Structure-Based High-Efficiency Homogeneous Antibody Platform by Endoglycosidase Sz Provides Insights into Its Transglycosylation Mechanism.

Monoclonal antibodies (mAbs) have gradually dominated the drug markets for various diseases. Improvement of the therapeutic activities of mAbs has become a critical issue in the pharmaceutical industry. A novel endo-ß-N-acetylglucosaminidase, EndoSz, from Streptococcus equisubsp. zooepidemicus Sz105 is discovered and applied to enhance the activities of mAbs. Our studies demonstrate that the mutant EndoSz-D234M possesses an excellent transglycosylation activity to generate diverse glycoconjugates on mAbs. We prove that EndoSz-D234M can be applied to various marketed therapeutic antibodies and those in development for antibody remodeling. The remodeled homogeneous antibodies (mAb-G2S2) produced by EndoSz-D234M increase the relative ADCC activities by 3-26-fold. We further report the high-resolution crystal structures of EndoSz-D234M in the apo-form at 2.15 Å and the complex form with a bound G2S2-oxazoline intermediate at 2.25 Å. A novel pH-jump method was utilized to obtain the complex structure with a high resolution. The detailed interactions of EndoSz-D234M and the carried G2S2-oxazoline are hence delineated. The oxazoline sits in a hole, named the oxa-hole, which stabilizes the G2S2-oxazoline in transit and catalyzes the further transglycosylation reaction while targeting Asn-GlcNAc (+1) of Fc. In the oxa-hole, the H-bonding network involved with oxazoline dominates the transglycosylation activity. A mobile loop2 (a.a. 152-159) of EndoSz-D234M reshapes the binding grooves for the accommodation of G2S2-oxazoline upon binding, at which Trp154 forms a hydrogen bond with Man (-2). The long loop4 (a.a. 236-248) followed by helix3 is capable of dominating the substrate selectivity of EndoSz-D234M. In addition, the stepwise transglycosylation behavior of EndoSz-D234M is elucidated. Based on the high-resolution structures of the apo-form and the bound form with G2S2-oxazoline as well as a systematic mutagenesis study of the relative transglycosylation activity, the transglycosylation mechanism of EndoSz-D234M is revealed.

Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Virtual MOLLI Target: Generative Adversarial Networks Toward Improved Motion Correction in MRI Myocardial T1 Mapping.

BACKGROUND: The modified Look-Locker inversion recovery (MOLLI) sequence is commonly used for myocardial T1 mapping. However, it acquires images with different inversion times, which causes difficulty in motion correction for respiratory-induced misregistration to a given target image. HYPOTHESIS: Using a generative adversarial network (GAN) to produce virtual MOLLI images with consistent heart positions can reduce respiratory-induced misregistration of MOLLI datasets. STUDY TYPE: Retrospective. POPULATION: 1071 MOLLI datasets from 392 human participants. FIELD STRENGTH/SEQUENCE: Modified Look-Locker inversion recovery sequence at 3 T. ASSESSMENT: A GAN model with a single inversion time image as input was trained to generate virtual MOLLI target (VMT) images at different inversion times which were subsequently used in an image registration algorithm. Four VMT models were investigated and the best performing model compared with the standard vendor-provided motion correction (MOCO) technique. STATISTICAL TESTS: The effectiveness of the motion correction technique was assessed using the fitting quality index (FQI), mutual information (MI), and Dice coefficients of motion-corrected images, plus subjective quality evaluation of T1 maps by three independent readers using Likert score. Wilcoxon signed-rank test with Bonferroni correction for multiple comparison. Significance levels were defined as P < 0.01 for highly significant differences and P < 0.05 for significant differences. RESULTS: The best performing VMT model with iterative registration demonstrated significantly better performance (FQI 0.88 ± 0.03, MI 1.78 ± 0.20, Dice 0.84 ± 0.23, quality score 2.26 ± 0.95) compared to other approaches, including the vendor-provided MOCO method (FQI 0.86 ± 0.04, MI 1.69 ± 0.25, Dice 0.80 ± 0.27, quality score 2.16 ± 1.01). DATA CONCLUSION: Our GAN model generating VMT images improved motion correction, which may assist reliable T1 mapping in the presence of respiratory motion. Its robust performance, even with considerable respiratory-induced heart displacements, may be beneficial for patients with difficulties in breath-holding. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 1.

Sub-MIC streptomycin and tetracycline enhanced Staphylococcus aureus Guangzhou-SAU749 biofilm formation, an in-depth study on transcriptomics.

Staphylococcus aureus is a major human pathogen, a potential "Super-bug" and a typical biofilm forming bacteria. With usage of large amount of antibiotics, the residual antibiotics in clinical settings further complicate the colonization, pathogenesis and resistance of S. aureus. This study aimed at investigating the phenotypical and global gene expression changes on biofilm formation of a clinical S. aureus isolate treated under different types of antibiotics. Firstly, an isolate Guangzhou-SAU749 was selected from a large sale of previously identified S. aureus isolates, which exhibited weak biofilm formation in terms of biomass and viability. Secondly, 9 commonly prescribed antibiotics for S. aureus infections treatment, together with 10 concentrations ranging from 1/128 to 4 minimum inhibitory concentration (MIC) with 2-fold serial dilution, were used as different antibiotic stress conditions. Then, biofilm formation of S. aureus Guangzhou-SAU749 at different stages including 8 h, 16 h, 24 h, and 48 h, was tested by crystal violet and MTS assays. Thirdly, the whole genome of S. aureus Guangzhou-SAU749 was investigated by genome sequencing on PacBio platform. Fourthly, since enhancement of biofilm formation occurred when treated with 1/2 MIC tetracycline (TCY) and 1/4 MIC streptomycin (STR) since 5 h, the relevant biofilm samples were selected and subjected to RNA-seq and bioinformatics analysis. Last, expression of two component system (TCS) and biofilm associated genes in 4 h, 8 h, 16 h, 24 h, and 48 h sub-MIC TCY and STR treated biofilm samples were performed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Although most antibiotics lowered the biomass and cell viability of Guangzhou-SAU749 biofilm at concentrations higher than MIC, certain antibiotics including TCY and STR promoted biofilm formation at sub-MICs. Additionally, upon genome sequencing, RNA-seq and RT-qPCR on biofilm samples treated with sub-MIC of TCY and STR at key time points, genes lytR, arlR, hssR, tagA, clfB, atlA and cidA related to TCS and biofilm formation were identified to contribute to the enhanced biofilm formation, providing a theoretical basis for further controlling on S. aureus biofilm formation.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.

BACKGROUND: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Antimicrobial Resistance, SCCmec, Virulence and Genotypes of MRSA in Southern China for 7 Years: Filling the Gap of Molecular Epidemiology.

As the prevalence of Staphylococcus aureus infections is of worldwide concern, phenotype and genotype in prevalent MRSA strains require longitudinal investigation. In this study, the antibiotic resistance, virulence gene acquisition, and molecular type were determined on a large scale of nosocomial S. aureus strains in Southern China during 2009-2015. Bacterial identification and antimicrobial susceptibility to 10 antibiotics were tested by Vitek-2. Virulence genes encoding staphylococcal enterotoxins (SEA, SEB, SEC, SED, and SEE), exfoliative toxins (ETA and ETB), Panton-Valentine leukocidin (PVL), and toxic shock syndrome toxin (TSST) were detected by PCR, with SCCmec typing also conducted by multiplex PCR strategy. Genotypes were discriminated by MLST and spaA typing. MLST was performed by amplification of the internal region of seven housekeeping genes. PCR amplification targeting the spa gene was performed for spa typing. No resistance to vancomycin, linezolid, or quinupristin and increase in the resistance to trimethoprim/sulfamethoxazole (55.5%) were identified. A total of nine SCCmec types and subtypes, thirteen STs clustered into thirteen spa types were identified, with ST239-SCCmec III-t037 presenting the predominant methicillin-resistant S. aureus (MRSA) clone. Typically, SCCmec type IX and ST546 were emergent types in China. Isolates positive for both pvl and tsst genes and for both eta and etb genes were also identified. Important findings in this study include: firstly, we have provided comprehensive knowledge on the molecular epidemiology of MRSA in Southern China which fills the gap since 2006 or 2010 from previous studies. Secondly, we have presented the correlation between virulence factors (four major groups) and genotypes (SCCmec, ST and spa types). Thirdly, we have shown evidence for earliest emergence of type I SCCmec from 2012, type VI from 2009 and type XI from 2012 in MRSA from Southern China.

