Search Results (997)

Histidine kinases are an integral component of bacterial two-component systems (TCSs), playing a pivotal role in signal transduction pathways, resulting in both resistance and virulence. However, their inherent membrane-bound nature often results in poor solubility, making them difficult to isolate and rendering them incompatible with most in vitro biochemical techniques. Consequently, much of the research on two-component systems has centered on response regulators, limiting both drug discovery efforts and our broader understanding of key signal transduction mechanisms. To address these challenges, we sought to straightforwardly generate cytosolic truncation mutants of histidine kinases that retain their autophosphorylation and phosphotransfer capabilities. Previously, we successfully developed a cytosolic truncation mutant of PmrB (PmrBc) that maintained these critical functions, demonstrating its suitability as a viable surrogate for in vitro investigations, including inhibitor compound screening. Building upon this foundation, we have refined our methods and here demonstrate these improvements by producing functional histidine kinase truncation mutants from the following diverse bacterial species: Escherichia coli; PhoQ, BasS and Klebsiella pneumoniae; and PmrB and PhoQ. Full article

Objectives: Global infertility rates are rapidly increasing. Assisted reproductive technologies combined with artificial intelligence are the next hope for overcoming infertility. In vitro fertilization (IVF) is gaining popularity owing to its increasing success rates. The success rate of IVF essentially depends on the assessment and inspection of blastocysts. Blastocysts can be segmented into several important compartments, and advanced and precise assessment of these compartments is strongly associated with successful pregnancies. However, currently, embryologists must manually analyze blastocysts, which is a time-consuming, subjective, and error-prone process. Several AI-based techniques, including segmentation, have been recently proposed to fill this gap. However, most existing methods rely only on raw grayscale intensity and do not perform well under challenging blastocyst image conditions, such as low contrast, similarity in textures, shape variability, and class imbalance. Methods: To overcome this limitation, we developed a novel and lightweight architecture, the microscopic multimodal interaction segmentation network (MiMics-Net), to accurately segment blastocyst components. MiMics-Net employs a multimodal blastocyst stem to decompose and process each frame into three modalities (photometric intensity, local textures, and directional orientation), followed by feature fusion to enhance segmentation performance. Moreover, MiMic dual-path grouped blocks have been designed, in which parallel-grouped convolutional paths are fused through point-wise convolutional layers to increase diverse learning. A lightweight refinement decoder is employed to refine and restore the spatial features while maintaining computational efficiency. Finally, semantic skip pathways are induced to transfer low- and mid-level spatial features after passing through the grouped and point-wise convolutional layers. Results/Conclusions: MiMics-Net was evaluated using a publicly available human blastocyst dataset and achieved a Jaccard index score of 87.9% while requiring only 0.65 million trainable parameters. Full article

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a prevalent global health issue driven by metabolic syndrome, with cardiovascular disease being the leading cause of mortality. This review synthesizes current knowledge on its multifactorial pathogenesis, the impact of sexual dimorphism, and key experimental models. The progression of MASLD involves interconnected pathways including dysregulated de novo lipogenesis, insulin resistance, mitochondrial dysfunction, gut dysbiosis, ferroptosis, and genetic and epigenetic predispositions. These mechanisms not only promote hepatic injury but also accelerate atherosclerosis. Notably, MASLD exhibits significant sexual dimorphism, influenced by physiological differences, sex hormones, genetic factors, and the microbiome. The study of these complex processes relies mostly on dietary-induced animal models, particularly in rats, which effectively recapitulate features of the human disease. Given the multifaceted nature of MASLD, the therapeutic focus is shifting from monotherapies to combination or dual-target strategies. To enable this transition, refinement of preclinical models is essential to better understand and target this complex disorder. Full article

