Search Results (20,832)

Background and Objectives: Routine preoperative chest radiography (CXR) has limited value for perioperative risk prediction when interpreted qualitatively. The Automated Diagnosis of Cardiovascular abnormalities (ADC) model enables automated quantification of cardiomediastinal vascular border (CVB) parameters on CXR. This study evaluated the associations of AI-derived CVB metrics with postoperative major adverse cardiovascular events (MACE) and their incremental predictive value beyond clinical factors. Materials and Methods: This retrospective cohort study included patients who underwent surgery under general anesthesia at a tertiary academic center. The ADC model quantified CVB parameters as raw measurements and age- and sex-adjusted z-scores. The primary outcome was postoperative in-hospital MACE. Associations were assessed using multivariable logistic regression, and discrimination was evaluated using receiver operating characteristic curve analysis. Incremental predictive performance was assessed by adding selected CVB parameters to a clinical reference model. Results: Among 101,531 patients, MACE occurred in 1655 patients (1.6%). All ADC-derived CVB parameters remained independently associated with MACE after multivariable adjustment. For cardiothoracic ratio, the adjusted OR was 1.64 (95% CI, 1.58–1.70; p < 0.001), with an AUC of 0.789. For the composite CVB z-score, the adjusted OR was 1.88 (95% CI, 1.79–1.97; p < 0.001), with an AUC of 0.762. Adding either the CT ratio or composite CVB z-score to the clinical reference model increased the AUC from 0.894 to 0.906 (p < 0.001 for both), with similar improvement in temporal validation. Conclusions: AI-derived CVB parameters from preoperative CXR were independently associated with postoperative in-hospital MACE and provided modest incremental predictive information beyond routinely available clinical factors. Automated CVB analysis may provide additional cardiovascular risk information from a routinely obtained preoperative CXR without additional imaging or patient burden, although further external validation is required to establish its clinical utility. Full article

Two-dimensional profiles extracted from three-dimensional pavement surface textures acquired by close-range photogrammetry are employed as geometrical input for modeling complex interfaces in finite element contact simulations of viscoelastic materials. The methodology allows to establish a robust computational framework for the systematic investigation of possible correlations between the simulated viscoelastic friction coefficients of profiles, the profile roughness descriptors, and the measured surface frictional performance assessed through the static skid resistance tester (SRT) parameter. The study shows that the proposed methodology successfully recovers consistent values of the friction coefficient in the range of velocities typical of the equivalent experimental tests. The methodology is suitable for large-scale statistical analyses and uncertainty quantification, which emerge as key aspects for future research. Full article

The yanglou buildings of Quanzhou are composite architectural heritage shaped by overseas Chinese and local traditions whose front facades provide an observable interface for comparing local and foreign cultural combinations. As existing studies emphasize plan typology and lack quantitative facade comparison, this study takes 40 yanglou buildings built by overseas Chinese between the late Qing dynasty and the 1960s in urban Quanzhou, Jinjiang, Shishi, and Nan’an and constructs a workflow of data survey, acculturation quantification, stage identification, and discriminative contribution analysis. The results show marked heterogeneity: the veranda presents strong object culture expressions, the wall elements are dominated by subject culture, and the hierarchy in the composition is unstable. FCM results show that the facades approximate continuous transitions rather than a single linear sequence. PLSR results show that the subject and object culture proportions in the veranda and wall elements and the object and renewal culture proportions in their compositions contribute more discriminatively to stage identification. This study shows that Quanzhou yanglou buildings are not direct transplantations of Western styles but composite heritage grounded in subject culture, absorbing object culture features and generating only limited renewal culture, providing a quantitative framework for stage identification, value interpretation, and differentiated conservation. Full article

