【摘要】：目的：探討結(jié)核分枝桿菌生物膜形成與初復(fù)治菌株及菌株耐藥之間的關(guān)系；探究藥物作用于相應(yīng)耐藥菌株后對(duì)該菌株生物膜生成的影響；為生物膜作為結(jié)核分枝桿菌在體內(nèi)以復(fù)雜系統(tǒng)模式生存的生物學(xué)基礎(chǔ)尋找間接的實(shí)驗(yàn)證據(jù)。方法：選取有代表性的43株結(jié)核分枝桿菌臨床菌株作為研究對(duì)象。為驗(yàn)證初復(fù)治菌株之間成膜性是否有差異，將以上菌株分為初治組（31株，其中包括8株敏感株）和復(fù)治組（12株，其中包括2株敏感株）。為尋找成膜性與耐藥程度之間的關(guān)系，根據(jù)耐藥程度分為，敏感組（10株）、耐1種藥物組（5株）、耐2、3、4、5種藥物組（各6株）以及耐6種藥物以上組（4株）。為驗(yàn)證藥物對(duì)相應(yīng)耐藥菌株生物膜產(chǎn)生的影響，我們分別選取耐INH20株和耐RFP19株，根據(jù)是否加用藥物分為加藥組和非加藥組。每株菌都經(jīng)過復(fù)蘇、增殖、成膜態(tài)培養(yǎng)等環(huán)節(jié)，最后用結(jié)晶紫染色吸光法進(jìn)行生物膜的測(cè)定。結(jié)果：初治組與復(fù)治組生物膜產(chǎn)生量的OD595值均值分別為2.095±0.821和2.733±0.644，復(fù)治組高于初治組，且有統(tǒng)計(jì)學(xué)意義(p=0.016)。生物膜的產(chǎn)生量與菌株耐藥強(qiáng)度呈正相關(guān)(r=0.412，p=0.006)，線性回歸方程為Y=1.780+0.185X，散點(diǎn)圖提示散點(diǎn)與回歸線之間的回歸聚集性并不明顯，對(duì)敏感組和各耐藥數(shù)目組方差分析，p為0.004，而組間均值的多重比較除耐6種以上藥物組分別與敏感組、耐1、2、4種藥物組之間的p 0.05外，其余均無(wú)統(tǒng)計(jì)學(xué)差異。在回顧性再分組{敏感組（10株）與耐藥組（33株）、初治敏感組（8株）與初治耐藥組（23株）及復(fù)治敏感組（2株）與復(fù)治耐藥組（10株）}比較耐藥性和成膜性的關(guān)系的數(shù)據(jù)探索后顯示三組比較均提示相應(yīng)的耐藥組的成膜性均值大于相應(yīng)的敏感組均值，但p值均大于0.05，未提示有統(tǒng)計(jì)學(xué)差異。INH (p=0.005)和RFP (p=0.002)均對(duì)相應(yīng)的耐藥菌株生物膜的產(chǎn)生有抑制作用。結(jié)論：結(jié)核分枝桿菌生物膜可能是結(jié)核分枝桿菌以復(fù)雜系統(tǒng)形式在宿主體內(nèi)生存的生物學(xué)基礎(chǔ)；結(jié)核分枝桿菌生物膜的產(chǎn)生可能是復(fù)治結(jié)核病發(fā)生的機(jī)制之一；目前尚無(wú)充分證據(jù)支持耐藥程度與菌株成膜性相關(guān)及有差異；抗INH與RFP對(duì)相應(yīng)的耐藥菌株生物膜的產(chǎn)生有抑制作用。 目的：將肺部CT全部數(shù)據(jù)視為復(fù)雜系統(tǒng)呈現(xiàn)的一部分，開發(fā)可用于測(cè)量肺結(jié)核CT影像病理?yè)p傷量的計(jì)算機(jī)輔助算法，并在不同個(gè)體、組別和時(shí)間序列間進(jìn)行初步評(píng)估、驗(yàn)證算法，為今后深入開發(fā)奠定基礎(chǔ)。方法：從影像中心數(shù)據(jù)庫(kù)中根據(jù)入選標(biāo)準(zhǔn)選取385人次的肺部CT數(shù)據(jù)作為研究對(duì)象。將樣本分為健康組（Normal）、PTB組（PTB）、PTB合并糖尿病組(PTB+DM)和因結(jié)核病而死亡的死亡組(Death)，此外將有不同階段CT數(shù)據(jù)的5名患者數(shù)據(jù)集中為一組進(jìn)行時(shí)間序列分析。根據(jù)肺結(jié)核肺部影像特征設(shè)計(jì)計(jì)算機(jī)輔助算法（CACTV-PTB），在全部CT資料中進(jìn)行演算，每份CT數(shù)據(jù)得到LV和TV值，并進(jìn)一步計(jì)算RLT和SRLT值。結(jié)果：LV和TV可通過CACTV-PTB進(jìn)行計(jì)算，所推導(dǎo)的RLT和SRLT值可用于個(gè)體、個(gè)體間、群體間和時(shí)間序列分析之中。正常人的LV與TV為正線性相關(guān)，其回歸方程為Y=-0.5+0.46X, R2=0.796, p 0.000。RLT在不同組的均值為Normal:4.01±1.04, PTB:3.66±1.26, PTB+DM:3.75±1.13,Death:2.22±0.55，組間比較有統(tǒng)計(jì)學(xué)差異。結(jié)論：應(yīng)用CACTV-PTB可對(duì)肺結(jié)核CT數(shù)據(jù)進(jìn)行處理計(jì)算LV和TV值，并推算RLT和SRLT值，該系列指標(biāo)可以用于個(gè)體、個(gè)體間、群體間的比較，以及時(shí)間序列分析。得病初期肺結(jié)核合并糖尿病患者的肺部病理?yè)p傷重于普通肺結(jié)核患者。 目的：嘗試提出“菌陰耐多藥結(jié)核�。╯nMDR-TB）”（因本概念之前從未被正式提出，因此本部分標(biāo)題和此處用引號(hào)加以標(biāo)注，以表示本概念尚有待進(jìn)一步的學(xué)術(shù)驗(yàn)證和探討，同時(shí)考慮到文章的可讀性在后續(xù)的論文中將引號(hào)去除）的概念，從復(fù)雜系統(tǒng)科學(xué)的角度將菌陰耐多藥結(jié)核病看做多因素關(guān)聯(lián)的一種表型，基于真實(shí)世界臨床數(shù)據(jù)，建立預(yù)測(cè)數(shù)學(xué)模型，初步評(píng)估菌陰耐多藥肺結(jié)核在住院環(huán)境下的數(shù)量規(guī)模和比例。方法：從首都醫(yī)科大學(xué)附屬北京胸科醫(yī)院住院病歷數(shù)據(jù)庫(kù)中以出院診斷包含“結(jié)核”且住院期間介于2009年到2013年的11950例患者信息中，，經(jīng)過入選標(biāo)準(zhǔn)篩選獲得6977例研究樣本。根據(jù)是否有MTB藥物敏感試驗(yàn)結(jié)果分為菌陽(yáng)組和菌陰組，將菌陽(yáng)組按隨機(jī)化1：1的比例分為訓(xùn)練集和驗(yàn)證集兩個(gè)亞組，菌陰組作為預(yù)測(cè)集。對(duì)訓(xùn)練集進(jìn)行Logistic回歸分析，并且建立預(yù)測(cè)數(shù)學(xué)模型。用驗(yàn)證集數(shù)據(jù)對(duì)預(yù)測(cè)數(shù)學(xué)模型進(jìn)行ROC分析，確立臨界值。應(yīng)用預(yù)測(cè)數(shù)學(xué)模型對(duì)預(yù)測(cè)集數(shù)據(jù)進(jìn)行演算評(píng)估。結(jié)果：發(fā)現(xiàn)了16項(xiàng)與MDR-TB的相關(guān)因素。預(yù)測(cè)數(shù)學(xué)模型ROC曲線下面積（AUC）為0.752（敏感度=61.3%，特異度=83.3%）。在住院患者中snMDR-TB/全部患者為28.7%±0.02，snMDR-TB/SN-PTB為26.5%±0.03，snMDR-TB/MDR-TB為2.09±0.33。結(jié)論：snMDR-TB是MDR-TB的早期階段或重要來源，可以嘗試用數(shù)學(xué)模型預(yù)測(cè)的方法進(jìn)行評(píng)估；snMDR-TB與MDR-TB的發(fā)展趨勢(shì)整體平行，局部有時(shí)間延遲現(xiàn)象；如更好的控制MDR-TB需更加重視snMDR-TB，并開展更為深入的研究。
