Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

<b>Background:</b>  Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.<p></p> <b>Methods:</b>  Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.<p></p> <b>Results:</b>  A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.<p></p> <b>Conclusions:<b>  Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.<p></p&gt

Population balances combined with computational fluid dynamics : a modeling approach for dispersive mixing in a high pressure homogenizer

High pressure homogenization is at the heart of many emulsification processes in the food, personal care and pharmaceutical industry. The droplet size distribution is an important property for product quality and is aimed to be controlled in the process. Therefore a population balance model was built in order to predict the droplet size distribution subject to various hydrodynamic conditions found in a high pressure homogenizer. The hydrodynamics were simulated using Computational Fluid Dynamics and the turbulence was modeled with a RANS k–e model. The high energy zone in the high pressure homogenizer was divided into four compartments. The compartments had to be small enough to secure nearly homogeneous turbulent dissipation rates but large enough to hold a population of droplets. A population balance equation describing breakage and coalescence of oil droplets in turbulent flow was solved for every compartment. One set of parameters was found which could describe the development of the droplet size distribution in the high pressure homogenizer with varying pressure drop. An improvement of 65% was found compared to the same model containing just one compartment. The compartment approach may provide an alternative to direct coupling of CFD and population balances

Lack of Effect of Leptin on Glucose Transport, Lipoprotein Lipase, and Insulin Action in Adipose and Muscle Cells

The effect of leptin on glucose transport, lipogenesis, and lipoprotein lipase activity was studied in cultured rat adipocytes and 3T3-L1 adipocytes. Leptin had no effect on basal and insulin stimulated glucose transport in isolated adipocytes from the rat and the genetically obese mouse. The incorporation of glucose into lipids was also unaffected. Lipoprotein lipase (LPL) activity remained unchanged in response to leptin in these cells, as well as in minced adipose tissue. Leptin also had no effect on both basal and insulin-stimulated glucose transport in cultured rat and human skeletal muscle cells. These studies showed that leptin had no effect on glucose transport, lipoprotein lipase activity, and insulin action in fat and muscle cells in vitro

Effects of Tumor Necrosis Factor-α on Glucose Metabolism in Cultured Human Muscle Cells from Nondiabetic and Type 2 Diabetic Subjects

The effects of tumor necrosis factor-a (TNFα) on glucose uptake and glycogen synthase (GS) activity were studied in human skeletal muscle cell cultures from nondiabetic and type 2 diabetic subjects. In nondiabetic muscle cells, acute (90-Min) exposure to TNFα (5 ng/ml) stimulated glucose uptake (73 ± 14% increase) to a greater extent than insulin (37 ± 4%; P \u3c 0.02). The acute uptake response to TNFα in diabetic cells (51 ± 6% increase) was also greater than that to insulin (31 ± 3%; P \u3c 0.05). Prolonged (24-h) exposure of nondiabetic muscle cells to TNFα resulted in a further stimulation of uptake (152 ± 31%; P \u3c 0.05), whereas the increase in cells from type 2 diabetics was not significant compared with that in cells receiving acute treatment. After TNFα treatment, the level of glucose transporter-1 protein was elevated in nondiabetic (4.6-fold increase) and type 2 (1.7-fold) cells. Acute TNFα treatment had no effect on the fractional velocity of GS in either nondiabetic or type 2 cells. Prolonged exposure reduced the GS fractional velocity in both nondiabetic and diabetic cells. In summary, both acute and prolonged treatment with TNFα up-regulate glucose uptake activity in cultured human muscle cells, but reduce GS activity. Increased skeletal muscle glucose uptake in conditions of TNFα excess may serve as a compensatory mechanism in the insulin resistance of type 2 diabetes

Promising Practices From Fiji in Empowering Women Economically: Learnings From Talanoa Treks, Ra Naari Parishad, Rise Beyond the Reef, and the Fiji Womens Fund

