Differences in looking to and processing of audiovisual speech have been theorized to contribute to heterogeneity in language ability in autistic children. Differential audiovisual speech processing has been indexed by event-related potentials (ERPs), specifically via amplitude suppression in response to audiovisual versus auditory-only speech, and linked with vocabulary in school-aged children. This study used an intact-group comparison and concurrent correlational design in infant siblings of autistic children (Sibs-Autism) and non-autistic children (Sibs-NA) to determine whether amplitude suppression is (a) present in infancy, (b) different in Sibs-Autism versus Sibs-NA, and (c) related to looking to audiovisual speech and language abilities. We collected EEG data from 54 infants aged 12-18 months (29 Sibs-Autism; 25 Sibs-NA) while they viewed videos of audiovisual and auditory-only speech, as well as eye tracking and language data. We found significant amplitude differences at the N2 ERP component in response to audiovisual versus auditory-only speech but no significant group differences in ERP amplitudes. Associations between looking to audiovisual speech, amplitude effects, and language were moderated by group, chronological age, and biological sex. Our findings suggest that differential audiovisual speech processing is present in 12-18-month-olds and may explain heterogeneity in looking to audiovisual speech and emerging language ability.
Over the past years, attention has been drawn to how narrative ideas could be integrated in care practices. For many years, we have inquired into, challenged, and stretched the concept of narrative care. For us, narrative care is more than to acknowledge or listen to people's stories. We see care itself as an intrinsically narrative endeavor. We think of narrative care as an active and relational co-composition of experience - a way of making sense of the world together, with or without spoken words in imaginative ways. In that sense, narrative care is forward looking rather than only dwelling on past experiences. Within this understanding, narrative care does not depend solely on the ability to tell or listen to stories, rather it depends on the ability to engage in and become part of experience, and to imagine the unimaginable. In this autobiographical narrative inquiry, we think with our experiences alongside older adult family members, as we look both backward and forward to continue to learn. We show how narrative care is about an opening to learn, to imagine the unimaginable, and to live with agency. It is in these moments that we can find ways in which we can retell and relive our lives in ways that allow us to become otherwise and to create forward-looking stories.
Parsing complex dynamic scenes is critical for navigating our visual world, and driving safely is a daily task that requires responding quickly to hazardous events. Theories of driver awareness suggest that drivers need to look at hazards to detect them, particularly in anticipation of hazard onset. Indeed, scene context may guide eye movements to likely hazard locations. However, given the complexity of real road scenes, drivers may rely instead on explicit cues of an impending collision before identifying the correct location. In 2024, we recorded eye position while 30 licensed drivers localized hazards in annotated dashcam footage. On correct trials, drivers started to look at where the hazard will be 2 s before onset, highlighting the importance of anticipatory processes in dynamic scene perception and safe driving. However, hazard-directed looking is not indicative of awareness, as 40% of missed hazards were foveated before response. Our results demonstrate early gaze guidance by scene context, which can help drivers anticipate hazards, an idea consistent with theories of driver awareness and with theories of visual search. However, looking directly at the hazard alone is not necessary or sufficient for hazard awareness and should not be used to index awareness in driver monitoring systems. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Leptomeningeal metastasis (LM) represents a devastating complication in patients with EGFR-mutant non-small cell lung cancer (NSCLC) who develop resistance to tyrosine kinase inhibitors (TKIs). The management of LM after TKI resistance poses significant clinical challenges due to the heterogeneity of tumor staging, diverse resistance mechanisms, and limited penetration of systemic therapies across the blood-brain barrier (BBB). Current treatment strategies lack standardized protocols and require careful consideration of tumor burden, resistance patterns, and patient-specific factors. This narrative review systematically summarizes recent clinical studies, guidelines, and expert consensus addressing therapeutic decision-making in this setting. This review emphasizes multimodal clinical decision-making, integrating targeted therapies, intrathecal drug administration, radiotherapy, and intracranial pressure relief devices, with discussion of emerging immunological strategies. Although combining these therapies can improve neurological prognosis and survival rates for some patients, toxicity, the unpredictability of efficacy, and the logistical challenges of multidisciplinary treatment remain significant obstacles. To address this issue, we outline a clinical decision-making pathway aimed at helping physicians optimize and personalize treatment regimens. Looking ahead, improving our methods for identifying drug resistance mechanisms, optimizing central nervous system (CNS) drug delivery, and building stronger collaborative care models will be crucial for improving the prognosis of this vulnerable group.
