Glycaemic control for patients with severe acute brain injury: Protocol for a systematic review

Background

Hyperglycaemia is common in patients with acute brain injury admitted to an intensive care unit (ICU). Many studies have found associations between development of hyperglycaemia and increased mortality in hospitalised patients. However, the optimal target for blood glucose control is unknown. We want to conduct a systematic review with meta-analysis and trial sequential analysis to explore the beneficial and harmful effects of restrictive versus liberal glucose control on patient outcomes in adults with severe acute brain injury.

Methods

We will systematically search medical databases including CENTRAL, Embase, MEDLINE and trial registries. We will search the following websites for ongoing or unpublished trials: http://www.controlled-trials.com/ , http://www.clinicaltrials.gov/ , www.eudraCT.com , http://centerwatch.com/ , The Cochrane Library's CENTRAL, PubMed, EMBASE, Science Citation Index Expanded and CINAHL. Two authors will independently review and select trials and extract data. We will include randomised trials comparing levels of glucose control in our analyses and observational studies will be included to address potential harms. The primary outcomes are defined as all-cause mortality, functional outcome and health-related quality of life. Secondary outcomes include serious adverse events including hypoglycaemia, length of ICU stay and duration of mechanical ventilation, and explorative outcomes including intracranial pressure and infection. Trial Sequential Analysis will be used to investigate the risk of type I error due to repetitive testing and to further explore imprecision. Quality of trials will be evaluated using the Cochrane Risk of Bias tool, and quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach.

Discussion

The results of the systematic review will be disseminated through peer-reviewed publication. With the review, we hope to inform future randomised clinical trials and improve clinical practice.     

异牡荆素对非小细胞性肺癌细胞自我更新和凋亡的影响

目的研究异牡荆素(ISO)对非小细胞性肺癌(NSCLC)细胞的影响和潜在的机制。方法将A549和H1650细胞分别分为空白组、低剂量实验组(4μmol·L^-1 ISO)和高剂量实验组(16μmol·L^-1 ISO)。用噻唑蓝法检测NSCLC细胞活性,用肿瘤球形成实验检测NSCLC细胞的细胞球形成率,用Western blot法检测NSCLC细胞凋亡、自我更新、无翅型MMTV整合位点家族/β-连环蛋白(Wnt/β-catenin)信号通路相关蛋白的表达水平。结果与空白组比较,低、高剂量实验组中A549和H1650细胞在24,48和72 h的细胞活性均显著降低。低、高剂量实验组和空白组中A549细胞的细胞球形成率分别为(4.18±0.45)%,(2.01±0.67)%和(6.02±0.57)%,切割的半胱氨酸蛋白酶-3相对表达量分别为0.24±0.08,1.25±0.13和0.06±0.07,SRY相关高迁移率族盒蛋白-2相对表达量分别为0.49±0.04,0.25±0.03和1.00±0.09,Kruppel样因子4相对表达量分别为0.68±0.04,0.44±0.03和1.01±0.06,Wnt1相对表达量分别为0.63±0.06,0.28±0.04和1.00±0.06,β-catenin相对表达量分别为0.41±0.05,0.22±0.03和1.01±0.09;与空白组比较,低、高剂量实验组中A549细胞的上述指标的差异均有统计学意义(P<0.05,P<0.01)。上述3组中H1650细胞的上述指标也呈现一致的现象。结论ISO可能通过抑制Wnt/β-catenin信号通路来抑制NSCLC细胞活性和自我更新,并促进其凋亡。     

