Receiver Operating Characteristic Values Research Articles

Are Clinical, Psychophysical or Psychological Variables Helpful for Discriminating Patients with tension type headache? A diagnostic accuracy study.

The aims of this study were: 1, to determine the ability of pain thresholds to differentiate between subjects with and without tension-type headache (TTH); and 2, to determine the capability of clinical, psychological and psychophysical variables to differentiate between individuals with frequent episodic (FETTH) or chronic (CTTH) tension-type headache. A diagnostic accuracy study was conducted. Pressure pain threshold (PPTs) over the temporalis muscle, cervical spine, second metacarpal, and tibialis anterior muscle and dynamic pain thresholds (DPT) were bilaterally assessed in 100 individuals with TTH and 50 comparable non-headache subjects. Clinical headache parameters (headache diary), headache-associated burden (HDI), anxiety and depressive levels (HADS), sleep quality (PSQI), and state (STAI-S) - trait (STAI-T) anxiety levels were also evaluated. The area under the receiver operating characteristic (ROC) curve, optimal cut-off point, sensitivity, specificity, and positive and negative likelihood ratios (LR) for each variable were calculated. Individuals with TTH exhibited lower PPTs and DPT than those without TTH. No significant differences in PPTs and DPT were found between FETTH and CTTH individuals. Overall, no clinical, psychological or psychophysical variable exhibited an acceptable ROC value (≥0.7) for identifying between TTH patients and non-headache controls or between subjects with FETTH and CTTH. Although individuals with TTH exhibit widespread pressure pain hyperalgesia, neither clinical nor psychological nor psychophysical variable had proper diagnostic accuracy to discriminate between individuals with/without TTH or between those with FETTH and CTTH. Further studies should clarify the clinical relevance of these findings.

Comparing Peristaltic and Direct-Drive Contrast Injection Systems for Thoracic Computed Tomography (CT): Effects on Dose, Image Quality, and Pathology.

Introduction To compare thoracic vasculature opacification between the direct-drive and the peristaltic drive contrast injection systems and the effect on quantitative and qualitative image quality during computed tomography. Method A retrospective chart review of 88 patients who underwent chest computed tomography (CT) following a direct-drive injector before 2016 (Group A) or a peristaltic injector after 2016 (Group B). Both protocols employed 80mL of iodinated contrast media volume. Quantitative measurements of the thoracic vascular opacification, contrast-to-noise ratio (CNR), effective radiation dose, and arterial venous contrast ratio (AVCR) for each vessel were computed and compared. The measured values of vascular opacification, CNR, and dose length product (DLP) were assessed utilizing paired t-test and Pearson's correlation. Receiver operating characteristic(ROC), visual grading characteristics (VGC), and Cohen's kappa methodology were measured. Results There was no significant difference in the mean opacification of the chest vasculature (p > 0.05). The CNR in Group B was significantly lower than in Group A (p < 0.05) except for the brachiocephalic artery, proximal descending aorta, and the right pulmonary artery. Arterial venous contrast ratio at all anatomic levels demonstrated no statistical significance between groups. There were no differences in ROC, VGC, or kappa values between groups. Conclusion When comparing two contrast media injection systems in chest CT, there was no qualitative difference in image quality or pathology detection. However, quantitatively, direct-drive injectors provided higher CNR. Direct-drive contrast injectors also provided a superior quantitative image quality of chest vasculature compared to peristaltic injectors.

Can Clinical, Psychophysical or Psychological Variables Help in Discriminating Women with Migraines from a Tertiary Center? A Diagnostic Accuracy Study.

