MA诱导方案与DA诱导方案对老年急性髓系白血病患者血清炎症因子及复发率的影响

目的:探讨米托蒽醌联合阿糖胞苷(MA方案)与柔红霉素联合阿糖胞苷(DA方案)对老年急性髓系白血病(AML)患者血清炎症因子及复发率的影响。方法:选取2015年1月~2018年1月期间深圳市人民医院收治的老年AML患者129例,根据治疗方案的不同将患者分为DA组(n=64,DA方案治疗)和MA组(n=65,MA方案治疗),比较两组患者疗效、炎症因子、不良反应及复发情况。结果:MA组治疗后的临床总有效率高于DA组(P<0.05)。两组患者治疗后血清干扰素γ诱导蛋白-10(IP-10)、巨噬细胞炎症蛋白-1α(MIP-1α)及可溶性细胞间黏附分子1(sICAM-1)水平均降低,且MA组低于DA组(P<0.05)。MA组完全缓解患者中累计复发率低于DA组(P<0.05)。两组不良反应发生率比较无差异(P>0.05)。结论:与DA诱导方案相比,MA诱导方案治疗老年AML患者,可有效改善炎症因子水平,减少复发,且用药安全性较好。     

优化两步输注美罗培南联合参麦注射液对重症感染患者血清感染指标、细菌清除率和T淋巴细胞亚群的影响

目的:探讨优化两步输注美罗培南联合参麦注射液对重症感染患者血清感染指标、细菌清除率和T淋巴细胞亚群的影响。方法:选取2017年1月-2018年10月在我院重症医学科(ICU)住院的153例重症感染患者,按随机数字表法将患者分为传统输注美罗培南组(A组,54例)、优化两步输注美罗培南组(B组,49例)、优化两步输注美罗培南联合参麦注射液组(C组,50例)。比较治疗后三组患者的总体疗效、血清感染指标和T淋巴细胞亚群水平及不良反应发生情况。结果:B组、C组的临床有效率、细菌清除率、28天生存率高于A组(P<0.05);B组、C组机械通气时间、ICU住院时间、总住院时间少于A组,且C组少于B组(P<0.05);治疗后B组、C组CD3⁺、CD4⁺、CD4⁺/CD8⁺高于A组,且C组高于B组(P<0.05);B组、C组CD8⁺、白细胞计数(WBC)、降钙素原(PCT)、C-反应蛋白(CRP)水平低于A组,且C组低于B组(P<0.05)。三组患者不良反应发生率比较无差异(P>0.05)。结论:优化两步输注美罗培南联合参麦注射液治疗重症感染患者疗效确切,可提高细菌清除率,改善患者免疫状态,促进患者康复。     

聚焦超声消融治疗子宫小肌瘤的疗效评价

摘要 目的：探讨聚焦超声消融治疗无症状子宫小肌瘤的临床价值。方法：采用对照回顾性研究,将2011年03月至2018年03月在遂宁市中心医院妇科收治入院行聚焦超声消融治疗的无症状的子宫小肌瘤患者设置为治疗组,将同期未经任何治疗的门诊随访的无症状子宫小肌瘤患者设置为对照组,比较两组在6个月、12个月、18个月、24个月后子宫肌瘤的体积变化、是否出现临床症状及妊娠情况。结果：治疗组在聚焦超声消融治疗后6个月、12个月、18个月、24个月,随着时间的推移,子宫小肌瘤的体积较治疗前逐渐缩小,均未出现临床症状;对照组子宫小肌瘤的体积逐渐增大,并出现临床症状;治疗组和对照组24个月内的妊娠率分别为17.48%和8.42%,比较两组差异有统计学意义(P＜0.05)。聚焦超声消融治疗后无严重并发症发生。结论：对于孕龄期有生育要求的黏膜下或肌壁间的无症状子宫小肌瘤患者,可行聚焦超声消融手术缩小病灶,维持子宫腔的正常形态,避免或延迟临床症状的出现,提高妊娠率。     

