AUC Values Research Articles

Predictive factors for lymph node metastasis in papillary thyroid cancer patients undergoing neck dissection: insights from a large cohort study

BackgroundThis study aimed to investigate the risk factors and metastatic patterns in papillary thyroid cancer (PTC) patients undergoing lymph node dissection, offering guidance for clinical practice.MethodsA total of 924 PTC patients who underwent thyroidectomy with central neck dissection (CND) or lateral neck dissection (LND) between January 2021 and November 2022 were included in the analysis. The study investigated the relationships between clinicopathological characteristics, lymph node metastasis, and various risk factor.ResultsAmong the 924 PTC patients, the cervical lymph node metastasis rate was 59.1% (546 patients). Of these patients, 381 had central neck metastasis (CNM, 41.2%), while the remaining 165 patients had lateral neck metastasis (LNM, 17.9%). Factors associated with increased risk of CNM and LNM included larger tumor diameter, presence of multiple tumors, and capsular invasion (p<0.05). Male sex, age <55 years, larger tumor diameter (>0.85 cm), multiple tumors, capsular invasion, and absence of Hashimoto’s disease were identified as independent risk factors for CNM (p<0.05), with an AUC value of 0.722. CNM, maximum diameter >1.15 cm, and multiple tumors were independent risk factors for LNM (p<0.05), with an AUC of 0.699.ConclusionThese findings suggest that tailored neck dissection based on individual risk factors is crucial, particularly in cases of suspected LNM with larger tumors, CNM, multiple tumors, and capsular invasion.

An optimized method for dose-effect prediction of traditional Chinese medicine based on 1D-ResCNN-PLS.

We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.

Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS)

ObjectivesTo enhance the identification of individuals at risk of developing clinically significant kidney stones.MethodsIn this study, data from the Fasa Adults Cohort Study were analyzed to explore factors linked to symptomatic and clinically significant kidney stone disease. After cleaning, 10,128 participants with 103 variables were studied. One outcome variable (presence of symptomatic kidney stones) and 102 predictor variables from surveys and tests were assessed. Five Machine learning (ML) algorithms (SVM, RF, KNN, GBM, XGB) were applied to examine kidney stone factors, with performance comparisons made. Data balancing was done using SMOTE, and metrics like accuracy, precision, sensitivity, specificity, F1 score, and AUC were evaluated for each algorithm.ResultsThe XGB model outperformed others with AUC of 0.60, while RF, GBM, SVC, and KNN had AUC values of 0.58, 0.57, 0.54, and 0.52. RF, GBM, and XGB showed good accuracy at 0.81, 0.81, and 0.77. Top predictors for kidney stones were serum creatinine, salt intake, hospitalization history, sleep duration, and BUN levels.ConclusionsML models show promise in evaluating an individual’s risk of developing painful kidney stones and recommending early lifestyle changes to reduce this risk. Further research can enhance predictive accuracy and tailor interventions for better prevention/management.

The accuracy of fine needle aspiration cytology and ultrasonography in assessing thyroid nodules in correlation with histopathology: a retrospective study

Background: Accurately diagnosing thyroid nodules is vital for preventing unnecessary surgeries and providing prompt therapy. Although fine needle aspiration cytology (FNAC) and ultrasonography (US) are widely used diagnostic methods, their reliability is questioned. This study investigates the effectiveness of US and FNAC in thyroid nodule diagnosis and differentiates benign from malignant nodules in relation to final histopathological diagnosis. Method: A retrospective study including 307 adult patients with thyroid diseases who underwent neck US and FNAC before surgery was conducted between April 2019 and May 2023. The diagnostic efficacy of US, FNAC, and their combination usage was compared to histopathological results. Result: Histopathological findings revealed that 187(61%) cases were benign, while 120 (39%) were malignant. The US features of “taller-than-wider” forms and hypoechoic appearance had the highest diagnostic accuracy in characterizing malignant thyroid nodules, with 83% and 73% accuracy, respectively. The combination of US parameters demonstrated high sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of 88.33%, 63.10%, 60.6%, and 89.4%, with a statistically significant area under the ROC curve (AUC: 0.828, P<0.001) than individual parameters. FNAC’s sensitivity, specificity, PPV NPV, and accuracy in detecting malignant lesions were 50%, 95%, 86%, 75%, and 77%, respectively, with acceptable discrimination and statistical significance (AUC: 0.723, P<0.0001). The combination of US parameters and FNAC significantly improved the AUC value (AUC: 0.878, P<0.0001), sensitivity (83.33%), and specificity (79.14%). Univariate analysis showed that hypoechoic appearance, heterogenicity, large mass size (>4 cm), “taller-than-wider,” infiltrative margins, and microcalcifications were risk factors for malignancy in thyroid nodules and were statistically significant (all P values <0.05). Conclusion: Combining US characteristics with FNAC results can afford the maximum analytical accuracy in distinguishing benign from malignant thyroid nodules. This strategy is practical due to its simplicity, minimal invasiveness, and cost-effectiveness, enabling robust management regimens and avoiding additional surgical procedures.

