Abstract: Wearable electronic textiles (e-textiles) are increasingly being explored for healthcare, sports, military, and smart wearable applications, creating a growing demand for sustainable and flexible energy harvesting systems. In this study, a cost-effective and ultra-flexible textile-assisted thermoelectric generator (TEG) was developed using recycled electronic and textile waste materials. Discarded copper and aluminum foils recovered from electronic waste were integrated into a recycled woven fabric composed of 70% cotton, 28% polyester, and 2% elastane to fabricate the wearable thermoelectric device. The fabricated system demonstrated a measurable thermoelectric response, producing a maximum output voltage of 180.75 mV under a temperature difference (ΔT) of 5.82 K. The results demonstrate the feasibility of utilizing waste-derived conductive materials and recycled textiles for flexible thermoelectric energy harvesting applications. In addition to its lightweight and wearable structure, the developed device highlights the potential of sustainable smart textile systems for low-power wearable electronics and self-powered sensing applications. This work contributes to the advancement of environmentally sustainable smart textiles by combining waste reutilization, wearable energy harvesting, and flexible electronic integration within a single textile platform. Future research may focus on improving thermal contact efficiency, long-term durability, output stability, and scalable fabrication strategies for practical wearable energy harvesting applications.

Abstract: Background: As the society ages, the number of patients with early cognitive impairment that can progress to Alzheimer’s disease also increases. Early diagnosis and risk as-sessment allows effectively initiate the necessary lifestyle changes and monitoring. The use of artificial intelligence (AI), when analyzing medical histories, enables more pro-ductive evaluation of large datasets and identify patterns that may go unnoticed in clinical practice. This kind of approach can improve early screening, reduce physicians’ workload and develop bigger support for personalized treatment. The aim of the study: To compare the performance of machine learning (ML) algorithm with a physician (neurologist) in assessing patient’s subjective cognitive decline and Alz-heimer’s disease risk in early stages. Research methods: The research was designed as a retrospective, comparative cohort study that used two data sources. Firstly, the National Alzheimer’s Coordination Center (NACC) longitudinal dataset to train the ML model. Secondly, medical records gathered from Pauls Stradins Clinical University Hospital dating from 2020 till May 2025 to evaluate the al-gorithm’s precision. Results: The research included 154 patients, predominantly women (68.8%), with a mean age of 80.3 years. Class distribution consisted of dementia (n=139); mild cognitive im-pairment (MCI) (n=13); subjective cognitive decline (SCD) (n=2). Dementia was identified the best – 128/139 (accuracy – 92.1%) with errors tending towards MCI. MCI was correct in 9/13 cases (accuracy – 69.2%) All SCD cases were classified as dementia. Overall model’s accuracy was 91.6% (141/154). Conclusions: ML algorithm can match to neurologist made diagnoses with high precision but is struggles to separate adjacent early-stage diagnoses. At this moment, ML models are great decision supporters, but no yet alone diagnosticians. Nevertheless, this technology has high potential to being integrated in the future to aid triage and early screening, especially when advanced diagnostics are limited.

Abstract: Two geometrical problems of negative time metric and abuse of distance factors for angular coordinates and other two physical problems of revisit redshift and covariant acceleration were put forward to investigate the traditional frames of general relativity. It is found that sub-indexes of Christoffel symbols in gravitational fields are not really alterable. The concept of trajectory derivative was carried out to clarify the derivatives on motion trajectories which perform far from field derivatives. Calculations on trajectory derivatives of frequency shift and acceleration lead to conclusions that light speed keeps general covariance in gravitational fields but light energy momentum would not, may as well, the motions of massive matters in gravitational fields do not perform general covariance thoroughly. The conservativeness of light angular momentum has been discovered in most surprising forms, as well as that of massive matters. Renovated kinematic equations for light ray propagations and massive matter motions have been carried out that forcefully impact the traditional methodologies on solutions of trajectory and time spending. Dynamic models of fluid planet rings were founded to interpret the evolutions of accretions of quasars and active galactic nuclei. Consequently, the mechanism of relativistic release was raised up based on light speed covariance and energy conservation, although it has not been completely proved. But the equations on relativistic release and relativistic frequency shifts so far as the line widths of emission and absorption could be astonishingly verified in observations, especially on the predictions of the broad line regions and narrow line regions. It could be imagined that the spectrums of relativistic emission and absorption may have been involved with fantastic mystery of matter’s intrinsic structures that we know less.

