Early management of diabetes risk and paying attention to the factors that cause someone to have the potential for diabetes are important to minimize cases of diabetes sufferers. Monitoring of Pre-diabetes patients is characterized by increasing certain parameters in the medical record data feature which is an important dynamic part of this study. Data mining techniques in diabetes disease prediction are used to determine a patient's risk of diabetes more quickly and accurately. This study uses the Knowledge Discovery in Database model process consisting of several stages such as Data Selection, Preprocessing, Transformation, Data Mining and Evaluation. The techniques that can be used to overcome these problems are Data Mining using the Navies Bayes algorithm, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest have various evaluation results with several input datasets used.
Randal S. Olson, R. Urbanowicz, Peter C. Andrews
et al.
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning—pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators—such as synthetic feature constructors—that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.
Background: Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), can affect the hepatobiliary system and pancreas, substantially impacting the life quality of patients. Objectives: To evaluate the quality of evidence and comprehensively assess the validity of associations of IBD with hepatobiliary and pancreatic diseases. Design: We performed an umbrella review of existing meta-analyses in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) recommendations. Data sources and methods: We systematically searched PubMed, Embase, and Web of Science from inception to April 2024, to identify and appraise meta-analyses examining IBD and risk of hepatobiliary and pancreatic manifestations. Methodologic quality was assessed with A Measurement Tool to Assess Systematic Reviews (AMSTAR 2) and the strength of evidence was graded according to prespecified criteria. Results: A total of 14 meta-analyses of observational studies were included. The strongest-validity evidence suggested the significant associations between IBD and risk of gallstones (odds ratio (OR) = 1.72; 95% confidence interval (CI) = 1.40–2.12) and acute pancreatitis (OR = 3.11; 95% CI = 2.93–3.30). Highly suggestive evidence indicated a significantly increased risk of hepatobiliary cancer in UC (incidence rate ratio (IRR) = 2.05; 95% CI = 1.52–2.76) and CD (IRR = 2.31; 95% CI = 1.25–4.28). In addition, highly suggestive evidence indicated that IBD was associated with portal venous system thrombosis. Suggestive evidence showed a significantly higher prevalence of primary sclerosing cholangitis, non-alcoholic fatty liver disease, autoimmune hepatitis, and autoimmune pancreatitis in IBD patients than in the general population. Conclusion: The associations between IBD and multiple hepatobiliary and pancreatic disorders showed varying levels of evidence and magnitude of risk. Further high-quality primary studies are needed to identify IBD patients who are more at risk and would benefit the most from screening and prevention programs. Trial registration (PROSPERO): CRD42023451461.
Diseases of the digestive system. Gastroenterology
F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi
et al.
This study provides an in-depth evaluation of sea level rise (SLR) and its varied effects across the coastal regions of southern Africa. Utilizing data collected between 1993 and 2022, we analyze SLR patterns alongside land subsidence phenomena, based on observations from 10 strategically located tide gauges and X-TRACK satellite altimetry datasets. To ensure greater accuracy, the Coastal Altimetry Approach was adopted to refine nearshore measurements. Findings indicate that in areas such as Cape Town, sea-level rise rates reach around 6.3 mm/year, which is nearly twice the current global average of 3.3 mm/year. The interaction between rapid sea-level rise and subsidence rates surpassing 2.2 mm/year presents significant threats to coastal communities, critical infrastructure, and natural ecosystems. Moreover, the study highlights how seismic activity contributes to coastal dynamics, illustrating the role of earthquake-induced subsidence in magnifying the impacts of SLR. By incorporating seismic factors into the analysis, a more comprehensive understanding of the interplay between natural and human-induced drivers of sea-level variability is achieved. Additionally, the study examines the broader effects of SLR on Africa’s culturally and historically important coastal heritage sites, emphasizing the urgent need for proactive coastal management and climate adaptation efforts.
Ghenwa Chamouni, Filippo Lococo, Carolina Sassorossi
et al.
