Nitrogen Forms Regulate the Response of Microcystis aeruginosa to Nanoplastics at Environmentally Relevant Nitrogen Concentrations.

As essential primary producers, cyanobacteria play a major role in global carbon and nitrogen cycles. Though the influence of nanoplastics on the carbon metabolism of cyanobacteria is well-studied, little is known about how nanoplastics affect their nitrogen metabolism, especially under environmentally relevant nitrogen concentrations. Here, we show that nitrogen forms regulated growth inhibition, nitrogen consumption, and the synthesis and release of microcystin (MC) in Microcystis aeruginosa exposed to 10 µg/mL amino-modified polystyrene nanoplastics (PS-NH2) with a particle size of 50 nm under environmentally relevant nitrogen concentrations of nitrate, ammonium, and urea. We demonstrate that PS-NH2 inhibit M. aeruginosa differently in nitrate, urea, and ammonium, with inhibition rates of 51.87, 39.70, and 36.69%, respectively. It is caused through the differences in impairing cell membrane integrity, disrupting redox homeostasis, and varying nitrogen transport pathways under different nitrogen forms. M. aeruginosa respond to exposure of PS-NH2 by utilizing additional nitrogen to boost the production of amino acids, thereby enhancing the synthesis of MC, extracellular polymeric substances, and membrane phospholipids. Our results found that the threat of nanoplastics on primary producers can be regulated by the nitrogen forms in freshwater ecosystems, contributing to a better understanding of nanoplastic risks under environmentally relevant conditions.

One-Pot facile synthesis of fluorescent copper nanoclusters for highly selective and sensitive detection of tetracycline.

Due to the excellent characteristics, fluorescent copper nanoclusters (Cu NCs) have aroused great interest in recent years. Herein, the simple prepared, environmentally friendly fluorescent Cu NCs were synthesized by using trypsin as the stabilizer and applied for the determination of tetracycline. Uniformly dispersed Try-Cu NCs were obtained with average size of 3.5 ± 0.3 nm and some excellent merits of good water solubility, UV light stability and salt stability. Emission peaks around 460.0 nm were visibly quenched by tetracycline based on static quenching mechanism and inner filter effect (IFE). Two excellent linear relationships were observed between ln(F0/F) and tetracycline concentrations in the range of 1-100 µM and 100-300 µM with limit of detection (LOD) of 0.084 µM. Meanwhile, this nanoprobe exhibited an apparent selectivity for tetracycline detection. Moreover, Try-Cu NCs were successfully employed to determine tetracycline in serum and milk samples after facile pretreatment with satisfactory recovery rates and credible standard deviation. The results suggested that this as-prepared Try-Cu NCs had excellent application prospects in the future.

Rapid chemical reduction synthesis of copper nanoclusters with blue fluorescence for highly sensitive detection of furazolidone.

Tannic acid (TA), as a stabilizing agent, was successfully utilized to establish blue-emitting copper nanoclusters (TA-Cu NCs) on the basis of a facile chemical reduction preparation method. Characterization results proved successful synthesis of TA-Cu NCs with uniform size and excellent stability. TA-Cu NCs exhibited a blue emission wavelength at 431 nm when excited at 364 nm. Interestingly, the as-prepared TA-Cu NCs were selectively quenched by furazolidone based on static quenching. In addition, this analysis platform for furazolidone detection had an excellent linear range from 0.5 to 120 µM with a detection limit of 0.074 µM (S/N = 3). Furthermore, the accuracy of this sensing method was successfully confirmed by detecting furazolidone in bovine serum samples, indicating that TA-Cu NCs had bright application prospects.

Histidine-capped copper nanoclusters for in situ amplified fluorescence monitoring of doxycycline through inner filter effect.

