Background: Deep brain stimulation (DBS) in Parkinson’s disease (PD) requires extensive trial-and-error programming, often taking over a year to optimize. An objective, rapid biomarker of stimulation success is needed. Our team developed a functional magnetic resonance imaging (fMRI)-based algorithm to identify optimal DBS settings. This study prospectively compared fMRI-guided programming with standard-of-care (SoC) clinical programming in a double-blind, crossover, non-inferiority trial. Methods: Twenty-two PD-DBS patients were prospectively enrolled for fMRI using a 30-sec DBS-ON/OFF cycling paradigm. Optimal settings were identified using our published classification algorithm. Subjects then underwent >1 year of SoC programming. Clinical improvement was assessed under SoC and fMRI-determined stimulation conditions. Results: fMRI optimization significantly reduced the time required to determine optimal settings (1.6 vs. 5.6 months, p<0.001). Unified Parkinson’s Disease Rating Scale (UPDRSIII) improved comparably with both approaches (23.8 vs. 23.6, p=0.9). Non-inferiority was demonstrated within a predefined margin of 5 points (p=0.0018). SoC led to greater tremor improvement (p=0.019), while fMRI showed greater bradykinesia improvement (p=0.040). Conclusions: This is the first prospective evaluation of an algorithm able to suggest stimulation parameters solely from the fMRI response to stimulation. It suggests that fMRI-based programming may achieve equivalent outcomes in less time than SoC, reducing patient burden while potentially enhancing bradykinesia response.

Background: Patients with severe traumatic brain injury (TBI) are at uniquely high risk of venous thromboembolism (VTE), but the benefits of VTE prophylaxis must be weighed against the risk of intracranial hemorrhage expansion. Current guidelines are heterogenous in their recommendations for chemical VTE prophylaxis (cVTEp) in this high-risk cohort. We conducted a systematic review to identify the optimal timing of cVTEp in severe TBI patients. Methods: We executed a systematic search of the literature to identify adult severe TBI patients treated with cVTEp. Results were pooled, analyzed using random-effects models, and presented as Forest plots and odds ratios. Results: We included 21 studies representing 322,735 patients. The odds of VTE were 0.47 (95% CI: 0.37,0.60) when using the authors’ own criteria for early initiation, and the odds of VTE remained significantly decreased in subgroup analysis (<24h, <48 and <72h). Early VTEp both as defined by authors and in subgroup analysis did not significantly impact the odds of hemorrhage progression or mortality; except for initiation <48h which showed a positive impact on mortality (OR: 0.74, 95% CI: 0.63-0.87). Conclusions: This study supports early initiation of cVTEp in reducing the odds of VTE events without significantly increasing the risk of adverse events.

Parkinson’s disease (PD) is a severe neurodegenerative disorder characterized by prominent motor and non-motor (e.g., cognitive) abnormalities. Notwithstanding Food and Drug Administration (FDA)-approved treatments (e.g., L-dopa), most persons with PD do not adequately benefit from the FDA-approved treatments and treatment emergent adverse events are often reasons for discontinuation. To date, no current therapy for PD is disease modifying or curative. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are central nervous system (CNS) penetrant and have shown to be neuroprotective against oxidative stress, neuroinflammation, and insulin resistance, as well as promoting neuroplasticity. Preclinical evidence suggests that GLP-1RAs also attenuate the accumulation of α-synuclein. The cellular and molecular effects of GLP-1RAs provide a basis to hypothesize putative therapeutic benefit in individuals with PD. Extant preclinical and clinical trial evidence in PD provide preliminary evidence of clinically meaningful benefit in the cardinal features of PD. Herein, we synthesize extant preclinical and early-phase clinical evidence, suggesting that GLP-1RAs may be beneficial as a treatment and/or illness progression modification therapeutic in PD.

The Edgerton crown is an iconic manifestation of drop impact splashing, with its prominent cylindrical edge decorated with detaching droplets. Herein, we identify the formation of an intriguing double-crown, when a high-viscosity drop impacts on a shallow pool of a lower-viscosity immiscible liquid. High-speed imaging shows that after the initial fine horizontal ejecta sheet, the first inner crown emerges vertically from the film liquid. This is followed by the second crown which forms near the outer base of the first crown, as the tip of the horizontally spreading viscous drop approaches the outer free surface. Axisymmetric numerical simulations, using the volume-of-fluid method with adaptive grid refinement, show that the flow squeezed out between the viscous drop and the solid surface, generates two counter-rotating vortex rings, which travel radially outwards together and drive out the second crown through the free surface. The bottom vortex emerges from the separated boundary layer at the solid wall, while the top one detaches from the underside of the viscous drop. We map out the narrow parameter regime, where this ephemeral structure emerges, in terms of viscosity ratio, impact velocity and film thickness.

Surveillance by clinical epidemiology teams for invasive fungal infections (IFIs) in healthcare settings can be challenging due to several factors including low sensitivity of noninvasive conventional microbiologic diagnostics, nonspecific clinical presentation, and complex patient populations. Recently, availability of microbial cell-free DNA testing (cfDNA) via the Karius Test has shown promise for increased diagnostic sensitivity of IFIs. However, how to best incorporate cfDNA results into IFI surveillance remains a vexing challenge. Herein, we provide perspectives on the benefits and challenges of use of cfDNA for IFI surveillance.