[Abstract]:OBJECTIVE: To explore the relationship between the biofilm formation of Mycobacterium tuberculosis and the drug resistance of the first retreated strain and the strain, and to explore the effect of drugs on the biofilm formation of the strain after the action of the corresponding drug-resistant strain. Methods: 43 clinical strains of Mycobacterium tuberculosis were selected as the research object. To verify the difference of film-forming ability between the first retreated strains, the above strains were divided into the first treatment group (31 strains, including 8 susceptible strains) and the second treatment group (12 strains, including 2 susceptible strains). According to the degree of drug resistance, they were divided into sensitive group (10 strains), resistant group (5 strains), resistant group (6 strains) and resistant group (4 strains). Result: The OD595 values of biofilm production in the first treatment group and the second treatment group were 2.095 (+ 0.821) and 2.733 (+ 0.644), respectively. The OD595 values of biofilm production in the second treatment group were higher than those in the first treatment group (p = 0.016). Resistance intensity was positively correlated (r = 0.412, P = 0.006), and the linear regression equation was Y = 1.780 + 0.185X. Scatter plot indicated that the regression aggregation between the scatter and the regression line was not obvious. The analysis of variance between the sensitive group and the number of drug resistance groups was 0.004, while the multiple comparisons of the mean values between the groups except the six or more drug resistant groups and the susceptible group, the resistance group to 1,2,4 drugs were not obvious. After retrospective grouping {sensitive group (10 strains) and resistant group (33 strains), sensitive group (8 strains) and resistant group (23 strains) and retreatment sensitive group (2 strains) and retreatment resistant group (10 strains)} the relationship between drug resistance and membrane formation was analyzed. Both INH (p = 0.005) and RFP (p = 0.002) inhibited the biofilm formation of the drug-resistant strains. Conclusion: Mycobacterium tuberculosis biofilm may exist in the host as a complex system. Biological basis; Mycobacterium tuberculosis biofilm production may be one of the mechanisms of retreatment of tuberculosis; there is no sufficient evidence to support the degree of drug resistance and film-forming strains and differences; anti-INH and RFP on the corresponding drug-resistant strains of biofilm production inhibition.