As the lightest metallic structural material, magnesium alloys face a fundamental trade-off between strength and ductility, limiting their broader application. This study investigates a processing approach to overcome this limitation by systematically comparing the effects of direct extrusion and a multi-directional forging (MDF) combined extrusion process on a Mg-8Gd-4Y-1Nd-0.5Zr alloy. The results demonstrate that MDF pretreatment effectively refines grains and enhances dynamic precipitation. It also significantly weakens the texture, reducing the intensity from 11.14 to 3.98 and tilting the {0001} basal planes by approximately 30° from the extrusion direction. This texture weakening is attributed to the combined effects of particle-stimulated nucleation (PSN) and the orientation diversity introduced by pre-forging, which promote orientation randomization during recrystallization. The alloy processed by the combined route exhibits an excellent strength–ductility synergy in the as-extruded state, with ultimate tensile strength, tensile yield strength, and elongation reaching 315 MPa, 228 MPa, and 13.1%, respectively. After peak aging, the strength further increases to 429 MPa and 323 MPa while maintaining a ductility of 7.3%. Schmid factor analysis confirms that the combined process facilitates the activation of non-basal slip and improves strain compatibility through multi-slip activity, providing an effective pathway for developing high-performance wrought magnesium alloys. Full article

This study evaluates whether a multimodal retrieval-augmented generation (RAG) pipeline originally developed for early modern woodcuts can be effectively transferred to the domain of medieval manuscript illumination. Using a dataset of Wenzelsbibel miniatures annotated with Iconclass, the pipeline combined page-level image input, LLM description generation, vector retrieval, and hierarchical reasoning. Although overall scores were lower than in the earlier woodcut study, the best-performing configuration still substantially surpassed both image-similarity and keyword-based search, confirming the advantages of structured multimodal retrieval for medieval material. Truncation analysis further revealed that many errors occurred only at the deepest Iconclass levels: removing levels raised precision to 0.64 and 0.73, with average remaining depths of 5.49 and 4.49 levels, respectively. These results indicate that the model’s broader hierarchical placement is often correct even when fine-grained specificity breaks down. Taken together, the findings demonstrate that a woodcut-oriented RAG pipeline can be meaningfully adapted to manuscript illumination and that its strengths lie in contextual reasoning and structured classification. Future improvements should incorporate available textual metadata, explore graph-based retrieval, and refine Iconclass-driven pathways. Full article

Despite recent progress in different treatment modalities, colorectal cancer remains a leading cause of cancer-related death, highlighting the need to further develop novel treatment strategies. In this context, over recent decades, several experimental studies have demonstrated that high doses of vitamin C provide anti-cancer benefits in various colorectal cancer models. Intravenous administrations of vitamin C are necessary to reach these high concentrations in tumors. Tumors harboring KRAS or BRAF mutations or driven by the HIF1α signaling pathway are particularly sensitive to high-dose vitamin C. In addition, high doses of vitamin C increase the efficacy of other treatments when used in combination, including chemotherapies, targeted therapies, and immunotherapies. Whilst results of experimental studies were promising, their translation into clinical protocols has been disappointing. So far, very few clinical data support the anti-cancer benefits of vitamin C in colorectal cancer patients. This lack of success highlights the need to ameliorate the selection of patients based on biomarkers and to refine treatment protocols to achieve high-dose vitamin C in tumors. In this review, we analyze in vitro and in vivo studies that investigated the effect of vitamin C in colorectal cancer cells and point out the anti-cancer mechanisms of vitamin C. We further examine clinical trials that tested vitamin C in colorectal cancer patients and address several points that still need to be investigated in order to fully define the role of vitamin C in the treatment of colorectal cancer. Full article

Venous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT), remains a major cause of morbidity and mortality worldwide, with significant clinical challenges in diagnosis and risk stratification. Traditional diagnostic tools, including clinical prediction scores, D-dimer testing, and imaging, are limited by suboptimal specificity or sensitivity. In this context, proteomics-based approaches have emerged as powerful tools to elucidate the molecular mechanisms of VTE and to identify novel diagnostic and prognostic biomarkers. This review synthesizes recent advances in proteomic research relevant to VTE. We searched four databases (PubMed, ScienceDirect, Springer Nature, and Wiley) using the keywords “acute pulmonary embolism”, “acute venous thromboembolism”, and “proteomics”. Thirty proteomic studies investigating VTE were examined. Across these studies, proteomic profiling consistently revealed alterations in pathways related to coagulation, inflammation, platelet activation, endothelial dysfunction, and fibrin clot structure. Multiple protein classes, including acute-phase reactants, complement components, coagulation factors, and platelet-derived proteins, have demonstrated potential value in improving diagnostic accuracy and refining prognostic stratification. Proteomic analyses have also revealed distinct molecular signatures between isolated PE and isolated DVT, supporting the concept of biologically heterogeneous VTE phenotypes. Furthermore, emerging evidence from COVID-19–associated thrombosis, cancer-associated VTE, and non-invasive sources such as exhaled breath condensate underscores the expanding clinical relevance of proteomic approaches. Although technical limitations and heterogeneity across studies remain challenges, the integration of proteomic data with clinical and genetic information holds promise for advancing precision medicine in VTE. Full article

