Faculty Bibliography

The acceptance of students to a medical school places a considerable emphasis on performance in standardized tests and undergraduate grade point average (uGPA). Traditionally, applicants may be judged as a homogeneous population according to simple quantitative thresholds that implicitly assume a linear relationship between scores and academic success. This 'one-size-fits-all' approach ignores the notion that individuals may show distinct patterns of achievement and follow diverse paths to success. In this study, we examined a dataset composed of 53 variables extracted from the admissions application records of 1,088 students matriculating to NYU School of Medicine between the years 2006-2014. We defined training and test groups and applied K-means clustering to search for distinct groups of applicants. Building an optimized logistic regression model, we then tested the predictive value of this clustering for estimating the success of applicants in medical school, aggregating eight performance measures during the subsequent medical school training as a success factor. We found evidence for four distinct clusters of students-we termed 'signatures'-which differ most substantially according to the absolute level of the applicant's uGPA and its trajectory over the course of undergraduate education. The 'risers' signature showed a relatively higher uGPA and also steeper trajectory; the other signatures showed each remaining combination of these two main factors: 'improvers' relatively lower uGPA, steeper trajectory; 'solids' higher uGPA, flatter trajectory; 'statics' both lower uGPA and flatter trajectory. Examining the success index across signatures, we found that the risers and the statics have significantly higher and lower likelihood of quantifiable success in medical school, respectively. We also found that each signature has a unique set of features that correlate with its success in medical school. The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.

The Drosophila visual system integrates input from 800 ommatidia and extracts different features in stereotypically connected optic ganglia. The development of the Drosophila visual system is controlled by gene regulatory networks that control the number of precursor cells, generate neuronal diversity by integrating spatial and temporal information, coordinate the timing of retinal and optic lobe cell differentiation, and determine distinct synaptic targets of each cell type. In this chapter, we describe the known gene regulatory networks involved in the development of the different parts of the visual system and explore general components in these gene networks. Finally, we discuss the advantages of the fly visual system as a model for gene regulatory network discovery in the era of single-cell transcriptomics.

Background: Trigeminal neuralgia (TN) is a recognized pain condition the treatment of which can be very challenging. Various surgical interventions can be applied in cases of therapy-resistance to drug treatments. The central lateral thalamotomy (CLT) against neurogenic (or neuropathic) pain is based on multiarchitectonic histological as well as physiopathological studies, and integrates the nucleus in a large thalamocortical (TC) and corticocortical network responsible for the sensory, cognitive and affective/emotional components of pain. The advent of the magnetic resonance imaging guided high intensity focused ultrasound (MRgFUS) brought a strong reduction in morbidity and increase in accuracy compared to penetration techniques. Objective: This study was aimed at analyzing the outcome of bilateral MRgFUS CLT for chronic therapy-resistant trigeminal pain, all performed in one single center. Methods: Patients were categorized in Classical, Idiopathic and Secondary TN. By definition, paroxysms lasted for seconds up to 2 min. All patients were screened for trigeminal neurovascular conflict. In case of classical TN, microvascular decompression was proposed. Therapy-resistance and thus indication for MRgFUS CLT was based on the lack of efficacy and/or side effects of antiepileptic and antidepressant drugs. Good outcome was defined by a pain relief ≥50%. Results: Eight patients suffering from chronic therapy-resistant trigeminal neuralgia were treated. All suffered from pain with paroxysmal character. Six patients reported additionally continuous pain. Mean follow-up was 53 months (range: 12-92, median: 60 months). The mean pain relief assessed by patients was 51% (median: 58%, range: 0-90%) at 3 months, 71% (median: 65%, range: 40-100%) at 1 year and 78% (median: 75%, range: 50-100%) at their longest follow-up. This represents 63% good outcomes at 3 months, 88% at 1 year and 100% at last follow-up. Frequency of the mean pain paroxysms decreased from 84 per day preoperative to 3.9 at 1 year postoperatively. There were no serious adverse events in this series. Conclusion: Our study provides preliminary support for the safety and efficacy of MRgFUS CLT, a histologically and pathophysiologically based medial thalamotomy against chronic therapy-resistant trigeminal neuralgia.

Regulating gene expression at the protein level is becoming increasingly important for answering basic questions in neurobiology. Several techniques using destabilizing domains (DD) on transgenes, which can be activated or deactivated by specific drugs, have been developed to achieve this goal. A DD from bacterial dihydrofolate reductase bound and stabilized by trimethoprim (TMP) represents such a tool. To control transgenic protein levels in the brain, the DD-regulating drugs need to have sufficient penetration into the central nervous system (CNS). Yet, very limited information is available on TMP pharmacokinetics in the CNS following systemic injection. Here, we performed a pharmacokinetic study on the penetration of TMP into different CNS compartments in the rat. We used mass spectrometry to measure TMP concentrations in serum, cerebrospinal fluid (CSF) and tissue samples of different CNS regions upon intraperitoneal TMP injection. We show that TMP quickly (within 10 min) penetrates from serum to CSF through the blood-CSF barrier. TMP also shows quick penetration into brain tissue but concentrations were an order of magnitude lower compared to serum or CSF. TMP concentration in spinal cord was lower than in any other analyzed CNS area. Nevertheless, effective levels of TMP to stabilize DDs can be reached in the CNS with half-lives around 2 h. These data show that TMP has good and fast penetration properties into the CNS and is therefore a valuable ligand for precisely controlling protein expression in the CNS in rodents.