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Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method based on FTIR near-infrared spectroscopy (NIRS) combined with machine learning strategy for clinical pleural effusion classification. NIRS spectra were recorded for 47 MPE samples and 35 BPE samples. The sample data were randomly divided into train set (n = 62) and test set (n = 20). Partial least squares, random forest, support vector machine (SVM), and gradient boosting machine models were trained, and subsequent predictive performance were predicted on the test set. Besides the whole spectra used in modeling, selected features using SVM recursive feature elimination algorithm were also investigated in modeling. Among those models, NIRS combined with SVM showed the best predictive performance (accuracy: 1.0, kappa: 1.0, and AUCROC: 1.0). SVM with the top 50 feature wavenumbers also displayed a high predictive performance (accuracy: 0.95, kappa: 0.89, AUCROC: 0.99). Our study revealed that the combination of NIRS and machine learning is an innovative, rapid, and convenient method for clinical pleural effusion classification, and worth further evaluation. [ABSTRACT FROM AUTHOR]

Self-microemulsifying drug delivery systems (SMEDDSs) have recently returned to the limelight of academia and industry due to their enormous potential in oral delivery of biomacromolecules. However, information on gastrointestinal lipolysis and trans-epithelial transport of SMEDDS is rare. Aggregation-caused quenching (ACQ) fluorescent probes are utilized to visualize the in vivo behaviors of SMEDDSs, because the released probes during lipolysis are quenched upon contacting water. Two SMEDDSs composed of medium chain triglyceride and different ratios of Tween-80 and PEG-400 are set as models, meanwhile Neoral® was used as a control. The SMEDDS droplets reside in the digestive tract for as long as 24 h and obey first order kinetic law of lipolysis. The increased chain length of the triglyceride decreases the lipolysis of the SMEDDSs. Ex vivo imaging of main tissues and histological examination confirm the trans-epithelial transportation of the SMEDDS droplets. Approximately 2%–4% of the given SMEDDSs are transported via the lymph route following epithelial uptake, while liver is the main termination. Caco-2 cell lines confirm the cellular uptake and trans-epithelial transport. In conclusion, a fraction of SMEDDSs can survive the lipolysis in the gastrointestinal tract, permeate across the epithelia, translocate via the lymph, and accumulate mainly in the liver. Aggregation-caused quenching (ACQ) probes are used to track the in vivo fate of self-microemulsifying drug delivery systems (SMEDDSs) via oral administration. A small fraction of SMEDDSs can survive the lipolysis, permeate across the epithelia, translocate via the lymph, and accumulate in the liver. [Display omitted] [ABSTRACT FROM AUTHOR]

The initiation and development of major inflammatory diseases, i.e. , cancer, vascular inflammation, and some autoimmune diseases are closely linked to the immune system. Biologics-based immunotherapy is exerting a critical role against these diseases, whereas the usage of the immunomodulators is always limited by various factors such as susceptibility to digestion by enzymes in vivo , poor penetration across biological barriers, and rapid clearance by the reticuloendothelial system. Drug delivery strategies are potent to promote their delivery. Herein, we reviewed the potential targets for immunotherapy against the major inflammatory diseases, discussed the biologics and drug delivery systems involved in the immunotherapy, particularly highlighted the approved therapy tactics, and finally offer perspectives in this field. The potential biological drugs and drug delivery systems against the inflammatory diseases, including cancer, atherosclerosis (AS), rheumatoid arthritis (RA), inflammatory bowel diseases (IBD) and pulmonary arterial hypertension (PAH). [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Panax notoginseng is a highly valuable medicinal plant. Reduced P. notoginseng yield is a common and serious problem that arises in a continuous cropping system. Variation in the composition and function of soil microbial community is considered the primary cause of yield reduction. Methods: This study used shotgun metagenomic sequencing approaches to describe the taxonomic and functional features of P. notoginseng rhizosphere microbiome and screen microbial taxa and functional traits related to yields. Results: At the family and genus level, a total of 43 families and 45 genera (relative abundance > 0.1%) were obtained, and the correlation with the yield of P. notoginseng was further analyzed. Nitrosomonadaceae, Xanthomonadaceae, Mycobacterium and Arthrobacter that were enriched in soils with higher yields were positively correlated with P. notoginseng yields, thereby suggesting that they might increase yields. Negative correlation coefficients indicated that Xanthobacteraceae, Caulobacteraceae, Oxalobacteraceae, Chitinophagaceae, Sphingomonas, Hyphomicrobium, Variovorax and Phenylobacterium might be detrimental to P. notoginseng growth. A total of 85 functional traits were significantly (P < 0.05) correlated with P. notoginseng yields. Functional traits, likely steroid biosynthesis and MAPK signaling pathway were positively correlated with P. notoginseng yields. In contrast, functional traits, such as bacterial secretion system, ABC transporters, metabolism of xenobiotics by cytochrome P450 and drug metabolism–cytochrome P450, were negatively associated with yields. Conclusions: This study describes an overview of the rhizosphere microbiome of P. notoginseng with discrepant yields and identifies the taxa and functional traits related to yields. Our results provide valuable information to guide the isolation and culture of potentially beneficial microorganisms and to utilize the power of the microbiome to increase plant yields in a continuous cropping system. [ABSTRACT FROM AUTHOR]