Part II: presentation,diagnosis, classification,treatment, and prevention of stress fractures in female athletes

ABSTRACT

Objectives: Stress fractures (SFx) occur as the result of repetitive loads over short periods of time, which leads to micro-damage of the bone through cortical resorption, ultimately leading to fracture. They are a common injury in female athletes and often cause significant morbidity. The goal of this study is to review the presentation, diagnosis, classification, treatment, and prevention of SFx in female athletes.

Results: A thorough history, physical exam, and appropriate imaging can facilitate early diagnosis of stress fracture (SFx) and faster resolution of symptoms with more conservative management. The female athlete triad is an especially important factor that contributes to the increased risk of SFx in females. The continuum of stress injuries ranges from mild microfailure to complete fracture, which has resulted in the development of newer grading schemas through MRI and radiographic findings. Stress fractures are also classified as low- or high-risk according to anatomic location, as blood supply and applied forces at different locations affect the likelihood of fracture propagation, displacement, delayed union, or non-union.

Conclusions: The ability to screen for at-risk athletes is paramount in preventing SFx. Recognition and prompt treatment of the female athlete triad requires a multidisciplinary approach in order to restore energy balance, correct menstrual irregularities, and improve bone health. This review provides a basis for understanding how to identify and treat stress fractures, which may allow treating physicians to diagnose this condition earlier and minimize any associated morbidity.     

Minocycline differentially modulates human spatial memory systems

Microglia play a critical role in many processes fundamental to learning and memory in health and are implicated in Alzheimer’s pathogenesis. Minocycline, a centrally-penetrant tetracycline antibiotic, inhibits microglial activation and enhances long-term potentiation, synaptic plasticity, neurogenesis and hippocampal-dependent spatial memory in rodents, leading to clinical trials in human neurodegenerative diseases. However, the effects of minocycline on human memory have not previously been investigated. Utilising a double-blind, randomised crossover study design, we recruited 20 healthy male participants (mean 24.6 ± 5.0 years) who were each tested in two experimental sessions: once after 3 days of Minocycline 150 mg (twice daily), and once 3 days of placebo (identical administration). During each session, all completed an fMRI task designed to tap boundary- and landmark-based navigation (thought to rely on hippocampal and striatal learning mechanisms respectively). Given the rodent literature, we hypothesised that minocycline would selectively modulate hippocampal learning. In line with this, minocycline biased use of boundary- compared to landmark-based information (t₉₈₀ = 3.140, p = 0.002). However, though this marginally improved performance for boundary-based objects (t₉₈₀ = 1.972, p = 0.049), it was outweighed by impaired landmark-based navigation (t₉₈₀ = 6.374, p < 0.001) resulting in an overall performance decrease (t₉₈₀ = 3.295, p = 0.001). Furthermore, against expectations, minocycline significantly reduced activity during memory encoding in the right caudate (t₉₇₇ = 2.992, p = 0.003) and five other cortical regions, with no significant effect in the hippocampus. In summary, minocycline impaired human spatial memory performance, likely through disruption of striatal processing resulting in greater biasing towards reliance on boundary-based navigation.Subject terms: Microglia, Hippocampus     

AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16 h−1Mpc and the HR halo mass function to within 10% down to 1011 M⊙. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

As telescopes and satellites become more powerful, observational data on galaxies, quasars, and the matter in intergalactic space becomes more detailed and covers a greater range of epochs and environments in the Universe. Our cosmological simulations (see, e.g., ref. 1) must also become more detailed and more wide-ranging in order to make predictions and test the effects of different physical processes and different dark-matter candidates. Even with supercomputers, we are forced to decide whether to maximize either resolution or volume, or else compromise on both. These limitations can be overcome through the development of methods that leverage techniques from the artificial intelligence (AI) revolution (see, e.g., ref. 2) and make superresolution (SR) simulations possible. In the present work, we begin to explore this possibility, combining knowledge and existing superscalable codes for petascale-plus cosmological simulations (3) with machine learning (ML) techniques to effectively create representative volumes of the Universe that incorporate information from higher-resolution models of galaxy formation. Our first attempts, presented here, involve simulations with dark matter and gravity only, and extensions to full hydrodynamics will follow. This hybrid approach, which will imply offloading simulations to neural networks (NNs) and other ML algorithms, has the promise to enable the prediction of quasar, supermassive black hole, and galaxy properties in a way that is statistically identical to full hydrodynamic models, but with a significant speed-up.Adding details to images below the resolution scale (SR image enhancement) has become possible with the latest advances in deep learning (DL; ML with NN; ref. 4), including generative adversarial networks (GANs; ref. 5). The technique has applications in many fields, from microscopy to law enforcement (6). It has been used for observational astronomical images by (7), to recover galaxy features from below the resolution scale in degraded Hubble Space Telescope images. Besides SR image enhancement, DL has started to find applications in cosmological simulations. For example, refs. 8 and 9 showed how NNs can predict the nonlinear formation of structures given simple linear theory predictions. NN models have also been trained to predict galaxies (10, 11) and 21-cm emission from neutral hydrogen (12) from simulations that only contain dark matter. GANs have been used in ref. 13 to generate image slices of cosmological models and to generate dark-matter halos from density fields (14). ML techniques other than DL find many applications, too. For example, Kamdar et al. (15) have applied extremely randomized trees to dark-matter simulations to predict hydrodynamic galaxy properties.Generating mocks for future sky surveys requires large volumes and high accuracy, a task that quickly becomes computationally prohibitive. To alleviate the cost, recently, Dai and Seljak (16) developed a Lagrangian-based parametric ML model to predict various hydrodynamical outputs from the dark-matter density field. In other work, Dai et al. (17, 18) sharpened the particle distribution using a potential gradient descent method starting from low-resolution (LR) simulations. Note, however, that these approaches did not aim to enhance the spatial or mass resolution of a simulation.On the DL side, recently, Ramanah et al. (19) explored using the SR technique to map density fields of LR cosmological simulations to that of the high-resolution (HR) ones. While the goal is similar, our work has the following three key differences. First, instead of focusing on the dark-matter density field, we aim to enhance the number of particles and predict their displacements, from which the density fields can be inferred. This approach allows us to preserve the particle nature of the N-body simulations and therefore to interpret the SR outputs as simulations themselves. Second, we test our technique at a higher SR ratio. Compared to ref. 19, which increased the number of Eulerian voxels by 8 times, we increase the number of particles and thus the mass resolution by a factor of 512. Finally, to facilitate future applications of SR on hydrodynamic simulations in representative volumes, we test our method at much smaller scales and in large simulations whose volume is much bigger than that of the training data.     

Genetic variation in the recognition of Porphyromonas gingivalis antigens in mice

T-cell cytokine profiles, anti-Porphyromonas gingivalis antibodies and Western blot analysis of antibody responses were examined in BALB/c, CBA/CaH, C57BL6 and DBA/2J mice immunized intraperitoneally with different doses of P. gingivalis outer membrane antigens. Splenic CD4 and CD8 cells were examined for intracytoplasmic interleukin (IL)-4, interferon (IFN)-gamma and IL-10 by FACS analysis and levels of anti-P. gingivalis antibodies in the serum samples determined by enzyme-linked immunosorbent assay. Western blot analysis was performed on the sera from mice immunized with 100 microg of P. gingivalis antigens. The four strains of mice demonstrated varying degrees of T-cell immunity, although the T-cell cytokine profiles exhibited by each strain were not affected by different immunizing doses. While BALB/c and DBA/2J mice exhibited responses that peaked at immunizing doses of 100-200 microg of P. gingivalis antigens, CBA/CaH and C57BL6 demonstrated weak T-cell responsiveness compared with control mice. Like the T-cell responses, serum antibody levels were not dose dependent. DBA/2J exhibited the lowest levels of anti-P. gingivalis antibodies followed by BALB/c with CBA/CaH and C57BL6 mice demonstrating the highest levels. Western blot analysis showed that there were differences in reactivity between the strains to a group of 13 antigens ranging in molecular weight from 15 to 43 kDa. Antibody responses to a number of these bands in BALB/c mice were of low density, whereas CBA/CaH and C57BL6 mice demonstrated high-density bands and DBA/2J mice showed medium to high responses. In conclusion, different immunizing doses of P. gingivalis outer membrane antigens had little effect on the T-cell cytokine responses and serum anti-P. gingivalis antibody levels. Western blot analysis, however, indicated that the four strains of mice exhibited different reactivity to some lower-molecular-weight antigens. Future studies are required to determine the significance of these differences, which may affect the outcome of P. gingivalis infection.