In vitro study of cold atmospheric plasma-activated liquids inhibits malignant melanoma by affecting macrophage polarization through the ROS/JAK2/STAT1 pathway.

Melanoma is a prevalent malignant skin tumor known for its high invasive ability and a high rate of metastasis, making clinical treatment exceptionally challenging. Tumor-associated macrophages (TAMs) are the most abundant immune cells in the tumor microenvironment and play a crucial role in tumor survival and development. Cold atmospheric plasma (CAP) is an emerging tool for tumor treatment that has garnered attention from scholars due to its interaction with non-tumor cells in the tumor microenvironment. Here, we used the macrophage lines THP-1 and RAW264.7, as well as the melanoma cell lines A375 and MV3, as research subjects to investigate the effect of plasma-activated liquid (PAL) on macrophage differentiation and its inhibitory effect on melanoma cell proliferation. We confirmed that the killing effect of PAL on melanoma cells was selective. Using flow cytometry and PCR, we discovered that PAL can influence macrophage differentiation. Through in vitro cell coculture, we demonstrated that PAL-treated macrophages can significantly impede tumor cell development and progression, and the effect is more potent than that of PAL directly targeting tumor cells. Furthermore, we have proposed the hypothesis that PAL promotes the differentiation of macrophages into the M1 type through the ROS/JAK2/STAT1 pathway. To test the hypothesis, we employed catalase and fludarabine to block different sites of the pathway. The results were then validated through Western Blot, qPCR and ELISA. This study illustrates that PAL therapy is an effective tumor immunotherapy and expands the scope of tumor immunotherapy. Furthermore, these findings establish a theoretical foundation for potential clinical applications of PAL.

Deep-learning architecture for PM2.5 concentration prediction: A review.

Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.

Molecular regulation of oil gland development and biosynthesis of essential oils in Citrus spp.

Secretory structures in terrestrial plants serve as reservoirs for a variety of secondary metabolites. Among these, the secretory cavity of the Rutaceae family is notable for containing essential oils with a wide range of applications. However, the molecular basis underlying secretory cavity development is unknown. Here, we reveal a molecular framework for Citrus oil gland formation. Using genetic mapping and genome editing, we demonstrated that this process requires LATE MERISTEM IDENTITY1 (LMI1), a key regulator of leaf serration. A conserved GCC box element of the LMI1 promoter recruits DORNROSCHEN-like (DRNL) for transcriptional activation. This DRNL-LMI1 cascade triggers MYC5 activation, facilitating the development of oil glands and the biosynthesis of essential oils. Our findings spotlight cis-regulatory divergence within leaf shape genes, propelling novel functional tissue formation.

Deep-learning assisted fast orbital angular momentum complex spectrum analysis.

Analyzing the orbital angular momentum (OAM) distribution of a vortex beam is critical for OAM-based applications. Here, we propose a deep residual network (DRN) to model the relationship between characteristics of the multiplexed OAM beam and their complex spectrum. The favorable experimental results show that our proposal can obtain both the intensity and phase terms of multiplexed OAM beams, dubbed complex spectrum, with a wide range of OAM modes, varying in intensity, phase ratio, and mode intervals at high accuracy and real-time speed. Specifically, the root mean square error (RMSE) of intensity and phase spectrum is evaluated as 0.002 and 0.016, respectively, with a response time of only 0.020 s. To the best of our knowledge, this work opens a new sight for fast OAM complex spectrum analysis and paves the way for numerous advanced domains that need real-time OAM complex spectrum diagnostic like ultrahigh-dimensional OAM tailoring.

Coupling suspension trapping-based sample preparation and data-independent acquisition mass spectrometry for sensitive exosomal proteomic analysis.

It has been a challenge to analyze minute amounts of proteomic samples in a facile and robust manner. Herein, we developed a quantitative proteomics workflow by integrating suspension trapping (S-Trap)-based sample preparation and label-free data-independent acquisition (DIA) mass spectrometry and then applied it for the analysis of microgram and even nanogram amounts of exosome samples. S-Trap-based sample preparation outperformed the traditional in-solution digestion-based approach and the commonly used filter-aided sample preparation (FASP)-based approach with regard to the number of proteins and peptides identified. Moreover, S-Trap-based sample preparation coupled with DIA mass spectrometry also showed the highest reproducibility for protein quantification. In addition, this approach allowed for identification and quantification of exosome proteins with low starting amounts (down to 50 ~ 200 ng). Finally, the proposed method was successfully applied to label-free quantification of exosomal proteins extracted from MDA-MB-231 breast cancer cells and MCF-10A non-tumorigenic epithelial breast cells. Prospectively, we envision the integrated S-Trap sample preparation coupled with DIA quantification strategy as a promising alternative for highly efficient and sensitive analysis of trace amounts of proteomic samples (e.g., exosomal samples).