Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm

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

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.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.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.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

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 (aiP 3 ) 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 AtheroEdge TM 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 AtheroEdge TM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

Different sex ratios of children born to Indian and Pakistani immigrants in Norway

<p>Abstract</p> <p>Background</p> <p>A low female-to-male ratio has been observed in different Asian countries, but this phenomenon has not been well studied among immigrants living in Western societies. In this study, we investigated whether a low female-to-male ratio exists among Indian and Pakistani immigrants living in Norway. In particular, we investigated whether the determination of sex via ultrasound examination, a common obstetric procedure that has been used in Norway since the early 1980 s, has influenced the female-to-male ratio among children born to parents of Indian or Pakistani origin.</p> <p>Methods</p> <p>We performed a retrospective cohort study of live births in mothers of Indian (n = 1597) and Pakistani (n = 5617) origin. Data were obtained from "Statistics Norway" and the female-to-male (F/M) sex ratio was evaluated among 21,325 children born, in increasing birth order, during three stratified periods (i.e., 1969-1986, 1987-1996, and 1997-2005).</p> <p>Results</p> <p>A significant low female-to-male sex ratio was observed among children in the third and fourth birth order (sex ratio 65; 95% CI 51-80) from mothers of Indian origin who gave birth after 1987. Sex ratios did not deviate from the expected natural variation in the Indian cohort from 1969 to 1986, and remained stable in the Pakistani cohort during the entire study period. However, the female-to-male sex ratio seemed less skewed in recent years (i.e., 1997-2005).</p> <p>Conclusion</p> <p>Significant differences were observed in the sex ratio of children born to mothers of Indian origin compared with children born to mothers of Pakistani origin. A skewed number of female births among higher birth orders (i.e., third or later) may partly reflect an increase in sex-selective abortion among mothers of Indian origin, although the numbers are too small to draw firm conclusions. Further research is needed to explain the observed differences in the female-to-male ratio among members of these ethnic groups who reside in Norway.</p

Effect of Incorporating Extruded Wheat Flour on the Quality of Goat Meat Sausages

241-245The effect of incorporating flour and its extruded counterparts from sound, 24 h and 48 h sprouted wheat at 5, 10 and 15 % levels on the quality of goat meal sausages is studied. It is found that the yield of sausages increases with increase in the level of incorporation ; however, the extrusion and sprouting show adverse effect. The incorporation of unextruded and extruded 48 h sprouted flour do not show any significant effect on the yield of sausages. The emulsion stability of sausage mix in terms of per cent separation on cooking correspond to the yield values. The organoleptic evaluation of cooked sausages reveals an improvement in texture, appearance and overall acceptability with increase in the level of incorporation of unextruded flour. The incorporation of extruded flour shows a deteriorating effect on all the sensory attributes. Sausages containing extruded or unextruded 24 h sprouted wheat flour are acceptable while those with 48 h sprouted wheat flour show poor acceptability. Warner Bratzler shear values increase with the addition of extruded and sprouted flour

Effects of A‐ and B‐type starch granules on composition, structural, thermal, morphological, and pasting properties of starches from diverse wheat varieties

Abstract The distribution of A‐ and B‐type‐sized starch granules plays a deciding role in controlling the physicochemical, structural, morphological, and functional attributes of wheat starch. Starches of three Indian wheat varieties, viz. DBW 16, WH 147, and WH 542, were fractionated into A‐ and B‐type starch granules and further evaluated for their influence on various attributes of wheat starches using different analytical tools like X‐ray diffraction, scanning electron microscopy (SEM), differential scanning calorimetry, and rapid viscoanalyzer. SEM revealed that the size of large granules (A‐type) ranged from 12.6 to 36.4 µm and small granules (B‐type) varied from 2.53 to 7.52 µm. The amylose content was significantly higher for A‐type starch ranging from 26.6% to 29.68% than B‐type starch ranging from 19.20% to 22.38%. The highest swelling power was observed for B‐type granules, followed by native and A‐type granules, and similar trend was noticed for water absorption. Pasting viscosities of A‐type granules were higher than B‐type for starches of all wheat varieties. A higher pasting temperature was observed in B‐type starch granules, suggesting more resistance to swell during the heating process. X‐ray diffraction of wheat starches showed A‐type pattern of crystallinity, variety DBW 16 (27%) showed the highest relative crystallinity and intensities of peaks in comparison to varieties WH 147 (23.5%) and WH 542 (22.4% as observed in diffractograms and well supported by Fourier transforms infrared spectroscopy. Fractionated large granules of wheat starches exhibited a higher gelatinization temperature than smaller granules and native starches for all the varieties. It was also observed that A‐type granules had higher onset temperature comparatively, which suggested that high energy is required in gelatinization due to more ordered arrangement of starch molecules

A comparative review of protein and starch characteristics and end-use quality of soft and hard wheat

Hardness of wheat endosperm is a physical property that depends on the genetic makeup of the grain and varies amongst different types/varieties. It is associated with proteins, particularly 14–15 kDa friabilins, which exist in higher amounts at the surface of starch in soft wheat grains while either absent or occur in smaller quantities on granules of hard wheat varieties. The storage proteins in hard wheat flours exhibit a higher proportion of polymeric proteins, β-sheets and β-turns. Soft wheat starches show lower crystallinity, amylose-lipid complex content and proportion of A-type granules (disk or lenticular-shaped granules with diameter >10 µm) while higher gelatinisation temperatures, swelling, breakdown susceptibility and retrogradation than hard wheat starches. Flours from hard and soft wheat also differ for processing and end-use quality based on differences in dough rheological properties, solvent retention capacities and particle size

Serial electrocardiographic changes as a predictor of cardiovascular toxicity in acute tricyclic antidepressant overdose.

Tricyclic antidepressant agents continue to be a leading cause of significant morbidity and mortality in reported poisonings involving pharmaceutical agents. Although the history and physical examination play an important role in the assessment of patients with tricyclic antidepressant overdose, the presence of anticholinergic features on examination cannot predict the severity of the overdose. Several clinical variables, in particular electrocardiographic (ECG) changes, have been proposed as a guide to determine the severity of the tricyclic antidepressant poisoning. The authors describe a patient with tricyclic antidepressant overdose who presented with altered mental status and whose serial ECG changes played a significant role in diagnosing and predicting the impending cardiovascular toxicity. The role of ECG changes in making the diagnosis and assessing the severity of the tricyclic antidepressant overdose is reviewed