Ultrasensitive Proteomics of Trace Cardiac Tissues with Anchor-Nanoparticles.

Pathological cardiac hypertrophy is a complex process that often leads to heart failure. Label-free proteomics has emerged as an important platform to reveal protein variations and to elucidate the mechanisms of cardiac hypertrophy. Endomyocardial biopsy is a minimally invasive technique for sampling cardiac tissue, but it yields only limited amounts of an ethically permissible specimen. After regular pathological examination, the remaining trace samples pose significant challenges for effective protein extraction and mass spectrometry analysis. Herein, we developed trace cardiac tissue proteomics based on the anchor-nanoparticles (TCPA) method. We identified an average of 6666 protein groups using ∼50 µg of myocardial interventricular septum samples by TCPA. We then applied TCPA to acquire proteomics from patients' cardiac samples both diagnosed as hypertrophic hearts and myocarditis controls and identified significant alterations in pathways such as regulation of actin cytoskeleton, oxidative phosphorylation, and cGMP-PKG signaling pathway. Moreover, we found multiple lipid metabolic pathways to be dysregulated in transthyretin cardiac amyloidosis compared to other types of cardiac hypertrophy. TCPA offers a new technique for studying pathological cardiac hypertrophy and can serve as a platform toolbox for proteomic research in other cardiac diseases.

Protecting effects of 4-octyl itaconate on neonatal hypoxic-ischemic encephalopathy via Nrf2 pathway in astrocytes.

BACKGROUND: Neonatal hypoxic-ischemic encephalopathy (HIE) is one of the most common neurological problems occurring in the perinatal period. However, there still is not a promising approach to reduce long-term neurodevelopmental outcomes of HIE. Recently, itaconate has been found to exhibit anti-oxidative and anti-inflammatory effects. However, the therapeutic efficacy of itaconate in HIE remains inconclusive. Therefore, this study attempts to explore the pathophysiological mechanisms of oxidative stress and inflammatory responses in HIE as well as the potential therapeutic role of a derivative of itaconate, 4-octyl itaconate (4OI). METHODS: We used 7-day-old mice to induce hypoxic-ischemic (HI) model by right common carotid artery ligation followed by 1 h of hypoxia. Behavioral experiments including the Y-maze and novel object recognition test were performed on HI mice at P60 to evaluate long-term neurodevelopmental outcomes. We employed an approach combining non-targeted metabolomics with transcriptomics to screen alterations in metabolic profiles and gene expression in the hippocampal tissue of the mice at 8 h after hypoxia. Immunofluorescence staining and RT-PCR were used to evaluate the pathological changes in brain tissue cells and the expression of mRNA and proteins. 4OI was intraperitoneally injected into HI model mice to assess its anti-inflammatory and antioxidant effects. BV2 and C8D1A cells were cultured in vitro to study the effect of 4OI on the expression and nuclear translocation of Nrf2. We also used Nrf2-siRNA to further validate 4OI-induced Nrf2 pathway in astrocytes. RESULTS: We found that in the acute phase of HI, there was an accumulation of pyruvate and lactate in the hippocampal tissue, accompanied by oxidative stress and pro-inflammatory, as well as increased expression of antioxidative stress and anti-inflammatory genes. Treatment of 4OI could inhibit activation and proliferation of microglial cells and astrocytes, reduce neuronal death and relieve cognitive dysfunction in HI mice. Furthermore, 4OI enhanced nuclear factor erythroid-2-related factor (Nfe2l2; Nrf2) expression and nuclear translocation in astrocytes, reduced pro-inflammatory cytokine production, and increased antioxidant enzyme expression. CONCLUSION: Our study demonstrates that 4OI has a potential therapeutic effect on neuronal damage and cognitive deficits in HIE, potentially through the modulation of inflammation and oxidative stress pathways by Nrf2 in astrocytes.

CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.

The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

Tetraazaisoindigos:Serpentine Syntheses and Their Expected and Unexpected Photophysical and Electronic Properties.

