Abstract: Classical Be stars are key laboratories for investigating how rapid rotation, pulsations, and mass loss couple to the formation and evolution of circumstellar decretion disks. However, few studies have combined Kepler/K2 photometry with multi-epoch Hα monitoring. Here we present four previously unclassified Be-type variable stars observed by K2 (three in Campaign 11 and one in Campaign 15) and followed up with ground-based spectroscopy. We analyzed public PDC light curves and extracted variability frequencies using Lomb–Scargle periodograms and iterative prewhitening with a conservative detection threshold of S/N≥5. Optical spectra obtained at the Observatório Pico dos Dias (Brazil) over a multi-year baseline (2017–2025) include repeated Hα observations and blue-region spectra for photospheric characterization. All targets show detectable K2 variability on timescales from hours to days, with frequency spectra ranging from close multi-periodic components producing beating patterns to power dominated by low frequencies. Each star exhibits Hα emission at multiple epochs, with long-term changes in line-profile morphology and equivalent width, indicating disk variability on year-long timescales. These results demonstrate that disk evolution can occur without conspicuous photometric outbursts over the time span of space-based observations, highlighting the diagnostic value of combining high-precision space photometry with long-term spectroscopy to characterize multiscale variability in Galactic Be stars.

Abstract: Early childhood caries (ECC) is routinely described as a complex, multifactorial disease shaped by biofilm ecology, host susceptibility, diet, behavior, and social context. Yet, a growing strand of public-health messaging and implementation practice increasingly treats ECC as a one-step problem solvable by a topical “magic paint” (most prominently silver diamine fluoride, SDF) and deliverable by non-dental or minimally trained providers. This commentary argues that the core contradiction—declaring ECC polycausal while operationalizing it as monocausal—drives a harmful evidence-to-policy drift: research designs favor short-term, easily marketable surrogate endpoints (e.g., “arrest” defined partly by SDF-induced black staining) and implementation strategies shift diagnosis and management to underprepared personnel without robust guardrails.Using a journal-style critical lens anchored in ROB-2, CONSORT, and STROBE principles, I examine recent Canadian work frequently cited to justify "paint-and-go" approaches, including open-label randomized trials of SDF application intervals and microbiome-focused substudies, and I integrate the delegation axis through the Canadian Caries Risk Assessment Tool (CCRAT) and its embedding into primary care workflows. While SDF and non-dental screening can be valuable adjuncts in a continuum of care, overselling them as substitutes for dentist-led diagnosis, pulpal assessment, and definitive rehabilitation risks institutionalizing a two-tier standard for children—especially for Indigenous and remote communities. I conclude with concrete research and policy guardrails: comparator-driven trials, multilevel modeling, lesion-specific sampling where mechanistic claims are made, patient-centered outcomes, defined referral timelines, and a dental-home–anchored pathway that treats SDF as a bridge—not a destination.

Abstract:

Ecological vulnerability of coral reefs contrasts sharply with their persistence through geologic time, creating a paradox from mis-scaled assumptions of time, mortality and organismal dimensionality, namely bleaching susceptibility, mortality, and recovery are treated as linear or sequential outcomes. Recursive definitions built on such mis-scaled assumptions generate straw-man inferences by conflating vulnerability with fragility and obscuring cryptic recovery dynamics. Using post hoc meta-analyses integrating datasets on coral bleaching, life history, reproductive strategy, morphology, and taxonomy, I evaluate system behavior across matrixed categories of thermal exposure and observation timing. Susceptibility emerges as a graded physiological response with weak coupling between predictor importance and variance, whereas mortality exhibits thresholded dynamics consistent with collapse behavior. Partial overlap in predictor structure indicates that bleaching does not represent a direct trajectory toward death, but rather a regulated buffering phase preceding potential tissue-level failure. Skeletal architecture consistently appears as a strong predictor across susceptibility and mortality, while taxonomic identity shows weak and variable effects. Recovery dynamics further indicate host–symbiont restructuring consistent with recursive evolutionary filtering rather than deterministic trait replacement. Together, these findings reframe coral bleaching as a regulated physiological state decoupled from mortality and demonstrate how recursive logic frameworks resolve paradoxes of timing, scale, and resilience in coral bleaching dynamics.

