PE/PPE mutations in the transmission of Mycobacterium tuberculosis in China revealed by whole genome sequencing.

OBJECTIVE: This study aims to examine the impact of PE/PPE gene mutations on the transmission of Mycobacterium tuberculosis (M. tuberculosis) in China. METHODS: We collected the whole genome sequencing (WGS) data of 3202 M. tuberculosis isolates in China from 2007 to 2018 and investigated the clustering of strains from different lineages. To evaluate the potential role of PE/PPE gene mutations in the dissemination of the pathogen, we employed homoplastic analysis to detect homoplastic single nucleotide polymorphisms (SNPs) within these gene regions. Subsequently, logistic regression analysis was conducted to analyze the statistical association. RESULTS: Based on nationwide M. tuberculosis WGS data, it has been observed that the majority of the M. tuberculosis burden in China is caused by lineage 2 strains, followed by lineage 4. Lineage 2 exhibited a higher number of transmission clusters, totaling 446 clusters, of which 77 were cross-regional clusters. Conversely, there were only 52 transmission clusters in lineage 4, of which 9 were cross-regional clusters. In the analysis of lineage 2 isolates, regression results showed that 4 specific gene mutations, PE4 (position 190,394; c.46G > A), PE_PGRS10 (839,194; c.744 A > G), PE16 (1,607,005; c.620T > G) and PE_PGRS44 (2,921,883; c.333 C > A), were significantly associated with the transmission of M. tuberculosis. Mutations of PE_PGRS10 (839,334; c.884 A > G), PE_PGRS11 (847,613; c.1455G > C), PE_PGRS47 (3,054,724; c.811 A > G) and PPE66 (4,189,930; c.303G > C) exhibited significant associations with the cross-regional clusters. A total of 13 mutation positions showed a positive correlation with clustering size, indicating a positive association. For lineage 4 strains, no mutations were found to enhance transmission, but 2 mutation sites were identified as risk factors for cross-regional clusters. These included PE_PGRS4 (338,100; c.974 A > G) and PPE13 (976,897; c.1307 A > C). CONCLUSION: Our results indicate that some PE/PPE gene mutations can increase the risk of M. tuberculosis transmission, which might provide a basis for controlling the spread of tuberculosis.

Transmission dynamics and phylogeography of Mycobacterium tuberculosis in China based on whole-genome phylogenetic analysis.

OBJECTIVES: This study aimed to describe the lineage-specific transmissibility and epidemiological migration of Mycobacterium tuberculosis in China. METHODS: We curated a large set of whole-genome sequences from 3204 M. tuberculosis isolates, including thousands of newly sequenced genomes, and applied a series of metrics to compare the transmissibility of M. tuberculosis strains between lineages and sublineages. The countrywide transmission patterns of major lineages were explored. RESULTS: We found that lineage 2 (L2) was the most prevalent lineage in China (85.7%), with the major sublineage 2.2.1 (80.9%), followed by lineage 4 (L4) (13.8%), which comprises major sublineages 4.2 (1.5%), 4.4 (6.2%) and 4.5 (5.8%). We showed evidence for frequent cross-regional spread and large cluster formation of L2.2.1 strains, whereas L4 strains were relatively geographically restricted in China. Next, we applied a series of genomic indices to evaluate M. tuberculosis strain transmissibility and uncovered higher transmissibility of L2.2.1 compared with the L2.2.2 and L4 sublineages. Phylogeographic analysis showed that southern, eastern, and northern China were highly connected regions for countrywide L2.2.1 strain spread. CONCLUSIONS: The present study provides insights into the different transmission and migration patterns of the major M. tuberculosis lineages in China and highlights that transmissible L2.2.1 is a threat to tuberculosis control.

Genomic analysis of lineage-specific transmission of multidrug resistance tuberculosis in China.

OBJECTIVES: We investigated the genetic diversities and lineage-specific transmission dynamics of multidrug-resistant tuberculosis (MDR-TB), with the goal of determining the potential factors driving the MDR epidemics in China. METHODS: We curated a large nationwide Mycobacterium tuberculosis (M. tuberculosis) whole genome sequence data set, including 1313 MDR strains. We reconstructed the phylogeny and mapped the transmission networks of MDR-TB across China using Bayesian inference. To identify drug-resistance variants linked to enhanced transmissibility, we employed ordinary least-squares (OLS) regression analysis. RESULT: The majority of MDR-TB strains in China belong to lineage 2.2.1. Transmission chain analysis has indicated that the repeated and frequent transmission of L2.2.1 plays a central role in the establishment of MDR epidemic in China, but no occurrence of a large predominant MDR outbreak was detected. Using OLS regression, the most common single nucleotide polymorphisms (SNPs) associated with resistance to isoniazid (katG_p.Ser315Thr and katG_p.Ser315Asn) and rifampicin (rpoB_p.Ser450Leu, rpoB_p.His445Tyr, rpoB_p.His445Arg, rpoB_p.His445Asp, and rpoB_p.His445Asn) were more likely to be found in L2 clustered strains. Several putative compensatory mutations in rpoA, rpoC, and katG were significantly associated with clustering. The eastern, central, and southern regions of China had a high level of connectivity for the migration of L2 MDR strains throughout the country. The skyline plot showed distinct population size expansion dynamics for MDR-TB lineages in China. CONCLUSION: MDR-TB epidemic in China is predominantly driven by the spread of highly transmissible Beijing strains. A range of drug-resistance mutations of L2 MDR-TB strains displayed minimal fitness costs and may facilitate their transmission.

