Introduction Both diabetes mellitus and being female significantly increase the risk of being diagnosed with major depressive disorder (MDD). The diagnosis of MDD, combined with diabetes mellitus, can be detrimental in terms of mortality and morbidity. We aimed at investigating the impact of diabetes mellitus on the gender gap in MDD over the course of a human lifetime. Research design and methods In a cross-sectional study over the course of 17 years, medical claims data of the general Austrian population (n=8 996 916) between 1997 and 2014 was analyzed. Of these, 123 232 patients with diabetes mellitus were extracted and compared with non-diabetic controls. Results In a cohort of 123 232 patients with diabetes mellitus and 1 933 218 controls (52% females, 48% males), women with diabetes had 2.55 times increased ORs to be diagnosed with MDD compared with women without diabetes (95% CI 2.48 to 2.62, p<0.001) between the age of 30 and 69 years. The effect of diabetes mellitus on the prevalence of MDD was significantly smaller in men (OR=1.85, 95% CI 1.80 to 1.91, p<0.001). Between 0 and 30 years and after age 70 years, the gender gap of MDD was not different between patients with and without diabetes mellitus. The peak of the gender gap in MDD in patients with diabetes mellitus was around the age of 40–49 years. A sensitivity analysis identified overweight, obesity and alcohol dependence as the most potent influencing factors of the widening of the gender gap among patients with diabetes mellitus. Conclusions Diabetes mellitus is a stronger risk factor for MDD in women than in men, with the greatest width of the gender gap between 40 and 49 years. High-risk patients for MDD, such as overweight female patients with diabetes, should be more carefully assessed and monitored.

In medium-size, spiny striatal neurons of the direct pathway, dopamine D1- and adenosine A1-receptors are coexpressed and are mutually antagonistic. Recently, a mutation in the gene encoding the A1-receptor (A1R), A1R-G279S7.44, was identified in an Iranian family: two affected offspring suffered from early-onset l-DOPA–responsive Parkinson’s disease. The link between the mutation and the phenotype is unclear. Here, we explored the functional consequence of the G279S substitution on the activity of the A1-receptor after heterologous expression in HEK293 cells. The mutation did not affect surface expression and ligand binding but changed the susceptibility to heat denaturation: the thermodynamic stability of A1R-G279S7.44 was enhanced by about 2 and 8 K when compared with wild-type A1-receptor and A1R-Y288A7.53 (a folding-deficient variant used as a reference), respectively. In contrast, the kinetic stability was reduced, indicating a lower energy barrier for conformational transitions in A1R-G279S7.44 (73 ± 23 kJ/mol) than in wild-type A1R (135 ± 4 kJ/mol) or in A1R-Y288A7.53 (184 ± 24 kJ/mol). Consistent with this lower energy barrier, A1R-G279S7.44 was more effective in promoting guanine nucleotide exchange than wild-type A1R. We detected similar levels of complexes formed between D1-receptors and wild-type A1R or A1R-G279S7.44 by coimmunoprecipitation and bioluminescence resonance energy transfer. However, lower concentrations of agonist were required for half-maximum inhibition of dopamine-induced cAMP accumulation in cells coexpressing D1-receptor and A1R-G279S7.44 than in those coexpressing wild-type A1R. These observations predict enhanced inhibition of dopaminergic signaling by A1R-G279S7.44 in vivo, consistent with a pathogenic role in Parkinson’s disease. SIGNIFICANCE STATEMENT Parkinson’s disease is caused by a loss of dopaminergic input from the substantia nigra to the caudate nucleus and the putamen. Activation of the adenosine A1-receptor antagonizes responses elicited by dopamine D1-receptor. We show that this activity is more pronounced in a mutant version of the A1-receptor (A1R-G279S7.44), which was identified in individuals suffering from early-onset Parkinson’s disease.

The plant Citrullus colocynthis, a member of the squash (Cucurbitaceae) family, has a long history in traditional medicine. Based on the ancient knowledge about the healing properties of herbal preparations, plant-derived small molecules, e.g., salicylic acid, or quinine, have been integral to modern drug discovery. Additionally, many plant families, such as Cucurbitaceae, are known as a rich source for cysteine-rich peptides, which are gaining importance as valuable pharmaceuticals. In this study, we characterized the C. colocynthis peptidome using chemical modification of cysteine residues, and mass shift analysis via matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry. We identified the presence of at least 23 cysteine-rich peptides in this plant, and eight novel peptides, named citcol-1 to -8, with a molecular weight between ~3650 and 4160 Da, were purified using reversed-phase high performance liquid chromatography (HPLC), and their amino acid sequences were determined by de novo assignment of b- and y-ion series of proteolytic peptide fragments. In silico analysis of citcol peptides revealed a high sequence similarity to trypsin inhibitor peptides from Cucumis sativus, Momordica cochinchinensis, Momordica macrophylla and Momordica sphaeroidea. Using genome/transcriptome mining it was possible to identify precursor sequences of this peptide family in related Cucurbitaceae species that cluster into trypsin inhibitor and antimicrobial peptides. Based on our analysis, the presence or absence of a crucial Arg/Lys residue at the putative P1 position may be used to classify these common cysteine-rich peptides by functional properties. Despite sequence homology and the common classification into the inhibitor cysteine knot family, these peptides appear to have diverse and additional bioactivities yet to be revealed.