Abstract: Background: Few currently available mental health group therapy programs have been co-designed with key stakeholders to meet the needs of autistic adult consumers. The current study formed part of a co-designed project with both autistic adults, and mental health clinicians. The goal of the study was to develop a fit-for-purpose mental health therapy program for autistic adults. This brief report outlines the major findings of the clinician portion of the project. Methods: Semi-structured interviews were conducted with mental health clinicians, asking about their experiences working with autistic adults and their thoughts and ideas for an autism specific group mental health therapy program. A constructivist grounded theory qualitative approach was used to analyse the qualitative data. Results: 18 mental health clinicians participated. Three main themes, and a further nine sub-themes, were identified. Main themes were: 1) capacity and experience of clinicians in identifying autistic clients; 2) how group sessions run: barriers and clinicians; 3) therapies that do/don’t work well and recommendations. Conclusions: Mental health clinicians reported varying confidence working effectively with autistic adult clients. Therapeutic alliance was discussed as key for stronger outcomes, along with a strengths-based approach and specific-skills based intervention.

Abstract: Antimicrobial resistance is globally known term in this 21st century. When antibiotic does not work against mi-crobes, then antimicrobial resistance occurs. Many people have become resistant to antibiotic because of their hap-hazard use of antibiotic. People does not maintain the proper use of antibiotic and resistance is developed. Antimi-crobial Resistance (AMR) is a broad term, has now become a global concern day by day. Antimicrobial agents are utilized to treat different microbiological infection in human and animals. Antimicrobial resistance refers to the re-sistance of antibiotics against specific microorganisms. However, there are few methods for detecting antibiotic re-sistance in the laboratory. Among of them conventional and molecular techniques are popular. This review article outlines a clear description of various techniques of antimicrobial resistance detection. Previous technology and in-novative future technology have been moderately described in this article.

Abstract:

We develop a structural bridge between relativistic Hamilton–Jacobi theory and the relativistic Schrödinger equation within the framework of tempered distributions and Schwartz linear algebra. For translation-invariant Hamiltonians, the principal functions \( S_p(x)=\langle p,x\rangle \) restricted to the mass shell form a complete integral of the Hamilton–Jacobi equation, while their exponential images \( \eta_p=\exp\!\left(\frac{i}{\hbar}S_p\right) \) constitute a Schwartz basis of the tempered state space. On each spectral fiber, both classical and quantum equations reduce to the same Einstein dispersion relation. We prove that the relativistic Schrödinger equation is precisely the Schwartz–von Neumann S–linear extension of the classical energy relation from certainty momentum states to arbitrary tempered superpositions. In the presence of scalar potentials, the Hamiltonian arises as a mixed (momentum-diagonal and position-diagonal) extension, showing that the extension principle is not restricted to the free case. We further demonstrate that exact quantum dynamics cannot, in general, be represented by a single exponential phase \( \exp\!\left(\frac{i}{\hbar}S\right) \) unless \( S \) is affine in space. Instead, quantum evolution is obtained by S–superpositions of the principal exponential family associated with a complete integral of the Hamilton–Jacobi equation. In this sense, classical elimination of parameters is replaced by linear spectral superposition. Geometrically, the exponential mapping transforms the flat affine space of Minkowski generators into a curved manifold of principal waves on which the nonlinear Hamilton–Jacobi flow pushes forward to a linear unitary Schrödinger flow. Through de Broglie–Maxwell isomorphisms, the construction extends to complex electromagnetic-like fields, preserving translation representation, dispersion relations, and polarization geometry. The results suggest that, for translation-invariant systems, quantization may be understood as an infinite-dimensional complex linearization of a classical certainty space rather than as a semiclassical approximation. Within the tempered-distribution setting, relativistic quantum dynamics emerges as the superpositional completion of a classical complete integral.

