ROC Curve Research Articles

Histopathological growth patterns of gastric cancer liver metastasis and their association with patient prognosis.

484 Background: The histopathological growth patterns (HGPs) of liver metastasis reflect the complicated and varied interactions between tumor cells and the host microenvironment. We studied HGPs of gastric cancer liver metastasis (GCLM) and sought to predict them using radiomic and clinical variables. Methods: Hepatectomy was performed in 62 patients with GCLM at our university hospital between 2011 and 2021. HGPs were evaluated according to international consensus guidelines, and were classified as either desmoplastic HGP (dHGP) or non-dHGP. CD4, CD8, VEGF and CD31 were analyzed by immunohistochemistry. Eight hundred and fifty-one radiomics features were extracted from CT portal venous phase images and the optimal radiomics signature was determined by univariate analysis and LASSO regression. Multivariable regression analyses and ROC curves were used to assess the predictive performance of HGPs with a radiomic and clinical combined model. Results: Among 62 patients, 14 were categorized as dHGP, and 48 as non-dHGP. Non-dHGP vs. d-HGP was associated with higher tumor N stage and CEA level, and with decreased CD8 + T cells, and lower VEGF and CD31 expression. HGP status was independently associated with patient cancer-specific survival (CSS) (P＜0.05) multivariately.Analysis of percent relative contribution revealed that HGP ranked first for CSS (35.9%), ahead of T (28.9%) and N (28.9%) stage. A model with combined radiomic and clinical features demonstrated robust performance with area under the curve (AUC) values of 0.993 and 0.933 in the training and validation sets, respectively. Conclusions: Non-invasive determination of radiomic and clinical features was shown to predict HGPs types. Compared to dHGP, GCLM with non-dHGP exhibited significantly lower immune cell infiltration and decreased angiogenesis. Among features examined in GCLM, HGP was the most robust for prognostication. Univariate and multivariate analysis of prognostic factors for cancer-specific survival. Characteristics Univariate analysis Multivariate rergression analysis HR (95%CI) P HR (95%CI) P Gender ( Women vs Men ) 1.13(0.61-2.09) 0.71 Age ( >60years vs ≤60years) 1.08(0.60-1.70) 0.98 HGP ( non-desmoplastic HGP vs desmoplastic HGP ) 3.63(1.78-7.41) 0.00 3.48(1.37-8.88) 0.01 T stage ( T3-T4 vs T1-T2 ) 2.03(1.08-3.80) 0.03 2.29(1.15-4.58) 0.02 N stage ( N1-N3 vs N0 ) 3.46(1.66-7.23) 0.00 2.54(1.17-5.56) 0.02 CEA ( >5ng/ml vs ≤5ng/ml ) 2.37(1.25-4.48) 0.01 1.56(0.70-3.48) 0.27

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the methods for identifying at-risk individuals need improvement. This study aims to evaluate the utility of Partial Least Squares Regression (PLSR) in predicting the onset of MDD among non-depressed adolescents, compared to traditional screening methods. The study recruited 1462 Portuguese adolescents aged 13-16, who were assessed using various self-report measures and followed for two years. Participants were randomly divided into training (70%, N=1023) and testing (30%, N=439) samples. PLSR models were developed to predict the occurrence of a major depressive episode (MDE) within two years, using 331 variables. The model's performance was compared to the Children's Depression Inventory (CDI) in predicting MDE onset. The best-fitting PLSR model with two components explained 19.1% and 16.9% of the variance in the training and testing samples, respectively, significantly outperforming the CDI, which explained 7.7% of the variance. The area under the ROC curve was 0.78 for PLSR, compared to 0.71 for CDI. An empirically derived cut-off point was used to create dichotomous risk categories, and it showed a significant difference in MDE rates between predicted high-risk and low-risk groups. The balanced accuracy of the PLSR model was 0.77, compared to 0.65 for the CDI method. The PLSR model effectively identified adolescents at risk for developing MDD, demonstrating superior predictive power over the CDI. This study supports the potential utility of ML techniques (e.g., PLSR) in enhancing early identification and prevention efforts for adolescent depression.

Interaction of inflammatory factors related to pulmonary infection and TLR4/NF-κB signaling pathway in patients with spontaneous intracerebral hemorrhage.

