An Exploratory Study On Tuberculosis - Related Problems Under The Guidance Of Scientific System Of Complex System Science

Objective: To investigate the relationship between Mycobacterium tuberculosis(MTB) biofilm formation with tuberculosis retreatment and drug resistant, and theeffect of biofilm formation when drug working on the drug resistant strains. And tofind the indirect evidence of biofilm as a basic of MTB living in the host body as acomplex system. Methods: Total43MTB clinical isolates were selected anddivided into initial treatment group (31strains, included8sensitive strains) andretreatment group (12strains, included2sensitive strains) to invest the relationshipbetween MTB biofilm formation and tuberculosis retreatment. To investigate therelationship between MTB biofilm formation with drug resistant, all strains weredivided into7groups, sensitive group (10strains),1drug resistant group (5strains),2drug resistant group (6strains),3drug resistant group (6strains),4drug resistantgroup (6strains),5drug resistant group (5strains) and more than6drug resistantgroup (4strains). To investigate the effect of biofilm formation, INH resistant group(20strains) and RFP resistant group (19strains) were selected and divided add-drugsubgroup and no-drug subgroup respectively. All strains were recovered, proliferatedand cultured by biofilm-forming state respectively. The biofilm formability wasdetected by crystal violet staining absorption method. Results: The mean opticaldensity (OD595) value of initial treatment group and retreatment group was2.095±0.821and2.733±0.644respectively. The OD595value of retreatment group washigher than that of the initial treatment group (p=0.016). MTB biofilm forming ispositive related with the number of resistant drugs (r=0.412，p=0.006). The linearregression equation is Y=1.780+0.185X. The p of ANOVA between sensitiveand different drug resistant groups is0.004. Except p <0.05among more than6drugresistant group with sensitive,1,2and4drug resistant groups, other p value ofmultiple comparisons is more than0.05. In the retrospective study in three pare groups of sensitive group (10strains) vs. drug resistance group (33strains), initialtreatment of drug sensitive group (8strains) vs. initial treatment of drug resistantgroup (23strains) and retreatment of drug sensitive group (2strains) vs. retreatmentof drug resistant group (10strains), it was presented that all three mean value ofbiofilm formation in drug resistant group was more than the mean value of biofilmformation in drug sensitive group, but the p value was greater than0.05. INH(p=0.005) and RFP (p=0.002) all can inhibit the biofilm production of drug resistantMTB strains respectively. Conclusions: MTB biofilm may be the biological basis ofthe living of MTB in human body as a complex system. MTB biofilm formation isone of the possible reasons for tuberculosis retreatment. No evidence supports thatMTB biofilm formation has positive related with the MTB drug resistance, or drugresistant MTB biofilm formation more than drug sensitive MTB biofilm formation.INH and RFP can inhibit biofilm formation of drug resistant MTB strains of INH andRFP. Objective: Considered thoracic computed tomography (CT) as a complexsystem, to develop a method to measure the lung injury level in CT of pulmonarytuberculosis (PTB) and making the quantitative comparisons and time series analysispossible. Methods:385different peoples CT were selected and divided into fivegroups: PTB group, PTB with diabetes mellitus (DM) group, death because of PTBgroup and time series group. Then we developed a method named CACTV-PTB thatmeasure the normal lung volume (LV) and the thoracic volume (TV), and calculatethe ration of LV and TV (RLT) and standard RLT (SRLT). Results: LV and TV canbe measured by CACTV-PTB. RLT and SRLT can be used in comparisons and timeseries analysis in different groups and individuals. In healthy people LV and TV arepositive correlation and linear regression (Y=-0.5+0.46X, R2=0.796, p <0.000).RLT in different groups (Normal:4.01±1.04, PTB:3.66±1.26, PTB+DM:3.75±1.13, Death:2.22±0.55) are significant difference. Conclusions: Using the PTBthoracic CT CACTV-PTB can measure LV and TV, calculate RLT and SRLT, andmake the comparisons and time series analysis possible at self-individual, amongindividuals and different groups; and in early stage the lung injury in PTB+DM areseverer than PTB. Objective: To find the essential characteristics from the surface features in acomplex system, we propose the concept of smear-negative multidrug-resistanttuberculosis (snMDR-TB), based on analysis of real world data to evaluate theoccurrence of snMDR-TB, and determine its relationship with MDR-TB. Methods:Medical records information of6977cases was included from11950inpatients from2009to2013. The data were divided into a training set, test set and prediction set.Logistic regression analysis was applied to the training set data to establish aprediction classification model, and the effect of which was then evaluated using thetest set by receiver operating characteristic (ROC) analysis. The model was thenapplied to the prediction set to identify incidence of snMDR-TB. Results: Sixteenfactors that correlate with MDR-TB were identified. The area under the ROC curve(AUC) of the prediction model was0.752(sensitivity=61.3%, specificity=83.3%).The percentage of all inpatients with snMDR-TB (snMDR-TB/Total) was28.7%±0.02, while that of all smear negative pulmonary (SN-PTB) with snMDR-TB(snMDR-TB/SN-PTB) was26.5%±0.03. The ratio of snMDR-TB to MDR-TB(snMDR-TB/MDR-TB) was2.09±0.33. Conclusions: snMDR-TB as an importantearly phrase or source of MDR-TB is a significant hidden problem for MDR-TBcontrol and can be identified by the prediction model; a kind of vicious circle with acertain delay effect exist between snMDR-TB and MDR-TB; to better controlMDR-TB it is necessary to pay greater attention to snMDR-TB, conduct furtherresearch and develop targeted therapeutic strategies.