Adhesins, Bacterial ; Biomedical Research ; Fusobacterium nucleatum

Objective:To understand the diagnostic value of tuberculosis (TB) pathogenic detection methods (TPDM) in pathological tissue for TB.Methods:A retrospective study was conducted with 190 pathological specimens from different tissues suspected with TB from Third People′s Hospital of Shenzhen during May 2016 and May 2019. Specimens were divided into four groups according to histomorphology: group one, necrotizing granulomatous inflammation (109 cases); group two, non-necrotic granulomatous inflammation (20 cases); group three, non-granulomatous inflammation (45 cases); group four, non-tuberculous lesions (16 cases). The positive rates of each TPDM among specimens from four groups were compared. The positive rates of all TPDM for specimens from group one were compared. Meanwhile, the influence of antituberculosis treatment course on the TPDM was analyzed. Chi-square test or Fisher′s exact test was used for statistical analysis.Results:The positive rates of Ziehl-Neelsen acid-fast staining among the four groups were 17.4%(19/109), 5.0%(1/20), 4.4%(2/45) and 0(0/16), respectively. The positive rates of Mycobacterium tuberculosis (MTB) complex culture were 32.0%(32/100), 4/19, 4.8%(2/42) and 0(0/16), respectively. The positive rates of Mycobacterium tuberculosis/rifampin resistance real-time quantitative nucleic acid amplification detection system (Xpert MTB/RIF) were 74.3%(81/109), 15.0%(3/20), 13.3%(6/45) and 0(0/16), respectively. The positive rates of fluorescent quantitative polymerase chain reaction (FQ-PCR) were 63.0%(58/92), 0(0/15), 2.6%(1/38) and 0(0/10), respectively. The positive rates of simultaneous amplification and testing (SAT) were 32.4%(24/74), 0(0/10), 0(0/15) and 0(0/10), respectively. The differences of each TPDM among four groups were all statistically significant (all P<0.05). The positive rate of Xpert MTB/RIF in group one specimens was significantly higher than those of acid-fast staining, MTB culture and SAT ( χ2=71.016, 37.162 and 35.679, respectively, all P<0.01), while the difference was not statistically significant when compared with FQ-PCR ( χ2=2.517, P=0.112). The positive rate of combined TPDM (85.3%(93/109)) was significantly higher than Xpert MTB/RIF(74.3%(81/109)) ( χ2=4.100, P=0.043). The positive rates of acid-fast staining group 1A (anti-tuberculosis treatment course was less than one month) and group 1B (anti-tuberculosis treatment course was longer than one month) were 14.3%(7/49) and 20.0% (12/60), respectively ( χ2=0.612, P=0.434); those of MTB culture were 48.9% (22/45) and 18.2% (10/55), respectively ( χ2=10.721, P=0.001); those of Xpert MTB/RIF were 69.4%(34/49) and 78.3%(47/60), respectively ( χ2=1.131, P=0.287); those of FQ-PCR were 55.0%(22/40) and 69.2%(36/52), respectively ( χ2=1.965, P=0.161); those of SAT were 43.3%(13/30) and 25.0%(11/44), respectively ( χ2=2.736, P=0.098). Conclusions:The results of TPDM correlate closely with the typical histomorphological features of tuberculosis. Xpert MTB/RIF possesses significantly higher sensitivity than any other single TPDM, and is not attenuated by early anti-tuberculosis treatment. Combined TPDM could significantly improve the sensitivity of TB pathogenic detection, which is suggested to be applied when the tissue specimen is sufficient.

Neuroimaging technologies have been applied to the diagnosis of schizophrenia. In order to improve the performance of the single-modal neuroimaging-based computer-aided diagnosis (CAD) for schizophrenia, an ensemble learning algorithm based on learning using privileged information (LUPI) was proposed in this work. Specifically, the extreme learning machine based auto-encoder (ELM-AE) was first adopted to learn new feature representation for the single-modal neuroimaging data. Random project algorithm was then performed on the learned high-dimensional features to generate several new feature subspaces. After that, multiple feature pairs were built among these subspaces to work as source domain and target domain, respectively, which were used to train multiple support vector machine plus (SVM+) classifier. Finally, a strong classifier is learned by combining these SVM+ classifiers for classification. The proposed algorithm was evaluated on a public schizophrenia neuroimaging dataset, including the data of structural magnetic resonance imaging (sMRI) and functional MRI (fMRI). The results showed that the proposed algorithm achieved the best diagnosis performance. In particular, the classification accuracy, sensitivity and specificity of the proposed algorithm were 72.12% ± 8.20%, 73.50% ± 15.44% and 70.93% ± 12.93%, respectively, on the sMRI data, and it also achieved the classification accuracy of 72.33% ± 8.95%, sensitivity of 68.50% ± 16.58% and specificity of 75.73% ± 16.10% on the fMRI data. The proposed algorithm overcomes the problem that the traditional LUPI methods need the additional privileged information modality as source domain. It can be directly applied to the single-modal data for classification, and also can improve the classification performance. Therefore, it suggests that the proposed algorithm will have wider applications.

Objective: To evaluate the value of preoperative diagnosis of extramural vascular invasion (EMVI) of rectal cancer with 3.0T high-resolution magnetic resonance imaging (MRI) and the MRI-related factors of EMVI in rectal cancer. Methods: The clinical and imaging data of 40 patients with rectal cancer were retrospectively analyzed. The postoperative pathological diagnosis was used as the gold standard to evaluate the diagnostic efficacy of preoperative diagnosis of EMVI of rectal cancer by high-resolution MRI, and to analyze the relationship between the EMVI and clinical and MRI features. Results: Of the 40 patients, 19 cases were diagnosed as positive EMVI and 21 were negative by MRI. Pathological diagnosis of EMVI was positive in 10 cases and negative in 30 cases. The sensitivity, specificity and accuracy of MRI in the diagnosis of EMVI were 100%, 70.0% and 77.5%, respectively. Preoperative MRI and postoperative pathology were moderately consistent in the diagnosis of EMVI in rectal cancer (Kappa=0.538, P<0.001). Pathological EMVI positivity were related to tumor size under MRI examination (P=0.028), degree of differentiation (P<0.001), depth of invasion (P=0.002), lymph node metastasis (P=0.001), liver metastasis (P=0.011), tumor apparent diffusion coefficient (ADC) value (P=0.010) and exponential apparent diffusion coefficient (eADC) value (P=0.003). It also related to extramural nerve invasion by pathological examination (P=0.005). Conclusion According to the EMVI imaging score of rectal cancer, preoperative MRI has a high value in the diagnosis of EMVI of rectal cancer.