To investigate the interaction of inflammatory factors related to pulmonary infection and the TLR4/NF-κB signaling pathway in patients with spontaneous intracerebral hemorrhage (ICH). A total of 325 critically ill ICH patients treated in our hospital from May 2021 to February 2024 were selected for this study. Based on whether the patient developed a pulmonary infection during treatment, they were divided into the infection group (n=86) and the non-infection group (n=239). The distribution characteristics of pathogens were observed, and changes in serum and defensin, hs-CRP, IL-4, TLR4 mRNA, and NF-κB mRNA were compared. Pearson correlation analysis was used to analyze the relationship between serum defensin, inflammatory factors, and the TLR4/NF-κB signaling pathway. A logistic regression model was used to construct a combined prediction model with defensin, hs-CRP, IL-4, HMGB1, TLR4 mRNA, and NF-κB mRNA. ROC curves were drawn to analyze the area under the curve (AUC), sensitivity, and specificity of defensin, hs-CRP, IL-4, HMGB1, TLR4 mRNA, NF-κB mRNA, and the six combined parameters for predicting pulmonary infection in critically ill ICH patients. In 86 ICH patients with pulmonary infection, 94 strains of pathogens were isolated, with 28 (29.79%) Gram-positive bacteria, 58 (61.70%) Gram-negative bacteria, and 8 (8.51%) fungi. The levels of defensin, hs-CRP, IL-4, HMGB1, TLR4 mRNA, and NF-κB mRNA in the infection group were significantly higher than those in the non-infection group (P<0.05). Pearson correlation analysis showed that defensin, hs-CRP, IL-4, and HMGB1 were positively correlated with TLR4 mRNA and NF-κB mRNA (P<0.05). ROC curve analysis showed that the AUC values for defensin, hs-CRP, IL-4, HMGB1, TLR4 mRNA, NF-κB mRNA, and the six combined parameters for predicting pulmonary infection were 0.789, 0.778, 0.690, 0.792, 0.741, 0.750, and 0.870, respectively. The main pathogens causing pulmonary infection in critically ill ICH patients are Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, and Streptococcus hemolyticus. Defensin, hs-CRP, IL-4, HMGB1, and other indicators are influenced by the TLR4/NF-κB signaling pathway in patients with secondary pulmonary infection.

Effect of tRF-28-ZJ47D112Z4DZ on the pathogenesis of gastric cancer through targeting cyclin D1/CDK4 and its early diagnostic value.

475 Background: Gastric cancer exhibits high incidence and mortality rates worldwide. Current research on gastric cancer aims to discover stable and effective early diagnostic markers, as well as uncover the disease's pathogenesis. tRNA-derived fragment (tRF) is a newly discovered regulatory non-coding RNA, which plays an important role in tumor diagnosis and treatment. Methods: This study, through transcriptome sequencing and MINTBase v2.0 database, screened out a new molecule tRF-28-ZJ47D112Z4DZ (tRF-28) related to gastric cancer. An absolute quantitative method was established to quantify the level of tRF-28 in plasma. ROC curve, survival curve, Cox regression model were constructed, and clinicopathological factors were collected to analyze the diagnostic value of tRF-28. "Overexpression" and "knockdown" tRF-28 gastric cancer cell models and xenograft tumor models were established to study the effects of tRF-28 on cell proliferation. Using bioinformatics, qRT-PCR, Western blot, dual luciferase reporter gene, fluorescence in situ hybridization, RNA binding protein immunoprecipitation assay, rescue experiment and immunohistochemistry to study the regulatory role of tRF-28 by binding to the 3'UTR of CCND1. Results: This study clarified the tRF-28 levels in the plasma of healthy individuals, and gastric benign and malignant lesions, providing a new candidate biomarker for the early diagnosis of gastric cancer. Through upregulating and downregulating the level of tRF-28 in gastric cancer cells, it was revealed that tRF-28 inhibits the proliferation of gastric cancer cells in vivo and in vitro by regulating key proteins such as Cyclin D1, CDK4, Rb, and p-Rb. In studying specific molecular mechanisms, tRF-28 and CCND1 were co-located in the cytoplasm. tRF-28 combines with Ago2 protein to form a complex and plays a role in silencing the target gene CCND1 in the cytoplasm. The rescue experiments revealed that silencing the target gene CCND1 can reverse the carcinogenesis effect of tRF-28 inhibitors, clarifying the biological functions of tRF-28 by regulating CCND1. Conclusions: Based on a series of research findings, we elucidated that tRF-28 form a complex with Ago2 protein, binding to the 3' UTR of CCND1 to downregulate Cyclin D1, CDK4, and other critical proteins, thereby inhibiting the proliferation of gastric cancer cells. Our study aims to offer new insights into identifying potential early diagnostic markers and therapeutic targets for gastric cancer.

Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.

