SEARCH · Search PubMed

Results for “Gaussian graphical model”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

696 records · Page 3Linked to original sources

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Effects of phytosterols supplementation on hepatic lipid metabolism and metabolic outcomes in obese rodent models: a systematic review and meta-analysis.

This study aimed to synthesize and quantitatively assess the available evidence on the effects of phytosterol supplementation on hepatic lipid metabolism and obesity-related metabolic outcomes in obese rodent models, integrating biochemical, histological, and molecular evidence. A systematic search was conducted in electronic databases (PubMed, EMBASE, and Web of Science). Data on study design, population, intervention, outcomes, and risk of bias were extracted and analyzed. A quantitative meta-analysis was performed. Meta-analysis showed reductions in body weight, serum triglycerides, total cholesterol, LDL-C, VLDL-C, glucose, liver weight, hepatic cholesterol, hepatic triglycerides, and nonalcoholic fatty liver disease activity score. No significant changes were observed for adiposity index, HDL-C, insulin, or hepatic expression of PPARα, FAS, and SREBP1c. Conversely, CPT1A expression was significantly increased following PS supplementation. Subgroup analyses indicated that the beneficial effects on lipid and hepatic outcomes were generally consistent across rodent species (mice, rats, and hamsters), obesity induction models, and routes of administration, although the magnitude of responses varied between strains, with C57BL/6 mice showing more pronounced metabolic improvements. Additional analyses suggested that treatment duration and phytosterol composition may modulate specific outcomes, whereas dose-response meta-regression identified dose-dependent associations for serum and hepatic cholesterol, and PPARα expression in dietary supplementation studies. Overall, the available preclinical evidence suggests that phytosterol supplementation may improve several metabolic and hepatic outcomes in rodent models of obesity. However, the substantial heterogeneity across studies highlights the need for standardized experimental protocols and future clinical studies before these findings can be translated to human health.

Animals↗

PubMed2026

An automated geometric modeling framework in GATE for the design and optimization of high-sensitivity converging-beam SPECT collimators.

Objective.The trade-off between detection sensitivity and spatial resolution is a fundamental challenge in designing organ-dedicated Single-photon emission computed tomography (SPECT) collimators. While converging-hole geometries offer a solution, their optimization is often hindered by the lack of flexible computational tools capable of modeling large-scale, non-parallel hole arrays. This study aims to develop an automated geometric modeling framework to facilitate the design and evaluation of complex converging- and diverging-hole collimators within standard Monte Carlo environments.Approach.We developed a specialized modeling framework by implementing custom C++ classes and a vector-based alignment algorithm within GATE. This platform enables automated, orientation-consistent construction of large-scale converging arrays not natively supported by standard implementations. A high-sensitivity pure cone-beam collimator (CBC) was designed using this framework. The evaluation used hot-rod, disc, and Jaszczak phantoms for physical characterization, while XCAT and dedicated brain models were employed for clinical tasks, including cardiac, brain perfusion, and DaTscan SPECT simulations.Main results.The CBC achieved a nearly fourfold sensitivity increase compared to a conventional low-energy high-resolution parallel-hole collimator at a 20 cm radius of rotation, while maintaining comparable spatial resolution. Despite a 52.3% field of view reduction, the CBC yielded a 2.2-fold noise reduction (CV: 11.7% vs 25.9%) and mitigated partial volume effects via geometric magnification. XCAT and brain phantom simulations confirmed enhanced anatomical definition and contrast recovery in cardiac, perfusion, and DaTscan tasks.Significance.This work provides an efficient computational tool for rapid design space exploration of advanced collimator geometries. The results demonstrate that the proposed CBC design offers a significant sensitivity advantage, making it highly suitable for high-performance, small-volume clinical applications such as brain and cardiac molecular imaging.

Tomography, Emission-Computed, Single-Photon↗

PubMed2026

Glucocorticoids and placental 11βHSD2 - A systematic review of human studies and animal models.