Comparison of Vancomycin AUC24 Calculation Methods for Neonates and Infants.

For neonates and infants receiving intermittent vancomycin infusions, the area under the concentration-timecurve during 24 h (AUC24) is often estimated with Bayesian forecasting using one or more measured vancomycin concentrations. When practical peak and trough concentrations are measured at steady state, AUC24 can also be calculated with first-order steady-state equations for a one-compartment model (Sawchuk-Zaske method), but previously this method has been applied only for adults. The objective of this studywas to compare AUC24 values obtained with the Sawchuk-Zaske method and two Bayesian models. AUC24 values were estimated retrospectively for 18 neonates and infants with steady-state peak and trough concentrations using traditional compartmental analysis with a one-compartment model (reference method), the Sawchuk-Zaske method, and Bayesian forecasting with two previously published models. In Bayesian forecasting, both original and modified residual error models were used. In the modified models, the residual error was reduced by setting the additive residual error to zero and the proportional error to 15%. AUC24 estimates obtained with the Sawchuk-Zaske method differed -2.7 to 0.9% from the reference method. When both peak and trough concentrations were used in Bayesian forecasting, 61% and 33% of AUC24 estimates obtained with two original models differed less than 15% from the reference method, and these fractions increased to 83% and 72% with the modified models, respectively. When practical peak and trough concentrations are measured at steady state, the simple Sawchuk-Zaske method is very useful for AUC24 estimation in neonates and infants. In Bayesian forecasting, the reduced residual error model can be used to improve the model fit.

Spatial modeling and ecological suitability of Ebola virus disease in Africa.

This paper looks into the MaxEnt model in a trial to comprehend the ecological and environmental conditions that propagate and drive the spread of Ebola Virus Disease in Africa. We use the MaxEnt model to assess risk determinants associated with the occurrence and distribution of EVD, taking into account non-correlated variables such as neighborhood mean temperature, rainfall, and human population density. Our findings indicate that among the factors that significantly shape the geographical distribution of EVD risk are human population density, annual rainfall, temperature variability, and seasonality. The model used is both reliable and accurate (the average value for training AUC was 0.987); it can be used as a valuable approach for the prediction of infectious disease outbreaks. High-risk areas are primarily identified in the western and central regions of Africa, with some of the others in the east also vulnerable. This further calls for specified public health interventions and enhanced surveillance in specified hotspots, contributing to global efforts to predict and mitigate risks associated with EVD outbreaks more adequately. The findings further support that it remains imperative to conduct additional research, including socio-economic and cultural variables, to enhance the understanding of how environmental factors contribute to the emergence and transmission of Ebola.

Effects of vagal nerve stimulation parameters on heart rate variability in epilepsy patients

