Search Results (54,876)

Background/Objectives: Breast cancer is the most common malignancy among women worldwide and poses a significant burden on healthcare systems. Despite evidence that treatment type is associated with hospital length of stay (LOS), current Germany-wide data on this topic are lacking. The present study aims to identify clinical and procedural factors associated with prolonged hospitalisation in women hospitalised for breast cancer in Germany. Methods: This multicentre cross-sectional study analysed anonymised inpatient data from 38 German hospitals (IQVIA database), including 16,062 female breast cancer hospitalisation episodes (inpatient cases; patient-level linkage was not possible due to anonymisation, so these may not represent 16,062 distinct women) treated between January 2019 and December 2024. The primary outcome was LOS; prolonged hospitalisation was defined as LOS > 7 days (75th percentile). Comorbidities were assessed using individual comorbidity categories defined according to the Elixhauser classification (retained at ≥1% prevalence). The primary multivariable analysis was restricted to characteristics documented independently of the in-hospital course (age, tumour site, metastatic status, chronic comorbidities). In-hospital complications, procedures, and diagnoses of ambiguous timing were analysed separately as secondary, descriptive associations. LOS was modelled using negative binomial regression (Poisson regression as a sensitivity analysis) and prolonged LOS using logistic regression, both with hospital-clustered robust (sandwich) standard errors. Results: The overall median LOS was 4 days (IQR 3–7); 19.3% of hospitalisation episodes were prolonged. In the primary analysis, longer LOS was associated with age > 70 years (RR 1.14; 95% CI 1.08–1.20), distant metastases (RR 1.76; 95% CI 1.51–2.04), lymph node metastases (RR 1.09; 95% CI 1.05–1.12), congestive heart failure (RR 1.44; 95% CI 1.31–1.59), anaemia (RR 1.41; 95% CI 1.14–1.75) and depression (RR 1.14; 95% CI 1.03–1.26). In the secondary descriptive analysis, in-hospital complications showed the strongest associations with longer LOS, particularly postoperative infection (RR 2.28), wound disruption (RR 1.92) and pneumonia (RR 1.45). Breast-conserving surgery and partial breast resection were associated with shorter LOS, while mastectomy, blood transfusions, and complex intensive care were associated with prolonged stays; the small inpatient radiotherapy subgroup (1.9%) most likely reflects a highly selected, predominantly palliative population rather than an association attributable to radiotherapy itself. Conclusions: Prolonged hospitalisation was associated with older age, documented lymph node and distant metastases, and chronic somatic comorbidities including congestive heart failure, anaemia, chronic kidney disease and depression, and, in secondary descriptive analyses, with perioperative complications and intensive procedures. The strongest correlates of prolonged stay—in-hospital complications and intensive procedures—are documented during rather than before the hospitalisation and therefore cannot themselves support early risk identification; however, the baseline characteristics documented independently of the in-hospital course (age, metastatic disease, chronic comorbidity burden) may usefully inform structured perioperative management and discharge planning in Germany. Full article

Seizures in several epilepsy syndromes cluster within narrow windows of the sleep–wake cycle, even though the underlying molecular defects are present continuously. This narrative review asks how constitutive molecular pathologies give rise to state-dependent seizures. We integrated epilepsy genetics with sleep neurophysiology, drawing on literature retrieved from PubMed/MEDLINE across eleven concept blocks, and organized the evidence around four gene families: nicotinic acetylcholine receptor subunits, KCNT1, the GATOR1–mTOR complex, and thalamocortical calcium and HCN channels. Across these pathways, the genetic evidence ranges from well-established monogenic causation to susceptibility alleles of insufficient effect. We propose, as an integrative working model, that non-rapid eye movement (NREM) sleep converts continuous defects into state-bound seizures by strengthening thalamocortical coupling, withdrawing cholinergic tone and destabilizing arousal, with circadian phase adding a further layer of temporal control. In this model, sleep-state dependence may reflect the interaction between molecular lesions and vigilance states rather than the properties of any single gene. The strength of evidence for this link varies across gene families and is weakest for the GATOR1 pathway; much of the supporting evidence derives from animal, expression, and computational studies, whose extension to human syndromes remains inferential. Full article

