Abstract:

This review discusses the microbiology of acne vulgaris, a chronic inflammatory condition of the pilosebaceous unit that affects most adolescents and can persist into adulthood. The current standard of care consists largely of antibacterial interventions, based on the traditional view of Cutibacterium acnes as a pathogen. Alternative treatments are suggested by the "comedo switch" hypothesis, which attributes acne to aberrant differentiation of Lrig1+ sebaceous progenitor cells. While there is strong evidence to support this idea, it does not explain the efficacy of antibacterial interventions. We propose a unified mechanism wherein C. acnes phylotype IA1 can act as a trigger for the comedo switch. Unlike commensal strains, phylotype IA1 has high lipase activity, hydrolyzing sebum triglycerides into free fatty acids, specifically palmitic acid. This metabolite stimulates Lrig1+ progenitors, inducing inflammation and initial comedo formation. The review discusses C. acnes phylotypes, emphasizing known virulence factors of IA1, such as enhanced biofilm formation. We evaluate the efficacy and limitations of both old and new antibacterials and in this context present a novel material that disrupts C. acnes biofilms (SmartArrow™). In a trial of 34 subjects with mild-to-moderate acne, this non-antibiotic agent demonstrated a statistically significant reduction in inflammatory lesions and selectively depleted phylotype IA1 by 37%, without compromising microbiome diversity. Our findings support the causal role of C. acnes IA1 in acne inflammation as well as in the comedo switch, and suggest that biofilm disruption represents a promising therapeutic avenue.

Abstract: Multi-agent systems often rely on long-term memory or shared knowledge bases to enhance collaborativeefficiency, yet this introduces risks of memory poisoning and cross-agent propagation. Addressing the covertdiffusion of poisoned information during collaboration, this study proposes a memory poisoning detection andrepair method tailored for multi-agent environments.This approach constructs an evidence graph based onmemory source credibility and content consistency to validate newly added memories. It combines contrastivelearning models to identify anomalous memories exhibiting command-induced characteristics. Upon detectingpoisoning, further propagation is suppressed through isolation, rewriting, and conflict resolution. Experimentsevaluated the method using 60 collaborative tasks, approximately 210,000 memory records, and 12,000injected poisoned samples.Results demonstrate an AUC of 0.94 in poisoning detection, reducing misbehaviorrates from 15.6% to 2.3% while decreasing cross-agent propagation by 78.1% on average, with minimal impacton overall task efficiency.

Abstract: Chronic kidney disease (CKD) is a progressively worsening condition that erodes renal function over time, reduces quality of life, and can ultimately culminate in kidney failure with far-reaching systemic complications. In addition to reduced filtration, worsening kidney function disrupts mineral homeostasis and leads to CKD–mineral and bone disorder (CKD-MBD). Dysregulated calcium handling and maladaptive endocrine responses contribute to bone pathology and increase cardiovascular calcification risk; therefore, serial calcium monitoring remains clinically relevant for longitudinal CKD management. Conventional calcium measurements are typically obtained with centralized analyzers or laboratory assays (e.g., colorimetry and electrode/optical readouts). Despite high accuracy, the required instrumentation, controlled operating conditions, and pretreatment steps complicate rapid point-of-care deployment, especially when only microliter-scale biofluids are available. Accordingly, this study develops a finger-actuated microfluidic colorimetric platform capable of determining calcium ion concentrations in human biofluids, such as whole blood, serum, and urine. The platform integrates a three-dimensional PMMA/paper microchip with a compact reader that maintains stable temperature control while enabling CMOS-based optical detection. With just 6 μL of sample, a brief finger press propels the biofluid across an internal filtration layer, generating serum or cleaned urine that subsequently reacts with a pre-deposited murexide reagent. Under optimized conditions (1.6% reagent, 50°C, 3 min), the signal follows a strong logarithmic relationship with calcium concentration (Y = 47.273 ln X + 28.890; R² = 0.9905), supporting quantification over 1–40 mg/dL and a detection limit of 0.2 mg/dL. Across 80 clinical CKD specimens spanning serum, whole blood, and urine, results aligned closely with the NM-BAPTA reference assay, with R² values exceeding 0.97.

