Bidirectional Causality Between Atopic Diseases and Nephrotic Syndrome: A Mendelian Randomization StudyXu Zheng1*, Yue Zheng2*, Zijing Wang3, Jingxin Ma1, Moli Wu1, Kaili Zhang1, Yue Du21Department of Medical Cell Biology, College of Basic Medical Sciences, Dalian Medical University, Dalian, China2 Department of Pediatrics, Shengjing Hospital of China medical university, Shenyang, China3Dalian Women and Children’s Medical Centre (Group), Dalian, China*Authors contributing equally to this article.

The potential use of the Banana (Musa paradisiaca L.) leaf which is commonly used in southern parts of India. The leaves are used in various functions, festivals for eating, packing. Most of them goes wasted to the land and gets polluted. The leaves have a wax coating on the surface of the leaf which is natural wax and are esters of fatty acids. This natural wax can be used for various commercial purposes as a coating, cosmetics, food coating, confections, medicines. In the present study the banana leaf wax was extracted and quantified for various studies anticancer, antidiabetic, XRD, IR, UV VIS, TGA, Homo Lumo analysis. The banana leaves were cut into small pieces and with solvent hexane, wax was extracted by reflux method. The precipitated wax was collected and analysed for anticancer, antidiabetic, IR, XRD, UV VIS, TGA, Homo Lumo spectral analysis for characterization. The wax was sent for analysis of anticancer and antidiabetic assay. The results obtained revealed that the anticancer activity exhibited against MDAMB-231 cell-line and results compared with the standard cisplatin drug. The IC50 of cell inhibition of banana wax was found to be 168.07 µg/ml and diabetic alpha amylase activity was measured with the standard acarbose which resulted a positive response with the plant extract 186.02 µg/ml. The presence of Arachidonic acid, Beharic acid, EPA, Lignoceric acid, linoleic acid, linolenic acid, Myristic acid, Oleic acid, Palmitic acid and Stearic acid revealed a significant use in the various useful products.

Distributed Acoustic Sensing (DAS) along seafloor fiber optic cables offers high-density, wide-aperture, real-time seismic data near subduction earthquakes, at a lower cost than conventional cabled ocean bottom seismic networks. It is thus a very promising approach to develop offshore observatories for hazard monitoring and mitigation and for fundamental research on earthquake processes. Here, we introduce a method for earthquake rupture imaging by back-projection of DAS data, taking full advantage of the data characteristics to achieve high resolution and accuracy. To develop and test the method, we use DAS data recorded along submarine telecom cables in Chile. The approach includes pre-processing steps, such as spatial integration and sediment time corrections, that greatly improve the back-projection performance. Our analysis of recordings of small earthquakes that can be considered as point sources demonstrate high accuracy in localizing seismic sources, with a resolution ranging from 2 to 5 km within a ‘high-resolution and high-robustness zone’ around the cable. We demonstrate the ability of the method to image large ruptures by applying it to simulated waveforms of a magnitude 7 earthquake, constructed by superposition of multiple empirical Green’s functions. We find that strong coda waves do not compromise the precise detection and location of sub-sources. Our method could enhance early warning systems and offer high-resolution observations crucial for studying fault mechanics.

