Receiver Operating Characteristic Curve Analysis Research Articles

Visceral adipose tissue measured by DXA predicts metabolic syndrome in low-income community-dwelling elderly: Insights from the São Paulo Aging & Health (SPAH) study

While visceral fat measured by dual-energy X-ray absorptiometry (DXA) is accurate in identifying middle-aged people at increased cardiometabolic risk, consistent data for the elderly are still lacking. We aimed to investigate the association between DXA-derived visceral adipose tissue (VAT) and metabolic syndrome (MetS) and to establish optimal cutoffs for VAT to predict MetS in a low-income elderly Brazilian cohort. A total of 449 women and 258 men (≥65 years) from the community were enrolled in this study. Participants underwent clinical and laboratory evaluations, along with body composition analysis by Hologic Discovery A densitometer. VAT was measured in the android region of the DXA scan. MetS was diagnosed using NCEP-ATPIII criteria. Multivariate logistic regression analyzed the relationship between VAT and MetS. Receiver-operating characteristic (ROC) curve analysis evaluated VAT's predictive accuracy for MetS, with optimal cutoffs determined by Youden's test to balance sensitivity and specificity. Mean ages were 76.6 ± 4.7 years for men and 77.1 ± 4.9 years for women. Mean BMIs were 26.5 ± 3.8 kg/m2 for men and 29.0 ± 5.2 kg/m2 for women. One hundred and seventy-five (41.5 %) men and 274 (61 %) women had MetS. After adjustments for confounders, multivariate analysis showed that VAT was independently associated with MetS in both men (OR 1.41, 95%CI 1.15–1.72) and women (OR 1.33, 95%CI 1.16–1.54, per each 100 g increase). Optimal VAT cutoffs to predict MetS were 642.5 g for men (AUC = 0.740) and 600.5 g for women (AUC = 0.729). Subanalysis for non-overweight/non-obese subjects yielded lower VAT cutoffs. Thus, VAT measured by DXA was significantly associated with MetS in older adults, regardless of BMI, emphasizing the critical role of VAT in predicting MetS. Therefore, VAT by DXA holds promise for evaluating MetS risk in the elderly. Further longitudinal studies are needed to investigate VAT's impact on major cardiovascular event incidence in this demographic.

Assessing Left Atrial Appendage Functions by Transesophageal Echocardiography and Speckle Tracking Imaging to Predict Recurring Atrial Fibrillation Post-Radiofrequency Catheter Ablation.

To predict recurring atrial fibrillation (AF) following radiofrequency catheter ablation by assessing the attributes of the left atrium and the left atrial (LA) appendage (LAA) using transesophageal echocardiography (TEE) and speckle tracking imaging. The structural and functional indices of the LA and LAA of 80 patients with AF who underwent preoperative transthoracic echocardiography and TEE were measured. Recurrence was recorded postoperatively at 3, 6, and 12 months. The independent determinants of recurring AF were identified by logistic regression, and their optimum cutoff values, sensitivity, and specificity were estimated from the receiver operating characteristic (ROC) curves. The recurrent and nonrecurrent groups comprised 17 and 63 patients, respectively. The LA internal diameter, LA end-diastolic and end-systolic volumes, LAA length, diameter and area of the opening of the LAA, and LAA end-diastolic volume were higher in the recurrent group. The LA strain in the reservoir phase, the ejection fraction and filling velocity of the LAA, the LAA emptying velocity (LAAeV), and the LAA strain (LAAS) reduced considerably. Multifactorial regression analyses demonstrated that the LAAeV and LAAS were independent determinants of recurring AF. ROC curve analysis revealed that the LAAeV and LAAS predicted postoperative recurrence at 34.5cm/s (area under the curve [AUC]: 0.954, sensitivity: 94.1%, and specificity: 92.1%) and 11.61% (AUC: 0.925, sensitivity: 82.4%, and specificity: 95.2%), respectively, while the AUC, sensitivity, and specificity of the combined predictors (LAAeV+LAAS) were 0.978, 94.1%, and 93.7%, respectively. The LAAeV and LAAS independently influenced the postoperative recurrence of AF.