Abstract: Post-traumatic stress disorder (PTSD) is a frequent psychiatric outcome following trauma, and adolescents may be particularly vulnerable. This cross-sectional study investigated the associations between PTSD, sleep quality, sleep problems, and chronotype in earthquake-exposed adolescents. The sample comprised 201 adolescents aged 12–18 years: 92 with PTSD and 109 earthquake-exposed controls without a DSM-5 psychiatric diagnosis. Participants completed the Children’s Posttraumatic Stress Reaction Index (CPTS-RI), Pittsburgh Sleep Quality Index (PSQI), Children’s Sleep Habits Questionnaire (CSHQ), and Children’s Chronotype Questionnaire (CCTQ). Adolescents with PTSD had significantly higher CPTS-RI, PSQI, CSHQ, and CCTQ scores compared with controls (all p < 0.001), indicating poorer sleep quality, more sleep problems, and greater eveningness tendency. Correlation analyses showed that PTSD severity was positively associated with sleep problems, impaired sleep quality, and eveningness. In binary logistic regression analysis, poor sleep quality (p < 0.001) and clinically significant sleep problems (p = 0.011) were independently associated with PTSD, whereas chronotype was not. Sleep disturbances appear to be more strongly related to PTSD than chronotype itself. Assessment and treatment of sleep problems may represent an important therapeutic target in adolescents exposed to large-scale trauma.

Abstract: Breast cancer represents a highly heterogeneous malignancy encompassing multiple molecular subtypes, each with distinct therapeutic responses and clinical outcomes. Conventional treatment strategies—including surgery, chemotherapy, radiotherapy, endocrine, and targeted therapies—have improved survival rates but continue to face major limitations due to tumor relapse, metastasis, and therapy-induced resistance. In recent years, chimeric antigen receptor (CAR) T-cell therapy has emerged as a transformative modality in cancer immunotherapy, offering targeted and durable antitumor activity. While its efficacy in hematological malignancies is well established, translation to solid tumors such as breast cancer remains hindered by a complex interplay of tumor-intrinsic and micro environmental barriers. This review delineates the mechanistic underpinnings of CAR T-cell function and highlights the multifaceted challenges posed by solid tumors, including antigen heterogeneity, an immunosuppressive tumor microenvironment, inadequate trafficking and infiltration, limited T-cell persistence, and safety-related cytotoxicities. Finally, emerging strategies and innovations aimed at overcoming these barriers are discussed, highlighting the potential and future direction of CAR T-cell therapy in breast cancer management. Collectively, this review underscores the translational potential of CAR T-cell therapy for breast cancer and outlines the rational strategies required to enhance its clinical applicability and therapeutic efficacy in solid tumor settings.

Abstract: We present a scalar-tensor extension of General Relativity (GR) in which a covariant coherence field Φ is non-minimally coupled to spacetime curvature through a variational action of the form S = R d⁴x √ −g [(1+λΦ)R− ω/2∇μΦ∇μΦ−V (Φ)]/(16πG)+S_m. Variation with respect to the metric yields modified Einstein equations Gμν + Cμν(Φ) = (8πG/c⁴) Tμν, where the coherence tensor C_μν encodes gradients of the scalar field and vanishes identically when Φ → 0, recovering GR exactly. We derive the effective correction to periapsis precession in the weak-field regime and show that it is governed by a single dimensionless combination Ξ = e²(1 − e²)⁻¹ · rg/a, where e is the orbital eccentricity, a the semi-major axis, and r_g = 2GM/c² the gravitational radius. The effective coupling λ_eff is bounded by precision pulsar timing to λ_eff < 1.95, which renders Solar System corrections undetectable at present but predicts corrections of order 10⁻³ for the S2 star orbiting Sagittarius A* — within reach of next-generation interferometric astrometry (GRAVITY+, ELT). The theory constitutes a phenomenological effective framework with a single effective parameter λ_eff , constrained by internal consistency and binary pulsar observations. We outline falsifiable predictions and identify the regimes where screening mechanisms may permit larger deviations, motivating future work on galactic-scale applications.

Abstract: High energy consumption in winter greenhouses challenges agricultural sustainability. This study integrates nonlinear cost-volume-profit (CVP) and data envelopment anal-ysis (DEA) to balance cucumber yields with energy costs in Northern China. Results show that while a 19°C treatment (T3) maximizes yield, it suffers from diminishing marginal returns (MR/MC = 0.72) and high sensitivity to energy price fluctuations. Conversely, the 16°C treatment (T2) emerged as the sole energy-efficient and econom-ically resilient configuration. We conclude that 16°C is the global optimal point for balancing biological potential and resource efficiency, providing a scientific basis for precision thermal management in cold-region greenhouses.

Abstract: Water supplies contaminated by heavy metals pose a serious threat to human health, especially in areas without access to centralized testing facilities. While copper is a necessary heavy metal in trace levels, high concentrations can have detrimental effects on health, such as oxidative stress, cognitive impairment, and liver damage. Due to their expense, complexity, and reliance on laboratories, conventional detection tech-niques are accurate but unsuitable for real-time, dispersed deployment. Machine learning offers a potent solution to these constraints by facilitating the automatic, pre-cise, and quick interpretation of complicated sensor data. It makes it possible to make decisions in real time without requiring a large laboratory infrastructure. In this work a dual-mode optical sensor was developed using the colorimetry and fluorometry images of carbon dots embedded in hydrogels with the Cu2+ concentration of 0, 20, 50, 100, 200, and 500 μM. Data augmentation was used to expand the RGB picture dataset for each modality, and these data were interpolated to provide re-sponses at 1 µM intervals (0–500 µM). We trained a comprehensive set of supervised machine learning models including Logistic Regression, Support Vector Machines, Random Forest, and XGBoost to categorize water samples into five risk-informed quality levels. The system achieved classification accuracies exceeding 96%. Further-more, we built a simple user interface to make the system practically deployable in mobile phone. Together, these results demonstrate a scalable, interpretable, cost-effective, and quick solution for real-time water quality monitoring in re-source-constrained environments.

Abstract: Candidozyma auris (formerly Candida auris) is an emerging multidrug-resistant pathogenic fungus with an increased ability to cause outbreaks in health care facilities leading to poor patient outcomes. Since its initial discovery in 2009, C.auris has spread rapidly across continents and is now classified by both the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) as a critical-priority pathogen. This review summarizes current knowledge on the origin, taxonomy, microbiology, and virulence mechanisms of C.auris, emphasizing its thermotolerance, osmotolerance, and biofilm-forming capacity on biotic and abiotic surfaces, as well as aspects related to its antifungal drug resistance and management. These features, together with its genomic plasticity, contribute to persistence, transmission, and drug resistance. Emerging evidence also supports a potential link between climate change and C. auris evolution, highlighting environmental adaptation as a driver of pathogenicity. Combating C. auris will require multidisciplinary efforts to mitigate its expanding global impact.