Tissue-specific autophagic remodeling drives larval tissue dismantling during lepidopteran metamorphosis, yet the metabolic regulators underlying tissue-autonomous autophagic heterogeneity remain incompletely defined. This study sought to dissect the linkage between lipid metabolic reprogramming and tissue-divergent autophagic potency in Bombyx mori. Spatiotemporal autophagic levels across larval tissues was profiled via BmAtg8–PE immunoblotting, BmAtg8 immunofluorescence, and LysoTracker Red lysosomal staining. Untargeted metabolomics was conducted on the fat body, midgut, posterior silk gland, and trachea at key metamorphic stages; differentially accumulated metabolites (DAMs) were processed through hierarchical clustering and KEGG functional enrichment. Autophagic activity was universally upregulated upon prepupal transition, with the posterior silk gland and trachea showing markedly higher autophagic levels, whereas the fat body and midgut maintained relatively lower autophagic activity in tissue-wide comparison. Lipids represented the most dynamically remodeled metabolite families; fatty acyls and their synthetic cascades were enriched in high-autophagy tissues, while glycerolipid and glycerophospholipid pathways prevailed in low-autophagy tissues. Integrative multi-omics and cytological evidence identify a correlative relationship between tissue-resolved lipid turnover and spatially segregated autophagic activation. This tissue-scale atlas establishes a fundamental resource for mechanistic exploration of lipid-autophagy crosstalk governing insect metamorphic development. Full article

Neoadjuvant therapy for breast cancer is planned by subtype: pathologic complete response (pCR) differs in frequency, meaning, and surrogate validity across HR+/HER2−, HER2+, and triple-negative breast cancer (TNBC). This review examines whether current evidence supports using artificial intelligence (AI) to move beyond predicting response under a fixed regimen to guiding systemic treatment tailoring. Here, tailoring means model-guided drug omission, switching, escalation, or de-escalation for an individual patient. We appraised 102 full-text studies of AI-based response prediction (2020–2026), organized by subtype and clinical decision, and graded each on an author-defined five-level clinical-readiness ladder (L1–L5) measuring validation and translational maturity rather than accuracy. Risk of bias was assessed with PROBAST and reporting against TRIPOD+AI. Readiness clustered low: 43 studies reached internal validation only (L1), 53 temporal or geographic external validation (L2), and 6 prospective observational validation (L3); none reached workflow integration (L4) or interventional evidence (L5). Most carried high overall risk of bias (89/102); external validation appeared in 52 (51%), fully reported decision-curve analysis in 44 (43%), and calibration in 19 (19%). Strategy comparison and individualized-treatment-effect analyses were essentially absent. Subtype-specific AI currently predicts response under fixed regimens with moderate-to-good discrimination; the evidence does not yet support changing an individual patient’s systemic treatment. Full article

This study evaluated changes in physicochemical properties, oxidative stability, colour, and texture of commercially available plant-based meat alternatives (PBMAs) and their meat counterparts during consumer-relevant post-opening refrigerated storage. Four product categories (salami, bacon, kabanos sausages, and frankfurters), each represented by a plant-based alternative and its corresponding conventional meat product, were analysed immediately after package opening and after 7 days of refrigerated storage (4 ± 1 °C). Physicochemical parameters (pH, oxidation–reduction potential, water activity), colour, cutting force, thiobarbituric acid reactive substances (TBARS), and ABTS radical-scavenging activity were determined. Frankfurters were additionally evaluated before and after heating. Storage-related quality changes depended primarily on product category rather than product origin. Plant-based salami and bacon showed substantially greater lipid oxidation than meat products, whereas kabanos sausages exhibited comparable oxidative stability. Plant-based kabanos maintained more stable colour and texture during storage, while frankfurters displayed formulation- and heating-dependent responses. Cluster analysis confirmed that quality characteristics were product-specific and did not consistently separate plant-based from meat products. PBMAs cannot be considered a homogeneous food category with respect to post-opening quality. Product-specific evaluation is therefore essential when assessing the dietetic value, storage stability, and practical suitability of PBMAs. Full article