Sleep value is the relative worth individuals assign to sleep. We previously found that individual differences in several sleep value subfactors relate to demographic, health and sleep variables. Given the pivotal role values play in health behavior and the positive association between sleep value and sleep disturbance, individual differences in sleep value may influence vulnerability/resilience to sleep and circadian disturbance. This survey study (N = 455) aimed to establish the latent factor structure of sleep value and identify whether sleep value profiles relate to demographic and sleep characteristics. Factor analysis on the Sleep Valuation Item Bank 2.0 identified five factors (wanting, prioritizing, devaluing, appreciating and preferring). Latent profile analyses revealed five distinct sleep value profiles (unconcerned, appreciative, devalue, ambivalent priority and concerned). Depression, sleep disturbance and sleep-related impairment were highest among those who highly value sleep (concerned profile) and lowest among those who neither value nor devalue sleep (unconcerned profile). Findings suggest sleep value is a complex aspect of sleep health rather than a “more is better” construct and highlight that individual differences in sleep value profiles may be associated with vulnerability/resilience to sleep disturbance.

A method is presented for estimating reliability using structural equation modeling (SEM) that allows for nonlinearity between factors and item scores. Assuming the focus is on consistency of summed item scores, this method for estimating reliability is preferred to those based on linear SEM models and to the most commonly reported estimate of reliability, coefficient alpha.

Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semiparametric Bayesian SEMs, which allow mixed categorical and continuous manifest variables while also allowing the latent variables to have unknown distributions. In order to include typical identifiability restrictions on the latent variable distributions, we rely on centered Dirichlet process (CDP) and CDP mixture (CDPM) models. The CDP will induce a latent class model with an unknown number of classes, while the CDPM will induce a latent trait model with unknown densities for the latent traits. A simple and efficient Markov chain Monte Carlo algorithm is developed for posterior computation, and the methods are illustrated using simulated examples, and several applications.

The general use of coefficient alpha to assess reliability should be discouraged on a number of grounds. The assumptions underlying coefficient alpha are unlikely to hold in practice, and violation of these assumptions can result in nontrivial negative or positive bias. Structural equation modeling was discussed as an informative process both to assess the assumptions underlying coefficient alpha and to estimate reliability

To investigate the flame acceleration to detonation in 2.0 and 0.5 mm planar glass combustion chambers, the experiments have been conducted utilising ethylene/oxygen mixtures at atmospheric pressure and temperature. The high-speed camera has been used to record the revolution of flame front and pressure inside the combustion chamber. Different equivalence ratios and ignition locations have been considered in the experiments. The results show that the detonation pressure in the 2 mm thick chamber is nearly three times of Chapman-Jouguet pressure, while detonation pressure in the 0.5 mm thick chamber is only 45.7% of the Chapman-Jouguet value at the stoichiometric mixture. This phenomenon is attributed to the larger pressure loss in the thinner chamber during the detonation propagation. As the value of equivalence ratio is 2.2, the detonation cannot be produced in the 2 mm thick chamber, while the detonation can be generated successfully in the 0.5 mm thick chamber. This phenomenon indicates that the deflagration is easily to be accelerated and transformed into the detonation, due to a larger wall friction and reflection. Besides, the micro-obstacle has been added into the combustor can shorten the detonation transition time and reduces the distance of the detonation transition.

Investigations are conducted on the effect of wall proximity on the flow around a cylinder under an axial magnetic field, using the electrical potential probe technology to measure the velocity of liquid metal flow. The study focused on the impact of the inlet velocity of the fluid, the magnetic field and wall proximity on the characteristics of velocity fields, particularly on the vortex-shedding mode. Based on different magnitudes of the magnetic field and the distance from the cylinder to the duct wall, three types of vortex-shedding modes are identified, (I) shear layer oscillation state, (II) quasi-two-dimensional vortex-shedding states and (III) transition of the magnetohydrodynamic to hydrodynamic Kármán street. The transitions between these modes are analysed in detail. The experimental results show that the weak wall-proximity effect leads to the formation of the Kármán vortex street, while a reverse Kármán vortex street and secondary vortices emerge under a strong wall-proximity effect. It is noticed that the Kelvin–Helmholtz instability drives vortex shedding under regime I, leading to an increase in the Strouhal number (St) with stronger magnetic fields. Additionally, under a strong axial magnetic field, the wall-proximity effect (‘Shercliff layer effect’) promotes the instability of shear layers on both sides of the cylinder. These unique coupling effects are validated by variations in modal coefficients and energy proportions under different vortex-shedding regimes using the proper orthogonal decomposition method.

Syphilis remains a serious public health problem in mainland China that requires attention, modelling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, hybrid SARIMA-LSTM model, and hybrid SARIMA-nonlinear auto-regressive models with exogenous inputs (SARIMA-NARX) model were used to simulate the time series data of the syphilis incidence from January 2004 to November 2023 respectively. Compared to the SARIMA, LSTM, and SARIMA-LSTM models, the median absolute deviation (MAD) value of the SARIMA-NARX model decreases by 352.69%, 4.98%, and 3.73%, respectively. The mean absolute percentage error (MAPE) value decreases by 73.7%, 23.46%, and 13.06%, respectively. The root mean square error (RMSE) value decreases by 68.02%, 26.68%, and 23.78%, respectively. The mean absolute error (MAE) value decreases by 70.90%, 23.00%, and 21.80%, respectively. The hybrid SARIMA-NARX and SARIMA-LSTM methods predict syphilis cases more accurately than the basic SARIMA and LSTM methods, so that can be used for governments to develop long-term syphilis prevention and control programs. In addition, the predicted cases still maintain a fairly high level of incidence, so there is an urgent need to develop more comprehensive prevention strategies.