Integrative Bayesian models using Post-selective inference: A case study in radiogenomics.

Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection of these associations with accuracy. In this paper, we develop selection-aware Bayesian methods, which (1) counteract the impact of model selection bias through a "selection-aware posterior" in a flexible class of integrative Bayesian models post a selection of promising variables via ℓ1 -regularized algorithms; (2) strike an inevitable trade-off between the quality of model selection and inferential power when the same data set is used for both selection and uncertainty estimation. Central to our methodological development, a carefully constructed conditional likelihood function deployed with a reparameterization mapping provides tractable updates when gradient-based Markov chain Monte Carlo (MCMC) sampling is used for estimating uncertainties from the selection-aware posterior. Applying our methods to a radiogenomic analysis, we successfully recover several important gene pathways and estimate uncertainties for their associations with patient survival times.

Comprehensive genome-wide analysis of calmodulin-binding transcription activator (CAMTA) in Durio zibethinus and identification of fruit ripening-associated DzCAMTAs.

BACKGROUND: Fruit ripening is an intricate developmental process driven by a highly coordinated action of complex hormonal networks. Ethylene is considered as the main phytohormone that regulates the ripening of climacteric fruits. Concomitantly, several ethylene-responsive transcription factors (TFs) are pivotal components of the regulatory network underlying fruit ripening. Calmodulin-binding transcription activator (CAMTA) is one such ethylene-induced TF implicated in various stress and plant developmental processes. RESULTS: Our comprehensive analysis of the CAMTA gene family in Durio zibethinus (durian, Dz) identified 10 CAMTAs with conserved domains. Phylogenetic analysis of DzCAMTAs, positioned DzCAMTA3 with its tomato ortholog that has already been validated for its role in the fruit ripening process through ethylene-mediated signaling. Furthermore, the transcriptome-wide analysis revealed DzCAMTA3 and DzCAMTA8 as the highest expressing durian CAMTA genes. These two DzCAMTAs possessed a distinct ripening-associated expression pattern during post-harvest ripening in Monthong, a durian cultivar native to Thailand. The expression profiling of DzCAMTA3 and DzCAMTA8 under natural ripening conditions and ethylene-induced/delayed ripening conditions substantiated their roles as ethylene-induced transcriptional activators of ripening. Similarly, auxin-suppressed expression of DzCAMTA3 and DzCAMTA8 confirmed their responsiveness to exogenous auxin treatment in a time-dependent manner. Accordingly, we propose that DzCAMTA3 and DzCAMTA8 synergistically crosstalk with ethylene during durian fruit ripening. In contrast, DzCAMTA3 and DzCAMTA8 antagonistically with auxin could affect the post-harvest ripening process in durian. Furthermore, DzCAMTA3 and DzCAMTA8 interacting genes contain significant CAMTA recognition motifs and regulated several pivotal fruit-ripening-associated pathways. CONCLUSION: Taken together, the present study contributes to an in-depth understanding of the structure and probable function of CAMTA genes in the post-harvest ripening of durian.

Solvent based optimization for extraction and stability of thymoquinone from Nigella sativa Linn. and its quantification using RP-HPLC.

