Receiver Operating Characteristic Curve Values Research Articles

Performance of the TIMI and GRACE scores in identifying the risk of death from acute coronary ischemic syndrome: a retrospective study in patients with chest pain at a Peruvian Hospital

Background. Acute coronary syndrome (ACS) is one of the significant causes of death in the adult population globally, and mortality risk assessment is essential to improve prevention strategies. This study aimed to determine the performance of the TIMI and GRACE predictive models in identifying the risk of death from acute coronary syndrome (ACS) in patients with chest pain at a public hospital in Tumbes, Peru. Methods. We designed a retrospective cross-sectional study of medical records of patients with ACS treated at the José Alfredo Mendoza Olavarría Regional Hospital in Tumbes, Peru, from May to December 2022. The performance of the scores TIMI and GRACE for risk of death from ACS was evaluated using the receiver operating characteristic (ROC) curve analysis and calculating the area under the ROC curve (AUC) value with its 95% confidence interval. The AUC values of both scores were compared using Pearson's chi-square test. Results. Data from 106 patients were analyzed. The average age of the patients was 65 years (interquartile range [IQR]: 55-73), with 63.2% being male. The AUC values for the TIMI and GRACE scores were 95.1% (CI95: 90.9-99.2%) and 95.2% (85.7-100.0%), respectively, p=0.982. Conclusion. No difference was found in using the TIMI and GRACE scores to assess the risk of death in patients with ACS who attend a hospital for chest pain. Hence, either of the two scores would be helpful for the prognosis of death in patients with ACS.

Derivation and validation of a new prediction model for sudden unexpected death in epilepsy based on a longitudinal prospective population-based cohort

ObjectiveWe conducted a population-based, prospective cohort study with a large sample size in Ningxia Province of the Northwest, a rural area in China, by developing a model to specifically assess risk factors of sudden unexpected death in epilepsy (SUDEP) in people with convulsive epilepsy by clinical variables. MethodsParticipants with convulsive epilepsy were recruited from January 1, 2008, to April 28, 2022, in rural Northwest China. They received regular assessments and management of epilepsy at the primary healthcare level and were followed up monthly. Information on the cause of death and relevant clinical details was obtained from death certificates or neurologist-conducted verbal autopsies. Survival analysis was employed to identify potential risk factors associated with SUDEP. ResultsFive variables were independently associated with SUDEP: generalized tonic-clonic seizures (GTCS) with ≥1 attack during the preceding month, GTCS with >3 attacks during the preceding year, body mass index (BMI) ≥24, age of onset ≤14 years, and duration >20 years. The area under receiver operator characteristic (ROC) curve (AUC) value (95% CI) of the model was 0.789 (0.735–0.843) in the derivation dataset and 0.830 (0.758–0.902) in the validation dataset. There was agreement between the observed and predicted probabilities of SUDEP. ConclusionsThis study establishes that high GTCS frequency, early age of onset, long duration of epilepsy, and being overweight are associated with an increased risk of SUDEP in individuals with convulsive epilepsy. The study also developed and validated a personalized prediction model to accurately assess the risk of SUDEP.

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT.

SEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings.

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology-based on wearable sensors and artificial intelligence-to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

Clinical Validation of a Primary Antibody Deficiency Screening Algorithm for Primary Care

PurposeThe diagnostic delay of primary antibody deficiencies (PADs) is associated with increased morbidity, mortality, and healthcare costs. Therefore, a screening algorithm was previously developed for the early detection of patients at risk of PAD in primary care. We aimed to clinically validate and optimize the PAD screening algorithm by applying it to a primary care database in the Netherlands.MethodsThe algorithm was applied to a data set of 61,172 electronic health records (EHRs). Four hundred high-scoring EHRs were screened for exclusion criteria, and remaining patients were invited for serum immunoglobulin analysis and referred if clinically necessary.ResultsOf the 104 patients eligible for inclusion, 16 were referred by their general practitioner for suspected PAD, of whom 10 had a PAD diagnosis. In patients selected by the screening algorithm and included for laboratory analysis, prevalence of PAD was ~ 1:10 versus 1:1700–1:25,000 in the general population. To optimize efficiency of the screening process, we refitted the algorithm with the subset of high-risk patients, which improved the area under the curve–receiver operating characteristics curve value to 0.80 (95% confidence interval 0.63–0.97). We propose a two-step screening process, first applying the original algorithm to distinguish high-risk from low-risk patients, then applying the optimized algorithm to select high-risk patients for serum immunoglobulin analysis.ConclusionUsing the screening algorithm, we were able to identify 10 new PAD patients from a primary care population, thus reducing diagnostic delay. Future studies should address further validation in other populations and full cost-effectiveness analyses.RegistrationClinicaltrials.gov record number NCT05310604, first submitted 25 March 2022