Abstract: Background/Objectives: Online health misinformation is an emerging public health concern, as it may influence health behaviors and vaccination decisions. This study addresses how susceptibility to online health misinformation shapes nurses' health behaviors and attitudes toward vaccination. Methods: A cross-sectional study was performed in Greece using an online questionnaire in September 2025. Online health misinformation susceptibility was measured with the Health-Related Online Misinformation Susceptibility Scale. Health behaviors were measured by the Health Behavior Inventory–Short Form (HBI-SF), and vaccine hesitancy was assessed through the Vaccine Hesitancy Scale (VHS). Multivariable analyses were performed to determine the independent effect of vulnerability to misinformation after adjusting for possible confounding variables. Results: The multivariable linear regression analyses showed that susceptibility to online health misinformation was positively associated with diet scores (adjusted beta = 0.033, 95% confidence interval [CI]: 0.016–0.051, p &lt; 0.001) and anger and stress score (adjusted beta = 0.065, 95% CI: 0.047–0.082, p &lt; 0.001). The misinformation susceptibility was positively associated with higher levels of vaccine hesitancy. In particular, we found a positive association between misinformation susceptibility and lack of confidence (adjusted beta = 0.021, 95% CI 0.012-0.030, P &lt; 0.001) and risk perception (adjusted beta = 0.032, 95% CI: 0.022–0.042, p = 0.001). Conclusions: Our findings suggest that higher susceptibility to online health misinformation is linked to poorer health behaviors and greater vaccine hesitancy.

Abstract: Background: Lipoprotein(a) [Lp(a)] is a casual and independent risk factor for atherosclerotic cardiovascular disease (ASCVD) and is largely genetically determined. However, recent studies indicate significant intra-individual variability, particularly among patients with intermediate Lp(a) levels (30–50 mg/dL). Yet, data on long-term variability are limited, and acute coronary syndrome (ACS) may further influence Lp(a) levels, questioning the optimal timing of assessment after ACS. Methods: We studied 235 ACS patients across two follow-up cohorts. Baseline Lp(a) was measured 24 hours before hospital discharge. Cohort A had follow-up measurements at 4 months, and 8 months; Cohort B at 5 years. Clinically meaningful intra-individual variability was defined as ≥20 mg/dL or ≥25% change. Results: A total of 57.9% patients exhibited clinically significant Lp(a) variability. Changes in risk category occurred in 15.3% of patients in the baseline high-risk group, 60.6% of patients in the intermediate-risk group, and 5.5% of patients in the baseline low-risk group. At multivariable analysis incomplete revascularization was an independent predictor of high Lp(a) variability (OR 2.22; 95% CI 1.14-4.31; p 0.02) while female sex, and age-adjusted menopause showed a trend (OR 1.92; 95% CI 0.93-4.00; p 0.08 and OR 11.18; 95% CI 0.79-157.58; p 0.07, respectively) without reaching formal statistical significance. The median absolute changes from baseline to 4-month and from baseline to 5-years follow-up were 7.9 mg/dL (IQR 3.0-18.9) and 10.7 mg/dL (IQR 3.0-21.7), respectively. Concordance between 4- and 8-month Lp(a) measurements was excellent. Conclusions: Early post-ACS intra-individual variability in Lp(a) is common, mainly affecting risk reclassification in intermediate-risk patients. In those patients, early targeted repeat Lp(a) measurement may improve cardiovascular risk stratification, whereas mid- to long-term reassessment appears unnecessary.

Abstract: Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process.

Abstract: Biomarkers are objective medical signals and can facilitate the diagnosis and monitoring of a multitude of diseases, including those whose symptomatology is largely subjective.Electrochemical biosensors offer an ideal platform for the application of emerging knowledge resulting from biomarker research. They combine the sensitivity of electrochemical detection techniques with the specificity of a biochemical reaction.In this review article, an introductory reference was made to the usefulness of biomarkers and the importance of their validation, to the problems encountered in the diagnosis of diseases, as well as to the structural characteristics and role of biosensors.ubsequently, applications of electrochemical biosensors for the determination of biomarkers from recent literature were presented, which were classified based on the biological mechanism of recognition of the sensor into enzymatic, immunochemical, DNA, other (biomimetic) and multipotent.

