ROC Curve Analysis Research Articles

P0623 Vedolizumab clearance as a predictor of remission in inflammatory bowel disease: a real-world pharmacokinetic study

Abstract Background Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), requires sustained control of gastrointestinal inflammation to improve patient outcomes. Vedolizumab (VDZ), a monoclonal antibody, has been approved for the induction and maintenance of remission in moderate to severe UC and CD. However, interpatient variability in VDZ pharmacokinetics (PK) complicates therapeutic drug monitoring (TDM). This study aimed to assess whether VDZ clearance (CL) could serve as a surrogate marker for remission, outperforming trough concentrations as a predictor. Methods This retrospective cohort study analysed 106 patients (61 UC and 45 CD) receiving 300 mg VDZ therapy at variable intervals (4, 6, or 8 weeks) after standard induction. Blood samples were collected at trough times, and VDZ concentrations were measured using ELISA. A population PK model was developed using NONMEM (version 7.5) to estimate VDZ CL. Statistical analyses included Fisher’s exact, t-test, and logistic regression to evaluate clinical predictors, while ROC curve analysis identified optimal cut-offs for clinical and endoscopic outcomes. Results A two-compartment model best described the PK of VDZ. The estimated CL was 0.159 L/day, increasing by 26.4% in patients previously treated with anti-TNF agents. Higher CL values correlated with thrombocytosis (Plt > 350·10^3/µL, AUROC = 76.9, 95% CI 62.8–91.1) and were significantly associated with reduced likelihood of remission. VDZ trough levels did not predict remission outcomes as effectively as CL, suggesting that AUC-based exposure metrics may better indicate clinical response. Conclusion This study demonstrates that VDZ CL, influenced by prior anti-TNF exposure and inflammatory markers, is a more reliable predictor of clinical and endoscopic remission than trough concentration. These findings support the integration of CL-based TDM for VDZ to optimize therapeutic outcomes in IBD management. Further research on AUC-based metrics may provide enhanced TDM guidance.

P1077 Early detection of non-response to non-anti-TNF treatment in Inflammatory Bowel Disease patients using neutrophil-to-lymphocyte ratio as a biomarker

Abstract Background The neutrophil-to-lymphocyte ratio (NLR) has been proposed as a potential marker of subclinical inflammation. Previous studies suggest NLR may predict therapeutic response to anti-tumor necrosis factor (anti-TNF) drugs1,2. However, there is a lack of published data investigating the role of NLR as a predictor for non-anti-TNF treatments. Methods In this retrospective analysis, medical records of 55 patients followed in the inflammatory bowel disease (IBD) outpatient department from January 1, 2020, to July 1, 2024, were reviewed. All patients initiated non-anti-TNF biologic treatment during this period. NLR was measured at week 4 of treatment, and non-response (NR) was assessed at week 16. Patients were divided into two groups: Group A (n = 11)/non-responders and Group B (n = 44)/responders. Statistical analysis of NLR measurements was performed using R. Logistic regression was conducted to evaluate the relationship between NR and ln(NLR). Results The study included 55 IBD patients (27 with ulcerative colitis [UC] and 28 with Crohn’s disease [CD]), with a mean age of 49.81 years. The cohort comprised 23 females and 32 males. Treatment distribution was as follows: ustekinumab (n = 26), vedolizumab (n = 22), upadacitinib (n = 6) and tofacitinib (n = 1).The non-parametric Wilcoxon test revealed statistically significant differences in median NLR between Group A and Group B (P = 0.01464 < 0.05). Logistic regression demonstrated a significant relationship between NR and ln(NLR) (P = 0.024844 < 0.05). The ROC curve analysis yielded an area under the curve (AUC) of 0.7407, with a threshold achieving a sensitivity of 0.9091 and a specificity of 0.5227. Conclusion NLR shows promise as a non-invasive, accessible, and cost-effective biomarker for the early detection of non-response to non-anti-TNF biologic therapy in IBD patients.

Diagnostic potential of CDK1 and STAT1 in acute kidney injury associated with gastrointestinal cancers: a bioinformatics-based study

