搜索结果：Matchmakers

In India, an estimated 41% of women report experiencing domestic violence in their lifetime and up to 28% report violence during pregnancy. Our study aims to understand local perceptions/attitudes towards GBV and lived experiences of GBVvictimisation and perpetration in marital relationships in rural Rajasthan. We conducted a focus group discussion with community health workers (n = 7) and interviews with young married men (n = 17) and women (n = 21), and local government officials (n = 2). The data was analysed using thematic analysis with triangulation conducted across methods and researchers. Our findings reveal that women experience multiform violence, including physical, sexual, and economic violence. In the continuum of GBV, violence stems from everyday conditions of disempowerment produced through early marriage and reproduction, norms of hegemonic masculinity, and community norms that normalise violence against women. These conditions of disempowerment lead to women's isolation through control of mobility and surveillance through technology, setting the stage for further violence to occur. Violence prevention efforts in the region must follow a multi-level approach to reduce child marriage through community education and improved enforcement of legislation, enhance reporting and identification alongside help-seeking behaviours through formal or informal channels, transform rigid gender norms, and leverage key actors in the family system such as the in-laws or matchmakers as potential agents of change.

Over the past few years there have been major changes impacting the Sleep Fellowship application process. These changes included a shift to virtual interviews for the 2020 interview season and alignment of the sleep fellowship match date with the American Medical Board Specialties match date for the 2021 interview season. Our study evaluates their effects on applicant numbers and feeder specialties as well as program directors' perspectives on outcomes of these changes. A survey was sent to all program directors of ACGME-accredited Sleep Fellowship programs in April 2022. In addition, a request for aggregate deidentified sleep applicant rank list and match data was submitted for the sleep match for academic years from 2012 to 2024. For the 2020 and 2021 interview seasons, program directors agreed that they received more applicants than the years before though they did not feel that this affected their match results or overall recruitment. Most program directors were in favor of these changes. The NRMP data supported these trends. The ratio of applicants to position increased over 1.0 for the first time in 2021 and has remained over 1.0 since then. However, the increase in applicants in 2022 and 2023 were entirely driven by those who ranked sleep as their non-preferred specialty. The advent of virtual interviews has allowed applicants to apply to more programs. The alignment of the sleep fellowship match further increased the number of applicants. However, the increase appears partially related to more applicants ranking sleep medicine as their non-preferred specialty.

Today's life science endeavours are complex and interconnected, and they face diverse challenges, such as budgetary insecurity and the reproducibility crisis. As a result, core facilities have become vital in research institutes, transitioning from mere conveniences to essential components that ensure instrument access. Core facilities actively support and steer research with staff advising on experimental design and data interpretation. However, core facilities must, likewise, continually demonstrate their value, and there is no clear consensus on their optimal operational structure. Integration into larger, geographically distributed infrastructures represents a crucial opportunity for consolidation and differentiation, provided expertise and focus are clearly defined. Here, we outline concrete steps to emphasise specific application know-how and increase visibility for imaging facilities to become future-proof, improve matchmaking to attract the right users, and build international recognition across European infrastructures. By clearly delineating and differentiating expertise, core facilities can accelerate research and position themselves as integral components of large-scale, durable infrastructures.

Resistance to lenvatinib has become a major obstacle in the clinical treatment of liver cancer, highlighting the significant research value and translational potential of developing synergistic drug combinations. In this study, deep learning models (MARSY and MatchMaker) were employed to predict potential synergistic partners for lenvatinib, with vincristine identified as a promising candidate. In vitro experiments confirmed that the combination synergistically inhibited the proliferation, migration, and clonogenic formation of liver cancer cells: CCK-8 and colony formation assays demonstrated a significant reduction in cell viability and clonogenic ability, while wound healing and Transwell assays indicated effective suppression of cell migration. The synergistic effect was quantitatively validated using the ZIP model. Furthermore, flow cytometry and Western blot analyses confirmed that the combination effectively induced apoptosis. Mechanistic studies revealed that the co-treatment led to excessive accumulation of intracellular reactive oxygen species (ROS), which activated the TNF-α/Caspase-8 signaling pathway, thereby inducing apoptosis in liver cancer cells. The cytotoxicity and pro-apoptotic effects were significantly attenuated by the ROS scavenger NAC. These findings provide a solid preclinical foundation for the further development of this combination therapy and underscore the importance of the "computational prediction-mechanistic validation" strategy in advancing cancer drug discovery.

