Post-prandial increases in liver-gut hormone LEAP2 correlate with attenuated eating behaviour in adults without obesity

Background: The novel liver-gut hormone LEAP2 is a centrally acting inverse agonist, and competitive antagonist of orexigenic acyl ghrelin (AG), at the growth hormone secretagogue receptor, reducing food intake in rodents. In humans, the effects of LEAP2 on eating behaviour and mechanisms behind the post-prandial increase in LEAP2 are unclear, though this is reciprocal to the post-prandial decrease in plasma AG. Methods: Plasma LEAP2 was measured in a secondary analysis of a previous study. Twenty-two adults without obesity attended after an overnight fast (Fasted-saline), consuming a 730kcal meal without (Fed-saline) or with (Fed-ghrelin) subcutaneous AG administration. Post-prandial changes in plasma LEAP2 were correlated with post-prandial changes in appetite, high-energy (HE) or low-energy (LE) food cue reactivity using functional MRI, ad libitum food intake, and plasma/serum AG, glucose, insulin and triglycerides. Results: Post-prandial plasma LEAP2 increased by 24.5-52.2% at 70-150 min, but was unchanged by exogenous AG administration. Post-prandial increases in LEAP2 correlated positively with post-prandial decreases in appetite, and cue reactivity to HE/LE and HE food in anterior/posterior cingulate cortex, paracingulate cortex, frontal pole, middle frontal gyrus, with similar trend for food intake. Post-prandial increases in LEAP2 correlated negatively with body mass index, but did not correlate positively with increases in glucose, insulin or triglycerides, nor decreases in AG. Conclusions: These correlational findings are consistent with a role for post-prandial increases in plasma LEAP2 in suppressing human eating behaviour in adults without obesity. Post-prandial increases in plasma LEAP2 are unrelated to changes in plasma AG and the mediator(s) remain uncertain

Gene therapy with Angiotensin-(1-9) preserves left ventricular systolic function after myocardial infarction

BACKGROUND: Angiotensin-(1-9) [Ang-(1-9)] is a novel peptide of the counter-regulatory axis of the renin angiotensin system previously demonstrated to have therapeutic potential in hypertensive cardiomyopathy when administered via osmotic minipump in mice. Here, we investigate whether gene transfer of Ang-(1-9) is cardioprotective in a murine model of myocardial infarction (MI). OBJECTIVES: To evaluate effects of Ang-(1-9) gene therapy on myocardial structural and functional remodeling post infarction. METHODS: C57BL/6 mice underwent permanent left anterior descending coronary artery ligation and cardiac function was assessed using echocardiography for 8 weeks followed by a terminal measurement of left ventricular (LV) pressure-volume loops. Ang-(1-9) was delivered by adeno-associated viral vector via single tail vein injection immediately following induction of MI. Direct effects of Ang-(1-9) on cardiomyocyte excitation–contraction coupling and cardiac contraction were evaluated in isolated mouse and human cardiomyocytes and in an ex vivo Langendorff perfused whole heart model. RESULTS: Gene delivery of Ang-(1-9) significantly reduced sudden cardiac death post-MI. Pressure–volume measurements revealed complete restoration of end systolic pressure, ejection fraction, end systolic volume and the end diastolic pressure–volume relationship by Ang-(1-9) treatment. Stroke volume and cardiac output were significantly increased versus sham. Histological analysis revealed only mild effects on cardiac hypertrophy and fibrosis, but a significant increase in scar thickness. Direct assessment of Ang-(1-9) on isolated cardiomyocytes demonstrated a positive inotropic effect via increasing calcium transient amplitude and increasing contractility. Ang-(1-9) increased contraction in the Langendorff model through a protein kinase A-dependent mechanism. CONCLUSIONS: Our novel findings show that Ang-(1-9) gene therapy preserves LV systolic function post-MI, restoring cardiac function. Furthermore, Ang-(1-9) has a direct effect on cardiomyocyte 3 calcium handling through a protein kinase A-dependent mechanism. These data highlight Ang-(1-9) gene therapy as a potential new strategy in the context of MI

Synthesis of Ce/Ru Doped ZnO photocatalysts to the degradation of emerging pollutants in wastewater

Semiconductor nanoparticles (NPs) and nanowires (NWs) of doped ZnO system with different dopant content have been synthesized by Polyol-Mediated Thermolysis (PMT) process and Vapour-Solid (VS) reaction. The average crystallite size, morphology, specific surface area, and direct band gap have been evaluated. The structural and functional characteristics have been studied by X-Ray Diffraction techniques (XRD), Field Emission Scanning Electron Microscope (FESEM), High Resolution Transmission Electron Microscopy (HRTEM), Brunauer, Emmett and Teller (BET) method, UV-Vis Diffuse Reflectance Spectra (DRS), UV-Vis Spectroscopy, and Photoluminescence measurements (PL). Also, the photocatalytic activities of pure and doped ZnONPs were evaluated by removal rate of Methylene Blue (MB) under UV irradiation (365 nm) at room temperature. XRD patterns revealed the common hexagonal ZnO Wurtzite-type structures with a preferred orientation of (101) plane. Secondary phases as CeO2, Ce2O3, Ce, RuO2, Ru3O4, Ruhave been identified. For both dopant, Ceand Ru, and for all the concentrations in the precursor solution, FESEM and HRTEM showed NPswith morphologies ranging from spherical/ellipsoidal to hexagonal. The size of NPs was observed to decrease (from ~30 to ~16 nm) with increasing doping concentration due to the interaction between the Ce-O-Zn or Ru-O-Zn ions. EDS results confirmed the incorporation of Ce or Ru ions into ZnO lattice.Using the Kubelka-Munk treatment on the diffuse reflectance spectra, the direct band gap energy has been estimated to be slightly lower than 3.0 eV in both, the Ce and Ru-doped samples. Compared with pure ZnO NPs, the PL spectra of the doped NPs showed red-shifted UV emission and an enhanced blue emission with the typical broad green-yellow emission. The results showed that photocatalytic efficiency of doped ZnO NPs was always enhanced

A Deep Learning-Based Multimodal Architecture to predict Signs of Dementia

This paper proposes a multimodal deep learning architecture combining text and audio information to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. The system was evaluated on the DementiaBank Pitt Corpus dataset, which includes audio recordings as well as their transcriptions for healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks (CNN) for audio classification, Transformers for text classification, and a combination of both in a multimodal ensemble. These models have been evaluated on a test set, obtaining the best results by using the text modality, achieving 90.36% accuracy on the task of detecting dementia. Additionally, an analysis of the corpus has been conducted for the sake of explainability, aiming to obtain more information about how the models generate their predictions and identify patterns in the data.We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00) and AICARE project (grant SPID202200X139779IV0). Furthermore, we would like to thank Nvidia for their generous hardware donation that made these experiments possible

Dynamical Processing of Geophysical Signatures based on SPOT-5 Remote Sensing Imagery

An intelligent post-processing computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of geophysical signatures based on Spot-5 imagery is proposed. As a matter of particular study, a robust algorithm is reported for the analysis of the dynamic behavior of geophysical indexes extracted from the real-world remotely sensed scenes. The simulation results verify the efficiency of the approach as required for decision support in resources management

Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient–doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21. HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning; CIAICO/2022/132 Consolidated group project “AI4Health” funded by the Valencian government and International Center for Aging Research ICAR funded project “IASISTEM.” This work has also been supported by a Valencian government grant for PhD studies, CIACIF/2022/175 and a research initiation grant from the University of Alicante, AII23-12