Visceral to subcutaneous fat area ratio predicts early postoperative small bowel obstruction after total gastrectomy for cardia cancer.

We aimed to investigate the relationship between the visceral to subcutaneous fat area ratio (V/S ratio) and incidence of early postoperative small bowel obstruction (EPSBO) following total gastrectomy for cardia cancer. We conducted a retrospective analysis among patients with cardia cancer who underwent elective total gastrectomy with esophagojejunostomy Roux-en-Y anastomosis at Nanjing Yimin Hospital between November 2019 and April 2024. Preoperative, intraoperative, and postoperative factors were meticulously monitored. The V/S ratio was calculated using computed tomography scans at the umbilical level with Slice-O-Matic software (Tomovision, Montreal, Canada). Statistical analyses included logistic regression and receiver operating characteristic (ROC) curve analysis. Among 175 patients, 27 (15.4%) developed EPSBO. The V/S ratio was significantly higher in the EPSBO group (1.76 ± 1.05 vs. 1.01 ± 0.54). Logistic regression identified the V/S ratio as a significant predictor of EPSBO (odds ratio [OR] = 1.612, 95% [CI]: 1.102-1.605). ROC curve analysis demonstrated high sensitivity (92%) and specificity (100%) for the V/S ratio in predicting EPSBO, with a 0.83 AUC. Our findings indicated a higher V/S ratio was a significant predictor of EPSBO following total gastrectomy for cardia cancer. Preoperative assessment of the V/S ratio can inform risk stratification and guide targeted interventions to improve postoperative outcomes.

Diagnostic and screening potential of plasma exosome miR‑99b‑5p and its combination with other miRNAs for colorectal cancer.

Extracellular vesicles (EVs) secreted by tumor cells have been documented to hold viable biomarker potential. Therefore, the present study evaluated the potential clinical value of EV-microRNAs (miRNAs or miRs) in the plasma exosomes of patients with colorectal cancer (CRC) for the early diagnosis and screening of CRC. In total, 95 plasma samples were collected at The Third Affiliated Hospital of Guangzhou Medical University (Guangzhou, China) between 2017 and 2019. Specifically, 68 samples were from patients with CRC and 27 were from healthy control (HC) donors. High-throughput sequencing was used to detect the expression of miRNAs in the isolated plasma EVs, which was subsequently verified by reverse transcription-quantitative PCR. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic potential of single and combined miRNAs for CRC. Bioinformatics analysis was employed to predict the target genes of candidate miRNAs. Compared with those in the HC group, the CRC group expressed higher levels of miR-99b-5p and miR-409-3p, especially during the early stages of CRC. Clinicopathological analysis confirmed the higher expression levels of miR-99b-5p during the early stages, as well as higher expression levels in the colon compared with those in the rectum. ROC curve analysis revealed that the area under the curve (AUC) of miR-99b-5p for the diagnosis of early CRC was 73.5% (P=0.007). The early diagnostic capability of miR-99b-5p combined with miR-409-3p for CRC was evaluated, and the AUC was found to be 74.1% (P=0.006). In addition, the AUC of the combination of miR-99b-5p, miR-409-3p and carcinoembryonic antigen was 81.2% (P<0.001), indicating that this three-parameter combination displayed higher diagnostic power compared with any single miRNA for early CRC screening. The results from the present study suggest that the expression of miR-99b-5p in plasma exosomes is significantly upregulated in CRC, which holds potential for the early diagnosis of this cancer type. Such potential can be enhanced further by combining it with other miRNAs. Therefore, the present study provides a comprehensive but preliminary insight for the viability of miR-99b-5p (alone or combined with other miRNAs) for CRC diagnosis, which requires further exploration in the future.