Pancreatic surgery is a technically demanding field associated with frequent morbidity, with pancreatic fistula representing the dominant driver of major complications in pancreaticoduodenectomy (PD). Although refinements in operative technique, perioperative management, and institutional systems have contributed to incremental improvements, the overall incidence of clinically relevant complications has remained largely unchanged over recent decades. This narrative review provides a comprehensive overview of current strategies aimed at reducing morbidity and mortality after pancreaticoduodenectomy, focusing on modifiable technical, pharmacological, nutritional, and systems-based interventions, whilst acknowledging the underlying biological determinants that remain difficult to alter. This review synthesises contemporary evidence on fistula risk modelling, anastomotic reconstruction, and adjunctive operative techniques. The role of pharmacological interventions is examined alongside an evaluation of perioperative nutritional optimisation and enhanced recovery frameworks. Systems-based strategies such as centralisation, failure-to-rescue performance, protocolised pathways, and algorithm-driven postoperative surveillance are highlighted as emerging areas with substantial potential to impact survival independently of complication rates. Finally, this review explores future directions, including radiomics-based risk stratification, intraoperative imaging, and tailored postoperative care. Together, these domains provide a platform for reducing complication severity, standardising postoperative care, and ultimately improving patient outcomes. By integrating these perspectives, this review aims to present a comprehensive and in-depth narrative of how to reduce complications in pancreas surgery. Overall, this narrative review proposes that meaningful improvements in outcomes after PD likely do not arise from the elimination of complications altogether, but rather from improved prediction, prevention where possible, and critically, more effective systems of care that reduce the severity and consequences of complications when they occur. Full article

Photodynamic therapy (PDT) is a technique based on the use of photosensitizers activated by light to destroy cancer cells in the presence of oxygen. This enables localized cancer treatment and, in some settings, fluorescence-guided visualization. However, the efficacy and clinical translation of PDT have been limited by the low specificity of traditional photosensitizers. The aim of the study is to create a ligand-guided PDT approach for pancreatic ductal adenocarcinoma (PDAC) using a peptide-conjugated photosensitizer binding to integrin αvβ6, which is a receptor linked to tumor growth and prevalent in PDAC cells. Current treatment options for this tumor are limited, with surgical resection and chemotherapy only effective when the tumor is detected early. Given the limited treatment options for PDAC, PDT via αvβ6 offers a new pathway for precision treatment. The cyclic peptide cyclo[FRGDLAFp(NMe)K], recognized for its high affinity to αvβ6, was chosen to guide a phthalocyanine-class photosensitizer toward αvβ6-expressing PDAC models. The PDT approach was further refined by developing 3D spheroid models and in vivo BxPc3 xenograft models in NOD/SCID mice, where its therapeutic efficacy was assessed. In the absence of a non-targeted control photosensitizer, a contribution from non-specific accumulation and EPR effects in the in vivo setting cannot be fully ruled out. This study highlights the potential of a peptide-guided photosensitizer, demonstrating uptake and photodynamic activity in spheroids, with moderate in vivo results addressing tumor microenvironment challenges. Optimization of PDT dosing, laser precision, and preclinical models, such as patient-derived xenografts, are crucial to enhance clinical translation. Full article

