Curve Values Research Articles

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation. Microarray and RNA-sequencing datasets (GSE47184, GSE96804, GSE104948, GSE104954, GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database. Differential expression analysis identified the differentially expressed genes (DEGs) between patients with DN and controls. Diverse machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, were used to enhance gene selection accuracy and predictive power. We integrated summary-level data from genome-wide association studies on DN with expression quantitative trait loci data to identify genes with potential causal relationships to DN. The predictive performance of the biomarker gene was validated using receiver operating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted to investigate potential mechanisms. Finally, the biomarker gene was validated using quantitative real-time polymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314 DEGs, seven characteristic genes with high predictive performance were identified using three integrated machine learning algorithms. MR analysis revealed 219 genes with significant causal effects on DN, ultimately identifying one co-expressed gene, carbonic anhydrase II (CA2), as a key biomarker for DN. The ROC curves demonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analysis indicated a significant association between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights. This biomarker was validated using clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we successfully identified and validated CA2 as a promising biomarker for DN with excellent predictive performance. The biomarker may play a role in the pathogenesis and progression of DN via immune-related pathways. These findings provide important insights into the molecular mechanisms underlying DN and may inform the development of personalized treatment strategies for this disease.

Plasma procalcitonin and urine interleukin-8, neutrophil gelatinase–associated lipocalin, and calprotectin in the diagnostic process of a urinary tract infection at the emergency department

ObjectivesThis study aimed to assess the usefulness of plasma procalcitonin and urine IL-8 (interleukin-8), NGAL (neutrophil gelatinase–associated lipocalin), and calprotectin for diagnosis of urinary tract infections (UTIs) at the emergency department (ED). MethodsIn adults presenting at the ED with UTI suspicion, biomarker performance was compared with that of routine diagnostics (urine dipstick, automated urinalysis). Patients with a urine catheter, leukopenia, or neither (standard) were analyzed separately. ResultsA UTI was clinically diagnosed in 91 of 196 episodes (46.4%) (standard: 29/67 [43.2%]; catheter: 46/73 [63.0%]; leukopenia: 17/60 [28.3%]; four patients had both). Procalcitonin did not discriminate between UTI and no UTI. Urinary biomarker levels were elevated in UTI episodes (median, µg/mmol creatinine: NGAL, 7.8 vs 46.3; IL-8, 6.1 vs 76.6; calprotectin, 23.9 vs 265.4); the three subgroups also had higher levels. Biomarker cut-off values (90% sensitivity) showed low specificity (range 20.8-64.9%) and moderate accuracy (58.6-75.4%). The biomarkers performed similarly to routine diagnostics, except for patients with leukopenia, who exhibited nonsignificantly higher area under the curve values. All urinary biomarkers correlated positively with urine leukocyte count. ConclusionPlasma procalcitonin could not accurately diagnose UTI. Urine IL-8, NGAL, and calprotectin showed no additional value relative to routine diagnostics, except a minor improvement in patients with leukopenia. These urine biomarkers seem to predominantly reflect leukocyturia.

A novel triage framework for emergency department based on machine learning paradigm

AbstractEmergency departments, crucial in managing patient emergencies, are often challenged by overcrowding and diagnostic errors during triage—the process that assesses the urgency of patients' conditions. Traditional triage systems, heavily dependent on human judgement, are prone to errors like under‐triage, where severe conditions are missed, delaying treatment, and over‐triage, where less severe conditions are overly prioritized, causing unnecessary resource use and morbidity. This thesis presents a novel multi‐model machine‐learning framework designed to enhance triage accuracy by evaluating multiple medical conditions concurrently, including chronic illnesses like heart disease. The framework employs various machine‐learning techniques—such as logistic regression, support vector machines, random forests, deep neural networks, and decision trees—to analyze different medical conditions in two comprehensive phases. In the first phase, patients' conditions are categorized, and a multi‐tiered analysis using multiple classifiers refines the assessment by considering probabilistic outcomes from each classifier. The second phase synthesizes these insights into a unified and precise triage decision, distinguishing between patients who require critical care and those needing less urgent hospitalization. This integration of diverse machine learning models allows for a fused and precise triage decision, overcoming traditional triage systems' limitations that usually focus on single conditions and rely on isolated model predictions. A hybrid feature selection method is also utilized to identify critical predictors, enhancing the decision‐making process. The framework is validated through a specially curated dataset that simulates multiple triage scenarios, evaluated by medical experts for efficacy. Comparative analysis with traditional triage methods demonstrates significant improvements in decision accuracy, as evidenced by higher area under curve (AUC) values—0.95 for critical care and 0.90 for hospitalization. The implementation of this data‐driven approach substantially reduces human error, boosts operational efficiency, and aids medical staff in making rapid, informed decisions. This research represents a significant advancement in emergency medical care, illustrating the benefits of integrating sophisticated machine learning techniques to improve patient outcomes and resource management.