Xenotransplantation received US Food and Drug Administration approval for clinical trials in humans, and highly sensitized patients-those with antibodies against major histocompatibility complex (MHC)-are among the groups that stand to benefit. There is no method to characterize and compare the MHC differences across different species. A database of all pig, rhesus, and human amino acid MHC sequences in the Immuno Polymorphism Database-MHC database was constructed, aligned against a common consensus, relative solvent accessibility scores calculated, and the results compared to a list of eplets from the human leukocyte antigen (HLA) Eplet Registry. Performance was compared to HLAMatchmaker using a linear regression of a random sampling of 1000 HLA alleles. Results compared favorably to HLAMatchmaker, with Pearson correlation coefficients of r2 of 0.72 and 0.74 for class I and class II MHC, respectively. We identified several HLA amino acid motifs registered in the HLA Eplet Registry that were not found across swine leukocyte antigen alleles. Our algorithm successfully compares and ranks MHC similarities and disparities across species for xenotransplantation or nonhuman primate-to-nonhuman primate allotransplantation. For highly sensitized patients, this tool may aid in risk assessments and predictions for negative crossmatch tests.

Multiplayer Online Battle Arena (MOBA) games rely on sustained player participation to maintain competitive matchmaking and community vitality, yet player churn remains a persistent challenge. Although gamification mechanics are widely embedded in MOBA design, how specific mechanics translate into long-term retention through internal psychological processes is not fully understood. Drawing on the Stimulus-Organism-Response (S-O-R) paradigm and integrating Self-Determination Theory and Flow Theory, we proposed a serial mediation model in which goal clarity, immediate feedback, and reward systems enhance continuance intention via perceived competence and flow experience. We collected cross-sectional survey data from 584 active MOBA players and tested the measurement and structural models using confirmatory factor analysis and structural equation modeling. Indirect effects were examined using bias-corrected bootstrapping, and hierarchical regression assessed whether task complexity moderates the perceived competence-flow relationship. The measurement model demonstrated strong reliability and validity. All three gamification mechanics positively predicted perceived competence, with immediate feedback showing the strongest effect. Perceived competence strongly predicted flow, which in turn strongly predicted continuance intention. Bootstrapped mediation tests supported significant serial indirect effects for all three stimuli, indicating partial mediation. Task complexity positively moderated the competence-flow link, such that competence translated into substantially stronger flow under high-complexity matches. MOBA retention is driven not only by gamification mechanics themselves but by their capacity to cultivate competence and facilitate flow, particularly in strategically demanding contexts. Optimizing immediate feedback and maintaining an adaptive challenge-skill balance appear to be key levers for sustaining long-term player engagement.

The "1/0" (Top/Bottom) system has long dominated Chinese gay culture, functioning as a rigid semiotic infrastructure that organizes sexual roles through heteronormative gender norms and hegemonic masculinity. The recent emergence of "Side" - a label for men who primarily engage in non-penetrative sex - poses a disruption to this binary order. Drawing on constructivist grounded theory and in-depth interviews with 15 gay men in China who identified as or practiced "Side," this study unpacks the lived meanings of this identity within a digitalized match-making landscape. Our findings reveal that "Side" is constructed not merely as a sexual preference, but as a multifaceted strategy of well-being, resistance, and digital agency. First, participants articulated "Side" as a praxis of bodily comfort and psychological safety, shifting the paradigm from a performance-oriented "homosexist" script to a well-being-centric ethos. Second, "Side" functions as a discursive shield against the effeminophobia and internalized hierarchies inherent in the 1/0 system, challenging the reproduction of patriarchal power dynamics in same-sex intimacy. Finally, within the algorithmic environment of dating apps, "Side" is deployed as a tactical tool for "data gaming," allowing individuals to strategically navigate visibility and manage relational expectations. By decoupling sexual legitimacy from penetrative acts, the rise of "Side" suggests an important and still unfolding development in contemporary Chinese gay communities, one that opens space for more flexible and well-being-centered understandings of intimacy.

