Perspectives for the Use of Neurotechnologies in Conjunction With Muscle Autotransplantation in Children
Blagovechtchenski E, Agranovich O, Kononova Y, Nazarova M, Nikulin VV. Perspectives for the use of neurotechnologies in conjunction with muscle autotransplantation in children. Frontiers in Neuroscience. 2019;13:99. https://doi.org/10.3389/fnins.2019.00099
Muscles autotransplantation is an important way to restore motor activity in case of injury or diseases associated with a loss of muscles ability. One of the typical examples of such pathology is arthrogryposis multiplex congenita (AMC). Arthrogryposis is one of the most serious congenital malformations of the musculoskeletal system. It is characterized by the presence of two or more major joint contractures, muscle damage, and motoneuronal dysfunction in the anterior horns of the spinal cord. One of the main problems that determines the limitation or even impossibility of self-care of patients suffering from arthrogryposis is the lack of active movements in the upper limb joints, which can be restored by autotransplantation of the muscles of various donor areas (Hall, 1997; Bamshad et al., 2009; Loeffler and Lewis, 2016).
Role of Debt and Loan Forgiveness/Repayment Programs in Osteopathic Medical Graduates’ Plans to Enter Primary Care
Scheckel CJ, Richards J, Newman JR, et al. Role of debt and loan forgiveness/repayment programs in osteopathic medical graduates’ plans to enter primary care. JAOA. 2019;119(4):227-235. https://doi.org/10.7556/jaoa.2019.038.
Context: Osteopathic medicine emphasizes partnering with patients to help them attain or maintain health. This philosophy encourages physicians to practice primary care and a mission of improving community health. However, there is currently a shortage of primary care physicians in many areas of the United States.
Objective: To determine whether intended practice patterns of recent graduates of colleges of osteopathic medicine favor primary care and whether practice patterns correlate with medical education debt.
Methods: Responses were analyzed from the American Association of Colleges of Osteopathic Medicine survey of pending medical school graduates from 2007 through 2016 regarding indebtedness and specialty selection.
Results: The percentage of graduating osteopathic medical students who chose a primary care specialty increased from 28.1% (676 students) in 2007 to 33.2% (1377 students) in 2016. Among graduates, those above the 75th percentile of debt had a general move toward more non–primary care positions, with a value of 74.4% in 2007 and 79.9% in 2016. Graduates below the 25th percentile had a gradual increase in primary care representation, moving from 24.6% in 2007 to 29.4% in 2016. In 2007, graduates with a loan forgiveness/repayment program were more likely to choose primary care over graduates without such a program (OR, 0.681 [95% CI, 0.505-0.920]; P=.02). Analysis of subsequent years showed a declining OR with increasing significance.
Conclusions: Results of this analysis indicated that increased educational debt loan directly influenced physician practice choice. Graduates with high debt burden were more likely to enter primary care fields and use loan forgiveness/repayment programs. Graduates with high debt burden who did not use loan forgiveness/repayment programs were more likely to enter non–primary care specialty fields, with this trend increasing as mean medical school debt increased. This association has implications for policies that could affect choice of primary care. However, further research is needed to fully understand the primary care choice by graduates of colleges of osteopathic medicine.
Towards Artificial Intelligence in Mental Health by Improving Schizophrenia Prediction with Multiple Brain Parcellation Ensemble-Learning
Kalmady SV, Greiner R, Agrawal R, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. npj Schizophrenia. 2019;5(1):2. https://doi.org/10.1038/s41537-018-0070-8.
In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model — EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.
Identification of common genetic risk variants for autism spectrum disorder
Grove J, Ripke S, Als TD, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431-444. https://doi.org/10.1038/s41588-019-0344-8.
Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.
Acceptability and Preliminary Efficacy of an Online HIV Prevention Intervention for Single Young Men Who Have Sex with Men Seeking Partners Online: The myDEx Project
Bauermeister JA, Tingler RC, Demers M, et al. Acceptability and preliminary efficacy of an online HIV prevention intervention for single young men who have sex with men seeking partners online: The myDEx project. AIDS and Behavior. 2019. https://doi.org/10.1007/s10461-019-02426-7.