Single breath-hold MR T1 mapping in the heart: Hybrid MOLLI combining saturation and inversion recovery.

The native T1 values of the myocardium provide valuable information for tissue characterization and assessment of cardiomyopathies. In this study, we proposed a novel hybrid MOLLI sequence for myocardial T1 mapping. Unlike the two groups of inversion-recovery sampling of the conventional MOLLI5(3 s)3 sequence, the hybrid MOLLI sequence consisted of an inversion-recovery block followed by a saturation-recovery block. Since the second block employed a saturation pulse to spoil the longitudinal magnetization, it did not require a waiting period as MOLLI5(3 s)3 did. As a result, the hybrid MOLLI required less acquisition time leading to a practical application for patients with breath-hold difficulties. Phantom and healthy subject experiments were performed to evaluate the proposed sequence against the MOLLI5(3 s)3 sequence. The phantom study showed that the heart-rate dependency of one variant of the hybrid MOLLI sequences, hbMOLLI4, was comparable to that of MOLLI5(3 s)3. In addition, both hbMOLLI4 and MOLLI53 derived T1 values under 2% variations with simulated heart rates from 50 to 90 beats-per-minute within the range of T1 values for myocardium and blood before contrast administration. Simulation results suggested slightly reduced T1 fitting precision in hbMOLLI4 compared with MOLLI5(3 s)3, but prominently better than saturation recovery. Bland-Altman analysis on accuracy assessment revealed that hbMOLLI4 partially reduced the T1 underestimation of MOLLI5(3 s)3. In the human study, The T1 values of both methods were consistent (hbMOLLI4 vs. MOLLI5(3 s)3, slope = 1.14, R2 > 0.97), with equal reproducibility. The results supported that hybrid MOLLI produced comparable T1 mapping results in terms of accuracy, reproducibility, and heart-rate dependency, at the expense of slightly reduced precision. We concluded that the hybrid MOLLI sequence presents a competitive alternative to the MOLLI5(3 s)3 sequence when a speedy acquisition is required.

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.

Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-scale database consisting of 7039 brain T1-weighted MRI data samples. We evaluated the method by using experiments focused on three distinct topics, namely, the necessity of preprocessing steps, cross-institutional and longitudinal reproducibility, and volumetric accuracy. The optimized model, MX_RW-where "MX" is a mix of RW and nonuniform intensity normalization data and "RW" is raw data with basic preprocessing-did not require time-consuming preprocessing steps, such as nonuniform intensity normalization or image registration, for brain MRI before segmentation. Cross-institutional testing revealed that MX_RW (Dice similarity coefficient: 0.809, coefficient of variation: 4.6%, and Pearson's correlation coefficient: 0.979) exhibited a performance comparable with that of FreeSurfer (Dice similarity coefficient: 0.798, coefficient of variation: 5.6%, and Pearson's correlation coefficient: 0.973). The computation time per dataset of MX_RW was generally less than 5 s (even when tested without graphics processing units), which was notably faster than FreeSurfer. Thus, for time-restricted applications, MX_RW represents a competitive alternative to FreeSurfer.

Blockade of interleukin-6 receptor attenuates apoptosis and modulates the inflammatory response in Mycoplasma pneumoniae infected A549 cells.

