COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background and novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Methodology: Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results: Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Conclusion: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Effect of parboiling treatment times on the physicochemical, cooking, textural, and pasting properties and amino acid, phenolic, and sugar profiles of germinated paddy rice from different rice varieties.

In this research, parboiling was carried out at different times (5 and 15 min) on germinated paddy rice (GPR) from various basmati and non-basmati varieties. The results showed that as the parboiling time increased from 5 to 15 min, Δ $\Delta $ E, ash content, total dietary fiber, mineral content, cooking time, and textural properties increased while L*, lipid content, total starch, gruel solid loss, water absorption, oil absorption, foaming capacity, sugar profile, and total phenolic and flavonoid content decreased as compared to GPR. All pasting properties of GPR increased except breakdown as the parboiling time increased from 5 to 15 min. Parboiling altered the properties of GPR due to starch gelatinization. Total essential amino acid and gamma-aminobutyric acid decreased as the parboiling time (5 to 15 min) increased. The germinated parboiled brown rice could create a highly nutritious alternative to regular brown rice as it offers improved texture and cooking qualities.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli.

BACKGROUND: There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data. METHODOLOGY: The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models. RESULTS: The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR. CONCLUSIONS: aiGeneR successfully detected the E. coli genes validating our four hypotheses.

Embracing nutritional, physical, pasting, textural, sensory and phenolic profile of functional muffins prepared by partial incorporation of lyophilized wheatgrass, fenugreek and basil microgreens juice powder.

BACKGROUND: Muffins are delightful baked food products that have earned a prominent place in the daily diet of a majority of people around the world. The incorporation of microgreens juice powder (MJP) into muffins boosts their nutritional value. The influence of the incorporation of wheatgrass, fenugreek and basil MJP at 1.5% and 3.0% levels on the nutritional composition, physical properties, pasting, sensory, textural and phenolic profile of functional muffins was evaluated. RESULTS: The results indicated a significant increase in the protein content, ash content, dietary fiber and total phenolic content of MJP incorporated muffins. The incorporation of MJP to the muffins led to a gradual reduction in the L*, a* and b* values. Baking characteristic such as bake loss decreased significantly as a result of MJP incorporation. Furthermore, the incorporation of various MJPs resulted in a significant decrease in the peak viscosity of the flour-MJP blends. Regarding texture, the hardness and chewiness of the muffins increased progressively with an increase in the level of MJP incorporation. The highest hardness (10.15 N) and chewiness (24.45 mJ) were noted for 3% fenugreek MJP incorporated muffins (FK 3.0). The sensory score of MJP incorporated muffins was acceptable and satisfactory. Additionally, 3% basil MJP incorporated muffins (BL 3.0) marked the dominant presence of majority of the detected phenolic acids such as ferulic acid, sinapic acid, chlorogenic acid, caffeic acid, quercetin, cinnamic acid, isothymosin and rosamarinic acid. The highest concentration of p-coumaric acid (11.95 mg kg-1), vanillic acid (26.07 mg kg-1) and kaempferol (8.04 mg kg-1) was recorded for FK 3.0 muffin. CONCLUSION: MJP incorporated muffins revealed the pool of phenolic acids and the reduced bake loss is of industrial interest. The present study concludes that wheatgrass, fenugreek and basil MJP can be incorporated by up to 3% into baked products as a source of functional ingredients for health benefits. © 2024 Society of Chemical Industry.

Deciphering the metabolic signatures of Trigonella microgreens as a function of photoperiod and temperature using targeted compound analysis and non-targeted UHPLC-QTOF-IMS based approach.

