Abstract: Feline infectious peritonitis (FIP) is responsive to treatment with the adenosine analogue GS-441524 (GS-44) and its prodrug, remdesivir (REM), both now available on veterinary prescription in many countries. Therapeutic drug monitoring (TDM) of GS-44 has the potential to support dose selection for individual cats however, TDM assays are currently only offered by one, UK based, laboratory. This study describes a simple, cost-effective, and environmentally conscious high performance liquid chromatography (HPLC) method for the quantification of GS-44 in feline plasma or serum. The method was validated in accordance with the International Council for Harmonisation M10 guidelines for bioanalytical methods. Calibration standards demonstrated linearity across a range of 15 to 5100 ng/mL, R² = 0.9978, with a lower limit of quantification 8.0 ng/mL and upper limit of quantification 5048 ng/mL; equivalent to an assay range of 0.33 to 208 µM. Precision, accuracy and spike recovery were within ±5% for most standard concentrations (±10% at the lower two). Carry-over, dilution integrity, and analyte stability under common storage conditions all met the guideline criteria. This is a simple, robust and accurate eco-friendly method suitable for being adopted by diagnostic laboratories, enabling routine TDM for cats undergoing treatment for FIP with GS-44 and/or REM.

Abstract: The rapid expansion of high-throughput sequencing technologies and the completion of telomere-to-telomere (T2T) assemblies have transformed genomics into a data-driven discipline, shifting the research focus from data generation to large-scale computational discovery. This literature review synthesizes foundational and emergent pathways in bioinformatics, genomics, and their integration into health applications. We examine the critical role of genomic reproducibility and benchmarking in establishing clinical trust, alongside mathematical models for comparative genomics, such as the Double-Cut-and-Join (DCJ) distance. A significant portion of this review is dedicated to methodological shifts in representation learning, specifically evaluating the impact of Byte-Pair Encoding (BPE) tokenization on genomic language models and the dominance of repetitive elements in sequence vocabularies. Furthermore, we explore the evolution of deep learning architectures, contrasting traditional convolutional and recurrent neural networks with recent advancements in State Space Models (SSMs). These emergent architectures, such as Caduceus and Mamba, demonstrate linear-time complexity and superior performance in capturing long-range regulatory dependencies across ultra-long genomic sequences. Finally, we discuss how these computational innovations converge to support the goals of precision medicine. By mapping these trajectories, this review provides a comprehensive overview of the technical and theoretical challenges inherent in modeling the complexity of the human genome for clinical and biological insights.

Abstract: Background: Accurate diagnosis of periprosthetic joint infection (PJI) remains challenging, particularly in culture-negative and borderline cases where current practices lead to high diagnostic uncertainty. SynTuition™, a machine-learning–based probability score integrating preoperative biomarkers, was developed to support clinical decision-making. This study compared its diagnostic performance and economic impact with standard physician practice. Methods: A total of 12 physicians provided diagnoses of 274 clinical vignettes representing suspected PJI cases. SynTuition probabilities were converted to binary Diagnostic classifications using a validated threshold. Diagnostic accuracy, agreement, indecision rates, decision-curve analysis, and misdiagnosis-related costs were evaluated. Results: SynTuition achieved an overall percent agreement of 96.0% when compared against the expert adjudicated clinical reference, outperforming the pooled physician group at 90.8%. Physicians showed high indecision (38–48%) in inconclusive 2018 ICM cases, whereas SynTuition generated a definitive diagnosis with an 86.7% agreement against expert adjudication. Decision curve analysis demonstrated higher net benefit for SynTuition across a broad range of thresholds, reducing projected unnecessary revision by up to 5.8%. Economic modeling showed a reduction in misdiagnosis-related costs from $6.9 million to $2.9 million per 1,000 suspected PJI cases, yielding estimated savings of $4,000 per suspected case. Conclusions: SynTuition demonstrated high diagnostic accuracy, lower uncertainty, and significant clinical and economic advantages over routine physician practice, supporting its integration into clinical decision-making for suspected PJI, particularly in diagnostically ambiguous cases.

Abstract: Pituitary neuroendocrine tumors (PitNETs) constitute a significant proportion of primary intracranial neoplasms and were historically differentiated based on clinical hormone excess syndromes and tinctorial properties. The 5th edition of the WHO classification introduces a paradigm shift towards the lineage-based taxonomy based on the cell-specific expression of transcription factors (TFs). This overview focuses on the biological justifications and diagnostic value of the core TFs of PIT1, TPIT and SF1 which signify the somatotroph, lactotroph, thyrotroph, corticotroph and gonadotroph lineages respectively. By focusing on TF expressions instead of hormone immunoreactivity, pathologists can better subtype clinically non-functioning tumors, effectively relegating the previously overutilized, null cell category, to about 1% of cases. The TF-based classification is also essential in discriminating high-risk histotypes of silent corticotroph tumors, sparsely granulated somatotrophs, and immature PIT1-lineage PitNETs, which are linked to a higher invasiveness and recurrence. We suggest a practical, stepwise immunohistochemical diagnostic algorithm with the integration of ancillary markers (e.g. GATA3 and ERα) to refine lineage assignment. New molecular correlates such as GNAS and USP8 mutations also add to this framework and guide the use of individualized treatment involving somatostatin analogs or dopamine agonists. And lastly, we discuss the ongoing issues of diagnosis of triple-negative and multilineage tumors and the growing importance of DNA methylation profiling and artificial intelligence in standardized reporting and improving precision management.

Abstract: Coastal lakes are vulnerable complex systems where potential contamination processes may affect the bottom sediments, especially if the coasts are intensively urbanized. In this respect, the sedimentological and ecological characterization of the bottom sediments may provide a fundamental background, particularly stringent in the cases of heavy metal contamination. In this paper, this multi-disciplinary approach was applied to Lake Ganzirri, a small-size and shallow coastal lake developed on an intensively urbanized territory of North-Eastern Sicily (Italy), where recent chemical investigations on the heavy metal contaminants of the sediments were carried out. The sediment textural features (in-cluded those of the malacofauna) and the bottom morpho-bathymetry were characterized and investigated by applying multivariate statistics and QGIS techniques. QGIS maps were finally compared with those of the heavy metal concentrations. The present research allowed to detect for the first time: i) a minor tectonic graben inside the main ENE-WSW trending Ganzirri graben; ii) mixed sediments composed of quartzo-lithic sands with sig-nificant contents in bioclastic calcareous remains; iii) sediment heterogeneous textures, mainly characterized by poorly sorted, leptokurtic, near symmetrical coarse-grained sands, with randomly distributed lenses of very coarse- grained sands with gravels and of medium-grained sands; iv) sediments testifying for actual high-energy conditions and environments at low confinement degree; v) no evidence of correlations between the hotspots of heavy metals (mainly related to prevalent geogenic origins) and the distribu-tions of sedimentological features and bottom depths.

Abstract: This paper addresses the critical intersection of demographic shifts and private land conservation, with a focus on the implications for wildlife management in rural private ecosystems. As private land ownership, resulting from the phenomenon of amenity migration, continues to fragment and diversify, understanding how these emerging landowners interact with wildlife and engage in management practices is essential to achieving large-scale conservation outcomes. The purpose of this paper is to shed light on this understudied intersection of literatures through a review that synthesizes existing scholarship, identifies critical gaps, and outlines opportunities for future research and institutional response. Building on socio-ecological systems perspectives, our results showcase four themes where wildlife is mentioned in the amenity migration literature, yet wildlife is rarely treated as a managed social-ecological system in this literature. Our call for action argues that the implications of amenity migration on wildlife management extend beyond individual landowners to include institutional systems, shifting com-munity dynamics, and new patterns of land use that together shape the conditions under which wildlife can persist.

