Phase Transition Behavior and Catalytic Activity of Poly(N-acryloylglycinamide-co-methacrylic acid) Microgels

Poly(N-acryloyl glycinamide) is a well-known thermoresponsive polymer possessing an upper critical solution temperature (UCST) in water. By copolymerizing N-acryloyl glycinamide (NAGA) with methacrylic acid (MAA) in the presence of a crosslinker, poly(N-acryloyl glycinamide-co-methacrylic acid) [P(NAGA-MAA)] copolymer microgels with an MAA molar fraction of 10-70 mol % were obtained. The polymerization kinetics suggests that the copolymer microgels have a random structure. The size of the microgels was between 60 and 120 nm in the non-aggregated swollen state in aqueous medium and depending on the solvent conditions, they show reversible swelling and shrinking upon temperature change. Their phase transition behavior was studied by a combination of methods to understand the process of the UCST-type behavior and interactions between NAGA and MAA. P(NAGA-MAA) microgels were loaded with silver nanoparticles (AgNPs) by the reduction of AgNO3 under UV light. Compared with the chemical reduction of AgNO3, the photoreduction results in smaller AgNPs and the amount and size of the AgNPs are dependent on the comonomer ratio. The catalytic activity of the AgNP-loaded microgels in 4-nitrophenol reduction was tested.Peer reviewe

Ovarian and Breast Cancer Risks Associated With Pathogenic Variants in RAD51C and RAD51D

Background: The purpose of this study was to estimate precise age-specific tubo-ovarian carcinoma (TOC) and breast cancer (BC) risks for carriers of pathogenic variants in RAD51C and RAD51D. Methods: We analyzed data from 6178 families, 125 with pathogenic variants in RAD51C, and 6690 families, 60 with pathogenic variants in RAD51D. TOC and BC relative and cumulative risks were estimated using complex segregation analysis to model the cancer inheritance patterns in families while adjusting for the mode of ascertainment of each family. All statistical tests were two-sided. Results: Pathogenic variants in both RAD51C and RAD51D were associated with TOC (RAD51C: relative risk [RR] = 7.55, 95% confidence interval [CI] = 5.60 to 10.19; P = 5 x 10(-40); RAD51D: RR = 7.60, 95% CI = 5.61 to 10.30; P = 5 x 10(-39)) and BC (RAD51C: RR =1.99, 95% CI = 1.39 to 2.85; P = 1.55 x 10(-4); RAD51D: RR = 1.83, 95% CI = 1.24 to 2.72; P = .002). For both RAD51C and RAD51D, there was a suggestion that the TOC relative risks increased with age until around age 60 years and decreased thereafter. The estimated cumulative risks of developing TOC to age 80 years were 11% (95% CI = 6% to 21%) for RAD51C and 13% (95% CI = 7% to 23%) for RAD51D pathogenic variant carriers. The estimated cumulative risks of developing BC to 80 years were 21% (95% CI = 15% to 29%) for RAD51C and 20% (95% CI = 14% to 28%) for RAD51D pathogenic variant carriers. Both TOC and BC risks for RAD51C and RAD51D pathogenic variant carriers varied by cancer family history and could be as high as 32-36% for TOC, for carriers with two first-degree relatives diagnosed with TOC, or 44-46% for BC, for carriers with two first-degree relatives diagnosed with BC. Conclusions: These estimates will facilitate the genetic counseling of RAD51C and RAD51D pathogenic variant carriers and justify the incorporation of RAD51C and RAD51D into cancer risk prediction models.Peer reviewe

Lupus Erythematosus: Dermatologic Perspectives on the Diversity

Lupus is one of the complex autoimmune disease, which is difficult to diagnose and consists of few subtypes that are required to be classified. During our clinical work, we found out that the dermoscopy can be of great benefit to diagnose discoid lupus erythematosus (DLE). The histopathological examination is very important to confirm the diagnosis. The cases of infant LE patients, may derive the autoimmune antibodies from their mothers in order to diagnose the neonatal lupus erythematosus. Thus, it is very important to examine the antibodies of the mother, who may also be a subclinical LE patient and need continuous follow-ups or even treatment managements. Here, we present the cases of lupus with particular characteristics including linear cutaneous lupus erythematosus, DLE, and neonatal lupus erythematosus

Cancer Risks Associated With Germline PALB2 Pathogenic Variants : An International Study of 524 Families