Trigonella foenum-graecum L. (Fenugreek) is an annual herb that belongs to Fabaceae family. The compositional make-up of microgreens depends on prevailing environmental conditions. So, Trigonella microgreens were cultivated under different photoperiod and temperature conditions and evaluated for plant height, total chlorophyll content (TCC), targeted compound analysis and non-targeted UHPLC-QTOF-IMS based metabolomic profile. The plant height and TCC of Trigonella microgreens increased by approximately 22 % and 20 %, respectively under T1 conditions (longer photoperiod of 22 h with 22 °C in light and 17 °C in dark). The targeted phenolic profile analysis revealed the dominant presence of gallic acid, p-coumaric acid and apigenin in Trigonella microgreens. Also, the concentration of p-coumaric acid concentration raised from 3.51 mg/g to 5.83 mg/g as a response of T1 conditions. The sugar profile revealed augmented concentration of myo-inositol, glucose, fructose, xylose, maltose, and sucrose in longer photoperiod with T1 conditions. The microgreens were also rich in amino acids like aspartic acid, glutamic acid, leucine, isoleucine, and phenylalanine. Notably, the concentration of proline increased from 10.40 mg/g to 16.92 mg/g as a response to T1 growth conditions. The concentration of these metabolites varied significantly under different photoperiod and temperature conditions. The comprehensive non-targeted UHPLC-QTOF-IMS analysis of microgreens revealed different class of metabolites like organic compounds, alkaloids, coumarin-derivatives, phenolic and flavonoid derivatives, terpenoids, sugars, amino acids and few nucleic acid derivatives. The multivariate PLS-DA explained different expression level of metabolites under different growing conditions. The T1 growing condition resulted in the increased biosynthesis of phenolic compounds and various metabolites. The expression level of terpenoid derivatives specifically of Trigonelloside C and Trigoneoside XIIa/b increased under T1 conditions. The substantial alteration in the metabolites due to growing conditions may alter the microgreen's dietary benefits. So, additional research may be warranted.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.

BACKGROUND AND MOTIVATION: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool.

BACKGROUND: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. OBJECTIVE: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm. METHOD: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. CONCLUSIONS: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.

A phyco-nanobionics biohybrid system for increased carotenoid accumulation in C. sorokiniana UUIND6.

Recent advancements in "phyco-nanobionics" have sparked considerable interest in the ability of microalgae to synthesize high-value natural bioactive compounds such as carotenoid pigments, which have been highlighted as an emergent and vital bioactive compound from both industrial and scientific perspectives. Such bioactive compounds are often synthesized by either altering the biogenetic processes existing in living microorganisms or using synthetic techniques derived from petroleum-based chemical sources. A bio-hybrid light-driven cell factory system was established herein by using harmful macroalgal bloom extract (HMBE) and efficient light-harvesting silver nanoparticles (AgNPs) to synthesize HMBE-AgNPs and integrating the synthesized HMBE-AgNPs in various concentrations (1, 2.5, 5 and 10 ppm) into the microalgae C. sorokiniana UUIND6 to improve the overall solar-to-chemical conversion efficiency in carotenoid pigment synthesis in microalgae. The current study findings found high biocompatibility of 5 ppm HMBE-AgNP concentration that can serve as a built-in photo-sensitizer and significantly improve ROS levels in microalgae (6.75 ± 0.25 µmol H2O2 g-1), thus elevating total photosynthesis resulting in a two-fold increase in carotenoids (457.5 ± 2.5 µg mL-1) over the native microalgae without compromising biomass yield. NMR spectroscopy was additionally applied to acquire a better understanding of pure carotenoids derived from microalgae, which indicated similar peaks in both spectra when compared to ß-carotene. Thus, this well-planned bio-hybrid system offers a potential option for the cost-effective and long-term supply of these natural carotenoid bio-products.

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

BACKGROUND AND MOTIVATION: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm.

Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing "seen" and "unseen" paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings.

Diversity in phenolics, amino acids, rheology and noodles glycemic response of brown rice from non-basmati and basmati rice.

Physicochemical, functional, phenolic and amino acids composition of brown rice (BR) from non-basmati and basmati varieties were evaluated. Higher a*, b*, ash content, dietary fiber, blue value, foaming capacity, oil absorption capacity and total phenolic content (TPC) were observed in BR from basmati varieties than the non-basmati varieties. In addition, higher accumulation of ferulic and p-Coumaric acid in bound form and gallic acid in free form was observed for BR from basmati varieties than non-basmati varieties. BR from basmati varieties contained higher concentration of valine, methionine, phenylalanine, histidine and threonine than BR from non-basmati varieties. Noodles from basmati varieties showed lower gruel solids loss, water uptake and glycemic index but higher storage modulus than from non-basmati varieties. BR from PB1121 had better functional, rheological and noodles making properties amongst all the varieties evaluated.

