Abstract: Due to its molecular structure we find a specific reaction mechanism of ozone in biological systems, which requires low doses. By contrast to disinfection, where ozone is added over a certain period of time (ct concept) until disinfection becomes effective, we must pay particular attention to concentrations and dosages so as not to increase oxidative stress in patients, e.g., those with chronic inflammatory diseases and high oxidative stress. Here we start with a deeper insight into the effect of ozone on Red Bood Cells (RBC) and the glutathione system which can be blocked at higher concentrations if needed, such as is the case in reducing the plasmodium falciparum growth. At low ozone concentrations, the RBC metabolism is activated, 2,3-diphosphoglycerate (2.3-DPG) increases and oxygen is easily released from hemoglobin, which is helpful in diabetes and sporting activities. In mononuclear cells low dose ozone acts as a redox bioregulator, e.g., by downregulating proinflammatory cytokines and oxidative stress.

Abstract: We present a novel framework for quantifying and tracking conceptual evolution in temporal document collections through multi-metric semantic analysis. Our methodology introduces three key innovations: (1) ensemble clustering validation combining silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin score for optimal semantic prototype discovery, (2) permutation-based statistical testing for establishing significant conceptual continuity across time periods, and (3) multi-dimensional conceptual change quantification through centroid shift analysis, distribution divergence via Wasserstein distance, and semantic space transformation measurement. Applied to sustainability discourse spanning 2018-2023, our framework reveals statistically significant paradigm shifts (p < 0.05) with centroid shift magnitudes ranging from 0.142 to 0.387, demonstrating the transition from Corporate Social Responsibility to ESG integration and finally to regulatory-driven net-zero frameworks. The system achieves 94.7% inter-annotator agreement on prototype classification and identifies semantic prototypes with mean intra-cluster coherence of 0.823. Our contributions include rigorous statistical foundations for semantic evolution analysis, automated prototype discovery with validated clustering, and a comprehensive framework for longitudinal discourse analysis applicable across domains from scientific literature to policy documents.

Abstract: This paper presents a comprehensive policy framework to position New Jersey as a national leader in artificial intelligence (AI) education and workforce development. Through analysis of current state initiatives—including the NJ AI Hub, AI Task Force reports, apprenticeship programs, and regulatory guidance—we identify strategic gaps and opportunities across K-12, higher education, and workforce development sectors. We propose a multi-layered approach visualized through interconnected frame works: an integrated AI education ecosystem, phased implementation roadmaps for K-12 AI literacy, a statewide AI curriculum consortium structure, multi-track workforce development pathways, and equity and access frameworks. Quantitative analysis reveals that while 25%+ of New Jersey’s workforce already uses AI technology daily, only 20-25% of educators feel prepared for AI integration. Our policy recommendations address this gap through a $165 million annual investment strategy with projected 3.8x return on investment, creating pathways for 15,000-20,000 new AI jobs by 2030. This framework provides actionable guidance for lawmakers, educators, and industry stakeholders to enhance New Jersey’s competitiveness, ensure ethical AI deployment, and foster inclusive economic growth in the AI era. Drawing from over recent sources including state publications, academic research, and industry reports, this paper offers concrete ecommendations for lawmakers, regulators, educators, and industry stakeholders to enhance New Jersey’s competitiveness, ensure ethical AI deployment, and foster inclusive economic growth in the AI era. Recommendations include establishing AI literacy standards for all K-12 students, creating specialized AI high schools, expanding community college AI programs, developing industry-aligned university curricula, and implementing statewide AI teacher training. We also address equity considerations, funding mechanisms, and implementation timelines.