Abstract: Granitic rocks dominate Earth's continental crust, yet the Hadean record is severely limited. Extraterrestrial evolved lithologies, crystallized under anhydrous, plate tectonics-free conditions analogous to those of early Earth, provide valuable analogues. This review synthesizes lunar, asteroidal, Martian, and candidate Venus/Mercury data, revealing that partial melting of mafic protoliths, not fractional crystallization or silicate liquid immiscibility, represents the dominant formation mechanism. Granitic magmatism persisted episodically from merely 2.3 Myr after Solar System formation through at least 3.87 Ga, with estimated abundances of 0.2–2% representing a conservative lower limit. These findings imply that Hadean Earth possessed the thermal and compositional prerequisites for analogous magmatism, potentially yielding a crustal inventory of 0.2–40% felsic material. By establishing a comparative planetary framework, this study illuminates pathways for reconstructing Earth's earliest crustal evolution and highlights priorities for future exploration missions targeting cryptic silicic reservoirs, particularly deep-crustal exposures in large lunar impact basins and in situ characterization of Venusian highland terrains.

Abstract: Time series forecasting represents one of the most critical challenges in contemporary data science and machine learning, with applications spanning finance, energy systems, weather prediction, traffic management, supply chain optimization, and healthcare. This comprehensive review examines and compares three prominent forecasting methodologies: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) neural networks, and Prophet. These models embody distinct paradigms—traditional statistical methods, deep learning architectures, and automated trend-based analysis respectively. Through systematic synthesis of recent literature and empirical studies from 2018–2025, this review analyzes theoretical foundations, practical implementations, strengths, limitations, and optimal application contexts. Our findings reveal that ARIMA exhibits superior performance for simple linear patterns (MAPE 3.2–13.6%), LSTM demonstrates exceptional capability in capturing complex non-linear dependencies with 84–87% error reduction vs. ARIMA, while Prophet excels in handling business time series with strong seasonality (MAPE 2.2–24.2%). Model selection depends critically on data characteristics, forecasting horizon, computational resources, and application requirements. This review synthesizes over two decades of empirical findings to provide principled guidance for practitioners in model selection and implementation.

Abstract: Introduction: Patients with major burn injuries are highly susceptible to hypothermia due to extensive skin loss, aggressive fluid resuscitation, repeated surgical procedures, and exposure during wound care. Hypothermia is associated with coagulation disorders, increased blood loss, impaired immune response, prolonged hospitalization, and increased mortality. When conventional warming strategies fail, intravascular temperature management systems may be employed, although they carry risks inherent to central venous catheters. Case Report: We report the case of a 26-year-old male with 66% total body surface area flame burns and inhalational injury, admitted to the Burns Intensive Care Unit with persistent hypothermia despite standard warming measures. An intravascular temperature management catheter was inserted via the femoral vein and successfully restored normothermia. Due to clinical instability, the catheter remained in situ beyond the recommended duration. During attempted catheter removal, significant resistance was encountered, raising concern for mechanical malfunction. Imaging confirmed catheter entrapment without fracture. Multidisciplinary management involving vascular surgery and interventional radiology enabled successful removal using endovascular snare techniques. A detached balloon fragment was identified and secured with venous stenting. Conclusions: This report describes the first documented case of complicated removal of an intravascular warming catheter due to balloon detachment in burn patients. Physicians using these devices should be aware of this possible complication and be prepared for its management.

Abstract: This paper discusses a multimodal AI system applied to legal reasoning for tax law. The results given here are very general and apply to systems developed for other areas besides tax law. A central goal of this work is to gain a better understanding of the relationships between LLMs (Large Language Models) and automated theorem-proving methodologies. To do this, we suppose (1) two cases for the theorem-proving system: one where it has a countable number of total meanings for its countable number of atoms and the other is where it has an uncountable number of total meanings for its countable number of atoms, and (2) LLMs can have an uncountable number of token meanings. With this in mind, the results given in this paper use the downward and upward Löwenheim–Skolem theorems and logical model theory to contrast these two AI modalities. One modality focuses on syntactic proofs and the other focuses on logical semantics based on LLMs. Particularly, one modality uses a rule-based first-order logic theorem-proving system to perform legal reasoning. The objective of this theorem-proving system is to provide proofs as evidence of valid legal reasoning when enacted laws are applied to particular situations. These proofs are syntactic structures that can be presented in the form of narrative explanations of how the answer to the legal question was determined. The second modality uses LLMs to analyze and transform a user's tax query so this query can be sent to a first-order logic theorem-proving system to perform its legal reasoning function. The main goal of our application of LLMs is to enhance and simplify user input and output for the theorem-proving system. Using logical model theory, we show how there can exist an equivalence between laws represented in logic of the theorem-proving system, fixed in time when the theorem-proving system was set up, and new semantics given by LLMs. These results are based on logical model theory and Löwenheim–Skolem theorems.

