Hepatitis B infection and risk factors among children living with HIV in Yaounde, Cameroon: an integrated management.

BACKGROUND: The endemicity of hepatitis B virus (HBV) prompted the systematic immunization of newborns in Cameroon since 2005. In the frame of a considerable burden of HIV/HBV co-infection (17.5%), monitoring HBV among children living with HIV (CLHIV) would guide toward HIV/HBV integrated paediatric care. We sought to ascertain the prevalence and determinants of HBV infection in the population of CLHIV and performance of commonly used rapid diagnosis tests (RDTs). METHODS: Cross-sectional study conducted from February through June 2017 in a subset of CLHIV ≤15 years old at the Essos Hospital Centre, Yaounde, Cameroon. HBV was tested by HBsAg ELISA sandwich in duplicates for each sample, and the mean optical density was calculated. The Determinants of HBV-prevalencewere evaluated, and p < 0.05 was the significance threshold. The performance of two HBV RDTs (Diaspot vs. HBV-5) was evaluated in comparison to ELISA (used as gold standard). RESULTS: Of the 83 CLHIV enrolled (54.2% female, mean age 8.7 [±3.8] years, 60% vaccinated against HBV, all breastfed), HBV-prevalence was 2.41% (2/83). HBV-positivity was significantly associated with unknown maternal HBV status (2.9% [2/69] vs. 0.0% [0/14], p = 0.0097) and vaginal delivery (2.4% [2/82] vs. 0.0% [0/1], p = 0.0018). Moreover, the most likely to be positive were aged 11 and 15 years, and had experienced neither anti-HBV vaccination nor anti-HBV serum administration, and both had not been treated with any antiseptic solution at birth. Regarding the performance of Diaspot vs. HBV-5 respectively, sensitivity was 100% (2/2) vs. 50% (1/2), while specificity was 100% (45/45) vs. 97.8% (44/45); positive and negative predictive values of Diaspot versus HBV-5 were respectively 100% (2/2) and 100% (45/45) versus 50% (1/2) and 97.8% (44/45). CONCLUSION: HBV-infection in the population of CLHIV appears at a moderate prevalence, suggesting a decreased burden likely due to preventive measures including the wide vaccine coverage. Focusing on mothers with unknown HBV status and promoting safer delivery mode (caesarean section) for HBV-positive motherswould contribute toward pediatric HBV elimination. In context of limited resources, Diaspot test appears more reliable to rollout HBV-infection in the population of CLHIV. As findings are limited to a small sample size, studies on a wider population would be relevant.

'Too-much-of-a-good-thing'? The role of advanced eco-learning and contingency factors on the relationship between corporate environmental and financial performance.

Inspired by the natural-resource-based view (NRBV) theory, we attempt to shed light on a controversy which has been persistent over the last decade, concerning the relationship between corporate environmental performance (CEP) and corporate financial performance (CFP). Using the 'too-much-of-a-good-thing' (TMGT) concept, which suggests that "too much can be worse than too little," we link mixed results and consider the roles of advanced eco-learning and contingency factors in influencing the CEP-CFP relationship. Based on a sample composed of ISO 14001 certified companies in Indonesia, and analyzing the data using consistent Partial Least Squares (PLSc), we found that: the CEP-CFP relationship follows an inverted U-shape; advanced eco-learning is a significant predictor of the CEP-CFP relationship, meaning that organizations able to develop higher eco-learning capability will be better able to identify the ideal boundaries of investment in environmental performance without reducing their financial performance; and that contingency factors such as environmental strategy and firm size have a significant role in influencing the CEP-CFP relationship. The study's limitations, implications for practitioners and a future research agenda are also detailed.

Assessing the potential of plastic waste management in the circular economy: a longitudinal case study in an emerging economy.

Plastic waste management represents a fundamental challenge in terms of environmental pollution and health in many emerging countries. Yet, some firms believe improved plastic waste management could lead to value creation and capture, especially from a circular economy perspective. This study draws on a longitudinal research approach that involved 12 organizations in assessing plastic waste management's contribution to Cameroon's circular economy. Our findings suggest that plastic waste management for value creation is still embryonic in Cameroon. Moving to the full value creation and capture stage will require overcoming various challenges identified and presented in the paper. We then discuss our findings and put forward several future research avenues. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-023-05386-3.

A Framework Based on Blockchain, Artificial Intelligence, and Big Data Analytics to Leverage Supply Chain Resilience considering the COVID-19.

In the global supply chains era, firms are more connected, integrated, and interdependent, bringing along a set of benefits and a number of risks. It is clear that the singular COVID-19 epidemic outbreak has led to unparalleled disruptions and considerable challenges for supply chains (SCs). For example, the sluggish economic environment provoked by the COVID-19 has negatively impacted the flow of goods, generating shortages and interruptions through the SCs. At the global level, many markets are enduring the effects of these disruptions. In this challenging context, the firms and their SCs must apply useful and efficient strategies to minimize and adapt their operations during and after these disruptions. In this view, this study aims to propose a novel framework based on Artificial Intelligence, Blockchain, and Big Data Analytics, to bring useful ideas and contribute to overcoming such disruptions. Besides, we propose novel categorizations that can support new insights for scholars and practitioners about the use of cutting-edge technologies during and after severe disruptions.

Artificial intelligence in E-Commerce: a bibliometric study and literature review.

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.