Search Results (186)

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant neurological plasticity in early childhood, where timely interventions like behavioral therapy, language training, and social skills development can mitigate symptoms. Contributions: We introduce a novel Multi-Atlas Guided Multi-View Contrast Learning (MAMVCL) framework for ASD classification, leveraging functional connectivity (FC) matrices from multiple brain atlases to enhance diagnostic accuracy. Methodology: The MAMVCL framework integrates imaging and phenotypic data through a population graph, where node features derive from imaging data, edge indices are based on similarity scoring matrices, and edge weights reflect phenotypic similarities. Graph convolution extracts global field-of-view features. Concurrently, a Target-aware attention aggregator processes FC matrices to capture high-order brain region dependencies, yielding local field-of-view features. To ensure consistency in subject characteristics, we employ a graph contrastive learning strategy that aligns global and local feature representations. Results: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 85.71%, outperforming most existing methods and confirming its effectiveness. Implications: The proposed model demonstrates superior performance in ASD classification, highlighting the potential of multi-atlas and multi-view learning for improving diagnostic precision and supporting early intervention strategies. Full article

Introduction: Genes in the endolysosome and autophagy pathways are major contributors to hereditary spastic paraplegia (HSP). A pathogenetic link between HSP and Alzheimer disease (AD) involving macroautophagy is well established. Sortilin-related receptor 1 (SORL1), an endosomal trafficking protein, plays a key role in glutamatergic neuron homeostasis and white matter tract integrity. Until now, SORL1 has only been associated with dominant AD and cerebral amyloid angiopathy. Methods: A case of HSP with cerebroretinal vasculopathy (CRV) negative on exome sequencing was further investigated using whole-genome sequencing. RNA-seq, Western blot, and immunofluorescence imaging were performed to explore a potential loss-of-function mechanism. Results: Sequencing revealed a biallelic SORL1 splice donor variant (c.1211 + 1G > A). Transcriptomics confirmed nonsense-mediated decay and aberrant splicing, predicting a disrupted reading frame. Reduced SORLA protein levels and significant enlargement of endolysosomes in patient-derived fibroblasts further cemented the pathogenicity of the variant. Conclusions: The probability that SORL1 acts as a recessive disease-causing gene gathers support from the following data: SORL1 genomic constraint score pRec = 1, high meiotic recombination rates on the locus, phenotype of Sorl1^−/− mice reminiscent of HSP with CRV, and endolysosomal enlargement in SORL1^−/− glutamatergic neurons in vitro. Taken together, SORL1 is probably a new candidate for a recessive form of complicated HSP. Full article

Background: Venetoclax-based combination therapies have provided treatment options for patients with acute myeloid leukemia (AML) who are unfit for intensive chemotherapy. However, venetoclax resistance is common, and for such patients, the prognosis is dismal, and treatment approaches with different mechanisms of action are urgently needed. γδ T cells are a promising candidate owing to their good safety profile and cytotoxic effects in various types of cancers but are mostly unstudied in AML. Methods: Here we used flow cytometry to profile the subtype and memory phenotype of peripheral blood γδ T cells in AML patients and investigate the feasibility of using donor-derived Vγ9Vδ2 T cells to treat AML as both a single agent and in combination with venetoclax. Additionally, we used bioluminescence imaging to examine the effect of donor-derived Vγ9Vδ2 T cells on AML xenograft models alone and in combination with venetoclax. Results: We observed that Vδ2 T cells were less abundant and the TEMRA (terminally differentiated effector memory) phenotype was more prevalent as compared with that of healthy donors, suggesting that replenishing patients with Vδ2 T cells may be an effective treatment option. We found that donor-derived Vγ9Vδ2 T cells that Vγ9Vδ2 T cells efficiently induced apoptosis in AML cells from eight cell lines and three primary cultures in an effector-to-target cell ratio-dependent manner. Moreover, Vγ9Vδ2 T cells showed potent cytotoxicity against the venetoclax-resistant OCI-AML3 cell line and remained potent in the presence of venetoclax. Treatment with Vγ9Vδ2 T cells significantly extended survival in two AML xenograft models established with the aggressive Molm-13 and the venetoclax-resistant OCI-AML3 cell lines. An additive effect of venetoclax and Vγ9Vδ2 T cells was observed in the latter model. Conclusions: Overall, these findings suggest Vγ9Vδ2 T cells as a promising “off-the-shelf” immunotherapy approach for AML patients, especially for patients with venetoclax-resistant disease. Full article

