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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

Influence of soil types with different soil-forming process on the qualitative and quantitative detection of microplastics by near-infrared spectroscopy.

Microplastics (MPs) have become a pressing global environmental threat, with soils-acting as sinks for MPs from multiple sources-gaining increasing attention. Near-infrared (NIR) spectroscopy offers a promising tool for MPs detection due to its rapid, non-destructive, and field-applicable features. Although previous studies have focused on the effects of individual soil components on the NIR detection performance of MPs, there is still a lack of systematic research on how the complex background-formed by the coupling of multiple physicochemical properties in natural soils-affects detection performance. This study focuses on soil types with different soil-forming processes, selected five representative agricultural soils to systematically evaluate how the combinations of physicochemical properties they represented affect the performance of NIR-based qualitative and quantitative analysis of MPs in soils. The results demonstrated that soil type significantly affected both the spectral response and detection performance of MPs. Brown Pedocals and Brown Earth exhibited clearer characteristic absorption and stronger linear responses, achieving higher identification accuracy under low (<1.5 %) or zero MPs concentrations and the best quantitative performance (R2 &#x2265; 0.988, prediction set root mean square error (RMSEP) &#x2264; 0.110 %). In contrast, Phaeozem and Red Soil were more prone to misclassification at low concentrations, while Fluvo-aquic Soil showed the poorest quantitative performance. This study is the first to reveal, at a holistic level, the critical constraints posed by natural soil complexity on the NIR detection of MPs, offering targeted empirical evidence to support the application of NIR technology in real-world soil environments.

Soil

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging

Tracking GAD-specific T-cell expansions in Type 1 diabetes by intradermal GAD-Alum challenge.

Identifying and monitoring autoreactive T cells that drive beta cell destruction remains a major obstacle to developing effective immunotherapies for type 1 diabetes (T1D). These cells are extremely rare in peripheral blood and cannot be accessed directly from the pancreas. We used intradermal injection of Glutamic Acid Decarboxylase (GAD)-Alum to recruit GAD-specific T cells to accessible sites in the skin and skin-draining lymph nodes (LNs), sampled by skin suction blisters and ultrasound-guided LN aspiration. Peripheral blood samples obtained before GAD injection were restimulated with GAD in vitro to detect reactive CD4+ T cells. Single-cell RNA sequencing (scRNAseq) followed by re-expression of selected T cell receptors (TCRs) confirmed antigen specificity. Up to 70% of T cells at the skin injection site were clonally-expanded and 4 of 14 (28%) re-expressed TCRs were GAD-reactive. In LNs 1 of 14 (4%) clonally-expanded TCRs was GAD-reactive, representing ~0.08% of all T-cells. GAD-reactive cells across compartments displayed Th1 and Th17-associated transcription signatures. These results demonstrate the intradermal autoantigen challenge and scRNAseq, enable direct identification and molecular profiling of autoreactive T cells in vivo. This minimally invasive approach provides a powerful platform for tracking antigen-specific T cells to monitor disease activity and evaluate immune interventions in T1D.

Autoimmunity

Temporal proteomic analysis reveals a three-phase adaptation strategy in Phytophthora cinnamomi during salinity stress.