Construction and validation of a predictive model for the risk of peritoneal dialysis-associated peritonitis after peritoneal dialysis catheterization.

To construct and validate a risk prediction model for the development of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritoneal dialysis (PD). This retrospective analysis included patients undergoing PD at the Department of Nephrology, the First Affiliated Hospital of Anhui University of Chinese Medicine, between January 2016 and January 2021. Baseline data were collected. The primary study endpoint was PDAP occurrence. Patients were divided into a training cohort (n = 264) and a validation cohort (n = 112) for model building and validation. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to optimize the screening variables. Predictive models were developed using multifactorial logistic regression analysis with column line plots. Receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow goodness-of-fit tests were used to verify and evaluate the discrimination and calibration of the prediction models. Decision curve analysis (DCA) was used to assess the clinical validity of the prediction models. Five potential predictors of PDAP after PD catheterization were screened using LASSO regression analysis, including neutrophil-to-lymphocyte ratio (NLR), serum ALBumin (ALB), uric acid (UA), high sensitivity C-reactive protein (hsCRP), and diabetes mellitus (DM). Predictive models were developed by multi-factor logistic regression analysis and plotted in columns. The area under the ROC curve (AUC) values were 0.891 (95% confidence interval [CI]: 0.829-0.844) and 0.882 (95% CI: 0.722-0.957) for the training and validation cohorts, respectively. The Hosmer-Lemeshow test showed a good fit (p = 0.829 for the training cohort; p = 0.602 for the validation cohort). The DCA curves indicated that the threshold probabilities for the training and validation cohorts were 4-64% and 3-90%, respectively, predicting a good net gain for the clinical model. NLR, ALB, UA, hsCRP, and DM are independent predictors of PDAP after PD catheterization. The column line graph model constructed based on the abovementioned factors has good discriminatory and calibrating ability and helps to predict the risk of PDAP after PD catheterization.

Tumor-associated endothelial cell prognostic risk model and tumor immune environment modulation in liver cancer based on single-cell and bulk RNA sequencing: Experimental verification

BackgroundTo build a prognostic and immunotherapeutic response prediction model for liver cancer based on marker genes of tumor-associated endothelial cell (TEC). MethodSingle cell sequencing data from Gene Expression Omnibus (GEO) liver cancer patients were utilized to identify TEC subpopulations. Models were built from transcriptomic and clinical data of TCGA liver cancer patients. The GSE76427 and ICGC databases were used as independent validation sets. Time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to verify the ability of the model to predict survival. XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA were applied to evaluate tumor immune cell infiltration. The TIDE score was used to predict the effect of immunotherapy. Immune blockade checkpoint gene, tumor mutational load and GSVA enrichment analyses were further explored. The expression levels of candidate genes were measured and validated by real-time PCR between liver cancer tissues and adjacent nontumor liver tissues. ResultsEighty-seven genes were identified as marker genes for TECs. IGFBP3, RHOC, S100A16, FSCN1, and CLEC3B were included in the constructed prognostic model. Time-dependent ROC curve values were higher than 0.700 in both the model and validation groups. The low risk group exhibited high immune cell infiltration and function than the higher risk group. The TIDE score indicated that the low-risk group benefited more from immunotherapy than the high-risk group. The risk score and multiple immune blockade checkpoint genes and immune-related pathways were strongly correlated. ConclusionNovel signatures of TEC marker genes showed a powerful ability to predict prognosis and immunotherapy response in patients with liver cancer.