Neural stem cell (NSC) therapeutics have emerged as a promising approach for addressing neurological disorders due to their inherent ability to self-renew, differentiate into neural lineages, and secrete neurotrophic factors. This narrative review explores the evolving clinical landscape of NSC applications, highlighting their therapeutic potential in neurodegenerative diseases, ischemic stroke, and spinal cord injuries. Recent clinical advancements demonstrate the safety and preliminary efficacy of NSC-based therapies in conditions like Parkinson's disease and amyotrophic lateral sclerosis. NSCs' capacity to promote neuroplasticity and tissue restoration underscores their potential in reversing synaptic and neuronal damage. Despite these advancements, significant challenges remain. Ethical considerations, particularly concerning cell sourcing and patient consent, must be carefully navigated. Technical barriers, including cell delivery, survival, and long-term integration, require innovative solutions. Furthermore, safety concerns such as tumor formation and immune rejection necessitate rigorous preclinical and clinical assessments. Regulatory challenges, including the standardization of manufacturing processes and international harmonization, are essential for widespread adoption. Looking ahead, the integration of precision medicine, advanced biomaterials, and patient-specific-induced pluripotent stem cells offers promising approaches to enhance NSC therapeutics. Collaborative efforts between researchers, clinicians, and regulatory agencies are crucial for overcoming existing barriers and translating NSC research into clinical practice, offering new hope for patients with complex neurological conditions.
Torpor is a reversible hypometabolic state marked by decreases in body temperature and energy expenditure. Leveraging advanced genetic and physiological techniques, recent studies have begun to identify hypothalamic and brainstem neurons that initiate and maintain this state. These tools nevertheless come with substantial limitations, and this manuscript discusses key interpretational caveats and practical considerations. Looking ahead, a central challenge is to test whether multi-day hibernation arises from retuning of the same core circuits that govern daily torpor.
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains challenging due to inherent subjectivity, variability, and scale. Large language models (LLMs) have achieved remarkable success, leading to "LLM-as-a-judge," where LLMs serve as evaluators for complex tasks. With their ability to process diverse data types and provide scalable assessments, LLMs present a compelling alternative to traditional expert-driven evaluations. However, ensuring the reliability of LLM-as-a-judge systems remains a significant challenge requiring careful design and standardization. This paper provides a comprehensive survey of LLM-as-a-judge, offering a formal definition and detailed classification while addressing the core question of how to build reliable LLM-as-a-judge systems. We explore strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse scenarios. We propose methodologies for evaluating reliability, supported by a novel benchmark. To advance development and deployment, we discuss practical applications, challenges, and future directions. Our contributions span multiple levels: we establish conceptual boundaries, reorganize fragmented literature into a unified framework, and propose a reliability-oriented benchmark. We articulate a forward-looking research agenda, offering theoretical foundations and practical guidance for constructing reliable and trustworthy LLM-as-a-judge systems.
