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Introduction: Smoking significantly burdens human health, contributing to an increasing incidence of mortality and morbidity. This study aims to explore the prevalence of smoking, cessation, and the association between various risk factors and smoking intensity measured in pack-years among Chinese adults., Methods: During 2020-2021, the China Stroke High-risk Population Screening and Intervention Program (CSHPSIP) invited participants aged ≥40 years from 31 provinces in mainland China. This cross-sectional study presents the standardized prevalence of smoking and cessation across various demographics, including age, sex, residence, income, education level, BMI, and geographical region of residence. Multivariable logistic regression was used to examine the associations between smoking pack-years and related factors., Results: Among 524741 participants (mean age: 61.9 ± 10.9 years; 41.1% male; 58.9% female), standardized smoking prevalence was 19.3% (95% CI: 19.2-19.4), with men (37.2%; 95% CI: 37.0-37.4) displaying significantly higher rates than women (1.3%; 95% CI: 1.2-1.3). Smoking cessation rate stood at 11.2% (95% CI: 11.0-11.4), with 11.3% (95% CI: 11.1-11.5) for men and 8.4% (95% CI: 7.5-9.2) for women. Urban residents and those with advanced education had lower smoking rates and higher cessation rates. Additionally, the dose-response relationship indicated a more pronounced association between higher smoking pack-years and elevated health risks, including hypertension (AOR=1.30; 95% CI: 1.24-1.36), diabetes (AOR=1.26; 95% CI: 1.20-1.33), hyperlipidemia (AOR=1.22; 95% CI: 1.16-1.28), heart disease (AOR=1.40; 95% CI: 1.26-1.54), and stroke (AOR=1.23; 95% CI: 1.10-1.36)., Conclusions: This comprehensive study emphasizes the profound impact of smoking on health in Chinese adults, indicating the critical need for tailored cessation programs, particularly for middle-aged individuals, men, rural residents, and those with lower level of education., Competing Interests: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported., (© 2024 Zeng Q. et al.)

CsPbX 3 (X = Br, Cl, I) perovskite quantum dots (PQDs) are the rising star for various display applications owing to their excellent opto-electrical properties, such as an adjustable spectrum, narrow emission linewidth and high quantum yield. However, these PQDs are well known to suffer from intrinsic instability under atmospheric conditions. In this work, a novel photosensitive ligand, phenylbis(2,4,6-trimethylbenzoyl)phosphine oxide (XBPO), was employed as a dual-functional reagent for PQD surface engineering. The XBPO ligand could cleave to produce phenylphosphinyl radicals and trimethylbenzoyl radicals under UV light irradiation. The phenylphosphinyl radicals with PO bonds could effectively passivate the PQD surface defects, leading to quantum yield improvement. The CsPbBr 3 and CsPbI 3 PQDs with XBPO modification could achieve a photoluminescence quantum yield (PLQY) of near unity and 92%, respectively. Additionally, the in situ encapsulation of the PQDs was achieved by the subsequent crosslinking polymerization, which significantly improved the stability of the PQDs against solvents and the environment. By combining a standard photolithography procedure, we demonstrated a micro-pattern of CsPbBr 3 PQDs. These results establish a universal route for PQD patterning, compatible with the existing photolithography processes, which could facilitate the application of PQDs in next-generation display technology.

Background: Due to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1-2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1-2 ESCA patients is of great significance to clinical practice., Methods: A total of 1,747 T1-2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively., Results: The age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1-2 ESCA patients ( p < 0.05). The age, T-stage, tumor grade, and tumor size were significant factors for predicting DM of T1-2 ESCA patients ( p < 0.05). The age, race, sex, histology, primary tumor site, tumor size, N-stage, M-stage, and surgery were significant factors for predicting OS of T1-2 ESCA patients ( p < 0.05). The C-indexes of the three nomograms constructed by these factors were 0.737, 0.764, and 0.740, respectively, suggesting that they were clinically effective., Conclusions: The newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1-2 ESCA patients, which contribute to the individualized decision making before clinical management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Qi, Wu, Tao, Xu, Chen, Feng, Lu, Wan and Li.)

