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PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

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

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

PubMed2026

Evaluation of the effects of domestic tomato processing on biopesticide residue using natural deep eutectic solvents (NADES) extractions.

The present study evaluated the fate of fourteen botanical biopesticides in processed tomato samples. Various processing methods were employed, including washing, dehydration, and the preparation of juice and sauce. The extraction was performed using more sustainable techniques, aimed at minimizing the environmental impact of conventional organic solvents by substituting them with natural deep eutectic solvents (NADES). Solid-liquid extraction (SLE) and dispersive liquid-liquid microextraction with solidification of floating organic drop (DLLME-SFOD) were utilized for solid and liquid tomato samples, respectively. The NADES used was choline chloride:2,3-butanediol (ChClBt) at a 1:4 molar ratio for both techniques, resulting in recovery values ranging from 69.2 to 106.2% for SLE, and extraction efficiencies reaching up to 46.2% for DLLME-SFOD. The impact of these processes was evaluated employing the processing factor (PF), yielding PF values of less than 1 in all cases. Compounds as pyrethrins, azadirachtin, and rotenone persisted after processing, posing a potential consumer risk.

Solanum lycopersicum↗

PubMed2026

Longitudinal Prediction of Retinal Sensitivity Based on Disease Progression Quantified From Optical Coherence Tomography in Geographic Atrophy.

PURPOSE: The purpose of this study was to analyze the association between disease progression of geographic atrophy (GA) from optical coherence tomography (OCT) with retinal sensitivity (RS) in microperimetry (MP) over a 2-year follow-up period. METHODS: This is a longitudinal analysis of the OAKS Phase-III clinical trial. Both study and fellow eyes with GA that underwent imaging with the Spectralis OCT and consecutive MP examination were eligible. Pointwise quantification of ellipsoid zone (EZ) thickness, EZ and retinal pigment epithelium (RPE) loss from OCT volumes was correlated with localized RS. A longitudinal predictive model using a Markov Chain framework was implemented to predict RS change over time based on OCT biomarkers. The modeling of morphological and functional progression was based on the fellow-eye cohort. RESULTS: A total of 39,681 MP points from 406 patients were analyzed. In the fellow eye cohort, baseline (BSL) EZ thickness was positively associated with RS (0.3 decibel [dB]/µm, P < 0.001). Decrease in EZ thickness between visits during follow-up was significantly associated with decrease in RS (0.1 dB / 1 µm change). RS was significantly lower in MP points within EZ loss during follow-up compared with MP points within the retina with measurable EZ (P < 0.001). The largest functional decline was observed within RPE loss, also associated with the highest probability of absolute scotoma (P < 0.001). Morphological progression to EZ and RPE loss was influenced by EZ thickness and the morphology of adjacent MP points (P < 0.001). CONCLUSIONS: Two exploratory endpoints were developed, namely quantification of EZ thickness and loss, and localized RS within high-risk OCT areas. RS decline during follow-up is associated with automatically quantified disease progression in OCT.

Humans↗

PubMed2026

Quantification of appetite-regulating hormones in children with hypothalamic and common obesity.

CONTEXT: The pathophysiology of hypothalamic obesity (HyOb) remains incompletely understood with no effective treatments. OBJECTIVE: We examined differences in appetite-regulating hormone concentrations between patients with HyOb, common obesity, and lean controls. DESIGN: Multiway cross-sectional case-control study of patients aged 2 through 19 years. SETTING: Two tertiary pediatric endocrinology centers. PATIENTS: Cases were obese (body mass index [BMI] > +2 SD score [SDS], "HyOb") and lean ("HyLean") patients with congenital (septo-optic dysplasia) or acquired (suprasellar brain tumor) hypothalamic disorders. Controls had common obesity ("Ob") or nonhypothalamic disorders and normal BMI ("Lean"). MAIN OUTCOME MEASURES: Relationships between the Dykens' Hyperphagia Questionnaire Score (DHQS), plasma or serum concentrations of leptin, insulin, α-melanocyte stimulating hormone (αMSH), brain-derived neurotrophic factor, oxytocin, acylated ghrelin, agouti-related peptide and copeptin, and BMI SDS. RESULTS: Dykens' Hyperphagia Questionnaire Score did not differ between HyOb and Ob patients (24 [17-34] vs 24 [18-31]) but correlated with BMI SDS in patients with hypothalamic disorders (P = 0.02). HyOb and Ob patients exhibited similarly increased anorexigens (insulin, leptin) and decreased orexigens (ghrelin, agouti-related peptide) compared to HyLean and Lean patients. The rate of BMI increase was independently associated with lower αMSH (β = -0.23 [-0.36 to -0.11], P = .0007) and ghrelin (β=-0.004 [-0.01 to 0.00], P = .001) concentrations, suggesting that αMSH replacement may be a therapeutic target for HyOb. HyLean patients demonstrated intermediate insulin responses to glucose compared to other subcohorts. CONCLUSION: In our cohort, patients with HyOb appeared indistinguishable from Ob in terms of their appetite and appetite-regulating neuroendocrine circuitry. Higher αMSH concentrations are associated with reduced weight gain and may be a target for therapeutic intervention.

