Complementary effects of CRM and social media on customer co-creation and sales performance in B2B firms: The role of salesperson self-determination needs

Highlights Social media, CRM technology & social CRM enrich the knowledge of salespeople. Social media, CRM technology & social CRM support value co-creation efforts. Knowledge mediates the effects of social media, CRM technology, and social CRM. Job autonomy & sales quota ease moderate the effect of knowledge on value co-creation. Value co-creation increases sales performance. Abstract This study examines the effects of salespeople\u27s social media and customer relationship management (CRM) technology use on value co-creation through knowledge and the downstream impact on sales performance. Based on task-technology fit and self-determination theories, the findings reveal that social media, CRM technology, and their interaction support salespeople in their value co-creation efforts through the mediating role of knowledge enriched by these tools. The results indicate a significant moderating effect of salesperson job autonomy and sales quota ease in enhancing the relationship between knowledge and value co-creation. The study concludes by discussing important implications that stem from our analyses

Heart Smart Diabetes Care

The world is growing smarter day by day, and so is health care. In spite of innumerable inventions and tech-tools, however, we struggle to contain chronic illnesses like diabetes and heart disease. We need to work together and design a rational, scientific and socially sustainable Heart Smart diabetes care ecosystem, with Heart Smart management strategies, to ensure happiness and harmony in persons who live with diabetes

Heart Smart Diabetes Care

The world is growing smarter day by day, and so is health care. In spite of innumerable inventions and tech-tools, however, we struggle to contain chronic illnesses like diabetes and heart disease. We need to work together and design a rational, scientific and socially sustainable Heart Smart diabetes care ecosystem, with Heart Smart management strategies, to ensure happiness and harmony in persons who live with diabetes

Estimation of adiponectin levels in diabetic, non-diabetic fatty liver diseases and healthy controls

Background:Estimation of adiponectin levels in diabetic and non-diabetic fatty liver and healthy controls.Methods:We studied 25 subjects for diabetic fatty liver, 25 subjects for non-diabetic fatty liver and 25 healthy controls. Clinical evaluation included anthropometric measurements, BMI, biochemical investigations and adiponectin estimation by ELISA.Results: There were 15 males (60%) and 10 (40%)females subjects in the DFL group, 18 males (72%) and 7 females (28%) subjects in the NDFL group and 13 males (52%) and 12 females (48%) subjects in the control group. 80% (20) of the DFL patients and 72% (18) subjects of NDFL group had BMI >25kg/m2. 80% (12 males and 8 females) of subjects in the DFL group and 68% (12 males and 5 females) had a waist circumference that indicated central obesity as per Indian cut-offs (>90 cm for females and >80 cm for males). The mean adiponectin (μg/ml) ± SD levels in DFL were 4.03 ± 0.43, NDFL was 5.01 ± 0.55 and in controls was 7.63 ± 0.66, the difference being statistically significant with P <0.001. The difference in the adiponectin levels was statistically significant between each of the three groups with P <0.001. There was no difference in serum adiponectin levels between males and females in all three groups.Conclusion:The chief conclusion of this study are that serum adiponectin levels are lower in subjects with NAFLD than those without it; adiponectin levels are inversely related to the degree of steatosis in NAFLD, with the lowest levels in more severe forms of steatosis.

Similar Events but Contrasting Impact: Appraising the Global Digital Reach of World Heart Day and Atrial Fibrillation Awareness Month

Background: With over 18.6 million deaths annually, cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. One such complication of CVDs that can result in stroke is atrial fibrillation (Afib). As part of global outreach and awareness, World Heart Day and Atrial Fibrillation Awareness Month are celebrated annually on 29 September and the month of September, respectively. Both of these events are important cardiovascular awareness initiatives to assist public education and develop awareness strategies, and they have received considerable support from leading international organizations. Objective: We studied the global digital impact of these campaigns via Google Trends and Twitter. Methods: We evaluated the overall number of tweets, impressions, popularity and top keywords/hashtags, and interest by region to determine the digital impact using various analytical tools. Hashtag network analysis was done using ForceAtlas2 model. Beyond social media, Google Trends web search analysis was carried out for both awareness campaigns to examine ‘interest by region’ over the past five years by analyzing relative search volume. Results: #WorldHeartDay and #UseHeart (dedicated social media hashtags for World Heart Day by the World Heart Federation) alone amassed over 1.005 billion and 41.89 million impressions as compared with the 1.62 million and 4.42 million impressions of #AfibMonth and #AfibAwarenessMonth, respectively. On Google Trends web search analysis, the impact of Afib awareness month was limited to the USA, but World Heart Day had a comparatively global reach with limited digital involvement in the African continent. Conclusions: World Heart Day and Afib awareness month present a compelling case study of vast digital impact and the effectiveness of targeted campaigning using specific themes and keywords. Though the efforts of the backing organizations are commended, planning and collaboration are needed to further widen the reach of Afib awareness month

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

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

PAPP-A2 and Inhibin A as Novel Predictors for Pregnancy Complications in Women With Suspected or Confirmed Preeclampsia

BACKGROUND: We aimed to evaluate the value of inhibin A and PAPP-A2 (pregnancy-associated plasma protein-A2) as novel biomarkers in the prediction of preeclampsia-related complications and how they compare with angiogenic biomarkers. METHODS AND RESULTS: Making use of a secondary analysis of a prospective, multicenter, observational study, intended to evaluate the usefulness of sFlt-1 (soluble Fms-like tyrosine kinase-1)/PlGF (placental growth factor) ratio, we measured inhibin A and PAPP-A2 levels in 524 women with suspected/confirmed preeclampsia. Women had a median gestational age of 35 weeks (range, 20–41 weeks) while preeclampsia occurred in 170 (32%) women. Levels of inhibin A and PAPP-A2 were significantly increased in women with preeclampsia and in maternal perfusate of preeclamptic placentas. Inhibin A and PAPP-A2 (C-index = 0.73 and 0.75) significantly improved the prediction of maternal complications when added on top of the traditional criteria; gestational age, parity, proteinuria, and diastolic blood pressure (C-index = 0.60). PAPP-A2 was able to improve the C-index from 0.75 to 0.77 when added on top of the sFlt-1/PlGF ratio for the prediction of maternal complications. To discriminate fetal/neonatal complications on top of traditional criteria, inhibin A and PAPP-A2 showed additive value (C-index = 0.79 to 0.80 and 0.82, respectively) but their discriminative ability remained inferior to that of sFlt-1/PlGF ratio or PlGF. Interestingly, the PAPP-A2/PlGF ratio alone showed remarkable value to predict pregnancy complications, being superior to sFlt-1/PlGF ratio in the case of maternal complications. CONCLUSIONS: Inhibin A and PAPP-A2 show significant potential to predict preeclampsia-related pregnancy complications and might prove beneficial on top of the angiogenic markers

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation