Abstract: Gender incongruence significantly impacts the family system, yet the subjective experiences of caregivers remain relatively underexplored. This narrative review synthesizes contemporary evidence regarding psychological distress, emotional burden, and quality of life among caregivers of transgender and gender-diverse individuals. A targeted literature search of PubMed, Scopus, PsycInfo, and Google Scholar (2015-2025) was conducted, identifying 16 studies for thematic synthesis. Results indicate that caregivers consistently report elevated emotional distress, characterized by chronic anxiety, hypervigilance, and ambiguous loss. This burden is primarily driven by prolonged exposure to uncertainty, the weight of complex medical decision-making - particularly regarding fertility and hormone therapy - and vicarious minority stress stemming from social stigma and systemic barriers. Notably, distress is often intensified by sociopolitical climates rather than the transition process itself. Conversely, access to peer support networks, healthcare relationships, and engagement in advocacy emerged as vital protective factors facilitating resilience and adaptive meaning-making. We can conclude that caregiver well-being is a multifaceted process deeply embedded in social and institutional contexts. These findings underscore the necessity of integrated, family-centered medical-psychological models that explicitly support caregivers to ensure more equitable and effective gender-affirming care pathways.

Abstract: The growing demand for high-protein dairy products, driven by the expanding markets for infant formula and nutritional supplements, has led to a higher incorporation of milk protein ingredients like milk protein concentrate (MPC) and whey protein isolate (WPI) in dairy formulations. However, the effects of these protein additives on the thermal stability and sensory attributes of dairy products remain insufficiently studied. This research examines the influence of thermal processing (80 °C for 30 min) and protein fortification (MPC, WPI, and their combination) on the denaturation of whey proteins, the formation of volatile compounds, and the sensory characteristics of milk. Specifically, whole milk was fortified with MPC, WPI, and their combination at concentrations of 4% MPC, 4% WPI, and 2% MPC + 2% WPI, respectively, to evaluate the impact of different protein fortifications on these properties. Our findings reveal that heat treatment significantly promoted the denaturation of β-lactoglobulin and α-lactalbumin, with protein fortification playing a role in modulating these changes. Notably, lactoferrin exhibited matrix-dependent antioxidant behavior, meaning its antioxidant activity varied based on the protein composition and structure of the milk matrix, influencing its stability and function under different fortification conditions. Volatile profiling indicated that MPC enhanced the formation of sulfur-containing compounds and aldehydes, whereas WPI favored ketones and Maillard-derived volatiles. Sensory analysis revealed that heated WPI fortified samples exhibited stronger cooked and dairy fat aromas, while unfortified milk retained milky and grassy notes. Correlation analysis highlighted the mechanistic links between protein denaturation and lipid-derived compounds. These results emphasize that protein type and composition play crucial roles in flavor development. The strategic blending of MPC and WPI offers a practical approach to balancing volatile profiles and mitigating off-flavors, providing insights for the formulation of thermally stable, protein-fortified dairy products with optimized sensory quality.

Abstract: This study investigated the kinetics of aptamer-cardiac troponin I (cTnI) interaction to establish a new dynamic quantitative indicator for the rapid, highly sensitive detection of cTnI, a critical myocardial infarction biomarker. The goal was to overcome the limitations of conventional diagnosis based on saturated binding amounts, which takes excessive time for point-of-care testing (POCT). Cyclic voltammetry (CV) was performed on a gold electrode immobilized with double-stranded aptamers, and the interaction kinetics were rigorously analyzed across cTnI concentrations from 10 pg/mL to 90 pg/mL. The adsorption process, quantified by changes in charge amount, was found to follow a similar first-order interaction model. The most significant findings were the establishment of a robust power function (R2=0.9515) relating the cTnI concentration to the derived interaction rate constant. This high explanatory power confirms the predictable and quantitative relationship between concentration and reaction speed. In conclusion, the interaction rate constant is proposed as a novel dynamic indicator for predicting cTnI concentration, providing a crucial technological foundation for developing next-generation, high-speed, high- sensitivity aptamer-based biosensors essential for time-critical POCT applications.

