Top 100 highly cited sustainability researchers.

The announcement of the UN Sustainable Development Goals (SDGs) provided a fresh direction to sustainability research that spans different disciplines. Consequently, scholarly databases made available the mapping of research publications to different SDGs, unleashing many opportunities for analysis. In this work, the top 100 Highly Cited Sustainability Researchers (HCSRs) and information related to them, such as the institutions they belong to, the type of these institutions, the geographical diversity of these researchers, and gender representation patterns, are analyzed. Also, from their publications, their publication pattern, including (i) the least and most researched SDGs, (ii) their Open Access publishing pattern, (iii) their collaboration pattern (iv) the pattern of their research impact, are analyzed. The most sought thematic areas of their research, top journals in which they publish, important research categories handled by these journals, etc., are also investigated. The most significant contribution of these researchers and their recent contributions are also discussed. The data indicates a significant disparity in research focus among the top 100 HCSRs, with most concentrating on "Good Health and Well Being," "Zero Hunger," and "Quality Education," while notably fewer researchers focus on "Decent Work and Economic Growth" and "No Poverty," underscoring the need for a more balanced research agenda across all SDGs. The study reveals that the United States, China, and the United Kingdom are the leading contributors to the top 100 HCSRs, suggesting that these countries are predominant in global sustainability research output, while nations like Iran and Saudi Arabia also make notable, albeit smaller, contributions. The institutional affiliations of HCSRs show a significant imbalance, with only 16 from private institutions compared to 84 from public ones. Specifically, it shows that out of the top 100 researchers, 93 are men, while only 7 are women. The analysis of authorship in publications by HCSRs reveals a tendency towards middle and last author positions, underscoring their collaborative and leadership roles within the research community. All these analyses can inform academia, industry, and policymakers about the most significant developments in research regarding SDGs.

ChatGPT: Literate or intelligent about UN sustainable development goals?

Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.

Fake news research trends, linkages to generative artificial intelligence and sustainable development goals.

In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013-2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. This work introduces three novel contributions: 1) a pioneering mapping of fake news research to Sustainable Development Goals (SDGs), indicating its influence on areas like health (SDG 3), peace (SDG 16), and industry (SDG 9); 2) the utilization of Prominence percentile metrics to discern critical and economically prioritized research areas, such as misinformation and object detection in deep learning; and 3) an evaluation of generative AI's role in the propagation and realism of fake news, raising pressing ethical concerns. These contributions collectively provide a comprehensive overview of the current state and future trajectories of fake news research, offering valuable insights for academia, policymakers, and industry.

Darkweb research: Past, present, and future trends and mapping to sustainable development goals.

The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world. This work carries out the bibliometric study on the research conducted on Darkweb over the last decade. The findings illustrate that most research on Darkweb can be clustered into four areas based on keyword co-occurrence analysis: (i) network security, malware, and cyber-attacks, (ii) cybercrime, data privacy, and cryptography, (iii) machine learning, social media, and artificial intelligence, and (iv) drug trafficking, cryptomarket. National Science Foundation from the United States is the top funder. Darkweb activities interfere with the Sustainable Development Goals (SDG) laid forth by the United Nations to promote peace and sustainability for current and future generations. SDG 16 (Peace, Justice, and Strong Institutions) has the highest number of publications and citations but has an inverse relationship with Darkweb, as the latter undermines the former. This study highlights the need for further research in bitcoin, blockchain, IoT, NLP, cryptocurrencies, phishing and cybercrime, botnets and malware, digital forensics, and electronic crime countermeasures about the Darkweb. The study further elucidates the multi-dimensional nature of the Darkweb, emphasizing the intricate relationship between technology, psychology, and geopolitics. This comprehensive understanding serves as a cornerstone for evolving effective countermeasures and calls for an interdisciplinary research approach. The study also delves into the psychological motivations driving individuals towards illegal activities on the Darkweb, highlighting the urgency for targeted interventions to promote pro-social online behavior.

