DNA-functionalized MOF fluorescent probes for the enzyme-free and pretreatment-free detection of MicroRNA in serum.

MicroRNA (miRNA) is a promising biomarker that plays an important role in various biomedical applications, especially in cancer diagnosis. However, the current miRNA detection technology has inherent limitations such as complex operation, expensive testing cost and excessive detection time. In this study, a dual signal amplification biosensor based on DNA-functionalized metal-organic frameworks (MOFs) fluorescent probes, MFPBiosensor, was established for the enzyme-free and pretreatment-free detection of the colon cancer (CC) marker miR-23a. DNA-functionalized MOFs NH2-MIL-53(Al) (DNA@MOFs) were synthesized as fluorescent probes with specific recognition functions. A single DNA@MOF carries a large number of fluorescent ligands 2-aminoterephthalic acid (NH2-H2BDC), which can generate strong fluorescence signals after alkaline hydrolysis. Combined with catalyzed hairpin assembly (CHA), an efficient isothermal amplification technique, the dual signal enhancement strategy reduced matrix interference and sensitized the signal response. The established MFPBiosensor successfully detected extremely low levels of miRNA in complex biological samples with acceptable sensitivity and specificity. With a single detection cost of $0.583 and a test time of 50 min, the excellent inexpensive and rapid advantage of the MFPBiosensor is highlighted. More importantly, the subtle design enables the MFPBiosensor to achieve convenient batch detection, where miRNA in serum can be directly detected without any pretreatment process or enzyme. In conclusion, MFPBiosensor is a promising biosensor with substantial potential for commercial miRNA detection and clinical diagnostic applications of CC.

Highly efficient amino-functionalized aluminum-based metal organic frameworks mesoporous nanorods for selective extraction of hydrocortisone in pharmaceutical wastewater.

Hydrocortisone (HC), as a common steroid hormone drug, is also one of the key intermediates involved in the synthesis of multiple steroid hormone drugs. Residual HC in pharmaceutical wastewater frequently pollutes environmental water as steroid hormone contaminant and possesses great threat to human health as well as sustainable development of the ecosystem. Herein, in order to develop a highly efficient adsorbent system for selective enrichment and detection of HC in pharmaceutical wastewater, a novel amino-functionalized aluminum-based metal organic frameworks (Al-MOFs@NH2) mesoporous nanorod is fabricated, in which 2-aminoterephthalic acid plays a dual role as organic linker and functional modification unit. The resultant Al-MOFs@NH2 not only exhibits stable mesoporous structure but also has large specific surface area (849.76 m2 g-1) and plentiful binding sites, which significantly increases the adsorption capacity for HC. Under the promotion of hydrogen bonding and hydrophobic interaction together, Al-MOFs@NH2 possesses high adsorption capacity (218.53 mg g-1) for HC, as well as shows satisfactory selectivity for HC and other steroid hormones. Moreover, a method using Al-MOFs@NH2 as solid phase extraction adsorbents combined with high performance liquid chromatography (HPLC) has been developed to specifically enrich and detect trace amount of HC in pharmaceutical wastewater. The developed method has a low limit of detection (LOD) (0.5×10-3 µg mL-1) and shows satisfactory recoveries for HC (75.9%-102.5%) with an acceptable relative standard deviation (RSD). These results demonstrate that the facile one-step preparation and excellent adsorption capacity makes Al-MOFs@NH2 attractive to capture and remove environmental steroid hormone pollutants. More importantly, the method proposed in this work is expected to provide a prospective solution for analysis of strong bioactive contaminants in pharmaceutical wastewater.

A Novel Nanomedicine Ameliorates Acute Inflammatory Bowel Disease by Regulating Macrophages and T-Cells.

Ramulus mori polysaccharide (RMP), one of the most important active components of R. mori, has been attracting increasing interest because of its potent bioactive properties, including anti-inflammatory, antitumor, and antidiabetic effects. Despite the great therapeutic potential of RMP, its inherent properties of low bioavailability and brief biological half-life have limited its applications to the clinic. Thus, RMP was packaged by poly(lactic-co-glycolic acid) (PLGA) nanoparticles to develop a novel anti-inflammatory nanomedicine (PLGA-RMP) in this study. The nanoparticles were synthesized via a double-emulsion solvent evaporation technique, and the average diameter of PLGA-RMP was about 202 nm. PLGA-RMP nanoparticles reduced the expression of inflammatory cytokines while promoting the production of IL-10, and boosted the phenotypic shift in macrophages in vitro. Furthermore, lipopolysaccharide (LPS)-induced inflammatory bowel disease (IBD) in mouse was used to examine the anti-inflammatory effect of PLGA-RMP in vivo. Oral administration of PLGA-RMP in LPS-induced IBD mice substantially mitigated the intestinal inflammation compared to treatment with LPS alone, as evidenced by attenuation of disease activity index scores and inflammatory damage in the intestine. Meanwhile, PLGA-RMP suppressed the expression and secretion of specific inflammatory cytokines including TNF-α, IL-6, IL-1ß, and PGE2 in the inflamed intestine while inhibiting the activation of CD3+CD8+ T-cells and increasing the number of activated Tregs in the intestine. These results indicated that PLGA-RMP deserves further consideration as a potential therapeutic nanomedicine to treat various inflammatory diseases, including IBD.

Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes.

OBJECTIVE: To screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC. METHODS: The GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs. RESULTS: We identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues. CONCLUSION: This prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

SIZ1 negatively regulates aluminum resistance by mediating the STOP1-ALMT1 pathway in Arabidopsis.

Sensitive to proton rhizotoxicity 1 (STOP1) functions as a crucial regulator of root growth during aluminum (Al) stress. However, how this transcription factor is regulated by Al stress to affect downstream genes expression is not well understood. To explore the underlying mechanisms of the function and regulation of STOP1, we employed a yeast two hybrid screen to identify STOP1-interacting proteins. The SUMO E3 ligase SIZ1, was found to interact with STOP1 and mainly facilitate its SUMO modification at K40 and K212 residues. Simultaneous introduction of K40R and K212R substitutions in STOP1 enhances its transactivation activity to upregulate the expression of aluminum-activated malate transporter 1 (ALMT1) via increasing the association with mediator 16 (MED16) transcriptional co-activator. Loss of function of SIZ1 causes highly increased expression of ALMT1, thus enhancing Al-induced malate exudation and Al tolerance. Also, we found that the protein level of SIZ1 is reduced in response to Al stress. Genetic evidence demonstrates that STOP1/ALMT1 is epistatic to SIZ1 in regulating root growth response to Al stress. This study suggests a mechanism about how the SIZ1-STOP1-ALMT1 signaling module is involved in root growth response to Al stress.

A machine learning approach for mortality prediction only using non-invasive parameters.

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine learning algorithms is proposed, which only uses non-invasive parameters readily available from ordinary monitors and manual measurement. A new feature selection method based on the Bayes error rate is developed to select valuable features. Based on non-invasive parameters, four machine learning models were trained for early mortality prediction. The subjects contained in this study suffered from general critical diseases including but not limited to cancer, bone fracture, and diarrhea. Comparison tests among five traditional scoring methods and these four machine learning models with and without laboratory measurement variables are performed. Only using the non-invasive parameters, the LightGBM algorithms have an excellent performance with the largest accuracy of 0.797 and AUC of 0.879. There is no apparent difference between the mortality prediction performance with and without laboratory measurement variables for the four machine learning methods. After reducing the number of feature variables to no more than 50, the machine learning models still outperform the traditional scoring systems, with AUC higher than 0.83. The machine learning approaches only using non-invasive parameters achieved an excellent mortality prediction performance and can equal those using extra laboratory measurements, which can be applied in rural areas and remote battlefield for mortality risk evaluation. Graphical abstract.

AtHB7/12 Regulate Root Growth in Response to Aluminum Stress.

Aluminum (Al) stress is a major limiting factor for plant growth and crop production in acid soils. At present, only a few transcription factors involved in the regulation of Al resistance have been characterized. Here, we used reversed genetic approach through phenotype analysis of overexpressors and mutants to demonstrate that AtHB7 and AtHB12, two HD-Zip I transcription factors, participate in Al resistance. In response to Al stress, AtHB7 and AtHB12 displayed different dynamic expression patterns. Although both AtHB7 and AtHB12 positively regulate root growth in the absence of Al stress, our results showed that AtHB7 antagonizes with AtHB12 to control root growth in response to Al stress. The athb7/12 double mutant displayed a wild-type phenotype under Al stress. Consistently, our physiological analysis showed that AtHB7 and AtHB12 oppositely regulate the capacity of cell wall to bind Al. Yeast two hybrid assays showed that AtHB7 and AtHB12 could form homo-dimers and hetero-dimers in vitro, suggesting the interaction between AtHB7 and AtHB12 in the regulation of root growth. The conclusion was that AtHB7 and AtHB12 oppositely regulate Al resistance by affecting Al accumulation in root cell wall.

Characterization of VuMATE1 Expression in Response to Iron Nutrition and Aluminum Stress Reveals Adaptation of Rice Bean (Vigna umbellata) to Acid Soils through Cis Regulation.

Rice bean (Vigna umbellata) VuMATE1 appears to be constitutively expressed at vascular system but root apex, and Al stress extends its expression to root apex. Whether VuMATE1 participates in both Al tolerance and Fe nutrition, and how VuMATE1 expression is regulated is of great interest. In this study, the role of VuMATE1 in Fe nutrition was characterized through in planta complementation assays. The transcriptional regulation of VuMATE1 was investigated through promoter analysis and promoter-GUS reporter assays. The results showed that the expression of VuMATE1 was regulated by Al stress but not Fe status. Complementation of frd3-1 with VuMATE1 under VuMATE1 promoter could not restore phenotype, but restored with 35SCaMV promoter. Immunostaining of VuMATE1 revealed abnormal localization of VuMATE1 in vasculature. In planta GUS reporter assay identified Al-responsive cis-acting elements resided between -1228 and -574 bp. Promoter analysis revealed several cis-acting elements, but transcription is not simply regulated by one of these elements. We demonstrated that cis regulation of VuMATE1 expression is involved in Al tolerance mechanism, while not involved in Fe nutrition. These results reveal the evolution of VuMATE1 expression for better adaptation of rice bean to acid soils where Al stress imposed but Fe deficiency pressure released.