In July 2024, interventional cardiology (IC) fellowship recruitment transitioned to the National Resident Matching Program (NRMP), representing a major step toward standardizing a process that had long lacked transparency and consistency. This transition followed years of advocacy by professional societies and aimed to improve applicant-program alignment while reducing administrative burden. Evaluating program participation, recruitment practices, and match performance during the inaugural cycle is essential for informing improvements to future recruitment. This descriptive study analyzed three data sources: NRMP match outcomes, Electronic Residency Application Service (ERAS) metrics, and a mixed-methods survey of IC program directors. The survey was developed using established research methodology, refined through pilot testing, and distributed electronically to all ERAS-registered IC programs. Quantitative and qualitative data on recruitment strategies, interview formats, outreach, and program satisfaction were collected. Participation was voluntary, responses were anonymized, and ethical approval was obtained when required. Thirty-five percent of eligible programs participated. Match outcomes closely paralleled historical trends; however, 16.6% of IC fellowship positions remained unfilled. Most programs reported satisfaction with the NRMP transition, citing improved structure and reduced stress. Challenges included overlapping timelines with general cardiology recruitment, limited opportunities for faculty-applicant engagement, and administrative strain. Programs that broadened applicant outreach, conducted structured interview days, and ranked more candidates per position achieved higher fill rates. Virtual interviews were widely favored for convenience, although some programs desired hybrid or in-person options. Lessons from electrophysiology's earlier NRMP transition underscored the value of clear communication, coordinated timelines, and technology-enhanced recruitment. The inaugural IC NRMP match represents a positive evolution in fellowship recruitment. Addressing challenges related to timelines, outreach, and interview logistics will be essential to optimizing future recruitment cycles and strengthening the specialty's long-term growth.

Artificial intelligence can substantially enhance law-enforcement capabilities, but its use in security research domains, including Fight Crime Terrorism, Border Management, INFRA, and DRS, raises significant legal, ethical, and operational challenges. Access to operational case data is typically restricted, making it unavailable for continuous ingestion or model training, while the adoption of third-party models, datasets and software artefacts introduces intellectual-property and licensing constraints. At the same time, EU regulations, notably the GDPR, the Law Enforcement Directive (LED), and the EU AI Act, impose procedural and technical safeguards that must be embedded throughout the development lifecycle . To address these challenges, this paper presents a practical, EU-centric lifecycle framework for developing AI systems in security-sensitive contexts. The methodology is structured into five stages: Matchmaking, Definition & Design, Development, Validation, and Monitoring. By mapping legal and ethical obligations to concrete engineering checkpoints, the framework supports data provenance, reproducibility, and software supply-chain assurance through artefacts such as dataset registries, Model Cards, and SBOMs. To address restricted access to operational data, the methodology also defines validation patterns for end-user evaluation, including on-premises bring-solution-to-data assessment. The main contributions of the paper are a tailored lifecycle methodology, a compliance mapping linking EU obligations to lifecycle evidence, and a practical assurance package for traceable and auditable development. The methodology is further illustrated through a worked example derived from the STARLIGHT ( https://starlight-h2020.eu/) European project, showing how operational validation can be conducted without exposing raw law-enforcement data. This paper provides a step-by-step guide for building Artificial Intelligence (AI) systems for security and law enforcement projects in the European Union (EU). Because AI systems in these high-stakes areas must be safe, ethical, and legally compliant, developers must follow strict rules like the EU AI Act and the General Data Protection Regulation (GDPR). The major challenges in building these systems are that law enforcement agencies cannot easily share highly sensitive, real-world data with developers, the compliance with the legal and ethical European framework and the intellectual-property and licensing constraints. To solve this, our paper proposes a five-stage methodology that allows developers to build, test, and monitor AI models reliably without needing direct access to private operational data and is compliant with both licensing and legal and ethical frameworks. Instead of treating the law as an afterthought or a final checklist, this framework builds legal and ethical checks directly into the daily work of writing code and managing data. By adopting this approach, technical teams and law enforcement agencies can work together to create AI tools that are effective, trustworthy, and fully aligned with EU regulations, ultimately protecting both the users of the technology and the public.

