Fully automatic mpMRI analysis using deep learning predicts peritumoral glioblastoma infiltration and subsequent recurrence.

Glioblastoma (GBM) is most aggressive and common adult brain tumor. The standard treatments typically include maximal surgical resection, followed adjuvant radiotherapy and chemotherapy. However, the efficacy of these treatment is often limited, as tumor often infiltrate into the surrounding brain tissue, often extending beyond the radiologically defined margins. This infiltration contributes to the high recurrence rate and poor prognosis associated with GBM patients, necessitating advanced methods for early and accurate detection of tumor infiltration. Despite the great promise traditional supervised machine learning shows in predicting tumor infiltration beyond resectable margins, these methods are heavily reliant on expert-drawn Regions of Interest (ROIs), which are used to construct multi-variate models of different Magnetic Resonance (MR) signal characteristics associated with tumor infiltration. This process is both time consuming and resource intensive. Addressing this limitation, our study proposes a novel integration of fully automatic methods for generating ROIs with deep learning algorithms to create predictive maps of tumor infiltration. This approach uses pre-operative multi-parametric MRI (mpMRI) scans, encompassing T1, T1Gd, T2, T2-FLAIR, and ADC sequences, to fully leverage the knowledge from previously drawn ROIs. Subsequently, a patch based Convolutional Neural Network (CNN) model is trained on these automatically generated ROIs to predict areas of potential tumor infiltration. The performance of this model was evaluated using a leave-one-out cross-validation approach. Generated predictive maps binarized for comparison against post-recurrence mpMRI scans. The model demonstrates robust predictive capability, evidenced by the average cross-validated accuracy of 0.87, specificity of 0.88, and sensitivity of 0.90. Notably, the odds ratio of 8.62 indicates that regions identified as high-risk on the predictive map were significantly more likely to exhibit tumor recurrence than low-risk regions. The proposed method demonstrates that a fully automatic mpMRI analysis using deep learning can successfully predict tumor infiltration in peritumoral region for GBM patients while bypassing the intensive requirement for expert-drawn ROIs.

Sex-specific Differences in IDH1-Wildtype Glioblastoma patients in the ReSPOND Consortium.

BACKGROUND: Understanding sex-based differences in glioblastoma patients is necessary for accurate personalized treatment planning to improve patient outcomes. PURPOSE: To investigate sex-specific differences in molecular, clinical and radiological tumor parameters, as well as survival outcomes in glioblastoma, isocitrate dehydrogenase-1 wildtype (IDH1-WT), grade 4 patients. METHODS: Retrospective data of 1832 glioblastoma, IDH1-WT patients with comprehensive information on tumor parameters was acquired from the Radiomics Signatures for Precision Oncology in Glioblastoma (ReSPOND) consortium. Data imputation was performed for missing values. Sex-based differences in tumor parameters, such as, age, molecular parameters, pre-operative KPS score, tumor volumes, epicenter and laterality were assessed through non-parametric tests. Spatial atlases were generated using pre-operative MRI maps to visualize tumor characteristics. Survival time analysis was performed through log-rank tests and Cox proportional hazard analyses. RESULTS: GBM was diagnosed at a median age of 64 years in females compared to 61.9 years in males (FDR = 0.003). Males had a higher Karnofsky Performance Score (above 80) as compared to females (60.4% females Vs 69.7% males, FDR = 0.044). Females had lower tumor volumes in enhancing (16.7 cm3 Vs. 20.6 cm3 in males, FDR = 0.001), necrotic core (6.18 cm3 Vs. 7.76 cm3 in males, FDR = 0.001) and edema regions (46.9 cm3 Vs. 59.2 cm3 in males, FDR = 0.0001). Right temporal region was the most common tumor epicenter in the overall population. Right as well as left temporal lobes were more frequently involved in males. There were no significant differences in survival outcomes and mortality ratios. Higher age, unmethylated O6-methylguanine-DNAmethyltransferase (MGMT) promoter and undergoing subtotal resection increased the mortality risk in both males and females. CONCLUSIONS: Our study demonstrates significant sex-based differences in clinical and radiological tumor parameters of glioblastoma, IDH1-WT, grade 4 patients. Sex is not an independent prognostic factor for survival outcomes and the tumor parameters influencing patient outcomes are identical for males and females. ABBREVIATIONS: IDH1-WT = isocitrate dehydrogenase-1 wildtype; MGMTp = O6-methylguanine-DNA-methyltransferase promoter; KPS = Karnofsky performance score; EOR = extent of resection; WHO = world health organization; FDR = false discovery rate.

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.

Preparation and in vitro evaluation of protective effects of Silibinin-loaded polymeric micelles on human hair against UV-B radiation.

BACKGROUND: The purpose of this study was to investigate the protective effect of Silibinin-loaded polymeric micelles from human hair against UV-B radiation. METHODS: Eight formulations with different concentrations of Silibinin, Pluronic F-127, and Labrasol-Labrafil were made by a solvent evaporation method, and the selected formulation was chosen by examining their properties like particle size and loading efficiency. Six groups of human hair, including a group that received the selected formulation, were exposed to UV-B radiation and by calculating its factors such as peak-to-valley roughness, RMS roughness, FTIR, and the amount of protein loss, the protective effect of the selected formulation was judged. RESULTS: According to the results, the loading efficiency and particle size of the selected formulation were 45.34% and 43.19 nm. The Silibinin release profile had two parts, fast and slow, which were suitable for creating a drug depot on hair. Its zeta potential also confirmed the minimum electrostatic interference between the formulation and hair surface. The zeta potential of selected formulation was -5.9 mv. Examination of AFM images showed that the selected formulation was able to prevent the increase in peak-to-valley roughness and RMS roughness caused by UV-B radiation. RMS roughness after 600 h of UV radiation in Groups 5 and 6 was significantly lower than the negative control group and the amount of this factor did not differ significantly between 0 and 600, so it can be concluded that the selected formulation containing Silibinin and the positive control group was able to prevent the increase of RMS roughness and hair destruction. In other hands, the two positive control groups and the selected formulation containing Silibinin were able to effectively reduce hair protein loss. CONCLUSION: Silibinin-loaded polymeric micelles were able to effectively protect hair from structural and chemical changes caused by UV-B radiation.

Adsorption of diazinon from aqueous solution using metal organic framework and functionalized graphene: Comparison of BBD, ANN models.

In the present study, metal organic frameworks (MOFs) and aminated graphitic carbonaceous structure (ACS-RGO) through chemical synthesis prepared by a simple precipitation method and used for diazinon removal. Several techniques such as XRD , FESEM and FTIR were applied for identification of MOF-5 and ACS-RGO. Also, response surface methodology (RSM) was employed in this work to look at the effectiveness of diazinon adsorption. To forecast pesticide removal, we applied artificial neural network (ANN) and Box-Behnken Design (BBD) models. For the ANN model, a sensitivity analysis was also performed. The effect of independent variables like solution pH, various concentrations of diazinon, MOFs and ACS-RGO adsorbent dose and contact time were assessed to find out the optimum conditions. Based on the model prediction, the optimal condition for adsorption ACS-RGO and MOF-5 were determined to be pH 6.6 and 6.6, adsorbent dose of 0.59 and 0.906 g/L, and mixing time of 52.15 and 36.96 min respectively. These conditions resulted in 96.69% and 80.62% diazinon removal using ACS-RGO and MOF-5, respectively. Isotherm studies proved the adsorption of ACS-RGO and MOF-5 following the Langmuir isotherm model for diazinon removal. Diazinon removal followed by the pseudo-second and Pseudo-first order kinetics model provides a better fit for analyzing the kinetic data associated with pesticide adsorption for ACS-RGO and MOF-5, respectively. Based on the obtained results, the predicted values for the efficiency of diazinon removal with the ANN and BBD were similar (R2=0.98). Therefore, two models were able to predict diazinon removal by ACS-RGO and MOF-5.