Isoindigo, an electron-withdrawing building block for polymeric field-effect transistors, has long been considered to be non-fluorescent. Moreover, using electron-deficient heterocycle to replace the phenyl ring in the isoindigo core for better electron transport behaviour is synthetically challenging. Here we report the syntheses of a series of tetraazaisoindigos, including pyrazinoisoindigo (PyrII), pyrimidoisoindigo (PymII) and their hybrid (PyrPymII), and the investigation on their photophysical and electric properties. Proper flanking groups need to be chosen to stabilize these highly electron-deficient bislactams. Both PyrII and PymII derivatives show lower LUMO energy levels than that of naphthalene bisimide (NDI). Interestingly, PyrII is instinctively unstable and can be easily reduced, while both PymII derivatives are stable. More surprisingly, PymII derivatives are highly fluorescent and their photoluminescence quantum yields are around 40%, 133 times higher than that of reported isoindigo derivatives. UV-vis spectroscopic results and theoretical calculations show that strong intramolecular hydrogen-bond exists in PymII, which prohibits it from non-radiative decay and accounts for its fluorescent behaviour.  PymII deriviatives are n-type semiconductors, while Ph-PyrII and the hybrid show balanced ambipolar charge transport behaviour, all among the best isoindigo derivatives. Our study not only discloses the structure-property relationship of tetraazaisoindigos, but also provides electron-deficient monomers for conjugated polymers.

Estimating smoking-attributable lung cancer mortality in Chinese adults from 2000 to 2020: a comparison of three methods.

BACKGROUND: Smoking is a significant public health concern in China and a leading cause of lung cancer deaths among adults. This study aims to employ three methods to estimate smoking-attributable lung cancer mortality among Chinese adults from 2000 to 2020. METHODS: Population attributable fractions (PAFs) of lung cancer deaths caused by smoking were estimated using lagged smoking prevalence, Peto-Lopez, and dose-response relationship methods, separately. Smoking exposure was obtained from national tobacco surveys in China, and relative risks (RR) were derived from a meta-analysis of state-of-the-art studies among the Chinese population. Finally, we estimated the sex- and age-stratified smoking-attributable lung cancer deaths in Chinese population in 2000, 2005, 2010, 2015, and 2020. RESULTS: The PAFs estimated using 5- and 10-year lagged smoking prevalence method (45-47%) and Peto-Lopez method (46-47%) were similar, while PAFs calculated using the dose-response method were highest (47-58%). The PAFs were consistently higher in males than in females. Age-specific PAFs estimated by lagged smoking prevalence method (54-60%) and the Peto-Lopez method (57-61%) in males were similar and relatively stable, with slight decreases in older populations, while the dose-response relationship-based PAFs increased with age and fluctuated by year. By using the above methods, smoking-attributable lung cancer deaths were estimated to be 134,100, 134,600, 136,600, and 155,300 in 2000 increasing to 310,300, 301,100, 306,000, and 314,700 in 2020, respectively. CONCLUSION: The estimation from dose-response methods could better reflect the smoking effect, however, high-quality data and accurate estimation of parameters are necessary.

Visual environment in schools and student depressive symptoms: Insights from a prospective study across multiple cities in eastern China.

OBJECTIVE: To investigate the effects of the school visual environment on depressive symptoms in children and adolescents based on cohort study in eastern China. The school visual environment-related indicators included in this study comprise personal factors (visual impairment) and school-related factors (classroom lighting, school green spaces and school air quality). METHOD: The follow-up cohort comprises 15,348 students from 283 primary and secondary schools in eastern China. This represents the one-year outcomes of a school-based myopia-mental health cohort study. Data collection includes basic demographics (age, gender, region, etc.), physical examination indicators, behavioral indicators, and school visual environment-related indicators. RESULT: After a one-year follow-up, we found that compared to the more severe vision impairment group (≤4.0), healthy vision group (≥5.0) had a positive effect against the occurrence of depressive symptoms during consecutive follow-ups, with an RR value of 0.61 (95% CI: 0.57-0.66). Higher values of blackboard illumination appear to be associated with greater positive effects, with an RR (Q75%∼Q100% range) value of 0.87(95% CI: 0.81-0.93). School green spaces seem to exhibit relatively good positive effects when in the Q25%∼Q75% range. The combination of physical activity (Weekly high-intensity exercise) with school air quality(PM2.5≤50%)showed a better positive effect, with an RR value of 0.51(95%CI:0.48-0.55). CONCLUSION: When addressing students' depressive symptoms, it is crucial to improve the visual environment both at the school level and in students' personal level. Paying appropriate attention to modifiable behaviors, like regular participation in high-intensity exercise sessions, can help alleviate students' depressive symptoms.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network.

Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness.

Deep Learning Segmentation and Reconstruction for CT of Chronic Total Coronary Occlusion.

Background CT imaging of chronic total occlusion (CTO) is useful in guiding revascularization, but manual reconstruction and quantification are time consuming. Purpose To develop and validate a deep learning (DL) model for automated CTO reconstruction. Materials and Methods In this retrospective study, a DL model for automated CTO segmentation and reconstruction was developed using coronary CT angiography images from a training set of 6066 patients (582 with CTO, 5484 without CTO) and a validation set of 1962 patients (208 with CTO, 1754 without CTO). The algorithm was validated using an external test set of 211 patients with CTO. The consistency and measurement agreement of CTO quantification were compared between the DL model and the conventional manual protocol using the intraclass correlation coefficient, Cohen κ coefficient, and Bland-Altman plot. The predictive values of CT-derived Multicenter CTO Registry of Japan (J-CTO) score for revascularization success were evaluated. Results In the external test set, 211 patients (mean age, 66 years ± 11 [SD]; 164 men) with 240 CTO lesions were evaluated. Automated segmentation and reconstruction of CTOs by DL was successful in 95% of lesions (228 of 240) without manual editing and in 48% of lesions (116 of 240) with the conventional manual protocol (P < .001). The total postprocessing and measurement time was shorter for DL than for manual reconstruction (mean, 121 seconds ± 20 vs 456 seconds ± 68; P < .001). The quantitative and qualitative CTO parameters evaluated with the two methods showed excellent correlation (all correlation coefficients > 0.85, all P < .001) and minimal measurement difference. The predictive values of J-CTO score derived from DL and conventional manual quantification for procedure success showed no difference (area under the receiver operating characteristic curve, 0.76 [95% CI: 0.69, 0.82] and 0.76 [95% CI: 0.69, 0.82], respectively; P = .55). Conclusion When compared with manual reconstruction, the deep learning model considerably reduced postprocessing time for chronic total occlusion quantification and had excellent correlation and agreement in the anatomic assessment of occlusion features. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Loewe in this issue.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients.

OBJECTIVES: To investigate the prognostic value of computed tomography fractional flow reserve (CT-FFR) in patients with diabetes and to establish a risk stratification model for major adverse cardiac event (MACE). METHODS: Diabetic patients with intermediate pre-test probability of coronary artery disease were prospectively enrolled. All patients were referred for coronary computed tomography angiography and followed up for at least 2 years. In the training cohort comprising of 957 patients, two models were developed: model1 with the inclusion of clinical and conventional imaging parameters, model2 incorporating the above parameters + CT-FFR. An internal validation cohort comprising 411 patients and an independent external test cohort of 429 patients were used to validate the proposed models. RESULTS: 1797 patients (mean age: 61.0 ± 7.0 years, 1031 males) were finally included in the present study. MACE occurred in 7.18% (129/1797) of the current cohort during follow- up. Multivariate Cox regression analysis revealed that CT-FFR ≤ 0.80 (hazard ratio [HR] = 4.534, p < 0.001), HbA1c (HR = 1.142, p = 0.015) and low attenuation plaque (LAP) (HR = 3.973, p = 0.041) were the independent predictors for MACE. In the training cohort, the Log-likelihood test showed statistical significance between model1 and model2 (p < 0.001). The C-index of model2 was significantly larger than that of model1 (C-index = 0.82 [0.77-0.87] vs. 0.80 [0.75-0.85], p = 0.021). Similar findings were found in internal validation and external test cohorts. CONCLUSION: CT-FFR was a strong independent predictor for MACE in diabetic cohort. The model incorporating CT-FFR, LAP and HbA1c yielded excellent performance in predicting MACE.

Radiomics features of pericoronary adipose tissue improve CT-FFR performance in predicting hemodynamically significant coronary artery stenosis.