Abstract: We present a unified algebraic framework, the "Golay-Hopf Machine," which synthesizes four distinct mathematical structures: Golay coding theory, Hida theory, Iwasawa theory, and Yang-Baxter integrability. By defining a Hopf algebra structure on the binary Golay weights W = {0, 8, 12, 16, 24}, we show that: (1) Hida transitions correspond to the coproduct ∆, (2) Galois height corresponds to the counit ε, and (3) the weight complement w 7 → 24 − w acts as the antipode S satisfying S2 = id. We formally verify in Lean 4 that this structure satisfies the Yang-Baxter compatibility condition for heights and the Iwasawa logarithmic identity. All core algebraic results are verified with zero axioms and zero sorry statements. Finally, we sketch a roadmap for extending this framework to Anabelian geometry.

Abstract: A quartz crystal microbalance-based biosensor for the specific detection of the first transgenic common bean (Phaseolus vulgaris L.) cultivar (BRS FC401 RMD) with resistance to bean golden mosaic virus (BGMV) was developed. The immobilization chemistry relies on the strong bond between the thiolated probe and the gold electrode surface. The probe sequence is internal to a region of the BGMV rep gene that was introduced into the common bean genome. The sensor's analytical performance was determined using synthetic oligonucleotides. Real samples of transgenic and wild-type bean seeds were also tested. Sample pretreatment consisted only of enzymatic fragmentation, followed by a thermal denaturation step combined with blocking oligonucleotides. Different biosensor regeneration approaches were studied. Immobilization showed good reproducibility (CV% of 5.8%). The biosensor proved specific for both synthetic oligonucleotides and non-amplified genomic DNA. A linear detection range of 0–1.4 ng/µL was observed, with a detection limit of 0.18 ng/µL. Three sequential detections were performed without loss of surface activity. The results demonstrate the biosensor's potential for direct, real-time, label-free detection of DNA samples for field screening of transgenic common bean cultivars.

Abstract: Low generic drug prices in the US have benefited from offshoring production to low-cost overseas plants in countries with a strong manufacturing base, such as China and India. However, the ongoing quality control and reliability issues with the supply chain of generic drugs have engendered discussions on the merits of “re-shoring”. This study examines the potential economic impact of re-shoring generic drug production from China and India back to US/EU. We use a convenience sample of ninety publicly traded generic drug manufacturers headquartered in China (43), India (32), and the US and Europe (15). All firms have at least one production plant inspected by the US Food and Drug Administration between 2017 and 2019. Nearly 90% are vertically integrated and manufacture both active pharmaceutical ingredients and final drug formulations for generic drugs. The US/EU-based firms face significantly higher labor compensation costs, and experience lower operating profit margins compared to China- and India-based firms. We estimate a Cobb-Douglas production function of generic drugs for each region, using the real value of cost of goods sold (COGS) and the real value of total capital stock as input factors, and the real value of sales as output. We employ a fixed effect regression model for each region. We find that output is more responsive to change in COGS among China- and India-based firms, but less responsive in US/EU-based firms, while the output response to capital stock is greater in the latter. We use the same production function to estimate total factor productivity (TFP), a measure of overall production efficiency. The 95% confidence intervals for estimated TFP in each region overlap, suggesting TFP is not statistically significantly different across regions. We project the COGS for generic drugs will rise by at least 35-40% if production is reshored from China or India. Findings from our analyses highlight an urgent need for more in-depth economic analyses on how reshoring may increase costs and ultimately prices for generic drugs in the US/EU.

Abstract: During the last decades, there has been a growing interest in and knowledge of the relationship between adverse childhood experiences (ACEs) or stressful life events and health issues in younger and adult populations. However, less is known in older populations. This comprehensive review aimed to summarize studies on the relationship between life course exposures and geriatric syndromes. We searched PubMed for articles published between 2015 and 2026 using specific keywords. We included 84 studies. There is substantial variability in the exposure measures used (e.g., for ACEs, from 1 question to more than 30) and in the outcomes (e.g., different diagnostic criteria for dementia or frailty). Our synthesis showed that ACEs, stressful events and other adversity measures are usually associated with greater probabilities for the occurrence of geriatric syndromes such as dementia, frailty, depression, falls, low muscle strength, multimorbidity, and functional decline. There are also some reports for mediators that may partially explain those associations.

