Transcriptome profiling of A549 non-small cell lung cancer cells in response to Trichinella spiralis muscle larvae excretory/secretory products

Trichinella spiralis (T. spiralis) muscle-larva excretory/secretory products (ML-ESPs) is a complex array of proteins with antitumor activity. We previously demonstrated that ML-ESPs inhibit the proliferation of A549 non-small cell lung cancer (NSCLC) cell line. However, the mechanism of ML-ESPs against A549 cells, especially on the transcriptional level, remains unknow. In this study, we systematically investigated a global profile bioinformatics analysis of transcriptional response of A549 cells treated with ML-ESPs. And then, we further explored the transcriptional regulation of genes related to glucose metabolism in A549 cells by ML-ESPs. The results showed that ML-ESPs altered the expression of 2,860 genes (1,634 upregulated and 1,226 downregulated). GO and KEGG analysis demonstrated that differentially expressed genes (DEGs) were mainly associated with pathway in cancer and metabolic process. The downregulated genes interaction network of metabolic process is mainly associated with glucose metabolism. Furthermore, the expression of phosphofructokinase muscle (PFKM), phosphofructokinase liver (PFKL), enolase 2 (ENO2), lactate dehydrogenase B (LDHB), 6-phosphogluconolactonase (6PGL), ribulose-phosphate-3-epimerase (PRE), transketolase (TKT), transaldolase 1 (TALDO1), which genes mainly regulate glycolysis and pentose phosphate pathway (PPP), were suppressed by ML-ESPs. Interestingly, tricarboxylic acid cycle (TCA)-related genes, such as pyruvate dehydrogenase phosphatase 1 (PDP1), PDP2, aconitate hydratase 1 (ACO1) and oxoglutarate dehydrogenase (OGDH) were upregulated by ML-ESPs. In summary, the transcriptome profiling of A549 cells were significantly altered by ML-ESPs. And we also provide new insight into how ML-ESPs induced a transcriptional reprogramming of glucose metabolism-related genes in A549 cells

A prognostic signature based on snoRNA predicts the overall survival of lower-grade glioma patients

IntroductionSmall nucleolar RNAs (snoRNAs) are a group of non-coding RNAs enriched in the nucleus which direct post-transcriptional modifications of rRNAs, snRNAs and other molecules. Recent studies have suggested that snoRNAs have a significant role in tumor oncogenesis and can be served as prognostic markers for predicting the overall survival of tumor patients. MethodsWe screened 122 survival-related snoRNAs from public databases and eventually selected 7 snoRNAs that were most relevant to the prognosis of lower-grade glioma (LGG) patients for the establishment of the 7-snoRNA prognostic signature. Further, we combined clinical characteristics related to the prognosis of glioma patients and the 7-snoRNA prognostic signature to construct a nomogram.ResultsThe prognostic model displayed greater predictive power in both validation set and stratification analysis. Results of enrichment analysis revealed that these snoRNAs mainly participated in the post-transcriptional process such as RNA splicing, metabolism and modifications. In addition, 7-snoRNA prognostic signature were positively correlated with immune scores and expression levels of multiple immune checkpoint molecules, which can be used as potential biomarkers for immunotherapy prediction. From the results of bioinformatics analysis, we inferred that SNORD88C has a major role in the development of glioma, and then performed in vitro experiments to validate it. The results revealed that SNORD88C could promote the proliferation, invasion and migration of glioma cells. DiscussionWe established a 7-snoRNA prognostic signature and nomogram that can be applied to evaluate the survival of LGG patients with good sensitivity and specificity. In addition, SNORD88C could promote the proliferation, migration and invasion of glioma cells and is involved in a variety of biological processes related to DNA and RNA

A voice recognition-based digital cognitive screener for dementia detection in the community: Development and validation study