2019年重庆市30岁及以上人群归因于被动吸烟的肺癌疾病负担研究

目的 分析重庆市肺癌发病死亡和疾病负担归因于被动吸烟的情况,为开展肺癌防治提供建议。 方法 肺癌死亡个案数据来源于2019年重庆市肿瘤登记报告系统,被动吸烟率来自2013年重庆市慢性病及危险因素监测。计算人群归因危险度百分比(population attributable risk percent, PAR%)、被动吸烟导致的肺癌发病、死亡和疾病负担。采用Excel 2010与SPSS 25.0进行统计分析,率的比较采用χ²检验。 结果 2013年30岁及以上成年人被动吸烟率为52.37%。2019年重庆市30岁及以上人群肺癌发病率与标化发病率分别为118.44/10万与80.83/10万,死亡率与标化死亡率分别为96.51/10万、63.58/10万。肺癌发病率和死亡率归因于被动吸烟的PAR%分别为19.76和19.04,归因发病率与归因标化发病率分别为23.41/10万和16.34/10万,归因死亡率与归因标化死亡率分别为18.38/10万和12.40/10万。2019年重庆市30岁及以上肺癌早死所致寿命损失年率(years of life lost,YLL)、残疾所致寿命损失年率(years lived with disability,YLD)、调整伤残寿命损失年率(disability adjusted life year,DALY)分别为21.16‰、0.31‰、21.47‰,YLL率、YLD率、DALY率归因于被动吸烟的PAR%分别为21.16、19.76和20.49,归因YLL率为4.34‰,归因YLD率为0.06‰,归因DALY率为4.40‰。 结论 2019年重庆市30岁及以上人群肺癌发病率、死亡率、YLL率、DALY率高,被动吸烟率高,肺癌归因于被动吸烟的疾病负担重,应加强落实控烟工作。     

Teach-Back健康教育模式对学龄期手足口病患儿家属疾病认知水平及防护行为的影响

目的:探讨Teach-Back健康教育模式对学龄期手足口病(HFMD)患儿家属疾病认知水平及防护行为的影响。方法:2018年7月—2020年11月选取我院学龄期113名HFMD患儿家属为研究对象,按照随机数字表法分为对照组56例与观察组57例,对照组给予常规健康教育干预,观察组给予Teach-Back健康教育模式干预,观察两组患儿家属疾病认知水平、行为干预遵从率、患儿症状改善状况。结果:干预后,观察组患儿家属发病原因、传播途径、隔离治疗、并发症预防认知评分高于对照组(P<0.05);观察组患儿家属消毒隔离措施、饭前便后洗手、勤晒衣被、居家开窗通风遵从率高于对照组(P<0.05);观察组患儿发热、口腔溃疡、皮疹、食欲减退改善时间短于对照组(P<0.05)。结论:对学龄期HFMD患儿家属开展Teach-Back健康教育模式,可明显提高家属对疾病认知水平,提高行为干预遵从率,促进患儿恢复。     

超声引导下经皮肺穿刺活检术术后并发症分析

目的分析超声引导下经皮肺穿刺活检术术后并发症的相关影响因素。 方法收集2015年1月至2017年12月于西部战区总医院住院并在超声引导下行经皮肺穿刺活检术的205例患者临床资料，分析205例患者穿刺术后病理结果及并发症发生率，探讨超声引导下经皮肺穿刺活检术术后并发症的相关影响因素。 结果205例患者行超声引导下经皮肺穿刺活检术，术后并发出血8例，占3.9%(8/205)，术后发热3例，占1.46% (3/205)，为低热，且予以对症治疗后均恢复正常，无严重并发症发生。术后出血的发生率在不同性别、年龄、BMI及病灶类型的患者中无显著差异(P>0.05)，且吸烟对患者术后出血率也无显著影响(P>0.05)；但有基础疾病的患者其术后出血率更高(P＝0.005)，且病灶直径越小，术后越容易并发出血(P＝0.006)。不同性别、年龄及病灶类型对术后发热的发生率无显著影响(P>0.05)，且术后发热与吸烟史及基础疾病史无关(P>0.05)，但BMI≥24或3 cm ≤病灶直径(d)<6 cm的患者术后更易发热(P<0.05)。 结论超声引导下经皮肺穿刺活检术术后并发症发生率低(约为5.37%)，主要为出血及低热，其中术后出血主要与基础疾病史和病灶直径有关，术后发热主要与病灶直径及BMI有关。     

