SEARCH · Search PubMed

Results for “Quantum computing”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

25 recordsLinked to original sources

PubMed2026

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↗

PubMed2026

The hidden threat from food-derived carbon dots: Formation, biodistribution, and potential health risks.

Food-derived carbon dots (CDs) are a new class of carbon-based nanoparticles generated during the thermal processing of food matrices. These nanomaterials have been extensively studied for their unique fluorescence, good biocompatibility, and tunable surface chemistry in food detection, intelligent packaging, and biomedical applications. However, their nanoscale size and high surface activity have raised safety concerns regarding biological interactions, in vivo biodistribution, and potential long-term health hazards. Although CDs have traditionally been regarded as low-toxicity materials due to their favorable biocompatibility, the potential hidden risks of CDs have not received sufficient attention. CDs exhibit dose-dependent toxicity, not only accumulating in various tissues and organs but also potentially inducing oxidative stress and interfering with cellular metabolic functions. Therefore, this review summarizes the advances in sources, synthetic strategies, and core properties of CDs, with a special focus on in vivo biological interactions, fates, and potential safety challenges. In addition, it is proposed that the standardized detection and risk assessment system should be established to further explore the long-term health effects of CDs under real dietary exposure, thereby ensuring their safety and sustainable application.

Carbon Quantum Dots↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2026

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↗

PubMed2026

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 = 55 each, 110 sides) successfully underwent RFT. No preprocedural PI differences were found (p > 0.05). Immediately post-RFT, PI in Group L was significantly higher than in Group U (left p = 0.025, right p = 0.013). No significant differences in HDSS grades were observed between groups before and at 1 day, 2 weeks, 1 month and 3 months post-procedure. However, at 6 and 12 months, Group L showed significantly lower HDSS grades (left p = 0.033 and 0.016; right p = 0.039 and 0.025) and lower DLQI scores (p = 0.037 and 0.024) than Group U. Patient satisfaction did not differ within 6 months, but Group L had significantly higher satisfaction at 12 months (p = 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↗

PubMed2026

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↗

PubMed2026

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 × 108 CFU/mL and a low detection limit of 1.66 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% ∼ 104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗

PubMed2026

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 months postoperatively. Levels with a cumulative sum of cranial and caudal subsidence greater than 4 mm were classified as severely subsided, while levels with cumulative subsidence less than 4 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 ± 1.9 mm. Non-severely subsided levels demonstrated a greater perioperative increase in neuroforaminal area (7.9 vs 2.1 mm2, p < 0.001), neuroforaminal height (1.1 vs 0.4 mm, p < 0.001) and neuroforaminal width (0.7 vs 0.1 mm, p < 0.001) compared to severely subsided levels. Interbody subsidence significantly predicted a decreased change in neuroforaminal height, width and area (p < 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↗

PubMed2026

The prevalence of isthmic and degenerative lumbar spondylolisthesis: an analysis of 1376 patients.

INTRODUCTION: Typically, spondylolisthesis is an asymptomatic spinal condition that is often captured accidently in radiographic studies. The limited studies reviewing incidence primarily used lateral radiographs, which lack the granularity of advanced imaging. In response, computed tomography (CT) has been recommended to enhance the accuracy of spondylolisthesis diagnosis (degenerative versus isthmic). In the present study, we sought to determine the prevalence of isthmic and degenerative spondylolisthesis using CT imaging. METHODS: We conducted a retrospective study of 1,680 patients who underwent abdominal/pelvic CT scans at a single level-1 trauma center from January 1, 2017, to January 31, 2017. RESULTS: A total of 1,680 CT scans were screened, of which 1,376 patient scans met the inclusion criteria of having undergone complete imaging (axial and sagittal images). The average age of the study population was 57.1 (standard deviation, 18.7) years; 51.1% were female, and 83.2% were Caucasian. The prevalence of isthmic spondylolisthesis was 5.4% (n = 71): 3.6% of cases were at the L5-S1 level, 2.1% were at the L4-L5 level, and 0.6% were at the L3-L4 level. The female-to-male ratio was 0.73:1. The prevalence of degenerative spondylolisthesis was higher at 21.5% (n = 285), and the level most commonly affected was L4-L5 (11.8%), followed by L5-S1 (9.7%) and L3-L4 (4.6%). The female-to-male ratio was 1.3:1. There was a higher prevalence of degenerative spondylolisthesis in women at L4-L5 (51.2% vs. 35.6%; P < 0.001). CONCLUSION: We found that degenerative spondylolisthesis was more prevalent, occurring primarily in older women, between the L4-L5 vertebrae. On the other hand, isthmic spondylolisthesis more commonly occurred within male patients between the L5-S1 vertebrae. Our study is one of the first to recognize a high rate of degenerative spondylolisthesis within the L5-S1 region, highlighting the utility of CT scan to visualize spinal translation. LEVEL OF EVIDENCE: IV.

Humans↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Post-intervention effectiveness of a computerized personalized cognitive stimulation program adapted according to cognitive reserve in older adults without cognitive impairment in Primary Care: A randomized clinical trial.