Abstract: Background: Hepatocellular carcinoma (HCC) is one of the most common and deadliest cancers worldwide. Alpha-fetoprotein (AFP), a widely used and accessible tumoral marker, has limited performance in the early detection of HCC among high-risk populations. This study aims to evaluate the potential added value of ccfDNA (circulating cell-free DNA), alone or in combination with AFP, using accessible, feasible ccfDNA analysis. Methods: A prospective cohort of 125 patients with chronic liver disease was analyzed. Patients with incomplete clinical or laboratory data and patients without cirrhosis were excluded from the final analysis. Nonparametric tests, logistic regression and ROC curve analysis were performed. ccfDNA concentration was assessed by fluorimetry and fragment size was measured using on-chip electrophoresis. Results: ccfDNA fragment size was significantly lower in the cirrhosis-HCC subgroup compared to the cirrhosis-only subgroup (p< 0.001). While AFP remains an independent predictor of HCC among cirrhosis patients, ccfDNA fragment size did not prove to be an independent predictor in this cohort. However, AUROC (Area Under the Receiver Operating Characteristic Curve) analysis revealed that a combined model of AFP and ccfDNA fragment size showed modest additional discriminatory value between the two groups, compared to either ccfDNA peak size or AFP alone. Conclusions: ccfDNA fragment size may provide modest complementary value within multimarker panels. However, the marker needs further validation in a larger cohort, and adequate assessment of potential confounders such as severity of liver dysfunction and age.

Abstract: Mineral waters represent unique limnological ecosystems with stable physicochemical conditions and specialised microbial communities adapted to extreme environments. Bulgarian mineral waters remain comparatively underexplored despite their considerable ecological and biotechnological significance. This review analyses current knowledge on the diversity, ecological functions, and biotechnological potential of microbial communities from Bulgarian mineral springs. A comprehensive literature survey covers studies published between 1990 and 2026. The study integrates hydrogeological, limnological, microbiological, and biotechnological data and encompasses both culture-dependent methods and molecular approaches. The available evidence demonstrates that microbial communities in Bulgarian mineral waters include diverse bacteria, archaea, cyanobacteria, microalgae that adapt to broad thermal and geochemical gradients. These microorganisms actively participate in element cycles, form complex biofilms, and show numerous physiological adaptations to oligotrophic and extreme conditions. Many taxa produce thermostable enzymes, antimicrobial compounds, exopolysaccharides with potential applications in medicine, industrial biotechnology, environmental remediation, and cosmeceutical technologies. The review identifies significant research gaps and emphasises the importance of integrated multi-omics approaches for future exploration of Bulgarian mineral water ecosystems.

Abstract: The ΛCDM cosmological model has been highly successful in describing the large-scale structure and evolution of the Universe, yet it continues to face persistent challenges, most notably the cosmological constant problem and the Hubble tension. Building upon a recently proposed conceptual framework, this work investigates the temporal evolution of the Universe’s total energy density and its constituent components—dark energy, matter, and radiation—under the assumptions that the Hubble parameter evolves inversely with cosmic time and that gravitationally repulsive dark energy remains in dynamical balance with attractive matter–radiation components. Within this framework, the Universe expands linearly with time and exhibits effective zero acceleration, sustained by a constant expectation value of an energy inflow rate attributed to gravity-driven vacuum energy fluctuations. Analytical results indicate that dark energy acts as a persistent energy reservoir, continuously supplying energy for the formation and evolution of matter and radiation throughout cosmic history. A simplified phenomenological description of the radiation–matter transition, while not derived from first principles, is shown to reproduce the broad thermal history of the Universe, yielding temperature estimates in good agreement with established cosmological epochs from the Planck era to the present day. Furthermore, the framework offers a potential pathway toward reconciling quantum field theory predictions of vacuum energy density with cosmological observations and provides a possible explanation for the unexpectedly rapid formation and maturity of early galaxies observed at high redshift. The analysis is further extended to a precritical, scale-dependent energetic regime, suggesting a unified balance principle operating across scales. The framework therefore provides a coherent phenomenological picture linking vacuum energetics, cosmic expansion, and early-Universe behavior, and offers a potential avenue toward addressing the cosmological constant problem.

