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Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n = 24) and direct mediator (n = 22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD = 1.49, 95% CI [0.55,2.43], p = 0.002) and skills (SMD = 0.66, 95% CI [0.02,1.31], p = 0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans

Comparison of summative assessments between simulated electronic health records versus traditional paper-based patient cases: A non-inferiority randomized controlled trial.

INTRODUCTION: Electronic health records are fundamental to contemporary pharmacy practice, yet evidence supporting their use in pharmacy education is lacking. This single-center, non-inferiority randomized controlled trial with blinded outcome assessment evaluated whether delivering patient cases via a simulated academic EHR (aEHR) was non-inferior to a traditional paper-based format in student exam performance. METHODS: 53 third-year PharmD students at the University of British Columbia were randomized 1:1 to complete a mock summative examination using either the aEHR or paper-based case delivery, stratified by self-reported EHR comfort level. The primary outcome was mean written exam score (%). Non-inferiority was pre-specified at a margin of 14%. Adjusted linear regression was used for the primary analysis, with a multiple imputation sensitivity analysis. Student perceptions were explored through post-exam focus groups analyzed using inductive thematic analysis. RESULTS: 42 students (21 per group) completed the exam and were included in the primary analysis. Mean scores were 66% (SD 11) in the aEHR group and 68% (SD 10) in the paper group. The adjusted mean difference (paper minus aEHR) was -2.2% (95% CI -9.2% to +4.8%), satisfying non-inferiority but not superiority. Sensitivity analysis (n = 53) yielded consistent results (-2.3%; 95% CI -7.1% to +4.1%). Focus groups revealed initial student anxiety with the aEHR but recognized its alignment with clinical practice. DISCUSSION: These findings support the feasibility of integrating simulated EHRs into summative pharmacy assessments without compromising performance. CONCLUSION: Simulated EHRs are a non-inferior assessment medium compared with paper-based formats and represent a viable step toward technology-driven pharmacy practice environments.

Humans

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans

Immersive virtual reality-assisted anatomy training improves endotracheal intubation performance in simulation: a randomized controlled trial among Chinese non-anesthesiology residents.

INTRODUCTION: This study aimed to compare immersive virtual reality (IVR)-assisted versus conventional anatomy training for teaching endotracheal intubation (ETI) to novice non-anesthesiology residents enrolled in China's Standardized Residency Training program. METHODS: A total of 90 non-anesthesiology residents without prior ETI experience were randomly assigned to either an IVR group receiving IVR-assisted anatomy training (n&#x2009;=&#x2009;45) or a control group receiving conventional anatomy training (n&#x2009;=&#x2009;45). All participants underwent a standardized teaching protocol. The primary endpoint was residents' ETI performance on a simulator, assessed using both the Global Rating Scale (GRS) and a task-specific checklist. The secondary endpoints included changes in written multiple-choice question (MCQ) scores and residents' evaluations of the course. RESULTS: In practical ETI assessments on a manikin, the IVR group achieved significantly higher scores on the task-specific checklist than the control group (90.34&#x2009;&#xb1;&#x2009;2.89 vs. 87.20&#x2009;&#xb1;&#x2009;3.29; p&#x2009;<&#x2009;0.001), whereas GRS scores were comparable between groups. Both groups showed significant post-training improvement in knowledge scores (p&#x2009;<&#x2009;0.001), with the IVR group showing a greater gain in theoretical knowledge (54.0% vs. 36.3%; p&#x2009;<&#x2009;0.001). Participants in the IVR group also expressed a stronger preference for their training method (80.8%) and reported higher levels of motivation, confidence, and enjoyment (all p&#x2009;<&#x2009;0.05). CONCLUSION: IVR-assisted anatomy training enhances the effectiveness of ETI training for novice non-anesthesiology residents, offering an interactive, engaging, and reproducible approach within China's Standardized Residency Training framework.

Humans

Detoxifying biotransformation of chloramphenicol by Exiguobacterium sp. CAP4 and its bioaugmentation of chloramphenicol biodegradation in simulated wastewater.