Abstract: Background/Objectives: Patients with breast cancer show substantial heterogeneity in psychological adjustment following diagnosis. We aim to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Data were drawn from the multicenter BOUNCE cohort. QoL (EORTC QLQ-C30) and depressive symptoms (HADS) were assessed repeatedly over 18 months. Latent Class Growth Analysis and Growth Mixture Modeling were used to identify distinct trajectory classes. Associations between candidate predictors and trajectory membership were ex-amined using logistic regression combined with elastic net regularization, including clinically motivated binary contrasts. Predictor robustness was evaluated under models with clinical site alternatively penalized and unpenalized. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high depression (25.3%), delayed-onset de-pression (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (13.2%) functioning to persistent low or deteriorating QoL (6.9%), with a distinct recovery trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, while selected clinical factors contributed to specific trajectories. Patterns of predictors dif-fered across trajectory contrasts. Conclusions: Distinct subgroups of women with breast cancer follow divergent QoL and depres-sion trajectories after diagnosis. Differences between trajectories are shaped by a combination of psychological, functional, and clinical factors, highlighting the multidimensional nature of resilience and recovery. These findings support the need for tailored interven-tions that move beyond risk reduction toward promoting long-term well-being and mental health.

Abstract: Thermoplastic starch (TPS) can be a sustainable alternative to petrochemical plastics for flexible packaging, especially in rainforests and tropical regions where native starch sources such as cassava are abundant. However, one problem preventing TPS packaging from widespread use is its susceptibility to moisture. This study evaluated TPS formulations based on Amazonian cassava starch reinforced with plantain leaf fibers, beeswax, and low-density polyethylene (LDPE) particles. The plastic compounds were extruded to obtain pellets and then films at 120-130 °C. The resulting films were then cut and heat-sealed to obtain flexible packaging. Different properties of the TPS packages were evaluated, such as mechanical strength, water vapor transmission (WVTR), color, infrared spectrum (FT-IR), and moisture adsorption. The results showed that the formulation with beeswax (2 % w/w), plantain leaves powder (1 % w/w), and LDPE powder (2 % w/w) had a higher tensile strength (5.99 MPa) and moisture barrier (WVTR = 366.6 g m-2 d-1) compared to the control formulation only with plasticizers (glycerol and water) but without reinforcements (0.48 MPa and 1486.6 g m-2 d-1, respectively). Films with only beeswax (4 % w/w) and plantain leaves powder (2.5 % w/w) had tensile strength = 5.53 MPa and WVTR = 716.8 g m-2 d-1, with higher moisture adsorption compared to the samples with LDPE. In both cases, homogeneous and heat-sealable bags were obtained. The reinforced TPS films can be used to reduce the environmental impact generated by single-use packaging applications such as food commercialization.

Abstract: The use of metal-based species bearing existing pharmaceuticals as ligands, often resulting in enhanced bioactivity, represents an attractive strategy for the development of novel therapeutic formulations. In the context, five well-known non-steroidal anti-inflammatory drugs (NSAIDs) were employed to substitute both PPh₃ and hydride ligands in [Ru(H)₂(CO)(PPh₃)₃] (1), selectively affording, via molecular hydrogen release, neutral κ²-(O,O)-chelate complexes in satisfactory yields. Among the obtained species, two complexes coordinating diclofenac (4) and aspirin (5) were further investigated by single-crystal X-ray diffraction (SCXRD). Preliminary biological studies on the ruthenium-salicylic acid species 2 showed promising antiproliferative activity against HeLa cancer cells, consistent with the fact that NSAID–ruthenium(II) complexes represent a well-established research area for the development of novel anticancer metallotherapeutics.

