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In this work, we describe the development of an imidazole functionalised colorimetric receptor (TDC) that can distinguish Cu2+ and Co2+ ions in aqueous acetonitrile medium with colour changes that are visible to the naked eye. With the addition of small amount of Cu2+ the colour changes from colourless to Persian green and for Co2+ it changes to light violet. Additionally, TDC exhibits exceptional sensitivity to Cu2+ and Co2+ cations in aqueous acetonitrile by fluorometric change, acting as a "turn-off" luminous chemosensor. Lower detection limits of 21.5 nM and 24.2 nM were used to validate fluorometric selectivity towards Cu2+ and Co2+ ions. It was evident that with passing the vapours of TFA the colour of the chemosensor TDC changes from colourless to mild green with the emergence of new peak at 638 nm. In order to examine the interaction site, determine the energies of the border molecular orbitals, and validate some of the experimental findings, a computational DFT research was conducted on the free ligand TDC and complex with Cu2+ and Co2+. The sensing ability of TDC was also supported by the silica based portable, easily disposable device studies.

The integration of artificial intelligence (AI) into addiction research has expanded rapidly, yet it remains unclear how psychosocial, behavioral, and social-structural determinants are incorporated into predictive models of addiction treatment outcomes. Because recovery is strongly shaped by psychological, social, and environmental context, assessing how AI approaches operationalize these dimensions is essential for developing clinically meaningful and equitable tools. We conducted a systematic review of peer-reviewed studies published from January 1, 2020 to October 30, 2025 across PubMed, Scopus, and Web of Science. Eligible studies applied artificial intelligence or machine learning (ML) models to addiction treatment outcomes and explicitly included psychosocial, behavioral, or social-structural predictors. Two reviewers independently screened studies, extracted data, and evaluated methodological quality using Joanna Briggs Institute (JBI) and Cochrane Risk of Bias 2 (RoB-2) domain structures. The protocol was prospectively registered on the Open Science Framework (OSF) and in PROSPERO. Fifteen studies met inclusion criteria, including electronic health record (EHR), administrative-, claims-based, program-level clinical models and psychosocial assessment datasets, digital phenotyping/ecological momentary assessment (EMA) studies, natural language processing/large language model (NLP/LLM) approaches, and one causal ML analysis of randomized controlled trial data. Across modalities, models consistently identified housing instability, psychiatric comorbidity, employment status, craving, stress, legal involvement, prior overdose, treatment history, medication adherence, and neighborhood disadvantage as influential predictors of treatment dropout, discontinuation, overdose risk, relapse, or poor engagement-often adding prognostic value beyond medication-related, diagnostic, and routinely available clinical variables. EMA and digital phenotyping showed the highest short-term predictive accuracy for near-term risk prediction, whereas structured EHR-, administrative-, claims-based, and program-level clinical models achieved moderate but clinically actionable performance. Methodological quality was moderate overall, with limited external validation and infrequent assessment of calibration, fairness, transportability, or reproducibility practices. Current evidence indicates that psychosocial, behavioral, and social-structural determinants are central to AI-based prediction of addiction treatment outcomes. Although findings are promising, existing models remain preliminary and should not yet guide clinical decisions without external validation and implementation evaluation. Future work should prioritize multi-site validation, transparent reporting, fairness evaluation, and co-development with clinicians and individuals with lived experience to ensure that AI tools strengthen person-centered and equitable addiction care.

Proper regulation of arousal maintains the balance of rest and activity and enables appropriate responses to stimuli; its disruption is a hallmark of many neurodevelopmental disorders. Although transcriptional mechanisms of arousal control are well defined, the contribution of posttranscriptional processes such as alternative splicing remains unclear. Here, we identify a critical role for the microexon splicing regulator srrm3 in maintaining arousal homeostasis in zebrafish. srrm3 mutants exhibit persistent hyperarousal characterized by sleep loss, sensory hypersensitivity, and elevated behavioral and neuronal activity. We identify the cyclic adenosine monophosphate (cAMP)-cAMP-dependent protein kinase (PKA)-cAMP response element-binding protein (CREB) signaling axis as a central driver of mutant hyperarousal. Specifically, pharmacological inhibition of cAMP signaling rescues mutant hyperactivity and associated transcriptional changes whereas wild-type cAMP activation phenocopies the mutant. Down-regulation of immediate early genes and reduced CREB phosphorylation further suggest adaptation to sustained neuronal activation. These findings establish srrm3-dependent microexon splicing as a key molecular layer of arousal regulation linking RNA-processing defects to neuromodulatory imbalance.