Abstract: This paper presents a complete cosmological model of Xuan-Liang, achieving a unified description of dark matter and dark energy. Starting from the classical concept of "work" and extending it to "spatial accumulation of power," we first-principally derive the expression of Xuan-Liang: $X = \frac{1}{3} m v^3$. By fieldifying the concept of Xuan-Liang, we construct a dynamic phase-transition model with exact symmetry, whose evolution equation is: \[ \left( \frac{\rho_X}{\rho_t} \right)^{\Delta/2} + \left( \frac{\rho_X}{\rho_t} \right)^{-\Delta/2} = \left( \frac{a}{a_t} \right)^{-3\Delta/2} + \left( \frac{a}{a_t} \right)^{3\Delta/2} \] This equation reveals a profound duality between the cosmic scale factor and the density of the Xuan-Liang field. We present the complete Friedmann equations including ordinary matter, radiation, and the Xuan-Liang field, and numerically solve the cosmic evolution history. Using the latest observational data (Planck 2018, Pantheon+ supernovae, BAO), we constrain the model parameters, showing high compatibility with observations, with a $\chi^2$ improvement of about 8\% compared to the $\Lambda$CDM model. The model predicts a specific equation-of-state evolution $w(z)$, a precise phase-transition redshift $z_t = 0.65 \pm 0.08$, and a weak early dark energy component ($\Omega_{Xe} \sim 10^{-5}$). Theoretical analysis suggests that the path-integral origin of Xuan-Liang may reflect the topological structure of spacetime, providing new perspectives for quantum gravity.

Abstract: This study aims to investigate the greenhouse gas (GHG) emissions and environmental footprint of BOCOM Petroleum, a mid-sized downstream oil company operating in Douala, Cameroon. In response to the critical need for empirical data on industrial emissions in Sub-Saharan Africa, a mixed-methods approach combining Life Cycle Assessment (LCA), carbon accounting, and stakeholder interviews was adopted. Emissions were categorized following the GHG Protocol into Scope 1 (direct), Scope 2 (energy-related), and Scope 3 (value chain). Results reveal total annual emissions of 51,734 kgCO₂-eq, with Scope 3 accounting for 38%, Scope 2 for 33%, and Scope 1 for 29%. Major emission sources include stationary combustion, laboratory processes, and the use of electricity-intensive heat-generating machines. An Environmental Management Plan (EMP) was developed, proposing actionable measures such as process optimization, adoption of energy-efficient equipment, electrification of vehicle fleets, and improved waste management. Findings underscore the need for systemic decarbonization strategies among mid-sized oil firms and highlight the alignment of corporate initiatives with Cameroon’s climate commitments. This study contributes a replicable methodological framework for emission auditing in industrial enterprises across the region and calls for further integration of environmental and financial planning in corporate sustainability strategies.

Abstract: The present study documents the diversity and ecological attributes of grass species (family Poaceae) recorded from Abheda Biological Park (ABP), Kota, Rajasthan. Floristic surveys conducted between July 2024 and December 2025 resulted in the documentation of 42 grass species. Species were evaluated based on life span (annual/perennial) and palatability, which are key indicators of grassland stability and forage potential. The dominance of palatable (65.8%) and perennial (53.6%) species suggests that ABP supports a productive and ecologically balanced grassland system. Herbarium specimens of selected taxa were prepared to ensure taxonomic accuracy and future reference. The study provides baseline information crucial for grassland management and conservation planning in southeastern Rajasthan.

Abstract: The challenges of inadequacies and disparity in quantity of fresh water supply as experience in many regions of the world Altogether, can be attributed to issues such as climate change, rapid population growth, institutional failures and growing water demand., thus resulting in water accessibility issues. While efforts have been made to analyse willingness to buy water or pay for improve water services no single study has pointed to the inequalities associated with informal water market (IWM) in Nigeria. Therefore, this study is an attempt to examine the impact of IWM on access to water in Ijebu-Ode Nigeria. Adopting questionnaire for the survey of 507 and connecting household socio-economic characteristics to household access to water sources. Analytical Method includes in investigating the inequalities associated with informal water market operation. Chi-Square, t-test, ANOVA Also further, inquiry into the associated inequality followed Lorenz Curve and Atkinson Index technique. The findings showed that, Gini index for the study area is 49.45, while that of water expenditure is 49.23 the Atkinson Index remains 0.361, showing existing inequality in access to water. Therefore, recommendations comprise the establishment of regulatory body, provision of loans services for the informal water market sector.

