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E3 ligase TRIM22 promotes melanoma proliferation by regulating cell cycle progression through K63-linked ubiquitination of p21
Abstract Melanoma, a highly aggressive skin cancer with limited therapeutic options, demonstrates poor prognosis in advanced stages. Tripartite motif-containing 22 (TRIM22), an E3 ubiquitin ligase of the tripartite motif (TRIM) family, is implicated in tumorigenesis, but its working mechanism remains poorly understood in melanoma. In this study, we found that expression of TRIM22 was abnormally upregulated in melanoma tissues, correlating with tumor stages. Functional analysis demonstrated that TRIM22 promoted melanoma cell proliferation in vitro. Furthermore, we found that in malignant melanoma, TRIM22 expression is negatively correlated to the level of p21, an inhibitor of cell cycle. With quantitative real-time PCR (qRT-PCR) assay and cycloheximide (CHX) treatment, we confirmed that TRIM22 suppressed p21 expression at protein level. Via S-Protein pull-down assay, we found that p21 could interact with TRIM22 at the SPRY domain. A ubiquitination assay proved that TRIM22 promoted the K63-linked ubiquitination of p21, and thereby induced p21 degradation through the proteasome pathway to accelerate cell cycle progression. Moreover, we discovered that overexpression of TRIM22 could not bring further boost of cell proliferation in p21 knockdown melanoma cells, indicating an epistatic role of p21 to TRIM22. Overall, our findings elucidated that TRIM22 acted as an E3 ligase targeting p21 for degradation to promote melanoma progression, which improved the understanding of TRIM22 function and provided more clues for developing TRIM22 as a potential target for malignant melanoma treatment.
Refractive index sensing using analytical calculation of angular momentum sidebands of a Laguerre Gaussian beam reflected from a plasmonic structure
ATPase-dependent duplex nucleic acid unwinding by SARS-CoV-2 nsP13 relies on facile binding and translocation along single-stranded nucleic acid
On the relationship between genes and environment
Fabrication and appraisal of axitinib loaded PEGylated spanlastics against MCF- 7 and OV- 2774 cell lines using molecular docking methods and in-vitro study
Axitinib is a second-generation tyrosine kinase inhibitor that works by selectively inhibiting vascular endothelial growth factor receptors (VEGFR-1, VEGFR-2, VEGFR-3). Through this mechanism of action, axitinib blocks angiogenesis, tumor growth and metastases and therefor it shows significant promise as a chemotherapeutic agent for various types of cancer. Nevertheless, the clinical efficacy of this substance is hindered by its restricted solubility in water and inadequate stability. To address these challenges, we developed spanlastics with polyethylene glycol (PEG) to improve the efficacy and stability of axitinib against breast and ovarian tumor malignancies in a targeted manner. Moreover, the study conducts a thorough examination of the interactions between the ligand Axitinib alone or after coating with PEG and a diverse array of protein types in breast (Dopamine, VEGFR) and ovarian cancer (EGFR, BCL-xL). The fabrication of axitinib- spanlastics was achieved through a thin-film hydration method. The evaluation of the impact of formulation factors on the features of nanovesicles was conducted using the I- optimal design. Subsequently, the optimum formulation was calculated. The optimal formulation was coated with polyethylene glycol (axitinib-PEG-spanlastics). An in vitro assessment was computed to evaluate the efficiency of the optimized axitinib-PEG-spanlastics against the MCF-7 breast cancer cell line and the OV-2774 ovarian cancer cell line. The optimized axitinib-PEG-spanlastics formulation exhibited a diameter of 563.42 ± 8.63 nm, accompanied by a zeta potential of −46.44 ± 0.09 mV. The formulation demonstrated an 84.32 ± 3.64% entrapment percent and a cumulative release of 73.58 ± 3.37% during a 4-hour period. The results obtained from the WST-1 assay showed a significant decrease in the percentage of cell survival, reaching 50% at a concentration of 0.68 µM for the PEG-spanlastics. In contrast, the axitinib free drug suspension exhibited 50% cell survival at a concentration of 1.1 µM in the breast cancer (MCF-7) cell line. In MCF-7 cells, the percentage of apoptotic cells generated by axitinib-PEG-spanlastics compared to the free drug suspension was 70.76 ± 4.971% vs. 32.6 ± 1.803%, while in OV-2774 cells, it was 43.55 ± 4.243% vs. 24.44 ± 4.950%. These results propose that Axitinib-PEG-spanlastics have the potential to be a successful nanoplatform for targeting breast and ovarian cancer and effectively managing tumors.
Trimodal machine learning based biometrics system
Comparison of genetic mutations in bladder cancers that arose following radiotherapy for prostate cancer with those in primary bladder cancers
Forest fragmentation and heterogeneity shape the occurrence of woodpecker species in Central Europe
Abstract The study investigates the impact of fragmentation metrics and other forest characteristics on the occurrence and richness of woodpecker species in 163 forest patches in Southern Poland. Generalised linear mixed models were used to estimate the influence of fragmentation metrics (patch size, nearest-neighbour distance, proximity index, patch shape) and forest stand features (age, proportion of coniferous tree species, proportion of dominant tree species) on woodpecker presence and woodpecker species richness. Eight woodpecker species were identified during surveys, and the study found that forest patch size positively correlated with the probability of occurrence for the great spotted woodpecker and black woodpecker but negatively with the occurrence of wryneck. The nearest-neighbour distance between two forest patches and the proximity index were negatively correlated with the occurrence of the lesser spotted woodpecker. The shape index negatively influenced the occurrence of the great spotted woodpecker but positively the occurrence of the wryneck. The European green woodpecker occurrence probability decreased with the proportion of coniferous tree species. Woodpecker species richness was positively associated with forest patch size and age, but negatively with the proportions of coniferous trees and dominant tree species. These findings indicate that forest fragmentation is a major driver of woodpecker species occurrence and richness, along with habitat quality characteristics.