IntroductionVagal nerve stimulation (VNS) is used as an alternative treatment in drug-resistant epilepsy patients. Effects of VNS on the cardiac autonomic system are controversial. In this study, we aimed to investigate the relationship between VNS parameters and heart rate variability (HRV) in epilepsy patients who underwent VNS treatment.MethodsOur study included 31 patients who underwent VNS for drug-resistant epilepsy. Patients were divided into groups according to response to VNS and VNS parameters. All patients underwent 24-h Holter ECG.ResultsThe mean age of 31 VNS-treated epilepsy patients included in the study was 33.87 ± 7.6 years. When patients were grouped according to VNS response, 25 patients were in the VNS responder group and six patients were in the VNS-nonresponder group. When comparing Holter parameters in the VNS responder and non-responder groups, the median HF was significantly lower in the VNS responder group. VNS duration and signal frequency had a positive effect on LF/HF, while output and off time had a negative effect on LF/HF. When ROC analysis was performed to determine the cut-off values of the parameters for the VNS-responsive state, the AUC value of the HF parameter was 0.780, which was statistically significant. The cut-off value to distinguish response to VNS was 156.9.ConclusionIn conclusion, the effects of VNS parameters on HRV parameters are quite complex. However, the conclusion is that VNS is a neuromodulation method that affects the autonomic system in a complex way. Different levels of VNS parameters may also contribute to this effect. Furthermore, HRV parameters can be used as biomarkers to predict the patient population that may benefit from VNS.

Clinical value of wideband acoustic immittance in the diagnosis of otitis media with effusion under negative intratympanic pressure in adults.

Normative clinical values for wideband acoustic immittance (WAI) are not well-established. This study aims to define the normative contour plot characteristics of WAI and evaluate its diagnostic value in detecting tympanic effusion. Data were collected from subjects with normal hearing (76 ears) and type C tympanograms on 226-Hz tympanometry (130 ears). Matlab was used to process and analyze the data. The maximum absorbance of WAI was used as the primary indicator, and the receiver operating characteristic (ROC) curve was drawn to determine the optimal cut-off value. WAI contour plots were compared to 226-Hz tympanometry and otoendoscopy, the latter being the gold standard. Mean WAI plots in the normal group showed single peaks (absorbance, >70%) and a nearly symmetrical distribution of the peak area around the 0 daPa pressure line. The ROC curve indicated an AUC value of 0.910, with 78% as the optimal cut-off value of maximum absorbance for detecting effusion. WAI demonstrated better diagnostic performance (sensitivity: 82.26%, specificity: 86.76%, kappa: 0.691) than 226-Hz tympanometry (sensitivity: 61.29%, specificity: 61.76%, kappa: 0.230). Normative WAI values were established, and WAI proved more accurate than 226-Hz tympanometry for identifying tympanic effusion, offering valuable guidance for selecting treatment options.

Multiparametric quantitative MRI of healthy adult pancreas: correlations with gender and age

BackgroundThe pancreas plays an important role in the nutrition and metabolism of the whole body. Many disease processes including obesity, diabetes mellitus (DM), acute or chronic pancreatitis, and pancreatic carcinoma result in abnormality of pancreas morphology and function. Magnetic resonance imaging (MRI) provides quantitative parameters including T1, T2, and apparent diffusion coefficient (ADC) values for evaluating normal and abnormal pancreas. Based on the normal range of these quantitative parameters, pancreatic abnormality could be detected early. However, the range and the relationship of T1, T2, and ADC values with gender and age groups using the same dataset have not been explored.PurposeTo establish the ranges of MRI tissue and functional parameters, including T1, T2, and ADC values, in healthy adult pancreas and their correlations with gender, subregion, and age.Materials and methodsThe T1, T2, and ADC values of healthy pancreas in 86 adults were measured using a 3.0-T MRI scanner. The average T1, T2, and ADC values were obtained in the whole pancreas and subregions (head, neck, body, and tail). Their correlations with gender and age were investigated.ResultsThe T1, T2, and ADC values of the whole pancreas from all subjects were 870.07 ± 61.86 ms, 44.07 ± 6.14 ms, and 1.072 ± 0.212 × 10−3 mm2/s, respectively. T2 values were significantly different between genders (P < 0.05). No significant differences were found between subregions. The T1, T2, and ADC values differed significantly among the age groups (P < 0.05). The T1 value revealed a moderately positive correlation, while the T2 and ADC values displayed negative correlations with age (r = 0.31, −0.45, and −0.39, respectively). The combination of T1, T2, and ADC values achieved the highest AUC value and showed a significant difference compared to T1, T2, and ADC values alone in predicting age older than 45 years.ConclusionThis study established the normal ranges of T1, T2, and ADC. We found that T2 is different between men and women, and T1, T2, and ADC are age-dependent. These results could be useful for quantitative MRI of pancreatic disease.