Structured expert elicitation (SEE) has become increasingly important in health technology assessment and economic evaluations. Complementing previous work, we aimed to synthesize recent developments in published SEE applications within health economics over the past 8 years. A systematic literature search was conducted in Medline and Embase databases from April 2017 to February 2026, supplemented with snowball sampling, to identify applications of SEE as part of economic evaluations. Data extraction and synthesis focused on expert selection, elicitation methods, and analytical techniques to identify commonalities and gaps. In total, 28 studies met the inclusion criteria. SEE applications covered diverse health interventions, from rare diseases treatments to diagnostic accuracy assessments. The number of experts recruited through purposive sampling varied from 1 to 18 clinicians per study. SEE processes remain bespoke and diverse, spanning from paper-based to software-assisted remote techniques. The studies used mainly variable and fixed interval methods (29% versus 67%) for encoding. Aggregation methods were mainly mathematical, with some studies using consensus approaches. Most studies (75%) directly incorporated pooled expert distributions into decision models. While SEE methods vary considerably across applications, suggesting that optimal approaches have yet to emerge, there is growing recognition of their potential for informing healthcare decision-making where empirical data are scarce, particularly in rare diseases and early-stage technology assessment. Future research should prioritize standardizing best practices, validating expert predictions against subsequently available empirical data, and developing enhanced bias mitigation strategies to improve the credibility of expert-informed health economic evaluations.

In this study, we examined the effectiveness of Theory of Mind (ToM) intervention programs on the ToM skills of individuals with Autism Spectrum Disorder (ASD) through a systematic review and meta-analysis. A comprehensive search was conducted in Web of Science, Scopus, EBSCO ERIC, Academic Search Complete, PubMed, ProQuest, and the Türkiye Council of Higher Education Thesis Center, completed on January 5, 2024. Studies were included if published in English or Turkish, involved participants with ASD, implemented interventions targeting ToM, used at least one ToM assessment tool, employed a randomized controlled trial (RCT) or quasi-experimental design, and provided sufficient data for meta-analysis. Methodological quality was evaluated using What Works Clearinghouse (WWC) standards. The meta-analysis used a fixed-effects model with Hedges' g in Comprehensive Meta-Analysis Software 4. We identified 20 studies (n = 924) meeting the inclusion criteria, of which 15 were included in the meta-analysis. Most interventions targeted understanding first- and second-order false beliefs and frequently used role-play and picture-based storytelling techniques. The results indicated a moderate positive effect (g = 0.492, 95% CI [0.322, 0.662]). Subgroup analyses showed no significant differences based on study characteristics. Findings should be interpreted cautiously due to publication bias, small sample sizes, and the lack of IQ, language, or prerequisite skill assessments. Crucially, insufficient reporting of social validity and generalization represents a major barrier to assessing real-world utility. Future research must prioritize rigorous RCTs and systematic functional reporting to strengthen the evidence base.

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This study introduces an AI-driven methodology for the personalized design of short femoral stems in total hip arthroplasty, addressing the challenge of stress shielding that compromises long-term implant survival. To improve pre-surgical implant suitability, a dual-input convolutional neural network (dual-CNN) was developed to predict the shielding directly from CT-like cross-sectional images that simultaneously capture femoral anatomy and stem geometry. A digitally generated dataset based on two segmented femurs and 392 stem designs was used for training and validation, while a third unseen femur assessed generalization. The influence of different dataset configurations was analyzed, with the combined dataset yielding the most accurate and robust predictions. The dual-CNN outperformed both single-anatomy models and a previously published random forest approach, reducing mean absolute error by approximately 30% and confirming the benefits of anatomically informed, image-based inputs. These findings demonstrate that the proposed model offers an efficient and scalable alternative to finite element analysis for evaluating stress/strain shielding and optimizing patient-specific short femoral stem designs.

