Identifying SLC2A6 as the novel protective factor in breast cancer by TP53-related genes affecting M1 macrophage infiltration.

The high heterogeneity of breast cancer (BC) caused by pathogenic gene mutations poses a challenge to immunotherapy, but the underlying mechanism remains unknown. The difference in the infiltration of M1 macrophages induced by TP53 mutations has a significant impact on BC immunotherapy. The aim of this study was to develop a TP53-related M1 macrophage infiltration molecular typing risk signature in BC and evaluate the biological functions of the key gene to find new immunotherapy biomarkers. Weighted correlation network analysis (WGCNA) and negative matrix factorization (NMF) were used for distinguishing BC subtypes. The signature and the nomogram were both constructed and evaluated. Biological functions of the novel signature gene SLC2A6 were confirmed through in vitro and in vivo experiments. RNA-Sequencing and protein profiling were used for detecting the possible mechanism of SLC2A6. The results suggested that four BC subtypes were distinguished by TP53-related genes that affect M1 macrophage infiltration. The signature constructed by molecular typing characteristics could evaluate BC's clinical features and tumor microenvironment. The nomogram could accurately predict the prognosis. The signature gene SLC2A6 was found to have an abnormally low expression in tumor tissues. Overexpression of SLC2A6 could inhibit proliferation, promote mitochondrial damage, and result in apoptosis of tumor cells. The HSP70 family member protein HSPA6 could bind with SLC2A6 and increase with the increased expression of SLC2A6. In summary, the risk signature provides a reference for BC risk assessment, and the signature gene SLC2A6 could act as a tumor suppressor in BC.

Toward accurate diagnosis and surveillance of bacterial infections using enhanced strain-level metagenomic next-generation sequencing of infected body fluids.

Metagenomic next-generation sequencing (mNGS) enables comprehensive pathogen detection and has become increasingly popular in clinical diagnosis. The distinct pathogenic traits between strains require mNGS to achieve a strain-level resolution, but an equivocal concept of 'strain' as well as the low pathogen loads in most clinical specimens hinders such strain awareness. Here we introduce a metagenomic intra-species typing (MIST) tool (https://github.com/pandafengye/MIST), which hierarchically organizes reference genomes based on average nucleotide identity (ANI) and performs maximum likelihood estimation to infer the strain-level compositional abundance. In silico analysis using synthetic datasets showed that MIST accurately predicted the strain composition at a 99.9% average nucleotide identity (ANI) resolution with a merely 0.001× sequencing depth. When applying MIST on 359 culture-positive and 359 culture-negative real-world specimens of infected body fluids, we found the presence of multiple-strain reached considerable frequencies (30.39%-93.22%), which were otherwise underestimated by current diagnostic techniques due to their limited resolution. Several high-risk clones were identified to be prevalent across samples, including Acinetobacter baumannii sequence type (ST)208/ST195, Staphylococcus aureus ST22/ST398 and Klebsiella pneumoniae ST11/ST15, indicating potential outbreak events occurring in the clinical settings. Interestingly, contaminations caused by the engineered Escherichia coli strain K-12 and BL21 throughout the mNGS datasets were also identified by MIST instead of the statistical decontamination approach. Our study systemically characterized the infected body fluids at the strain level for the first time. Extension of mNGS testing to the strain level can greatly benefit clinical diagnosis of bacterial infections, including the identification of multi-strain infection, decontamination and infection control surveillance.

Emerging technologies for engineering of extracellular vesicles.

Extracellular vesicles (EVs) are lipid-bilayer membrane-enclosed vesicles that are secreted by all cell types. Natural EVs contain biological information such as proteins, nucleic acids, and lipids from their parent cells. Therefore, EVs have been extensively studied as diagnostic biomarkers and therapeutic tools under normal and pathological conditions. However, some drawbacks, including low yield, poor therapeutic effects, lack of imaging, and targeting capacity of natural EVs, still need to be improved. Emerging engineering technologies have rendered EVs new properties or functionalities that broadened their applications in the biomedical field. Herein, in this review, we gave a brief overview of advanced strategies for EV engineering. We focused on pre-treatment of parent cells to regulate their released EVs. Meanwhile, we summarized and discussed the direct modification of EVs to achieve drug loading, imaging, and targeting functionalities for downstream applications.