Abstract: Background: Transferrin receptor-targeting monoclonal antibodies (TfRMAbs) enhance brain drug delivery by facilitating TfR-mediated transcytosis across the blood-brain barrier (BBB). Data suggest that chronic TfRMAb dosing reduces their plasma exposure in a dose- and fusion partner-dependent manner; however, the underlying mechanisms remain unclear. The neonatal Fc receptor (FcRn) extends IgG half-life via recycling, but its saturation after repeated doses may alter the pharmacokinetics (PK) of IgG-fusion proteins. This study evaluated the role of the FcRn on PK and biodistribution of TfRMAb fusion proteins. Methods: We examined TfRMAb alone and TfRMAb fused to erythropoietin (TfRMAb-EPO) or TNFα receptor (TfRMAb-TNFR) in wild-type (WT) and FcRn knockout (KO) mice following acute (single dose) or chronic (3× weekly for 4 weeks) subcutaneous administration at 3 mg/kg. Plasma levels, tissue biodistribution, and FcRn binding were measured using immunoassays. Results: Our results show that fusion partners influenced FcRn-mediated recycling and PK of TfRMAb-fusion proteins. After acute dosing, TfRMAb-TNFR exhibited the greatest reduction in plasma exposure in FcRn KO versus WT mice, compared to TfRMAb and TfRMAb-EPO. Chronic dosing reduced the plasma persistence of all fusion proteins in WT mice. In FcRn KO mice, plasma exposure of TfRMAb and TfRMAb-EPO decreased with chronic dosing, whereas TfRMAb-TNFR showed no further reduction. Differences in FcRn binding affinity likely explain these patterns. Tissue distribution largely mirrored plasma concentrations. Conclusion: FcRn regulates plasma concentrations of TfRMAb-fusion proteins in a fusion partner-dependent manner. While FcRn-mediated protection regulates plasma exposure with acute dosing, additional mechanisms beyond FcRn saturation appear to regulate plasma exposure during chronic dosing.

Abstract: The conventional formulation of quantum mechanics explains the Einstein, Podolsky, and Rosen (EPR) experiments with “spooky action at a distance" and wavefunction collapse. A time-symmetric and retrocausal formulation of quantum mechanics explains the same experiments without spooky action at a distance or wavefunction collapse. An experiment that can distinguish between the conventional and time-symmetric formulations is described.

Abstract: Placental abruption is detachment of the placenta before delivery from the implantation site that may have a potential to develop life-threating emergency clinic syptoms. The multifactorial nature of this disorder and no lab testing or procedures that can diagnose placental abruption. makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study purposed on predictive 15 ML models for placental abruption high-lighting input characteristics, performance metrics, and validation. The medical records of 564 patients were analyzed between 2021 and 2025 for studies using AI to develop predictive models for placental abruption. Findings were analyzed with Python software and Pycaret library. The model integrated data for 5 variables (features) for the prediction. Among 15 machine learning algorithms, Logistic regression was chosen as the best model. The performance metrics were determined as follows: accuracy rate of 0.85, AUC of 0.91, recall of 0.85, precision of 0.85, and F1 score of 0.85. In the ranking based on their importance in the classification model, gestational age at delivery was observed to have the highest importance for classification. Twenty-eight unseen cases were utilized for an extra validation step. The model achieved a high accuracy on this set, with 21 cases correctly predicted. The presented 15 ML models in our study had significant accuracy in predicting placental abruption , but these models require further development before they can be applied in a clinical setting.

Abstract: Bacillus amyloliquefaciens is an important agricultural microbial resource. This study focuses on the whole genome analysis and functional characterization of B. amyloliquefaciens SH-53, isolated from the Wuliang Mountain National Nature Reserve in Dali, Yunnan. The genomic feature analysis revealed that the genome of SH-53 contains 27 ribosomal RNA operons, 4,078 protein-coding genes, and 250 prophage-related genes. Additionally, 12 biosynthetic gene clusters (BGCs) for secondary metabolites were predicted, of which 7 are novel gene clusters with unknown functions, showing significant differences compared to the known BGCs of conventional biocontrol strains.Functional potential analysis indicates that SH-53 possesses potential antagonistic activity against plant pathogenic bacteria and can colonize the plant rhizosphere through various mechanisms to exert growth-promoting effects. It is capable of synthesizing multiple antibacterial secondary metabolites, indole-3-acetic acid (IAA), iron carriers, secreting amylase, and efficiently utilizing sulfur sources. The genome also harbors a complete core gene network related to the induced systemic resistance (ISR) and supporting genes that maintain secondary metabolism homeostasis.In conclusion, B. amyloliquefaciens SH-53 exhibits rich biocontrol-related characteristics and unique secondary metabolic potential, indicating promising prospects for its development as an excellent biocontrol agent.

