Upper Extremity Strength Characteristics in Female Recreational Tennis Players With and Without Lateral Epicondylalgia

STUDY DESIGN: Descriptive, cross-sectional. OBJECTIVES: To compare static strength characteristics of the upper extremity musculature in female recreational tennis players with lateral epicondylalgia to those of nonsymptomatic tennis players and a control group of women who did not play tennis. BACKGROUND: There is a paucity of research describing the relationship between lateral epicondylalgia and strength characteristics of the upper extremity musculature, despite the functional relationship between the shoulder, elbow, and wrist. METHODS: Sixty-three women were recruited into 3 groups (n = 21 per group): symptomatic tennis players (SIP) with lateral epicondylalgia, nonsymptomatic tennis players, and controls. Data collection was performed during a single session, during which the strength of selected muscle groups of the dominant upper extremity was measured using a combination of force transducers. Strength ratios of selected muscle groups were then calculated. RESULTS: The SIP group reported median pain level of 3/10 on a numeric pain rating scale and a symptom duration of 16 weeks. The SIP group had weaker lower trapezius strength (mean difference, -9.0 N; 95% confidence interval [CI]: -13.5, -4.4) and wrist extensor strength (-12.7 N; 95% CI: -24.4, -1.1), and a higher shoulder internal/external rotation strength ratio (0.19; 95% CI: 0.02, 0.35) and upper/lower trapezius strength ratio (1.32; 95% CI: 0.41, 2.23), compared to those of the nonsymptomatic group. Compared to the control group, the SIP group demonstrated a significantly higher shoulder internal/external rotation strength ratio (0.21; 95% CI: 0.04, 0.38) and wrist flexion/extension strength ratio (0.14; 95% CI: 0.01, 0.27). CONCLUSION: In this group of recreational female tennis players, significant differences in strength and strength ratio characteristics were identified. Although the design of the study precludes establishing a cause-and-effect relationship, the results suggest further study and treatment of the muscle groups of interest

Photodynamic therapy offers a novel approach to managing miltefosine-resistant cutaneous leishmaniasis

Funding: Engineering and Physical Sciences Research Council of the UK (grants EP/R035164/1 and EP/L015110/1) and the Scottish Funding Council (ODA GCRF fund grant SFC/AN/12/2017).Cutaneous leishmaniasis (CL) is a neglected disease caused by Leishmania parasites. The oral drug miltefosine is effective, but there is a growing problem of drug resistance, which has led to increasing treatment failure rates and relapse of infections. Photodynamic therapy (PDT) combines a light source and a photoactive drug to promote cell death by oxidative stress. Although PDT is effective against several pathogens, its use against drug resistant Leishmania parasites remains unexplored. Herein, we investigated the potential of organic light-emitting diodes (OLEDs) as wearable light sources, which would enable at-home use or ambulatory treatment of CL. We also assessed its impact on combating miltefosine resistance in Leishmania amazonensis-induced CL in mice. Thein vitro activity of OLEDs combined with 1,9-dimethyl-methylene blue (DMMB) (OLED-PDT) was evaluated against wild-type and miltefosine-resistant L. amazonensis strains in promastigote (EC50 = 0.034 μM for both strains) and amastigote forms (EC50 = 0.052 μM and 0.077 μM, respectively). Cytotoxicity in macrophages and fibroblasts was also evaluated. In vivo, we investigated the potential of OLED-PDT in combination with miltefosine using different protocols. Our results demonstrate that OLED-PDT is effective in killing both strains of L. amazonensis by increasing reactive oxygen species and stimulating nitric oxide production. Moreover, OLED-PDT showed great antileishmanial activity in vivo, allowing the reduction of miltefosine dose by half in infected mice using a light dose of 7.8 J/cm2 and 1.5 μM DMMB concentration. In conclusion, OLED-PDT emerges as a new avenue for at-home care and allows a combination therapy to overcome drug resistance in cutaneous leishmaniasis.Peer reviewe

The quantum mechanical geometric phase of a particle in a resonant vibrating cavity

We study the general-setting quantum geometric phase acquired by a particle in a vibrating cavity. Solving the two-level theory with the rotating-wave approximation and the SU(2) method, we obtain analytic formulae that give excellent descriptions of the geometric phase, energy, and wavefunction of the resonating system. In particular, we observe a sudden $\pi$-jump in the geometric phase when the system is in resonance. We found similar behaviors in the geometric phase of a spin-1/2 particle in a rotating magnetic field, for which we developed a geometrical model to help visualize its evolution.Comment: 15pages,6figure

