Gender Disparities in Deep Brain Stimulation for Parkinson's Disease.

OBJECTIVES: This study sought to determine whether there is a gender disparity in patients undergoing deep brain stimulation (DBS) surgery for Parkinson's disease (PD) at a single health system, and better understand the reasons for this discrepancy. MATERIALS AND METHODS: We analyzed data from the University of Miami DBS Database, which included 3251 PD patients, using chi-square, repeated measures ANOVA, and t tests to examine gender differences in the number of patients referred for surgery, reasons for referral, number receiving/not receiving surgery, reasons for not receiving surgery, and postsurgical outcomes. RESULTS: During the study period, 207 PD patients were referred for DBS (75.8% male), and 100 underwent surgery (77.0% male). Of those who did not receive surgery, the most common reasons were need for further medical optimization (26.2%), suboptimal performance on neuropsychological evaluation (22.4%), other reason (20.6%), lost to follow-up (18.7%), or patient preference (12.2%). However, in women one of the most common reasons was patient preference (28.0%), and this was significant compared to men (p < 0.001). Men were more likely to be lost to follow-up (p = 0.046). There was no statistically significant difference in postsurgical outcomes. CONCLUSIONS: Despite similar postsurgical improvements, women were less likely to undergo DBS surgery due to their own preference, while men were more likely to be lost to follow-up. These data underscore the need for increased education and awareness of DBS so that all patients with PD who qualify for surgery can benefit from this procedure.

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms.

Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups-algae, aquatic invertebrates and fish-and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.

Presence of Spodoptera frugiperda Multiple Nucleopolyhedrovirus (SfMNPV) Occlusion Bodies in Maize Field Soils of Mesoamerica.

The occlusion bodies (OBs) of lepidopteran nucleopolyhedroviruses can persist in soil for extended periods before being transported back on to the foliage for transmission to the host insect. A sensitive insect bioassay technique was used to detect OBs of Spodoptera frugiperda multiple nucleopolyhedrovirus (SfMNPV) in 186 soil samples collected from maize fields in the southern Mexican states of Chiapas, Tabasco, Campeche, Yucatán, and Quintana Roo, as well Belize and Guatemala. Overall, 35 (18.8%) samples proved positive for SfMNPV OBs. The frequency of OB-positive samples varied significantly among Mexican states and countries (p < 0.05). Between 1.7 and 4.4% of S. frugiperda larvae that consumed OB-positive samples died from polyhedrosis disease. Restriction endonuclease analysis using PstI and HindIII confirmed that the soil-derived isolates were strains of SfMNPV and that genetic diversity was evident among the isolates. The prevalence of OB-positive soil samples did not differ with altitude or extension (area) of the maize field, but it was significantly higher in fields with the presence of living maize plants compared to those containing dead plants or crop residues (p < 0.05). Georeferenced soil samples were used to identify soil types on digitized soil maps. Lithosol and Luvisol soils had a higher than average prevalence of OB-positive samples (42−45% positive) (p = 0.006), as did Andosol, Gleysol, and Vertisol soils (33−60% OB-positive), although the sample sizes were small (<5 samples) for the latter three soils. In contrast, Cambisol soils had a lower than average prevalence of OB-positive samples (5% positive). Bioassays on Acrisol, Fluvisol, Phaeozem, and Rendzina soils resulted in intermediate levels of OB-positive samples. We conclude that certain soil types may favor OB persistence and virus-mediated biological pest control. The soil is also likely to provide a valuable source of genetic diversity for the design of virus-based insecticides against this pest.

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data.

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.