Difference in hematocrit and plasma albumin levels as an early biomarker of severity and prognosis in patients with severe fever and thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is a widespread infectious disease with high mortality. Hence, identifying valuable biomarkers for detecting the early changes in SFTS is crucial. In this study, we investigated the relationship between the difference in hematocrit (HCT) and serum albumin (ALB) levels (HCT-ALB) and the prognosis of patients with SFTS virus infection. After excluding the patients who did not meet the SFTS diagnostic criteria, those with SFTS from the First Affiliated Hospital of Wannan Medical College were divided into a fatal and Nonfatal group based on their disease prognosis. A dynamic analysis of the daily laboratory data was conducted for 14 days following SFTS onset. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of HCT-ALB. Another sample of patients with SFTS admitted to the First Affiliated Hospital of Nanjing Medical University was utilized to verify the study conclusions. A total of 158 patients with SFTS were included. Among them, 126 patients were categorized in the Nonfatal group and 32 in the fatal group, leading to a mortality rate of 20.25% (32/158). Univariate analysis of the laboratory test findings and ROC curve analysis showed that alanine aminotransferase (ALT), aspartate aminotransferase (AST), HCT-ALB, and lactate dehydrogenase (LDH) had a relatively better ability to discriminate the disease condition of the patients with SFTS. Moreover, HCT-ALB served as a predictor of SFTS prognosis. Additionally, an area under the ROC curve (AUC) of 0.777 and a critical HCT-ALB value of 4.75 on day 7 were associated with a sensitivity of 83.3% and a specificity of 73.9%. On day 8 (AUC = 0.882), the critical value of HCT-ALB was 9.25, while the sensitivity was 100% and specificity was 76.5%. Further verification based on the data of 91 patients with SFTS admitted to the First Affiliated Hospital of Nanjing Medical University demonstrated a mortality rate of 51% (24/47) among those with HCT-ALB values >4.75 on day 7 of the disease course, highlighting the potential of the HCT-ALB value of >4.75 for predicting SFTS prognosis. High HCT-ALB values are closely related to the mortality of patients with SFTS. HCT-ALB is a sensitive and independent predictor of early disease in patients with SFTS.

Examining the Long-Term Prognostic Significance of Serum sST2: Influence of Myocardial Infarction History and Subgroup Level Standardization.

In this study, the predictive value of soluble growth stimulation expressed gene 2 protein (sST2) for long-term clinical outcomes in patients with acute heart failure (AHF) is assessed. In addition, the influence of a history of myocardial infarction on the levels of sST2 in patients with HF, as well as its impact on outcome events, is explored. We also aim to establish a specific standard for sST2 levels in this subgroup. We conducted an ambispective cohort study involving hospitalized patients with AHF, measuring their sST2 levels and following their progress over three years. The primary endpoint was major adverse cardiovascular events (MACEs), encompassing heart failure readmission and all-cause mortality over three years. Cox regression analysis was used to evaluate the prognostic significance of sST2 levels, along with a subgroup analysis using propensity score matching (PSM) to adjust for confounding variables. Receiver operating characteristic (ROC) curve analysis was utilized to determine the optimal sST2 threshold using Youden's J statistics, and a sensitivity analysis included Kaplan-Meier survival curves. The study included 149 patients with a median age of 68 years, of whom 57% were male. Both univariate and multivariate Cox regression analyses confirmed sST2 as an independent predictor of MACEs. Post-PSM analysis, 124 samples were grouped by MI history ROC curve analysis revealed an area under the curve of 0.726 for predicting MACEs in patients with MI, demonstrating a significant predictive value for sST2 levels above 34 ng/mL, which correlated with increased readmission and mortality rates. In contrast, sST2 levels in patients without MI history showed no significant predictive relevance. sST2has significant long-term predictive value for clinical outcomes in patients with AHF, particularly for those with a prior MI history, indicating a need for heightened clinical attention and thorough follow-up to mitigate long-term adverse cardiovascular outcomes.

Association Between Pericoronary Fat Attenuation Index as Evaluated by Coronary Artery CT Angiography and Clinical Interventions in Lipid Management Among Patients with Coronary Artery Disease.

