The Hippo transducer TAZ as a biomarker of pathological complete response in HER2-positive breast cancer patients treated with trastuzumab-based neoadjuvant therapy

Activation of the Hippo transducer TAZ is emerging as a novel oncogenic route in breast cancer and it has been associated with breast cancer stem cells. Additionally, TAZ expression has been linked with HER-2 positivity. We investigated the association between TAZ expression and pathological complete response in HER2-positive breast cancer patients treated with trastuzumab-based neoadjuvant therapy. TAZ was assessed in diagnostic core biopsies by immunohistochemistry. To categorize samples with low TAZ and samples with high TAZ we generated a score by combining staining intensity and cellular localization. The pathological complete response rate was 78.6% in patients with low TAZ tumors and 57.6% in patients with high TAZ tumors (p=0.082). In HER2-enriched tumors there was no significant association between TAZ and pathological complete response, whereas in the luminal B subtype the pathological complete response rate was 82.4% in tumors with low TAZ and 44.4% in tumors with high TAZ (p=0.035). This association remained statistically significant when restricting our analysis to triple-positive tumors with expression of both estrogen receptor and progesterone receptor \ue2\u89\ua5 50% (p=0.035). Results from this exploratory study suggest that the TAZ score efficiently predicts pathological complete response in Luminal B, HER2-positive breast cancer patients who received neoadjuvant chemotherapy and trastuzumab

The genetic basis of alopecia areata

Alopecia areata (AA) is a common autoimmune disorder, characterized by circle patches of hair loss, in which genetic and environmental factors influence the disease development and progression. In this chapter, we will focus on the genetic loci that have been associated with AA. Some of these loci contain genes involved in innate and adaptive immunity and are shared with other autoimmune diseases, suggesting an overlap of the genetic mechanisms involved in the development of such disorders. Linkage and association studies underline the major region of AA susceptibility coming from the HLA system (6p21.32), specifically HLA-DQB1*03 alleles coding for DQ7 heterodimers. Modern technological innovations have advanced our understanding of the genetic basis of AA. Genome wide association studies have recently identified new chromosomal regions linked to AA liability in 2q33.2 (CTLA4), 4q27 (IL-2/IL-21), 6q25.1 (ULBP), 10p15.1 (IL-2RA) and 12q13 (IKZF4). A significant association was also evident for single-nucleotide polymorphisms in 9q31.1 and 11q13, harboring genes expressed in the hair follicle (STX17 and PRDX5, respectively), and in an intronic region of SPATA5 gene. These association studies may provide mechanistic insights into the AA pathogenesis and can improve the predictive models of the genetic risk. Follow-up of individuals with a high genetic risk of AA could also help to elucidate the role of environmental factors (such as stressful events, diet, infections etc) with the general aim to develop novel clinical approaches for AA treatment. © 2012 Nova Science Publishers, Inc

Clinical, neuropsychological, neurophysiologic, and genetic features of a new Italian pedigree with familial cortical myoclonic tremor with epilepsy

We studied the clinical, neuropsychological, neurophysiologic, and genetic features of an Italian family with familial cortical myoclonic tremor with epilepsy (FCMTE). Clinically affected members of the family had limb and voice tremor, seizures, and myoclonus involving the eyelids during blinking. Neuropsychological testing disclosed visuospatial impairment, possibly due to temporal lobe dysfunction. Neurophysiologic findings suggested increased primary motor cortex excitability with normal sensorimotor integration. Linkage analysis excluded the 8q24 locus, where patients shared a common haplotype spanning 14.5 Mb in the pericentromeric region of chromosome 2

The role of data sample size and dimensionality in neural network based forecasting of building heating related variables

Energy consumed in buildings represents a challenge in the context of reduction of greenhouse gases emission. For this reason and due to the growing interest in operative costs reduction the energy used by buildings (tertiary and privates) for heating, ventilating, and air conditioning (HVAC) is even more investigated. Due to the nature of the energy consumption profile a predictive optimization method is one of the solution the scientific literature spreads even more. However optimization techniques need a good and reliable prediction of the variables of interest over a time horizon. This work focuses on methods to obtain a robust and reliable predictor based on artificial neural networks. For the optimization purposes the neural model predicts total heating energy consumption (gas), internal air temperature and aggregated thermal discomfort 12 h ahead. Training and testing data are simulated using a simulator based on heat, air and moisture model for building and systems evaluation (HAMBASE), by which a real office building was modeled. Influence of training data sample size and selection of predictor inputs is examined. Several combinations of early stopping condition and network complexity are tested for different training sample sizes. It is observed that the early stopping mechanism is crucial especially but not only for small training data, because it reliably overcomes overfitting problems. Surprisingly, relatively small networks were sufficient or performed best, although examined range of training sample covered up to five heating seasons. The use of a model tuning is thus supported by the results. Further, two strategies of selection of suitable input variables are demonstrated. While the input selection does not degrade the prediction performance, it is able to reduce the dimensionality and thus to save computational, communication, time, and data acquisition demands. The importance of inputs selection in HVAC modeling is thus pointed out and demonstrated