Search Results (383)

Tomato is a globally significant horticultural crop with substantial economic and nutritional value. High-precision phenotypic analysis of tomato fruit characteristics, enabled by computer vision and image-based phenotyping technologies, is essential for varietal selection and automated quality evaluation. An intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits, including fruit morphology, structure, color and so on. First, a dataset of tomato fruit section images was developed using a depth camera. Second, the SegFormer model was improved by incorporating the MLLA linear attention mechanism, and a lightweight SegFormer-MLLA model for tomato fruit phenotype segmentation was proposed. Accurate segmentation of tomato fruit stem scars and locular structures was achieved, with significantly reduced computational cost by the proposed model. Finally, a Hybrid Depth Regression Model was designed to optimize the estimation of optimal depth. By fusing RGB and depth information, the framework enabled efficient detection of key phenotypic traits, including fruit longitudinal diameter, transverse diameter, mesocarp thickness, and depth and width of stem scar. Experimental results demonstrated a high correlation between the phenotypic parameters detected by the proposed model and the manually measured values, effectively validating the accuracy and feasibility of the model. Hence, we developed an equipment automatically phenotyping tomato fruits and the corresponding software system, providing reliable data support for precision tomato breeding and intelligent cultivation, as well as a reference methodology for phenotyping other fruit crops. Full article

Background/Objectives: Lithium remains the first choice for long-term prophylaxis of mood episodes in bipolar disorder (BD), but only 30% of patients will respond, and there is no reliable method by which to predict treatment response. Ketamine is a rapid antidepressant therapy which ostensibly yields greater results in patients with clinical phenotypes that are classically associated with lithium non-response. This inspired a scoping review to map the overlapping and divergent clinical and mechanistic evidence for acute ketamine response and long-term prophylactic lithium therapy in BD. Methods: We conducted a scoping review of clinical and preclinical studies that examine convergent and divergent predictors and mechanisms of acute response to ketamine and long-term response to lithium. Results: Data from 19 preclinical studies show mechanistic convergence of ketamine and lithium on the GSK-3β/mTOR pathways, and enhancement of synaptic plasticity. Furthermore, lithium appears to consistently limit ketamine-related oxidative stress and hyperlocomotion. However, data from the 23 clinical studies suggest divergence of predictors of response to ketamine and lithium in BD, with ketamine response associated with metabolic risk factors, anxiety/mixed features, and non-melancholic presentations, which are generally predictors of poorer prophylactic lithium response. No study directly tested ketamine response as a predictor of prophylactic lithium response. An important limitation is that clinical studies of ketamine are enriched for lithium-refractory populations and have often included mixed unipolar and bipolar cases. Conclusions: Overall, existing data support mechanistic overlap but clinical divergence between ketamine and lithium responders, though this is confounded by sampling bias. We must therefore undertake longitudinal studies of prophylactic lithium therapy among patients with BD who received ketamine for acute antidepressant treatment in order to investigate if responsiveness to ketamine predicts response to lithium, and establish control over BD earlier in the course of illness. Full article

Background: Cardiorenal syndrome (CRS) reflects bidirectional heart–kidney injury whose mechanisms extend far beyond hemodynamics. High-throughput genomics and multi-omics now illuminate the molecular circuits that couple cardiac and renal dysfunction. Methods: We narratively synthesize animal and human studies leveraging transcriptomics, proteomics, peptidomics, metabolomics, and non-coding RNA profiling to map convergent pathways in CRS and to highlight biomarker and therapeutic implications. Results: Across acute and chronic CRS models, omics consistently converge on extracellular matrix (ECM) remodeling and fibrosis (e.g., FN1, POSTN, collagens), immune–inflammatory activation (IL-6 axis, macrophage/complement signatures), renin–angiotensin–aldosterone system hyperactivity, oxidative stress, and metabolic/mitochondrial derangements in both organs. Single-nucleus and bulk transcriptomes reveal tubular dedifferentiation after cardiac arrest-induced AKI and myocardial reprogramming with early CKD, while quantitative renal proteomics in heart failure demonstrates marked upregulation of ACE/Ang II and pro-fibrotic matricellular proteins despite near-normal filtration. Human translational data corroborate these signals: urinary peptidomics detects CRS-specific collagen fragments and protease activity, and circulating FN1/POSTN and selected microRNAs (notably miR-21) show diagnostic potential. Epigenetic and microRNA networks appear to integrate these axes, nominating targets such as anti-miR-21 and anti-fibrotic strategies; pathway-directed repurposing exemplifies dual-organ benefit. Conclusions: Genomics and multi-omics recast CRS as a systems disease driven by intertwined fibrosis, inflammation, neurohormonal and metabolic programs. We propose a translational framework that advances (i) composite biomarker panels combining injury, fibrosis, and regulatory RNAs; (ii) precision, pathway-guided therapies; and (iii) integrated, longitudinal multi-omics of well-phenotyped CRS cohorts to enable prediction and personalized intervention. Full article