Study on the classification of benign and malignant breast lesions using a multi-sequence breast MRI fusion radiomics and deep learning model

PurposeTo develop a multi-modal model combining multi-sequence breast MRI fusion radiomics and deep learning for the classification of benign and malignant breast lesions, to assist clinicians in better selecting treatment plans. MethodsA total of 314 patients who underwent breast MRI examinations were included. They were randomly divided into training, validation, and test sets in a ratio of 7:1:2. Subsequently, features of T1-weighted images (T1WI), T2-weighted images (T2WI), and dynamic contrast-enhanced MRI (DCE-MRI) were extracted using the convolutional neural network ResNet50 for fusion, and then combined with radiomic features from the three sequences. The following models were established: T1 model, T2 model, DCE model, DCE_T1_T2 model, and DCE_T1_T2_rad model. The performance of the models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The differences between the DCE_T1_T2_rad model and the other four models were compared using the Delong test, with a P-value < 0.05 considered statistically significant. ResultsThe five models established in this study performed well, with AUC values of 0.53 for the T1 model, 0.62 for the T2 model, 0.79 for the DCE model, 0.94 for the DCE_T1_T2 model, and 0.98 for the DCE_T1_T2_rad model. The DCE_T1_T2_rad model showed statistically significant differences (P < 0.05) compared to the other four models. ConclusionThe use of a multi-modal model combining multi-sequence breast MRI fusion radiomics and deep learning can effectively improve the diagnostic performance of breast lesion classification.

The spatial-temporal evolution patterns of landslide-oriented resilience in mountainous city: A case study of Chongqing, China

Cities, as complex systems with multi-interconnected subsystems, face significant challenges from both rapid urbanization and climate change. Ensuring high resilience in urban areas is essential for managing these dynamic risks effectively. This study introduces an innovative, data-driven approach to quantitatively analyze the spatial-temporal evolution patterns of urban resilience, validated through a case study of Chongqing, a representative mountainous city in China. Based on historical landslide data from Chongqing (2010–2020), which includes 4464 events, along with indicator data from the Chongqing Statistical Yearbook, we developed a comprehensive assessment framework. This framework incorporates 33 variables, covering indicators of physical-environmental resilience (PER) and socio-economic resilience (SER). The model integrates the Random Forest (RF) algorithm, Analytic Hierarchy Process (AHP), and Coupling Coordination Degree (CCD) model. Key findings include: (1) Social development in mountainous cities like Chongqing follows a point-to-area pattern. Although there is an overall increase in SER, the CCD in more developed areas (Chongqing urban circle) was generally higher than in less developed areas (northeastern and southeastern Chongqing) (2) The PER model demonstrated exceptional performance (AUC values consistently above 0.95). Spatiotemporal evolution models reveal that Chongqing maintains a high overall PER. Notably, from 2019 to 2020, the proportion of administrative units classified as highly resilient peaked at 24.5%, marking a historical high. (3) Multi-year average rainfall primarily impacts PER (ranked first), while Gross Domestic Product (GDP) significantly affect SER. The development of multi-dimensional recovery indicators provides a robust framework for assessing resilience against landslides in mountainous cities. The CCD model illustrates the importance of regional dynamic coordinated development in resilience trajectories. This study provides a detailed blueprint for the scientific development of resilient mountainous cities, emphasizing the need for a spatial-temporal perspective on resilience and the benefits of coordinated regional development.

Prediction on Yellowfin Tuna (Thunnus albacares) Fishing Ground in Waters Near the Marshall Islands Based on SMOTETomek‐RF