IntroductionArtificial intelligence (AI) is increasingly integrating into the healthcare field, particularly in lung cancer care, including screening, diagnosis, treatment, and prognosis. While these applications offer promising advancements, they also raise complex challenges that must be addressed to ensure responsible implementation in clinical practice. This scoping review explores the ethical and legal aspects of AI applications in lung cancer.MethodsA search was conducted across PubMed, Scopus, Web of Science, Cochrane Library, PROSPERO, OAIster, and CABI. A total of 581 records were initially retrieved, of which 20 met the eligibility criteria and were included in the review. The PRISMA guidelines were followed.ResultsThe most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. The most frequently raised legal concerns were data protection and privacy, although issues relating to cybersecurity, liability, safety and effectiveness, the lack of appropriate regulation, and intellectual property law were also noted. Solutions proposed ranged from technical approaches to calls for regulatory and policy development. However, many studies lacked comprehensive legal analysis, and most included papers originated from high-income countries. This highlights the need for a broader global perspective.DiscussionThis review found that data privacy and protection are the most prominent ethical and legal concerns in AI applications for lung cancer care. Deep Learning (DL) applications, especially in diagnostic imaging, are closely tied to data privacy, lack of transparency, and algorithmic bias. Hybrid and multimodal AI systems raise additional concerns regarding informed consent and the lack of proper regulations. Ethical issues were more frequently addressed than legal ones, with limited consideration for global applicability, particularly in low- and lower middle-income countries. Although technical and policy solutions have been proposed, these remain largely unvalidated and fragmented, with limited real-world feasibility or scalability.
IntroductionEthiopia’s livestock sector is critically vulnerable to a wide range of geological and hydrometeorological hazards that undermine animal health, productivity, and the livelihoods of pastoral communities. The country’s geographic location along the East African Rift System increases its susceptibility to geological threats such as volcanic eruptions, earthquakes, and landslides, while climate variability exacerbates hydrometeorological risks including droughts and floods.MethodsThis systematic review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and employs a structured search strategy across major academic databases including Scopus, Web of Science, and Google Scholar. Studies were selected based on predefined inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed.ResultsThe review synthesizes findings from high-quality studies to qualitatively assess the compound impacts of geological and hydrometeorological hazards on livestock production in Ethiopia, particularly within pastoral and agro-pastoral systems. Drought emerges as the most significant hazard, with more than 6.8 million livestock deaths reported since 2020 due to successive failed rainy seasons. Floods have also caused severe damage; for instance, the 2006 flooding in the Southern Nations, Nationalities, and Peoples’ Region (SNNPR) resulted in the loss of approximately 15,600 livestock. In contrast, direct data on geological hazards such as volcanic eruptions and earthquakes remain limited, though their indirect effects—such as ashfall on grazing lands, water contamination, and disruption of grazing routes—further compromise livestock productivity and resilience.DiscussionThe review highlights critical gaps in data and research, particularly regarding the direct impacts of geological hazards. It identifies key adaptation and mitigation strategies, including early warning systems, hazard mapping, veterinary service enhancement, livestock diversification, and the promotion of livestock insurance schemes. Strengthening policy frameworks, community engagement, and economic instruments is essential to build resilience in the livestock sector. Evidence-based interventions are urgently needed to safeguard livelihoods, ensure food security, and promote sustainable adaptation in Ethiopia’s hazard-prone regions.
Victor Dante AYAVIRI-NINA, Sofia Alejandra MATOS AROCA, Gabith Miriam QUISPE FERNANDEZ
et al.
The present research develops a bibliometric analysis of innovation in the social economy, the study provides a
comprehensive vision of the current state of the field and highlights the most relevant publications, authors, sources and research
topics, taking as its general objective to analyze the behavior and advances in the literature about innovation in the social economy
through a bibliometric analysis, in this way Scopus and Web of Science (WoS) were considered as data sources, forming a database
of 304 registered articles, of which 111 belong to Scopus and 193 Web of Science (WoS). In the processing and representation of
data, the Bibliometrix and VoSviewer programs were used, which highlights information on trends, citation analysis, H index,
analysis of co-occurrences, keywords, affiliations on innovation research in the social economy. The growing scientific production
underlines the growing importance of the social economy as a driver of sustainable economic development. Geographic distribution,
thematic trends, and identification of influential contributors contribute to future research and practical efforts within this field.