Phytophthora cinnamomi, a highly invasive hemibiotrophic oomycete, threatens global agriculture, forestry, and native ecosystems. Although drought and temperature effects on P. cinnamomi-host interactions are well studied, current knowledge of abiotic stress responses in P. cinnamomi remains largely centered on infection and phytopathology, with limited molecular insight into the pathogen's direct response to salinity independent of its host. To address this gap, we combined growth assays, time-resolved proteomics, and network analysis to define how P. cinnamomi responds and adapts to salinity exposure. Growth assays showed that NaCl-modified agar enhanced mycelial expansion in a concentration-dependent manner, with 100&#xa0;mM NaCl significantly increasing growth at 48, 72, and 96&#xa0;h compared with controls, while 50&#xa0;mM NaCl remained comparable to control conditions. Temporal proteomic analysis of 100&#xa0;mM NaCl treatment at 0, 1, 6, 12, and 24&#xa0;h post treatment revealed dynamic shifts in protein abundance. Early induction of ROS (Reactive Oxygen Species)-detoxifying enzymes, including glutathione S-transferases and peroxidases, was consistent with ROS-specific staining assays. Network analysis identified modules enriched for redox regulation, ATP generation, ion transport, and translational control, highlighting multi-layered adaptation to elevated NaCl levels. Notably, clusters of conserved hypothetical proteins were strongly upregulated, indicating unexplored stress tolerance components in Phytophthora species. Here, we propose that P. cinnamomi rapidly activates a three-phase strategy involving metabolism readjustments, redox defenses, and cellular structure alterations under salinity conditions. With increasing soil salinization due to climate change, our study provides first mechanistic insights into P. cinnamomi's adaptive plasticity and ecological resilience to abiotic stress. SIGNIFICANCE: This study represents the first temporal proteomic analysis of salinity stress adaptation in Phytophthora cinnamomi, revealing a sophisticated three-phase adaptation strategy. This research fundamentally advances our understanding of how this globally destructive plant pathogen, P. cinnamomi, maintains environmental resilience. Our findings reveal proteome remodelling as a mechanistic framework for understanding stress tolerance in oomycetes, a group of microorganisms responsible for some of the world's most destructive agricultural and forest diseases. Our results show proteins involved in emergency damage control through metabolic recalibration to sustained adaptation. These findings have relevance for predicting pathogen behavior under climate change scenarios, where increasing soil salinity threatens agricultural productivity while simultaneously enhancing pathogen survival and virulence. Understanding how P. cinnamomi responds to prolonged salinity exposure may inform targeted biocontrol strategies and improve predictive models of disease pressure in salt-affected agricultural regions. The temporal analysis framework we present offers a broadly applicable approach for understanding microbial stress adaptation, with implications extending beyond plant pathology to environmental microbiology and biotechnology applications where stress tolerance is paramount.

Phytophthora

Volumetric bone marrow cellularity (VBMC) assessment from routinely processed trephines using three-dimensional x-ray histology and gaussian peak modelling.

Objective.Bone marrow cellularity is routinely estimated from a small number of two-dimensional histology sections, making assessment sensitive to section representativeness, processing artefacts and observer interpretation. Three-dimensional (3D) x-ray histology (XRH), using x-ray computed microtomography (&#xb5;CT), enables non-destructive whole-block imaging of trephine biopsies. This study evaluated whether XRH combined with Gaussian peak modelling could provide a pragmatic whole-block volumetric bone marrow cellularity (VBMC) estimate from formalin-fixed paraffin-embedded (FFPE) trephine biopsy blocks.Approach.Six routinely processed FFPE bone marrow trephine blocks were imaged using &#xb5;CT-based XRH at &#x223c;15 &#xb5;m spatial resolution. VBMC was defined as the red-marrow (RM) fraction of the marrow soft-tissue compartment, RM/(RM + intra-biopsy wax), with wax serving as the volumetric proxy for adipocyte/yellow marrow space. Whole-volume greyscale histograms were modelled using a three-peak Gaussian approach representing intra-biopsy wax, RM and demineralised trabecular matrix. Peak-height and area-under-the-curve metrics were compared with whole-volume 3D segmentation and clinical two-dimensional (2D) cellularity estimates.Main Results.Gaussian peak modelling successfully approximated the segmented tissue-phase distributions. The peak-height-derived VBMC metric showed the closest agreement with whole-volume 3D segmentation, with an average absolute percentage difference of 9.3%, compared with 18.6% for clinical expert 2D cellularity estimates. The area-under-the-curve metric followed similar trends but consistently overestimated VBMC. Clinical 2D cellularity broadly followed whole-biopsy trends but showed one discordant case not explained by slice-position sampling alone. XRH also enabled unrestricted virtual reslicing and visualisation of sectioning-associated artefacts prior to further microtomy.Significance.Pre-sectioning XRH combined with Gaussian peak modelling provides a rapid, segmentation-free route to volumetric cellularity estimation from intact clinical FFPE trephine blocks. The approach supports objective whole-biopsy assessment while remaining compatible with routine histopathology workflows, reflecting the expected limitations of section-based visual estimation despite its role as the current clinical standard. In the near term, it could provide a non-disruptive adjunct to conventional 2D cellularity reporting, pending larger validation studies.

Imaging, Three-Dimensional

The Factors That Contribute to Dysfunctional Behavior in Active Military Personnel: An Umbrella Review.

Dysfunctional behavior in active military personnel is a complex and challenging issue for military forces worldwide. Effective management of this issue requires a comprehensive understanding of the factors that contribute to dysfunctional behavior in military populations. The current study presents an umbrella review that synthesized and analyzed the existing literature on contributory factors to dysfunctional behavior in active military personnel using a systems thinking-based framework. Eleven systematic reviews were identified as eligible for inclusion in the umbrella review. The synthesis identified 14 contributory factors to the following types of dysfunctional behavior: suicidal behavior, substance misuse, domestic violence perpetration, and destructive leadership. The analysis indicated that existing literature focuses on contributory factors relating to the military personnel themselves and not influences in the broader military system or wider society. Additionally, few studies have sought to understand how factors interact to create dysfunctional behavior. Future research would benefit from the use of systems thinking-based frameworks and methods to investigate the factors, across the broader military system and society, that contribute to dysfunctional behavior in active military personnel.

Humans

Urinary Small Extracellular Vesicle DNA as a Biomarker for the Non-Invasive Diagnosis of Bladder Cancer.

Existing diagnostic technologies for bladder cancer (BC) suffer from low sensitivity, low specificity, or a lack of validation. Therefore, validated, non-invasive diagnostic biomarkers with high sensitivity and specificity for early detection of BC are needed to complement and improve upon the limitations of existing diagnostic methods. We used low-pass whole genome sequencing (LP-WGS) technology to detect copy number variations (CNVs) in small extracellular vesicle (sEV) DNA isolated from urine samples of patients. Based on these results, we constructed and validated a diagnostic model to differentiate between benign and malignant bladder lesions. We conducted a receiver operating characteristic analysis and calculated the area under the curve (AUC) to evaluate the performance of the diagnostic model. The urine sEV-DNA LP-WGS data revealed CNV differences between benign and malignant samples. The diagnostic model achieved an AUC of 0.953, a sensitivity of 86.7%, and a specificity of 100% in the training cohort and an AUC of 0.985, a sensitivity of 90%, and a specificity of 100% in the validation cohort. Even at the lowest coverage depth of 0.01X, the performance of the diagnostic model remained relatively robust. Notably, the performance of this diagnostic model surpassed that of the biomarker neuron-specific enolase (sensitivity: 85.7% vs. 64.3%; specificity: 100% vs. 87.5%) and urinary cytology (sensitivity: 100% vs. 66.7%; specificity: 100% vs. 94.1%). Our study demonstrates that urine sEV-DNA exhibits high discriminatory power in distinguishing between benign and malignant bladder lesions, making it a promising tool for auxiliary diagnosis of BC.

Humans

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans

Enhancing Hemoglobin Bart's hydrops fetalis syndrome prevention: a single-tube multiplex real-time PCR assay for the comprehensive detection of four significant &#x3b1;0-thalassemia deletions (--SEA, --THAI, --CR, and --SA) found in Thailand.

BACKGROUND: Hemoglobin (Hb) Bart's hydrops fetalis is a major public health concern in Southeast Asia, particularly in Thailand. Current screening strategies target the two most common &#x3b1;0 -thalassemia deletions (--SEA and --THAI). METHOD: In this study, we developed a single-tube multiplex real-time PCR assay for the simultaneous detection of four clinically relevant &#x3b1;0-thalassemia deletions (--SEA, --THAI, --CR, and --SA). The assay was validated using 538 clinical samples with diverse thalassemia genotypes and compared against conventional gap-PCR as the reference method. Analytical performance, including sensitivity, specificity, and limit of detection (LOD), was evaluated. In addition, clinical utility was assessed in 22 prenatal diagnosis cases at risk of Hb Bart's hydrops fetalis. RESULTS: The study cohort demonstrated substantial genetic heterogeneity, comprising 43 distinct genotypes. The developed assay achieved 100% sensitivity and specificity for all targeted deletions, with complete concordance with gap-PCR results. No cross-reactivity was observed with &#x3b1;+-thalassemia. The assay demonstrated a high analytical sensitivity with a LOD of 9.76&#x2009;&#xd7;&#x2009;10-3&#x2009;ng per reaction. Whereas in prenatal diagnosis, all 22 fetal genotypes were accurately identified, including five cases of homozygous --SEA and one rare compound heterozygous --SEA/--CR fetus. CONCLUSIONS: This study presents a rapid, accurate, and cost-effective multiplex real-time PCR assay capable of detecting both common and rare &#x3b1;0-thalassemia deletions in a single reaction. The assay demonstrates strong potential for implementation in routine clinical laboratories and large-scale population screening, contributing to improved prevention and control of severe thalassemia syndromes in high-prevalence regions.

Humans

Inactivation of Aspergillus flavus spores by dielectric barrier discharge cold plasma: Kinetics, physiological properties and proteomic analysis.

A. flavus, as a pathogen, poses a grave threat to both human and livestock health, significantly influencing agricultural production as well. This study aimed to investigate the inactivation effect and mechanism of dielectric barrier discharge cold plasma (DBD-CP) on A. flavus spores. The results exhibited that DBD-CP effectively inactivated A. flavus spores by the Weibull + Tail model. Furthermore, the physiological and proteomic analysis revealed that DBD-CP destructed cell wall and membrane integrity, causing cellular protein leakage and increasing membrane penetration of ROS generated from DBD-CP. Although intracellular ROS was excessively accumulated, the protein levels and activities of SOD and CAT were decreased, indicating that intracellular redox homeostasis was disrupted by DBD-CP. Subsequently, DBD-CP treatment induced cellular protein oxidation and changed protein structures, resulting in unstable protein structures. Meanwhile, protein synthesis and degradation in A. flavus spores were disturbed by inhibiting ribosome biogenesis, initiation process and NEDD8-mediated UPS, which did not compensate for the loss of protein caused by oxidative damage and leakage, leading to A. flavus spore inactivation. Besides, DBD-CP could attenuate A. flavus virulence by downregulating hydrolytic enzymes and CFEM-related proteins. This study provides novel insight into the inactivation mechanism of DBD-CP against A. flavus spores, which establishes a basis for the application of DBD-CP in controlling pathogenic fungi contamination in grains and crops, promoting the development of DBD-CP in food and agricultural decontamination.

Spores, Fungal

Assessing the threat of Bacillus cereus: From toxin characterization to modern detection strategies.

Bacillus cereus is a spore-forming pathogen responsible for both diarrheal and emetic foodborne illnesses worldwide. Its significance in food safety has received growing attention. Recent advances, including the discovery of novel virulence factors and the development of emerging detection technologies, have provided new insights into its pathogenic mechanisms and surveillance strategies. This review critically examines the global burden of B. cereus infections, and molecular mechanisms of its major virulence factors, and the performance characteristics of current detection knowledge gaps such as the viable-but-non-culturable state and regulatory blind spots for emetic toxins, and discuss unresolved challenges in clinical management. By integrating epidemiological, microbiological, and technological perspectives with critical lens, this review aims to provide a valuable reference for future research and food safety practices.

Bacillus cereus

Dynamics of soil fungal communities restored with biochar from a quarry site.

Quarrying activities have intensified due to population expansion, leading to landscape degradation and ecological destruction. Quarry restoration is usually mandatory in Hong Kong, China. Although biochar is used for sustainable soil amendment, its effectiveness in restoring quarry soil with poor properties has rarely been investigated. A 24-month field study was conducted to evaluate the ecological feasibility of restoring a quarry site by using native species (that is, Castanopsis fissa and Cyclobalanopsis edithiae) and biochar amendment. The results revealed that after 24 months, the application of biochar increased the organic carbon, phosphorus and potassium of the vegetated soil by at least 120 %, 31 % and 12 %, respectively, due to higher cation exchange capacity and better plant growth. The relative abundance of Ascomycota and Basidiomycota increased by 24 % and 47 % with biochar application when C. fissa was planted, which was likely associated with the improved nutrient cycling and soil fertility. Even though adding biochar to bare soil was found to increase the complexity of fungal co-occurrence networks, biochar application only increased fungal diversity in vegetated quarry soil but usually reduced its fungal richness. Moreover, fungal co-occurrence networks in vegetated soil became less complex, suggesting that biochar potentially helped plants to assemble specific, beneficial fungal communities. This effect is most pronounced in the soil planted with C. edithiae, where the structure of fungal communities after 24 months was significantly different from that at other restoration times. This study identifies key fungal phyla enhanced by biochar in quarry soil and provides an effective strategy for facilitating the restoration and management of degraded lands, especially quarry sites.

Charcoal

Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.

AIM: To synthesise existing evidence concerning the application of AI methods in detecting depression through behavioural cues among adults in healthcare and community settings. DESIGN: This is a diagnostic accuracy systematic review. METHODS: This review included studies examining different AI methods in detecting depression among adults. Two independent reviewers screened, appraised and extracted data. Data were analysed by meta-analysis, narrative synthesis and subgroup analysis. DATA SOURCES: Published studies and grey literature were sought in 11 electronic databases. Hand search was conducted on reference lists and two journals. RESULTS: In total, 30 studies were included in this review. Twenty of which demonstrated that AI models had the potential to detect depression. Speech and facial expression showed better sensitivity, reflecting the ability to detect people with depression. Text and movement had better specificity, indicating the ability to rule out non-depressed individuals. Heterogeneity was initially high. Less heterogeneity was observed within each modality subgroup. CONCLUSIONS: This is the first systematic review examining AI models in detecting depression using all four behavioural cues: speech, texts, movement and facial expressions. IMPLICATIONS: A collaborative effort among healthcare professionals can be initiated to develop an AI-assisted depression detection system in general healthcare or community settings. IMPACT: It is challenging for general healthcare professionals to detect depressive symptoms among people in non-psychiatric settings. Our findings suggested the need for objective screening tools, such as an AI-assisted system, for screening depression. Therefore, people could receive accurate diagnosis and proper treatments for depression. REPORTING METHOD: This review followed the PRISMA checklist. PATIENTS OR PUBLIC CONTRIBUTION: No patients or public contribution.

Humans

Autodissemination stations suppress Aedes notoscriptus mosquitoes and reduce Buruli ulcer risk in urban Australia: a randomized controlled field trial.

Aedes notoscriptus are mosquito vectors implicated in transmission of Mycobacterium ulcerans. This bacterium causes a destructive infection of skin and soft tissue called Buruli ulcer. Here we ran a randomized controlled trial in an urban Buruli ulcer endemic area in Melbourne, Australia to test whether autodissemination mosquito control stations, containing pyriproxyfen (larvicide) and Beauveria bassiana (entomopathogenic fungus), suppress Ae. notoscriptus populations. Six geographic areas each received 100 autodissemination stations for 8&#x2009;weeks, and six control areas received no stations between 25 January 2024 and 21 March 2024. The primary outcome measure was mosquito population numbers. After the trial, there was a 70% average reduction in mosquito egg counts among the six intervention areas compared to control areas (P&#x2009;=&#x2009;0.0076). In an ad hoc analysis, we then explored human Buruli ulcer notifications in treatment and control areas. After accounting for the 4.8-month mean incubation period, there was an 83% reduction in infection likelihood coinciding with peak intervention effect (intervention zones 1 case, control zones 6 cases, incidence ratio rate 0.167, 95% CI 0.0026-1.054, P&#x2009;=&#x2009;0.047). The effect was not observed during the same time period in the year previous or following 2024, when no interventions were undertaken. A strong correlation (R2&#x2009;=&#x2009;0.85) was observed between decreased disease risk and mosquito suppression. These data show that autodissemination traps can effectively lower urban mosquito populations and reduce the threat of Buruli ulcer in humans.

Animals

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&#xa0;min, offering a faster alternative to conventional methods while maintaining high sensitivity and reproducibility. Detection limits reach 0.96-1.22&#xa0;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