新辅助化疗后腋窝病理完全缓解乳腺癌的预后分析

摘要 目的：研究乳腺癌患者新辅助化疗（NAC）后达到腋窝淋巴结病理完全缓解（pathologic complete response of axillary,apCR）的远期生存以及影响远期生存的相关因素分析。方法：回顾性分析哈尔滨医科大学附属肿瘤医院乳腺外科624例乳腺癌患者的住院资料,采用Kaplan-Meier生存分析以及COX回归分析的统计学分析方法,分析乳腺癌患者新辅助化疗后腋窝状态与无病生存（DFS）和总生存（OS）的关系及影响apCR预后的因素。结果：apCR与非apCR患者比较DFS（P=0.013）和OS（P=0.037）差异具有统计学意义,apCR患者的预后与年龄、肿瘤大小、肿瘤受体状态、HER-2、ki67状态、分子分型等因素无相关性。结论：与非apCR患者相比,乳腺癌患者新辅助化疗后apCR患者预后更好,但apCR患者预后良好的因素仍需进一步临床试验分析。     

Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study

BackgroundMachine learning (ML) has been gradually integrated into oncologic research but seldom applied to predict cervical cancer (CC), and no model has been reported to predict survival and site-specific recurrence simultaneously. Thus, we aimed to develop ML models to predict survival and site-specific recurrence in CC and to guide individual surveillance.MethodsWe retrospectively collected data on CC patients from 2006 to 2017 in four hospitals. The survival or recurrence predictive value of the variables was analyzed using multivariate Cox, principal component, and K-means clustering analyses. The predictive performances of eight ML models were compared with logistic or Cox models. A novel web-based predictive calculator was developed based on the ML algorithms.ResultsThis study included 5112 women for analysis (268 deaths, 343 recurrences): (1) For site-specific recurrence, larger tumor size was associated with local recurrence, while positive lymph nodes were associated with distant recurrence. (2) The ML models exhibited better prognostic predictive performance than traditional models. (3) The ML models were superior to traditional models when multiple variables were used. (4) A novel predictive web-based calculator was developed and externally validated to predict survival and site-specific recurrence.ConclusionML models might be a better analytic approach in CC prognostic prediction than traditional models as they can predict survival and site-specific recurrence simultaneously, especially when using multiple variables. Moreover, our novel web-based calculator may provide clinicians with useful information and help them make individual postoperative follow-up plans and further treatment strategies.     

Quantifying energetic and fitness consequences of seasonal heterothermy in an Arctic ungulate

Animals have adapted behavioral and physiological strategies to conserve energy during periods of adverse conditions. Heterothermy is one such adaptation used by endotherms. While heterothermy—fluctuations in body temperature and metabolic rate—has been shown in large vertebrates, little is known of the costs and benefits of this strategy, both in terms of energy and in terms of fitness. Hence, our objective was to model the energetics of seasonal heterothermy in the largest Arctic ungulate, the muskox (Ovibos moschatus), using an individual‐based energy budget model of metabolic physiology. We found that the empirically based drop in body temperature (winter max ~−0.8°C) overwinter in adult females resulted in substantial fitness benefits in terms of reduced daily energy expenditure and body mass loss. Body mass and energy reserves were 8.98% and 14.46% greater in modeled heterotherms compared to normotherms by end of winter. Based on environmental simulations, we show that seasonal heterothermy can, to some extent, buffer the negative consequences of poor prewinter body condition or reduced winter food accessibility, leading to greater winter survival (+20%–30%) and spring energy reserves (+10%–30%), and thus increased probability of future reproductive success. These results indicate substantial adaptive short‐term benefits of seasonal heterothermy at the individual level, with potential implications for long‐term population dynamics in highly seasonal environments.     