Abstract: Vascular anomalies (VAs), including hemangiomas and vascular malformations, present a significant diagnostic challenge due to their high prevalence, complex classification (nearly 100 subtypes), and visual mimicry. Current Multimodal Large Language Models (MLLMs) struggle in this specialized domain, often failing to capture fine-grained visual features or lacking evidence-based reasoning. To address these limitations, we introduce HevaDx, an agentic diagnostic system that explicitly decouples visual perception from clinical reasoning. Leveraging a newly constructed large-scale dataset of VA patients, HevaDx employs a lightweight visual specialist for precise feature extraction and a reasoning specialist equipped with Retrieval-Augmented Generation (RAG) for therapeutic planning. This cooperative architecture mitigates the "reasoning gap" observed in end-to-end models by grounding decisions in up-to-date clinical guidelines. Experimental results demonstrate that HevaDx markedly outperforms state-of-the-art open-source MLLMs, achieving a top-3 diagnostic accuracy of 94.8% and a treatment recommendation accuracy of 83.3%. By bridging visual precision with transparent, verifiable logic, HevaDx offers a reliable framework for AI-assisted management of vascular anomalies.

Abstract: This paper proposes a lightweight full-stack execution framework integrating Serverless architecture with WebAssembly runtime optimization to enhance performance and energy efficiency in edge deployments. The system employs modular task decomposition and Light-Container Isolation (LCI) technology to achieve cross-node function reuse on AWS Lambda and Cloudflare Workers platforms. An Reinforcement Learning Scheduler (RL-Scheduler) predicts request distribution in real-time, dynamically allocating CPU cycles and memory limits. Targeted testing demonstrates a 52% reduction in cold start time, a 33% decrease in average execution latency, and a 21% reduction in energy consumption under 3,000 concurrent tasks. Results confirm the framework effectively enhances execution autonomy and cross-platform portability for edge Serverless systems in multi-tenant environments.

Abstract: Does structure and parameter determine function in complex coupled oscillator systems? Conventional theory of synchronization stability is built upon this premise, relying crucially on knowledge of network topology and system parameters. We challenge this view by discovering a universal synchronization stability boundary defined solely by the states of oscillators, which is independent of configuration (encompasses both the interaction topology and oscillator parameters). Through exhaustive validation in two disparate test systems and rigorous mathematical proof, we demonstrate that this boundary is rooted in physical reality, not in any specific model. Furthermore, a novel type of spontaneous synchronization, a non-equilibrium critical phenomenon, has also been discovered near this boundary and likewise exhibits configuration-independence. These findings challenge the structural basis of the synchronization stability boundary (a key function) on complex networks. They demonstrate that the loss of synchronization stability is governed by an intrinsic, configuration-independent critical condition. Consequently, our work challenges the theoretical foundation of the “from configuration to function” principle for predicting collective behaviors in complex systems.

Abstract: Ischemic heart disease remains the most significant cause of morbidity and mortality worldwide. Althoughconventional therapies such as β-blockers, ACE inhibitors, statins, and percutaneous coronary intervention havereduced mortality in industrialized nations, progress has plateaued, and global ischemic burden continues to rise.Renewed scientific attention has turned to canonical cardioprotective signaling pathways with a level of molecularprecision not previously feasible. Parallel advances in exercise biology and mesenchymal stem-cell derived exosome(MSC-EXO) research suggest an opportunity for integrative cardioprotection. Exercise, once understood primarilyin descriptive physiologic terms, is now recognized as a complex molecular stimulus that can activateredox-sensitive kinases, autophagy regulators, and metabolic remodeling pathways. New state-of-the-artinvestigations have gone towards decoding this “exercise secretome” and developing tools to modulate thesechemical cascades. Multiple experimental studies report that MSC-derived exosomes function as biologically activeparacrine vectors that deliver regulatory microRNAs and proteins to recipient cells and promote angiogenesis,suppress apoptosis, and support mitochondrial function. This narrative review assesses the effectiveness ofinterventions on canonical pathways such as ERK and AKT/mTOR on ISO-induced ischemic injury models to theheart based on recent animal and human studies. Supporting literature on stem cell biology, exosome deliverystrategies, and translational barriers is discussed to create an integrated framework that reinforces and sustainsreparative signaling in ISO-induced ischemic environments. Together, these two rapidly evolving fields, exercisebiology and MSC-ECO, may define the next frontier in regenerative ischemic cardiomyopathy by harnessing innaterepair pathways and bioengineered tools to potentiate the crosstalk among metabolic, kinase, and paracrine survivalcircuits.