Surface-enhanced Raman spectroscopy (SERS) is rapidly emerging as a transformative technology in dermatological diagnostics, offering ultra-sensitive, noninvasive detection of molecular markers associated with skin diseases. With nearly five billion individuals affected worldwide and conventional diagnostic methods often limited by invasiveness or subjective interpretation, there is an urgent need for rapid, accessible, and real-time diagnostic solutions. The present review systematically examines the integration of SERS into dermatological practice, with a focus on recent advancements in biosensing platforms, nanostructure engineering, and point-of-care devices. Innovative methodologies, including microneedle-based SERS biosensors and microfluidic-integrated detection systems, are discussed in the context of their ability to enhance diagnostic accuracy for early-stage skin cancers, microbial infections, and inflammatory dermatoses. Furthermore, the review highlighted the role of AI-driven spectral analysis in improving data interpretability and clinical decision-making. Critical evaluation of the challenges of substrate reproducibility, clinical standardization, and device scalability, while outlining emerging strategies that aim to bridge laboratory innovations with clinical applications, are explored. Looking ahead, the development of portable, low-cost SERS platforms for continuous skin health monitoring, combined with personalized diagnostic pathways, is poised to redefine dermatological care and expand the scope of precision medicine. Skin diseases impact 4.8 billion people globally, imposing substantial mortality and economic burdens, highlighting the urgent need for accurate, timely diagnostics. Current approaches are often invasive, time-intensive, or lack sensitivity. The present review critically examines the transformative potential of surface-enhanced Raman spectroscopy (SERS) as a minimally invasive, point-of-care solution for dermatological diseases and effective care management. The primary focus of this review is to explore the potential use of SERS in dermatological diagnostics, an area where traditional Raman spectroscopy has seen limited application. Integrating advanced spectroscopic techniques, lab-on-a-chip platforms and opto-microfluidic systems, it explores progressive SERS capabilities for early detection and disease management. By bridging medical engineering, medicine and biochemistry, this work addresses a critical knowledge gap, fostering interdisciplinary innovation to advance dermatological diagnostics and improve global health outcomes.
Childhood stunting remains a major global health challenge, reflecting the cumulative effects of inadequate nutrition, recurrent infection, and chronic intestinal dysfunction during early life. Beyond conventional micronutrient supplementation, nutraceutical interventions have emerged as complementary strategies to address the complex biological pathways underlying impaired linear growth. This review synthesizes current evidence on nutraceutical approaches to stunting, including improvements in macronutrient quality, bioactive food components, and microbiome-targeted strategies such as probiotics, prebiotics, synbiotics, postbiotics, and microbiota-directed foods. Evidence from clinical and preclinical studies indicates that nutraceutical effects on growth are generally modest and heterogeneous, with more consistent effects on weight gain than on height-for-age (HAZ). Variability in efficacy is strongly influenced by baseline nutritional status, environmental enteric dysfunction (EED), infection burden, dietary quality, and water, sanitation, and hygiene (WASH) conditions. Mechanistically, nutraceuticals may act through modulation of gut barrier integrity, inflammatory tone, microbial metabolism, and endocrine signaling pathways, particularly those involving the growth hormone-insulin-like growth factor-1 (GH-IGF-1) axis. Recent microbiota-directed food trials provide proof-of-concept that targeted correction of microbiome immaturity and gut dysfunction can support linear growth. Looking forward, advances in nutrigenomics, microbiome science, and epigenetics support a shift toward precision nutrition strategies that tailor interventions to biological responsiveness and context. Systems biology approaches integrating multi-omics data, network pharmacology, and interpretable artificial intelligence are expected to refine mechanistic understanding and guide intervention design. Effective translation will require rigorous trial designs, regulatory clarity, and integration of nutraceuticals within broader stunting reduction frameworks in low- and middle-income countries.
As mobile genetic elements, transposons play a crucial role in the adaptive evolution and genome engineering of industrial microorganisms. Their applications range from high-throughput functional genomics, enabling systematic genotype-phenotype mapping via transposon sequencing, to the construction of random integration libraries for chassis development and directed evolution. Despite the emergence of precise editing tools such as CRISPR-Cas, transposon technology remains indispensable in non-model industrial strains owing to its operational simplicity and high efficiency. Recent discoveries of novel transposon systems, along with their functional enhancement, have further expanded their utility. Looking ahead, integrating transposon technology with strategies such as AI-assisted design and CRISPR-Cas-based systems will greatly advance our ability to decipher and engineer industrial microbial cell factories. This review summarizes the principles, challenges, and opportunities of transposon-associated technologies in industrial microorganisms, offering new insights into their roles in industrial biotechnology.