To establish, evaluate and compare three recurrence prediction models for keloid patients using machine learning methods. We enrolled 301 keloid patients who underwent surgery and postoperative radiotherapy, dividing them into a training set (70%) and a validation set (30%). Three recurrence prediction models were established in the training set: the logistic regression model, the decision tree model, and the random forest model. We then evaluated and compared the performance of these models in the validation set, using metrics such as accuracy, sensitivity, specificity, recall, precision, kappa coefficient, and the area under the ROC curve (AUC). We developed three machine learning-based prediction models for keloid recurrence. KAAS, mean arterial pressure levels, postoperative complications, and the proportion of inflammatory cells played crucial roles in these models. The decision tree model outperformed both the random forest and logistic regression models in terms of accuracy, and it also exhibited the highest overall precision. Regarding AUC, logistic regression performed the best, followed by random forest and decision trees. This study established three prediction models for keloid recurrence using machine learning techniques, highlighting the significance of KAAS, blood pressure levels, postoperative complications, and inflammatory cell proportions. When compared from various dimensions, the logistic regression model demonstrated the most favorable prognostic performance in terms of AUC.

Role of cfDNA and DNA integrity index (DII) in colon cancer in response to surgery.

236 Background: Colorectal cancer diagnosis requires new biomarkers for improved detection and monitoring. cfDNA has shown potential in detecting minimal residual disease, evaluating treatment response, and providing prognostic information. Recent studies highlight cfDNA as a promising molecular marker for early disease detection and progression monitoring. This study aims to evaluate the role of cfDNA and DII in assessing surgical response in CRC patients. The primary objective is to assess cfDNA levels pre- and post-curative resection. Secondary objectives include identifying factors influencing baseline cfDNA levels, evaluating factors affecting DII, and comparing cfDNA and CEA levels in response to surgery. Statistical analyses included descriptive statistics, ROC curve, Student T tests and Fisher's exact test. Methods: The study was designed as a prospective investigation conducted over a period from September 2022 to April 2024 at the Regional Cancer Centre. Forty pathologically proven non metastatic colonic carcinoma planned for primary surgery were included and patients with sepsis and chronic diseases were excluded from the study. Blood samples (10 ml) were collected pre-surgery and at four weeks post-surgery. Plasma cfDNA was extracted, quantified, and analyzed via quantitative real-time PCR using human β-actin as a reference. The DII was calculated as the ratio of 394 bp to 99 bp amplicons of β-actin. Results: The mean age was 62.15 years (range 39-85), with 55% over 60 years. High-risk features included poor differentiation (4.2%), preoperative CEA &gt;5 ng/ml (17.5%), LVI (20.8%), and PNI (6.3%). Surgical approaches included 10 laparoscopic, 29 open, and 1 converted from laparoscopic to open due to excessive bleeding. Overall stage grouping showed 18 patients in Stage III, and 11 each in Stage I and II. Median pre- and postoperative cfDNA levels were 24.2 ng/μl and 29.6 ng/μl (p=0.83), and median DNA integrity index levels were 1.1 and 1.0 (p=0.95). Preoperative cfDNA levels averaged 24.2 ng/μl and postoperative levels 29.6 ng/μl (p = 0.83); DII levels were 1.1 preoperatively and 1.0 postoperatively (p = 0.95). Postoperative cfDNA levels decreased in 21 patients and increased in 19, while DII decreased in 20 and increased in 20. Significant associations were noted for cfDNA changes with surgery type and high-risk features. ROC analysis revealed cfDNA cut-off at 275 ng/μl, DII at 1.5, and CEA at 4 µg/L. Sensitivities for cfDNA and DII were 66.67% and 100%, respectively, with combined sensitivity reaching 100%. Conclusions: Persistent high cfDNA levels post-surgery suggest potential minimal residual disease despite radical resection. The residual disease is a harbinger of systemic failure. Monitoring cfDNA and DII can identify patients at risk for recurrence, guiding intensified adjuvant therapy. Further studies with extended follow-up are recommended to validate these findings. Clinical trial information: CTRI/2023/10/058468 .

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes. In this work, we present a novel framework that uses vector-quantization GAN and a transformer incorporating masked token modeling to generate high-resolution and diverse 3D brain tumor ROIs that can be used as additional data for tumor ROI classification. We apply our method to two imbalanced datasets where we augment the minority class: (1) low-grade glioma (LGG) ROIs from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 dataset; (2) BRAF V600E Mutation genetic marker tumor ROIs from the internal pediatric LGG (pLGG) dataset. We show that the proposed method outperforms various baseline models qualitatively and quantitatively. The generated data was used to balance the data to classify brain tumor types. Our approach demonstrates superior performance, surpassing baseline models by 6.4% in the area under the ROC curve (AUC) on the BraTS 2019 dataset and 4.3% in the AUC on the internal pLGG dataset. The results indicate the generated tumor ROIs can effectively address the imbalanced data problem. Our proposed method has the potential to facilitate an accurate diagnosis of rare brain tumors using MRI scans.