Abstract: Background: The increasing use of electronic cigarettes (ECIGs), especially among youth, has raised concerns about the impact of vaping on oral health. While ECIGs are often marketed as a safer alternative, existing literature suggests their use may have detrimental effects on the pulmonary and cardiovascular systems. The oral cavity is the first point of contact for ECIG aerosol, and new reports link vaping to the onset of periodontal disease. It is critical to understand the potential effects of vaping on the oral microbiome, which affects systemic health. This study investigates how flavored E-liquids and commensal bacteria influence the growth of Porphyromonas gingivalis, a periodontal pathogen, under planktonic and biofilm conditions. Methods: P. gingivalis was grown planktonically in the presence of the supernatants of four streptococcal species (Streptococcus gordonii, Streptococcus intermedius, Streptococcus mitis, and Streptococcus oralis) and flavored E-liquids (tobacco, menthol, cinnamon, strawberry, and blueberry) under anaerobic conditions. Multispecies biofilms, including all species above and Fusobacterium nucleatum, were also grown anaerobically and quantified by crystal violet assays, qPCR, and CFU counts. Re-sults: Although E-liquids inhibit P. gingivalis growth under planktonic conditions, the presence of commensal supernatants partially mitigates this effect. However, P. gingivalis growth in multispecies biofilms is increased by E-liquid treatments. Conclusion: This study highlights the enhanced growth of P. gingivalis as part of an oral microbial community in the presence of E-liquids. This dysbiosis can lead to oral diseases, such as periodontitis, and ultimately systemic pathologies.

Abstract: Cognitive aging is characterized by declines in executive functions, yet the molecular mechanisms underlying the dissociation between cortical control and emotional reactivity remain unclear. This article proposes a conceptual model based on divergent transcriptomic erosion in the prefrontal cortex (PFC) compared to the relative resilience of the limbic system. We summarize data showing that the PFC exhibits marked reductions in the expression of genes critical for synaptic integrity and layer II/III glutamatergic signaling, such as PTGS2, DRD4, SST, and CREB1. Furthermore, we propose that postnatal attenuation of human-specific developmental factors, including ARHGAP11B, may limit "cortical reserve," increasing the vulnerability of the neocortex to mitochondrial and oxidative stress. In contrast, phylogenetically older limbic structures, such as the amygdala, exhibit a more conserved expression profile, with relative retention of early response genes (ARC, FOS). FAT4 gene expression in subcortical limbic structures (such as the amygdala) remains relatively constant after brain development is complete. It is less sensitive to momentary neurotransmitter fluctuations, resulting in a flatter expression profile. We posit that this "transcriptomic mismatch" leads to a disruption of descending disinhibition, in which stable limbic reactivity is no longer modulated by weakening prefrontal cortex activity. This evolutionary tradeoff provides a molecular basis for age-related increases in impulsivity and emotional lability, suggesting that more recently evolved brain regions are the first to succumb to the molecular pressures of aging, compared to the more conservative and stable limbic system. This confirms and illustrates how the brain has evolved over the course of evolution and how new cortical areas often become unstable or incompletely developed as they develop further.

Abstract: Astronomy is probably the oldest science of mankind and thus older than writing. With the development of exact natural sciences, various astronomical sciences such as astrochemistry, astrobiology and some more have developed. The current rapid development of space travel is leading to the formation of further sub-disciplines. In principle, there can be a new extraterrestrial astronomical science for every natural science that has so far been earthbound.

Abstract: Celiac disease (CD) is an autoimmune disorder triggered by immunogenic gluten peptides that resist gastrointestinal digestion. The only current treatment is a strict gluten-free diet, which is challenging to maintain. Lactic acid bacteria (LAB) with specific proteolytic systems offer a promising strategy for gluten detoxification. This study aims to isolate and characterize gluten-degrading LAB from traditional Spanish and Algerian dairy products. A total of 27 artisanal dairy samples were collected. LAB were isolated on MRS and Elliker agar. Gluten-degrading activity was screened using a well diffusion assay with cell-free supernatants and a spot assay with live cultures. Active isolates were identified by 16S rRNA gene sequencing. Out of 123 isolates, 40 (32.5%) were positive in the well assay, while 67 (54.5%) were positive in the spot assay, indicating the latter's higher sensitivity for detecting cell-associated proteases. Halo diameters ranged from 6 to 16 mm. Algerian isolates exhibited significantly stronger activity (mean halo: 12.6 ± 2.1 mm) compared to Spanish isolates (10.2 ± 2.0 mm; p < 0.001). Molecular identification of the 32 most active isolates revealed dominant species as Lactiplantibacillus plantarum, L. pentosus, Levilactobacillus brevis, and Enterococcus faecium. This study confirms that artisanal dairy fermentations are rich sources of LAB with robust gluten-degrading potential. The superior activity of Lactiplantibacillus spp. aligns with their complex peptidase systems. The geographical variation highlights the influence of local fermentation practices. Selected strains represent excellent candidates for developing adjunct cultures to produce gluten-reduced foods and novel probiotic therapeutics.

Abstract: The standard cosmological model, ΛCDM, despite its observational success, relies on three components whose physical nature remains unconfirmed: inflation, dark matter, and dark energy. This work proposes an alternative geometric framework that offers a unified solution to these enigmas based on a single and fundamental hypothesis: our universe is a three-dimensional hypersphere in expansion, embedded in a five-dimensional spacetime. We argue that the intrinsic perspective of standard cosmology (the FLRW metric) provides an incomplete description of reality, forcing the introduction of “dark” components to explain effects that arise naturally from the extrinsic geometry and dynamics of the fifth dimension. In our model, the “dark” phenomena are not exotic substances or epochs, but rather the manifestations in our 4D spacetime of this higher-dimensional geometric reality. Moreover, the model requires only three initial parameters—the baryonic mass of the universe, its radiation content, and the current value of H₀ that fixes the proper time τ—highlighting its simplicity compared to the ΛCDM paradigm. First, we show that the apparent accelerated expansion inferred from Type Ia supernova observations can be consistently reinterpreted as the consequence of a mild evolution of their intrinsic luminosity with redshift, parametrized as L(z)∝(1+z)⁻¹. When this effect is taken into account, the supernova Hubble diagram is accurately reproduced within a decelerating, matter-dominated universe, eliminating the need for a dark energy component. This reinterpretation also leads to a revised value of the Hubble constant and resolves the associated age problem. Second, we postulate a fundamental relation between curvature and inhomogeneity (|Ω_k| = 1/2 δ_ρ), which resolves the flatness and horizon problems without the need for an inflationary epoch. When applied to the CMB, this hypothesis constrains the fundamental parameters of the universe and reproduces the angular scale of the first acoustic peak with an accuracy of about 7%, as well as the BAO angular scale with an error of 2.3%. Third, the global deceleration of the hypersphere projects an additional acceleration into our 3D space, which—using the parameters derived from the CMB—quantitatively explains galactic rotation curves, the dynamics of clusters, and provides a new Tully–Fisher relation of the form M ∝ v³. From this formulation, the MOND law and the Virial-like relation M ∝ σ_v³ for galaxy clusters naturally emerge without the need to invoke dark matter. In summary, we present a self-consistent, purely geometric cosmological model that addresses several of the major puzzles of modern cosmology within a unified framework, offering a potentially simpler alternative to ΛCDM and making testable predictions for a range of astrophysical and cosmological observations. While the model provides a radical alternative to the current paradigm, it should be regarded as an initial and basic proposal, requiring further mathematical development and more detailed confrontation with observations. Its simplicity, together with the breadth of phenomena it accounts for, suggests it may serve as a viable starting point for dialogue and further research aimed at testing and refining this geometric approach.

