5-Oxo-dihydropyranopyran derivatives as anti-proliferative agents; synthesis, biological evaluation, molecular docking, MD simulation, DFT, and in-silico pharmacokinetic studies

A series of ethyl 2-amino-7-methyl-5-oxo-4-phenyl-4,5-dihydropyrano[4,3-b]pyran-3-carboxylate derivatives (4a-j) bearing different substitutions on the C4-phenyl ring was synthesized. The anti-proliferative activity of all the synthesized compounds was assessed against two human cancer-cell lines, including SW-480 and MCF-7, by using MTT method. Derivatives 4g, 4i, and 4j, possessing 4-NO2, 4-Cl, and 3,4,5-(OCH3)3 substitutions, were found to be the most potent compounds against both cell lines. The obtained IC50 values for 4g, 4i, and 4j were 34.6, 35.9, and 38.6 μM against SW-480 cells and 42.6, 34.2, and 26.6 μM against MCF-7 cells, respectively. Evaluation of the free radical scavenging potential of the compounds against DPPH radicals showed the highest result for compound 4j with an EC50 value of 580 μM. Molecular docking studies revealed the compounds were well accommodated within the binding site of cyclin-dependent kinase-2 (CDK2) with binding energies comparable to those of DTQ (the co-crystallized inhibitor) and BMS-265246 (a well-known CDK2 inhibitor). Molecular dynamics simulation studies confirmed the interactions and stability of the 4g-CDK2 complex. All derivatives, except 4g, were predicted to comply with the drug-likeness rules. Compound 4j may be proposed as an anti-cancer lead candidate for further studies due to the promising findings from in-silico pharmacokinetic studies, such as high GI absorption, not being a P-gp substrate, and being a P-gp inhibitor. Density functional theory (DFT) analysis was performed at the B3LYP/6-311++G (d,p) level of theory to examine the reactivity or stability descriptors of 4d, 4g, 4i, and 4j derivatives. The highest value of energy gap between HOMO and LUMO and thermochemical parameters were obtained for 4i and 4j.

Pyrano[2,3-b]chromone derivatives as novel dual inhibitors of α-glucosidase and α-amylase: Design, synthesis, biological evaluation, and in silico studies

Inhibition of α-glucosidase and α-amylase is an important target for treatment of type 2 diabetes. In this work, a novel series of pyrano[2,3-b]chromene derivatives 5a-m was designed based on potent α-glucosidase and α-amylase inhibitors and synthesized by simple chemical reactions. These compounds were evaluated against the latter enzymes. Most of the title compounds exhibited high inhibitory activity against α-glucosidase and α-amylase in comparison to standard inhibitor (acarbose). Representatively, the most potent compound, 4-methoxy derivative 5d, was 30.4 fold more potent than acarbose against α-glucosidase and 6.1 fold more potent than this drug against α-amylase. In silico molecular modeling demonstrated that compound 5d attached to the active sites of α-glucosidase and α-amylase with a favorable binding energies and established interactions with important amino acids. Dynamics of compound 5d also showed that this compound formed a stable complex with the α-glucosidase active site. In silicodrug-likeness as well as ADMET prediction of this compound was also performed and satisfactory results were obtained.

Assessing the Effectiveness of Stabilized Cysteamine 5% Cream Compared to Hydroquinone 4%/Ascorbic Acid 3% Combination Cream in Treating Acne-induced Post-inflammatory Hyperpigmentation: A Randomized, Controlled Study

Objective

Postinflammatory hyperpigmentation (PIH) is a common sequela of acne vulgaris. Topical treatment with hydroquinone is the standard treatment, but may be associated with complications. Cysteamine is a relatively safe depigmenting agent with an observed depigmenting effect. We designed this study to assess the efficacy of a cysteamine 5% cream in treating acne-induced PIH.

Methods

Twenty-eight out of 32 participants finalized this investigator-blind, randomized, and controlled trial (registered in Iranian Registry of Clinical Trials [IRCTID: IRCT20140212016557N5]). We randomized the patients to apply either cysteamine 5% or hydroquinone 4%/ascorbic acid 3% (HC) cream. Postacne hyperpigmentation index (PAHPI) and melanin index were the assessment measures after four months of treatment. We evaluated the quality of life by the Dermatology Life Quality Index (DLQI) questionnaire.

Results

Both cysteamine and HC cream significantly decreased the PAHPI score and melanin index of acne-induced PIH patients (p<0.05). The decrease in PAHPI score and melanin index were not significantly different in treatment groups after four months (p>0.05). Quality of life ameliorated significantly only with cysteamine treatment. However, no significant change in quality of life was observed between groups.

Limitations

Limitations of our study include the relatively small sample size and absence of follow-up.

Conclusion

Cysteamine cream is an effective treatment of post-acne PIH, with similar efficacy to the accepted treatment of PIH, i.e., hydroquinone cream.

The association of dietary carbohydrate quality and quantity with obesity among Iranian adolescents: a case-control study

BACKGROUND

Adolescent obesity is considered as a major health concern worldwide which is closely linked to the quality of diet. The purpose of the present study was to assess the carbohydrate quality and quantity in relation to odds of overweight and obesity in adolescents.

METHODS

This case-control study with a 1:1 ratio was conducted on 406 adolescents (14 to 18 years old) matched for age and gender. Participants were selected by multistage cluster random sampling method from March to October 2019 in Shiraz, Iran. Dietary intakes of the study population were assessed by a validated semi-quantitative food frequency questionnaire. Also anthropometric indices were measured using standard methods and demographic information was recorded via face to face interview. The relation between low carbohydrate diet score (LCDS) and carbohydrate quality index (CQI), and odds of obesity was evaluated by multiple Logistic regression.

RESULTS

After adjusting the role of potential confounders, the participants in the third tertiles of LCDS (OR = 0.443, 95% CI = (0.260 to 0.755)) and CQI (OR = 0.005, 95% CI = (0.001 to 0.025)) had less odds of being overweight and obese compared to the first tertile.

CONCLUSION

The present study found an inverse relationship between dietary quantity and quality of carbohydrate intake and the odds of overweight and obesity in a sample of Iranian adolescents.