Evidence on early achievements, challenges and opportunities would help low-income and middle-income countries (LMICs) accelerate implementation of health and health-related sustainable development goals (HHSDGs). A series of country-specific and multicountry consultative meetings were conducted during 2018–2019 that involved 15 countries across five regions to determine the status of implementation of HHSDGs. Almost 120 representatives from health and non-health sectors participated. The assessment relied on a multidomain analytical framework drawing on existing public health policy frameworks. During the first 5 years of the sustainable development goals (SDGs) era, participating LMICs from South and Central Asia, East Africa and Latin America demonstrated growing political commitment to HHSDGs, with augmentation of multisectoral institutional arrangements, strengthening of monitoring systems and engagement of development partners. On the other hand, there has been limited involvement of civic society representatives and academia, relatively few capacity development initiatives were in place, a well-crafted communication strategy was missing, and there is limited evidence of additional domestic financing for implementing HHSDGs. While the momentum towards universal health coverage is notable, explicit linkages with non-health SDGs and integrated multisectoral implementation strategies are lacking. The study offers messages to LMICs that would allow for a full decade of accelerated implementation of HHSDGs, and points to the need for more implementation research in each domain and for testing interventions that are likely to work before scale-up.

In the modern telecommunication systems, mobility is one of the key advantage of wireless communications, given that it is possible to transmit/receive data, without caring of having a static position into the network. Of course, mobility poses special issues such as degradations, channel quality fluctuations, fast topology changes, and so on. Modern researches focus their attention on predicting mobile future node positions, in order to a-priori know, for example, what the evolution of the network topology will be or which level of stability each node will reach. Each prediction scheme is based on the storage and analysis of several historical mobility trajectories, in order to train the proper prediction algorithm. In this paper, we focus our attention on the optimization of the space needed to store historical mobility samples, encoding their values and evaluating the conversion error, comparing different encoding functions. Several simulation campaigns have been carried out in order to evaluate the goodness and feasibility of our proposal.

Abstract Epilepsy is a neurological disorder characterised by unusual brain activity widely known as seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently based on analysis of the electroencephalography (EEG) signals in the time-frequency domain. The analysis is performed applying various algorithms that yield high performance, however the challenge of effective real-time epilepsy diagnosis persists. To address this, we have developed a Field Programmable Gate Array (FPGA) based solution for the classification of generalized and focal epileptic seizure types using a feed-forward multi-layer neural network architecture (MLP ANN). The neural network algorithm is trained, validated and tested on 822 captured signals from Temple University Hospital Seizure Detection Corpus (TUH EEG Corpus) database. Inputs into the system were five main features obtained from EEG signals by time-frequency analysis followed by Continuous Wavelet Transform (CWT) and subsequent statistical analysis. Out of the total number of samples, 583 (70 %) of them were utilised during the system development in MATLAB and TensorFlow and 239 (30 %) samples were further used for subsequent testing of the model performance on the FPGA. Subsequently, the adequate parameters of the ANN model were determined by using k-Fold Cross-Validation. Finally, the best performing ANN model in terms of average validation data accuracy achieved during cross-validation was implemented on the FPGA for real-time seizure classification. The digital ANN solution was coded in Very High-Speed Integrated Circuit Hardware Description Language (VHDL) and tested on the FPGA using 30 % reaming data. The results of this research demonstrate that epilepsy diagnosis with quite high accuracy (95.14 %) can be achieved with (5-12-3) MLP ANN implemented on FPGA. Also, the results show the steps towards appropriate implementation of ANN on the FPGA. These results can be utilised as the basis for the design of an application-specific integrated circuit (ASIC) allowing large serial production.

This paper analyzes the vollatility spillover between the conventional index in Malaysia FTSE Malaysia KLCI (KLSE) and the Islamic index in Malaysia FTSE Bursa Malaysia Shariah Index (FTFBMHS). Monthly observations spanning in a period from 2002 to 2018 are obtained from investing.com database. GARCH model and Johansen cointegration test are used to investigate volatility spillover and the relationship between two indices. The results of the analysis indicate that in the short-run there is volatility spillover between FTSE Malaysia KLCI and FTSE Bursa Malaysia Shariah Index, while in the long-run there is no relationship between the two indices. The methodology of compiling Islamic indeces is based on Shariah law. Keywords: Conventional