Chlorophyll is crucial for crop photosynthesis and useful for monitoring crop growth and predicting yield. Its content can be indicated by SPAD meter readings. However, SPAD-based monitoring of rice is time- and labor-intensive, whereas remote sensing offers non-destructive, rapid, real-time solutions. Compared with mainstream unmanned aerial vehicle, emerging phenotyping robots can carry multiple sensors and acquire higher-resolution data. Nevertheless, the feasibility of estimating rice SPAD using multi-sensor data obtained by phenotyping robots remains unknown, and whether the integration of machine learning algorithms can improve the accuracy of rice SPAD monitoring also requires investigation. This study utilizes phenotyping robots to acquire multispectral and RGB images of rice across multiple growth stages, while simultaneously collecting SPAD values. Subsequently, four machine learning algorithms—random forest, partial least squares regression, extreme gradient boosting, and boosted regression trees—are employed to construct SPAD monitoring models with different features. The random forest model combining vegetation indices, color indices, and texture features achieved the highest accuracy (R² = 0.83, RMSE = 1.593). In summary, integrating phenotyping robot-derived multi-sensor data with machine learning enables high-precision, efficient, and non-destructive rice SPAD estimation, providing technical and theoretical support for rice phenotyping and precision cultivation. Full article

Background: Structurally active phospholipoproteomic formulations that lack pharmacodynamic targets or systemic absorption present unique challenges for validation. Designed for immune compatibility or structural modulation—rather than therapeutic effect—these platforms cannot be evaluated through conventional clinical or molecular frameworks. Methods: This study introduces a standardized, non-invasive ex vivo protocol using real-time kinetic imaging to document biological behavior under neutral conditions. Eight human tumor-derived adherent cell lines were selected for phenotypic stability and imaging compatibility. Phospholipoproteomic preparations were applied under harmonized conditions, and cellular responses were recorded continuously over 48 h. Results: Key parameters included signal continuity, morphological integrity, and inter-batch reproducibility. The system achieved high technical consistency without labeling, endpoint disruption, or destructive assays. Outputs included full kinetic curves and viability signals across multiple cell–fraction pairings. Conclusions: This method provides a regulatorily compatible foundation for functional documentation in non-pharmacodynamic programs where clinical trials are infeasible. It supports early-stage screening, batch comparability, and audit-ready records within SAP, CTD, or real-world evidence (RWE) ecosystems. By decoupling validation from systemic exposure, the protocol enables scalable, technically grounded decision-making for structurally defined immunobiological platforms. Full article

Positron emission tomography (PET) has become a powerful tool in Alzheimer’s disease (AD) research by enabling in vivo visualization of pathological biomarkers. Recent efforts have aimed to integrate PET-derived imaging phenotypes with genome-wide association studies (GWASs) to better elucidate the genetic architecture underlying AD. This systematic review examines studies that leverage PET imaging in the context of GWASs (PET-GWASs) to identify genetic variants associated with disease risk, progression, and brain region-specific pathology. A comprehensive search of PubMed and Embase databases was performed on 18 February 2025, yielding 210 articles, of which 10 met pre-defined inclusion criteria and were included in the final synthesis. Studies were eligible if they included AD populations, employed PET imaging alongside GWASs, and reported original full-text findings in English. No formal protocol was registered, and the risk of bias was not independently assessed. The included studies consistently identified APOE as the strongest genetic determinant of amyloid burden, while revealing additional significant loci including ABCA7 (involved in lipid metabolism and amyloid clearance), FERMT2 (cell adhesion), CR1 (immune response), TOMM40 (mitochondrial function), and FGL2 (protective against amyloid deposition in Korean populations). The included studies suggest that PET-GWAS approaches can uncover genetic loci involved in processes such as lipid metabolism, immune response, and synaptic regulation. Despite limitations including modest cohort sizes and methodological variability, this integrated approach offers valuable insight into the biological pathways driving AD pathology. Expanding PET-genomic datasets, improving study power, and applying advanced computational tools may further clarify genetic mechanisms and contribute to precision medicine efforts in AD. Full article

