92 Hz/°C (resonator I), 1.98 Hz/°C (resonator II). Following temperature compensation, the fitting error of the microsensor was within the range of 0.006% FS and the measurement accuracy was as high as 0.017% FS in the pressure range of 200 ~ 7000 kPa and the temperature range of -40 °C to 80 °C.Melanoma is the deadliest form of skin cancer, primarily due to its high metastatic propensity and therapeutic resistance in advanced stages. https://www.selleckchem.com/products/k03861.html The frequent inactivation of the p53 tumour suppressor protein in melanomagenesis may predict promising outcomes for p53 activators in melanoma therapy. Herein, we aimed to investigate the antitumor potential of the p53-activating agent SLMP53-2 against melanoma. Two- and three-dimensional cell cultures and xenograft mouse models were used to unveil the antitumor activity and the underlying molecular mechanism of SLMP53-2 in melanoma. SLMP53-2 inhibited the growth of human melanoma cells in a p53-dependent manner through induction of cell cycle arrest and apoptosis. Notably, SLMP53-2 induced p53 stabilization by disrupting the p53-MDM2 interaction, enhancing p53 transcriptional activity. It also promoted the expression of p53-regulated microRNAs (miRNAs), including miR-145 and miR-23a. Moreover, it displayed anti-invasive and antimigratory properties in melanoma cells by inhibiting the epithelial-to-mesenchymal transition (EMT), angiogenesis and extracellular lactate production. Importantly, SLMP53-2 did not induce resistance in melanoma cells. Additionally, it synergized with vemurafenib, dacarbazine and cisplatin, and resensitized vemurafenib-resistant cells. SLMP53-2 also exhibited antitumor activity in human melanoma xenograft mouse models by repressing cell proliferation and EMT while stimulating apoptosis. This work discloses the p53-activating agent SLMP53-2 which has promising therapeutic potential in advanced melanoma, either as a single agent or in combination therapy. By targeting p53, SLMP53-2 may counteract major features of melanoma aggressiveness.In solid malignancies, the glucocorticoid receptor (GR) signalling axis is associated with tumour progression and GR antagonists are in clinical development. Therefore, GR expression may be a useful potential prognostic or predictive biomarker for GR antagonist therapy in cancer. The aim of this review is to investigate if GR expression in tumours is predictive of overall survival or progression free survival. Twenty-five studies were identified through systematic searches of three databases and a meta-analysis conducted using a random effects model, quantifying statistical heterogeneity. Subgroup analysis was conducted for cancer types and publication bias was assessed via funnel plots. There was high heterogeneity in meta-analysis of the studies in all cancer types, which found no association between high GR expression with overall survival (pooled unadjusted HR 1.16, 95% CI (0.89-1.50), n = 2814; pooled adjusted HR 1.02, 95% CI (0.77-1.37), n = 2355) or progression-free survival (pooled unadjusted HR 1.12, 95% CI (0.88-1.42), n = 3365; pooled adjusted HR 1.04, 95% CI (0.6-1.81), n = 582) across all cancer types. However, subgroup meta-analyses showed that high GR expression in gynaecological cancers (endometrial and ovarian) (unadjusted HR 1.83, 95% CI (1.31-2.56), n = 664) and early stage, untreated triple negative breast cancers (TNBCs) (unadjusted HR 1.73, 95% CI (1.35-2.23), n = 687) is associated with disease progression. GR expression in late stage, chemotherapy treated TNBC was not prognostic (unadjusted HR 0.76, 95% CI (0.44, 1.32), n = 287). In conclusion, high GR expression is associated with an increased risk of disease progression in gynaecological and early stage, untreated TNBC. Additional studies are required to elucidate the tumour specific function of the GR receptor in order to ensure GR antagonists target the correct patient groups.Thrombin activatable fibrinolysis inhibitor (TAFI), a proenzyme, is converted to a potent attenuator of the fibrinolytic system upon activation by thrombin, plasmin, or the thrombin/thrombomodulin complex. Since TAFI forms a molecular link between coagulation and fibrinolysis and plays a potential role in venous and arterial thrombotic diseases, much interest has been tied to the development of molecules that antagonize its function. This review aims at providing a general overview on the biochemical properties of TAFI, its (patho)physiologic function, and various strategies to stimulate the fibrinolytic system by interfering with (activated) TAFI functionality.High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt-Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametion (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.