Effects of flaxseed supplementation on weight loss, lipid profiles, glucose, and high-sensitivity C-reactive protein in patients with coronary artery disease: A systematic review and meta-analysis of randomized controlled trials.

This meta-analysis aimed to evaluate the effects of flaxseed supplementation on weight loss, lipid profiles, high-sensitivity C-reactive protein (hs-CRP), and glucose levels in patients with coronary artery disease (CAD). A systematic search was performed using various online databases, including Scopus, PubMed, Web of Science, EMBASE, and Cochrane Library, to identify relevant randomized controlled trials (RCTs) until June 2023. To evaluate heterogeneity among the selected studies, the Q-test and I2 statistics were employed. Data were combined using either a fixed- or random-effects model and presented as a weighted mean difference (WMD) with a 95% confidence interval (CI). Of the 428 citations, six RCTs were included. The pooled results did not show significant changes in the WMD of lipid factors (high-density lipoprotein cholesterol, triglycerides (TG), low-density lipoprotein cholesterol, and total cholesterol) following flaxseed intake. However, after performing a sensitivity analysis to determine the source of heterogeneity, flaxseed supplementation resulted in a significant decrease in TG levels (WMD = -18.39 mg/dL; 95% CI: -35.02, -1.75). Moreover, no significant differences were observed in either weight or BMI following flaxseed intake. However, the circulating levels of fasting blood glucose (WMD = -8.35 mg/dL; 95% CI: -15.01, -1.69, p = .01) and hs-CRP (WMD = -1.35 mg/L; 95% CI: -1.93, -0.77, p < .01) significantly decreased after the intervention. Flaxseed supplementation was associated with lowering FBS, hs-CRP, and TG levels but did not affect weight loss parameters and other lipid markers in CAD.

Tocilizumab degradation via photo-catalytic ozonation process from aqueous.

Following the advent of the coronavirus pandemic, tocilizumab has emerged as a potentially efficacious therapeutic intervention. The utilization of O3-Heterogeneous photocatalytic process (O3-HPCP) as a hybrid advanced oxidation technique has been employed for the degradation of pollutants. The present study employed a solvothermal technique for the synthesis of the BiOI-MOF composite. The utilization of FTIR, FESEM, EDAX, XRD, UV-vis, BET, TEM, and XPS analysis was employed to confirm the exceptional quality of the catalyst. the study employed an experimental design, subsequently followed by the analysis of collected data in order to forecast the most favorable conditions. The purpose of this study was to investigate the impact of several factors, including reaction time (30-60 min), catalyst dose (0.25-0.5 mg/L), pH levels (4-8), ozone concentration (20-40 mMol/L), and tocilizumab concentration (10-20 mg/L), on the performance of O3-HPCP. The best model was discovered by evaluating the F-value and P-value coefficients, which were found to be 0.0001 and 347.93, respectively. In the given experimental conditions, which include a catalyst dose of 0.46 mg/L, a reaction time of 59 min, a pH of 7.0, and an ozone concentration of 32 mMol/L, the removal efficiencies were found to be 92% for tocilizumab, 79.8% for COD, and 59% for TOC. The obtained R2 value of 0.98 suggests a strong correlation between the observed data and the predicted values, indicating that the reaction rate followed first-order kinetics. The coefficient of synergy for the degradation of tocilizumab was shown to be 1.22. The catalyst exhibited satisfactory outcomes, but with a marginal reduction in efficacy of approximately 3%. The sulfate ion (SO42-) exhibited no influence on process efficiency, whereas the nitrate ion (NO3-) exerted the most significant impact among the anions. The progress of the process was impeded by organic scavengers, with methanol exhibiting the most pronounced influence and sodium azide exerting the least significant impact. The efficacy of pure BiOI and NH2-MIL125 (Ti) was diminished when employed in their pure form state. The energy consumption per unit of degradation, denoted as EEO, was determined to be 161.8 KWh/m3-order.