While Zanthoxylum bungeanum Maxim. (Z. bungeanum) pericarps are a globally prized spice, their leaves are frequently discarded as agricultural waste. This study systematically characterizes the aromatic potential of leaf by-products compared with traditional pericarps under diverse extraction strategies, utilizing an integrated flavoromics and sensomics approach. Qualitative GC-MS-O analysis revealed that leaf-derived fractions possess superior aromatic diversity: leaf essential oil and volatile solvent extract yielded 71 and 68 odorants, respectively, significantly surpassing pericarp counterparts (65 and 43 compounds). Concurrently, HS-GC-IMS profiling confirmed that targeted extraction allows leaf-derived flavors to replicate and exceed traditional spice complexity. Specifically, the leaf solvent extract achieved aromatic parity with pericarps by effectively mirroring the core spicy-citrus profile through cuminaldehyde and limonene retention. Conversely, distilled leaf essential oil unlocked a distinctive herbal-woody sensory innovation, driven by eucalyptol and a broader variety of aldehydes and ketones. Sensomics validation, incorporating aroma recombination, omission experiments, and partial least-squares regression modeling, conclusively identified β-myrcene, limonene, caryophyllene, and humulene as core molecular markers dictating these perceptual shifts. Ultimately, this research provides a robust theoretical foundation for upcycling Z. bungeanum leaves into valuable flavoring resources, facilitating circular bio-economy practices by delivering functional equivalence and entirely novel sensory experiences for the global food industry.
Conventional displacement reactions often suffer from drawbacks such as high background signal and low reaction rates. To overcome this limitation, we propose a G-quadruplex (G4) folding-aided displacement amplification (G4DA) strategy to illuminate the ratiometric fluorescence of allosteric Ag nanoclusters as bicolor signaling reporters (aAg554 and aAg610) for the rapid and sensitive detection of a specific targeting trigger (tT). The recognizable element is totally blocked in the stem of a modular hairpin for minimizing nonspecific background responses, while it can be exclusively unlocked by a short-stranded key effector via disturbing the sticky toehold. Preferably, red aAg610 emitters are developed only in two proximal template splits through directional complementary hybridization. Upon the effector invasion and affinity binding, the strand-exchange events are executed to drive progressive G4DA operation, which is kinetically sped up by the rapid intramolecular folding of rigid G4 structures with more stable geometry, thereby displacing tT for repetitive recycling amplification. During this process, the disassembly of a duplex complex switches red aAg610 into green aAg554 to produce reversely changed fluorescence for conformation-dependent ratiometric signaling. Without enzyme participation and tedious chemical modification, this G4DA-based approach is achievable with simplified operation, reaction dynamics, productive yield, and assay sensitivity, further suggesting a new methodological paradigm for potential biosensor, bioanalysis, and therapeutic applications.
The exponential growth of publicly available genomic data has created unprecedented opportunities for sequence-based discovery. Locating specific k-mers is fundamental to diverse applications, including metagenomic classification, pathogen and cancer detection, and variant calling yet efficient identification of multiple k-mer patterns across large sequencing data and massive databases remains a significant computational challenge. We implement two quantum algorithms for DNA multi-pattern string matching for k-mer detection, leveraging Grover's amplitude amplification under the idealized quantum random access memory (QRAM) framework. The first algorithm uses an enumerate-m oracle that sequentially checks a loaded text substring against all m patterns achieving O (√S) query complexity for S text positions but requiring O (m · L) work per oracle call. The second algorithm employs nested Grover search with an outer loop over text positions and an inner loop over pattern space, reducing oracle complexity to O(L) while performing O (√S · √m) in total. These asymptotic gains highlight the potential advantages that could be unlocked by future large-scale, low-noise QRAM architectures, positioning our results as a promising proof-of-concept foundation. This work introduces two quantum implementations of multi-pattern string matching tailored for k-mer detection. Leveraging quantum parallelism and Grover-inspired search primitives, our methods accelerate dictionary-based pattern matching, particularly in contexts involving large sequences, such as genomic data, and extensive pattern sets. While implementation challenges such as QRAM overhead remain, this study demonstrates both the promise and current limitations of quantum-enhanced string matching, establishing a foundational step toward quantum readiness in bioinformatics. To maximize accessibility and practical use, we provide our methodology at: https://github.com/Georgakopoulos-Soares-lab/quantum-multi-motif-finder.
Incretin-based obesity pharmacotherapies have revolutionized patient care but act predominantly by reducing food intake. Approaches that increase energy expenditure could improve efficacy but remain challenging to harness. Recently, neurokinin 2 receptor (NK2R) activation was shown to both lower food intake and stimulate energy expenditure in preclinical models. However, the endogenous NK2R ligand, NKA, crossreacts with other receptor family members that are linked to unwanted adverse effects. Therefore, understanding NK2R selectivity is the key to unlocking its therapeutic potential. Here we generated cryo-electron microscopy complexes of NK2R bound to NKA and several synthetic agonists to discover candidate interactions driving selectivity. Targeted receptor and ligand mutagenesis was then used to functionally validate the specific residues in the NK2R binding pocket and the C terminus of synthetic peptide agonists that were responsible for selectivity. These findings provide a structural framework for defining neurokinin selectivity and enable the development of improved NK2R agonists for clinical investigation.
Unlocking extreme fast charging in lithium-ion batteries requires ultra-early detection of lithium plating. While macroscopic expansion tracking is a promising tool, current methods rely on empirical slopes and lack a fundamental physical boundary. This work establishes an absolute mechanical baseline for lithium plating by bridging first-principles calculations with operando tracking. We quantitatively decouple the intrinsic volumetric strain of intercalation from the massive partial molar volume surge of metallic deposition. This process yields a rigorous theoretical threshold of 1.20 × 10-4 cm3/C without the need for post-mortem fitting. Experiments on a pouch-cell platform demonstrate that this bottom-up baseline acutely captures mechanical anomalies during nascent lithium nucleation. The diagnostic remains robust under overcharge, subzero temperatures, and high charging rates. Finally, we translate this physical boundary into an active feedback loop for millisecond-level current derating. This framework successfully halts dendrite growth and promotes the reintercalation of dead lithium.
Human gut microbiome research has expanded remarkably over the past two decades, revealing the fundamental role of gut microbes in human health and disease. Despite these advances, translation into evidence-based clinical practice and public health implementation remains exceptionally limited. This integrative translational perspective review evaluates human gut microbiome research across four critical aspects: translational successes, barriers to effective translation, applicability of frameworks from other medical disciplines, and strategies to enhance translational progress. Human gut microbiome research was evaluated through the lens of translational medical research principles, as summarised below. (1) Translational successes in human gut microbiome research are explored by analysing the developmental pathways of major microbiome-based or microbiome-targeted approaches, including faecal microbiota transplantation, probiotics, postbiotics, prebiotics, and dietary interventions, despite overall limited clinical and public health translation. (2) Established translational medical research frameworks served as a foundation to identify missing elements in current human gut microbiome research, including progression through T0-T4 phases, bidirectional knowledge flow, prioritization of unmet patient and societal health needs, patient-centric approaches, stakeholder engagement, and interdisciplinary collaboration. Integration of these principles is discussed in light of the specific characteristics, challenges, and limitations of human gut microbiome research. (3) Translational barriers in human gut microbiome research were analysed beyond limited integration of translational medical principles. These arise from the inherent complexity and high-dimensional nature of the gut microbiome, temporal and inter-individual variability, confounding factors, inconsistent methodological standardization and validation, and fragmentation across research efforts. Collectively, these barriers hinder causal inference, resulting in a low-quality evidence base and limiting effective translation. (4) A framework to advance translational human gut microbiome research is proposed based on the previous findings, including strategic priorities such as education and training in translational research principles for gut microbiome researchers. Human gut microbiome research remains largely confined to early translational phases, with progression toward effective translation limited by intrinsic and methodological barriers that hinder causal inference and high-level evidence generation. Integration of core translational medical research principles offers a pathway to bridge these gaps, with education and training of gut microbiome researchers emerging as a key priority for advancing translational progress.