Neutrophil CD64-a prognostic marker of sepsis in intensive care unit: a prospective cohort study.

Little is known about the prognostic ability of nCD64 in critically ill patients. This study aimed to assess the prognostic values of nCD64 in adult ICU patients with sepsis. A prospective cohort study was conducted at the ICU of Cho Ray Hospital in Vietnam between January 2019 to September 2020. All newly admitted 86 septic patients diagnosed based on sepsis-3 criteria were included. An evaluation of nCD64 was performed at admission (T0) and 48 h thereafter (T48). Delta nCD64 (nCD64 T48 - nCD64 T0), %delta nCD64 [(nCD64 T48 - nCD64 T0)/nCD64 T0 x 100%], APACHE II and SOFA scores were calculated and examined. Serum procalcitonin levels and white blood cell counts were documented. Spearman's rank correlation coefficient was used to test the correlation between nCD64 and severity scores. Receiver-operating characteristic (ROC) curve was performed to evaluate the predictive efficacy of the sepsis parameters. Patients with septic shock had significantly higher nCD64 levels than septic patients [3,568 (2,589; 5,999) vs. 1,514 (1,416;2,542) molecules/cell, p < 0.001]. nCD64 T0 and SOFA scores had a moderately positive linear correlation (R = 0.31, p = 0.004). In the survivor group, nCD64 levels significantly decreased within the first 48 h of admission (p < 0.001), while this trend was not statistically significant in the non-survivor group (p = 0.866). The area under the ROC curve (AUC) value of %delta nCD64 combined with APACHE II score (0.81) was higher than that of any other parameter alone or in combination with each other. The nCD64 index may serve as a valuable biomarker for predicting the course of sepsis. Monitoring changes in nCD64 during the initial 48 h of admission can aid in predicting the prognosis of septic patients. The use of a combination of the trends of nCD64 index in the first 48 h with APACHE II score would further enhance the predictive accuracy. More studies with longer follow-ups are needed to fully understand the implications of serial trend and kinetics of nCD64 in septic patients.

A new predictor of coronary artery disease in acute ischemic stroke or transient ischemic attack patients: pericarotid fat density.

The study aims to evaluate the incremental predictive value of pericarotid fat density (PFD) on head and neck computed tomography angiography (CTA) for the obstructive coronary artery disease (CAD) (≥ 50% stenosis) relative to a clinical risk model (Framingham risk score (FRS)) and the degree of carotid artery stenosis and plaque type in acute ischemic stroke (AIS) or transient ischemic attack (TIA) patients without a known history of CAD. In a cohort of 134 consecutive stable patients diagnosed with AIS or TIA undergoing head and neck CTA between January 2010 and December 2021, pericarotid adipose tissue density (PFD) was quantified using a dedicated software. We collected demographic and clinical data, assessed the risk of CAD using the FRS, and analyzed coronary and carotid artery CTA images. Univariate and multivariate logistic regression analyses were performed to assess associations between FRS, PFD, CTA variables, and obstructive CAD risk. Four prediction models were established to evaluate the incremental predictive value of PFD relative to FRS, stenosis degree, and plaque types. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUC) were compared. Increasing FRS, stenosis degree, and PFD values were positively correlated with obstructive CAD (all p < 0.05). In the predictive models for obstructive CAD, the model incorporating carotid stenosis exhibited superior predictive performance compared to FRS alone (p < 0.05). Moreover, the predictive model integrating PFD demonstrated enhanced performance and yielded the highest AUC of the receiver operator characteristic curve (AUC = 0.783), with sensitivity and specificity values of 86.89% and 65.75%, respectively. CTA-derived PFD measurements offer supplementary predictive value for obstructive CAD beyond FRS and stenosis, thereby facilitating improved risk stratification of TIA or stroke patients without a history of CAD history. CTA-derived PFD provides incremental predictive value for obstructive coronary artery disease in acute ischemic stroke or transient ischemic attack patients without CAD history, beyond Framingham risk score and carotid artery stenosis degree, improving risk stratification. • Pericarotid fat density is associated with obstructive coronary artery disease in acute ischemic stroke or transient ischemic attack patients. • Higher pericarotid fat density corresponds to an increased risk of obstructive coronary artery disease. • Estimation of pericarotid fat density using computed tomography angiography imparts additional predictive value for obstructive CAD in risk stratification of acute ischemic stroke or transient ischemic attack patients.