The Nigella sativa pharmacological properties are mainly ascribed to its volatile oil, of which thymoquinone is an important bioactive component. Surprisingly, till date, no standard formulation or thymoquinone rich N. sativa extract is under clinical use probably due to its poor extraction and lesser stability in the already used solvents. In the present investigation solubility, extraction, percent composition and total antioxidant activity from the seeds of N. sativa was explored using five solvents. An HPLC method was standardized in an isocratic system (C-18 column, flow rate of 1.0 ml/min, mobile phase-water:methanol: 30:70, detection wavelength-254 nm, retention time-8.77 min) for quantification of thymoquinone. To further confirm the presence of thymoquinone in the respective extracts absorbance spectra analysis has been carried out and compared with pure thymoquinone. Additionally total antioxidant activity of Nigella sativa extracts has been evaluated using ascorbic acid as standard. Our results showed maximum percentage yield in aqueous extract while methanol having the least yield and the ethanol, benzene and hexane extracts exhibited moderate yields. A linear standard calibration curve of thymoquinone showed R2 as 0.999 and % RSD as 7.166. The HPLC analysis revealed maximum percentage composition of thymoquinone in the benzene extract, whereas in the hexane and methanol extracts the content was less. Aqueous and ethanol extracts displayed insignificant thymoquinone content. Absorbance spectra analysis confirms the presence of thymoquinone peak in the benzene, hexane and methanol extracts while aqueous and ethanol extracts showed minimal absorbance. Maximum total antioxidant activity was observed in the aqueous extract while minimum was observed in the methanolic extract. Weak positive (+ 0.3676) correlation was established between percent composition of thymoquinone and antioxidant activity among different extracts indicating that thymoquinone may not be the only factor for antioxidant activity, but other phytochemicals might also contribute. However, we for the first time demonstrated that the benzene extract of N. sativa has better solubility and percent composition of thymoquinone as compared to other solvents. It can be concluded that the solubility, differential composition of bioactive components among these extracts may have diverse effects on the total antiradical activity. Thus, our study provides insights on optimization and standardization of bioactive rich formulation of N. sativa.

Modeling health and well-being measures using ZIP code spatial neighborhood patterns.

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Later Midline Shift Is Associated with Better Outcomes after Large Middle Cerebral Artery Stroke.

Background/Objective: Space occupying cerebral edema is the most feared early complication after large ischemic stroke, occurring in up to 30% of patients with middle cerebral artery (MCA) occlusion, and is reported to peak 2-4 days after injury. Little is known about the factors and outcomes associated with peak edema timing, especially when it occurs after 96 hours. We aimed to characterize differences between patients who experienced maximum midline shift (MLS) or decompressive hemicraniectomy (DHC) in the acute (<48 hours), average (48-96 hours), and subacute (>96 hours) groups and determine whether patients with subacute peak edema timing have improved discharge dispositions. Methods: We performed a two-center, retrospective study of patients with ≥1/2 MCA territory infarct and MLS. We constructed a multivariable model to test the association of subacute peak edema and favorable discharge disposition, adjusting for age, admission Alberta Stroke Program Early CT Score (ASPECTS), National Institute of Health Stroke Scale (NIHSS), acute thrombolytic intervention, cerebral atrophy, maximum MLS, parenchymal hemorrhagic transformation, DHC, and osmotic therapy receipt. Results: Of 321 eligible patients with MLS, 32%, 36%, and 32% experienced acute, average, and subacute peak edema. Subacute peak edema was significantly associated with higher odds of favorable discharge than non-subacute swelling, adjusting for confounders (aOR, 1.85; 95% CI, 1.05-3.31). Conclusions: Subacute peak edema after large MCA stroke is associated with better discharge disposition compared to earlier peak edema courses. Understanding how the timing of cerebral edema affects risk of unfavorable discharge has important implications for treatment decisions and prognostication.

Tumor radiogenomics in gliomas with Bayesian layered variable selection.

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations.

Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.

Differential expression of cancer stem cell markers and pro-inflammatory cytokine IL-1ß in the oral squamous cell carcinoma and oral submucosal fibrosis.