Abstract:

Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a corresponding region of interest (ROI) improves classification accuracy for both normal–abnormal and benign–malignant classifications. Our methodology involves independently training models and performing predictions on each subgroup with subsequent integration of the results. We used several DL models, including ResNet, EfficientNet, SwinTransformer, ConvNeXt, and MobileNet. For experimentation, we used the publicly available VinDr., CDD-CESM, and DMID datasets. Our comparison with prediction results obtained without ROI-based stratification demonstrated that the utility of considering ROI presence to enhance diagnostic accuracy in mammography increases along with the data volume. These findings support the usefulness of our stratification approach, particularly as a dataset size grows.

Abstract: Let 1 < a1 < a2 < · · · be integers with \( \sum_{k=1}^\infty a_k^{-1}<\infty \), and set \( F(s)=1+\sum_{k=1}^\infty a_k^{-s}, \qquad \Re s>1. \) A question of Erdős and Ingham, recorded as Erdős Problem #967 in a compilation by T. F. Bloom (accessed 2025--12--01), asks whether one always has \( F(1+it)\neq 0 \) for all real t. This paper does not resolve the problem; instead, it develops a modern dynamical-systems framework for its study. Using the Bohr transform, we realise $F$ as a Hardy-function on a compact abelian Dirichlet group and interpret \( F(1+it) \)as an observable along a Kronecker flow. Within this setting we establish a quantitative reduction of the nonvanishing question to small-ball estimates for the Bohr lift, formulated as a precise conjecture, and we obtain partial results for finite Dirichlet polynomials under Diophantine conditions on the frequency set. The approach combines skew-product cocycles, ergodic and large-deviation ideas, and entropy-type control of recurrence to small neighbourhoods of -1, aiming at new nonvanishing criteria on the line \( \Re s=1 \).

Abstract: Background: Invisible or humanized care (High-Touch) is inherent to the nursing profession. Professionals with sensory processing sensitivity perceive and process more sensory information simultaneously and more deeply than usual, which may be more closely linked to invisible care. Objective: To analyze the influence of sensory processing sensitivity on nursing professionals' perception of invisible care. Method: A cross-sectional descriptive observational study. Seventy-nine professionals from a level III hospital completed an online form assessing various situations encountered by nursing staff in their daily practice related to the different dimensions of invisible care (Care-Q) and the sensory processing sensitivity temperament trait (HSPS). Results: Showed that 15% (12) of nursing professionals were highly sensitive. A statistically significant relationship was also found between the components of invisible care and the overall Care-Q score for professionals in general and for highly sensitive professionals. The invisible care component "maintains a trusting relationship" with the HSPS overall (ρ = 0.224), but no significant correlations were observed when professionals were identified as highly sensitive with scores ≥ 160 points with the different Care-Q components. Conclusion: The perception of invisible care is inherent to the nursing profession and is not strongly influenced by the SPS trait. Therefore, invisible care is an essential component of nursing practice.

Abstract: Environmental, Social and Governance (ESG) risk is increasingly influenced by inter-firm relationships embedded in global supply chains. Challenging firm-level approaches that treat ESG exposure as independent across companies. This study examines whether firms’ structural positions within supply-chain networks are associated with ESG risk exposure and whether incorporating network information improves ESG risk prediction. The analysis draws on an international dataset integrating validated supplier-buyer relationships, shipment-level trade data. ESG incident records and sentiment derived from ESG-related news. Network-based econometric models and graph-oriented learning approaches are evaluated against conventional firm-level benchmarks. The results indicate that ESG risk clusters within connected groups of firms, with higher exposure observed among firms occupying central or intermediary positions in supply networks. In addition, ESG-related media sentiment exhibits predictive power for subsequent ESG incidents, supporting its role as an early warning signal. Overall, models that explicitly account for network structure deliver more accurate and better-calibrated predictions than standard econometric and machine-learning approaches. These findings highlight the value of a network-informed perspective for ESG risk assessment in complex international production systems.

Abstract: Travel-associated Legionnaires’ disease (TALD) events can generate public concern when environmental surveillance findings are communicated without adequate ex-planation of the results. This study examined how surveillance data on Legionella spp. were framed and amplified during a TALD-related investigation in Crete, Greece, between June and July 2025. A mixed infodemiology and environmental surveillance approach was applied, including analysis of 95 online media items across nine lan-guages, Google Trends search-interest data, and hotel water-system surveillance data from epidemiologically linked facilities. Sampling conducted in a limited number of hotels associated with TALD cases indicated that approximately 50% of water samples exceeded the laboratory reporting limit of ≥50 CFU/L, a numerically correct but con-text-specific finding. Numerical misframing occurred in 83.7%, 41.7%, and 18.2% of Greek, German, and English language items, respectively, with significant differences across language markets (χ² (8) = 43.75, p < 0.0001; Cramér’s V = 0.679). Public search-interest signals were transient and geographically limited. Environmental sur-veillance showed no increase in Legionella pneumophila risk, with similar proportions of samples ≥50 CFU/L in the pre-/peri-infodemic (Jan–Jul 2025) and post-infodemic (Aug–Nov 2025) periods (23.11% [95% CI: 18.21–28.87] vs. 24.45% [19.34–30.41]) and similar exceedance of ≥1000 CFU/L (13.45% [9.69–18.36] vs. 14.41% [10.45–19.55]). Overall, loss of contextual interpretation of surveillance results and conflation of laboratory re-porting limits with regulatory thresholds were associated with inconsistent public risk perception, without evidence of increased environmental hazard.

Abstract: The importance of the infrastructure is associated with the value of the infrastructure, the greater the importance of infrastructure, the greater its value. The concept of the importance of road infrastructure can take on a different value instead of different points of view. For example, roads can be evaluated from an economic, social, political, and military, among others. In 2021, the Lazio Regional Road Authority (ASTRAL) requested assistance from the Research Center for Transport and Logistics (CTL) to develop a composite scoring index (Regional Index, Ri) that would rank the relative importance of ASTRAL–maintained roadway network. The Ri index is expressed numerically between values from 1 to 5 (with 5 representing the highest importance). It includes the following variables: Population density, AADT, road traffic crashes, accessibility to point of interest, maintenance cost, air emissions, and noise pollution. The methodology includes the following steps. First, the variables were selected on the basis of their reliability, measurability, coverage and relevance to the phenomenon to be measured. Then, the data collection and normalization of the variables on a scale of 1 to 5 were carried out. Subsequently, through a multicriteria analysis, the variables were weighted and added. Finally, a sensitivity analysis was performed to evaluate which variables had the most influence on the final output of the formula. The methodology proposed has been implemented on the Region Lazio roadway network in order to obtain the Ri of the road segments.

Abstract: The Bateman-Horn conjecture is a conjecture on prime values in polynomials. We prove it by Golomb's method.

Abstract: The increased demand for food worldwide has led to the widespread use of synthetic chemical fertilizers. Since the Green Revolution, the use of such chemical fertilizers has been in high demand as a nutrient input in agriculture. The increased application of ferti-lizer to upsurge crop yields is not suitable for the long term and leads to nutrient loss, as well as severe environmental and ecological consequences. Contrasted to conventional fertilizers, nano-fertilizers, which are designed at the 1–100 nm size, provide focused nu-trient delivery, decreased leaching, and improved plant absorption. They accomplish this by greatly increasing crop yields, enhancing fertilizer usage efficiency, and facilitating sustainable farming in the face of obstacles, including resource scarcity, climate change, and a projected 10 billion people by 2050. In comparison to typical NPK fertilizers at equal nutrient rates, nano-fertilizers enhanced crop yields by an average of 20-23% across cere-als, legumes, and horticulture crops, according to studies conducted between 2015 and 2024. In particular, using nano-urea to rice increased grain yield by 28.6% with 44% less nitrogen input, and applying nano-zinc to wheat increased yields by 31.2% and improved grain Zn content by 41%. Through targeted foliar or soil application, nano fertilizers in-crease nutrient use efficiency (NUE) by frequently more than 50% as opposed to 30-50% for conventional fertilizers. Nano fertilizer is prepared based on the encapsulation of plant essential minerals and nutrients with a suitable polymer matrix as a carrier and delivered as nano-sized particles or emulsions to the plants. Natural plant openings like stomata and lenticels in plant parts facilitate the uptake and diffusion, leading to higher NUE. This review provides an overview of current knowledge on the development of advanced nano-based and smart agriculture using nano fertilizer that has improved nutritional management. Furthermore, nano-scale fertilizers and their formulation, and nano-based approaches to increase crop production, along with the different types of fertilizers that are currently available and the mechanism of action of the nano fertilizers, are discussed. Thus, it is expected that a properly designed nano fertilizer could synchronize the release of nutrients in crop plants as and when needed.