Background: Migraine diagnosis is mainly clinically based on symptomatology. The objectives of this study were (1) to determine the ability of pain thresholds to differentiate between women with and without migraines and (2) to determine the ability of clinical, psychological and psychophysical variables to differentiate between women with episodic and chronic migraines. A diagnostic accuracy study was conducted. Methods: Pressure-pain thresholds (PPTs) at one trigeminal (temporalis muscle) and one extra-trigeminal (cervical spine) and two distant-pain free (second metacarpal and tibialis anterior muscle) areas, as well as dynamic pain thresholds (DPTs), were bilaterally assessed in 100 women with migraines, recruited from tertiary hospitals (50% episodic, 50% chronic), and 50 comparable women without headaches. Migraine pain features (headache diary), migraine-associated burden (HDI), anxiety and depressive levels (HADS) and state (STAI-S)-trait (STAI-T) anxiety were also evaluated. The area under the receiver operating characteristic (ROC) curve, with optimal cut-off points, as well as the sensitivity, specificity and positive/negative likelihood ratios (LR) for each variable, were calculated. The women with migraines showed lower PPTs and DPTs than those without migraines. Results: The women with chronic migraines showed lower PPTs in the temporalis muscle than the women with episodic migraines. No clinical, psychological or psychophysical variables exhibited acceptable ROC values (≥0.7) for differentiating between women with and without migraines or between women with episodic and chronic migraines. Conclusions: Although the women with migraines had widespread pressure-pain hyperalgesia, neither the clinical, psychological nor psychophysical (pain threshold) variable exhibited the proper diagnostic accuracy to distinguish between women with and without migraines or between women with episodic and chronic migraines. New studies should clarify the clinical relevance of the findings of the current study.

Diagnostic Accuracy of Ultrasound Imaging and Shear Wave Elastography to Discriminate Patients with Chronic Neck Pain from Asymptomatic Individuals

Objectives: The aim of this study was to determine and compare the capability of several B-mode ultrasound (US) and shear wave elastography (SWE) metrics to differentiate subjects with chronic non-specific neck pain from asymptomatic subjects. Methods: A diagnostic accuracy study recruiting a sample of patients with chronic neck pain and asymptomatic controls was conducted. Data collection included sociodemographic information (i.e., gender, age, height, weight and body mass index), clinical information (pain intensity assessed using the Visual Analogue Scale and pain-related disability using the Neck Disability Index) and B-mode ultrasound and shear wave elastography features of the cervical multifidus muscle (cross-sectional area, perimeter, mean echo intensity, fat infiltration, shear wave speed and Young’s modulus). After analyzing between-group differences for left/right sides, cases and controls, and males and females, the area under the receiver operating characteristic (ROC) curve, the optimal cut-off point, the sensitivity, the specificity, the positive likelihood ratio (LR) and negative LR for each metric were calculated. A total of 316 individuals were recruited in this study (n = 174 cases with neck pain and n = 142 asymptomatic controls). Results: No significant differences (p &gt; 0.05) were found between cases and controls for most variables, except for fatty infiltration, which was significantly higher in chronic neck pain cases (p &lt; 0.001). Gender differences were significant across all US and SWE metrics (all, p &lt; 0.001 except p = 0.015 for fatty infiltrates). A slight asymmetry was observed between the left and right sides for area (p = 0.038). No significant interactions between group, gender and side (all metrics, p &gt; 0.008) were identified. Fatty infiltration was the most effective discriminator, with a ROC value of 0.723, indicating acceptable discrimination. The optimal cut-off point for fatty infiltration was 25.77, with a moderate balance between sensitivity (59.8%) and specificity (20.5%). However, its positive likelihood ratio (LR) of 0.75 suggests limited usefulness in confirming the condition. Conclusions: Fatty infiltration was significantly higher in individuals with chronic idiopathic neck pain compared to those without symptoms, while other muscle metrics were similar between both groups. However, since fat infiltration had moderate diagnostic accuracy and the other metrics showed poor discriminatory power, US cannot be used solely to discriminate patients with idiopathic neck pain.

A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients. ObjectiveThe aim of our observational study was to build a machine learning model to identify NASH in NAFLD patients. MethodsThe clinical characteristics of 259 NAFLD patients and their initial laboratory data (Cohort 1) were collected to train the model and carry out internal validation. We compared the models built by five machine learning algorithms and screened out the best models. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were used to evaluate the performance of the model. In addition, the NAFLD patients in Cohort 2 (n = 181) were externally verified. ResultsWe finally identified six independent risk factors for predicting NASH, including neutrophil percentage (NEU%), aspartate aminotransferase/alanine aminotransferase (AST/ALT), hematocrit (HCT), creatinine (CREA), uric acid (UA), and prealbumin (PA). The NASH-XGB6 model built using the XGBoost algorithm showed sufficient prediction accuracy, with ROC values of 0.95 (95 % CI, 0.91–0.98) and 0.90 (95 % CI, 0.88–0.93) in Cohort 1 and Cohort 2, respectively. ConclusionsNASH-XGB6 can serve as an effective tool for distinguishing NASH patients from NAFLD patients.

Associated Factors for Liver Fibrosis and Histological Activity Index in Treatment-Naïve HBeAg-Positive Chronic Hepatitis B Infection: Insights from a Retrospective Analysis.

