Tumor Mutation Burden,Immune Cell Infiltration,and Construction of Immune-Related Genes Prognostic Model in Head and Neck Cancer

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors.Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model.Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis.Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635.Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.     

中美战伤急救止血器材现状及思考

本文分析目前中美两军战伤急救止血器材实际效果与研究进展,阐明中美两军急救止血器材实际应用的优缺点,总结中美急救止血器材的特征,比较中美两军多种战伤急救止血器材的研究,为新器材的改进设计提供依据.提出我军战伤急救止血器材应朝着多样化、实战化、智能化、规范化和新型化方向发展目标,以更符合未来战争需要.     

伤口组织与电刺激调控的相关性研究*

慢性伤口迁延不愈。外源性电刺激提供促进慢性伤口愈合的电流，其独特优势是使电荷调控伤口，促进伤 口愈合，具有良好的应用前景，并成为目前组织学研究者和临床医生的研究热点之一。现介绍一些常见慢性伤口 的形态学和电生理特性，主要探讨目前电荷调控伤口愈合的形态学改变、可能作用机制及其影响因素。     

肝内胆汁淤积对联合弹性成像评估肝纤维化的影响

目的评估肝内胆汁淤积对联合弹性成像诊断肝纤维化分期的影响。方法收集入组患者的弹性成像、生化学和组织学数据。比较不同总胆红素(TB)组的剪切波速度(Vs)及肝纤维化指数(LF index)。使用ROC曲线下面积计算联合成像的准确度。结果以肝活检为金标准,TB异常组的Vs高于正常组(P<0.001),LF index在TB正常组及异常组中无区别(P>0.05)。不同TB分组中联合成像的准确度并无明显差异。结论肝内胆汁淤积对LF index无任何影响,使Vs轻微升高,但不会降低联合弹性成像评估肝纤维化分期的整体诊断效能。     

观察调神针法对慢性失眠症患者过度觉醒状态的影响＊

目的 观察调神针法治疗慢性失眠症的疗效，并基于“过度觉醒”探讨针刺治疗慢性失眠症的潜在机制。方法 将60例慢性失眠症（Chronic insomnia， CI）患者，随机分为调神针刺组（试验组）30例和假针刺组（对照组）30例。调神针刺组选用百会、印堂、神门（双）、三阴交（双），假针刺组采用非病症相关穴位浅刺的方法。两组均隔日治疗1次，每周3次，连续4周。比较两组患者治疗前后及结束治疗1月随访时匹兹堡睡眠指数量表（Pittsburgh sleep quality index， PSQI）总积分及各因子积分、过度觉醒量表（Hyperarousal scale， HAS）积分、血清皮质醇（Cortisol， CORT）浓度变化。结果 治疗后与随访时比较，调神针刺组各量表积分较治疗前均明显下降（P < 0.01）；治疗后血清CORT较治疗前明显下降（P < 0.01）。治疗后，假针刺组PSQI总分及血清CORT与治疗前比，均明显下降（P < 0.05）；随访时假针刺组各量表积分与治疗前比较，均无统计学意义（P > 0.05）。调神针刺组各量表积分及血清CORT降低均明显优于假针刺组（P < 0.01）。结论 调神针法可明显改善慢性失眠症患者睡眠质量、过度觉醒状态，降低血清CORT浓度可能是潜在作用机制之一。     

Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer

PURPOSEWhether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with locally advanced rectal cancer (LARC).METHODSThis retrospective study enrolled patients with LARC (06/2014–08/2017) and divided them into nCRT-sensitive and nCRT-resistant groups according to postoperative tumor regression grading results. Radiomics features from preoperative MRI were extracted, followed by dimension reduction using the minimum redundancy maximum relevance filter. Three machine-learning classifiers and an ensemble classifier were used for therapeutic response prediction. Radiomics nomogram incorporating clinical parameters were constructed using logistic regression. The receiver operating characteristic (ROC), decision curves analysis (DCA) and calibration curves were also plotted to evaluate the prediction performance.RESULTSThe machine learning classifiers showed good prediction performance for therapeutic responses in LARC patients (n=189). The ROC curve showed satisfying performance (area under the curve [AUC], 0.830; specificity, 0.794; sensitivity, 0.815) in the validation group. The radiomics signature included 30 imaging features derived from axial T1-weighted imaging with contrast and sagittal T2-weighted imaging and exhibited good predictive power for nCRT. A radiomics nomogram integrating carcinoembryonic antigen levels and tumor diameter showed excellent performance with an AUC of 0.949 (95% confidence interval, 0.892–0.997; specificity, 0.909; sensitivity, 0.879) in the validation group. DCA confirmed the clinical usefulness of the nomogram model.CONCLUSIONThe radiomics method using multiple MRI sequences can be used to achieve individualized prediction of nCRT in patients with LARC before treatment.

Colorectal cancer is one of the most common malignancies. It ranks fourth for morbidity and third for mortality among malignant tumors, among which the proportion of rectal cancer with poor prognosis is over 60% (1, 2). Neoadjuvant therapy, combined with total mesorectal excision, has become a common strategy for rectal cancer (3). Response to neoadjuvant chemoradiotherapy (nCRT) is a marker of good prognosis in patients with locally advanced rectal cancer (LARC) (4). Tumor regression grading (TRG) is a reliable biomarker for evaluating the efficacy of nCRT (5, 6). TRG reflects the treatment effect of nCRT by evaluating fibrosis and the ratio of residual tumor cells (4). The accurate nCRT evaluation can only be achieved by postoperative histopathological TRG (3, 4), and there is still no technology that can noninvasively evaluate the therapeutic response.Magnetic resonance imaging (MRI) is commonly used in the diagnosis, preoperative staging, and therapeutic efficacy evaluation of rectal cancer. Prediction of the efficacy of nCRT by MRI has been rarely reported, partly due to the heterogeneity of the tumor combined with the prevalence of fibrosis and edema of lesions and surrounding tissue after nCRT. Over the recent years, a magnetic resonance TRG system was proposed for the evaluation of nCRT efficacy by using MRI and evaluating residual tumor and fibrosis. Nevertheless, the magnetic resonance TRG method has a low predictive value for pathological TRG and poor consistency, which hinders its clinical applications (7, 8).In recent years, radiomics has drawn increasing attention in oncology. Radiomics features selected from medical images have shown to be highly associated with the diagnosis and prognosis of cancers, and even with gene expression patterns (9). Studies highlighted the value of radiomics approaches in determining tumor status, preoperative staging, and efficacy evaluation (9, 10). Nevertheless, the application of the radiomics methods in evaluating therapeutic responses to nCRT is limited (11).Accordingly, the aim of the present study was to establish an nCRT prediction model based on multiple MRI sequences combined with tumor anatomy and biological characteristics so as to achieve a comprehensive preliminary prediction of nCRT efficacy for rectal cancer before treatment, to provide an essential basis for the rational formulation of clinical diagnosis and treatment decisions, and to avoid unnecessary exposure to radiotherapy and chemotherapy and the related risks such as toxicity and delayed definitive surgery.