Understanding barriers to reducing antimicrobials on Australian dairy farms: A qualitative analysis

INTRODUCTION

Reducing antibiotic use in production animal systems is one strategy which may help to limit the development of antimicrobial resistance. To reduce antimicrobial use in food-producing animals, it is important to first understand how antibiotics are used on farm and what barriers exist to decreasing their use. In dairy production systems, mastitis is one of the most common reasons for administering antimicrobials. Therefore, it is important to understand the motivations and behaviours of dairy farmers in relation to the diagnosis, treatment and prevention of mastitis.

MATERIALS AND METHODS

In this study, we interviewed a sample of dairy farmers and dairy industry professionals from the major dairying regions of eastern Australia regarding their current practices used to diagnose, treat, and control subclinical and clinical mastitis. Inductive thematic analysis was used to code interview transcripts and identify the recurrent themes.

RESULTS

Four overarching themes were identified: (1) the challenges associated with the detection and diagnosis of clinical mastitis, including with laboratory culture, (2) the motivations behind treatment decisions for different cases, (3) decisions around dry cow therapy and the role of herd recording, and (4) concerns regarding the development of antimicrobial resistance.

DISCUSSION

This study identifies several challenges which may limit the ability of Australian dairy farmers to reduce antimicrobial use on farm, such as the need for rapid and reliable diagnostic tests capable of identifying the pathogenic causes of mastitis and the difficulties associated with conducting herd recording for the implementation of selective dry cow therapy. Industry professionals were concerned that farmers were not using individual cow records to aid in treatment decisions, which could result in unnecessary antimicrobial use. The results of this study can act as the basis for future research aimed at assessing these issues across the broader Australian dairy industry.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Discovery of novel 4,5-diphenyl-imidazol-α-aminophosphonate hybrids as promising anti-diabetic agents: Design, synthesis, in vitro, and in silico enzymatic studies

Herein, a novel series of 4,5-diphenyl-imidazol-α-aminophosphonate hybrids 4a-m was designed, synthesized, and evaluated as new anti-diabetic agents. These compounds were evaluated against two important target enzymes in the diabetes treatment: α-glucosidase and α-amylase. These new compounds were synthesized in three steps and characterized by different spectroscopic techniques. The in vitro evaluations demonstrated that all the synthesized compounds 4a-m were more potent that standard inhibitor acarbose against studied enzymes. Among these compound, the most potent compound against both studied enzymes was 3-bromo derivative 4l. The latter compound with IC50 = 5.96 nM was 18-times more potent than acarbose (IC50 = 106.63 nM) against α-glucosidase. Moreover, compound 4l with IC50 = 1.62 nM was 27-times more potent than acarbose (IC50 = 44.16 nM) against α-amylase. Molecular docking analysis revealed that this compound well accommodated in the binding site of α-glucosidase and α-amylase enzymes with notably more favorable binding energy as compared to acarbose.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net

Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treatment effects and surgical cavities are not present. Thus, existing automatic segmentation models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmentation on glioma images with different phenotypic and treatment appearance. We assessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained technicians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Evaluation of Lipid Profile and Serum Adiponectin Levels in Patients with Lichen Planus

The Article Abstract is not available.  

NIMG-19. IMAGE-BASED MODELING MAP OF EDEMA IS CORRELATED WITH MULTIPLE BLOOD-BRAIN-BARRIER PERMEABILITY RELEVANT TRANSCRIPTOMIC MARKERS IN BRAIN TUMOR PATIENTS

Brain tumor associated edema is a significant cause of patient morbidity and mortality often requiring continual treatment with differing levels of success across patients. Edema is a broad term that can indicate increased local water as well as diffusely infiltrating tumor cells. Edema is visualized as hyperintense regions on T2-weighted (T2W) magnetic resonance imaging (MRI) and is typically associated with blood-brain barrier (BBB) breakdown and tumor aggressiveness. Understanding the molecular mechanisms driving imaging patterns of “edema” could provide insights into clinical imaging interpretation. We have an ongoing image-guided biopsy study that allows us to link biopsy molecular markers with locoregional MRI patterns of edema. Further, we previously developed a physics-based method to estimate edema abundance (i.e., edema map) from T2W MRIs. Our goal was to identify connections between BBB-associated molecular factors and edema abundance in brain tumors. Our cohort included 38 patients (female: 15, male: 23) with 129 image-guided biopsies (female: 62, male: 67). We correlated image-localized edema map values with the mean transcriptional frequency for 57 genes related to BBB function. Additionally, we examined correlations separately according to patient reported sex (i.e., male and female) and imaging phenotype (i.e., ENH: enhancing and NE: non-enhancing). We utilized multiple comparisons corrections with a 5% false discovery rate to determine significance. For the overall cohort, we observed significant positive correlations for the HIF1A (p&lt; 0.001) and SOX2 markers with edema. For NE samples, significant correlations included APOE (p=0.001), HIF1A (p&lt; 0.001), PIK3CA (p&lt; 0.001), PTCH1 (p&lt; 0.001), and SOX2 (p&lt; 0.001). Amongst female samples, a significant correlation with PTCH1 (p=0.002) was observed. There were no significant correlations noted for male and enhancing sub-cohorts. Significant correlations between molecular markers of BBB and edema map values could lead to clinical biomarkers for edema or tumor aggressiveness.