Quantum Particle-Trajectories and Geometric Phase

"Particle"-trajectories are defined as integrable $dx_\mu dp^\mu = 0$ paths in projective space. Quantum states evolving on such trajectories, open or closed, do not delocalise in $(x, p)$ projection, the phase associated with the trajectories being related to the geometric (Berry) phase and the Classical Mechanics action. High Energy Physics properties of states evolving on "particle"-trajectories are discussed.Comment: 4 page

Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods

The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a library of candidate prediction models. The SL is not restricted to a single prediction model, but uses the strengths of a variety of learning algorithms to adapt to different databases. While the SL has been shown to perform well in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated the performance of the SL in its ability to predict treatment assignment using three electronic healthcare databases. We considered a library of algorithms that consisted of both nonparametric and parametric models. We also considered a novel strategy for prediction modeling that combines the SL with the high-dimensional propensity score (hdPS) variable selection algorithm. Predictive performance was assessed using three metrics: the negative log-likelihood, area under the curve (AUC), and time complexity. Results showed that the best individual algorithm, in terms of predictive performance, varied across datasets. The SL was able to adapt to the given dataset and optimize predictive performance relative to any individual learner. Combining the SL with the hdPS was the most consistent prediction method and may be promising for PS estimation and prediction modeling in electronic healthcare databases

Rho GTPase Transcriptome Analysis Reveals Oncogenic Roles for Rho GTPase-Activating Proteins in Basal-like Breast Cancers

The basal-like breast cancer (BLBC) subtype accounts for a disproportionately high percentage of overall breast cancer mortality. The current therapeutic options for BLBC need improvement; hence, elucidating signaling pathways that drive BLBC growth may identify novel targets for the development of effective therapies. Rho GTPases have previously been implicated in promoting tumor cell proliferation and metastasis. These proteins are inactivated by Rho-selective GTPase-activating proteins (RhoGAPs), which have generally been presumed to act as tumor suppressors. Surprisingly, RNA-Seq analysis of the Rho GTPase signaling transcriptome revealed high expression of several RhoGAP genes in BLBC tumors, raising the possibility that these genes may be oncogenic. To evaluate this, we examined the roles of two of these RhoGAPs, ArhGAP11A (also known as MP-GAP) and RacGAP1 (also known as MgcRacGAP), in promoting BLBC. Both proteins were highly expressed in human BLBC cell lines, and knockdown of either gene resulted in significant defects in the proliferation of these cells. Knockdown of ArhGAP11A caused CDKN1B/p27-mediated arrest in the G1 phase of the cell cycle, whereas depletion of RacGAP1 inhibited growth through the combined effects of cytokinesis failure, CDKN1A/p21-mediated RB1 inhibition, and the onset of senescence. Random migration was suppressed or enhanced by the knockdown of ArhGAP11A or RacGAP1, respectively. Cell spreading and levels of GTP-bound RhoA were increased upon depletion of either GAP. We have established that, via the suppression of RhoA, ArhGAP11A and RacGAP1 are both critical drivers of BLBC growth, and propose that RhoGAPs can act as oncogenes in cancer

A rapid extraction method for glycogen from formalin-fixed liver

Liver glycogen, a highly branched polymer, acts as our blood-glucose buffer. While past structural studies have extracted glycogen from fresh or frozen tissue using a cold-water; sucrose-gradient centrifugation technique, a method for the extraction of glycogen from formalin-fixed liver would allow the analysis of glycogen from human tissues that are routinely collected in pathology laboratories. In this study, both sucrose-gradient and formalin-fixed extraction techniques were carried out on piglet livers, with the yields, purities and size distributions (using size exclusion chromatography) compared. The formalin extraction technique, when combined with a protease treatment, resulted in higher yields (but lower purities) of glycogen with size distributions similar to the sucrose-gradient centrifugation technique. This formalin extraction procedure was also significantly faster, allowing glycogen extraction throughput to increase by an order of magnitude. Both extraction techniques were compatible with mass spectrometry proteomics, with analysis showing the two techniques were highly complementary. (C) 2014 Elsevier Ltd. All rights reserved