Laser Speckle Contrast Imaging Guides Needling Treatment of Vascular Complications from Dermal Fillers.

BACKGROUND: Although laser Doppler imaging (LDI) accurately delineates a hypoperfused area to help target hyaluronidase treatment, laser speckle contrast imaging (LSCI) is more appropriate for assessing microvascular hemodynamics and has greater reproducibility than LDI. This study investigated the use of LSCI in the evaluation and treatment of six patients who developed vascular complications after facial dermal filler injections. METHODS: The areas of vascular occlusion were accurately defined in real time by LSCI and were more precise than visual inspections or photographic evidence for guiding needling and hyaluronidase treatment. RESULTS: All patients had achieved satisfactory outcomes as early as Day 2 of treatment and no procedure-related complications were reported after a median follow-up of 9.5 (7-37) days. CONCLUSION: LSCI accurately and noninvasively delineated vascular occlusions in real time among patients experiencing complications of facial dermal filler injections. Moreover, LSCI was more accurate than visual and photographic evaluations. Clinicians can use LSCI to reliably follow-up therapeutic outcomes after salvage interventions for vascular occlusions. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Structural basis of transcriptional activation by the OmpR/PhoB-family response regulator PmrA.

PmrA, an OmpR/PhoB-family response regulator, triggers gene transcription responsible for polymyxin resistance in bacteria by recognizing promoters where the canonical-35 element is replaced by the pmra-box, representing the PmrA recognition sequence. Here, we report a cryo-electron microscopy (cryo-EM) structure of a bacterial PmrA-dependent transcription activation complex (TAC) containing a PmrA dimer, an RNA polymerase σ70 holoenzyme (RNAPH) and the pbgP promoter DNA. Our structure reveals that the RNAPH mainly contacts the PmrA C-terminal DNA-binding domain (DBD) via electrostatic interactions and reorients the DBD three base pairs upstream of the pmra-box, resulting in a dynamic TAC conformation. In vivo assays show that the substitution of the DNA-recognition residue eliminated its transcriptional activity, while variants with altered RNAPH-interacting residues resulted in enhanced transcriptional activity. Our findings suggest that both PmrA recognition-induced DNA distortion and PmrA promoter escape play crucial roles in its transcriptional activation.

Kimura Disease of the Thigh Treated With Surgical Excision and Dupilumab.

ABSTRACT: Kimura disease (KD) is a rare, chronic inflammatory disorder presenting with solitary or multiple masses. Treatment options include surgical excision, corticosteroids, and radiotherapy; however, optimal therapy remains to be established. Moreover, efficacy of a humanized monoclonal antibody, dupilumab (Dupixent), requires to be demonstrated. Here, we present a 36-year-old male patient with an enlarging mass in the left medial thigh and chronic eczema over the abdomen and lower legs. Kimura disease was diagnosed after surgical excision. Postoperative treatment with dupilumab was applied with an initial dose of 600 mg followed by 300 mg every 2 weeks for 8 months. No recurrence of KD was observed in the 1-year follow-up. The eczematous lesions improved greatly. To our knowledge, this is the first report of using dupilumab for treating KD.

A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.

OBJECTIVES: To propose and evaluate a set of radiomic features, called morphological dynamics features, for pulmonary nodule detection, which were rooted in the dynamic patterns of morphological variation and needless precise lesion segmentation. MATERIALS AND METHODS: Two datasets were involved, namely, university hospital (UH) and LIDC datasets, comprising 72 CT scans (360 nodules) and 888 CT scans (2230 nodules), respectively. Each nodule was annotated by multiple radiologists. Denoted the category of nodules identified by at least k radiologists as ALk. A nodule detection algorithm, called CAD-MD algorithm, was proposed based on the morphological dynamics radiomic features, characterizing a lesion by ten sets of the same features with different values extracted from ten different thresholding results. Each nodule candidate was classified by a two-level classifier, including ten decision trees and a random forest, respectively. The CAD-MD algorithm was compared with a deep learning approach, the N-Net, using the UH dataset. RESULTS: On the AL1 and AL2 of the UH dataset, the AUC of the AFROC curves were 0.777 and 0.851 for the CAD-MD algorithm and 0.478 and 0.472 for the N-Net, respectively. The CAD-MD algorithm achieved the sensitivities of 84.4% and 91.4% with 2.98 and 3.69 FPs/scan and the N-Net 74.4% and 80.7% with 3.90 and 4.49 FPs/scan, respectively. On the LIDC dataset, the CAD-MD algorithm attained the sensitivities of 87.6%, 89.2%, 92.2%, and 95.0% with 4 FPs/scan for AL1-AL4, respectively. CONCLUSION: The morphological dynamics radiomic features might serve as an effective set of radiomic features for lung nodule detection. KEY POINTS: • Texture features varied with such CT system settings as reconstruction kernels of CT images, CT scanner models, and parameter settings, and so on. • Shape and first-order statistics were shown to be the most robust features against variation in CT imaging parameters. • The morphological dynamics radiomic features, which mainly characterized the dynamic patterns of morphological variation, were shown to be effective for lung nodule detection.

Heterologous overexpression, purification and functional analysis of plant cellulose synthase from green bamboo.

BACKGROUND: The cellulose synthase complex (CSC), composed of cellulose synthase (CesA) proteins, is a catalytic enzyme complex involved in cellulose synthesis in the plant cell. CesA proteins synthesize cellulose microfibrils corresponding to the microtubule direction and export linear products across the plasma membrane. However, the CSC arrangement and the mechanism of cellulose synthesis in plant cells remain unclear. Purified CesA proteins are required to determine biochemical and biophysical characteristics. RESULTS: In this study, we constructed, expressed, and purified six heterologously expressed cellulose synthases from Bambusa oldhamii (BoCesA) and analyzed the associated enzyme activity. The conjugating sequences of the maltose-binding protein (MBP) gene and the BoCesA genes were constructed into the expression vector pYES2/CT and were further transformed into yeast cells (BCY123) for fermentation culturing. Purified BoCesA recombinant proteins were obtained by a two-step purification procedure, consisting of immobilized metal affinity chromatography to purify MBP-BoCesAs and size-exclusion chromatography (Superdex-200) to isolate BoCesAs in oligomeric form. The enzymatic activity of oligomeric BoCesAs with 80% purity was determined by partially methylated alditol acetate (PMAA)-coupled gas chromatography-mass spectrometry (GC-MS) analysis. Furthermore, the long fiber-like products synthesized by oligomeric BoCesAs were observed under a transmission electron microscope (TEM) and were further confirmed as cellulose microfibril products. CONCLUSIONS: In this study, we successfully established a heterologous expression and purification system for BoCesAs. The purified recombinant BoCesA proteins display enzyme activity and can produce protein in milligram quantities for further studies on molecular composition and structure.

Efficiency optimisation of proteins on a chip.

This study elucidates that the protein reorientation on a chip can be changed by an external electric field (EEF) and optimised for achieving strong effective binding between proteins. Protein A and its binding protein immunoglobulin G (IgG) were used as an example, in addition to an anticancer peptide (CB1a) and its antibody (anti-CB1a). The binding forces (BFs) were measured by atomic force microscopy (AFM) with EEFs applied at different angles (EEF°). The optimal angle (OA) of the EEF (OAEEF°) corresponding to the maximum binding force (BFmax) was obtained. The results showed that the BFmax values between IgG/Protein A and anti-CB1a/CB1a were 6424.2 ± 195.3 pN (OAEEF° = 45°) and 729.1 ± 33.2 pN (OAEEF° = 22.5°), respectively. Without an EEF, the BF values were only 730.0 ± 113.9 pN and 337.3 ± 35.0 pN, respectively. Based on these observations, we concluded that the efficient optimisation of protein-protein interaction on a chip is essential. This finding is applicable to the industrial fabrication of all protein chips.

Inhibition effect of a custom peptide on lung tumors.

Cecropin B is a natural antimicrobial peptide and CB1a is a custom, engineered modification of it. In vitro, CB1a can kill lung cancer cells at concentrations that do not kill normal lung cells. Furthermore, in vitro, CB1a can disrupt cancer cells from adhering together to form tumor-like spheroids. Mice were xenografted with human lung cancer cells; CB1a could significantly inhibit the growth of tumors in this in vivo model. Docetaxel is a drug in present clinical use against lung cancers; it can have serious side effects because its toxicity is not sufficiently limited to cancer cells. In our studies in mice: CB1a is more toxic to cancer cells than docetaxel, but dramatically less toxic to healthy cells.

Fast incorporation of primary amine group into polylactide surface for improving C2C12 cell proliferation using nitrogen-based atmospheric-pressure plasma jets.

In this article, we report the development of the fast incorporation of primary amine functional groups into a polylactide (PLA) surface using the post-discharge jet region of an atmospheric-pressure nitrogen-based dielectric barrier discharge (DBD). Plasma treatments were carried out in two sequential steps: (1) nitrogen with 0.1% oxygen addition, and (2) nitrogen with 5% ammonia addition. The analyses show that the concentration of N/C ratio, surface energy, contact angle, and surface roughness of the treated PLA surface can reach 19.1%, 70.5 mJ/m(2), 38° and 73.22 nm, respectively. In addition, the proposed two-step plasma treatment procedure can produce a PLA surface exhibiting almost the same C2C12 cell attachment and proliferation performance as that of the conventional gelatin coating method. Most importantly, the processing/preparation time is reduced from 13-15 h (gelatin coating method) to 5-15 min (two-step plasma treatment), which is very useful in practical applications.

Real value prediction of protein solvent accessibility using enhanced PSSM features.

BACKGROUND: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM). RESULTS: This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction. CONCLUSION: Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.