Objectives: The poor prognosis of oral squamous cell carcinoma (OSCC) is vastly due to late diagnosis. The oral submucosal fibrosis (OSMF) is often unnoticed pathology linked with high risk of malignancy. Recently, we demonstrated that the clinicopathological alterations in OSMF and OSCC patients were correlated with cancer stem cell (CSCs) markers (CD133 and CD44). However, the parallel alterations of interleukin-1 beta (IL-1ß) with CSCs expression are largely unexplored. Thus, we aimed to investigate the relationship between IL-1ß alterations and CSC marker expression in both OSMF and OSCC situations. Materials and Methods: A total of 135 people have signed up for the study. There were sixty each in OSMF and OSCC groups, as well as 15 healthy controls. Levels of serum IL-1ß were examined by ELISA. Immunohistochemistry (IHC) was used to examine the expression of CD133 and CD44. For evaluating differential CSCs expression, IHC scoring (0-4) was utilized. Results: The IHC results showed maximum subjects in the OSMF and OSCC displaying CD44 and CD133 positivity, although the extent of expression in terms of IHC scoring found variable. CD133 and CD44-positive subjects showed increased levels of IL-1ß in the OSMF and OSCC group. Nevertheless, the enhancement of IL-1ß is more pronounced in the OSCC cases. Further, we observed a direct link of IL-1ß levels with IHC scoring. Multivariate regression analysis demonstrated a significant role for CD44 and CD133 positivity in the increase of IL-1ß levels. Conclusion: We concluded that concurrent simultaneous changes in CSC biomarkers and IL-1ß may help with early detection of OSMF and OSCC conditions.

GaWRDenMap: a quantitative framework to study the local variation in cell-cell interactions in pancreatic disease subtypes.

Spatial pattern modelling concepts are being increasingly used in capturing disease heterogeneity. Quantification of heterogeneity in the tumor microenvironment is extremely important in pancreatic ductal adenocarcinoma (PDAC), which has been shown to co-occur with other pancreatic diseases and neoplasms with certain attributes that make visual discrimination difficult. In this paper, we propose the GaWRDenMap framework, that utilizes the concepts of geographically weighted regression (GWR) and a density function-based classification model, and apply it to a cohort of multiplex immunofluorescence images from patients belonging to six different pancreatic diseases. We used an internal cohort of 228 patients comprised of 34 Chronic Pancreatitis (CP), 71 PDAC, 70 intraductal papillary mucinous neoplasm (IPMN), 16 mucinous cystic neoplasm (MCN), 29 pancreatic intraductal neoplasia (PanIN) and 8 IPMN-associated PDAC patients. We utilized GWR to model the relationship between epithelial cells and immune cells on a spatial grid. The GWR model estimates were used to generate density signatures which were used in subsequent pairwise classification models to distinguish between any two pairs of disease groups. Image-level, as well as subject-level analysis, were performed. When applied to this dataset, our classification model showed significant discrimination ability in multiple pairwise comparisons, in comparison to commonly used abundance-based metrics, like the Morisita-Horn index. The model was able to best discriminate between CP and PDAC at both the subject- and image-levels. It was also able to reasonably discriminate between PDAC and IPMN. These results point to a potential difference in the spatial arrangement of epithelial and immune cells between CP, PDAC and IPMN, that could be of high diagnostic significance. Further validation on a more comprehensive dataset would be warranted.

Calcium Mediated Cold Acclimation in Plants: Underlying Signaling and Molecular Mechanisms.

Exposure of plants to low temperatures adversely affects plant growth, development, and productivity. Plant response to cold stress is an intricate process that involves the orchestration of various physiological, signaling, biochemical, and molecular pathways. Calcium (Ca2+) signaling plays a crucial role in the acquisition of several stress responses, including cold. Upon perception of cold stress, Ca2+ channels and/or Ca2+ pumps are activated, which induces the Ca2+ signatures in plant cells. The Ca2+ signatures spatially and temporally act inside a plant cell and are eventually decoded by specific Ca2+ sensors. This series of events results in the molecular regulation of several transcription factors (TFs), leading to downstream gene expression and withdrawal of an appropriate response by the plant. In this context, calmodulin binding transcription activators (CAMTAs) constitute a group of TFs that regulate plant cold stress responses in a Ca2+ dependent manner. The present review provides a catalog of the recent progress made in comprehending the Ca2+ mediated cold acclimation in plants.

Cellular engagement and interaction in the tumor microenvironment predict non-response to PD-1/PD-L1 inhibitors in metastatic non-small cell lung cancer.

Immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 agents have improved the survival of patients with metastatic non-small cell lung cancer (mNSCLC). Tumor PD-L1 expression is an imperfect biomarker as it does not capture the complex interactions between constituents of the tumor microenvironment (TME). Using multiplex fluorescent immunohistochemistry (mfIHC), we modeled the TME to study the influence of cellular distribution and engagement on response to ICI in mNSCLC. We performed mfIHC on pretreatment tissue from patients with mNSCLC who received ICI. We used primary antibodies against CD3, CD8, CD163, PD-L1, pancytokeratin, and FOXP3; simple and complex phenotyping as well as spatial analyses was performed. We analyzed 68 distinct samples from 52 patients with mNSCLC. Patients were 39-79 years old (median 67); 44% were male and 75% had adenocarcinoma histology. The most used ICI was atezolizumab (48%). The percentage of PD-L1 positive epithelial tumor cells (EC), degree of cytotoxic T lymphocyte (CTL) engagement with EC, and degree of CTL engagement with helper T lymphocytes (HTL) were significantly lower in non-responders versus responders (p = 0.0163, p = 0.0026 and p = 0.0006, respectively). The combination of these 3 characteristics generated the best sensitivity and specificity to predict non-response to ICI and was also associated with shortened overall survival (p = 0.0271). The combination of low CTL engagement with EC and HTL along with low expression of EC PD-L1 represents a state of impaired endogenous immune reactivity. Together, they more precisely identified non-responders to ICI compared to PD-L1 alone and illustrate the importance of cellular interactions in the TME.

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management.

Rice (Oryza sativa) is an imperative staple crop for nearly half of the world's population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, "omics" techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

Plant Defense Responses to Biotic Stress and Its Interplay With Fluctuating Dark/Light Conditions.

Plants are subjected to a plethora of environmental cues that cause extreme losses to crop productivity. Due to fluctuating environmental conditions, plants encounter difficulties in attaining full genetic potential for growth and reproduction. One such environmental condition is the recurrent attack on plants by herbivores and microbial pathogens. To surmount such attacks, plants have developed a complex array of defense mechanisms. The defense mechanism can be either preformed, where toxic secondary metabolites are stored; or can be inducible, where defense is activated upon detection of an attack. Plants sense biotic stress conditions, activate the regulatory or transcriptional machinery, and eventually generate an appropriate response. Plant defense against pathogen attack is well understood, but the interplay and impact of different signals to generate defense responses against biotic stress still remain elusive. The impact of light and dark signals on biotic stress response is one such area to comprehend. Light and dark alterations not only regulate defense mechanisms impacting plant development and biochemistry but also bestow resistance against invading pathogens. The interaction between plant defense and dark/light environment activates a signaling cascade. This signaling cascade acts as a connecting link between perception of biotic stress, dark/light environment, and generation of an appropriate physiological or biochemical response. The present review highlights molecular responses arising from dark/light fluctuations vis-à-vis elicitation of defense mechanisms in plants.

Nigella sativa callus treated with sodium azide exhibit augmented antioxidant activity and DNA damage inhibition.

