Search Results (717)

Alzheimer’s disease and related dementias (ADRD) are neurodegenerative conditions characterized by progressive cognitive and functional decline. AD pathology is associated with extracellular amyloid-β plaques, intracellular tau neurofibrillary tangles, synaptic dysfunction, and neuronal loss. AD accounts for approximately 60–80% of dementia cases globally. In 2022, AD was the seventh leading cause of death in the United States, and the number of Americans aged 65 and older living with Alzheimer’s dementia is projected to increase substantially by 2060. Despite decades of research, AD/ADRD data resources remain fragmented across clinical, imaging, genetic, genomic, and therapeutic domains. This paper addresses that gap by providing a centralized review of widely used AD/ADRD databases and computational methods. We first summarize computational approaches used to analyze these datasets, including machine learning (ML), natural language processing (NLP), and biomedical imaging. We then review eight databases classified into three categories: Clinical and Population Data, Genetics and Genomics, and Drug Discovery and Therapeutics. Finally, we discuss real-world applications, including early diagnosis, clinical decision support, personalized medicine, and drug-mechanism analysis. This review identifies opportunities for future work in data harmonization, cross-database compatibility, and robust, generalizable AI models for AD/ADRD research. Full article

Background/Objectives: Single-case studies represent a sophisticated and rigorous methodological framework, widely established in clinical research for providing high-resolution data on individual functional responses. This study evaluates the clinical utility of integrating immersive Virtual Reality (VR) gaming as a novel “functional ingredient” within the Video Game Therapy (VGT) protocol. Given the exploratory single-case nature of this intervention, clinical state-modulations cannot be rigorously validated using standard aggregated group statistics. Therefore, the core objective of this paper is to investigate the therapeutic potential of the VR session on psychological state-modulation, introducing the Single-Case Normative Analytics (SCNA) framework as the mandatory statistical vehicle required to validate individual longitudinal shifts against normative data. Methods: The study treats individual VR exposures as independent, short-term clinical probes embedded within a real-world clinical journey. The SCNA framework was deployed by integrating Crawford’s modified t-tests with longitudinal percentile tracking against an empirical normative reference group (n = 20). Acute state-anxiety variations (STAI-Y1), psychological well-being (PGWBI), and flow dynamics were tracked across three distinct sessions to monitor the patient’s relative repositioning within the normative distribution. Results: The inferential analysis indicates that the immersive 20-min environment facilitated reliable, statistically significant changes in acute state anxiety and flow dimensions, systematically exceeding standard measurement error boundaries and successfully moving the patient’s psychometric profile toward healthy normative ranges. Conclusions: While these findings focus on individual, idiographic reactivity, they demonstrate the utility of the SCNA framework in providing clinicians with objective, evidence-based feedback on the clinical viability of specific VR-based functional units. This approach allows for a rigorous evaluation of standalone digital tools independently of a full, holistic VGT protocol, offering a structured alternative to traditional designs focused on identifying general patterns across groups. Full article

As the core sensor of inertial measurement units, the reliability of Micro-Electro-Mechanical Systems (MEMS) gyroscopes is critical for long-term navigation and motion control applications. To bridge the mechanism-data gap in MEMS multi-mechanism degradation modeling, this paper proposes a physics-informed dual-indicator reliability assessment framework based on Wiener processes. Two degradation indicators under consideration are frequency-related degradation caused by stiffness degradation and Q-factor degradation caused by damping degradation, for which corresponding physics-embedded stochastic degradation models are formulated. The two indicators are normalized and fused through a generalized weighted limit state function, where failure is defined as gyroscope-level performance failure. Closed-form reliability expressions are derived for linear limit states, while Monte Carlo simulation is used for nonlinear cases. Reduced-order multiphysics simulation cases, including a double-ended fixed beam and a cantilevered MEMS mass block, are used to demonstrate the mechanism-to-indicator-to-reliability modeling procedure. The results show that the proposed dual-indicator framework provides more balanced reliability assessment than single-indicator analysis under the simulation setting. The proposed method offers an alternative mechanism-informed approach for reliability analysis and lifetime prediction of other MEMS devices. Full article