Abstract: As reasoning becomes a defining capability of large language models, reasoning benchmarks have moved to the center of evaluation. However, despite the rapid growth in the number of benchmarks and reported scores, benchmark results are often not directly comparable. This is because benchmarks may differ not only in the reasoning capabilities they target, but also in the conditions under which models are evaluated and the criteria used to assess success. To address this challenge, we present the first survey of reasoning benchmarks for large language models across three dimensions: Object, Setting, and Evaluation. Object defines the reasoning capability under examination. Setting specifies the conditions that shape model behavior. Evaluation determines how success is measured. We further introduce extended scenarios to account for special conditions. Based on this analysis, we identify two major weaknesses in current practice, namely heterogeneous benchmark objects and weakly justified settings, and derive practical guidance for benchmark selection, construction, and reporting, along with future directions for benchmark development. We hope this survey will help advance reasoning evaluation beyond score comparison alone toward benchmarks that are more interpretable, better justified, and easier to implement. A repository for the related papers is available at https://github.com/chenyuanTKCY/Awesome-Benchmarks-for-LLM-Reasoning.

Abstract: Recurrent lumbar disc herniation remains a prominent clinical challenge following microdiscectomy, particularly at the L5–S1 level, which has distinct biomechanical characteristics. While various radiological and clinical predictors have been proposed, the influence of surgical laterality has not been adequately explored. This study aimed to identify independent predictors of recurrence following L5–S1 microdiscectomy, with a specific focus on surgical laterality. This retrospective cohort study included patients who underwent primary L5–S1 microdiscectomy at a single center with a minimum follow-up of 12 months. Recurrence was defined as same-level, same-side herniation confirmed by imaging and clinical findings. Demographic, clinical, and radiological parameters—including facet tropism, disc height index, Modic changes, and multifidus morphology—were analyzed. Recurrence-free survival was assessed using Kaplan–Meier analysis, and independent predictors were identified through Cox proportional hazards regression. Patients with recurrence were significantly older and had higher rates of preoperative neurological deficits and systemic comorbidities. Radiological parameters showed no significant association with recurrence. In multivariable Cox regression analysis, left-sided surgery emerged as the strongest independent predictor, associated with a 3.5-fold increased hazard of recurrence (aHR: 3.506; 95% CI: 1.537–7.999; p = 0.003). Systemic comorbidities also independently increased recurrence risk (aHR: 2.051; 95% CI: 1.000–4.208; p = 0.050). Kaplan–Meier analysis demonstrated significantly shorter recurrence-free survival in patients undergoing left-sided procedures. Recurrence after L5–S1 microdiscectomy appears to be driven more by surgical and systemic factors than by conventional radiological parameters. Surgical laterality, particularly left-sided procedures, represents a novel and significant predictor of recurrence. These findings highlight the potential role of technical and patient-related factors in surgical outcomes.

Abstract: Background/Objectives: Mental health issues among adolescents have increased during the pandemic necessitating targeted intervention programs. Improving health literacy (HL) of adolescents and parents could be a meaningful concept. This study aimed to explore the HL of parents and adolescents and its association with mental health screening outcomes in a bilingual region. Methods: A population-based anonymous online survey was conducted in South Tyrol, Italy. About 3,229 questionnaires provided information on HL and adolescents’ mental health concerns using standardized inter-nationally validated instruments. Parental and self-reported data were compared; the associations of HL with social support, problematic Internet use, language and mental health outcomes were explored. Results: Adolescents’ HL was associated with ques-tionnaire language, with better results observed for the German language (23.1% high HL) than for Italian (14.3%). Higher levels of HL among both parents and adolescents were related to better mental health outcomes in adolescents with higher associations to adolescents’ HL. Social support and problematic internet use were associated with both mental health outcomes and parental- and self-reported HL. HL accounted as a mediator partly for of the relationship between these two variables and self-reported mental health outcomes. Conclusions: Enhancing HL among parents and adolescents through school-based programs might be a promising strategy to improve adolescents’ mental health. In the bilingual context of South Tyrol, existing international German school-based programs can be adapted to fit the Italian health care and educational system. Further research is essential to evaluate the implementation of such programs and their effects on adolescents’ mental health and HL outcomes. South Tyrol offers the unique opportunity to apply German actual school HL knowledge and adapt it to Italian needs.

Abstract: Domain Generation Algorithm(DGA) is widely used by botnets to evade detection by generating numerous pseudo-random domains to communicate with commandandcontrol servers. While existing Graph Neural Networks attempt to detect DGA botnets by exploiting the feature similarity of these domains to model semantic associations via similarity graphs, they are restricted to binary relationships, causing information decay during multi-hop propagation. To overcome this, we propose HyperDGA. Treating domains as nodes, HyperDGA utilizes K Nearest Neighbors to construct hyperedges, explicitly capturing high order group semantic correlations. Subsequently, a Local Topology Aggregation module employs multi-head node attention-based hypergraph convolution to dynamically assign distinct aggregation weights to intra hyperedge nodes, extracting fine-grained structural features. To mitigate the limited receptive field of hypergraph convolutions, a Global Node Association module integrates the selective state space model, Mamba, to capture long-range dependencies across all nodes. Experiments on two public datasets demonstrate that HyperDGA outperforms all baselines and achieves over 99% accuracy, validating the efficacy of high-order semantic modeling for DGA botnet detection.

Abstract: Mangrove wetlands are important coastal ecosystems and are increasingly vulnerable to heavy metal contamination. The accumulation of heavy metals in man-grove ecosystems is well studied; however, studies on the seasonal variations of heavy metals in mangrove wetlands are scarce. This study investigated heavy metal (Cd, Cr, Cu, As, Pb, and Zn) accumulation in surface sediments of six typical mangrove wet-lands (DZG, QLH, XCP, SYR, SBW, and XY) in Hainan Island, China, during wet and dry seasons. In addition, potential ecological concerns and relationships between sedimentary physicochemical parameters and metal accumulation were assessed. The findings demonstrated significant spatial differences in heavy metal accumulation, with higher concentrations in the northern localities and lower concentrations in the southern areas. There were notable seasonal fluctuations in heavy metal concentrations, with higher levels in the dry season. Risk assessment models exhibited that Cadmium (Cd) and Arsenic (As) were the principal contaminants of concern in most research sites with moderate levels of contamination and posed at least moderate ecological concerns in both wet and dry seasons. The overall ecological risk index indicated a moderate risk to the environment, especially in the dry season. The principal component analysis (PCA) and correlation analysis results indicated that the physicochemical properties of sediments, mainly total organic carbon (TOC), total phosphorus (TP), total nitrogen (TN), and salinity, had significant effects on the heavy metals accumulation in the mangrove sediments. The present study helps raise awareness of seasonal fluctuations in heavy metal pollutants and provides strategies for the prevention and monitoring of metal pollution in mangrove wetlands.