Background/Objectives: Quantification of human DNA before short tandem repeat (STR) typing is an essential step in forensic DNA analysis, and the DNA quantity recovered from trace material can itself be weighed in court, as illustrated by a recently concluded Japanese criminal case in which the amount of DNA on a complainant’s skin was a central point of contention. For one specific instrument–assay combination, we characterized within- and between-operator variability of real-time PCR quantification and quantified how much an estimate changes when the calibration curve used to convert it originates from a different run. Methods: Three examiners quantified three oral-swab DNA extracts from three donors (low, medium and high concentration) on a SmartCycler II with a human genomic DNA quantification kit (Ver. 2), each reaction in duplicate, over four consecutive days and, separately, in five consecutive runs within one day (27 runs, 162 unknown measurements). Each unknown quantification cycle (Cq) was then re-converted, with its Cq held constant, using the calibration curve of every other run in the same experiment block (2052 paired comparisons), stratified by whether the operator and the day changed. Results: All 27 curves had R-squared ≥ 0.995, yet slopes ranged from −3.13 to −3.90 (amplification efficiency 80.4–108.8%), so a single Cq of 16.80 mapped to 0.51–1.22 ng/μL, a 2.4-fold range, according only to which run supplied the curve. Coefficients of variation per sample and operator were 7.6–19.5% between days and 7.3–18.7% within a day; repeatability CV was 4.9–13.7% and intermediate-precision CV 10.9–17.6%. Applying a non-contemporaneous curve changed the estimate by a median of 14.7% (interquartile range 7.0–27.6) when the curve came from another run by the same operator on the same day, 13.6% (6.3–20.7) when it came from a run by the same operator on a different day, and 17.7–18.6% (8.5–30.1) for cross-operator pairings, with single changes up to 102%. Changes occurred in both directions within every stratum; mean signed changes of 1.9–3.3% are reported descriptively. Conclusions: In this system, applying a calibration curve generated in a different run changed the reported concentration by a median of approximately 14–19%. Within the same operator, separation of the runs by days rather than minutes was not associated with a larger discrepancy. Because the three examiners measured sequentially and never on the same day, operator identity was fully confounded with calendar period, so operator-specific effects could not be estimated independently and the larger cross-operator values are reported descriptively only. R-squared alone does not reveal this cross-run variability. These findings support the generation and preservation of run-specific calibration data whenever quantitative DNA results may carry evidential weight. Full article

Snake venomics and antivenomics approaches have increasingly relied on mass spectrometry to characterize the venom components and to evaluate the antivenom neutralization potential. However, accurate quantification remains essential for distinguishing biologically relevant toxins from snake venom and for translating proteomic data into meaningful clinical interpretations. This review article examines the role of quantitative analysis in snake venomics and antivenomics, with special emphasis on massspectrometry-based strategies used to estimate toxin abundance. This review summarizes the main applications and limitations of mass spectrometry in venom research covering bottom-up and top-down proteomics, venom databases, toxin quantification, and how MS-based quantification determines the efficacy of antivenom through antivenomics approaches. This review also explains why abundance alone cannot determine the biological or therapeutic importance of a toxin. The article discusses label and label-free based quantification, absolute quantification approaches, top-down venomics, mass spectrometry-based antivenomics, and the integration of proteo-transcriptomic workflows in snake venom research. These approaches can be affected by several factors including the problem of measuring toxin isoforms that share similar protein structures and toxin components that are important for antivenom recognition within complex venom samples. A conceptual overview is provided of how inaccurate quantification can influence venom proteome comparisons among similar and different species, as well as the interpretation of antivenom binding and neutralization potential. Reliable mass-spectrometry-based quantification will be critical for linking the venom composition with toxicity, antivenom performance evaluations in preclinical settings, and evidence-based strategies for snake venom research and snakebite management. Full article

Campylobacter spp., particularly Campylobacter jejuni and Campylobacter coli, remain the leading bacterial causes of foodborne gastroenteritis worldwide, with poultry serving as the dominant reservoir and additional transmission routes involving livestock, raw milk, water, fresh produce, and food-processing environments. Accurate detection and quantification are essential for risk assessment, process hygiene monitoring, outbreak investigation, and One Health surveillance; however, reliable measurement is complicated by the organism’s fastidious microaerophilic growth requirements, stress sensitivity, low or uneven distribution in complex matrices, and ability to enter viable but non-culturable (VBNC) states. This review examines current and emerging approaches for Campylobacter detection and quantification across the food production continuum, including pre-harvest, harvest and processing, post-harvest, and retail, as well as consumer-level settings. Conventional culture, colony-count enumeration, most probable number (MPN) methods, and immunological assays are compared with molecular approaches, including polymerase chain reaction (PCR), qPCR, digital PCR, viability PCR, whole-genome sequencing, metagenomics, biosensors, microfluidics, and artificial intelligence-assisted surveillance. Regulatory and standardization frameworks, including the International Organization of Standards (ISO), Food and Drug Administration (FDA), United States Department of Agriculture-Food Safety and Inspection Service (USDA-FSIS), and European Union (EU) process hygiene criteria are discussed, together with One Health surveillance systems. Overall, future Campylobacter monitoring will require integrated, matrix-specific strategies that combine standardized culture-based enumeration, viability-informed molecular quantification, genomic source attribution, and harmonized metadata to support more rapid, accurate, and risk-based food-safety decision-making. Full article