Objective:To develop a computer-aided algorithm for measuring pathological lesions in CT images of pulmonary tuberculosis, and to validate the algorithm in different individuals, groups and time series. The subjects were divided into three groups: Normal, PTB, PTB with diabetes mellitus (PTB + DM) and death due to tuberculosis. Five patients with different stages of CT data were collected for time series analysis. Feature design computer-aided algorithm (CACTV-PTB) was used to calculate the LV and TV values in all CT data, and the RLT and SRLT values were calculated further. Results: LV and TV can be calculated by CACTV-PTB. The derived values of LT and SRLT can be used in the analysis of individual, individual time, group time and time series. The regression equation was Y=-0.5+0.46X, R2=0.796, P 0.000.RLT in different groups was Normal:4.01+1.04, PTB:3.66+1.26, PTB+DM:3.75+1.13, Death:2.22+0.55. There was statistical difference between groups. Conclusion: CACTV-PTB can be used to process CT data of pulmonary tuberculosis to calculate LV and TV values, and to calculate RLT and SRT values. Indicators can be used for individual, inter-individual, inter-group comparisons, and time series analysis.
OBJECTIVE: To propose the concept of "bacterial-negative multidrug-resistant tuberculosis (snMDR-TB)" (since the concept has never been formally proposed before, the title of this section and quotation marks are used here to indicate that the concept needs further academic verification and discussion, while taking into account the readability of the article in subsequent papers will be removed from the quotation marks). From the point of view of complex systems science, bacterial-negative multidrug-resistant tuberculosis is regarded as a phenotype associated with multiple factors. Based on real-world clinical data, a predictive mathematical model is established to preliminarily evaluate the quantity scale and proportion of bacterial-negative multidrug-resistant tuberculosis in hospitalized environment. Among 11 950 patients diagnosed as "tuberculosis" and hospitalized between 2009 and 2013, 6977 study samples were screened according to inclusion criteria. According to the results of MTB drug susceptibility test, the patients were divided into two groups: bacterial positive group and bacterial negative group. The bacterial positive group was divided into training set and validation set according to the ratio of randomized 1:1. Logistic regression analysis was carried out on the training set, and the prediction mathematical model was established. ROC analysis was carried out on the prediction mathematical model with the verification set data, and the critical value was established. The area under the C-curve (AUC) was 0.752 (sensitivity = 61.3%, specificity = 83.3%). SnMDR-TB/all patients were 28.7%+0.02, snMDR-TB/SN-PTB was 26.5%+0.03, and snMDR-TB/MDR-TB was 2.09+0.33. Conclusion: SnMDR-TB is an early stage or an important source of MDR-TB, which can be assessed by means of mathematical prediction. Parallel with the development trend of MDR-TB, there are local time delays. For better control of MDR-TB, more attention should be paid to snMDR-TB and more in-depth research should be carried out.
【學(xué)位授予單位】：北京市結(jié)核病胸部腫瘤研究所
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R52