alpha-MSH↗

PubMed2026

Comparison of deep and nondeep hypothermia in thoracic and thoracoabdominal aortic surgery: A systematic review and meta-analysis.

OBJECTIVE: Deep hypothermic circulatory arrest (DHCA) remains a cornerstone technique for neuroprotection and end-organ preservation during ascending aorta and arch surgeries. However, its benefits and risks compared with non-DHCA strategies in thoracic and thoracoabdominal aortic aneurysm (TAAA) repair are uncertain owing to conflicting evidence and variable institutional practices. METHODS: A systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis and Cochrane guidelines. PubMed, Embase, and Cochrane Library were searched for comparative studies evaluating DHCA and non-DHCA techniques for open thoracic and TAAA repair. Random-effects models were applied to calculate pooled effect estimates. Effect sizes were risk ratio (RR) for binary end points and mean difference for continuous end points, both with 95% confidence intervals. Statistical significance was set at P < .05. Between-study heterogeneity was estimated using the I2 statistic. Metaregression analyses were used to explore the sources of heterogeneity. RESULTS: Nine observational studies, including 1041 patients, were analyzed. DHCA use was associated with a significantly lower risk of spinal cord injury (RR, 0.44; P = .012) compared with non-DHCA. However, DHCA was also associated with prolonged postoperative ventilation time (RR, 1.34; P = .003). No significant differences were observed in overall mortality, length of hospital and intensive care unit stay, stroke, or renal complications. Metaregression identified patient age as a moderator of length of stay variability, with older cohorts demonstrating longer recovery periods. CONCLUSIONS: DHCA is associated with a lower risk of spinal cord injury during TAAA repair without increasing mortality or stroke risk, although it is associated with longer ventilation times.

Humans↗

PubMed2026

Vortex-assisted liquid-liquid microextraction based on natural deep eutectic solvents for the determination of pyrethroid pesticides in urine.

A novel, facile, and environmentally friendly analytical method was developed based on vortex-assisted liquid-liquid microextraction and high-performance liquid chromatography with diode-array detection for detecting pyrethroid pesticides (PPs) in urine. Natural deep eutectic solvents (NADESs) were prepared using plant essential oil-derived monoterpenoids (thymol, carvacrol, and menthol) combined with aromatic primary alcohols (benzyl alcohol, phenethyl alcohol, and phenylpropyl alcohol) as hydrogen bond donors and acceptors. These solvents served as environmentally benign extraction media, thereby avoiding the use of conventional volatile, toxic organic solvents. NADESs are naturally derived, easy to prepare, biodegradable, and environmentally friendly solvents. Hydrophobic and π-π interactions between the NADESs and PPs may contribute to enhancing the affinity of PPs toward the NADESs phase. Vortex technology, accelerating mass transfer between the sample and extractant phases, enables fast extraction of PPs. Under optimized conditions, the method achieved a low detection limit (0.002 mg L-1), satisfactory precision with relative standard deviations (0.3%-2.4%), and acceptable recovery (80.7%-86.2%). The method demonstrated excellent performance in urine analysis and was feasible as a facile and green strategy for monitoring the content of PPs in biological matrices and assessing exposure risk.

Liquid Phase Microextraction↗

PubMed2026

Target Capture of Ancient Shell DNA Enables Phylogenetic Reconstruction of Deep-Sea Molluscs.