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer screening. Meanwhile, evidence in Lynch syndrome surveillance is sparse and inconsistent. We assessed the effect of CADe on adenoma detection during Lynch syndrome surveillance. Computer-aided optical diagnosis (CADx) performance for optical differentiation of colorectal lesions was evaluated as a secondary aim. METHODS: CADLY2 was an international, multicentre, open-label, randomised controlled superiority trial at nine specialised hereditary cancer surveillance centres in Belgium, Germany, the Netherlands, and Spain. Adults aged 18 years or older with genetically confirmed Lynch syndrome scheduled for surveillance colonoscopy were randomly assigned (1:1) to high-definition white-light (HD-WL) colonoscopy alone or to HD-WL colonoscopy with computer-aided assistance from CAD EYE (Fujifilm, Tokyo, Japan). CAD EYE was used for CADe during withdrawal and for CADx after lesion detection. Randomisation was done centrally through a secure web-based system using Pocock's minimisation algorithm with a stochastic component and was stratified by centre, sex, previous colorectal cancer, underlying pathogenic variant, and interval since previous colonoscopy. Allocation concealment was ensured through the centralised web-based system. Patients were masked to group allocation until the start of withdrawal in procedures with mild sedation, or until completion of the procedure in procedures with propofol-based sedation. Endoscopists were not masked. The primary outcome was adenoma detection rate, defined as the proportion of patients with at least one histopathologically confirmed adenoma, analysed in the full analysis set (defined as all randomly allocated patients with available data for the primary outcome). The diagnostic performance of the CADx system was evaluated as a secondary outcome. The safety analysis set comprised all randomly allocated patients who underwent a study colonoscopy. This study is registered with the German Clinical Trials Register, DRKS00030695, and is completed. FINDINGS: Between May 9, 2023, and Oct 30, 2025, 757 patients were randomly allocated to HD-WL colonoscopy (377 patients) or to AI-assisted colonoscopy (380 patients); 733 patients were included in the full analysis set (369 HD-WL and 364 AI-assisted). The median age was 49 years (IQR 38-59) in the HD-WL group and 50 years (38-59) in the AI-assisted group; 213 (58%) were female and 156 (42%) male in the HD-WL group, and 207 (57%) were female and 157 (43%) male in the AI-assisted group. The adenoma detection rate was 30&#xb7;9% (114 of 369 patients) with HD-WL versus 33&#xb7;8% (123 of 364 patients) with CADe assistance (odds ratio 1&#xb7;14 [95% CI 0&#xb7;83-1&#xb7;57], p=0&#xb7;41). For CADx differentiation of neoplastic versus non-neoplastic lesions in the paired lesion-level analysis, with histopathology as the reference standard and sessile serrated lesions and traditional serrated adenomas classified as non-neoplastic, CADx sensitivity was 85&#xb7;9% (95% CI 82&#xb7;0-89&#xb7;1) and specificity was 91&#xb7;4% (89&#xb7;4-93&#xb7;0). Three adverse events occurred in the AI-assisted group: two mild post-polypectomy bleedings and one serious pulmonary embolism or deep venous thrombosis unrelated to the procedure. No adverse events occurred in the HD-WL group. INTERPRETATION: CADe-assisted colonoscopy did not show the absolute improvement in adenoma detection rate that was assumed in the prespecified sample-size calculation. CADx did not clearly improve lesion differentiation beyond expert optical diagnosis in expert Lynch syndrome surveillance settings. FUNDING: Third-party research funding of the National Center for Hereditary Tumor Syndromes, University Hospital Bonn.

Humans

Microbial allies in a cotton pest: A descriptive account of associated microbiota dynamics in Dysdercus cingulatus across development.

BACKGROUND: Hemipteran insects harbour several symbiotic partners, mainly bacteria, which play pivotal roles for hosts like dietary provision, support overall physiology, xenobiotic degradation and manipulate/regulate behaviour. Most of these symbionts usually reside and operate from the digestive tracts of the animals. Cotton is one of the major cash crops in India and Dysdercus cingulatus (D. cingulatus) though a secondary pest, is causing significant destruction of cotton bolls, poor lint quality and reduce oil content of seeds. Premature opening of cotton bolls often leads to bacterial and fungal infections, thus resulting in extensive economic loss worldwide. D. cingulatus is a hemimetabolous insect that comprises of developmental stages like egg, nymph (5 instar stages), and adult. The present work explored the ontogeny specific diversity in the associated microbiota and predicted their probable functional inputs in D. cingulatus. RESULTS: The data obtained using 16S rRNA gene sequencing (NovaSeq 6000) revealed presence of members of Proteobacteria (65.83%), Firmicutes (24%), Actinobacteria (10%) phyla throughout the ontogeny of D. cingulatus. Highest alpha diversity of these symbiotic bacteria was recorded in the third instar nymphs in contrast to rest of the developmental stages. Among all the observed genera, Stenotrophomonas, Hungatella and Glutamicibacter were predominant from egg to adult stages. MicFunPred, a tool used for predicting the probable functional inputs of these symbionts, hinted at their probable stage specific contribution in crucial biochemical pathways such as polyketide biosynthesis, ascorbate/aldarate metabolism, pentose phosphate and glyoxylate cycles, steroid hormone and peptidoglycan biosynthesis, and glycolysis/pyruvate metabolism. CONCLUSIONS: The primary investigations on the ontogenetic composition and diversity of associated microbiota, suggest dynamic shifts in D. cingulatus, concurrent with their probable functions/roles in the host development and metabolism. To the best of our knowledge, this is the first report on symbiotic microbiota variation across the developmental stages of D. cingulatus that provides preliminary descriptive observations that may guide future functional and experimental investigations into microbiota-based pest management.

Animals