IntroductionPatients with gastrointestinal cancers are prone to acute kidney injury (AKI) due to treatment or disease progression, and current diagnostic methods exhibit insufficient sensitivity and specificity. This study aims to evaluate the potential value of CDK1 and STAT1 in the diagnosis of AKI in this patient population.MethodsA retrospective analysis was conducted on adjacent tissue, cancerous and the clinical data tissue from 150 gastrointestinal cancer patients treated at our hospital from May 2022 to May 2023. Differentially expressed genes (DEGs) associated with gastrointestinal cancer and kidney injury were identified through bioinformatics analysis. The expression of DEGs proteins in cancerous and adjacent tissues was assessed using immunohistochemical scoring (H scores). Patients were classified into AKI (n = 42) and non-AKI groups (n = 108) according to KDIGO AKI criteria. Univariate and multivariate logistic regression analyses were performed to investigate the influencing factors of AKI occurrence. Spearman correlation analysis was used to explore the relationship between DEGs and AKI biomarkers (Scr, BUN, MAU, and UA). The application value of DEGs in early diagnosis of AKI was evaluated using ROC curves.ResultsBioinformatics analysis identified CDK1, STAT1, COL1A2, and COL1A1 as DEGs related to AKI in gastrointestinal cancer. Immunohistochemical analysis revealed elevated H scores for CDK1, STAT1, COL1A2, and COL1A1 in tumor tissues. Univariate analysis showed no significant differences in age, sex, marital status, education level, monthly income, disease type, cancer stage, or tumor markers (CEA, CA242, CA50) between AKI and non-AKI groups (P > 0.05). However, the AKI group exhibited significantly higher levels of MAU, UA, and H scores for CDK1, STAT1, COL1A2, and COL1A1 compared to the non-AKI group (P < 0.05). Multivariate logistic regression confirmed that MAU, UA, CDK1, and STAT1 are independent risk factors for AKI in gastrointestinal cancer patients. Correlation analysis indicated a significant positive association between CDK1, STAT1, and AKI biomarker levels (P < 0.05). ROC curve analysis demonstrated that CDK1 and STAT1 possess high diagnostic value for early detection of AKI in patients with gastrointestinal cancer, with enhanced efficacy when used in combination.ConclusionCDK1 and STAT1 serve as early diagnostic indicators for the occurrence of AKI in gastrointestinal cancer patients.

Intraoperative ex-vivo epifluorescent diagnostics of stereotactic brain biopsies using EndoScell scanner: diagnostic accuracy study.

Brain biopsy is commonly employed for the histological diagnosis of complex intracranial diseases. To improve the positive diagnostic rate, the precision of intraoperative tissue sampling is critical. This study evaluated the accuracy of fluorescence imaging technology in rapidly distinguishing tumours from nontumour tissue during surgery, thus providing real-time feedback to surgeons and optimizing the surgical workflow. Biopsy samples from 65 patients were selected for this study. The lesion tissues were sequentially stained with sodium fluorescein and methylene blue, followed by fluorescence imaging via a handheld EndoScell scanner under an intraoperative cellular microscope. Frozen section examinations and haematoxylin-eosin (HE) staining were performed on the same lesion tissue by the pathology department. The time required for fluorescence imaging and pathology of frozen sections was recorded. The results of fluorescence imaging (whether the tissue was a tumour or nontumour tissue) and frozen pathology (whether the tissue was a tumour or nontumour tissue) were also recorded. The HE staining results were used as the final gold standard for diagnosis. The sensitivity, specificity, area under the curve (AUC), Kappa consistency test, and diagnostic efficiency of both methods were calculated. Lesion tissue and diagnostic results were successfully obtained from all 65 patients. When HE-stained histopathology was used as the gold standard, the sensitivity of fluorescence imaging was 100% (95% CI: 0.917-1.000), and the specificity was 63.6% (95% CI: 0.316-0.876). In comparison, the sensitivity of frozen section pathology was 88.9% (95% CI: 0.767-0.954), and the specificity was 100% (95% CI: 0.679-1.000). Both methods demonstrated high diagnostic accuracy. ROC curve analysis revealed that the AUCs for fluorescence imaging and frozen pathology were 0.818 and 0.944, respectively, with no significant difference observed in diagnostic performance (Z = 1.597, P > 0.05). Kappa consistency tests indicated that the Kappa value for frozen pathology compared with HE staining was 0.730 (P < 0.001); for fluorescence imaging compared with HE staining, the Kappa value was 0.744 (P < 0.001), thus demonstrating strong agreement with the HE staining results for both methods. In terms of time efficiency, fluorescence imaging was significantly faster than frozen section pathology [6 (4, 7) min vs. 48 (46, 55) min, Z=-9.856, P < 0.001], thus showing a clear advantage regarding time efficiency for fluorescence imaging. Intraoperative fluorescence imaging via an EndoScell scanner, which represents a novel method for histopathological diagnosis, has high diagnostic accuracy and efficiency. This method provides real-time guidance for tissue sampling strategies in brain biopsy, thereby improving the positive diagnostic rate and reducing surgical risk.

Analysis of Platelet Morphological Parameters as Clinical Risk Stratification in Acute Coronary Syndrome.