Diagnostic Value and Thyroid Hormone Variations in Pregnant Women During Pregnancy in Hui'an County in China.

To evaluate the diagnostic value of thyroid hormones in pregnancy-related thyroid conditions and ascertain the reference range of thyroid hormones in Hui'an pregnant women. We examined 760 pregnant women (406 early, 172 mid, and 182 late pregnancy) and 114 non-pregnant controls. A 95% medical reference range was computed based on measurements of thyroid hormone levels (FT3, FT4, and TSH). Group comparisons were conducted, and the diagnostic accuracy was assessed using ROC curves. There were significant differences in thyroid hormone levels between pregnant women at different stages and non-pregnant women (P < 0.05). In contrast to mid-pregnancy and controls, FT3 and FT4 were higher in the early and late stages of pregnancy (P < 0.05), although TSH was decreased during pregnancy. Compared to healthy or hypothyroid pregnant women, the diagnostic positive rate for hyperthyroidism was greater (P < 0.05). TSH, FT3, and FT4 had larger AUCs in ROC analysis for identifying hyperthyroidism than hypothyroidism (P < 0.05), and the combination of these markers improved diagnostic accuracy (P < 0.05). In Hui'an, thyroid hormone levels vary over the weeks of pregnancy and between pregnant and non-pregnant women. By establishing local reference ranges, thyroid problems in pregnancy can be diagnosed, and complications can be avoided.

Using integrative bioinformatics approaches and machine-learning strategies to identify potential signatures for atrial fibrillation.

Atrial fibrillation (AF) is the most common tachyarrhythmia and seriously affects human health. Key targets of AF bioinformatics analysis can help to better understand the pathogenesis of AF and develop therapeutic targets. The left atrial appendage tissue of 20 patients with AF and 10 patients with sinus rhythm were collected for sequencing, and the expression data of the atrial tissue were obtained. Based on this, 2578 differentially expressed genes were obtained through differential analysis. Different express genes (DEGs) were functionally enriched on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), mainly focusing on neuroactive ligand-receptor interactions, neuronal cell body pathways, regulation of neurogenesis, and neuronal death, regulation of neuronal death, etc. Secondly, 14 significant module genes were obtained by analyzing the weighted gene co-expression network of DEGs. Next, LASSO and SVM analyzes were performed on the differential genes, and the results were in good agreement with the calibration curve of the nomogram model for predicting AF constructed by the weighted gene co-expression network key genes. The significant module genes obtained by the area under the ROC curve (AUC) analysis were analyzed. Through crossover, two key disease characteristic genes related to AF, HOXA2 and RND2, were screened out. RND2 was selected for further research, and qPCR verified the expression of RND2 in sinus rhythm patients and AF patients. Patients with sinus rhythm were significantly higher than those in AF patients. Our research indicates that RND2 is significantly associated with the onset of AF and can serve as a potential target for studying its pathogenesis.

Evaluating systemic immune-inflammation indices as predictive markers for endometriosis diagnosis: A retrospective observational study.

Endometriosis is a chronic inflammatory disease for which there is currently no accurate screening test to identify or predict the probability of the disease in individuals. This can often lead to delays in diagnosis. Several systemic immune-inflammation indices are currently used as predictive or supportive markers for several inflammation-associated diseases. In this study, we investigated whether these immune-inflammation indices, such as systemic immune inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), and pan-immune inflammation value (PIV) could serve as predictive or supplementary markers for diagnosing endometriosis. A total of 434 women with confirmed endometriosis and 517 controls were included in this study. Data on peripheral blood tests, including total counts of white cells, neutrophils, monocytes, lymphocytes, and platelets, were collected, and systemic immune-inflammation indices were calculated. SII, SIRI, NLR, and PIV values were significantly higher in women diagnosed with endometriosis compared to controls. Using the cut-off values of 538 for SII, 0.814 for SIRI, 2.03 for NLR, and 210 for PIV, we achieved 76 % sensitivity and 70 % specificity. When the four indices were combined, the area under the ROC curve (AUC) was 0.796 (95 % CI: 0.766, 0.827), with 76 % sensitivity and 70 % specificity. In conclusion, while ultrasonography or MRI remains the gold standard for visualizing the lesions in diagnosing endometriosis, followed by laparoscopy and histologic verification, routine peripheral blood tests in combination with clinical symptoms, could provide additional clinical diagnostic value for endometriosis as a non-invasive and cost-effective test.