The study aimed to identify predictors of advanced fibrosis score and histological activity index (HAI) in HBeAg-positive patients to facilitate early disease detection and reduce the need for invasive biopsies. The single-center retrospective study included treatment-naïve HBeAg-positive chronic hepatitis B (CHB) patients. Patients with HAI ≥6 and/or fibrosis ≥2 were considered to have significant liver damage. Independent determinants were identified by univariate and multivariate logistic regression analysis. Cut-off values for variables were determined by receiver operating characteristics (ROCs) curve analysis. The study enrolled a cohort of 66 patients, with 51.5% male and a median age of 26 (22.7-34.2) years. In assessing necroinflammation, no significant differences were observed in age and gender between patients with HAI <6 and HAI ≥6. However, patients with HAI ≥6 exhibited higher aspartate aminotransferase (AST) levels compared to those with HAI <6. Furthermore, lower albumin levels and platelet (PLT) counts, along with higher fibrosis-4 (FIB-4) scores, were associated with HAI ≥6. In the evaluation of fibrosis, while gender distribution did not differ significantly, patients with fibrosis grade ≥2 were older and had higher HAI scores, HAI ≥6 ratios, and FIB-4 scores compared to those with fibrosis grade <2. Multivariate analysis identified albumin as a significant predictor for both HAI ≥6 and fibrosis grade ≥2. The area under ROC (AUROC) values of albumin for predicting HAI ≥6 and fibrosis ≥2 were 0.696 and 0.698, respectively, indicating moderate predictive ability. Albumin was found to be an independent predictor of liver damage in HBeAg-positive CHB patients. The fact that the optimal threshold values detected for both HAI ≥6 and fibrosis ≥2 in this patient group were close to normal values suggests that clinicians should be more cautious in monitoring albumin levels and other pre-defined parameters to avoid delays in diagnosis.

MME and PTPRC: key renal biomarkers in lupus nephritis

Background Lupus nephritis (LN) is an autoimmune-related kidney disease with a poor prognosis, however the potential pathogenic mechanism remains unclear and there is a lack of precise biomarkers. Therefore, a thorough screening and identification of renal markers in LN are immensely beneficial to the research on its pathogenic mechanisms and treatment strategies. Methods We utilized bioinformatics to analyze the differentially expressed genes (DEGs) at the transcriptome level of three clusters: total renal, glomeruli, and renal tubulointerstitium in the GEO database to discover potential renal biomarkers of LN. We utilized NephroSeq datasets and measured mRNA and protein levels in the kidneys of MRL/lpr mice to confirm the expression of key DEGs. Results Seven significantly differential genes (EGR1, MME, PTPRC, RORC, MX1, ZBTB16, FKBP5) were revealed from the transcriptome database of GSE200306, which were mostly enriched in the pathway of the hematopoietic cell lineage and T cell differentiation respectively by KEGG and GO analysis. The seven hot differential genes were verified to have consistent change trends using three datasets from NephroSeq database. The receiver operating characteristic (ROC) curve indicated that five DEGs (PTPRC, MX1, EGR1, MME and RORC) exhibited a higher diagnostic ROC value in both the glomerulus and tubulointerstitium group. Validation of core genes using MRL/lpr mice showed that MME and PTPRC exhibit significantly differential mRNA and protein expression patterns in mouse kidneys like the datasets. Conclusions This study identified seven key renal biomarkers through bioinformatics analysis using the GEO and NephroSeq databases. It was identified that MME and PTPRC may have a high predictive value as renal biomarkers in the pathogenesis of LN, as confirmed by animal validation.

An initial exploration of factors that may impact radiographer performance in reporting mammograms