EPCO-12. SEX-SPECIFIC PATTERNS CONNECTING LOCOREGIONAL MRI FEATURES AND IMMUNOHISTOCHEMISTRY OF IMAGE-LOCALIZED BIOPSIES OF GLIOBLASTOMA

Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of 14 months. GBMs are challenging to treat due to their heterogeneous nature. It has also been seen that these tumors have sex differences in their cellular subtypes as well as imaging. Radiomics has the potential to provide a non-invasive, spatial understanding of genetic and epigenetic diversity in these complex tumors and to aid in treatment planning. We have an ongoing study to obtain image-localized biopsies from GBM patients, allowing us to complete radiomic analysis and make connections between immunohistochemistry (IHC) and magnetic resonance imaging (MRI) features. We sought to determine if the patterns on imaging were correlated with underlying tumor biology. We focused on immunohistochemistry (IHC) markers of key features of tumor biology including SOX2 for stem-like tumor cells, CD68 for immune response and Ki67 for proliferation kinetics. Our study included 38 patients with a total of 99 biopsies (bxs): 27 males with 77 bxs and 11 females with 22 bxs. Biopsies were sectioned and stained for the SOX2, CD68, and KI67 markers. We computed 18 first-order radiomic features at each biopsy location for patients’ multimodal MRIs: T1W, T1Gd, T2W, FLAIR, apparent diffusion coefficient, diffusion weighted imaging (DWI) and susceptibility weighted imaging. We then performed correlation analysis between each radiomic feature and marker abundance for each IHC stain. Overall, we found sex-distinct patterns connecting imaging with these IHC markers. For example, amongst female patients, DWI held more prominent correlations with SOX2 than in males. Whereas there were more correlations between CD68 IHC abundance and T1Gd imaging features in males compared to females. Taken together, the overall patterns connecting locoregional imaging features to these IHC markers showed sex-distinct patterns suggesting the potential for sex to be an important biological variable when interpreting the biology underlying imaging changes.

Effects of psychotropic switches on weight change: a prospective cohort study

Introduction

Many psychotropic drugs can induce weight gain with differences in their metabolic risk profiles (i.e. high, medium or low-risk).

Objectives

To compare the weight evolution of patients switching versus patients keeping their psychotropic drugs with different risk-profiles.

Methods

Data for patients switching or keeping the same drug were obtained from the Psyclin (from 2007 to 2015) and Psymetab (2007- 2019) cohort studies, conducted at the Lausanne University Hospital, Switzerland. Patients either switched from a high to a low-risk, a high to a medium-risk, a medium to a low-risk drug, or for a drug with the same risk category. Patients not switching either kept a high, medium or low-risk drug. The evolution of weight is currently being analyzed using a linear mixed-effect model.

Results

Preliminary results showed that switching from a high to low-risk molecule had the strongest impact on weight changes. The analysis being ongoing, the quantitative results will be presented at the congress.

Conclusions

Switching from a high-risk to a low-risk molecule is likely to have the strongest impact on weight changes.

Disclosure

No significant relationships.

Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients

Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.

The Association between Energy-Adjusted Dietary Inflammatory Index, Body Composition, and Anthropometric Indices in COVID-19-Infected Patients: A Case-Control Study in Shiraz, Iran

BACKGROUND AND AIMS

Inflammation is strongly associated with the severity and mortality rate of SARS-CoV-2 disease (COVID-19). Dietary factors have a crucial role in preventing chronic and systemic inflammation. This study aimed to evaluate the association between energy-adjusted dietary inflammatory index (E-DII) scores and body composition parameters in COVID-19-infected patients compared to noninfected controls.

METHODS

A total of 133 COVID-19-infected patients and 322 noninfected controls were selected and enrolled from the Cohort Study of Employees of Shiraz University of Medical Sciences. E-DII score was calculated based on a validated food frequency questionnaire (FFQ) and body composition was measured using In-Body 770 equipment. Logistic regression models were utilized to estimate the odds ratio (OR).

RESULTS

In the control group, the mean E-DII score was significantly lower than the case group (-2.05 vs. -0.30, P ≤ 0.001), indicating that the diet of COVID-19-infected subjects was more proinflammatory than the controls. For every 1 unit increase in E-DII score, the odds of infection with COVID-19 was nearly triple (OR: 2.86, CI: 2.30, 3.35, P ≤ 0.001). Moreover, for each unit increase in body mass index (BMI), the odds of infection to COVID-19 increased by 7% (OR: 1.07, CI: 1.01, 1.13, P = 0.02). No significant difference was observed for other anthropometric parameters.

CONCLUSION

The findings revealed that obese people and those consuming a more proinflammatory diet were more susceptible to coronavirus infection. Therefore, maintaining ideal body weight and consuming a more anti-inflammatory diet can decrease the probability of COVID-19 infection.

NIMG-40. MRI-BASED ESTIMATION OF THE ABUNDANCE OF IMMUNOHISTOCHEMISTRY MARKERS IN GBM BRAIN USING DEEP LEARNING

Glioblastoma (GBM) is a devastating primary brain tumor known for its heterogeneity with a median survival of 15 months. Clinical imaging remains the primary modality to assess brain tumor response, but it is nearly impossible to distinguish between tumor growth and treatment response. Ki67 is a marker of active cell proliferation that shows inter- and intra-patient heterogeneity and should change under many therapies. In this work, we assessed the utility of a semi-supervised deep learning approach for regionally predicting high-vs-low Ki67 in GBM patients based on MRI. We used both labeled and unlabeled datasets to train the model. Labeled data included 114 MRI-localized biopsies from 43 unique GBM patients with available immunohistochemistry Ki67 labels. Unlabeled data included nine repeat routine pretreatment paired scans of newly-diagnosed GBM patients acquired within three days. Data augmentation techniques were utilized to enhance the size of our data and increase generalizability. Data was split between training, validation, and testing sets using 65-15-20 percent ratios. Model inputs were 16x16x3 patches around biopsies on T1Gd and T2 MRIs for labeled data, and around randomly selected patches inside the T2 abnormal region for unlabeled data. The network was a 4-conv layered VGG-inspired architecture. Training objective was accurate prediction of Ki67 in labeled patches and consistency in predictions across repeat unlabeled patches. We measured final model accuracy on held-out test samples. Our promising preliminary results suggest potential for deep learning in deconvolving the spatial heterogeneity of proliferative GBM subpopulations. If successful, this model can provide a non-invasive readout of cell proliferation and reveal the effectiveness of a given cytotoxic therapy dynamically during the patient's routine follow up. Further, the spatial resolution of our approach provides insights into the intra-tumoral heterogeneity of response which can be related to heterogeneity in localization of therapies (e.g. radiation therapy, drug dose heterogeneity).

NIMG-75. ANALYZING THE INTERFACE BETWEEN MRI AND DRUG DISTRIBUTION USING ORTHOTOPIC GBM-DERIVED XENOGRAFT (PDX) MODELS

INTRODUCTION

Glioblastoma (GBM) is a diffusely invasive primary brain tumor with significant spread of tumor cells to the periphery of visible image abnormality. Enhancement of Gadolinium (Gd) contrast agent on magnetic resonance imaging (MRI) has historically been considered a confirmation of local breakdown of the blood brain barrier (BBB) and sufficient drug delivery to the bulk of tumors. In this work, we used GBM-derived xenograft (PDX) models to compare drug delivery in GBM brain for high and low BBB-permeable drugs.

MATERIALS AND METHODS

Five patient-derived orthotopic xenograft models from two GBM cell lines (GBM39 and GBM12) were co-dosed with erlotinib and osimertinib, two drugs with low and high BBB-permeability, respectively. T1Gd and T2-weighted MRIs were acquired from all animals prior to model sacrifice. Tumors were manually segmented on denoised and standardized MRIs and intensity patterns were captured using first and second order statistical features in the moving 3x3 kernel. We compared drug levels found in Matrix Assisted Laser Desorption Ionization (MALDI) in T1Gd enhancement, T2 enhancement, and normal brain. We also performed linear regression modeling to predict drug levels using MRI features. Model performance was measured using root mean squared error (RMSE).

RESULTS

Our analysis showed correlations between imaging features and MALDI drug levels. Osimertinib had a uniform distribution across the brain for all animals and all cell lines, consistent with our expectation for a high BBB-penetrant drug. Erlotinib showed the highest drug levels in T2 for GBM39 and in T1Gd for GBM12. Regression models showed promising results for predicting Erlotinib with a low RMSE of 0.037.

CONCLUSION

Our preliminary results suggest MRI can be predictive of drug levels for low-BBB penetrant drugs. Understanding the relationship between MRIs and drug distribution in diffuse tumors can be beneficial to developing effective treatment.

Benzylidene-6-hydroxy-3,4-dihydronaphthalenone chalconoids as potent tyrosinase inhibitors

Background and purpose:

Tyrosinase enzyme has a key role in melanin biosynthesis by converting tyrosine into dopaquinone. It also participates in the enzymatic browning of vegetables by polyphenol oxidation. Therefore, tyrosinase inhibitors are useful in the fields of medicine, cosmetics, and agriculture. Many tyrosinase inhibitors having drawbacks have been reported to date; so, finding new inhibitors is a great need.

Experimental approach:

A variety of 6-hydroxy-3,4-dihydronaphthalenone chalcone-like analogs (C1-C10) have been synthesized by aldol condensation of 6-hydroxy tetralone and differently substituted benzaldehydes. The compounds were evaluated for their inhibitory effect on mushroom tyrosinase by a spectrophotometric method. Moreover, the inhibition manner of the most active compound was determined by Lineweaver-Burk plots. Docking study was done using AutoDock 4.2. The drug-likeness scores and ADME features of the active derivatives were also predicted.

Results/Findings:

Most of the compounds showed remarkable inhibitory activity against the tyrosinase enzyme. 6-Hydroxy-2-(3,4,5-trimethoxybenzylidene)-3,4-dihydronaphthalen-1(2H)-one (C2) was the most potent derivative amongst the series with an IC₅₀ value of 8.8 μM which was slightly more favorable to that of the reference kojic acid (IC₅₀ = 9.7 μM). Inhibitory kinetic studies revealed that C2 behaves as a competitive inhibitor. According to the docking results, compound C2 formed the most stable enzyme-inhibitor complex, mainly via establishing interactions with the two copper ions in the active site. In silico drug-likeness and pharmacokinetics predictions for the proposed tyrosinase inhibitors revealed that most of the compounds including C2 have proper drug-likeness scores and pharmacokinetic properties.

Conclusion and implications:

Therefore, C2 could be suggested as a promising tyrosinase inhibitor that might be a good lead compound in medicine, cosmetics, and the food industry, and further drug development of this compound might be of great interest.

Wooden hoof blocks: are we using the right wood?

AIMS

To investigate the association between the density of wooden hoof blocks and resistance to wear in pasture-based dairy herds, and to assess the density of commercially available wooden hoof blocks.

METHODS

Three types of wooden hoof blocks with different densities (low, medium and high) were attached to 36 lactating dairy cows with parity ≤2 and sound locomotion (score ≤2 on a scale of 1-4). The height of wooden blocks was measured in three different regions, front, abaxial and caudal on Days 7, 11, 14, 18, 21, 25 and 28 after application. Due to the loss of low-density wooden blocks, the data for these blocks were analysed for only two measurements on Days 7 and 11. The data for medium and high-density wooden blocks were analysed from Days 7-25. A linear mixed model with repeated measures was used to analyse the repeated observations. Height, density and surface area of commercially available hoof blocks (n = 19) were measured and compared to the blocks used in this study.

RESULTS

The magnitude of wear, in the front and the abaxial point of the blocks were greater in blocks made of low-density wood compared to those made of medium and high-density wood (p < 0.001). The amount of wear increased over time for all groups (p < 0.001). Wood density was negatively associated with wear and loss. Measurements of commercial wooden blocks revealed that the 13/19 (63%) had lower density and 12/19 (68%) less surface area than the wooden blocks with medium density used in this study.

CONCLUSION

In this study, the density of the wood was significantly associated with the longevity of hoof blocks when applied to hooves of pasture-based dairy cows.

CLINICAL RELEVANCE

The longevity of the wooden hoof blocks applied to treat lame cows plays a significant role in the healing of the claw horn lesions. The density of a wooden hoof block affects the rate of wear of the block, and this should be considered by manufacturers and those treating lame cows.

Soy intake is associated with lowering blood pressure in adults: A systematic review and meta-analysis of randomized double-blind placebo-controlled trials

BACKGROUND

Soy has several beneficial effects on cardiovascular disease (CVD); however, results of clinical trial studies are equivocal. Thus, the present study sought to discern the efficacy of soy intake on blood pressure.

METHODS

The search process was conducted in PubMed, Scopus, Web of Science, and Cochrane Library, to ascertain studies investigating the efficacy of soy intake on blood pressure in adults, published up to June 2020. A random-effects model was applied to pool mean difference and 95 % confidence interval (CI). Begg's and Egger's methods were conducted to assess publication bias.