Prevention of new cases of HIV among young gay, bisexual and other men who have sex with men (YGBMSM; ages 18–24) remains a priority. We developed and pilot tested an online intervention (myDEx) using a pilot randomized trial design with 180 online-recruited single YGBMSM who reported recent unprotected anal intercourse, self-reporting as HIV negative or status-unaware, and who met sexual partners through online dating applications. myDEx participants reported higher overall satisfaction (d = 0.46) and willingness to recommend the intervention to friends (d = 0.48) than controls. myDEx participants were less likely to report foregoing condoms to achieve an emotional connection with a partner (d =0 .43), and more likely to report greater emotional regulation during their partner-seeking behaviors (d = 0.44). myDEx participants reported fewer partners with whom they had condomless receptive anal sex (d = 0.48). Our pilot results demonstrate the potential of the myDEx intervention, suggesting that a larger efficacy trial may be warranted in the future.
Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data
Masino AJ, Harris MC, Forsyth D, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLOS ONE. 2019;14(2):e0212665. https://doi.org/10.1371/journal.pone.0212665.
Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition.
Methods and findings
We performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children’s Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive–positive blood culture for a known pathogen (110 evaluations); and clinically positive–negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80–0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85–0.87, again with no significant differences.
Machine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial.
Functional selectivity profiling of the angiotensin II type 1 receptor using pathway-wide BRET signaling sensors
Namkung Y, LeGouill C, Kumar S, et al. Functional selectivity profiling of the angiotensin II type 1 receptor using pathway-wide BRET signaling sensors. Sci Signal. 2018;11(559). http://dx.doi.org/10.1126/scisignal.aat1631
G protein–coupled receptors (GPCRs) are important therapeutic targets that exhibit functional selectivity (biased signaling), in which different ligands or receptor variants elicit distinct downstream signaling. Understanding all the signaling events and biases that contribute to both the beneficial and adverse effects of GPCR stimulation by given ligands is important for drug discovery. Here, we report the design, validation, and use of pathway-selective bioluminescence resonance energy transfer (BRET) biosensors that monitor the engagement and activation of signaling effectors downstream of G proteins, including protein kinase C (PKC), phospholipase C (PLC), p63RhoGEF, and Rho. Combined with G protein and β-arrestin BRET biosensors, our sensors enabled real-time monitoring of GPCR signaling at different levels in downstream pathways in both native and engineered cells. Profiling of the responses to 14 angiotensin II (AngII) type 1 receptor (AT1R) ligands enabled the clustering of compounds into different subfamilies of biased ligands and showed that, in addition to the previously reported functional selectivity between Gαq and β-arrestin, there are also biases among G protein subtypes. We also demonstrated that biases observed at the receptor and G protein levels propagated to downstream signaling pathways and that these biases could occur through the engagement of different G proteins to activate a common effector. We also used these tools to determine how naturally occurring AT1R variants affected signaling bias. This suite of BRET biosensors provides a useful resource for fingerprinting biased ligands and mutant receptors and for dissecting functional selectivity at various levels of GPCR signaling.
The Emerging Evidence of the Parkinson Pandemic
Dorsey ER, Sherer T, Okun MS, Bloem BR. The emerging evidence of the parkinson pandemic. Journal of Parkinson’s Disease. 2018;8(s1):S8. http://dx.doi.org/10.3233/JPD-181474.
Neurological disorders are now the leading source of disability globally, and the fastest growing neurological disorder in the world is Parkinson disease. From 1990 to 2015, the number of people with Parkinson disease doubled to over 6 million. Driven principally by aging, this number is projected to double again to over 12 million by 2040. Additional factors, including increasing longevity, declining smoking rates, and increasing industrialization, could raise the burden to over 17 million. For most of human history, Parkinson has been a rare disorder. However, demography and the by-products of industrialization have now created a Parkinson pandemic that will require heightened activism, focused planning, and novel approaches.
