ROC Curve Research Articles

Predictive biomarkers for low-dose IL-2 therapy efficacy in systemic lupus erythematosus: a clinical analysis

BackgroundLow-dose IL-2 (Ld-IL2) has shown favorable therapeutic effects in systemic lupus erythematosus (SLE) therapy. However, previous clinical trials reported an SLE Responder Index-4 (SRI-4) response rate of 65.52%-68%, with approximately half failing to achieve the primary endpoint by week 24. Our study aims to determine the real-world use of Ld-IL2 and to identify determinants of its effectiveness in SLE.MethodsWe pooled data from 342 SLE patients undergoing sequential Ld-IL2 treatment, with 314 persisting for over 3 months were included in effectiveness and prediction analyses. All patients were categorized into responder (n = 136) and non-responder group (n = 178) according to SRI-4. Lupus Low Disease Activity State (LLDAS) was also analyzed to validate our results.ResultsRash, lower complement 3 (C3), and renal involvement including urine protein, urine occult blood and urine casts emerged as prominent predictors of achieving SRI-4. Adjusting for baseline values using the ratio of change to baseline revealed significant differences in CD4 + T cell immune profiles between responders and non-responders. ROC analysis confirmed a satisfactory performance of rash, renal involvement, percentage change of CD4 + T cells, and C3 in predicting SRI-4, yielding an AUC of 0.933. LLDAS analysis showed that hematological involvements and lower CLA + Treg were potent predictive markers in LLDAS attainment. Conversely, renal involvement failed to have significant association in achieving LLDAS. The analysis of background therapy in SLE patients showed that MMF was more likely to reach the SRI-4 response with the combination of Ld-IL2.ConclusionsThese findings uncovered the predictors of Ld-IL2 treatment efficacy in SLE patients and provided guidance to physicians for rational utilization.

The Predictive Value of Pan-Immune-Inflammation Value for Saphenous Vein Graft Disease in Post-Coronary Artery Bypass Grafting Patients

Background: Coronary artery bypass grafting (CABG) remains the gold standard treatment for patients with significant coronary artery disease (CAD) and high-risk profiles for percutaneous coronary intervention. Despite the frequent use of saphenous vein grafts (SVGs) in CABG, their patency rates are lower than those of arterial grafts. Identifying noninvasive methods to determine SVG patency is crucial. Aims: This study investigates the relationship between a novel inflammation marker, pan-immune-inflammation value (PIV), and SVG patency in post-CABG patients. Methods: The study included 507 patients who underwent coronary angiography (CAG) due to clinical indications between 2016 and 2023. Patients who had undergone CABG at least one year prior with at least one SGV used were divided into two groups based on the presence or absence of SVG stenosis (SGVS). Results: Among the 507 patients, 244 had SVGS. Patients with SVGS exhibited higher levels of diabetes mellitus and inflammatory markers such as NLR, SII, CAR, and PIV. Multivariate analysis identified PIV as an independent predictor of SVGS. ROC analysis showed that a PIV cut-off value > 315.5 predicted SVGS with 75.8% sensitivity and 68.6% specificity. Conclusions: PIV, a simple and easily measurable marker, demonstrated strong predictive value for SVGS in post-CABG patients.

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome.

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk and enhance clinical management. Optimal efficacy of COVID-19 antiviral medications relies on early administration within the initial five days of symptoms, assisting high-risk patients in avoiding hospitalization and improving survival chances. The complete blood count can be an efficient and affordable option to find biomarkers that predict the COVID-19 prognosis due to infection-induced alterations in various blood parameters. This study aimed to associate hematological parameters with different COVID-19 clinical forms and utilize them as disease outcome predictors. We performed a complete blood count in blood samples from 297 individuals with COVID-19 from Belo Horizonte, Brazil. Statistical analysis, as well as ROC Curves and machine learning Decision Tree algorithms were used to identify correlations, and their accuracy, between blood parameters and disease severity. In the initial four days of infection, traditional hematological COVID-19 alterations, such as lymphopenia, were not yet apparent. However, the monocyte percentage and granulocyte-to-lymphocyte ratio proved to be reliable predictors for hospitalization, even in cases where patients exhibited mild symptoms that later progressed to hospitalization. Thus, our findings demonstrate that COVID-19 patients with monocyte percentages lower than 7.7% and a granulocyte-to-lymphocyte ratio higher than 8.75 are assigned to the hospitalized group with a precision of 86%. This suggests that these variables can serve as important biomarkers in predicting disease outcomes and could be used to differentiate patients at hospital admission for managing therapeutic interventions, including early antiviral administration. Moreover, they are simple parameters that can be useful in minimally equipped health care units.