ABSTRACTThis study monitored 37 longliners fishing in waters near the Marshall Islands from 2020 to 2022 by Liancheng Overseas Fishery (Shenzhen) Co., Ltd.'s operation management system. This study developed nine predictive models on the relationship between catch per unit effort (CPUE) data for yellowfin tuna (Thunnus albacares) and the environmental data. The environmental data integrate 48 variables, including eddy kinetic energy, chlorophyll a concentration, sea surface height, and additional measures of vertical oceanic conditions, alongside spatiotemporal parameters (year, month, day, longitude, and latitude). This study employed four spatial resolutions (0.25° × 0.25°, 0.5° × 0.5°, 1° × 1°, and 2° × 2°) to develop nine predictive models: KNN, RF, GBDT, CART, LightGBM, XGBoost, CatBoost, AdaBoost, and Stacking (RF, KNN, GBDT, and LR). These models, with a daily time resolution, were trained using 75% of the data and tested with the remaining 25%. The optimal spatial resolution and model were determined through a comprehensive comparison of model evaluation metrics across these spatial resolutions. The SMOTETomek algorithm was then applied to resample 75% of the data at the optimal spatial resolution, forming a new training dataset. This dataset was used to refine the model, subsequently tested with the remaining 25% of the data. Results indicated that (1) the optimal spatial resolution is 0.25° × 0.25° and the optimal model is RF; (2) the SMOTETomek algorithm enhances the model's predictive performance; and (3) the developed SMK‐RF model, exhibiting Acc and AUC values of 76.73% and 82.47%, respectively, accurately predicts the central fishing grounds for yellowfin tuna, consisting closely with actual fishing activity.

An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.

Platelet-to-albumin ratio: a potential biomarker for predicting all-cause and cardiovascular mortality in patients undergoing peritoneal dialysis

BackgroundAlthough peritoneal dialysis (PD) is an efficient therapy for renal replacement, the long-term survival rate of patients undergoing PD remains low. The platelet-to-albumin ratio (PAR), recently identified as a parameter of inflammatory and nutritional status, is associated with an adverse prognosis for various diseases. However, the association between the serum PAR and prognosis of patients undergoing PD is poorly understood. This study aimed to evaluate whether the PAR is a reliable predictor of cardiovascular disease (CVD) and all-cause mortality in patients undergoing PD.MethodsThis multicenter cohort study enrolled patients undergoing PD from January 1, 2009, to September 30, 2018. The patients were divided into four groups according to the quartiles of their baseline PAR. The primary endpoint was all-cause and CVD-related mortality. Cox proportional hazard models were used to determine the association between the PAR and all-cause or CVD-related mortality. The receiver operating characteristic (ROC) curve was utilized to compare the performance among PAR and other inflammatory indicators. C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were applied to examine the incremental prognostic value of PAR compared with baseline model for predicting all-cause and CVD mortality.ResultsA total of 2825 patients were included. During the follow-up period of 47.5 ± 28.3 months, 747 (26.4%) mortality cases were observed, of which 415 (55.6%) were CVD-related. Compared with the Q1 (PAR < 4.43), placement in Q4 (PAR > 7.27) was associated with an increased risk of all-cause mortality and CVD mortality (p < 0.001). The adjusted restricted cubic spline analysis indicated that the relationship of the PAR with all-cause and cardiovascular mortality was linear (p for nonlinearity = 0.289 and 0.422, respectively). No positive correlations were shown in the interaction tests. PAR exhibited superior predictive value for mortality compared to other inflammatory indicators, with a respective AUC value of 0.611 (P < 0.001) for all-cause mortality and 0.609 (P < 0.001) for cardiovascular mortality. According to the C-statistic, continuous NRI and IDI, the addition of PAR to the baseline model yielded a moderate but significant improvement in outcome prediction.ConclusionsThe PAR is an independent prognostic factor associated with all-cause and cardiovascular mortality in patients undergoing PD.

Novel troponin fragmentation assay to discriminate between Takotsubo syndrome and acute myocardial infarction.

