Validación de test de flexión-relajación para definir un déficit lumbar con electrodos tetrapolares / Validation of the flexion-relaxation test to define a lumbar deficit with tetrapolar electrodes

Introducción y objetivo: Obtener un nuevo punto de corte (PC) para un test de flexión-relajación (FR) lumbar efectuado con electrodos (e.) tetrapolares, desde valores ya definidos con dispositivos bipolares.Materiales y métodosLa muestra del estudio consta de 47 pacientes en situación de incapacidad temporal por dolor lumbar (DL). Fueron evaluados mediante un test de dinamometría isométrica, una prueba cinemática y una valoración del fenómeno FR.Se plantean dos experimentos con curvas ROC. El primero, con 47 pacientes que efectuaron de modo consecutivo el test FR con ambos tipos de electrodos, utilizándose como variable de clasificación el punto de corte conocido para los e. bipolares (2,49uV). En el segundo, con los datos de la EMGs registrados con e. tetrapolares en 17 pacientes, se efectúa un test de DeLong que compara las 2 curvas ROC que construimos, por un lado, al clasificar la muestra desde pruebas de dinamometría y cinemática, y por el otro, al clasificarlos con los valores de la EMGs bipolar.ResultadosUn total de 34 pacientes completaron adecuadamente las valoraciones del primer experimento y 17 pacientes el segundo. El primer estudio arroja un punto de corte de 1,2uV, con un AUC del 87,7%; sensibilidad 84,2% y especificidad 80%. El segundo muestra un PC para los e. bipolares de 1,21uV (AUC 87,5%) y para los e. tetrapolares de 1,43 (AUC 82,5%) con un test de DeLong sin diferencias significativas entre ambas curvas (p>0,4065).ConclusionesLa metodología de validación con curvas ROC ha permitido obtener un nuevo PC para la prueba FR de modo práctico, simplemente simultaneando ambos test sobre el mismo grupo de pacientes hasta obtener una muestra significativa. (AU)

Introduction and objective: To obtain a new cut-off point (CP) for a lumbar flexion-relaxation (RF) test established with tetrapolar (e.) electrodes, from values already defined with bipolar devices.Materials and methodsThe study sample consists of 47 patients in a situation of temporary disability due to low back pain (DL). They were evaluated by means of an isometric dynamometry test, a kinematic test and an assessment of the FR phenomenon.Two experiments with ROC curves are proposed. The first, with 47 patients who consecutively performed the RF test with both types of electrodes, using the cut-off point (CP) known for the e. bipolar (2.49μV). In the second, with the EMG data recorded with e. tetrapolar in 17 patients, a DeLong test was performed that compares the 2 ROC curves that were constructed on the one hand, by classifying the sample from dynamometry and kinematic tests, and on the other, by classifying them with the bipolar EMG values.ResultsA total of 34 patients adequately completed the evaluations of the first experiment and 17 patients the second. The first study shows a cut-off point of 1.2μV, with an AUC of 87.7%; Sensitivity 84.2% and Specificity 80%. The second shows a PC for e. bipolars of 1.21μV (AUC 87.5%) and for e. tetrapolar values of 1.43 (AUC 82.5%) with a DeLong test without significant differences between both curves (p>0.4065).ConclusionsThe validation methodology with ROC curves has made it possible to obtain a new PC for the RF test in a practical way, simply by simultaneously performing both tests on the same group of patients until a significant sample is obtained. (AU)

Value of third-generation of VNCa dual-energy CT for differentiating diffuse marrow infiltration of multiple myeloma from red bone marrow.