Target capture is widely used to enrich endogenous DNA from calcium phosphate skeletal material in vertebrates, but its performance on calcium carbonate hard parts widely produced by invertebrates remains poorly understood. Here, we compared DNA recovery from four fresh and 12 ancient (eight radiocarbon-dated to 1671-1135 years old before present) deep-sea vesicomyid clam shells, including species Archivesica marissinica, A. nanshaensis and A. okutanii, using whole-genome sequencing (WGS) or target capture of ultraconserved elements (UCEs). WGS achieved 16.65% on-target read recovery of UCEs from fresh soft tissue, but < 1% from shell specimens. By contrast, UCE capture in the same specimen increased on-target reads by up to 155-fold, reaching 29.84% in fresh shells and up to 72-fold, reaching 19.89% in ancient shells. Target capture of UCEs recovered 142-1001 loci per sample compared to 0-230 with WGS alone. Ancient shells of A. marissinica and A. okutanii, based on reads mapped with bwa-mem2 and bbmap, exhibited characteristic post-mortem DNA damage signals, with average 5'-end C-to-T misincorporation rates of 3.46% and 15.97%, respectively, exceeding the levels observed in fresh A. marissinica shells (maximum 1.24%). UCE-based phylogenetic reconstructions incorporating shell ancient DNA recovered two major clades within Pliocardiinae, consistent with published phylogenomic trees. Together, these findings demonstrate that target-capture enrichment enables effective recovery of highly degraded DNA from ancient mollusc shells and supports robust phylogenetic inference at the intrageneric scale, expanding the utility of shells-one of the most abundant invertebrate remains-for evolutionary, biogeographic and conservation studies.

Animals↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Efficacy of dapagliflozin on hepatic steatosis and fibrosis in patients with type 2 diabetes mellitus and metabolic dysfunction-associated steatotic liver disease: a pre-specified single-arm analysis from a randomized controlled trial.

AIM: To evaluate the association of dapagliflozin therapy with changes in hepatic steatosis and non-invasive fibrosis surrogate markers in patients with type 2 diabetes mellitus (T2DM) and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) over 12 months. METHODS: This is a pre-specified single-arm analysis from a randomised, open-label, parallel-group trial. Of 54 participants randomised to dapagliflozin 10 mg daily, 50 (92.6%) completed the 12-month follow-up and were included in the per-protocol analysis. Assessments at baseline, 3, 6, and 12 months included transient elastography (CAP and LSM), ultrasonography, and biochemical tests. Primary endpoints were changes in hepatic steatosis (CAP) and non-invasive fibrosis surrogates (LSM). RESULTS: Significant reductions were observed in hepatic steatosis (CAP: 316.7 to 245.7 dB/m; mean change - 71.02 dB/m, 95% CI: -63.4 to - 78.6; p < 0.001) and in liver stiffness as a non-invasive fibrosis surrogate (LSM: 8.59 to 7.28 kPa; mean change - 1.31 kPa, 95% CI: -0.92 to - 1.70; p < 0.001). Improvements were also observed in glycaemic control, body weight, lipid profile, liver enzymes, ultrasonographic steatosis grading, and serum fibrosis markers. Genitourinary infections were the most frequently reported adverse events (32%); no serious adverse events were recorded. CONCLUSIONS: Dapagliflozin was associated with significant improvements in hepatic steatosis, non-invasive fibrosis surrogate markers, metabolic parameters, and liver function in T2DM patients with MASLD over 12 months. These findings provide region-specific evidence for an Indian population and support further controlled investigation. However, these findings should be interpreted in light of the pre-specified single-arm design of this analysis, the open-label methodology, relatively small sample size, and the absence of liver biopsy confirmation.

Humans↗

PubMed2026

The Effect of Slow Deep Breathing Relaxation Exercise on Pain and Anxiety Levels During and Post-Chest Tube Removal After CABG.

Chest tube removal after coronary artery bypass graft is frequently reported by patients as stressful and painful, highlighting the need for effective nonpharmacological interventions. Slow deep breathing relaxation exercises (SDBREs) may serve as a simple nursing strategy to reduce discomfort. In this study, we aimed to evaluate the effect of SDBRE on pain and anxiety during and after chest tube removal following coronary artery bypass grafting in Nablus hospitals. An experimental design was used with 80 patients recruited from 2 hospitals. Participants were randomly assigned to either an intervention group (n = 40) that practiced SDBRE or a control group (n = 40) that received standard care. Data were collected through a self-administered questionnaire, the Numeric Pain Scale, and the Visual Anxiety Scale. Data collection occurred from March to October 2024. The intervention group reported significantly lower pain scores during removal (M: 5.325 vs 7.125, P < .001) and after removal (P < .001). Anxiety scores were significantly lower both during and after removal (P < .001). Pain correlated with duration, with more complex operations and prolonged chest tube insertion linked to higher scores. SDBRE significantly reduced pain and anxiety during and after chest tube removal, supporting its integration into routine postoperative nursing care.

Humans↗

PubMed2026

Natural deep eutectic solvent in situ formation-based extraction method coupled to high-performance anion-exchange chromatography with pulsed amperometric detection for multiclass carbohydrates in hot pot bases.