RESULTS

Pooled effects from 17 effect sizes revealed a significant improvement in systolic blood pressure (SBP) (-1.70; -3.34 to -0.06 mmHg; I² = 45.4 %) and diastolic blood pressure (DBP) (-1.27; -2.36 to -0.19 mmHg, I² = 43.9 %) following soy consumption, in comparison with controls. Subgroup analysis demonstrated a reduction in both SBP and DBP in younger participants with lower baseline DBP and intervention durations of <16 weeks.

CONCLUSION

Our results suggest that soy intake is associated with an ameliorating effect on blood pressure in adults.

Novel Cytotoxic Phenanthro-triazine-3-thiol Derivatives as Potential DNA Intercalators and Bcl-2 Inhibitors

Novel phenanthro-triazine-3-thiol derivatives were designed as potential DNA intercalators and Bcl-2 inhibitors. After being synthesized, the compounds were evaluated for their cytotoxic activity against MOLT-4 (human acute lymphoblastic leukemia) and MCF-7 (human breast adenocarcinoma) cells by MTT assay. P1 (bearing hydrogen substitution) was the most potent derivative against MOLT-4 with an IC₅₀ value of 7.1 ± 1.1 μM, whereas P11 (bearing phenyl substitution) demonstrated considerable cytotoxicity against MCF-7 with an IC₅₀ value of 15.4 ± 2.9 μM. Compounds P7, P8, P14 and P15 exhibited moderate cytotoxic effects. Furthermore, to confirm the potential DNA intercalation and Bcl-2 inhibitory activities of phenanthro-triazine scaffolds, molecular docking analysis was performed. Molecular docking studies indicated that these compounds not only bind to DNA by intercalation mainly through stacking interactions but also are well accommodated in the active site of Bcl-2. Therefore, P1 and P11 having phenanthro-triazine-3-thiol scaffold could be presented as cytotoxic agents with dual DNA intercalation and Bcl-2 inhibitory activities.

Anti-Toxoplasma gondii activity of 5-oxo-hexahydroquinoline derivatives: synthesis, in vitro and in vivo evaluations, and molecular docking analysis

Background and purpose

The aim of this study was to evaluate the in vitro and in vivo anti-Toxoplasma gondii (T. gondii) effect of 5-oxo-hexahydroquinoline compounds. Moreover, molecular docking study of the compounds into the active site of enoyl-acyl carrier protein reductase (ENR) as a necessary enzyme for the vitality of apicoplast was carried out.

Experimental approach

A number of 5-oxo-hexahydoquinoline derivatives (Z1-Z4) were synthesized. The T. gondii tachyzoites of RH strain were treated by different concentrations (1-64 μg/mL) of the compounds. The viability of the encountered parasites with compounds was assessed using flow cytometry and propidium iodide (PI) staining. Due to the high mortality effect of Z3 and Z4 in vitro, their chemotherapy effect was assessed by inoculation of tachyzoites to four BALB/c mice groups (n = 5), followed by the gavage of various concentrations of the compounds to the mice. Molecular docking was done to study the binding affinity of the synthesized 5-oxo-hexahydroquinolines into ENR enzyme active site byusing AutoDock Vina® software. Docking was performed by a Lamarckian Genetic Algorithm with 100 runs.

Findings / Results

Flow cytometry assay results indicated compounds Z3 and Z4 had relevant mortality effect on parasite tachyzoites. Besides, in vivo experiments were also performed and a partial increase of mice longevity between control and experiment groups was recorded. Molecular docking of Z3 and Z4 in the binding site of ENR enzyme indicated that the compounds were well accommodated within the binding site. Therefore, it could be suggested that these compounds may exert their anti-T. gondii activity through the inhibition of the ENR enzyme.

Conclusion and implications

Compounds Z3 and Z4 are good leads in order to develop better anti-T. gondii agents as they demonstrated both in vitro and in vivo inhibitory effects on tachyzoites viability and infection. Further studies on altering the route of administration along with additional pharmacokinetics evaluations are needed to improve the anti-T. gondii impacts of 5-oxo-hexahydroquinoline compounds.

Effect of plasma-induced oxidative stress on the glycolysis pathway of Escherichia coli

Antibiotic resistance is one of the world's most urgent public health problems. Due to its antibacterial properties, cold atmospheric plasma (CAP) may serve as an alternative method to antibiotics. It is claimed that oxidative stress caused by CAP is the main reason of bacteria inactivation. In this work, we computationally investigated the effect of plasma-induced oxidation on various glycolysis metabolites, by monitoring the production of the biomass. We observed that in addition to the significant reduction in biomass production, the rate of some reactions has increased. These reactions produce anti-oxidant products, showing the bacterial defense mechanism to escape the oxidative damage. Nevertheless, the simulations show that the plasma-induced oxidation effect is much stronger than the defense mechanism, causing killing of the bacteria.

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations.

Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap).

Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p<0.00007). The DL method achieved a mean Dice coefficient of 0.87 on the test cases.

Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its “human teachers” and produced output that was better, on average, than its training data.

1,2,3-Triazole-linked 5-benzylidene (thio)barbiturates as novel tyrosinase inhibitors and free-radical scavengers

In this study, benzyl-1,2,3-triazole-linked 5-benzylidene (thio)barbiturate derivatives 7a-d and 8a-h were designed as potential tyrosinase inhibitors and free-radical scavengers. The twelve derivatives were synthesized via the [3+2] cycloaddition reaction of the corresponding benzyl azide as a dipole and the corresponding alkyne as a dipolarophile in the presence of copper(I) species, generated in situ from copper(II)/ascorbate. The thiobarbiturate derivative 8h and the barbiturate derivative 8b bearing 4-fluoro and 4-bromo groups on the benzyl-triazole moiety were found to be the most potent tyrosinase inhibitors with IC₅₀ values of 24.6 ± 0.9 and 26.8 ± 0.8 μM, respectively. Almost all the compounds showed a good radical scavenging activity with EC₅₀ values in the range of 29.9-324.9 μM. Derivatives 7a, 8f, and 8h were the most potent free-radical scavengers with EC₅₀ values of 29.9 ± 0.8, 36.8 ± 0.9, and 39.2 ± 1.1 μM, respectively. The kinetic analysis revealed that compound 8h was a mixed-type tyrosinase inhibitor. The molecular docking analysis indicated that 8b and 8h were well accommodated in the active site of the tyrosinase enzyme and possessed the most negative binding energy values of -8.55 and -8.81 kcal/mol, respectively. Moreover, it was found that the two residues, Asn81 and Glu322, played a significant role in forming stable enzyme-inhibitor complexes.