Deep learning has become the default toolkit for mortality forecasting, yet essentially all existing models are purely data-driven: they fit well in-sample but can produce biologically implausible surfaces and extrapolate poorly to the oldest ages, and rarely deliver calibrated uncertainty. Classical parametric laws (Gompertz–Makeham, Kannisto) have the opposite profile—smooth and well-behaved under extrapolation, but rigid. We introduce Physics-Informed Neural Mortality (PINM), a continuous neural model of the log force of mortality whose training objective couples the Poisson likelihood of observed deaths with (i) a physics residual that softly imposes a mortality law in the age dimension, including a Kannisto-type deceleration penalty for old-age closure, and (ii) an ODE-structured, learned improvement field in the time dimension (a state-independent special case of a neural ODE), so that forecasts are produced by integrating the learned dynamics forward rather than by a separate time-series step. We give a compact mathematical foundation—existence of minimisers, well-posed forecast dynamics, constrained approximation, and consistency of the fitted rates—and evaluate the method candidly on controlled ground-truth simulation and on real data for six European populations (Eurostat). On the simulation, PINM leads at the older, annuity-relevant ages (RMSE 0.131 versus 0.143 for Lee–Carter) and retains substantially higher interval coverage under the specific misspecification scenario studied (44.8% versus 12.2% for Lee–Carter), though both remain materially below the nominal 95% level; in a held-out closure experiment on the same simulated surface, continuous neural surfaces extrapolate mortality from age 90 to age 110 with RMSE 0.196 against 0.301 for Lee–Carter completed with a Kannisto tail. On the six-population backtest (2010–2019), classical Lee–Carter and Renshaw–Haberman remain the most accurate forecasters overall (mean RMSE 0.123 versus 0.170 for PINM), with the neural surfaces competitive at older ages and on the longest series; PINM matches or improves on an unconstrained network of equal capacity in all six cases (outright in five). PINM’s contribution is a novel, unified differential-equation framework for fitting, structured forecasting, structured old-age closure, and empirically evaluated uncertainty quantification in one model, together with an explicit discussion of the method’s robustness and tail-estimation limitations. Full article

A targeted method for the determination of 10 active components in 4 characteristic honeys from Yunnan, China, was established using ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). The lower calibration limit differed among analytes, ranging from 0.01 to 0.5 μg·mL⁻¹. The detection limits and quantification limits ranged from 0.03–1.5 mg·kg⁻¹ and 0.1–5.0 mg·kg⁻¹, respectively. The precision, stability, and repeatability were satisfactory. After partial least squares discriminant analysis (PLS-DA) discriminant analysis was applied to four honey varieties, Brassica rapa var. oleifera DC. honey (BRH) and Hevea brasiliensis honey (HBH) clustered into one group but were clearly distinguishable from Amomum tsao-ko Crevost et Lemarié honey (ATH) and Apis cerana honey (ACH). BRH and HBH could be further differentiated using the OPLS-DA model. Gentisic acid, trigonelline, and naringenin were differentially abundant active components among the four honeys, whereas trigonelline and naringenin exhibited differential abundance between BRH and HBH. Volcano plot analysis revealed that homoeriodictyol, quercetin, and naringenin may serve as key compounds for distinguishing ACH and ATH, BRH, and HBH honeys; that trigonelline may be a key compound for differentiating ATH and HBH, ACH, and BRH honeys; and that quercetin and apigenin may be key compounds for distinguishing HBH and ATH, ACH, and BRH honeys. Full article

Short-term typhoon wind-field prediction across multiple lead times is challenging because forecast error grows with lead time and inappropriate sample construction may leak future storm-position information. We adopt STL-Net, a lightweight spatiotemporal framework integrating convolutional encoding, long short-term memory (LSTM) temporal modeling, squeeze-and-excitation recalibration, and multi-branch fusion for 10 m wind-field prediction at 6, 12, and 24 h lead times. A fixed-t₀ protocol anchors the target patch at the initialization-time storm position, avoiding future best-track leakage. Trained in 2020–2021, validated in 2022, and tested in 2023, STL-Net achieves a root mean square error (RMSE) of 3.11, 4.06, and 5.31 m·s⁻¹ at 6, 12, and 24 h. Among deep-learning baselines, U-Net achieves the lowest errors at 6 and 12 h and convolutional neural network (CNN) the lowest 24 h RMSE and mean absolute error (MAE), while STL-Net remains competitive with 1.673 million parameters and low inference latency. Ten-seed ablation identifies multi-resolution fusion as the most consistently beneficial component. An out-of-year evaluation with 2020 as the test year reproduces the error-growth pattern, supporting cross-year robustness. Models are deterministic without uncertainty quantification; error growth is deterministic, not calibrated uncertainty. Diagnostic interpretation links the error structure to the multi-scale organization of typhoon winds, storm translation, and wind-field asymmetry. Results constitute an offline proof-of-concept, not an operational system. Full article

