Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

S1PR3 suppresses the inflammatory response and extracellular matrix degradation in human nucleus pulposus cells.

Sphingosine 1-phosphate receptor 3 (S1PR3) participates in the inflammatory response in multiple types of diseases. However, the biological role of S1PR3 in intervertebral disc degeneration and the underlying mechanism are unclear. The aim of the present study was to investigate the functional role and the mechanism of S1PR3 in lipopolysaccharide (LPS)-induced human nucleus pulposus cells. The expression of S1PR3 and Toll-like receptor (TLR) 2 in LPS-induced nucleus pulposus (NP) cells was investigated using western blotting. The Cell Counting Kit-8 assay was used to detect cell proliferation, and the levels of inflammatory factors were detected using ELISA. Flow cytometry and western blotting were used for the assessment of apoptosis. The deposition of extracellular matrix (ECM) proteins was investigated using reverse transcription-quantitative PCR and western blotting. In addition, western blotting was used to investigate the protein expression levels of phosphorylated (p)-STAT3, STAT3, p-JNK, JNK, p-ERK, ERK, p-p38 and p38associated with STAT3 and MAPK signaling. S1PR3 expression was reduced, while TLR2 expression was elevated in LPS-induced human nucleus pulposus cells (HNPC). S1PR3 overexpression increased HNPC viability, inhibited the inflammatory response and suppressed apoptosis. Meanwhile, S1PR3 overexpression regulated the expression of ECM-related proteins. Additionally, overexpression of S1PR3 inhibited the expression of the TLR2-regulated STAT3 and MAPK pathways in LPS-induced HNPCs. Furthermore, TLR2 overexpression partially offset the impacts of S1PR3 overexpression on HNPC viability, apoptosis level, inflammation and as ECM degradation. In conclusion, STAT3 overexpression suppressed viability injury, the inflammatory response and the level of apoptosis and alleviated ECM protein deposition in HNPCs through the TLR2/STAT3 and TLR2/MAPK pathways, which may offer a promising candidate for the amelioration of intervertebral disc degeneration.

Brexpiprazole Prevents the Malignant Progression of Human Colorectal Cancer Cells and Increases Its Sensitivity to EGFR Inhibition.

BACKGROUND: Colorectal cancer (CRC) is a major cause of mortality and morbidity. A study proved that brexpiprazole, as a novel dopamine receptor partial agonist, can also prevent CRC cell proliferation. Therefore, clarifying the molecular mechanism of brexpiprazole is vital to developing a novel therapeutic strategy for CRC. METHODS: The effect of brexpiprazole on human colorectal cancer cell proliferation was measured with Cell Counting Kit-8 (CCK-8) kits. Cell migration capability was measured using wound healing and transwell. Cell apoptosis was evaluated with a flow cytometer. Western blots and immunohistochemical staining were used to evaluate protein expression. The effects observed in vitro were also confirmed in xenograft models. RESULTS: Brexpiprazole remarkably inhibited the proliferation, suppressed the migration ability, and induced apoptosis of colorectal cancer cells. Mechanism study showed that brexpiprazole exerted these effects by inhibiting the EGFR pathway. Brexpiprazole enhanced HCT116 cells' sensitivity to cetuximab, and a combination of brexpiprazole and cetuximab inhibited xenograft tumor growth in vivo. CONCLUSIONS: Our finding suggested that brexpiprazole inhibits proliferation, promotes apoptosis, and enhances CRC cells' sensitivity to cetuximab by regulating the EGFR pathway and it might be an efficacious treatment strategy for CRC.

p53 promotes antiviral innate immunity by driving hexosamine metabolism.

The tumor suppressor p53 controls cell fate decisions and prevents malignant transformation, but its functions in antiviral immunity remain unclear. Here, we demonstrate that p53 metabolically promotes antiviral innate immune responses to RNA viral infection. p53-deficient macrophages or mice display reduced expression of glutamine fructose-6-phosphate amidotransferase 2 (GFPT2), a key enzyme of the hexosamine biosynthetic pathway (HBP). Through transcriptional upregulation of GFPT2, p53 drives HBP activity and de novo synthesis of UDP-GlcNAc, which in turn leads to the O-GlcNAcylation of mitochondrial antiviral signaling protein (MAVS) and UBX-domain-containing protein 1 (UBXN1) during virus infection. Moreover, O-GlcNAcylation of UBXN1 blocks its interaction with MAVS, thereby further liberating MAVS for tumor necrosis factor receptor-associated factor 3 binding to activate TANK-binding kinase 1-interferon (IFN) regulatory factor 3 signaling cascades and IFN-ß production. Genetic or pharmaceutical inhibition of GFPT efficiently reduces MAVS activation and abrogates the antiviral innate immunity promoted by p53 in vitro and in vivo. Our findings reveal that p53 drives HBP activity and O-GlcNAcylation of UBXN1 and MAVS to enhance IFN-ß-mediated antiviral innate immunity.