Shock Wave Therapy in the Treatment of Erection Dysfunction: How to Define Clinical Outcomes? A Comparison Between Penile Doppler Ultrasound and a New Visual Erec-tion Hardness Score (V-EHS) During a Blinded, Sham-Controlled Trial.

In the last decade, several studies have proven the effectiveness of low-intensity shock waves (LI-ESWT), but with several factors that make it difficult to carry out systematic reviews. To demonstrate the effectiveness of LI-ESWT and define the best tool for routine clinical assessment of erectile dysfunction. Twenty-one participants with purely vasculogenic erectile dysfunction were selected and randomized to LI-ESWT or placebo. All patients underwent evaluation with The International Index of Erectile Function (IIEF-5), V-EHS (new visual scale), and standardized penile doppler ultrasound before and after shock wave therapy. LI-ESWT has proven effective in the treatment of moderate erectile dysfunction, and the new V-EHS has demonstrated greater accuracy than Doppler in the diagnosis and follow-up of erectile dysfunction. Using the IIEF-5 as a control tool, we observed a clinical response after 1 month, with a greater increase in the shock wave therapy arm of +3.21 points compared to + 0.57 in the sham group. At six months, the treated group showed a mean increase of 4.71 points compared to baseline (p = 0.006), while those who received sham therapy had a decrease (case = +4.71 points vs. sham control = -1.0, p = 0.006). Based on this observed difference, we performed a comparative analysis between the V-EHS and penile doppler ultrasound to observe whether the test results corroborated the IIEF-5 findings. The correlation between V-EHS and IIEF-5 in the therapy group in the pre-therapy period was strong (r = 0.816, p < 0.001), and at 6 months it increased to very strong (r = 0.928, p < 0.001). Penile Doppler ultrasound did not show the same correlation strength with IIEF-5, presenting a moderate correlation at 6 months (Pearson correlation score = 0.540), as also demonstrated in the ROC curve through the V-EHS AUC = 0.963 (p = 0.001) vs. Doppler AUC = 0.713 (p = 0,290). Strengths and Limitations: The main strengths of the present study are the blinded, randomized, placebo-controlled clinical trial and the comparison between penile Doppler and a new visual classification for erection hardness score. The limitations are the number of patients and the short follow-up. LI-ESWT has proven effective in the treatment of moderate vasculogenic erectile dysfunction, with optimal results at 6 months. The new V-EHS offers a simple, reliable and reproducible assessment of erectile function.

The impact of diabetes on Sepsis-induced cardiomyopathy.

This study investigated the association between diabetes and Sepsis-induced cardiomyopathy (SIC), focusing on how changes in inflammatory response and cardiac function influence SIC prognosis. The aim is to provide clinicians with more accurate treatment and management strategies, ultimately enhancing patient outcomes and quality of life. This retrospective cohort study analyzed 258 Sepsis-induced cardiomyopathy (SIC) patients, stratified by diabetes status and HbA1C levels. Data were collected from electronic medical records. Statistical tests included the Kolmogorov-Smirnov, t-test, Mann-Whitney U, Kruskal-Wallis, chi-square, and Spearman correlation. Univariate and multivariate logistic regression assessed diabetes' impact on SIC severity. Model fit was evaluated with the Hosmer-Lemeshow and negative log-likelihood ratio tests. A nomogram was constructed and validated using ROC curves, calibration curves, and decision curve analysis. Subgroup and interaction analyses were performed (P<0.05). Diabetes worsened inflammation and immune responses in SIC, significantly affecting markers like LVEF, TnI, CK-MB, BNP, NLR, IL-6, PCT, CRP, APACHE II, and SOFA scores (P<0.05). Grouping by HbA1C levels revealed no significant differences in LVEF (P=0.078), Alb (P=0.105), or L/A (P=0.211), but differences were found for TnI, CK-MB, BNP, NLR, IL-6, PCT, CRP, APACHE II, and SOFA (P<0.05). HbA1C strongly correlated with CRP (rs=0.8664). BNP (OR 1.001) and HbA1C (OR 1.302) were significant risk factors for SIC, with the nomogram showing good predictive performance (AUC 0.693). No significant interaction between HbA1C and BNP on SIC severity was observed (P=0.791). Diabetes exacerbates inflammatory and immune responses in Sepsis-induced cardiomyopathy patients, leading to worsened cardiac function.

Hospital frailty risk score predicts postoperative outcomes after endoscopic endonasal resection of non-functioning pituitary adenomas.