As learners engage with Intelligent Tutoring Systems (ITSs) by responding to a series of questions, their performance data, such as correct or incorrect responses, is crucial for assessing and predicting their knowledge states through analysis and modeling. However, data sparsity, often arising from skipped or incomplete responses, poses challenges for accurately assessing learning and delivering personalized instruction. To address this, we propose a generative data imputation method based on Generative Adversarial Imputation Networks (GAIN) to complete missing learning performance data. Our approach employs a three-dimensional (3D) framework structured by learners, questions, and attempts, with an adaptable design along the attempts dimension to manage varying sparsity levels. Enhanced by convolutional neural networks in the input and output layers and optimized with a least squares loss function, our GAIN-based method aligns the input and output shapes with the dimensions of question-attempt matrices across the learners' dimension. Extensive experiments on datasets from three types of ITSs, including AutoTutor Adult Reading Comprehension (ARC), ASSISTments and MATHia, demonstrate that our approach generally outperforms baseline methods, e.g., tensor factorization-based methods and other Generative Adversarial Network (GAN) variants, in imputation accuracy across different setting of maximum attempts. Bayesian Knowledge Tracing (BKT) modeling further validates the imputed data's efficacy by estimating learning parameters, including initial knowledge P (L0), learning rate P (T), guess rate P (G), and slip rate P (S). Results reveal that the imputed data not only enhances model fit but also closely aligns with the original sparse distributions by capturing underlying learning behaviors, indicating greater reliability in learner assessments. Kullback-Leibler (KL) divergence measurements of all these learning parameters confirm that the imputed data effectively preserve essential learning characteristics, maintaining low divergence

Conifers generally exhibit narrow, deep crowns, whereas broadleaf trees typically form spherical crowns. A widely accepted hypothesis attributes this difference to variation in solar angles: conifers, which prevail in high-latitude regions with lower solar angles, optimize light capture differently than broadleaf trees that dominate low-latitude areas with higher solar angles. Previous studies have suggested that differences in crown morphology mitigate light competition in mixed forests, facilitating coexistence and enhancing productivity. However, these studies relied on simplified structural models that did not fully account for the physiological constraints of crown morphology or the dynamics of crown competition. In this study, we employed the Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM) to examine the effects of crown morphology on competition dynamics and ecosystem productivity in mixed forests. The model introduces several novel elements: (1) competition for space during canopy expansion, (2) self-pruning due to shading (i.e., dieback of lower branches), (3) reductions in crown basal area resulting from self-pruning, and (4) reductions in leaf area following the decrease in crown basal area. A 100-year simulation of conifer- and broadleaf-type saplings with distinct crown morphologies revealed that their relative advantages depended on tree density, solar angle, and the composition of solar radiation (direct vs. diffuse light). However, contrary to prior assumptions, adverse frequency-dependent selection—expected to promote coexistence—was not observed. Moreover, crown shape diversity did not enhance forest productivity. These findings challenge previous models and suggest that factors beyond crown morphology may drive species coexistence and ecosystem productivity in mixed ecosystems.

The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating external data through Information Retrieval, thereby enhancing their generation ability. As a recent advancement, RAG improves the selection of knowledge sources for response generation in dialogues. Although LLMs generate answers to questions, these answers may sometimes be of suboptimal quality and contain inaccuracies. The RAG framework includes a fine-tuning process that refines models using feedback and examples based on relevance. This process further enhances Open Domain Question Answering by incorporating external data through Information Retrieval. The RAG end2end extension dynamically updates external data during the training of both the retriever and generator, as well as during the training of Dense Passage Retrieval (DPR) models with QA pairs. This process eliminates the need for large continuous improvements in prediction. RAG goes beyond merely creating a smarter ChatGPT; it enables conversations by integrating external sources, adding personalized external sources, and implementing metrics to evaluate these sources, thereby generating beneficial sources. The framework also employs metrics to evaluate answers, refines them through dialogue and feedback, and reduces hallucinations by augmenting with up-to-date knowledge. In summary, these instances highlight the power of RAG and its potential applications for optimizing language models. However, RAG has some limitations. The quality of the generated responses may be impacted by the quality of the incorporated external data. If the data is inaccurate or biased, this could negatively affect the responses. Furthermore, hallucinations remain a challenge because inaccuracies can arise if the input does not contain sufficient information or metrics for evaluation. Future work should focus on enhancing data integration, educating the prompt query, developing real-time correction mechanisms, and adapting RAG for specific domains.