BACKGROUND: Cognitive reserve may influence responsiveness to cognitive interventions, yet it is rarely used to tailor computerized stimulation. OBJECTIVE: To evaluate the effectiveness of a computerized cognitive stimulation program personalized according to cognitive reserve on cognition, reserve-related activities, and digital competence in community-dwelling older adults without cognitive impairment in Primary Care. METHODS: In this randomized clinical trial, 102 adults aged ≥65 years with normal cognitive performance were recruited from three primary care centers in Zaragoza, Spain, and stratified by cognitive reserve level before random allocation to intervention or control. The intervention comprised digital literacy sessions followed by 8 weeks of home-based computerized cognitive stimulation tailored to participants' cognitive reserve profiles and life history. Controls received a single group-based health education session focused on maintaining everyday cognitive activity. Outcomes were assessed at baseline and post-intervention using global cognition (MEC-35), the Cognitive Reserve Questionnaire, the Mobile Device Proficiency Questionnaire-16, and domain-specific neuropsychological tests. A total of 100 participants completed the final evaluation and were included in complete-case analyses. RESULTS: Compared with controls, the intervention group showed greater adjusted post-intervention improvements in global cognition (MEC-35 between-group difference: 1.8 points) and several cognitive measures, including temporal orientation, calculation, attention, praxis, verbal fluency, processing speed, executive functions, and verbal learning. CRQ scores and digital competence also improved, with small-to-large effect sizes. CONCLUSIONS: A computerized cognitive stimulation program adapted according to cognitive reserve appears feasible in Primary Care and may improve cognition, engagement in reserve-related activities, and digital competence in older adults without cognitive impairment.

Humans↗

PubMed2026

Enhancement of secondary organic aerosol formation from isoprene photooxidation by ammonia.

Ammonia (NH3) can participate in atmospheric secondary organic aerosol (SOA) formation by reacting with organic acids and carbonyl compounds in particle phase, but its influence on the gas phase chemistry remains unclear. This study performed a series of smog chamber experiments to investigate the influence of NH3 on the formation of SOA from isoprene photooxidation by OH radicals. Both gas and particle phase products were measured with a series of state-of- art instruments including a nitrate ion chemical ionization mass spectrometer (nitrate-CIMS) and high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS). Our results showed that in the presence of NH3 SOA in the chamber significantly increased, along with an enhanced oxidation of isoprene. CIMS analysis further showed that NH3 in the chamber homogeneously reacts not only with gas-phase organic acids but also with gaseous low volatility oxygenated organic molecules (OOMs) to generate extremely low volatility and ultralow volatility NH3-OOMs clusters. Quantum chemical calculation showed that NH3 can spontaneously interact with OOMs to form NH3-OOMs clusters by forming hydrogen bonds with RCOOH, R-OOH, and R-OH. These clusters can promote new particles formation and particle growth through nucleation and condensation, directly enhancing the isoprene SOA production with a contribution of 78% to the enhanced SOA. Moreover, the formation of NH3-OOMs clusters also results in more isoprene consumed by OH radicals, indirectly increasing the SOA production with a contribution of 22 % to the enhanced SOA. Our work for the first time clarified a synergetic effect of NH3 on isoprene SOA formation, which should be accounted for by models.

Aerosols↗

PubMed2026

A STORM-based protocol for nanoscale imaging and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber.

Stochastic Optical Reconstruction Microscopy (STORM) enables nanoscale mapping of molecular components beyond the diffraction limit; however, its reproducible implementation in hydrophobic polymer matrices remains challenging because fluorescence-labeling specificity, fluorophore photoswitching, three-dimensional localization, chromatic registration, and quantitative image analysis must be carefully controlled. This protocol presents a standardized experimental workflow for dual-color labeling, astigmatism-based three-dimensional STORM acquisition, and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber (NR). The workflow covers sample pretreatment, Cy5 NHS ester labeling of protein-associated primary amines, DiI labeling of phospholipid-rich domains, STORM imaging-buffer preparation, three-dimensional single-molecule localization, dual-channel registration, generation of standardized xy projections, aggregate-size analysis, and projected lateral spatial correlation assessment. Reproducibility is supported by defined acquisition and localization criteria, three independent sample preparations with at least five fields of view analyzed per condition, and unlabeled, single-color, dye-only matrix, and processing-associated Cy5 controls. Mean lateral localization precisions of 11.8 ± 2.3 nm for Cy5 and 13.5 ± 2.9 nm for DiI were obtained, while two-dimensional Fourier ring correlation analysis of the xy projections yielded effective lateral image resolutions of approximately 25 and 28 nm, respectively. Image-based particle segmentation and localization-coordinate-based density-based spatial clustering of applications with noise (DBSCAN) were applied to standardized xy projections as complementary quantitative approaches. Application of the protocol to untreated, centrifuged, and protease-treated NR samples demonstrated treatment-associated changes in the detected abundance and projected size distributions of protein- and phospholipid-associated aggregates, together with a non-monotonic change in their projected lateral spatial correlation. These observations describe alterations in nanoscale organization but do not, by themselves, establish stable protein-phospholipid complex formation. Unlike previous studies that primarily demonstrated the feasibility of STORM imaging in rubber materials, the principal contribution of this work is an end-to-end, step-by-step protocol incorporating defined controls, three-dimensional localization, image-quality metrics, chromatic-registration procedures, and complementary quantitative-analysis pipelines for non-expert users. The workflow may be adaptable to other hydrophobic polymers and soft-material systems after appropriate optimization and validation.

Rubber↗