Background/Objectives: Chest compression quality is a key determinant of survival after cardiac arrest. Debriefing is an essential educational tool in cardiopulmonary resuscitation training; however, limited evidence exists regarding the comparative effectiveness of specific debriefing approaches incorporating objective feedback. The objective of this study was to evaluate whether a structured Plus/Delta debriefing approach incorporating graphical performance feedback was associated with greater improvements in chest compression quality parameters than unstructured conversational debriefing in nursing students. Methods: A pre–post quasi-experimental study with a control group was conducted in nursing students. Participants were assigned to experimental or control groups at the academic subgroup level. The experimental group received a structured Plus/Delta debriefing approach incorporating graphical performance feedback, whereas the control group received conversational debriefing. Performance outcomes were assessed before and after the intervention. The primary outcome was the time spent with compression rate, compression depth, and residual leaning simultaneously within their recommended ranges, expressed in seconds. Data were primarily analyzed using linear mixed-effects models to account for repeated measures and clustering. Sensitivity analyses using ANCOVA models adjusted for baseline values and age were performed to assess the robustness of the findings. Results: A total of 112 participants were included (n = 56 per group), with no baseline differences between groups. Improvements were observed in both groups, although they were larger in the experimental group. Mixed-effects models showed significant group × time interactions, with the experimental group showing greater improvements in the primary outcome (β = 91.40 s, 95% CI 82.51–100.28; p < 0.001). Significant group × time interactions were also observed for the secondary compression-quality outcomes. The percentage of total test duration during which all three parameters were simultaneously within the recommended ranges increased in both groups and showed a greater improvement in the experimental group. A complementary analysis using a fixed 120 s observation period showed a greater proportion of time with all three parameters simultaneously within the recommended ranges in the experimental group (50.7% ± 24.8% vs. 42.8% ± 11.0%; p = 0.042). ANCOVA sensitivity analyses adjusted for baseline values and age yielded results consistent with the primary analysis. Conclusions: A structured Plus/Delta debriefing approach incorporating graphical performance feedback was associated with greater improvements in the consistency of guideline-compliant chest compression performance than unstructured conversational debriefing in undergraduate nursing students during simulated CPR training. However, given the quasi-experimental design, group-level allocation, potential practice effects, residual confounding, and the inability to isolate the independent contributions of structured debriefing and graphical feedback, causal inference should be made with caution. Randomized controlled studies are needed to confirm these findings, evaluate their long-term effects, and determine whether similar findings are observed in other populations and settings. Full article

Canine parvovirosis is a fatal disease state traditionally caused by canine parvovirus (CPV), which evolved from feline panleukopenia virus (FPV). While FPV was historically restricted to felids, FPV-like strains were recently reported to be causing clinical disease in dogs. This study aims to characterize an FPV-like strain detected in an eight-week-old unvaccinated female dog in Spain exhibiting hemorrhagic diarrhea and severe leukopenia. After fecal DNA isolation, a 1745-base-pair fragment of the VP2 gene was sequenced for phylogenetic and molecular analysis. Our results revealed that this strain shared high homology with local feline FPV strains; the sequence was clustered near to other FPV strains detected in dogs elsewhere. Molecular analysis identified an isoleucine-to-threonine substitution at position 101 (I101T) of the VP2 protein. This mutation, common in contemporary FPV and CPV variants, has been described as a structural modification that optimizes interaction with the canine transferrin receptor, suggesting a potential role in canine host adaptation. In conclusion, this study represents the first report in Spain of FPV associated with clinical gastroenteritis in dogs. These findings underscore the necessity of continuous genomic surveillance of these highly evolving viruses for monitoring host-switching events. Full article