Promoting fast MR imaging pipeline by full-stack AI.

Magnetic resonance imaging (MRI) is a widely used imaging modality in clinics for medical disease diagnosis, staging, and follow-up. Deep learning has been extensively used to accelerate k-space data acquisition, enhance MR image reconstruction, and automate tissue segmentation. However, these three tasks are usually treated as independent tasks and optimized for evaluation by radiologists, thus ignoring the strong dependencies among them; this may be suboptimal for downstream intelligent processing. Here, we present a novel paradigm, full-stack learning (FSL), which can simultaneously solve these three tasks by considering the overall imaging process and leverage the strong dependence among them to further improve each task, significantly boosting the efficiency and efficacy of practical MRI workflows. Experimental results obtained on multiple open MR datasets validate the superiority of FSL over existing state-of-the-art methods on each task. FSL has great potential to optimize the practical workflow of MRI for medical diagnosis and radiotherapy.

Tomographic Image Reconstruction Using an Advanced Score Function (ADSF).

Computed tomography (CT) reconstructs volumetric images using X-ray projection data acquired from multiple angles around an object. For low-dose or sparse-view CT scans, the classic image reconstruction algorithms often produce severe noise and artifacts. To address this issue, we develop a novel iterative image reconstruction method based on maximum a posteriori (MAP) estimation. In the MAP framework, the score function, i.e., the gradient of the logarithmic probability density distribution, plays a crucial role as an image prior in the iterative image reconstruction process. By leveraging the Gaussian mixture model, we derive a novel score matching formula to establish an advanced score function (ADSF) through deep learning. Integrating the new ADSF into the image reconstruction process, a new ADSF iterative reconstruction method is developed to improve image reconstruction quality. The convergence of the ADSF iterative reconstruction algorithm is proven through mathematical analysis. The performance of the ADSF reconstruction method is also evaluated on both public medical image datasets and clinical raw CT datasets. Our results show that the ADSF reconstruction method can achieve better denoising and deblurring effects than the state-of-the-art reconstruction methods, showing excellent generalizability and stability.

Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, deep learning (DL)-based methods have achieved promising results in CT reconstruction, but these methods usually require the centralized collection of large amounts of data for training from specific scanning protocols, which leads to serious domain shift and privacy concerns. To relieve these problems, in this article, we propose a hypernetwork-based physics-driven personalized federated learning method (HyperFed) for CT imaging. The basic assumption of the proposed HyperFed is that the optimization problem for each domain can be divided into two subproblems: local data adaption and global CT imaging problems, which are implemented by an institution-specific physics-driven hypernetwork and a global-sharing imaging network, respectively. Learning stable and effective invariant features from different data distributions is the main purpose of global-sharing imaging network. Inspired by the physical process of CT imaging, we carefully design physics-driven hypernetwork for each domain to obtain hyperparameters from specific physical scanning protocol to condition the global-sharing imaging network, so that we can achieve personalized local CT reconstruction. Experiments show that HyperFed achieves competitive performance in comparison with several other state-of-the-art methods. It is believed as a promising direction to improve CT imaging quality and personalize the needs of different institutions or scanners without data sharing. Related codes have been released at https://github.com/Zi-YuanYang/HyperFed.

Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT.

Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To address this challenge, here we introduce an iterative reconstruction algorithm regularized by a diffusion prior. Drawing on the exceptional imaging prowess of the denoising diffusion probabilistic model (DDPM), we merge it with a reconstruction procedure that prioritizes data fidelity. This fusion capitalizes on the merits of both techniques, delivering exceptional reconstruction results in an unsupervised framework. To further enhance the efficiency of the reconstruction process, we incorporate the Nesterov momentum acceleration technique. This enhancement facilitates superior diffusion sampling in fewer steps. As demonstrated in our experiments, our method offers a potential pathway to high-definition CT image reconstruction with minimized radiation.

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction.