Pragmatic national fire outbreak intervention planning requires intelligent data generative models capable of integrating the varied benefits of decentralization underlying the data generation processes. This paper focuses on developing realistic national fire outbreak data generative models based on regional data generation processes with region-specific spatial data issues controls. The concept of compound count random variables is adopted to activate the natural assumption of empirical mixtures with non-parametric weights such that the resulting data generative model is robust to data issues at the region-level. This allows data challenges to be treated at the observation level within regions so that regional resources are factored in the analysis with avoidance of bias. The resulting data is a consolidated national fire data that avoids large observations which may yield low probabilities of occurrence of event whiles indeed the event can rapidly occur. Results based on real fire data example provides evidence on the effectiveness of the methods in generating better national data with unique calibration of regional contribution towards national intervention planning.

This multidisciplinary study employs advanced Bayesian analysis and psychohistorical methods to examine the probabilistic linkages between childhood trauma and geopolitical decision-making. Utilizing datasets from 23 conflict archives and trauma registries (1945-2025), we identify risk patterns while rigorously adhering to UNESCO’s ethical AI research protocols.

Success in competitive environments hinges on complex psychological and social dynamics. In these contexts, performance can be disrupted when there is a mismatch between societal expectations and individual feedback competitors receive. This study examined how expectations tied to social identity influence physiological stress in competitive settings by manipulating aspects of a math competition task. Participants were randomly assigned to receive either negative or positive individual-level feedback, creating personal expectations of high/low performance on the upcoming task. To manipulate social expectations (i.e., based on social identity), competitions occurred in a same-sex, interracial dyad where one person belonged to a member of a racial group positively stereotyped in the mathematics domain (i.e., Asians) and the other was not (i.e., Whites). Asian participants exhibited greater sympathetic arousal, as evidenced by larger decreases in pre-ejection period (PEP), during both preparation and competition phases compared to White participants, suggesting heightened engagement in the competition task. Regardless of race, participants receiving negative personal feedback exhibited stronger physiological threat reactivity while anticipating the competition, compared to those who received positive feedback. Exploratory analyses examined moderation of effects by stress appraisals, group identification, and achievement goals. This study highlights the complex interplay between expectations, identity, and stress in competitive environments, providing insights into how psychological factors influence physiological outcomes.

Traditional manual inspection and operational maintenance methods for industrial equipment and infrastructure face multifaceted technical challenges. Although robots demonstrate superior operational efficiency and safety compared to human operators, existing solutions remain limited by inadequate reachability and insufficient structural compactness. To solve this problem, inspired by the coiling behavior of arboreal snakes, a multi-coiled cable-driven robot (MC-CDR) with enhanced structural compactness and excellent exploration reachability is designed. The robot consists of a rotating platform, six fully constrained rigid links driven by double-cable synchronous traction module, and a rotating base. A multi-level kinematics mapping framework is established to express complex motion relationships. A variable degree-of-freedom kinematic model (VDOFKM) is developed to solve the motion interference problem of the coiled redundant robot by dynamically determining the minimum number of activated joints. Based on the VDOFKM, a multi-constraint motion planning (MCMP) method is proposed to realize global path planning in complex environments, which integrates joint constraints, base constraints and obstacle avoidance constraints. Simulation results demonstrate that MCMP achieves superior computational efficiency, smoother joint configurations, and less motion compared to conventional methods. Additionally, the MCMP enables continuous collision-free joint configurations acquisition during path tracking. The prototype experiments validate that the integration of VDOFKM and MCMP equips MC-CDR with higher solution efficiency, excellent environmental reachability and maneuverability in complex environments.

The tumor-associated microbiota represents a novel cancer hallmark, yet the interplay between diverse microorganisms, such as bacteria, viruses and fungi, and tumorigenesis remains elusive. Gastric cancer (GC) has long been considered a type of microbiome-associated tumor because of the epidemiological identification of Helicobacter pylori and Epstein–Barr virus as key carcinogenic microorganisms. Nevertheless, the detailed molecular and cellular mechanisms by which these microbial infections eventually drive GC are still debated. Furthermore, recent advancements in technology have enabled the identification of an increasing number of novel microorganisms implicated in the pathogenesis of cancer development, especially from the perspectives of fungal ecology and intracellular bacteria, as well as the microbiota ecosystem. Here, we summarize the recent progress in the study of GC-related pathogens, emphasizing the emerging roles of fungal ecology and bacterial‒fungal interactions (BFIs) in gastric tumorigenesis and highlighting new opportunities for developing microbiome-engineered therapeutic strategies for GC.

Autonomous remotely operated vehicles (ROVs) have emerged as a promising solution for fish pen inspections, replacing labor-intensive manual inspections and enhancing operational efficiency. However, deploying ROVs in these environments presents significant challenges, including unreliable localization, system uncertainties, and dynamic environmental disturbances. To address these issues, we propose a fully autonomous ROV system designed specifically for fish pen inspection in this paper. The system integrates a vision-aided approach for underwater localization and an effective path-planning algorithm to ensure safe navigation during inspections. To improve the system’s robustness against uncertainties and disturbances, we introduce a robust control scheme that combines two components: a nominal feedback controller that stabilizes the partially known nominal model of the ROV dynamics, and a sliding-mode compensator (SMC) that mitigates the effects of unknown dynamics and external disturbances. This robust control scheme, referred to as RC-SMC, minimizes the need for extensive parameter tuning while ensuring precise path-tracking underwater. Comprehensive experiments have been conducted in both laboratory and field environments to validate the efficacy and robustness of the proposed system. The results demonstrate that our ROV system can effectively perform autonomous inspections while maintaining improved stability and tracking precision compared to existing algorithms in the presence of various uncertainties and disturbances.

Red blood cells drug delivery systems for biomedical applicationsHuizi Deng, Dr.a, Xiaobei Cheng, Dr.a, Yi Li, Dr.a, Yameng Ling, Ms.a,b, Yuli Wang, Prof.a, Yang Yang, Prof.a,*, Chunsheng Gao, Prof.a,*aState Key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing 100850, ChinabHenan University, Kaifeng, Henan 475004, China* Corresponding authors.E-mail addresses: amms2013@126.com (Y. Yang), gaocs@bmi.ac.cn (C. Gao).

Ulcerative colitis (UC) is a chronic idiopathic inflammatory bowel disorder characterized by complex polygenic inheritance and refractory clinical manifestations. As one of the most challenging gastrointestinal conditions with unclear etiology, UC frequently exhibits persistent mucosal inflammation extending from the rectum to proximal colon regions. The limited efficacy of existing pharmacological regimens, coupled with its recurrent and treatment-resistant progression, highlights an imperative demand for advanced therapeutic innovations targeting this multifactorial pathology. Qing-Chang-Hua-Shi granule (QCHS), a traditional Chinese medicine for the treatment of UC, which has been widely used in clinical practice. Here, it is shown that QCHS improves the inflammatory response and intestinal barrier in DSS-induced colitic mice by regulating gut microbiota and their secreted metabolites. Specifically, QCHS increased the relative abundance of Lactobacillus gasseri ( L. gasseri) and altered the composition of metabolite in feces. Further analysis showed that the metabolites altered by QCHS were significantly enriched in the ferroptosis metabolic pathway. QCHS can also promote the proliferation of L. gasseri in vitro and effectively repair ferroptosis-like injury in the colon of UC mice. Overall, these results indicate that QCHS is an effective agent against UC mainly through the regulation of ferroptosis associated metabolic pathway by modulating Lactobacillus gasseri novelty.

Humans can learn to use repeated spatial arrangements of irrelevant, non-target items to direct the focus of attention towards behaviorally relevant – target – items, a phenomenon known as contextual cueing (CC). However, whether CC is itself dependent on attentional resources is a controversial issue. Here, we used visual search to test how CC is affected when attention varies through two types of manipulations: perceptual load (as induced by target-distractor similarity) and postural load (sitting vs. standing). For easy searches (low target-distractor similarity), we observed reliable facilitation of search in repeated-context displays, which was independent of participants’ body posture. For difficult searches (high target-distractor similarity), contextual facilitation was evident only with standing posture. Posture-related benefits remained significant even after controlling for heart rate variability (HRV), body mass index, and physical activity. Decomposing aggregated reaction times by drift-diffusion modeling revealed that CC in difficult searches decreased the amount of evidence required for target-response decisions. Our results suggest that statistical learning is effectively supplemented during standing posture when visual search is challenging, possibly because posture manipulation and contextual manipulation affect common response-selection stages of processing.

