Expression of hypoxia-inducible factor 1α in thyroid carcinomas

Hypoxia-inducible factor 1α (HIF-1α) is upregulated by hypoxia and oncogenic signalling in many solid tumours. Its regulation and function in thyroid carcinomas are unknown. We evaluated the regulation of HIF-1α and target gene expression in primary thyroid carcinomas and thyroid carcinoma cell lines (BcPAP, WRO, FTC-133 and 8505c). HIF-1α was not detectable in normal tissue but was expressed in thyroid carcinomas. Dedifferentiated anaplastic tumours (ATCs) exhibited high levels of nuclear HIF-1α staining. The HIF-1 target glucose transporter 1 was expressed to a similar level in all tumour types, whereas carbonic anhydrase-9 was significantly elevated in ATCs. In vitro studies revealed a functionally active HIF-1α pathway in thyroid cells with transcriptional activation observed after graded hypoxia (1% O2, anoxia) or treatment with a hypoxia mimetic cobalt chloride. High basal and hypoxia-induced expression of HIF-1α in FTC-133 cells that harbour a phosphatase and tensin homologue (PTEN) mutation was reduced by introduction of wild-type PTEN. Similarly, pharmacological inhibition of the phosphoinositide 3-kinase (PI3K) pathway using LY294002 inhibited HIF-1α and HIF-1α targets in all cell lines, including those with B-RAF mutations (BcPAP and 8505c). In contrast, the effects of inhibition of the RAF/MEK/extracellular signal-regulated kinase pathway were restricted by environmental condition and B-RAF mutation status. HIF-1 is functionally expressed in thyroid carcinomas and is regulated not only by hypoxia but also via growth factor signalling pathways and, in particular, the PI3K pathway. Given the strong association of HIF-1α with an aggressive disease phenotype and therapeutic resistance, this pathway may be an attractive target for improved therapy in thyroid carcinomas

Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics

Cathelicidin suppresses lipid accumulation and hepatic steatosis by inhibition of the CD36 receptor.

Background and objectivesObesity is a global epidemic which increases the risk of the metabolic syndrome. Cathelicidin (LL-37 and mCRAMP) is an antimicrobial peptide with an unknown role in obesity. We hypothesize that cathelicidin expression correlates with obesity and modulates fat mass and hepatic steatosis.Materials and methodsMale C57BL/6 J mice were fed a high-fat diet. Streptozotocin was injected into mice to induce diabetes. Experimental groups were injected with cathelicidin and CD36 overexpressing lentiviruses. Human mesenteric fat adipocytes, mouse 3T3-L1 differentiated adipocytes and human HepG2 hepatocytes were used in the in vitro experiments. Cathelicidin levels in non-diabetic, prediabetic and type II diabetic patients were measured by enzyme-linked immunosorbent assay.ResultsLentiviral cathelicidin overexpression reduced hepatic steatosis and decreased the fat mass of high-fat diet-treated diabetic mice. Cathelicidin overexpression reduced mesenteric fat and hepatic fatty acid translocase (CD36) expression that was reversed by lentiviral CD36 overexpression. Exposure of adipocytes and hepatocytes to cathelicidin significantly inhibited CD36 expression and reduced lipid accumulation. Serum cathelicidin protein levels were significantly increased in non-diabetic and prediabetic patients with obesity, compared with non-diabetic patients with normal body mass index (BMI) values. Prediabetic patients had lower serum cathelicidin protein levels than non-diabetic subjects.ConclusionsCathelicidin inhibits the CD36 fat receptor and lipid accumulation in adipocytes and hepatocytes, leading to a reduction of fat mass and hepatic steatosis in vivo. Circulating cathelicidin levels are associated with increased BMI. Our results demonstrate that cathelicidin modulates the development of obesity

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE GLOBECOM 202

Climate-smart spatial planning assessment in support of conservation and blue growth in Da Nang city’s marine environment.

This study assessed ocean climate modelling datasets to establish what sensitivities to climate change could be identified for species of commercial and conservation value in the waters of Da Nang City, Vietnam, and what actions could be taken to support their adaptation to these pressures. Commissioned via the UK Research Councils Official Development Assistance national Capability funded project ‘Addressing Challenges of Coastal Communities through Ocean Research for Developing Economies’ (ACCORD), and co-developed with the Da Nang Da Nang Department of Natural Resources and Environment with the support of PEMSEA, our main ambition was to highlight what spatial management activities could be undertaken in the waters off the city to support climate change adaptation in its resources. We identified substantial sensitivities of species of commercial and conservation value across the whole bay and its offshore waters to climate change under increasing global greenhouse emissions. For species that occupy the water column (as opposed t the seabed), this sensitivity appeared to be concentrated in the southern part of the bay. Importantly, fishing pressure exacerbated the pressure of climate change on pelagic target species, highlighting the challenges of delivering food security and a growing blue economy imposed by a changing climate. Additionally, lowered emissions, in line with the Paris Agreement, would deliver clear benefits to all types of species assessed, supporting a more sustainable path for the exploration of Da Nang’s marine resources and it’s blue economy. Recommendations are made about how the Coastal Use Zoning Plan for Da Nang City could be adapted to support climate change adaptation in these species and habitats, and well as the broader sustainability of these ecosystems

Genome sequences of seven foot-andmouth disease virus isolates collected from serial samples from one persistently infected carrier cow in Vietnam

Several foot-and-mouth disease virus (FMDV) carrier cattle were identified in Vietnam by the recovery of infectious virus from oropharyngeal fluid. This report contains the first near-complete genome sequences of seven viruses from sequential samples from one carrier animal collected over the course of 1 year. The characterization of within-host viral evolution has implications for FMDV control strategies