performance, including the time window duration, the damage duration, and the number of principal components. We then propose a monotonic decreasing sampling strategy that reduces these issues and improves damage detection. Results show that the best receiver operating curve area under the curve score, using an 80-day window of guided wave data and 20-day damage duration, increases from 0.88 to 0.92 while the running time and required memory reduces to less than 1/4 of the original cost.

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality

Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination. To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients. This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone. During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient's assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos. The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support. Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%. In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure.

Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data. We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models. TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE.

Validation of updated antenatal vaginal birth after caesarean section prediction model without race and ethnicity in Australia.

The Grobman antenatal nomogram to predict likelihood of successful vaginal birth after caesarean section (VBAC) has been validated in multiple institutions. However, due to concerns regarding inclusion of ethnicity, a new nomogram has been developed. The aim was to evaluate the efficacy of the updated Grobman nomogram without ethnicity in a regional hospital in Australia. This was a retrospective cohort study of women electing to have a VBAC at a regional hospital over a nine-year period. Maternal demographics and obstetric outcomes were collected. Women were assigned a predicted likelihood of successful VBAC using the updated Grobman nomogram, with variables such as age, pre-pregnancy weight, height and arrest disorder as indications for previous caesarean birth, previous vaginal birth, previous VBAC and treated chronic hypertension. The predicted likelihood of successful VBAC was compared with actual successful VBAC rates. A total of 541 women attempted VBAC with a VBAC success rate of 74.3% (402/541). The nomogram demonstrated good fit, with a receiver operating curve area under the curve of 0.707 (95% confidence interval 0.659-0.755). Using a cut-off value of 0.5, the success rate of classification with this model was 74.3%. On comparing each predicted decile, the nomogram performed poorly in those predicted to have a <40% chance of successful VBAC. This study confirms the use of the updated Grobman nomogram without ethnicity, alongside usual counselling, to provide individualised advice for informed decision-making. However, clinicians should be mindful of the limitation of poor accuracy in women with a low predicted probability of VBAC.

Comparative performances of soluble urokinase plasminogen activator receptor and Mid-regional proADM to predict composite death and new heart failure rehospitalisation in acutely breathless patients

Abstract Background Soluble urokinase plasminogen activator receptor (suPAR) is a pleotropic receptor, capable of orchestrating plaque vulnerability and vascular immune dysfunction. Mid-regional pro-adrenomedullin (MR-proADM) is the stable peptide precursor of adrenomedullin, with concentrations reflective of vasodilation and cardiac remodelling. We compared the prognostic performances of suPAR and MR-proADM for the composite clinical endpoint of death and new heart failure (HF) in patients with undifferentiated breathlessness. Methods Patients presenting to hospital with the primary complaint of acute dyspnoea were recruited in New Zealand (n=612) and in Singapore (n=483)]. Baseline plasma measurements were undertaken for suPAR (ViroGates) and MR-proADM (Thermo Scientific). Cardiac biomarker levels of NT-proBNP (Roche) was available on all patients. Statistical assessment was made using SPSS v28 (IBM), with all biomarkers treated as continuous variables and presented as median [interquartile range (IQR)]. Prognostic performance of suPAR, MR-proADM and NT-proBNP to predict the composite clinical endpoint of death/new HF at 90-days and 1-yr were assessed using receiver operator curve (ROC) area under the curve (AUC) analysis (Z-scores) and Cox hazard regression analysis (per doubling of biomarker concentrations) after adjustment for traditional risk factors. P-value &amp;lt;0.05 was considered statistically significant. Results In the entire acutely breathless cohort [median age: 65 years (IQR: 52.9–76.0), 63.1% males], 343/1095 of patients had the final adjudication of ADHF. suPAR and MR-proADM concentrations were higher with increasing age (Spearmans rho, r&amp;gt;0.46, P&amp;lt;0.0001), lower eGFR (r&amp;gt;0.58, P&amp;lt;0.0001) and in those with ADHF (r&amp;gt;0.40, P&amp;lt;0.0001). During the follow-up period, 122 patients were categorised with death/new HF by 90-days, rising to 281 at 1-year. suPAR and MR-proADM were able to predict death/new HF at 90-days (both ROC-AUC &amp;gt;0.77) and at 1-year (both ROC-AUC ≥0.78) (Table 1). All markers were however less accurate in predicting this endpoint in the presence of ADHF (ROC-AUC &amp;lt;0.71). After adjustment in Cox-regression modelling, suPAR obtained HR &amp;gt;1.35 per doubling of suPAR concentrations (P=0.001) for outcomes at 90-days and at 1-year (Table 2), achieving the highest prognostic performance for this clinical endpoint, followed by NT-proBNP (HR &amp;gt;1.29) (Table 2), whilst MR-proADM was not an independent predictor of death/HF in this cohort. suPAR was also an independent predictor of death/HF for patients with ADHF, obtaining HR &amp;gt;1.35 per doubling of concentrations. Above a cut-off concentration of 3.6 ng/mL, suPAR was associated with a HR of 2.1 (95% CI: 1.55–2.91) for death/HF at 1-year for acutely dyspnoeic patients. Conclusion suPAR concentrations is superior than MR-proADM in predicting the clinical end-point of death/HF at 1-year in this cohort. It may aid in risk-stratification strategies for the management of acutely breathless patients. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): National Heart Foundation of New ZealandHealth Research Council of New Zealand

Proteomic biomarkers of Kleine-Levin syndrome.

Kleine-Levin syndrome (KLS) is characterized by relapsing-remitting episodes of hypersomnia, cognitive impairment, and behavioral disturbances. We quantified cerebrospinal fluid (CSF) and serum proteins in KLS cases and controls. SomaScan was used to profile 1133 CSF proteins in 30 KLS cases and 134 controls, while 1109 serum proteins were profiled in serum from 26 cases and 65 controls. CSF and serum proteins were both measured in seven cases. Univariate and multivariate analyses were used to find differentially expressed proteins (DEPs). Pathway and tissue enrichment analyses (TEAs) were performed on DEPs. Univariate analyses found 28 and 141 proteins differentially expressed in CSF and serum, respectively (false discovery rate <0.1%). Upregulated CSF proteins included IL-34, IL-27, TGF-b, IGF-1, and osteonectin, while DKK4 and vWF were downregulated. Pathway analyses revealed microglial alterations and disrupted blood-brain barrier permeability. Serum profiles show upregulation of Src-family kinases (SFKs), proteins implicated in cellular growth, motility, and activation. TEA analysis of up- and downregulated proteins revealed changes in brain proteins (p < 6 × 10-5), notably from the pons, medulla, and midbrain. A multivariate machine-learning classifier performed robustly, achieving a receiver operating curve area under the curve of 0.90 (95% confidence interval [CI] = 0.78-1.0, p = 0.0006) in CSF and 1.0 (95% CI = 1.0-1.0, p = 0.0002) in serum in validation cohorts, with some commonality across tissues, as the model trained on serum sample also discriminated CSF samples of controls versus KLS cases. Our study identifies proteomic KLS biomarkers with diagnostic potential and provides insight into biological mechanisms that will guide future research in KLS.

Earlier detection of SARS-CoV-2 infection by blood RNA signature microfluidics assay.

Earlier detection of SARS-CoV-2 infection by blood RNA signature microfluidics assay.

Using machine learning techniques to predict antimicrobial resistance in stone disease patients.

Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department. Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species. The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier. Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24-48h before test results are known.

When Will You Succeed Casting Patients With Early-onset Scoliosis? Prospective Evaluation of Predictive Radiographic Parameters.

In a recent retrospective study, in cast correction of the major curve correlated with final curve size in patients with early-onset scoliosis treated with casting. We therefore sought to perform a prospective study with controlled methodology to determine if there are parameters associated with reduction of coronal deformity. A prospective, observational study was conducted between 2014 and 2019 at selected sites willing to comply with a standard radiographic and follow-up protocol. Radiographic data was collected at time points of precast, in traction, initial in-cast, and at minimum 1 year follow-up. Multivariate linear regression models were utilized to control for potential confounders using a stepwise procedure. Twenty-nine patients met inclusion criteria. On multivariate analysis, traction major curve (P=0.043) and initial in-cast (P=0.011) major curve Cobb angles were independently associated with final out of cast major curve Cobb angle. The only factor that was independently associated with failure to cure (<15-degree major curve) was traction major curve Cobb angle (P=0.046). A threshold traction major curve Cobb angle of 20 degrees was found to have good accuracy with 81% sensitivity and 73% specificity (receiver operator curve area: 0.869, P<0.001). A traction major curve Cobb angle over 20 degrees would accurately predict failure of casting treatment to cure scoliosis in 79% of cases. A threshold in-cast major curve Cobb angle of 21 degrees was found to have slightly less accuracy than traction with 69% sensitivity, 82% specificity, and 74% accuracy (receiver operator curve area: 0.830, P=0.004). Radiographic measurements in traction and initially in the cast are predictive of curve size at follow-up for children with early-onset scoliosis treated with casting. The standardization and utility of traction films should be further explored. Level II.

Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things.

The deep neural network is used to establish a neural network model to solve the problems of low accuracy and poor accuracy of traditional algorithms in screening differentially expressed genes and function prediction during the walnut endocarp hardening stage. The paper walnut is used as the research object to analyze the biological information of paper walnut. The changes of lignin deposition during endocarp hardening from 50 days to 90 days are observed by microscope. Then, the Convolutional Neural Network (CNN) and Long and Short-term Memory (LSTM) network model are adopted to construct an expression gene screening and function prediction model. Then, the transcriptome and proteome sequencing and biological information of walnut endocarp samples at 50, 57, 78, and 90 days after flowering are analyzed and taken as the training data set of the CNN + LSTM model. The experimental results demonstrate that the endocarp of paper walnut began to harden at 57 days, and the endocarp tissue on the hardened inner side also began to stain. This indicates that the endocarp hardened laterally from outside to inside. The screening and prediction results show that the CNN + LSTM model’s highest accuracy can reach 0.9264. The Accuracy, Precision, Recall, and F1-score of the CNN + LSTM model are better than the traditional machine learning algorithm. Moreover, the Receiver Operating Curve (ROC) area enclosed by the CNN + LSTM model and coordinate axis is the largest, and the Area Under Curve (AUC) value is 0.9796. The comparison of ROC and AUC proves that the CNN + LSTM model is better than the traditional algorithm for screening differentially expressed genes and function prediction in the walnut endocarp hardening stage. Using deep learning to predict expressed genes’ function accurately can reduce the breeding cost and significantly improve the yield and quality of crops. This research provides scientific guidance for the scientific breeding of paper walnut.