Imaging features of cardioembolic stroke on 4-dimensional computed tomography angiography.

Identifying cardioembolic stroke is important for the decision-making of endovascular treatment and anticoagulation therapy. We aimed to explore the features of cardioembolic stroke on 4-dimensional (4D) computed tomography angiography (4D-CTA) and assess whether these features can assist in classifying stroke etiology. In this retrospective study, we analyzed the images of 294 patients with acute ischemic stroke (AIS) from July 2020 to February 2022 at the First Affiliated Hospital of Chongqing Medical University, which had been consecutively collected. The data of 110 patients with occlusion of the M1/M2 segment of the middle cerebral artery (MCA) with/without intracranial internal carotid artery (ICA) occlusion were analyzed to calculate the clot burden score (CBS) and collateral score (CS), and the data of 88 patients with a clear origin and distal part were analyzed to measure clot length. Maximum intensity projection (MIP) and time MIP (tMIP) post-processing were used to assess the clot features. The Mann-Whitney U test was used to compare the clot characteristics between the 2 groups. Binary logistic regression was performed to assess the association between the image characteristics and cardioembolic stroke. Moreover, the receiver operating characteristic (ROC) curve was used to test the diagnostic efficacy of MIP/tMIP clot features in classifying cardioembolic stroke. Age, high-risk factors for cerebrovascular disease, high/medium-risk sources of cardioembolic stroke, clot length, CBS, and CS were significantly different between the cardioembolic stroke group and non-cardioembolic stroke group (P<0.05). In the cardioembolic stroke group, the median MIP and tMIP clot length was 12 mm [interquartile range (IQR), 8.3-17.4 mm] and 9.3 mm (IQR, 6.8-14.3 mm), respectively. In the non-cardioembolic stroke group, the median MIP and tMIP clot length was 6.5 mm (IQR, 4.7-11.5 mm) and 5.8 mm (IQR, 3.9-10.6 mm), respectively. Binary logistic regression showed that cardioembolic stroke was significantly associated with MIP-clot length [odds ratio (OR), 1.15; 95% confidence interval (CI): 1.02-1.29; P<0.05], tMIP-clot length (OR, 1.18; 95% CI: 1.02-1.36; P<0.05), and tMIP-CBS (OR, 3.96; 95% CI: 1.08-14.58; P<0.05). The area under the ROC curve (AUC) values of MIP clot length for identifying cardioembolic stroke were 0.75 (95% CI: 0.65-0.84, P<0.05), with a cut-off value of >7.4 mm [sensitivity: 84.62% (95% CI: 69.50-94.10%); specificity: 59.18% (95% CI: 44.20-73.00%)]. The AUC value of tMIP clot length was 0.72 (95% CI: 0.61-0.81, P<0.05), with a cut-off value of >5.4 mm [sensitivity: 92.31% (95% CI: 79.10-98.40%); specificity: 48.98% (95% CI: 34.40-63.70%)]. Clot length and CBS were overestimated on MIP images. Among the clot characteristics, clot length could identify cardioembolic stroke.