OBJECTIVE: To investigate the effect of silencing the interleukin (IL)-6 gene on the induction of inflammation, oxidative stress and apoptosis in Mycoplasma pneumoniae (MP) infected A549 cells and its mechanism of action. METHODS: IL-6 small interfering RNA (siRNA) was synthesized and transfected into A549 cells, which were divided into a blank control group, a negative control group, and an IL-6 siRNA group. The mRNA and protein expression of IL-6 and the protein expression of CyclinD1, Cleaved caspase-3, Bax, B-cell lymphoma 2 (Bcl-2), signal transducer and activator of transcription 3 (STAT3), phosphorylated STAT3 (p-STAT3), matrix metalloproteinase (MMP)-2 and MMP-9 were measured. Besides, cell viability and apoptosis were determined. Additionally, the levels of malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX), IL-1ß, IL-8 and tumor necrosis factor (TNF)-α were measured. RESULTS: The mRNA and protein levels of IL-6 in the IL-6 siRNA group were lower than those in the blank and negative control groups (P < 0.05). The IL-6 siRNA group had higher viability but lower apoptosis rate of A549 cells at 24 h, 48 h and 72 h than the blank and negative control groups (P < 0.05). The IL-6 siRNA group had lower protein expression levels of Cleaved caspase-3 and Bax, but higher protein expression levels of CyclinD1 and Bcl-2 than the blank and negative control groups (P < 0.05). The IL-6 siRNA group had lower levels of IL-6, IL-8, TNF-α and MDA, but higher levels of SOD and GSH-PX than the blank and negative control groups (P < 0.05). CONCLUSION: Silencing the IL-6 gene can reduce the MP-induced inflammatory response and oxidative stress of A549 cells, enhance cell viability and inhibit apoptosis. Meanwhile, it was also found that STAT3 expression was inhibited after silencing IL-6 gene expression. Therefore, it is speculated that IL-6 may play a role by regulating STAT3, but its exact molecular biological mechanism still needs to be further explored.

Deep learning-based classification for lung opacities in chest x-ray radiographs through batch control and sensitivity regulation.

In this study, we implemented a system to classify lung opacities from frontal chest x-ray radiographs. We also proposed a training method to address the class imbalance problem presented in the dataset. We participated in the Radiological Society of America (RSNA) 2018 Pneumonia Detection Challenge and used the datasets provided by the RSNA for further research. Using convolutional neural networks, we implemented a training procedure termed batch control to manipulate the data distribution of positive and negative cases in each training batch. The batch control method regulated and stabilized the performance of the deep-learning models, allowing the adaptive sensitivity of the network models to meet the specific application. The convolutional neural network is practical for classifying lung opacities on chest x-ray radiographs. The batch control method is advantageous for sensitivity regulation and optimization for class-unbalanced datasets.

Corrigendum: First report on the rapid detection and identification of methicillin-resistant Staphylococcus aureus (MRSA) in viable but non-culturable (VBNC) under food storage conditions.

[This corrects the article DOI: 10.3389/fmicb.2020.615875.].

Microbial Interaction between Lactiplantibacillus plantarum and Saccharomyces cerevisiae: Transcriptome Level Mechanism of Cell-Cell Antagonism.

Lactiplantibacillus plantarum and Saccharomyces cerevisiae are frequently co-isolated in food, although playing different roles. This study aimed at investigating the microbial interaction between L. plantarum and S. cerevisiae, especially cell-cell direct interaction and their mechanism. Cell-cell and supernatant-cell coculture models were set up, with CFU counting, OD600 measurement, optical and atomic force microscopy performed to examine the growth and morphology of L. plantarum and S. cerevisiae cells. In cell-cell coculture model, L. plantarum cells inhibited S. cerevisiae growth (inhibition rate ~80%) with its own growth pattern unaffected. Cell-cell aggregation happened during coculture with surface roughness changed and partial S. cerevisiae cell lysis. Mature (24 h) L. plantarum cell-free culture supernatant showed inhibition (35%-75%) on S. cerevisiae growth independent of pH level, while supernatant from L. plantarum-S. cerevisiae coculture showed relatively stronger inhibition. Upon transcriptomics analysis, hypothesis on the mechanism of microbial interaction between L. plantarum and S. cerevisiae was demonstrated. When L. plantarum cell density reached threshold at 24 h, all genes in lamBDCA quorum sensing (QS) system was upregulated to potentially increase adhesion capability, leading to the aggregation to S. cerevisiae cell. The downregulation of whole basic physiological activity from DNA to RNA to protein, cell cycle, meiosis, and mitogen-activated protein kinase (MAPK) signaling pathways, as well as growth maintenance essential genes ari1, skg6, and kex2/gas1 might induce the decreased growth and proliferation rate and partial death of S. cerevisiae cells in coculture. IMPORTANCE L. plantarum and S. cerevisiae are frequently co-isolated in food, although playing different roles. The co-existence of L. plantarum and S. cerevisiae could result in variable effects, raising economic benefits and safety concerns in food industry. Previous research has reported the microbial interaction between L. plantarum and S. cerevisiae mainly rely on the signaling through extracellular metabolites. However, cell-cell aggregation has been observed with mechanism remain unknown. In the current study, the microbial interaction between L. plantarum and S. cerevisiae was investigated with emphasis on cell-cell direct interaction and further in-depth transcriptome level study showed the key role of lamBDCA quorum sensing system in L. plantarum. The results yield from this study demonstrated the antagonistic effect between L. plantarum and S. cerevisiae.