Biodiversity in Southeast Asia is currently facing a significant decline, with primate species particularly impacted due to deforestation, poaching, global warming, and various other challenges. All 20 recognized gibbon species are considered at risk of extinction due to rapidly decreasing population sizes. The northern white-cheeked gibbon (Nomascus leucogenys) is among six gibbon species belonging to the genus Nomascus documented in Vietnam. This gibbon has been recorded in several protected areas, but significant populations persist only in a few locations, potentially including Vu Quang National Park (52.733 km2). In our study, we utilized the auditory point count method to collect data in the field and applied a distance sampling method to estimate the abundance of northern white-cheeked gibbons in Vu Quang National Park. A total of 27 gibbon groups were documented during our field survey. The estimated gibbon group density was approximately 0.48 groups/km², leading us to estimate the gibbon population size in Vu Quang National Park at about 155 groups. Vu Quang National Park therefore has the largest documented population of northern white-cheeked gibbons in Vietnam, highlighting the urgent need for prioritizing the conservation of this species.

Community-academic partnerships are increasingly recognized as essential for advancing equitable public health outcomes. Yet many partnerships struggle to move beyond short-term, project-based collaboration toward sustained, trust-based engagement with communities. This commentary draws on the experience of the University of California San Diego Center for Community Health (CCH) and its long-standing partnerships with immigrant, refugee, and other underserved communities in San Diego County. Over more than two decades of practice, CCH and its community partners developed the Community-Led Transformation (CLT) approach to guide authentic community-academic collaboration. We describe three interdependent pillars of CLT: valuing community expertise, fostering trust-based partnerships, and ensuring fair access to resources and power-sharing. Examples from CCH programs, coalitions, and research collaborations illustrate how these principles are operationalized in practice. We also reflect on structural challenges within academic institutions, including funding instability, administrative barriers, and limitations in partnership infrastructure, and strategies used to navigate these constraints while sustaining community partnerships. We provide specific recommendations for academic partners, community partners, and funders to facilitate community-academic partnerships via increased capacity building, infrastructural supports, and greater relationship and trust building. The CLT framework has therefore been a success within CCH, and can provide practical insights for a variety of partners and institutions seeking to build authentic, durable partnerships that meaningfully advance public health and health equity.

Serine integrases enable precise genome engineering but remain underused for iterative genome integration because current workflows are often labor-intensive and difficult to scale. Here, we report SARGE, a serine integrase-assisted rapid genome integration platform, that enables iterative insertion of large DNA fragments into the Escherichia coli genome through cassette exchange. By combining the orthogonal serine integrases PhiC31 and Bxb1 with rational donor plasmid design, SARGE supports rapid, programmable integration cycles without the need for resistance marker excision between rounds. To simplify the identification of correct recombinants, we incorporated a dual-fluorescence reporter system based on sfGFP and mKate and developed a green-red screening strategy for direct, naked-eye colony selection. This visual screen identified the desired recombinants with 100% accuracy among the colonies tested. SARGE achieved cassette exchange efficiencies of up to 95% and maintained efficiencies of approximately 90% for cargoes as large as 10 kb. Together, these features substantially streamline iterative genome integration and establish SARGE as a robust and accessible platform for genome engineering and synthetic biology in E. coli, with potential for extension to other genetically tractable microbial hosts.

Aging is associated with declining mitochondrial function and translational regulation-processes modulated by interventions such as dietary restriction (DR) and cold-induced longevity (CHIL). Both DR and CHIL inhibit global protein synthesis but selectively enhance translation of proteins that support mitochondrial efficiency, stress resistance, and lifespan extension. These translational shifts are mediated, at least in part, by the 4E-BP/eIF4E pathway, which regulates translation according to mRNA 5'-untranslated region (5'-UTR) length and structure. To identify compounds that mimic the beneficial effects of DR/CHIL, we developed a cell-based phenotypic screen that reports on mRNA translation as a function of 5'-UTR length. A pilot screen identified compounds that preferentially increased the expression of mRNAs with short 5'-UTRs relative to those with long 5'-UTRs, and these hits were enriched for known lifespan-extending agents, such as curcumin and rapamycin. Among the novel candidates, fluspirilene significantly extended life in both Drosophila melanogaster and Caenorhabditis elegans, and mitigated age-related locomotor decline in female flies. Fluspirilene-mediated longevity in C. elegans required the DAF-16/FOXO and HLH-30/TFEB transcription factors and the autophagy gene, atg-18. Fluspirilene failed to extend lifespan in two other Caenorhabditis species, as well as in flies maintained on a high-yeast diet, indicating that its pro-longevity effects are constrained by evolutionary divergence and nutrient status. Together, our findings identify fluspirilene as a novel modulator of translation that extends life and preserves healthspan via an autophagy-dependent mechanism and support the promise of drug discovery efforts that modulate translation state as a therapeutic strategy for healthy aging.

