Computed Tomography-Based Biomarker for Longitudinal Assessment of Disease Burden in Pulmonary Tuberculosis

Serum pro-inflammatory biomarkers associated with improvement in quality of life in pulmonary tuberculosis

Introduction

Pulmonary dysfunction is an underestimated complication in tuberculosis (TB) infection, affecting quality of life (QoL). Although respiratory function tests objectively reflect lung disturbances in a specific moment, predictors of illness severity at the time of diagnosis are still lacking.

Methods

We measured serum pro-inflammatory cytokines (TNF-α and IL-8), eicosanoids (PGE2, LTB4, RvD1, Mar1, and LXA4), a marker of tissue damage (cell-free nucleosomes), and indicators of redox status (malonaldehyde, 8-isoprostane, total oxidants, and antioxidants), as well as a score of radiological abnormalities (SRA) and a QoL questionnaire, in 25 patients with pulmonary TB at the time of diagnosis (t0) and two months after the initiation of treatment (t2).

Results

We found higher antioxidant levels in the patients with the worst QoL at t0, and all the indicators of the prooxidant state were significantly reduced at t2, while the total antioxidant levels increased. LTB4, a pro-inflammatory eicosanoid, was diminished at t2, while all the pro-resolutory lipids decreased substantially. Significant correlations between the SRA and the QoL scores were observed, the latter showing a substantial reduction at t2, ranking it as a reliable tool for monitoring disease evolution during TB treatment.

Discussion

These results suggest that evaluating a combination of these markers might be a valuable predictor of QoL improvement and a treatment response indicator; in particular, the oxidation metabolites and eicosanoid ratios could also be proposed as a future target for adjuvant therapies to reduce inflammation-associated lung injury in TB disease.

Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia

The objective of this study was to explore the accuracy of low-dosage computed tomography (CT) images based on the expectation maximization algorithm denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia. 120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group. The low-dosage CT images of patients in the control group were not processed (nonalgorithm group), and the low-dosage CT images of patients in the observation group were denoised using the EM algorithm (algorithm group). In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis. The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients. The results showed that the peak signal-to-noise ratio (PSNR) (15.9 dB) of the EM algorithm was higher than the regularized adaptive matching pursuit (RAMP) algorithm (1.69 dB) and the mean filter (4.3 dB) (P < 0.05). The time consumption of EM algorithm (21 s) was shorter than that of PWLS algorithm (34 s) and MS-PWLS algorithm (39 s) (P < 0.05). The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group (P < 0.05), which were 66.67% vs. 90%, 60% vs. 88.89%, 71.42% vs. 100%, and 60% vs. 88.89%, respectively. The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (ADPKD) (56.77%) was much higher than that of the other diseases (P < 0.05). After denoising by the EM algorithm, low-dosage CT image could improve the diagnostic accuracy of several types of renal dysplasia except ADPKD, showing certain clinical application value. In addition, ADPKD was easy to cause hypertension.

One Size Fits All? Not in In Vivo Modeling of Tuberculosis Chemotherapeutics

Tuberculosis (TB) remains a global health problem despite almost universal efforts to provide patients with highly effective chemotherapy, in part, because many infected individuals are not diagnosed and treated, others do not complete treatment, and a small proportion harbor Mycobacterium tuberculosis (Mtb) strains that have become resistant to drugs in the standard regimen. Development and approval of new drugs for TB have accelerated in the last 10 years, but more drugs are needed due to both Mtb's development of resistance and the desire to shorten therapy to 4 months or less. The drug development process needs predictive animal models that recapitulate the complex pathology and bacterial burden distribution of human disease. The human host response to pulmonary infection with Mtb is granulomatous inflammation usually resulting in contained lesions and limited bacterial replication. In those who develop progressive or active disease, regions of necrosis and cavitation can develop leading to lasting lung damage and possible death. This review describes the major vertebrate animal models used in evaluating compound activity against Mtb and the disease presentation that develops. Each of the models, including the zebrafish, various mice, guinea pigs, rabbits, and non-human primates provides data on number of Mtb bacteria and pathology resolution. The models where individual lesions can be dissected from the tissue or sampled can also provide data on lesion-specific bacterial loads and lesion-specific drug concentrations. With the inclusion of medical imaging, a compound's effect on resolution of pathology within individual lesions and animals can also be determined over time. Incorporation of measurement of drug exposure and drug distribution within animals and their tissues is important for choosing the best compounds to push toward the clinic and to the development of better regimens. We review the practical aspects of each model and the advantages and limitations of each in order to promote choosing a rational combination of them for a compound's development.

Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.