There is a significant need to accurately measure doxycycline concentrations in view of the adverse effects of an overdose on human health. A fluorescence (FL) detection method was adopted and copper nanoclusters (CuNCs) were synthesized using chemical reduction technology. Based on FL quenching with doxycycline, the prepared CuNCs were used to explore a fluorescent nanoprobe for doxycycline detection. In an optimal sensing environment, this FL nanosensor was sensitive and selective in doxycycline sensing and displayed a linear relationship in the range 0.5-200 µM with a detection limit of 0.092 µΜ. A characterization test demonstrated that CuNCs offered active functional groups for identifying doxycycline using electrostatic interaction and hydrogen bonds. Static quenching and the inner filter effect (IFE) resulted in weakness in the FL of His@CuNCs with doxycycline with great efficiency. This suggested nanosensor was revealed to be a functional model for simple and rapid detection of doxycycline in real samples with very pleasing accuracy.

Horizontal and vertical migration of cyanobacterial blooms in two eutrophic lakes observed from the GOCI satellite.

Under the variations of natural conditions (temperature, wind speed, light, et al.) and self-regulation of buoyancy, cyanobacterial blooms can change rapidly in a short time. The Geostationary Ocean Color Imager (GOCI) can provide hourly monitoring of the dynamics of algal blooms (eight times per day), and has potential in observing the horizontal and vertical movement of cyanobacterial blooms. Based on the fractional floating algae cover (FAC), the diurnal dynamics and migration of floating algal blooms were evaluated, and the horizontal and vertical migration speed of phytoplankton was estimated from the proposed algorithm in two eutrophic lakes, Lake Taihu and Lake Chaohu in China. The locations, number, and area of algal bloom patches showed the hotspots and horizontal movement of bloom patches. The spatial and seasonal variations of the vertical velocities indicated that both the rising and sinking speed were higher in summer and autumn than those in spring and winter. The factors affecting diurnal horizontal and vertical migrations of phytoplankton were analyzed. Diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and temperature had significant positive relationships with FAC in the morning. Wind speed contributed 18.3 and 15.1% to the horizontal movement speed in Lake Taihu and Lake Chaohu, respectively. The rising speed was more related to DNI and DHI in Lake Taihu and Lake Chaohu with contribution of 18.1 and 16.6%. The horizontal and vertical movement of algae provide important information for understanding phytoplankton dynamics and the prediction and warning of algal blooms in lake management.

Selective detection of nitrofurantoin by histidine-capped silver nanoclusters with blue luminescence.

In view of the significance of nitrofurantoin, there is an urgent need for efficient analytical methods for accurate detection of nitrofurantoin. Considering their superior fluorescence performance and rarity of reports regarding nitrofurantoin detection by fluorescent silver nanoclusters (Ag NCs), Ag NCs with good stability and uniform size were synthesized through a simple method by protection of histidine (His) and reduction of ascorbic acid (AA). Based on the quenching by nitrofurantoin, Ag NCs were applied successfully in the detection of nitrofurantoin with high sensitivity. In the range of 0.5-150 µM, a linear relationship was found between ln(F0 /F) and nitrofurantoin amounts. Static quenching and inner filter effect were proved to be the main quenching mechanisms. Significantly superior selectivity and satisfactory recovery results in bovine serum indicate that Ag NCs provide a better choice for nitrofurantoin detection.

Effect of Collagenase Chemonucleolysis on Residual Pain After Transforaminal Lumbar Endoscopic Discectomy.

Purpose: To investigate the short-term clinical effect of collagenase chemonucleolysis (CCNL) in the treatment of residual pain after transforaminal lumbar endoscopic discectomy (TLED). Patients and Methods: The clinical data of 40 patients with residual pain after TLED for lumbar disc herniation (LDH) in our hospital from January 2018 to December 2020 were retrospectively analyzed. The visual analogue scale (VAS) and quality of recovery-15 (QoR-15) were used to evaluate the effect of the operation. Modified MacNab method was used to evaluate the efficacy of CCNL at 6 months and 12 months after operation. Results: All patients successfully completed the operation without serious complications, such as intervertebral space infection, vascular injury and nerve injury. The VAS 3 days after TLED was not significantly lower than that Pre-TLED (P > 0.05). The VAS at each time point after CCNL was significantly lower than that at Pre-TLED, and the QoR-15 score at each time point after CCNL was significantly higher than that 3 days after CCNL (P < 0.05). The modified MacNab criteria for the last follow-up was 95%. Conclusion: CCNL can effectively relieve the residual pain after TLED, and the quality of life of patients after TLED is improved with time, and the safety of CCNL is higher.