IGFL2 expression and surgical volume: Independent predictors of survival in gastric cancer.

This study aimed to assess the impact of surgeons' annual volume and insulin-like growth factor-like family member 2 (IGFL2) expression on gastric cancer prognosis. Clinicopathological data from 475 patients who underwent D2 lymph node dissection were analyzed. IGFL2 expression was evaluated using immunohistochemistry. Patients were divided into training (70%) and validation (30%) groups. Univariate and multivariate Cox regression identified risk factors for overall survival (OS) and disease-free survival (DFS), leading to a clinical prediction model. Model performance was evaluated using C-index. High IGFL2 expression and low surgical volume independently predicted poorer OS and DFS (hazard ratio = 2.13, 2.17, all P < .01). Surgeons performing >26 cases annually had higher OS and DFS (hazard ratio = 1.65, 1.58, all P < .01). Nomograms integrating surgical volume, IGFL2 expression, grade, TNM staging, and carcinoembryonic antigen showed superior predictive accuracy for OS and DFS compared to TNM alone, with robust C-indices and area under the curve values. Surgeons' annual volume and IGFL2 expression independently predict gastric cancer prognosis, emphasizing the need for specialized training and further research on IGFL2's molecular mechanisms to enhance patient outcomes.

Maxent modelling reveals suitable habitat remains unchanged for Carcinus maenas in Australia

ABSTRACT This study assesses the habitat suitability of the invasive green crab, Carcinus maenas, in Australia using MaxEnt modelling under current and future climate scenarios. The species has significant ecological impacts, displacing native species and altering habitats. Using 90 occurrence points from the Global Biodiversity Information Facility, the MaxEnt model shows high performance (Area Under the Curve value of 0.99), indicating reliable predictions. Currently, the green crab's habitat suitability along the Australian coast ranges from high to medium in Tasmania, Victoria, New South Wales and South Australia and low in Western Australia and South Queensland. Future projections suggest a decrease in suitable habitats, confined to Tasmania, Victoria, Adelaide and New South Wales. These findings can guide the development of long-term management and conservation plans for C. maenas, with an emphasis on areas with consistent habitat suitability. Additionally, further field and laboratory experiments are recommended to understand the species’ tolerance to environmental changes. Recognising the ecological impact of C. maenas is crucial for informed decision-making to preserve Australia's ecosystems and ensure the sustainable coexistence of native species amidst invasive threats.

Wideband tympanometry in otitis Media

BackgroundOtitis media with effusion (OME) is one of the most frequent infectious diseases in children and is the most common cause of acquired hearing loss in childhood. Diagnosis of OME is mainly dependent on the single-frequency tympanometry. Wideband tympanometry (WBT) is an emerging technology that has been utilized to overcome the limitations of conventional tympanometric procedures. This study aimed to evaluate the diagnostic value of wideband acoustic absorbance in children suffering from otitis media.MethodsThis study included a control group of 50 children with 100 ears with bilateral normal peripheral hearing and bilateral normal middle ear function and a study group which included 100 children with 176 ears complaining of otitis media. Measures included subjects history, basic audiological evaluation and wideband tympanometry. Resonant frequency, tympanometric peak pressure and middle ear absorbance were measured from wideband tympanometry and compared in both groups.ResultsThere was a statistically significant reduction of resonsnce frequency in the study group in comparison to the control group. Also, there was a statistically significant reduction of tympanometric peak pressure in the study group in comparison to the control group. As regards the absorbance, there was a statistically significant reduction of absorbance in the study group in comparison to the control group at all tested frequencies (250Hz- 8000Hz). The best area under the receiver operating characteristic curve values for diagnosing OM were obtained at all tested frequencies. Data of this work showed that higher values were mainly at 250Hz, 500Hz and 1kHz (0.84, 0.88 and 0.93 respectively).ConclusionFindings of this study suggested that wide band tympanometry can be used efficiently in various degrees of OM.