Byproduct formation during the biosynthesis of spinosyn A and evidence for an enzymatic interplay to prevent its formation.

Biosynthesis of spinosyn A in Saccharopolyspora spinosa involves a 1,4-dehydration followed by an intramolecular [4 + 2]-cycloaddition catalyzed by SpnM and SpnF, respectively. The cycloaddition also takes place in the absence of SpnF leading to questions regarding its mechanism of catalysis and biosynthetic role. Substrate analogs were prepared with an unactivated dienophile or an acyclic structure and found to be unreactive consistent with the importance of these features for cyclization. The SpnM-catalyzed dehydration reaction was also found to yield a byproduct corresponding to the C11 = C12 cis isomer of the SpnF substrate. This byproduct is stable both in the presence and absence of SpnF; however, relative production of the SpnM product and byproduct could be shifted in favor of the former by including SpnF or the dehydrogenase SpnJ in the reaction. This result suggests a potential interplay between the enzymes of spinosyn A biosynthesis that may help to improve the efficiency of the pathway.

Establishment and application of a rapid visual detection method for Listeria monocytogenes based on polymerase spiral reaction (PSR).

Listeria monocytogenes is a common foodborne pathogen that presents in various food products, posing important threat to public health. The aim of this study was to establish a rapid and sensitive method with visualization to detect L. monocytogenes based on polymerase spiral reaction (PSR). Primers targeting conserved hlyA gene sequence of L. monocytogenes were designed based on bioinformatics analyses on the current available L. monocytogenes genomes. The isothermal amplification PSR can be completed under constant temperature (65ᵒC) within 60 min with high specificity and sensitivity. Twenty-five reference strains were used to evaluate the specificity of the developed reaction. The results showed that the sensitive of the reaction for L. monocytogenes in purified genomic DNA and artificially contaminated food samples were 41 pg/µL and 103 CFU/mL, respectively. It was 100-fold more sensitive than conventional PCR. In conclusion, this novel PSR method is rapid, cost-efficient, timesaving, and applicable on artificially contaminated food samples, providing broad prospects into the detection of foodborne microbes with the promising on-site inspection.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Regulatory network controls microbial biofilm development, with Candida albicans as a representative: from adhesion to dispersal.

Microorganisms mainly exist in the form of biofilm in nature. Biofilm can contaminate food and drinking water system, as well as cause chronic wound infections, thereby posing a potential threat to public health safety. In the last two decades, researchers have made efforts to investigate the genetic contributors control different stages of biofilm development (adherence, initiation, maturation, and dispersal). As an opportunistic pathogen, C. albicans causes severe superficial or systemic infections with high morbidity and mortality under conditions of immune dysfunction. It has been reported that 80% of C. albicans infections are directly or indirectly associated with biofilm formation on host or abiotic surfaces including indwelling medical devices, resulting in high morbidity and mortality. Significantly, the outcome of C. albicans biofilm development includes enhanced invasion, exacerbated inflammatory responses and intrinsic resistance to antimicrobial chemotherapy. Thus, this review aimed at providing a comprehensive overview of the regulatory network controls microbial biofilm development, with C. albicans as a representative, served as reference for therapeutic targets.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

Development of a Direct and Rapid Detection Method for Viable but Non-culturable State of Pediococcus acidilactici.