The SMC5/6 complex recruits the PAF1 complex to facilitate DNA double-strand break repair in Arabidopsis.

DNA double-strand breaks (DSBs) are one of the most toxic forms of DNA damage, which threatens genome stability. Homologous recombination is an error-free DSB repair pathway, in which the evolutionarily conserved SMC5/6 complex (SMC5/6) plays essential roles. The PAF1 complex (PAF1C) is well known to regulate transcription. Here we show that SMC5/6 recruits PAF1C to facilitate DSB repair in plants. In a genetic screen for DNA damage response mutants (DDRMs), we found that the Arabidopsis ddrm4 mutant is hypersensitive to DSB-inducing agents and is defective in homologous recombination. DDRM4 encodes PAF1, a core subunit of PAF1C. Further biochemical and genetic studies reveal that SMC5/6 recruits PAF1C to DSB sites, where PAF1C further recruits the E2 ubiquitin-conjugating enzymes UBC1/2, which interact with the E3 ubiquitin ligases HUB1/2 to mediate the monoubiquitination of histone H2B at DSBs. These results implicate SMC5/6-PAF1C-UBC1/2-HUB1/2 as a new axis for DSB repair through homologous recombination, revealing a new mechanism of SMC5/6 and uncovering a novel function of PAF1C.

Unsupervised Landmark Detection-Based Spatiotemporal Motion Estimation for 4-D Dynamic Medical Images.

Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In addition, the correct anatomical topology is difficult to be preserved as the image global context is not well incorporated into motion estimation. In this study, we provide a novel motion estimation framework of dense-sparse-dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ's anatomical topology, and discard the redundant information that is unnecessary for motion estimation. For this purpose, we introduce an unsupervised 3-D landmark detection network to extract spatially sparse but representative landmarks for the target organ's motion estimation. In the second stage, we derive the sparse motion displacement from the extracted sparse landmarks of two images of different time points. Then, we present a motion reconstruction network to construct the motion field by projecting the sparse landmarks' displacement back into the dense image domain. Furthermore, we employ the estimated motion field from our two-stage DSD framework as initialization and boost the motion estimation quality in light-weight yet effective iterative optimization. We evaluate our method on two dynamic medical imaging tasks to model cardiac motion and lung respiratory motion, respectively. Our method has produced superior motion estimation accuracy compared to the existing comparative methods. Besides, the extensive experimental results demonstrate that our solution can extract well-representative anatomical landmarks without any requirement of manual annotation. Our code is publicly available online: https://github.com/yyguo-sjtu/DSD-3D-Unsupervised-Landmark-Detection-Based-Motion-Estimation.

Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics.

BACKGROUND: Metabolomics is a primary omics topic, which occupies an important position in both clinical applications and basic researches for metabolic signatures and biomarkers. Unfortunately, the relevant studies are challenged by the batch effect caused by many external factors. In last decade, the technique of deep learning has become a dominant tool in data science, such that one may train a diagnosis network from a known batch and then generalize it to a new batch. However, the batch effect inevitably hinders such efforts, as the two batches under consideration can be highly mismatched. RESULTS: We propose an end-to-end deep learning framework, for joint batch effect removal and then classification upon metabolomics data. We firstly validate the proposed deep learning framework on a public CyTOF dataset as a simulated experiment. We also visually compare the t-SNE distribution and demonstrate that our method effectively removes the batch effects in latent space. Then, for a private MALDI MS dataset, we have achieved the highest diagnostic accuracy, with about 5.1 ~ 7.9% increase on average over state-of-the-art methods. CONCLUSIONS: Both experiments conclude that our method performs significantly better in classification than conventional methods benefitting from the effective removal of batch effect.

Positive association of glucagon with bone turnover markers in type 2 diabetes: A cross-sectional study.