Asymmetric heat transfer is an intrinsic and defining feature of phase change material (PCM) systems, arising from the nonlinear coupling between conduction, buoyancy-driven convection, interfacial motion, and geometric or operational non-uniformities. This review synthesizes the physical, numerical, and application-specific mechanisms through which asymmetry emerges and shapes the thermal behavior of PCM-based energy systems. We first examine the fundamental origins of asymmetry, highlighting how natural convection, material heterogeneity, and spatially uneven boundary conditions distort temperature fields and melt front evolution even in nominally symmetric enclosures. We then provide a comprehensive assessment of state-of-the-art modeling approaches—including full-domain CFD, advanced interface tracking methods, stability and bifurcation analysis, and reduced-order modeling—emphasizing their capacity to resolve asymmetric flow structures and capture the complex dynamics governing phase transition. Experimental observations from optical, infrared, and flow visualization techniques further validate the prevalence of asymmetric patterns and underscore the need for high-resolution multi-field datasets. Building upon these foundations, the review analyzes the implications of asymmetry across key energy applications such as thermal energy storage, building envelopes, solar receivers, electronics cooling, transportation systems, and industrial heat exchangers. We also provide a literature review on the importance of using multicriteria evaluation as a key tool in the design of multidimensional symmetric and asymmetric PCM systems. In this context, we address and analyze the performance criteria, evaluation metrics, case studies, and optimization strategies to be considered in the design of these systems. Finally, we identify critical research gaps—including multiphysics coupling, uncertainty quantification, CFD–machine learning integration, and the exploration of emerging asymmetric applications—and outline pathways toward next-generation PCM-based technologies that not only accommodate asymmetry but strategically exploit it for enhanced thermal performance. Full article

The use of herbicides to control weeds in crops, especially in soybeans, corn, and sugarcane, is a common practice, but it poses a risk to nearby sensitive crops, since herbicide drift is common and has caused phytotoxicity, resulting in reduced crop yield. Thus, the determination of acid herbicides in plant leaves is important but challenging due to the complexity of the matrix and compound characteristics. Considering this, an analytical method was developed and validated for the determination of 22 acid herbicides in plant leaves, using a modified QuEChERS procedure and ultra-high performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) with a run time of 10 min. The optimized method involved the extraction of the hydrated sample with acetonitrile containing formic acid, partitioning with salts, and cleanup with graphitized carbon black. Validation presented recoveries from 71 to 118%, with RSD ≤ 19% and method limit of quantification of 5 µg kg⁻¹ for most of the herbicides indicating satisfactory performance. The proposed method was applied to 50 plant leaf samples, 36 of which contained residues of at least one of the evaluated herbicides. The developed method is rapid and efficient and can be applied in routine analyses of acid herbicides in plant leaves. Full article

Background/Objective: Co-exposure to heavy metals from environment and a high-fat diet (HFD) represents a growing health concern, contributing to various diseases, but their combined effects on brain health remain less explored. This study aimed to evaluate the neuroprotective potential of Shogaol-enriched ginger extract (SEGE) against heavy metals- and HFD-induced neuropathology. Plant-derived isolated pure compounds can be costly and less accessible; SEGE may offer a translatable, multi-target dietary intervention. Methods: Male Balb/c mice (8–11 weeks old) were exposed to a metal mixture of arsenic (As), lead (Pb), and aluminum (Al; 25 mg/kg/day each) in drinking water and 40% HFD in feed for 60 days. SEGE was administered via feed at two different doses (2 mg/kg/day and 12 mg/kg/day). Assessments including gene expression analyses (using Quantitative Reverse Transcription Real Time Polymerase Chain Reaction (qRT-PCR)) of synaptic and cholinergic markers, spectrophotometric measurement of acetylcholine (ACh) levels, neuronal counting in the cortex and hippocampus via histology, and Gas chromatography mass spectrometric (GC/MS) analysis for brain metabolic quantification were performed. Results: The combined toxic exposure significantly downregulated the expression of synaptic plasticity markers (Synaptophysin, Polysynaptic Density Protein 95 (PSD95), and Calcium/Calmodulin-Dependent Protein Kinase-IV (CAMK-4)) and α and β Nicotinic Acetylcholine Receptors (α7nAChR, α4nAChR, and β2nAChR) in the hippocampus and cortex. ACh levels were also reduced along with significant neuronal loss in the cortical layers and the hippocampal regions. Met + HFD disrupted the brain’s metabolic profile. SEGE treatment significantly restored these markers and attenuated the cholinergic deficits. SEGE treatment, specifically at a higher dose, demonstrated a rescuing effect on the brain’s metabolic profile when compared to the metals and HFD. SEGE further preserved the neuronal count at a higher dose (12 mg/kg/day), exhibiting superior protective effects. Conclusions: These findings suggest that SEGE mitigates neurotoxicity induced by the interaction of toxic metals and dietary factors, likely by preserving synaptic plasticity, metabolic profile, and cholinergic functions, particularly at a higher dose. Full article