Frailty indices are invaluable resources in risk stratification and predicting high-value care outcomes for neurosurgical patients. The Hospital Frailty Risk Score (HFRS) is a recently developed and validated method for evaluating frailty; however, its implementation has yet to be assessed in patients with non-functioning pituitary adenomas undergoing endoscopic endonasal resection. In this study, we aimed to evaluate HFRS's predictive ability for high-value care outcomes, namely postoperative complications, length of stay (LOS), and hospital charges, and to compare it to other traditionally used frailty indices. A retrospective review of electronic medical records from 2017 to 2020. A total of 109 ICD-10 codes corresponding to various frailty-related conditions were used to identify the components of HFRS. These components were then used to calculate the HFRS for each patient, with higher scores indicative of elevated frailty. Standard multivariate logistic regression models were employed to explore the association between HFRS and high-value care outcomes. Model discrimination was assessed using the area under the ROC curves, and the DeLong test was used to compare AUCs. A total of 172 patients were included, with a mean age of 57.27 ± 12.95 years and an average HFRS score of 3.65 3.27. Among patients, 56% were male, 5.2% experience postoperative complications, 23.3% endured extended LOS, 25.0% incurred high hospital charges. In multivariate regression models, greater HFRS was significantly and independently associated with postoperative complications (OR = 1.51, P < 0.001), extended LOS (OR = 1.17, P = 0.006) and high hospital charges (OR = 1.18, P = 0.004). HFRS had the highest AUC compared to other frailty indices and was the most parsimonious model, with AUC values of 0.82, 0.64, and 0.63 for predicting complications, extended LOS, and higher charges, respectively. Higher HFRS scores are significantly associated with postoperative complications, prolonged LOS, and high hospital charges for patients undergoing pituitary surgery.

Dysregulation of the kynurenine pathway is related to persistent cognitive impairment in tick-borne encephalitis.

Tick-borne encephalitis (TBE) patients frequently suffer cognitive and neuropsychiatric sequelae, which may be severe and long-lasting. The underlying mechanisms remain poorly understood, but likely involve dysregulation of immunological and neuroinflammatory processes. Here, we investigated the activity of the kynurenine pathway (KP), an immunologically induced set of enzymes generating neuroactive metabolites via degradation of the amino acid tryptophan and their correlation with the cognitive performance in TBE patients. KP metabolite concentrations were measured in serum and cerebrospinal fluid (CSF) samples from patients taken during hospitalization for acute TBE (n = 87) and a control group (n = 12) by UPLC/MS-MS. At two follow-up occasions, 6 and 18 months after hospital discharge, additional serum samples were obtained from TBE patients and their cognitive performance was evaluated by the Matrics Consensus Cognitive Battery. Relative to controls, TBE patients exhibited a robust induction of the KP in both the central nervous system and peripheral blood during the acute phase of the disease, with drastic elevations of the neuroactive QUIN and KYNA in CSF. KP activity remained significantly elevated in serum of TBE patients at the 6- and 18-month follow-ups. Several inverse associations between cognitive performance and measurements of KP activity recorded in both CSF and serum were found by rank correlation analysis. Further, via ROC analysis, serum rKT was found to be an accurate predictor of impaired overall cognition (<35 T-score) 6 months after acute TBE (AUC 0.79, CI 0.64-0.93, p < 0.01 for rKT measured during acute TBE; AUC 0.68, CI 0.50-0.86, p < 0.05 for rKT measured at 6 months). We have found evidence of both acute and chronic dysregulation of KP activity in TBE patients, which correlated with degree of cognitive impairment at long-term follow-up. Our findings suggest that KP metabolites may serve as biomarkers for TBE-related cognitive sequelae, and more broadly, that the KP may offer avenues for development of novel treatments.

Plasma Phosphorylated Tau 217 as a Discriminative Biomarker for Cerebral Amyloid Angiopathy.

Blood-based biomarkers may offer a non-invasive approach to diagnose cerebral amyloid angiopathy (CAA), especially in early-stage. We evaluated the ability of plasma phosphorylated tau-217 (p-tau 217) to differentiate CAA from Alzheimer's disease (AD) and deep perforator arteriopathy (DPA). Patients with AD (age 73.7 ± 8.1 years), probable CAA (74.8 ± 6.9 years), or DPA (66.1 ± 10.4 years) were enrolled from memory and stroke clinics at a medical center in Taiwan. All participants received amyloid and tau PET scans. Plasma biomarkers were measured via a SIMOA immunoassay platform. The diagnostic utility of p-tau 217 was assessed using ROC analyses and the Youden cutoff. Associations between plasma p-tau 217 and neuroimaging variables in CAA were explored. Patients with CAA had lower plasma p-tau 217 (0.69 ± 0.76 vs. 1.28 ± 0.97 pg/mL, p < 0.001) and a lower p-tau 217/Aβ40 ratio (0.003 ± 0.002 vs. 0.006 ± 0.003, p < 0.001) than the AD group but higher levels than the DPA group (p-tau 217, 0.27 ± 0.13 pg/mL, p = 0.001; p-tau 217/Aβ40, 0.001 ± 0.0005, p < 0.001), although adjustment attenuated the difference in p-tau 217 between CAA and DPA. Plasma Aβ40, Aβ42, and Aβ40/Aβ42 were not significantly different between groups. Plasma p-tau 217 had moderate to good diagnostic utility to differentiate CAA vs. AD (sensitivity, 64.4%; specificity, 89.5%; AUC, 0.809) and CAA vs. DPA (sensitivity, 67.8%; specificity, 100%; AUC, 0.855). In CAA, p-tau 217 significantly correlated with the severity of CAA, amyloid PET signal intensity, and lobar microbleed count (p < 0.001). Plasma p-tau 217 may represent a non-invasive biomarker for distinguishing cerebral amyloid angiopathy (CAA) from other conditions, including AD and DPA.

