Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. / åŒºåˆ†èƒ¶è´¨æ¯ç»†èƒžç˜¤å’Œå•å‘æ€§è„‘è½¬ç§»ç˜¤çš„å¤šæ¨¡æ€èžåˆæ·±åº¦å­¦ä¹ æ¨¡åž‹çš„å¼€å‘å’ŒéªŒè¯.

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Predictive value of peripheral blood leukocytes-based methylation of Long non-coding RNA MALAT1 and H19 in the chemotherapy effect and prognosis of gastric cancer.

BACKGROUND: The predictive value of the methylation of Long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and H19 promoters in peripheral blood leukocytes as a non-invasive biomarker for the chemotherapy effect and prognosis gastric cancer (GC) is unclear. METHODS: The DNA methylation of H19 and MALAT1 between chemotherapy-sensitive and non-sensitive groups and between groups with better and worse survival of GC was compared using regression analyses. Several predictive nomograms were constructed. The genetic alteration of MALAT1 and H19 and the association between gene expression and immune status in GC were also investigated using bioinformatics analysis. RESULTS: Higher genetic methylations in peripheral blood were noticed in GC groups with poorer survival. The constructed nomograms presented strong predictive values for the chemotherapy effect and 3-year survival of disease-free survival, progression-free survival, and overall survival, with the area under the curve as 0.838, 0.838, 0.912, and 0.925, respectively. Significant correlations between MALAT1 or H19 expression and marker genes of immune checkpoints and immune pathways were noticed. The high infiltration of macrophages in H19-high and low infiltration of CD8+ T cells in MALAT1-high groups were associated with worse survival of GC. CONCLUSIONS: MALAT1 and H19 have the potential to predict the chemotherapy response and clinical outcomes of GC.

Deep convolutional network-based chest radiographs screening model for pneumoconiosis.

Background: Pneumoconiosis is the most important occupational disease all over the world, with high prevalence and mortality. At present, the monitoring of workers exposed to dust and the diagnosis of pneumoconiosis rely on manual interpretation of chest radiographs, which is subjective and low efficiency. With the development of artificial intelligence technology, a more objective and efficient computer aided system for pneumoconiosis diagnosis can be realized. Therefore, the present study reported a novel deep learning (DL) artificial intelligence (AI) system for detecting pneumoconiosis in digital frontal chest radiographs, based on which we aimed to provide references for radiologists. Methods: We annotated 49,872 chest radiographs from patients with pneumoconiosis and workers exposed to dust using a self-developed tool. Next, we used the labeled images to train a convolutional neural network (CNN) algorithm developed for pneumoconiosis screening. Finally, the performance of the trained pneumoconiosis screening model was validated using a validation set containing 495 chest radiographs. Results: Approximately, 51% (25,435/49,872) of the chest radiographs were labeled as normal. Pneumoconiosis was detected in 49% (24,437/49,872) of the labeled radiographs, among which category-1, category-2, and category-3 pneumoconiosis accounted for 53.1% (12,967/24,437), 20.4% (4,987/24,437), and 26.5% (6,483/24,437) of the patients, respectively. The CNN DL algorithm was trained using these data. The validation set of 495 digital radiography chest radiographs included 261 cases of pneumoconiosis and 234 cases of non-pneumoconiosis. As a result, the accuracy of the AI system for pneumoconiosis identification was 95%, the area under the curve was 94.7%, and the sensitivity was 100%. Conclusion: DL algorithm based on CNN helped screen pneumoconiosis in the chest radiographs with high performance; thus, it could be suitable for diagnosing pneumoconiosis automatically and improve the efficiency of radiologists.

Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study.