This study aims to evaluate the relationship between the pericoronary fat attenuation index (FAI), derived from coronary artery computed tomography angiography, and post-lipid management levels of low-density lipoprotein cholesterol in patients with coronary artery disease (CAD). Additionally, the study investigates coronary inflammation across different lipid management strategies. We selected a cohort comprising 521 CAD patients who met the inclusion criteria. Patients were categorized into well-managed (LDL-C<2.6mmol/L) and poorly managed (LDL-C≥2.6mmol/L) groups based on lipid management efficacy. We collected anthropometric measures (height, weight, body mass index, and body surface area) and clinical indicators, including Gensini score, and FAI-related parameters for coronary atherosclerotic lesions. We analyzed the interrelations along these parameters and lipid management using statistical methods and assessed diagnostic value via receiver operating characteristic (ROC) curve analysis of these parameters was assessed through. The poorly managed group exhibited significantly higher levels of total cholesterol, triglycerides, and lower levels of high-density lipoprotein compared to the well-managed group (P < 0.05). Significant differences were observed between the groups in terms of lesion length in the proximal segment of the left anterior descending artery, FAI value in the proximal segment of lesions in the right coronary artery (RCA), volume thickness in the middle segment of RCA lesions, and lesion length in the distal segment of RCA (P < 0.05). ROC curve analysis revealed areas under the curve ranging from 0.484 to 0.660 for the parameters, indicating limited diagnostic efficacy. The FAI in the RCA varies with lipid management strategies, suggesting it as a valuable metric for monitoring both perivascular inflammation and lipid status in CAD patients. However, its current diagnostic efficacy is limited, indicating the need for further research to improve its clinical utility.

Clinical values of preoperative red blood cell distribution width and platelet parameters in patients with papillary thyroid carcinoma.

The prevalence of thyroid carcinoma is increasing, and papillary thyroid carcinoma (PTC) is the most frequent subtype. More and more attention is being concentrated on the association between inflammation indicators and malignant tumors. The aim of the present study was to analyze whether the preoperative red blood cell distribution width (RDW) and platelet parameters, including mean platelet volume (MPV) and platelet distribution width (PDW), can be applied to distinguish between patients with PTC or papillary thyroid microcarcinoma (PTMC) and healthy controls, and to explore the associations with clinicopathological characteristics. The study retrospectively compared the RDW, MPV and PDW values of 780 patients with PTC or PTMC against a healthy control group. Receiver operating characteristic (ROC) curves were conducted to determine diagnostic accuracy. Furthermore, the clinicopathological features of the patients with PTC or PTMC were compared between higher and lower platelet parameter groups based on the RDW, MPV and PDW values. Significantly higher preoperative RDW, MPV and PDW values were found in patients with PTC or PTMC compared with those of the healthy group. ROC curve analysis showed that the area under the curve (AUC) plus 95% confidence interval (95% CI) values of RDW, MPV and PDW were 0.808 (0.780-0.835), 0.771 (0.743-0.799) and 0.711 (0.681-0.742), respectively. When RDW and MPV were combined together, the AUC (95% CI) value was enhanced to 0.858 (0.835-0.881) for the patients with PTC. For the patients with PTMC, RDW, MPV and PDW had AUC (95% CI) values of 0.812 (0.783-0.840), 0.779 (0.749-0.808) and 0.718 (0.685-0.751), respectively. When RDW and MPV were combined together, the AUC (95% CI) value was enhanced to 0.858 (0.835-0.881). A higher RDW was significantly associated with being female, deeper tumor infiltration, and normal FT3 and FT4 levels. A higher PDW was significantly associated with elevated thyrotropin receptor antibody levels. In conclusion, as convenient and available inflammation indicators, RDW, PDW and MPV have diagnostic ability and can distinguish between patients with PTC or PTMC and healthy controls. In addition, the combined application of RDW and MPV can improve the diagnostic power. The values of RDW and MPV were associated with clinicopathological characteristics. To the best of our knowledge, this is the first study to prove the usefulness of preoperative RDW combined with MPV in diagnosing patients with PTC or PTMC.

Clinical significance of S100B protein in pregnant woman with early- onset severe preeclampsia.