Although bloodstain pattern analysis (BPA) can be performed on various types of patterns, there is a lack of research looking into whether the area of origin (AO) can be determined for expirated patterns. Expirated patterns are formed when blood is pushed out of an opening in the body by the force of air, in some cases forming a pattern of radiating bloodstains which can be analyzed similarly to impact patterns. This research aims to compare the true versus observed AO of expirated patterns using a mechanical rig. In addition, a series of blind expirated patterns was created by human participants. The resultant patterns were analyzed in HemoVision to deduce the AO and determine what kinds of errors manifest. Using the rig, five expirated patterns were produced from 20 cm and 40 cm from a target, at 90° and 45° towards the wall (n = 20). The errors in each direction (X, Y, Z) were assessed. One-sample t-tests of the 3D coordinates found statistically significant differences between the sampled and hypothesized mean (μ = 0) at 90° and 20 cm (x = 11.89, p = 7.220E-04), 90° and 40 cm (x = 22.54, p = 5.764E-06), 45° and 20 cm (x = 13.41, p = 9.888E-04), and 45° and 40 cm (x = 28.16, p = 4.040E-05). This indicates that the AO may not be accurately calculated for expirated patterns, with the largest difference being the distance from the wall. This suggests a unique limitation compared to impact patterns, which tend to have the greatest errors in height. Blind tests were also analyzed to determine the quality of AO analysis using samples from human participants (n = 11).
Bone regeneration remains constrained by incomplete osteogenic commitment of mesenchymal stem cells (MSCs), underscoring the need for precise lineage control. CRISPR/Cas-based epigenome editing provides programmable access to chromatin regulators without altering the DNA sequence, and catalytically inactive Cas9 (dCas9) fused to transcriptional activators, repressors, or chromatin modifiers enables locus-specific modulation of key osteogenic networks, including RUNX2, OSX, and BMP2, while suppressing inhibitory loci such as PPARG, SOST, and DKK1. Multiplex strategies further allow the concurrent activation of osteogenic genes and repression of adipogenic or Wnt antagonists, reshaping lineage allocation in vitro and in vivo. Delivery innovations-from AAV vectors and lipid nanoparticles to biomaterial scaffolds and extracellular vesicles-support local and systemic applications with increasing precision, while whole-genome chromatin profiling and high-fidelity Cas variants reduce off-target risk, and CRISPRoff/on platforms provide reversible and heritable control of transcriptional states. Proof-of-concept studies in small animals demonstrate bone repair in preclinical models, with emerging large-animal data highlighting translational potential. Remaining challenges include payload size, immunogenicity, durability of epigenetic states, GMP-grade manufacturing, and regulatory classification. Looking ahead, advances such as AI-guided gRNA libraries, mechano-responsive scaffolds, and long-term tracking of epigenetic memory may yield durable "smart" osteo-epigenetic therapies. Collectively, CRISPR/dCas9-based epigenome editing is progressing from mechanistic exploration toward clinically viable strategies for skeletal regeneration.
Due to the under diagnosis of bipolar disorder, screening instruments such as the hypomania checklist 32 items (HCL-32) is used to differentiate between Bipolar Disorder (BD) and Major Depressive Disorder (MDD). However due to its lengthy format, efforts were done to validate a shorter alternative without compromising its ability to differentiate between BD and MDD. We aimed to shorten the HCL-32 and assess the screening performance of the three Lebanese Arabic abbreviated HCL versions (HCL-20, -16, and -8) relative to the full HCL-32 in a sample of clinically diagnosed patients with BD and MDD in Lebanon. In a sample of 760 patients (BD-I=29, BD-II=142, MDD=589) clinically diagnosed with BD and MDD, the screening performance of the three Lebanese Arabic abbreviated HCL versions (HCL-20, -16, and -8) as well as the full HCL-32, was assessed, looking at the reliability, sensitivity, and specificity. All the shortened HCL versions showed strong reliability (a=0.78-0.90.) They also demonstrated good screening ability (AUC=0.8520- 0.8835) in differentiating BD from MDD. For the sensitivities across the shortened versions, they were consistently higher in BD-II vs MDD compared to BD-I vs MDD across all scales showing that the shortened versions have the ability to detect BD-II cases much more effectively. This study is the first to validate the shortened HCL versions in an Arabic speaking population. The HCL- 16 appears to be the most optimal shortened scale for distinguishing between BD versus MDD. However, these findings should be interpreted in light of the study's limitations including the use of retrospective data collection and item interdependence of the HCL-32.
