Expression mapping of GREM1 and functional contribution of its-secreting-cells in the brain using transgenic mouse models

Gremlin 1 (Grem1) is a secreted protein that antagonizes bone morphogenetic proteins (BMPs). While abnormal Grem1 expression has been reported to cause behavioral defects in postpartum mice, the spatial and cellular distribution of GREM1 in the brain and the influence of the Grem1-secreating cells on brain function and behavior remain unclear. To address this, we designed a genetic cassette incorporating a 3 × Flag-TeV-HA-T2A-tdTomato sequence, resulting in the creation of a novel Grem1Tag mouse model, expressing an epitope tag (3 × Flag-TeV-HA-T2A) followed by a fluorescent reporter (tdTomato) under the control of the endogenous Grem1 promoter. This design facilitated precise tracking of the cell origin and distribution of GREM1 in the brain using tdTomato and Flag (or HA) markers, respectively. We confirmed that the Grem1Tag mouse exhibited normal motor, cognitive, and social behaviors at postnatal 60 days (P60), compared with C57BL/6 J controls. Through immunofluorescence staining, we comprehensively mapped the distribution of Grem1-secreting cells across the central nervous system. Pervasive Grem1 expression was observed in the cerebral cortex (Cx), medulla, pons, and cerebellum, with the highest levels in the Cx region. Notably, within the Cx, GREM1 was predominantly secreted by excitatory neurons, particularly those expressing calcium/calmodulin-dependent protein kinase II alpha (Camk2a), while inhibitory neurons (parvalbumin-positive, PV+) and glial cells (oligodendrocytes, astrocytes, and microglia) showed little or no Grem1 expression. To delineate the functional significance of Grem1-secreting cells, a selective ablation at P42 using a diphtheria toxin A (DTA) system resulted in increased anxiety-like behavior and impaired memory in mice. Altogether, our study harnessing the Grem1Tag mouse model reveals the spatial and cellular localization of GREM1 in the mouse brain, shedding light on the involvement of Grem1-secreting cell in modulating brain function and behavior. Our Grem1Tag mouse serves as a valuable tool for further exploring the precise role of Grem1 in brain development and disease

Novel synthetic bio-mimic polymers for potential cell delivery therapy

Cell-based therapy is one of the major potential therapeutic strategies for cardiovascular, neuronal and degenerative diseases in recent years. Synthetic biodegradable polymers have been utilized increasingly in pharmaceutical, medical and biomedical engineering. Control of the interaction of living cells and biomaterials surfaces is one of the major goals in the design and development of new polymeric biomaterials in tissue engineering. The aims of this study is to develop a novel bio-mimic polymeric materials which will facilitate the delivery cells, control cell bioactivities and enhance the focal integration of graft cells with host tissues

Anti-inflammatory and antiosteoclastogenic activities of parthenolide on human periodontal ligament cells in vitro

Periodontitis is an inflammatory disease that causes osteolysis and tooth loss. It is known that the nuclear factor kappa B (NF-κB) signalling pathway plays a key role in the progression of inflammation and osteoclastogenesis in periodontitis. Parthenolide (PTL), a sesquiterpene lactone extracted from the shoots of Tanacetum parthenium, has been shown to possess anti-inflammatory properties in various diseases. In the study reported herein, we investigated the effects of PTL on the inflammatory and osteoclastogenic response of human periodontal ligament-derived cells (hPDLCs) and revealed the signalling pathways in this process. Our results showed that PTL decreased NF-κB activation, I-κB degradation, and ERK activation in hPDLCs. PTL significantly reduced the expression of inflammatory (IL-1β, IL-6, and TNF-α) and osteoclastogenic (RANKL, OPG, and M-CSF) genes in LPS-stimulated hPDLCs. In addition, PTL attenuated hPDLC-induced osteoclastogenic differentiation of macrophages (RAW264.7 cells), as well as reducing gene expression of osteoclast-related markers in RAW264.7 cells in an hPDLC-macrophage coculture model. Taken together, these results demonstrate the anti-inflammatory and antiosteoclastogenic activities of PTL in hPDLCs in vitro. These data offer fundamental evidence supporting the potential use of PTL in periodontitis treatment

Complex Dynamics and Chaos Control on a Kind of Bertrand Duopoly Game Model considering R&D Activities

We study a dynamic research and development two-stage input competition game model in the Bertrand duopoly oligopoly market with spillover effects on cost reduction. We investigate the stability of the Nash equilibrium point and local stable conditions and stability region of the Nash equilibrium point by the bifurcation theory. The complex dynamic behaviors of the system are shown by numerical simulations. It is demonstrated that chaos occurs for a range of managerial policies, and the associated unpredictability is solely due to the dynamics of the interaction. We show that the straight line stabilization method is the appropriate management measure to control the chaos

A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks

Gratitude practice helps undergraduates who experienced an earthquake in China find meaning in life

Abstract This study was conducted following a magnitude 6.8 earthquake that occurred in early September 2022, coinciding with the commencement of a positive psychology course for the affected students. A sample of 479 Chinese undergraduates was recruited for an intervention focused on weekly gratitude practice. Data were collected through an online questionnaire package at 3 time points: the first week of the course (Time 1), the fifth week (Time 2), and the ninth week (Time 3), assessing gratitude, learning engagement, and the meaning of life. Findings revealed that gratitude significantly predicted meaning in life through learning engagement over time. This highlights the significant mediating role of learning engagement in the context of earthquakes and provides insights for positive interventions aimed at facilitating personal growth among emerging adults in higher educational settings, particularly those who have experienced traumatic events such as earthquakes