Preeclampsia is one of the most feared complications of pregnancy, which can progress rapidly to serious complications such as death of both mother and fetus. To present, the leading cause of preeclampsia is still debated. The purpose of this article was to explore the clinical significance of S100B protein, a kind of Ca2+ -sensor protein, in the early-onset severe preeclampsia. Nine pregnant women with early-onset severe preeclampsia (the study group) and 13 healthy pregnant women (the control group) were included in this study. The level of S100B in the amniotic fluid, maternal blood, and umbilical cord blood were detected by enzyme-linked immunosorbent assay (ELISA) and surface plasmon resonance imaging (SPRi) methods. Diagnostic values of S100B for early-onset severe preeclampsia were assessed by Receiver Operating Characteristic (ROC) curve analysis. The levels of S100B in maternal blood and amniotic fluid in the study group were higher than those in the control group (p < 0.05). ROC curve analysis showed that S100B detected by SPRi method (SPRi-S100B) showed a cut-off level of 181 ng/mL with sensitivity of 100%, a specificity of 84.6%, and a Youden index of 0.846 in the maternal blood, which had better clinical significance and diagnostic value (at than that detected by ELISA (ELISA-S100B). The levels of S100B detected by SPRi in maternal blood can indicate early-onset severe preeclampsia and perinatal brain injury.

Association between triglyceride glucose-body mass index and the staging of non-alcoholic steatohepatitis and fibrosis in patients with non-alcoholic fatty liver disease

Objective The objective of this study was to thoroughly investigate the clinical value of triglyceride glucose-body mass index (TyG-BMI) in patients diagnosed with non-alcoholic fatty liver disease (NAFLD). Specifically, we aimed to determine its association with non-alcoholic steatohepatitis (NASH) and the progression of liver fibrosis. Methods The study included 393 patients diagnosed with NAFLD after liver biopsy. The patients were divided into two distinct cohorts: a training cohort (N = 320) and a validation cohort (N = 73). The training cohort was further divided into four groups based on TyG-BMI quartiles. The clinical characteristics of the patients in each group were compared in detail, and the association between TyG-BMI and NASH, NAFLD Activity Score (NAS) ≥ 4, at-risk NASH, significant fibrosis, advanced fibrosis, and cirrhosis was analyzed using multiple models. Additionally, we generated receiver operating characteristic (ROC) curves to evaluate the predictive ability of TyG-BMI for NASH and fibrosis staging in patients with NAFLD. Results Patients with higher TyG-BMI values had a significantly higher prevalence of NASH, NAS ≥ 4, at-risk NASH, significant fibrosis, advanced fibrosis, and cirrhosis (all p < .05). TyG-BMI was an independent predictor of these diseases in both unadjusted and adjusted models (all p < .05). ROC curve analysis further revealed the excellent performance of TyG-BMI in predicting NASH, NAS ≥ 4, at-risk NASH, significant fibrosis, advanced fibrosis, and cirrhosis. The validation cohort yielded analogous results. Furthermore, we constructed three multivariate models of TyG-BMI in conjunction with elastography metrics, which demonstrated elevated diagnostic AUC values of 0.782, 0.792, 0.794, 0.785, 0.834, and 0.845, respectively. Conclusion This study confirms a significant association between insulin resistance and NAFLD, including at-risk NASH and fibrosis staging, as assessed using the TyG-BMI index. TyG-BMI and its associated multivariate models can be valuable noninvasive indicators for NAFLD diagnosis, risk stratification, and disease course monitoring.

ANXA3 as a novel biomarker for sepsis diagnosis: Evidence from integrative WGCNA analysis

