Search Results (3,803)

Small-scale two-phase flows are subject to intense capillary accelerations that must be treated with care in order to avoid artifacts often associated with the numerical methodologies used, such as excessive fragmentation of structures. This analysis proposes a formulation of capillary actions for compressible viscous two-phase flows within the framework of discrete mechanics, where the concept of mass is abandoned in favor of a law of motion that describes the conservation of accelerations, one related to inertia and the other to external actions. With the introduction of the capillary term, the sum of a capillary potential gradient and the dual curl of a vector potential is consistent with the other terms of the law of motion, a formal Helmholtz–Hodge decomposition. This fully compressible formulation reproduces the capillary waves generated by the source terms and the contact and shock discontinuities in the two immiscible fluids. This methodology completely eliminates parasitic currents due mainly to the presence of residual curl in the capillary source terms. Several classic examples demonstrate the validity of this approach. Full article

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne zoonotic pathogen of significant public health concern in South Korea, where human cases continue to occur at high levels; however, information on the molecular epidemiology and genotype diversity of SFTSV in goats—an increasingly important livestock species—remains limited. In this study, blood samples were collected from 750 clinically healthy goats during nationwide surveillance in 2024. Viral RNA was detected by RT-PCR targeting the S and M genomic segments. Epidemiological characteristics were analyzed according to season, region, farm size, breed, and sex. Positive samples were subjected to sequencing and phylogenetic analysis to determine SFTSV genotypes. SFTSV RNA was detected in 10 of 750 goats (1.3%), with significantly higher detection rates in autumn compared with summer, in southern regions compared with northern regions, and in female goats compared with males, while no significant association was observed with farm size or breed. Phylogenetic analysis showed that goat-derived SFTSV strains belonged to genotypes B2, D, and F; notably, genotypes D and F were identified in goats for the first time in South Korea. These findings indicate that goats are exposed to genetically diverse SFTSV strains circulating in tick populations and exhibit epidemiological patterns consistent with tick ecology and human SFTS incidence, supporting the role of goats as incidental or sentinel hosts. Continuous molecular surveillance of goats, integrated with vector monitoring programs, may enhance understanding of regional SFTSV transmission dynamics and facilitate early detection of emerging genotypes with public health implication. Full article

This study aims to investigate the prevalence and risk factors associated with canine vector-borne diseases (CVBDs) in the Aegean Region of Türkiye. Using a questionnaire-based approach, this study intends to fill the gaps in existing knowledge regarding the prevalence and determinants of these infections. A retrospective analysis of 781 dogs presented to Aydın Adnan Menderes University Small Animal Clinic from 2019 to 2024 was conducted. Among these, 205 dogs were confirmed to have at least one CVBD using rapid diagnostic tests (SNAP 4DX PLUS and SNAP Leishmania) with confirmatory methods. Data on dog demographics, lifestyle, and environmental exposure were collected using structured questionnaires. Prevalence rates were calculated based on the at-risk population, and logistic regression determined associations between risk factors and disease occurrence. Overall CVBD prevalence was 26.3%, with Ehrlichiosis (9.9%) and Leishmaniasis (7.4%) being the most common infections. Co-infections were present in 8.3% of cases. Geographical factors significantly influenced infection rates, particularly in Aydın compared to İzmir and Muğla, while demographics like age, breed size, gender, and outdoor activity had no significant impact. This highlights the necessity for region-specific control measures and the need for consistent adherence to preventive protocols to mitigate CVBD prevalence in high-risk areas. Full article

