Diffusion-weighted imaging in oral squamous cell carcinoma using 3 Tesla MRI: is there a chance for preoperative discrimination between benign and malignant lymph nodes in daily clinical routine?

Background Preoperative staging of cervical lymph nodes is important to determine the extent of neck dissection in patients with oral squamous cell carcinoma (OSCC). Purpose To evaluate whether a preoperative discrimination of benign and malignant cervical lymph nodes with diffusion-weighted imaging (DWI) (3T) is feasible for clinical application. Material and Methods Forty-five patients with histological proven OSCC underwent preoperative 3T-MRI. DWI (b=0, 500, and 1000s/mm(2)) was added to the standard magnetic resonance imaging (MRI) protocol. Mean apparent diffusion coefficients (ADC(mean)) were measured for lymph nodes with 3mm or more in short axis by two independent readers. Finally, these results were matched with histology. Results Mean ADC was significantly higher for malignant than for benign nodes (1.1430.188 * 10(-3) mm(2)/s vs. 0.987 +/- 0.215 * 10(-3) mm(2)/s). Using an ADC value of 0.994 * 10(-3) mm(2)/s as threshold results in a sensitivity of 80%, specificity of 65%, positive predictive value of 31%, and negative predictive value of 93%. Conclusion Due to a limited sensitivity and specificity DWI alone is not suitable to reliably discriminate benign from malignant cervical lymph nodes in daily clinical routine. Hence, the preoperative determination of the extent of neck dissection on the basis of ADC measurements is not meaningful

Applications of Machine Learning in Palliative Care: A Systematic Review

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception

Mood and Performance Anxiety in High School Basketball Players: A Pilot Study

International Journal of Exercise Science 10(4): 604-618, 2017. Participation in competitive sport may impact psychological measures, such as mood and performance anxiety, which in turn may impact enjoyment, adherence, continued participation, and so on. This study assessed the feasibility – in terms of process, resources, management, and potential scientific value– of measuring the effect of varying competitive challenges upon the mood and performance anxiety measures of high school athletes. The participants (n=12) consisted of the boys’ varsity basketball team at a high school in a rural Midwestern community. Participants completed the Profile of Mood States (POMS) to assess mood and the Sport Anxiety Scale-2 (SAS-2) to assess performance anxiety, respectively. Survey administration occurred at baseline and prior to games designated as non-conference, conference, and state tournament. A-priori feasibility measures were achieved in this prospective design. Significant correlations on the subscale measures were found on the POMS and SAS-2 administered before the four conditions in this study; Chronbach’s alpha ranged from 0.54- 0.94 across conditions for POMS subscales, and Chronbach’s alpha ranged from 0.73-0.97 across all conditions for SAS-2 subscales, respectively. Significant differences were found across conditions in the POMS subscale confusion [F(3,33) = 5.71, p = 0.01] and in the SAS-2 subscale worry [F(3,33) = 6.13, p=0.01]. These preliminary findings suggest that the competitive conditions in this study significantly affected some measures of mood and performance anxiety in high school basketball players. These findings warrant further investigation, as well as suggest coaches could gather such information from their players, ultimately aiding in player development and team performance

Applications of Machine Learning in Palliative Care: A Systematic Review.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception

Stability of Attention Performance of Adults with ADHD over Time:Evidence from Repeated Neuropsychological Assessments in One-Month Intervals

Neuropsychological assessments of attention are valuable sources of information in the clinical evaluation of adults with attention-deficit/hyperactivity disorder (ADHD). However, it is unclear whether the attention performance of adults with ADHD is stable or fluctuates over time, which is of great importance in the interpretation of clinical assessments. This study aimed to explore the stability of attention performance of adults with ADHD in repeated assessments at one-month intervals. Twenty-one adults diagnosed with ADHD took part in this study by completing selective attention and vigilance tests three times, each one month apart. Test scores of participants were compared with and interpreted based on test norms. A considerable proportion of 'below average' performance scores were observed in most of the variables of selective attention and vigilance in all three assessments. Further, selective attention and vigilance performance scores did not differ significantly between the three repeated assessments. Finally, the majority of participants received consistent test score interpretations across the three repeated assessments. This study confirms previous research and highlights abnormal selective attention and vigilance performance in adults with ADHD. Further, this study preliminarily demonstrates relatively stable attention performance across repeated assessments, which has the potential to support clinical assessment, treatment planning, and evaluation

ExpaRNA-P : simultaneous exact pattern matching and folding of RNAs

Background: Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable. Results: The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P webcite. Conclusions: ExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts

An Equine Model for Vaccination against a Hepacivirus: Insights into Host Responses to E2 Recombinant Protein Vaccination and Subsequent Equine Hepacivirus Inoculation

Equine hepacivirus (EqHV) is the closest known genetic homologue of hepatitis C virus. An effective prophylactic vaccine is currently not available for either of these hepaciviruses. The equine as potential surrogate model for hepacivirus vaccine studies was investigated, while equine host responses following vaccination with EqHV E2 recombinant protein and subsequent EqHV inoculation were elucidated. Four ponies received prime and booster vaccinations (recombinant protein, adjuvant) four weeks apart (day −55 and −27). Two control ponies received adjuvant only. Ponies were inoculated with EqHV RNA-positive plasma on day 0. Blood samples and liver biopsies were collected over 26 weeks (day −70 to +112). Serum analyses included detection of EqHV RNA, isotypes of E2-specific immunoglobulin G (IgG), nonstructural protein 3-specific IgG, haematology, serum biochemistry, and metabolomics. Liver tissue analyses included EqHV RNA detection, RNA sequencing, histopathology, immunohistochemistry, and fluorescent in situ hybridization. Al-though vaccination did not result in complete protective immunity against experimental EqHV inoculation, the majority of vaccinated ponies cleared the serum EqHV RNA earlier than the control ponies. The majority of vaccinated ponies appeared to recover from the EqHV-associated liver insult earlier than the control ponies. The equine model shows promise as a surrogate model for future hepacivirus vaccine research

IL-17+ CD8+ T cell suppression by dimethyl fumarate associates with clinical response in multiple sclerosis

IL-17-producing CD8+ (Tc17) cells are enriched in active lesions of patients with multiple sclerosis (MS), suggesting a role in the pathogenesis of autoimmunity. Here we show that amelioration of MS by dimethyl fumarate (DMF), a mechanistically elusive drug, associates with suppression of Tc17 cells. DMF treatment results in reduced frequency of Tc17, contrary to Th17 cells, and in a decreased ratio of the regulators RORC-to-TBX21, along with a shift towards cytotoxic T lymphocyte gene expression signature in CD8+ T cells from MS patients. Mechanistically, DMF potentiates the PI3K-AKT-FOXO1-T-BET pathway, thereby limiting IL-17 and RORγt expression as well as STAT5-signaling in a glutathione-dependent manner. This results in chromatin remodeling at the Il17 locus. Consequently, T-BET-deficiency in mice or inhibition of PI3K-AKT, STAT5 or reactive oxygen species prevents DMF-mediated Tc17 suppression. Overall, our data disclose a DMF-AKT-T-BET driven immune modulation and suggest putative therapy targets in MS and beyond