Platelet activation plays a central role in the pathogenesis of acute coronary syndrome (ACS). Platelet morphological parameters, including MPV, PDW, and P-LCR, are emerging as biomarkers for predicting the severity of ACS and prognosis. This study aims to assess the relationship between these parameters and coronary severity and to evaluate their predicting adverse outcomes. A total of 134 ACS patients and 50 healthy controls were included in this prospective observational study. Platelet morphological parameters (MPV, PDW, and P-LCR) were measured at admission, and coronary artery lesion severity was determined using the Gensini score from coronary angiography. Multivariate logistic regression analysis assessed the predictive value of these platelet parameters for adverse outcomes, and ROC curve analysis was used to evaluate their diagnostic performance. MPV, PDW, and P-LCR were significantly higher in ACS patients compared to healthy controls (p < 0.001). A strong positive correlation was found between platelet parameters and the Gensini score (MPV: r = 0.778, PDW: r = 0.800, P-LCR: r = 0.761; p < 0.001). Multivariate logistic regression identified MPV (OR = 1.807, p < 0.001), PDW (OR = 1.700, p = 0.001), and P-LCR (OR = 1.287, p < 0.001) as independent predictors of advent prognosis. ROC curve analysis showed that the combined use of MPV, PDW, and P-LCR provided superior predictive accuracy (AUC = 0.927) compared to the individual parameters. Elevated platelet morphological parameters are strongly associated with coronary artery lesion severity and serve as independent predictors of adverse outcomes in ACS patients. The combined assessment of MPV, PDW, and P-LCR enhances risk stratification, offering a valuable tool for guiding therapeutics and improving prognosis in management.

Landslide hazard assessment of an urban agglomeration in central Guizhou Province based on an information value method and SVM, bagging, DNN algorithm

The urban agglomeration in central Guizhou is located in a crustal deformation area caused by tectonic uplift between the Mesozoic orogenic belt of East Asia and the Alpine-Tethys Cenozoic orogenic belt, with high mountains, steep slopes, fractured rock masses and a fragile ecological environment; this area is the most affected by landslides in Guizhou Province, China. In the past decade, there were a total of 613 medium and large landslide disasters, resulting in 137 deaths and a direct economic loss of 1.032 billion yuan. Therefore, this study selected 12 indicators from the topography, geological structure, and external inducing factors, and conducted factor collinearity analysis using the variance expansion coefficient to construct a landslide hazard assessment index system. The statistical analysis model was combined with a variety of machine learning models, and the selection of negative sample points was restricted in various ways to improve training data accuracy and enable machine learning model predictions with sufficiently supervised prerequisites. The accuracy of the model was validated by ROC curve analysis. The AUC values of the SVM, DNN, and bagging models were all greater than 0.85, indicating that the results were credible. However, the overall accuracy was SVM > DNN > Bagging; that is, SVM was more suitable for landslide hazard assessment of the urban agglomeration in central Guizhou. Finally, field surveys were used to validate multiple sites with historical landslides in extremely high-hazard areas and analyse their development characteristics. The evaluation results can provide strong guidance for engineering design, construction and disaster prevention decision-making of urban agglomeration in central Guizhou.

Serum connexin 26 as a potential marker of non-syndromic hearing loss

BackgroundHearing loss (HL) represents the most common form of sensory impairment in both children and adults. In children, genetic causes account for over 50%. The genetic causes of HL include multiple syndromes associated with HL. In non-syndromic hearing loss (NSHL), patients have only hearing impairment or loss. 35delG, 167delT are common mutations that lead to severe HL. The prevalence of these mutations varies according to region, population characteristics, and within the same region according to the genetic pools. There is currently no approval for laboratory investigations to diagnose non-syndromic hearing loss (NSHL) except few studies that are based on expensive genetic tests. This study evaluates the value of connexin 26 as a screening test for NSHL.MethodsOne hundred forty children participated in our study; they were divided into two groups. Seventy children with NSHL and 70 children as control group. All children were subjected to Audiometry, measurement frequency of 35delG, 167delT by PCR–RFLP, and connexin26 level by ELISA.ResultsWe found that the connexin 26 level by ELISA was significantly lower in patients compared to controls (p = 0.0001). In addition, there was a significant negative correlation between connexin 26 levels and the degree of hearing loss by audiometry (r = − 0.403, p < 0.0001). In ROC curve analysis, serum levels of connexin 26 have sensitivity (94.3%) and specificity (100%). Measuring of connexin 26 by ELISA is more sensitive and specific than 35 del G mutation where sensitivity was 11.45 and 5.7% for both hetero and homo mutation.ConclusionSerum connexin 26 could be used as a good diagnostic marker to detect NSHL in children. It could be used as a potential marker for NSHL in newborns as it is more rapid and easily available than genetic study, especially in developing countries.

Exploring PANoptosis Related Novel Diagnostic Biomarkers and Potential Drugs for Sarcopenia based on Machine Learning and Experimental Validation.

Sarcopenia, an aseptic chronic inflammatory disease, is a complex and debilitating disease characterized by the progressive degeneration of skeletal muscle. PANoptosis, a novel proinflammatory programmed cell death pathway, has been linked to various diseases. However, the precise role of PANoptosis-related features in sarcopenia remains uncertain. According to the intersection of differentially expressed genes (DEGs) in the sarcopenia dataset GSE167186 and the PANoptosis gene set, we classified patients into PANoptosis-related subtypes (PANRS) using consensus clustering. The DEGs of PANRS were intersected with weighted gene co-expression network analysis (WGCNA). Proteinprotein interaction network and cytoHubba algorithms were employed to further identify potential genes related to PANoptosis. The most characteristic genes were selected using LASSO regression and validated by ROC curve analysis, followed by relevant immune infiltration analysis. Additionally, small-molecule drug screening was performed using Cmap. The relative expression levels of hub genes in sarcopenia were confirmed by PCR. Finally, single-cell analysis and GSEA were used to examine the distribution and function of hub genes. Thirty-five candidate genes were identified through WGCNA and PANRS. Machine learning and ROC curve analysis revealed three core genes: LTBP2, ETS2, and H3.3B, all of which were up-regulated in patients with sarcopenia (p<0.01). Immune infiltration analysis indicated that these three diagnostic genes were linked to the activation of NK cells and macrophages. Single-cell analysis demonstrated that LTBP2 was mainly localized in fibroblasts, while ETS2 and H3.3B exhibited a uniform distribution. Enrichment analysis indicated that the three hub genes were predominantly associated with the inhibition of energy metabolism. In this study, the hub genes LTBP2, ETS2, and H3.3B associated with PANoptosis in sarcopenia were successfully identified through a combination of bioinformatics and experimental verification methods. This establishes a foundation for new candidate diagnostic and therapeutic targets for sarcopenia.

The fibrosis-4 index and its association with carotid atherosclerosis in type 2 diabetes: a cross-sectional study in China

BackgroundThe medical community has long been concerned about the cardiovascular disease risk in patients with type 2 diabetes. While liver fibrosis scores were originally designed for application in individuals with liver steatosis, an increasing number of studies have shown that they are also associated with cardiovascular disease risk. However, the association between Fibrosis-4 (Fib-4) in liver fibrosis scores and carotid atherosclerosis (CA) in patients with type 2 diabetes remains unclear.ObjectiveThe aim of this study is to investigate the association between the Fib-4 index and CA in patients with Type 2 diabetes. Additionally, it seeks to determine whether this relationship is influenced by factors including gender, age, body mass index (BMI), hypertension, and other variables.MethodsScreening based on inclusion and exclusion criteria identified 2658 hospitalized patients with type 2 diabetes. Subsequently, patients were divided into three groups according to Fib-4 values (Fib-4 < 1.3, 1.3 ≤ Fib-4 < 2.67, Fib-4 ≥ 2.67). Logistic regression analysis was then applied to evaluate the association between Fib-4 and the presence of CA in type 2 diabetes. Further stratified analyses were conducted considering gender, age (using 60 years as the threshold), hypertension status, smoking, alcohol consumption, and BMI groups (using 24 kg/m2 as the threshold), aiming to investigate potential effect heterogeneity within predefined subgroups. ROC curve analysis was used to evaluate the predictive power of the Fib-4 value for CA, increased CIMT, and carotid plaques.ResultsThe study encompassed 2658 patients diagnosed with type 2 diabetes, comprising 1441 males and 1217 females, with an average age of 56.71 ± 10.22 years. Among them, 1736 individuals (65.3%) exhibited CA, 1243 (46.8%) had increased carotid intima-media thickness (CIMT), and 1273 (47.9%) manifested carotid plaques. Following adjustments for various factors, the prevalence of CA exhibited a progressive increase in the Fib-4 < 1.3, 1.3 ≤ Fib-4 < 2.67, and Fib-4 ≥ 2.67 groups, with statistically significant differences (P < 0.05). Moreover, the prevalence of increased CIMT and carotid plaques in the Fib-4 ≥ 2.67 group remained significantly higher than that in the Fib-4 < 1.3 group after considering various factors (P < 0.05). In the 1.3 ≤ Fib-4 < 2.67 group, subsequent to adjustments for gender, smoking, and drinking, the prevalence of increased CIMT and carotid plaques surpassed that in the Fib-4 < 1.3 group (P < 0.05). Despite further adjustments for multiple factors, the prevalence of increased CIMT and carotid plaques persisted higher than that in the Fib-4 < 1.3 group, yet the difference lacked statistical significance (P > 0.05). The results of the ROC curve analysis indicated that the AUC for Fib-4 predicting CA was 0.602 (P < 0.001, 95% CI: 0.579–0.625), while the AUC values for increased CIMT and carotid plaques were 0.561 (P < 0.001, 95% CI: 0.540–0.583) and 0.580 (P < 0.001, 95% CI: 0.558–0.601), respectively.ConclusionElevated Fib-4 levels (Fib-4 ≥ 1.3) are positively associated with CA in patients with type 2 diabetes, including increased CIMT and the presence of carotid plaques. As such, Fib-4 may serve as a potential biomarker for the detection of CA in patients with type 2 diabetes. However, its clinical utility needs further validation, particularly in larger sample sizes and multicenter studies.