The principal of generic product development is to match the critical quality attributes. Most of the time during complex product development, life cycle management; biowaiver, pre- and post-change approvals the significant efforts are made by scientist to match the drug release profile. In order to get the vivo bioequivalence testing waived based on in vitro performance of drug product; the dissolution testing is mostly act as a surrogate or performance indicator. Hence, assessment of similarity or equivalence of release profile is most critical aspect with respect to regulatory decision making. Available guideline defines the methodologies and acceptance criteria for same based on data structure e.g., application of mathematical and statistical model like similarity factor (F2), bootstrapped F2, model independent and model dependent approach etc. However, during regulatory review lot of discrepancies usually raise by regulators with respect to similarity demonstration like selection of proper methodology, define suitable acceptance criteria in case of high variability. Current article emphases on the visions behind regulatory expectations, with respect to dissolution profile comparison and highlights the prerequisites and answer the common question like how to choose the correct methodology, what are the limitations, way forward and regulatory expectations and alternative methodologies in order to evaluate the dissolution data statistically to make wise decision on in vitro equivalence. Overall, various approaches are available for dissolution similarity analysis. However, the intension of statistical comparability should be like; neither force the dissimilar product to pass the criteria, nor fail the product which are similar. This comprehensive review will enhance the overall understanding and help the formulation and biopharmaceutics scientists; how to ensure regulatory compliance during similarity evaluation.

In the context of smart cities, Non-Fungible Tokens (NFTs) are transforming digital art markets by enabling secure, decentralized transactions. As NFT trading grows, incorporating intelligence and adaptability becomes crucial-making Machine Learning (ML) integration essential. However, existing models, particularly Cooperative Game Theoretic Trading (CoGTT) frameworks, underutilize ML across all trading phases. Key gaps include limited real-time adaptability, suboptimal negotiation strategies, and inadequate buyer-seller matchmaking. This research addresses these gaps by integrating ML into a three-phase CoGTT framework-ML-augmented Naive Trading, Min-Max Price Negotiation, and Equilibrium-Based Trading-to enhance decision-making and pricing. The methodology applies ML algorithms such as decision trees, clustering, and reinforcement learning (Q-learning) within a public blockchain-based simulation environment using smart contracts. The simulation uses a customized dataset reflecting both market dynamics and artist credibility. The dataset is synthetically generated to emulate an NFT marketplace while maintaining controlled experimental conditions, which may limit direct applicability to volatile real-world markets. Zero-knowledge proofs (ZKPs) are employed to preserve privacy. ZKPs are employed to preserve privacy. A comparative analysis of ML models for NFT price estimation and strategic bidding demonstrates the effectiveness of combining predictive algorithms with reinforcement learning. Linear Regression and Random Forest models both accurately estimate NFT prices, with Random Forest achieving higher real-time prediction accuracy (R2 = 0.9920). K-Means clustering effectively segments market participants to support targeted negotiation, achieving a silhouette score of 0.8178. Integrating Q-learning with Random Forest enables dynamic bidding strategies that minimize the gap between recommended and actual prices. The discrete action set (decrease, stay, increase) supports interpretable, real-time bid adjustments. These findings highlight the potential for ML-driven NFT trading systems to support scalable, privacy-compliant digital marketplaces in smart cities, aligning trading behavior with market demands through automated, data-driven processes.