Background: Health accessibility is a key determinant of equitable cancer care. In many countries, specialized oncology services are concentrated in urban and socioeconomically advantaged regions, forcing many patients to travel long distances for treatment. Consequently, geographic and social characteristics may be impactful in determining cancer healthcare outcomes. Objective: The aim of this study was to evaluate the association between the municipal-level Social Progress Index (SPI) and geographic travel burden, stage at diagnosis, treatment, and survival in patients with pancreatic cancer in São Paulo state, Brazil. Methods: We conducted a population-based study using data from “Fundação Oncocentro” on adults with pancreatic adenocarcinoma (2005–2025). The SPI (0–100 scale), a composite measure of municipal social and environmental development, was the primary exposure. It is structured into 3 dimensions and 12 components: Basic Human Needs (nutrition, medical care, water and sanitation, housing, safety); Foundations of Well-being (education, information access, health, environmental quality); and Opportunity (rights, freedom of choice, social inclusion, higher education). Municipal residence and cancer center locations were geocoded, and travel distance (km) was estimated. Multivariable Cox, logistic, and linear regression models assessed associations between SPI and overall survival, stage IV at diagnosis, surgery, and travel distance. Results: A total of 13,478 patients were included (mean follow-up 15.1 ± 27.2 months; mean age 62.3 years; 50.4% male). Stage IV disease was frequent (46.3%), and surgery was performed in 33% of cases. Over half of patients (53.2%) traveled more than 10 km for treatment. Increasing SPI was strongly associated with shorter travel distance (β −62.6 km per SPI unit; p < 0.001) and higher odds of surgery (OR 1.04; p < 0.001) and remained independently associated with a higher likelihood of undergoing surgical treatment (adjusted OR 1.04; p < 0.001). The proportion of stage IV disease did not decrease with increasing SPI and was slightly higher in the highest quartile (49.3%). In survival analysis, SPI demonstrated a protective effect in univariate modeling (HR 0.987; p < 0.001), but lost significance in multivariable analysis (p = 0.125). Travel burden was not retained as an independent predictor of survival after adjustment. Conclusions: Municipal-level SPI was a strong determinant of healthcare access and the likelihood of receiving surgical treatment for pancreatic cancer. Social and geographic vulnerability directly influence care pathways, revealing structural inequities in access to treatment. SPI-based stratification may serve as a practical tool to identify priority regions for transport support and equitable allocation of oncology services. Full article

Fire is an essential component of species, ecosystems, and atmospheric dynamics. However, human activity has caused changes in fire regimes over the past two decades. In many cases, the spatial patterns of vegetation change after fire at the landscape scale remain unknown. The aim of this study was to evaluate spatial vegetation trends in burned areas across the Americas (2001–2024), using non-parametric tests and analyzing Normalized Difference Vegetation Index (NDVI) remote sensing products. Over a period of 24 years, fire activity burned a total area of 429.7 million hectares in 44 countries or territories and 269 ecoregions in the Americas. Regarding fire recurrence, the data indicates that 244.7 Mha (56.9%) burned only once (≤1), while 185.0 Mha (43.1%) burned multiple times (≥2), with certain regions experiencing up to 39 fires. The NDVI trend analysis showed that burned areas with increasing trends (p < 0.05) represented a total of 149.6 Mha (34.8%), primarily in Brazil (54.6 Mha, 12.7%), Argentina (17.8 Mha, 4.2%), the United States (14.4 Mha, 3.4%). In terms of decreasing NDVI trends (p < 0.05), these represented a total of 91.8 Mha (21.37%), primarily in Brazil (29.1 Mha, 6.8%), Canada (23.4 Mha, 5.4%), and the United States (14.2 Mha, 3.3%). The ecoregions with the largest areas showing increasing NDVI trends (p < 0.05) were the Cerrado (33.8 Mha, 7.8%), the Llanos (13.3 Mha, 3.1%) and the Humid Chaco (7 Mha, 1.6%). In contrast, the ecoregions with the largest areas showing decreasing NDVI trends (p < 0.05) were the Dry Chaco (9.2 Mha, 2.1%), the Cerrado (8.6 Mha, 2.0%), and the Boreal Shield (8.3 Mha, 1.9%). In terms of land cover types, savannas (37.2%) exhibited the highest proportions of increasing NDVI trends (p < 0.05), while decreasing trends were also present in savannas (28.0%) and grasslands (22.1%). Identifying spatiotemporal trends in vegetation change after fires is a fundamental step in implementing strategies and public policies to ensure ecological restoration. Moreover, given the high costs of restoration efforts, governments must work together to prevent these ecosystems from burning repeatedly. Full article

