Search Results (22)

Molecular diagnosis has advanced rapidly in neurogenetic disorders, yet translating genotype into patient-specific predictions of brain network dysfunction and progression remains limited. Virtual brain models provide a structured solution by embedding individual anatomy and connectomics into biophysical whole-brain simulations. The critical step is to position genetics not as a diagnostic label, but as a constructive input to model design. This review outlines a genetics-centered framework for virtual brain modeling. First, atlas-derived transcriptomic and cell-type maps can define region-specific molecular priors, constraining vulnerability or excitability parameters and reducing model degeneracy. Second, when reproducible genotype-linked network phenotypes exist, mutation groups can inform stratified initialization and progression regimes. Third, at the patient level, exome and CNV data—summarized as pathway burdens and, where appropriate, calibrated polygenic modifiers—can be translated into individualized priors or regularizers, provided that mapping rules are explicit and externally validated. By integrating genetics at multiple levels of evidence, virtual brain models gain mechanistic plausibility, improved calibration, and explicit uncertainty quantification. The most realistic impact over the next few years is likely to be improved stratification, progression-aware forecasting, and scenario-based decision support in rare neurogenetic diseases, especially where longitudinal cohort infrastructure and validated biomarker inputs are already available, rather than deterministic individual prediction. Full article

Major depressive disorder (MDD) affects over 330 million people globally, yet up to 30% of patients fail initial pharmacotherapy due to genetic variability in drug metabolism. This narrative review synthesizes evidence on pharmacogenomic (PGx) guided approaches for MDD, emphasizing their integration with POC diagnostics and engineering solutions. Approximately 40–50% of patients carry actionable variants in CYP2C19 or CYP2D6, which govern the metabolism of selective serotonin reuptake inhibitors. Landmark trials (GUIDED, PRIME Care, GAPP-MDD) and meta-analyses demonstrate that PGx-informed prescribing modestly but significantly improves remission and response rates, particularly in treatment-resistant depression. Established guidelines from CPIC and the Dutch Pharmacogenetics Working Group provide actionable recommendations for CYP2D6 and CYP2C19 phenotypes. Emerging POC platforms, including Genomadix Cube and Genedrive, now deliver CYP2C19 results within one hour, supporting rapid clinical decisions. However, psychiatric-specific implementation data remain limited compared to cardiology; current POC devices lack multi-gene capabilities, and most studies underrepresent diverse populations. Persistent barriers include variable reimbursement, limited clinician education, and fragmented electronic health record integration. Future directions include pre-emptive genotyping, expanded multi-gene panels, and embedded clinical decision support. With continued engineering innovation and rigorous validation, PGx-guided care holds promise for reducing the trial-and-error burden and advancing precision psychiatry. Full article

Background: Hospital wastewater (HWW) is a critical reservoir for carbapenem-resistant Gram-negative bacteria. Methods: Between November 2024 and August 2025, sixty 24 h composite wastewater samples were collected from five tertiary hospitals. Of the 244 carbapenem-resistant isolates recovered, 34 bla_NDM-1-positive Acinetobacter isolates were subjected to phenotypic, genotypic, and plasmid analyses. Results: Eleven species were identified among the 34 carbapenem-resistant Acinetobacter isolates, predominantly non-baumannii Acinetobacter (NBA). All isolates were carbapenem-resistant (34/34, 100%) with high-level MICs (meropenem MIC_50/90, 32/64 mg/L; imipenem MIC_50/90, >128/>128 mg/L); 21% (7/34) of isolates were resistant to colistin, and resistance to ceftazidime, cefepime, and trimethoprim-sulfamethoxazole was 100%, 94%, and 76%, respectively. Core-genome SNP analysis revealed highly similar isolates across hospitals within the same season (1-2 SNPs) or within the same hospital across seasons (19 SNPs). Genomic analysis showed that bla_NDM-1 was present in all isolates (34/34, 100%), with plasmid carriage in 85.3% (29/34); bla_OXA-58 co-occurred in 62.1% (18/29), mainly on Rep_3 plasmids (19/29), especially R3-T28 (15/29) that frequently carried bla_OXA-58 (10/15). Two unclassified plasmids co-harboring bla_NDM-1 and bla_OXA-23 were detected in Acinetobacter tandoii isolates. The bla_NDM-1 gene was embedded in a conserved Tn125-like structures with variable flanks. Conclusions: Overall, carbapenem-resistant Acinetobacter from hospital wastewater frequently carried Rep_3 plasmid-borne bla_NDM-1, especially R3-T28 and often co-occurring with bla_OXA-58, within a conserved Tn125-like core structures. These findings highlight HWW as a potential hotspot for dissemination of carbapenem resistance and support routine genomic surveillance under a One Health framework. Full article

Background/Objectives: Bacterial conjunctivitis is a common ocular infection requiring prompt treatment, particularly in vulnerable patients, and may influence perioperative outcomes. This study aimed to characterize conjunctival bacterial isolates phenotypically and genotypically, to evaluate their biofilm-forming capacity, and to investigate the relationship between resistance gene carriage, resistance phenotypes, and biofilm-associated antimicrobial resistance (AMR). Methods: A prospective, single-center, cross-sectional study was conducted on bacterial isolates from conjunctival samples of patients examined in an ophthalmology department. Antimicrobial susceptibility testing (AST) was performed to determine the minimum inhibitory concentrations (MICs). Resistance genes were detected by quantitative PCR. Biofilm-forming capacity was assessed using the microtiter plate assay, and biofilm susceptibility to amikacin (AK) and levofloxacin (LEV) was evaluated using a biofilm susceptibility assay. Results: A total of 78 isolates were analyzed; Gram-positive cocci prevailed (GPC, 84.6%), being significantly more frequent than Gram-negative bacilli (GNB, p < 0.001). Among GPC, 65.2% were multidrug-resistant, with Staphylococcus epidermidis emerging as the most frequent species (p < 0.001). Resistance gene carriage was detected in 33.3% of GNB. Strong biofilm formation was observed in 22.7% of GPC versus 58.3% of GNB. It should be noted that the relatively small number of GNB may limit the statistical robustness of comparisons between Gram-positive and Gram-negative groups. A statistically significant association between resistance genes and biofilm capacity was found only in Staphylococcus aureus (p = 0.027). Biofilm-embedded bacteria showed increased antimicrobial tolerance, particularly for AK in S. aureus and for both AK and LEV in S. epidermidis (p < 0.001). Conclusions: The prevalence of multidrug-resistant conjunctival isolates and their biofilm-forming capacity highlights the clinical importance of biofilm-related resistance and support integrating AMR profiling with biofilm assessment to optimize empirical therapy in bacterial conjunctivitis. Full article

Primary Failure of Eruption (PFE) is a disorder characterized by aberrant tooth eruption, in which one or more teeth fail to follow the physiological eruptive pathway and remain partially or completely embedded within the bone or soft tissues. Although the etiopathogenesis of PFE is not yet fully elucidated, several contributing factors have been identified, including genetic alterations, hormonal disturbances, and systemic conditions. An expanding body of evidence points to the centrality of genetic determinants in the etiopathogenesis of PFE, supporting its occurrence in both syndromic contexts and non-syndromic presentations. Non-syndromic forms are closely related to heterozygous variants in the Parathyroid Hormone 1 Receptor (PTH1R) gene, located on chromosome 3p21, which encodes a receptor essential for the regulation of bone and dental growth and development. In most cases, pathogenic variants result in a non-functional receptor. To date, a substantial number 50 PTH1R variants have been documented in individuals exhibiting a phenotype consistent with PFE, underscoring the central involvement of this gene in the disorder’s molecular basis. Advances in understanding the genetic contribution to PFE emphasize the need for early diagnosis, as timely identification of the condition can prevent secondary dental complications and reduce reliance in adulthood on invasive orthodontic or surgical interventions, including extractions, orthognathic surgery, and implant-supported rehabilitation. This review aims to provide a comprehensive analysis of the spectrum of PTH1R variants implicated in PFE, examining genotype–phenotype correlations and their implications for diagnostic strategies and clinical management. Full article

The cultivation of potatoes is essential for rural food security, and the use of Unmanned Aerial Vehicle Red-Green-Blue (UAV-RGB) imagery allows for precise and cost-effective estimation of yield and identification of varieties, overcoming the limitations of manual assessment. We evaluated four INIA varieties (Bicentenario, Canchán, Shulay and Tahuaqueña) by integrating agronomic measurements (height, number and weight of tubers, leaf health) with color and textural indices derived from RGB orthomosaics. Yield prediction was modeled using Random Forest (RF) and Gradient Boosting (GB); varietal identification was approached with (i) a Convolutional Neural Network (CNN) that classifies RGB images and (ii) classical models such as Random Forest, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Decision Trees and Logistic Regression trained on EfficientNetB0 embeddings. The results showed significant genotypic differences in yield (p < 0.001): Tahuaqueña 13.86 ± 0.27 t ha⁻¹ and Bicentenario 6.65 ± 0.27 t ha⁻¹. The number of tubers (r = 0.52) and plant height (r = 0.23) correlated with yield; RGB indices showed low correlations (r < 0.3) and high redundancy (r > 0.9). RF achieved a better fit (Coefficient of determination, R² = 0.54; Root Mean Square Error, RMSE = 2.72 t ha⁻¹), excelling in stolon development (R² = 0.66) and losing precision in maturation due to foliar senescence. In classification, the CNN and RF on embeddings achieved F1-macro ≈ 0.69 and 0.66 (Receiver Operating Characteristic—Area Under the Curve, ROC AUC RF = 0.89), with better identification of Bicentenario and Shulay. We conclude that UAV-RGB is a cost-effective alternative for phenotypic monitoring and varietal selection in high Andean contexts. These findings support the integration of UAV-RGB imagery into breeding and monitoring pipelines in resource-limited Andean systems. Full article

Targeted next-generation sequencing (tNGS) closes the gap between point-of-care rapid tests and phenotypic drug susceptibility testing (pDST) in drug-resistant tuberculosis (DR-TB). The 2025 World Health Organization (WHO) consolidated guidelines and the operational handbook place tNGS after initial automated nucleic acid amplification tests (aNAATs) for the delivery of catalogue-linked molecular drug susceptibility testing (DST) for a broad drug panel, reserving whole-genome sequencing (WGS) and/or pDST for discordance resolution, confirmation, and surveillance. This review summarizes (i) the core tNGS principles and panel design; (ii) platform-specific workflows for Illumina and Nanopore, including direct-from-sample implementations and typical turnaround times; (iii) catalogue-based interpretation against the 2023 WHO Mycobacterium tuberculosis mutation catalogue, with emphasis on bedaquiline/clofazimine (BDQ/CFZ) resistance and management of uncertain variants; (iv) pooled accuracy and sources of genotype–phenotype discordance; and (v) practical requirements for bioinformatics, quality assurance/external quality assessment (QA/EQA), and standardized reporting. We summarize operational and economic considerations (throughput, batching, and network design) to clarify where tNGS adds value compared with alternative strategies and to outline priority research needs, including (i) performance standards for culture-free tNGS, (ii) robust heteroresistance detection, (iii) standardized variant curation, and (iv) data-sharing frameworks to refine genotype–phenotype links. When embedded within validated QA/EQA frameworks and catalogue-linked reporting systems, tNGS can shorten the time to effective therapy by rapidly informing fluoroquinolone (FQ) susceptibility and providing early, tiered resistance signals for newer agents (e.g., BDQ), with indeterminate findings prompting reflex pDST/WGS. Full article

The reasons for the aggressive clinical phenotype of signet ring cell carcinoma (SRCC) have not been fully elucidated. Previous studies suggest similarities in the genotype of colorectal and gastric SRCC and a clear distinction from non-SRCC. The immune microenvironments of gastric and colorectal SRCC have not been comprehensively examined. We isolated RNA from formalin-fixed, paraffin-embedded (FFPE) sections of 34 tumor specimens, 10 colorectal SRCC, 24 gastric SRCC, 4 non-SRCC colorectal (CCC), and 3 gastric adenocarcinoma (GCC) samples. The PanCancer Immune Profiling Panel was used to evaluate the expression of 770 immune-related genes. We compared the expression profiles of colorectal and gastric SRCC and non-SRCC adenocarcinoma. We found that the immune-related gene expression profiles (GEPs) of colorectal SRCC (CR-SRCC) and gastric SRCC (G-SRCC) were distinct from the non-SRCC. A total of 127 genes were upregulated and 32 downregulated in CR-SRCC compared to CCC. Only two genes (CCL27 and LAIR2 reached statistical significance (p-adj < 0.05)) among the differentially expressed genes in G-SRCC compared to GCC. None of the clinically relevant immune checkpoints were significantly differentially expressed in SRCC vs. non-SRCC. Overall, we noted a relative abundance of CD8+ cells in CR-SRCC and G-SRCC and relative overexpression of genes involved in innate immune response including the complement pathway. Finally, we identified IL13RA2 as a potential biomarker and therapeutic target candidate for CR-SRCC. The immune microenvironments of CR-SRCC and G-SRCC are distinct from non-SRCC. Broadly, both CR-SRCC and G-SRCC are characterized by a complex immune microenvironment that features cytotoxic cells and innate immune activity that may facilitate immune evasion. Full article

Plant phenotyping plays a vital role in connecting genotype to environmental adaptability, with important applications in crop breeding and precision agriculture. Traditional leaf measurement methods are laborious and destructive, while modern 3D sensing technologies like LiDAR provide high-resolution point clouds but face challenges in automatic leaf segmentation due to occlusion, geometric similarity, and uneven point density. To address these challenges, we propose MRSliceNet, an end-to-end deep learning framework inspired by human visual cognition. The network integrates three key components: a Multi-scale Recursive Slicing Module (MRSM) for detailed local feature extraction, a Context Fusion Module (CFM) that combines local and global features through attention mechanisms, and an Instance-Aware Clustering Head (IACH) that generates discriminative embeddings for precise instance separation. Extensive experiments on two challenging datasets show that our method establishes new state-of-the-art performance, achieving AP of 55.04%/53.78%, AP₅₀ of 65.37%/64.00%, and AP₂₅ of 74.68%/73.45% on Dataset A and Dataset B, respectively. The proposed framework not only produces clear boundaries and reliable instance identification but also provides an effective solution for automated plant phenotyping, as evidenced by its successful implementation in real-world agricultural research pipelines. Full article

Background/Objectives: Pompe disease (PD) is a rare autosomal recessive disorder caused by a deficiency of the lysosomal acid α-1,4-glucosidase (GAA) encoded by the GAA gene, leading to muscular dysfunctions due to pathological accumulation of glycogen in skeletal and cardiac muscles. PD has been reported in several animals and Japanese quails (JQ; Coturnix japonica), but a causative mutation has yet to be found in JQs with PD. Here, we aimed to identify a pathogenic mutation in JQs associated with PD. Methods: Paraffin-embedded skeletal muscle blocks from four JQs stored since the 1970s were used in this study. After confirming the histopathological phenotypes of PD, Sanger sequencing was performed to identify a pathological mutation in the GAA I gene of JQs. A genotyping survey was conducted using a real-time polymerase chain reaction assay targeting a candidate mutation using DNA samples extracted from 70 new-hatched JQs and 10 eggs from commercial farms. Results: Microscopic analysis confirmed the presence of the PD phenotype in three affected JQs based on abnormal histopathological changes and accumulated glycogen in the affected muscles, while one JQ was unaffected and served as a control. Sanger sequencing revealed that the three affected JQs were homozygous for the deletion of guanine at position 1096 in the open reading frame (c.1096delG). A genotyping survey of 70 JQs and 10 eggs from commercial farms showed that none carried this deletion mutation. Conclusions: This study identified c.1096delG as the pathogenic mutation for PD in JQs. This mutation induces a frameshift and substitution of amino acids at position 366 (alanine to histidine), resulting in premature termination at the 23rd codon (p.A366Hfs*23). This suggests that this mutation causes the deficient activity of GAA in JQs with PD. The identification of the c.1096delG mutation enabled the systematic maintenance of the flock colony in the PD model. Furthermore, this PD model can be used to clarify unknown aspects of PD pathogenesis and develop therapeutic strategies. Full article

Background/Objectives: Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed models, such as Genomic Best Linear Unbiased Prediction (GBLUP), and conventional machine learning methods like Support Vector Regression (SVR). Traditional methods are limited in handling high-dimensional data and nonlinear relationships. Thus, deep learning methods have also been applied to genomic prediction in recent years. Methods: We proposed iADEP, Integrated Additive, Dominant, and Epistatic Prediction model based on deep learning. Specifically, single nucleotide polymorphism (SNP) data integrating latent genetic interactions and genome-wide association study results as biological prior knowledge are fused to an SNP embedding block, which is then input to a local encoder. The local encoder is fused with an omic-data-incorporated global decoder through a multi-head attention mechanism, followed by multilayer perceptrons. Results: Firstly, we demonstrated through experiments on four datasets that iADEP outperforms existing methods in genotype-to-phenotype prediction. Secondly, we validated the effectiveness of SNP embedding through ablation experiments. Third, we provided an available module for combining other omics data in iADEP and propose a novel method for fusing them. Fourthly, we explored the impact of feature selection on iADEP performance and conclude that utilizing the full set of SNPs generally provides optimal results. Finally, by altering the partition of training and testing sets, we investigated the differences between transductive learning and inductive learning. Conclusions: iADEP provides a new approach for AI breeding, a promising method that integrates biological prior knowledge and enables combination with other omics data. Full article

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens. Full article

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson’s correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer. Full article

Microfluidic droplets accommodating a single cell as independent microreactors are frequently demanded for single-cell analysis of phenotype and genotype. However, challenges exist in identifying and reducing the covalence probability (following Poisson’s distribution) of more than two cells encapsulated in one droplet. It is of great significance to monitor and control the quantity of encapsulated content inside each droplet. We demonstrated a microfluidic system embedded with a weakly supervised cell counting network (WSCNet) to generate microfluidic droplets, evaluate their quality, and further recognize the locations of encapsulated cells. Here, we systematically verified our approach using encapsulated droplets from three different microfluidic structures. Quantitative experimental results showed that our approach can not only distinguish droplet encapsulations (F1 score > 0.88) but also locate each cell without any supervised location information (accuracy > 89%). The probability of a “single cell in one droplet” encapsulation is systematically verified under different parameters, which shows good agreement with the distribution of the passive method (Residual Sum of Squares, RSS < 0.5). This study offers a comprehensive platform for the quantitative assessment of encapsulated microfluidic droplets. Full article

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine. Full article