Background/Objectives: Malnutrition is highly prevalent in patients with acute leukemia and is frequently underrecognized at diagnosis. Traditional screening tools based on anthropometry often fail to identify early nutritional deterioration. This study aimed to evaluate the prognostic utility of a comprehensive morphofunctional assessment—including bioelectrical impedance vector analysis (BIVA), handgrip strength (HGS), and muscle ultrasound—conducted at diagnosis and after induction therapy, to evaluate the prognostic association with 12-month mortality. Methods: In this prospective cohort study, 52 adult patients with newly diagnosed acute leukemia were enrolled between November 2022 and November 2024 at two tertiary hospitals in Málaga, Spain. Nutritional status was determined using GLIM criteria. Morphofunctional assessment included BIVA-derived phase angle (PhA), HGS via dynamometry, and rectus femoris ultrasound. A second evaluation was performed prior to haematopoietic stem cell transplantation. Mortality at 12 months was the primary outcome. Logistic regression and ROC analysis were used to assess prognostic associations. Results: At baseline, 65.4% of patients were classified as malnourished. After three months, patients showed significant declines in PhA (−0.55°, p < 0.001), body cell mass (−3.15 kg, p < 0.01), skeletal muscle mass (−1.66 kg, p < 0.01), and rectus femoris cross-sectional area (−0.36 cm², p = 0.011). Baseline malnutrition (OR = 6.88; 95% CI: 1.17–40.38; p = 0.033) and PhA decline ≥ 0.90° were both independently associated with higher 12-month mortality. Conclusions: Early morphofunctional assessment using GLIM criteria, BIVA, and muscle ultrasound identifies patients at nutritional and functional risk. PhA decline during treatment was associated with higher 12-month mortality, supporting the need for early, personalized nutritional intervention in leukemia care. Full article

Pear decline (PD), associated with ‘Candidatus Phytoplasma pyri’, is one of the most severe diseases affecting pear cultivation in Europe and the United States. Several psyllid species act as vectors of phytoplasmas belonging to the 16SrX group and play a key role in the epidemiology of the disease. This study aimed to characterize the epidemiology of pear decline in Sicily using integrated field, molecular, vector, and remote sensing approaches, four years after the first detection of PD in the region. Visual surveys and molecular analyses were conducted over two years in eight pear orchards. A total of 115 plant samples and 101 Cacopsylla spp. specimens, selected from 1435 collected individuals, were analysed, confirming the presence of ‘Ca. P. pyri’ in 69% of symptomatic plants and in 4.6% of C. pyri individuals. Genetic characterization revealed a high degree of similarity among the phytoplasma isolates analysed. Remote sensing analyses conducted since 2018, combined with vector population monitoring, confirmed the epidemic nature of PD and indicated the persistence of a risk of further pathogen spread within the region, supporting the use of remote sensing as a complementary tool for large-scale disease monitoring. Full article

The rise in antimicrobial resistance (AMR) among the most clinically significant bacteria presents a global threat. The coexistence of resistance mechanisms to both carbapenems and colistin is particularly concerning, as these are last-line treatments, specifically reserved for the most challenging infections caused by clinically multidrug-resistant Enterobacterales. Natural aquatic environments have become environmental reservoirs for the transmission of AMR, particularly concerning mechanisms against these two types of critically important drugs. The crucial role of environmental settings as a driving force for the spread and evolution of AMR associated with these drugs is underestimated, and scientific knowledge on this topic is limited. This review aims to fill an important gap in the scientific literature and comprehensively consolidate the available data on carbapenem- and colistin-associated AMR in the aquatic environment. This study provides a comprehensive synthesis of the current knowledge by integrating bibliographic data with a detailed genomic analysis of 278 bacterial genomes sourced from natural waters. It explores the distribution of carbapenemase and mobile colistin resistance (mcr) genes, identifying their hosts, geographical spread, and complex gene–plasmid–host associations. This review distinguishes two critical host groups for genes that provide resistance to last-resort drugs, Enterobacterales and autochthonous aquatic microbiota, highlighting both confirmed and potential interactions between them. Crucially, genomic analysis highlights the alarming co-occurrence of carbapenem and colistin resistance in single cells and on single plasmids, contributing to the spread of multidrug resistance phenotypes. These findings clearly indicate that aquatic environments are not merely passive recipients but active, evolving hubs for high-risk AMR determinants. Future research should focus on the interplay between allochthonous vectors and autochthonous microbiota to better understand the long-term stabilization of carbapenemase and mcr genes. Such efforts, combined with advanced sequencing technologies, are essential to ensure that carbapenems and colistin remain viable treatment options in clinical settings. Full article