Correlation of HLA-A and HLA-B/C Expression With ESR1 Expression in Patients With Metastatic Breast Cancer as a Potential Prognosticator of Favorable Distant Disease-free Survival.

The loss of breast cancer cell differentiation during metastatic progression leads to a down-regulation of class 1 human leukocyte antigen (HLA) expression, which in turn hinders cytotoxic T lymphocytes from effectively preventing tumor cell proliferation. Consequently, one would expect that decreased HLA expression would correlate with decreased 5-year survival. However, estrogen receptor alpha (ESR1) is known to be positively associated with overall survival. The study aimed to determine the expression levels of HLA-A, HLA-B/C, and ESR1 and to assess their influence on distant disease-free survival (DDFS). This retrospective subgroup analysis of the initial prospective, single-center, double-blind cohort study included a total of 34 patients who underwent a new treatment line for metastatic breast cancer (MBC). The MBC cells were examined using RT-qPCR. The acquired data and the subsequent survival and ROC analyses indicated a positive association of reduced expression of HLA-A and HLA-B/C with DDFS. A statistically significant association of ESR1 with DDFS could not be shown. A potential positive association between reduced expression of HLA-A and HLA-B/C and DDFS is observed. This contrasts with the generally observed association between HLA expression loss and poor prognosis, as reported in previous protein-based studies. In metastatic settings, reduced expression of particular HLA subsets, measured at the mRNA level, might have a protective effect against disease progression.

Association of First-Trimester Combined Inflammatory Markers (NLR, SII, SIRI, and AISI) With Poor Obstetric Outcomes in Pregnancies With Ulcerative Colitis.

To compare the first-trimester combined inflammatory markers between pregnant women diagnosed with ulcerative colitis (UC) and healthy pregnant women. Furthermore, to investigate the predictive value of these markers for adverse obstetric outcomes in pregnant women affected by UC. In this retrospective study, data were collected from pregnant women with UC who gave birth at our hospital between January 2021 and May 2024 and from a control group of healthy pregnant women. A comparison was made between the two groups in terms of their demographic characteristics, neonatal outcomes, and the values of the first-trimester neutrophil-to-lymphocyte ratio (NLR), systemic inflammation index (SII), systemic inflammatory response index (SIRI), and aggregated index of systemic inflammation (AISI). Furthermore, the relationship between these combined inflammatory markers and adverse obstetric outcomes in pregnant women with UC was investigated. No notable discrepancy was observed between the UC group and the control group with respect to SIRI and AISI. The first-trimester NLR and SII values were observed to be higher in the UC group in comparison to the control group. The data indicated that pregnant women with UC who experienced poor obstetric outcomes exhibited elevated levels of NLR, SII, SIRI, and AISI during the first trimester. In the ROC analysis, the predictive capacity of all four parameters for poor obstetric outcomes in UC patients was statistically significant. There is an association between elevated combined inflammatory markers in the first trimester and poor obstetric outcomes in pregnant women with UC.

Thermographic images for screening oral health problems in older adults: A pilot study

