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A novel Bi6O5(OH)3(NO3)5·2H2O/AgBr (6535BBN/AgBr) composite with long-lasting antibacterial efficacy was prepared.

Targeting the limitation of antimicrobial peptides (AMPs) application

The gut microbiota, regarded as “the second genome” of human, is responsible for a considerable number of key physiological responses in the host, including the regulation of host immunity, the prevention of pathogen infection, the synthesis and metabolism of critical molecules, and others. More importantly, recent research highlighted that it could also play an indispensable role during anticancer therapy. By interacting with the host immunity or producing direct modifications on the drugs, the gut microbiota can ultimately contribute to the effectiveness of anticancer treatments and also overcome the therapy-induced adverse effects. In this review, we discuss the potential mechanisms of gut microbiota in facilitating anticancer therapy and illustrate the applications of different commensal bacterial species in preclinical and clinical trials, which may provide insights into how the gut microbiota could be used as a promising adjuvant therapy option for the future treatment.

In order to better investigate the geological research, a tilt photogrammetry technology based on the unmanned aerial vehicle is proposed. This paper makes use of UAV tilt photogrammetry technology to make geological macroscopic analysis and establishes 3d color digital ground model, forming a set of technical methods for geological survey assisted by UAV. The experimental results show that the coordinate calculation is significantly improved, and the root mean square error of plane coordinates reaches 0.2 m, which can meet the requirements of later cartography and spatial measurement. The feasibility and efficiency of carrying out a geological survey based on the unmanned aerial vehicle are verified by the practical application in landslide hazard geological survey. It is proved that DEM and DOM data obtained based on oblique photogrammetry data can form various thematic maps by superimposing them with vector GIS data, which will provide a basic data model for subsequent engineering investigation, site management, and image display.

To analyze the possible factors causing fatty liver in children based on ultrasound data of children in south Texas, and to establish machine learning models of fatty liver in children to provide ideas for the prevention and treatment of fatty liver in children.The binary classification model of fatty liver problem in obese children in Texas was established under the multiple model. First, we selected important features using the CatBoost algorithm. Second, the best parameters of the algorithm were selected on the training set and the validation set by using the grid search method, and all six models were tested on the test set. The six models then were compared by area under the curve value, precision, accuracy, recall rate, and F1 score in a model evaluation. Then, two algorithms, logic regression and CatBoost, were selected to establish prediction models of fatty liver disease in children.We selected body mass index, height, liver size, kidney volume, glomerular filtration rate, and liver diameter as the features used in the machine learning model. The prediction models we chose showed that children with higher body mass index at the same age tended to have a greater probability of fatty liver.Based on the analysis of the results of the two prediction models established by logistic regression and CatBoost, we determined that the mean probability of fatty liver in severely obese children was between 74.47% and 92.22%, 73.45% and 85.41% in obese children, and slightly higher in boys than in girls, with a mean difference of 3.00% to 3.95%.

With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p

Objectives The objective of this study was to construct a novel strategy for the mutation detection of knockdown resistance(kdr)gene in Aedes albopictus using multiplex PCR-mass spectrometry minisequencing technique (mPCR-MS minisequencing). Methods Based on the single-base mass probe extension of multiplex PCR amplification products in wild and mutant genotype, a matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) method was established to detect 3 mutated sites in Ae. albopictus kdr gene: locus 1016,1532 and 1534. Results The detection of the three sites can be conducted simultaneously by double PCR amplification combined with MALDI-TOF MS, achieving a detection limit of 20fg/ul. This method is extensible and flexible, and can be used in a high-throughput manner, easily allowing the addition of new mutation sites as needed to identify and track new kdr gene as they emerge. Conclusions mPCR-MS minisequencing provides a new option for the detection of kdr gene in Ae. Albopictus.