Cardiac troponin levels are elevated in Takotsubo syndrome (TTS) with significant overlap to acute myocardial infarction (MI). Long and intact cardiac troponin T (cTnT) forms are typical for MI. This study sought to assess whether the fragmentation composition of cTnT release in TTS differs from MI. The concentration of long molecular forms of cTnT (long cTnT) was measured with a novel upconversion luminescence immunoassay and total cTnT with a commercial high-sensitivity cTnT assay in 24 TTS patients and in 84 Type 1 MI patients. The ratio of long to total cTnT (troponin ratio) was determined as a measure of cTnT fragmentation. Troponin ratio was lower in TTS patients 0.13 [0.10-0.20] vs. 0.62 [0.29-0.96], p<0.001). In the ROC curve analyses, troponin ratio showed a better predictive power than total cTnT in discriminating TTS and MI patients (AUC 0.869 [CI95% 0.789-0.948)] vs. 0.766 [CI95% 0.677-0.855], p=0.047). When restricting the analysis to patients with total cTnT below 1200 ng/L (maximal value in TTS patients), the respective AUC values for total cTnT and troponin ratio were 0.599 (CI 95% 0.465-0.732) and 0.816 (CI95% 0.712-0.921), p =0.003). At a cutoff point of 0.12, troponin ratio correctly identified 95% of MI patients and 50% of TTS patients. In contrast to Type 1 MI, only a small fraction of circulating cTnT in TTS exists in intact or long molecular forms. This clear difference in troponin composition could be of diagnostic value when evaluating patients with cTnT elevations and suspicion of TTS. URL: https://www.clinicaltrials.gov; Unique identifier: NCT04465591.

Elucidating the Role of Autophagy-related Genes in Polycystic Ovary Syndrome: Implications for Diagnostic Models and Immune Response Regulation.

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder that negatively affects female reproductive capacity. Although the association between autophagy and PCOS is known, there are few detailed studies on the association between autophagy-related genes and PCOS. Publicly available gene expression datasets (GSE102293, GSE138518, GSE34526, GSE114419, GSE137684, GSE155489) were used in a comprehensive analysis to identify a role for autophagy in PCOS. Batch effects were mitigated using the sva package, followed by WGCNA (weighted gene correlation network analysis) and ss- GSEA (single sample gene set enrichment analysis) to identify autophagy-related genes. Recursive feature elimination (RFE) and LASSO COX methods were used to identify important hub genes, and their correlation with immune cell activity was assessed using ssGSEA and Pearson correlation analysis. High autophagy scores were observed in PCOS samples, and the dark green gene module with the highest autophagy correlation was identified. The differential analysis identified a total of 169 up-regulated genes versus 2 down-regulated genes in the PCOS samples, which were intersected by taking the intersection with the deep green module genes and resulted in 121 key genes. Subsequently, 6 hub genes (MMP25, CSF3R, SLPI, MMP9, CLEC4E, and SIGLEC10) were further identified based on RFE and LASSO algorithms. Diagnostic efficacy based on ROC curves showed six autophagy- associated hub genes with AUC values as high as 0.959 and 0.896 in the training and validation sets, respectively. Finally, we observed that these hub genes are strongly associated with immune function, especially chronic inflammation and aberrant immune activation pathways. In this study, we identified autophagy genes closely related to PCOS and constructed a gene model with high diagnostic accuracy. These findings not only provided potential new biomarkers for the diagnosis of PCOS but also revealed the key role of autophagy in the pathogenesis of PCOS.

Comparative Preclinical Pharmacokinetics and Disposition of Favipiravir Following Pulmonary and Oral Administration as Potential Adjunct Therapy Against Airborne RNA Viruses.

Favipiravir is administered orally, even against airborne RNA viruses, in a loading-dose/maintenance dose regimen. We investigated whether-(a) pulmonary delivery of favipiravir would generate high concentrations in the luminal side of the respiratory tract; and (b) avoiding first-pass metabolism by the liver by inhaled drug would generate comparable pharmacokinetics (PK) with doses significantly smaller than the oral maintenance dose. A dry powder inhalation (DPI) of favipiravir formulated by mixing with Inhalac 400® was prepared and characterized. Inhalations of ~ 120µg dose strength, with or without a prior oral loading dose were administered to mice. Comparator mice received human-equivalent oral doses (3mg). Three mice per sampling time point were sacrificed and favipiravir concentrations in the blood plasma, bronchio-alveolar lavage fluid (BALF) and lung tissue homogenate determined by HPLC. One-compartment PK modeling of concentration-time data indicated that the area under the curve (AUC0-24h) generated in the BALF recovered from mice receiving inhalations of ~ 1/25th of the oral dose subsequent to an oral loading dose was 86.72 ± 4.48µg⋅mL-1⋅h. This was consistently higher than the AUC observed in the BALF of orally-dosed mice (56.71 ± 53.89µgmL-1⋅h). In blood serum, the respective values of AUC were 321.55 ± 124.91 and 354.71 ± 99.60µg⋅mL-1⋅h. Pulmonary delivery of significantly smaller doses of favipiravir generates meaningful drug disposition and pharmacokinetics at the site of respiratory viral infections. We provide the rationale for designing a self-administered, non-invasive, low-cost, targeted drug delivery system against airborne RNA virus infection.