Background: In 2020, our center established a Tanner-Whitehouse 3 (TW3) artificial intelligence (AI) system using a convolutional neural network (CNN), which was built upon 9059 radiographs. However, the system, upon which our study is based, lacked a gold standard for comparison and had not undergone thorough evaluation in different working environments. Methods: To further verify the applicability of the AI system in clinical bone age assessment (BAA) and to enhance the accuracy and homogeneity of BAA, a prospective multi-center validation was conducted. This study utilized 744 left-hand radiographs of patients, ranging from 1 to 20 years of age, with 378 boys and 366 girls. These radiographs were obtained from nine different children's hospitals between August and December 2020. The BAAs were performed using the TW3 AI system and were also reviewed by experienced reviewers. Bone age accuracy within 1 year, root mean square error (RMSE), and mean absolute error (MAE) were statistically calculated to evaluate the accuracy. Kappa test and Bland-Altman (B-A) plot were conducted to measure the diagnostic consistency. Results: The system exhibited a high level of performance, producing results that closely aligned with those of the reviewers. It achieved a RMSE of 0.52 years and an accuracy of 94.55% for the radius, ulna, and short bones series. When assessing the carpal series of bones, the system achieved a RMSE of 0.85 years and an accuracy of 80.38%. Overall, the system displayed satisfactory accuracy and RMSE, particularly in patients over 7 years old. The system excelled in evaluating the carpal bone age of patients aged 1-6. Both the Kappa test and B-A plot demonstrated substantial consistency between the system and the reviewers, although the model encountered challenges in consistently distinguishing specific bones, such as the capitate. Furthermore, the system's performance proved acceptable across different genders and age groups, as well as radiography instruments. Conclusions: In this multi-center validation, the system showcased its potential to enhance the efficiency and consistency of healthy delivery, ultimately resulting in improved patient outcomes and reduced healthcare costs.

Dual consistency regularization with subjective logic for semi-supervised medical image segmentation.

Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical image analysis. It significantly reduces the time and cost involved in labeling data. Current methods primarily focus on consistency regularization and the generation of pseudo labels. However, due to the model's poor awareness of unlabeled data, aforementioned methods may misguide the model. To alleviate this problem, we propose a dual consistency regularization with subjective logic for semi-supervised medical image segmentation. Specifically, we introduce subjective logic into our semi-supervised medical image segmentation task to estimate uncertainty, and based on the consistency hypothesis, we construct dual consistency regularization under weak and strong perturbations to guide the model's learning from unlabeled data. To evaluate the performance of the proposed method, we performed experiments on three widely used datasets: ACDC, LA, and Pancreas. Experiments show that the proposed method achieved improvement compared with other state-of-the-art (SOTA) methods.

Health-related quality of life in Chinese SLE patients: evidence from 1568 SLE patients and 2610 healthy controls.

OBJECTIVE: To investigate the effects of systemic lupus erythematosus (SLE) on health-related quality of life (HRQOL), the relationship between disease activity and HRQOL, and potential factors affecting HRQOL in Chinese SLE patients. METHODS: This study recruited 1568 patients and 2610 controls to explore the effects of SLE on HRQOL. The association between disease activity and HRQOL, and the influencing factors of HRQOL were determined in 1568 patients. Then, we prospectively followed 1096 patients to explore the association between reduced disease activity and improved HRQOL, and the influencing factors of improved HRQOL. The Short-Form 36 (SF-36) and SLE disease activity index (SLEDAI) were used to evaluate HRQOL and disease activity. RESULTS: Chinese SLE patients had lower HRQOL than controls in all domains (P < 0.001), especially in role-physical (RP) and role-emotional (RE). Compared with SLE patients from outside China, the HRQOL of Chinese patients appeared to be higher in mental component summary (MCS) but lower in RP and RE. SLEDAI was negatively correlated with HRQOL, which was validated using the results of a follow-up study, where SLEDAI reduction was positively associated with HRQOL improvements (P < 0.05). Furthermore, personality, life nervous and experiences of adverse life events may influence HRQOL and HRQOL improvements. CONCLUSION: SLE significantly affected the HRQOL of Chinese patients, especially in RP and RE. Disease activity was negatively correlated with HRQOL. We also found for the first time some factors affecting HRQOL, which can be regarded as the basis for improving the HRQOL of SLE patients.

BNIP3 and DAPK1 methylation in peripheral blood leucocytes are noninvasive biomarkers for gastric cancer.