A Toolkit for Detecting Fallacious Calls for Papers from Potential Predatory Journals.

Purpose: Flattering emails are crucial in tempting authors to submit papers to predatory journals. Although there is ample literature regarding the questionable practices of predatory journals, the nature and detection of spam emails need more attention. Current research provides insight into fallacious calls for papers from potential predatory journals and develops a toolkit in this regard. Methods: In this study, we analyzed three datasets of calls for papers from potential predatory journals and legitimate journals using a text mining approach and R programming language. Results: Overall, most potential predatory journals use similar language and templates in their calls for papers. Importantly, these journals praise themselves in glorious terms involving positive words that may be rarely seen in emails from legitimate journals. Based on these findings, we developed a lexicon for detecting unsolicited calls for papers from potential predatory journals. Conclusion: We conclude that calls for papers from potential predatory journals and legitimate journals are different, and it can help to distinguish them. By providing an educational plan and easily usable tools, we can deal with predatory journals better than previously.

Ayurveda research: Emerging trends and mapping to sustainable development goals.

Ayurveda is India's prominent traditional medical system. The World Health Organization has stated the need for more evidence and data from conventional medicine methods to inform policymakers, regulatory bodies, healthcare stakeholders, and the public about its safe, effective, and equitable use. This study aims to provide a comprehensive analysis of the emerging trends in Ayurveda research, mapping research to the UN Sustainable Development Goals (SDG) and examining the impact of COVID-19. Using bibliometric methods, the researchers analyzed a total of 11,773 publications between 1993 and 2022 to understand the temporal evolution of publications, open-access publications, patterns of author collaboration, top-performing countries, and co-citation networks. The keyword co-occurrence analysis identifies networks of concentrated studies on Ayurveda research themes relating to the four clusters, Alternative and Traditional Medicine, Bioactive Compounds and Biological Activities, Analytical Techniques and Herbal Standardization, and Herbal Medicines and Immunomodulation, reflecting the diverse research areas within Ayurveda. The last cluster included research related to the SARS-CoV-2 virus, suggesting research on herbal approaches to immune modulation in the context of COVID-19. The most prominent SDG among these research themes was Good Health and Well-being (SDG 3), emphasizing the potential of natural products and traditional medicine in promoting holistic health and combating antibiotic resistance.

Menstrual practices, fertility intentions and decision-making regarding family planning by women belonging to various indigenous groups of Wayanad- A qualitative study.

Background: Among the different social groups in India, the tribes are the most vulnerable and socioeconomically deprived. The tribal population is distinct from the general population in terms of their unique cultural, traditional, and reproductive health practices. Therefore, the aim of the study was to conduct an exploratory analysis of the menstrual practices, fertility intentions and decision-making regarding family planning among various indigenous tribal women of Kerala. Methods: A qualitative study was conducted among the tribal women of Wayanad district using the grounded theory approach. Using purposive sampling 16 in-depth interviews, 6 key informant interviews and 2 focus group discussion (FGD) s were conducted. Interview guides were developed for in-depth interviews, key-informant interviews, and focus group discussion through extensive formative research with literature reviews and taking expert opinions. The interviews were conducted among women of reproductive age and their spouses hailing from Paniya and Kurichiya tribal groups in Kalpetta and Mananthavady areas of Wayanad district. Key informant interviews were also conducted among doctors, pharmacists, and community health workers. Results: The key findings of this study were the identification of a web of cultural practices pertaining to menstruation among persons of tribal origin. Unique traditional practices such as "Valayamapura" and "Thirandukalyanam" were reported and most of the women were keen to carry it forward. Fertility desires among couples were found to be not significantly influenced by any gender bias. Decision-making regarding family size were found to be on a mutual agreement between the spouses. The tribal women were aware of modern spacing methods, but preferred natural methods of contraception for temporary use and tubectomy as the permanent method. Non-contraceptive use of oral contraceptive pills (OCP) was prevalent, and the majority took it occasionally for postponement of menstruation to attend various social events. Conclusion: Menstruation-related myths and practices are prevalent and require educational interventions. More focus needs to be given to male sterilization as the permanent method of contraception. The study underscores the need to address gender inequalities and attitudes among tribal populations and to increase efforts to promote higher education among the tribes for busting cultural myths and practices.