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4-D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires a manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this article, we propose a sparse and low-rank unrolling network (SOUL-Net) for spectral CT image reconstruction, that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction.

METTL3-mediated ANXA2 inhibition confers neuroprotection in ischemic stroke: Evidence from in vivo and in vitro studies.

Given the important role of m6A, the most common and reversible mRNA modification, in the pathogenesis of ischemic stroke, this study investigates the mechanisms of m6A methyltransferase METTL3 in neuronal damage in ischemic stroke. In silico analysis was used to pinpoint the expression of ANXA2, which was verified in clinical peripheral blood samples. SD rats were used for middle cerebral artery occlusion (MCAO) establishment. The experimental data suggested that T lymphocytes were increased in peripheral blood samples of ischemic stroke patients and MCAO rats. The MCAO rats were treated with anti-ANXA2 alone or combined with RP101075 (T lymphocyte infiltration inhibitor), followed by brain injury assessment. Oxygen-glucose deprivation/reoxygenation (OGD/R) was induced in primary cortical neurons, where shRNAs targeting ANXA2 or METTL3, or overexpression plasmids of METTL3 were introduced to verify the regulatory function for METTL3. Inhibition of T lymphocyte migration to the ischemic brain reduced brain injury in MCAO rats and neuronal damage in OGD/R-exposed neurons. Ablation of ANXA2 in T lymphocytes inhibited the migration of T lymphocytes to the ischemic brain and reduced neuronal damage. Mechanistically, METTL3 reduced ANXA2 expression in T lymphocytes through m6A modification and inhibited p38MAPK/MMP-9 pathway activation, exerting protective effects against neuronal damage in ischemic stroke. Overall, this study reveals the neuroprotective effects of METTL3-mediated ANXA2/p38MAPK/MMP-9 inhibition against ischemic stroke.

Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning.

Accurate and automatic segmentation of individual tooth and root canal from cone-beam computed tomography (CBCT) images is an essential but challenging step for dental surgical planning. In this paper, we propose a novel framework, which consists of two neural networks, DentalNet and PulpNet, for efficient, precise, and fully automatic tooth instance segmentation and root canal segmentation from CBCT images. We first use the proposed DentalNet to achieve tooth instance segmentation and identification. Then, the region of interest (ROI) of the affected tooth is extracted and fed into the PulpNet to obtain precise segmentation of the pulp chamber and the root canal space. These two networks are trained by multi-task feature learning and evaluated on two clinical datasets respectively and achieve superior performances to several comparing methods. In addition, we incorporate our method into an efficient clinical workflow to improve the surgical planning process. In two clinical case studies, our workflow took only 2 min instead of 6 h to obtain the 3D model of tooth and root canal effectively for the surgical planning, resulting in satisfying outcomes in difficult root canal treatments.

DeSeg: auto detector-based segmentation for brain metastases.

Delineation of brain metastases (BMs) is a paramount step in stereotactic radiosurgery treatment. Clinical practice has specific expectation on BM auto-delineation that the method is supposed to avoid missing of small lesions and yield accurate contours for large lesions. In this study, we propose a novel coarse-to-fine framework, named detector-based segmentation (DeSeg), to incorporate object-level detection into pixel-wise segmentation so as to meet the clinical demand. DeSeg consists of three components: a center-point-guided single-shot detector to localize the potential lesion regions, a multi-head U-Net segmentation model to refine contours, and a data cascade unit to connect both tasks smoothly. Performance on tiny lesions is measured by the object-based sensitivity and positive predictive value (PPV), while that on large lesions is quantified by dice similarity coefficient (DSC), average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD95). Besides, computational complexity is also considered to study the potential of method in real-time processing. This study retrospectively collected 240 BM patients with Gadolinium injected contrast-enhanced T1-weighted magnetic resonance imaging (T1c-MRI), which were randomly split into training, validating and testing datasets (192, 24 and 24 scans, respectively). The lesions in the testing dataset were further divided into two groups based on the volume size (smallS: ≤1.5 cc,N= 88; largeL: > 1.5 cc,N= 15). On average, DeSeg yielded a sensitivity of 0.91 and a PPV of 0.77 on S group, and a DSC of 0.86, an ASSD 0f 0.76 mm and a HD95 of 2.31 mm onLgroup. The results indicated that DeSeg achieved leading sensitivity and PPV for tiny lesions as well as segmentation metrics for large ones. After our clinical validation, DeSeg showed competitive segmentation performance while kept faster processing speed comparing with existing 3D models.

