Skovgaardthompson3753

Collectively, these data showed that the new ONs presented in this study could be ideal candidates for PMs in biological and photoelectric applications.

Collectively, these data showed that the new ONs presented in this study could be ideal candidates for PMs in biological and photoelectric applications.

Medical gas plasma therapy has been successfully applied to several types of cancer in preclinical models. Hexadimethrine Bromide First palliative tumor patients suffering from advanced head and neck cancer benefited from this novel therapeutic modality. The gas plasma-induced biological effects of reactive oxygen and nitrogen species (ROS/RNS) generated in the plasma gas phase result in oxidation-induced lethal damage to tumor cells.

This study aimed to verify these anti-tumor effects of gas plasma exposure on urinary bladder cancer.

2D cell culture models, 3D tumor spheroids, 3D vascularized tumors grown on the chicken chorion-allantois-membrane (CAM) in ovo, and patient-derived primary cancer tissue gas plasma-treated ex vivo were used.

Gas plasma treatment led to oxidation, growth retardation, motility inhibition, and cell death in 2D and 3D tumor models. A marked decline in tumor growth was also observed in the tumors grown in ovo. In addition, results of gas plasma treatment on primary urothelial carcinoma tissues ex vivo highlighted the selective tumor-toxic effects as non-malignant tissue exposed to gas plasma was less affected. Whole-transcriptome gene expression analysis revealed downregulation of tumor-promoting fibroblast growth factor receptor 3 (FGFR3) accompanied by upregulation of apoptosis-inducing factor 2 (AIFm2), which plays a central role in caspase-independent cell death signaling.

Gas plasma treatment induced cytotoxicity in patient-derived cancer tissue and slowed tumor growth in an organoid model of urinary bladder carcinoma, along with less severe effects in non-malignant tissues. Studies on the potential clinical benefits of this local and safe ROS therapy are awaited.

Gas plasma treatment induced cytotoxicity in patient-derived cancer tissue and slowed tumor growth in an organoid model of urinary bladder carcinoma, along with less severe effects in non-malignant tissues. Studies on the potential clinical benefits of this local and safe ROS therapy are awaited.Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, the understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models of predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.Lysine acetylation is a reversible and dynamic post-translational modification that plays vital roles in regulating multiple cellular processes including aging. However, acetylome-wide analysis in the aging process remains poorly studied in mammalian tissues. Nicotinamide adenine dinucleotide (NAD+), a hub metabolite, benefits health span at least in part due to the activation of Sirtuins, a family of NAD+-consuming deacetylases, indicating changes in acetylome. Here, we combine two antibodies for the enrichment of acetylated peptides and perform label-free quantitative acetylomic analysis of mouse livers during natural aging and upon the treatment of beta-nicotinamide mononucleotide (NMN), a NAD+ booster. Our study describes previously unknown acetylation sites and reveals the acetylome-wide dynamics with age as well as upon the treatment of NMN. We discover protein acetylation events as potential aging biomarkers. We demonstrate that the life-beneficial effect of NMN could be partially reflected by the changes in age-related protein acetylation. Our quantitative assessment indicates that NMN has mild effects on acetylation sites previously reported as substrates of Sirtuins. Collectively, our data analyze protein acetylation with age, laying critical foundation for the functional study of protein post-translational modification essential for healthy aging and perhaps disease conditions.The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.Modern medicine continues to evolve, and the treatment armamentarium for various diseases grows more individualized across a breadth of medical disciplines. Cure rates for infectious diseases that were previously pan-fatal approach 100% because of the identification of the specific pathogen(s) involved and the use of appropriate combinations of drugs, where needed, to completely extinguish infection and hence prevent emergence of resistant strains. Similarly, with the assistance of technologies such as next-generation sequencing and immunomic analysis as part of the contemporary oncology armory, therapies can be tailored to each tumor. Importantly, molecular interrogation has revealed that metastatic cancers are distinct from each other and complex. Therefore, it is conceivable that rational personalized drug combinations will be needed to eradicate cancers, and eradication will be necessary to mitigate clonal evolution and resistance.The treat-to-target strategy has been recently suggested in the management of Systemic Lupus Erythematosus (SLE). Lupus Low Disease Activity State (LLDAS) and Definitions Of Remission In SLE (DORIS) remission were outlined as two concentric targets. The achievement of LLDAS was shown to be associated with lower frequency of SLE flare, decreased damage progression, better quality of life, and reduced mortality. In addition, LLDAS has successfully been tested in post-hoc analyses of a number of randomized controlled trials. However, it has been recently underlined that LLDAS includes a high proportion of patients in remission, raising the question if these endpoints are sufficiently distinct to consider their separation clinically relevant. Some studies suggest that the protective effect of LLDAS on damage might be due to the inclusion of patients who are in remission. Notably, clinical low disease activity (LDA) seems to be uncommon in SLE due to the relapsing-remitting pattern of the disease, in which low level of activity only occurs transiently. Moreover, since the domains included in LLDAS have several limitations, such as the use of a binomial disease activity index, the exclusion of some mild manifestations and the consideration of items subjected to variability (physician global assessment and glucocorticoids dose), not all patients in LDA are adequately represented by LLDAS.

Patients with systemic sclerosis (SSc) are at increased risk of cancer, a growing cause of non-SSc-related death among these patients. We analyzed the increased cancer risk among Spanish patients with SSc using standardized incidence ratios (SIRs) and identified independent cancer risk factors in this population.

Spanish Scleroderma Registry data were analyzed to determine the demographic characteristics of patients with SSc, and logistic regression was used to identify cancer risk factors. SIRs with 95% confidence intervals (CIs) relative to the general Spanish population were calculated.

Of 1930 patients with SSc, 206 had cancer, most commonly breast, lung, hematological, and colorectal cancers. Patients with SSc had increased risks of overall cancer (SIR 1.48, 95% CI 1.36-1.60; P<0.001), and of lung (SIR 2.22, 95% CI 1.77-2.73; P<0.001), breast (SIR 1.31, 95% CI 1.10-1.54; P=0.003), and hematological (SIR 2.03, 95% CI 1.52-2.62; P<0.001) cancers. Cancer was associated with older age at SSc onset (odds ratio [OR] 1.22, 95% CI 1.01-1.03; P<0.001), the presence of primary biliary cholangitis (OR 2.35, 95% CI 1.18-4.68; P=0.015) and forced vital capacity <70% (OR 1.8, 95% CI 1.24-2.70; P=0.002). The presence of anticentromere antibodies lowered the risk of cancer (OR 0.66, 95% CI 0.45-0.97; P=0.036).

Spanish patients with SSc had an increased cancer risk compared with the general population. Some characteristics, including specific autoantibodies, may be related to this increased risk.

Spanish patients with SSc had an increased cancer risk compared with the general population. Some characteristics, including specific autoantibodies, may be related to this increased risk.