Abstract: Ambient air pollution significantly contributes to respiratory illnesses, yet little is known about how industrial emissions are linked to preventable hospitalizations across atmospheric basins in middle-income countries. This study develops a basin-based geo-matics framework to examine the spatial and temporal relationship between industrial pollutants and age- and sex-adjusted avoidable hospitalizations for community-acquired pneumonia (PQI 11) in Mexico from 2013 to 2020. Using state-level data grouped into eight macro-regions, we combine bivariate choropleth maps, Pearson correlations, linear regression, and longitudinal time-series analysis to identify spatial clusters of high risk and to estimate regional sensitivities to changes in PM2.5, SO2, NOx, and volatile organic compound emissions. The findings reveal notable regional differences: northern border states and the Mexico City metropolitan basin form persistent high–high clusters where elevated emissions coincide with high PQI 11 rates, while coastal and peninsular regions show lower hospitalization burdens despite medium emission levels. Although national industrial PM2.5 emissions decreased over the study period, several macro-regions—particularly CDMX_Edomex, Centro, and Centro Norte—experienced significant increases in avoidable hospitalizations and decoupled emission–health patterns. Correlation matrices and regression slopes suggest that the strength and even direction of links between pollutants and PQI 11 vary across macro-regions, with emission-responsive patterns in Centro Norte and weak or inverse relationships in Peninsula and Pacifico Sur. These findings demonstrate that national averages obscure critical spatial disparities and highlight the value of basin-based geomatics approaches for regional air-quality governance, spatial decision support, and primary-care planning aimed at reducing preventable respiratory hospitalizations.

Abstract: Estimating Remaining Useful Life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven bearing fault prognosis and RUL prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyzes how these approaches have been individually and implicitly combined to cope with nonstationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions.

Abstract: Plastic recycling technologies are rapidly being reoriented toward process-, operations-, and quality-centered innovation, driven by the expansion of the circular economy and digital transformation. This study uses patent data to quantify long-term trends in plastic recycling and to compare technological structures and thematic shifts before and after 2015, thereby identifying core technological axes and convergence patterns. We collected and curated 64,639 triadic patents (2005–2024) and conducted IPC portfolio analysis, IPC co-occurrence network analysis, and period-split topic modeling. The results indicate that, since 2015, technologies related to data- and AI-enabled sorting, quality assurance, and process optimization (G06), along with tracking and connectivity (H04), collection and logistics (B65), water treatment (C02), and quality modification/compounding (C09), have expanded, while the relative prominence of some synthesis- and conversion-oriented technologies has declined. Convergence has shifted from material formulation–centered combinations toward stronger linkages with downstream processing–productization–standardization and operational infrastructure. Topic trends likewise show the rising salience of reuse-oriented packaging take-back, washing and standardization, remanufacturing, and data governance in the later period. Overall, these shifts suggest that recycling technologies are evolving beyond isolated process improvements toward maximizing circularity performance across the value chain, supporting sustainability objectives such as reducing environmental burdens and carbon emissions and improving resource efficiency.

Abstract: This study develops a nonlinear macro-labor framework to analyze dynamic adjustment mechanisms in European labor markets using harmonic stability theory, panel econometric modeling, and frequency-domain propagation analysis. The research investigates whether labor market interactions exhibit partial harmonic conjugacy, asymmetric transmission structures, and regime-dependent convergence behavior. Employing panel VAR estimation, DCC-GARCH volatility modeling, threshold regression, structural break testing, and spectral coherence analysis, the study provides empirical evidence of nonlinear shock propagation and spatial heterogeneity across European economies.The results confirm the existence of partial harmonic equilibrium structures, where macro-labor variables satisfy local propagation symmetry but fail to maintain global analytic consistency under crisis conditions. Structural break analysis reveals significant regime shifts during the 2008 financial crisis and the COVID-19 shock. Convergence tests indicate fragmentation between core and peripheral economies, with peripheral regions exhibiting stronger persistence, higher crisis amplification, and slower adjustment speeds. Threshold regression results demonstrate state-dependent labor market responses, particularly under high unemployment regimes.Simulation and counterfactual policy analysis show that structural reforms and coordinated policy packages generate the largest welfare gains by reducing system instability and improving harmonic synchronization. Youth unemployment dynamics display higher volatility and stronger amplification effects, highlighting demographic vulnerability. Overall, the findings suggest that European macro-labor systems operate as nonlinear, spatially heterogeneous networks characterized by regime-switching propagation dynamics.The study contributes to nonlinear macroeconomics by introducing harmonic propagation analysis as a complementary framework for understanding labor market adjustment. Policy implications emphasize the importance of structural flexibility, institutional coordination, and crisis-response mechanisms in maintaining macroeconomic stability.