Sepsis is a dysregulated immune response to infection that comes with multiple organ dysfunction and high mortality. The management of sepsis relies heavily on early recognition and diagnosis, but current diagnostic methods have limitations in timeliness, sensitivity, and discriminability. This study aims to discover novel biomarkers for sepsis diagnosis. Four datasets from different regions were analyzed using weighted gene co-expression network analysis (WGCNA), and genes with high Gene Significance values across these datasets were overlapped. Finally, two genes, CD177 and ANXA3, were identified. ANXA3 was validated as a potential sepsis biomarker by checking multiple datasets and Receiver Operating Characteristic (ROC) Curve Analysis. Of note, ANXA3 could distinguish not only between adult and child sepsis patients and healthy controls, but also between septic shock and cardiogenic shock. Moreover, a murine sepsis model was established and the results showed that the transcription of ANXA3 in peripheral blood of septic mice was significantly higher than that of healthy controls, while Escherichia coli infection alone did not significantly increase the transcription level of this gene. Subsequent studies of sepsis in mice revealed that the predictive effect of Anxa3 on sepsis could be observed as early as 6 h post-modeling. Interestingly, ANXA3 expression was predominantly up-regulated in myeloid cells, up-regulated in spleen, down-regulated in lung, and not detected in liver after sepsis modeling. Taken together, this study provides a way for the discovery of biomarkers and finds that ANXA3 may be a novel diagnostic biomarker for sepsis.

Monocyte-to-Albumin Ratio Is Associated With Hematoma Expansion in Spontaneous Intracerebral Hemorrhage.

Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a severe complication that independently predicts poor prognosis. In this study, we aimed to investigate whether monocyte-to-albumin ratio (MAR), a novel marker of systemic inflammation, could predict HE in patients with ICH. We retrospectively assessed the data of patients with ICH. The clinical, imaging, and laboratory test data including, the MAR on admission, were analyzed. A multivariate logistic regression analysis was carried out to explore the relationship between MAR and hematoma growth. The receiver operating characteristic (ROC) curve was employed to investigate the predictive value of MAR for HE after ICH. A total of 246 patients were included in the present study. Multivariate logistic regression analysis demonstrated that the MAR was associated with HE (odds ratio [OR]=1.179; 95% confidence interval, 1.093-1.272; p=0.000). ROC curve analysis showed that MAR could predict HE, with an area under the curve of 0.802 (95% CI: 0.744-0.859, p<0.001). The optimal predictive cutoff value of MAR for HE was 10.01 (sensitivity: 72.43%, specificity: 77.05%). Our results suggested that a high MAR on admission was associated with an increased risk of HE in ICH patients, and MAR can become an independent predictor of HE in ICH patients.

A Risk Correlative Model for Sleep Disorders in Chinese Older Adults Based on Blood Micronutrient Levels: A Matched Case-Control Study.

The physical abilities of older adults decline with age, making them more susceptible to micronutrient deficiency, which may affect their sleep quality. This study aimed to construct a risk correlative model for sleep disorders in Chinese older adults based on blood micronutrient levels. In this matched case-control study, we recruited 124 participants with sleep disorders and 124 matched controls from the Tianjin Elderly Nutrition and Cognition cohort in China. Micronutrient levels in whole blood were measured using the dried blood spot technique. We compared the differences in micronutrient levels between the two groups and also constructed a receiver operating characteristic (ROC) model and nomogram for sleep disorders. In comparison to the control group, the sleep disorders group showed lower levels of blood vitamin A, vitamin E (VE), folate, magnesium, copper, iron, and selenium (Se) in the univariate analysis (p < 0.05). The ROC curve analysis indicated that the combination of VE + folate + Se may have an excellent diagnostic effect on sleep disorders, with an area under the curve of 0.964. This VE + folate + Se was integrated into a nomogram model to demonstrate their relationship with sleep disorders. The consistency index of the model was 0.88, suggesting that the model assessed sleep disorders well. The sleep disorders risk correlative model constructed by the levels of VE, folate, and Se in whole blood might show good performance in assessing the risk of sleep disorders in older adults.