This study aims to investigate the ability of bone marrow imaging using third-generation dual-energy computed tomography (CT) virtual noncalcium (VNCa) to differentiate between multiple myeloma (MM) with diffuse bone marrow infiltration and red bone marrow (RBM). Bone marrow aspiration or follow-up results were used as reference. We retrospectively reviewed 188 regions of interests (ROIs) from 21 patients with confirmed MM and diffuse bone marrow infiltrations who underwent VNCa bone marrow imaging between May 2019 and September 2022. At the same time, we obtained 98 ROIs from 11 subjects with RBM for comparative study, and 189 ROIs from 20 subjects with normal yellow bone marrow for the control group. The ROIs were delineated by 2 radiologists independently, the interobservers reproducibility was evaluated by interclass correlation coefficients. The correlation with MRI grade results was analyzed by Spearman correlation coefficient. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal threshold for differentiating between these groups and to assess diagnostic performance. There were statistically significant differences in VNCa CT values of bone marrow among the MM, RBM, and control groups (all P < .001), with values decreasing sequentially. A strong positive rank correlation was observed between normal bone marrow, subgroup MM with moderately and severe bone marrow infiltration divided by MRI and their corresponding CT values (ρ = 0.897, 95%CI: 0.822 to 0.942, P < .001). When the CT value of VNCa bone marrow was 7.15 HU, the area under the curve (AUC) value for differentiating RBM and MM was 0.723, with a sensitivity of 50.5% and a specificity of 89.8%. When distinguishing severe bone marrow infiltration of MM from RBM, the AUC value was 0.80 with a sensitivity 70.9% and a specificity 78.9%. The AUC values for MM, RBM, and the combined group compared to the control group were all >0.99, with all diagnostic sensitivity and specificity exceeding 95%. VNCa bone marrow imaging using third-generation dual-energy CT accurately differentiates MM lesions from normal bone marrow or RBM. It demonstrates superior diagnostic performance in distinguishing RBM from MM with diffuse bone marrow infiltration.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. METHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. RESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. CONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

The role of the immune system in early-onset schizophrenia: identifying immune characteristic genes and cells from peripheral blood.

BACKGROUND: Early-onset schizophrenia (EOS) is a type of schizophrenia (SCZ) with an age of onset of < 18 years. An abnormal inflammatory immune system may be involved in the occurrence and development of SCZ. We aimed to identify the immune characteristic genes and cells involved in EOS and to further explore the pathogenesis of EOS from the perspective of immunology. METHODS: We obtained microarray data from a whole-genome mRNA expression in peripheral blood mononuclear cells (PBMCs); 19 patients with EOS (age range: 14.79 ± 1.90) and 18 healthy controls (HC) (age range: 15.67 ± 2.40) were involved. We screened for differentially expressed genes (DEGs) using the Limma software package and modular genes using weighted gene co-expression network analysis (WGCNA). In addition, to identify immune characteristic genes and cells, we performed enrichment analysis, immune infiltration analysis, and receiver operating characteristic (ROC) curve analysis; we also used a random forest (RF), a support vector machine (SVM), and the LASSO-Cox algorithm. RESULTS: We selected the following immune characteristic genes: CCL8, PSMD1, AVPR1B and SEMG1. We employed a RF, a SVM, and the LASSO-Cox algorithm. We identified the following immune characteristic cells: activated mast cells, CD4+ memory resting T cells, resting mast cells, neutrophils and CD4+ memory activated T cells. In addition, the AUC values of the immune characteristic genes and cells were all > 0.7. CONCLUSION: Our results indicate that immune system function is altered in SCZ. In addition, CCL8, PSMD1, AVPR1B and SEMG1 may regulate peripheral immune cells in EOS. Further, immune characteristic genes and cells are expected to be diagnostic markers and therapeutic targets of SCZ.

Impulse oscillometry in the diagnosis of cough variant asthma in children.