Background: No Fourier transform infrared (FTIR) spectroscopy studies have directly evaluated adrenalectomy vessels, the technique’s established ability to probe collagen/elastin-associated spectral features and lipid peroxidation-related signatures, and protein structural damage. Stable gastric pentadecapeptide BPC 157 therapy was found to maintain the vascular function under severe stress, as FTIR spectroscopy recently demonstrated rapid peptide-induced molecular changes in healthy rat blood vessels, particularly in lipid content and protein secondary structure. Methods: To extend these findings and highlight the BPC 157 vascular background in the special circumstances of the course following unilateral adrenalectomy, abdominal aortas were collected at 15 min, 5 h, and 24 h after unilateral adrenalectomy for the FTIR spectroscopy assessment. Results: FTIR spectra were acquired, preprocessed, and analyzed using principal component analysis (PCA), support vector machine discriminant analysis (SVMDA), and band-specific statistics. BPC 157 (10 ng/kg intragatrically immediately after unilateral adrenalectomy) produced a clear, reproducible separation of aortic spectra from control samples at all time points. The main discriminatory spectral signatures were observed in three regions, including amide I and amide II (protein-related bands, consistent with collagen/elastin contributions) and lipid C–H stretching bands. These spectral signatures are consistent with early extracellular matrix reinforcement and membrane preservation in the vascular wall and align with the recovering effect on the lesions in counteraction of the severe vascular and multiorgan failure, attenuation/elimination of thrombosis and blood pressure disturbances in various occlusion/occlusion-like syndromes. Conclusions: Together, after unilateral adrenalectomy, the FTIR data provide molecular-level spectral signatures consistent with rapid remodeling of the aortic wall toward a more structurally stable and functionally favorable state. Full article

Background and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratification remains challenging with conventional models that rely on linear assumptions and limited variable integration. This study aimed to evaluate and compare the predictive performance of multiple machine learning (ML) algorithms with traditional logistic regression and the Mehran risk score for CA-AKI prediction and to explore key determinants of risk using explainable artificial intelligence methods. Materials and Methods: This retrospective, single-center study included 1741 patients with AMI who underwent coronary angiography. CA-AKI was defined according to KDIGO criteria. Multiple ML models, including gradient boosting machine (GBM), random forest (RF), XGBoost, support vector machine, elastic net, and standard logistic regression were developed using routinely available clinical and laboratory variables. A weighted ensemble model combining the best-performing algorithms was constructed. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model interpretability was evaluated using feature importance and SHapley Additive exPlanations (SHAP). Results: CA-AKI occurred in 356 patients (20.4%). In multivariable logistic regression, lower left ventricular ejection fraction, higher contrast volume, lower sodium, lower hemoglobin, and higher neutrophil-to-lymphocyte ratio (NLR) were independently associated with CA-AKI. Among ML approaches, the weighted ensemble model demonstrated the highest discriminative performance (AUC 0.721), outperforming logistic regression and the Mehran risk score (AUC 0.608). Importantly, the ensemble model achieved a consistently high NPV (0.942), enabling reliable identification of low-risk patients. Explainability analyses revealed that inflammatory markers, particularly NLR, along with sodium, uric acid, baseline renal indices, and contrast burden, were the most influential predictors across models. Conclusions: In patients with AMI undergoing coronary angiography, interpretable ML models, especially ensemble and gradient boosting-based approaches, provide superior risk stratification for CA-AKI compared with conventional methods. The high negative predictive value highlights their clinical utility in safely identifying low-risk patients and supporting individualized, risk-adapted preventive strategies. Full article

