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Background: To evaluate the population-based frequency and severity of multiple sclerosis (MS)-related ocular diseases. Methods: Retrospective, population-based study examining patients with MS between January 1, 1998 and December 31, 2011. Patients were identified using the Rochester Epidemiology Project, which is a record-linkage system of medical records for all patient–physician encounters among Olmsted County, Minnesota residents. Diagnosis of MS was confirmed based on neuroimaging, cerebrospinal fluid studies, and serum studies for each patient according to the 2017 McDonald criteria. Patient data were obtained using the medical records and followed through April 1, 2018. Results: Of the 116 patients with MS, 66% were female and the median age of onset was 36 years (interquartile range 27.5–43.5 years). About half (61/116, 53%) had MS-related neuro-ophthalmic manifestations during their disease course, and about one-fourth (33/116, 28%) had visual symptoms as their presenting symptom of MS, most commonly as optic neuritis (26/116, 22%). Optic neuritis was the leading MS-related ocular condition (37%), followed by internuclear ophthalmoplegia (16%) and nystagmus (13%). Optic neuritis was mostly unilateral (40/43, 93%), with 16% (6/43) having a visual acuity of 20/200 or worse at nadir but ultimately 95% (35/37) improving to a visual acuity of 20/40 or better. Conclusions: This study provides the population-based frequency of MS-related ocular disease, which demonstrates a high frequency of ocular manifestations in MS both at disease onset and during the disease course, emphasizing the utility of neuro-ophthalmologists, or collaboration between neurologists and ophthalmologists, in the care of patients with MS. [ABSTRACT FROM AUTHOR]

Nowadays, the "flipped classroom" approach is taking the center stage within medical education. However, very few reports on the implementation of the flipped classroom in neurology have been published to date, and this educational model still represents a challenge for students and educators alike. In this article, neurology educators from the American Academy of Neurology's A. B. Baker Section on Neurological Education analyze reports of flipped classroom in other medical/surgical subspecialties, review the current implementation in neurology, and discuss future strategies to flip the neurology curriculum through contextualization of the benefits and the consequences. ANN NEUROL 2020;87:4–9 [ABSTRACT FROM AUTHOR]

Quantitative structure-activity relationship (QSAR) modeling of 40 DEET-related mosquito repellents was carried out using calculated molecular descriptors. When the four different classes of descriptors (topochemical, topostructural, geometrical and quantum chemical) were used in a hierarchical fashion, topochemical descriptors were found to explain much of the variance in the data and this indicated the importance of the chemical nature of the atoms and groups towards repellency of these compounds. Ridge regression (RR) outperformed partial least square regression (PLS) and principal component regression (PCR). We also used descriptor thinning via a modified Gram-Schmidt algorithm prior to ridge regression. This resulted in a four-parameter model with a 20-fold cross-validated R2 of 0.734. The q2 (R2cv) reported here is the "true-q2" because the descriptor thinning was embedded within the cross-validation step. Inclusion of any calculated physicochemical property (secondary descriptor) did not result in improvement of the models built with the calculated molecular descriptors (primary descriptors). This result has great implications in the QSAR assisted design of new mosquito repellents because calculation of the primary descriptors does not require any input other than the molecular structure. [ABSTRACT FROM AUTHOR]

Competing Interests: The authors report no conflicts of interest.

Competing Interests: The authors report no conflicts of interest.

Quantitative structure-activity relationship (QSAR) modelers often encounter the problem of multicollinearity owing to the availability of large numbers of computable molecular descriptors. Sparsity of the variables while using descriptors such as atom pairs increases the complexity. Three different predictor-thinning methods, namely, a modified Gram-Schmidt algorithm, a marginal soft thresholding algorithm, and LASSO (least absolute shrinkage and selection operator), were utilized to reduce the number of descriptors prior to developing linear models. Juvenile hormone (JH) activity of 304 compounds on Culex pipiens larvae was taken as the model data set, and predictor trimming of a large number of diverse descriptors comprising 268 global molecular descriptors (topostructural, topochemical, and geometrical), 13 quantum chemical descriptors, and 915 atom pairs (substructural counts) was applied prior to linear regression by the ridge regression method. The data set (N = 304) was split into five calibration data sets of random samples of sizes 60/110/160/210/260, and the remaining 244/194/144/94/44 compounds were used for validations. LASSO was not found to be a very effective method in handling a large set of descriptors because the number of predictors retained could not exceed the number of observations. The results indicated that the modified Gram-Schmidt algorithm could be used to trim the number of predictors in the global molecular descriptor set where collinearity of the descriptors was the major concern. On the contrary, the soft thresholding approach was found to be an effective tool in subset selection from a diverse set of descriptors having both sparsity and multicollinearity, as in the case of the combined set of atom pairs and global molecular descriptors. The final model developed after variable selection was dominated more by atom pairs, which indicated the important structural moieties that affect JH activity of the compounds. The success of the method reiterates the fact that QSAR or quantitative structure-property relationship (QSPR) models can be developed for a diverse set of compounds using properly parametrized and diverse sets of descriptors, of course, with the selection of the appropriate statistical tools.

In view of the wide distribution of halocarbons in our world, their toxicity is a public health concern. Previous work has shown that various measures of toxicity can be predicted with standard molecular descriptors. In our work, biodescriptors of the effect of halocarbons on the liver were obtained by exposing hepatocytes to 14 halocarbons and a control and by producing two-dimensional electrophoresis gels to assess the expressed proteome. The resulting spot abundances provide additional biological information that might be used in toxicity prediction. QSAR models were fitted via ridge regression to predict eight dependent toxicity measures: d37, arr, EC50MTT, EC50LDH, EC20SH, LECLP, LECROS, and LECCAT. Three predictor sets were used for each-the chemodescriptors alone, the biodescriptors alone, and the combined set of both chemo- and biodescriptors. The results differed somewhat from one dependent to another, but overall it was shown that better results could be obtained by using both chemo- and biodescriptors in the model than by using either chemo- or biodescriptors alone. The library of compounds used was small and quite homogeneous, so our immediate conclusions are correspondingly limited in scope, but we believe the underlying methodologies have broad applicability at the interface of chemical and biological descriptors.