BACKGROUND: Cough variant asthma (CVA) is one of the most common causes of chronic cough in children worldwide. The diagnosis of CVA in children remains challenging. This study aimed to assess the diagnostic utility of impulse oscillometry (IOS) pulmonary function in children with CVA. METHODS: This study included children aged 4 to 12 years diagnosed with CVA who underwent IOS pulmonary function and bronchodilation (BD) tests. A control group of healthy children was matched. Pre- and post-BD IOS parameters were recorded and presented as mean ± standard deviation or median. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the discriminatory potential of the IOS parameters for diagnosing CVA. RESULTS: A total of 180 patients with CVA and 65 control subjects were included. The baseline IOS parameters in the CVA group, except X5%pred, were significantly greater compared to the control group. After inhalation of salbutamol sulfate, all IOS parameters improved significantly in the CVA group. However, Z5%pred, R5%pred, and R20%pred remained greater in the CVA group compared to the control group. The improvement rates of IOS parameters in the CVA group significantly surpassed those in the control group. The ROC curve results for pre-BD IOS parameters and the improvement rate during the BD test showed that the combinations of pre-Z5%pred+△Z5% and pre-R5%pred+△R5% achieved the highest AUC value of 0.920 and 0.898, respectively. The AUC values of these combined parameters surpassed those of individual ones. CONCLUSIONS: This study highlights that children with CVA exhibit greater IOS parameters compared to healthy children. The changes in IOS parameters during the BD test provided valuable diagnostic information for CVA, and the combination of various parameters can help pediatricians accurately identify CVA in children.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments.

BACKGROUND: Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients. METHODS: Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients. RESULTS: The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker. CONCLUSION: The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients.

Background: The triglyceride-glucose (TYG) index is a novel and reliable marker reflecting insulin resistance. Its predictive ability for cardiovascular disease onset and prognosis has been confirmed. However, for advanced chronic heart failure (acHF) patients, the prognostic value of TYG is challenged due to the often accompanying renal dysfunction (RD). Therefore, this study focuses on patients with aHF accompanied by RD to investigate the predictive value of the TYG index for their prognosis. Methods and Results: 717 acHF with RD patients were included. The acHF diagnosis was based on the 2021 ESC criteria for acHF. RD was defined as the eGFR < 90 mL/(min/1.73 m2). Patients were divided into two groups based on their TYG index values. The primary endpoint was major adverse cardiovascular events (MACEs), and the secondary endpoints is all-cause mortality (ACM). The follow-up duration was 21.58 (17.98-25.39) months. The optimal cutoff values for predicting MACEs and ACM were determined using ROC curves. Hazard factors for MACEs and ACM were revealed through univariate and multivariate COX regression analyses. According to the univariate COX regression analysis, high TyG index was identified as a risk factor for MACEs (hazard ratio = 5.198; 95% confidence interval [CI], 3.702-7.298; P < 0.001) and ACM (hazard ratio = 4.461; 95% CI, 2.962-6.718; P < 0.001). The multivariate COX regression analysis showed that patients in the high TyG group experienced 440.2% MACEs risk increase (95% CI, 3.771-7.739; P < 0.001) and 406.2% ACM risk increase (95% CI, 3.268-7.839; P < 0.001). Kaplan-Meier survival analysis revealed that patients with high TyG index levels had an elevated risk of experiencing MACEs and ACM within 30 months. Conclusion: This study found that patients with high TYG index had an increased risk of MACEs and ACM, and the TYG index can serve as an independent predictor for prognosis.

Racial Differences in Diagnostic Accuracy of Retinal Nerve Fiber Layer Thickness in Primary Open-Angle Glaucoma.

Purpose: To evaluate the diagnostic accuracy of retinal nerve fiber layer thickness (RNFLT) by spectral-domain optical coherence tomography (OCT) in primary open-angle glaucoma (POAG) in eyes of African (AD) and European descent (ED). Design: Comparative diagnostic accuracy analysis by race. Participants: 379 healthy eyes (125 AD and 254 ED) and 442 glaucomatous eyes (226 AD and 216 ED) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. Methods: Spectralis (Heidelberg Engineering GmbH) and Cirrus (Carl Zeiss Meditec) OCT scans were taken within one year from each other. Main Outcome Measures: Diagnostic accuracy of RNFLT measurements. Results: Diagnostic accuracy for Spectralis-RNFLT was significantly lower in eyes of AD compared to those of ED (area under the receiver operating curve [AUROC]: 0.85 and 0.91, respectively, P=0.04). Results for Cirrus-RNFLT were similar but did not reach statistical significance (AUROC: 0.86 and 0.90 in AD and ED, respectively, P =0.33). Adjustments for age, central corneal thickness, axial length, disc area, visual field mean deviation, and intraocular pressure yielded similar results. Conclusions: OCT-RNFLT has lower diagnostic accuracy in eyes of AD compared to those of ED. This finding was generally robust across two OCT instruments and remained after adjustment for many potential confounders. Further studies are needed to explore the potential sources of this difference.