Background: Rhodopseudomonas palustris is a metabolically versatile bacterium with significant biotechnological potential, including the ability to catabolize lignin and its heterogeneous breakdown products. Understanding the molecular determinants of growth on lignin-derived compounds is essential for advancing lignin valorization strategies under both aerobic and anaerobic conditions. Methods: R. palustris was cultivated on multiple lignin breakdown products (LBPs), including p-coumaryl alcohol, coniferyl alcohol, sinapyl alcohol, p-coumarate, sodium ferulate, and kraft lignin. Condition-specific transcriptomics and proteomics datasets were generated and used as input features to train machine-learning models, with experimentally measured growth rates as the prediction target. Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) models were evaluated and compared. Permutation feature importance analysis was applied to identify genes and proteins most influential for growth. Results: Among the tested models, ANNs achieved the highest predictive performance, with accuracies of 94% for transcriptomics-based models and 96% for proteomics-based models. Feature importance analysis identified the top twenty growth-associated genes and proteins for each omics layer. Integrating transcriptomic and proteomic results revealed eight key transport proteins that consistently influenced growth across LBP conditions. Re-training ANN models using only these eight transport proteins maintained high predictive accuracy, achieving 86% for proteomics and 76% for transcriptomics. Conclusions: This study demonstrates the effectiveness of ANN-based models for predicting growth-associated genes and proteins in R. palustris. The identification of a small set of key transport proteins provides mechanistic insight into lignin catabolism and highlights promising targets for metabolic engineering aimed at improving lignin utilization. Full article

The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning approaches for lake total nitrogen to total phosphorus ratios (TN:TP), utilizing Zhuhai-1 hyperspectral satellite imagery to develop a CNN-SVR ensemble model integrating convolutional neural networks and support vector regression for remote sensing inversion of lake TN:TP ratios. Performance is evaluated against random forest (RF) and convolutional neural network (CNN) models, systematically analyzing spatial distribution patterns and primary drivers. Results indicate that the CNN-SVR model demonstrated superior performance among the tested models, with R², RMSE, MAPD, and RPD values of 0.856, 2.675, 9.516%, and 2.390, respectively. Spatially, the nitrogen-to-phosphorus ratio in lakes during the growing season exhibits an increasing trend from the western to the eastern half of the lake, progressing from northwest to southeast. When TN:TP falls below 9, algal growth becomes nitrogen-limited, indicating a higher degree of eutrophication; when TN:TP exceeds 22.6, phosphorus becomes the limiting factor, indicating lower eutrophication levels. A similar distribution pattern is observed during the non-growing season. Regarding driving mechanisms, the nitrogen-to-phosphorus ratio during the growing season is primarily influenced by TN accumulation and shows significant correlations with dissolved oxygen (DO) and pH. During the non-growing season, while still affected by TN input, its association with other water quality parameters is weaker. The results indicate that the combined use of CNN and SVR improves feature extraction and model fitting in nitrogen-to-phosphorus ratio inversion and helps clarify its ecological significance as an indicator of algal growth. This provides methodologies and evidence for precise diagnosis and ecological management of lake eutrophication. Full article

CRISPR-Cas9 systems have enabled unprecedented advances in genome engineering, particularly in developing treatments for human diseases, like cancer. Despite potential applications, limitations of Cas9 include its relatively large size and strict targeting requirements. Cas12j2, a variant ofCasΦ-2, shows promise for overcoming these limitations. However, its effectiveness in mammalian cells remains relatively unexplored. This study sought to develop an optimized CRISPR-Cas12j2 system for targeted knockout of the E6 oncogene in HPV-associated cancers. A combination of computational tools (ColabFold, CCTop, Cas-OFFinder, HADDOCK2.4, and Amber for Molecular Dynamics) was utilized to investigate the impact of engineered modifications on structural integrity and gRNA binding of Cas12j2 fusion constructs, in potential intracellular conditions. Cas12j2_F2, a Cas12j2 variant designed and evaluated in this study, behaves similarly to the wild-type Cas12j2 structure in terms of RMSD/RMSF profiles, compact Rg values, and minimal electrostatic perturbation. The computationally validated Cas12j2 variant was incorporated into a custom expression vector, co-expressing the engineered construct along with a dual gRNA for packaging into a viral vector for targeted knockout of HPV-associated cancers. This study provides a structural and computational foundation for the rational design of Cas12j2 fusion constructs with enhanced stability and functionality, supporting their potential application for precise genome editing in mammalian cells. Full article