The extensive use of chloramphenicol (CAP) in livestock leads the accumulation of CAP in livestock manures, threatening environmental and human health. Therefore, eliminating or reducing CAP concentration in manures before its re-utilization and application through microbial remediation is necessary. Exiguobacterium sp. CAP4, isolated from the plastisphere in duck manures, was capable of degrading CAP with the biodegradation efficiency of 97.8 % at initial CAP concentration of 5 mg/L within 4 days. A total of twenty-four biotransformation products were determined, including two novel transformation products, TP166 and TP203, enriched the integrity of CAP biodegradation pathways. Furthermore, the biotransformation process was proposed as a detoxifying process through biotransformation products toxicity evaluation. Notably, Exiguobacterium sp. CAP4 successfully colonized in the cow manures after inoculation, and bioaugmented the biodegradation of CAP in virgin cow manures. This study significantly extended our understanding of the CAP biotransformation fate, and provided a promising bacterial strain for bioremediation of CAP containing wastewater in situ.

Chloramphenicol

Inhibitory mechanism of phloretin on the AgrA LytTR domain-agr operon complex formation and its application in beef.

Staphylococcus aureus (S. aureus) represents a major foodborne pathogen whose enterotoxin production poses significant challenges to food safety due to its high environmental resistance and limited efficacy of conventional sterilization. Since the expression of enterotoxins is predominantly governed by the agr quorum sensing system, targeting this regulatory pathway has become a strategic choice for virulence control. This study elucidated the mechanism by which phloretin, a potential quorum sensing inhibitor, interferes with the agr system to attenuate virulence. To achieve this, the recombinant AgrA LytTR domain was expressed and purified, and its interaction with phloretin was characterized using thermal shift assays (TSA), electrophoretic mobility shift assays (EMSA), and molecular dynamics (MD) simulations. The results showed that phloretin specifically binds to the AgrA LytTR domain, enhancing its thermal stability and disrupting AgrA LytTR-agr operon binding by reducing the free energy of interaction between them, without causing significant structural rearrangement. Mechanistic analysis indicated that phloretin sterically hinders key &#x3b2;-sheet turn residues (HIS169, ASN201, ARG233), thereby impairing DNA recognition, downregulating RNAIII transcription, and inhibiting agr signaling. In cooked beef, phloretin significantly inhibited the secretion of enterotoxins and &#x3b1;-hemolysin, while delaying lipid oxidation and protein degradation, and maintaining the meat texture. These findings suggested that phloretin is a multifunctional substance with anti-virulence, antioxidant, and preservative properties, demonstrating its potential as a natural food preservative.

Phloretin

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans

Evaluation of Physical and Mental Workload and Transfusion Time in Trauma Resuscitation.

BACKGROUND: Trauma resuscitation is time sensitive and complex. Whole blood (WB) and blood components are standard treatments for trauma related hemorrhage, yet their nursing workload and transfusion time have not been well evaluated. PURPOSE: To assess feasibility of a simulation-based crossover trial and obtain preliminary estimates comparing nursing workload and transfusion completion time between WB and blood component administration. METHODS: A randomized crossover pilot study using in situ simulation was conducted with experienced trauma nurses. Time-motion analysis measured transfusion completion time, and the National Aeronautical and Space Administration Task Load Index assessed workload domains. RESULTS: Strong feasibility was demonstrated across recruitment, retention, adherence, and completion. WB was associated with significantly shorter transfusion time, lower overall workload and mental demand, less effort, and better perceived performance. CONCLUSIONS: These findings support the feasibility and justify a fully powered trial. WB may improve resuscitation efficiency and reduce cognitive burden, with potential implications for patient outcomes and nursing workflow.

Humans

Affective reactivity to a remote computer-based Trier Social Stress Test during a planned quit attempt: associations with short-term cigarette smoking lapse risk.