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis

BackgroundImmune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.MethodsIn this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib’s efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.ResultsWe developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.ConclusionThe GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Using Cerebrospinal Fluid Biomarkers To Enhance Diagnostic Testing For Alzheimer's Disease In A Tertiary Memory Service

Abstract Background Cerebrospinal Fluid (CSF) biomarkers are useful in the definitive diagnosis of Alzheimer’s disease (AD) when basic investigations and neuroimaging are inconclusive. In our tertiary memory clinic, the standard CSF biomarker panel consists of Amyloid Beta (AB) 42, Total tau, and Phosphorylated tau. AB42/40 is an additional CSF biomarker which has been shown to improve diagnostic accuracy for AD but is not part of our standard CSF biomarker panel (1). We examined the use of AB42/40 in patients attending our service. Methods In our service, diagnosis of dementia is based on a multidisciplinary consensus meeting (including Geriatricians, Neurologists, Psychiatrists) who review clinical presentation and neuroimaging. In a minority of cases, CSF analysis is performed. We retrospectively evaluated how the addition of AB40/42 to our standard CSF panel impacted diagnostic accuracy for AD, taking a ratio of &amp;lt;0.067 to indicate AD (2). Results Of 1853 patients attending the Memory service between September 2022 and December 2023, CSF was available for 107 patients. Of these 107, average age was 68. 56 were female (52%) and 51 were male (48%). At the multidisciplinary consensus meeting, 81 of these 107 patients were diagnosed with AD (75%), with the remaining 26 (24%) patients being diagnosed with a non-AD process (Vascular disease, Lewy Body etc) or an inconclusive diagnosis. After the addition of AB42/40 to CSF, 8 of the 26 patients (31%) with non-AD or an inconclusive diagnosis were identified as potential AD. Of the 81 with a diagnosis of AD, none were reclassified after addition of AB42/40. Receiver Operating Characteristic (ROC) curve analysis was performed to determine relevant cut off values. Conclusion This study suggests the addition of AB42/40 ratio to our standard CSF biomarker panel improves identification of potential AD cases in our sample. This biomarker is being added to the standard CSF panel for our Memory service.

PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research.

Analyzing the relationship between processing speed impairment and Rey-15 item test performance

ABSTRACT Objective This study investigated the relationship between processing speed impairment severity and performance on the Rey 15-Item Test (RFIT) and RFIT + Recognition. Method Cross-sectional data from 285 examinees (228 valid/57 invalid) referred for neuropsychological assessment who were administered the RFIT, Weschler Adult Intelligence Scale-Fourth Edition (WAIS-IV) Processing Speed Index (PSI), Brief Visuospatial Memory Test – Revised, Rey Auditory Verbal Learning Test, and three independent criterion PVTs were included. PSI bands were operationalized as Intact (≥85SS; n = 163), Reduced/Possibly Impaired (77-84SS; n = 36), or Impaired (≤76 SS; n = 29). Receiver operator characteristic (ROC) curve analyses tested the RFIT and RFIT + Recognition’s classification accuracy for detecting invalid performance for the overall sample and by PSI impairment status. Results Those with intact processing speed performed significantly better on the RFIT and RFIT + Recognition than those with reduced/possibly impaired and impaired processing speed. Though verbal/visual memory predicted RFIT scores independently, PSI contributed additional variance. ROC curves for RFIT and RFIT + Recognition were significant (AUC=.64-.84). Optimal cut-scores yielded modest sensitivity (30%-63%) and high specificity (89%-93%) among those with intact and reduced processing speed but yielded unacceptable accuracy in those with impaired speed (AUC=.59-.62). Conclusions Although the RFIT and RFIT + Recognition demonstrated acceptable classification accuracy in those with intact processing speed, accuracy diminished with increasing speed impairment. This finding was more pronounced for RFIT + Recognition compared to the traditional RFIT. As such, the RFIT may have limited clinical utility in examinees with more significant processing speed deficits.

Radiomics and Clinical Features for Distinguishing Kidney Stone-Associated Urinary Tract Infection: A Comprehensive Analysis of Machine Learning Classification.