Background/Objectives: Sepsis detection remains challenging due to clinical heterogeneity and limitations of traditional scoring systems. This study developed and validated a hospital-wide machine learning model for sepsis detection using retrospectively developed data from prospectively expert-validated cases, aiming to improve diagnostic accuracy beyond conventional approaches. Methods: This retrospective cohort study analysed 218,715 hospital episodes (2014–2018) at a tertiary care centre. Sepsis cases (n = 11,864, 5.42%) were prospectively validated in real-time by a Multidisciplinary Sepsis Unit using modified Sepsis-2 criteria with organ dysfunction. The model integrated structured data (26.95%) and unstructured clinical notes (73.04%) extracted via natural language processing from 2829 variables, selecting 230 relevant predictors. Thirty models including random forests, support vector machines, neural networks, and gradient boosting were developed and evaluated. The dataset was randomly split (5/7 training, 2/7 testing) with preserved patient-level independence. Results: The BiAlert Sepsis model (random forest + Sepsis-2 ensemble) achieved an AUC-ROC of 0.95, sensitivity of 0.93, and specificity of 0.84, significantly outperforming traditional approaches. Compared to the best rule-based method (Sepsis-2 + qSOFA, AUC-ROC 0.90), BiAlert reduced false positives by 39.6% (13.10% vs. 21.70%, p < 0.01). Novel predictors included eosinopenia and hypoalbuminemia, while traditional variables (MAP, GCS, platelets) showed minimal univariate association. The model received European Medicines Agency approval as a medical device in June 2024. Conclusions: This hospital-wide machine learning model, trained on prospectively expert-validated cases and integrating extensive NLP-derived features, demonstrates superior sepsis detection performance compared to conventional scoring systems. External validation and prospective clinical impact studies are needed before widespread implementation. Full article

Diabetic nephropathy (DN) is the most common cause of kidney failure worldwide. Mesenchymal stromal cells (MSCs) have demonstrated promise for treating DN by promoting kidney repair and regulating inflammation. Allogeneic (Allo)-MSCs may have similar or superior anti-inflammatory effects to autologous (Auto)-MSCs but also have potential to elicit adverse immune responses due to major histocompatibility complex (MHC) mismatches. To better understand how MSC-delivered allo-antigens influence therapeutic effects of Allo-MSCs compared to Auto-MSCs in DN, lentiviral transduction was used to generate adipose-derived MSCs (ADSCs) from DBA/2J (H-2^d) mice which expressed an allogeneic class I MHC protein (H-2K^b). H-2K^b-ADSCs were injected intravenously into male DBA/2J mice at 11 and 13 weeks after initiation of diabetes, and their effects on renal functional and structural indices were compared at week 15 with those of diabetic DBA/2J recipients of vehicle alone or of empty vector-transduced DBA/2J ADSCs (EV-ADSCs). Both EV-ADSCs and H-2K^b-ADSCs resulted in reduced kidney/total body weight ratio, blood urea nitrogen (BUN), urine albumin creatinine ratio (uACR), mesangial matrix expansion (MME) and renal fibrosis compared to vehicle alone, without influencing glycemia or survival. However, H-2K^b-ADSCs recipients had greater reductions in BUN and uACR, reduced intra-renal myeloid cell infiltration, increased splenic regulatory T cell (Treg) proportions and increased intra-renal Treg infiltration and FOXP3 and IL-10 mRNA. Nonetheless, recipients of H-2K^b-ADSCs also had decreased splenic CD4/CD8 T cell ratios, increased circulating anti-H-2K^b IgG antibodies and histological and biochemical evidence of inflammatory liver injury. These novel findings demonstrated that ADSCs expressing an MHC-I allo-antigen had superior beneficial effects on DN than fully autologous ADSCs. Improved DN severity was associated with immune modulation, including Treg enhancement, but also had potentially detrimental immunological effects in mice with established diabetes. The results highlight the need for further investigation of the immune modulatory effects of Allo-MSCs in diabetes and its organ-specific complications. Full article