Epigenomic biomarkers insights in PBMCs for prognostic assessment of ECMO-treated cardiogenic shock patients

ObjectiveAs the global use of extracorporeal membrane oxygenation (ECMO) treatment increases, survival rates have not correspondingly improved, emphasizing the need for refined patient selection to optimize resource allocation. Currently, prognostic markers at the molecular level are limited.MethodsThirty-four cardiogenic shock (CS) patients were prospectively enrolled, and peripheral blood mononuclear cells (PBMCs) were collected at the initiation of ECMO (t0), two-hour post-installation (t2), and upon removal of ECMO (tr). The PBMCs were analyzed by comprehensive epigenomic assays. Using the Wilcoxon signed-rank test and least absolute shrinkage and selection operator (LASSO) regression, 485,577 DNA methylation features were analyzed and selected from the t0 and tr datasets. A random forest classifier was developed using the t0 dataset and evaluated on the t2 dataset. Two models based on DNA methylation features were constructed and assessed using receiver operating characteristic (ROC) curves and Kaplan–Meier survival analyses.ResultsThe ten-feature and four-feature models for predicting in-hospital mortality attained area under the curve (AUC) values of 0.78 and 0.72, respectively, with LASSO alpha values of 0.2 and 0.25. In contrast, clinical evaluation systems, including ICU scoring systems and the survival after venoarterial ECMO (SAVE) score, did not achieve statistical significance. Moreover, our models showed significant associations with in-hospital survival (p < 0.05, log-rank test).ConclusionsThis study identifies DNA methylation features in PBMCs as potent prognostic markers for ECMO-treated CS patients. Demonstrating significant predictive accuracy for in-hospital mortality, these markers offer a substantial advancement in patient stratification and might improve treatment outcomes.

Seminal oxidative stress index can be used as a marker in the prediction of bull semen cryotolerance

In this study, it was aimed to evaluate the effect of seminal plasma activity levels of enzymatic antioxidants such as catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), non-enzymatic antioxidant alpha-tocopherol (Vit-E), and also total antioxidant status (TAS), total oxidant status (TOS), and oxidative stress index (OSI) on post-thaw sperm quality. As well as it was aimed to investigate the possibility of the use of OSI as a marker for the estimation of bull semen freezability. For this study, 72 ejaculates were collected from 6 bulls and separated into two aliquots. The first one was centrifuged to separate seminal plasma. The latter one was cryopreserved and stored in liquid nitrogen until analysis. Post-thaw semen quality was examined in two groups (good-freezable semen (GFS) and poor-freezable semen (PFS)) through cluster analyses based on post-thaw total motility and plasma membrane and acrosome integrity. As a result of the analyses, seminal TAS, CAT, and Vit-E values were higher (P<0.05) in the GFS group, while TOS and OSI values were higher (P<0.01) in the PFS group. We also performed an ROC curve analysis to determine whether the seminal OSI value could be used to predict semen freezeability. The area under curve (AUC) value was found as 0,70 (P=0.006). In conclusion, it has been revealed that the seminal plasma antioxidant content is responsible for the freezability of semen, and the OSI value, which can be determined by performing TAS and TOS analyses instead of looking for separate antioxidant enzymes, can be used as a marker for the estimation of post-thaw semen quality at artificial insemination centers.

Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence

Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of internal cancers. Traditionally, human arsenic exposure is estimated through arsenic levels in biological tissues; however, these methods are invasive and time-consuming. This study aims to develop a noninvasive approach to predict arsenic exposure using artificial intelligence (AI) to analyze photographs of hands and feet. By incorporating well water consumption data and arsenic concentration levels, we developed an AI algorithm trained on 9988 hand and foot photographs from 2497 subjects. This algorithm correlates visual features of palmoplantar hyperkeratosis with arsenic exposure levels. Four pictures per patient, capturing both ventral and dorsal aspects of hands and feet, were analyzed. The AI model utilized existing arsenic exposure data, including arsenic concentration (AC) and cumulative arsenic exposure (CAE), to make binary predictions of high and low arsenic exposure. The AI model achieved an optimal area under the curve (AUC) values of 0.813 for AC and 0.779 for CAE. Recall and precision metrics were 0.729 and 0.705 for CAE, and 0.750 and 0.763 for AC, respectively. While biomarkers have traditionally been used to assess arsenic exposure, efficient noninvasive methods are lacking. To our knowledge, this is the first study to leverage deep learning for noninvasive arsenic exposure assessment. Despite challenges with binary classification due to imbalanced and sparse data, this approach demonstrates the potential for noninvasive estimation of arsenic concentration. Future studies should focus on increasing data volume and categorizing arsenic concentration statistics to enhance model accuracy. This rapid estimation method could significantly contribute to epidemiological studies and aid physicians in diagnosis.

Predicting implementation of response to intervention in math using elastic net logistic regression.

The primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. Due to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net logistic regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Method50 and Methodcoef emerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.

The value of adjusted PSAD in prostate cancer detection in the Chinese population.

To investigate the value of adjusted prostate-specific antigen density (PSADadj) in the diagnosis of prostate cancer (PCa). Data from 410 patients who underwent transrectal ultrasound-guided prostate biopsy were retrospectively analyzed in Beijing Tsinghua Changgung Hospital between November 2014 and March 2024. All patients were divided into PCa and benign prostatic hyperplasia (BPH) groups according to pathological results. Multivariate logistic regression analyses were performed to evaluate the odd ratios (ORs) of predictors for PCa occurrence. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) values were used to assess and compare the diagnostic accuracies of total PSA (tPSA), free-to-total (f/t) PSA, free PSA (fPSA), PSAD, and PSADadj (PSAD×weight). There were 166 patients in the PCa group and 244 in the BPH group. Multivariate analyses demonstrated that PSAD was positively correlated with the presence of PCa, with the highest OR value among all PSA-related parameters (OR = 19.075, p<0.001). tPSA, fPSAD, PSAD, and PSADadj had high accuracy in predicting PCa, with AUC values of 0.633, 0.730, 0.778, and 0.780. Of note, PSADadj had the highest AUC with a sensitivity of 63.3% and specificity of 81.6%. Similarly, in patients with a PSA level in the gray zone, the diagnostic accuracy of PSADadj in predicting PCa (AUC, 0.709; 95% CI, 0.616-0.802) remained better than other PSA-related markers. PSADadj has an advantage over other PSA-related markers in detecting PCa and could be used for making biopsy decisions.

Factor Xa Inhibitors and Low Molecular Weight Heparins in Perioperative Surgical Thromboprophylaxis: A Network Meta-Analysis.

venous thromboembolism (VTE) prophylaxis is crucial for reducing the risk of deep vein thrombosis (DVT) and pulmonary embolism (PE). This network metaanalysis was carried out to determine the most effective intervention among selective Xa inhibitors and low molecular weight heparins (LMWHs) for perioperative surgical thromboprophylaxis in major abdominal, pelvic, lumbar spine, and lower limb surgeries. A systematic literature search was conducted for randomized controlled trials (RCTs) comparing selective factor Xa inhibitors, LMWHs, and placebo as thromboprophylaxis agents in major abdominal, pelvic, lumbar spine, and lower limb surgeries. A Bayesian network metaanalysis was performed to compare all interventions for the risk of developing DVT, VTE, major VTE, total bleeding, and major bleeding. The surface under the cumulative ranking curves was used to rank all interventions. Of 1788 retrieved references, 42 RCTs comparing 11 anticoagulants were included. As compared to enoxaparin, the risk of DVT was significantly reduced in patients treated with fondaparinux [RR: 0.53 (95% CrI: 0.31, 0.93)] and rivaroxaban [RR: 0.42 (95% CrI: 0.27, 0.64)]; VTE in patients treated with bemiparin [RR: 0.09 (95% CrI: 0, 0.7)], edoxaban [RR: 0.43 (95% CrI: 0.18, 0.96)], fondaparinux [RR: 0.55 (95% CrI: 0.34, 0.91)] and rivaroxaban [RR: 0.56 (95% CrI: 0.34, 0.85)]; major VTE in patients treated with rivaroxaban [RR: 0.26 (95% CrI: 0.11, 0.6)]. According to the surface under the cumulative ranking curves (SUCRA) value, fondaparinux and bemiparin increase the risk of serious bleeding more than other factor Xa inhibitors and LMWHs. Rivaroxaban, fondaparinux, edoxaban, and bemiparin are superior perioperative thromboprophylaxis agents than enoxaparin in major surgeries. Fondaparinux and bemiparin have shown the highest risk of major bleeding compared to other factor Xa inhibitors and LMWHs.

