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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↗

PubMed2026

Physician-Modified Fenestrated Stent-Grafts Planned Using Three-Dimensional Techniques for Complex Aortic Pathology: A Systematic Review and Meta-Analysis.

BACKGROUND: Complex aortic pathology involving the visceral arteries remains a significant therapeutic challenge. Open repair is associated with considerable perioperative risk, particularly in patients with multiple comorbidities, while standard endovascular aneurysm repair (EVAR) is often not feasible because of inadequate proximal sealing zones. Fenestrated and branched endovascular repair (F/BEVAR) represents an established treatment strategy; however, the use of custom-made devices is limited by manufacturing time and availability. Physician-modified stent grafts (PMSGs) have therefore emerged as a pragmatic alternative. Three-dimensional planning techniques have been increasingly used to facilitate accurate graft modification. The aim of this systematic review and meta-analysis was to evaluate the effectiveness and safety of PMSG procedures planned with three-dimensional techniques. Technical success, target vessel patency, early mortality, endoleak occurrence, and reintervention rates were analyzed. METHODS: A systematic search was conducted in the PubMed/MEDLINE and Embase databases. Studies describing the use of physician-modified fenestrated stent grafts planned with three-dimensional tools were included. Meta-analyses were performed using a random-effects model with restricted maximum likelihood estimation. A logit transformation was used for the analysis of proportions. RESULTS: The analysis included five studies involving 172 patients. The estimated weighted mean follow-up duration was 14.9 months. The overall technical success rate was 92.9% (95% confidence interval [CI]: 84.5-96.9%), with low-to-moderate heterogeneity. Target vessel patency was 96.9% (95% CI: 93.6-98.5%). Early mortality was 5.5% (95% CI: 2.1-13.3%). The incidence of endoleaks was 13.3% (95% CI: 5.8-27.4%), with significant heterogeneity among studies. Reinterventions were reported in 6.6% of patients (95% CI: 2.3-17.5%). CONCLUSION: The results indicate that PMSG procedures planned with three-dimensional techniques are associated with a high rate of technical success and preserved patency of target vessels in patients with complex aortic pathology. The observed variability in endoleak and reintervention rates likely reflects differences in anatomical complexity and patient selection among studies. Further prospective studies are needed to confirm long-term outcomes.

Humans↗

PubMed2026

Three-dimensional porous nano-hydroxyapatite@gelatin composite as efficient adsorbent for uranyl ion removal from low-level radioactive wastewater.

The contamination of water resources by uranyl (UO22+) ions poses significant environmental and health risks, requiring the development of efficient and sustainable remediation strategies. Adsorption-based techniques have emerged as promising approaches in the field of UO22+ removal, but the design of cost-effective, high-capacity, and environmentally friendly adsorbents remains challenging. In this study, a three-dimensional porous nano-hydroxyapatite@gelatin (nHAP@Ge) composite was synthesized through glutaraldehyde cross-linking, combining the structural stability of Ge with the high uranium affinity of nHAP. The optimized nHAP@Ge, with a nHAP:Ge mass ratio of 1:0.5, exhibited exceptional UO22+ removal efficiency (97 %), along with high adsorption capacity (364.03 mg/g). Systematic characterizations using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS) methods revealed that the porous structure and surface functional groups (-OH, Ca2+, and PO43-) of the material synergistically contributed to binding UO22+ species. Furthermore, the incorporation of nHAP into the Ge framework resulted in enhanced thermal stability while significantly improving the UO22+ adsorption performance. This work presents a scalable, eco-friendly, and recyclable strategy for the effective treatment of uranium-contaminated water, with potential applications in nuclear wastewater treatment and environmental remediation.

Adsorption↗

PubMed2026

Three-dimensional source apportionment and quantitative characterization of horizontal and vertical transport fluxes of O3 and its precursors in the Beijing-Tianjin-Hebei region, China.