OBJECTIVE: The objective of this study is to comprehensively investigate the potential value of BNIP3 and DAPK1 methylation in peripheral blood leukocytes as a non-invasive biomarker for the detection of gastric cancer (GC), prediction of chemotherapy efficacy, and prognosis assessment. PATIENTS AND METHODS: Initially, multiple bioinformatic analyses were employed to explore the genetic landscape and biological effects of BNIP3 and DAPK1 in GC tissues. Subsequently, case-control and prospective follow-up studies were conducted to compare the differences in BNIP3 and DAPK1 methylation levels in peripheral blood leukocytes among GC patients and healthy controls, as well as between patients exhibiting sensitivity and resistance to platinum plus fluorouracil treatment, and between patients with varying survival outcomes of GC. Additionally, several predictive nomograms were constructed based on the identified CpG sites and relevant clinical parameters to forecast the occurrence of GC, chemotherapy efficacy, and prognosis. RESULTS: The upregulation of BNIP3 and DAPK1 was found to be associated with the development and poorer survival outcomes of GC. Furthermore, the expression of BNIP3/DAPK1 exhibited an inverse relationship with their DNA methylation levels and demonstrated a positive correlation with immune cell infiltration, as well as the IC50 values of 5-Fluorouracil and Cisplatin in GC tissues. Increased infiltration of macrophages in the high-expression groups was observed to be linked to unfavorable GC survival. In the case-control and follow-up studies, lower methylation levels of BNIP3 and DAPK1 were identified in the peripheral leukocytes of GC patients compared to healthy controls. Hypomethylation was also associated with more aggressive subtypes, diminished chemotherapy efficacy, and poorer survival outcomes in GC. CONCLUSION: The DNA methylation of BNIP3 and DAPK1 in peripheral blood leukocytes holds promise as a novel non-invasive biomarker for predicting the occurrence of GC, chemotherapy efficacy, and prognosis assessment.

Global disease burden attributed to high sugar-sweetened beverages in 204 countries and territories from 1990 to 2019.

High sugar-sweetened beverages (SSBs) are a controllable risk factor for chronic non-communicable diseases (NCDs), but their effect on the global disease burden is uncertain. The study aims to assess the global burden of high SSBs from 1990 to 2019. Global Burden of Disease (GBD) 2019 provides data on deaths, disability-adjusted life years (DALYs), years of life with disabilities (YLDs) and years of life lost (YLLs) ascribe to high SSBs by ages, genders, regions and countries. For the past 30 years, overall exposure to high SSBs decreased for males and increased for females. The number of deaths from chronic NCDs ascribed to high SSBs increased from 149,988 (110,278-182,947) to 242,218 (172,045-302,250), DALYs increased from 3,698,578 (2,693,476-4,559,740) to 6,307,562 (4,300,765-8,079,556), especially the males. Age-standardized YLDs rate (ASYLDs) increased from 11.58 to 17.03. The number of ischemic heart disease (IHD) and diabetes mellitus (DM) deaths and DALYs ascribed to high SSBs has been increasing. Age-standardized death rate (ASDR) for DM risen from 0.56 to 0.62, age-standardized DALYs rate (ASDALYs) risen from 21.41 to 28.21. The burden of disease ascribed to high SSBs was in the elderly significantly higher than in the young and middle-aged, mainly concentrated in Central Asia and Oceania. The disease burden was highest in regions with moderate sociodemographic index (SDI). More extraordinary efforts should be made to raise awareness among the general public about interventions aimed at limiting the use of high SSBs, to reduce disease burden ascribed to high SSBs.

Global, regional, and national burden of digestive diseases: findings from the global burden of disease study 2019.

Background: The global burden of digestive diseases has been rising in the last 30 years. The rates and trends of incidence, deaths, and disability-adjusted life-years (DALYs) for digestive diseases need to be investigated. Methods: We extracted the data on overall digestive diseases and by cause between 1990-2019 from the Global Burden of Diseases 2019 website, including the absolute number and the corresponding age-standardized rates of incidence (ASIR), deaths (ASDR), and DALYs (ASDALYs). Results: Globally, the incident cases, deaths, and DALYs of digestive diseases in 2019 increased by 74.44, 37.85, and 23.46%, respectively, compared with that in 1990, with an increasing ASIR of 0.09%, as well as decreasing ASDR and ASDALYs of 1.38 and 1.32% annually. The sociodemographic index (SDI) of overall digestive diseases showed a slight increase in ASIR from low to middle-low regions. The downtrend in ASDR and ASDALYs was found in all SDI regions. The burden of incidence was higher in females, while the burden of deaths and DALYs was higher in males for the overall digestive diseases and most causes. The estimated annual percentage changes were significantly associated with the baseline ASIR, ASDR, and ASDALYs for the overall digestive diseases, and the negative correlations between ASDR, ASDALYs, and human development index both in 1990 (R = -0.68, R = -0.69) and 2019 (R = -0.71, R = -0.73) were noticed. Conclusion: The findings indicate that digestive diseases remain a significant public health burden, with substantial variation across countries, sexes, and age groups. Therefore, implementing age, gender, and country-specific policies for early screening and targeted interventions could significantly reduce the global burden of digestive diseases.

