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PubMed2026

Vesicoureteral reflux and anorectal malformations.

BACKGROUND: Renal and urinary tract anomalies are frequently associated with anorectal malformations (ARMs) and may adversely affect long-term renal outcomes, if not detected early. However, reliable clinical predictors for significant urologic abnormalities across different ARM phenotypes remain poorly defined. OBJECTIVE: To determine the prevalence and grade distribution of vesicoureteric reflux (VUR) in neonates with ARMs, and to explore its association with renal and urinary tract anomalies, the complexity of the ARM phenotype, and other factors are associated with high-grade VUR. METHODS: In this retrospective cross-sectional study, medical records of 64 neonates diagnosed with ARMs and managed at a tertiary children's hospital between 2018 and 2025 were reviewed. All patients underwent renal and urinary tract ultrasonography. Voiding cystourethrography (VCUG) was performed for all neonates according to our institutional protocol, regardless of ultrasound findings or ARM phenotype. Demographic characteristics, ARM phenotype (less-complex vs. complex), urologic findings, urinary tract infection (UTI) history, and associated anomalies were analyzed. Multivariable logistic regression models were used to identify independent predictors of complex ARM phenotype and high-grade VUR. RESULTS: The cohort consisted of 64 neonates (75% male) with a mean gestational age of 37.36 ± 1.83 weeks and a mean birth weight of 2940 ± 601 g. Renal and urinary tract anomalies were common, with hydronephrosis observed in 48.4%, VUR of any grade in 39.1% and hydroureter in 35.9%,of patients. High-grade VUR was identified in 21.9% of patients, and a documented history of UTI was present in 18.8% of the entire cohort. In multivariable analyses, birth weight, presence of VUR, and UTI history were not independently associated with complex ARM phenotype. Additionally, no demographic or clinical variables reliably predicted high-grade VUR. The predictive performance of the regression model for high-grade VUR was limited (AUC = 0.60). CONCLUSION: Renal and urinary tract anomalies are highly prevalent among neonates with ARMs, with VUR representing a prominent finding. The lack of robust clinical predictors for complex ARM phenotype or high-grade VUR underscores the limitations of selective screening strategies and supports the role of comprehensive urologic evaluation in neonates with ARM, regardless of anatomic subtype.

Humans↗

PubMed2026

Effects of faba bean-based crisping culture on phenotypic characteristics, muscle quality, and serum metabolome in Nile tilapia: Screening biomarkers to assess the degree of crisping.

Feeding Nile tilapia (Oreochromis niloticus) a faba bean-based crisping diet enhances muscle hardness (crispness) and overall flesh quality. However, the underlying mechanisms and reliable biomarkers remain insufficiently defined. This study integrated phenotypic traits, muscle texture, collagen content, serum antioxidant enzyme activities (SOD, CAT, and GSH-Px), MDA levels, and serum metabolomics to understand the determinants of muscle crisping. Fish were assigned to a crisping diet or a control group for 90 days. Individuals in the crisping group were implanted with passive integrated transponder (PIT) tags to enable correlation analyses among phenotypic traits (body weight/length/frame changes), serum indicators (NAM, FAD, and GSH-Px) and muscle hardness. Compared with controls, the crisping diet significantly increased muscle hardness, gumminess, and chewiness, accompanied by elevated collagen content. Antioxidant profiles were altered, with higher activities of serum SOD and CAT, together with elevated MDA levels and reduced GSH-Px activity (P < 0.05). Metabolomic analysis identified 830 differential metabolites (682 upregulated and 148 downregulated), predominantly comprising carboxylic acids and derivatives, glycerophospholipids, and benzene derivatives. Enrichment analysis indicated significant involvement in general metabolic pathways, ATP-binding cassette (ABC) transporters, amino acid biosynthesis, and glycine, serine, and threonine metabolism (P < 0.05). Notably, acetylpyruvate was upregulated in glutathione metabolism, nicotinate and nicotinamide metabolism, and galactose metabolism; pantothenic acid was upregulated in glycine, serine, and threonine metabolism; whereas δ-tocotrienol was downregulated. Correlation analysis revealed weak negative associations between muscle hardness and phenotypic traits (body weight/length/frame changes D5-7, D5-10, D7-8) (P < 0.05). In contrast, serum NAM and FAD were weakly positively correlated with muscle hardness, whereas GSH-Px showed a weak negative correlation (P < 0.05). Collectively, these findings suggest that body weight, body length, frame measurements (D5-7, D5-10, and D7-8), and serum NAM, FAD, and GSH-Px are associated with the degree of muscle crispness in Nile tilapia fed a faba bean-based crisping diet and may serve as candidate biomarkers under these culture conditions.

Animals↗

PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗

PubMed2026

ZrO₂@C-based colorimetric/photothermal dual-mode immunosensor coupled with a novel monoclonal antibody for quantification of Aspergillus ochraceus biomass.