【共引文獻(xiàn)】

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2 鄭志剛;;XpertMTB/RIF檢測(cè)結(jié)核桿菌和利福平耐藥的研究及應(yīng)用進(jìn)展[J];應(yīng)用預(yù)防醫(yī)學(xué);2014年02期

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10 田生科;王羽佳;;某市2008~2012年肺結(jié)核病流行狀況的分析[J];中國(guó)醫(yī)藥指南;2013年31期

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1 邱月琴;結(jié)核病人中研究Tim-3調(diào)節(jié)T細(xì)胞的免疫應(yīng)答作用及細(xì)胞因子之間的關(guān)系[D];暨南大學(xué);2013年

2 周媛;中國(guó)人群藥物性肝損害的流行病學(xué)：系統(tǒng)分析患者21789例[D];第三軍醫(yī)大學(xué);2013年

3 邱奕;復(fù)蘇促生長(zhǎng)因子結(jié)構(gòu)域及其突變體蛋白誘導(dǎo)小鼠免疫應(yīng)答的研究[D];第四軍醫(yī)大學(xué);2013年

4 鐘雋鐫;早產(chǎn)兒真菌性敗血癥流行病學(xué)特點(diǎn)及病原菌分析研究[D];福建醫(yī)科大學(xué);2013年

5 鐘雋雋;早產(chǎn)兒真菌性敗血癥流行病學(xué)特點(diǎn)及病原菌分析研究[D];福建醫(yī)科大學(xué);2013年

6 馮金棟;四種重組結(jié)核分枝桿菌抗原在結(jié)核病血清學(xué)診斷中應(yīng)用價(jià)值的研究[D];河北北方學(xué)院;2013年

7 袁明麗;結(jié)核性胸腔積液中胸膜間皮細(xì)胞以粘附分子依賴途徑調(diào)節(jié)CD4~+T細(xì)胞[D];華中科技大學(xué);2013年

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9 張瑾鈺;綠原酸、異綠原酸對(duì)煙曲霉菌生物被膜抑制作用的體外研究[D];廣西醫(yī)科大學(xué);2014年

10 槐鵬程;復(fù)治肺結(jié)核患者耐多藥的危險(xiǎn)因素研究[D];山東大學(xué);2014年

本文編號(hào)：2183908