<h4>Objective</h4>Hand osteoarthritis poses a significant health challenge globally due to its increasing prevalence and the substantial burden on individuals and the society. In current clinical practice, treatment options for hand osteoarthritis encompass a range of approaches, including biological agents, antimetabolic drugs, neuromuscular blockers, anti-inflammatory drugs, hormone medications, pain relievers, new synergistic drugs, and other medications. Despite the diverse array of treatments, determining the optimal regimen remains elusive. This study seeks to conduct a network meta-analysis to assess the effectiveness and safety of various drug intervention measures in the treatment of hand osteoarthritis. The findings aim to provide evidence-based support for the clinical management of hand osteoarthritis.<h4>Methods</h4>We performed a comprehensive search across PubMed, Embase, Web of Science, and Cochrane Central Register of Controlled Trials was conducted until September 15th, 2022, to identify relevant randomized controlled trials. After meticulous screening and data extraction, the Cochrane Handbook's risk of bias assessment tool was applied to evaluate study quality. Data synthesis was carried out using Stata 15.1 software.<h4>Results</h4>21 studies with data for 3965 patients were meta-analyzed, involving 20 distinct Western medicine agents. GCSB-5, a specific herbal complex that mainly regulate pain in hand osteoarthritis, showed the greatest reduction in pain [WMD = -13.00, 95% CI (-26.69, 0.69)]. CRx-102, s specific medication characterized by its significant effect for relieving joint stiffness symptoms, remarkably mitigated stiffness [WMD = -7.50, 95% CI (-8.90, -6.10)]. Chondroitin sulfate displayed the highest incidence of adverse events [RR = 0.26, 95% CI (0.06, 1.22)]. No substantial variation in functional index for hand osteoarthritis score improvement was identified between distinct agents and placebo.<h4>Conclusions</h4>In summary, GCSB-5 and CRx-102 exhibit efficacy in alleviating pain and stiffness in HOA, respectively. However, cautious interpretation of the results is advised. Tailored treatment decisions based on individual contexts are imperative.
Abstract Background Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined. Methods Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies’ titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity. Results This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research. Conclusion This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Background The number of patients undergoing joint replacement procedures is continuously increasing. Tele-equipment is progressively being employed for postrehabilitation of total hip and knee replacements. Gaining a comprehensive understanding of the experiences and requirements of patients undergoing total hip and knee arthroplasty who participate in telerehabilitation can contribute to the enhancement of telerehabilitation programs and the overall rehabilitation and care provided to this specific population. Objective To explore the needs and experiences of total hip and knee arthroplasty patients with telerehabilitation. Design Systematic review and qualitative synthesis. Methods Electronic databases PubMed, Web of Science, The Cochrane Library, Embase, CINAHL, Scopus, ProQuest, CNKI, Wanfang Data, VIP, and SinoMed were systematically searched for information on the needs and experiences of telerehabilitation for patients with total hip arthroplasty and total knee arthroplasty in qualitative studies. The search period was from the creation of the database to March 2024. Literature quality was assessed using the 2016 edition of the Australian Joanna Briggs Institute Centre for Evidence-Based Health Care Quality Assessment Criteria for Qualitative Research. A pooled integration approach was used to integrate the findings inductively. Results A total of 11 studies were included and 4 themes were identified: the desire to communicate and the need to acquire knowledge; accessible, high-quality rehabilitation services; positive psychological experiences; the dilemmas of participating in telerehabilitation. Conclusions This study's findings emphasize that the practical needs and challenges of total hip and knee arthroplasty patients’ participation in telerehabilitation should be continuously focused on, and the advantages of telerehabilitation should be continuously strengthened to guarantee the continuity of patients’ postoperative rehabilitation and to promote their postoperative recovery.
Computer applications to medicine. Medical informatics
While recognizing the vital role of teachers in augmented reality (AR) integration, a noticeable literature gap exists regarding how science educators address challenges related to technology, pedagogy, and content during AR instructional design and implementation. Conducted in a secondary school in Taiwan, this study addressed this gap by conducting a qualitative single-case analysis of a science teacher’s integration of AR technology into her biology lessons. The teacher’s pedagogical reasoning and action processes were observed and analyzed over 10 weeks, with a focus on micro-level exploration across two iterations of pedagogical analysis, design, implementation, reflection, and revision. The primary data collection includes teacher interviews, supplemented by teacher reflective notes, lesson plans, teaching materials, researcher observations and field notes taken during the weekly, one-hour teacher learning community meetings, and the AR-integrated lessons, student assessment results, and feedback. The study was informed by both the Technology Integration Planning model and the Technological Pedagogical Content Knowledge framework. Data analysis techniques involved deductive coding and thematic analysis. The findings reveal the teacher’s developmental proficiency in AR, a reimagined depiction of AR-enhanced instructional content, a shift from didactic-based to inquiry-based teaching approaches, and an intertwined development of technological pedagogical knowledge, technological content knowledge, and pedagogical content knowledge. This study provides valuable insights into how the educator became a pedagogical designer, overcame individual and contextual challenges, and leveraged reflective strategies to enhance biology lessons using AR technology, emphasizing technology’s potential to enrich pedagogy in science education.
<p>Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM<span class="inline-formula"><sub>2.5</sub></span> and <span class="inline-formula">O<sub>3</sub></span>, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30 %–42 % using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.</p>