Introduction: Current clinical guidelines recommend comprehensive management of multiple healthy lifestyles as fundamental to prediabetes care; however, the evidence base regarding the status and clustering of these risk factors remains insufficient. We identified distinct latent classes of healthy lifestyle in individuals with prediabetes and examined their impact on glycemic progression. Methods: This study included 2529 baseline prediabetic participants from the China Health and Retirement Longitudinal Study (CHARLS). We performed latent class analysis (LCA) to stratify participants according to healthy lifestyle factors: physical activity, smoking, alcohol consumption, sleep duration, body mass index (BMI), and blood pressure. Multiple imputation was used for missing covariate data. Multinomial logistic regression was used to examine associations with glycemic transition. Results: Three healthy lifestyle classes were identified: Class 1 (27.6%, the smoking and drinking group), Class 2 (28.2%, the high-BMI group), and Class 3 (44.3%, the healthy lifestyle group). Compared with Class 3, Class 1 (OR, 0.736; 95% CI, 0.554 to 0.978) and Class 2 (OR, 0.628; 95% CI, 0.489 to 0.806) had significantly decreased odds of returning to normoglycemia. Conversely, Class 2 (OR, 2.167; 95% CI, 1.626 to 2.887) had higher odds of progressing to diabetes than Class 3. Conclusions: Prediabetic individuals fall into three distinct healthy lifestyle latent classes. The distinct associations between the three lifestyle latent classes and glycemic progression to diabetes and regression to normoglycemia support the potential value of lifestyle-based risk stratification in prediabetes management, pending validation in prospective cohorts. Full article

Sedimentary heavy metal background levels vary among lake types in heterogeneous floodplain lake systems worldwide. Uniform regional values may therefore bias contamination assessment. We developed a source–process–sink framework linking lake typology to type-specific geochemical baselines for 91 lakes in the Middle and Lower Yangtze River Plain. Ward hierarchical clustering of 24 indicators spanning freshwater, terrestrial and human landscapes plus two spatial metrics identified four groups: shallow river network (Group I, n = 28), small peri-urban lakes (Group II, n = 20), hill–plain transition lakes (Group III, n = 23) and large river-connected lakes (Group IV, n = 20). Pre-industrial layers of four dated representative sediment cores yielded type-specific Cr, Cu, Pb and Zn baselines. Surface heavy metal enrichment and contamination were assessed using the single-factor pollution index (Pi) and geo-accumulation index (Igeo), respectively. Baselines differed markedly with lake groups, with maximum-to-minimum ratios of 2.18, 3.82, 2.39 and 3.64 for Cr, Cu, Pb and Zn, respectively. Greater catchment development triggered the strongest multi-metal enrichment in Group I: P_i values for all four metals exceeded 1 in 92.9% of lakes. Igeo values for Pb exceeded 0 in 85.0% and 78.3% of Group II and III lakes, respectively. Group IV showed the weakest overall enrichment under lower catchment development. Although P_i values for Pb exceeded 1 in 85.0% of its lakes, no lake reached moderate or higher contamination for any metal. Coupling lake typology with type-specific baselines supports enrichment assessment and differentiated management at the lake group scale in China’s Middle and Lower Yangtze River Plain. Full article