The Use of the Arm Circumference as a Measure to Detect Underweight in Individuals Aged 60 Years or Older Living in Costa Rica

[Objective]: This investigation focuses on the association between arm circumference and body mass index, and the estimation of cutoff values of this arm measurement for identifying low weight in Costa Rican residents aged 60 years and over. [Methods]: The study included a total of 2514 persons 60 years old or older who participated in the project “Costa Rica: Study of Longevity and Healthy Aging.” The analysis included calculation of Spearman’s correlation coefficient between arm circumference and the body mass index, the area under the Receiver Operating Characteristic (ROC) curve, and sensitivity and specificity values for measurements of arm circumference corresponding to a body mass index lower than 22 kg/m2. [Results]: The Spearman’s correlation coefficient between the body mass index and the arm circumference was 0.794 (0.774 for men and 0.806 for women). The optimum cutoff point was estimated at 26.5 cm, with a Youden’s Index of 0.7256, a sensitivity of 87.79 %, and a specificity of 84.77 %. Specific cutoff points for men and women were 26.5 cm and 25.9 cm, respectively. [Conclusions]: There is a strong relationship between arm circumference and body mass index in Costa Rican residents aged 60 years and over. A first approach was established to determine an adequate cutoff point in the measurement of arm circumference that will allow the detection of persons with low weight and greater nutritional risk in this population.

Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression.

We aimed to investigate the potential diagnostic value of five serum neuroactive substances in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) complicated with depression. A total of 103 patients with AECOPD were enrolled between August 2020 and August 2021. All patients were assessed using a self-rating depression scale and divided into AECOPD with or without depression groups. Baseline data and serum neuroactive substance levels were compared between the two groups. Logistic regression was used to identify the risk factors. The diagnostic performance of neuroactive substances was evaluated using receiver operating characteristic (ROC) curves. Patients with AECOPD complicated with depression exhibited higher partial pressure of CO2 values and higher chronic obstructive pulmonary disease assessment test (CAT) scores. An elevated proportion of patients with more than two acute exacerbations (AEs) in the previous year was observed in this patient group (all P < 0.001). The CAT score and number of AEs during the previous year were identified as independent risk factors for AECOPD complicated with depression. No significant differences were observed in the levels of aspartic acid and glutamate between the two groups (P > 0.05). Serum γ-aminobutyric acid (GABA) and glycine (Gly) levels were decreased. In contrast, serum nitric oxide (NO) levels were increased in the AECOPD complicated with the depression group (P < 0.05). Serum GABA and Gly levels exhibited a negative correlation, and NO levels positively correlated with the number of AEs in the previous year and the CAT score. The area under the ROC curve values for GABA, Gly, and NO were 0.755, 0.695, and 0.724, respectively. Serum GABA exhibited a sensitivity of 85.1% and a specificity of 58.9%, below the cut-off value of 4855.98 nmol/L. Serum GABA, Gly, and NO may represent potential biomarkers for AECOPD complicated with depression.

Measurement of Hounsfield units on proximal femur computed tomography for predicting regional osteoporosis.

This study was designed to investigate the use of proximal femoral Hounsfield units (HU) in conventional abdominal and pelvic computed tomography (CT) to predict hip osteoporosis by coupling with data from quantitative CT (QCT). In this study, 315 patients who underwent routine abdominal and pelvic CT with the proximal femur included in the scanning range were also subjected to QCT of the proximal femur. Pearson correlation test was performed to analyze the correlations of the femoral head, femoral neck, proximal femur, and femoral trochanter CT HU with the femoral neck, femoral trochanter, and intertrochanteric femur bone mineral density (BMD) values from QCT. The diagnostic performance of CT HU measurement of the proximal femur for osteoporosis was analyzed using receiver operating characteristic (ROC) curves. The CT HU of the proximal femur showed the highest correlation with the BMD value of the hip (r = 0.826; p < 0.01). The mean CT HU of the proximal femur differed significantly (all p < 0.01) for the three QCT-defined BMD categories of osteoporosis (192.23 HU vs. 188.71), of osteopenia (247.86 HU vs. 248.36 HU), and of normal individuals (308.13 HU vs. 310.41 HU) in left and right sides, respectively. In the ROC curve analysis, the area under the ROC curve values to predict osteoporosis in the left and right proximal femurs were 0.942 and 0.941, respectively. The CT HU of the proximal femur was significantly associated with the BMD value of the hip measured by QCT. The CT HU of the proximal femur is highly effective in diagnosing osteoporosis and could be used for hip osteoporosis screening.