High-entropy boride ceramics hold great promise as ultra-high-temperature structural materials but are hindered by the well-known strength-toughness trade-off. Conventional extrinsic toughening approaches, such as composite reinforcement and microstructural refinement, offer limited improvements as they fail to modify the inherent intragranular tendency for brittle fracture. Here, we report an approach to overcome this limitation by constructing intragranular energy dissipation units through an extreme non-equilibrium process. By employing heavy direct current sintering with TiSi2 addition, high densification (> 93% relative density) was achieved at a substantially reduced sintering temperature of 1000°C and an ultrahigh heating rate exceeding 5300°C/min. This process promotes selective diffusion of cations, leading to compositional redistribution within grains and forming compositional gradients and dislocation networks. These microscopic features collectively hinder crack propagation. The resulting ceramic demonstrates attractive mechanical properties with a flexural strength of 887 MPa and a fracture toughness of 7.1 MPa·m1/2. These findings demonstrate a viable pathway for the intrinsic toughening of high-entropy ceramics through intragranular microstructural engineering.
Aqueous zinc metal batteries (AZMBs) employing halogen- or manganese-based cathodes and anode-free design possess the highest energy density among the aqueous battery families. However, their performance is severely limited by interfacial side reactions and anode-cathode cross-talk, which undermine energy density and cycle life. Conventional strategies focusing on a single interface are inadequate to address these systemic issues. The optimal strategy cluster from literature data mining guided the material system creation via a data-driven process of molecular descriptor screening. This guided the creation of an asymmetric dual-interphase separator, featuring a Zn2+-supplying interphase (ZSI) on the anode side that suppresses polyhalide shuttling and accelerates desolvation, and a composite conductive interphase (CCI) on the cathode side that enhances multi-electron reaction kinetics and active species utilization. Enabled by the asymmetric dual-interphase engineering, the constructed anode-free Zn||MnO2 full cell achieves a high voltage efficiency of 90% and stable cycling over 1000 cycles. Concurrently, a state-of-the-art anode-free Zn||I2 battery delivers an energy efficiency exceeding 90% at high areal loading of 38.14 mg cm-2. Furthermore, a universal zinc metal anode/electrolyte interphase descriptor (ZMAEID) was proposed, mechanistically linking interfacial electrochemical behavior with mechanical stability. This systematic, data-driven, and theory-guided strategy establishes a new paradigm for next-generation anode-free AZMBs.
Traditional drug discovery is a high-risk, time-consuming, and costly endeavor. Drug repurposing has emerged as a pivotal strategy to overcome these challenges by identifying new therapeutic indications for approved drugs, thereby significantly reducing development timelines, costs, and safety risks. This review aims to provide a comprehensive methodological survey of computational strategies for drug repurposing. It seeks to clarify the core principles, applicability, and limitations of various approaches, offering a clear technological landscape and valuable insights for future research directions. We categorize and elaborate on the prevailing methodologies, following a logical progression. The review begins with biological mechanism-driven methods, including structure-based, omics-based, fuzzy logic-based, and adverse event-based methods. It then details network-based methods that integrate multi-source data, encompassing graph mining and matrix factorization/completion techniques. Finally, we explore data-driven paradigms, tracing the evolution from traditional text mining-based methods to cutting-edge large language model (LLM)-based methods. Each methodological category presents unique advantages and challenges. While structure-based, omics-based, fuzzy logic-based, and adverse event-based methods provide deep mechanistic insights, network-based methods enable systematic prediction. Text mining unlocks information from vast literature, a potential greatly amplified by LLMs. This review highlights that the future of drug repurposing lies in the intelligent integration of diverse methodologies. In the future, we believe that network-based methods and data-driven methods will mark the beginning of large-scale drug repurposing, but ultimately, biological mechanism-driven methods will still be necessary for rigorous validation and explanation.