Vaccines have greatly improved public health since their proper establishment, but there are fundamental disconnects in the way that vaccines are developed, namely from cells and animals, then to humans, leading to their failure in final clinical trials. Fortunately, organoids, especially immune and tumor organoids, can replicate sophisticated insights regarding human tissue architecture and functionality in organ-like 3D structures, thereby emerging as a powerful tool to bridge the gap between animals and humans. In this review, we first highlight the mechanisms of vaccine-induced protection, then discuss the development of immune and tumor organoids as novel tools for vaccine testing, design, and reverse translation. We also indicate specific hurdles facing the application of organoids in vaccinology and offer forward-looking perspectives.
Artificial intelligence (AI) chatbots are transforming the delivery of urological care, evolving from simple digital assistants into emerging clinical partners that support patients and clinicians across the care continuum. This review traces this transformation by examining the development, clinical applications, and persistent challenges of AI-driven chatbots in urology. Across the patient journey, these systems are reshaping how urological care is accessed and experienced, from early symptom screening and patient education to lifestyle management, clinical decision support, and postoperative follow-up. Although these advances show considerable promise, important challenges remain regarding accuracy, data privacy, and empathic communication. Looking ahead, next-generation multimodal and on-device AI systems may further advance this transformation, positioning chatbots as increasingly important clinical partners in the delivery of high-quality, personalized, and patient-centered urological care.
Genetic literacy is an integral measure for examining society's interaction with genetics, but widely-used "genetic literacy" measures lack both knowledge comprehension measures and psychometric validation. To address these issues, we validated the Education and Assessment of Genetic Literacy measure (EAGL) in a sample of 2708 US participants, using both exploratory and confirmatory factor analysis. In addition to standard subjective and objective knowledge subscales, our measure's distinct knowledge comprehension subscale focuses on autism as an example of a complex condition. Regression analyses showed a statistically significant interaction when looking at education and personal connection to autism in relation to knowledge comprehension (F=3.68, p=0.003). Separately, those in our sample with a connection to autism scored higher on the subjective knowledge section (F=19.52, p<0.001) only, concurring with previous demonstrations of a subjective-objective knowledge gap in science literacy. We explored geographic location as one potential factor in genetic literacy and found that metropolitan vs non-metropolitan status had no significant main effects on overall levels. After the validation process, we have two multi-domain measures which accurately capture the construct of genetic literacy and are available for wide use: the multi-faceted EAGL-long, which has previously been tested in thousands of participants, or the validated three-factor EAGL-short.
Leaf biomass, particularly ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO)-enriched fractions, represents a viable secondary source of protein for sustainable food systems; this review presents a focused, comparing green extraction approaches deep eutectic solvents, enzymatic pretreatments, ultrasound- and microwave-assisted methods, and hybrid combinations with respect to protein yield, purity and functional properties. Enzymatic pretreatment most consistently enhances protein recovery while preserving solubility and emulsification capacity; ultrasound and microwave assistance reduce processing time but require careful parameter control to avoid denaturation. Key barriers to scale-up include inconsistent reporting of performance metrics, solvent-recovery inefficiencies, limited pilot-scale validation, and gaps in techno-economic and safety data. Finally, a forward-looking roadmap proposed to overcome persistent challenges such as solvent recovery and the absence of standardized protocols, highlighting the promise of artificial intelligence (AI)-guided process optimization and tailored solvent design. Collectively, this review provides a distinct framework for advancing leaf protein valorization toward sustainable food system transformation.