Mapping analytical hierarchy process research to sustainable development goals: Bibliometric and social network analysis.

The drive to achieve the Sustainable Development Goals (SDGs) becomes more urgent as the 2030 deadline draws near, increasing research in various sectors. Nevertheless, studies that systematically map Analytical Hierarchy Process (AHP) publications with the SDGs need to be more conspicuously lacking. Our study adds a new perspective to the field by creatively bridging this knowledge gap using the Elsevier SDG Mapping Initiative. To find research clusters, trends, and themes linked to SDGs and their connection to environmental sustainability, we thoroughly analyzed 29,897 publications from 2012 to 2022. The analysis showed that SDG 15, SDG 7, SDG 12, SDG 13, and SDG 11 were the top five SDGs, with an environmental focus among the 17 SDGs. These top SDGs had many clusters connected to them, illustrating various sustainability-related problems. The study also looked at connections between SDGs, the nations with the highest rates of productivity, the top contributors, and the journals with the highest citation counts. We discovered three separate SDG clusters using co-occurrence network analysis, each representing a different SDG. We discovered relevant SDGs using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis and centrality indicators like eigenvector and betweenness. This novel method for publishing analysis combines an AHP focus that aligns with the SDGs with social network analysis and centrality metrics. Our research advances knowledge of how the AHP technique can assess initiatives supporting the SDGs. We offer essential insights into prioritizing sustainable development measures by identifying research clusters, trends, and issues related to environmental sustainability. This study highlights the subject's most important SDGs, productive nations, helpful organizations, and significant journals.

Mapping sustainability reporting research with the UN's sustainable development goal.

Non-financial reporting (NFR) has become crucial to corporate sustainability strategies as companies demonstrate their commitment to the environmental, social, and governance actions outlined in the United Nations Sustainable Development Goals (SDGs) Agenda 2030. Among the various mandatory NFR initiatives, Sustainability Reporting (SR) has emerged as a widely adopted practice by companies worldwide. A gap that the study addresses is the theoretical perspectives on SR in the context of SDG. Then we conduct a bibliometric and science mapping analysis of research trends on SR and precisely map SR research to SDGs which is also a gap in the current literature. We find an exponential increase in the number of publications and citations on SR, particularly after 2015, which coincides with increased public awareness and scrutiny of the SDGs. At the country level, Australia leads with a total of 13 SDGs, followed by the UK, Spain, and Italy, which each address 12 SDGs. Emerging economies such as Indonesia, Malaysia, and India have also increased their contributions since 2019. A keyword co-occurrence analysis identified three main clusters: stakeholder engagement, corporate governance, and accountability; sustainable development goals and climate change; and sustainability reporting and global reporting initiatives. All three clusters had highly cited publications related to SDG 8 (decent work), SDG 9 (industry innovation), and SDG 12 (responsible consumption). This highlights the interdisciplinary nature of SR and its relevance to multiple SDGs. The study is distinctive in that we utilized social network analysis to examine the SDG network based on SR publications, which also affirmed the centrality of SDG 9 and 12. We utilized the prominence percentile, which indicates the momentum of a particular topic, to identify future topics in SR that align with the SDGs. These include cause-related marketing, environmentally preferable purchasing decisions, environmental management systems, education for sustainability, and green computing.

Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study.