OBJECTIVES: To evaluate the value of radiomics-based model of pericoronary adipose tissue (PCAT) combined with CT fractional flow reserve (CT-FFR) in predicting hemodynamically significant coronary stenosis. METHODS: Patients with suspected or known coronary artery disease, who had coronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR within 1 month, were retrospectively included. Radiomics features of lesion-based PCAT were extracted. The lesion-specific CT-FFR values, CCTA-derived diameter stenosis, lesion length, and PCAT attenuation were also measured. FFR values were used as the reference standard to assess the diagnostic performance of radiomics model, CT-FFR, and combined model for detection of flow-limiting stenosis. RESULTS: A total of 146 patients with 180 lesions were included in the study. All lesions were divided into training and validation cohorts at a ratio of 2:1. CT-FFR model exhibited the highest area under the curve (AUC) (0.803 for training, 0.791 for validation) in predicting hemodynamically significant stenosis, followed by radiomics model (0.776 for training, 0.744 for validation). However, no statistically significant difference was found between the AUCs of the above two models (p > 0.05). When CT-FFR was combined with radiomics model, the AUC reached 0.900 for training cohort and 0.875 for validation cohort, which were significantly higher than that of CT-FFR and radiomics model alone (both p < 0.05). CONCLUSION: The diagnostic performance of PCAT radiomics model was comparable to that of CT-FFR for identification of ischemic coronary stenosis. Adding PCAT radiomics model to CT-FFR showed incremental value in discriminating flow-limiting from non-flow-limiting lesions. KEY POINTS: • Radiomics analysis of lesion-based PCAT is potentially an alternative method to identify hemodynamic significance of coronary artery stenosis. • Adding radiomics model of PCAT to CT-FFR improved diagnostic performance for the detection of flow-limiting coronary stenosis. • Radiomics features + CT-FFR is a promising noninvasive method for comprehensive evaluation of hemodynamic significance of coronary artery stenosis.

Financial and clinical outcomes of CT myocardial perfusion imaging and coronary CT angiography-guided versus coronary CT angiography-guided strategy.

OBJECTIVES: To compare the financial and clinical outcomes of CT myocardial perfusion imaging (CT-MPI) + coronary CT angiography (CCTA)-guided versus CCTA-guided strategy in patients suspected of chronic coronary syndrome (CCS). MATERIALS AND METHODS: This study retrospectively included consecutive patients suspected of CCS and referred for CT-MPI+CCTA-guided and CCTA-guided treatment. The details of medical costs within 3 months after index imaging, including downstream invasive procedures, hospitalization, and medications, were recorded. All patients were followed up for major adverse cardiac events (MACE) at a median time of 22 months. RESULTS: A total of 1335 patients (559 in the CT-MPI+CCTA group and 776 in the CCTA group) were finally included. In the CT-MPI+CCTA group, 129 patients (23.1%) underwent ICA and 95 patients (17.0%) received revascularization. In the CCTA group, 325 patients (41.9%) underwent ICA whereas 194 patients (25.0%) received revascularization. An addition of CT-MPI in the evaluation strategy remarkably reduced the healthcare expenditure, compared with CCTA-guided strategy (USD 1441.36 vs. USD 232.91, p < 0.001). After adjustment for potential cofounders after inverse probability weighting, the CT-MPI+CCTA strategy was significantly associated with lower medical expenditure [adjusted cost ratio (95% CI) for total costs: 0.77 (0.65-0.91), p < 0.001]. In addition, there was no significant difference regarding the clinical outcome between the two groups (adjusted HR= 0.97; p = 0.878). CONCLUSIONS: CT-MPI+CCTA considerably reduced medical expenditures in patients suspected of CCS, compared to the CCTA strategy alone. Moreover, CT-MPI+CCTA led to a lower rate of invasive procedures with a similar long-term prognosis. CLINICAL RELEVANCE STATEMENT: CT myocardial perfusion imaging + coronary CT angiography-guided strategy reduced medical expenditure and invasive procedure rate. KEY POINTS: • CT-MPI+CCTA strategy yielded significantly lower medical expenditure than did the CCTA strategy alone in patients with suspected CCS. • After adjustment for potential confounders, the CT-MPI+CCTA strategy was significantly associated with lower medical expenditure. • No significant difference was observed regarding the long-term clinical outcome between the two groups.

A Bioartificial Pancreas with "Immune Stealth" and Continuous Oxygen Supply for Islet Transplantation.