Polysaccharide-Based Hydrogels for Wound Dressing: Design Considerations and Clinical Applications.

Wound management remains a worldwide challenge. It is undeniable that patients with problems such as difficulties in wound healing, metabolic disorder of the wound microenvironment and even severely infected wounds etc. always suffer great pain that affected their quality of lives. The selection of appropriate wound dressings is vital for the healing process. With the advances of technology, hydrogels dressings have been showing great potentials for the treatment of both acute wounds (e.g., burn injuries, hemorrhage, rupturing of internal organs/aorta) and chronic wounds such as diabetic foot and pressure ulcer. Particularly, in the past decade, polysaccharide-based hydrogels which are made up with abundant and reproducible natural materials that are biocompatible and biodegradable present unique features and huge flexibilities for modifications as wound dressings and are widely applicable in clinical practices. They share not only common characteristics of hydrogels such as excellent tissue adhesion, swelling, water absorption, etc., but also other properties (e.g., anti-inflammatory, bactericidal and immune regulation), to accelerate wound re-epithelialization, mimic skin structure and induce skin regeneration. Herein, in this review, we highlighted the importance of tailoring the physicochemical performance and biological functions of polysaccharide-based hydrogel wound dressings. We also summarized and discussed their clinical states of, aiming to provide valuable hints and references for the future development of more intelligent and multifunctional wound dressings of polysaccharide hydrogels.

FNDC3B and BPGM Are Involved in Human Papillomavirus-Mediated Carcinogenesis of Cervical Cancer.

Human papillomavirus (HPV)-mediated cervical carcinogenesis is a multistep progressing from persistent infection, precancerous lesion to cervical cancer (CCa). Although molecular alterations driven by viral oncoproteins are necessary in cervical carcinogenesis, the key regulators behind the multistep process remain not well understood. It is pivotal to identify the key genes involved in the process for early diagnosis and treatment of this disease. Here we analyzed the mRNA expression profiles in cervical samples including normal, cervical intraepithelial neoplasia (CIN), and CCa. A co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to reveal the crucial modules in the dynamic process from HPV infection to CCa development. Furthermore, the differentially expressed genes (DEGs) that could distinguish all stages of progression of CCa were screened. The key genes involved in HPV-CCa were identified. It was found that the genes involved in DNA replication/repair and cell cycle were upregulated in CIN compared with normal control, and sustained in CCa, accompanied by substantial metabolic shifts. We found that upregulated fibronectin type III domain-containing 3B (FNDC3B) and downregulated bisphosphoglycerate mutase (BPGM) could differentiate all stages of CCa progression. In patients with CCa, a higher expression of FNDC3B or lower expression of BPGM was closely correlated with a shorter overall survival (OS) and disease-free survival (DFS). A receiver operating characteristic (ROC) analysis of CIN and CCa showed that FNDC3B had the highest sensitivity and specificity for predicting CCa development. Taken together, the current data showed that FNDC3B and BPGM were key genes involved in HPV-mediated transformation from normal epithelium to precancerous lesions and CCa.

Genomic characterization of a multidrug-resistant Salmonella enterica serovar Goldcoast sequence type 358 strain in China.

OBJECTIVES: Salmonella enterica serovar Goldcoast has been identified as the agent responsible for multistate epidemic outbreaks in humans. However, knowledge regarding the antimicrobial resistance and transmission of S. Goldcoast is scarce. This study aimed to investigate the genomic features of a multidrug-resistant S. Goldcoast sequence type 358 strain in China. METHODS: Antimicrobial susceptibility of S. enterica strain SalGC_ZJ_53 was determined by microdilution broth assay. Whole genomic DNA was extracted and sequenced using the Illumina NovaSeq platform. De novo genome assembly was performed using Unicycler and the draft genome was annotated using the NCBI Prokaryotic Genome Annotation Pipeline. The sequence type (ST), antimicrobial resistance genes and Salmonella pathogenicity islands were identified from the genome sequence. The core genome multilocus sequence typing (cgMLST) analysis between S. enterica SalGC_ZJ_53 and all of the S. Goldcoast strains retrieved from the public database was performed using BacWGSTdb server. RESULTS: Salmonella enterica SalGC_ZJ_53 was resistant to ampicillin, amikacin, cefotetan, cefazolin, ciprofloxacin, gentamicin and tetracycline. The genome size was calculated as 4,904,788 bp, with 4595 protein-coding sequences and GC content of 52.0%. This isolate was assigned to ST358. Several antimicrobial resistance genes and salmonella pathogenicity islands, as well as multiple insertion sequences were identified. The closest relative of S. enterica SalGC_ZJ_53 was another isolate recovered from the UK, differing by only six cgMLST loci. CONCLUSIONS: This study reports the first genome sequence of a multidrug-resistant S. Goldcoast isolate in China. These data may help to understand the antimicrobial resistance mechanisms and transmission dynamics of this rare serovar of Salmonella infection in humans.

