Effect of chloride concentration on the cytotoxicity,bioavailability, and bioreactivity of copper and silver in the rainbow trout gut cell line,RTgutGC

Ecotoxicology - Chloride (Cl?) influences the bioavailability and toxicity of metals in fish, but the mechanisms by which it influences these processes is poorly understood. Here, we...     

Surgical treatment of radial head isolated Mason III fractures

Introduction

In this retrospective study we have analyzed a consecutive series of patients affected by isolated radial head Mason III fractures and treated with bone resection or prosthesis.

Increased expression of glutamine transporter SNAT2/SLC38A2 promotes glutamine dependence and oxidative stress resistance,and is associated with worse prognosis in triple-negative breast cancer

Background Glutamine (Gln) is an abundant nutrient used by cancer cells. Breast cancers cells and particularly triple-receptor negative breast cancer (TNBC) are reported to be dependent on Gln to produce the energy required for survival and proliferation. Despite intense research on the role of the intracellular Gln pathway, few reports have focussed on Gln transporters in breast cancer and TNBC.Methods The role and localisation of the Gln transporter SLC38A2/SNAT2 in response to Gln deprivation or pharmacological stresses was examined in a panel of breast cancer cell lines. Subsequently, the effect of SLC38A2 knockdown in Gln-sensitive cell lines was analysed. The prognostic value of SLC38A2 in a cohort of breast cancer was determined by immunohistochemistry.Results SLC38A2 was identified as a strongly expressed amino acid transporter in six breast cancer cell lines. We confirmed an autophagic route of degradation for SLC38A2. SLC38A2 knockdown decreased Gln consumption, inhibited cell growth, induced autophagy and led to ROS production in a subgroup of Gln-sensitive cell lines. High expression of SLC38A2 protein was associated with poor breast cancer specific survival in a large cohort of patients (p = 0.004), particularly in TNBC (p = 0.02).Conclusions These results position SLC38A2 as a selective target for inhibiting growth of Gln-dependent breast cancer cell lines.Subject terms: Breast cancer, Cancer metabolism     

Metastasis-directed Therapy (SBRT) Guided by PET-CT 18F-CHOLINE Versus PET-CT 68Ga-PSMA in Castration-sensitive Oligorecurrent Prostate Cancer: A Comparative Analysis of Effectiveness

BackgroundThe present analysis aims to compare the impact of 18F-fluorocholine (¹⁸F-choline) and gallium-68 prostate-specific membrane antigen (⁶⁸Ga-PSMA) positron emission tomography (PET)-computed tomography (CT)–guided metastases-directed therapies (MDTs) in patients with castration-sensitive oligorecurrent prostate cancer (PC).Materials and MethodsInclusion criteria were: (1) histologically proven prostate adenocarcinoma; (2) evidence of biochemical relapse after primary tumor treatment; (3) ≤ 3 hypermetabolic oligorecurrent lesions detected by ¹⁸F-choline or ⁶⁸Ga-PSMA PET-CT; (4) PET-CT imaging performed in a single nuclear medicine department; (5) patients treated with upfront stereotactic body radiotherapy (SBRT) without hormone therapy; and (6) SBRT delivered with a dose per fraction ≥ 5 Gy. In the case of oligoprogression (≤ 3 lesions outside the previous RT field) after MTD, a further course of SBRT was proposed; otherwise, androgen deprivation therapy (ADT) was administered.ResultsA total of 118 lesions in 88 patients were analyzed. Forty-four (50%) patients underwent ⁶⁸Ga-PSMA PET-guided SBRT, and the remaining underwent choline PET-based SBRT. The median follow-up was 25 months (range, 5-87 months) for the entire cohort. Overall survival and local control were both 100%. Distant progression occurred in 48 (54.5%) patients, for a median distant progression-free survival of 22.8 months (range, 14.4-28.8 months). The median pre-SBRT prostate-specific antigen was 2.04 ng/mL in the choline PET cohort and 0.58 ng/mL in the PSMA-PET arm. Disease-free survival rates were 63.6% and 34%, respectively, in the ⁶⁸Ga-PSMA and choline PET group (P = .06). The ADT administration rate was higher after choline-PET–guided SBRT (P = .00) owing to the higher incidence of polymetastatic disease after first-course SBRT compared with ⁶⁸Ga-PSMA-based SBRT.ConclusionIn the setting of oligorecurrent castration-sensitive PC, PSMA-PET-guided SBRT produced a higher rate of ADT-free patients when compared with the ¹⁸F-choline-PET cohort. Randomized trials are advocated.     

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.     

Mesenchymal stromal cell secretome in liver failure: Perspectives on COVID-19 infection treatment

Due to their immunomodulatory potential and release of trophic factors that promote healing, mesenchymal stromal cells (MSCs) are considered important players in tissue homeostasis and regeneration. MSCs have been widely used in clinical trials to treat multiple conditions associated with inflammation and tissue damage. Recent evidence suggests that most of the MSC therapeutic effects are derived from their secretome, including the extracellular vesicles, representing a promising approach in regenerative medicine application to treat organ failure as a result of inflammation/fibrosis. The recent outbreak of respiratory syndrome coronavirus, caused by the newly identified agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has forced scientists worldwide to use all available instruments to fight the infection, including the inflammatory cascade caused by this pandemic disease. The use of MSCs is a valid approach to combat organ inflammation in different compartments. In addition to the lungs, which are considered the main inflammatory target for this virus, other organs are compromised by the infection. In particular, the liver is involved in the inflammatory response to SARS-CoV-2 infection, which causes organ failure, leading to death in coronavirus disease 2019 (COVID-19) patients. We herein summarize the current implications derived from the use of MSCs and their soluble derivatives in COVID-19 treatment, and emphasize the potential of MSC-based therapy in this clinical setting.