Background: Right heart catheterization (RHC) is the gold-standard for diagnosis of pulmonary hypertension (PH) but is a terminal procedure in neonatal mice. The objective was to validate echocardiographic measures of PH to establish the diagnostic capability against pulmonary vascular histology in neonatal mice. Methods: Adult mice, exposed to hypoxia or normoxia, were assessed by echocardiography and RHC to evaluate right ventricle (RV) morphometry and function. Echocardiographic measures identified in adult mice were then used to evaluate PH characteristics in hypoxia-exposed neonatal mice. Physiological parameters were compared to histopathology in all mice. Results: Hypoxia-challenged adult mice developed PH with RHC, demonstrating confirmed elevated RV systolic pressure (RVSP), RV hypertrophy, and increased cross-sectional area and neomuscularization of pulmonary vessels. Echocardiography-derived RV free wall (RVFW) thickness correlated with RV mass. Tricuspid valve annulus tissue Doppler imaging (TV TDI), tricuspid annular plane systolic excursion (TAPSE), pulmonary artery acceleration measures (PAAT), and TAPSE × PAAT (a measure of RV work) all correlated with RVSP determined by RHC. In neonatal mice exposed to hypoxia, PAAT, TV TDI, TAPSE, and TAPSE × PAAT were decreased and RVFW thickness was increased, correlating with the histologic phenotype of PH. Conclusions: Echocardiographic indices of RV morphology and function provide reliable estimates of invasive RV hemodynamics in hypoxia-induced PH. Full article

The development of scalable, non-invasive tools to assess tumor responsiveness to structurally active immunoformulations remains a critical unmet need in solid tumor immunotherapy. Here, we introduce a real-time, ex vivo functional system to classify tumor cell lines exposed to a phospholipoproteomic platform, without relying on cytotoxicity, co-culture systems, or molecular profiling. Tumor cells were monitored using IncuCyte^® S3 (Sartorius) real-time imaging under ex vivo neutral conditions. No dendritic cell components or immune co-cultures were used in this mode. All results are derived from direct tumor cell responses to structurally active formulations. Using eight human tumor lines, we captured proliferative behavior, cell death rates, and secretomic profiles to assign each case into stimulatory, inhibitory, or neutral categories. A structured decision-tree logic supported the classification, and a Functional Stratification Index (FSI) was computed to quantify the response magnitude. Inhibitory lines showed early divergence and high IFN-γ/IL-10 ratios; stimulatory ones exhibited a proliferative gain under balanced immune signaling. The results were reproducible across independent batches. This system enables quantitative phenotypic screening under standardized, marker-free conditions and offers an adaptable platform for functional evaluation in immuno-oncology pipelines where traditional cytotoxic endpoints are insufficient. This approach has been codified into the STIP (Structured Traceability and Immunophenotypic Platform), supporting reproducible documentation across tumor models. This platform contributes to upstream validation logic in immuno-oncology workflows and supports early-stage regulatory documentation. Full article

Dry bean, the fourth-largest pulse crop in Canada is increasingly impacted by climate variability, needing efficient methods to support cultivar development. This study investigates the potential of unmanned aerial vehicle (UAV)-based Light Detection and Ranging (LiDAR) and multispectral imaging (MSI) for high-throughput phenotyping of dry bean traits. Image data were collected across two dry bean field trials to assess plant height, lodging and seed yield. Multiple LiDAR-derived features accessing canopy height, crop lodging and digital biomass were evaluated against manual height measurements, visually rated lodging scale and seed yield, respectively. At the same time, three MSI-derived data were used to estimate seed yield. Classification- and regression-based machine learning models were used to estimate key agronomic traits using both LiDAR and MSI-based crop features. The canopy height derived from LiDAR showed a good correlation (R² = 0.86) with measured plant height at the mid-pod filling (R6) stage. Lodging classification was most effective using Gradient Boosting, Random Forest and Logistic Regression, with R8 (physiological maturity stage) canopy height being the dominant predictor. For seed yield prediction, models integrating LiDAR and MSI outperformed individual datasets, with Gradient Boosting Regression Trees yielding the highest accuracy (R² = 0.64, RMSE = 687.2 kg/ha and MAE = 521.6 kg/ha). Normalized Difference Vegetation Index (NDVI) at the R6 stage was identified as the most informative spectral feature. Overall, this study demonstrates the importance of integrating UAV-based LiDAR and MSI for accurate, non-destructive phenotyping in dry bean breeding programs. Full article

Background: Three-dimensional skeletal muscle organoids (3D SkMO) are becoming of increasing interest for preclinical studies in Duchenne muscular dystrophy (DMD), provided that the used platform demonstrates the possibility to form functional and reproducible 3D SkMOs, to investigate on potential patient-related phenotypic differences. Methods: In this study, we employed fibrin-based 3D skeletal muscle organoids derived from immortalized myogenic precursors of DMD patients carrying either a stop codon mutation in exon 59 or a 48–50 deletion. We compared dystrophic lines with a healthy wild-type control (HWT) by assessing microtissue formation ability, contractile function at multiple timepoints along with intracellular calcium dynamics via calcium imaging, as well as expression of myogenic markers. Results: We found patient-specific structural and functional differences in the early stages of 3D SkMO development. Contractile force, measured as both single twitch and tetanic responses, was significantly lower in dystrophic 3D SkMOs compared to HWT, with the most pronounced differences observed at day 7 of differentiation. However, these disparities diminished over time under similar culturing conditions and in the absence of continuous nerve-like stimulation, suggesting that the primary deficit lies in delayed myogenic maturation, as also supported by gene expression analysis. Conclusions: Our results underline that, despite the initial maturation delay, DMD muscle precursors retain the capacity to form functional 3D SkMOs once this intrinsic lag is overcome. This suggests a critical role of dystrophin in early myogenic development, while contraction-induced stress and/or an inflammatory microenvironment are essential to fully recapitulate dystrophic phenotypes in 3D SkMOs. Full article

Alfalfa is a deep-rooted perennial forage crop with diverse drought-tolerant traits. This study evaluated 250 alfalfa half-sib populations over three growing seasons (2021–2023) under irrigated and rainfed conditions in the Mediterranean drought-prone region of Central Chile (Cauquenes), aiming to identify high-yielding, drought-tolerant populations using remote sensing. Specifically, we assessed RGB-derived indices and canopy temperature difference (CTD; Tc − Ta) as proxies for forage yield (FY). The results showed considerable variation in FY across populations. Under rainfed conditions, winter FY ranged from 1.4 to 6.1 Mg ha⁻¹ and total FY from 3.7 to 14.7 Mg ha⁻¹. Under irrigation, winter FY reached up to 8.2 Mg ha⁻¹ and total FY up to 25.1 Mg ha⁻¹. The AlfaL4-5 (SARDI7), AlfaL57-7 (WL903), and AlfaL62-9 (Baldrich350) populations consistently produced the highest yields across regimes. RGB indices such as hue, saturation, b*, v*, GA, and GGA positively correlated with FY, while intensity, lightness, a*, and u* correlated negatively. CTD showed a significant negative correlation with FY across all seasons and water regimes. These findings highlight the potential of RGB imaging and CTD as effective, high-throughput field phenotyping tools for selecting drought-resilient alfalfa genotypes in Mediterranean environments. Full article

Background/Objectives: Peripheral neuroblastic tumors (pNT) are a biologically heterogeneous group of embryonal tumors that derive from the neural crest and affect the sympathetic nervous system. So far, little is known about the complex immune landscape in these rare childhood cancers. Methods: We focused on the immune cell infiltrate of treatment-naïve pNT from 24 patients, including high-risk neuroblastoma (HR-NBL), non-high-risk neuroblastoma (NHR-NBL), ganglioneuroblastoma (GNBL), and rare ganglioneuroma (GN). To gain novel insights into the immune architecture of these pNT subtypes, we used multiplex immunohistochemistry, multispectral imaging, and algorithm-based data evaluation to detect and characterize T cells, B cells, neutrophils, macrophages, and tertiary lymphoid structures (TLS). Results: The majority of the investigated tumor-infiltrating immune cells were macrophages and T cells. Their detailed phenotypic characterization revealed high proportions of M2-like macrophages as well as activated GrzB⁺ CD8⁺ and PD-1⁺ T lymphocytes. Proportions of these T cell phenotypes were significantly increased in GN compared to HR-NBL, NHR-NBL, or GNBL. In addition, TLS occurred in 11 of 24 patients, independent of immune cell frequencies in the whole tissues. Interestingly, all GN, most GNBL, but only a few NBL contained TLS. We distinguished between three TLS maturation stages that were present irrespective of the pNT subtype. The majority belonged to mature TLS of the primary follicle state. Mature LAMP3⁺ dendritic cells were also found, predominantly in T cell zones of TLS. Furthermore, TLS presence identified pNT patients with significantly prolonged progression-free survival in contrast to all other analyzed immunological features. Conclusions: We propose TLS to be a potential prognostic marker for pNT to predict patient outcomes. Full article

Identifying blueberry characteristics such as the wax bloom is an important task that not only helps in phenotyping (for novel variety development) but also in classifying berries better suited for commercialization. Deep learning techniques for image analysis have long demonstrated their capability for solving image classification problems. However, they usually rely on large architectures that could be difficult to implement in the field due to high computational needs. This paper presents a small (only 1502 parameters) Bayesian–CNN ensemble architecture that can be implemented in any small electronic device and is able to classify wax bloom content in images. The Bayesian model was implemented using Keras image libraries and consists of only two convolutional layers (eight and four filters, respectively) and a dense layer. It includes a statistical module with two metrics that combines the results of the Bayesian ensemble to detect potential misclassifications. The first metric is based on the Euclidean distance (${\mathcal{L}}_2$) between Gaussian mixture models while the second metric is based on a quantile analysis of the binary class predictions. Both metrics attempt to establish whether the model was able to find a good prediction or not. Three experiments were performed: first, the Bayesian–CNN ensemble model was compared with state-of-the-art small architectures. In experiment 2, the metrics for detecting potential misclassifications were evaluated and compared with similar techniques derived from the literature. Experiment 3 reports results while using cross validation and compares performance considering the trade-off between accuracy and the number of samples considered as potentially misclassified (not classified). Both metrics show a competitive performance compared to the state of the art and are able to improve the accuracy of a Bayesian–CNN ensemble model from $96.98\%$ to $98.72\pm 0.54\%$ and $98.38\pm 0.34\%$ for the ${\mathcal{L}}_2$ and $r^2$ metrics, respectively. Full article

Hyperspectral plant phenotyping is a method that has a wide range of applications in various fields, including agriculture, forestry, food processing, medicine and plant breeding. It can be used to obtain a large amount of spectral and spatial information about an object. However, it is important to acknowledge the inherent limitations of this approach, which include the presence of noise and the redundancy of information. The present study aims to assess a novel approach to hyperspectral data preprocessing, namely Random Reflectance (RR), for the classification of plant species. This study employs machine learning (ML) algorithms, specifically Random Forest (RF) and Gradient Boosting (GB), to analyse the performance of RR in comparison to Min–Max Normalisation (MMN) and Principal Component Analysis (PCA). The testing process was conducted on data derived from the proximal hyperspectral imaging (HSI) of leaves from three different maple species, which were sampled from trees at 7–10-day intervals between 2021 and 2024. The RF algorithm demonstrated a relative increase of 8.8% in the F1-score in 2021, 9.7% in 2022, 11.3% in 2023 and 11.8% in 2024. The GB algorithm exhibited a similar trend: 6.5% in 2021, 13.2% in 2022, 16.5% in 2023 and 17.4% in 2024. It has been demonstrated that hyperspectral data preprocessing with the MMN and PCA methods does not result in enhanced accuracy when classifying species using ML algorithms. The impact of preprocessing spectral profiles using the RR method may be associated with the observation that the synthesised set of spectral profiles exhibits a stronger reflection of the general parameters of spectral reflectance compared to the set of actual profiles. Subsequent research endeavours are anticipated to elucidate a mechanistic rationale for the RR method in conjunction with the RF and GB algorithms. Furthermore, the efficacy of this method will be evaluated through its application in deep machine learning algorithms. Full article

Background/Objectives: Observational studies have suggested a correlation between brain imaging alterations and Parkinson’s disease (PD). However, data on causal relationships are still lacking. This study aimed to examine the causal relationship between brain imaging-derived phenotypes (IDPs) and PD. Methods: A bidirectional two-sample Mendelian randomization analysis was conducted to explore the causal association between IDPs and PD. Summary-level data for IDPs (n = 39,691), PD (n = 482,730), and PD symptoms (n = 4093) were obtained from genome-wide association studies of European ancestry. Clinical validation was performed in an Asian cohort, which involved healthy controls (n = 81), patients with idiopathic rapid-eye-movement sleep behavior disorder (iRBD) (n = 47), and patients with PD (n = 85). Results: We found 13 IDPs with significant causal effects on PD and seven reciprocal effects of PD on IDPs. For instance, increased median T2star in the right caudate (odds ratio = 1.23, 95% confidence interval 1.08–1.40, p = 0.0057) and bilateral putamen (left: odds ratio = 1.25, 95% confidence interval 1.09–1.43, p = 0.0056; right: odds ratio = 1.25, 95% confidence interval 1.10–1.43, p = 0.0056) were associated with PD. Enlargement of the left thalamus (odds ratio = 1.50, 95% confidence interval 1.14–1.96, p = 0.016) demonstrated causal links with PD. No reverse causal effects were detected. Observational analyses results in the Asian cohort (healthy controls, iRBD, PD) aligned with the Mendelian randomization results. Conclusions: Our results suggest bidirectional causal links between IDPs and PD, offering insights into disease mechanisms and potential imaging biomarkers for PD. Full article