This study investigated the abilities of radiomics and clinical feature models to distinguish kidney stone-associated urinary tract infections (KS-UTIs) using computed tomography. A retrospective analysis was conducted on a single-center dataset comprising computed tomography (CT) scans and corresponding clinical information from 461 patients with kidney stones. Radiomics features were extracted from CT images and underwent dimensionality reduction and selection. Multiple machine learning (Three types of shallow learning and four types of deep learning) algorithms were employed to construct radiomics and clinical models in this study. Performance evaluation and optimal model selection were done using receiver operating characteristic (ROC) curve analysis and Delong test. Univariate and multivariate logistic regression analyzed clinical and radiomics features to identify significant variables and develop a clinical model. A combined model integrating radiomics and clinical features was established. Model performance was assessed by ROC curve analysis, clinical utility was evaluated through decision curve analysis, and the accuracy of the model was analyzed via calibration curve. Multilayer perceptron (MLP) showed higher classification accuracy than other classifiers (area under the curve (AUC) for radiomics model: train 0.96, test 0.94; AUC for clinical model: train 0.95, test 0.91. The combined radiomics-clinical model performed best (AUC for combined model: train 0.98, test 0.95). Decision curve and calibration curve analyses confirmed the model's clinical efficacy and calibration. This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.

Investigating soluble CD40 ligand as a prognostic factor among acute coronary syndromes patients: A multi-center prospective case-control study.

Soluble CD40 ligand (sCD40L) is a protein that plays a crucial role in the inflammatory response associated with the development and progression of acute coronary syndrome (ACS). Recent studies have suggested that sCD40L may be a useful prognostic factor for ACS, but the data are conflicting. This study aimed to investigate the potential of sCD40L as a prognostic marker among ACS patients and provide valuable insights for clinical practice. To our knowledge, this is the first study of its type in the Arabic World. A multi-center prospective case-control study was conducted in Damascus, Syria, involving 158 participants with different ACS subtypes (STEMI, NSTEMI, UA) and a control group of healthy individuals. Sociodemographic data, medical history, and sCD40L levels were collected. The predictive ability of sCD40L for ACS, STEMI, NSTEMI, and UA was assessed using receiver operating characteristic (ROC) curves. Statistical analysis was performed using IBM SPSS version 25. The study included 58 STEMI, 33 NSTEMI, 36 UA patients, and 30 healthy individuals. The mean age of participants was 55 years (SD 10.7 years). Analysis of sCD40L levels revealed significantly higher concentrations in ACS patients compared to the control group (P < .001). ROC curve analysis demonstrated that sCD40L had a significant predictive ability for ACS, STEMI, and NSTEMI (P < .05), while its predictive value for UA was not statistically significant. This study provides evidence supporting the potential of sCD40L as a prognostic factor in ACS. The elevated levels of sCD40L observed in these subtypes indicate its potential usefulness in risk stratification and predicting adverse cardiovascular events. Further investigations are warranted to establish standardized sCD40L cutoff values and evaluate its clinical implications in the management of ACS patients.

The added value of MRI in distinguishing malignant and benign ampullary strictures: a multicenter retrospective study.

To investigate the added value of using contrast-enhanced magnetic resonance imaging (MRI) in conjunction with contrast-enhanced computed tomography (CT) for differentiating malignant and benign ampullary strictures. The present retrospective study included 90 patients with ampullary strictures who underwent preoperative contrast-enhanced CT and contrast-enhanced MRI at two tertiary institutions. The image sets (i.e., CT alone vs. combined CT and MRI) were evaluated by three abdominal radiologists, who used a five-point Likert scale to score their confidence for diagnosing malignancy in patients with ampullary strictures. Diagnostic accuracy was calculated using receiver-operating characteristic (ROC) curve analysis, sensitivity, specificity, and accuracy. Additionally, interobserver agreement regarding the scoring of potential malignancies of the ampullary strictures was assessed. The addition of contrast-enhanced MRI to contrast-enhanced CT showed a significant improvement in predicting malignant ampullary strictures in all three observers (p = 0.007, 0.001, and 0.002) using ROC curve analysis, and a significant improvement was observed in diagnostic sensitivity and accuracy for predicting malignancy (p = 0.016 and 0.029 for observer 1; p = 0.023 and 0.010 for observer 2; and p = 0.010 and 0.011 for observer 3). The interobserver agreement for the five-point scale in determining malignancies of the ampullary strictures was 0.86 for CT alone and 0.93 for the combined set of CT and MRI. The addition of contrast-enhanced MRI to CT provided added value for differentiating malignant from benign ampullary strictures.