Comparison between Injury Severity Score (ISS) and New Injury Severity Score (NISS) in predicting mortality of thoracic trauma in a tertiary hospital.

INTRODUCTION: measuring the severity of traumatic injuries is crucial for predicting clinical outcomes. Whereas the Injury Severity Score (ISS) has limitations in assigning scores to injuries at the same site, the New Injury Severity Score (NISS) corrects for this problem by taking into account the three most severe injuries regardless of the region of the body. This study seeks to comprehend the clinical and epidemiological profile of trauma patients while comparing the effectiveness of scales for predicting mortality. METHODS: a descriptive, observational and retrospective study using records of patients who underwent thoracotomy at the Hospital das Clínicas of the Federal University of Triângulo Mineiro between 2000 and 2019. Demographic data, mechanisms of injury, affected organs, length of stay and mortality were analyzed. Injury severity was assessed using the ISS and NISS, and statistical analyses were conducted using MedCalc and SigmaPlot. RESULTS: 101 patients were assessed, on average 29.6 years old, 86.13% of whom were men. The average duration of hospitalization was 10.9 days and the mortality rate was 28.7%. The ROC curve analysis revealed a sensitivity of 68.97%, specificity of 80.56% and area under the curve of 0.837 for the ISS, and 58.62%, 94.44% and 0.855 for the NISS, respectively. The Youden index was 0.49 for the ISS and 0.53 for the NISS. CONCLUSION: the study demonstrated comparable efficacy of NISS and ISS in predicting mortality. These findings hold significance in the hospital setting. Professionals must be familiar with these scales to utilize them competently for each patient.

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosis and therapeutic guidance of bladder cancer. This study focuses on evaluating the potential of high-definition computed tomography (CT) imagery coupled with RNA-sequencing analysis to accurately predict bladder tumor stages, utilizing deep residual networks. Data for this study, including CT images and RNA-Seq datasets for 82 high-grade bladder cancer patients, were sourced from the TCIA and TCGA databases. We employed Cox and lasso regression analyses to determine radiomics and gene signatures, leading to the identification of a three-factor radiomics signature and a four-gene signature in our bladder cancer cohort. ROC curve analyses underscored the strong predictive capacities of both these signatures. Furthermore, we formulated a nomogram integrating clinical features, radiomics, and gene signatures. This nomogram's AUC scores stood at 0.870, 0.873, and 0.971 for 1-year, 3-year, and 5-year predictions, respectively. Our model, leveraging radiomics and gene signatures, presents significant promise for enhancing diagnostic precision in bladder cancer prognosis, advocating for its clinical adoption.

Predictive Value of Serum microRNA-29b-3p in Recurrence of Atrial Fibrillation After Radiofrequency Catheter Ablation.