Pathomics models for CD40LG expression and prognosis prediction in glioblastoma

Glioblastoma (GBM) is the most prevalent primary malignant tumor of the nervous system. In this study, we utilized pathomics analysis to explore the expression of CD40LG and its predictive value for the prognosis of GBM patients. We analyzed the expression differences of CD40LG in GBM tissue and normal brain tissue, along with performing survival prognosis analysis. Additionally, histopathological sections of GBM were used to screen for pathological features. Subsequently, SVM and LR pathomics models were constructed, and the models’ performance was evaluated. The pathomics model was employed to predict CD40LG expression and patient prognosis. Furthermore, we investigated the potential molecular mechanisms through enrichment analysis, WGCNA analysis, immune correlation analysis, and immune checkpoint analysis. The expression level of CD40LG was significantly increased in GBM. Multivariate analysis demonstrated that high expression of CD40LG is a risk factor for overall survival (OS) in GBM patients. Five pathological features were identified, and SVM and LR pathomics models were constructed. Model evaluation showed promising predictive effects, with an AUC value of 0.779 for the SVM model, and the Hosmer–Lemeshow test confirmed the model’s prediction probability consistency (P > 0.05). The LR model achieved an AUC value of 0.785, and the Hosmer–Lemeshow test indicated good agreement between the LR model’s predicted probabilities and the true value (P > 0.05). Immune infiltration analysis revealed a significant correlation between the pathomics score (PS) and the degree of infiltration of activated DC cells, T cells CD4 naïve, Macrophages M2, Macrophages M1, and T cells CD4 memory resting. Our results demonstrate that the pathomics model exhibits predictability for CD40LG expression and GBM patient survival. These findings can be utilized to assist neurosurgeons in selecting optimal treatment strategies in clinical practice.

Circulating miR-18a and miR-532 Levels in Extrahepatic Cholangiocarcinoma.

Background: Cholangiocarcinoma (CCA) is a highly aggressive cancer of the bile ducts with a poor prognosis and limited diagnostic markers. This study aims to investigate the potential of miR-18a and miR-532 as biomarkers for CCA by exploring their correlations with clinical parameters and traditional tumor markers such as CA19.9, CEA, and AFP. Methods: This study involved a cohort of patients diagnosed with CCA. Serum levels of miR-18a and miR-532 were measured and analyzed in relation to various clinical parameters, including age, tumor markers, and histological features. Results: Serum levels of miR-18a and miR-532 were upregulated in patients with extrahepatic cholangiocarcinoma (eCCA) compared to healthy controls (p < 0.05). MiR-18a and miR-532 levels were correlated with each other (p = 0.011, Spearman's rho = 0.482) but showed no significant correlation with age or traditional tumor markers (CA19.9, CEA, AFP). No significant differences in miR-18a and miR-532 levels were observed concerning tumor localization or histological grading. For predicting tumor resectability, miR-532 at a cut-off point of 2.12 showed a sensitivity of 72.73%, specificity of 81.25%, and an AUC of 71.3%, while miR-18a, at a cut-off of 1.83, had a sensitivity of 63.64%, specificity of 75%, and an AUC of 59.7%. ROC curve analysis suggested moderate diagnostic potential for miR-18a and miR-532, with AUC values of 0.64 and 0.689, respectively. Conclusions: Although miR-18a and miR-532 showed significant upregulation in eCCA patients compared to healthy controls, they did not demonstrate significant associations with key clinical parameters, limiting their effectiveness as standalone diagnostic biomarkers. Further research involving larger, multi-center cohorts and additional molecular markers is necessary to validate these findings and explore the broader diagnostic potential of miRNAs in CCA.

Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?

BackgroundTo compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).MethodsTwo hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors’ 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).ResultsThere were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.ConclusionsBoth 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.