With the growth of data and intelligent technologies, the healthcare sector opened numerous technology that enabled services for patients, clinicians, and researchers. One major hurdle in achieving state-of-the-art results in health informatics is domain-specific terminologies and their semantic complexities. A knowledge graph crafted from medical concepts, events, and relationships acts as a medical semantic network to extract new links and hidden patterns from health data sources. Current medical knowledge graph construction studies are limited to generic techniques and opportunities and focus less on exploiting real-world data sources in knowledge graph construction. A knowledge graph constructed from Electronic Health Records (EHR) data obtains real-world data from healthcare records. It ensures better results in subsequent tasks like knowledge extraction and inference, knowledge graph completion, and medical knowledge graph applications such as diagnosis predictions, clinical recommendations, and clinical decision support. This review critically analyses existing works on medical knowledge graphs that used EHR data as the data source at (i) representation level, (ii) extraction level (iii) completion level. In this investigation, we found that EHR-based knowledge graph construction involves challenges such as high complexity and dimensionality of data, lack of knowledge fusion, and dynamic update of the knowledge graph. In addition, the study presents possible ways to tackle the challenges identified. Our findings conclude that future research should focus on knowledge graph integration and knowledge graph completion challenges.

Mapping autism's research landscape: trends in autism screening and its alignment with sustainable development goals.

Introduction: Autism Spectrum Disorder is a complex neurodevelopmental syndrome that profoundly affects social interactions, communication, and sensory perception. The research traced the evolution of autism research from 2011-2022, specifically focusing on the screening and diagnosis of children and students. Methods: Through an analysis of 12,262 publications using the PRISMA framework, bibliographic coupling, science mapping, and citation analysis, this study illuminates the growth trajectory of ASD research and significant disparities in diagnosis and services. Results: The study indicates an increasing trend in autism research, with a strong representation of female authorship. Open Access journals show a higher average citation impact compared to their closed counterparts. A keyword co-occurrence analysis revealed four central research themes: Child Development and Support Systems, Early Identification and Intervention, Prevalence and Etiology, and Mental Health. The pandemic's onset has prioritized research areas like mental health, telehealth, and service accessibility. Discussion: Recommendations on a global level stress the importance of developing timely biological markers for ASD, amplifying Disability Inclusion research, and personalizing mental health services to bridge these critical service gaps. These strategies, underpinned by interdisciplinary collaboration and telehealth innovation, particularly in low-resource settings, can offer a roadmap for inclusive, context-sensitive interventions at local levels that directly support SDG3's aim for health and well-being for all.

Micronutrient interventions among vulnerable population over a decade: A systematic review on Indian perspective.

Background: Micronutrient deficiency has long been recognized as a public health problem, particularly among vulnerable groups such as children, adolescents, pregnant and lactating women. Micronutrient deficiency could not be ruled out in spite of the implementation of various intervention strategies. Different interventions are being used to prevent and treat micronutrient deficiencies at the national and global level. The aim of this study is to systematically review the intervention strategies among different vulnerable age groups in India. Methods: The review was focused on identifying various interventions published based on the internet databases and the peer-reviewed papers from 2011 to 2021, on the predefined inclusive/exclusive criteria. The major intervention strategies implemented in India were recognized and evaluated based on dietary supplementation, micronutrient supplementation, knowledge interventions and food fortification among various age groups. Results: The results show that there are still considerable gaps in identifying the effective intervention strategies, research initiatives, programs and policies addressing to tackle micronutrient deficiencies in India. Multiple interventions are effective that could lead the road to innovations in approaches with diverse dietary intake, developing multiple micronutrient supplements, fortifying foods and nutrition interventions to address calcium, zinc, iodine, vitamin D and vitamin A deficiencies among the vulnerable population. Conclusion: Evidence-based multiple intervention studies covering a large population, in the long term cross-sectional, is the need for the hour to design policies and programs for improving the micronutrient status of vulnerable population in the community.

Virtual Laboratories- A historical review and bibliometric analysis of the past three decades.