We introduce MonSter++, a geometric foundation model for multi-view depth estimation, unifying rectified stereo matching and unrectified multi-view stereo. Both tasks fundamentally recover metric depth from correspondence search and consequently face the same dilemma: struggling to handle ill-posed regions with limited matching cues. To address this, we propose MonSter++, a novel method that integrates monocular depth priors into multi-view depth estimation, effectively combining the complementary strengths of single-view and multi-view cues. MonSter++ fuses monocular depth and multi-view depth into a dual-branched architecture. Confidence-based guidance adaptively selects reliable multi-view cues to correct scale ambiguity in monocular depth. The refined monocular predictions, in turn, effectively guide multi-view estimation in ill-posed regions. This iterative mutual enhancement enables MonSter++ to evolve coarse object-level monocular priors into fine-grained, pixel-level geometry, fully unlocking the potential of multi-view depth estimation. MonSter++ achieves new state-of-the-art on both stereo matching and multi-view stereo. By effectively incorporating monocular priors through our cascaded search and multi-scale depth fusion strategy, our real-time variant RT-MonSter++ also outperforms previous real-time methods by a large margin. As shown in Fig. 1, MonSter++ achieves significant improvements over previous methods across eight benchmarks from three tasks-stereo matching, real-time stereo matching, and multi-view stereo, demonstrating the strong generality of our framework. Besides high accuracy, MonSter++ also demonstrates superior zero-shot generalization capability. We will release both the large and the real-time models to facilitate their use by the open-source community. The code will be released at: https://github.com/Junda24/MonSter-plusplus.
Automated analysis of peripheral nerve ultrastructure is bottlenecked by heterogeneous electron microscopy (EM) datasets, where varying staining protocols and resolutions create domain shifts that confound deep learning. To address this, we developed a generalized segmentation pipeline. Using a custom pre-processing workflow (CLAHE and noise suppression) integrated into ZEISS Arivis Pro, we standardized inputs across three disparate domains: traditional osmium-based Palade, lanthanide-based "green" Uranyl-free method, and low-resolution Ellisman preparations. A U-Net trained on a highly constrained 15-image composite dataset achieved peak internal Intersection over Union (IoU) scores >0.95 for myelin and Schwann cells. Crucially, during open-world, zero-shot inference on an expanded independent testing cohort (N = 40), the model sustained robust Dice Similarity Coefficients of 0.854 for myelin and 0.597 for mitochondria. This demonstrates that integrating classical image standardization with deep learning effectively mitigates EM domain gaps, enabling comprehensive 3D multi-organelle reconstructions from challenging data. To ensure transparency and community utility, the pre-trained models and standardization scripts are provided in a public, open-access repository. Ultimately, this pipeline supports the transition to sustainable, non-toxic EM protocols and provides a robust pathway for unlocking historical clinical archives for automated organellomics.
Regioselective introduction of functional groups at distal positions of carbonyl compounds remains a significant challenge, often necessitating inefficient stepwise routes to access synthetically and medicinally important targets such as 1,6-aminocarbonyls. Direct radical α-functionalization of carbonyls can unlock broad scope strategies for position-specific installation of diverse functionalities and can address the inherent limitations of enolate-based approaches. However, the development of radical α-functionalization has remained underexplored due to the scarcity of catalytic systems capable of efficiently promoting both the direct generation of α-carbonyl radicals and the regioselective construction of distally functionalized frameworks. We report herein the development of a tricomponent cobalt(salen)-catalyzed α-alkylation of carbonyl compounds, providing a direct approach to 1,6-aminocarbonyl frameworks from diverse unactivated carbonyls and aromatic amines. The reaction proceeds through a cobalt(salen)-catalyzed sequence involving polar-radical crossover (PRC), which enables direct formation of α-carbonyl radicals, and radical-polar crossover (RPC), which mediates regiospecific installation of the distal amino group. The method tolerates a wide range of carbonyl substrates, including α-substituted aldehydes that are typically prone to deleterious hydrogen atom transfer from the reactive carbonyl group. Mechanistic studies reveal the roles of cobalt(salen) O- and C-enolates in the PRC step and the involvement of α-carbonyl radicals in enabling the RPC process.
Nagatani, T, Kendall, KL, Vial, S, Comfort, P, Searle, P, Klaver, M, and Haff, GG. Unlocking greater load potential: How cluster sets enable higher external loads. J Strength Cond Res XX(X): 000-000, 2026-The aim of this study was to examine how altering external loads within cluster sets acutely affects barbell kinematics, compared with traditional sets performed at lighter loads. Twenty strength-trained individuals performed 3 sets of 9 power cleans (relative 1 repetition maximum [1RM]: 1.21 ± 0.16 kg·kg-1) using 3 experimental protocols performed in a randomized repeated-measures design: traditional sets with 70% 1RM (TRAD), cluster sets of 3 with 30-second intraset rest between every 3 repetitions and 85% 1RM (CLU-3), and cluster sets of 1 with 30-second inter-repetition rest and 90% 1RM (CLU-1). A linear mixed-effects model was used to examine the effects of session and repetition on barbell peak velocity (PV). In addition, statistical parametric mapping was used to conduct waveform analysis of the vertical displacement-time and horizontal displacement-time curve data, respectively. TRAD resulted in the highest PV, but subjects experienced clear signs of fatigue over the course of a high-volume power clean bout, as indicated by declines in PV (velocity loss = -0.17 ± 0.02 m·s-1) and significant changes in vertical and horizontal barbell displacements (p ≤ 0.05) across the set. Conversely, both CLU-3 and CLU-1 allowed subjects to better maintain PV (velocity loss = -0.11 ± 0.03 and -0.03 ± 0.03 m·s-1, respectively) and relatively consistent barbell trajectories across the set, despite showing lower PV overall due to the use of greater external loads when compared with TRAD. Based on the results of this study, cluster sets can be designed to not only modulate exercise-induced fatigue and maintain lifting technique but also increase external loads during high-volume power clean sessions.
Usnic acid (UA), a prominent lichen secondary metabolite, exhibits a unique dual therapeutic profile in dermatology, though its clinical translation is limited by systemic hepatotoxicity and poor solubility. This review comprehensively evaluates the topical efficacy, molecular mechanisms, and advanced formulation strategies of UA enantiomers and UA-rich extracts. A literature search across PubMed, Scopus, and Google Scholar identified 36 original publications focusing on anti-melanoma activity, photoprotection, and tissue regeneration. In vitro studies demonstrate that UA induces apoptosis in resistant melanoma cell lines (A375, HTB-140) via extrinsic/intrinsic pathways, with (-)-UA effectively overcoming doxorubicin resistance. Conversely, in non-cancerous models, low concentrations of UA accelerate wound and burn healing by upregulating vascular endothelial growth factor (VEGF), stimulating fibroblast proliferation, and optimizing extracellular matrix remodeling while preventing hypertrophic scarring. To mitigate skin sensitization and systemic risks, advanced drug delivery systems-including liposomes, nanoemulsions, chitosan nanogels, and electrospun scaffolds-have been developed, significantly enhancing skin permeability and localized dermal retention. Ultimately, the development of bio-functionalized smart dressings and targeted nano-formulations represents the most viable path toward unlocking the full clinical potential of UA in modern dermatological and oncological care.
Prompt engineering has the potential to enhance large language models' (LLM) ability to solve tasks through improved in-context learning. In clinical research, the use of LLMs has shown expert-level performance for a variety of tasks ranging from pathology slide classification to identifying suicidality. We introduce clickBrick, a modular prompt-engineering framework, and rigorously test its effectiveness. Here, we explore the effects of increasingly structuring prompts with the clickBrick framework for a comprehensive psychopathological assessment of 100 index patients from psychiatric electronic health records. We compare the performance of two locally-run LLMs against an expert-labeled ground truth for a variety of successively built-up prompts for the extraction of 12 transdiagnostic psychopathological criteria. Potential clinical value was explored by training linear support vector machines on outputs from the strongest and weakest prompts to predict discharge ICD-10 main diagnoses for a historical sample of 1692 patients. We could reliably extract information across 12 distinct psychopathological classification tasks from unstructured clinical text with balanced accuracies spanning 71% to 94%. Across tasks, we observed a substantially improved extraction accuracy (between +19% and +36%) using clickBrick for the most reactive model. The comparison unveiled great variations between prompts with a reasoning prompt performing best in 7 out of 12 domains. Clinical value and internal validity were approximated by downstream classification of eventual psychiatric diagnoses for 1,692 patients. Here, clickBrick led to an improvement in overall classification accuracy from 71% to 76%. ClickBrick prompt engineering, i.e., iterative, expert-led design and testing, is critical for unlocking LLMs' clinical potential. The framework offers a reproducible, explainable pathway for deploying trustworthy generative AI across mental health and other clinical fields.