Transcriptome Dynamics of Common Bean Roots Exposed to Various Heavy Metals Reveal Valuable Target Genes and Promoters for Genetic Engineering.

Understanding the gene regulatory basis of plant response to heavy metals (HMs) is fundamental for the management of food safety and security. However, a comprehensive and comparative view of the plant responses to different HMs is still lacking. Here, we compared root transcriptomes in common bean under 9 HM treatments at 50 µM for three time points each. Cd, Cr, Co, Ni, and Pb caused most severe morphological and/or biochemical retardations. A total of 448 genes were found to be responsive to all nine HMs, which were mostly involved in photosynthesis, oxidization-reduction, and ion binding. Cd and Cu triggered the greatest number of unique differentially expressed genes (DEG)s, which were predominantly related to cellular transport/localization in the former and RNA binding in the latter. Short-term and prolonged HM treatments shaped very different DEG patterns. Weighted gene co-expression network analysis identified six co-expression modules showing exceptionally high transcripts abundance in specific HM × time scenarios. We experimentally verified the promoter activity of the gene GIP1 and the novel function of XTH23 under Cu/Cd stress. Collectively, the transcriptomic atlas provides valuable resources for better understanding the common and unique mechanisms of plant response to different HMs and offers a mass of candidate target genes/promoters for genetic engineering.

M3NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising.

Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent diagnosis and analysis. Recently, convolutional neural networks have achieved promising results in removing noise from LDCT images. The network architectures that are used are either handcrafted or built on top of conventional networks such as ResNet and U-Net. Recent advances in neural network architecture search (NAS) have shown that the network architecture has a dramatic effect on the model performance. This indicates that current network architectures for LDCT may be suboptimal. Therefore, in this paper, we make the first attempt to apply NAS to LDCT and propose a multi-scale and multi-level memory-efficient NAS for LDCT denoising, termed M3NAS. On the one hand, the proposed M3NAS fuses features extracted by different scale cells to capture multi-scale image structural details. On the other hand, the proposed M3NAS can search a hybrid cell- and network-level structure for better performance. In addition, M3NAS can effectively reduce the number of model parameters and increase the speed of inference. Extensive experimental results on two different datasets demonstrate that the proposed M3NAS can achieve better performance and fewer parameters than several state-of-the-art methods. In addition, we also validate the effectiveness of the multi-scale and multi-level architecture for LDCT denoising, and present further analysis for different configurations of super-net.

Incidence and risk factors of postoperative delirium after pancreatic cancer surgery: a retrospective study.

PURPOSE: Postoperative delirium (POD) commonly occurs after major abdominal surgery and is associated with increased morbidity and mortality. There have been many studies on the relationship between POD and various surgeries, but research on POD after pancreatic cancer surgery is limited. The aim of this study was to identify the incidence and risk factors of POD after pancreatic cancer surgery. METHODS: The subjects of this retrospective analysis were 196 patients who were transferred for postoperative care after pancreatic cancer surgery, to a 12-bed critical care medicine ward at Shandong Provincial Hospital, affiliated with Shandong First Medical University, between January 2015 and December 2019. The patients were divided according to whether they suffered POD into a delirium group and a non-delirium group. Delirium was assessed using the Confusion Assessment Method for the Intensive Care Unit and two independent medical practitioners analyzed all the data. Univariate and multiple logistic regression analyses were performed. RESULTS: The overall delirium incidence was 20.41%, which increased to 29.03% for patients aged ≥ 70 years. POD was associated with age, smoking, the American Society of Anesthesiologists classification, the Acute Physiology and Chronic Health Evaluation II score, and the TNM stage of the cancer. The variables concerning sex, drinking, hypertension, a history of cerebral disease, surgery type, operation time, amount of bleeding, and the intraoperative use of dexmedetomidine did not differ significantly between the two groups. There was no significant difference in the length of ICU stay, with the exclusion of long-term stay for complications, between the groups, but POD tended to prolong the postoperative hospital stay and increase the risk of mortality. There was also a gradual decline in the incidence of POD between 2015 and 2019, especially from 2015 to 2018, after preventive measures were implemented. CONCLUSION: POD is related to many risk factors and worthy of attention. Appropriate management can reduce its incidence or at least shorten its duration.

Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey.

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome.