ObjectiveThis pilot study assessed the feasibility of using facial thermography to detect intra-oral problems in older adults with cognitive decline and care-resistant behaviors, who are often unable to communicate pain or early symptoms. MethodsTwenty-three older adults (mean age 73.7 ± 13.2 years) with cognitive decline were enrolled. Thermal images of four facial views were taken using a smartphone-connected FLIR thermal camera. Intra-oral examinations were conducted, and the thermographic data were analyzed to extract temperature values in the regions of interest (ROI). Point-biserial correlations and ROC curve analyses were performed to evaluate associations between temperature data and clinical findings, with a significance level of p<0.05. ResultsIntra-oral issues requiring treatment were found in 12 participants, with six reporting clinical pain. The overall mean temperature in the ROI was 33.5 ± 3.9°C, and significant temperature differences were found between the body and ROI temperatures. Correlations were observed between clinical problems and various temperature metrics, including the minimum and maximum ROI temperatures (rpb=-0.327, p=0.002 and rpb=-0.309, p=0.003). ROC analysis indicated that ROI temperature could predict the presence or absence of clinical problems, with AUC values ranging from 0.651 to 0.796 for different metrics. ConclusionThermographic facial imaging shows significant potential as a non-invasive tool for detecting oral health problems in vulnerable older adults. While promising, further research is essential to enhance image quality, streamline the technique, and incorporate AI for improved diagnostic accuracy and ease of use. Clinical SignificanceThis non-invasive, inexpensive technique is easy to perform, independent of patient compliance and, is promising to detect early oral problems in noncommunicative patients.

Abstract TP168: Automated CT perfusion Parameters as Predictors of Hemorrhagic Transformation After Mechanical Thrombectomy in Large Vessel Ischemic Stroke

Introduction: Hemorrhagic transformation is a common complication of mechanical thrombectomy (MT), often as a result of reperfusion injury. We investigated the correlation between automated CT perfusion (CTP) parameters and Hemorrhagic transformation after MT in acute ischemic stroke. Method: We conducted a retrospective cohort analysis of patients consecutively admitted to the Providence Health System between January 2018 and February 2022 with acute ischemic stroke who underwent MT and had a CTP on admission. We also addressed each CTP parameter’s predictive power through bivariate logistic regression and ROC/AUC analysis. We compared the median and IQR ranges of volumes of automated CTP parameters between patients with and without ICH. Any degree of hemorrhagic transformation in the ECASS II classification system was considered ICH. Symptomatic intracranial hemorrhage (sICH) was defined by The Heidelberg Bleeding Classification. (4 point increase in NIHSS, or 2 in one NIHSS subcategory, major medical/surgical intervention involved) Results: We included 513 patients with a ischemic stroke who had an MT. Median age was 75(63, 85) years old. Higher blood sugar (p=0.003) and chronic kidney disease (p=0.001) were significantly associated with sICH Table1. Cerebral blood flow( CBF)&lt;30%, cerebral blood volume (CBV)&lt;38%, CBV&lt;42%, mismatch ratio, and CBV index were associated to sICH as shown in Table 2. ROC analysis for CBV index showed an AUC of 0.64 in the bivariate analysis. Other CTP parameter logistic regression and an ROC/AUC analysis are shown in Table 3. Conclusion: Higher CBF&lt;30%, CBV&lt;38%, and CBV&lt;42% volumes were associated with an increased frequency of ICH and sICH. Higher mismatch ratios correlated with a lower frequency of ICH and sICH. Lower hypoperfusion index; and, elevated Tmax volumes &gt;10s and &gt;8s, were associated to a higher frequency of ICH. However, these parameters were insufficiently powered to show a significant association with sICH. CBV index outperformed the other CTP parameters in the ROC analysis. Larger studies are needed to clarify these associations.

Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoadjuvant therapy in borderline resectable pancreatic ductal adenocarcinoma.

To investigate whether multiparametric quantitative diffusion weighted magnetic resonance imaging (DWI) can effectively predict the neoadjuvant therapy (NAT) response in borderline resectable pancreatic ductal adenocarcinoma (BRPC). The clinicopathological data, including tumor size, location, and CA19-9 values, as well as DWI parameters(ADC, D, and f values) from 72 patients with BRPC, were analyzed. The differences and changes in these factors before and after NAT were compared to identify those most accurately reflect the response to NAT. ROC analysis was used to evaluate the diagnostic efficacy, and Kaplan-Meier survival analysis explored the relationship between DWI parameters and prognosis. Subgroup survival analysis to further identify populations potentially benefiting from NAT based on multiparametric DWI. After-NAT, the response group showed significantly higher ADC and D values and lower f values compared to the non-response group. The ΔADC (OR: 12.24, P=0.013) emerged as the most important independent factor for tumor response, demonstrating the highest diagnostic accuracy for NAT response with an AUC of 0.936. Kaplan-Meier showed the high ADC value group, high D value group and low f value group were associated with better prognosis after NAT; and the ΔADC≥0 group, ΔD≥0 group, and Δf<0 group was significantly associated with better prognosis. In addition, subgroup analysis suggested two groups of patients might potentially benefit from NAT. Multiparametric quantitative DWI may offer valuable insights into the efficacy and prognosis of NAT in BRPC. These findings have the potential to support the evaluation and decision-making process for patients undergoing NAT.