A novel method was developed for the simultaneous extraction of fourteen multiclass carbohydrates from high-fat foods via the in situ formation of deep eutectic adducts from analytes and acetate ions. Different natural deep eutectic solvents (NADESs) composed of fructose and organic acids were tested as extraction solvents. A model NADES formulated with sodium acetate and fructose was characterized using Fourier transform infrared (FTIR) spectroscopy and hydrogen nuclear magnetic resonance (1H-NMR) spectroscopy. The critical extraction parameters were systematically optimized using multi-response surface methodology (MRSM) with a central composite design (CCD). The extract was analyzed using high-performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD) using a sodium hydroxide-sodium acetate eluent, which did not require organic solvents. This approach exhibited good linearity over the concentration range of 0.02-10 mg L-1, with correlation coefficients (r) ranging from 0.9994 to 0.9999. The limits of detection and quantification were in the ranges of 0.06-0.42 mg kg-1 and 0.19-1.3 mg kg-1, respectively, which were significantly lower than those of liquid chromatography (LC). The protocol was successfully applied to the determination of fourteen carbohydrates in forty-five hotpot seasoning samples. The recoveries ranged from 86.3% to 104.1%, with relative standard deviations (RSDs) of 0.9-7.1%. By integrating multiple techniques, this strategy simplifies operations, shortens extraction time, and achieves baseline separation of three carbohydrate classes that exhibit poor resolution using a conventional LC method. This study describes an efficient procedure for the simultaneous determination of multiple trace-level carbohydrates in complex samples using HPAEC-PAD.

Journal Article↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Diversity and population connectivity of members of the family Eunicidae inhabiting deep-water corals in the North Atlantic.

Eunicid polychaetes are often found in association with Cold Water Corals (CWCs), even establishing symbiotic relationships, such as those described between Desmophyllum pertusum and Eunice norvegica. While genetic connectivity of CWCs across the North Atlantic has been widely studied, little is known about their associated fauna in this regard. Here, we present a study combining a focused analysis of the genetic and genomic connectivity of E. norvegica with a regional assessment of the distribution and evolutionary relationships of three CWC-associated eunicid species from the Cantabrian Sea and the North of the United Kingdom (190-1,230 m depth). An integrative approach using genetic (16S, COI and 18S), morphological and ecological data allowed the identification of the eunicids studied, with new records of Eunice cf. nicidioformis and Leodice cf. antarctica in the Cantabrian Sea, as well as previously undocumented associations with CWC species. In addition, RADseq data contributed to the delimitation of the closely related species E. norvegica and Eunice philocorallia. Moreover, the genetic connectivity of E. norvegica was studied trough a RADseq (1,067 neutral SNPs) approach. Our results indicate a single panmictic population across approximately 2,000 km, suggesting that oceanographic currents facilitate passive dispersal of E. norvegica lecithotrophic larvae, aided by coral host stepping-stones. The connectivity patterns observed for E. norvegica mirror those of D. pertusum, on which the worm is ecologically dependent. Our study highlights the importance of using integrated genetic, morphological and ecological data to characterise and delineate understudied CWC-associated species and improve our understanding of their dispersal capabilities and genetic connectivity to inform future conservation recommendations.

Animals↗

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↗

PubMed2026

Transcutaneous vagus nerve stimulation influences sleep quality and insomnia: A systematic review and meta-analysis.

Impairments in sleep quality, timing, or duration disrupt normal sleep patterns. This systematic review and meta-analysis investigated the effects of transcutaneous vagus nerve stimulation (tVNS) protocols on sleep outcomes. Thirteen randomized controlled trials with parallel or crossover designs that applied tVNS intervention and assessed sleep quality (Pittsburgh Sleep Quality Index) and insomnia severity (Athens Insomnia Scale and Insomnia Severity Index) were included. Effect sizes were calculated by comparing changes between the active tVNS and control groups. Moderator analyses examined whether stimulation of different targeted regions influences sleep outcomes. Meta-regression analyses examined potential relationships between the effects of tVNS protocols on sleep quality and demographic characteristics and multiple tVNS parameters, respectively. The random-effects meta-analysis indicated that tVNS protocols influenced better sleep quality and lower insomnia severity. Moderator variable analysis revealed that tVNS targeting the concha region induced better sleep quality. Meta-regression analysis revealed that better sleep quality was associated with lower ages of participants. These findings suggest that tVNS protocols, particularly those targeting the concha, were associated with favorable changes in sleep quality and insomnia severity, with age potentially moderating the treatment response.

Humans↗