PIGM encodes a critical enzyme in the glycosylphosphatidylinositol (GPI)-anchor biosynthesis pathway. While promoter-region mutations in PIGM have been associated with a relatively mild phenotype characterized by portal vein thrombosis and absence seizures, recent evidence suggests that coding-region mutations result in a more severe multisystemic disorder. Whole-exome sequencing reanalysis was performed in a patient with early-onset developmental and epileptic encephalopathy, and subsequent matchmaking identified another patient with a similar phenotype. The pathogenicity of the variant was evaluated through multiple functional assays. To further delineate the genotypic and phenotypic spectrum of PIGM, we reviewed all patients reported to date with PIGM variants. We describe two unrelated patients, both carrying the same homozygous missense variant in PIGM (c.1001A > C, p.Gln334Pro), presenting with early-onset developmental and epileptic encephalopathy, profound neurodevelopmental impairment, multi-organ involvement, and distinctive neuroimaging findings including hypomyelination. Both patients died in infancy due to super-refractory status epilepticus. Treatment with sodium phenylbutyrate was attempted in one patient without clinical benefit. Flow cytometry revealed partial GPI-anchor deficiency. Comparative analysis with previously reported cases highlights a potential genotype-phenotype correlation between coding region variants and disease severity. Our findings establish PIGM as a causative gene of early-onset developmental and epileptic encephalopathy and expand the clinical and radiological spectrum of PIGM deficiency to include hypomyelination and prenatal onset. This study underscores the importance of including PIGM in the differential diagnosis of developmental and epileptic encephalopathy and leukoencephalopathies and provides further insight into the molecular mechanisms underlying phenotypic variability of GPI-anchor disorders.

The CRL4CRBN E3 ubiquitin ligase is the target of molecular glue degrader compounds that reprogram ligase specificity to induce the degradation of clinically relevant neosubstrate proteins. Known cereblon (CRBN) neosubstrates share a generalizable β-hairpin G-loop recognition motif that allows for the systematic exploration of the CRBN target space. Computational mining approaches using structure- and surface-based matchmaking algorithms predict more than 1600 CRBN-compatible G-loop proteins across the human proteome, including the newly discovered helical G-loop motif, and identify the noncanonical neosubstrate binding mode of VAV1 that engages CRBN through a molecular surface mimicry mechanism. This work broadens the CRBN target space, redefines rules for neosubstrate recognition, and establishes a platform for the elimination of challenging drug targets by repurposing CRL4CRBN through next-generation molecular glue degraders.

Patients awaiting a solid organ transplant require pretransplant testing including high-resolution HLA typing and screening of HLA antibodies. However, the standard HLA antibody testing cannot encompass all the HLA alleles available in the Australian donor pool. With the advent of high-resolution HLA typing for organ allocation, there is an increased prevalence of detecting uncommon HLA alleles. It is challenging to manually determine whether patients have donor-specific HLA antibodies against these antigens. To address this issue, a bioinformatic pipeline, HLA-EP-RESOLVER, was developed to assess eplet reactivity for rare HLA unacceptable antigens (UAs). The HLA-EP-RESOLVER pipeline was validated against sensitized patients, and a list of candidate UAs was identified for each patient. The candidate UAs must meet the following criteria: (1) match at the first field with existing UAs, (2) share eplets or serological types with the current UAs, (3) do not share the patient's self-eplets or eplets on negative beads, and/or (4) in the exception rules. The Australian deceased donor pool was used as the initial model. The predicted UAs were then confirmed with physical crossmatching. The pipeline proved to be highly accurate at predicting high-risk antigens by utilizing eplet analysis through HLA Matchmaker, and the HLA data extracted from the Australian donor population. The reactivity of several candidate UAs were confirmed by cellular verification. The HLA-EP-RESOLVER program was shown to be rapid, useful, and highly accurate in determining acceptable or unacceptable HLA alleles in the new era of rapid high-resolution donor typing before allocation.