This paper is jointly authored by eight women who work with the Fiji Women's Fund and three of the Fund's partner organisations - Talanoa Treks, Ra Naari Parishad, and, Rise Beyond the Reef. The paper aims to contribute to improved women's economic empowerment programs by sharing the experiences of these three partners. The authors document the learnings of practitioners in Fiji and compare these with the existing literature for the audience of practitioners in the Pacific and abroad. The Fiji Women's Fund supports the documentation of research from practice, so that the expertise of practitioners is recognised, and, to increase the body of knowledge generated from the Global South.The paper examines the experiences and learnings of the three partners using the Gender at Work framework, developed by Rao and Kelleher, which highlights the inter-linked dimensions of change required to achieve sustainable progress on gender equality and women's empowerment. The paper documents the similar journey taken by all three partner organisations, through each of the four quadrants of this framework. All three entities supported the establishment of a formal, collective structure being established, to provide women access to training and income-generating opportunities. Women accessed these opportunities to improve their skills, capabilities, income and assets. These changes, in turn, had an influence on the way the women themselves, and the men in their lives, think about what it means to be a woman or a man and the possibilities available. For example, there is evidence of positive changes to what women and men are doing in their households. Husbands, sons and partners are helping women beneficiaries by taking on some of the care tasks that were previously left to the women. The greatest evidence of change is within households, as changes to exclusionary practices at the village level are less evident

Improving Power Delivery of Grid-Connected Induction Machine Based Wind Generators under Dynamic Conditions Using Feedforward Linear Neural Networks

In the conventional grid-connected Wind Energy Conversion System (WECS), the generator side inverter is typically controlled via Field Oriented Control (FOC), while Voltage Oriented Control (VOC) controls the grid side inverter. However, robust operation cannot be guaranteed during sudden changes in wind speeds and weak grid connections. This paper presents a novel method to improve the overall dynamic performance of on-grid induction machine-based wind generators. An online mechanical parameter estimation technique is devised using Recursive Least Squares (RLS) to compute the machine inertia and friction coefficient iteratively. An adaptive feedforward neural (AFN) controller is also proposed in the synchronous reference frame, which is constructed using the estimated parameters and the system's inverse. The output of the neural controller is added to the output of the speed PI controller in the outer loop of the FOC to enhance the speed response of the wind generator. A similar approach is taken to improve the classical VOC structure for the grid-side inverter. In this case, the RLS estimates the equivalent Thevenin's grid impedance in real-time. As for the adaptive action, two identical neural networks are integrated with the inner loop direct and quadrature axis current PI controllers. Under nominal operating conditions, it is observed that the PI+AFN provides a faster settling time for the generator's speed and torque response. Upon being subjected to variations in the wind speed, the PI+AFN outperforms the classical PI controller and attains a lower integral-time error. In addition, the proposed PI+AFN controller has a better ability to maintain the grid-side inverter stability during stochastic variations in grid impedance. One significant advantage of the proposed control approach is that no data for training or validation is required since the neural network weights are directly the output of the RLS estimator. Hardware verification for the improved FOC for wind generators using the adaptive controller is also made using the DSPACE 1007 AUTOBOX platform

Correction to: Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

The article “Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England”, written by Adrian H. Heald, David A. Jenkins, Richard Williams, Rajshekhar N. Mudaliar, Amber Khan, Akheel Syed, Naveed Sattar, Kamlesh Khunti, Asma Naseem, Kelly A. Bowden-Davies, J. Martin Gibson, William Ollier, on behalf of the CVD-COVID-UK/COVID-IMPACT Consortium was originally published electronically on the publisher’s Internet portal (currently SpringerLink) on August 25, 2023, without open access. Now, the article is updated with open access as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The original article has been corrected

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D. Methods: This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England’s Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression. Results: Median age of the people living with T1D was 37 (interquartile range 25–52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m2, and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62–1.80]), age (OR 1.02, 95% CI 1.02–1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06–1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974–0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06–1.33/OR Asian 1.21, 95% CI 1.05–1.39) and having asthma (OR 1.27, 95% CI 1.19–1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89–2.32), severe mental illness (OR 1.83, 95% CI 1.57–2.12) or hypertension (OR 1.44, 95% CI 1.37–1.52). Conclusion: In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics