Activity

k squamous cell carcinoma; HSP90 heat shock protein 90; TAM tumour-associated macrophage.

CDC37 cell division control 37; EMT epithelial-mesenchymal transmission; EV extracellular vesicles; HNSCC head and neck squamous cell carcinoma; HSP90 heat shock protein 90; TAM tumour-associated macrophage.Periodontitis is a bacterial biofilm-induced oral disease, mostly caused by Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) and Porphyromonas gingivalis (P. gingivalis). Oral administration of chicken egg yolk antibody (IgY) is a promising nutritional strategy to control pathogen infections. The objective of this study was to produce an A. check details actinomycetemcomitans- and P. gingivalis-specific IgY and evaluate its effects on bacterial agglutination and biofilm formation. Thirty laying hens were immunized with a complex of lysate containing typical molecular weights of membrane proteins of A. actinomycetemcomitans and P. gingivalis. IgY was isolated by polyethylene glycol 6000 and ammonium sulfate and purified by dialysis. The results of enzyme-linked immunosorbent assay showed that the obtained IgY were specific to both A. actinomycetemcomitans and P. gingivalis. In addition, immunoelectron microscopy scanning and crystal violet staining showed that the IgY could bind to cell wall of the pathogens and efficiently accelerate agglutination and inhibit biofilm formation. Furthermore, the activity of the IgY remained stable at different temperature, pH, and storage period. This is the first report that a novel two-in-one IgY was produced to modulate the agglutination and biofilm formation of A. actinomycetemcomitans and P. gingivalis, suggesting the potential of IgY to control periodontitis caused by oral pathogens.In order to improve the efficiency of disease diagnosis and environmental monitoring, it is desirable to detect the concentration of proteins and metal ions simultaneously, since the current popular diagnostic platform can only detect proteins or metal ions independently. In this work, we developed a colorimetric microfluidic paper-based analytical device (µPAD) for simultaneous determination of protein (bovine serum albumin, BSA) and metal ions [Fe(III) and Ni(II)]. The µPAD consisted of one central zone, ten reaction zones and ten detection zones in one device, in which reaction solutions were effectively optimized for different types of chromogenic reactions. Fe(III), Ni(II) and BSA can be easily identified by the colored products, and their concentrations are in good accordance with color depth based on the established standard curves. The detection limits are 0.1 mM for Fe(III), 0.5 mM for Ni(II) and 1µM for BSA, respectively. Best of all, we demonstrated the efficiency of the µPAD with accurate detection of Fe(III), Ni (II) and BSA from river water samples within 15 minutes. The µPAD detection is efficient, instrument-free, and easy-to-use, holding great potential for simultaneous detection of cross type analytes in numerous diagnostic fields.[This corrects the article DOI 10.18632/oncotarget.6744.].Juxtapapillary retinal capillary hemangioblastoma (JRCH), a benign intraocular vascular tumor, is usually progressive and may lead to severe vision loss due to various complications. We herein present a case of JRCH observed with laser speckle flowgraphy (LSFG) before and after laser photocoagulation (LPC). A 21-year-old Japanese woman underwent LSFG evaluations. Right eye showed an orange-colored tumor consistent with JRCH on the papillomacular bundle, where LSFG showed a mild warm-color blood flow signal. Eight months after the first examination, JRCH in the right eye increased redness with vasodilatation, and the size enlarged, where LSFG showed a stronger warm-color blood flow signal. She underwent direct yellow laser ablation for the JRCH lesion. One week after LPC, JRCH became paler and LSFG eventually depicted a weakened blood flow signal at the same site. In conclusion, non-invasive and reproducible LSFG is a useful tool for assessing not only JRCH activity but also therapeutic effect.

Alpelisib is a first-in-class α-specific phosphatidylinositol 3-kinase inhibitor approved for the treatment of patients with estrogen receptor-positive metastatic breast cancer. High absolute risk (AR) of relevant toxicities has been observed with this treatment. This meta-analysis aimed to improve the precision of the estimated AR of selected adverse events (AEs) associated with this new agent.

A literature search was conducted in August 2019 to identify trials analyzing the anti-tumor efficacy and toxicity profile of alpelisib. Heterogeneity was assessed by using

statistics. Data were analyzed using random effect meta-analyses for AR. Eleven trials and 511 patients were included.

There was no evidence of heterogeneity between studies regarding the AR of most AEs except for all-grade weight loss and grade 3-4 stomatitis. The number of serious AEs was clearly reported in only one study, of which the most common was hyperglycemia; the most common all-grade AEs were hyperglycemia (59%), diarrhea (56%), nausea (44%), and rash (38%). Grade 3/4 hyperglycemia and rash occurred in 28% and 10% of patients, respectively. No treatment-associated deaths were observed, and 18% of patients had to stop treatment due to toxicities.

Alpelisib is associated with clinically relevant AEs that can lead to treatment discontinuation. The most common AE was hyperglycemia. No treatment-related deaths were observed.

Alpelisib is associated with clinically relevant AEs that can lead to treatment discontinuation. The most common AE was hyperglycemia. No treatment-related deaths were observed.

To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).

100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derived (20 QUS-Tex

), which were further processed to create texture derivatives (80 QUS-Tex

-Tex

). Patients were classified into responders and non-responders based on clinical/pathological responses to treatment. Three machine learning algorithms based on linear discriminant (FLD),

-nearest-neighbors (KNN), and support vector machine (SVM) were used for developing radiomic models of response prediction.

A KNN-model provided the best results with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 81%, 82%, and 0.86, respectively. The most helpful features in separating the two response groups were QUS-Tex

-Tex

features.