B-240 Multi-omic blood test for early detection of cancer and pre-cancer

Abstract Background The earlier diagnosis of cancer is vital to improve patient survival. Many liquid biopsy technologies are focused on single tumor-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed sufficient genetic material into the bloodstream. The Dxcover liquid biopsy employs Multi-Omic Spectral Analysis (MOSA) that interrogates a blood sample with Infrared (IR) radiation and produces a distinctive signature that represents the whole biomolecular profile of the sample. This multi-omic blood test analyzes the full complement of tumor and immune-derived markers present within blood derivatives and could facilitate the earlier detection of cancer. The technology can be tuned for high sensitivity (rule out) or high specificity (rule in) depending on the desired application, clinical priorities and international healthcare markets. Methods Three clinical studies have been completed, assessing the technology for the detection of: (1) brain cancer, (2) multiple cancers, and (3) colorectal cancer (CRC) and pre-cancerous lesions. In the first study, we recruited 988 patients prospectively with non-specific symptoms associated with a brain tumor. Secondly, we carried out a large-scale multi-cancer evaluation (n = 2092 patients), targeting eight different cancers. Moreover, the CREATE (ColoREctal cancer &amp; Adenoma Test Evaluation) feasibility study (3) examined 100 CRC, 92 adenoma samples and 104 colonoscopy screening control patients. Results In study 1, the brain cancer algorithm detected 96% of the patients with brain cancer, and 100% of glioblastomas, the most aggressive primary brain tumor. In the multi-cancer study (2), area under the receiver operating characteristic curve values were calculated for the eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We also assessed the test performance when all eight cancer types were pooled to classify ‘any cancer’ against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%). In the CREATE study, the test reported promising results for pre-cancer detection; at 90% specificity, the test reported sensitivities of 80% for CRC and 59% for advanced adenomas. Conclusions A rapid liquid biopsy that is sensitive to pre-cancerous lesions and early-stage cancer could substantially improve patient outcomes. There is a low barrier to integrating this blood test into existing diagnostic pathways as the technology is rapid, simple to use, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.

Value of cardiac enzyme spectrum for the risk assessment of mortality in critically ill children: a single-centre retrospective study

ObjectivesIdentifying high-risk paediatric patients with a poor prognosis and providing timely and adequate treatment are critical. This study aimed to evaluate the effects of different types of cardiac enzyme spectrum...

The analysis of credit governance in the digital economy development under artificial neural networks

This paper aims to provide new avenues for innovation in credit governance in the digital economy to provide more reliable credit evaluation solutions for financial, commercial, and social interactions. This paper integrates the potential value of Internet of Things (IoT) technology in credit governance and proposes a credit governance method that utilizes IoT data and an improved Long Short-Term Memory model. The proposed model introduces an adaptive mechanism to monitor changes in data in real-time and automatically adjust network parameters to improve the model performance. Experimental validation is conducted on the University of California Irvine dataset. It is found that the proposed model performs well in terms of accuracy, F1 value, and Area Under the Curve value, with values of 0.9, 0.91, and 0.94, respectively. This indicates that the proposed model has higher classification accuracy and performance in classification tasks while maintaining efficient training time. This indicates that the presented method has potential application prospects in credit governance research.

How did coronavirus disease 2019 affect autonomic balance in young individuals? Analysis by heart rate variability.