Comparison of effect of using hard and soft wheat on the high molecular weight-glutenin subunits profile and the quality of produced cookie.

Twelve wheat genotypes with variable grain hardness were evaluated for grain, flour, pasting, dough rheological properties, high molecular weight glutenin subunits (HMW-GS) and their relationship with cookie quality characteristics. The degree of hardness played an important role in the expression of characters under study. Genotypes with higher grain hardness index (GHI) showed higher dough development time and dough stability. GHI and solvent retention capacity were positively related to each other and negatively to spread factor. GluD1 locus of majority of hard wheat genotypes showed 5 + 10 subunit while soft wheat (SW) genotypes with 2 + 12 subunit related to gluten quality and dough properties. Overall, variation in subunits at GluD1 locus led to greater variation amongst studied genotypes followed by GluB1 and GluA1. Subunits Null at GluA1, 20, 7 + 8 and 7 + 9 at GluB1, and 2 + 12 and 5 + 10 at GluD1 showed a profound effect on flour, dough and cookie quality. Distribution of different HMW-GS, gluten characteristics and GHI, thus emerged as major parameters for selection of wheat genotypes for development of cookies. SW (QBP 13-11) with the lowest GHI and HMW-GS profile (2*, 7 and 2 + 12 subunit) showed the highest cookie SF and the lowest BS, thereby, turning out to be the best suitable genotype for producing cookies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-021-05272-5.

Colour, composition, digestibility, functionality and pasting properties of diverse kidney beans (Phaseolus vulgaris) flours.

The present work evaluated nine diverse kidney bean accessions for colour, composition, digestibility, protein profile, starch crystallinity, techno-functional properties, pasting properties and microstructure with the objective of identifying key attributes affecting their digestibility and functionality. The accessions exhibited dry matter digestibility, resistant starch (RS) content, water absorption capacity, fat absorption capacity, emulsifying activity index (EAI), foaming capacity (FC) and foam stability (FS) of 14.6-47.2%, 32.0-50.5%, 1.7-2.7 g/g, 1.4-1.7 g/g, 50.1-70.1 m2/g, 70.8-98.3% and 82.4-91.3%, respectively. Starch-lipid complexes (SLC), proteins and non-starch carbohydrates contributed to lower starch and dry matter-digestibility. Principal component analysis revealed positive relation of emulsification, foaming and water absorption capacity with proteins, starch, RS and ash-content while negative with crystallinity and amount of lipids, non-starch carbohydrates and digestible starch. Hydration ability of proteins promoted foaming whereas flour with lower vicilins level was less surface active and exhibited the lowest EAI, FC and FS. Pasting temperature related positively with SLC, while average starch granule size was in strong positive relationship with RS content, peak viscosity and breakdown viscosity. The results could be useful for enhanced utilization of kidney beans in different foods.

Influence of dry air and infrared pre-treatments on oxidative stability, Maillard reaction products and other chemical properties of linseed (Linum usitatissimum L.) oil.

In this study, the influence of dry air and infrared pre-treatments on linseed oil (LO) yield, chemical properties, colour, pigment content, total phenolic content (TPC), Maillard reaction products (MRPs), fatty acid composition (FAC), radical scavenging activity (RSA), and oxidative stability index (OSI) were investigated. An increase in dry air and infrared roasting temperature had increased the LO yield, pigment content, a* value, TPC, RSA, OSI, and browning index (BI) while lowered the L* and b* values of LO. Higher OSI (2.24 h), chlorophylls (2.29 mg/kg), carotenoids (3.87 mg/kg), TPC (63.67 mg GAE/100 g), RSA (62.53%), BI (0.330), and MRPs (2.10 mg/kg) were detected in LO by dry air roasting at 180°C for 10 min. Dry air and infrared roasting had slightly affected the FAC of LO. Both dry air and infrared pre-treatments had influenced the LO quality characteristics. However, dry air roasting of linseed at 180°C for 10 min proved more effective in improving oxidative stability, antioxidant activity and other quality characteristics of LO. SUPPLEMENTARY INFORMATION: The online version of this article at 10.1007/s13197-021-05023-6.