Targeted pollutant exposure is widely used to acclimate microbial communities for enhanced biodegradation of recalcitrant contaminants, yet the evolutionary mechanisms underlying functional reinforcement remain poorly understood. Here, we acclimated a methanotrophic consortium achieving efficient removal of 3-amino-5-methyl-isoxazole (3A5MI) (>90%, >5 mg/L/d) and elucidated the adaptive evolutionary processes behind it. Analyses of mobile genetic elements (MGEs) and horizontal gene transfer (HGT) revealed that dominant Methylococcaceae members served as genetic exchange hubs in the acclimation bioreactor. Integrated metagenomic and metatranscriptomic analyses showed that prolonged 3A5MI exposure activated their MGEs and promoted extensive HGT of genes related to energy generation, oxidative stress defense, and biosynthesis. This adaptive evolution enabled community-level metabolic rewiring, including optimized carbon metabolism to relieve energy limitation, niche differentiation, and specialized transcription of C-N bond catalytic functions. Furthermore, batch experiments and transformation product analyses confirmed that 3A5MI-induced functional traits (e.g., heterocycle hydroxylation and C-N bond catalysis) facilitated complete sulfamethoxazole (SMX) biodegradation. Overall, this study demonstrates the evolutionary plasticity of methanotrophic consortia under targeted acclimation and highlights MGE-driven genetic exchange and metabolic adaptation as key mechanisms that both underpin functional enhancement and support the development of methanotroph-based strategies for the biodegradation of recalcitrant isoxazole-based pollutants.
Hepatocellular carcinoma (HCC) represents the primary liver cancer among adults with diverse tissue appearance, high disease severity, and negative treatment expectancy. The molecular heterogeneity, extensive invasion, and propensity for relapse of HCC present a substantial challenge for oncologists. Hepatoma cells display deregulated genomic pathways interacting with epigenetic modifications. Epigenetic changes are crucial in HCC research, serving as potential biomarkers for tumor classification, prognosis, and drug targeting. Histone PTMs and chromatin regulation control gene expression during malignant transformation and tumor progression. The development of HCC is influenced by the alteration in the expression of genes that encode acetyltransferases and deacetylases (KAT6A, SIRT2, SIRT7, HDAC4, 6, 9) and lysine and arginine methyltransferases (G9a, SUV39H1, and SETDB1). Furthermore, HCC cell lines exhibit an upregulation of proteins from the sumoylation pathway, such as E1 (SAE1), E2 (Ubc9) components, and a SUMO-specific protease (SENP1) and TGM2 from serotonylation respectively. The latest generation of HDAC inhibitors, protein arginine methyltransferase (PRMT) inhibitors, and bromodomain inhibitors are being studied in preclinical and clinical research for HCC treatment. The article provides an extensive breakdown of contemporary HCC epigenetic research focusing on histone modifications while exploring epigenetic therapy as an available treatment approach for HCC. This review is a summary on the existing knowledge on the various epigenetic mechanisms that shape HCC biology with a special focus on histone-modifying enzymes, newly identified epigenetic regulators, and their therapeutic potential.