The 1944 Water Treaty recognizes irrigation return flows; however, neither the Treaty nor subsequent Minutes (323/2017, 330/2024) regulate irrigation efficiency, creating a critical institutional gap under increasing water scarcity. This review provides a comparative institutional and policy evidence across Mexico–United States transboundary aquifer systems, anchored by the Valle de Juárez/Hueco Bolson system as a representative case study. It identifies a quantifiable irrigation efficiency paradox, whereby increases in field-scale efficiency may reduce basin-scale recharge by decreasing recoverable return flows. In the Valle de Juárez system, irrigation return flows account for 51.1% of total recharge (64.4 mm³/year), while natural recharge contributes only 2.6%, resulting in a current groundwater deficit of −73.3 mm³/year. Scenario comparisons reported in the literature indicate that progressive irrigation modernization may further reduce recharge and increase the deficit by up to 65.9%, despite reductions in applied water. The reviewed evidence suggests that in highly arid transboundary systems strongly dependent on anthropogenic recharge, efficiency-focused policies that do not distinguish between consumptive use and recoverable return flows may unintentionally intensify groundwater depletion. These findings highlight the need for binational management frameworks that explicitly incorporate return flow dynamics, hydrogeological connectivity, uncertainty analysis, and basin-scale water balance for future research. Full article

The rapid growth of electric vehicle (EV) adoption requires the scalable and cost-effective deployment of publicly accessible charging infrastructure, where cost-effectiveness is understood in terms of infrastructure reuse rather than explicit economic optimisation. Integrating slow AC charging units into existing public lighting networks represents a promising infrastructure reuse strategy, though spatial feasibility, electrical constraints, and regulatory requirements must be addressed. This study proposes an integrated GIS–MCDA–MILP framework for the optimal allocation of EV charging stations within public lighting systems. GIS-based spatial analysis identifies feasible poles based on parking accessibility and demand indicators, while MCDA ranks candidate locations and a MILP model determines optimal deployment under capacity constraints and phased rollout scenarios. The framework also incorporates AFIR-based policy benchmarking to assess compliance under current and future EV adoption levels. A real-world case study identifies 1223 feasible poles with a structural hosting capacity of 368 chargers. The results demonstrate that such integration is viable at the spatial and cabinet-capacity planning level but structurally limited, with a critical fleet growth multiplier of approximately 3.4 identified as the threshold beyond which lighting-integrated deployment alone becomes insufficient for AFIR compliance. The proposed framework advances the state of practice by coupling spatial, electrical, and regulatory analysis within a single reproducible methodology, offering a transferable decision-support tool for sustainable urban EV charging planning. Full article