Expression patterns of cluster of differentiation 147 impact the prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) has very low overall survival. According to global cancer statistics, approximately 905677 new cases were reported in 2020, with at least 830180 of them being fatal. Cluster of differentiation 147 (CD147) is a novel, transmembrane glycoprotein that is expressed in a wide variety of tumor cells and plays an important role in various stages of tumor development. Based on the reports described previously, we theorize that CD147 may be used as a novel biological indicator to predict the prognosis of HCC. To study this possibility, expression profiles of CD147 and corresponding clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed, and a hazard ratio (HR) was established. To explore the pattern of CD147 expression and its applicability in the prognosis of HCC. To establish HRs and probability points for predicting the prognosis of HCC by correlating CD147 expression with clinical characteristics. To determine if CD147 can be a reliable biomarker in HCC prognosis. The CD147 expression profile in HCC and corresponding clinical data were obtained from TCGA database. The expression patterns of CD147 were then validated by analyzing data from the GEO database. In addition, CD147 immunohistochemistry in HCC was obtained from the Human Protein Atlas. CD147 expression patterns and clinical characteristics in the prognosis of HCC were analyzed by accessing the UALCAN web resource. Accuracy, sensitivity, and specificity of the CD147 expression profile in predictive prognosis were determined by the time-dependent receiver operating characteristic (ROC) curves. Kaplan-Meier curves were plotted to estimate the HR of survival in HCC. Univariate and multivariate Cox regression proportional hazards analyses of CD147 expression levels and clinical characteristics as prognostic factors of HCC were performed. Nomograms were used to establish probability points and predict prognosis. Data from TCGA and GEO databases revealed that CD147 was significantly overexpressed in HCC (P = 1.624 × 10-12 and P = 1.2 × 10-5, respectively). The expression of CD147 and prognosis of HCC were significantly correlated with the clinical characteristics of HCC as per the data from the UALCAN web resource (P < 0.05). Kaplan-Meier analysis of CD147 expression in HCC revealed that the high expression groups showed poor prognosis and an HR of survival > 1 [log-rank test, P = 0.000542, HR (in high expression group): 1.856, 95% confidence interval (CI): 1.308 to 2.636]. ROC curves were plotted to analyze the 1-year, 3-year, and 5-year survival rates. The area under the ROC curve values were 0.675 (95%CI: 0.611 to 0.740), 0.623 (95%CI: 0.555 to 0.692), and 0.664 (95%CI: 0.582 to 9.745), respectively. Univariate Cox analysis of CD147 expression and clinical characteristics of HCC and multivariate Cox analysis of CD147 patterns and pathological tumor-node-metastasis stage showed significant differences (univariate Cox, P = 0.00013, HR: 1.424, 95%CI: 1.884 to 1.707 and P = 0.00066, HR: 1.376, 95%CI: 1.145 to 1.654, respectively; multivariate Cox, P = 0.00578, HR: 1.507, 95%CI: 1.126 to 2.018 and P = 0.00336, HR: 1.443, 95%CI: 1.129 to 1.844, respectively). Nomograms were plotted to establish the probability points and predict prognosis. The total points ranged from 0 to 180, and the C-index value was 0.673 (95%CI: 0.600 to 1.000, P < 0.01). Overexpression of CD147 was correlated with poor prognosis in HCC. The CD147 expression profile combined with clinical characteristics can reliably predict the prognosis of HCC. CD147 can serve as a biomarker to predict the prognosis of HCC.

Metabolomics analysis of serum metabolites during endometrial transformation: association with recurrent implantation failure in hormonal replacement therapy-frozen embryo transfers cycles.