Aspergillus ochraceus contaminates agricultural products and produces nephrotoxic, carcinogenic ochratoxin A (OTA), posing severe food safety hazards. A dual-signal lateral flow immunochromatographic assay (dLFIA) based on ZrO₂@C nanoprobes was established for quantitative detection of A. ochraceus biomass. A novel monoclonal antibody (mAb 4B4) was prepared as the capture antibody to immobilize A. ochraceus mycelial lysate antigen on the test line, and a rabbit polyclonal antibody (pAb G2801) as the detection antibody to modify ZrO₂@C composites (synthesized via UiO-66 pyrolysis) into 200 nm colorimetric/photothermal nanoprobes. This dLFIA achieved limits of detection of 0.164 μg/mL (colorimetric) and 0.517 μg/mL (photothermal). This efficient and reliable method allows quantitative analysis of A. ochraceus biomass, which is suitable for routine monitoring of fungal contamination in agro-food matrices.

Antibodies, Monoclonal↗

PubMed2026

Comprehensive quality profiling and comparative metabolic characterization of seven dominant fresh-eating Chinese olive (Canarium album Lour.) cultivars in Southern China.

Fresh-eating Chinese olive (Canarium album Lour.) is a subtropical fruit endemic to southern China with considerable commercial value, yet systematic quality characterization of dominant cultivars remains scarce. This study established a multi-dimensional quality dataset for seven dominant cultivars from Fujian and Guangdong provinces, integrating nutritional components, soluble sugars, organic acids, mineral elements, volatile profiles, and non-targeted metabolomics. Significant cultivar-specific differences were observed across all evaluated dimensions: "Lingfeng" exhibited a sugar-dominant low-acid profile, whereas "Sanleng" showed elevated phenolic constituents accumulation. Volatile profiling identified terpenoid-based candidate discriminatory biomarkers, and metabolomic analysis revealed phenylpropanoid biosynthesis, tryptophan metabolism, and starch and sucrose metabolism as the most variable pathways. Correlations between untargeted profiling and targeted absolute quantification validated untargeted result reliability and revealed their complementarity in nutritional evaluation. These findings provide baseline data for FECO germplasm evaluation and targeted industrial utilization.

China↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

The journey of fluxapyroxad, mandipropamid and mefentrifluconazole residues in two morphologically distinct chilli peppers: A comprehensive risk assessment from field to processing.

Understanding the residue fate of novel pesticides in crops is crucial for ensuring their safe application and safeguarding public health. This study examined the dissipation, processing factors (PFs), and risk assessment of fluxapyroxad, mandipropamid, and mefentrifluconazole in two morphologically distinct varieties of chilli peppers from field to processing. The half-lives of the three pesticides ranged from 5.42 to 10.05 days, following first-order kinetics. The initial residues were higher in Chaotian chilli peppers (CCP) than in long green chilli peppers (GCP). However, dissipation occurred more rapidly in CCP. Washing notably reduced the residues (PF: 0.60-0.89), whereas sun drying and oven drying concentrated them (PF: 1.92-3.74), with oven drying leading to greater concentrations. Both chronic and acute dietary risk assessments suggested acceptable risk levels for the general population. This study offers reliable guidance for the rational application of these three pesticides in chilli pepper cultivation.

Capsicum↗

PubMed2026

A point-of-use SERS assay for rapid detecting difenoconazole and flusilazole residues in fruit juices using Au/COF substrate.

We developed a ready-to-use surface-enhanced Raman scattering (SERS) sensor for rapid, pretreatment-free detection of difenoconazole (DIF) and flusilazole (FLU) in peach and lychee juices. The substrate combines Au nanoparticles (AuNPs) with covalent organic frameworks (COF) and is implemented on a portable 25-well plate, enabling in situ testing. Juices can be directly applied to the SERS-active Au/COF composite, allowing simultaneous adsorption and signal generation. The correlation between SERS intensity and logarithmic concentration yielded R-values between 0.925 and 0.986, meeting the monitoring needs of non-laboratory scenarios. The entire workflow completes within 12 min, offering a faster alternative to conventional methods while maintaining high sensitivity and reproducibility. Detection limits reach 0.96-1.22 ppb for DIF and FLU, both of which are below the regulatory maximum residue limits. Distinct SERS fingerprints enable reliable discrimination of mixed residues across juice matrices, supporting rapid on-site monitoring and cost-effective pesticide surveillance.

Triazoles↗

PubMed2026

Beyond Photometric Consistency: Addressing Loss Insensitivity to Depth Noise in Endoscopic Estimation via Error Calibration.