Background: Oropharyngeal cancer (OPC) incidence has been increasing among males in the United States, reflecting a complex interplay among social, behavioral, and biological determinants of health. This study aimed to quantify cumulative risk profiles and their relationship with the burden of comorbid conditions in men with OPC using the All of Us Research Program cohort. Methods: We developed a cumulative risk index from nine biological, clinical, and social variables for males with OPC in the United States. Comorbidity burden was measured by the number of unique comorbid diagnoses per patient, excluding HIV/AIDS and primary OPC to reduce circularity. Poisson regression was performed to estimate incidence rate ratios (IRR) for comorbidity by risk group/count. Results: Under strict criteria requiring data for each risk factor, mean comorbidity was 1.90 in the low-risk and 2.29 in the moderate-risk groups; in an inclusive, ‘liberal’ analysis, most cases (74%) were moderate risk with much lower mean comorbidities (mean = 0.050–0.205), with only 5% having any comorbidity recorded. Each additional risk factor was associated with an 81% increase in unique comorbidities (IRR = 1.81, 95% CI: 1.16–2.91; p = 0.01). The high-risk group had substantially higher comorbidity but comprised only two individuals. The most common comorbid diagnoses were essential hypertension and hyperlipidemia, and the most frequent risk factor co-occurrence was having a family history of head and neck cancer and having no insurance. Conclusions: Our analysis demonstrated male OPC patients to have multiple risk factors, but comorbidity burden was concentrated in a small minority, supporting the need for risk stratification and integrated, multidomain prevention and care strategies. Full article

Background and Clinical Significance: NMDAR autoimmune encephalitis is a rare but potentially life-threatening autoimmune disorder that can be hard to recognize initially because it has nonspecific symptoms. In the early phase of the disease, clinical presentation is often dominated by psychiatric symptoms, which can be misleading. A diagnosis is established by demonstrating specific anti-NMDA receptor antibodies, with cerebrospinal fluid analysis considered the most reliable diagnostic method. Timely initiation of immunomodulatory therapy, including corticosteroids, intravenous immunoglobulins, and therapeutic plasmapheresis, significantly improves disease outcomes, while second-line therapies are used in refractory cases. Case Presentation: A 21-year-old female patient (M.B.) was admitted to the Psychiatry Clinic at the University Clinical Center of Vojvodina due to the sudden onset of behavioral changes, including social withdrawal, absence of verbal communication, and unusual orofacial grimacing. During hospitalization, the patient was intermittently in a state of severe psychomotor agitation and poorly communicative, with pronounced orofacial dyskinesias and involuntary tongue movements. Anti-NMDA receptor autoantibodies were detected in both serum and cerebrospinal fluid, and the patient was subsequently transferred to the Intensive Care Unit of the Neurology Clinic. Due to the lack of an adequate clinical response to pulse corticosteroid therapy, six cycles of therapeutic plasmapheresis were performed. Following this treatment, significant clinical improvement was observed. Conclusions: Timely recognition of this condition and a multidisciplinary approach allow for early initiation of immunomodulatory therapy and significantly improve treatment outcomes. Full article

Flat bands exhibit vanishing group velocity and marked sensitivity to lattice geometry, making them a useful setting for studying localization driven by destructive interference. In this work, electrical-circuit simulations are employed to investigate flat-band systems in one, two, and three dimensions. A one-dimensional two-band circuit is first considered, and its flat-band response is characterized through node-to-ground impedance spectra and steady-state voltage distributions. The analysis is then extended to two- and three-dimensional Lieb lattice circuits characterized by sublattice imbalance. In the two-dimensional Lieb circuit, the flat band touches the dispersive bands at a Dirac point, so hybridization with dispersive modes affects the observed localization. Under periodic boundary conditions, wave vector quantization also produces responses that depend on whether the number of unit cells is even or odd. By contrast, in the three-dimensional Lieb circuit, the flat band is spectrally isolated from the dispersive bands, allowing stronger spatial confinement and clearer sublattice selectivity. The one-dimensional, two-dimensional, and three-dimensional models therefore represent three different situations: a singular flat band, a flat band that touches dispersive bands, and a spectrally isolated flat band. Comparing these cases shows how different degeneracy conditions shape impedance responses and localization patterns in electrical circuit systems. At the flat band frequency, the localized voltage response can also be used to generate spatial patterns in both two-dimensional and three-dimensional circuits, pointing to a possible route for spatial mode control of compact localized states in electrical systems. Full article