Transplantation of microencapsulated islet cells remains a promising strategy for the normalization of glucose metabolism control in type 1 diabetes mellitus. However, vigorous host immunologic rejection, fibrotic overgrowth around the microcapsules, and poor oxygen supply often lead to graft failure. Herein, a bioartificial pancreas is constructed, which incorporates the "stealth effect" based on polyethylene glycol copolymers and the high oxygen-carrying performance of fluorinated nanoparticles. Polycationic poly(l-lysine)-grafted-poly(ethylene glycol) is successfully coated on the surface of alginate microcapsules through electrostatic interaction, which can not only resist fibrinogen adhesion and avoid excessive fibrosis around the microcapsules but also isolate the host immune system from attacking, achieving a "stealth effect" of microencapsulated islet cells. Furthermore, the coloading of fluoride-based O2 nanocarriers gives them enhanced oxygen-carrying and continuous oxygen supply capabilities, thereby effectively prolonging the survival of islet cells. The intracapsular islet cells still display similar cell viability and almost normal insulin secretion function even in long-term culture under hypoxic conditions. Collectively, here a new approach is opened for microencapsulated islets to efficiently evade host immune attack and improve oxygen supply and a promising strategy is provided for islet transplantation in type 1 diabetes mellitus.

Predictive value of intravascular ultrasound for the function of intermediate coronary lesions.

BACKGROUND: Intravascular ultrasound (IVUS) can provide detailed coronary anatomic parameters. The purpose of our study was to evaluate the parameters measured by IVUS for the prediction of intermediate coronary lesions function by referencing quantitative fraction ratio (QFR) ≤ 0.80 (vs. > 0.80). METHODS: Eighty four cases with 92 intermediate coronary lesions in vessels with a diameter ≥ 2.50 mm were enrolled. Paired assessment of IVUS and cQFR was available, and vessels with cQFR ≤ 0.8 were considered the positive reference standard. Logistic regression was used to select model variables by a maximum partial likelihood estimation test and receiver operating characteristic curve (ROC) analysis to evaluate the diagnostic value of different indices. RESULTS: Plaque burden (PB) and lesion length (LL) of IVUS were independent risk factors for the function of coronary lesions. The predictive probability P was derived from the combined PB and LL model. The area under the curve (AUC) of PB, (minimum lumen area) MLA, and LL and the predicted probability P are 0.789,0.732,0731, and 0.863, respectively (P < 0.01). The AUC of the predicted probability P was the biggest among them; the prediction accuracy of cQFR ≤ 0.8 was 84.8%, and the sensitivity of the diagnostic model was 0.826, specificity was 0. 725, and P < 0.01. CONCLUSION: PB and LL of IVUS were independent risk factors influencing the function of intermediate coronary lesions. The model combining the PB and LL may predict coronary artery function better than any other single parameter.

Dose-response relationship between active smoking and lung cancer mortality/prevalence in the Chinese population: a meta-analysis.

BACKGROUND: The dose‒response relationship-based relative risk (RR) of smoking exposure could better predict the risk of lung cancer than the dichotomous RR. To date, there is a lack of large-scale representative studies illustrating the dose‒response relationship between smoking exposure and lung cancer deaths, and no study has systematically pooled the current evidence in the Chinese population. OBJECTIVES: To elucidate the dose‒response relationship of smoking and the risk of lung cancer mortality in the Chinese population. METHODS: Data were derived from studies on dose‒response relationships of smoking exposure and the risk of lung cancer among Chinese adults published before June 30th, 2021. Based on smoking exposure indicators and RR of lung cancer mortality, a series of dose‒response relationship models were developed. For smokers, 10 models were built to fit the dose‒response relationships between pack-years and RR of lung cancer deaths. For quitters, quit-years and corresponding RRs were used, and the pooled dichotomous RR value was used as the starting point to avoid overestimation. Finally, the results were compared with the estimates from 2019 Global Burden of Disease (GBD) study. RESULTS: A total of 12 studies were included. Among 10 dose‒response relationship models of pack-years with the RR of lung cancer mortality, the integrated-exposure-response (IER) model achieved the best fit. In all models, less than 60 pack-years presented RRs below 10. For former smokers, the RR decreased to 1 when quit-years reached up to 7 years. Both smokers and quitters had much lower RRs than that of the global level estimated by GBD. CONCLUSION: The risk of lung cancer mortality rose with pack-years and decreased with quit-years among Chinese adults, and both values were far below global level. The results suggested that the dose-response RR of lung cancer deaths associated with smoking in China should be estimated separately.