Abstract: Precise assessment of landslide potential in tectonically active mountain areas like Darjeeling Sikkim Himalaya (DSH) is a scientific challenge due to the complexity of different landslide conditioning factors that control the slope stability. Despite several studies for landslide susceptibility mapping, most of the conventional methods struggle to capture the nonlinear relationships and spatial heterogeneity that characterize landslides. Besides, the current use of pixel-based methods is insufficient to depict geomorphological units and slope-scale processes, thus limiting their effectiveness in boundary demarcation of landslide-prone areas. These limitations highlight the need for more robust machine learning frameworks that integrate geomorphology-based terrain segmentation with advanced machine learning models, which would not only facilitate modeling the multifaceted interactions among environmental components but also improve the understanding of the landslide driving forces. In this study, we have used slope unit based landslide susceptibility mapping with 4380 slope units integrated with 17 conditioning factors, and 8373 total updated inventories using six models Random Forest (RF), Generalized Additive Model (GAM), Categorical Boosting (CatBoost), Tabular Neural Network (TabNet), Bayesian Additive Regression Trees (BART), and Convolutional Neural Network (CNN). The model hyperparameters were optimized using Bayesian optimization, except for the BART model. Among the six models, RF (AUC = 0.848) and CatBoost (AUC = 0.846) were the best two performing models. Furthermore, SHAP analysis reveals that elevation, aspect, slope, distance to faults, NDVI, and proximity to roads and drainage networks are the main landslide controlling factors in DSH. The interaction analysis using SHAP indicates that the occurrence of landslides is controlled by nonlinear and threshold-dependent relations, especially among slope-rainfall, rainfall-soil moisture, and slope-distance to roads and faults, which represents a complex interaction between the hydrological triggering factor, geomorphic processes, tectonic activity, and human interventions.

Abstract:

Caryocar coriaceum Wittm. (Caryocaraceae) is a native Brazilian species predominantly distributed in Cerrado areas and transitional regions with the Caatinga in Northeastern Brazil, whose fruits exhibit significant nutritional, technological, and biofunctional potential. This review systematizes and critically analyzes the available scientific evidence regarding the fixed oil extracted from its fruits, addressing extraction methods, chemical composition, physicochemical parameters, nutritional value, technological applications, and the main bioactivities described in experimental models. Chromatographic and bromatological studies demonstrate that the oil presents a lipid profile characterized by the predominance of monounsaturated and saturated fatty acids, especially oleic acid and palmitic acid, in addition to the presence of carotenoids, phenolic compounds, and other bioactive lipophilic constituents. Available preclinical evidence indicates antioxidants, anti-inflammatory, wound-healing, gastroprotective, respiratory, anticonvulsant, and microbial resistance-modulating properties, suggesting potential applications in the food, pharmaceutical, cosmetic, and biotechnological fields. From the perspective of Food Science, the oil demonstrates characteristics compatible with lipid matrices of functional interest, although aspects related to oxidative stability, compositional standardization, sensory acceptability, and industrial scale-up remain insufficiently explored. Additionally, important limitations persist regarding the scarcity of systematic toxicological studies, the absence of clinical trials in humans, and the limited elucidation of the molecular mechanisms involved in the observed bioactivities. Thus, although C. coriaceum presents promising biotechnological potential, the advancement of its translational application will depend on multidisciplinary approaches capable of integrating chemical standardization, toxicological safety, and applied technological development.

Abstract: Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent- to-human spillover remain incompletely characterised. The timeliness of this frame- work is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard 9 the MV Hondius cruise ship – the first such cluster in a maritime setting, with three deaths reported across multiple countries (WHO Disease Outbreak News: https://www.who.int/emergencies/disease-outbreak-news/item/2026-DON599). This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Here, we present an integrated computational study that combines three complementary analyses. Preliminarly, we performed the first phylogenetic analysis of such virus, idenifying as Orthoantavirus andensense the responsible for the vessel outbreak. Second, we performed a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs) using publicly available RNA-seq data (GEO accession GSE133751, n = 3 per group), identifying 184 upregulated and 19 downregulated evidencing the role of dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We constructed a protein–protein interaction (PPI) network from STRING (176 nodes, 3,210 edges) and applied a composite network centrality score to rank regulatory hubs, identifying ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis con- firms strong activation of interferon signalling (Reactome, p = 1.3×10⁻⁶³), antiviral defence (Gene Ontology, p = 3.8 × 10⁻⁵⁸), and NF-κB pathways, with concurrent suppression of ribosomal translation. We finally developed a coupled SEIRD epi-demiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and 35 public-health intervention design.