Background: In recent years, case reports and case series have suggested an association between the use of second- (SGAs), but not first-generation antipsychotics (FGAs), also known as atypical and typical APDs, respectively, and hyperglycemic complications, notably diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS). Although this evidence is informative, there is a need for more observational studies to strengthen this body of knowledge. Objective: To conduct a systematic review of evidence established in observational studies on adverse drug events, specifically DKA and HHS, associated with the use of FGAs and SGAs. Methods: Pertinent bibliographic databases (MEDLINE, EMBASE, PsycINFO, and the Cochrane Central Register of Controlled Trials (CENTRAL)) were searched using appropriate index phrases and keywords through October 17, 2025. Exposure included at least one United States Food and Drugs Administration (US FDA)-approved antipsychotic drug (APD); outcomes were limited to DKA and HHS. Results: A total of 15 observational studies were included in this review, including seven analytical and eight descriptive studies. These studies varied in scope and used different case definitions, study populations, exposures, and outcomes. The observational studies support existing evidence of an association between atypical APDs and DKA, mainly. As a class, typical APDs were associated with an increased risk of DKA, when compared to non-antipsychotic drug use. Although some studies evaluated this association in relation to HHS, there is insufficient information to draw conclusions for this outcome at this time. Conclusions: This analysis provides additional evidence of an association between use of atypical APDs and DKA. Additional analytical studies using administrative health databases are needed to clarify this association. Full article

Background: A unique group of non-small cell lung cancer (NSCLC) is driven by alterations in HER2. Early studies, mostly from advanced NSCLC, suggest that NSCLC with HER2 alterations confers a poor prognosis. However, modern studies are scant. Methods: We performed an analysis of a United States-based database on stage I–III NSCLC patients diagnosed during 2019–2024. Cases with complete data on EGFR, ALK, and HER 2 alterations were included. Study cohorts were divided into: EGFR or ALK alterations (EGFR/ALK group), HER2 alterations (HER2 group), or negative for these alterations (triple-negative group). Outcomes of interest were survival. Results: Analyses were performed on data from 3486 patients: 515 patients (15%) in the EGFR/ALK group, 173 patients (5%) in the HER2 group, and 2798 patients (80%) in the triple-negative group. Median disease-free survival (DFS) was 41.3 months, 26.6 months, and 21.2 months, respectively. Median overall survival (OS) was 62.5 months, 63.7 months, and 40.1 months, respectively. Multivariable analysis showed that DFS and OS were significantly worse among the triple-negative group than HER2 group: adjusted HRs 1.44 (95% CI: 1.08–1.90, p = 0.01) and 1.94 (95% CI: 1.25–3.01, p = 0.003), respectively. In the subgroup of patients with HER2 alterations, no significant difference in DFS or OS was found among patients with HER2 mutation, HER2 amplification, or HER2 overexpression in this exploratory analysis. Conclusions: When classified by the status of EGFR, ALK and HER2, early-stage NSCLC patients with HER2 alterations had significantly better DFS and OS than those with triple-negative biomarkers. Full article