预测年轻乳腺癌患者生存率列线图的建立和验证

目的:构建预测年轻乳腺癌患者生存情况的列线图，以期帮助临床诊疗。方法:收集SEER数据库中5 525例年轻乳腺癌患者的临床信息，通过单因素Log-rank检验和多因素Cox生存分析筛选出独立预后因素,用于构建预测患者3、5年总生存率(overall survival,OS)和癌症特异性生存率（cancer special survival,CSS)的列线图，将我院就诊的147例年轻乳腺癌患者作为验证集进行外部验证。结果:单因素和多因素分析结果显示，种族、病理类型、组织学分级、T分期、N分期、M分期、ER状态、HER-2状态、手术方式是与患者OS和CSS相关的独立危险因素，将这些因素纳入并建立预测患者OS和CSS的列线图模型。内部和外部验证结果显示模型具有良好的预测性能。基于建立的OS和CSS列线图模型对患者进行了风险分层，能够准确地将年轻乳腺癌患者分成预后有显著差异的三个风险亚组。结论:本研究构建的预测模型能较为准确的预测年轻乳腺癌患者的预后情况，为临床的诊疗提供科学依据。     

Use and Applicability of the Gail Model to Calculate Breast Cancer Risk: A Scoping Review

Objective: To perform a scoping review of the applicability of the Gail model in different countries for different ethnicities. Methods: The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and search strategies based on the PICOS approach. The reviewed articles were included if they were published between 2013 and 2018 in English, Portuguese, or Spanish; were original articles available in full online; and described the use of the Gail model. The PubMed, Embase, and Web of Science data bases were searched. Results: A total of 38 articles eligible for analysis were identified, of which 16 used the Gail model to assess breast cancer risk in women, eight analyzed the applicability of this tool in their population, seven compared the tool and/or modified it according to the specific risk factors of their population, and seven cited the model in determining eligibility for chemoprevention. Conclusion: The Gail model has different applicabilities Greater effectiveness and breast cancer risk are found in developed countries.     

Impact of COVID-19 and malaria coinfection on clinical outcomes: a retrospective cohort study

ObjectivesDespite the possibility of concurrent infection with COVID-19 and malaria, little is known about the clinical course of coinfected patients. We analysed the clinical outcomes of patients with concurrent COVID-19 and malaria infection.MethodsWe conducted a retrospective cohort study that assessed prospectively collected data of all patients who were admitted between May and December 2020 to the Universal COVID-19 treatment center (UCTC), Khartoum, Sudan. UCTC compiled demographic, clinical, laboratory (including testing for malaria), and outcome data in all patients with confirmed COVID-19 hospitalized at that clinic. The primary outcome was all-cause mortality during the hospital stay. We built proportional hazard Cox models with malaria status as the main exposure and stepwise adjustment for age, sex, cardiovascular comorbidities, diabetes, and hypertension.ResultsWe included 591 patients with confirmed COVID-19 diagnosis who were also tested for malaria. Mean (SD) age was 58 (16.2) years, 446/591 (75.5%) were males. Malaria was diagnosed in 270/591 (45.7%) patients. Most malaria patients were infected by Plasmodium falciparum (140/270; 51.9%), while 121/270 (44.8%) were coinfected with Plasmodium falciparum and Plasmodium vivax. Median follow-up was 29 days. Crude mortality rates were 10.71 and 5.87 per 1000 person-days for patients with and without concurrent malaria, respectively. In the fully adjusted Cox model, patients with concurrent malaria and COVID-19 had a greater mortality risk (hazard ratio 1.43, 95% confidence interval 1.21-1.69).DiscussionCoinfection with COVID-19 and malaria is associated with increased all-cause in-hospital mortality compared to monoinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).