Serum Lactic Acid: A Predictor of Septic Shock in Childhood Cancers with Febrile Neutropenia in a Tertiary Care Hospital in Bangladesh

Background: Febrile neutropenia (FN) is a common and life-threatening complication in children undergoing treatment for cancer, often leading to septic shock. Early identification of septic shock is critical. Serum lactic acid levels have emerged as a potential biomarker for predicting septic shock. This study aimed to determine the implications of serum lactic acid levels as a predictor of septic shock in childhood cancer patients with febrile neutropenia. Methods: This cross-sectional study was from June 2023 to July 2024. One hundred pediatric oncology patients with neutropenia who were admitted to the Department of Pediatric Hematology and Oncology at Bangabandhu Sheikh Mujib Medical University (BSMMU) were enrolled in the study. Clinical examinations and laboratory investigations were conducted for all participants, and the relevant information was recorded. Data were analyzed using SPSS 24.0. Results: Shock patients exhibited significantly higher initial serum lactic acid levels (3.52±0.81 mg/dl) compared to non-shock patients (1.93±0.75 mg/dl). The ROC curve analysis yielded an AUC of 0.934, with a sensitivity of 100.00%, specificity of 81.6%, and a cut-off value of 2.50 mg/dl. The diagnostic performance test demonstrated a PPV of 63.16%, NPV of 100.00%, and overall accuracy of 86.00%. Conclusion: This study demonstrates the potential utility of serum lactic acid levels as a predictive biomarker for identifying febrile neutropenic patients at risk of developing septic shock within 48 hours. Serum lactic acid level &gt;2.5 mmol/L could be considered as a threshold for early risk stratification.

Correlation between the Observer's Assessment of Alertness/Sedation score and bispectral index in patients receiving propofol titration during general anesthesia induction.

To explore the relationship between the Observer's Assessment of Alertness/Sedation (OAAS) score and the bispectral index (BIS) during propofol titration for general anesthesia induction and analyze the impact of BIS monitoring delay on anesthetic depth assessment. This study was conducted among 90 patients (ASA class I-II) undergoing elective surgery under general anesthesia. For anesthesia induction, the patients received propofol titration at the rate of 0.5 mg·kg-1·min-1 till OAAS scores of 4, 3, 2, and 1 were reached. After achieving an OAAS score of 1, remifentanil (2 μg·kg⁻¹) and rocuronium (0.6 mg·kg⁻¹) were administered, and tracheal intubation was performed 2 min later. BIS values, mean arterial pressure (MAP), heart rate (HR), and propofol dosage at each OAAS score were recorded, and the correlation between OAAS scores and BIS values was analyzed. The diagnostic performance of BIS values for determining when the OAAS score reaches 1 was analyzed using ROC curve. All the patients successfully completed tracheal intubation. BIS values of the patients at each of the OAAS scores differed significantly (P<0.01), and the mean BIS value decreased by 4.08, 8.32, 5.43 and 5.24 as the OAAS score decreased from 5 to 4, from 4 to 3, from 3 to 2, and from 2 to 1, respectively. There was a significant correlation between the OAAS score and BIS values (ρ=0.775, P<0.001). The median BIS value for an OAAS score of 1 was 76, at which point 83.33% of the patients had BIS values exceeding 60. ROC curve analysis showed that for determining an OAAS score of 1, BIS value, at the optimal cutoff value of 84, had a sensitivity of 88.9%, a specificity of 73.3%, and an area under the curve of 0.842 (0.803-0.881). OAAS score during induction of general anesthesia is strongly correlated with BIS value and is a highly sensitive and timely indicator to compensate for the delay in BIS monitoring.

Enhancing cardiac diagnostics: a deep learning ensemble approach for precise ECG image classification

Cardiovascular diseases are a global health challenge that necessitates improvements in diagnostic accuracy and efficiency. This study examines the potential of deep learning (DL) models for the classification of electrocardiogram (ECG) images to assist in the identification of various cardiac conditions. We initiated a two-tiered experimental framework to investigate the effectiveness of several neural network architectures in this medical application. In the first experiment, eight distinct neural network models were selected based on their top-5 accuracy on the ImageNet validation dataset and were fine-tuned using transfer learning techniques. These models were assessed using a cross-validation scheme, focusing on balanced accuracy, precision, recall, and the F1-score to evaluate their classification capabilities across four cardiac conditions: Myocardial Infarction (MI), abnormal heartbeat, historical MI, and normal ECG patterns. The second experiment extended our inquiry into the power of ensemble learning. By testing all possible combinations of the chosen models, we explored 120 ensemble configurations. The resulting analysis identified the best-performing ensemble set, which did not include the least effective model based on F1 score rankings. The most effective ensemble, composed of Inception, MobileNet, and NASNetLarge, achieved an F1 score of 0.9651 and a balanced accuracy of 0.9640, indicating a superior predictive performance. The ROC curve analysis yielded near-perfect Area Under the Curve (AUC) values for all classes, underscoring the ensemble’s proficiency in distinguishing between the specified cardiac conditions. The outcomes of this research highlight the synergistic benefit of ensembles in DL applications for medical imaging and suggest a promising approach for the early detection and diagnosis of cardiac diseases, potentially improving clinical outcomes and patient care.