Self-supervised monocular depth estimation in endoscopy is fundamentally constrained by the ill-posed nature of photometric supervision. In this work, we identify a critical yet overlooked cause of this ambiguity: the inherent insensitivity of photometric loss to depth noise. To overcome this intrinsic limitation, we propose Depth Error Calibration Learning (DECL), a two-stage framework that suppresses prediction variance and mitigates residual errors in self-supervised depth estimation. In Stage I (Variance Reduction), a cyclic depth generation strategy produces multiple depth hypotheses for the input image. The per-pixel empirical variance is quantified and integrated into a dedicated variance loss term, which penalizes inconsistent predictions and encourages the network to generate more stable and reliable depth estimates. In Stage II (Bias Calibration), an image-conditioned diffusion model refines the Stage-I depth prior and mitigates structured residuals through iterative denoising, thereby improving geometric accuracy and global consistency. Extensive experiments on three public endoscopic datasets demonstrate that DECL achieves consistent improvements over representative self-supervised monocular depth estimation methods under the evaluated protocols. Moreover, ablation studies on two representative backbones indicate that DECL is not restricted to a single network implementation, while broader validation on additional backbone families remains necessary. The source code is publicly available at https://github.com/DavidLuBit/EndoDenoising.

Journal Article↗

PubMed2026

From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

Biosensing Techniques↗

PubMed2026

Longitudinal associations between family factors and the neurodevelopmental and psychosocial outcomes of children with congenital heart disease: A systematic review.

Family factors have been gaining increased attention in understanding adverse neurodevelopmental and psychosocial outcomes for children with congenital heart disease (CHD). To clarify relevance, we undertook a systematic review of only longitudinal studies which assessed such associations. Comparisons with the contribution of disease/surgical factors were also made where included studies considered such. We included longitudinal studies which assessed dynamic family factors (e.g. parent mental health, attachment, family functioning) and later child outcomes. Searches were conducted across CINAHL, Medline-Pubmed, PsychInfo and SCOPUS Web of Science. The NIH Quality Assessment Tool was used to evaluate study quality and risk of bias. Eighteen studies, utilizing data from 11 study samples and 2109 participants, met inclusion criteria. These studies included samples from infancy, with follow-up periods stretching into young adulthood, and with various degrees of CHD severity. The quality of studies was "good" to "fair", with key limitations of attrition and limited sociocultural diversity in samples. Findings suggested that family factors predicted later child psychosocial outcomes and more consistently than severity of disease indicators. This contrasted with a much smaller number of studies examining family factors and child neurodevelopmental outcomes, where no reliable conclusions could be reached. Findings highlight the importance of screening and family focused interventions for this population.

Child↗

PubMed2026

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30 weeks) and late laying (50 weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid β-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals↗

PubMed2026

The cold case of state transition 7 (stt7) mutants of Chlamydomonas reinhardtii, solved by whole-genome sequencing.

The process of State Transitions (ST) corresponds to an STT7 kinase-driven redistribution of the transmembrane LHCII antenna proteins between Photosystem II (PSII) and Photosystem I (PSI), which results from changes in their phosphorylation state. For the past two decades, two LHCII-kinase mutants, stt7-1 and stt7-9, have been instrumental in the study of STs in Chlamydomonas reinhardtii, the former being a null mutant for the kinase but quasi-sterile in crosses, while the latter, although fertile, has a leaky phenotype. Using long-read sequencing, this study further characterized the genetic lesions of the stt7 mutant strains through whole-genome reconstruction and de novo chromosome assembly. In addition, two new stt7 null mutants were generated, one derived by crosses from the original stt7-1 and one obtained by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (Cas9) technology. This work provides a comprehensive genomic characterization of the original stt7-1 null mutant, revealing extensive chromosomal rearrangements and high levels of aneuploidy, associated with increased cell size and meiotic dysfunction. Reassessment of their physiology and genetic backgrounds highlights the need for caution in interpreting genetic information. We thus produced more reliable null mutants for the LHCII-kinase, amenable to genetic crosses for the study of STs in a variety of genetic backgrounds.

Chlamydomonas reinhardtii↗

PubMed2025

Comparison of three tracking methods to assess usage of two pediatric powered mobility devices for young children with cerebral palsy.

Powered mobility devices are underutilized for promoting self-initiated mobility in young children with cerebral palsy due to prioritization of walking, caregiver effort, device characteristics, and environmental factors. Understanding device usage patterns is important to assess the impact of powered mobility interventions on child outcomes. As part of clinical trial (NCT04684576), three objective tracking methods including integrated loggers, Global Positioning System trackers (GPST), and caregiver reported activity logs (CRAL) were compared to provide insights into device usage patterns. Metrics included play session frequency, session duration, total usage minutes, and unique usage days for two powered mobility devices: Modified Ride-on Cars (MROC) and the Permobil Explorer Mini (EM). Twelve children with cerebral palsy used each device for eight consecutive weeks in home and community settings (16 weeks total), with device ordered randomized. Results showed no significant differences among tracking methods for the MROC. For the EM, only session duration differed between GPST and CRAL. Correlation analysis revealed variable relationships amongst tracking methods for both devices. The EM was used more frequently than the MROC, with significantly greater total usage minutes and session duration via CRAL, not GPST. The findings highlight the need for reliable tracking technologies that can be used across powered mobility devices.

Child, Preschool↗

PubMed2026

A homogeneous immunoassay based on AlphaLICA technology for detecting florfenicol residues in animal-derived foods.

Florfenicol (FF), a broad-spectrum amide antibiotic widely used in livestock, poultry, and aquaculture, poses potential threats to food safety and public health due to its residual accumulation. In this study, a novel homogeneous immunoassay based on Amplified Luminescent Proximity Homogeneous Assay (AlphaLICA) technology was developed for the first time for rapid screening of FF residues in milk and egg matrices. By covalently immobilizing the FF-BSA conjugate and goat anti-mouse IgG onto luminescent and photosensitive microspheres, respectively, the method achieved wash-free, homogeneous quantitative detection through a competitive immunoreaction. Under optimized conditions, the assay exhibited a linear range of 0.2-16.2 ng mL-1, with a limit of detection of 9.7 pg mL-1 and a limit of quantification of 183 pg mL-1. The intra- and inter-batch coefficients of variation ranged from 3.08% to 5.70% and 2.44% to 7.09%, respectively. Spike recovery rates in milk and egg matrices ranged from 93.18% to 107.17% (RSD ≤ 5.57%). Cross-reactivity with 11 other common antibiotics, including chloramphenicol and thiamphenicol, was below 0.1%, demonstrating excellent specificity. Comparative analysis with a commercial ELISA kit showed high consistency (r2 = 0.9332, p < 0.001). With high sensitivity, strong specificity, simple operation, and a detection time of only 10 min, this method provides a reliable technical platform for high-throughput, rapid monitoring of FF residues in milk and egg matrices.

Journal Article↗

PubMed2026

Electrospun Nanofiber Dressings for Diabetic Wounds: From Single-Layer to Intelligent Composite Systems.

Diabetic chronic wounds have become a major challenge for clinical treatment due to their complex pathological microenvironment, including persistent inflammatory response, angiogenesis disorder, excessive oxidative stress, and susceptible infection. Traditional dressings as a passive barrier have difficulty meeting the above multiple treatment needs. Electrospinning technology, with its ability to mimic the fibrous network structure of the natural extracellular matrix (ECM), offers a high specific surface area, controllable porosity, and excellent drug-loading capacity, making it an ideal platform for developing a new generation of multifunctional wound dressings. This article provides a systematic review of the research progress on electrospun nanofiber dressings in the treatment of diabetic wounds, focusing on the design evolution from basic single-layer structures to advanced complex structures and elucidating the mechanisms of action and quantifiable effects of each structural type in addressing specific pathological challenges. We also compared the current status of clinical translation for electrospun dressings with that of other advanced wound care platforms and proposed a standardized preclinical evaluation framework. A large number of research data show that these advanced designs can effectively improve the quality of healing. Finally, this paper points out the challenges faced by this field, such as scalable fabrication, in vivo reliability of smart systems, and long-term biosafety, and provides theoretical basis and technical reference for the design of efficient and intelligent electrostatic spinning diabetic wound dressings.

Nanofibers↗

PubMed2026

Nonviral transposon‑engineered stem cells characterization: dose‑dependency between vector copy number and transgene expression.

Genetically engineered stem cells hold substantial promises for advancing regenerative medicine, yet ensuring their genomic safety remains a critical challenge. A key safety concern is vector copy number (VCN), which defines the number of integrated transgene copies per genome. Although ddPCR is used to assess VCN in virally transduced cells, its application in transposon‑engineered systems is limited. In this study, we extended VCN determination to non‑viral, transposon‑engineered stem cells. In alignment with FDA recommendations, the primary objective was to establish a robust and quantitative framework for interim VCN determination at the time of lot release. Specifically, we demonstrate that reliable interim VCN estimates increase in a dose‑dependent manner with increasing plasmid input. In addition, strong linear correlations between VCN and both EGFP median fluorescence intensity (MFI) and gene‑of‑interest (GOI) protein expression validate the accuracy of this framework. Furthermore, comparison of two distinct GOIs revealed gene‑specific differences in expression efficiency. Together, these findings validate a standardized VCN determination workflow that quantitatively links plasmid dose, genomic integration, and functional transgene expression. This workflow provides a systematic characterization of engineered cells, offering comprehensive information to support downstream risk‑based analyses to ensure the genomic safety and stability of the final cell product.

Transgenes↗

PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