Abstract: The algebraic analysis of linear code parameters reveals deep connections with cryptographic constructions, including the information dispersal algorithms (IDAs) and secret-sharing schemes. In this work, we propose an algebraic method for constructing bases of binary linear codes from subsets of codewords selected according to their generalized Hamming weights (GHWs). The approach employs a degree-compatible monomial ordering on the polynomial ring F₂[x₁, . . . , x_n] and imposes the conditions d₁(C) = 1 and d_k (C) = n. Under these assumptions, we prove the existence of a generator matrix containing an invertible k × k submatrix, which guarantees correct information reconstruction. This structural property enables the direct application of binary linear codes to information dispersal and recovery mechanisms without the need for larger finite fields. We validate the proposed framework through algebraic proofs and an explicit example illustrating both the dispersal and recovery procedures. These results provide a theoretical foundation for the design of information dispersal schemes relying exclusively on binary linear codes.

Abstract: This study aims to explore how sports businesses can be reimagined in the digital era by identifying key transformative indicators shaped by virtualization. The research is of mixed type (qualitative-quantitative). The statistical population of the qualitative section included all IT specialists in the sports industry and university professors with expertise in sports management and digitalization from executive and academic fields. Purposive sampling method was used to select the research samples. The sample size continued until theoretical saturation (15 people) was reached. In the initial stage, the data of this research were collected through semi-structured interviews with the selected samples. To ensure the validity, various methods such as tracking data over time (audit tracking) and consolidating the findings through repeated assessments of data and patterns were used. After the coding processes and identification of main themes and indicators, a questionnaire was developed to validate the extracted components. For qualitative data, content analysis was used, and in the quantitative phase, second-order confirmatory factor analysis was used using SPSS version 25 and Smart PLS version 3 software. Based on the findings, the foundations for developing digital business included business virtualization contexts, assumptions of the digital workplace in business; digital business processes included the efficiency of the virtual business system and business virtualization processes; digital business functions encompassed the development of digital business and the sustainability of the digital work environment. Also, the results of the validity and reliability analyses and confirmatory factor analysis of the research components indicated the appropriate validity of all items of the questionnaire obtained from the qualitative section. The examination of the indicators of the final research model also indicated the high validity of the model. According to other results, the desired model had appropriate fit indicators. This research provides a scientific and practical basis for policymakers and managers in the sports industry to better focus on advancing the virtualization and digitalization of sport businesses in their decision-making processes.

Abstract: Physics-based constitutive modelling remains a cornerstone for predicting ductile damage and fracture in metallic materials, particularly where microstructural mechanisms govern macroscopic response. Over the past two decades, a wide range of crystal plasticity, porous plasticity, and void-based fracture models have been proposed to capture deformation localisation, void growth, and coalescence under complex loading paths. However, these developments are often presented in isolation, obscuring their shared physical assumptions and limiting their transferability across material systems and length scales.This article provides a microstructure-sensitive perspective on constitutive modelling of ductile damage and fracture, with particular emphasis on crystal plasticity-based frameworks, void growth and coalescence mechanisms, and interface-driven fracture. Rather than attempting an exhaustive review, this review highlights unifying concepts, modelling trade-offs, and recurring challenges related to parameter identifiability, scale bridging, and predictive robustness. It further clarifies how physics-based constitutive descriptions can be systematically integrated into modern fatigue and fracture assessments and situate these developments relative to emerging data-assisted and machine-learning-enhanced modelling strategies.By reframing established constitutive models within a coherent physical narrative, this perspective aims to support more transparent model selection, improve interpretability, and guide future developments in multiscale damage and fracture modelling of metallic materials.