OBJECTIVE: The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models. METHODS: The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model. RESULTS: Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase. CONCLUSION: The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

Study on Dynamic Progression and Risk Assessment of Metabolic Syndrome Based on Multi-State Markov Model.

Aim: Metabolic syndrome (MetS) coexists with the occurrence and even death of cardiovascular disease and diabetes mellitus. It is essential to study the factors in the dynamic progression of MetS in the interest of prevention and control. Purpose: The aim of this study was to analyze the dynamic progression of Mets and explore the potential factors influencing the progression or reversal of MetS. Patients and Methods: This study involved 5581 individuals from two waves of the China Health and Retirement Longitudinal Study: 2011 and 2015. A multistate Markov model containing 4 states (free of metabolic disorder (FMD), mild metabolic disorder (MMD), severe metabolic disorder (SMD) and MetS) was adopted to study the dynamic progression of MetS and its influencing factors. Results: After follow-up, a total of 2862 cases (50.28% of the total number) had disease state transition. The intensity of transition from MetS to SMD is the same as that from SMD to MMD, and is greater than that from MMD to Mets (0.06 vs 0.05). For the MetS state, a mean of 1/0.08=12.5 years was spent in the MetS state before recovery. The exercise, smoke, drink, BMI level, hyperuricemia had statistically significant effects on progression of MetS status (P<0.05). The obesity or overweight, little exercise, smoke, drink and hyperuricemia increased the risk of forward progression of MetS disease status. There were significant nonmodifiable (age, gender) and modifiable factors (exercise, drink, BMI level, or high HbA1c) associated with reversion of MetS state. Conclusion: The likelihood of progression from MMD to MetS is less likely than that of reversion from MetS to SMD and SMD to MMD. Old females were more resistant to recover from worse states than males. Prevention and intervention measures should be adopted early when MMD or SMD onset occurs.

Relationship between vitamin D receptor gene polymorphisms and second acid-fast bacilli smear-positive during treatment for tuberculosis patients.

PURPOSE: Tuberculosis (TB) treatment is associated with Vitamin D. This study aimed to explore the relationship between Vitamin D receptor (VDR) gene polymorphisms and second acid-fast bacilli (AFB) smear-positive during treatment for TB patients. METHODS: This was a cross-sectional study. Seven hundred and thirty-one TB patients whose single nucleotide polymorphism site (SNPs) of VDR gene were detected from December 2019 to December 2020 in XinJiang of China. The genotypic distributions in each group were tested separately for Hardy-Weinberg equilibrium. The tetragram test was used to construct haplotypes to evaluate the association between each haplotype and second AFB smear-positive occurrence. RESULTS: No significant deviations were observed with all the four polymorphism sites in the genotypic distributions (P>0.05). Linkage disequilibrium (LD) analysis showed that there was LD between SNPs of VDR gene (r2=0.74, D`>0.9). Each haplotype was not considered to be the influencing factor of second AFB smear-positive. CONCLUSIONS: There is no association between VDR gene polymorphism (ApaI, BsmI, FokI and TaqI) and second AFB smear-positive.

The relationship between vitamin D level and second acid-fast bacilli (AFB) smear-positive during treatment for TB patients was inferred by Bayesian network.

BACKGROUND: Vitamin D is related to human immunity, so we used Bayesian network model to analyze and infer the relationship between vitamin D level and the acid-fast bacilli (AFB) smear-positive after two months treatment among pulmonary tuberculosis (TB) patients. METHODS: This is a cross-sectional study. 731 TB patients whose vitamin D level were detected and medical records were collected from December 2019 to December 2020 in XinJiang of China. Logistic regression was used to analyze the influencing factors of second AFB smear-positive. Bayesian network was used to further analyze the causal relationship among vitamin D level and the second AFB smear-positive. RESULTS: Baseline AFB smear-positive (OR = 6.481, 95%CI: 1.604~26.184), combined cavity (OR = 3.204, 95%CI: 1.586~6.472), full supervision (OR = 8.173, 95%CI:1.536~43.492) and full management (OR = 6.231, 95%CI:1.031~37.636) were not only the risk factors and can also be considered as the reasons for second AFB smear-positive in TB patients (Ensemnle > 0.5). There was no causal relationship between vitamin D level and second AFB smear-positive (Ensemnle = 0.0709). CONCLUSIONS: The risk factors of second AFB smear-positive were baseline AFB smear-positive, combined cavity, full supervision and full management. The vitamin D level in TB patients was not considered as one of the reasons for the AFB smear-positive.