Abstract: Humanity faces not isolated problems but a PolyCrisis, which is a set of 26 tightly interwoven existential crises spanning ecological collapse, planetary overheating, chronic disease epidemics, institutional fragility and social breakdown. Each crisis amplifies the others through cascading feedback loops, and sixteen possess the independent capacity to cause human extinction. We are not entering an emergency, but we are already in a state of emergency. Multiple planetary boundaries have been transgressed, and climate tipping points are being crossed now. Extinction rates match historical great mass extinction events, while our food systems, primarily responsible for almost half these crises, simultaneously drive hunger, obesity and chronic diseases. This PolyCrisis is not the result of isolated failures, but the predictable outcome of Planet A, the Operating System of our mainstream civilization, characterized by economics of unbounded extraction and hoarding, violence-based and profit-based food systems, short-term thinking, and unlimited growth imperatives on a finite planet. Planet B is our proposed PolySolution framework, a complete alternative Operating System grounded in empirical reality and proven solutions. It integrates animal-free food systems releasing up to 5 billion hectares for rewilding, regenerative economics measuring non-violence and biocapacity, circular economy minimizing waste, technological restraint with democratic governance, seven-generation thinking, and PolyCommunity coordination, collaboration and co-creation of the PolySolution. It calls for the immediate emergency implementation of two planetary-scale MegaSolutions: a) Hungerless, implementing universal, free access to gourmet whole-foods, plant-based Vegan meals worldwide, eliminating hunger and accelerating food and health systems transformation, and b) Cool, halting planetary overheating through agricultural emissions elimination, massive rewilding for carbon sequestration, and comprehensive stabilization of the life-support systems of our planet.

Abstract: Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road-network distances. We term this range-feasibility blindness and derive its analytical radius Δ=Rmax(α−1)/(2α), where α is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms α∈[1.40,1.52] and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road-network range constraints simultaneously with depot-opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments across all three cities show consistent cost reductions of 20.6–28.2% over sequential baselines, with gains increasing monotonically with instance scale. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning.

Abstract: The rapid advancement of Machine Learning (ML) has significantly transformed polymer science by enabling efficient prediction and design of polymer properties through high‑throughput screening. However, current methods still struggle with nonlinear Structure–Property Relationships (SPRs), limited dataset standardization, and computational inefficiency, which restrict prediction accuracy and interpretability. This study proposes a comprehensive ML‑based framework for predicting polymer properties and identifying SPRs. The approach integrates data preprocessing, molecular descriptor and topological index–based feature extraction, iterative feature selection, and XGBoost predictive modeling. Model hyperparameters are optimized using the Starfish Optimization Algorithm (SOA) to enhance performance and efficiency. Model interpretability is achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing both global and local insights into the influence of molecular features on polymer properties. Experimental evaluation on the PolyOne dataset demonstrates strong predictive performance, with R² values exceeding 0.92, mean absolute error (MAE) below 0.08, and root mean square error (RMSE) under 0.12 for key physical and optical polymer properties. Overall, the proposed framework effectively balances accuracy, computational efficiency, and interpretability, offering a robust and practical tool for accelerating polymer design while enhancing understanding of molecular structure–property relationships.

Abstract: With the large-scale commercialization of 5G and rapid evolution of 6G wireless systems, planar interdigital bandpass filters (BPFs) have become the core passive components for low-power RF front-ends. However, state-of-the-art filter design methods either rely heavily on empirical trial-and-error with 8–10 simulation iterations, or fail to resolve the inherent trade-off between center frequency tuning and stopband performance degradation, which cannot meet the demands of rapid customized design for 5G/6G multi-band scenarios. In this paper, a symmetric five-resonator three-segment patch-type interdigital BPF is taken as the research object. Through theoretical derivation, full-wave electromagnetic simulation, parametric scanning and orthogonal experiments, the quantitative mapping between structural parameters and filter performance is established. Notably, the directional tuning mechanism of the resonator’s narrow segment width on the first stopband is first revealed, which realizes lossless stopband optimization without disturbing the center frequency. On this basis, a three-stage standardized design procedure is proposed, which reduces design iterations from 8–10 to 3, shortens the design cycle by over 70%, and achieves 100% compliance of core design indexes. This work provides an implementable, low-threshold engineering method for rapid customized design of planar interdigital BPFs for 5G/6G RF front-ends.

