27941 research outputs found
Sort by
In-Hospital Mortality Prediction among Intensive Care Unit Patients with Acute Ischemic Stroke: A Machine Learning Approach
Full text linkBackground: Acute ischemic stroke is a leading cause of death in the United States. Identifying patients with stroke at high risk of mortality is crucial for timely intervention and optimal resource allocation. This study aims to develop and validate machine learning-based models to predict in-hospital mortality risk for intensive care unit (ICU) patients with acute ischemic stroke and identify important associated factors. Methods: Our data include 3,489 acute ischemic stroke admissions to the ICU for patients not discharged or dead within 48 h from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Demographic, hospitalization type, procedure, medication, intake (intravenous and oral), laboratory, vital signs, and clinical assessment [e.g., Glasgow Coma Scale Scores (GCS)] during the initial 48 h of admissions were used to predict in-hospital mortality after 48 h of ICU admission. We explored 3 machine learning models (random forests, logistic regression, and XGBoost) and applied Bayesian optimization for hyperparameter tuning. Important features were identified using learned coefficients. Results: Experiments show that XGBoost tuned for area under the receiver operating characteristic curve (AUC ROC) was the best performing model (AUC ROC 0.86, F1 0.52), compared to random forests (AUC ROC 0.85, F1 0.47) and logistic regression (AUC ROC 0.75, F1 0.40). Top features include GCS, blood urea nitrogen, and Richmond RASS score. The model also demonstrates good fairness for males versus females and across racial/ethnic groups. Conclusions: Machine learning has shown great potential in predicting in-hospital mortality risk for people with acute ischemic stroke in the ICU setting. However, more ethical considerations need to be applied to ensure that performance differences across different racial/ethnic groups will not exacerbate existing health disparities and will not harm historically marginalized populations.No embarg
Obstructive Sleep Apnea and Sleep Disorders in Children with Attention Deficit Hyperactivity Disorder
Full text linkIntroduction: Sleep disorders are common yet often underdiagnosed in children with attention deficit/hyperactivity disorder (ADHD). These disturbances can exacerbate ADHD symptoms and negatively affect cognitive, emotional, and behavioral functioning. This study aimed to describe the prevalence of obstructive sleep apnea (OSA) and other sleep disorders in children with ADHD using standardized diagnostic criteria and to identify associated clinical and behavioral factors.
Methods: A cross-sectional study was conducted on 629 children aged 6-12 years (mean age: 7.8 ± 1.5 years) who were diagnosed with ADHD. Sleep disturbances were assessed using the Children's Sleep Habits Questionnaire (CSHQ), the Pediatric Sleep Questionnaire (PSQ), and respiratory polygraphy. Sleep disorders were classified on the basis of the International Classification of Sleep Disorders, Third Edition (ICSD-3). Multivariate logistic regression was used to identify associated risk factors.
Results: Sleep disorders were diagnosed in 70.0% of children with ADHD. The most common disorders were insomnia (40.2%), OSA (23.4%), parasomnias (27.8%), restless legs syndrome (10.5%), and delayed sleep-wake phase disorder (4.8%). The inattentive ADHD subtype, psychiatric comorbidities, tonsil and adenoid hypertrophy, iron-deficiency anemia, and sleep-related behaviors in children with ADHD were significantly associated with sleep disturbances.
Conclusions: Sleep disorders are highly prevalent and diverse in children with ADHD. Early identification and targeted management of sleep disturbances, particularly OSA and insomnia, are essential to improving sleep quality and optimizing ADHD outcomes. Routine sleep screening should be integrated into clinical ADHD evaluations. Graphical abstract available for this article.No embarg
Sarcoidosis Presenting as a Giant Pulmonary Bulla With Concurrent COVID-19 Infection
No full textSarcoidosis is a systemic granulomatous disease that predominantly affects the lungs. However, its presentation as a giant pulmonary bulla is exceptionally rare. Its association with COVID-19 has raised new concerns regarding disease exacerbation and misdiagnosis. We report a case of a 38-year-old man who developed a large left lower lobe bulla in the context of recent COVID-19 infection. Initial misinterpretation of the bulla as loculated pneumothorax nearly led to an unnecessary chest tube placement. A subsequent thoracotomy with lobectomy revealed nonnecrotizing granulomas, confirming sarcoidosis. The patient showed spontaneous remission without requiring treatment. This case highlights the importance of multidisciplinary discussions in atypical lung presentations to prevent mismanagement.No embarg
Epigenetic Inheritance of Parental Effects in Mammals
No full textEpigenetic inheritance, the transmission of phenotypic traits across generations without changes to the DNA sequence, provides a mechanism by which parental environmental and physiological influences shape offspring development and health. Here we investigate the molecular mechanisms underlying epigenetic inheritance, focusing on parental dietary and genetic perturbations affecting offspring phenotypes.
Refining the methods for profiling epigenomes in sperm, we revealed the significant impact of cell-free DNA/chromatin contamination in prior studies. This enabled more accurate characterization of sperm epigenomes. Using a high-fat diet (HFD) exposure model, we demonstrated that metabolic phenotypes in offspring can be inherited from parents with metabolic disorders. Molecular analyses revealed paternal HFD effects had a stronger influence on early embryonic transcriptomes and chromatin states than maternal effects. Interestingly, HFD-induced chromatin alterations in sperm were largely erased in early embryos. Instead, changes in sperm small RNA payloads, particularly nuclear- and mitochondrial-derived tRNA fragments, were implicated as non-chromatin carriers of epigenetic inheritance.
Using the X-linked gene Rlim, we developed a novel genetic model demonstrating that paternal Rlim knockout epigenetically transmitted an obesity resistance phenotype to offspring. Conditional Rlim knockout (cKO) pinpointed Sertoli cells as critical mediators of soma-germline communication, transferring epigenetic information to germ cells. RNA-seq analysis of Rlim cKO Sertoli cells identified Amh as a downstream target of this pathway. Profiling of Rlim cKO sperm further implicated nuclear- and mitochondrial- derived tRNA fragments as key epigenetic carriers, rather than chromatin accessibility or DNA methylation.Interdisciplinary Graduate Program1 year2026-02-2
COVID-19 Vaccination Timing, Relative to Acute COVID-19, and Subsequent Risk of Long COVID [preprint]
Full text linkThis article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.Objectives: Long COVID is a debilitating condition that impacts millions of Americans, but patients and clinicians have little information on how to prevent this disorder. Vaccination is a vital tool in preventing acute COVID-19 and may confer additional protection against Long COVID. There is limited evidence regarding the optimal timing of COVID-19 vaccination (i.e., vaccination schedule) to minimize the risk of Long COVID.
Methods: We applied Longitudinal Targeted Maximum Likelihood Estimation to electronic health record (EHR) data from a retrospective cohort of patients vaccinated against COVID-19 between December 2021 and September 2022. We evaluated the association between binary COVID-19 vaccination status (two or more doses vs. zero doses) and 12-month Long COVID risk among patients diagnosed with acute COVID-19 between December 2021 and September 2022. In addition, we compared the 12-month cumulative risk of Long COVID (ICD-10 code U09.9) among patients diagnosed with acute COVID-19 one to three months after vaccination, three to five months after vaccination, or five to seven months after vaccination while adjusting for relevant high-dimensional baseline and time-dependent covariates.
Results: We analyzed EHR data from a retrospective cohort of 1,558,018 patients. In our binary cohort (n = 519,980), we found that vaccinated patients had a lower risk of Long COVID than unvaccinated patients (adjusted marginal risk ratio 0.84 (0.81, 0.88)). In our longitudinal cohort (n = 1,085,291), we did not find a significant difference in Long COVID risk comparing patients who were diagnosed with acute COVID-19 one to three months after vaccination versus patients who were diagnosed with COVID-19 three to five months (adjusted marginal risk ratio 0.93 (95% CI 0.62, 1.41) or 5 to 7 months (adjusted marginal risk ratio 1.06 (95% CI 0.72, 1.56)) after vaccination.
Conclusions: We found that COVID-19 vaccination before SARS-CoV-2 infection was protective against Long COVID, and we did not find that this protection significantly waned within 7 months after vaccination. These findings suggest that COVID-19 vaccination protects against Long COVID.The UMass Center for Clinical and Translational Science (UMCCTS), UL1TR001453, helped fund this study.No embarg
4D marmoset brain map reveals MRI and molecular signatures for onset of multiple sclerosis-like lesions
No full textInferring cellular and molecular dynamics of multiple sclerosis (MS) lesions from postmortem tissue collected decades after onset is challenging. Using magnetic resonance image (MRI)-guided spatiotemporal RNA profiling in marmoset experimental autoimmune encephalitis (EAE), we mapped lesion dynamics and modeled molecular perturbations relevant to MS. Five distinct lesion microenvironments emerged, involving neuroglial responses, tissue destruction and repair, and brain border regulation. Before demyelination, MRI identified a high ratio of proton density-weighted signal to relaxation time, capturing early hypercellularity, and elevated astrocytic and ependymal senescence signals marked perivascular and periventricular areas that later became demyelination hotspots. As lesions expanded, concentric glial barriers formed, initially dominated by proliferating and diversifying microglia and oligodendrocyte precursors, later replaced by monocytes and lymphocytes. We highlight SERPINE1 astrocytes as a signaling hub underlying lesion onset in both marmoset EAE and MS.No embarg
Modulation of Ischemia-Reperfusion Injury in Organ Transplantation with Therapeutic Small Interfering RNAs
No full textOrgan transplantation is the only cure for end-stage disease, yet many patients die while waiting for a transplant due to a perpetual shortage of donor organs. Insufficient donor supply is further diminished by ischemia-reperfusion injury (IRI) during procurement and preservation of organs, driving primary graft dysfunction and resulting in significant patient morbidity and mortality. Mitigating IRI is critically needed to increase the number of viable donor organs and improve patient survival. Temporarily reducing, not eradicating, organ expression levels of IRI mediators during the transplant period may provide an opportunity for therapeutic modulation of pathogenic gene expression and subsequently, promote organ rehabilitation to enable suitability for transplant.
Small interfering RNAs (siRNAs) are a revolutionary new class of medicine that enable potent, temporary, yet durable modulation of gene expression by leveraging the endogenous RNA interference (RNAi) pathway to degrade mRNA, rendering downstream protein translation ineffective. The use of chemically-stabilized siRNAs in organ transplantation is ideal due to their robust duration of effect, where a single systemic injection supports 6-12 months of efficacy, overlapping with the critical period of primary graft dysfunction and acute organ rejection. Here, we leverage siRNA-based technologies to prophylactically intervene during the transplantation process to rehabilitate and optimize organs for transplantation.
First, we establish normothermic ex vivo machine perfusion as a feasible and robust platform for functional delivery of lipophilic docosanoic acid conjugated (DCA)-siRNA targeting the master inflammatory regulator, JAK1, in rat, pig, and discarded human heart models whereby we efficiently transduce all major cardiac cell types. We translated these results to a porcine orthotopic heart transplant model, demonstrating successful transplantation of the siRNA-treated organs during ex vivo machine perfusion and confirm lack of recipient secondary organ exposure. Next, we validated the platform of ex vivo lung perfusion for robust and widespread delivery of DCA-conjugated siRNA in a porcine model. Finally, we investigated the impact of silencing known IRI mediators pre-transplant in a rat donor pre-treatment model using hepatocyte-specific GalNAc-conjugated siRNAs. We identified lead siRNAs targeting mediators of IRI cell death and inflammation, Fas and Hmgb1, and employed lead candidates in a model of rat liver IRI, validating that therapeutic silencing of these targets modulates liver expression post-ischemia and drives improvement in liver function post-injury.
These studies support the application of targeted and programmable therapies using siRNA technologies in solid organ transplantation, enabling organ-specific treatment for rehabilitation and recovery, and establishes an RNAi platform for translational and clinical transplant applications.MD/PhD2 years2027-06-0
Telehealth utilization and perceptions among deaf or hard of hearing adults: A cross-sectional analysis of the HINTS6 national dataset
Full text linkObjective: Telehealth has emerged as a vital medium for healthcare delivery and has been integrated increasingly in clinic and hospital settings in the post-COVID-19 era. However, accessibility of telehealth for individuals who are deaf or hard of hearing (DHH) remains underexplored. As effective communication is critical to high-quality healthcare, a deeper understanding of how DHH individuals interact with telehealth and identifying specific barriers they face can inform targeted interventions to improve care.
Methods: We conducted a cross-sectional analysis of the 2022 Health Information National Trends Survey (HINTS 6), a nationally representative dataset. Respondents were stratified by self-identified DHH status. Demographic, internet access, health behavior, and telehealth perception variables were compared between DHH and normal hearing individuals. Statistical analyses were performed using chi-square tests and t-tests.
Results: Among 5694 respondents, 521 identified as DHH. Chi-square testing found that DHH patients reported poorer general health (p < 0.01), lower internet use (p < 0.01), and less engagement with online health resources (p < 0.01), with similar rates of telehealth being offered and utilized. However, DHH individuals were less likely to perceive telehealth as convenient (p = 0.04) and more likely to cite difficulty using the platform (p = 0.01). They were also more likely to value the inclusion of others in their telehealth visits (p < 0.01) and report technical issues (p < 0.01).
Conclusions: While DHH individuals use telehealth at similar rates to the general population, they face significant barriers related to convenience, usability, and communication. Enhancing platform accessibility and expanding support for these patients can help reduce difficulties and further promote equity in telehealth.No embarg
A Case for Automated Segmentation of MRI Data in Neurodegenerative Diseases: Type II GM1 Gangliosidosis
Full text linkBackground: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls.
Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum.
Results: We found volBrain's vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer's recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain's vol2Brain and SimNIBS' headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL's FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable.
Conclusions: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.No embarg
Natural history progression of MRI brain volumetrics in type II late-infantile and juvenile GM1 gangliosidosis patients
No full textObjective: GM1 gangliosidosis is a rare lysosomal storage disorder characterized by the accumulation of GM1 gangliosides in neuronal cells, resulting in severe neurodegeneration. Currently, limited data exists on the brain volumetric changes associated with this disease. This study focuses on the late-infantile and juvenile subtypes of type II GM1 gangliosidosis, aiming to quantify brain volumetric characteristics to track disease progression.
Methods: Brain volumetric analysis was conducted on 56 MRI scans from 24 type II GM1 patients (8 late-infantile and 16 juvenile) and 19 healthy controls over multiple time points. The analysis included the use of semi-automated segmentation of the whole brain, ventricles, cerebellum, corpus callosum, thalamus, caudate, and lentiform nucleus. A generalized linear model was used to compare the volumetric measurements between the patient groups and healthy controls, accounting for age as a confounding factor.
Results: Both late-infantile and juvenile GM1 patients exhibited significant whole-brain atrophy compared to healthy controls, even after adjusting for age. Notably, the late-infantile subtype displayed more pronounced atrophy in the cerebellum, thalamus, and corpus callosum compared to the juvenile subtype. Both late-infantile and juvenile subtypes showed significantly higher ventricular volumes and a significant reduction in all other structure volumes compared to the healthy controls. The volumetric measurements also correlated well with disease severity based on clinical metrics.
Conclusions: The findings underscore the distinct brain volumetrics of the late-infantile and juvenile subtypes of GM1 gangliosidosis compared to healthy controls. These quantifications can be used as reliable imaging biomarkers to track disease progression and evaluate responses to therapeutic interventions.No embarg