BACKGROUND: The Trier Social Stress Test (TSST) elicits affective responses and has been linked to smoking behavior. However, its remote use during a planned quit attempt-when stress reactivity may influence early lapse-remains understudied. OBJECTIVE: To quantify affective reactivity to a remotely administered TSST on a planned quit date following overnight abstinence and evaluate associations with cigarette use and lapse within 48 h. METHODS: This secondary analysis used data from a randomized controlled trial of adult smokers completing a remotely administered TSST following overnight nicotine abstinence. Urge, anxiety, and stress were assessed using visual analog scales and summarized using area under the curve (AUC) metrics. Smoking outcomes included cigarette count and lapse within 48 h. Associations were estimated using generalized estimating equations. RESULTS: In adjusted models, anxiety reactivity-but not urge or stress-was associated with cigarette count and lapse. Greater anxiety exposure (AUCtot) and change above baseline (AUCab) were associated with higher cigarette count (IRR=1.0004, 95%CI:1.0002-1.001, p=.002; IRR=1.01, 95%CI: 1.002-1.01, p=.002) and increased odds of lapse (OR=1.001, 95%CI: 1.0001-1.002, p=.03; OR=1.02, 95%CI: 1.001-1.03, p=.03). Effect sizes were small. CONCLUSIONS: Anxiety reactivity under nicotine deprivation was associated with increased cigarette use and lapse 48 h post quit attempt, suggesting individual differences in stress-evoked anxiety may serve as a behavioral marker for early lapse. Remote TSST administration appears feasible for eliciting affective responses on a quit date.

Humans

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5&#x200b; concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution

Clinical performance of two lithium disilicate CAD/CAM materials in posterior Class II inlay restorations: A 48-month randomised split-mouth clinical trial.

OBJECTIVES: To compare the clinical performance of Amber Mill (AM) and IPS e.max CAD (EM) lithium disilicate computer-aided design/computer-aided manufacturing (CAD/CAM) materials in posterior Class II inlay restorations and characterise their baseline properties. METHODS: Thirty-four adults received paired AM and EM posterior Class II inlays (68 restorations) in a triple-blind randomised split-mouth trial followed for 48 months. Restorations were evaluated at baseline and annually using revised World Dental Federation (FDI) criteria, with fracture and retention as the primary endpoint. Baseline characterisation included flexural strength, shear bond strength, translucency parameter, and scanning electron microscopy. McNemar, Wilcoxon signed-rank, Friedman, one-way analysis of variance, Tukey post hoc, and inter-rater agreement analyses were used. RESULTS: At 48 months, 18 paired participants were available for primary analysis. Failures occurred in 2 of 18 AM restorations and in 3 of 18 EM restorations, corresponding to success rates of 88.9% and 83.3%, respectively, with no significant between-material difference (McNemar p = 1.000). No catastrophic bulk ceramic fracture was observed. Secondary FDI scores remained mostly within the clinically acceptable range; marginal staining deteriorated over time in both groups (p < .001) without significant between-material differences. Baseline material testing showed significant material- and translucency-dependent differences in flexural strength, shear bond strength, and translucency. CONCLUSIONS: Within the limitations of the 48-month follow-up and the tested Class II inlay indication, AM showed clinical performance comparable to EM. Observed clinical complications were related to retention or marginal/interface behaviour. CLINICAL SIGNIFICANCE: For posterior Class II lithium disilicate CAD/CAM inlays, medium-term complications were mainly retention/interface-related, suggesting adhesive-interface durability may be as important as baseline ceramic strength.

Humans

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products

Efficacy and safety of rib-guided percutaneous thoracic sympathetic radiofrequency thermocoagulation at two different targets for primary palmar hyperhidrosis: a randomized controlled trial.