A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT.

Introduction: Accurate delineation of tumor targets is crucial for stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC). This study aims to develop a deep learning-based segmentation approach to accurately and efficiently delineate NSCLC targets using diagnostic PET-CT and SBRT planning CT (pCT). Methods: The diagnostic PET was registered to pCT using the transform matrix from registering diagnostic CT to the pCT. We proposed a 3D-UNet-based segmentation method to segment NSCLC tumor targets on dual-modality PET-pCT images. This network contained squeeze-and-excitation and Residual blocks in each convolutional block to perform dynamic channel-wise feature recalibration. Furthermore, up-sampling paths were added to supplement low-resolution features to the model and also to compute the overall loss function. The dice similarity coefficient (DSC), precision, recall, and the average symmetric surface distances were used to assess the performance of the proposed approach on 86 pairs of diagnostic PET and pCT images. The proposed model using dual-modality images was compared with both conventional 3D-UNet architecture and single-modality image input. Results: The average DSC of the proposed model with both PET and pCT images was 0.844, compared to 0.795 and 0.827, when using 3D-UNet and nnUnet. It also outperformed using either pCT or PET alone with the same network, which had DSC of 0.823 and 0.732, respectively. Discussion: Therefore, our proposed segmentation approach is able to outperform the current 3D-UNet network with diagnostic PET and pCT images. The integration of two image modalities helps improve segmentation accuracy.

Impact of climate factors and climate-gene interaction on systemic lupus erythematosus patients' response to glucocorticoids therapy.

BACKGROUND: Glucocorticoids (GCs) were the essential drugs for systemic lupus erythematosus (SLE). However, different patients differ substantially in their response to GCs treatment. Our current study aims at investigating whether climate variability and climate-gene interaction influence SLE patients' response to the therapy of GCs. METHODS: In total, 778 SLE patients received therapy of GCs for a study of 12-week follow-up. The efficacy of GCs treatment was evaluated using the Systemic Lupus Erythematosus Disease Activity Index. The climatic data were provided by China Meteorological Data Service Center. Additive and multiplicative interactions were examined. RESULTS: Compared with patients with autumn onset, the efficacy of GCs in patients with winter onset is relatively poor (ORadj = 1.805, 95%CIadj : 1.181-3.014, padj = 0.020). High mean relative humidity during treatment decreased the efficacy of GCs (ORadj = 1.033, 95%CIadj : 1.008-1.058, padj = 0.011), especially in female (ORadj = 1.039, 95%CIadj : 1.012-1.067, padj = 0.004). There was a significant interaction between sunshine during treatment and TRAP1 gene rs12597773 on GCs efficacy (Recessive model: AP = 0.770). No evidence of significant interaction was found between climate factors and the GR gene polymorphism on the improved GCs efficacy in the additive model. Multiplicative interaction was found between humidity in the month prior to treatment and GR gene rs4912905 on GCs efficacy (Dominant model: OR = 0.470, 95%CI: 0.244-0.905, p = 0.024). CONCLUSIONS: Our findings suggest that climate variability influences SLE patients' response to the therapy of GCs. Interactions between climate and TRAP1/GR gene polymorphisms were related to GCs efficacy. The results guide the individualized treatment of SLE patients.

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion.

Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to severe complications such as bleeding and headache. Therefore, forecasting necrosis of the nasopharynx and initiating timely clinical intervention has important implications for reducing complications caused by re-irradiation. This research informs clinical decision-making by making predictions on re-irradiation of recurrent nasopharyngeal carcinoma using deep learning multi-modal information fusion between multi-sequence nuclear magnetic resonance imaging and plan dose. Specifically, we assume that the hidden variables of model data can be divided into two categories: task-consistency and task-inconsistency. The task-consistency variables are characteristic variables contributing to target tasks, while the task-inconsistency variables are not apparently helpful. These modal characteristics are adaptively fused when the relevant tasks are expressed through the construction of supervised classification loss and self-supervised reconstruction loss. The cooperation of supervised classification loss and self-supervised reconstruction loss simultaneously reserves the information of characteristic space and controls potential interference simultaneously. Finally, multi-modal fusion effectively fuses information through an adaptive linking module. We evaluated this method on a multi-center dataset. and found the prediction based on multi-modal features fusion outperformed predictions based on single-modal, partial modal fusion or traditional machine learning methods.