ObjectivesIn the United Kingdom, radiographers with a qualification in image interpretation have interpreted mammograms since 1995. These radiographers work under the title of radiography advanced practitioners (RAP) or Consultant Radiographer. This study extends upon what has been very recently published by exploring further clinical, non-clinical and experiential factors that may impact the reporting performance of RAPs. MethodsFifteen RAPs interpreted an image test set of 60 2D mammograms of known truth using the Detected-X software platform. Unknown to the reader, twenty cases contained a malignancy. Sensitivity, specificity, lesion sensitivity, receiver operating characteristic (ROC) and jack-knife free response operating characteristic (AFROC) values were established for each RAP. Specific features that had significant impact on accuracy were identified using Student's-T and Mann Whitney tests. ResultsRAPs with more than 10 years' experience in image interpretation, compared to those with less than 10 years’ experience, demonstrated lower specificity (51.3% vs 84.8%, p = 0.0264), ROC (0.83 vs 0.91, p = 0.0264) and AFROC (0.75 vs 0.87, p = 0.0037) values. Further, higher sensitivity values of 90.7% were seen in those RAPs who had an eye test in the last year compared to those who had not, 82% (p = 0.021). Other changes are presented in the paper. ConclusionThese data reveal previously unidentified factors that impact the diagnostic efficacy of RAPs when interpreting mammographic images. Highlighting such findings will empower screening authorities to better examine ways of standardising performance and offer a baseline for performance benchmarks. Implications for practiceThis study for the first time performs an initial exploration of the factors that may be associated with RAP performance when interpreting screening mammograms.

Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees

Accurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (Cc) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and Cc-Euclidean, 0.996 for Cc-Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, Cc-Euclidean, and Cc-Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.

Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan–Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

Predicting COPD exacerbations based on quantitative CT analysis: an external validation study.

Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations. Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification. A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82-0.92) in the internal cohort and 0.86 (95%CI, 0.81-0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events. The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.

Multi-sequence MRI-based radiomics: An objective method to diagnose early-stage osteonecrosis of the femoral head

ObjectivesThis study investigated the use of radiomics for diagnosing early-stage osteonecrosis of the femoral head (ONFH) by extracting features from multiple MRI sequences and constructing predictive models. Materials and methodsWe conducted a retrospective review, collected MR images of early-stage ONFH (102 from institution A and 20 from institution B) and healthy femoral heads (102 from institution A and 20 from institution B) from two institutions. We extracted radiomics features, handled batch effects using Combat, and normalized features using z-score. We employed the Least absolute shrinkage and selection operator (LASSO) algorithm, along with Max-Relevance and Min-Redundancy (mRMR), to select optimal features for constructing radiomics models based on single, double, and multi-sequence MRI data. We evaluated performance using receiver operating characteristic (ROC) and precision-recall (PR) curves, and compared area under curve of ROC (AUC-ROC) values with the DeLong test. Additionally, we studied the diagnostic performance of the multi-sequence radiomics model and radiologists, compared the diagnostic outcomes of the model and radiologists using the Fisher exact test. ResultsWe studied 122 early-stage ONFH and 122 normal femoral heads. The multi-sequence model exhibited the best diagnostic performance among all models (AUC-ROC, PR-AUC for training set: 0.96, 0.961; validation set: 0.96, 0.97; test set: 0.94, 0.94), and it outperformed three resident radiologists on the external testing group with an accuracy of 87.5 %, sensitivity of 85.00 %, and specificity of 90.00 % (p < 0.01), highlighting the robustness of our findings. ConclusionsOur study underscored the novelty of the multi-sequence radiomics model in diagnosing early-stage ONFH. By leveraging features extracted from multiple imaging sequences, this approach demonstrated high efficacy, indicating its potential to advance early diagnosis for ONFH. These findings provided important guidance for enhancing early diagnosis of ONFH through radiomics methods, offering new avenues and possibilities for clinical practice and patient care.

Model development for anemia prediction in pregnancy

BackgroundThe number of pregnant women who suffer from anemia is still high. Anemia has an impact on the health status of mothers, fetuses, and newborns. This paper aims to develop an anemia risk prediction model for pregnant women in Indonesia. MethodsThe data was obtained from the Indonesian basic health research database years 2013 and 2018. A bivariate method the user to select which variables are significantly affecting anemia in pregnant women. The result was then analyzed using the multivariate for model development and the data was then plotted into a receiver operator characteristic (ROC) to illustrate the relationship between clinical sensitivity and specificity. ResultsThe variables that significantly to the anemia risk status of pregnant women are iron supplement during pregnancy (p = 0.003) and MUAC (p = 0.001) from 2013 data. After analyzing using multivariate, only MUAC and iron supplement during pregnancy could be included in the prediction model since the p-value is less than 0.05. The ROC value is 60.7 % with a sensitivity of 87.8 % and a specificity of 75.7 % for the 2013 data and we got the same ROC score is 60.7 % for the 2018 data with a sensitivity of 94.3 % and a specificity of 90 %. ConclusionResults suggest that the anemia risk prediction model developed based on data from pregnant women in 2013 and 2018 consisted of 2 variables, namely iron consumption and mid-upper arm circumference.