Abstract: This study takes Liu Tongfang's article Marx's Intellectual Measure, published in Guangming Daily, as its sole subject of investigation. Its objective is to examine the theoretical validity and interpretative boundaries of the author's approach to synthesising Marx's thought through the concept of ‘measure’. This analysis is conducted across three dimensions: conceptual legitimacy, historical interpretative mechanisms, and consistency with the Sinicisation of Marxism in the new era. The research thereby addresses the core question: ‘Does this article possess an academic argumentative structure that is reviewable, reproducible, and testable?’ Methodologically, this paper constructs and implements a triple-algorithm review process comprising ‘formal logical audit—generative verification through intellectual history—contemporaneous consistency testing.’ Employing a Chinese clause-numbering system and rule-driven quantitative metrics, it conducts structured, reproducible evidence audits on: the semantic stability of core concepts; the sufficiency of boundaries in social stage delineation; the explicitness of contradiction mechanism chains; and the operationality of era mapping. Calculations yield the following indices: Boundary Adequacy Index (Boundary Adequacy Index ≈ 0.389), Normative Substitution Index for Mechanism Explanation (Normative Substitution Index ≈ 0.161), Mechanism Explicitness Score (Mechanism Explicitness ≈ 0.738), and Sentence Coverage Rate (Sentence Coverage Rate ≈ 0.421). These quantitative outcomes anchor the scope of argumentation and strength of reasoning. Findings indicate that ‘scale’ concurrently fulfils dual functions of empirical description and normative evaluation within the text. Its transdisciplinary migration from physical or existential spatial extension to the boundaries of consciousness, cognition, and value lacks requisite mediating rules and verifiable derivation chains, thereby generating auditable semantic slippage risks. The text exhibits strong macro-level coherence in its phased narrative of ‘prehistory and true human history’ alongside ‘human dependency, material dependency, and free individuality.’ However, insufficient articulation of boundary conditions concerning mutual exclusivity, exhaustiveness, and transitional forms renders the phasing closer to a value hierarchy than a falsifiable explanatory model. Though multiple passages simultaneously present the contradiction between productive forces and relations of production alongside the developmental goal of free individuality, key arguments exhibit a tendency to substitute normative objectives for mechanism-chain decomposition, thereby weakening the testability of historical materialist explanations. The integration of Marx's theoretical resources across different periods within the intellectual history lacks explicit annotation of generative differences and methodological shifts, while the world-historical narrative insufficiently bridges the stage structure of capitalism with the deepening of imperialism theory. Within the framework of Sinicised Marxism in the new era, the indicator-based mapping interface for ‘people-centred development, practical verification, and Chinese-style modernisation’ remains relatively weak, hindering its direct translation into an operational evaluation system. The research concludes that Marx's Measure of Thought demonstrates theoretical ambition in its comprehensive exposition and value synthesis, yet its pivotal arguments require enhanced reviewability and reproducibility through conceptual semantic constraints, explicit phase boundary conditions, and the explicitation of contradiction mechanism chains. The proposed ‘logical-historical-epochal’ triple-audit framework and quantitative indicator system can provide transferable, top-tier structural assessment tools and standardised rewriting pathways for similar comprehensive philosophical texts.

Abstract: Introduction and aim: Liver disease is associated with obesity, diabetes, and steatotic liver, aside from viral causes and alcohol consumption. Likewise, chronic kidney disease shares metabolic risk factors and a viral etiology with liver disease, contributing to its development and accelerated progression. The clinical data for both pathologies is very similar, which makes early identification of liver damage difficult when they overlap. The aim of this study was to identify chronic liver disease using the fibrosis-4 index (FIB4) in end-stage renal disease undergoing hemodialysis patients, also describing etiology and biochemical variables. Patients and Methods: The study was realized at a secondary-level referral hospital for hemodialysis of the Mexican Social Security Institute in Northeast Mexico. Results: All patients with end-stage renal disease undergoing hemodialysis between 2017 and 2019 were included. Of the 362 patients evaluated, 56.6% were men with an average age of 58 years. The main etiology attributable to chronic kidney disease was hypertension in 92.8%, followed by type 2 diabetes in 71.8%, primary glomerulopathies in 6.9%, and hepatitis C and human immunodeficiency viruses in 0.3% each. The time in hemodialysis was 19 months. Anemia was identified in 93%. The risk of liver fibrosis was identified at 29.5%, and of these, 8% had a FIB4 > 2.67, indicating advanced liver fibrosis. Conclusions: The FIB4 is an accessible and useful method for identifying the risk of liver fibrosis in end-stage renal disease patients undergoing hemodialysis and can be used as an initial tool for assessing liver disease.