FEV1/FEV6 Cutoff Points to Avoid False Negatives When Using Portable Devices, PICO-6® and COPD-6®, in COPD Detection in Primary Healthcare Services.

Background: Chronic obstructive pulmonary disease (COPD) is a frequent but underdiagnosed disease, primarily due to the lack of access to forced spirometry (FS) in primary care. Portable, easy-to-use expiratory flow meters like Piko-6® and COPD-6® that measure FEV1, FEV6, and FEV1/FEV6 ratio provide an alternative. Given that Piko-6® and COPD-6® devices measure FEV6 but not FVC, the aim of the study is to determine the optimal cutoff value for the FEV1/FEV6 ratio of each device to avoid false negatives when these devices are used for COPD screening in primary care (PC). Methods: A total of 664 patients of 35 years of age or older with a cumulative tobacco consumption of 10 or more packs/year were recruited at two university hospitals. FS (gold standard) was performed and FEV1, FVC, and FEV1/FVC measurements were compared with FEV1, FEV6, and FEV1/FEV6 measurements acquired using Piko-6® and COPD-6® devices. The devices were compared using statistical methods including Pearson correlation coefficients, the Youden index (YI), kappa coefficient, Bland-Altman plots, and ROC curves analysis. Results: Correlations between FEV1/FEV6 using Piko-6® and COPD-6® and FEV1/FVC with FS were 0.79 and 0.73, respectively. Piko-6® achieved the best YI in FEV1/FEV6 (0.73), whereas for COPD-6®, it was 0.80. Concordance between Piko-6® and FS was 83.9% (kappa 0.67 ± 0.028) and for COPD-6®, it was 68.7% (kappa 0.42 ± 0.02). Conclusions: This is the first study that compares two hand-held expiratory flow meters with FS. Piko-6® and COPD-6® devices are effective tools for COPD detection, as their measurements provide a good correlation with FS. In order to avoid false negative results, the FEV1/FEV6 cutoff point needs to be increased to 0.73 and 0.80 with Piko-6® and COPD-6®, respectively.

Effectively capturing memory deficits: New advances in the use of the In-out-Test for cognitive Test post-stroke

Objective This study evaluated the reliability and validity of the In-Out-Test for detecting episodic memory deficits in stroke patients and explored its potential as a clinical test. Methods A total of 75 stroke patients and 120 healthy controls underwent tests, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Picture-Based Memory Impairment Screen (PMIS), and In-Out-Test. Reliability metrics (Cronbach’s α, inter-scorer reliability, test-retest reliability), criterion validity, corrected item-total correlation, hierarchical regression analysis and ROC curve analysis were performed to determine the sensitivity and specificity of the In-Out-Test. Results Stroke patients scored lower across all tests (p < 0.001), with the largest difference in the In-Out-Test (d = 0.99). The In-Out-Test correlated strongly with other cognitive tests (r = 0.79–0.85 in stroke patients; r = 0.66–0.78 in controls). It explained an additional 4.5% of variance in MoCA-MIS scores (p < 0.001). Reliability was high (Cronbach’s α = 0.835; inter-rater ICCs = 0.911–0.925; test-retest ICCs = 0.764–0.802). ROC analysis showed an AUC of 0.747, with a sensitivity of 0.708 and specificity of 0.680 at a cutoff of 10.5. Conclusion Preliminary findings indicated that the In-Out-Test showed potential in detecting episodic memory impairments in stroke patients, warranting further validation in larger cohorts.

Validation of the role of apolipoproteins in coronary artery disease patients with impaired kidney function for prognosis: a prospective cohort study in China

