Background: White matter (WM) damage is a consistent finding in HIV-infected (HIV+) individuals. Previous studies have evaluated WM fiber tract-specific brain regions in HIV-associated neurocognitive disorders (HAND) using diffusion tensor imaging (DTI). However, DTI might lack an accurate biological interpretation, and the technique suffers from several limitations. Fixel-based analysis (FBA) and free water corrected DTI (fwcDTI) have recently emerged as useful techniques to quantify abnormalities in WM. Here, we sought to evaluate FBA and fwcDTI metrics between HIV+ and healthy controls (HIV−) individuals. Using machine learning classifiers, we compared the specificity of both FBA and fwcDTI metrics in their ability to distinguish between individuals with and without cognitive impairment in HIV+ individuals.Methods: Forty-two HIV+ and 52 HIV– participants underwent MRI exam, clinical, and neuropsychological assessments. FBA metrics included fiber density (FD), fiber bundle cross section (FC), and fiber density and cross section (FDC). We also obtained fwcDTI metrics such as fractional anisotropy (FAT) and mean diffusivity (MDT). Tract-based spatial statistics (TBSS) was performed on FAT and MDT. We evaluated the correlations between MRI metrics with cognitive performance and blood markers, such as neurofilament light chain (NfL), and Tau protein. Four different binary classifiers were used to show the specificity of the MRI metrics for classifying cognitive impairment in HIV+ individuals.Results: Whole-brain FBA showed significant reductions (up to 15%) in various fiber bundles, specifically the cerebral peduncle, posterior limb of internal capsule, middle cerebellar peduncle, and superior corona radiata. TBSS of fwcDTI metrics revealed decreased FAT in HIV+ individuals compared to HIV– individuals in areas consistent with those observed in FBA, but these were not significant. Machine learning classifiers were consistently better able to distinguish between cognitively normal patients and those with cognitive impairment when using fixel-based metrics as input features as compared to fwcDTI metrics.Conclusion: Our findings lend support that FBA may serve as a potential in vivo biomarker for evaluating and monitoring axonal degeneration in HIV+ patients at risk for neurocognitive impairment.

Objective: There is a high incidence of concussion and frequent utilization of rapid weight loss (RWL) methods among combat sport athletes, yet the apparent similarity in symptoms experienced as a result of a concussion or RWL has not been investigated. This study surveyed combat sports athletes to investigate the differences in symptom onset and recovery between combat sports and evaluated the relationships between concussion and RWL symptoms. Design: Cross-sectional study. Setting: Data were collected through an online survey. Participants: One hundred thirty-two (115 male athletes and 17 female athletes) combat sport athletes. Interventions: Modified Sport Concussion Assessment Tool (SCAT) symptom checklist and weight-cutting questionnaire. Main Outcome Measures: Survey items included combat sport discipline, weight loss, medical history, weightcutting questionnaire, and concussion and weight-cutting symptom checklists. Results: Strong associations (r s 5 0.6-0.7, P , 0.05) were observed between concussion and RWL symptoms. The most frequently reported symptom resolution times were 24 to 48 hours for a weight cut (WC; 59%) and 3 to 5 days for a concussion (43%), with 60% to 70% of athletes reporting a deterioration and lengthening of concussion symptoms when undergoing a WC. Most of the athletes (65%) also reported at least one WC in their career to "not go according to plan," resulting in a lack of energy (83%) and strength/power (70%). Conclusions: Rapid weight loss and concussion symptoms are strongly associated, with most of the athletes reporting a deterioration of concussion symptoms during a WC. The results indicate that concussion symptoms should be monitored alongside hydration status to avoid any compound effects of prior RWL on the interpretation of concussion assessments and to avoid potential misdiagnoses among combat athletes.

Heat-induced hypo-hydration (hyperosmotic hypovolemia) can reduce prolonged skeletal muscle performance; however, the mechanisms are less well understood and the reported effects on all aspects of neuromuscular function and brief maximal contractions are inconsistent. Historically, a 4–6% reduction of body mass has not been considered to impair muscle function in humans, as determined by muscle torque, membrane excitability and peak power production. With the development of magnetic resonance imaging and neurophysiological techniques, such as electromyography, peripheral nerve, and transcranial magnetic stimulation (TMS), the integrity of the brain-to-muscle pathway can be further investigated. The findings of this review demonstrate that heat-induced hypo-hydration impairs neuromuscular function, particularly during repeated and sustained contractions. Additionally, the mechanisms are separate to those of hyperthermia-induced fatigue and are likely a result of modulations to corticospinal inhibition, increased fibre conduction velocity, pain perception and impaired contractile function. This review also sheds light on the view that hypo-hydration has ‘no effect’ on neuromuscular function during brief maximal voluntary contractions. It is hypothesised that irrespective of unchanged force, compensatory reductions in cortical inhibition are likely to occur, in the attempt of achieving adequate force production. Studies using single-pulse TMS have shown that hypo-hydration can reduce maximal isometric and eccentric force, despite a reduction in cortical inhibition, but the cause of this is currently unclear. Future work should investigate the intracortical inhibitory and excitatory pathways within the brain, to elucidate the role of the central nervous system in force output, following heat-induced hypo-hydration.

The estimation of the ultimate capacity of rectangular or circular shaped steel tubular members filled with concrete, such as columns, beams, and beam-column connections, requires a detailed structural study to be carried out. Therefore, identify the concrete strength the member subjected to axial-load only. Using the Levenberg-Marquardt artificial neural network, this paper investigates the concrete-filled steel tubular (CFT) members axial strength. 201 experimental specimens were collected from the literature to obtain the best results, and a wide range of geometric and material properties of CFT members were included. The proposed design and specimens illustrate the practicality and effectiveness of the chosen CFT column approach to classify real structural results.