Captive breeding plays an important role in the conservation of large carnivores. Yet, factors influencing offspring survival remain poorly quantified. Using long-term reproductive records from the Jaguar Conservation Fund, Brazil, we examined sources of variation in cub survival within a highly productive jaguar (Panthera onca) population. We analysed 34 litters (66 cubs) produced by 10 females and 11 males between 2014 and 2025. Data were evaluated using both litter-level binomial mixed-effects models and individual-level survival analyses. Overall cub survival was high (86.4%), but varied predictably with maternal parity and management context. Litters from multiparous females showed near-complete survival, whereas primiparous litters experienced significantly lower survival probabilities. Paternal presence at birth was associated with reduced cub survival and elevated early mortality risk. In contrast, a history of maternal hand-feeding was associated with improved survival. Proximity to humans showed no statistically supported effect after accounting for other variables. Survival analyses restricted to the first 90 days post-birth confirmed that these factors influenced early-life mortality risk rather than cumulative losses over time. Random effects for dam and sire identity explained little additional variation, indicating that observed differences in survival were largely attributable to identifiable life-history and management factors rather than persistent individual effects. Together, these results demonstrate that high reproductive success in captive jaguars is compatible with structured human intervention, and that early postnatal survival is shaped primarily by maternal experience and social context. By identifying specific, actionable risk factors, this study provides evidence-based guidance for refining captive management strategies for large felids.

The case mix index (CMI) measures the complexity and severity of hospitalized patients and is used to determine hospital reimbursement rates. Better documentation is associated with higher complexity assigned to cases as measured by CMI but remains understudied in the trauma population. The authors hypothesized that standardized templates for neurotrauma history and physical (H&P) notes are associated with increased CMI and reimbursement. A multidisciplinary team consisting of trauma program professionals, trauma data specialists, neurosurgeons, revenue integrity specialists, and financial analysts created traumatic brain injury (TBI) and traumatic spine injury (TSI) H&P templates targeting Medicare Severity Diagnosis Related Groups (MS-DRGs). All neurotrauma cases from 2015 to 2023 were extracted from the electronic medical records with their accompanying diagnosis-related group weights and divided into pretemplate (pre-T) and posttemplate (post-T) groups. The hospital CMI and reimbursement were calculated for the TBI group, the TSI group, and a control group of patients with nonbrain or spine trauma. Comparisons were made between the pre-T and post-T groups. The authors included 5884 neurotrauma patients. TBI and TSI patients were similar in age and sex across time periods. The Injury Severity Score was significantly higher in the post-T period (p < 0.001). CMI was significantly higher for TBI and TSI in the post-T period (2.99 vs 2.51 for TBI, p < 0.001; 4.03 vs 3.42 for TSI, p < 0.001). CMI showed no increase in the post-T period for the nonbrain or spine trauma group (2.41 vs 2.38, p = 0.57). The post-T period demonstrated a significant increase in hospital reimbursement per discharge with a 13% increase for TSI and a 22% increase for TBI. Implementation of a neurotrauma documentation template was associated with increases in hospital CMI and reimbursement for TBI and TSI patients. Further studies are needed to explore how trauma documentation in other subpopulations is associated with hospital CMI and reimbursement.