LINC01518 predicts poor prognosis of prostate cancer and promotes its progression by regulating hsa-miR-320a/CNKSR2 axis

BackgroundProstate cancer (PCa) is the most common malignancy of the male genitourinary system. Understanding the molecular mechanism of PCa and its prognostic markers will assist in the selection of better treatment.AimTo explore the role of LINC01518 in the development of PCa and its prognostic value, and to understand its specific regulatory mechanism.MethodsThe levels of LINC01518, hsa-miR-320a, and CNKSR2 mRNA were detected by RT-qPCR. ROC curve was constructed to evaluate the prognostic value of LINC01518 in PCa. The effects of LINC01518 on the functions of PCa cells were demonstrated by CCK-8, Transwell test, and the detection of apoptotic markers, after LINC01518 knockdown. The interaction between hsa-miR-320a and LINC01518 or CNKSR2 mRNA was examined by constructing luciferase vectors.ResultsLINC01518 was abnormally expressed in PCa cells and tumor tissues, and knockdown of it inhibited the growth of PCa cells. LINC01518 predicted castration-resistant prostate cancer (CRPC) outcomes in PCa patients with AUC of 0.803, sensitivity and specificity of 75.4% and 74.6%, respectively. Hsa-miR-320a mimics reduced luciferase activity in PCa cells transfected with WT-LINC01518/CNKSR2 plasmids. Knocking down LINC01518 reduced CNKSR2 level, and this regulatory effect disappeared with the inhibition of hsa-miR-320a.ConclusionHigh levels of LINC01518 predicted poor prognosis in PCa patients and promoted CNKSR2 expression by competitively binding hsa-miR-320a, contributing to the progression of PCa.

The potential for species distribution models to distinguish source populations from sinks.

While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics. Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web-available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks. Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.

Integrative Human Genetic and Cellular Analysis of the Pathophysiological Roles of AnxA2 in Alzheimer's Disease.

Alzheimer's disease (AD) is an intractable and progressive neurodegenerative disease. Amyloid beta (Aβ) aggregation is the hallmark of AD. Aβ induces neurotoxicity through a variety of mechanisms, including interacting with membrane receptors to alter downstream signaling, damaging cellular or organelle membranes, interfering with protein degradation and synthesis, and inducing an excessive immune-inflammatory response, all of which lead to neuronal death and other pathological changes associated with AD. In this study, we extracted gene expression profiles from the GSE5281 and GSE97760 microarray datasets in the GEO (Gene Expression Omnibus) database, as well as from the Human Gene Database. We identified differentially expressed genes in the brain tissues of AD patients and healthy persons. Through GO, KEGG, and ROC analyses, annexin A2 (AnxA2) was identified as a putative target gene. Notably, accumulating evidence suggests that intracellular AnxA2 is a key regulator in various biological processes, including endocytosis, transmembrane transport, neuroinflammation, and apoptosis. Thus, we conducted a series of cell biology experiments to explore the biological function of AnxA2 in AD. The results indicate that AnxA2 gene knockdown primarily affects oxidative phosphorylation, cell cycle, AD, protein processing in the endoplasmic reticulum, SNARE interactions in vesicular transport, and autophagy. In SH-SY5Y cells secreting Aβ42, AnxA2 gene knockdown exacerbated Aβ42-induced cytotoxicity, including cell death, intracellular ROS levels, and neuronal senescence, altered cell cycle, and reduced ATP levels, suggesting its critical role in mitochondrial function maintenance. AnxA2 gene knockdown also exacerbated the inhibitory effect of Aβ42 on cell migration. AnxA2 overexpression reduced the inflammatory response induced by Aβ42, while its absence increased pro-inflammatory and decreased anti-inflammatory responses. Furthermore, AnxA2 gene knockdown facilitated apoptosis and decreased autophagy. These results indicated potential pathophysiological roles of AnxA2 in AD.

Left Atrial Low-Voltage Extent Predicts the Recurrence of Supraventricular Arrhythmias.