Antimicrobial resistance (AMR) is a growing global health threat, and agricultural pesticide exposure has been proposed as an environmental driver of resistance alongside antibiotic consumption. However, long-term, multi-country evidence linking pesticide use to AMR in Escherichia coli remains limited. Panel data analysis was applied to data from 28 European countries between 2013 and 2023 to examine lagged associations between agricultural pesticide use and E. coli resistance to fluoroquinolones, third-generation cephalosporins, aminoglycosides, and aminopenicillins, alongside spatial analysis of resistance distribution. Country-level panel data on E. coli resistance, antibiotic consumption, and pesticide use per cultivated area were compiled for 28 European countries, except for the aminopenicillin resistance model, for which Sweden was excluded owing to insufficient longitudinal data, yielding a 27-country panel for that indicator. Four random-effects generalized least squares (GLS) panel regression models were constructed for each resistance indicator, incorporating same-year and one-, two-, and three-year lagged pesticide use, adjusted for the corresponding antibiotic consumption. Global Moran’s I and Local Indicators of Spatial Association (LISA) analyses assessed spatial autocorrelation and clustering of resistance across countries. Pesticide use was significantly and positively associated only with fluoroquinolone resistance at a three-year lag; no significant associations were found for third-generation cephalosporin, aminoglycoside, or aminopenicillin resistance at any lag. Antibiotic consumption was consistently and positively associated with resistance across all models. All four resistance indicators showed statistically significant positive spatial autocorrelation, with persistent high-resistance clusters in Southeast Europe and low-resistance clusters in Northern Europe. Antibiotic consumption remains the dominant determinant of E. coli resistance, whereas pesticide use shows only a delayed, class-specific association restricted to fluoroquinolone resistance. Full article

Accurate point-cloud segmentation of cotton organs is essential for precise phenotypic characterization. However, reliable instance segmentation of cotton bolls in field-derived point clouds remains challenging because foliage occlusion, contact between adjacent bolls and incomplete reconstruction obscure instance boundaries. Here we present RQ-PointNeXt, an end-to-end framework that directly maps input point clouds to boll instance masks within a unified trainable network. Built on PointNeXt, it incorporates relative-elevation geometric channel attention in the shallow encoder to fuse global channel context with elevation and surface-normal cues. Its Query–mask branch integrates semantic guidance, center-seeded queries and a center-aware mask prior to suppress background responses, localize instances and constrain mask extent. Hungarian matching and multitask optimization establish one-to-one query–instance assignments, whereas query-based decoding produces instance masks without external geometric clustering. We evaluated the framework on 226 field-grown cotton plants containing 720 annotated boll instances reconstructed from UAV multi-view imagery using neural radiance fields. On the held-out test set, overall accuracy, mean class accuracy and mean intersection over union reached 0.8942, 0.8980 and 0.8076, respectively. AP25, AP50 and AP75 were 0.7359, 0.5585 and 0.2777, yielding an mAP25/50/75 of 0.5240. The framework provides instance-level outputs for boll counting and spatial analysis in high-throughput field phenotyping. Full article

Water-distribution-system failure records often contain nonlinear, heterogeneous patterns that are difficult to summarize with conventional statistics alone. This study evaluates how Random Forest and XGBoost reconstruct normalized water-pipe failure-rate patterns in a field-derived Malatya, Türkiye dataset and uses distributional diagnostics, empirical ranking, and multi-method explainability to identify stable model drivers. The analysis includes 1231 records from nine source-defined clusters and four modelling inputs: cluster identifier, normalized pipe length, normalized pipe diameter, and normalized second-failure age. Because the models were fitted to and evaluated on the same records, the reported performance represents apparent/full-data goodness of fit rather than independent predictive validation. XGBoost provided the closer reconstruction (R² = 0.9189 versus 0.8523 for Random Forest; MAE = 0.0230 versus 0.0321). Across permutation importance, SHAP attribution, and variable-removal sensitivity, pipe length and second-failure age emerged as the most robust model-relevant features, whereas native importance was more method-dependent. The framework demonstrates how ensemble models can support transparent retrospective database screening and pattern interpretation while maintaining a clear boundary between historical fit and forecasting. Temporal, spatial, pipe-grouped, or external validation is required before prospective decision use. Full article