Experimental and numerical modelling of desiccation shrinkage process of kaolin clays
Proteomic risk scores for predicting common diseases using linear and neural network models in the UK biobank
Abstract Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological processes. Leveraging data from 2,923 proteins measured in 53,030 UK Biobank participants, we developed proteomic risk scores for 27 common outcomes over 5- and 15-year follow-up periods using two approaches: a linear ElasticNet regression model and a deep learning neural network (NN) model. Using Cox regression, we assessed the discrimination of proteomic risk scores either in isolation or as incremental improvements over clinical risk factors. We also studied the shared and unique protein predictors across conditions. Proteomic risk scores demonstrated strong discrimination for most outcomes, with a C-index > 0.80 for 12 diseases. NN models outperformed linear models for 11 outcomes, particularly for diseases such as Parkinson’s disease (C-index 0.84) and pulmonary embolism (C-index 0.83), where nonlinear relationships contributed significantly to prediction. Across all outcomes, the addition of proteomic scores to clinical models improved predictive accuracy (ΔC-index 0.03), with the greatest gains observed in 9 diseases (ΔC-index > 0.1), including end-stage renal disease, pulmonary embolism, and Parkinson’s disease. Analysis of protein contributions revealed shared predictors across multiple diseases, such as growth differentiation factor 15 (GDF15), as well as unique predictors like PAEP for endometriosis. While NN models may capture complex relationships, linear models provided value through simplicity and interpretability. These findings underscore the importance of tailoring predictive approaches to specific diseases and demonstrate the pivotal potential of proteomics in advancing risk stratification and early detection.
A compact MIMO antenna with high gain and dual circular polarization using a T divider for WLAN applications
Abstract This paper presents a circularly polarized (CP) multiple-input multiple-output (MIMO) antenna with compact size and high-gain features for wireless local area network (WLAN) applications. The proposed approach employs two compact dual-CP antennas and T-junction power dividers to design 2-port MIMO antenna. The use of T-junction divider can excite both radiators simultaneously, resulting in high gain operation. For validation, an antenna prototype with overall dimensions of 1.06 $$\lambda$$ $$\times$$ 0.65 $$\lambda$$ $$\times$$ 0.03 $$\lambda$$ at 2.45 GHz is fabricated and measured. The measured operating bandwidth is from 2.43 to 2.485 GHz, in which the matching is less than $$-10$$ dB, the isolation is higher than 10 dB, and the axial ration is smaller than 3 dB. Additionally, the antenna also performs high gain radiation of about 8.0 dBi and good MIMO diversity performance in terms of envelop correlation coefficient, diversity gain, and so on. In comparison with the related works, the proposed antenna is beneficial in terms of CP radiation and overall dimensions with less number of required radiating elements, while achieving comparable performance.
Predictive value of AMH in late reproductive age: a retrospective cohort study
Investigation of heavy metal levels in canned tomato paste, olives, and pickled
Dimerization of the BAR domain–containing protein FAM92A modulates lipid binding and interaction with CBY1
A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets
The application of hidden Markov models (HMMs) to neural data has uncovered hidden states and signatures of neural dynamics that are relevant for sensory and cognitive processes. However, training an HMM on cortical data requires a careful handling of model selection, since models with more numerous hidden states generally have a higher likelihood on new (unseen) data. A potentially related problem is the occurrence of very rapid state switching after decoding the data with an HMM. The first problem can lead to overfitting and over-segmentation of the data. The second problem is due to intermediate-to-low self-transition probabilities and is at odds with many reports that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the ‘sticky Poisson-HMM’ (sPHMM). The sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. When used together with the Bayesian Information Criterion for model selection, the sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.
Topical application of the HSP90 inhibitor 17-AAG reduces skin inflammation and partially restores microbial balance: implications for atopic dermatitis therapy
Abstract Heat shock proteins belonging to the HSP90 family promote inflammation and are potential therapeutic targets in inflammatory and autoimmune diseases. Here the effects of the HSP90 inhibitor 17-AAG applied topically were evaluated in a DNCB-induced murine model of atopic dermatitis (AD). The use of 17-AAG improved clinical disease activity without causing toxicity in the animals. Topical application of 17-AAG resulted in reduced epidermal hyperplasia, decreased expression of TSLP, IL-5, and IL-6, as well as reduced activation of NF-κB in the skin. In addition, the eosinophil proportion in the blood and eosinophil peroxidase (EPX) activity in the skin were significantly reduced in 17-AAG-treated AD mice. The inhibitory effects of 17-AAG on the production of epidermal alarmins, T-helper cell-associated cytokines, and ROS release were demonstrated in cultures of activated human keratinocytes, CD4 + T lymphocytes, and eosinophils, respectively. Finally, next-generation sequencing metagenomic approaches revealed that topical application of 17-AAG partially restored the normal gut microbiome in AD mice. Moreover, 17-AAG inhibited Staphylococcus aureus biofilm formation in vitro. The findings of this study, combined with the observed increase in HSP90 and EPX activity in the leukocytes of the analyzed cohort of AD patients, support the potential therapeutic use of HSP90 inhibitors in individuals with AD.