Floodplain wetlands are crucial for waterbird survival and reproduction. Droughts can significantly reduce food availability and habitat quality for waterbirds, leading to a decline in their abundance and diversity. The aim of this study was to assess the impact of extreme drought on waterbird populations in Dongting Lake by analyzing alterations in hydrological, vegetation, and land use variables. We conducted waterbird surveys (2018–2023) and collected habitat data over six years to determine the effects of extreme drought on waterbird conservation. Key environmental factors influencing waterbird feeding guilds were identified, and their effects were analyzed across normal, extreme drought, and flood years. The results indicated that the Normalized Difference Water Index and water recession depth are critical hydrological variables that influence waterbird abundance. During extreme drought years, high-temperature vegetation dryness index values led to significant habitat and resource shortages, challenging waterbird survival. During normal and flood years, waterbird populations can partially buffered moderate drought effects by adjusting their foraging strategies and habitat selection. The temperature vegetation dryness index affected waterbird feeding guilds through multiple ecological pathways by altering habitat and water quality. This study also examined how environmental factors affected migratory bird guilds. These findings provide valuable insights into the spatial distribution of overwintering waterbirds and inform conservation efforts involving wetland ecosystems, highlighting the necessity for adaptive water management strategies to mitigate the adverse effects of drought and hydrological changes on waterbird populations.

In light of global climate change, identifying critical marine habitats and conserving them is essential. Marine conservation planning recommends designating cooler habitats as marine protected areas. The ‘deep-reef refugia’ hypothesis suggests that deeper, suitable habitats may allow species to undergo the evolutionary changes necessary to adapt to the growing environmental threats they face. This hypothesis has rarely been tested outside tropical ecosystems, where it has been fully or partially rejected. This study, using a systematic approach, is the first to evaluate this hypothesis regarding fish communities in the East Mediterranean Sea (EMS), which is warming at an unprecedented rate. Fish were surveyed twice a year from 2015 to 2022 across three rocky habitats: shallow (10 m depth, 23% ± 11 of 1 m Photosynthetically Active Radiation, PAR), upper mesophotic (25 m depth; 8% ± 4 of 1 m PAR), and lower mesophotic (45 m depth; 3% ± 2 of 1 m PAR), using closed-circuit rebreather systems. Data collected from 357 belt transects indicate that: 1) species and functional diversity of the shallow habitat are encompassed within those of the deeper habitats; 2) gamma diversity is greater in the upper mesophotic community; 3) alpha diversity in the upper mesophotic is higher compared to shallow depths; 4) beta diversity increases with depth. Unlike most findings on tropical coral ecosystems, our results suggest that a fish community is currently thriving in the rapidly warming Eastern Mediterranean Sea (EMS) at upper mesophotic depths. This community appears to act as a climate change refuge for a less diverse, shallower community. The unique position of the EMS as a transitional marine environment emphasizes its potential role as an early indicator of changes in fish depth distributions that could globally impact subtropical ecosystems.

Animals adjust their sociality, including grouping behaviours, as a common strategy to adapt to human disturbances and maintain individual fitness. While there is extensive research on animal sociality from evolutionary perspectives, gaps remain in integrating human impacts on group sizes, especially across a range of species taxa and disturbance contexts. We synthesise 151 studies (129 species and 8 disturbance types) to develop a conceptual framework that maps how human disturbances influence terrestrial mammal group sizes, covering anthropogenic proximate drivers and wildlife’s ecological and behavioural responses. We reveal some general trends and how outcomes are mediated by concurrent human disturbances, as well as species traits, habitat characteristics, and local human behaviours. We can improve population management and conservation efforts by disentangling the complex relationship between human disturbance and wildlife sociality.

Background: Responding to the COVID-19 pandemic, kindergarten through 12 th grade schools implemented non-pharmaceutical interventions (NPIs). The effects of school-based NPIs on broader community levels of acute respiratory infection (ARI) have not been defined. We utilized an existing longitudinal cohort of households reporting weekly ARI cases to evaluate effects of evolving school districtwide NPIs on ARI activity at eight transition points from December 2019 through October 2022. Methods: Household ARI data was reported through GROVES (the GReat ORCHARDS Vaccine Effectiveness Study—a prospective cohort study based in the OSD). Participating GROVES families completed weekly online surveys with respiratory illness updates. Mixed effects logistic regression was used to examine the association between eight school-related transition events during the COVID-19 pandemic and changes in trajectory of ARI risk for GROVES family members, while accounting for family clusters. Transition events were assessed using a ±4-week window of community data. Results: Opening schools with maximal NPIs (mandated masking and physical distancing, with hybrid education) was not associated with increased community ARI activity. The four transition events associated with significant ARI risk trajectory increases included summer breaks (June 2020, p=0.001; June 2021, p=0.002), and the start of school with mandatory masking only (September 2021, p<0.001) or without NPIs (September 2022, p<0.001). Conclusions: School-based NPI implementation was associated with reduced risks for community ARI activity. Enhanced surveillance platforms such as the weekly online surveys used in this study are valuable tools for better understanding and monitoring SARS-CoV-2 and respiratory virus transmission in schools and surrounding communities.

Background: Our study examined the prevalence, types of human respiratory syncytial virus (HRSV), and sequence variability of the attachment glycoprotein (G) gene among symptomatic children under 18 in Hungary between 2017 and 2023. Methods: We retrospectively determined the type of HRSV strains and analysed their G protein sequence and aminoacid polymorphism in respiratory samples before and after the SARS-CoV-2 pandemic. Results: We confirmed the presence of HRSV-A in 233 (12.74%), HRSV-B in 235 (13.85%), and both in 5 cases (0.27%). The subtype pattern was HRSV-B in 2017 and 2022-2023, and HRSV-A in 2017-2022. The highest HRSV positivity (38%) could be observed between 2018 and 2019, while the lowest was in the 2017-2018 season. The median age of HRSV-positive patients was 7.09 months, and we did not show a significant difference in their median age between the seasons. During the SARS-CoV-2 pandemic, the HRSV season has an earlier onset and longer duration than before. All HRSV-A isolates are classified into the A.D clade, HRSV-B strains have belonged to B.D. clade. We found characteristic mutations in the HRV1 region of the G gene in the HRSV-A A.D strains during the 2019 and 2020 seasons, forming a distinct lineage closely related to only two German isolates. The HRSV-B B.D strains between 2022 and 2023 carried mutations in the HRV2 region, reported from several countries. Discussion: The SARS-CoV-2 pandemic caused changes in the seasonal appearance of HRSV infections. G protein sequence modifications in Hungarian HRSV strains can support the need for their molecular characterisation.

This paper introduces a medical suggestion system that effectively aids in the diagnosis and prognosis of heart disease. In order to enhance cardiac disease prediction and medicine use across electronic health environments, fast Fourier transformation is used in conjunction with ensemble learning approaches. The suggested system's accuracy and applicability are assessed in relation to the current heart disease prediction methods. The suggested system helps with cardiovascular risk prediction and recommendation procedures and builds on the existing data mining approaches. It is presumable that hybrid classification approaches will be the foundation for the coupling of quick Fourier transformations with machine learning models. It is presumable that the ensemble model supports accurate prediction and the medical recommendation process, and the use of quick Fourier transformation facilitates time series analysis of the patient's data. Additionally, it is assumed that the input dataset for the proposed system is devoid of noise and missing values.