The incidence of left atrial (LA) supraventricular arrhythmias is increasing. Even after LA ablation, recurrence of these tachycardias is common. MRI studies show that LA cardiomyopathy is a significant risk factor for recurrence and correlates with low voltage areas detected via 3D electroanatomic mapping (EAM). There are limited data on the impact of low voltage extent detected by EAM on recurrence-free survival. Voltage thresholds defining low voltage vary across different studies. This study aims to investigate the impact of the extent of low voltage areas in the LA on recurrence-free survival and to assess whether defining low voltage areas using thresholds of 0.5, 0.4, or 0.3 mV offers better predictive performance. Patients with atrial arrhythmia who underwent LA EAM at Ulm University Heart Center between September 2018 and September 2022 were included from the ATRIUM registry. ROC analysis determined the voltage threshold for predicting recurrence-free survival. Kaplan-Meier and logistic regression models adjusted for patient variables were used to analyze recurrence-free survival. Of 1089 screened patients, 108 met the inclusion criteria. ROC analysis indicated that a 0.4 mV threshold for low voltage provided the best predictive performance. Logistic regression showed a 1.039-fold increase in recurrence risk per percent increase in LA low voltage area (odds ratio = 1.039, 95% CI 1.014-1.064). Low voltage extent in EAM correlates with 1-year recurrence rate after ablation of left atrial supraventricular arrhythmias. The threshold of 0.4 mV is the most suitable for predicting recurrences of those examined.

Ultrasound guided transcutaneous phrenic nerve stimulation in critically ill patients: a new method to evaluate diaphragmatic function.

Diaphragm dysfunction is common in intensive care unit and associated with weaning failure and mortality. Diagnosis gold standard is the transdiaphragmatic or tracheal pressure induced by magnetic phrenic nerve stimulation. However, the equipment is not commonly available and requires specific technical skills. We aimed to evaluate ultrasound guided transcutaneous phrenic nerve stimulation for daily bedside assessment of diaphragm function by targeted electrical phrenic nerve stimulation. In this randomized cross-over study we compared a new method of ultrasound guided transcutaneous electrical phrenic nerve stimulation (SONOTEPS method) using a peripheral nerve stimulator, with the magnetic phrenic nerve stimulation. Intensive care unit adult patients under mechanical ventilation with a Richmond-Agitation-Sedation-Scale score of -4 or -5 were included. Each patient received the two methods of stimulation, in a randomized order. The primary outcome was the tracheal pressure (Ptrach) induced by stimulation. We analyzed 232 measures of Ptrach from 116 patients of whom 77 presented a diaphragm dysfunction (Ptrach < 11 cmH2O) and 50 a severe diaphragm dysfunction (Ptrach < 8 cmH2O). The Passing-Bablok regression showed no significant differences (intercept A of -0.03 [CI95:-0.83-0.52] and slope B of 0.98 [CI95:0.90-1.05]) between SONOTEPS method and magnetic stimulation which were positively correlated (R²=0.639). The mean bias was -1.08 (CI95 5.02, -7.18) cmH2O. The receiver operating curves showed an excellent performance for the diagnosis of diaphragm dysfunction and severe diaphragm dysfunction with respectively an area under curve of 0.90 (CI95 0.83-0.97) and 0.88 (CI95 0.82-0.95). This performance was not significantly affected by the body mass index or the presence of a neck catheter. The SONOTEPS method is a simple and accurate tool for bedside assessment of diaphragm function with ultrasound guided transcutaneous phrenic nerve stimulation in sedated patients with no or minimal spontaneous respiratory activity.

Diagnostic Value of the Voltage-to-Mass Ratio in Biopsy-Proven Cardiac Amyloidosis.

The calculation of left ventricular mass varies in different studies, and reference values of the voltage-to-mass ratio for diagnosing cardiac amyloidosis (CA) are lacking. This study aimed to determine the value of the voltage-to-mass ratio in diagnosing CA and provide an optimal cut-off value for different calculation methods. We reviewed the electrocardiograms and echocardiograms of 213 consecutive biopsy-proven CA patients, 236 hypertrophic cardiomyopathy (HCM) patients, 100 hypertensive heart disease patients, and 181 healthy controls. Left ventricular mass was calculated using linear and cross-sectional area (CSA) methods. The voltage-to-mass ratios were compared between the CA group and other groups. The voltage-to-mass ratio obtained was used to build multivariate logistic regression models that predicted the log odds of developing CA. The CA group had a significantly lower voltage-to-mass ratio than the HCM, hypertensive heart disease, and healthy control groups. The voltage-to-mass ratio was an independent factor significantly associated with the CA diagnosis after adjusting for baseline characteristics. Linear and CSA methods yielded areas under the ROC curve of 0.86 and 0.90, respectively. Using the CSA method, the optimal cut-off was 16.42 mV/mm2/m2, with 89.0% sensitivity and 80.8% specificity. The voltage-to-mass ratio could differentiate patients with CA, HCM, and hypertensive heart disease from healthy controls, potentially providing an accurate and non-invasive alternative to current expensive and invasive diagnostic techniques.