Enhanced recovery after elective spinal and peripheral nerve surgery: pilot study from a single institution
Ali ZS, Flanders TM, Ozturk AK, et al. Enhanced recovery after elective spinal and peripheral nerve surgery: Pilot study from a single institution. Journal of Neurosurgery: Spine SPI. 2019:1-9. https://dx.doi.org/10.3171/2018.9.SPINE18681.
Enhanced recovery after surgery (ERAS) protocols address pre-, peri-, and postoperative factors of a patient’s surgical journey. The authors sought to assess the effects of a novel ERAS protocol on clinical outcomes for patients undergoing elective spine or peripheral nerve surgery.
The authors conducted a prospective cohort analysis comparing clinical outcomes of patients undergoing elective spine or peripheral nerve surgery after implementation of the ERAS protocol compared to a historical control cohort in a tertiary care academic medical center. Patients in the historical cohort (September–December 2016) underwent traditional surgical care. Patients in the intervention group (April–June 2017) were enrolled in a unique ERAS protocol created by the Department of Neurosurgery at the University of Pennsylvania. Primary objectives were as follows: opioid and nonopioid pain medication consumption, need for opioid use at 1 month postoperatively, and patient-reported pain scores. Secondary objectives were as follows: mobilization and ambulation status, Foley catheter use, need for straight catheterization, length of stay, need for ICU admission, discharge status, and readmission within 30 days.
A total of 201 patients underwent surgical care via an ERAS protocol and were compared to a total of 74 patients undergoing traditional perioperative care (control group). The 2 groups were similar in baseline demographics. Intravenous opioid medications postoperatively via patient-controlled analgesia was nearly eliminated in the ERAS group (0.5% vs 54.1%, p < 0.001). This change was not associated with an increase in the average or daily pain scores in the ERAS group. At 1 month following surgery, a smaller proportion of patients in the ERAS group were using opioids (38.8% vs 52.7%, p = 0.041). The ERAS group demonstrated greater mobilization on postoperative day 0 (53.4% vs 17.1%, p < 0.001) and postoperative day 1 (84.1% vs 45.7%, p < 0.001) compared to the control group. Postoperative Foley use was decreased in the ERAS group (20.4% vs 47.3%, p < 0.001) without an increase in the rate of straight catheterization (8.1% vs 11.9%, p = 0.51).
Implementation of this novel ERAS pathway safely reduces patients’ postoperative opioid requirements during hospitalization and 1 month postoperatively. ERAS results in improved postoperative mobilization and ambulation.
Neuraminidase inhibition contributes to influenza A virus neutralization by anti-hemagglutinin stem antibodies
Kosik I, Angeletti D, Gibbs JS, et al. Neuraminidase inhibition contributes to influenza A virus neutralization by anti-hemagglutinin stem antibodies. J Exp Med. 2019. dx.doi.org/10.1084/jem.20181624.
Broadly neutralizing antibodies (Abs) that bind the influenza virus hemagglutinin (HA) stem may enable universal influenza vaccination. Here, we show that anti-stem Abs sterically inhibit viral neuraminidase (NA) activity against large substrates, with activity inversely proportional to the length of the fibrous NA stalk that supports the enzymatic domain. By modulating NA stalk length in recombinant IAVs, we show that anti-stem Abs inhibit virus release from infected cells by blocking NA, accounting for their in vitro neutralization activity. NA inhibition contributes to anti-stem Ab protection in influenza-infected mice, likely due at least in part to NA-mediated inhibition of FcγR-dependent activation of innate immune cells by Ab bound to virions. Food and Drug Administration–approved NA inhibitors enhance anti-stem–based Fc-dependent immune cell activation, raising the possibility of therapeutic synergy between NA inhibitors and anti-stem mAb treatment in humans.