Objective: Atrial fibrillation (AF) is a common arrhythmia. This study explored serum miR-29b-3p expression in AF patients and its value in predicting AF recurrence after radiofrequency catheter ablation (RFCA). Methods: Totally 100 AF patients who underwent RFCA were enrolled, with 100 individuals without AF as controls. Serum miR-29b-3p expression in participants was determined using RT-qPCR. The correlation between miR-29b-3p and atrial fibrosis markers (FGF-21/FGF-23) was assessed by Pearson analysis. The diagnostic efficacy of serum miR-29b-3p and FGF-21/FGF-23 in predicting AF recurrence after RFCA was analyzed by the receiver operating characteristic (ROC) curves. The Kaplan-Meier method was adopted to evaluate the effect of miR-29b-3p expression on the incidence of AF recurrence after RFCA. The independent risk factors for AF recurrence after RFCA were analyzed by logistic regression analysis. Results: Serum miR-29b-3p was poorly expressed in AF patients. After RFCA, AF patients showed elevated serum miR-29b-3p expression. Serum miR-29b-3p expression in AF patients negatively correlated with serum FGF-21 and FGF-23 concentrations. The cut-off values of serum miR-29b-3p, FGF-21, and FGF-23 in identifying AF recurrence were 0.860 (sensitivity: 100.00%, specificity: 39.71%), 222.2 pg/mL (sensitivity: 96.88%, specificity: 32.35%) and 216.3 ng/mL (sensitivity: 53.13%, specificity: 70.59%), respectively. Patients with low miR-29b-3p expression had a significantly higher incidence of AF recurrence than patients with high miR-29b-3p expression. Serum miR-29b-3p expression was one of the independent risk factors for AF recurrence after RFCA. Conclusion: Low miR-29b-3p expression in AF patients has certain predictive values and is one of the independent risk factors for AF recurrence after RFCA.

Association of the CUN-BAE body adiposity estimator and other obesity indicators with cardiometabolic multimorbidity: a cross-sectional study.

Cardiometabolic multimorbidity (CM), defined as the coexistence of two or three cardiometabolic disorders, is one of the most common and deleterious multimorbidities. This study aimed to investigate the association of Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) with the prevalence of CM. The data were obtained from the 2021 health checkup database for residents of the Electronic Health Management Center in Xinzheng, Henan Province, China. 81,532 participants aged ≥ 60 years were included in this study. Logistic regression models were used to estimate the odd ratios (ORs) and 95% confidence intervals (CIs) for CUN-BAE, BMI, WC, and WHtR in CM. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of different anthropometric indicators for CM. The multivariable-adjusted ORs (95% CIs) (per 1 SD increase) of CM were 1.799 (1.710-1.893) for CUN-BAE, 1.329 (1.295-1.364) for BMI, 1.343 (1.308-1.378) for WC, and 1.314 (1.280-1.349) for WHtR, respectively. Compared with BMI, WC and WHtR, CUN-BAE had the highest AUC in both males and females (AUC: 0.642; 95% CI 0.630-0.653 for males, AUC: 0.614; 95% CI 0.630-0.653 for females). CUN-BAE may be a better measure of the adverse effect of adiposity on the prevalence of CM than BMI, WC, and WHtR.

Preoperative peripheral inflammatory markers are predictors of postoperative central diabetes insipidus in craniopharyngioma patients: a retrospective study.

BACKGROUND: Postoperative central diabetes insipidus (CDI) is commonly observed in craniopharyngioma (CP) patients, and the inflammatory response plays an important role in CPs. We aimed to evaluate the predictive value of preoperative peripheral inflammatory markers and their combinations regarding CDI occurrence in CPs. METHODS: The clinical data including preoperative peripheral inflammatory markers of 208 CP patients who underwent surgical treatment were retrospectively collected and analyzed. The preoperative peripheral white blood cells (WBC), neutrophils, lymphocytes, monocytes, platelet (PLT), neutrophil-to-lymphocyte ratio (NLR), derived-NLR (dNLR), monocyte-to-lymphocyte ratio (MLR) and PLT-to-lymphocyte ratio (PLR) were assessed in total 208 CP patients and different age and surgical approach CP patient subgroups. Their predictive values were evaluated by the receiver operator characteristic curve analysis. RESULTS: Preoperative peripheral WBC, neutrophils, NLR, dNLR, MLR, and PLR were positively correlated and lymphocyte was negatively associated with postoperative CDI occurrence in CP patients, especially when WBC ≥ 6.66 × 109/L or lymphocyte ≤ 1.86 × 109/L. Meanwhile, multiple logistic regression analysis showed that WBC > 6.39 × 109/L in the > 18 yrs age patients, WBC > 6.88 × 109/L or lymphocytes ≤ 1.85 × 109/L in the transcranial approach patients were closely associated with the elevated incidence of postoperative CDI. Furthermore, the area under the curve obtained from the receiver operator characteristic curve analysis showed that the best predictors of inflammatory markers were the NLR in total CP patients, the MLR in the ≤ 18 yrs age group and the transsphenoidal group, the NLR in the > 18 yrs age group and the dNLR in the transcranial group. Notably, the combination index NLR + dNLR demonstrated the most valuable predictor in all groups. CONCLUSIONS: Preoperative peripheral inflammatory markers, especially WBC, lymphocytes and NLR + dNLR, are promising predictors of postoperative CDI in CPs.

Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer.

BACKGROUND: Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration's (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC. METHODS: In this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs). RESULTS: Analyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001). CONCLUSION: ICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients' responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient's unique response situations.

Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.

BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Purpose: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON. Methods: Subjects with TED who underwent clinical evaluation and orbital CT imaging were included. Patients with clinical features of CON were classified as having severe TED, and those without were classified as having mild TED. Normal subjects were used for controls. A U-Net DL-model was used for automatic segmentation of orbital muscle and fat volumes from orbital CTs, and ensemble of Random Forest Classifiers were used for volumetric analysis of muscle and fat. Results: Two hundred eighty-one subjects were included in this study. Automatic segmentation of orbital tissues was performed. Dice coefficient was recorded to be 0.902 and 0.921 for muscle and fat volumes, respectively. Muscle volumes among normal, mild, and severe TED were found to be statistically different. A classification model utilizing volume data and limited patient data had an accuracy of 0.838 and an area under the curve (AUC) of 0.929 in predicting normal, mild TED, and severe TED. Conclusions: DL-based automated segmentation of orbital images for patients with TED was found to be accurate and efficient. An ML-based classification model using volumetrics and metadata led to high diagnostic accuracy in distinguishing TED and TED with CON. By enabling rapid and precise volumetric assessment, this may be a useful tool in future clinical studies.

GALAD score as a prognostic model for recurrence of hepatocellular carcinoma after local ablation.

BACKGROUND: Currently, the high recurrence rate still forms severe challenges in hepatocellular carcinoma (HCC) treatment. The GALAD score, including age, gender, alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), and des-gamma-carboxyprothrombin (DCP) was developed as a diagnostic model. However, evidence is still lacking to confirm the capability of the GALAD score to predict the recurrence of HCC. METHODS: This study included 390 HCC patients after local ablation at Beijing You'an Hospital from January 1, 2018, to December 31, 2022. Firstly, the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the predictive capability of the GALAD score. Then, the Kaplan-Meier (KM) curve and log-rank test were used to compare the prognosis between two groups classified by GALAD score. Finally, a nomogram for high-risk patients was established by Lasso-Cox regression. It was assessed by ROC curves, calibration curves, and decision curve analysis (DCA). RESULTS: The ROC curve (AUC: 0.749) and KM curve showed the GALAD score had good predictive ability and could clearly stratify patients into two groups through the risk of recurrence. Prognostic factors selected by Lasso-Cox regression contained tumor number, tumor size, and globulin. The nomogram for high-risk patients showed reliable discrimination, calibration, and clinical utility. CONCLUSION: This research displayed that the GALAD score is an effective model for predicting the recurrence of HCC. Meanwhile, we found the poor prognosis of the high-risk group and created a nomogram for these patients.

Prognostic significance of preoperative lymphocytes, albumin, and neutrophils (LANR) index in resectable pancreatic ductal adenocarcinoma.