Online and virtual teaching-learning has been a panacea that most educational institutions adopted from the dire need created by COVID-19. We provide a comprehensive bibliometric study of 9523 publications on virtual laboratories in higher education covering the years 1991 to 2021. Influential bibliometrics such as publications and citations, productive countries, contributing institutions, funders, journals, authors, and bibliographic couplings were studied using the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. A new metric to complement citations called Field Weighted Citation Impact was introduced that considers the differences in research behavior across disciplines. Findings show that 72% of the research work was published between 2011-and 2021, most likely due to digitalization, with the highest number of publications in 2020-2021 highlighting the impact of the pandemic. Top contributing institutions were from the developed economies of Spain, Germany, and the United States. The citation impact from publications with international co-authors is the highest, highlighting the importance of co-authoring papers with different countries. For the first time, Altmetrics in the context of virtual labs were studied though a very low correlation was observed between citations and Altmetrics Attention Score. Still, the overall percentage of publications with attention showed linear growth. Our work also highlights that virtual laboratory could play a significant role in achieving the United Nations Sustainable Development Goals, specifically SDG4-Quality Education, which largely remains under-addressed.

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning.

The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results.

Pattern of contraceptive use, determinants and fertility intentions among tribal women in Kerala, India: a cross-sectional study.

OBJECTIVES: To assess the pattern of contraceptive use and its determinants, knowledge regarding contraceptives including oral contraceptive pills and fertility intentions among tribal women in the reproductive age group. DESIGN: Community-based cross-sectional study. SETTING: Community development blocks in a predominantly tribal district of Wayanad in Kerala, India. PARTICIPANTS: Women in the reproductive age group (15-49 years) from the tribal groups in the district numbering 2495. OUTCOME MEASURES: PRIMARY: Prevalence of contraceptive use and its determinants. SECONDARY: Knowledge regarding contraceptives in general, oral contraceptives and fertility intentions. RESULTS: The mean age of the study participants was 30.8 years (SD=9.8) and belonged to various tribal groups such as Paniya (59.2%), Kurichiyar (13.6%) and Adiya (10.9%). Current use of contraceptive was reported by about a fourth, 658 (26.4%) (95% CI 27.9 to 24.9) of women. Following logistic regression, belonging to Paniya tribe (adjusted OR (aOR) 2.67, 95% CI 1.49 to 4.77; p<0.001) and age at menarche >13 years (aOR 1.69, 95% CI 1.14 to 2.52; p<0.009) had significantly higher use of contraceptives whereas social vulnerability as indicated by staying in a kutcha house had a lesser likelihood of use of contraceptive (aOR 0.55, 95% CI 0.31 to 0.95; p<0.03). Oral contraceptive use was low (4.8%) among this population and no abuse was observed.Less than half (47%) of the respondents had an above average knowledge on contraception. Multivariable logistic regression indicated that above average knowledge was 2.2 times more likely with higher education (95% CI 1.2 to 3.9), lesser among those who desired more than two children (aOR 0.59; 95% CI 0.38 to 0.94; p<0.02).Two children per family was the preferred choice for 1060 (42.5%) women. No gender bias in favour of the male child was observed. CONCLUSION: Awareness and use of contraceptives are poor though the fertility is not commensurately high. Along with developing targeted responses to contraceptive use among Indigenous people with indigenous data, awareness also requires attention. Ethnographic studies are also necessary to determine the differences in contraceptive use including traditional methods among the various Indigenous groups.

Community-based approach to combat micronutrient deficiencies among irular tribal women: An education intervention.

Tribal women may suffer from poor nutritional health, lack of awareness of micronutrients, reduced dietary diversity, underutilization of micronutrient supplements and locally available food resources, poor hygiene, and sanitation. This study aims to examine the impact of educational intervention on the micronutrient status of the tribal women (n = 714, 15-60 years) in 15 hamlets of Coimbatore district, Tamil Nadu, by census sampling method. Self-structured pretested questionnaires, participatory learning methods, and focus group discussions were adopted to record the background information (anthropometry, clinical signs of micronutrient deficiency, hemoglobin, and dietary assessments). Even though there was no increase in body mass index (BMI), there was a significant change in age, income, and BMI with hemoglobin levels. Impact analysis showed significant behavior change in the utilization of locally available micronutrient-rich foods, improved access to supplements, and dietary diversity. Sustained attempts to educate tribal women proved to be effective in attaining their nutritional security and in the families.