Operational redundancies in Nigeria’s airport logistics, including duplicated clearance procedures, fragmented data systems, and repeated physical inspections, generate substantial inefficiencies that hamper service delivery, increase costs, and raise sustainability concerns. Despite growing interest in blockchain technology (BCT) as a logistics innovation, empirical evidence from Sub-Saharan African aviation contexts is largely limited. This study examines how stakeholders perceive BCT as a potential mechanism for reducing procedural, informational, and structural redundancies across Nigerian airport operations. A qualitative research design was employed, drawing on 45 semi-structured interviews with airport managers, airline officials, regulatory bodies, and aviation experts. Data were analyzed using Leximancer, yielding eight thematic clusters across 115 concepts. To augment thematic rigor, three quantitative measures—Normalized Pointwise Mutual Information (NPMI), Jaccard similarity, and degree centrality—were applied to the exported Concept Co-occurrence Matrix (CCMatrix; 6555 pairwise relationships). The blockchain theme exhibited the highest intra-theme cohesion (mean NPMI = 0.615) and the second highest normalized centrality (0.638), suggesting that it functions as a semantically tight, cross-cutting concept in participants’ discourse. The strongest concept pair, inefficiency–reliability (NPMI = 0.989; Jaccard = 0.909), is consistent with participants’ recurring association of service failures with procedurally generated redundancies. Stakeholders perceived BCT as potentially addressing these redundancies through shared ledger architectures, smart contract automation, and consolidated identity management, with anticipated sustainability co-benefits across energy, documentation and processing domains. The study offers an exploratory, contextualized application of the Technology–Organization–Environment (TOE) framework, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Dynamic Capabilities Framework (DCF) to a resource-constrained developing-country aviation context and proposes a stakeholder-informed, phased BCT adoption framework—not yet tested through implementation—with direct policy and managerial implications for the Nigerian airport administration. Full article

Background/Objectives: This study examined whether sharing a class with a peer with physician-diagnosed food allergy (FA) was associated with post-intervention FA comprehension among preschool children. Methods: A four-month ARCS motivation model-based digital nutrition education program was delivered to 163 five-year-old preschoolers from nine public kindergartens. The program provided instruction on knowledge of balanced diets and FA. Preschoolers were classified as inclusive (at least one peer with physician-diagnosed FA in the same class; n = 75) or non-inclusive (n = 88). Dietary balance and FA comprehension were assessed using illustrated worksheets. Ordinal logistic generalized estimating equations (GEE) accounted for clustering within kindergartens. Results: The inclusive environment was associated with higher FA comprehension (Wald χ²(1) = 4.750, p = 0.029; OR = 4.219, 95% CI: 1.156–15.399), whereas no association was observed for dietary balance (Wald χ²(1) = 0.053, p = 0.818; OR = 0.912, 95% CI: 0.414–2.008). Conclusions: The presence of a peer with FA was associated with higher post-intervention FA comprehension. Full article

Malicious packages in open source-software supply chains are a growing security concern, yet machine learning detectors built on registry metadata are difficult to interpret and are typically evaluated under protocols susceptible to data leakage. We construct a dataset of 3330 package versions from NPM and PyPI in which every attribute is reconstructed at its exact publication timestamp, and we partition the data so that all versions of a package, all packages of a maintainer, and all members of a name-similarity family reside within a single fold. The primary analysis is restricted to NPM, where 18 of 456 point-in-time attributes attain an AU-PR of 0.9972 against a positive-class baseline of 0.8755 and an AUC-ROC of 0.9828 on 763 held-out versions. A secondary pooled analysis over both ecosystems is reported: the class priors differ by nearly a factor of nine, a classifier using only the ecosystem of origin attains an AUC-ROC of 0.8036, and the pooled figures should be read with that composition in mind. Confidence intervals are obtained by cluster-bootstrap resampling at the composite-group level and are 3.7-times wider than version-level intervals in AU-PR (0.0201 against 0.0055); at that scale, none of the differences between feature subsets, selection-stage orderings, or metadata families reported here is distinguishable from sampling variation. At an operational prevalence of 0.1%, the positive predictive value is 4.3% and recall at the selected threshold is 0.537, indicating that metadata-based detection functions as a triage filter rather than a definitive verdict. Full article