Lipid levels and insulin resistance markers in gastric cancer patients: diagnostic and prognostic significance.

Gastric cancer (GC) is a highly heterogeneous and aggressive malignant tumor that seriously affects the life safety of people all over the world. Its early manifestations are subtle. The present study aimed to investigate the clinical significance of serum lipid profiles, insulin resistance markers including the triglyceride-glucose (TyG) index and the atherosclerotic index (AI), in GC patients. A retrospective analysis encompassed 215 GC patients and 827 healthy individuals. The study results show that the total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein levels, and the TyG index of GC patients were significantly lower than those of the control group before and after propensity score matching analysis. In the GC group, the levels of CEA, CA199, CA125, and CA724 tumor markers were higher than those in the healthy control group. Patients in advanced stages exhibited lower serum levels of serum lipids and TyG index compared to those in early stages. ROC analysis revealed that the TyG index, CA125, and CA199 combination yielded the highest positive prediction rate for GC at 98.6%. TyG index is significantly associated with the risk of adverse reactions after chemotherapy (OR = 1.104, 95% CI 1.028-1.186, P < 0.01). Multiple tumor markers and the TyG index combined detection showed correlations with five adverse reactions caused by chemotherapy (r < 0.6, P < 0.05). Preoperative lipid profiles in the serum show a strong correlation with patients diagnosed with GC. Evaluating a combination of various serum lipids and cancer markers significantly improves diagnostic precision for GC and the ability to predict chemotherapy side effects.

Prediction values of fat-soluble vitamin of growth retardation in children aged 1–6 years

BackgroundRetardation among children is a persistent global health concern. Vitamin deficiency in childhood may contribute to growth retardation; however, its causal effects are not fully understood.ObjectiveHere, we aimed to explore the prediction values of fat-soluble vitamin levels on GR in children aged 1–6 years.Methods614 children aged from 1 to 6 years at Nantong Maternal and Child Care Health Hospital between January 2021 and December 2022 in this retrospective observational study participated in the assessment of developmental status and blood detection of vitamin from peripheral blood (PB). The relationship between vitamin levels and GR was analyzed by Multivariable logistic regression analysis.ResultsDevelopmental assessment results showed that 132 cases from 614 with growth retardation (21.50%). Statistical analysis showed children with GR were more likely to be males (59.45% vs. 40.55%, p = 0.191). The age of children with GR was significantly higher than those without GR (p &amp;lt; 0.01). Importantly, the levels of various fat-soluble vitamins in GR individuals were significantly lower than those in normal individuals. ROC analysis showed that vitamin E, vitamin A and 25(OH) D3 were less effective in predicting GR model (AUC: 0.87, 0.74, and 0.65,). However, the combination of vitamin E, vitamin A and 25(OH)D3 with age was effective in predicting GR. (AUC: 0.84, 0.77, 0.75).ConclusionThe combination of 25(OH)D3, Vitamin E, Vitamin A with age may have good predictive performance for children GR aged 1–6 years.

Impact of burden and distribution of aortic valve calcification on the hemodynamic performance and procedural outcomes of a self-expanding, intra-annular transcatheter aortic valve system.