Iterative multiple instance learning for weakly annotated whole slide image classification.

Objective.Whole slide images (WSIs) play a crucial role in histopathological analysis. The extremely high resolution of WSIs makes it laborious to obtain fine-grade annotations. Hence, classifying WSIs with only slide-level labels is often cast as a multiple instance learning (MIL) problem where a WSI is regarded as a bag and tiled into patches that are regarded as instances. The purpose of this study is to develop a novel MIL method for classifying WSIs with only slide-level labels in histopathology analysis.Approach.We propose a novel iterative MIL (IMIL) method for WSI classification where instance representations and bag representations are learned collaboratively. In particular, IMIL iteratively finetune the feature extractor with selected instances and corresponding pseudo labels generated by attention-based MIL pooling. Additionally, three procedures for robust training of IMIL are adopted: (1) the feature extractor is initialized by utilizing self-supervised learning methods on all instances, (2) samples for finetuning the feature extractor are selected according to the attention scores, and (3) a confidence-aware loss is applied for finetuning the feature extractor.Main results.Our proposed IMIL-SimCLR archives the optimal classification performance on Camelyon16 and KingMed-Lung. Compared with the baseline method CLAM, IMIL-SimCLR significantly outperforms it by 3.71% higher average area under curve (AUC) on Camelyon16 and 4.25% higher average AUC on KingMed-Lung. Additionally, our proposed IMIL-ImageNet achieve the optimal classification performance on TCGA-Lung with the average AUC of 96.55% and the accuracy of 96.76%, which significantly outperforms the baseline method CLAM by 1.65% higher average AUC and 2.09% higher average accuracy respectively.Significance.Experimental results on a public lymph node metastasis dataset, a public lung cancer diagnosis dataset and an in-house lung cancer diagnosis datasets show the effectiveness of our proposed IMIL method across different WSI classification tasks compared with other state-of-the-art MIL methods.

The global burden and trends of four major types of heart disease, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

OBJECTIVES: The global burden of heart disease is severe and increasing in the coming years. This study aims to analyze the global burden of heart disease. STUDY DESIGN: Rheumatic heart disease (RHD), ischemic heart disease (IHD), hypertensive heart disease (HHD), and non-rheumatic valvular heart disease (NRVHD) were selected and analyzed from the Global Burden of Disease Study 2019. METHODS: The prevalence, deaths, disability-adjusted life years and their corresponding age-standardized rates were obtained from the Global Burden of Disease Study 2019. In addition, estimated annual percentage change was calculated to better assess epidemiological trends. In addition, we performed an age-period-cohort analysis using the Nordpred package in R program to predict death trends over the next 20 years. RESULTS: Globally, the prevalence of four heart diseases (RHD, IHD, HHD, and NRVHD) increased by 70.5%, 103.5%, 137.9%, and 110.0% compared with 1990, respectively. The deaths cases of RHD decreased by 15.6%, whereas IHD, HHD, and NRVHD increased by 60.4%, 76.6%, and 110.6%. Compared with absolute values, their corresponding age-standardized rates only showed a slight increase trend or even decreased in some areas with high sociodemographic index. In the next 20 years, the absolute values of deaths will continue to increase, whereas their age-standardized rates of deaths will flatten out. CONCLUSIONS: Globally, the absolute values of heart disease have increased over the past 30 years and will continue to increase over the next 20 years. Targeted prevention and control strategies and measures need to be developed and improved to reduce this burden.

Impact of diurnal temperature range on other infectious diarrhea in Tongcheng, China, 2010-2019: a distributed lag non-linear analysis.

Our study aimed to quantify the exposure-lag-response effects of the diurnal temperature range (DTR) on other infectious diarrhea (OID) in Tongcheng city and examine the vulnerable populations. Distributed lag non-linear model (DLNM) and generalized additive model (GAM) were applied jointly to quantify the association between DTR and the daily number of OID cases compared with the median DTR. Stratified analysis was performed by gender, age, and seasons of onset. There are a total of 8231 cases during this decade. We observed a j-shaped relationship between DTR and OID, with a peak point at the maximum DTR (RR: 2.651, 95% CI: 1.320-5.323) compared to the median DTR. As DTR increased from 8.2 to 10.9 °C, we found the RRs started to decrease and then rise from day 0, and the minimum value occurred on day 7 (RR:1.003, 95% CI: 0.996-1.010). From stratified analysis, we observed that females and adults are more likely to be affected by high DTR significantly. In addition, the influence of DTR was different in cold and warm seasons. High DTR in warm seasons affects the number of OID daily cases, but no statistical significance was identified in cold seasons. This study suggests a significant relationship between high DTR and the incidence risk of OID.