IntroductionTo facilitate community-based dementia screening, we developed a voice recognition-based digital cognitive screener (digital cognitive screener, DCS). This proof-of-concept study aimed to investigate the reliability, validity as well as the feasibility of the DCS among community-dwelling older adults in China.MethodsEligible participants completed demographic, clinical, and the DCS. Diagnosis of mild cognitive impairment (MCI) and dementia was made based on the Montreal Cognitive Assessment (MoCA) (MCI: MoCA < 23, dementia: MoCA < 14). Time and venue for test administration were recorded and reported. Internal consistency, test-retest reliability and inter-rater reliability were examined. Receiver operating characteristic (ROC) analyses were conducted to examine the discriminate validity of the DCS in detecting MCI and dementia.ResultsA total of 103 participants completed all investigations and were included in the analysis. Administration time of the DCS was between 5.1–7.3 min. No significant difference (p > 0.05) in test scores or administration time was found between 2 assessment settings (polyclinic or community center). The DCS showed good internal consistency (Cronbach’s alpha = 0.73), test-retest reliability (Pearson r = 0.69, p < 0.001) and inter-rater reliability (ICC = 0.84). Area under the curves (AUCs) of the DCS were 0.95 (0.90, 0.99) and 0.77 (0.67, 086) for dementia and MCI detection, respectively. At the optimal cut-off (7/8), the DCS showed excellent sensitivity (100%) and good specificity (80%) for dementia detection.ConclusionThe DCS is a feasible, reliable and valid digital dementia screening tool for older adults. The applicability of the DCS in a larger-scale community-based screening stratified by age and education levels warrants further investigation

Population pharmacokinetics of Amisulpride in Chinese patients with schizophrenia with external validation: the impact of renal function

Introduction: Amisulpride is primarily eliminated via the kidneys. Given the clear influence of renal clearance on plasma concentration, we aimed to explicitly examine the impact of renal function on amisulpride pharmacokinetics (PK) via population PK modelling and Monte Carlo simulations.Method: Plasma concentrations from 921 patients (776 in development and 145 in validation) were utilized.Results: Amisulpride PK could be described by a one-compartment model with linear elimination where estimated glomerular filtration rate, eGFR, had a significant influence on clearance. All PK parameters (estimate, RSE%) were precisely estimated: apparent volume of distribution (645 L, 18%), apparent clearance (60.5 L/h, 2%), absorption rate constant (0.106 h−1, 12%) and coefficient of renal function on clearance (0.817, 10%). No other significant covariate was found. The predictive performance of the model was externally validated. Covariate analysis showed an inverse relationship between eGFR and exposure, where subjects with eGFR= 30 mL/min/1.73 m2 had more than 2-fold increase in AUC, trough and peak concentration. Simulation results further illustrated that, given a dose of 800 mg, plasma concentrations of all patients with renal impairment would exceed 640 ng/mL.Discussion: Our work demonstrated the importance of renal function in amisulpride dose adjustment and provided a quantitative framework to guide individualized dosing for Chinese patients with schizophrenia

China's green building revolution: Path to sustainable urban futures

This paper critically examines the significant strides China has made toward sustainable urban development, emphasizing the evolution of green building practices since 2015. Through a detailed analysis of green building policies under China's 13th and 14th Five-Year Plans, as well as the General Code for Building Energy Conservation and Renewable Energy Utilization, this study highlights the progressive integration of sustainability in construction. Notable advancements include the implementation of stringent energy efficiency standards and the incorporation of renewable energy technologies, which have collectively shifted the paradigm of architectural practices in urban China. Moreover, the paper draws upon several case studies, such as the Shanghai Chenghuaxinyuan Project and Mini Sky City, to illustrate the tangible outcomes and the effectiveness of these policies in real-world settings. These examples not only demonstrate significant reductions in energy consumption and carbon emissions but also reflect the scalability and replicability of such initiatives in other urban contexts. Key findings suggest that China's proactive policy landscape has catalyzed a national movement towards environmentally responsible construction, setting a global benchmark for sustainable urban development. The paper argues that these policy-driven initiatives have important implications for practitioners, who are encouraged to adopt innovative construction technologies and sustainable practices. For policymakers, the study underscores the necessity of continuous support for research and development in green technology, coupled with robust regulatory frameworks to sustain momentum in the green building sector. Finally, the paper offers comprehensive recommendations aimed at enhancing the efficacy and adoption of green building practices, proposing that future policy directions should focus on integrating smart city technologies to further optimize energy use and urban sustainability

Boosting Solar Steam Generation by Using AIE Photothermal Molecule-Doped 3D Nanofibrous Aerogel with Self-Pumping Water Function