Linear body measurements and productivity of subtropical Holstein-Friesian dairy calves

OBJECTIVE

The study aimed to describe growth parameters and to quantify the association between linear body measurements as predictors of liveweight (LW) of Holstein-Friesian (HF), and HF crossbred dairy calves in Queensland. A secondary objective was to quantify the effect of disease events on LW change.

STUDY DESIGN

Longitudinal study.

METHODS

Fortnightly LW, hip height (HH), hip width (HW) and heart girth (HG) measurements were recorded from 16 male and 28 female HF calves from birth until weaning. The association between linear body measurement and the effect of a disease event on LW change were explored using linear mixed-effects modelling with random intercepts and random slopes.

RESULTS

HG was the best body measure used individually as a predictor of calf LW (R2 = 82%; P < 0.001), while the combined use of HG, HW and HH was the most accurate predictor of calf LW between birth and weaning (R2 = 90%; P < 0.001). HW, average feed intake and total feed intake were significantly affected by disease events (P < 0.05). On average, total average LW loss associated with a single pneumonia event was estimated at 14.6 kg (95% CI = 10.5 to 18.7 kg; P < 0.05).

CONCLUSIONS

Calves of this study performed at a level consistent with the previously published reports. Growth performance was significantly compromised by pneumonia. HW was found to be the least predictive individual measure, and the combined use of HH, HW and HG had the most accurate prediction of calf liveweight from birth to weaning.

Farmers' perceptions and approaches to detection, treatment and prevention of lameness in pasture-based dairy herds in New South Wales, Australia

AIMS

The objective of this study was to investigate farmers' perception of lameness in comparison to the estimated prevalence of lameness in NSW pasture-based dairies to evaluate farmers' perceptions and approaches to detection, treatment and prevention of lameness.

METHODS

Across-sectional study was conducted on 62 pasture-based dairy farms across NSW, Australia. The prevalence of lameness in these farms was estimated using locomotion scoring (1-4 scale). A survey was also conducted, using a questionnaire and face-to-face interview, to explore farmers' perceived prevalence of lameness and approaches to treatment and prevention.

RESULTS

The prevalence of lameness estimated by farmers was 3.7 times less (mean: 5%; range 0% to 26%) than that determined by locomotion scoring (mean: 19.1%; range 5.0%-44.5%). Approaches to treatment included antimicrobial therapy, hoof inspection with or without application of wooden blocks. In 28% of the farms, the lame cows were managed by farmers or farm staff with no official training in treatment of lame cows. The mean interval from detection of lameness to examination of the affected hoof was almost 55 hours (range 2-720 hours). A very low percentage of farms kept lameness records or implemented lameness preventive strategies such as footbaths and prophylactic foot trimming.

CONCLUSIONS

Farmers and farm managers were found to underestimate the prevalence of lameness which could be due to the low level of awareness and can contribute to subsequent lack of implementation of prophylactic procedures and preventive management strategies for lameness. These findings accentuate the need to improve farmers' ability to detect lame cows and to emphasise the importance of recording in order to facilitate the management of lameness in dairy herds.

Evaluating prevalence of depression, anxiety and hopelessness in patients with Vitiligo on an Iranian population

INTRODUCTION

Vitiligo is caused by partial or complete destruction of melanocytes in the affected skin area and influences the patient's quality of life. Besides physical involvement, vitiligo patients experience a high level of stress. Depression and Anxiety are common psychiatric disorders in vitiligo patients.

AIM

This study, as the first study, evaluates hopelessness, anxiety, depression and general health of vitiligo patients in comparison with normal controls in an Iranian population.

METHOD

Hundred patients with vitiligo and hundred healthy controls were examined. General health, depression, hopelessness and anxiety were evaluated based on general health questionnaire. Anxiety, depression and hopelessness levels were analyzed using Chi-Square, and the mean value of general health was evaluated through t-test.

RESULTS

The results showed that anxiety and hopelessness levels were significantly higher in vitiligo patients than those who are in healthy controls. This significant difference refers to high levels of anxiety and hopelessness among women with vitiligo. It was also found that the single patients were more anxious, hopeless and depressive, while the married patients were only more anxious and hopeless than those who are in the control group, respectively. General health of patients was significantly worse than in healthy controls. The low level of general health in patients was related to poorer level of general health among women with vitiligo.

CONCLUSION

It seems that women with vitiligo are more mentally stressed than men with vitiligo. Both singles and married vitiligo patients suffer from anxiety and hopelessness.

P1759 How an echocariologist can work off-line in a personal computer on the echocardiography images at the level of mitral valve?

Background

The objective of this presentation is to provide an educational survey on the mitral valve geometrical indices measurements using MATLAB software offline in a personal computer as an echo desk for further information and recommendation before mitral valve repairs or the other treatments.

Methods and Results:

Utilizing mathematical equation and MATLAB software (a simple echo desk) to measure the geometrical mitral valve variables offline based on 2D or 3D conventional echocardiographic images.

MATLAB software as an echocardiography desk

Open the MATLAB software. Write down the command pic = imread(‘’) in the command window. You can use this command to read an echocardiographic image from your computer and in a variable like a put. You must write the image address, image name and photo extensions in parentheses. For example, your echocardiographic image is in the following address: C:\Users\USER\Desktop\echo image 1.jpg. You should write the above command like this: pic = imread(‘C:\Users\USER\Desktop\echo image 1.jpg’); Finally, call that image by imshow(pic) command and then save the program and click the RUN bottom. You will see that a new window is opened and the image you are looking for is displayed. You can edit or measure the distances of the image using the menus above this observed image (Figure A). You can also extract frames from an echocardiography video clip of a patient by writing down the command Avi2frame (‘1.avi’, ‘OutputFolder’,’jpg’) in the command window and then you can work offline on echocardiography images frame by frame to calculate quantitative data (Figures B and C).