OBJECTIVES: To compare the efficacy and safety of computed tomography-guided percutaneous thoracic sympathetic radiofrequency thermocoagulation (RFT) targeting the upper versus lateral margin of the fourth rib head for severe primary palmar hyperhidrosis (PPH). METHODS: Patients with severe PPH were randomly divided into Group U (upper margin target) and Group L (lateral margin target). Outcome measures included 1-year recurrence rate, Hyperhidrosis Disease Severity Scale (HDSS), Dermatology Life Quality Index (DLQI), palm skin temperature, finger perfusion index (PI), compensatory hyperhidrosis and patient satisfaction. RESULTS: Both groups (n&#x2009;=&#x2009;55 each, 110 sides) successfully underwent RFT. No preprocedural PI differences were found (p&#x2009;>&#x2009;0.05). Immediately post-RFT, PI in Group L was significantly higher than in Group U (left p&#x2009;=&#x2009;0.025, right p&#x2009;=&#x2009;0.013). No significant differences in HDSS grades were observed between groups before and at 1&#x2009;day, 2&#x2009;weeks, 1&#x2009;month and 3&#x2009;months post-procedure. However, at 6 and 12&#x2009;months, Group L showed significantly lower HDSS grades (left p&#x2009;=&#x2009;0.033 and 0.016; right p&#x2009;=&#x2009;0.039 and 0.025) and lower DLQI scores (p&#x2009;=&#x2009;0.037 and 0.024) than Group U. Patient satisfaction did not differ within 6&#x2009;months, but Group L had significantly higher satisfaction at 12&#x2009;months (p&#x2009;=&#x2009;0.036). CONCLUSIONS: Targeting the lateral margin of the fourth rib head for RFT achieved greater efficacy, better quality of life and higher patient satisfaction compared to the upper margin target.

Humans

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7&#xa0;&#xd7;&#xa0;108&#xa0;CFU/mL and a low detection limit of 1.66&#xa0;CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19%&#xa0;&#x223c;&#xa0;104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer

The radiographic effect of cage subsidence on neuroforamina after anterior cervical discectomy and fusion.

STUDY DESIGN: Retrospective Cohort Study. OBJECTIVE: The objective of this study is to investigate the effect of cage subsidence on neuroforaminal area after anterior cervical discectomy and fusion (ACDF) utilizing computed tomography (CT). SUMMARY OF BACKGROUND DATA: Restoration of disc height via implantation of an interbody device provides an indirect decompression of the cervical neuroforamina. Interbody cage subsidence is a potential postoperative occurrence, but the effect of this on neuroforaminal area has yet to be characterized. METHODS: A retrospective review was conducted of patients who underwent one- to four-levels of ACDF utilizing an interbody device with anterior plating. Cage subsidence, neuroforaminal area, height and width were measured on CT scans preoperatively and at least 6&#xa0;months postoperatively. Levels with a cumulative sum of cranial and caudal subsidence greater than 4&#xa0;mm were classified as severely subsided, while levels with cumulative subsidence less than 4&#xa0;mm were classified as non-severely subsided. RESULTS: A total of 83 patients (151 levels) were included in this retrospective analysis. Average endplate subsidence was 3.2&#xa0;&#xb1;&#xa0;1.9&#xa0;mm. Non-severely subsided levels demonstrated a greater perioperative increase in neuroforaminal area (7.9 vs 2.1&#xa0;mm2, p&#xa0;<&#xa0;0.001), neuroforaminal height (1.1 vs 0.4&#xa0;mm, p&#xa0;<&#xa0;0.001) and neuroforaminal width (0.7 vs 0.1&#xa0;mm, p&#xa0;<&#xa0;0.001) compared to severely subsided levels. Interbody subsidence significantly predicted a decreased change in neuroforaminal height, width and area (p&#xa0;<&#xa0;0.001). Severe subsidence was associated with an increased rate of pseudarthrosis, but similar reoperation rates and recurrent neurologic deficits between the two groups. CONCLUSIONS: Severe subsidence of interbody cages after an ACDF was associated with a decreased perioperative change in neuroforaminal dimensions. This decrease in the size of the neuroforamen may reduce the effect of indirect decompression of the nerve root.

Humans