Treatment of children diagnosed with cancer requires coordinated, multimodal therapy that is often resource intensive and can occur over several years. In addition to physical suffering, children diagnosed with cancer experience an emotional burden that also impacts their families and communities. To best support these children and their families, we must understand society’s perspective and assess its readiness to take action to improve the situation of children affected by cancer. Objective The aim of this study was to solicit the attitudes of the Polish community towards childhood cancer. Gaining an understanding of the Polish public’s awareness and knowledge regarding childhood cancer will highlight areas for future exploration and education to improve childhood cancer outcomes. Methodology This study was conducted in December 2023, as a component of a collaborative research effort (Institute of Mother and Child, SYNO and ABR SESTA). The Computer – Assisted Web Interview via website research technique was used 1,002 respondents from Poland participated, maintaining the structure of Poles according to gender, age and size of place of residence. Results Pediatric cancer is an important topic that concerns everyone - young people, as parents or future parents, or older people, as grandparents who often take care of children. Therefore, everyone should have elementary knowledge on this subject. This is admitted by the respondents themselves, with 65% of the total declaring that they would like to know more about childhood cancer. This is emphasized even more by parents - 72%. More than half of respondents believe that the availability and quantity of materials on this topic is insufficient, and only 34% admit that in Poland there is talk about childhood cancer. Respondents agree on increasing the coverage of the topic in the media (72%) [1]. Conclusions The perspective of Polish society, faced with the challenges related to childhood cancer, seems to be focused on the need to increase awareness,

Background: Luminal A breast cancer (LABC) is the most common subtype with bone metastasis. Identifying high-risk patients for bone metastasis early is essential for improving outcomes. This research focused on creating and validating a nomogram to assess the risk of BM in patients with LABC. Methods: We extracted data for 236,132 LABC patients from the SEER database covering the years 2010 to 2015. Patients diagnosed between 2010 and 2013 composed the training set (n=152,850), whereas those diagnosed between 2014 and 2015 composed the validation set (n=83,282). Logistic regression analyses identified predictive factors for BM. A nomogram was developed and validated through ROC curve analysis and calibration plots. A total of 2.1% of the training cohort and 2.2% of the validation cohort developed BM. T stage, N stage, and marital status were significant predictors of BM risk. The nomogram exhibited strong discriminative performance, with an AUC of 0.894 (95% CI: 0.890-0.899) in the training set and 0.846 (95% CI: 0.837-0.856) in the validation set. The calibration plots demonstrated strong concordance between the predicted and observed BM rates across both cohorts. Conclusion: This study established a clinically relevant nomogram for predicting BM risk in LABC patients. The model’s strong predictive ability suggest its potential as a valuable tool for risk stratification and personalized patient management. Further external validation is warranted to confirm its generalizability across diverse populations. This study focused on developing a risk prediction model for bone metastasis in LABC patients using a nomogram based on data from the SEER database

The adult population of tri-spine horseshoe crabs (Tachypleus tridentatus) in Taiwan has been considered nearly extinct for decades, resulting in its exclusion from data moderation assessments in previous studies. This highlights a significant gap between historical baseline data and recent research efforts in the Taiwan Strait. In this study, the population size was estimated using five years of mark-recapture data and identified potential habitat to simulate suitable habitats through the MaxEnt model. This is the first comprehensive assessment of the adult T. tridentatus population in the Taiwan Strait in decades. The result of the population size of T. tridentatus was estimated to be approximately 64,243 individuals in Taiwan. Habitat predication was identified not only the well-documented inshore where function was breeding and nursery areas critical for juveniles but also highlighted central strait regions, including the Taiwan Bank and Changyun Ridge, as high probability of occurrence for potential adult foraging ground with that may been overlooked in the historical baseline data. These findings indicated that the importance ecological niches of the Taiwan Strait for T. tridentatus, functioning as a breeding ground, conservation zone, foraging area, and migration corridor that connects Taiwan and the eastern coast of China. Based on these results, we propose a regional conservation strategy emphasizing international collaborative monitoring, cross-border conservation measures, the implementation of conservation practices, and benefit assessments. This work provides valuable insights for developing and implementing an action plan for the conservation of Asian horseshoe crabs.