Conclusion

In spite of the introduced methods applied for echocardiographic datasets at the level of mitral valve for practical applications and clinical verifications. The most important applications, however, may be pedagogical. A computer desk (online or offline) can help an Echocardiologist to work on the echocardiographic images to evaluate the data.

Abstract P1759 Figure. working offline on echo images

Analytical methodologies for determination of methotrexate and its metabolites in pharmaceutical, biological and environmental samples

Methotrexate (MTX) is a folate antagonist drug used for several diseases, such as cancers, various malignancies, rheumatoid arthritis (RA) and inflammatory bowel disease. Due to its structural features, including the presence of two carboxylic acid groups and its low native fluorescence, there are some challenges to develop analytical methods for its determination. MTX is metabolized to 7-hydroxymethotrexate (7-OH-MTX), 2,4-diamino-N10-methylpteroic acid (DAMPA), and the active MTX polyglutamates (MTXPGs) in the liver, intestine, and red blood cells (RBCs), respectively. Additionally, the drug has a narrow therapeutic range; hence, its therapeutic drug monitoring (TDM) is necessary to regulate the pharmacokinetics of the drug and to decrease the risk of toxicity. Due to environmental toxicity of MTX; its sensitive, fast and low cost determination in workplace environments is of great interest. A large number of methodologies including high performance liquid chromatography equipped with UV-visible, fluorescence, or electrochemical detection, liquid chromatography-mass spectroscopy, capillary electrophoresis, UV-visible spectrophotometry, and electrochemical methods have been developed for the quantitation of MTX and its metabolites in pharmaceutical, biological, and environmental samples. This paper will attempt to review several published methodologies and the instrumental conditions, which have been applied to measure MTX and its metabolites within the last decade.

NIMG-64. IMPACT OF TUMOR LOCATION ON IMAGE-DERIVED VOLUME, PROLIFERATION RATE AND GROWTH VELOCITY IN GLIOBLASTOMA PATIENTS

INTRODUCTION

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults with a median overall survival (OS) of 15months. Despite advancements in treatments, prognosis is dismal and the prognostic significance of tumor location is not entirely understood. METHODOLOGY: In our study, we investigated sex-specific volumetric, tumor growth kinetics, and outcome differences among GBMs in various brain locations. Primary GBM patients with pretreatment magnetic resonance imaging (MRI) data (N=289, 173 males, 116 females) were selected from our brain tumor repository. Tumor abnormality was segmented on T1-weighted post-gadolinium contrast agent (T1Gd) MRIs. We utilized the Harvard-Oxford brain atlases to determine the location of GBMs.

RESULTS

Overall, our study found smaller tumors in the left hemisphere. This may be expected as left-hemispheric GBM symptoms could present earlier, leading to earlier diagnosis and treatment. However, when the cohort was split by sex, we found this observation significant for females only in the parietal lobe (p < 0.0001). Further, female GBMs demonstrated smaller necrotic volume in the left hemisphere (p = 0.030). Sex-specific differences in incidence were noted in the temporal and occipital lobes (2M:1F). Comparing tumor growth kinetics in different brain locations and hemispheres, females had significantly lower tumor proliferation rates in the left hemisphere (p = 0.009) and lower tumor proliferation rates in the left frontal lobe (p = 0.031). Controlling for treatment, patients with frontal lobe tumors had significantly longer OS compared to those with GBMs in the temporal lobe (p = 0.046, 312 days). Differences in growth velocities were noted between frontal and parietal lobe with frontal GBMs having lower velocities in comparison to parietal lobe GBMs.

CONCLUSION

Together, our results demonstrate that tumor growth and proliferation rates may vary based on location and sex. Additional research is needed to further explore the clinical significance of tumor location.

NIMG-37. PREDICTING SEIZURE IN GLIOMA PATIENTS USING A RANDOM FOREST CLASSIFIER TRAINED ON SEX-SPECIFIC AND MIXED COHORTS

PURPOSE

Brain tumor related epilepsy (BTE) is a major co-morbidity in patients with glioma. It is difficult to determine whether the use of anti-epileptic drugs is necessary. We attempted to build a machine-learning model to predict the probability of seizure presentation (SP) with glioma.

METHODS

We trained a random forest classifier using the following variables: volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log(ρ)), invasiveness (log(D)) and their relative ratio (log(ρ/D)). Our cohort consisted of 221 patients total. Using an 80-20 ratio, we used 176 patients (76 SP, 100 nSP) for training and the remaining 45 patients (19 SP, 26 nSP) were used for testing. We also trained on male-only and female-only cohorts to evaluate any sex differences in prediction. For training, 108 males (53 SP, 55 nSP) were used and 28 for testing (14 SP, 14 nSP). We used 72 females (21 SP, 49 nSP) for training and 15 (7 SP, 8 nSP) for testing. We corrected for class imbalance in the female cohort before training. Using 10-fold cross-validation and a separate testing set, we measured performance by ROC curve (AUC), accuracy, sensitivity, and specificity of predictions (average of folds in cross validation).

RESULTS

The female model achieved the highest AUC (0.853) followed by the mixed model (0.726) and the male model (0.651). In the validation set, the accuracy/sensitivity/specificity of the three cohorts were as follows: mixed (0.726/0.696/0.750), female (0.853/0.830/0.875), and male (0.651/0.577/0.722). The performance of the testing set, in terms of accuracy/sensitivity/specificity were: mixed (0.733/0.74/0.73), female (0.8/0.57/1), and male (0.714/0.64/0.79).

CONCLUSION

We found a negative correlation between seizure probability and size and invasiveness of tumors. Our model shows promising performance on testing set data. Further cohort studies and training is warranted.

