Prostate cancer (PCa) has different molecular features along progression, including androgen profile, which is associated to therapy inefficiency leading to more aggressive phenotype. Docosahexaenoic acid (DHA) has antiproliferative and pro-apoptotic properties in different cancers associated to cell metabolism modulation. The latter is of particular interest since metabolic reprogramming is one of PCa hallmarks, but is not clear how this occurs among disease progression. Therefore, we evaluated DHA antiproliferative potential in distinct androgenic backgrounds associated to metabolism modulation and androgen-regulated genes. For this purpose, pre-malignant PNT1A and tumor AR-positive 22rv1, and AR-negative PC3 cells were incubated with DHA at 100 μM-48 h. DHA reduced at least 26% cell number for all lineages due to S-phase decrease in AR-positive and G2/M arrest in AR-negative. Mitochondrial metabolic rate decreased in PNT1A (~38%) and increased in tumor cells (at least 40%). This was associated with ROS overproduction (1.6-fold PNT1A; 2.1 22rv1; 2.2 PC3), lipid accumulation (3-fold PNT1A; 1.8 22rv1; 3.6 PC3) and mitochondria damage in all cell lines. AKT, AMPK and PTEN were not activated in any cell line, but p-ERK1/2 increased (1.5-fold) in PNT1A. Expression of androgen-regulated and nuclear receptors genes showed that DHA affected them in a distinct pattern in each cell line, but most converged to metabolism regulation, response to hormones, lipids and stress. In conclusion, regardless of androgenic or PTEN background DHA exerted antiproliferative effect associated to cell cycle impairment, lipid deregulation and oxidative stress, but differentially regulated gene expression probably due to distinct molecular features of each pathologic stage. The purpose of this study was to predict and classify the gamma passing rate (GPR) value by using new features (3D dosiomics features and combined with plan and dosiomics features) together with a machine learning technique for volumetric modulated arc therapy (VMAT) treatment plans. A total of 888 patients who underwent VMAT were enrolled comprising 1255 treatment plans. Further, 24 plan complexity features and 851 dosiomics features were extracted from the treatment plans. The dataset was randomly split into a training/validation (80%) and test (20%) dataset. The three models for prediction and classification using XGBoost were as follows (i) plan complexity features-based prediction method (plan model); (ii) 3D dosiomics feature-based prediction model (dosiomics model); (iii) a combination of both the previous models (hybrid model). The prediction performance was evaluated by calculating the mean absolute error (MAE) and the correlation coefficient (CC) between the predicted and measured GPRs. ML324 chemical structure The classification performance was evaluated by calculating the area under curve (AUC) and sensitivity. MAE and CC at γ2%/2mm in the test dataset were 4.6% and 0.58, 4.3% and 0.61, and 4.2% and 0.63 for the plan model, dosiomics model, and hybrid model, respectively. AUC and sensitivity at γ2%/2mm in test dataset were 0.73 and 0.70, 0.81 and 0.90, and 0.83 and 0.90 for the plan model, dosiomics model, and hybrid model, respectively. A combination of both plan and dosiomics features with machine learning technique can improve the prediction and classification performance for GPR.A combination of both plan and dosiomics features with machine learning technique can improve the prediction and classification performance for GPR. Isavuconazole is a triazole previously shown to have potent invitro activity against Aspergillus spp., Mucorales, and Candida spp. Unlike for other azoles, it is unclear if isavuconazole may induce a trailing effect. We studied isavuconazole MICs for a large collection of Candida isolates from blood samples and determined the extent of the trailing effect when using the EUCAST Edef 7.3.1 method. 761 molecularly identified Candida isolates from blood samples of 742 patients admitted to the hospital (January 2007 to September 2017) were evaluated and further tested for invitro susceptibility to isavuconazole following the EUCAST E.Def 7.3.1 test method. C.albicans showed the highest susceptibility, followed by C.parapsilosis and C.tropicalis (geometric mean MIC 0.003 vs 0.005/0.006, respectively; P<0.001). In contrast, C.glabrata, and C.krusei had significantly higher MIC values (geometric mean MIC 0.094 vs 0.093, respectively). Isavuconazole MIC distributions were not truncated at the lowest concentration tested, except for C.albicans. Overall, the mean percentage of trailing was 12.9% but differences among species were observed C.glabrata, C.albicans, and C.tropicalis exhibited higher trailing in comparison to C.parapsilosis and non-Candida yeasts (P<0.001). The percentage of non-wild-type C.albicans (considering the heavy trailer isolates as wild-type), C.parapsilosis and C.glabrata isolates were 0.56% (2/355), 1.5% (3/200), and 4.65% (4/86), respectively. Isavuconazole showed high invitro activity against Candida spp., particularly against C.albicans. Trailing effect is commonly observed with isavuconazole, particularly with C.glabrata.Isavuconazole showed high in vitro activity against Candida spp., particularly against C. albicans. Trailing effect is commonly observed with isavuconazole, particularly with C. glabrata. Motor development research has seen substantial recent growth. However, much remains to be understood about the nature and extent of motor impairments in neurodevelopmental disorders, including their potential as early markers and/or causal determinants of downstream functioning in other domains. In this narrative review, drawing primarily on the autism literature by way of example, we review current accounts of the nature and consequences of motor functioning. We consider conventional approaches to measurement and study design, and current limited approaches to tackling heterogeneity. We argue that ongoing adherence to traditional diagnostic outcome classification stands in the face of mounting evidence that characteristics of neurodevelopmental disorders lie on a continuum with variability in the general population, and that three broad research avenues stand to offer a better understanding of motor functioning The use of technology and advanced statistical methods for a more nuanced understanding of motor abilities; exploiting the prospective longitudinal tracking of at-risk infants to understand developmental consequences of early motor difference; and employing randomized controlled trials to test the utility of motor therapies whilst also testing causal hypotheses about the role of motor functioning.