Abstract: This study aimed to analyze the temporal trends and spatial distribution of mortality from malignant central nervous system (CNS) neoplasms in Brazil from 2000 to 2023. An ecological, time-series study using Joinpoint regression for temporal analysis and Global and Local Moran’s I for spatial patterns. Data were retrieved from the Mortality Information System (SIM). A total of 187,551 deaths were recorded. The Average Annual Percent Change (AAPC) showed a significant growth of +2.1% (95% CI: 1.8 – 2.4). However, a significant inflection point was identified in 2013; between 2000–2013, the APC was +4.1% (p < 0.05), becoming non-significant (+0.8%) thereafter, likely reflecting methodological data inconsistencies in the national system. Spatially, high-high clusters were concentrated in the South and Southeast regions (Moran’s I p < 0.05), while the North and Northeast presented low-low clusters, suggesting significant underreporting. While mortality trends appear to increase, they are heavily influenced by regional diagnostic disparities and information system transitions. This is the first nationwide study to integrate spatio-temporal dynamics to highlight these inequities in Brazil.

Abstract: Background: Transthyretin amyloidosis (ATTR) is a rare, often underdiagnosed and undertreated, autosomal dominantly inherited, progressive disease that affects multiple systems of the body. It results from the extracellular accumulation of misfolded transthyretin (TTR) protein as insoluble amyloid fibrils, predominantly causing cardiomyopathy, polyneuropathy or mixed phenotypes. It can occur in a hereditary form (ATTRv) and in a wild-type form (ATTRwt), with over 150 different pathogenic mutations having been identified worldwide. The clinical presentation is highly variable, leading to a diagnostic delay of 2–5 years. Transthyretin (TTR) amyloidosis is an inherited disease for which recent advances in pathogenesis, diagnosis and treatment have revolutionized its management. Objectives: The aims of this review are to provide an update on the epidemiology and genotype-phenotype correlation, on current diagnostic techniques and on emerging and individualized treatments for this rare hereditary disease. Particular attention will be given to the early diagnosis Methods: A systematic literature search was conducted across major databases to identify studies addressing clinical characteristics, diagnostic modalities, and treatment outcomes in hereditary and wild-type ATTR amyloidosis. Registry data from THAOS and other multinational cohorts were analyzed to evaluate phenotypic variability across genotypes and geographic regions. Results: Clinical presentation of TTR related amyloidosis (h-Amyloid) can range from early onset to late onset with late onset having worse neurological and cardiac involvement at time of diagnosis. The Val30Met mutation is the most common TTR mutation worldwide, however patients with non-V30M mutations can have very different presentations of h-amyloidosis. Identifying “red flag” symptoms in a patient with suspicious clinical presentation can initiate correct diagnostic pathway. Non-invasive imaging, especially bone scintigraphy, has greatly facilitated the diagnosis of patients with Transthyretin related cardiac amyloidosis (ATTR-CM). First generation h-amyloid treatments or TTR stabilizers such as tafamidis have been shown to significantly improve survival in patients with h-amyloidosis. The second generation treatments such as RNA silencers (patisiran, vutrisiran, inotersen, eplontersen) have been shown to halt the disease progression. Present data from small to moderate-sized patient cohorts demonstrate that TTR-targeting therapy is associated with reduction of cardiovascular events and improvement in survival compared with current standard of care. Early recognition of key clinical features and application of a diverse diagnostic strategy, in conjunction with timely initiation of disease-modifying therapy, are critical to optimal management of patients with hereditary transthyretin (ATTR) amyloidosis. Conclusions: The therapeutic options have evolved and improved in recent years, and with current diagnostic tools, the opportunity to alter the natural history of a disease that was once invariably fatal is better than ever. Because the disease is systemic, a thorough, multidisciplinary approach to patient management is ideal.