Abstract: Large Language Model (LLM) based multi-agent systems have demonstrated remarkable potential in automating complex software engineering tasks. However, existing frameworks such as MetaGPT and AutoGen suffer from critical limitations including static role assignments, cascading hallucinations in long-horizon tasks, and the absence of experience accumulation mechanisms. We propose Eco-Evolve, a self-reflective multi-agent collaboration framework that addresses these challenges through three key innovations: (1) a dynamic topology generation mechanism that adaptively constructs agent communication graphs based on task complexity, (2) a system reflection module featuring a dedicated Critic Agent for deliberate verification at critical checkpoints, and (3) an error-driven self-evolution mechanism inspired by Hindsight Experience Replay (HER) that enables prompt optimization through experiential learning. We evaluate Eco-Evolve on SWE-bench Verified and DevBench, achieving62.3% and 73.5% respectively, representing improvements of26.6% and 14.7% over the strongest baseline. Comprehensive ablation studies validate the contribution of each component, demonstrating that integrating dynamic collaboration, deliberate reflection, and continuous evolution significantly advances the state of the art in automated software engineering.

Abstract: Background: People with an intellectual disability (ID) are at increased risk of bowel cancer. However, evidence on their presenting symptoms, diagnostic pathways, treatments and survival remains limited. Methods: A matched cohort study was conducted using linked primary care (Clinical Practice Research Datalink), hospital, cancer, and mortality records. Outcomes included symptoms associated with bowel cancer, faecal immunochemical or faecal occult blood (FIT/FOB) testing, urgent suspected cancer (USC) referral, endoscopy, surgery, systemic anticancer therapy (SACT), and bowel cancer-specific mortality. Adjusted incidence rate ratios (aIRRs), risk ratios (aRRs), and hazard ratios (aHRs) were estimated using Poisson, modified Poisson and Cox regression. Results: A total of 111,034 individuals with an ID were matched to 1,964,420 comparators. ID was associated with increased risk of bowel cancer (aHR 1.30, 1.18-1.44), particularly before age 50 years (aRR 2.19, 1.68-2.85). People with an ID presented more frequently with symptoms associated with bowel cancer (aIRR 2.59, 2.53-2.65) but, following such symptoms, were less likely to undergo FIT/FOB testing (aRR 0.74, 0.67-0.83), USC referral (aRR 0.57, 0.52-0.62), endoscopy (aRR 0.45, 0.42-0.49), or receive a diagnosis within 56 days (aRR 0.52, 0.41-0.67). They were also less likely to be diagnosed via screening (aRR 0.27, 0.14-0.50) or USC referral (aRR 0.62, 0.50-0.76), and more likely to be diagnosed via emergency presentation (aRR 1.76, 1.52-2.02), on the date of death (aRR 5.08, 2.92-8.84), or with stage IV disease (aRR 1.25, 1.01-1.56). ID was associated with similar proportions receiving curative surgery for stage I-III disease (aRR 0.98, 0.79-1.19), but markedly lower proportions receiving SACT for stage IV (aRR 0.15, 0.05-0.46), and higher bowel cancer-specific mortality across all stages (aHR 2.00, 1.71-2.33). Conclusions: People with an ID experience worse outcomes across nearly all stages of the bowel cancer care pathway, including referral, investigation, treatment and survival. Earlier screening may be justified given the elevated risk in those under age 50 years.