Abstract: Post-annealing treatments constitute a simple and cost-effective strategy to tailor the structure and photocatalytic performance of polymeric carbon nitride (PCN). In this work, PCNs synthesized from melamine and urea were subjected to post-annealing at 580 °C under air and CO₂ atmospheres to elucidate the role of hydrogen bonding, as well as other structural modifications induced by oxidizing atmospheres, on photocatalytic water splitting. Comprehensive structural, chemical, and textural characterization (XRD, FTIR spectroscopy, XPS, SSNMR, HRTEM, BET, TGA, and UV–Vis DRS) reveals that post-annealing induces markedly different effects depending on the precursor. For melamine-derived PCN, the treatment selectively disrupts hydrogen bonds between melon strands without introducing nitrogen vacancies, amorphization, or framework shortening. This structural rearrangement increases surface area, reduces particle size, slightly widens the band gap, and enhances water–framework interactions, resulting in a twofold improvement in the hydrogen evolution rate (HER), reaching ~3300 µmol h⁻¹ g·cat⁻¹ under visible-light irradiation. In contrast, urea-derived PCN undergoes only minor structural modifications, including slight exfoliation and possible nitrogen deficiency, which do not translate into a measurable enhancement of photocatalytic activity. These results demonstrate that selective hydrogen-bond disruption is a key factor governing charge transport and photocatalytic efficiency in PCN. Importantly, the optimized melamine-derived PCN achieves HER values comparable to those of urea-derived PCN while maintaining a substantially higher synthesis yield, highlighting its potential for scalable solar hydrogen production.

Abstract: Mpox is a zoonosis caused by the monkeypox virus. Here, we report Mali’s index Mpox case, which was clinically identified at the Mali-Guinea border by the national telemedicine centre and confirmed by PCR. The library prepared with NextGenPCR™ MPXV Sequencing Library Prep and sequenced on Minion MK1C revealed a genome length length of 177,122 bp with an average depth 1284.4x. The strain belonged to clade IIb G1 lineage and exhibited 85 mutations relative to NC_063383.1. To decipher genomic epidemiology, genomes ≥195 kb were retrieved from NCBI and aligned with MAFFT. Time-resolved phylogenetic reconstruction and ancestral trait inference were performed with TreeTime v0.11.4. Phylogeographic analysis revealed clustering with clade IIb (G.1 lineage) linked to the May 2025 outbreak in Sierra Leone.

Abstract: Airborne terminals increasingly rely on OTA updates, yet their performance is limited by high satellite-link delays and the overhead of kernel-based packet handling. This study designs a DPDK–SR-IOV transmission path that moves packet processing to user space and assigns fixed queues to OTA traffic. Tests on an airborne terminal and a co-simulation platform show that the new path raises link utilization from 68.4% to 91.7%, reduces median delay by 36.2%, and lowers the 99th-percentile jitter by 47.9%. The retransmission rate stays below 0.4% across 1000 update cycles, indicating stable behavior under long runs. These findings show that kernel-bypass methods, when applied with controlled queue and CPU settings, can support high-throughput and low-jitter OTA updates in aircraft. The study also notes the need for broader testing across different hardware and mixed traffic conditions before deployment at fleet scale.