Neurexin (nrxn) genes encode synaptic cell-adhesion molecules that have been repetitively associated with neurodevelopmental and mental disorders. While the zebrafish animal model offers tremendous advantages for dissecting neural development/function, no complete loss-of-function (LOF) nrxn zebrafish models are currently available. In this study, we generated the first collection of zebrafish knockout lines for each nrxn gene, with mutations ranging from transmembrane domain- to full genomic locus-deletions. Surprisingly, all homozygous lines developed normally, presenting no gross neurodevelopmental or obvious early behavioural abnormalities. However, this absence of early phenotypes translated into profound, paralog-specific behavioural alterations emerging during later developmental stages. All neurexin knockouts affected mating behaviour, complicating the generation and maintenance of homozygous lines. Except for this shared behavioural alteration, nrxn1, but not nrxn2 or nrxn3, led to marked changes in affiliative social behaviour and aggression. In contrast, nrxn2 mutants exhibited severe anxiety-like behaviours, including bottom-dwelling and repetitive freezing/seizure events. Strikingly, nrxn1 full-locus deletion mutants showed opposing behaviour, spending most of their time near the surface. The two also displayed opposite responses to open/closed field transitions; confinement alleviated nrxn2 anxiety but enhanced nrxn1 surface-dwelling. Meanwhile, nrxn3 mutants behaved normally in all our initial tests. In summary, our study introduces a complete set of zebrafish mutants covering the whole nrxn gene family, presenting striking adult behavioural alterations despite the absence of noticeable early defects; echoing the delayed onset of human psychiatric disorders such as schizophrenia. This work confirms the value of zebrafish to study mental disorders and unlock a novel platform to unravel the pathogenic contribution of neurexins and associated subtle neurodevelopmental changes/timing that drive the emergence of mental illnesses.
Tin-based perovskite light-emitting diodes (PeLEDs) have emerged as promising candidates for environmentally benign optoelectronic applications. However, their practical performance remains limited by severe interfacial defect states, particularly at the buried interface. In this work, we introduce a self-assembled monolayer (SAM) molecule [4-(3,6-dibromo-9H-carbazol-9-yl)butyl]phosphonic acid (Br-4P), combining NiOx to effectively mitigate interfacial defects for PEA2SnI4-based (PEA = phenethylammonium) pure-red PeLEDs. This SAM is meticulously designed with tailored dimensions to achieve interfacial structural synergy with PEA2SnI4, serving as a bridging component between NiOx and the perovskite. The insertion of Br-4P promotes superior crystallinity and more uniform crystal orientation, enables the passivation of undercoordinated Sn2+ species, and the phosphate group of Br-4P functions to anchor onto NiOx. As a result, the optimized PeLEDs achieved a peak external quantum efficiency of 6.19% at 623 nm with a narrow full width at half-maximum of 27 nm and a luminance of 226 cd·m-2. This work highlights the critical role of interface engineering in unlocking the potential of Sn-based perovskite emitters for high-efficiency, nontoxic light-emitting applications.
The ongoing Russian-Ukrainian war has triggered a significant refugee crisis, resulting in widespread trauma, displacement, and mental health challenges among affected populations. This study aimed to explore the potential usefulness of logotherapy, a meaning-centered therapy, in addressing the mental health needs of Ukrainian refugees. The research was conducted in a naturalistic scenario of a group of 20 Ukrainian refugees residing in Europe who received a tailored logotherapy or meaning-centered psychological support, compared with controls who did not. Meaning-oriented techniques, namely, Socratic Dialogue, Modification of Attitude, Paradoxical Intention, and Dereflection were used during the intervention. Participants were assessed in a pre-post quasi-experimental design using validated self-report measures for anxiety (Generalized Anxiety Disorder-7), depression (Beck Depression Inventory), and general health (General Health Questionnaire-12). The improvements were clearly observed as reductions in their self-reported anxiety and depressive symptoms after the meaning-centeres psychological support. Overall, these preliminary findings indicate that logotherapy may be a promising and feasible approach to psychological support for refugee populations. However, given its pilot nature and quasi-experimental design, causal conclusions cannot be drawn, and further research using larger, randomized, and methodologically rigorous designs is warranted to examine long-term effects and broader applicability.