Abstract: The endocannabinoid system (ECS) has been extensively mapped at the level of receptors, ligands, enzymes, and signaling pathways, forming a detailed component inventory of a major homeostatic network. However, prevailing ECS models largely omit the nutritional substrates required to sustain ligand synthesis, membrane composition, signaling capacity, and regenerative function, leaving the system operationally incomplete from a systems-biology perspective. This Hypothesis identifies this gap by integrating evidence from nutritional biochemistry, lipid metabolism, and regenerative physiology, and argues that inclusion of dietary inputs is necessary to advance toward a nutritionally complete model of the ECS.By reframing the ECS as a metabolically sustained regulatory network rather than a purely signaling system, this framework has implications for understanding resilience, regeneration, and system failure under chronic stress, nutritional insufficiency, and environmental disruption. This synthesis is intended as a hypothesis-generating foundation to guide future experimental and clinical investigation.

Abstract: This scoping review represents the first concerning 2020–2025, peer-reviewed publications to investigate possible relationships among burnout, nutrition, and nutrition/food literacy during and following the COVID-19 pandemic. 9 January 2026 searches were of the keywords (burnout OR job stress) AND (nutrition OR diet OR eating pattern OR food intake) AND (nutrition literacy OR food labeling) AND (food literacy OR health literacy). Eight databases were searched (CINAHL Plus, Google Scholar, JSTOR, OVID, PubMed, Science Direct, Scopus, Web of Science). With 160 returns, the included reports from these searches were from Google Scholar alone (n = 6). The addition of four relevant reports from the 3 June 2025 searches of “burnout AND nutrition AND (nutrition literacy OR food literacy)” increased the included studies to 10. Although 2020 was the lower date limit, publication of the results was between 2023 and 2025. The finding is that research con-ducted during this period reports co-occurring issues of burnout, nutrition, and nutrition/food literacy in specific populations. The primary discovery is that assessing the relationships among these terms was not the aim of the included studies. This lack of dedicated research on this topic presents an opportunity for burnout and nutrition re-searchers to investigate these relationships intentionally.

Abstract: Background: COVID-19 severity shows marked interindividual variability, suggesting a role for host genetic factors. Polymorphisms in genes involved in the renin–angiotensin system and inflammatory response, such as the angiotensin-converting enzyme (ACE) and the tumor nrecorsis factor-alpha (TNF-α), have been proposed as potential modulators of disease severity. Objectives: To evaluate the association between the ACE I/D (rs4646994) and TNF-α −308 G/A (rs1800629) polymorphisms and COVID-19 severity in a Mexican population. Methods: A total of 236 individuals with RT-PCR–confirmed SARS-CoV-2 infection were included. Patients were classified as hospitalized (severe, n = 155) or non-hospitalized (asymptomatic–mild, n = 81). Genotyping was performed by PCR–RFLP. Genotype distributions were analyzed using χ² tests under dominant and recessive genetic models, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: The ACE I/D polymorphism showed a significant association with COVID-19 severity. Carriers of the I allele (ID + II) had a higher risk of hospitalization compared with DD homozygotes (OR = 2.78, 95% CI: 1.53–5.06, p = 0.001). Sex-stratified analysis revealed that this association was significant only in male patients. No significant association was observed between the TNF-α-308 G/A polymorphism and disease severity. Conclusions: The ACE I/D polymorphism is associated with COVID-19 severity in a Mexican population, with a stronger effect observed in males. These findings underscore the relevance of host genetic background and sex-specific effects in COVID-19 outcomes. Further studies including larger cohorts, healthy controls, and multivariable analyses are required to confirm these associations.

Abstract: Background. The factors driving Coronavirus disease 2019 (COVID-19) severity and its long-term respiratory sequelae remain poorly understood. This study evaluates whether baseline lung function (LF) influences COVID-related clinical outcomes, mortality, and post-infection LF decline. Methods. Data from 602 participants in the Prospective Urban Rural Epidemiology (PURE)-Colombia study were analyzed. Among these, 200 with con-firmed SARS-CoV-2 infection and 402 controls (65% women; 68% aged ≥60 years). All underwent baseline spirometry prior to 2010 and follow-up testing 1-40 months post-recovery. Among infected individuals, 51 (26%) died. Spirometric parameters Forced Expiratory Volume in 1 Second (FEV1), Forced Vital Capacity (FVC), and Peak Expiratory Flow (PEF) were compared using paired t-tests and Cohen's d. Non-parametric data were compared using Wilcoxon s (z statistic). Results. Compared to baseline LF, hospitalized COVID-19 patients showed significant declines in follow-up LF: FEV1 (2.84 vs 2.34 liters; p=0.002), FVC (3.01 vs 2.53 liters; p=0.006), and PEF (399 vs 328 liters; p=0.001). Non-hospitalized COVID-19 cases showed a non-significant downward trend, while con-trols maintained stable LF. Risk factors for post-COVID FEV1 < 80% predicted included hospitalization, elevated waist-to-hip ratio, and incomplete or absent COVID-19 vaccina-tion. Moderate-to-high physical activity was protective. Post-COVID PEF< 80% predicted was associated with female sex, diabetes mellitus, and subsidized healthcare enrollment. Mortality risk was elevated among individuals with low baseline LF, age>65, male sex, hypertension, obesity, low physical activity, and reduced handgrip strength. Discussion. Significant LF decline was observed in hospitalized COVID-19 patients, with minimal changes in outpatients and controls. Identifying clinical and demographic predictors of post-COVID LF impairment may inform targeted interventions to mitigate long-term pulmonary complications.

Abstract: Objective of the study: This study utilized Latent Profile Analysis (LPA) to identify distinct personality profiles in a sample of 523 adults with Substance Use Disorder (SUD) from Punjab, Pakistan. Methodology: Using the NEO-120-IPIP inventory, a statistically optimal four-profile solution demonstrated an excellent model fit (Entropy = 0.875). One-way ANOVA confirmed highly significant differences between profiles across all five personality domains (p < 0.001), with particularly large effect sizes for Conscientiousness (η² = 0.75) and Agreeableness (η² = 0.55). Results: The derived profiles were labeled as: (1) Conscious-Warrior (high neuroticism and conscientiousness); (2) Socially Expressive (high extraversion, lower agreeableness); (3) Highly Expressive and Emotionally Intense (extremely high extraversion, neuroticism, openness); and (4) Agreeable-Achiever (high conscientiousness and extraversion). These results reveal significant personality heterogeneity within the SUD population. Conclusions: The findings highlight the clinical utility of person-centered approaches for culturally informed assessment, individualized treatment planning, and targeted relapse prevention strategies.

Abstract: Large language models have demonstrated remarkable capabilities across diverse reasoning tasks, yet their performance on algorithmic reasoning remains limited. To handle this limitation, we propose PRIME (Policy-Reinforced Iterative Multi-agent Execution), a framework comprising three specialized agents, an executor for step-by-step reasoning, a verifier for constraint checking, and a coordinator for backtracking control, optimized through group relative policy optimization. For comprehensive evaluation, we introduce PRIME-Bench, the largest algorithmic reasoning benchmark to date, comprising 86 tasks across 12 categories with 51,600 instances. Tasks span sorting algorithms, graph and tree structures, automata and state machines, symbolic reasoning, and constraint-based puzzles, with execution traces reaching over one million steps. Compared to baseline approach, PRIME improves average accuracy from 26.8% to 93.8%, a 250% relative gain. The largest improvements occur on tasks requiring sustained state tracking, with Turing machine simulation improving from 9% to 92% and long division from 16% to 94%. Ablation studies identify iterative verification as the primary contributor, preventing the error propagation that causes baseline approaches to fail catastrophically. Analysis across model scales (8B–120B parameters) reveals that smaller models benefit disproportionately, achieving accuracy comparable to models 8× larger.