Adoption of online proctored examinations by university students during COVID-19: Innovation diffusion study.

COVID-19 global pandemic pushed a large number of higher educational institutions to use Online Proctored Exams (OPE) because of government-imposed lockdowns. Treating OPE as an educational technology innovation, we apply the diffusion of innovation theory in predicting factors affecting its adoption by university students which we believe is the first of its kind research study. The study presented here reviews OPE, its types, architecture, challenges, and prospects and then focuses on the student adoption experience at a large, multi-campus higher educational institution. We have used the fine-grained Aspect Level Sentiment Analysis to check the university students' attitudes towards the Online Proctored Exams. We then used linguistic features to extract the aspect terms present in the feedback comments which showed that 55% of university students having a positive attitude towards OPE. Results of our study show that innovation characteristics such as relative advantage, compatibility, ease of use, trialability, and observability were found to be positively related to acceptance of OPE.

Data-Driven Methods for Advancing Precision Oncology.

PURPOSE OF REVIEW: This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice. RECENT FINDINGS: Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care. SUMMARY: Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.

Personalized Health Monitoring System for Managing Well-Being in Rural Areas.

Rural India lacks easy access to health practitioners and medical centers, depending instead on community health workers. In these areas, common ailments that are easy to manage with medicines, often lead to medical escalations and even fatalities due to lack of awareness and delayed diagnosis. The introduction of wearable health devices has made it easier to monitor health conditions and to connect doctors and patients in urban areas. However, existing initiatives have not succeeded in providing adequate health monitoring to rural and low-literate patients, as current methods are expensive, require consistent connectivity and expect literate users. Our design considerations address these concerns by providing low-cost medical devices connected to a low-cost health platform, along with personalized guidance based on patient physiological parameters in local languages, and alerts to medical practitioners in case of emergencies. This patient-centric integrated healthcare system is designed to manage the overall health of villagers with real-time health monitoring of patients, to offer guidance on preventive care, and to increase health awareness and self-monitoring at an affordable price. This personalized health monitoring system addresses the health-related needs in remote and rural areas by (1) empowering health workers in monitoring of basic health conditions for rural patients in order to prevent escalations, (2) personalized feedback regarding nutrition, exercise, diet, preventive Ayurveda care and yoga postures based on vital parameters and (3) reporting of patient data to the patient's health center with emergency alerts to doctor and patient. The system supports community health workers in the diagnostic procedure, management, and reporting of rural patients, and functions well even with only intermittent access to Internet.

Complementing Neurophysiology Education for Developing Countries via Cost-Effective Virtual Labs: Case Studies and Classroom Scenarios.

Classroom-level neuroscience experiments vary from detailed protocols involving chemical, physiological and imaging techniques to computer-based modeling. The application of Information and Communication Technology (ICT) is revolutionizing the current laboratory scenario in terms of active learning especially for distance education cases. Virtual web-based labs are an asset to educational institutions confronting economic issues in maintaining equipment, facilities and other conditions needed for good laboratory practice. To enhance education, we developed virtual laboratories in neuroscience and explored their first-level use in (Indian) University education in the context of developing countries. Besides using interactive animations and remotely-triggered experimental devices, a detailed mathematical simulator was implemented on a web-based software platform. In this study, we focused on the perceptions of technology adoption for a virtual neurophysiology laboratory as a new pedagogy tool for complementing college laboratory experience. The study analyses the effect of virtual labs on users assessing the relationship between cognitive, social and teaching presence. Combining feedback from learners and teachers, the study suggests enhanced motivation for students and improved teaching experience for instructors.