#1970 Mortality prediction of plasma presepsin in sepsis-associated acute kidney injury patients requiring continuous kidney replacement therapy

Abstract Background and Aims Presepsin has recently introduced as a more specific biomarker for sepsis. Presepsin is greatly affected by kidney function, so its reliability may be reduced in acute kidney injury (AKI) patient, especially requiring continuous kidney replacement therapy (CKRT). For this reason, there has been no study to date analyzing presepsin in AKI patients requiring CKRT. This study analyzed the usefulness of plasma presepsin for predicting mortality in AKI patients requiring CKRT, especially in sepsis-associated AKI (SA-AKI). Method This single-center, retrospective study included the patients who underwent CKRT with plasma presepsin measurement from April 2022 to March 2023. A total of 57 patients were enrolled, of which 35 were SA-AKI. 28-day mortality predictive values of plasma presepsin, Acute Physiology and Chronic Health Evaluation-II (APACHE-II), and Sequential Organ Failure Assessment (SOFA) scores were analyzed using receiver operating characteristics (ROC) curve analysis. Multivariate Cox regression analysis was performed to identifying risk factors for 28-day mortality in the SA-AKI subgroup. Results In the overall cohort, plasma presepsin showed the lower area under the ROC (AuROC) value of 0.636 than those of APACHE-II (0.663) and SOFA (0.731) scores. However, in SA-AKI subgroup, AuROC value of plasma presepsin was much increased to 0.799, which was the higher than those of APACHE-II (0.651) and SOFA (0.697) scores. Moreover, in multivariate Cox regression analysis, high presepsin was observed as an independent risk factor for 28-day mortality in the SA-AKI subgroup (HR 6.868, P=0.005). Conclusion Presepsin can be used as a good prognostic marker in SA-AKI patients requiring CKRT. The usefulness of plasma presepsin in the patients undergoing CKRT needs to be further studied in the future.

A non-destructive predictive model for estimating the freshness/spoilage of packaged chicken meat using changes in drip metabolites

This study investigates the possibility of utilizing drip as a non-destructive method for assessing the freshness and spoilage of chicken meat. The quality parameters [pH, volatile base nitrogen (VBN), and total aerobic bacterial counts (TAB)] of chicken meat were evaluated over a 13-day storage period in vacuum packaging at 4 °C. Simultaneously, the metabolites in the chicken meat and its drip were measured by nuclear magnetic resonance. Correlation (Pearson's and Spearman's rank) and pathway analyses were conducted to select the metabolites for model training. Binary logistic regression (model 1 and model 2) and multiple linear regression models (model 3-1 and model 3-2) were trained using selected metabolites, and their performance was evaluated using receiver operating characteristic (ROC) curves. As a result, the chicken meat was spoiled after 7 days of storage, exceeding 20 mg/100 g VBN and 5.7 log CFU/g TAB. The correlation analysis identified one organic acid, eight free amino acids, and five nucleic acids as highly correlated with chicken meat and its drip during storage. Pathway analysis revealed tyrosine and purine metabolism as metabolic pathways highly correlated with spoilage. Based on these findings, specific metabolites were selected for model training: ATP, glutamine, hypoxanthine, IMP, tyrosine, and tyramine. To predict the freshness and spoilage of chicken meat, model 1, trained using tyramine, ATP, tyrosine, and IMP from chicken meat, achieved a 99.9 % accuracy and had an ROC value of 0.884 when validated using drip metabolites. This model 1 was improved by training with tyramine and IMP from both chicken meat and its drip (model 2), which increased the ROC value for drip metabolites from 0.884 to 0.997. Finally, selected two metabolites (tyramine and IMP) can predict TAB and VBN quantitatively through models 3-1 and 3-2, respectively. Therefore, the model developed using metabolic changes in drip demonstrated the capability to non-destructively predict the freshness and spoilage of chicken meat at 4 °C. To make generic predictions, it is necessary to expand the model's applicability to various conditions, such as different temperatures, and validate its performance across multiple chicken batches.