Clinical and pathological significance of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) expression in high grade serous ovarian cancer

We investigated programmed cell death 1 (PD-1) / programmed cell death ligand 1 (PD-L1) expression in high grade serous ovarian cancer (HGSOC) and its relationship to tumor infiltrating lymphocytes (TIL) and prognosis. Formalin fixed paraffin embedded (FFPE) samples of 94 HGSOC cases were included in the study. Immunohistochemical analysis (CD3, CD4, CD8, PD-1 and PD-L1) was performed. Samples were analyzed for expression of immune proteins in the peritumoral stromal and intratumoral areas, scored, and expression was correlated with overall survival, stage, and age. PD-L1 staining ratio with a score greater than 0 was found to have lower survival. There were two positive staining patterns, patchy/diffuse and patchy/focal patterns, in 24 (25.5%) cases. Considering the threshold value ≥5%, we demonstrated that the PD-L1 positive cancer cell membrane immunoreactivity rate and patchy/diffuse PD-L1 expression were 9.6% (n = 9). There was statistically significant relationship between high PD-1 scores and PD-L1 cases of ≥ 5%. A statistically significant difference was found between PD-L1 staining and survival in patients with a threshold ≥ 5%. However an appropriate rate for treatment was determined in 9.6% cases. There was a statistically significant correlation between PD-1 positive TIL score and intratumoral CD3, peritumoral stromal CD3, intratumoral CD4 and intratumoral CD8 positive cells. Survival was lower in cases with higher PD-L1 positive stromal TIL score.     

Cell-to-cell interaction analysis of prognostic ligand-receptor pairs in human pancreatic ductal adenocarcinoma

Cell-to-cell interactions (CCIs) through ligand-receptor (LR) pairs in the tumor microenvironment underlie the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). However, there is scant knowledge of the association of CCIs with PDAC prognosis, which is critical to the identification of potential therapeutic candidates. Here, we sought to identify the LR pairs associated with PDAC patient prognosis by integrating survival analysis and single-cell CCI prediction. Via survival analysis using gene expression from cancer cohorts, we found 199 prognostic LR pairs. CCI prediction based on single-cell RNA-seq data revealed the enriched LR pairs associated with poor prognosis. Notably, the CCIs involved epithelial tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages through integrin-related and ANXA1–FPR pairs. Finally, we determined that CCIs involving 33 poor-prognostic LR pairs were associated with tumor grade. Although the clinical implication of the set of LR pairs must be determined, our results may provide potential therapeutic targets in PDAC.     

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are affected both by the doubling time of the microorganism and by any delay in growth upon transfer from one condition to another, the lag. The ScanLag method enables the characterization of these two independent properties at the level of colonies originating each from a single cell, generating a two-dimensional distribution of the lag time and of the growth time. In ScanLag, measurement of the time it takes for colonies on conventional nutrient agar plates to be detected is automated on an array of commercial scanners controlled by an in house application. Petri dishes are placed on the scanners, and the application acquires images periodically. Automated analysis of colony growth is then done by an application that returns the appearance time and growth rate of each colony. Other parameters, such as the shape, texture and color of the colony, can be extracted for multidimensional mapping of sub-populations of cells. Finally, the method enables the retrieval of rare variants with specific growth phenotypes for further characterization. The technique could be applied in bacteriology for the identification of long lag that can cause persistence to antibiotics, as well as a general low cost technique for phenotypic screens.     

IL-4: an important cytokine in determining the fate of T cells

The pleiotropic effect of cytokines has been well documented, but the effects triggered by unique cytokines in different T cell types are still under investigation. The most relevant findings on the influence of interleukin-4 (IL-4) on T cell activation, differentiation, proliferation, and survival of different T cell types are discussed in this review. The main aim of our study was to correlate the observed effect with the corresponding molecular mechanism induced on IL-4/IL-4R interaction, in an effort to understand how the same extracellular stimuli can trigger a wide spectrum of signaling pathways leading to different responses in each T cell type.