ObjectiveThis study aims to evaluate the relationship between apolipoproteins (ApoA1, ApoB, and the ApoB/A1 ratio) and the incidence of major adverse cardiovascular events (MACE) in patients with coronary artery disease (CAD) and impaired kidney function, assessing their potential role in secondary prevention.MethodA prospective cohort of 1,640 patients with impaired kidney function who underwent percutaneous coronary intervention in China was analyzed. Patients were categorized based on the measurements of ApoA1, ApoB, and ApoB/A1 ratio. MACE, defined as a composite of all-cause mortality, cardiovascular death, nonfatal myocardial infarctions, strokes, and unplanned revascularizations, was tracked post-procedure, with statistical analyses including Kaplan–Meier survival curves and Cox regression models to identify associations with apolipoproteins. Subgroup analyses according to kidney function were conducted.ResultDuring a median follow-up of 3.1 years, 324 MACE events were observed. Multivariable Cox regression analyses illustrated higher levels of ApoB and the ApoB/A1 ratio were significantly associated with increased MACE incidence (adjusted HR [95%CI] 1.668[1.044–2.666]; adjusted HR [95%CI] 2.231[1.409–3.533], respectively), while lower ApoA1 levels correlated with a higher risk (adjusted HR [95%CI] 0.505[0.326–0.782]). ROC curve analyses indicated comparable predictive performances to traditional risk factors like LDL cholesterol. Subgroup analysis revealed that the above association was not statistically significant in the moderate-to-severe renal impairment CAD patients (eGFR < 45 mL/min/1.73 m2).ConclusionOur findings illustrate that apolipoproteins, specifically ApoA1 and ApoB, along with their ratio, are significant predictors of major adverse cardiovascular events in CAD patients with impaired kidney function. These results emphasize the need for incorporating apolipoprotein measurements in secondary prevention strategies for this high-risk population.

Exploring the mechanism of Radix Bupleuri in the treatment of depression combined with SARS-CoV-2 infection through bioinformatics, network pharmacology, molecular docking, and molecular dynamic simulation.

Radix Bupleuri is commonly used in treating depression and acute respiratory diseases such as SARS-CoV-2 infection in China. However, its underlying mechanism in treating major depressive disorder combined with SARS-CoV-2 infection remains unclear. This study aims to elucidate the pharmacological mechanisms of Radix Bupleuri in treating major depressive disorder combined with SARS-CoV-2 infection, employing bioinformatics, network pharmacology, molecular docking, and dynamic simulation techniques. Active ingredients and drug target genes of Radix Bupleuri were collected from TCMSP, PubChem, SwissTargetPrediction, and SuperPred databases. Differentially expressed genes were analyzed using datasets of SARS-CoV-2 infection and major depression disorder from the GEO database. The key genes were identified by using GO and KEGG functional analyses and STRING database. Machine learning methods were employed to predict core target gene, and ROC curve analysis validated the models' accuracy and the core gene expression had been analyzed and validated with other datasets. Molecular docking and dynamic simulation were conducted to verify the affinity of the active ingredients with core target gene. Finally, immune infiltration and correlation analyses between core target genes and immune cells were performed. A total of 15 active ingredients, 1898 differentially expressed genes related to SARS-CoV-2 infection, and 814 differentially expressed genes related to major depression disorder were collected. 18 common genes were identified at the intersection of Radix Bupleuri, major depression disorder, and SARS-CoV-2 infection. The key gene JAK2 was identified through PPI network construction and machine learning model predictions. Molecular docking showed that the binding energies of the active ingredients with JAK2 were all below - 5kcal/mol, with petunidin exhibiting the highest affinity. Molecular dynamic simulations further suggested stable interactions with JAK2. Immune infiltration analysis suggested that Radix Bupleuri in the context of depression combined with SARS-CoV-2 infection may promote the activation and generation of B cells, CD4 T cells, and CD8 T cells, while inhibiting the activation of mature dendritic cells, macrophages, natural killer cells, and neutrophils. Correlation analysis of JAK2 with immune cells indicated an association with macrophage activation and the inhibition of memory B cells and activated B cells. The active ingredients of Radix Bupleuri may exhibit both antidepressant and antiviral pharmacological effects in the progression of major depression disorder combined with SARS-CoV-2 infection, through a mechanism closely associated with the JAK2 target.

Predictive risk model of mild cognitive impairment in patients with malignant haematological diseases after haematopoietic stem cell transplantation

ObjectiveThis study is to develop and validate a robust risk prediction model for mild cognitive impairment (MCI) in patients with malignant haematological diseases after haematopoietic stem cell transplantation (HSCT).MethodsIn this study, we analysed the clinical data of the included patients. Logistic regression analysis was used to identify independent risk factors for cognitive impairment after HSCT in patients with malignant haematological diseases, and a risk prediction model was constructed. Multiple cohorts of patients with haematological malignancies after HSCT (282 cases) from the Affiliated Hospital of Xuzhou Medical University and the First People’s Hospital of Yancheng City between April 2019 and February 2022, and patients from the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University between March 2022 and July 2023 were used for external validation. Logistic regression analysis was performed to develop the predictive model. The predictive value and consistency of the model were evaluated using the area under the curve (AUC) and calibration method, respectively. Decision curve analysis (DCA) was performed to access the utility of the model.ResultsApproximately half (52.26%) of the patients in the study developed mild cognitive impairment (MCI). Older age, allogeneic HSCT, anxiety, graft-versus-host disease, and longer hospital stay were associated with a higher risk of developing MCI. ROC curve analysis confirmed the sound performance of the predictive model and external validation, with AUC of 0.897 and 0.789 respectively. The direction of the calibration curves of the training and validation sets is closer to the diagonal (ideal curve), indicating good model consistency; the DCA curves also show that the model has good predictive ability and stability.ConclusionsWe conclude that it is possible to predict mild cognitive impairment with readily available, mostly pretransplant predictors. The accuracy of the risk prediction models can be improved for use in clinical practice, possibly by adding pretransplant patient-reported functioning and comorbidities.

Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients

ObjectiveThe objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy.MethodsWe applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD.ResultsThe final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794–0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786–0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium.ConclusionOur results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium.

The effect of cardiac catheterization on thyroid functions in infants with congenital heart diseases: a prospective observational study

This study aims to determine the incidence, clinical course, and risk factors of hypothyroidism following cardiac catheter (CC) in infants with congenital heart diseases (CHD). This prospective study involved 115 patients with CHD, all aged 3 years or younger, who underwent CC, as well as 100 healthy age- and sex-matched controls. Baseline thyroid function tests (TFTs) were conducted for both the patients and controls. In the CHD cohort, TFTs were reassessed 4 weeks after the CC, and for those with abnormal TFT values at this time, the tests were repeated after 2 weeks. Levothyroxine was started for patients with persistent abnormal TFTs, at 4 weeks and 6 weeks assessments after CC. Four weeks after CC, 12% of the studied group exhibited hypothyroidism. Univariate analysis identified significant predictors of hypothyroidism following CC: aortic stenosis (RR = 10.0 (1.49–66.99), P = 0.018), duration of fluoroscopy during CC (RR = 2.12 (0.99–4.26), P = 0.05), and total iodinated contrast media (iCM) during CC (RR = 2.5 (1.35–3.55), P = 0.019). Multivariate analysis indicated that iCM dose was the sole significant predictor of developing hypothyroidism (RR = 2.10 (1.01–3.23), P = 0.04). ROC curve analysis showed that the cut-off point of iCM dose for prediction of hypothyroidism evolution is 8.7 gm/kg, (sensitivity: 83%, specificity: 65%), while the cut-off point of fluoroscopy duration which predicts the development of hypothyroidism is 24 min (sensitivity: 83%, specificity: 66%). Acquired hypothyroidism after CC persisted in 4% of this cohort for 6 months.Conclusion: Higher doses of iCM and longer duration of fluoroscope during CC are risk factors for the evolution of hypothyroidism post-CC. We recommend assessing thyroid profile 4 weeks after CC, particularly in patients who received an iCM dose greater than 8.7 gm/kg and/or exposed to fluoroscopy for more than 24 min. What is known:• The use of excess iodine leads to transient inhibition of thyroid hormones biosynthesis via the Wolff-Chaikoff effect.• Infants with congenital heart diseases (CHD) are more prone to hypothyroidism due to higher frequency of abnormal thyroid morphology and routine exposure to supraphysiological doses of iodine.What is new:• Exposure to a total dose of iodinated contrast media more than 8.7 gm/kg and a fluoroscopy duration more than 24 min during cardiac catheter are risk factors for the evolution of thyroid hypofunction following cardiac catheter.

Plasma Humanin and Non-Coding RNAs as Biomarkers of Endothelial Dysfunction in Rheumatoid Arthritis: A Pilot Study

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder associated with an increased risk of cardiovascular disease (CVD), largely driven by peripheral endothelial dysfunction (ED). Humanin, a mitochondrial-derived peptide, has been suggested to play a protective role in endothelial function. However, the relationship between Humanin levels and ED in RA, as well as the interaction between Humanin and non-coding RNAs such as Long Non-Coding RNA GAS5, microRNA-21 (miR-21), and microRNA-103 (miR-103), remains unclear. Objective: This study aimed to investigate the relationship between circulating Humanin levels, non-coding RNAs (GAS5, miR-21, miR-103), and endothelial dysfunction (ED) in patients with RA. Additionally, we explored the correlation between Humanin expression and specific non-coding RNAs (GAS5, miR-21, and miR-103) to better understand their potential role in vascular health. Methods: Peripheral ED was assessed using flow-mediated pulse amplitude tonometry, with Ln-RHI values &lt;0.51 indicating dysfunction. Humanin levels, GAS5, miR-21, and miR-103 were measured in RA patients. Univariate and multivariate analyses were conducted to determine the relationship between these biomarkers and ED. Kaplan–Meier survival analysis and ROC curve analysis were used to assess the prognostic value of Humanin. Results: Higher Humanin levels were significantly associated with better endothelial function (OR = 0.9774, p = 0.0196). Kaplan–Meier analysis demonstrated that higher Humanin levels correlated with improved survival (p &lt; 0.0001). The non-coding RNAs (GAS5, miR-21, and miR-103) did not show significant associations with ED. Conclusions: Humanin is a potential protective biomarker for endothelial dysfunction and survival in RA patients. Further research is needed to explore the interaction between Humanin and non-coding RNAs in the context of vascular health.