The WW-FINGERS network has demonstrated the efficacy of multidomain non-pharmaceutical interventions (NPIs) but left their real-world implementation largely unexplored, prompting this study in Changxing County to identify key determinants and develop actionable strategies for community-based delivery. An embedded mixed-methods retrospective evaluation using the Consolidated Framework for Implementation Research (CFIR) was conducted. Data from 42 stakeholders across six communities were analyzed via a hybrid deductive-inductive approach and coincidence analysis (CNA). Strategies were matched using the ERIC compendium and refined by a stakeholder panel. We identified 202 determinants, revealing six core facilitator themes (e.g., policy-academia-community synergy) and six barrier themes(e.g., unsustainable funding). CNA delineated potential pathways. Three strategy bundles were finalized: Capacity Building, Collaborative Network Building, and an AI-enabled digital platform. This study provides a practical, theory-informed framework for implementing complex NPIs, bridging the science-to-practice gap in dementia prevention. The AI-enabled platform offers a forward-looking approach for sustained delivery.
Acute appendicitis (AA) is one of the most common clinical conditions for emergency surgery. The diagnosis of acute appendicitis remains challenging for surgeons. Researchers are still looking for a parameter that is less expensive and easily available for diagnosis. The main aim of the study was to determine the diagnostic accuracy of inflammatory biomarkers for AA and the severity of inflammation. A prospective comparative cross-sectional study was conducted among 161 participants (80 with acute appendicitis and 81 non-AA abdominal pain controls) recruited using a consecutive sampling technique. The study was conducted from August 21, 2024 to January 16, 2025. In this study, we have investigated the diagnostic utility of white blood cells (WBC), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII) and systemic inflammation response index (SIRI) for AA. Normality was assessed using the Shapiro-Wilk test and visual inspections. Mean differences for WBC were compared using the Independent sample t-test, while the Mann-Whitney U test was employed for others. ROC curve analysis was done for statistically significant parameters (p value < 0.05 with 95%CI). Post-hoc power analysis was performed using G*Power 3.1.9.7 to ensure study robustness. Males account for 57.76% of participants. The mean age of non-AA and AA group was 26.47 ± 8.53 and 24.34 ± 9.77 years, respectively. The AA group demonstrated significantly higher WBC, NLR, SII and SIRI levels compared to non-AA. Significantly higher WBC, NLR, SII and SIRI levels were observed in complicated AA (CAA) groups relative to simple AA (SAA) groups. The area under the ROC curve (AUC) of WBC, NLR, SII, and SIRI to diagnose AA was 0.814, 0.834, 0.816, and 0.814, respectively. WBC, NLR, SII, and SIRI can predict CAA with AUC of 0.722, 0.781, 0.770, and 0.715, respectively. Inflammatory markers, including WBC, NLR, SII, and SIRI have acceptable diagnostic performance for AA. Additionally, WBC, NLR, SII and SIRI possess potential for differentiating CAA from SAA. It may be better for surgeons to consider the level of NLR, SII and SIRI for the diagnosis of AA and to determine its severity. However, further studies on a larger sample size are recommended to validate their clinical utility.
Rechargeable zinc-air batteries (R-ZABs) have surfaced as a quintessential representative for next-generation energy technologies, appealing for applications in renewable energy storage, electric vehicles, and electronic devices, owing to their inherent safety, high energy density, and sustainable material supply. Nonetheless, the commercialization of traditional R-ZABs has been hindered by limitations such as insufficient cycle life, excessive overpotential, and subpar rate performance. This review outlines recent innovative strategies overcoming these barriers, with a focus on cost-effective and performance-driven designs. It begins by analyzing the fundamental constraints of traditional R-ZABs. Subsequently, we systematically categorize and evaluate eight distinct classes of emerging R-ZAB configurations including quasi-solid-state, neutral, asymmetric acid/alkali, metal hybrid, small-molecule hybrid, seawater-based, light-assisted, and dual-cathode R-ZABs, highlighting design innovations in cell architecture and material composition aimed at achieving superior functionality. Specific cases studies illustrate the critical link between structural design and electrochemical performance, alongside rationales for optimizing electrolytes and air-cathode catalysts. Finally, we provide a forward-looking perspective on R-ZABs, identifying key research directions to bridge the gap between laboratory achievements and viable market applications. This review aims to light on the intriguing potential for R-ZAB advancements and directs the pursuit of high-performance R-ZABs toward commercialization.