NIMG-39. REVEALING THE TUMOR-IMMUNE LANDSCAPE THROUGH SPATIALLY-RESOLVED RADIOMICS: CASE STUDIES

BACKGROUND

Conventional magnetic resonance imaging (MR) guides patient care in GBM. However, there is mounting awareness that MR enhancement is non-specific reflecting either tumor progression or non-tumoral inflammatory changes. Histological evaluation of GBM is held as the gold standard for disease assessment. However, the invasiveness of this methodology and the sample sparsity limit its usefulness. Methods to infer histological underpinnings of MRI are needed to improve clinical care.

METHODS

A transfer learning mixed effects model based on T1Gd and FLAIR MR voxel based image features trained and cross-validated to predict FPKM values of CD68, CASP3, CD8A and IL13RA2. Training data included RNAseq from 38 image-localized biopsies from 15 newly-diagnosed GBM patients. These models were then applied to two independent patients, chosen based on therapy and the quality of image registration, at three different time points, just prior to receiving IL13RA2 targeted CAR-T therapy for rGBM, after 2 cycles and after 4 cycles, resulting in a voxel-based prediction of the relative expression levels of these genes.

RESULTS

Cross-validation of the proposed machine learning models demonstrated a Pearson Correlation coefficient between predicted and observed FPKM values of 0.89 (CD68), 0.92 (CASP3), 0.93 (CD8A), and 0.88 (IL13RA2). When the models were applied to the two CAR-T patients, CD68-modeled expression levels were seen to increase throughout therapy for one patient, while remaining stable for the other. Survival from first CAR-T infusion was, respectively, 412 and 83 days suggesting that the increased CD68 was indicative of therapeutic effectivity. Further, the spatial activity predictions were consistent with expected therapeutic action as the correlation coefficient between CASP3 and the product of IL13R2 and CD8A expression was 0.81, suggesting the T-cells targeting IL13RA2 were inducing cell death.

CONCLUSIONS

Preliminary results with our model highlights the potential of spatially resolved radiomic maps to provide insight into regional therapeutic effectivity.

NIMG-71. DETECTION OF CYSTIC GLIOBLASTOMA FROM MAGNETIC RESONANCE IMAGING USING DEEP LEARNING TECHNIQUES

Glioblastoma (GBM) is the most aggressive primary brain tumor with an average survival of 15 months with standard of care treatment. GBM patients typically present with necrosis surrounded by enhancement on T1-weighted post gadolinium magnetic resonance imaging (T1gd MRI), however some patients present with a significant cystic component. Cysts are caused by different underlying biological mechanisms to necrosis and are important to identify for future clinical investigations. These cystic components can be manually identified through MRI but this process can be time consuming for large patient cohorts. Over the last two decades, our lab has collected serial MRI data of brain tumor patients. With over 70,000 images now in the database and that number increasing daily, it is clear that we have a unique resource for clinical investigation and a need to automate this process. To this end, the aim of this work was to develop and assess the performance of a convolution neural network (CNN) model for automatic detection of cystic GBMs. In this retrospective IRB-approved work, we collected pretreatment MRIs of a patient cohort consisting of 85 patients with a significant cystic component at presentation along with 400 non-cystic GBM, both identified manually through MRI. Image slices with a view of the cystic component were used as positive samples for training. Data were randomly split into training, validation, and test sets using a 70:15:15 ratio. The proportion of positive to negative cases was comparable between sets. Prior to training, we used image augmentation techniques to compensate for the class imbalance in our data. Our results showed that deep learning networks can automatically detect cystic GBMs on MRIs with high accuracy and thus illustrates the potential use of this technique in clinically relevant settings.

Unsymmetric dihydropyridines bearing 2-pyridyl methyl carboxylate as modulators of P-glycoprotein; synthesis and biological evaluation in resistant and non-resistant cancer cells

Multi-drug resistance (MDR) in cancer cells is often associated with overexpression of P-glycoprotein (P-gp or ABCB1 or MDR1); therefore, modulators of this transporter might be helpful in overcoming MDR. In this study, 16 novel unsymmetrical dihydropyridine (DHP) derivatives bearing 2-pyridyl methyl carboxylate at C3 and a nitroimidazole or nitrophenyl ring at C4 positions of the DHP ring were synthesized. Their cytotoxicity was tested against four human cancer cells by MTT assay. The reversal capacity of MDR was examined in P-gp overexpressing cells (MES-SA/DX5) by measuring the alteration of doxorubicin’s IC50 and performing flow cytometric determination of intracellular rhodamine 123 accumulation. The calcium channel blocking (CCB) activity, as a side effect of DHPs, was tested on the ileum of a guinea pig. Molecular docking was performed to explain the binding mode of compounds. Two derivatives, 4a and 4c, containing 4-nitrophenyl at C4 and possessing methyl (4a) and iso-propyl (4c) carboxylates at the C5 position of DHP core demonstrated superior cytotoxic and MDR reversal activities and lower CCB effect. Docking analysis confirmed the importance of the 4-nitrophenyl ring for P-gp inhibitory activity. Some of the synthesized DHP derivatives with considerable MDR reversal capacity could be promising compounds for further discovery of useful agents for management of drug resistant cancer.

Brain MR Radiomics to Differentiate Cognitive Disorders

OBJECTIVE

Subtle and gradual changes occur in the brain years before cognitive impairment due to age-related neurodegenerative disorders. The authors examined the utility of hippocampal texture analysis and volumetric features extracted from brain magnetic resonance (MR) data to differentiate between three cognitive groups (cognitively normal individuals, individuals with mild cognitive impairment, and individuals with Alzheimer's disease) and neuropsychological scores on the Clinical Dementia Rating (CDR) scale.

METHODS

Data from 173 unique patients with 3-T T₁-weighted MR images from the Alzheimer's Disease Neuroimaging Initiative database were analyzed. A variety of texture and volumetric features were extracted from bilateral hippocampal regions and were used to perform binary classification of cognitive groups and CDR scores. The authors used diagonal quadratic discriminant analysis in a leave-one-out cross-validation scheme. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to assess the performance of models.

RESULTS

The results show promise for hippocampal texture analysis to distinguish between no impairment and early stages of impairment. Volumetric features were more successful at differentiating between no impairment and advanced stages of impairment.

CONCLUSIONS

MR radiomics may be a promising tool to classify various cognitive groups.