Persistent surface ozone (O3) pollution in the Beijing-Tianjin-Hebei (BTH) region is driven by coupled precursor emissions and multi-scale transport, yet its altitude-dependent transport and source contributions remain insufficiently quantified. Here we integrated the Weather Research and Forecasting and the Comprehensive Air Quality Model with Extensions with the Ozone Source Apportionment Technology and a quantitative transport-flux framework to characterize three-dimensional source apportionment and horizontal/vertical fluxes of O3, Volatile Organic Compounds‌ (VOCs), and Nitrogen Oxides (NOx) across dynamic meteorological scenarios. Simulations showed that VOCs and NOx were dominated by local emissions near the surface (73.61 %-82.18 %), whereas surface O3 was primarily controlled by regional transport, with local contributions of only 11.01 %-13.75 %. Notably, the transport dominance further strengthened with altitude, exceeding 93 % at 1.8 km. Industrial and transportation emissions together contributed more than 75 % of precursor emissions and account for approximately 80 % of O3 formation, while favorable/unfavorable meteorological years modulated long-range transport efficiency and the vertical distribution of contributions. Horizontal flux analysis highlighted three major pathways (Northwest-Southeast, Southeast-Northwest, and Southwest-Northeast), with Shijiazhuang serving as a critical pollutant "sink" across altitude layers. Vertical fluxes revealed an altitude transition near 600 m: net downward transport dominated below 600 m, whereas enhanced summer convection promoted upward transport above 600 m. These results support altitude-dependent, scenario-specific strategies for coordinated regional O3 mitigation in the BTH region.

Ozone↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2025

Coupling of spectroscopy and nitrogen-oxygen isotopes unveils the mechanisms of dissolved organic matter and nitrate pollution in lakes within the agro-pastoral transition zone.

Lakes in arid and semi-arid regions are subjected to severe ecological stress, such as organic pollution, eutrophication, and salinization, due to climate change and human activities. This study investigates Chagannur Lake, a typical arid-region lake that is representative and ecologically sensitive in Northern China's agro-pastoral ecotone, to uncover its pollution characteristics and mechanisms. We employed fluorescence spectroscopy and stable isotope analysis to trace dissolved organic matter (DOM) and nitrate sources. The DOM composition was dominated by microbial metabolic byproducts and protein-like substances, suggesting that microbial processes are key to organic matter transformation. Source apportionment revealed that pollutants primarily originated from livestock and poultry manure (37.6 %), agricultural fertilizers (35.6 %), and soil erosion (24.7 %), with agricultural fertilizers contributing most significantly in the Gogstai River (63.3 %). A structural equation model (SEM) coupling spectral and mass spectrometric data revealed that microbial transformation significantly impairs the lake's self-purification capacity, thereby promoting pollutant accumulation (path coefficient = 0.91,*p < 0.05). Moreover, microbial processes link endogenous and exogenous pollution, a mechanism effectively traced by isotopic and fluorescence indices (path coefficient = 0.55, ⁎⁎p < 0.01). These findings enhance the understanding of pollution sources and transformation mechanisms in arid-region lakes and offer foundational theoretical support for policymakers engaged in pollution control strategies.

Lakes↗

PubMed2026

Fused Deposition Modeling (FDM) of polyether-ether-ketone (PEEK) dental implants: A systematic review of the effect of printing parameters on mechanical behaviour and surface quality.

PURPOSE: This systematic review evaluated how FDM printing parameters influence mechanical behaviour and surface characteristics of 3D-printed PEEK and identified parameter combinations linked to the most favourable mechanical performance and surface quality. MATERIALS AND METHODS: An electronic search was conducted in: MEDLINE (Ovid), PubMed, Embase, Web of Science, Scopus, and Compendex (last update: January 2025). Studies that evaluated the effect of FDM printing parameters on mechanical and surface properties of PEEK were included. Outcomes comprised compressive, tensile, and flexural strengths, elastic modulus, fracture toughness, surface hardness, roughness, and wettability. RESULTS: Of 4005 reports screened, 54 manuscripts were included. 92.6% (n = 50) of articles showed low risk-of-bias, while 7.4% (n = 4) showed medium risk-of-bias. Tensile strength was the most investigated mechanical parameter (78%), followed by elastic modulus (41%), flexural strength (30%), compressive strength (20%), and fracture toughness (6%). Surface roughness was the most evaluated surface property (30%), followed by hardness (17%) and wettability (6%). Across studies, higher printing temperatures, lower printing speed, thinner layer thickness, and maximum infill ratio in a horizontal printing orientation were associated with higher strengths, less warpage, increased accuracy, and improved surface quality. CONCLUSION: Specific combinations of FDM printing parameters can significantly improve the mechanical and surface properties of PEEK. However, it is difficult to meet all the optimal conditions simultaneously. Thus, balancing between different parameters must be considered in practical production.

Benzophenones↗

PubMed2026

Retrosigmoid craniotomy surgical guide: The way forward for precise exposure of the transverse-sigmoid sinuses.