Abstract: Cardio-renal syndrome (CRS) is a term referring to a bidirectional group of disorders in which there is a concomitant compromise of both organs, the heart and the kidney, leading to a significant increase in morbidity and mortality. In recent years, numerous publications have addressed this complex entity from different points of view.For better understanding, five subtypes have been established: depending on its form of presentation, acute or chronic; the organ initially affected and whether there is another responsible systemic disease.CRS represents a complex interaction between both organs with several neurohormonal, inflammatory and hemodynamic pathophysiological mechanisms involved. Its different forms of presentation and the difficulty of its management requires a multidisciplinary and comprehensive therapeutic approach targeting all the mechanisms involved in its pathogenesis. Throughout this review we will analyze all relevant aspects of CRS from its classification to current diagnosis and treatment.

Abstract:

To address the practical challenges of diverse anomaly patterns, strongly coupled dependencies, and high labeling costs in large-scale complex infrastructures, this paper presents an unsupervised anomaly detection method that integrates graph neural networks with Transformer models. The approach learns normal system behavior and identifies deviations without relying on anomaly labels. Infrastructure components are abstracted as nodes in a dependency graph, where nodes are characterized by multiple source observability signals. A graph encoder aggregates neighborhood information to produce structure-enhanced node representations. Self-attention mechanisms are introduced along the temporal dimension to capture long-range dynamic dependencies. This design enables joint modeling of structural relations and temporal evolution. A reconstruction-based training strategy is adopted to constrain the learning of normal patterns. Reconstruction error is used to derive anomaly scores for detection. To ensure reproducibility and ease of deployment, complete specifications of data organization, training procedures, and key hyperparameter settings are provided. Comparative experiments on public benchmarks demonstrate overall advantages across multiple evaluation metrics and confirm the effectiveness of the proposed framework in representing anomaly propagation and temporal drift characteristics in complex systems.

Abstract: Acute myeloid leukemia (AML) continues to pose significant therapeutic challenges, with high relapse rates driven largely by leukemic stem cells (LSCs), a rare, therapy-resistant population with self-renewal capacity, niche adaptation, and the ability to re-initiate disease. In this state-of-the-art review, we synthesize recent advances in LSC biology, addressing (i) how LSCs differ functionally and phenotypically from normal hemato-poietic stem cells, (ii) practical approaches for LSC quantification using multiparameter flow cytometry and LSC-enriched marker panels, (iii) the metabolic and epigenetic programs that enable LSC persistence under chemotherapy and contribute to measurable residual disease, and (iv) current therapeutic strategies targeting LSC eradication, in-cluding antibody-based therapies, apoptosis and metabolic inhibitors, and emerging epigenetic agents. We also examine the key translational barriers, particularly antigen overlap with normal progenitors, microenvironmental protection, and the need for assay harmonization, and propose a practical framework for integrating LSC assessment into risk stratification and therapeutic development.

Abstract: In the modern microservice environment, library dependencies for inter-system communication have become bloated, and conflicts and complications during build and operation have become problems. In particular, in the conventional communication architecture that depends on the MySQL database, the multi-layer dependencies included in \texttt{libmysqlclient} restrict the flexibility of system design. In this study, a replication-protocol-compatible patch was applied to the lightweight MySQL client library Trilogy, and a loosely coupled, low-footprint IPC library connecting the control plane and the data plane was implemented. The proposed method eliminates dependencies on the internal static library group of MySQL Server, while enabling binary log events to be processed directly at the application layer. Stable operation has been achieved for more than one year in a commercial system environment, and its effectiveness has been verified through long-term operation.

Abstract:

This paper discusses the theory of productivity maximisation in relation to human productive potential. If productivity is considered as means to attain certain outcomes, it must have practical implications. Herein, human productive potential is considered as a neurocognitive concept having its significance felt in personal and professional frontier, for human beings are always in search to maximise their productivity by tapping untapped potential latent within. This paper addresses this issue, while at the same time, it examines of the role of cognitive constraints in constraining human potential, which has important implications for the individual and industrial frontiers. In this respect, we have also discussed, in brief, the concept of anti-productivity, its nature, and practical implications.