Measurement of 7-dehydrocholesterol and cholesterol in hair can be used in the diagnosis of Smith-Lemli-Opitz syndrome

7-dehydrocholesterol (7-DHC) and cholesterol (CHOL) are biomarkers of Smith-Lemli-Opitz Syndrome (SLOS), a congenital autosomal recessive disorder characterized by elevated 7-DHC level in patients. Hair samples have been shown to have great diagnostic and research value, which has long been neglected in the SLOS field. In this study, we sought to investigate the feasibility of using hair for SLOS diagnosis. In the presence of antioxidants (2,6-ditert-butyl-4-methylphenol and triphenylphosphine), hair samples were completely pulverized and extracted by micro-pulverized extraction in alkaline solution or in n-hexane. After microwave-assisted derivatization with N,O-Bis(trimethylsilyl)trifluoroacetamide, the analytes were measured by GC-MS. We found that the limits of determination for 7-DHC and CHOL were 10 ng/mg and 8 ng/mg, respectively. In addition, good linearity was obtained in the range of 50–4000 ng/mg and 30–6000 ng/mg for 7-DHC and CHOL, respectively, which fully meets the requirement for SLOS diagnosis and related research. Finally, by applying the proposed method to real hair samples collected from 14 healthy infants and two suspected SLOS patients, we confirmed the feasibility of hair analysis as a diagnostic tool for SLOS. In conclusion, we present an optimized and validated analytical method for the simultaneous determination of two SLOS biomarkers using human hair.     

NP7 protects from cell death induced by oxidative stress in neuronal and glial midbrain cultures from parkin null mice

Parkin mutations produce Parkinson’s disease (PD) in humans and nigrostriatal dopamine lesions related to increased free radicals in mice. We examined the effects of NP7, a synthetic, marine derived, free radical scavenger which enters the brain, on H₂O₂ toxicity in cultured neurons and glia from wild-type (WT) and parkin null mice (PK-KO).NP7, 5-10 μM, prevented the H₂O₂ induced apoptosis and necrosis of midbrain neuronal and glial cultures from WT and PK-KO mice. NP7 suppressed microglial activation and the H₂O₂ induced drop-out of dopamine neurons_. Furthermore, NP7 prevented the increased phosphorylation of ERK and AKT induced by H₂O₂. NP7 may be a promising neuroprotector against oxidative stress in PD.     

Incidence,prevalence, mortality,disability-adjusted life years and risk factors of cancer in Australia and comparison with OECD countries, 1990–2015: findings from the Global Burden of Disease Study 2015

BackgroundComparative evidence on the burden, trend, and risk factors of cancer is limited. Using data from the Global Burden of Disease (GBD) study, we aimed to assess cancer burden – incidence, prevalence, mortality, disability-adjusted life years (DALYs) – and attributable risk factors for Australia between 1990 and 2015, and to compare them with those of 34 members of the Organisation for Economic Co-operation and Development (OECD).MethodsThe general GBD cancer estimation methods were used with data input from vital registration systems and cancer registries. A comparative risk assessment approach was used to estimate the population-attributable fractions due to risk factors.ResultsIn 2015 there were 198,880 (95% uncertainty interval [UI]: 183,908–217,365) estimated incident cancer cases and 47,562 (95% UI: 46,061–49,004) cancer deaths in Australia. Twenty-nine percent (95% UI: 28.2–29.8) of total deaths and 17.0% (95% UI: 15.0–19.1) of DALYs were caused by cancer in Australia in 2015. Cancers of the trachea, bronchus and lung, colon and rectum, and prostate were the most common causes of cancer deaths. Thirty-six percent (95% UI: 33.1–37.9) of all cancer deaths were attributable to behavioral risks. The age-standardized cancer incidence rate (ASIR) increased between 1990 and 2015, while the age-standardized cancer death rate (ASDR) decreased over the same period. In 2015, compared to 34 other OECD countries Australia ranked first (highest) and 24^th based on ASIR and ASDR, respectively.ConclusionThe incidence of cancer has increased over 25 years, and behavioral risks are responsible for a large proportion of cancer deaths. Scaling up of prevention (using strategies targeting cancer risk factors), early detection, and treatment of cancer is required to effectively address this growing health challenge.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Filamin structure,function and mechanics: are altered filamin-mediated force responses associated with human disease?

The cytoskeleton framework is essential not only for cell structure and stability but also for dynamic processes such as cell migration, division and differentiation. The F-actin cytoskeleton is mechanically stabilised and regulated by various actin-binding proteins, one family of which are the filamins that cross-link F-actin into networks that greatly alter the elastic properties of the cytoskeleton. Filamins also interact with cell membrane-associated extracellular matrix receptors and intracellular signalling proteins providing a potential mechanism for cells to sense their external environment by linking these signalling systems. The stiffness of the external matrix to which cells are attached is an important environmental variable for cellular behaviour. In order for a cell to probe matrix stiffness, a mechanosensing mechanism functioning via alteration of protein structure and/or binding events in response to external tension is required. Current structural, mechanical, biochemical and human disease-associated evidence suggests filamins are good candidates for a role in mechanosensing.     

植物蛋白SUMO化修饰检测方法

SUMO化是一种重要的蛋白质翻译后修饰,对植物正常生长发育不可或缺。到目前为止已筛选到上千个可能的SUMO底物,但由于SUMO化修饰水平普遍很低,其生物学功能研究相对较少。该文详细描述了检测蛋白SUMO化修饰的常用方法,包括体外和体内SUMO化实验,以及SUMO化修饰位点的检测方法,旨在为深入研究植物蛋白SUMO化修饰提供技术支持。     

Automated extraction of bank angles from bird‐borne video footage using open‐source software

The use of miniaturized video cameras to study the at‐sea behavior of flying seabirds has increased in recent years. These cameras allow researchers to record several behaviors that were not previously possible to observe. However, video recorders produce large amounts of data and videos can often be time‐consuming to analyze. We present a new technique using open‐source software to extract bank angles from bird‐borne video footage. Bank angle is a key facet of dynamic soaring, which allows albatrosses and petrels to efficiently search vast areas of ocean for food. Miniaturized video cameras were deployed on 28 Wandering Albatrosses (Diomedea exulans) on Marion Island (one of the two Prince Edward Islands) from 2016 to 2018. The OpenCV library for the Python programming language was used to extract the angle of the horizon relative to the bird’s body (= bank angle) from footage when the birds were flying using a series of steps focused on edge detection. The extracted angles were not significantly different from angles measured manually by three independent observers, thus being a valid method to measure bank angles. Image quality, high wind speeds, and sunlight all influenced the accuracy of angle estimates, but post‐processing eliminated most of these errors. Birds flew most often with cross‐winds (58%) and tailwinds (39%), resulting in skewed distributions of bank angles when birds turned into the wind more often. Higher wind speeds resulted in extreme bank angles (maximum observed was 94°). We present a novel method for measuring postural data from seabirds that can be used to describe the fine‐scale movements of the dynamic‐soaring cycle. Birds appeared to alter their bank angle in response to varying wind conditions to counter wind drift associated with the prevailing westerly winds in the Southern Ocean. These data, in combination with fine‐scale positional data, may lead to new insights into dynamic‐soaring flight.     

Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.