Aortic valve calcification (AVC) has been explored as a powerful predictor of procedural complications in patients undergoing transcatheter aortic valve implantation (TAVI). However, little evidence exists on its impact on intra-annular devices' performance. We aimed to investigate the impact of AVC burden and distribution pattern on the occurrence of paravalvular leak (PVL), conduction disturbances requiring permanent pacemaker implantation (PPI) and 30-day clinical outcomes in patients undergoing TAVI with a self-expanding, intra-annular device. According to AVC, 103 patients enrolled in a single medical centre from November 2019 to December 2022 were divided into tertiles. Valve Academic Research Consortium (VARC)-3 definitions were used to classify procedural complications and outcomes. Patients in the highest AVC tertile showed an increased occurrence of mild or more PVL and conduction disorders (p < 0.001 and p = 0.006). AVC tertiles (highest tertile) emerged as an independent predictor of PVL (OR 7.32, 95%CI 3.10-17.28, p < 0.001) and post-TAVI conduction disturbances (OR 3.73, 95%CI 1.31-10.60, p = 0.013) but not of PPI (OR 1.44, 95%CI 0.39-5.35, p = 0.579). Considering calcium distribution, ROC analyses revealed that annular AVC but not left ventricle outflow tract (LVOT) calcium burden significantly indicated the development of PVL (AUC 0.863, 0.77-0.93, p < 0.001) and conduction disorders/PPI (AUC 0.797, 0.70-0.89, p < 0.001 and 0.723, 0.58-0.86, p = 0.018, respectively). After adjustment for age and sex, the highest tertile remained an independent predictor of the 30-day composite outcome (death, myocardial infarction, stroke, major vascular complications, type 3/4 bleedings, acute kidney injury, PPI and ≥ moderate PVL) (OR 3.26; 95%CI 1.26-8.40, p = 0.014). A higher AVC is associated with an increased risk of PVL and conduction disturbances after TAVI with a self-expanding, intra-annular device. However, our findings suggest a minor role for LVOT calcification compared with annular AVC in the performance of this specific prosthesis.

LncRNA NRIR serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by a malfunction of the body's immune defense system. The objective of the present investigation was to examine the expression and diagnostic significance of NRIR in SLE and to prove whether it is involved in the progression of SLE. The study involved 110 participants, including 55 healthy individuals and 55 SLE patients. The expression levels of NRIR, miR-31-5p, and ICAM-1 were measured using qRT-PCR. The ROC curve was performed to assess the diagnostic significance of NRIR in SLE patients. Pearson correlation analysis was utilized to explore the relationship between NRIR and other indicators. Cytokines including IL-4, IL-6, and IL-21, along with IgG levels, were assessed using ELISA. The interaction between NRIR and miR-31-5p was validated using a dual-luciferase reporter assay. Upregulated expression of NRIR was observed in individuals with SLE, serving a diagnostic function for SLE. Additionally, abnormal expression of NRIR impacted the viability of CD4+ T cells within SLE patients. NRIR could negatively modulate the expression of miR-31-5p. LncRNA NRIR may be a potential biomarker for SLE and is likely involved in the progression of SLE.

Quantifying the degree of scientific innovation breakthrough: Considering knowledge trajectory change and impact

Scientific breakthroughs have the potential to reshape the trajectory of knowledge flow and significantly impact later research. The aim of this study is to introduce the Degree of Innovation Breakthrough (DIB) metric to more accurately quantify the extent of scientific breakthroughs. The DIB metric takes into account changes in the trajectory of knowledge flow, as well as the deep and width of impact, and it modifies the traditional assumption of equal citation contributions by assigning weighted citation counts. The effectiveness of the DIB metric is assessed using ROC curves and AUC metrics, demonstrating its ability to differentiate between high and low scientific breakthroughs with high sensitivity and minimal false positives. Based on ROC curves, this study proposes a method to calculate the threshold for high scientific breakthrough, reducing subjectivity. The effectiveness of the proposed method is demonstrated through a dataset consisting of 1108 award-winning computer science papers and 9832 matched control papers, showing that the DIB metric surpasses single-dimensional metrics. The study also performs a granular analysis of the innovation breakthrough degree of non-award-winning papers, categorizing them into four types based on originality and impact through 2D histogram visualization, and suggests tailored management strategies. Through the adoption of this refined classification strategy, the management of innovation practices can be optimized, ultimately fostering the enhancement of innovative research outcomes. The quantitative tools introduced in this paper offer guidance for researchers in the fields of science intelligence mining and science trend prediction.

Android Malware Detection Using Support Vector Regression for Dynamic Feature Analysis

Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from Android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model’s superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model’s capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing Android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges.

Dietary Regulation of Lipid Metabolism in Gestational Diabetes Mellitus: Implications for Fetal Macrosomia.