The aim of this study was to demonstrate the effect of coronavirus disease 2019 on the cardiovascular autonomic system using heart rate variability in young individuals. The study was designed retrospectively by scanning the 24-h Holter electrocardiography records of patients who applied to the Ankara Abdurrahman Yurtaslan Oncology Education and Research Hospital Cardiology outpatient clinic. The study group consisted of 492 patients under the age of 40 years, who did not have additional comorbidities or medication use and had prolonged symptoms after coronavirus disease 2019 during the pandemic. The control group, including 401 patients, was determined during the pre-pandemic period (before December 2019). Heart rate variability parameters were evaluated by scanning the 24-h Holter electrocardiography records of the patients and compared with the non-coronavirus disease 2019 group. The median age of participants was 30 years. Standard deviation of normal RR intervals (SDNN) ≤100 ms was more prevalent in the study group (27 (6.7%) vs 73 (14.8%), p<0.001). In univariate logistic regression analysis, the presence of coronavirus disease 2019 [(OR 2.41, 95%CI 1.52-3.83), p<0.001] and age [(OR 1.04, 95%CI 1.01-1.07), p=0.016] had a significant effect on the probability of SDNN≤100. In multivariate logistic regression analysis, the presence of coronavirus disease 2019 [(OR 2.42, 95%CI 1.52-3.85), p<0.001] and age [(OR 2.42, 95%CI 1.52-3.85), p=0.016] had a significant effect on the probability of SDNN≤100. Frequency domain measures such as, high-frequency values were significantly higher in the study group (p=0.029). The study group's low-frequency/high frequency ratio was significantly lower (p=0.019). The low-frequency/high-frequency ratio's cut-off value was ≤2.77. for determining the differentiation between coronavirus disease 2019 positive and negative cases in the receiver operating characteristic analysis. The sensitivity rate was 80.7%. The area under the curve value is 0.546 (p=0.019). This study showed that coronavirus disease 2019 causes reduced heart rate variability and increased parasympathetic activity in young patients. This may explain the prolonged symptoms after coronavirus disease 2019 infection.

Identification and validation of a novel innate lymphoid cell-based signature to predict prognosis and immune response in liver cancer by integrated single-cell RNA analysis and bulk RNA sequencing.

Innate lymphoid cells (ILCs) exert tumor suppressive and tumor promoting effects. However, the prognostic significance of ILC-associated genes remains unclear in hepatocellular carcinoma (HCC). Hence, the aim of this research was to develop an innovative predictive risk classification system using bioinformatics examination. We explored the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to gather data pertaining to HCC and its clinical details. Significantly different ILC-associated genes were investigated by Seurat analysis. The number of signaling interactions of ILCs with other cells was discovered by CellPhoneDB analysis. ClusterProfiler and Metascape were utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on ILC genes. In order to identify potential ILC predictors, we utilized univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, subsequently validating these predictors in TCGA and GEO groups. The multi-omics ILC signature model's clinical predictive capabilities, along with drug sensitivity and immune factor relations, were assessed using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and pRRophetic. We investigated the possible molecular pathways in our predictive ILC signature through the utilization of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Five key genes were screened out by constructing a competing endogenous RNA (ceRNA) network using Cytoscape and their values in clinical indexes were demonstrated. Immunohistochemistry (IHC) in HCC cases confirmed the expression of these genes. ILC cell subsets were identified, and cell-cell communication analysis revealed that the signaling pathways involving ILC cell subsets were mostly abundant in the HCC microenvironment. Subsequently, 270 marker genes of the ILC clusters were subjected to GO and KEGG enrichment analysis. Furthermore, a total of 58 prognostically relevant genes were screened as features for prognostic prediction models. Next, the models were validated and clinically evaluated (P values of Kaplan-Meier survival curves below 0.05). Five key genes (C2, STK4, CALM1, IL7R, and RORA) were further screened by multi omics analysis of immune cell and factor and drug sensitivity and correlation analysis of tumor regulatory genes in liver cancer. Furthermore, the potential clinical value of the 5 key genes was confirmed in HCC patients. Finally, the IHC results confirmed the expression of C2, STK4, CALM1, IL7R, and RORA in HCC. Our experimental results provided preliminary evidence supporting the oncogenic roles of STK4 and CALM1, as well as the tumor-suppressive roles of C2, RORA, and IL7R in HCC. A novel prognostic feature of ILC potentially involved in HCC was discovered. It showed high values in predicting patient overall survival (OS) as well as good differences in immunity and drug sensitivity. Therefore, targeting these ILC signatures may be a potential effective approach in HCC treatment.