Abstract: Modern requirements for critical threads, such as drilling lock threads or running trapezoidal threads of heavy machine tools dictate the need for very durable and at the same time very accurate thread cutters. Conventional thread cutters supplied to the world market have the same profile as the thread for which they are intended. However, for durability and productivity, such tools should have effective geometric parameters of the cutting part, namely: the rake angle and the angle of inclination of the cutting edge. However, there are no known algorithms for profiling such cutters in order to ensure their maximum possible accuracy. This analytical study is specifically designed to identify an algorithm that makes it possible to make highly productive and at the same time highly accurate thread cutters with straight sides of the profile for the manufacture of threads with trapezoidal, triangular and buttress profiles, including for parts made of difficult-to-machine materials.

Abstract: As the key enzyme catalyzing the final step in the biosynthesis of heme and chlorophyll, protoporphyrinogen oxidase (PPO) has become a crucial target for herbicide development. To date, more than 40 PPO-inhibiting herbicides have been developed, exhibiting multiple advantageous characteristics: they combine high efficacy with environmental friendliness, feature low effective concentrations, rapid action, long-lasting effects, and excellent control of both monocotyledonous and dicotyledonous weeds. In recent years, significant progress has been made in the structural biology of PPO—five crystal structures from tobacco, humans, and various bacteria have been resolved, most of which are presented as enzyme-inhibitor complexes. Although the development of such herbicides spans over five decades, novel PPO inhibitors still hold broad potential for innovation due to the resistance of early applied PPOs. This review systematically summarizes the three-dimensional structures of PPO from different sources, the interaction mechanisms between the enzyme and inhibitors, studies on quantitative structure-activity relationships of inhibitors, and outlines molecular design directions for the next generation of PPO inhibitors.

Abstract:

Background: Respiratory infections in young children are a common health problem that is determined by some factors. This study aimed to learn the principles of respiratory infection prevention in young children in the context of parents' sense of self-efficacy and the level of health locus of control. Materials and Methods: A cross-sectional study was conducted among 150 parents of young children. The research tools used were an original questionnaire and a standardized scale of the Generalized Self-Efficacy Survey (GSES) and the Multidimensional Health Locus of Control Scale (MHLC - version A). The study material was collected online using Google Forms software. Data from 134 respondents were included in the statistical analysis. Results: A significant relationship was found between the frequency of respiratory infections in children aged 3-4 years and the parents' care for their hygiene, spending time outdoors, and dressing appropriately for the ambient temperature (Chi²=4.10; p=0.040). Based on the sten scores for the GSES scale, it was found that most parents (66.42%; n = 89) had a high level of self-efficacy (scores of 7-10 sten). According to the MHLC scale - version A, health control was the highest in the internal dimension (Me=26), and chance had the least impact on health control (Me=20). Conclusions: Parents took various actions to prevent respiratory infections in their children. Most parents scored high on the GSES and MHLC – Version A, which may have translated into better health management skills and the implementation of appropriate health-promoting practices in their children.

Abstract: This paper examines the limits of artificial intelligence by distinguishing two dimensions that are frequently conflated in contemporary discussions: machine realization and task completion. While advances in AI are often understood as improvements in realization—better architectures, larger models, or more efficient learning—such advances do not necessarily imply that a task, as specified, is completable at all. The paper argues that this assumption obscures a class of structural limits that are independent of computational power or intelligence. To make this distinction precise, a task-relative framework based on logical time is introduced. Logical time characterizes the organization of conceptually required transitions leading toward a task’s completion conditions, abstracting away from implementation details. Within this framework, the notion of logical-time incompletability is defined as a property of tasks whose completion conditions cannot be reached along any logically bounded trajectory. Several general structural sources of incompletability are identified. On this basis, the paper formulates the AI Boundary Thesis: if a task is logically-time incompletable under its own specification, then no machine—regardless of realization—can fully complete it as specified. This perspective helps explain persistent phenomena in AI, including reliance on approximation, the appearance of pseudo-completion, and the contrast between superhuman performance on formal tasks and fragility in everyday reasoning.

Abstract: Introduction: The clinical outcome of switching to levodopa-entacapone-carbidopa intestinal gel (LECIG) after failure of subcutaneous foslevodopa/foscarbidopa (fLD/fCD) is unknown. We analyze it in people with Parkinson's disease (PwP) treated in Spain. Methods: Retrospective analysis of PwP who had previously received fLD/fCD but dropped out for different reasons and started before this LECIG in Spain up to November 30, 2025. Non-parametric tests were applied to evaluate the changes between the pre- (Vpre) and post-treatment (Vpost) (LECIG) periods. Results: Data about 14 patients (57.1% males; 66.6 ± 8.6 years old) from 12 hospitals out of a total of 15 who were treated with LECIG were included. The mean time with fLD/fCD was 98.6 ± 92.3 days, with 92.9% and 57.1% experiencing side effects and lack of response, respectively. Specifically, significant subcutaneous nodules were reported in up to 64.3% of the patients. LECIG was a direct switch from fLD/fCD in 35.7% of the patients. LECIG was well tolerated, with only 1 dropout due to complications related to dementia. Adverse events were reported in 28.6% and 35.7% of the patients in the optimization and final follow-up evaluation (mean follow-up of 233.7 ± 157.4 days) phases, respectively. Daily OFF time was reduced from Vpre to Vpost in 2.9 ± 1.9 hours (p=0.002). Conclusion: PwP improved significantly from Vpre to Vpost in motor symptoms (p=0.013), whereas a trend of significance was found for non-motor symptoms burden and quality of life. LECIG could be a good therapeutic option in PwP who failed fLD/fCD.

Abstract: Aiming at the problems of difficult hard constraint enforcement, weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard constraint safety assurance: a Constraint-Discounted Soft Actor-Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensure the smoothness and feasibility of the trajectory, and has a good adaptive capacity to dynamic environments, thus providing an efficient solution for the autonomous and safe operation of manipulators.

Abstract: The rapid development of Large Language Models (LLMs) has made machine unlearning essential for privacy and compliance. This technology erases specific information without retraining the whole model. However, the inherent complexity of LLMs leads to fundamental differences in machine unlearning compared to traditional models. To analyze these distinctions, this survey conducts a detailed comparison of machine unlearning in traditional models and LLMs. This comparison reveals four major challenges: performance degradation, unlearning completeness, efficiency and cost, and black-box constraints. Instead of broadly categorizing algorithms, we structure our taxonomy around these core challenges, systematically evaluating how existing methodologies mitigate these specific risks, and finally discuss promising directions for future research.

Abstract: This narrative review conceptually integrates neurodevelopmental and infectious disease research to examine whether shared metabolic and immune dysregulation provides a biologically plausible framework for understanding COVID-19 risk and recovery in individuals with Autism Spectrum Disorder (ASD). Rather than inferring causality, the aim is to critically evaluate existing evidence, identify gaps in current knowledge, and propose testable hypotheses to guide future longitudinal, mechanistic, and biomarker-driven investigations. By situating ASD within a systems-level metabolic–immune framework, this work seeks to inform more precise strategies for risk stratification and clinical monitoring in vulnerable populations.A structured literature search was conducted using PubMed, PsycINFO, ScienceDirect, and Scopus, covering publications from 2007 to 2025. Search terms included ASD, COVID-19, cholesterol, glucose, ferritin, and white blood cells. Priority was given to peer-reviewed original research, reviews, and meta-analyses with mechanistic or biomarker relevance. Preclinical studies were included to support pathway-level discussion, while case reports and non-peer-reviewed sources were excluded.Evidence suggests overlapping biomarker patterns in ASD and COVID-19, including reduced HDL, elevated glucose, and increased neutrophil-to-lymphocyte ratios. Ferritin, often reduced in ASD, may rise during acute infection. These shared alterations underscore the importance of early biomarker monitoring and targeted intervention strategies.