Comparison of Short-Tau Inversion Recovery With Short-Tau Inversion Recovery-Slice Encoding for Metal Artifact Correction for Spine Imaging at 1.5 T in the Setting of Metallic Implants.

OBJECTIVE: To compare the image quality of short-tau inversion recovery (STIR) and the STIR-slice encoding for metal artifact correction (SEMAC) sequence for postsurgery spine magnetic resonance imaging (MRI). METHODS: Twenty-nine patients with metallic spinal implants who underwent spinal 1.5 T MRI with STIR and STIR-SEMAC sequences between July 2016 and November 2020 were retrospectively enrolled. Qualitative assessments were performed using 5-point scales; higher scores indicated better image quality. For screw metal artifact analysis, scores were obtained for artifacts on vertebral bodies and neural foramina, screw artifact widths, and bone marrow signal intensities. For patient-based analysis, scores were obtained for imaging quality and fat suppression quality, signal intensity, and cerebrospinal fluid noise. A paired t test was performed for statistical analyses. RESULTS: We analyzed 163 screws in 29 patients. In the screw metal artifact analysis, the vertebral body and neural foramen scores were significantly higher for the STIR-SEMAC images than for the STIR (all P < 0.001). The artifact width in the STIR-SEMAC images (9.8 ± 3.4 mm) was significantly smaller than that in the STIR images (16.0 ± 4.7 mm, P < 0.001). In patient-based analysis, the fat suppression and imaging quality scores were significantly higher for the STIR-SEMAC images than for the STIR images (all P < 0.001). The cerebrospinal fluid signal intensity, noise, and signal-to-noise ratios were significantly higher for the STIR images (all P < 0.005). CONCLUSIONS: Short-tau inversion recovery-SEMAC sequences provide good metallic artifact reduction and fat suppression for postsurgery spine 1.5 T MRI.

A pharmacokinetic model based on the SSA-1DCNN-Attention method.

To solve the problem of the lack of representativeness of the training set and the poor prediction accuracy due to the limited number of training samples when the machine learning method is used for the classification and prediction of pharmacokinetic indicators, this paper proposes a 1DCNN-Attention concentration prediction model optimized by the sparrow search algorithm (SSA). First, the SMOTE method is used to expand the small sample experimental data to make the data diverse and representative. Then a one-dimensional convolutional neural network (1DCNN) model is established, and the attention mechanism is introduced to calculate the weight of each variable for dividing the importance of each pharmacokinetic indicator by the output drug concentration. The SSA algorithm was used to optimize the parameters in the model to improve the prediction accuracy after data expansion. Taking the pharmacokinetic model of phenobarbital (PHB) combined with Cynanchum otophyllum saponins to treat epilepsy as an example, the concentration changes of PHB were predicted and the effectiveness of the method was verified. The results show that the proposed model has a better prediction effect than other methods.

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. METHODS: A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. RESULTS: The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). CONCLUSION: Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

Role of the extra Fe in K2-x Fe4+y Se5 superconductors.

The exact superconducting phase of K2-x Fe4+y Se5 has so far not been conclusively decided since its discovery due to its intrinsic multiphase in early material. In an attempt to resolve this mystery, we have carried out systematic structural studies on a set of well-controlled samples with exact chemical stoichiometry K2-x Fe4+x Se5 (x = 0-0.3) that are heat-treated at different temperatures. Using high-resolution synchrotron radiation X-ray diffraction, our investigations have determined the superconducting transition by focusing on the detailed temperature evolution of the crystalline phases. Our results show that superconductivity appears only in those samples that have been treated at high-enough temperature and then quenched to room temperature. The volume fraction of superconducting transition strongly depends on the annealing temperature used. The most striking result is the observation of a clear contrast in crystalline phase between the nonsuperconducting parent compound K2Fe4Se5 and the superconducting K2-x Fe4+y Se5 samples. The X-ray diffraction patterned can be well indexed to the phase with I4/m symmetry in all temperatures investigated. However, we need two phases with similar I4/m symmetry but different parameters to best fit the data at a temperature below the Fe vacancy order temperature. The results strongly suggest that superconductivity in K2-x Fe4+y Se5 critically depends on the occupation of Fe atoms on the originally empty 4d site.