Pattern of lifestyle behaviors and associated risk of being bullied at schools: A latent class analysis of 25,379 adolescents in Jiangsu Province of China.

School bullying is a worldwide problem. Although previous studies examined the association between different lifestyle behaviors and bullying victimization, the complex co-occurrence of these behaviors was not identified, and their association with the risk of being bullied remains unclear. We aimed to identify the behavioral patterns of adolescents and to explore their association with bullying victimization. This cross-sectional study employed data from the "Surveillance for Common Diseases and Health Risk Factors among Students" project implemented in Jiangsu Province of China in 2019, and a total of 25,379 school-enrolled students were included. We used a latent class analysis to identify behavioral patterns and a regression mixture model to explore various demographic characteristics, such as age, sex, and family structure in relation to bullying victimization across different patterns. We considered respondents having targeted behaviors, including smoking, alcohol consumption, illicit drug use, sugar consumption, no fruit consumption, low physical activity, electronic media use, and insufficient sleep. Four behavioral patterns were identified, including the "adolescents without apparent targeted behaviors" (19.65%), "substance and electronic media users" (12.76%), "typical electronic media users" (54.49%), and "typical substance users" (8.10%). The risk of being bullied was the highest in the "substance and electronic media users" (probability: 0.33), tripled that in "adolescents without apparent targeted behaviors" (odds ratio: 3.60, 95% confidence interval: 3.01-4.30). Risk of being bullied was reduced for those "substance and electronic media users" living with a nuclear family. Behavioral patterns and their association with being bullied differ between groups of school-aged adolescents. To better inform decision-making based on the current real-world findings, the implementation of bullying prevention programs could target specific behavioral patterns.

Sulfate reduction behavior in pressure-bearing leachate saturated zone.

Attention should be paid to the sulfate reduction behavior in a pressure-bearing leachate saturated zone. In this study, within the relative pressure range of 0-0.6 MPa, the ambient temperature with the highest sulfate reduction rate of 50°C was selected to explore the difference in sulfate reduction behavior in a pressure-bearing leachate saturated zone. The results showed that the sulfate reduction rate might further increase with an increase in pressure; however, owing to the effect of pressure increase, the generated hydrogen sulfide (H2S) could not be released on time, thereby decreasing its highest concentration by approximately 85%, and the duration extended to about two times that of the atmospheric pressure. Microbial community structure and functional gene abundance analyses showed that the community distribution of sulfate-reducing bacteria was significantly affected by pressure conditions, and there was a negative correlation between disulfide reductase B (dsrB) gene abundance and H2S release rate. Other sulfate reduction processes that do not require disulfide reductase A (dsrA) and dsrB genes may be the key pathways affecting the sulfate reduction rate in the pressure-bearing leachate saturated zone. This study improves the understanding of sulfate reduction in landfills as well as provides a theoretical basis for the operation and management of landfills.

Functional and structural characterization of a GH3 ß-N-acetylhexosaminidase from Akkermansia muciniphila involved in mucin degradation.

Akkermansia muciniphila is a probiotic that colonizes the outer layer of intestinal mucus and is negatively associated with metabolic disorders. Amuc_2109 protein, a ß-N-acetylhexosaminidase from A. muciniphila, may be involved in the degradation of mucins and is associated with intestinal health. Here, we reported the crystal structure of Amuc_2109, which belongs to the GH family 3 enzymes and fell into the canonical (α/ß)8 TIM barrel structure with GlcNAc bound to the active center. Biochemical assay characterization of Amuc_2109 revealed that Amuc_2109 is a GlcNAc-specific glycosidase active over a wide temperature and pH range, reflecting the survival advantage of Amuc_2109 in the intestinal environment. Our structural and biochemical results will contribute to the understanding of the catalytic mechanism of the GH3 ß-N-acetylhexosaminidase and help to gain insight into the molecular mechanism of complex carbohydrate utilization and restoration of the intestinal barrier in A. muciniphila.