The primary therapeutic approach for managing hyperglycemia today is diet therapy. Lipids are not only a source of nutrients but also play a role in initiating adipocyte differentiation in the fetus, which may explain the development of fetal macrosomia and future metabolic disorders in children born to mothers with gestational diabetes mellitus (GDM). Alterations in the maternal blood lipid profile, influenced by adherence to a healthy diet in mothers with GDM and the occurrence of fetal macrosomia, represent a complex and not fully understood process. The aim of this study was to examine the characteristics of the blood plasma lipid profile in pregnant women with GDM across all trimesters based on adherence to diet therapy. The clinical part of the study followed a case-control design, including 110 women: 80 in the control group, 20 in a GDM group adhering to the diet, and 10 in a GDM group not adhering to the diet. The laboratory part was conducted as a longitudinal dynamic study, with venous blood samples collected at three time points: 11-13, 24-26, and 30-32 weeks of pregnancy. A significant impact of diet therapy on the composition of blood lipids throughout pregnancy was demonstrated, starting as early as the first trimester. ROC analysis indicated high effectiveness of the models developed, with an AUC of 0.98 for the 30- to 32-week model and sensitivity and specificity values of 1 and 0.9, respectively. An association was found between dietary habits, maternal blood lipid composition at 32 weeks, and newborn weight. The changes in lipid profiles during macrosomia development and under diet therapy were found to be diametrically opposed, confirming at the molecular level that diet therapy can normalize not only carbohydrate metabolism but also lipid metabolism in both the mother and fetus. Based on the data obtained, it is suggested that after further validation, the developed models could be used to improve the prognosis of macrosomia by analyzing blood plasma lipid profiles at various stages of pregnancy.

AI-DPAPT: a machine learning framework for predicting PROTAC activity.

Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .

Optimal timing for video assisted thoracic surgery decortication for improved survival in chronic empyema

BackgroundVideo-assisted thoracic surgery decortication for phase 3 thoracic empyema is widely accepted, but its optimal timing has not been established. We aim to investigate and assess this timing, in terms of overall survival, for chronic empyema.MethodsTwo hundred four patients with pneumonia-caused phase 3 empyema were treated with video-assisted thoracic surgery decortication over 10-years at Changhua Christian Hospital. The 90-day post-operative survival status was analyzed, and we compared the survivor group versus the non-survivor group. A receiver operating characteristic curve was used to identify the optimal decortication timing.ResultsA comparison between survivors and non-survivors showed statistical differences among age (p=0.004), presence of cardiovascular disease (p=0.018), presence of end-stage renal disease (p=0.002), duration to surgery (p=0.013), length of intensive care unit stay (p=0.010), and overall length of hospital stay (p=0.015). ROC curve analysis determined the cut-off for video-assisted thoracic surgery decortication, based on optimal 90-day post-operative survival, to be 7.5 days after hospitalization; mortality increases threefold thereafter (14.2% vs 44.6%, p<0.001). Multivariate analysis revealed that age, end-stage renal disease, pleural effusion pH≦7.2 and duration to surgery >7.5 days negatively impacted 90-day post-operative survival.ConclusionsPatients receiving decortication surgery within 7.5 days of hospital admission had better overall survival.

Maternal Fasting Plasma Glucose, Age and Body Mass Index as Prediction of Gestational Diabetes Mellitus in Iran

Background: Gestational diabetes mellitus (GDM) is a common complication of pregnancy that can lead to adverse outcomes. In this retrospective cohort study, maternal fasting blood sugar (FBS) in the second trimester of pregnancy, body mass index (BMI), and age were assessed as potential screening indicators of later GDM. Methods: The study population included information on 4007 Iranian pregnant women documented by the Integrated Health Record System (SIB) record system (2019-2020). Results: In the adjusted analysis, FBS maintained a significant relationship with GDM (P ≤0.001). In the simple ROC analysis, the AUC (SE) of FBS for the prediction of GDM was 0.905(0.09), and considering the cut-off point as 85.95, sensitivity (Se) and specificity (Sp) were equal to 0.81 and 0.71, respectively, but by stillbirth, abortion, prematurity, neonatal weight, height, and head circumference not obtained acceptable AUC (≥.60) for detection of FBS cut-off point. The cut-off point of FBS in the presence of maternal age (AUC&gt;0.6) and BMI (AUC&gt;0.6) by GDM was 83.75(Se= 86.4%, Sp= 80.0%). Conclusion: Based on the evidence presented, maternal weight and BMI are important in predicting hyperglycemia leading to GDM. It is necessary to conduct more precise national studies to standardize the FBS cut-off point by controlling age and BMI variables.