Global, regional, and national deaths, disability-adjusted life years, years lived with disability, and years of life lost for the global disease burden attributable to second-hand smoke, 1990-2019: A systematic analysis for the Global Burden of Disease Study.

BACKGROUND: Smoke-free policies have led to a decline in smoking prevalence. Nevertheless, as the global population grows, more non-smokers are exposed to second-hand smoke (SHS) hazards. Mitigating SHS hazards requires a systematic analysis of the global disease burden attributable to SHS. METHODS: Data on SHS was extracted from the Global Burden of Disease Study 2019. First, we measured the disease burden of SHS by the number of cases and age-standardized rates of deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) from 1990 to 2019. Second, trends in the disease burden of SHS in different periods were estimated based on the annual percentage change (APC) by joinpoint regression analysis. Finally, using histogram plots, world maps, Pearson correlation analysis, and population attributable fraction (PAF), we conducted a stratified analysis of SHS exposure by sex, age, geographic location, sociodemographic index (SDI) level, and disease. RESULTS: The number of deaths caused by SHS remained stable between 1990 and 2019, and the number of YLDs more than doubled in three decades. In contrast, the number of DALYs and YLLs caused by SHS decreased. The declining trend in deaths (APC = -1.42 % [95 % UI -1.79 %, -1.05 %]), DALYs (APC = -1.91 % [95 % UI -2.15 %, -1.67 %]), and YLLs (APC = -1.28 % [95 % UI -1.93 %, -0.64 %]) had slowed down in recent years, while SHS-related YLDs were still increasing (APC = 1.84 % [95 % UI 0.74 %, 2.96 %]). From 2010 to 2019, we found that SHS exposure increased the risk of tracheal, bronchus, and lung cancer (PAF increased by 11.75 %), breast cancer (PAF increased by 5.36 %), diabetes mellitus (PAF increased by 8.24 %), and ischemic heart disease (PAF increased by 4.46 %). In addition, the disease burden caused by SHS was highest in middle SDI and low-middle SDI countries. CONCLUSION: The global disease burden attributable to SHS is still severe, and policymakers need to implement more effective measures to reduce the harm of SHS.

Associations of RPEL1 and miR-1307 gene polymorphisms with disease susceptibility, glucocorticoid efficacy, anxiety, depression, and health-related quality of life in Chinese systemic lupus erythematosus patients.

OBJECTIVE: Our present study intended to examine the associations of RPEL1 and miR-1307 gene polymorphisms (rs4917385 and rs7911488) with susceptibility, glucocorticoids (GCs) efficacy, anxiety, depression, and health-related quality of life (HRQoL) in Chinese systemic lupus erythematosus (SLE) patients. METHODS: Initially, 1000 participants (500 SLE cases and 500 controls) were recruited for the case-control study. Then, 429 cases who received GCs were followed through 12 weeks to explore GCs efficacy, depression, anxiety, and HRQoL. We selected the iMLDR technique for genotyping: RPEL1: rs4917385 (G/T) and miR-1307: rs7911488 (A/G). RESULTS: The minor G allele of rs7911488 reduced the risk of SLE (p = .024). Four haplotypes consisting of rs4917385 and rs7911488 were associated with SLE susceptibility (p < .025). Both rs4917385 and rs7911488 were associated with anxiety symptoms and physical function (PF) in SLE patients (p < .025). The rs4917385 was associated with depression and its improvement. No statistical significance was found between RPEL1 and miR-1307 gene polymorphisms with GCs efficacy. Meanwhile, additive interaction analysis showed a significant association between RPEL1 and miR-1307 gene polymorphisms with tea consumption in anxiety. CONCLUSION: RPEL1 and miR-1307 gene polymorphisms (rs4917385 and rs7911488) might be related to SLE susceptibility in Chinese population. Additionally, the two polymorphisms were possibly associated with depression, anxiety, and HRQoL in Chinese SLE population.

An association study on the risk, glucocorticoids effectiveness, and prognosis of systemic lupus erythematosus: insight from mitochondrial DNA copy number.