Osteomyelitis (OM), an inflammatory condition of the bone tissue, is a complex orthopedic condition marked by chronic inflammation, diagnostic uncertainty, and recurrent infections. Despite standard treatments—including surgical debridement, antimicrobial therapy, and bone reconstruction—many patients continue to experience recurrence and treatment failure. Growing molecular evidence indicates that host genetic factors play a crucial role in shaping immune responses and influencing disease progression in OM. This narrative review synthesizes current knowledge from candidate gene single-nucleotide polymorphism (SNP) association studies to illustrate how specific genetic variations contribute to OM pathogenesis, diagnostic refinement, and treatment outcomes. We examined key immunogenetic variants within genes involved in inflammatory signaling, pathogen recognition, and neutrophil regulation. Our synthesis identifies a landscape of pro-inflammatory SNPs, such as IL-1β rs16944 and NLRP3 rs10754558, that are associated with increased susceptibility to chronic or post-traumatic OM, as well as SNPs that are associated with protective effects that may favor infection resolution, such as within the NOS2 and VDR genes. These SNP-driven differences in inflammasome activity, cytokine pathways, and oxidative stress responses highlight emerging opportunities for individualized therapeutic strategies. This review consolidates these variants, providing a genetic framework to analyze host susceptibility and differentiating high risk from protective genetic profiles. Integrating genomic insights into OM management represents a promising shift toward personalized medicine, enhancing diagnostic precision, informing targeted interventions, and improving prognostic assessment. Continued large-scale validation of candidate SNPs and translational genomic models will be essential to support their future clinical application. Full article

Stroke is a major global health concern, particularly among the elderly, who frequently present with multiple comorbidities, most notably cardiovascular diseases. Importantly, atrial fibrillation confers a nearly fivefold increase in stroke risk and accounts for up to one-quarter of ischemic strokes in older adults. Stroke is a neurological disease characterised by a strong cardiovascular interplay, and its multifactorial nature requires an integrated preventive approach. This review focuses on primary and secondary prevention in this population, with a frailty-informed perspective. We synthesise evidence on blood pressure control, lipid-lowering (including LDL-C targets), glycemic management, and antithrombotic strategies—particularly oral anticoagulation for atrial fibrillation—as well as the role of frailty indices in guiding individualised risk–benefit decisions. We also discuss practical care pathways, including structured post-discharge programs, continuity of care, and the need for multidisciplinary collaboration involving cardiologists, neurologists, and primary care. We highlight how frailty indices refine risk–benefit assessments without justifying therapeutic nihilism, and how sex- and age-related factors shape treatment effectiveness and safety. By narrowing scope and emphasising practical, multidisciplinary prevention strategies, this review aims to support clinicians in reducing recurrent events, disability, and mortality in very old patients. Future work should prioritise pragmatic trials, including those involving the oldest old and the use of standardised frailty metrics, to inform prevention decisions. Full article

Peroxisome-proliferator–activated receptor delta (PPARδ) regulates metabolic, mitochondrial, and inflammatory pathways implicated in neurodegeneration, making it an attractive therapeutic target for amyotrophic lateral sclerosis (ALS). In this study, we evaluated two PPARδ agonists, KD3010 and T3D-959, in two established ALS/FTD mouse models: an AAV-mediated C9orf72 G4C2-repeat expansion model (C9-149R) and the TDP-43^Q331K transgenic model. Drug treatment was initiated prior to the emergence of key disease features and continued for 9–10 months. Comprehensive behavioral, neuropathological, and biomarker analyses revealed marked differences between the two models. C9-149R mice exhibited reduced body weight and subtle behavioral alterations without robust motor deficits, whereas TDP-43^Q331K mice developed pronounced, progressive motor and cognitive impairments accompanied by a ~7-fold elevation in plasma neurofilament light chain (NfL). Despite effective target engagement—particularly for T3D-959—neither PPARδ agonist improved motor performance, cognitive behavior, neuroanatomical measures, plasma NfL levels, or disease-associated molecular phenotypes in either model. Prolonged KD3010 treatment resulted in loss of target engagement, consistent with drug tolerance, while T3D-959 sustained PPARδ activation without therapeutic benefit. Together, these findings demonstrate that PPARδ agonism is insufficient to modify disease progression in these ALS/FTD mouse models and underscore the importance of publishing well-powered negative preclinical studies to refine therapeutic strategies for ALS. Full article

Conventional material balance methods, typically based on single- or dual-porosity models solvable via single-step linearization, are inadequate for hydraulically fractured shale oil reservoirs due to their pronounced heterogeneity and contrasting interzonal connectivity. Specifically, dual-zone models fail to represent the realistic characteristics of shale oil reservoirs because they treat artificially created hydraulic fractures and natural fractures as equivalent, despite their substantially different properties. To address this gap, this paper proposes a novel three-zone conceptual model, segmenting the reservoir into the matrix zone (MZ), the Weakly Stimulated Zone (WSZ, low-conductivity zone), and the Strongly Stimulated Zone (SSZ, high-conductivity zone). A corresponding three-zone gas injection replenishment material balance model is developed. This model explicitly captures interactions between injected gas and formation fluids and incorporates dynamic variations in pore volume and fluid saturation induced by imbibition. To solve the complexities introduced by the triple-porosity system, a dedicated two-step linearization solution procedure is proposed. Utilizing conventional production performance and basic PVT data, the method enables simultaneous estimation of zone-specific developed reserves and prediction of the Estimated Ultimate Recovery (EUR) through a least squares algorithm. Validation against actual well cases and multi-well statistics confirms that the method provides stable and reliable zonal reserve characterization and EUR forecasting. The results indicate that the MZ contributes the majority of the geological reserves, accounting for >70%. The WSZ contributes approximately 29.5% of the reserves and serves as the primary source for energy replenishment in the shale oil reservoir. In contrast, the SSZ contributes less than 0.5% of the reserves but acts as the dominant channel for flow convergence, controlling the main fluid production pathways. The proposed framework not only offers a practical tool for refined reserve assessment in shale oil reservoirs but also provides a computational basis and decision support for the design and injection parameter optimization of pre-pad CO₂ energy storage fracturing schemes. Full article

Bio-inspired engineering draws on principles refined by natural evolution to tackle persistent challenges in fluid mechanics, structural dynamics, and thermal transport. This article presents a critical, mechanism-driven narrative review that integrates recent advances across three complementary domains that are often treated independently, namely: flow-control strategies such as leading-edge tubercles, alula-like devices, riblets, superhydrophobic skins, and hybrid low-Reynolds-number fliers; fluid-structure interactions inspired by aquatic and aerial organisms that leverage compliant foils, flexible filaments, ciliary arrays, and piezoelectric fluttering plates for propulsion, wake regulation, mixing, and energy harvesting; and phase-change heat-transfer surfaces modeled after stomata, porous biological networks, and textured cuticles that enhance nucleation control, liquid replenishment, and droplet or bubble removal. Rather than providing an exhaustive catalog of biological analogues, this review emphasizes the underlying physical mechanisms that link these domains and enable multifunctional performance. These developments reveal shared physical principles, including multiscale geometry, capillary- and vortex-mediated transport, and compliance-enabled flow tuning, which motivate the integrated treatment of aerodynamic, hydrodynamic, and thermal systems in applications spanning aerospace, energy conversion, and microscale thermal management. The review assesses persistent challenges associated with scaling biological architectures, ensuring long-term durability, and modeling tightly coupled fluid-thermal-structural interactions. By synthesizing insights across flow control, fluid-structure interaction, and phase-change heat transfer, this review provides a unifying conceptual framework that distinguishes it from prior domain-specific reviews. Emerging opportunities in hybrid multi-mechanism designs, data-driven optimization, multiscale modeling, and advanced fabrication are identified as promising pathways to accelerate the translation of biological strategies into robust, multifunctional thermal–fluid systems. Full article

Fractures are a leading cause of morbidity and mortality in Thoroughbred racehorses, posing a significant threat to their welfare and careers. This study introduces a deep learning model specifically designed to facilitate fracture detection in equine athletes. By leveraging extensive training on human fracture data and refining the model with equine imaging, it highlights the transformative potential of transfer learning across species and medical contexts. This approach is not limited to equine fractures but could be adapted for use in detecting injuries or conditions in other veterinary species and even human healthcare applications. A comprehensive databank of radiographs, sourced from public archives and equine hospitals, was curated to encompass diverse conditions (fracture and non-fracture), ensuring robust pattern recognition. The architecture integrates a Vision Transformer for global context modelling with a ResNet backbone and loss function to optimize local feature extraction and cross-species adaptability. The pipeline achieved 96.7% accuracy for modality classification, 97.2% accuracy for projection recognition, and fracture localization intersection over union values of 0.71–0.84 across equine datasets. This work bridges advancements in human and veterinary medicine, opening pathways for AI-driven solutions that extend beyond fractures, fostering improved diagnostic precision and broader applications across species (felines, canines, etc.). By integrating advanced imaging techniques with AI, this study aims to set a foundation for more comprehensive and versatile health monitoring systems. Full article