An immune-related gene pair signature predicts the prognosis and immunotherapeutic response in glioblastoma

BackgroundGlioblastoma (GBM) has the feature of aggressive growth and high rates of recurrence. Immunotherapy was not included in standard therapy for GBM due to lacking the predictive biomarkers. In the present study, we performed an immune-related gene pair (IRGP) signature to predict the prognosis and immunotherapy response of GBM. MethodsA total of 160 GBM patients from TCGA were included. ssGSEA was conducted to evaluate the immune infiltration level. Univariate Cox, LASSO regression analysis, ROC analysis, and Kaplan-Meier survival analysis were applied to construct and evaluate the risk model. Moreover, the association between immune infiltration and the risk score was assessed. Finally, the expression of immune checkpoints between different risk groups was explored. ResultsAccording to the normal/tumor, high-/low-immunity group, we identified 125 differentially expressed immune-related genes. Subsequently, a prognostic model including 22 IRGPs was established. The area under the ROC curve to predict 1, 3, and 5-year was 0.811, 0.958, and 0.99 respectively. According to the optimal cut-off value of the 3-year ROC curve, patients were classified into high- and low-risk groups. The Kaplan-Meier analysis result indicated that patients in the low-risk group have longer survival time. The risk score was an independent prognostic predictor (P < 0.001). Moreover, PDCD1 was positively correlated with the risk score (P < 0.01). We also found that patients with high PDCD1 expression had worse survival. ConclusionsThe IRGP signature was built to predict the prognosis of GBM patients. This signature can serve as a tool to predict the response to immunotherapy in GBM.

Development of a predictive model for patients with bone metastases referred to palliative radiotherapy: Secondary analysis of a multicenter study (the PRAIS trial).

The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short-, intermediate-, and long-term overall survival (OS) for patients in this clinical setting. This study constitutes a sub-analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer-induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non-bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL-8], chloride levels, C-reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL-8 included both in the PMs for short- and long-term OS. The AUC values for ROC curves for 3-week, 24-week, and 52-week OS were 0.901, 0.767, and 0.806, respectively. We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases.

Construction and Validation of a Predictive Model for Culture Results of Mycobacterium Tuberculosis in Superficial Lymph Nodes.

To establish and validate a nomogram for predicting the culture results of Mycobacterium tuberculosis in superficial lymph nodes. The clinical data of patients with superficial lymph node tuberculosis admitted to Xi'an City Chest Hospital from November 23, 2018, to May 30, 2024, were selected and divided into a training set and a validation set according to a ratio of 7:3. Influencing factors were identified through multivariate logistic regression analyses. Using R version 4.3.2, we developed a predictive model and generated a nomogram based on this model. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curve analysis (CCA), and decision curve analysis (DCA). The positive rate of superficial lymph node tuberculosis culture was 23.0% (103/446). Multivariate Logistic regression analysis showed that anti-tuberculosis treatment duration (OR=0.98, 95% CI: 0.97 ~ 0.99), initial treatment or retreatment (OR=0.12, 95% CI: 0.05 ~ 0.28), and adenosine deaminase (OR=1.12, 95% CI: 1.03 ~ 1.22) were independent factors affecting the culture results of Mycobacterium tuberculosis in superficial lymph nodes. The areas under the ROC curves were 0.86 (95% CI: 0.82-0.91) for the training set and 0.89 (95% CI: 0.84-0.95) for the validation set. The P values of calibration curves were 1.000 and 0.961, respectively, and the predicted values were in good agreement with the actual values. The threshold probabilities of clinical decision curves were 3%~64% and 1%~68%, respectively. The positive rate of Mycobacterium tuberculosis culture in superficial lymph nodes is low. The increase in retreatment patients and anti-tuberculosis treatment time are obstacle factors for Mycobacterium tuberculosis culture positivity, while an increase in adenosine deaminase is a promoting factor for Mycobacterium tuberculosis culture positivity. The nomogram model established based on these factors can be used to predict the results of Mycobacterium tuberculosis culture in superficial lymph nodes.