The purpose of this study was to investigate alterations in serum metabolites during endometrial transformation and possibleassociations with recurrent implantation failure (RIF) in hormonal replacement therapy (HRT)-frozen embryo transfer (FET) cycles. We performed a prospective study involving 100 patients scheduled for HRT-FET cycles during January 2022 to April 2022. Blood serum samples were collected on the day of progesterone administration (dPA) and on the third day of progesterone administration (d3PA). Gas chromatography-mass spectrometry (GC-MS) analysis was performed to identify and quantify serum metabolites. A nested case-control study including 19 RIF patients and 19 matching controls was conducted to explore the predictive value of serum metabolites for RIF. Partial least squares discriminant analysis (PLS-DA) and receiver operating characteristic (ROC) curve analysis were performed to establish prediction models. We identified 105 serum metabolites, with 76 of them exhibiting significant alterations during the initial 3 days of endometrial transformation. Metabolites involved in amino acid metabolism and tricarboxylic acid (TCA) cycle showed lower levels during endometrial transformation. In the nested case-control study, the prediction model based on the ratio of serum metabolites between d3PA and dPA showed the highest area under the ROC curve (AUC), accuracy, and R2 and Q2 values. Eight metabolites, including indol-3-propionic acid, beta-alanine, myristoleic acid, malic acid, indole, DL-isocitric acid, proline, and itaconic acid, exhibited high predictive values for RIF. This study demonstrates alterations in serum metabolites during endometrial transformation, particularly in amino acid metabolism and TCA cycle. The identified metabolites, especially indol-3-propionic acid and malic acid, show potential as predictive markers for RIF. These findings contribute to a better understanding of the metabolic changes associated with endometrial receptivity and provide insights for the development of personalized approaches to improve implantation outcomes in FET cycles.

A data-driven machine learning approach for discovering potent LasR inhibitors

ABSTRACTThe rampant spread of multidrug-resistant Pseudomonas aeruginosa strains severely threatens global health. This severity is compounded against the backdrop of a stagnating antibiotics development pipeline. Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the las quorum sensing (QS) system remains an attractive therapeutic strategy to combat P. aeruginosa infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. In this work, we demonstrated using a Multilayer Perceptron (MLP) algorithm boosted with AdaBoostM1 to discriminate between active and inactive LasR inhibitors. The optimal model performance was evaluated using 5-fold cross-validation and test sets. Our best model achieved a 90.7% accuracy in distinguishing active from inactive LasR inhibitors, an area under the Receiver Operating Characteristic Curve value of 0.95, and a Matthews correlation coefficient value of 0.81 when evaluated using test sets. Subsequently, we deployed the model against the Enamine database. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies, Molecular Dynamics simulations, MM-GBSA analysis, and Free Energy Landscape analysis. Our data indicate that several of our chosen top hits showed better ligand-binding affinities than naringenin, a competitive LasR inhibitor. Among the six top hits, five of these compounds were predicted to be LasR inhibitors that could be used to treat P. aeruginosa-associated infections. To our knowledge, this study provides the first assessment of using an MLP-based QSAR model for discovering potent LasR inhibitors to attenuate P. aeruginosa infections.

Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma.

Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics. To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors. The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC). The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p=0.029). The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI.

To evaluate a novel method of semisupervised learning (SSL) guided by automated sparse information from diagnostic reports to leverage additional data for deep learning-based malignancy detection in patients with clinically significant prostate cancer. This retrospective study included 7756 prostate MRI examinations (6380 patients) performed between January 2014 and December 2020 for model development. An SSL method, report-guided SSL (RG-SSL), was developed for detection of clinically significant prostate cancer using biparametric MRI. RG-SSL, supervised learning (SL), and state-of-the-art SSL methods were trained using 100, 300, 1000, or 3050 manually annotated examinations. Performance on detection of clinically significant prostate cancer by RG-SSL, SL, and SSL was compared on 300 unseen examinations from an external center with a histopathologically confirmed reference standard. Performance was evaluated using receiver operating characteristic (ROC) and free-response ROC analysis. P values for performance differences were generated with a permutation test. At 100 manually annotated examinations, mean examination-based diagnostic area under the ROC curve (AUC) values for RG-SSL, SL, and the best SSL were 0.86 ± 0.01 (SD), 0.78 ± 0.03, and 0.81 ± 0.02, respectively. Lesion-based detection partial AUCs were 0.62 ± 0.02, 0.44 ± 0.04, and 0.48 ± 0.09, respectively. Examination-based performance of SL with 3050 examinations was matched by RG-SSL with 169 manually annotated examinations, thus requiring 14 times fewer annotations. Lesion-based performance was matched with 431 manually annotated examinations, requiring six times fewer annotations. RG-SSL outperformed SSL in clinically significant prostate cancer detection and achieved performance similar to SL even at very low annotation budgets.Keywords: Annotation Efficiency, Computer-aided Detection and Diagnosis, MRI, Prostate Cancer, Semisupervised Deep Learning Supplemental material is available for this article. Published under a CC BY 4.0 license.

