Effect of parathyroid hormone on the structural, densitometric and failure behaviours of mouse tibia in the spatiotemporal space

Parathyroid hormone (PTH) is an anabolic bone drug approved by the US Food and Drug Administration (FDA) to treat osteoporosis. However, previous studies using cross-sectional designs have reported variable and sometimes contradictory results. The aim of the present study was to quantify the localized effect of PTH on the structural and densitometric behaviors of mouse tibia and their links with the global mechanical behavior of bone using a novel spatiotemporal image analysis approach and a finite element analysis technique. Twelve female C57BL/6J mice were divided into two groups: the control and PTH treated groups. The entire right tibiae were imaged using an in vivo micro-computed tomography (μCT) system eight consecutive times. Next, the in vivo longitudinal tibial μCT images were rigidly registered and divided into 10 compartments across the entire tibial space. The bone volume (BV), bone mineral content (BMC), bone tissue mineral density (TMD), and tibial endosteal and periosteal areas (TEA and TPA) were quantified in each compartment. Additionally, finite element models of all the tibiae were generated to analyze the failure behavior of the tibia. It was found that both the BMC and BV started to increase in the proximal tibial region, and then the increases extended to the entire tibial region after two weeks of treatment (p < 0.05). PTH intervention significantly reduced the TEA in most tibial compartments after two weeks of treatment, and the TPA increased in most tibial regions after four weeks of treatment (p < 0.05). Tibial failure loads significantly increased after three weeks of PTH treatment (p < 0.01). The present study provided the first evidence of the localized effect of PTH on bone structural and densitometric properties, as well as their links with the global mechanical behaviors of bone, which are important pieces of information for unveiling the mechanism of PTH intervention

ProgClust: A progressive clustering method to identify cell populations

Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations and detection of rare cells. ProgClust represents the single-cell data with clustering trees where a progressive searching method is designed to select cell population-specific genes and cluster cells. The obtained trees reveal the structure of both abundant cell populations and rare cell populations. Additionally, it can automatically determine the number of clusters. Experimental results show that ProgClust outperforms the baseline method and is capable of accurately identifying both common and rare cells. Moreover, when applied to real unlabeled data, it reveals potential cell subpopulations which provides clues for further exploration. In summary, ProgClust shows potential in identifying subpopulations of complex single-cell data

Excess Deaths of Gastrointestinal, Liver, and Pancreatic Diseases During the COVID-19 Pandemic in the United States

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic.Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases.Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4–2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis.Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community

Expression and Gene Regulation Network of Metabolic Enzyme Phosphoglycerate Mutase Enzyme 1 in Breast Cancer Based on Data Mining

The metabolic enzyme phosphoglycerate mutase enzyme 1 (PGAM1) is a key enzyme in the glycolysis pathway, and glycolysis is closely related to cancer progression, suggesting that PGAM1 may have important functions in breast cancer. We used sequencing data from the Oncomine database and UALCAN database to analyze the expression of PGAM1 and its influence on the clinicopathological characteristics of breast cancer. LinkedOmics was used to identify genes related to PGAM1 expression, kinases, miRNAs, and transcription factors that were significantly related to PGAM1 through GSEA. cBioPortal was used to identify the alternation frequency and form of PGAM1 in breast cancer. The expression level of PGAM1 in breast cancer was significantly higher than that in normal tissues. Moreover, the expression level of PGAM1 is closely related to the molecular subtype and TP53 mutation status. The expression level of PGAM1 in HER2-positive and triple-negative tumors was significantly higher than that of luminal type. The expression level of PGAM1 in TP53-mutant tumors was higher than that in non-TP53-mutant tumors. In addition, the overall survival of patients with high PGAM1 expression was significantly worse than that of patients with low expression (P=0.0077). Through GSEA analysis, we found multiple kinases, miRNAs, and transcription factors significantly related to PFKFB4. cBioPortal analysis showed that the mutation rate of PGAM1 in breast cancer was relatively low (4%), and the main form of mutation was high mRNA expression. This study suggests that PGAM1 is a potential diagnostic and prognostic marker in breast cancer. Through data mining, we revealed the potential regulatory network information of PGAM1, laying a foundation for further research on the role of PGAM1 in breast cancer

Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI

Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with a power projective base method. Then we were applied a decision-making model, the sequential probability ratio testing (SPRT), for single-trial classification of motor imagery movement events. The unique strength of this proposed classification method lies in its accumulative process, which increases the discriminative power as more and more evidence is observed over time. The properties of the method were illustrated on thirteen subjects’ recordings from three datasets. Results showed that our proposed power projective method outperformed two benchmark methods for every subject. Moreover, with sequential classifier, the accuracies across subjects were significantly higher than that with nonsequential ones. The average maximum accuracy of the SPRT method was 84.1%, as compared with 82.3% accuracy for the sequential Bayesian (SB) method. The proposed SPRT method provides an explicit relationship between stopping time, thresholds, and error, which is important for balancing the time-accuracy trade-off. These results suggest SPRT would be useful in speeding up decision-making while trading off errors in BCI

Methane Emissions Offset Net Carbon Dioxide Uptake From an Alpine Peatland on the Eastern Qinghai-Tibetan Plateau

Peatlands store large amounts of carbon (C) and actively exchange greenhouse gases (GHGs) with the atmosphere, thus significantly affecting global C cycle and climate. Large uncertainty exists in C and GHG estimates of the alpine peatlands on Qinghai-Tibetan Plateau (QTP), as direct measurements of CO2 and CH4 fluxes are scarce in this region. In this study, we provided 32-month CO2 and CH4 fluxes measured using the eddy covariance (EC) technique in a typical alpine peatland on the eastern QTP to estimate the net C and CO2 equivalent (CO2-eq) fluxes and investigate their environmental controls. Our results showed that the mean annual CO2 and CH4 fluxes were -68 +/- 8 g CO2-C m(-2) yr(-1) and 35 +/- 0.3 g CH4-C m(-2) yr(-1), respectively. While considering the traditional and sustained global warming potentials of CH4 over the 100-year timescale, the peatland acted as a net CO2-eq source (1,059 +/- 30 and 1,853 +/- 31 g CO2-eq m(-2) yr(-1), respectively). The net CO2-eq emissions during the non-growing seasons contributed to over 40% of the annual CO2-eq budgets. We further found that net CO2-eq flux was primarily influenced by global radiation and soil temperature variations. This study was the first assessment to quantify the net CO2-eq flux of the alpine peatland in the QTP region using EC measurements. Our study highlights that CH4 emissions from the alpine peatlands can largely offset the net cooling effect of CO2 uptake and future climate changes such as global warming might further enhance their potential warming effect