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 age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic