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1. A mouse model of seizures in anti– N ‐methyl‐ d ‐aspartate receptor encephalitis

   https://doi.org/10.1111/epi.14662  

   Taraschenko, Olga ; Fox, Howard S. ; Pittock, Sean J. ; Zekeridou, Anastasia ; Gafurova, Maftuna ; Eldridge, Ember ; Liu, Jinxu ; Dravid, Shashank M. ; Dingledine, Raymond ( February 2019 , Epilepsia)

   Seizures develop in 80% of patients with anti–N‐methyl‐d‐aspartate receptor (NMDAR) encephalitis, and these represent a major cause of morbidity and mortality. Anti‐NMDAR antibodies have been linked to memory loss in encephalitis; however, their role in seizures has not been established. We determined whether anti‐NMDAR antibodies from autoimmune encephalitis patients are pathogenic for seizures.

   We performed continuous intracerebroventricular infusion of cerebrospinal fluid (CSF) or purified immunoglobulin (IgG) from the CSF of patients with anti‐NMDAR encephalitis or polyclonal rabbit anti‐NMDAR IgG, in male C57BL/6 mice. Seizure status during a 2‐week treatment was assessed with video‐electroencephalography. We assessed memory, anxiety‐related behavior, and motor function at the end of treatment and assessed the extent of neuronal damage and gliosis in the CA1 region of hippocampus. We also performed whole‐cell patch recordings from the CA1 pyramidal neurons in hippocampal slices of mice with seizures.

   Prolonged exposure to rabbit anti‐NMDAR IgG, patient CSF, or human IgG purified from the CSF of patients with encephalitis induced seizures in 33 of 36 mice. The median number of seizures recorded in 2 weeks was 13, 39, and 35 per mouse in these groups, respectively. We observed only 18 brief nonconvulsive seizures in 11 of 29 control mice (median seizure count of 0) infused with vehicle (n = 4), normal CSF obtained from patients with noninflammatory central nervous system (CNS) conditions (n = 12), polyclonal rabbit IgG (n = 7), albumin (n = 3), and normal human IgG (n = 3). We did not observe memory deficits, anxiety‐related behavior, or motor impairment measured at 2 weeks in animals treated with CSF from affected patients or rabbit IgG. Furthermore, there was no evidence of hippocampal cell loss or astrocyte proliferation in the same mice.

   Our findings indicate that autoantibodies can induce seizures in anti‐NMDAR encephalitis and offer a model for testing novel therapies for refractory autoimmune seizures.

    

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2. Regenerative mesenchymal stem c ell‐derived extracellular vesicles: A potential alternative to c ell‐based therapy in viral infection and disease damage control

   https://doi.org/10.1002/wsbm.1574  

   Ipinmoroti, Ayodeji O. ; Pandit, Rachana ; Matthews, Qiana L. ( September 2022 , WIREs Mechanisms of Disease)

   Extracellular vesicles (EVs) released by regenerative cells such as mesenchymal stem cells are effective facilitators of healing, therapy, and repair. Conversely, EVs released from infected and/or diseased cells could be useful as markers in the detection and diagnosis of disease conditions such as cancer at their earliest most detectable, and treatable stage. A very important type of EVs, termed exosomes offer a hypothetical new paradigm in disease detection, diagnosis, and treatment. A broad range of exosome‐based biomedical and therapeutic applications are now being evaluated in recent clinical trials. Exosomes are found in virtually all bodily fluids and cells and are capable of crossing tight junctions and toughly regulated boundaries such as the blood–brain barrier. Exosomes' expedition ends when they are taken up by bystander cells which corroborates the fact that they are conduits for cells releasing them. Exosomes released by diseased cells have been associated with cell‐to‐cell progression of diseases like viral disease, neurodegeneration, and certain cancers. Due to high discrimination in most disease conditions, exosome uptake is usually cell‐specific. Lots of research evidence have revealed that infusion of exosomes derived from regenerative cells such as stem cells could impede the development of certain infections and age‐related diseases by activating self‐repair machinery through RNA, DNA, protein, and lipid transfer between cells in patients. They have also been demonstrated in the restoration of the circulating population of exosomes in tissues and the fluid of recipients. The first human clinical trials of exosome therapies are now underway, establishing the future of regenerative exosome in regenerative medicine.

   This article is categorized under:

   Cancer > Stem Cells and Development

   Immune System Diseases > Stem Cells and Development

   Immune System Diseases > Molecular and Cellular Physiology

    

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3. Association of type 2 diabetes mellitus with dementia‐related and non–dementia‐related mortality among postmenopausal women: A secondary competing risks analysis of the women's health initiative

   https://doi.org/10.1002/alz.13416  

   Titcomb, Tyler J. ; Richey, Phyllis ; Casanova, Ramon ; Phillips, Lawrence S. ; Liu, Simin ; Karanth, Shama D. ; Saquib, Nazmus ; Nuño, Tomas ; Manson, JoAnn E. ; Shadyab, Aladdin H. ; et al ( August 2023 , Alzheimer's & Dementia)

   Alzheimer's disease (AD) and AD‐related dementias (ADRD) are leading causes of death among older adults in the United States. Efforts to understand risk factors for prevention are needed.

   Participants (n = 146,166) enrolled in the Women's Health Initiative without AD at baseline were included. Diabetes status was ascertained from self‐reported questionnaires and deaths attributed to AD/ADRD from hospital, autopsy, and death records. Competing risk regression models were used to estimate the cause‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the prospective association of type 2 diabetes mellitus (T2DM) with AD/ADRD and non‐AD/ADRD mortality.

   There were 29,393 treated T2DM cases and 8628 AD/ADRD deaths during 21.6 (14.0–23.5) median (IQR) years of follow‐up. Fully adjusted HRs (95% CIs) of the association with T2DM were 2.94 (2.76–3.12) for AD/ADRD and 2.65 (2.60–2.71) for the competing risk of non‐AD/ADRD mortality.

   T2DM is associated with AD/ADRD and non‐AD/ADRD mortality.

   Type 2 diabetes mellitus is more strongly associated with Alzheimer's disease (AD)/AD and related dementias (ADRD) mortality compared to the competing risk of non‐AD/ADRD mortality among postmenopausal women.

   This relationship was consistent for AD and ADRD, respectively.

   This association is strongest among participants without obesity or hypertension and with younger age at baseline, higher diet quality, higher physical activity, higher alcohol consumption, and older age at the time of diagnosis of type 2 diabetes mellitus.

    

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4. Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy

   https://doi.org/10.1186/s12859-023-05389-8  

   Guerler, Aysam ; Baker, Dannon ; van den Beek, Marius ; Gruening, Bjoern ; Bouvier, Dave ; Coraor, Nate ; Shank, Stephen D. ; Zehr, Jordan D. ; Schatz, Michael C. ; Nekrutenko, Anton ( December 2023 , BMC Bioinformatics)

   Protein–protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein–protein interactions and produce high-quality multimeric structural models.

   Application of our method to the Human and Yeast genomes yield protein–protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3. We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4.

   The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

    

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5. Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches

   https://doi.org/10.3389/fmed.2023.1165912  

   Kobayashi, Yuta ; Peng, Yu-Chung ; Yu, Evan ; Bush, Brian ; Jung, Youn-Hoa ; Murphy, Zachary ; Goeddel, Lee ; Whitman, Glenn ; Venkataraman, Archana ; Brown, Charles H. ( September 2023 , Frontiers in Medicine)

   Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance.

   Adults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data.

   2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features.

   Postoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management.

    

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