INTRODUCTION: The retrosigmoid craniotomy is the workhorse approach to the cerebellopontine angle. Accurate localisation of the transverse-sigmoid junction (TSJ) is key for optimised exposure and cerebellar retraction. Various methods, both anatomical and navigational, have been used but with suboptimal results. We utilised a 3D-printed retrosigmoid surgical guide in an attempt to overcome this and report our early outcomes and experiences in the design, production and utilisation of the guide. METHODS: This is a prospective cohort study of the patients with retrosigmoid craniotomies performed using the surgical guides. Patient demographics and diagnoses, along with the accuracy of the planned burrhole and craniotomy, need for craniotomy extension, presence of venous sinus injury, set-up time, and cost were reported. RESULTS: There were ten cerebellopontine angle cases in which the surgical guides were utilised, three petrous meningiomas, two trigeminal neuralgias, two metastasis, and three other tumours. The planned burrhole and craniotomy were precise in all cases with accurate exposure of the TSJ and no requirement for craniotomy extension. The mean set up time was 3.9 min, and the mean cost of the surgical guides was USD 470.90. One elderly patient had an intraoperative transverse sinus injury related to adherent dura that was planned for exposure. CONCLUSION: The 3D-printed surgical guide is a potential solution to the rapid, precise and consistent identification of the TSJ when performing a retrosigmoid craniotomy. We present our early experience and discuss nuances in the designing, production, and intraoperative phases to optimise the precision of this guide. We suggest two methods to avoid sinus injury in elderly patients: either to plan the craniotomy to the edge of the sinus, or to plan sinus exposure but to use burr drills rather than the osteotome, as in our case, to expose the sinus.

Humans↗

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

Effectiveness of psychologically informed physical therapy, tendon-specific exercise program and routine physical therapy in prolonged unilateral shoulder pain and symptom correlations with imaging: a single-center, randomized, parallel-group, three-arm study (RESPECT).

BACKGROUND: Shoulder complaints are one of the most common musculoskeletal ailments. Patient-specific characteristics such as obesity, depression and physical labor are established risk factors, whereas imaging findings are common and associations between specific imaging findings and symptomatology is limited. General exercises are considered useful in treatment whereas evidence for specific tendon exercises is lacking. Biopsychosocial model is also recommended, but has not been extensively studied concerning shoulder symptoms. This article describes the study protocol designed to evaluate the effectiveness and the cost-effectiveness of routine and specific physical therapy (PT) interventions. Imaging is performed for descriptive, longitudinal and imaging-symptom correlation studies. METHODS: The Rehabilitation of Shoulder Pain: Evaluation and Clinical Trial (RESPECT) is a randomized three-arm parallel-group study involving 300 participants aged 20 to 60 years with prolonged unilateral shoulder pain. Participants will receive either routine PT, physiotherapist-guided tendon-specific exercise program or psychologically informed PT. Bilateral shoulder radiographs, ultrasound and magnetic resonance imaging will be done at the baseline and at 12 and 36 months. Electronic surveys will be completed at the baseline and at 3, 6, 12 and 36 months. The primary outcome will be patient-specific functional scale (PSFS) at 12 months, analyzed using analysis of covariance (ANCOVA), adjusted for baseline PSFS. DISCUSSION: RESPECT will provide systematic and controlled data regarding different PT interventions in prolonged shoulder symptoms, which is currently limited. Being one of the most common sources of musculoskeletal pain, improved management could reduce symptom-related burden and prolonged functional impairment at individual and population level. CLINICALTRIALS: gov; Registration number NCT07235969; Registered November 18th, 2025; Version: 1.0.

Humans↗

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

E-cigarette product characteristics and packaging features and interest in e-cigarette use: Results from a randomized within-person trial nested in three prospective cohorts.