To assess how three virtual articulator programming strategies, including jaw motion tracking, reproduce a predefined reference occlusal contact scheme (ROCS) after crown fabrication by evaluating occlusal contact presence/absence and interference intensity. Maxillary and mandibular casts with preparations on the maxillary left central incisor, maxillary left first premolar, and mandibular right second molar were mounted in a semi-adjustable articulator. Forty-five crowns were fabricated and allocated to three groups: grou,1 designed with full dynamics recorded by an electronic jaw tracking device; group,2 designed with a virtual articulator programmed using individualized parameters from dynamic recordings; and group,3 designed with default CAD settings. Occlusal contacts in maximum intercuspation and eccentric movements were evaluated with articulating paper. Weighted kappa measured agreement with the ROCS, and chi-square test evaluated contact intensity. Significant differences were observed among groups for contact presence/absence and interference distribution. Agreement with the ROCS was low: group 1 (&#x3ba; = 0.291), group 2 (&#x3ba; = 0.254), and group 3 (&#x3ba; = 0.163). Anterior guidance showed higher agreement: group 1 (&#x3ba; = 0.500), group 2 (&#x3ba; = 0.409), and group 3 (&#x3ba; = 0.233). Groups 1 and 2 generated fewer and weaker interferences, whereas group 3 showed more strong contacts. Arbitrary virtual articulator programming increased the likelihood of strong occlusal contacts after milling, whereas dynamic and individualized programming improved agreement with the ROCS. However, agreement remained only fair in all groups, indicating that further refinement of current programming strategies is required.

Individual transactions involving pharmaceutical products via social networking service (SNS) are considered an inappropriate distribution route and may serve as a guise for illicit business-to-consumer activities. In Japan, individual transactions of pharmaceutical products via the internet are recognized as inappropriate distribution routes, which not only lead to the inappropriate use of pharmaceutical products but also require more active monitoring and guidance from the viewpoint of pharmaceutical security and quality assurance. This study aimed to develop a method to accurately detect SNS tweets suspected of involving individual transactions of pharmaceutical products, using text data from Twitter (subsequently rebranded as X), the primary platform for such activities in Japan. We applied text mining to 1389 text tweets suspected of involving individual pharmaceutical transactions. Using the hashtag "#Okusuri mogumogu," which was identified through manual searching and is commonly associated with trading psychotropic pharmaceuticals, we collected 7499 tweets posted in 2022 and 6461 tweets posted from January 1 to March 31, 2023, using our web crawler program. After manually categorizing whether each tweet was related to individual pharmaceutical transactions, we extracted words and summarized their occurrences and frequencies using the 2022 dataset. A decision tree model was then generated using the 2022 dataset and validated using the 2023 dataset to evaluate the reliability of detecting transaction-related tweets. Using web crawling, the number of tweets identified using the hashtag "#Okusuri mogumogu" was 7499 in 2022 and 6461 in the first 3 months of 2023. The crawling results also showed that the number of detectable tweets increased closer to the crawl date, suggesting that SNS tweets may frequently be deleted. From 3228 extracted words in the 2022 dataset, 452 were significantly associated with tweets suspected of involving individual transactions. Highly indicative terms included "kyuu" (request), "yuzuri" (transfer), "DM" (direct message), and transaction-related hashtags. The chi-square automatic interaction detection model demonstrated stable discriminative performance (area under the receiver operating characteristic curve values: training 0.83 and 0.84; Gini coefficient: training 0.65 and test 0.68). The overall accuracy using the 2023 validation dataset was 82.31%, indicating reasonable generalizability despite linguistic fragmentation and the presence of partial word forms characteristic of Japanese text. Using transaction-related tags, text mining, and machine learning, we identified key terms linked to individual pharmaceutical transactions and developed a predictive model. This approach may aid in preventing inappropriate online transactions of pharmaceutical products.

Echocardiography is the guideline- recommended tool for risk stratification in asymptomatic high-risk patients, but its reproducibility in sub-Saharan Africa remains largely undocumented. This study assessed the intra- and inter-rater repeatability of the risk-carrying echocardiographic traits among 42 patients enrolled in the UPRIGHT-HTM trial at three Nigerian sites. The echocardiographic images (168 for intra- and 84 for inter-rater agreement) were acquired according to current recommendations and blindly analysed by randomising the order of the digitised images. The study focused on left ventricular mass (LVM) index to body surface area of height 2.7 and ejection fraction (EF) and left ventricular diastolic dysfunction (LVDD). Repeatability was assessed by the Bland and Altman approach, the coefficient of variation (CV), the intra-class correlation coefficient (ICC) and Cohen's &#x3ba; statistic. The current findings were compared with a systematic literature review of 27 publications. LVM showed a slight but significant intra-rater bias, but no inter-rater bias. CVs for LVM were approximately 30%, ICCs ranged from 0.74 to 0.84, and &#x3ba; statistics for LV hypertrophy (LVH) varied from 0.49 to 0.64. For EF, intra- and inter-rater CVs ranged from 13.0% to 14.5%, ICCs from 0.60 to 0.69, and statistics were 0.71 and 0.53, respectively. LVDD reproducibility was moderate, with &#x3ba; values around 0.50. These findings were consistent with most of the reviewed literature. The risk-carrying echocardiographic traits were obtained with moderate repeatability in three Nigerian tertiary referral centres. As data quality augments with expertise, these findings call for a Nigerian training programme in echocardiography with certification.

