Bossenmcbride2236

The reporters can also be used for assessing HDR efficiencies of the Acidaminococcus sp. 8-Bromo-cAMP (As)Cas12a nuclease. The results suggest that the ΔEGFP mouse models serve as valuable tools for evaluation of in vivo genome editing.Hypoxia induces a series of cellular adaptive responses that enable promotion of inflammation and cancer development. Hypoxia-inducible factor-1α (HIF-1α) is involved in the hypoxia response and cancer promotion, and it accumulates in hypoxia and is degraded under normoxic conditions. Here we identify prostate cancer associated transcript-1 (PCAT-1) as a hypoxia-inducible long non-coding RNA (lncRNA) that regulates HIF-1α stability, crucial for cancer progression. Extensive analyses of clinical data indicate that PCAT-1 is elevated in breast cancer patients and is associated with pathological grade, tumor size, and poor clinical outcomes. Through gain- and loss-of-function experiments, we find that PCAT-1 promotes hypoxia-associated breast cancer progression including growth, migration, invasion, colony formation, and metabolic regulation. Mechanistically, PCAT-1 directly interacts with the receptor of activated protein C kinase-1 (RACK1) protein and prevents RACK1 from binding to HIF-1α, thus protecting HIF-1α from RACK1-induced oxygen-independent degradation. These findings provide new insight into lncRNA-mediated mechanisms for HIF-1α stability and suggest a novel role of PCAT-1 as a potential therapeutic target for breast cancer.Chronic stress has been proven to accelerate the development and progression of ovarian cancer, but the underlying molecular mechanisms have not been fully elucidated. In a combination survey of ovarian cancer with chronic stress (OCCS) mouse models and high-throughput sequencing, a key lncRNA named LOC102724169 on chromosome 6q27 has been identified, which functions as a dominant tumor suppressor in OCCS. Transcriptionally regulated by CCAAT enhancer binding protein (CEBP) beta (CEBPB), LOC102724169 shows low expression and correlates with poor progression-free survival (PFS) in OCCS patients. LOC102724169 is an instructive molecular inhibitor of malignancy of ovarian cancer cells, which is necessary to improve the curative effect of cisplatin therapy on ovarian cancer. link2 This function stems from the inactivation of molecules in phosphatidylinositol 3-kinase (PI3K)/AKT signaling, repressing MYB expression and retaining the responsiveness of cancer cells to cisplatin. These findings provide a mechanistic understanding of the synergistic anti-tumor purpose of LOC102724169 as a bona fide tumor suppressor, enhancing the therapeutic effect of cisplatin. The new regulatory model of "lncRNA-MYB" provides new perspectives for LOC102724169 as a chronic stress-related molecule and also provides mechanistic insight into exploring the cancer-promoting mechanism of MYB in OCCS, which may be a promising therapeutic strategy for ovarian cancer.

The COVID-19 pandemic revealed existing gaps in the medical educational system that is heavily dependent on the presence of medical students and teachers in laboratory and class for instruction. This affects continuity in the implementation of the neuroanatomy component of the medical neuroscience laboratory activities during COVID-19. We hypothesized that pivoting wet laboratory neuroanatomy activities to online using an adaptive flexible blended method might represent an effective approach in the implementation of laboratory neuroanatomy activities during a pandemic.