BACKGROUND: Product characteristics and packaging may be key targets for regulation to reduce e-cigarette use among youth, but existing data are limited. METHODS: Data are from an experimental study (2018-2020) nested within three prospective cohorts (age 14-26) in southern California (N = 3565). Participants were shown five e-cigarette (e-liquid) packages in a random order that varied in flavor (sweet vs. tobacco), flavor name (descriptive ["blueberry cheesecake"] vs. concept ["smurf cake"] vs. none [number only]), and cartoon image on package (yes/no). For each stimuli, survey items assessed the following outcomes: product appeal (self-enjoyment, others' enjoyment; Likert scale [1-5]), susceptibility to use (use if friends offered, curiosity; 4 ordered responses [definitely not-definitely yes]), peer acceptability (definitely not-definitely yes), and perceived harm (definitely not-definitely yes). Mixed effects proportional odds models evaluated within-person effects of each factor (flavor, flavor name, cartoon) with each outcome. RESULTS: Participants reported greater appeal, susceptibility, and peer acceptability (OR range=3.7-16.9; ps<0.05), and lower perceived harm (OR=0.61; 95%CI=0.53, 0.70) for sweet (vs. tobacco-flavored) e-cigarettes; effects were progressively stronger for younger cohorts. The descriptive flavor name rated higher than the concept flavor (OR range=1.13-2.02; ps<0.05) or number only (OR range=1.18-1.69; ps<0.05) for appeal and susceptibility measures; no differences for concept vs. number were found. The cartoon image rated higher for appeal, curiosity, and peer acceptability (OR range=1.24-1.51; ps<0.05). CONCLUSIONS: Sweet flavors, descriptive flavor names, and cartoon images may increase the appeal of e-cigarettes among youth and young adults with no history of e-cigarette use, and are key targets for regulation.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Patient experiences of diagnostic uncertainty in musculoskeletal care: a systematic review of qualitative studies.

BACKGROUND: Diagnosis plays a central role in musculoskeletal care. However, establishing a clear diagnosis is often challenging, and diagnostic uncertainty is common. OBJECTIVES: To explore patient experiences of diagnostic uncertainty in musculoskeletal care. METHODS: Five databases (CINAHL, Embase, MEDLINE, AMED, Web of Science) were searched from inception to October 2025. Qualitative studies involving semi-structured interviews with adults receiving care for MSK conditions were included. Methodological quality was appraised using the Joanna Briggs Institute Qualitative Checklist. Data were synthesised using thematic synthesis, and confidence in findings was assessed using the Grading of Recommendations Assessment, Development, and Evaluation Confidence in the Evidence from Reviews of Qualitative Research approach (GRADE-CERQual). RESULTS: Twenty-six studies involving 462 participants were included. Critical appraisal identified 23 studies with varying methodological limitations; all studies were included in the synthesis. Nine descriptive themes were synthesised into three analytical themes: (1) patient expectations and perceived meanings of a diagnosis and interpretations of diagnostic uncertainty; (2) the multi-dimensional experience of diagnostic uncertainty; and (3) the role of contextual factors, particularly communication and the therapeutic relationship, in shaping experiences of diagnostic uncertainty. Using GRADE-CERQual, confidence in these themes was rated as low, moderate and very low, respectively. CONCLUSION: Diagnostic uncertainty is a subjective and multi-dimensional experience shaped in part by patients' expectations and the meanings attributed to diagnosis. Its impact spans predominantly cognitive and affective domains and may influence clinical presentation. Patient-centred communication and strong therapeutic relationships may support patients in navigating diagnostic uncertainty in musculoskeletal care.

Adult↗

PubMed2026

Engineered MXene-based nanozyme platform: NIR-II photothermal and dual enzyme-mimetic potentiated chemodynamic synergy for precision tumor eradication.

The antioxidant defense barrier in the tumor microenvironment, particularly glutathione (GSH), considerably restricts the therapeutic efficacy of chemodynamic therapy (CDT). Moreover, CDT generally exhibits relatively mild therapeutic efficacy owing to its intrinsic reaction kinetics, making it difficult to achieve complete tumor eradication within a short time. To address these issues, we construct a functionalized nanotherapeutic platform, Nb2CTx@Ru-PEG2000-FA (NCRPF), for tumor photothermal ablation and enhanced CDT resulting from GSH depletion. NCRPF possesses three key advantages: 1. Efficient near-infrared II photothermal conversion (η = 42.08%), raising the tumor temperature above 45 °C within 90 s for rapid ablation; 2. Dual peroxidase-like and glutathione peroxidase-like activities, simultaneously depleting GSH and generating a burst of ·OH to eliminate residual tumors; 3. Targeted tumor accumulation with 2.9-fold higher efficiency than passive diffusion. Both in vitro and in vivo results confirm that this combined strategy achieves complete tumor eradication with favorable biosafety. Collectively, the NCRPF nanotherapeutic system provides a powerful new paradigm with high translational potential for the complete eradication of breast cancer.

Animals↗

PubMed2026

Dual signal-enhanced immunochromatographic test strip based on Au@PtNPs: From sensitive detection of thiamethoxam to multiplex pesticide screening in vegetables.