Nigella sativa L. (NS) is an herbaceous plant, possessing phytochemicals of therapeutic importance. Thymoquinone is one of the active phytochemicals of NS that confers noteworthy antioxidant properties. Sodium azide, an agent of abiotic stress, can modulates antioxidant system in plants. In the present investigation, sodium azide (0, 5 µM, 10 µM, 20 µM, 50 µM, 100 µM and 200 µM) doses administered to the in vitro NS callus cultures for production/modification of secondary metabolites with augmented activity. 200 µM sodium azide treated NS callus exhibited maximum peroxidase activity (1.286 ± 0.101 nanokatal mg-1 protein) and polyphenol oxidase activity (1.590 ± 0.110 nanokatal mg-1 protein), while 100 µM sodium azide treated NS callus for optimum catalase activity (1.250 ± 0.105 nanokatal mg-1 protein). Further, 200 µM sodium azide treated NS callus obtained significantly the highest phenolics (3.666 ± 0.475 mg g-1 callus fresh weight), 20 µM sodium azide treated NS callus, the highest flavonoids (1.308 ± 0.082 mg g-1 callus fresh weight) and 100 µM sodium azide treated NS callus, the highest carotenes (1.273 ± 0.066 mg g-1 callus fresh weight). However, NS callus exhibited a decrease in thymoquinone yield/content vis-à-vis possible emergence of its analog with 5.3 min retention time and an increase in antioxidant property. Treatment with 200 µM sodium azide registered significantly the lowest percent yield of callus extract (4.6 ± 0.36 mg g-1 callus fresh weight) and thymoquinone yield (16.65 ± 2.52 µg g-1 callus fresh weight) and content (0.36 ± 0.07 mg g-1 callus dry weight) and the highest antioxidant activity (3.873 ± 0.402%), signifying a negative correlation of the former with the latter. DNA damage inhibition (24.3 ± 1.7%) was recorded significantly maximum at 200 µM sodium azide treatment. Sodium azide treated callus also recorded emergence of a new peak at 5.3 min retention time (possibly an analog of thymoquinone with augmented antioxidant activity) whose area exhibits significantly negative correlation with callus extract yield and thymoquinone yield/content and positive correlation with antioxidant activity and in vitro DNA damage inhibition. Thus, sodium azide treatment to NS callus confers possible production of secondary metabolites or thymoquinone analog (s) responsible for elevated antioxidant property and inhibition to DNA damage. The formation of potent antioxidants through sodium azide treatment to NS could be worthy for nutraceutical and pharmaceutical industries.

A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas.

In cancer radiomics, textural features evaluated from image intensity-derived gray-level co-occurrence matrices (GLCMs) have been studied to evaluate gray-level spatial dependence within the regions of interest in the brain. Most of these analysis work with summary statistics (or texture-based features) constructed using the GLCM entries, and potentially overlook other structural properties in the GLCM. In our proposed Bayesian framework, we treat each GLCM as a realization of a two-dimensional stochastic functional process observed with error at discrete time points. The latent process is then combined with the outcome model to evaluate the prediction performance. We use simulation studies to assess the performance of our method and apply it to data collected from individuals with lower grade gliomas. We found our approach to outperform competing methods that use only summary statistics to predict isocitrate dehydrogenase (IDH) mutation status.

Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography.

Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.

Therapeutic Promises of Chlorogenic Acid with Special Emphasis on its Anti-Obesity Property.

BACKGROUND: Chlorogenic acid (CGA) is a quinic acid conjugate of caffeic acid. It is an ester formed between caffeic acid and the 3-hydroxyl of L-quinic acid. This polyphenol is naturally present in substantial amount in the green coffee beans. Minor quantities of CGA are also reported in apples, eggplant, blueberries, tomatoes, strawberries and potatoes. CGA is reported to be beneficial in hypertension, hyperglycemia, antimicrobial, antitumor, memory enhancer, weight management etc. Further, it is also reported to have anticancer, antioxidant and anti-inflammatory activities. Since the last decade, CGA drew public attention for its widely recommended use as a medicine or natural food additive supplement for the management of obesity. OBJECTIVE: The current review explores the medicinal promises of CGA and emphasizes on its antiobese property as reported by various scientific reports and publication. CONCLUSION: CGA shows promises as an antioxidant, glycemic control agent, anti-hypertensive, antiinflammatory, antimicrobial, neuro-protective and anti-obesity agent. It primarily activates the AMPactivated protein kinase, inhibits 3-hydroxy 3-methylglutaryl coenzyme-A reductase and strengthens the activity of carnitine palmitoyltransferase to control the obesity.

Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.

Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post-T1pre and T2-FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.

Predictions, role of interventions and effects of a historic national lockdown in India's response to the COVID-19 pandemic: data science call to arms.

With only 536 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25. The lockdown was first extended to May 3 soon after the analysis of this paper was completed, and then to May 18 while this paper was being revised. In this paper, we use a Bayesian extension of the Susceptible-Infected-Removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other less severe non-pharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its healthcare and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown between 42-56 days is preferable to substantially "flatten the curve" when compared to 21-28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and, thus, inform policymakers about one aspect of this multi-faceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org.