PURPOSE To estimate age-specific relative and absolute cancer risks of breast cancer and to estimate risks of ovarian, pancreatic, male breast, prostate, and colorectal cancers associated with germline PALB2 pathogenic variants (PVs) because these risks have not been extensively characterized. METHODS We analyzed data from 524 families with PALB2 PVs from 21 countries. Complex segregation analysis was used to estimate relative risks (RRs; relative to country-specific population incidences) and absolute risks of cancers. The models allowed for residual familial aggregation of breast and ovarian cancer and were adjusted for the family-specific ascertainment schemes. RESULTS We found associations between PALB2 PVs and risk of female breast cancer (RR, 7.18; 95% CI, 5.82 to 8.85; P = 6.5 x 10(-76)), ovarian cancer (RR, 2.91; 95% CI, 1.40 to 6.04; P = 4.1 x 10(-3)), pancreatic cancer (RR, 2.37; 95% CI, 1.24 to 4.50; P = 8.7 x 10(-3)), and male breast cancer (RR, 7.34; 95% CI, 1.28 to 42.18; P = 2.6 x 10(-2)). There was no evidence for increased risks of prostate or colorectal cancer. The breast cancer RRs declined with age (P for trend = 2.0 x 10(-3)). After adjusting for family ascertainment, breast cancer risk estimates on the basis of multiple case families were similar to the estimates from families ascertained through population-based studies (P for difference = .41). On the basis of the combined data, the estimated risks to age 80 years were 53% (95% CI, 44% to 63%) for female breast cancer, 5% (95% CI, 2% to 10%) for ovarian cancer, 2%-3% (95% CI females, 1% to 4%; 95% CI males, 2% to 5%) for pancreatic cancer, and 1% (95% CI, 0.2% to 5%) for male breast cancer. CONCLUSION These results confirm PALB2 as a major breast cancer susceptibility gene and establish substantial associations between germline PALB2 PVs and ovarian, pancreatic, and male breast cancers. These findings will facilitate incorporation of PALB2 into risk prediction models and optimize the clinical cancer risk management of PALB2 PV carriers. (C) 2019 by American Society of Clinical OncologyPeer reviewe

Parameter estimation and error calibration for multi-channel beam-steering SAR systems

Multi-channel beam-steering synthetic aperture radar (multi-channel BS-SAR) can achieve high resolution and wide-swath observations by combining beam-steering technology and azimuth multi-channel technology. Various imaging algorithms have been proposed for multi-channel BS-SAR but the associated parameter estimation and error calibration have received little attention. This paper focuses on errors in the main parameters in multi-channel BS-SAR (the derotation rate and constant Doppler centroid) and phase inconsistency errors. These errors can significantly reduce image quality by causing coarser resolution, radiometric degradation, and appearance of ghost targets. Accurate derotation rate estimation is important to remove the spectrum aliasing caused by beam steering, and spectrum reconstruction for multi-channel sampling requires an accurate estimate of the constant Doppler centroid and phase inconsistency errors. The time shift and scaling effect of the derotation error on the azimuth spectrum are analyzed in this paper. A method to estimate the derotation rate is presented, based on time shifting, and integrated with estimation of the constant Doppler centroid. Since the Doppler histories of azimuth targets are space-variant in multi-channel BS-SAR, the conventional estimation methods of phase inconsistency errors do not work, and we present a novel method based on minimum entropy to estimate and correct these errors. Simulations validate the proposed error estimation methods

p53 Dependent Centrosome Clustering Prevents Multipolar Mitosis in Tetraploid Cells

BACKGROUND: p53 abnormality and aneuploidy often coexist in human tumors, and tetraploidy is considered as an intermediate between normal diploidy and aneuploidy. The purpose of this study was to investigate whether and how p53 influences the transformation from tetraploidy to aneuploidy. PRINCIPAL FINDINGS: Live cell imaging was performed to determine the fates and mitotic behaviors of several human and mouse tetraploid cells with different p53 status, and centrosome and spindle immunostaining was used to investigate centrosome behaviors. We found that p53 dominant-negative mutation, point mutation, or knockout led to a 2∼ 33-fold increase of multipolar mitosis in N/TERT1, 3T3 and mouse embryonic fibroblasts (MEFs), while mitotic entry and cell death were not significantly affected. In p53-/- tetraploid MEFs, the ability of centrosome clustering was compromised, while centrosome inactivation was not affected. Suppression of RhoA/ROCK activity by specific inhibitors in p53-/- tetraploid MEFs enhanced centrosome clustering, decreased multipolar mitosis from 38% to 20% and 16% for RhoA and ROCK, respectively, while expression of constitutively active RhoA in p53+/+ tetraploid 3T3 cells increased the frequency of multipolar mitosis from 15% to 35%. CONCLUSIONS: p53 could not prevent tetraploid cells entering mitosis or induce tetraploid cell death. However, p53 abnormality impaired centrosome clustering and lead to multipolar mitosis in tetraploid cells by modulating the RhoA/ROCK signaling pathway

Fast synthesis of platinum nanopetals and nanospheres for highly-sensitive non-enzymatic detection of glucose and selective sensing of ions

Novel methods to obtain Pt nanostructured electrodes have raised particular interest due to their high performance in electrochemistry. Several nanostructuration methods proposed in the literature use costly and bulky equipment or are time-consuming due to the numerous steps they involve. Here, Pt nanostructures were produced for the first time by one-step template-free electrodeposition on Pt bare electrodes. The change in size and shape of the nanostructures is proven to be dependent on the deposition parameters and on the ratio between sulphuric acid and chloride-complexes (i.e., hexachloroplatinate or tetrachloroplatinate). To further improve the electrochemical properties of electrodes, depositions of Pt nanostructures on previously synthesised Pt nanostructures are also performed. The electroactive surface areas exhibit a two order of magnitude improvement when Pt nanostructures with the smallest size are used. All the biosensors based on Pt nanostructures and immobilised glucose oxidase display higher sensitivity as compared to bare Pt electrodes. Pt nanostructures retained an excellent electrocatalytic activity towards the direct oxidation of glucose. Finally, the nanodeposits were proven to be an excellent solid contact for ion measurements, significantly improving the time-stability of the potential. The use of these new nanostructured coatings in electrochemical sensors opens new perspectives for multipanel monitoring of human metabolism

Learning with multiple pairwise kernels for drug bioactivity prediction

Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.Peer reviewe