Immunochromatographic test strip (ICTS) is a rapid analytical technique widely used in environmental and food detection owing to its merits of simple operation and short analysis time. Herein, three-dimensional nanoflower-structured gold‑platinum nanoparticles (Au@PtNPs) were synthesized via a seed-growth method. Compared with conventional gold nanoparticles (AuNPs), Au@PtNPs exhibited stronger signal intensity, excellent catalytic performance, and efficient antibody binding efficiency. Colorimetric Au@PtNPs-ICTS and catalytic colorimetric Au@PtNPs-ICTS were developed for the sensitive detection of thiamethoxam (THI) in vegetables. The limits of detection (LODs) for colorimetric Au@PtNPs-ICTS and catalytic colorimetric Au@PtNPs-ICTS quantitative analysis were 0.18 ng/mL and 0.093 ng/mL, respectively, representing approximately 3-fold and 6-fold improvement compared to AuNPs-ICTS (0.56 ng/mL). Furthermore, highly sensitive detection of multiple pesticide residues (chlorpyrifos, acetamiprid, and imidacloprid) was achieved by replacing the corresponding target antigens and antibodies, which further verified the universality of this immunochromatographic strategy.

Thiamethoxam↗

PubMed2026

Depression and amyloid-β across CSF, PET, and plasma biomarkers: a systematic review and meta-analysis.

Alzheimer's disease is increasingly defined by biomarker evidence of amyloid-β and tau pathology, sharpening questions about whether late-life depression contributes to, or instead reflects, this pathology. We conducted a systematic review and meta-analysis of studies published between 2000 and 2025 that compared amyloid-β biomarkers in adults with and without depression, with depression defined by validated clinical diagnoses or symptom rating scales. Twenty-four studies were included, spanning three biomarker sources: cerebrospinal fluid, positron emission tomography imaging, and plasma. Across all sources, the pooled difference in amyloid-β burden between depressed and non-depressed individuals was small and clustered near zero, indicating only a weak, statistically non-significant tendency toward higher amyloid in depression. When the three sources were examined separately, each yielded a similar near-null result, although between-study heterogeneity was considerable for cerebrospinal fluid and plasma and moderate for imaging. Importantly, a prespecified subgroup analysis showed that imaging results diverged by quantification method: studies using the simpler standardized uptake value ratio clustered around zero, whereas the smaller group of studies using kinetic distribution volume ratio modelling showed a significant positive association, suggesting that methodological choices critically influence the observed relationship. Taken together, these findings indicate that depression is not consistently accompanied by greater amyloid-β burden across widely used biomarker platforms. The distribution volume ratio signal nonetheless raises the possibility of subtle associations that cruder methods may obscure, and suggests that depression may shape Alzheimer's disease trajectories more by modifying the clinical impact of amyloid than by altering its amount.

Humans↗

PubMed2026

Subtle cortical thinning in the temporal pole in middle-aged APOE-ε4 and PICALM (rs3851179) AA/AG carriers without dementia.

The symptoms of Alzheimer's disease (AD) are caused by neurodegeneration and atrophy in particular brain regions, especially in the temporal lobe. However, the influence of genetic risk on cortical thickness prior to dementia onset, remains unclear. This study aimed to explore the relationship between AD genetic risk (related to APOE and PICALM genes) and cortical thickness in selected regions of interest (ROIs) in middle-aged individuals without dementia. Sixty-nine (N = 69) participants (34 females, 35 males; age: 55.45 ± 3.19) underwent magnetic resonance imaging (MRI). They were divided into three groups based on their genetic AD risk: A+ P+ (APOE/PICALM risk variants), A+P- (APOE risk variant, PICALM neutral variants), and the N group (APOE/PICALM neutral alleles). Cortical thickness was analyzed using CAT12 software (surface-based morphometry with the Destrieux atlas) based on T1-weighted MR images in five ROIs referred to as "the cortical signature of AD" in previous studies. The A+P- group had a thinner right temporal pole cortex than non-carriers after controlling for sex, age, and Raven's Progressive Matrices scores. Although this finding did not survive FDR correction across the 10 tested regions, it is consistent with our hypotheses and prior literature. No other differences in cortical thickness were found in the analyzed regions of AD "signature". The observed effect was restricted to single-risk APOE carriers without PICALM risk alleles. Therefore, further research is needed to understand the genetic interplay between these two genes in conferring AD risk.

Humans↗