Background: Constructing a medical image feature database according to the category of disease can achieve a quick retrieval of images with similar pathological features. Therefore, this approach has important application values in the fields such as auxiliary diagnosis, teaching, research, and telemedicine., Methods: Based on the deep convolutional neural network, an image classifier applicable to brain disease was designed to distinguish between the image features of the different brain diseases with similar anatomical structures. Through the extraction and analysis of visual features, the images were labelled with the corresponding semantic features of a specific disease category, which can establish an association between the visual features of brain images and the semantic features of the category of disease which will permit to construct a disease category feature database of brain images., Results: Based on the similarity measurement and the matching strategy of high-dimensional visual feature, a high-precision retrieval of brain image with semantics category was achieved, and the constructed disease category feature database of brain image was tested and evaluated through large numbers of pathological image retrieval experiments, the accuracy and the effectiveness of the proposed approach was verified., Conclusion: The disease category feature database of brain image constructed by the proposed approach achieved a quick and effective retrieval of images with similar pathological features, which is beneficial to the categorization and analysis of intractable brain diseases. This provides an effective application tool such as case-based image data management, evidence-based medicine and clinical decision support., Competing Interests: The authors have declared that no competing interests exist

In order to improve the level of health decision-making, based on health information resources and decision support function types, this study summarized five core functions of health decision support system: information support, monitoring and early warning, analysis and evaluation, trend prediction, comprehensive optimization; And from the perspective of business functions, business processes and business activities of business domains, the demand of Health Decision Support System is refined in to six parts, such as public health, medical care, drug management, medical insurance, comprehensive management, grass-roots health. On this basis, the overall design of the system is carried out.

Background: We presented the running state of rural basic medical insurance system in Henan and discussed the enforcements and development experiences of underdeveloped areas. We provided data evidence to support the improvement and development of a basic rural medical insurance system., Methods: We selected Henan Province, China as a sample, using the method of cluster sampling, from policy documents published in the national and provincial level of the new rural cooperative medical policy and work documents, data from 2004 to 2014, the National Health Statistical Yearbook of health statistics yearbook of Henan Province and relevant statistical data of the province., Results: The new rural cooperative policy has covered the whole population in Henan Province. The number of individual received benefits is increasing. In 2013, the number of persons counted has reached to 270 million, funds raised and expenditures reached 38.5 billion and 26 billion, respectively. The operational task force has been developed rapidly. In 2013, on average each staff managed the cases for 16.4 thousand rural residents., Conclusion: The major implementation and development experience from the new rural cooperative policy of Henan province include: education of related knowledge, optimization of compensation plan, development of operational system and framework, improvement of management rules, reinforcement of information system development and financial supervision and increment of investment in rural medical healthcare., Competing Interests: Conflict of Interests The authors declare that there is no conflict of interests.

Background: Compared to the rigid image registration task, the non-rigid image registration task faces much more challenges due to its high degree of freedom and inherent requirement of smoothness in the deformation field. The purpose was to propose an efficient coarse-to-fine non-rigid medical image registration algorithm based on a multilevel deformable model., Methods: In this paper, a robust and efficient coarse-to-fine non-rigid medical image registration algorithm is proposed. It contains three level deformation models, i.e., the global homography model, the local mesh-level homography model, and the local B-spline FFD (Free-Form Deformation) model. The coarse registration is achieved by the first two level models. In the global homography model, a robust algorithm for simultaneous outliers (error matched feature points) removal and model estimation is applied. In the local mesh-level homography model, a new similarity measure is proposed to improve the robustness and accuracy of local mesh based registration. In the fine registration, a local B-spline FFD model with normalized mutual information gradient is employed., Results: We verified the effectiveness of each stage of the proposed registration algorithm with many non-rigid transformation image pairs, and quantitatively compared our proposed registration algorithm with the HBFFD method which is based on the control points of multi-resolution. The experimental results show that our algorithm is more accurate than the hierarchical local B-spline FFD method., Conclusion: Our algorithm can achieve high precision registration by coarse-to-fine process based on multi-level deformable model, which ourperforms the state-of-the-art methods., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest.

This paper proposes a quasi-dense matching approach to the automatic acquisition of camera parameters, which is required for recovering 3-D information from 2-D images. An affine transformation-based optimization model and a new matching cost function are used to acquire quasi-dense correspondences with high accuracy in each pair of views. These correspondences can be effectively detected and tracked at the sub-pixel level in multiviews with our neighboring view selection strategy. A two-layer iteration algorithm is proposed to optimize 3-D quasi-dense points and camera parameters. In the inner layer, different optimization strategies based on local photometric consistency and a global objective function are employed to optimize the 3-D quasi-dense points and camera parameters, respectively. In the outer layer, quasi-dense correspondences are resampled to guide a new estimation and optimization process of the camera parameters. We demonstrate the effectiveness of our algorithm with several experiments.