Pediococcus acidilactici may significantly reduce the pH-value, and thus has different influence, including serving as a probiotic in human microbiota but a spoilage in human food as it could change the flavor. Pediococcus acidilactici is also capable of entering into the viable but non-culturable (VBNC) state causing false negative results of standard culture-based detection method. Thus, development of detection method for VBNC state P. acidilactici is of great significance. In this study, propidium monoazide (PMA) combined with cross priming amplification (CPA) was developed to detect the VBNC cells of P. acidilactici and applied on the detection in different systems. With detection limit of 104 cells/ml, high sensitivity, and 100% specificity, PMA-CPA can successfully detect VBNC cells of P. acidilactici and be applied in with high robustness.

Pathogenic and Virulence Factor Detection on Viable but Non-culturable Methicillin-Resistant Staphylococcus aureus.

Food safety and foodborne infections and diseases have been a leading hotspot in public health, and methicillin-resistant Staphylococcus aureus (MRSA) has been recently documented to be an important foodborne pathogen, in addition to its recognition to be a leading clinical pathogen for some decades. Standard identification for MRSA has been commonly performed in both clinical settings and food routine detection; however, most of such so-called "standards," "guidelines," or "gold standards" are incapable of detecting viable but non-culturable (VBNC) cells. In this study, two major types of staphylococcal food poisoning (SFP), staphylococcal enterotoxins A (sea) and staphylococcal enterotoxins B (seb), as well as the panton-valentine leucocidin (pvl) genes, were selected to develop a cross-priming amplification (CPA) method. Limit of detection (LOD) of CPA for sea, seb, and pvl was 75, 107.5, and 85 ng/µl, indicating that the analytical sensitivity of CPA is significantly higher than that of conventional PCR. In addition, a rapid VBNC cells detection method, designated as PMA-CPA, was developed and further applied. PMA-CPA showed significant advantages when compared with PCR assays, in terms of rapidity, sensitivity, specificity, and accuracy. Compared with conventional VBNC confirmation methods, the PMA-CPA showed 100% accordance, which had demonstrated that the PMA-CPA assays were capable of detecting different toxins in MRSA in VBNC state. In conclusion, three CPA assays were developed on three important toxins for MRSA, and in combination with PMA, the PMA-CPA assay was capable of detecting virulent gene expression in MRSA in the VBNC state. Also, the above assays were further applied to real samples. As concluded, the PMA-CPA assay developed in this study was capable of detecting MRSA toxins in the VBNC state, representing first time the detection of toxins in the VBNC state.

Preclinical Studies of OBI-999: A Novel Globo H-Targeting Antibody-Drug Conjugate.

Globo H (GH), a hexasaccharide, is expressed at low levels in normal tissues but is highly expressed in multiple cancer types, rendering it a promising target for cancer immunotherapy. OBI-999, a novel antibody-drug conjugate, is derived from a conjugation of a GH-specific mAb with a monomethyl auristatin E (MMAE) payload through a site-specific ThioBridge and a cleavable linker. OBI-999 high homogeneity with a drug-to-antibody ratio of 4 (>95%) was achieved using ThioBridge. OBI-999 displayed GH-dependent cellular internalization and trafficked to endosome and lysosome within 1 and 5 hours, respectively. Furthermore, OBI-999 showed low nanomolar cytotoxicity in the assay with high GH expression on tumor cells and exhibited a bystander killing effect on tumor cells with minimal GH expression. Tissue distribution indicated that OBI-999 and free MMAE gradually accumulated in the tumor, reaching maximum level at 168 hours after treatment, whereas OBI-999 and free MMAE decreased quickly at 4 hours after treatment in normal organs. Maximum MMAE level in the tumor was 16-fold higher than in serum, suggesting that OBI-999 is stable during circulation and MMAE is selectively released in the tumor. Excellent tumor growth inhibition of OBI-999 was demonstrated in breast, gastric, and pancreatic cancer xenograft or lung patient-derived xenograft models in a dose-dependent manner. The highest nonseverely toxic dose in cynomolgus monkeys is 10 mg/kg determined by a 3-week repeated-dose toxicology study demonstrating an acceptable safety margin. Taken together, these results support further clinical development of OBI-999, which is currently in a phase I/II clinical study in multiple solid tumors (NCT04084366). OBI-999, the first GH-targeting ADC, displayed excellent tumor inhibition in animal models across multiple cancer types, including breast, gastric, pancreatic, and lung cancers, warranting further investigation in the treatment of solid tumors.