Although Androgenetic Alopecia (AGA) is classified as a non-inflammatory alopecia, histological evidence of microinflammation has long been recognized. However, the changes in the immune microenvironment, the immune-related pathway and the expression of Immune-related genes (IRGs) involved in AGA remain unclear. The microarray gene expression data (GSE36169) from patients with male AGA were analyzed. Gene Set Enrichment Analysis (GSEA) among statistically changed genes was done. KEGG and GO analyses among differentially expressed genes (DEGs) were performed. DEGs were screened to identify IRGs based on the ImmPort database. The cytohubba-MCC plugin of Cytoscape was applied to screen hub immune genes. The infiltration levels of 28 immune cells were quantified adopting single-sample GSEA (ssGSEA) algorithm. The microarray gene expression data (GSE90594) of male AGA was analyzed to validate hub IRGs genes and differential infiltrated immune cells. The ssGSEA revealed γδT cell, central memory CD8+ T cell, mast cell, immature B cell, activated CD8+ T cell, effector memory CD4+ T cell, eosinophil and neutrophil were significantly increased infiltration in the bald scalp. GSEA showed statistically changed genes were most enriched in immune related pathways, including innate immune system, adaptive immune system, cytokine signaling, interferon-γ signaling, interferon signaling and interleukins signaling. The four hub IRGs, including MMP9, PTPRC, BMP2 and THBS1, were enriched in the pathways of allograft rejection, coagulation and interferon-γ response. In summary, we proposed that the increase in γδ T cells, central memory CD8+ T cells, activated CD8+ T cell as well as the infiltration of mast cells contributed to immune microenvironment changes in male AGA. The 4 hub IRGs may be involved in the development and progression of hair loss in male AGA through interferon-γ signal pathways.

Background: The present study aims to evaluate how nutritional status may affect transcatheter aortic valve implantation (TAVI) outcomes. Materials and Methods: This is a retrospective study of 383 TAVI patients. In-hospital, 1-month, and 12-month survival was evaluated. Since most patients undergoing TAVI are over 75 years old, the NRI definition for a geriatric population (GNRI) was used. Preoperative baseline clinical and laboratory data were collected and then the corresponding nutritional status was calculated, including Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNRI), and Controlling Nutritional Status Score (CONUT). Survival analysis and receiver operating characteristic curve (ROC) analysis were used to evaluate the correlation between these parameters and TAVI outcome. Results: By CONUT and GNRI scores, 168 (58.9%) and 40 (14.0%) patients were considered to have mild malnutrition, respectively. By using PNI, CONUT, and GNRI scores, 16 (5.7%), 29 (10.3%), and 39 (13.7%) patients were moderately or severely malnourished. Survival analysis showed that patients with worse nutritional status had a worse prognosis regardless of the nutritional score used. Subgroup analysis showed that these differences remained significant in subgroups of patients over age 75. COX multivariate analysis showed that GNRI, PNI, and CONUT were independently associated with all-cause mortality during the follow-up. Conclusion: Patients with worse nutritional status had a worse prognosis regardless of the nutritional score used. Subgroup analysis showed that these differences remained significant in subgroups of patients over age 75. GNRI, PNI, and CONUT were independent predictors of all-cause mortality after TAVI.

Potent antioxidative drugs are urgently needed to treat ischemia-reperfusion (I/R) induced reactive oxygen species (ROS)-mediated cerebrovascular and neural injury during ischemia strokes. However, current antioxidative agents have limited application in such disease due to low blood-brain barrier (BBB) penetration. We herein designed a "neutrophil piggybacking" strategy based on albumin opsonized nanoparticles co-encapsulated with antioxidases catalase (CAT) and superoxide dismutase 1 (SOD1). The system utilized the natural potential of neutrophils to target inflamed tissues to deliver antioxidases to injured sites in the brain. In addition, the system was integrated with a selenium (Se)-containing crosslinker to inhibit ferroptosis. We showed that the nanoparticles opsonized in the hybrid form rather than with an albumin-shell structure exhibited enhanced neutrophil targeting and efficient BBB penetration

An overall superior ammonia/syngas model: the optimized rate parameters are supported by experiments; the optimized model reproduces reasonably an extensive set of macroscopic data including ignition delay, flame speed and species concentration.