CONTEXT: Elevated prenatal glucocorticoid (GC) exposure is linked to adverse offspring outcomes. The placental enzyme 11β-hydroxysteroid-dehydrogenase-type-2 (11βHSD2) protects the fetus by converting maternal derived cortisol to inactive cortisone. Although in vitro studies suggest GC mediated upregulation of 11βHSD2, in vivo evidence remains inconclusive. METHODS: PubMed, Embase, and PsycInfo were searched in October 2024 for human and mammalian animal studies on endogenous or exogenous GCs during pregnancy and associations with placental 11βHSD2 (mRNA, protein, activity, gene methylation). Narrative synthesis was conducted due to heterogeneity precluding meta-analysis. RESULTS: Eighteen studies (eight human, ten animal populations) met inclusion criteria. Exogenous GC exposure was associated with modifications in placental 11βHSD2 expression in animal models, with effects varying by substance, timing, and species. Dexamethasone trended towards increased expression in rodents, whereas betamethasone increased expression in non-human primates but not rodents. Human studies on endogenous GCs showed inconsistent associations with 11βHSD2 changes. In asthmatic pregnancies, moderate inhaled GC-use maintained enzyme activity compared to untreated patients. No convincing sex-specific trend emerged. CONCLUSIONS: GC exposure alters placental 11βHSD2 in a substance- and species-specific way; translational relevance remains limited based on current literature. Future studies should employ technological advances and include GC-sensitive biomarkers to clarify mechanisms of maternal-fetal stress transmission.

Female↗

PubMed2026

Non-linear predictive modeling and comprehensive meta-analysis of rectal temperature in Santa Inês sheep: a systematic review of thermal challenges and biometerological trends.

A systematic and bibliometric review, combined with a meta-analysis, was used to adjust an equation for estimating the physiological responses of Santa Inês sheep subjected to different thermal challenges. The systematic review compiled data on physiological responses and the thermal environment, which were then used in the meta-analysis to adjust regression models. The bibliometric analysis mapped the relationships among studies, highlighting their usefulness in interpreting research findings and biases. Addressing prior methodological critiques, the core of this study involves replacing the linear approach with a non-linear segmented regression model to accurately define the Thermal Neutral Zone (TNZ). The Segmented Regression Model was crucial, establishing the upper limit of the Thermal Neutral Zone (TNZ) at an air temperature (tair) of 34.64 °C, where trectal begins to increase abruptly. The model, while identifying a biologically significant breakpoint, exhibited a moderate Multiple R-squared of 0.3529, highlighting the high heterogeneity and methodological variability in the current Santa Inês literature. This non-linear approach offers a biologically superior tool for identifying the onset of thermal distress.

Animals↗

PubMed2026

Are there any common effects in preclinical models of micro- and nanoplastic (MNP) exposure? A systematic review.

Micro- and nanoplastics (MNPs) are emerging contaminants detected in food sources and the marine food chain, raising concerns about human health. Although no causal relationship has been established between MNP exposure and specific diseases, growing evidence suggests adverse developmental, behavioral, cognitive and biochemical effects. This systematic review synthesized evidence from common preclinical neurotoxicology models, including C. elegans, D. rerio, D. melanogaster, in vitro systems and rodents, to identify convergent developmental, behavioral and biochemical outcomes. The protocol was preregistered in OSF, followed PRISMA-P guidelines, applied PICOS criteria, and assessed methodological quality using the European Commission's ToxRTool. Overall, 185 studies were included. Consistent findings showed impaired survival and disrupted development across all models. Behavioral alterations affecting anxiety, memory, learning, sociability and locomotor activity were also consistently reported. In addition, numerous studies identified disruptions in the serotonergic (5-HT) system, including changes in neurotransmitter levels, transporters and metabolic enzymes. Despite methodological heterogeneity, these findings indicate that MNP exposure produces reproducible neurodevelopmental and neurochemical alterations across experimental models. Future studies should improve methodological harmonization, strengthen cross-model comparability and identify robust biomarkers and key mechanisms underlying MNP-induced neurotoxicity, facilitating translation to human health risk assessment frameworks.

Animals↗

PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗

PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2026

"Orphaned bereavement": Toward a public health model for bereavement.

Bereavement is increasingly recognized as a public health concern, yet support systems in many welfare states continue to allocate support according to the circumstances of death rather than the functional needs of bereaved families. Existing bereavement frameworks have substantially advanced understanding of social recognition and public legitimacy but provide more limited guidance for understanding how institutional responsibility for bereaved families is organized. using Israel as a bereavement-saturated case, this study introduces the concept of orphaned bereavement to describe bereavement in which no institution holds clearly defined and continuing responsibility for identifying needs, coordinating support, and ensuring continuity of care. Drawing on 25 semi-structured interviews with five bereaved family members and 20 professionals, analyzed using reflexive thematic analysis, the analysis generated three interrelated themes: institutionalized invisibility and unequal recognition; reorganizing life in the absence of institutional support; and pathways toward a needs-based model of bereavement support. The findings extend existing theories of disenfranchized grief and grievability by introducing institutional responsibility as a complementary lens for understanding bereavement inequality and support a needs-based public health approach in which support is organized according to families' evolving functional needs rather than the circumstances of death.

Journal Article↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

A multi-model genome-wide association study identifies genetic variants underlying resistance to Largemouth Bass Ranavirus (LMBV) in Micropterus salmoides.

Largemouth bass (Micropterus salmoides) is an economically important freshwater aquaculture species, yet recurrent outbreaks of Largemouth Bass Ranavirus (LMBV) continue to impair production and cause substantial losses. The genetic basis of host variation in LMBV resistance remains insufficiently characterized. Here, we applied a multi-model genome-wide association study (GWAS) to identify loci associated with resistance following a controlled challenge with the LMBV-23PY strain. Whole-genome resequencing was performed for 146 phenotyped fish, including 72 susceptible and 74 resistant individuals. After stringent quality control, 877,262 high-quality variants were retained and tested using six GWAS models. Across binary survival status and survival time phenotypes, 32 shared suggestive variants were consistently detected across models, representing suggestive loci for LMBV-23PY resistance. Genes within ±50 kb of these loci were annotated, and functional enrichment highlighted immune- and redox-related biological processes. Three prioritized candidates-GSTT3L (glutathione S-transferase theta-3-like), CGRP2 (calcitonin gene-related peptide 2), and NPPC (natriuretic peptide C)-were associated with pathways involved in oxidative stress responses and immune regulation. Collectively, these results provide insight into the genetic architecture of LMBV-23PY resistance in largemouth bass and identify suggestive variants and associated candidate genes for downstream validation, functional interrogation, and the development of marker-assisted and genome-enabled breeding strategies.

Animals↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗

PubMed2026

Innovation-related perception as a key driver of alternative protein acceptance: evidence from an early-stage model for cultivated meat and algae-/microalgae-based alternative protein products in Italy.

Alternative proteins are increasingly considered part of the transition toward more sustainable food systems, yet their diffusion depends critically on consumer acceptance. This study investigates the early-stage acceptance of two alternative protein categories in Italy-cultivated meat and algae-/microalgae-based alternative protein products. Focusing on the first three phases of acceptance, the analysis examines how innovation-related perception (IRP) shapes consumer perceived value (CPV), consumer perceived risk (CPR), and subsequent affective (AFF), cognitive (COG), and conative (CON) responses. Data were collected through an online survey administered to 238 Italian respondents and analysed using partial least squares structural equation modelling (PLS-SEM). The results show that IRP is the main upstream driver of early-stage acceptance in both product domains: more favourable perceptions strongly increase perceived value and reduce perceived risk. In turn, CPV exerts a much stronger influence than CPR on both affective and cognitive attitudes. A tentative cross-model comparison suggests only a descriptive variation in the final transition toward conative acceptance: affective and cognitive responses were both significant in the two models, with a relatively larger affective coefficient for cultivated meat and more balanced coefficients for algae-/microalgae-based products. Overall, the findings support a process-based interpretation of alternative protein acceptance and highlight the central role of innovation-related perception in shaping early consumer responses. These results provide relevant implications for communication strategies, product positioning, and policy actions aimed at improving the acceptability of alternative proteins in food cultures characterised by strong culinary traditions.

Italy↗

PubMed2026

Optimized AAV5-RPGR ORF15 Gene Therapy Rescues Photoreceptor Structure and Function in X-Linked Retinitis Pigmentosa Mouse Model.

PURPOSE: To develop and evaluate an rAAV5-based gene therapy vector expressing an optimized human RPGR ORF15 transgene (rAAV5-RPGR) for the treatment of X-linked retinitis pigmentosa caused by RPGR mutations, addressing the challenges of cloning the unstable wild-type ORF15 sequence. DESIGN: This was a prospective experimental study. SUBJECTS: This was an animal study. METHODS: An optimized RPGR ORF15 sequence was designed to eliminate problematic secondary structures and cryptic splice sites. In vitro expression was validated in HEK 293T and photoreceptor-like 661 W cells. A complete Rpgr knockout mouse model (Rpgr-knockout [KO]) was generated and characterized phenotypically. Therapeutic efficacy was assessed in Rpgr-KO mice via subretinal injection of rAAV5-RPGR at low (1 × 10⁹ vg/eye), medium (3 × 10⁹ vg/eye), or high (1 × 10¹⁰ vg/eye) doses. Structural and functional outcomes were evaluated at 12- and 14-month postinjection. Short-term safety was assessed in rabbits 1 month after subretinal injection. MAIN OUTCOME MEASURES: Level of RPGR protein expression and Protein isoform profile (elimination of truncated isoforms), Cellular localization of transgene expression and Dose-dependence of expression, outer nuclear layer thickness, and electroretinography parameters. RESULTS: (1) The optimized vector increased RPGR protein expression 3.3-fold in vitro compared to wild-type and eliminated truncated isoforms. (2) Subretinal delivery of rAAV5-RPGR in mice demonstrated dose-dependent transgene expression localized correctly to photoreceptor inner segments. (3) In Rpgr-KO mice, high-dose treatment significantly preserved outer nuclear layer thickness at the injection site (42% greater than controls at 14 months, P < .01) and central retina (P < .05), reduced aberrant rhodopsin mislocalization (P < .01), and partially restored retinal function. ERG showed significantly improved scotopic a-wave (≥100 vs <90 µV in controls at 10 cd·s/m²) and photopic b-wave amplitudes (49-66 vs 31-46 µV at 30 cd·s/m²) in treated mice. (4) No vector-related toxicity was observed in rabbits. CONCLUSIONS: rAAV5-RPGR mediated efficiently, targeted expression of optimized RPGR-ORF15, significantly preserved photoreceptor structure and function in a severe X-linked retinitis pigmentosa mouse model, and demonstrated a favorable safety profile. This study provides preclinical proof-of-concept for RPGR-targeted gene replacement therapy.

Animals↗

PubMed2026

De Novo 2.2 Mb 19q13.42-q13.43 Microdeletion Encompassing U2AF2: Support for a Haploinsufficiency Model.

U2 small nuclear RNA auxiliary factor 2 (U2AF2) is an essential pre-mRNA splicing factor involved in the early stages of pre-mRNA splicing. To date, multiple individuals have been reported with predominantly heterozygous missense variants presenting intellectual disability, speech and motor delays, seizures, hypotonia, and thin or hypoplastic corpus callosum. Here, we describe a patient with a de novo 2.2 Mb interstitial deletion involving chromosome 19q13.42-q13.43, encompassing U2AF2, presenting with intellectual disability, epilepsy, corpus callosum hypoplasia, dysmorphic features, and congenital heart disease. The patient's clinical features overlap substantially with those reported in individuals harboring heterozygous U2AF2 variants, supporting haploinsufficiency as a plausible disease mechanism. To our knowledge, this represents the first postnatal report of complete U2AF2 gene deletion. In addition, this is the first detailed phenotypic characterization of a distal 19q chromosomal interstitial deletion, further delineating the clinical spectrum associated with this genomic region.

Humans↗

PubMed2026

Ramu stunt virus genome reveals previously unreported segments and nucleocapsid domain duplication in Mechlorovirus.

Ramu stunt virus (RmSV), a member of the genus Mechlorovirus within the family Phenuiviridae, was previously described as a six-segmented RNA virus infecting sugarcane. In this study, we re-examined type material and additional isolates using high-throughput sequencing and RT-PCR validation, revealing that RmSV possesses a nine-segmented genome, making it the largest reported in the Phenuiviridae. This expanded architecture includes duplicated RNA segments (RNA 2a and RNA 2b) encoding nucleocapsid-like proteins and two novel segments (RNA 7 and RNA 8). Comparative analysis showed that RNA 2a and 2b share about 84% amino acid identity, while RNA 5 encodes a third nucleocapsid homolog, indicating unprecedented domain redundancy. Structural modeling confirmed that all three nucleocapsid proteins maintain a conserved fold despite low sequence identity, with electrostatic mapping suggesting differential RNA-binding potential. Additionally, RNA 6 encodes a hypothetical protein structurally similar to the rice stripe virus disease-specific S-protein, implicating a role in symptom development. Transcript abundance analysis revealed RNA 6 as the most highly expressed segment across isolates. These findings revise the genomic composition of RmSV, highlight mechanisms of genome plasticity and adaptive evolution in plant-infecting bunyaviruses, and underscore practical implications for diagnostic assay design, resistance breeding, and biosecurity surveillance.

Genome, Viral↗