This study aimed to explore the role of mitochondrial DNA copy number (mtDNAcn) in the risk, glucocorticoid (GC) effectiveness, and prognosis of systemic lupus erythematosus (SLE) and its interactions with environmental factors and tumor necrosis factor receptor-associated protein 1 (TRAP1) genetic polymorphisms. We first conducted a case-control study of 1198 subjects (595 SLE patients and 603 healthy controls). Subsequently, we followed up with patients to assess the effectiveness of GC treatment and the prognosis of SLE. Real-time fluorescent quantitative PCR (qPCR) was used to quantify mtDNAcn. Associations were estimated using logistic regression, and prognosis analysis was performed using Kaplan-Meier analysis and Cox proportional hazards models. Interactions on multiplicative and additive scales were also evaluated. Individuals with low mtDNAcn had an increased risk of SLE (P < 0.001). Low mtDNAcn was associated with poor GC effectiveness in patients with spicy food consumption or with arthritis (P < 0.05). mtDNAcn was significantly related to the prognosis of SLE in the drinking subgroup (P = 0.018). Furthermore, we found significant interactions between mtDNAcn and environmental factors/TRAP1 genetic polymorphisms on the risk, GC effectiveness, and prognosis of SLE. Our data suggest that low mtDNAcn is associated with an increased risk of SLE. Alteration of mtDNAcn may be associated with GC effectiveness and prognosis in certain subgroups of SLE. The interactions between mtDNAcn, environmental factors, and TRAP1 gene polymorphisms may jointly affect the risk, GC effectiveness, and prognosis of SLE.

Multimolecular characteristics of cell-death related hub genes in human cancers: a comprehensive pan-cancer analysis.

Failure of the normal process of cell death pathways contributes to the defection of immune systems and the occurrence of cancers. The key genes, the multimolecular mechanisms, and the immune functions of these genes in pan-cancers remain unclear. Using online databases of The Cancer Genome Atlas, GEPIA2, TISIDB, HPA, Kaplan-Meier Plotter, PrognoScan, cBioPortal, GSCALite, TIMER, and Sangerbox, we identified the key genes from the six primary cell death-related pathways and performed a comprehensive analysis to investigate the multimolecular characteristics and immunological functions of the hub genes in 33 human cancers. We identified five hub genes in the six primary cell death-related pathways (JUN, NFKB1, CASP3, PARP1, and TP53). We found that CASP3, PARP1, and TP53 were overexpressed in 28, 23, and 27 cancers. The expression of the five genes was associated with the development and prognosis of many cancers. Particularly, JUN, NFKB1, CASP3, and TP53 have prognostic values in Brain Lower Grade Glioma (LGG), while PARP1 and CASP3 could predict the survival outcomes in Adrenocortical carcinoma (ACC). In addition, an extensive association between five genes' expression, DNA methylation, and tumor-immune system interactions was noticed. The five cell death-related hub genes could function as potential biomarkers for various cancers, particularly LGG and ACC. The immunological function analysis of the five genes also proposes new targets for developing immunosuppressants and improving the immunotherapy efficacy of cancers. However, further extensive clinical and experimental research are required to validate their clinical values.

Polycystic ovary syndrome and the risk of endometrial, ovarian and breast cancer: An updated meta-analysis.

BACKGROUND AND AIMS: This updated meta-analysis aimed to further quantify the risk of endometrial, ovarian, and breast cancer in patients with polycystic ovary syndrome (PCOS), thus providing updated and more reliable estimates. METHODS AND RESULTS: We identified relevant articles by searching electronic databases of PubMed, Embase, Web of Science, and Chinese Biological Medical Literature (CBM) published up to March 20, 2021. The pooled effect estimates and their 95% confidence intervals (CIs) were calculated using the random-effect model or the fixed-effect model. A total of 26 eligible studies were included. We found that PCOS was significantly associated with endometrial cancer (odds ratios [OR]: 3.66, 95%CI: 2.05-6.54, P < 0.001), but not with ovarian or breast cancer (OR: 1.23, 95%CI: 0.99-1.53, P = 0.059; OR: 0.94, 95%CI: 0.78-1.14, P = 0.551, respectively). However, in subgroups of high-quality studies, cohort studies, younger women (54 years or less or premenopausal), and studies with unadjusted body mass index (BMI), PCOS patients had a significantly higher risk of ovarian cancer. CONCLUSION: These results indicated that PCOS is a significant risk factor for endometrial cancer independent of BMI, but not for breast cancer. PCOS may increase the risk of ovarian cancer in younger women.