Extracellular vesicles (EVs) are membrane-enclosed nanoparticles involved in intercellular communication and numerous physiological and pathological processes. Their molecular cargo reflects the state of the cell of origin, making EVs promising sources of biomarkers and potential therapeutic agents. However, efficient and selective isolation of EVs from complex biological fluids remains challenging. Immunoaffinity-based approaches offer high selectivity through the recognition of EV-associated surface markers by specific affinity ligands. In this study, silica-coated magnetite nanoparticles were functionalized with a mixture of five VHH–eGFP constructs and evaluated as a solid phase for immunoaffinity isolation of EVs from human plasma. Surface modification of the nanoparticles was confirmed by FTIR spectroscopy, while protein-binding studies showed a maximum binding capacity (Qmax) of 118.2 mg/g. The developed material was then applied for EV isolation from plasma, and the resulting EV-enriched preparations were characterized by protein and lipid quantification, nanoparticle tracking analysis, flow cytometry, and atomic force microscopy. NTA revealed a median particle diameter of 124 nm and a particle concentration of 6.9 × 10⁹ particles/mL. The presence of the EV-associated markers CD9, CD63, and CD81, together with their reduced signal following Triton X-100 treatment, supported the vesicular nature of the isolated particles. The affinity material could be reused over five consecutive isolation cycles, and EVs could be detected from plasma volumes as low as 25 µL. These results demonstrate the potential of VHH-functionalized magnetic nanoparticles as a reusable and adaptable platform for immunoaffinity-based EV isolation from human plasma. Full article

Earthquake and post-earthquake fire cascading hazards impose disproportionate risks on dense informal historic districts across South Asia. Conventional multi-hazard evaluation methodologies fail to accommodate non-engineered mixed-use constructions and ultra-compact unplanned urban morphologies prevalent in regional megacities, owing to two inherent limitations: decoupled quantification of seismic deterioration and structural fire performance, and fire spread parameters calibrated for regular Western urban grids rather than narrow, congested historic streetscapes. This work develops a localized probabilistic risk framework integrating seismic damage-induced fire degradation, and advances two dedicated methodological improvements to address the identified research voids. Seismic damage-dependent fire resistance reduction coefficients are embedded within cellular automaton iterations to dynamically modulate effective burnout durations of seismically compromised buildings; urban morphological correction factors are further incorporated to refine inter-building fire propagation probabilities tailored to compact informal settlements. Field inventories, nonlinear pushover finite element analysis, morphology-modified fire simulation and large-sample Monte Carlo stochastic sampling are integrated to execute full-process quantitative risk assessment, with model calibration and validation conducted against field survey data and the 2019 Chawkbazar chemical fire archive in Dhaka. The empirically validated framework (note: the validation primarily applies to the fire spread submodel; the coupled seismic–fire mechanism is supported by numerical simulation rather than empirical observation) delivers transferable quantitative benchmarks for multi-hazard governance of analogous South Asian historic agglomerations, and provides actionable technical evidence to inform targeted urban renewal schemes and the formulation of earthquake-fire coupled design specifications within Bangladesh’s national building regulatory codes. Full article

Polyether ether ketones (PEEK) and polydimethylsiloxanes (PDMS) are widely used biomaterials; however, their susceptibility to microbial colonization remains a limitation in clinical applications. This study comparatively evaluated biofilm formation by Candida albicans and Nakaseomyces glabratus on PEEK and PDMS and characterized the effect of TiO₂/ZrO₂-based coatings applied to PEEK. Biofilm development was analyzed during adhesion, maturation, and dispersion using biomass quantification, metabolic activity assays, and scanning electron microscopy. Surface modifications were also evaluated. Both species formed a biofilm on the biomaterials, with a significantly higher biomass on the control, followed by PEEK and PDMS. TiO₂/ZrO₂-based coating affected biofilm maturation, with coating B as the one producing the greatest reduction in biofilm development. These findings suggest that surface modifications, such as hydrophobicity, using TiO₂/ZrO₂ coatings may influence fungal biofilm reduction and improve the antimicrobial performance of biomedical materials. Full article