XPO1 functions in key cellular processes, including nucleo-cytoplasmic export and mitosis. The gene is deleted in a subset of patients with the 2p15p16.1 microdeletion syndrome; however, no monogenic XPO1-related disorder has been described to date. We collected clinical data of individuals with de novo XPO1 variants through online matchmaking. We used Drosophila to study XPO1 function in development and habituation learning. A total of 22 individuals met the criteria to be included in the main study cohort. Of these, half have putative loss-of-function variants, and half have coding variants (10 missense and 1 in-frame deletion variant). We found an overlapping phenotype, consistent with a monogenic neurodevelopmental disorder. We demonstrate XPO1 functions in development by ubiquitous and neuron-specific knockdown in Drosophila. GABAergic neuron specific knockdown flies demonstrated impaired habituation. Our results establish XPO1 as a novel dominant monogenic neurodevelopmental disorder gene and demonstrate a central role for XPO1 in development.

Glycoproteins are of significant value to liquid biopsy of human diseases. Herein, we present a universal electrochemical platform for the amplified detection of glycoproteins, taking advantage of the glycan-matchmade multivalent decoration of enzyme labels for the enzymatic signal amplification. Briefly, the glycan-matchmade multivalent decoration involves two steps, i.e., the site-directed decoration of the phenylboronic acid-coated gold nanoparticles (PBA-AuNPs) to the cis-diol-containing glycans of glycoproteins and the subsequent decoration of enzyme labels via the affinity cross-linking between the glycan moieties of enzyme labels and the remaining PBA groups on the PBA-AuNP cross-linkers. As the glycan matchmaking-based strategy enables the decoration of each glycoprotein with multiple enzyme labels, this electrochemical platform exhibits a high sensitivity toward glycoprotein detection. Using alkaline phosphatase (ALP) as the proof-of-concept enzyme label in combination with the solid-state voltammetric stripping assay of the enzymatically deposited metallic silver, the detection limits at the pg mL-1 level have been obtained for the electrochemical aptamer-based detection of thrombin and prostate-specific antigen. Overall, this work illustrates an efficient and versatile strategy for the multivalent decoration of enzyme labels for electrochemical detection of glycoproteins at ultralow concentration levels, holding the desirable advantages of simplicity and cost-effectiveness over sandwich enzyme-linked immunosorbent assays.

Syndromic cardiac malformations can result in morbidity, yet their genetic etiology is only understood for a subset of individuals. Genome sequencing efforts in congenital anomaly cohorts may identify disease-associated variants in previously unrecognized genes. Through international matchmaking efforts, we identified eighteen individuals in total with de novo or loss-of-function variants in EIF3A (n = 4) or EIF3B (n = 14). The clinical phenotype varied but predominantly included cardiac defects, craniofacial dysmorphisms, mild developmental delays, and behavioral abnormalities. These genes encode core subunits of the eukaryotic initiation factor 3 (eIF3) complex, which plays a critical role in binding mRNA transcripts to the 40S ribosomal subunit during translation initiation. Both genes are highly constrained against loss of function, and animal models have demonstrated that disruptions in the eIF3 complex result in a range of developmental defects, including cardiovascular malformations. Additionally, EIF3B is located within the minimally overlapping region implicated in cardiac anomalies associated with 7p22.3 microdeletions. We sought to further study the role of these genes in syndromic congenital heart disease. To explore their functional impact, we generated zebrafish models with mutations in the orthologous eif3s10 and eif3ba genes, which resulted in developmental abnormalities, including thin heart tubes, lack of craniofacial cartilage, and embryonic lethality. We propose that pathogenic variants in EIF3A, as well as pathogenic variants or microdeletions involving EIF3B, cause a distinct autosomal-dominant neurodevelopmental syndrome characterized by cardiovascular and craniofacial manifestations.