Effect of photoperiod and growth media on yield and antioxidant properties of wheatgrass juice of Indian wheat varieties.

The effect of photoperiod durations (16 h light:8 h dark vs 22 h light:2 h dark) and different doses (0.5x and 1x) of Murashige and Skoog medium on the yield and antioxidant characteristics of wheatgrass from hard, medium-hard and soft wheat varieties were analyzed. The average wheatgrass height and wheatgrass yield increased in MS media both under normal photoperiod as well as in water under prolonged photoperiod. An increase in total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) of wheatgrass in different strengths of MS media under normal photoperiod was observed. Whereas, increase in protein content, chlorophyll (Chl) a, Chl b, total Chl, average TPC, DPPH inhibition and FRAP values were observed for wheatgrass of different varieties grown in water under prolonged photoperiod. The accumulation of polypeptides (PPs) of 92 kDa, 33 kDa, 23 kDa, 14 kDa, 12 kDa, and 10 kDa for wheatgrass shoot powder of different varieties was affected by strength of MS media and duration of photoperiod. On the contrary, wheatgrass juice powder showed major changes in the accumulation of PPs 33 kDa and 23 kDa PPs under varied strength of MS media and prolonged photoperiod.

Novel Gellan Gum-Based In Situ Nanovesicle Formulation of Docetaxel for Its Localized Delivery Using Depot Formation.

In the present study, different in situ hydrogel formulations of docetaxel (DTX) based on biocompatible polymers such as Hyaluronic Acid (HA), poloxamer-407, chitosan and gellan gum were formulated to increase its therapeutic efficacy and reduce toxicity. DTX was loaded in nanovesicles (20 mg/mL equivalent to commercial strength) and further incorporated into the hydrogel bases to possess a dual rationale of protection against burst release and enhanced solubility of the drug. The optimized hydrogel formulation (NV-TPGS-3-GG-4) showed ideal rheological behavior and in situ characteristics at 37±0.5°C with sustained release of more than 144 h. The optimized formulation had instant in vitro gelation (2.8±0.3 min) with good injectability in comparison to the conventional commercial DTX injectable formulation having instant release (<2 h). Additionally, developed formulation exhibited an improved biodisponibility (25.1±0.2 h) in comparison to the commercially available formulation (1.7±0.1 h). The Solid Tumor Carcinoma model in Swiss albino mice revealed that the optimized formulation (based on gellan gum) showed better tumor reduction (85.7±1.2%) and lower toxicity as compared to the commercial formulation (77.3±1.3%). Pharmacokinetic and biodistribution studies demonstrated 3 to 4 times higher localization of drug in tumors. Our findings suggested that injectable gellan gum-based in situ hydrogel formulation can be an effective delivery system for DTX with enhanced solubility, reduced toxicity, and better targeting to the tumors for improved therapeutics.Graphical abstract.

Physicochemical, functional and structural characteristics of grains, flour and protein isolates of Indian quinoa lines.

The physicochemical and functional properties of grains, flour and protein isolates (QPI) from different quinoa lines were evaluated. Quinoa flour and protein isolates were characterised by high protein (14.1-15.4% and 78.7-86%, respectively) and phenolic content (419 to 655 mg GAE/100 g). Water and oil absorption capacity of QPI (2.0-3.6 g/g and 2.71-3.32 g/g, respectively) was significantly higher than quinoa flour (1.22 and 2.17 gand 0.9 and1.54 g, respectively). QPI showed desirable functional properties in terms of high foaming capacity (50.8 to 60.2%), foam stability (38.5 to 50.4%), protein solubility (30 and 53.3%), and emulsification index (EAI- 19.2-26.1 m2/g and ESI- 96.7-149.8 min, respectively). Digestibility of QPI ranged between 70.8 and 77.8% and was negatively correlated to random coils. Quinoa lines with higher grain density (GD) showed higher total phenol content (TPC) and WAC and lower FRSA and OAC. QPI with higher proportions of ß-structure and random coils had enhanced protein surface activities leading to improved emulsification and foaming capabilities. Among the highly conserved polypeptides (PPs), variations were observed in accumulation of 53, 59 and 32 kDa PPs for grains and 33 and 21-24 kDa PPs for QPI.