Development and external validation of a nomogram for predicting short-term prognosis in patients with acute pulmonary embolism

BackgroundAccurate assessment and timely intervention play a crucial role in ameliorating poor short-term prognosis of acute pulmonary embolism (APE) patients. The currently employed scoring models exhibit a degree of complexity, and some models may not comprehensively incorporate relevant indicators, thereby imposing limitations on the evaluative efficacy. Our study aimed to construct and externally validate a nomogram that predicts 30-day all-cause mortality risk in APE patients. MethodsClinical data from APE patients in Intensive Care-IV database was included as a training cohort. Additionally, we utilized our hospital's APE database as an external validation cohort. The nomogram was developed, and its predictive ability was evaluated using receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis. ResultsA collective of 1332 patients and 336 patients were respectively enrolled as the training cohort and the validation cohort in this study. Five variables including age, malignancy, oxygen saturation, blood glucose, and the use of vasopressor, were identified based on the results of the multivariate Cox regression model. The ROC value for the nomogram in the training cohort yielded 0.765, whereas in the validation group, it reached 0.907. Notably, these values surpassed the corresponding ROC values for the Pulmonary Embolism Severity Index, which were 0.713 in the training cohort and 0.754 in the validation cohort. ConclusionsThe nomogram including five indicators had a good performance in predicting short-term prognosis in patients with APE, which was easier to apply and provided better recommendations for clinical decision-making.

Viscoelastic parameters derived from multifrequency MR elastography for depicting hepatic fibrosis and inflammation in chronic viral hepatitis

ObjectivesThe capability of MR elastography (MRE) to differentiate fibrosis and inflammation, and to provide precise diagnoses is crucial, whereas the coexistence of fibrosis and inflammation may obscure the diagnostic accuracy.MethodsIn this retrospective study, from June 2020 to December 2022, chronic viral hepatitis patients who underwent multifrequency MRE (mMRE) were included in, and further divided into, training and validation cohorts. The hepatic viscoelastic parameters [shear wave speed (c) and loss angle (φ) of the complex shear modulus] were obtained from mMRE. The logistic regression and receiver operating characteristic (ROC) curves were generated to evaluate performance of viscoelastic parameters for fibrosis and inflammation.ResultsA total of 233 patients were assigned to training cohort and validation cohorts (mean age, 52 years ± 13 (SD); 51 women; training cohort, n = 170 (73%), and validation cohort, n = 63 (27%)). Liver c exhibited superior performance in detecting fibrosis with ROC (95% confidence interval) of ≥ S1 (0.96 (0.92–0.99)), ≥ S2 (0.86 (0.78–0.92)), ≥ S3 (0.89 (0.84–0.95)), and S4 (0.88 (0.83–0.93)). Similarly, φ was effective in diagnosing inflammation with ROC values of ≥ G2 (0.72 (0.63–0.81)), ≥ G3 (0.88 (0.83–0.94)), and G4 (0.92 (0.87–0.98)). And great predictive discrimination for fibrosis and inflammation were shown in validation cohort (all AUCs > 0.75).ConclusionThe viscoelastic parameters derived from multifrequency MRE could realize simultaneous detection of hepatic fibrosis and inflammation.Critical relevance statementFibrosis and inflammation coexist in chronic liver disease which obscures the diagnostic performance of MR elastography, whereas the viscoelastic parameters derived from multifrequency MR elastography could realize simultaneous detection of hepatic fibrosis and inflammation.Key points• Hepatic biomechanical parameters derived from multifrequency MR elastography could effectively detect fibrosis and inflammation.• Liver stiffness is useful for detecting fibrosis independent of inflammatory activity.• Fibrosis could affect the diagnostic efficacy of liver viscosity in inflammation, especially in early-grade of inflammation.Graphical

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis.

Apoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI). The tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated. Six biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators. The six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection.

Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity.

In order to meet the needs of refined landslide risk management, the extended correlation framework of dynamic susceptibility modeling desiderates to be further explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, with a transportation channel with significant economic value to carry out innovative research in two stages. (i) Five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries. (ii) Based on the PS-InSAR technology, the dynamic factor of deformation intensity was obtained. Subsequently, the dynamic factor was combined with proposed static factors (topography conditions, geological conditions, hydrological conditions, and human activities) to generate dynamic landslide susceptibility mapping (DLSM). The receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1 score were proposed as evaluation metrics. Compared with ignoring the dynamic factor, the predictive accuracy of some models was further improved when considering the dynamic factor. Especially the DT model, the area under the curve of ROC (AUC) value increased by 2%, and obtained the highest AUC value (93.1%). The susceptibility results of introducing the dynamic factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.