Cabled underwater information networks (CUINs) are a focal point and priority in the field of global marine science and technology. Reliability and economic viability are among the primary constraints on the large-scale deployment of such networks. The remote power supply system for grid-like CUINs is the component with the highest technical risk, exerting a significant impact on both network reliability and economic feasibility. This paper designs and constructs a minimal model and a basic model of a constant-current remote power supply system (CCRPSS) for grid-like CUINs. Through simulation modeling and analysis, the system’s capability to handle faults in a single underwater unit or multiple underwater units in different power supply link segments (PSLSs) is validated, and the impact of underwater unit faults on the system’s operational state is analyzed. Based on this, a descriptive method for determining the power supply reliability (PSR) of observation equipment (OE) is proposed, and the variation patterns of this reliability across different power supply links (PSLs) are derived. Building on this foundation, a constrained engineering design method for the grid-like CCRPSS is proposed. This method aims to deploy a larger number of secondary nodes (SNs) at a lower cost. By integrating constraints including the PSR of OE for each PSL, the open-circuit and short-circuit fault rates of underwater units, and the allowable number of SNs per PSLS, it optimizes the system engineering design problem. This approach yields an optimal solution for the number of longitudinally and transversely deployed SNs as well as the reliability requirements for each underwater unit. Case simulation results validate the descriptive method for the PSR of OE and the variation patterns of such reliability, thereby confirming the feasibility of the constrained engineering design approach. The research findings presented in this paper can provide theoretical references for the reliability analysis, scale design, and long-term planning of CUINs and their remote power supply systems. Full article

Many tertiary mathematics departments are seeking to improve equity in their programs; however, they may struggle to translate these goals for equity into action. This longitudinal, qualitative study focuses on a Networked Improvement Community (NIC) within the mathematics department at a public, doctoral degree-granting university located in the Southeast United States. This NIC worked together for two years (Spring 2023 to Spring 2025) to become more reflective practitioners and critically transform the mathematics program at their institution. We used Cultural Historical Activity Theory (CHAT) to examine relationships between objects, tools, and outcomes for the NIC. Data included multiple interviews and journals from eleven (n = 11) participants, and was triangulated with observer field notes of monthly NIC meetings. Thematic analysis revealed three pathways that connected NIC members’ individual and collective goals (objects), NIC activities and resources (tools), and NIC members’ perspectives on teaching and students (outcomes). We found that sometimes objects, mediated by tools, led to aligned outcomes, but not always. Specific tools could lead the NIC to adopt a new and collective object (and outcome). In other cases, the lack of the right tool led to unrealized outcomes or even secondary outcomes within the NIC. Ultimately, the critical transformations that NIC members envisioned were not realized; however, the experience of examining student data and discussing with colleagues shaped their thinking about teaching and students in impactful ways that inform faculty development for institutional change efforts on a broader scale. Our findings highlight the importance of identifying the right tools to support critical transformation, including the value of examining data as a collaborative group. We also extend NIC scholarship by using second-generation CHAT to distinguish objects over time and specify pathway models linking tools to outcomes. Full article

Minimum wage policies have become a central instrument for promoting social and economic sustainability by ensuring sufficient income to cover basic needs and reduce inequalities. They align with recent predistribution approaches in the literature and with goal 10.4 of the United Nations 2030 Agenda. In the European context, these policies are explicitly embedded within the sustainable development and just transition agenda, where the European Union emphasises that securing fair wages is a necessary condition for inclusive, balanced and equality-enhancing growth. At the same time, the methodological debate has evolved from early time-series-based approaches to a new generation of quasi-experimental studies, which provide more rigorous and less biased evidence. Within this framework, Spain represents a relevant case due to the scale and persistence of its minimum wage reforms since 2019, yet the Spanish case has lacked a systematic synthesis comparable to those available for other advanced economies (e.g., Germany, the UK, the USA). This article offers the first systematic synthesis of empirical evidence on the effects of the minimum wage in Spain from the 1990s to 2025, following the PRISMA 2020 methodology. This process yielded a large number of articles, from which an initial selection of 249 was made. Following the full screening and eligibility assessment, 34 articles were retained. The results allow for an analysis of the current state of research on the effects of the minimum wage across multiple dimensions, especially on employment and inequality. Other aspects, such as productivity, prices, other business adjustments, administrative obstacles, and public finances, are still poorly addressed in the available literature. In any case, this is a valuable exercise in understanding how wage policies can help to clarify the relationship between minimum wage policies and the transformation of labour markets. Full article