Acupuncture to Promote Recovery of Disorder of Consciousness after Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Traumatic brain injury (TBI) has become an economic and social burden for patients and their families. While acupuncture is an effective tool for promoting recovery of disorder of consciousness (DOC) following TBI, there have been no comprehensive meta-analyses and/or systematic reviews addressing this topic. The present systematic review and meta-analysis aimed to assess the therapeutic efficacy of acupuncture for DOC after TBI. All randomized controlled trials (RCTs) incorporating acupuncture, or acupuncture combined with other interventions for DOC after TBI, were included and assessed by two independent investigators. Six outcome indicators were assessed: Glasgow Coma Scale (GCS); Glasgow Outcome Scale (GOS); mortality; efficacy rate; activities of daily living (ADL); and functional comprehensive assessment. Direct comparisons were performed using RevMan 5.3.0 software, with results presented as mean difference (MD) for continuous outcomes and relative risk (RR) for binary outcomes. A total of 3511 patients from 49 trials were included. Pooled analyses indicated that acupuncture may have a superior effect on GCS score (MD=2.03, 95% CI :1.92 2.43, Z=16.54, and P<0.00001); GOS score (RR=1.23, 95%CI: 1.18 1.35, Z=6.65, and P<0.00001); efficacy rate (RR=1.48, 95%CI: 1.40 1.56, Z=13.49, and P<0.00001); ADL (MD=9.20, 95% CI:8.19 10.21, Z=17.84, and P<0.00001); and mortality (RR=0.50, 95% CI:0.38 0.67, Z=4.70, and P<0.00001). The results demonstrated that the acupuncture group fared better than the control group in the treatment of DOC after TBI. However, studies were generally of poor quality, and publication bias favoring positive studies was obvious. Therefore, rigorous evaluation standards and well-designed studies are necessary in future studies.

Scatter correction of cone-beam CT using a deep residual convolution neural network (DRCNN).

Scatter correction is an essential technique to improve the image quality of cone-beam CT (CBCT). Although different scatter correction methods have been proposed in the literature, a standard solution is still being studied due to the limitations including accuracy, computation efficiency and generalization. In this paper, we propose a novel scatter correction scheme for CBCT using a deep residual convolution neural network (DRCNN) to overcome the limitations. The proposed method combines the deep convolution neural network (CNN) and the residual learning framework (RLF) to train the mapping function from the uncorrected image to the corrected image. Two residual network modules (RNMs) are built based on the RLF to improve the accuracy of the mapping function by strengthening the propagation of the gradient. The dropout operations are applied as the regularizer of the network to avoid the overfitting problem. The RMSE of the corrected images reconstructed using the DRCNN is reduced from over 200 HU to be about 20 HU. The structural similarity (SSIM) is slightly increased from 0.95 to 0.99, indicating that the proposed scheme maintains the anatomical structure. The proposed DRCNN has a higher accuracy of scatter correction than the networks without the RLF or the dropout operations. The proposed network is effective, efficient and robust as a solution to the CBCT scatter correction.

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy.

BACKGROUND: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. METHODS: Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for 'Cox' method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. FINDINGS: The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656-0.801, 95% CI) and 0.705 (0.628-0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670-0.952, 95%CI) in training and 0.805 (0.638-0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. INTERPRETATION: The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FUND: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.