Gut-derived uremic toxins may play a key role in neurodevelopmental conditions such as autism spectrum disorder (ASD) via host-microbe metabolic interactions. We evaluated five uremic toxins—p-cresyl sulfate (PCS), indoxyl sulfate (IS), trimethylamine N-oxide (TMAO), asymmetric dimethylarginine (ADMA), and symmetric dimethylarginine (SDMA)—in urine samples of 97 children with ASD and 71 neurotypical controls, stratified by Bristol Stool Chart (BSC) consistency types. Four of these toxins (PCS, IS, TMAO, ADMA) were integrated into a novel composite biomarker called the Metabolic Index of Gut Dysfunction (MIGD), while SDMA was measured as a complementary renal function marker. While individual metabolite levels showed no statistically significant differences, group-wise analysis by stool phenotype revealed distinct trends. ASD children with hard stools (BSC 1–2) showed elevated PCS levels and the MIGD score (median 555.3), reflecting phenolic fermentation dominance with reduced indolic detoxification. In contrast, children with loose stools (BSC 6–7) had the lowest MIGD values (median 109.8), driven by higher IS and lower ADMA concentrations, suggestive of enhanced indole metabolism. These findings indicate that MIGD may serve as a novel biomarker to stratify metabolic phenotypes in ASD, linking urinary metabolite patterns to gut function. Further validation in larger and longitudinal cohorts is warranted to confirm its potential utility in precision microbiota-targeted interventions. Full article

Hypertrophic cardiomyopathy (HCM) is a complex and heterogeneous myocardial disorder, best evaluated with echocardiography for initial diagnosis, risk stratification, and longitudinal monitoring. This focused review explores the echocardiographic assessment of various morphologic phenotypes of HCM, emphasizing their diagnostic nuances. Distinct phenotypes, including asymmetric septal hypertrophy, concentric hypertrophy, and the less common apical HCM, present unique imaging challenges. Additionally, the review outlines essential techniques and practical tips for assessing left ventricular apical aneurysm flow patterns and dynamic intraventricular gradients. A thorough understanding of mitral valve anatomy and its role in left ventricular outflow tract obstruction is also crucial. Finally, anatomical variants of the mitral valve, papillary muscles and left ventricular myocardium are examined for their contribution to systolic anterior motion and mid-ventricular obstruction as well as for constituting additional phenotypical expressions of HCM, beyond left ventricular hypertrophy. Full article

Background/Objectives: Cardiovascular and pulmonary diseases are leading comorbidities n individuals with Down syndrome (DS). Although clinically well described, preclinical models fully characterizing these cardiopulmonary alterations are lacking. Our objective is to characterize the cardiopulmonary and immunological phenotype in a commonly used DS mouse model, the Ts65Dn mice, and investigate the modulatory effects of green tea extract enriched in epigallocatechin-3-gallate (GTE-EGCG); Methods: Treatment started at embryonic day 9 and continued until postnatal day (PD) 180. Mice were longitudinally monitored using micro-computed tomography, and structural, functional, and immunological alterations were evaluated at PD210 to determine the persistent effects of GTE-EGCG administration; Results: Ts65Dn mice displayed normal structural lung development and presented with right ventricular hypertrophy and reduced B-cell lymphocytes, indicating that this model may find applications in immunological respiratory research specific to the context of DS. GTE-EGCG administration induced transient lung immaturity, persistent decreases in lung function, and airway hyperreactivity, while normalizing arterial and right ventricular morphology and partially restoring B-cell lymphocyte numbers; Conclusions: These findings underscore the dual nature of EGCG modulation, both beneficial and adverse, and highlight the importance of a multiorgan, holistic approach when evaluating therapeutic interventions in DS models. Full article

Background: Multiple sclerosis (MS) is a chronic auto-immune neuroinflammatory disorder presenting as a range of systemic and neurological symptoms, including cognitive impairment. Emerging evidence suggests that diets targeting brain health—such as the Mediterranean (MED) and Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diets—may improve cognitive function; however, studies examining their role in people living with MS are limited. Methods: We examined cross-sectional associations between diet and cognition data from 967 participants in the United Kingdom Multiple Sclerosis Register (UKMSR). Dietary pattern scores (alternate Mediterranean; aMED, and MIND) were derived from the 130-item EPIC-Norfolk food frequency questionnaire. Cognition was assessed using the MS-specific Cognitron-MS (C-MS) battery (13 tasks) and summarised as overall cognition (global G factor) and four domains (object memory, problem solving, information processing speed [IPS], and words memory). Cognitive outcomes were expressed as Deviation-from-Expected (DfE) scores standardised to demographic and device characteristics using external regression-based norms. Linear models were adjusted for total energy intake, MS phenotype, disease duration since diagnosis, and current disease-modifying therapy (DMT) use. Interactions tested moderation by MS phenotype (relapsing vs. progressive MS) and current DMT use (yes vs. no). Sensitivity analyses included within-domain multiple-comparison control, rank-based inverse-normal transformation, and winsorisation. Results: Greater alignment with aMED and MIND dietary patterns were associated with higher scores in specific cognitive domains but not in overall cognition. Higher aMED scores were associated most consistently with better IPS, while higher MIND scores were additionally associated with better words memory. In categorical models, participants with the middle or highest tertiles of aMED or MIND scores performed up to ~0.4 SD better on tasks of Verbal Analogies, Word Definitions, Simple Reaction Time, Words Memory Immediate, or Words Memory Delays compared with those in the lowest tertile. These findings were robust across sensitivity analyses. Stratified analyses showed differential cognitive performance and diet-cognition associations by MS phenotype and DMT use. Conclusions: Mediterranean and MIND dietary patterns showed modest cross-sectional associations with specific cognition domains, with differential cognitive performance in different subgroups according to MS phenotype and DMT use. Although causal inference is not possible, our findings indicate future MS-related dietary studies (longitudinal and/or randomised controlled trials) examining cognitive function domains across different MS subgroups are warranted. Full article

Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling—by capturing the metabolic phenotype of tumors—has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring. Full article

Background: Hypertrophic cardiomyopathy (HCM) is a heterogeneous myocardial disease in which conventional prognostic models, primarily focused on sudden cardiac death, often fail to identify patients at risk of clinically relevant events such as heart failure progression or rehospitalization. Cardiopulmonary exercise testing (CPET) quantifies functional capacity, while stress echocardiography (SE) provides mechanistic insights into exercise-induced hemodynamic changes. Their combined application (CPET–SE) may enhance risk stratification in patients with HCM. Methods: In this retrospective study, 388 patients with obstructive and non-obstructive HCM (mean age 48 ± 15 years, 63.1% male) underwent baseline CPET–SE between 2010 and 2022 and were followed for a median of 7.4 years [IQR 4.3–10.2]. Echocardiographic parameters were assessed at rest and peak exercise, and CPET indices included peak oxygen consumption (pVO₂), ventilatory efficiency, and anaerobic threshold. The primary outcome was a composite of heart failure hospitalization or progression to end-stage HCM. Results: Over a median follow-up of 7.4 years, 63 patients (16.2%) experienced an event of the primary outcome. Patients who developed a primary outcome had greater left atrial diameter (45.0 vs. 41.0 mm, p < 0.001) and indexed volume at rest (36.4 vs. 29.0 mL/m², p < 0.001), with further dilation during stress (p = 0.046); increased LV wall thickness (p = 0.001); higher average E/e′ at rest and during stress (p ≤ 0.004); and higher pulmonary artery systolic pressure at rest (p = 0.027) and during stress (p = 0.044). CPET findings included lower pVO₂ (16.0 vs. 19.5 mL/kg/min, p = 0.001), reduced % predicted pVO₂ (p = 0.006), earlier anaerobic threshold (p = 0.032), impaired ventilatory efficiency (p = 0.048), and chronotropic incompetence (p < 0.001) in patients who experienced a primary outcome. Multivariable analysis identified dyslipidemia (OR 2.58), higher E/e′ (OR 1.06), and lower pVO₂ (OR 0.92) as independently associated with the primary outcome. Conclusions: CPET–SE provided a comprehensive evaluation of patients with HCM, associating aerobic capacity to its hemodynamic determinants. Reduced pVO₂ showed the strongest association with adverse outcomes, while exercise-induced diastolic dysfunction and elevated pulmonary pressures identified a high-risk phenotype. Incorporating CPET–SE into longitudinal management of patients with HCM may enable earlier detection of physiological decompensation and guide personalized therapeutic strategies. Full article

Background: Hereditary spastic paraplegias (HSPs) are a group of neurodegenerative disorders marked by progressive corticospinal tract dysfunction and wide phenotypic variability. Their genetic heterogeneity has so far limited the identification of biomarkers that are broadly applicable across different subtypes. Objective: We aim to define a balanced review on the use of biomarkers in HSP. Methods: This review focuses on fluid biomarkers already available in clinical or research settings—primarily validated in other neurodegenerative diseases—and assesses their potential translation to the HSP context. Biomarkers such as neurofilament light chain, brain-derived tau, glial fibrillary acidic protein, and soluble TREM2 reflect key converging mechanisms of neurodegeneration, including axonal damage, neuronal loss, and glial activation. These shared downstream pathways represent promising targets for disease monitoring in HSP, independently of the underlying genetic mutation. Results: An integrative framework of fluid biomarkers could assist in defining disease progression and stratify patients in both clinical and research settings. Moreover, recent advances in ultrasensitive assays and remote sampling technologies, such as dried blood spot collection, offer concrete opportunities for minimally invasive, longitudinal monitoring. When combined with harmonized multicenter protocols and digital infrastructure, these tools could support scalable and patient-centered models of care. Conclusions: The integration of already available biomarkers into the HSP field may accelerate clinical translation and offer a feasible strategy to overcome the challenges posed by genetic and clinical heterogeneity. Full article

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous syndrome with a continuing high mortality rate. Despite intensive research, established therapies consist mainly of supportive measures, while pharmacological approaches have not yet shown any consistent survival benefits. In recent years, it has become clear that the great clinical and biological diversity of ARDS contributes significantly to the difficulty of demonstrating therapeutic effects. The phenotyping of ARDS has therefore become a central field of research. Different approaches—from clinical parameters and imaging to inflammatory and cardiovascular profiles and multi-omics analyses—have repeatedly identified reproducible subphenotypes that differ in prognosis and, in some cases, in response to therapies. Hypo- and hyperinflammatory subphenotypes have been described as particularly consistent. These are prognostically relevant and, in retrospective analyses, have also shown a differentiated response to glucocorticoids, statins, or fluid strategies. However, endotypes based on causal pathophysiological mechanisms are still largely theoretical. The concept of treatable traits illustrates the potential of personalized therapy but is currently based predominantly on retrospective findings. Future studies should use standardized terminology and multimodal approaches, take longitudinal data into account, and aim for prospective validation to define robust subphenotypes and causal endotypes. This could lay the foundation for true precision medicine in ARDS. Full article

Senescence is a dynamic, multifaceted process implicated in tissue aging, organ dysfunction, and intricately associated with numerous chronic diseases. As senescent cells accumulate, they drive inflammation, fibrosis, and metabolic disruption through the senescence-associated secretory phenotype (SASP). Despite its clinical relevance, senescence remains challenging to detect non-invasively due to its heterogeneous nature and the lack of universal biomarkers. Recent advances in the development of specific imaging probes for positron emission tomography (PET) enable in vivo visualization of senescence-associated pathways across key organs, such as the lung, heart, kidney, and metabolic processes. For instance, [¹⁸F]FPyGal, a β-galactosidase-targeted tracer, has demonstrated selective accumulation in senescent cells in both preclinical and early clinical studies, while FAP-targeted radioligands are emerging as tools for imaging fibrotic remodeling in the lung, liver, kidney, and myocardium. This review examines a new generation of PET radioligands targeting hallmark features of senescence, with the potential to track and measure the process, the ability to be translated into clinical interventions for early diagnosis, and longitudinal monitoring of senescence-driven pathologies. By integrating organ-specific imaging biomarkers with molecular insights, PET probes are poised to transform our ability to manage and treat age-related diseases through personalized approaches. Full article

Left ventricular (LV) hypertrabeculation is increasingly recognized as a phenotype that may reflect physiological adaptation, particularly in athletes exposed to chronic overload, although its functional relevance remains uncertain. This study evaluated the prevalence of excessive trabeculation and its physiological correlation with LV remodeling. We conducted a single-center, cross-sectional study involving 320 Olympic-level athletes without cardiovascular disease. All underwent cardiac magnetic resonance (CMR). Hypertrabeculation was defined by the Petersen criteria. Athletes meeting these criteria were classified as hypertrabeculated and compared with non-hypertrabeculated matched for age, sex, and sport category. LV morphology, function, strain parameters, and hemodynamic forces (HDFs) were analyzed. Hypertrabeculation was identified in 9% of the cohort. No significant differences were observed between groups for training exposure (p = 0.262), body surface area (p = 0.762), LV volumes (end-diastolic volume indexed p = 0.397 end-systolic volume indexed p = 0.118), ejection fraction (p = 0.101), mass (p = 0.919), sphericity index (p = 0.419), myocardial wall thickness (p = 0.394), tissue characterization (T1 mapping p = 0.366, T2 mapping p = 0.833), global longitudinal strain (GLS p = 0.898), global circumferential strain (GCS p = 0.219), or HDFs. All values were within the normal range. In our cohort, LV hypertrabeculation, evaluated by CMR, was relatively common but not associated with structural or functional abnormalities, supporting its interpretation as a benign variant in asymptomatic athletes with normal cardiac function. Full article

Neurodevelopmental disorders (NDDs), including autism spectrum disorder, intellectual disability, and attention-deficit/hyperactivity disorder, are genetically and phenotypically heterogeneous conditions affecting millions worldwide. High-throughput omics technologies—transcriptomics, proteomics, metabolomics, and epigenomics—offer a unique opportunity to link genetic variation to molecular and cellular mechanisms underlying these disorders. However, the high dimensionality, sparsity, batch effects, and complex covariance structures of omics data present significant statistical challenges, requiring robust normalization, batch correction, imputation, dimensionality reduction, and multivariate modeling approaches. This review provides a comprehensive overview of statistical frameworks for analyzing high-dimensional omics datasets in NDDs, including univariate and multivariate models, penalized regression, sparse canonical correlation analysis, partial least squares, and integrative multi-omics methods such as DIABLO, similarity network fusion, and MOFA. We illustrate how these approaches have revealed convergent molecular signatures—synaptic, mitochondrial, and immune dysregulation—across transcriptomic, proteomic, and metabolomic layers in human cohorts and experimental models. Finally, we discuss emerging strategies, including single-cell and spatially resolved omics, machine learning-driven integration, and longitudinal multi-modal analyses, highlighting their potential to translate complex molecular patterns into mechanistic insights, biomarkers, and therapeutic targets. Integrative multi-omics analyses, grounded in rigorous statistical methodology, are poised to advance mechanistic understanding and precision medicine in NDDs. Full article