Objective: To develop and validate a genetic diagnostic model for colorectal cancer (CRC). Methods: First, differential expression genes (DEGs) between colorectal cancer and normal groups were screened using the TCGA database. Subsequently, a two-sample Mendelian randomization analysis was performed using the eQTL genomic data from the IEU OpenGWAS database and colorectal cancer outcomes from the R12 Finnish database to identify associated genes. The intersecting genes from both methods were selected for the development and validation of the CRC genetic diagnostic model using nine machine learning algorithms: Lasso Regression, XGBoost, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT). Results: A total of 3716 DEGs were identified from the TCGA database, while 121 genes were associated with CRC based on the eQTL Mendelian randomization analysis. The intersection of these two methods yielded 27 genes. Among the nine machine learning methods, XGBoost achieved the highest AUC value of 0.990. The top five genes predicted by the XGBoost method—RIF1, GDPD5, DBNDD1, RCCD1, and CLDN5—along with the five most significantly differentially expressed genes (ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) in the GSE87211 dataset, were selected for the construction of the final colorectal cancer (CRC) genetic diagnostic model. The ROC curve analysis revealed an AUC (95% CI) of 0.9875 (0.9737–0.9875) for the training set, and 0.9601 (0.9145–0.9601) for the validation set, indicating strong predictive performance of the model. SHAP model interpretation further identified IFITM1 and DBNDD1 as the most influential genes in the XGBoost model, with both making positive contributions to the model’s predictions. Conclusions: The gene expression profile in colorectal cancer is characterized by enhanced cell proliferation, elevated metabolic activity, and immune evasion. A genetic diagnostic model constructed based on ten genes (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) demonstrates strong predictive performance. This model holds significant potential for the early diagnosis and intervention of colorectal cancer, contributing to the implementation of third-tier prevention strategies. Full article

The granulation of pomelo (Citrus maxima) juice sacs severely compromises fruit quality and is closely associated with lignin accumulation, a process catalyzed by peroxidases (PODs). Analysis of ‘Sanhong’ pomelo juice sacs collected 175–215 days after flowering revealed that bound peroxidase (BPOD) activity paralleled changes in lignin content, suggesting a potential role for BPOD in lignin biosynthesis. A total of 71 CmPOD genes were identified in the pomelo genome through integrated HMMER and BLAST analyses. Among them, CmPOD52 was selected for functional characterization based on its alkaline peroxidase properties, absence of a CE domain, predicted extracellular localization, and gradually increasing expression pattern revealed by RT-qPCR. Its transient overexpression in ‘Sanhong’ pomelo juice sacs for 36 h increased BPOD activity 2.06-fold (p < 0.01) compared to the empty vector control, indicating that CmPOD52 may be a BPOD gene. The recombinant CmPOD52 protein was expressed in a prokaryotic system, purified, and used in enzymatic assays with sinapyl alcohol as the substrate. The recombinant CmPOD52 protein, assayed at 272 nm with controls (substrate-only blank and heat-inactivated protein), showed an activity of 13.67 ± 0.9 U. The experimental group showed new products, identified by mass spectrometry as sinapyl alcohol dimers, thus suggesting that the recombinant protein catalyzes the dehydrogenation and polymerization of sinapyl alcohol monomers. This study identified CmPOD52, a gene potentially involved in lignin polymerization in pomelo juice sacs, offering a key candidate for further in vivo validation. Full article