Serum identification of at-risk MASH: The metabolomics-advanced steatohepatitis fibrosis score (MASEF).

Early identification of those with NAFLD activity score ≥ 4 and significant fibrosis (≥F2) or at-risk metabolic dysfunction-associated steatohepatitis (MASH) is a priority as these patients are at increased risk for disease progression and may benefit from therapies. We developed and validated a highly specific metabolomics-driven score to identify at-risk MASH. We included derivation (n = 790) and validation (n = 565) cohorts from international tertiary centers. Patients underwent laboratory assessment and liver biopsy for metabolic dysfunction-associated steatotic liver disease. Based on 12 lipids, body mass index, aspartate aminotransferase, and alanine aminotransferase, the MASEF score was developed to identify at-risk MASH and compared to the FibroScan-AST (FAST) score. We further compared the performance of a FIB-4 + MASEF algorithm to that of FIB-4 + liver stiffness measurements (LSM) by vibration-controlled transient elastography (VCTE). The diagnostic performance of the MASEF score showed an area under the receiver-operating characteristic curve, sensitivity, specificity, and positive and negative predictive values of 0.76 (95% CI 0.72-0.79), 0.69, 0.74, 0.53, and 0.85 in the derivation cohort, and 0.79 (95% CI 0.75-0.83), 0.78, 0.65, 0.48, and 0.88 in the validation cohort, while FibroScan-AST performance in the validation cohort was 0.74 (95% CI 0.68-0.79; p = 0.064), 0.58, 0.79, 0.67, and 0.73, respectively. FIB-4+MASEF showed similar overall performance compared with FIB-4 + LSM by VCTE ( p = 0.69) to identify at-risk MASH. MASEF is a promising diagnostic tool for the assessment of at-risk MASH. It could be used alternatively to LSM by VCTE in the algorithm that is currently recommended by several guidance publications.

The Predictive Value of Lung Ultrasound Score on Hemodynamically Significant Patent Ductus Arteriosus among Neonates ≤25 Weeks.

Lung ultrasound (LU) is increasingly used to diagnose and monitor neonatal pulmonary disorders; however, its role in hemodynamically significant patent ductus arteriosus (hsPDA) has not been elucidated. This prospective study investigated the predictive value of the LU score (LUS) for hsPDA in preterm infants with gestational age (GA) ≤ 25 weeks. Preterm infants with GA ≤ 25 weeks were enrolled in this study. LU was conducted on the fourth day of life (DOL). Six lung regions in every lung were scanned, with each region rated as 0-4 points. The performance of the LUS in predicting hsPDA among infants aged ≤25 weeks was analyzed by plotting the receiver operating characteristic (ROC) curve. A total of 81 infants were included in this study. GA, birth weight (BW), gender, Apgar score, delivery mode, antenatal steroids, meconium-stained amniotic fluid, premature rapture of membrane, and early-onset sepsis were not significantly different, but infants in the hsPDA group had increased LUS (38.2 ± 2.8 vs. 30.3 ± 4.3, p < 0.001) compared with non-hsPDA group. The area under the ROC curve (AUC) value of the LUS on the fourth DOL was 0.94 (95% CI: 0.93-0.99) in predicting hsPDA. The LUS threshold at 33 achieved 89% sensitivity and 83% specificity, with the positive and negative predictive values (PPV and NPV) being 87 and 86%, respectively. The LUS can predict hsPDA in extremely preterm infants at an early stage.