Hospital-at-home is an initiative to move health care services that have traditionally been provided in a hospital setting to a patient's home. The objective of this study was to assess the impact of the oncology Huntsman at Home (HH) hospital-at-home program on health care costs from the health care system's perspective. Using a difference-in-difference approach, we compared health care costs between 169 oncology patients enrolled in HH and 198 similar patients who would have been eligible for HH but lived outside the HH service area. Costs were measured from the health system perspective using an innovative cost-accounting tool. We constructed longitudinal datasets spanning the 2 patient-quarters before enrollment and the 2 patient-quarters following an acute episode. Outcomes were total direct medical costs of health care encounters as well as subcategories of cost, including facility, imaging, supplies, pharmacy, labs, and other. We ran fixed effects linear regression models to assess the impact of HH on health care cost outcomes. We found that HH was associated with a statistically significant reduction in cost for the 6 months post-admission (total -$8,337, P=0.012) and the first quarter post-admission (-$10,516, P=0.009), with significant reductions in pharmacy, facility, and other costs. We also examined a subset of patients with gastrointestinal or gynecologic cancers as exemplars of patients at considerable risk for extended complications and found similar cost reductions 6 months (-$8006, P=0.006) and in the first quarter (-$10,438, P=0.004). We found that an oncology hospital at home lowers health care costs, particularly during the 3 months following a care episode.

In order to discover highly bioactive Fenjuntong compounds, 10 2'-acyloxyfenjuntong derivatives (3a-j) were synthesized by structural modification of Fenjuntong, and their structures were well characterized by 1H NMR, 13C NMR, and HRMS. Furthermore, the bioactivities of these compounds as anti-oomycete, antifungal, and nematicidal agents against three serious agricultural pests, Phytophthora capsici, Fusarium graminearum, and Heterodera glycines were evaluated. Among all tested compounds, first, compound 3a exhibited promising anti-oomycete against P. capsici, with an EC50 value of 35.00&#xa0;mg/L. Second, compounds 3d and 3f displayed promising antifungal against F. graminearum, with EC50 values of 52.60 and 39.41&#xa0;mg/L, respectively. Third, compounds 3g, 3i, and 3j showed significant nematicidal activity against H. glycines, with LC50 values of 36.29, 30.19, and 35.43&#xa0;mg/L, respectively. The results demonstrate that introducing acyloxy pharmacophores at the phenolic hydroxyl position of Fenjuntong not only significantly enhances its biological activity against P. capsici, F. graminearum, and H. glycines, but also yields target compounds that uniformly more active than Fenjuntong itself. Moreover, these important results lay the foundation for further optimization of Fenjuntong toward the development of potential new pesticides.

Wuchereria bancrofti, a major causative agent of lymphatic filariasis, poses a significant public health burden in tropical and subtropical countries. Despite the availability of antifilarial drugs used in mass drug administration programs, issues such as non-compliance, low coverage, emerging resistance, and incomplete parasite clearance underscore the need for novel therapeutic targets. In this study, we analyzed potential drug targets identified from our previous subtractive proteomic analysis of W. bancrofti. Computational analysis of these targets highlighted Profilin as a potential candidate based on its essential biological role and druggability. We carried out detailed in-silico analyses of Profilin, including homology modeling, molecular docking, SwissADME based pharmacokinetic profiling, and molecular dynamics to assess binding affinity of the protein along with the protein-ligand complex. One of the lead compounds demonstrated strong and stable interactions with Profilin, alongside favorable pharmacokinetic properties. These findings support Profilin as a viable therapeutic target in W. bancrofti and highlight the identified compound as promising candidate for further validation.