Sep 2026· Health Science Reports· Vol 9 9, pp.
e73144
· 0 citations
Medicine
Abstract
Background and Aims
Hypertension is a long-term condition marked by consistently high arterial blood pressure. Hypertensive patients often experience altered serum albumin and uric acid, attributed to factors like inflammation, kidney damage, and the activation of the renin angiotensin aldosterone system. However, localized evidence regarding these biomarkers remains scarce within the Ethiopian context. This study aimed to assess the level of serum albumin and uric acid among hypertensive patients.
Methods
A comparative cross-sectional study was conducted from March 1 to May 30, 2025, involving 232 study participants selected using systematic random sampling. Of these, 116 were patients with hypertensive and 116 were normotensives. Sociodemographic, behavioral, and clinical data were measured by structured questionnaires. Serum albumin and uric acid levels were measured by spectrophotometric principle. Data were analyzed using SPSS version 25 software. Descriptive statistics, independent t-tests, and binary logistic regression were performed with significance set at p < 0.05.
Results
The mean age of the study participants was 46 ± 11.4 and 45 ± 11.6 for the case and control groups. There was a statistically significant difference in mean serum albumin and uric acid levels between the case and control groups. The prevalence of hypoalbuminemia and hyperuricemia among hypertensive patients were 25.9% (95% CI: 18.2%, 34.8%) and 23.3% (95% CI: 15.9%, 32%), respectively. Multivariable logistic regression showed hypertensive patients with albuminuria had 3.3 and 16.1 odds of developing hypoalbuminemia and hyperuricemia.
Conclusion
This study demonstrates that the prevalence of hypoalbuminemia and hyperuricemia was higher in hypertensive patients than the controls. Albuminuria was a significant predictor of hypoalbuminemia and hyperuricemia. This underscores the potential metabolic alterations that may be associated with hypertension. Thus, it is important to monitor these parameters in hypertensive individuals.
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
Albérick Euraste Djiré, Iyiola E. Olatunji, Melissa Tessa et al.· 1 citation
MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.
Yunhao Liang, Chengguang Gan, Ruixuan Ying et al.· 0 citations
BACKGROUND
Underreporting is a defining problem in the registration of workplace violence across public services. This is reinforced by inconsistent, often poor incident descriptions and the absence of a clear, shared nomenclature. Without an ontology-backed structure, similar events are recorded differently across settings or remain uncodable and effectively invisible. Moreover, WPV is typically registered and studied within sector- and even organization-specific categories and local reporting logics rather than through a shared semantic framework, limiting comparability and cumulative understanding across sectors. To address this, we propose creating and publicizing a workplace violence ontology (WPV-ONTO) to unify the representation of WPV events across public services.
OBJECTIVE
The objective of this protocol is to describe and justify a research methodology for developing a cross-sector ontology and reference nomenclature for WPV in public services (WPV-ONTO), including the scoping review, expert-consensus, and evaluation procedures used to build it. Promoting openness and high standards during its creation and encouraging its uptake once available are broader project goals that this protocol is designed to support, rather than measurable objectives that this protocol itself tests.
METHODS
Using the Protégé ontology editor and the METHONTOLOGY ontology development life-cycle guidelines, we will create an ontology that captures the cross-sector WPV domain in Web Ontology Language (OWL). In order to find common WPV concepts, definitions, and synonyms, the modeling process will employ a methodologically defined, iterative workflow that combines (1) focused scoping searches (Web of Science/PubMed/APA PsycInfo/ERIC/Sociological Abstracts); (2) structured extraction from industry-standard incident reporting tools and code lists; and (3) expert consensus. Based on iterative rounds of scientific literature reviews and industry-standard code lists, a team of domain experts will use a hybrid top-down and bottom-up approach to define and identify key ideas and relationships. A small pilot with 6-8 frontline workers from two sectors will test the prototype reporting form against usability criteria before version 1.0 release.
RESULTS
The primary output will be a comprehensive, versioned WPV-ONTO accommodating key WPV concepts relevant to public services, augmented with synonyms, definitions, and references. WPV-ONTO will include an explicit hierarchical structure and relations supporting inheritance and compositional incident encoding. WPV-ONTO seeks to integrate the needs and conceptualizations of frontline workers, safety and aftercare professionals, sector policymakers, WPV researchers, and health/information systems experts. Recruitment of the expert panel is planned to begin in March 2027, and WPV-ONTO version 1.0 is expected at the end of the 24-month development period.
CONCLUSIONS
WPV-ONTO is expected to enable reasoning, inference, and consistent representation of relationships among WPV concepts for application in multiple contexts, including cross-sector reporting harmonization, software and API development, research data integration, and evaluation of prevention and aftercare initiatives. By providing a shared vocabulary and explicit structure, WPV-ONTO may also facilitate linkage with other relevant information systems, such as electronic medical records, and justice and police information systems, reducing methodological fragmentation and supporting coordinated cross-sector learning while retaining the contextual specificity necessary for public service environments.
CLINICALTRIAL
Not applicable.
I. Steenhout, Ronald Buyl· JMIR Research Protocols· 0 citations
This prototype MRG image translocation software was helpful to 69% of patients with binocular diplopia, but limited by large angle strabismus because of the limited instrument field of view.
Edsel B Ing, Kevin Sha, Sarosh Dandoti et al.· Journal of neuro-ophthalmolo...· 0 citations
A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.
Vedamurthy D R, Dr. Anup Ritti, A. Bibi et al.· International Journal for Re...· 0 citations
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