PURPOSE: The index composed of preoperative lymphocytes, albumin, and neutrophils (LANR), a new composite score based on inflammatory response and nutritional status, has been reported to be associated with the prognosis of multiple types of cancer, but the role of LANR in the prognosis of resectable pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. PATIENTS AND METHODS: The data of 142 patients with PDAC who underwent radical resection in the Affiliated Hospital of Jiangnan University from January 2015 to December 2018 were retrospectively analyzed. Receiver Operating Characteristic (ROC) curves were generated to determine the optimal cut-off values for these parameters, as well as the sensitivity and specificity of LANR in predicting survival. The Kaplan-Meier method was used to draw the survival curves. Log rank test was used for univariate analysis, and Cox proportional hazards regression model was used for multivariate analysis.  RESULTS: The optimal cut-off value of LANR was 18.145, and a low preoperative LANR was significantly correlated with the location of the tumor (p = 0.047). Multivariate analysis showed that tumor differentiation degree (HR:2.357, 95%CI:1.388-4.003,p = 0.002), lymph node metastasis (HR:1.755, 95%CI: 1.115-2.763, p = 0.015), TNM stage (HR:4.686, 95%CI: 2.958-7.425, p < 0.001), preoperative cancer antigen 19 - 9 levels (HR:1.001, 95%CI: 1.000-1.001, p < 0.001) and preoperative LANR (HR:0.221, 95%CI: 0.111-0.441, p < 0.001) were independent risk factors for a poor prognosis in patients undergoing radical resection of PDAC. CONCLUSION: This study found that preoperative LANR can be used to assess the prognosis of radical resection in patients with PDAC; those with low preoperative LANR had a worse outcome.

Investigating the risk of reintubation by cough force assessment using cough peak expiratory flow: a single-center observational pilot study.

BACKGROUND: No objective indicator exists for evaluating cough strength during extubation of tracheally intubated patients. This study aimed to determine whether cough peak expiratory flow (CPEF) can predict the risk of reintubation due to decreased cough strength. METHODS: This was a retrospective cohort study of patients who were admitted to our Emergency Intensive Care Unit between September 1, 2020 and August 31, 2021 and were under artificial ventilation management for ≥ 24 h. The patients were divided into two groups: successful extubation and reintubation groups, and the relationship between CPEF immediately before extubation and reintubation was investigated. RESULTS: Seventy-six patients were analyzed. In the univariate analysis, CPEF was significantly different between the successful extubation (90.7 ± 25.9 L/min) and reintubation (57.2 ± 6.4 L/min) groups (p < 0.001). In the multivariate analysis with age and duration of artificial ventilation as covariates, CPEF was significantly lower in the reintubation group (p < 0.01). The cutoff value of CPEF for reintubation according to the receiver operating characteristic curve was 60 L/min (area under the curve, 0.897; sensitivity, 78.5%; specificity, 90.9%; p < 0.01). CONCLUSION: CPEF in tracheally intubated patients may be a useful indicator for predicting the risk of reintubation associated with decreased cough strength. The cutoff CPEF value for reintubation due to decreased cough strength was 60 L/min.

A proposed simplified definition of metabolic syndrome in children and adolescents: a global perspective.

Metabolic syndrome (MetS) is becoming prevalent in the pediatric population. The existing pediatric MetS definitions (e.g., the International Diabetes Federation (IDF) definition and the modified National Cholesterol Education Program (NCEP) definition) involve complex cut-offs, precluding fast risk assessment in clinical practice.We proposed a simplified definition for assessing MetS risk in youths aged 6-17 years, and compared its performance with two existing widely used pediatric definitions (the IDF definition, and the NCEP definition) in 10 pediatric populations from 9 countries globally (n = 19,426) using the receiver operating characteristic (ROC) curve analyses. In general, the total MetS prevalence of 6.2% based on the simplified definition was roughly halfway between that of 4.2% and 7.7% estimated from the IDF and NCEP definitions, respectively. The ROC curve analyses showed a good agreement between the simplified definition and two existing definitions: the total area under the curve (95% confidence interval) of the proposed simplified definition for identifying MetS risk achieved 0.91 (0.89-0.92) and 0.79 (0.78-0.81) when using the IDF or NCEP definition as the gold standard, respectively.The proposed simplified definition may be useful for pediatricians to quickly identify MetS risk and cardiometabolic risk factors (CMRFs) clustering in clinical practice, and allow direct comparison of pediatric MetS prevalence across different populations, facilitating consistent pediatric MetS risk monitoring and the development of evidence-based pediatric MetS prevention strategies globally.