Utilizing solar energy to generate clean water by interface solar steam generation is considered to be a promising strategy to address the challenge of water shortage globally. However, high evaporation rate and long-term sustainability have rarely been achieved simultaneously, due to salt accumulation, discontinuous water supply and insufficient photothermal conversion. Herein, we demonstrate that a three-dimensional nanofibrous aerogel (3D NA) with Janus layers enables floating on the surface water by hydrophobic layer and continues pumping water by hydrophilic layer and interconnected porous structure. More notably, an aggregation-induced emission (AIE) photothermal molecule is doped into nanofibers for the first time, which was endowed with superior capacity of transferring solar energy into heat. Combining these unique benefits, the presented 3D NA exhibits extremely high evaporation rate (1.99 kg m-2 h-1) and solar-to-vapor conversion efficiency (89%) under irradiation of 1 sun. Besides, there is no significant change in evaporation performance after 21 cycles in the case of seawater treatment, suggesting that the designed 3D NA possess sustainable stability and self-cleaning function to restrain salt deposition. With highly efficient evaporation rate and long-term sustainable solar steam generation, such 3D NA can offer new strategy for desalination and sewage treatment. </p

Reverse Thinking of Aggregation-Induced Emission Principle: Amplifying Molecular Motions to Boost Photothermal Efficiency of Nanofibers

Development of efficient photothermal nanofibers is of vital importance, but remaining a big challenge. Herein, with reverse thinking of aggregation-induced emission (AIE) principle, we demonstrate an ingenious and universal protocol for amplifying molecular motions to boost photothermal efficiency of nanofibers. Core-shell nanofibers having the olive oil solution of AIE-active molecules as the core surrounded by PVDF-HFP shell were constructed by coaxial electrospinning. The molecularly dissolved state of AIE-active molecules allows them to freely rotate and/or vibrate in nanofibers upon photoexcitation and thus significantly elevates the proportion of non-radiative energy dissipation, affording impressive heat-generating efficiency

The critical role of energy transition in addressing climate change at COP28

The paper outlines the urgency and strategies for transitioning from fossil fuels to renewable energy discussed at the 2023 Climate Change Conference (COP28). It emphasizes the Paris Agreement's role and highlights the environmental harm of fossil fuels, advocating for sustainable alternatives like solar, wind, hydro, and geothermal power. This transition is crucial for reducing greenhouse gas emissions and promoting economic and health benefits. Key findings reveal advancements in renewable technologies, resulting in job creation, energy independence, and improved health due to reduced pollution. However, the transition faces challenges such as high initial costs and the need for advanced infrastructure, particularly in developing countries. The paper underscores the importance of sustainable mining for essential materials like lithium and cobalt, and references António Guterres's “five-point energy plan” as a strategic approach to address these issues. In conclusion, the paper stresses the necessity of global collaboration among governments, businesses, and civil societies. It asserts that the path to sustainable energy is rich with opportunities for innovation, growth, and equitable progress, providing a comprehensive roadmap for a feasible and just energy transition for all nations

Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions

International audienceAnomaly detection(AD) is an important task of machines’ condition monitoring(CM). Data-driven policies can be used in a more intelligent way to achieve anomaly detection and effectively avoid the introduction of expert experience, thus having a broader scope of application. However, Machines like wind turbines often work under time-varying operating conditions(TVOCs), and the performance of traditional data-driven AD methods is significantly degraded because TVOCs can lead to “false alarms” and “missed alarms” in the implementation due to the monitoring data shifting caused by variation of operating conditions(OCs). To address this problem, this paper proposes a novel conditional feature disentanglement learning framework to solve the disturbance in AD on account of entanglement between OCs and health states. The proposed approach performs conditional self-supervised AD by utilizing the variational autoencoder(VAE) and OCs information. Then, a feature disentanglement conditional VAE(FDCVAE) network is developed to realize the disentanglement of OCs and health states. Subsequently, An anomaly indicator(ANI) is constructed by the dimension reduction of the disentangled health state-related feature and combined with the statistics anomaly threshold for AD. Experiments on accelerated fatigue degradation of bearings under TVOCs validate the effectiveness of the proposed method and further demonstrate the superiority of the constructed ANI in eliminating TVOC interference, compared with common fault mechanisms-based and data-driven ANI. The proposed method not only achieves anomaly detection under TVOCs but also provides a new way for representation learning under variable working conditions in machine health management applications in the foreseeable future

Hybrid system response model for condition monitoring of bearings under time-varying operating conditions

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