The current study describes an adaptive flexible blended learning approach that systematically mixes virtual face-to-face interaction activities with the online learning of brain structures, and the discussion of clinical cases. link3 Learning materials are delivered through both synchronous and asynchronous modes, and Year 1 medical students learn neuroanatomy laboratory activities at different locations and different times. Student performancesboratory activities provided a unique educational experience for Year 1 medical students to learn neuroscience laboratory activities during the COVID-19 pandemic.Coronavirus disease 2019 (COVID-19) pandemic has proven to be tenacious and shows that the global community is still poorly prepared to handling such emerging pandemics. Enhancing global solidarity in emergency preparedness and response, and the mobilization of conscience and cooperation, can serve as an excellent source of ideas and measures in a timely manner. The article provides an overview of the key components of risk communication and community engagement (RCCE) strategies at the early stages in vulnerable nations and populations, and highlight contextual recommendations for strengthening coordinated and sustainable RCCE preventive and emergency response strategies against COVID-19 pandemic. Global solidarity calls for firming governance, abundant community participation and enough trust to boost early pandemic preparedness and response. Promoting public RCCE response interventions needs crucially improving government health systems and security proactiveness, community to individual confinement, trust and resilience solutions. To better understand population risk and vulnerability, as well as COVID-19 transmission dynamics, it is important to build intelligent systems for monitoring isolation/quarantine and tracking by use of artificial intelligence and machine learning systems algorithms. Experiences and lessons learned from the international community is crucial for emerging pandemics prevention and control programs, especially in promoting evidence-based decision-making, integrating data and models to inform effective and sustainable RCCE strategies, such as local and global safe and effective COVID-19 vaccines and mass immunization programs.Mucus consistency affects voice physiology and is connected to voice disorders. Nevertheless, the rheological characteristics of human laryngeal mucus from the vocal folds remain unknown. Knowledge about mucus viscoelasticity enables fabrication of artificial mucus with natural properties, more realistic ex-vivo experiments and promotes a better understanding and improved treatment of dysphonia with regard to mucus consistency. We studied human laryngeal mucus samples from the vocal folds with two complementary approaches 19 samples were successfully applied to particle tracking microrheology (PTM) and five additional samples to oscillatory shear rheology (OSR). Mucus was collected by experienced laryngologists from patients together with demographic data. The analysis of the viscoelasticity revealed diversity among the investigated mucus samples according to their rigidity (absolute G' and G″). Moreover some samples revealed throughout solid-like character (G' > G″), whereas some underwent a change from solid-like to liquid-like (G' less then G″). This led to a subdivision into three groups. We assume that the reason for the differences is a variation in the hydration level of the mucus, which affects the mucin concentration and network formation factors of the mucin mesh. The demographic data could not be correlated to the differences, except for the smoking behavior. Mucus of predominant liquid-like character was associated with current smokers.Smart nanoparticles for medical applications have gathered considerable attention due to an improved biocompatibility and multifunctional properties useful in several applications, including advanced drug delivery systems, nanotheranostics and in vivo imaging. Among nanomaterials, zinc oxide nanoparticles (ZnO NPs) were deeply investigated due to their peculiar physical and chemical properties. The large surface to volume ratio, coupled with a reduced size, antimicrobial activity, photocatalytic and semiconducting properties, allowed the use of ZnO NPs as anticancer drugs in new generation physical therapies, nanoantibiotics and osteoinductive agents for bone tissue regeneration. However, ZnO NPs also show a limited stability in biological environments and unpredictable cytotoxic effects thereof. To overcome the abovementioned limitations and further extend the use of ZnO NPs in nanomedicine, doping seems to represent a promising solution. This review covers the main achievements in the use of doped ZnO NPs for nanomedicine applications. Sol-gel, as well as hydrothermal and combustion methods are largely employed to prepare ZnO NPs doped with rare earth and transition metal elements. For both dopant typologies, biomedical applications were demonstrated, such as enhanced antimicrobial activities and contrast imaging properties, along with an improved biocompatibility and stability of the colloidal ZnO NPs in biological media. The obtained results confirm that the doping of ZnO NPs represents a valuable tool to improve the corresponding biomedical properties with respect to the undoped counterpart, and also suggest that a new application of ZnO NPs in nanomedicine can be envisioned.In spite of machine learning has been successfully used in a wide range of healthcare applications, there are several parameters that could influence the performance of a machine learning system. One of the big issues for a machine learning algorithm is related to imbalanced dataset. An imbalanced dataset occurs when the distribution of data is not uniform. This makes harder the implementation of accurate models. In this paper, intelligent models are implemented to predict the hematocrit level of blood starting from visible spectral data. The aim of this work is to show the effects of two balancing techniques (SMOTE and SMOTE+ENN) on the imbalanced dataset of blood spectra. Four different machine learning systems are fitted with imbalanced and balanced datasets and their performances are compared showing an improvement, in terms of accuracy, due to the use of balancing.An accurate tumour segmentation in brain images is a complicated task due to the complext structure and irregular shape of the tumour. In this letter, our contribution is twofold (1) a lightweight brain tumour segmentation network (LBTS-Net) is proposed for a fast yet accurate brain tumour segmentation; (2) transfer learning is integrated within the LBTS-Net to fine-tune the network and achieve a robust tumour segmentation. To the best of knowledge, this work is amongst the first in the literature which proposes a lightweight and tailored convolution neural network for brain tumour segmentation. The proposed model is based on the VGG architecture in which the number of convolution filters is cut to half in the first layer and the depth-wise convolution is employed to lighten the VGG-16 and VGG-19 networks. Also, the original pixel-labels in the LBTS-Net are replaced by the new tumour labels in order to form the classification layer. Experimental results on the BRATS2015 database and comparisons with the state-of-the-art methods confirmed the robustness of the proposed method achieving a global accuracy and a Dice score of 98.11% and 91%, respectively, while being much more computationally efficient due to containing almost half the number of parameters as in the standard VGG network.The rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time-series data to identify abnormal morphology. However, such algorithms are less reliable than gold-standard human expert labels (where the latter are typically difficult and expensive to obtain), due to their large inter- and intra-subject variabilities. Actions taken in response to these algorithms can therefore result in sub-optimal patient care. In a typical scenario where only unevenly sampled continuous or numeric estimates are provided, without access to the "ground truth", it is challenging to choose which algorithms to trust and which to ignore, or even how to merge the outputs from multiple algorithms to form a more precise final estimate for individual patients. In this work, the novel application of two previously proposed parametric fully-Bayesian graphical models is demonstrated for fusing labels from (i) independent and (ii) potentially correlated algorithms, validated on two publicly available datasets for the task of respiratory rate (RR) estimation.