AIMS: The osteo-metabolic changes in type 2 diabetes (T2D) patients are intricate and have not been fully revealed. It is not clear whether glucagon is entirely harmful in the pathogenesis of diabetes or a possible endocrine counter-regulation mechanism to reverse some abnormal bone metabolism. This study aimed to investigate the association between glucagon and bone turnover markers (BTMs) in T2D patients. METHODS: A total of 3984 T2D participants were involved in a cross-sectional study in Shanghai, China. Serum glucagon was measured to elucidate its associations with intact N-terminal propeptide of type I collagen (P1NP), osteocalcin (OC), and ß-C-terminal telopeptide (ß-CTX). Glucagon was detected with a radioimmunoassay. Propeptide of type I collagen, OC, and ß-CTX were detected using chemiluminescence. The diagnosis of T2D was based on American Diabetes Association criteria. RESULTS: The concentration of glucagon was positively correlated with two BTMs [OC-ß: 0.034, 95% CI: 0.004, 0.051, p = 0.024; CTX-ß: 0.035, 95% CI: 0.004, 0.062, p = 0.024]. The result of P1NP was [P1NP-regression coefficient (ß): 0.027, 95% CI: -0.003, 0.049, p = 0.083]. In the glucagon tertiles, P for trend of the BTMs is [P1NP: 0.031; OC: 0.038; CTX: 0.020], respectively. CONCLUSIONS: Glucagon had a positive effect on bone metabolism. The concentrations of the three BTMs increased as glucagon concentrations rose. This implied that glucagon might speed up skeletal remodelling, accelerate osteogenesis, and promote the formation of mature bone tissue. At the same time, the osteoclastic process was also accelerated, providing raw materials for osteogenesis to preserve the dynamic balance. In view of the successful use of single-molecule as well as dual/triple agonists, it would be feasible to develop a preparation that would reduce osteoporosis in diabetic patients.

Relation Regularized Scene Graph Generation.

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph convolution networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on the visual genome dataset for three SGG tasks (i.e., predicate classification, scene graph classification, and scene graph detection), demonstrating the effectiveness of our proposed method. Ablation studies also verify the key roles of our proposed components in performance improvement.

One facile fluorescence strategy for sensitive determination of baicalein using trypsin-templated copper nanoclusters.

Herein, we established a fluorescent detection platform for baicalein (Bai) based on copper nanoclusters, which were prepared by using copper sulfate as the precursor, trypsin (Tryp) as the template and hydrazine hydrate as the reducing agent. The entire preparation and testing process were rapid, facile and green. Many characterization methods, such as UV-vis absorption spectroscopy, fluorescence spectroscopy, fourier transform infrared spectroscopy (FT-IR), fluorescence lifetime, transmission electron microscope (TEM) and X-ray photoelectron spectroscopy (XPS), were applied for the analysis of trypsin-templated copper nanoclusters (Cu NCs@Tryp). The Cu NCs@Tryp released green fluorescence at maximum emission wavelength of 457 nm under maximum excitation wavelength of 377 nm. More importantly, the fluorescence of Cu NCs@Tryp was efficiently quenched by Bai. According to this phenomenon, a facile, rapid and selective turn-off fluorescence probe for Bai sensing was developed. Under the optimized testing conditions, the ln(F0/F) value and concentration of Bai displayed excellent linear relationship changing from 0.5 to 60 µM (R2 = 0.9969), and the detection limit was 0.078 µM. Furthermore, the Cu NCs@Tryp has been successfully employed to measure the amount of Bai in bovine serum samples with satisfactory recoveries.

Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images.

Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net.

Impact of treatment-duration-ratio of traditional Chinese medicine on survival period of primary liver cancer -A real-world study.

Introduction: In China, traditional Chinese medicine (TCM) is regarded as an effective treatment for primary liver cancer (PLC). The present study analyzed the effect of TCM on the survival period of patients with PLC by analyzing the relationship between the treatment-duration-ratio of traditional Chinese medicine (C-TDR, (traditional Chinese medicine treatment duration)/(Overall treatment duration) × 100%) and the survival time of 1002 patients with PLC. Methods: In this study, 1002 patients with PLC admitted to TCM Oncology Department of Changhai Hospital from January, 2015 to December, 2019 were enrolled. The univariate and multivariate Cox regression equation, propensity score matching (PSM) were performed to identify independent prognostic factors for survival outcomes of PLC patients at different stages and estimate the influence of C-TDR on survival time. Results: Cox regression analysis indicated that C-TDR was an independent prognostic factor for survival outcome (P＜0.05) and a corresponding reduction of relative risk of death of 75.67% (relative risk (RR) = 0.2433; 95%Confedential Interval (CI) = 0.1747-0.3388). Similarly, it is also an independent prognostic factor for patients outcome of each stage (P＜0.05). The 251 patients of BCLC-A reduced 96.09% risk of mortality (RR = 0.0391; 95%CI = 0.0151-0.1012). The 396 BCLC-B patients decreased risk of death of 81.24% (RR = 0.1876, 95%CI = 0.1112-0.3163). Moreover, 355 patients of stage C demonstrated a 51.36% lower risk of death (RR = 1.0016, 95%CI = 0.9885-1.0149). Significant differences were found in the median overall survival (OS) both higher and lower C-TDR of all patients. Even after PSM, the overall survival of two groups were significantly improved following each stage. Conclusion: Earlier administration of traditional Chinese medicine can reduce the risk of mortality and prolong survival in patients with liver cancer.

Relationship between thick or greasy tongue-coating microbiota and tongue diagnosis in patients with primary liver cancer.

Tongue diagnosis is a unique aspect of traditional Chinese medicine for diagnosing diseases before determining proper means of treatment, but it also has the disadvantage of relying on the subjective experience of medical practitioners and lack objective basis. The purpose of this article is to elucidate tongue-coating microbiota and metabolic differences in primary liver cancer (PLC) patients with thick or greasy tongue coatings. Tongue-coating samples were analyzed in 60 PLC patients (30 PLC with thick or greasy tongue-coating patients and 30 PLC with tongue-coating neither thick nor greasy) and 25 healthy controls (HC) using 16S rRNA gene sequencing technology. As compared to healthy individuals, tongue coatings of patients with PLC had elevated levels of Firmicutes and Actinobacteria. The abundance of Fusobacteria, SR1_Absconditabacteria_, and Spirochaete were higher in tongue coatings of healthy controls compared to samples in patients with PLC. In addition to site-specific differences, higher abundances of Fusobacteria and Actinobacteria were observed in thick or greasy tongue-coating patients as compared to non-thick and greasy tongue-coating patients. The inferred metagenomic pathways enriched in the PLC tongue-coating patients were mainly those involved in replication, recombination, and repair of protein. We also identify a tongue-coating microbiome signature to discriminate HC and PLC, including 15 variables on genus level. The prediction performance of the signature showed well in the training and validation cohorts. This research illustrates specific clinical features and bacterial structures in PLC patients with different tongue coatings, which facilitates understanding of the traditional tongue diagnosis.

Automatic left ventricular cavity segmentation via deep spatial sequential network in 4D computed tomography.

Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (consisting of multiple time-points) is a fundamental requirement for quantitative analysis of cardiac structural and functional changes. Deep learning methods for segmentation are the state-of-the-art in performance; however, these methods are generally formulated to work on a single time-point, and thus disregard the complementary information available from the temporal image sequences that can aid in segmentation accuracy and consistency across the time-points. In particular, single time-point segmentation methods perform poorly in segmenting the end-systole (ES) phase image in the cardiac sequence, where the left ventricle deforms to the smallest irregular shape, and the boundary between the blood chamber and the myocardium becomes inconspicuous and ambiguous. To overcome these limitations in automatically segmenting temporal LVCs, we present a spatial sequential network (SS-Net) to learn the deformation and motion characteristics of the LVCs in an unsupervised manner; these characteristics are then integrated with sequential context information derived from bi-directional learning (BL) where both chronological and reverse-chronological directions of the image sequence are used. Our experimental results on a cardiac computed tomography (CT) dataset demonstrate that our spatial-sequential network with bi-directional learning (SS-BL-Net) outperforms existing methods for spatiotemporal LVC segmentation.

Highly fluorescent carbon dots from coix seed for the determination of furazolidone and temperature.

In this work, blue emission fluorescent carbon dots (CDs) were fabricated by using the hydrothermal strategy from coix seed for the first time. We found that the prepared CDs possessed many excellent characteristics including excitation-dependent properties, good solubility and strong photostability. The optimal excitation and emission wavelength of CDs were 363 and 435 nm, respectively. Unbelievably, the fluorescence of CDs was selectively and effectively quenched with the addition of furazolidone (Fu). The quenching mechanisms might be assumed to the static quenching and inner filter effect (IFE). Based on this principle, a novel fluorescence probe was developed for the determination of Fu. At the same time, the proposed probe showed excellent sensitivity and selectivity towards Fu with a wide linear range from 0.5 to 100 µM, and the corresponding detection limit was 0.096 µM. Moreover, the CDs also could be applied for the sensing of temperature. The practical application of the CDs for Fu detection in real samples was also confirmed with the satisfactory recoveries changing from 96.6% to 108.5%, which provided huge possibility in the field of medical analysis.

Novel luteolin sensor of tannic acid-stabilized copper nanoclusters with blue-emitting fluorescence.

In this work, the fluorescent copper nanoclusters (Cu NCs) were firstly adopted to detect luteolin with excellent performance. The blue-emitting Cu NCs was successfully prepared through a facile one-pot approach by protection of tannic acid (TA) and chemical reduction of ascorbic acid (AA). The water-soluble nanoclusters possessed uniform size and displayed good stability. The TA-Cu NCs showed maximum luminescence at 434 nm when excited at 366 nm. Based on the static quenching and inner filter effect (IFE) mechanism, the TA-Cu NCs was efficiently and selectively quenched by luteolin. The detection limit was 0.12 µM and linear relationship existed in the range of 0.2-100 µM. Moreover, the TA-Cu NCs probe was successfully employed to detect luteolin in bovine serum samples with satisfactory recoveries. This novel platform was expected to expand the possible detection method based on fluorescence properties.

Lead exposure and its association with cardiovascular disease and diabetic kidney disease in middle-aged and elderly diabetic patients.

AIMS: Previous studies have suggested that a high blood lead level (BLL) is associated with cardiovascular outcomes and impaired renal function in the general population; however, studies investigating the effect of a high BLL on diabetic vascular complications have been limited. We aimed to investigate whether a higher BLL is associated with cardiovascular disease (CVD) and diabetic kidney disease (DKD). METHODS: We analyzed 4234 individuals out of 4813 diabetic participants enrolled from seven communities in China in 2018 in this cross-sectional study. Macrovascular measurements, including assessment of common carotid artery (CCA) plaques and their diameters, were performed with ultrasound. CVD was defined as a composite measure including a previous diagnosis of coronary heart disease, myocardial infarction, or stroke. The definition of DKD was an albumin to creatinine ratio (ACR) ≥30 mg/g or an estimated glomerular filtration rate (eGFR) < 60 ml/min per 1.73 m2. Linear and logistic regression analyses were used to measure the associations. RESULTS: The median age and BLL of the participants were 67 years (interquartile range, 62-72 years) and 26 µg/L (interquartile range, 18-36 µg/L). Compared with the first quartile, the odds ratio (OR) (95% CI) of CCA plaques ranging from none to bilateral in the ordinal logistic regression analysis associated with BLL was 1.53 (1.29, 1.82) in the fourth BLL quartile (P for trend <0.01), and the odds of having CVD was significantly increased by 44% for participants in the fourth quartile (1.44 (1.17, 1.76)) (P for trend <0.01). The odds of DKD in the fourth BLL quartile increased by 36% (1.36 (1.06, 1.74)) compared with that in the first quartile (P for trend <0.05). These associations were adjusted for potential confounders. CONCLUSIONS: A high BLL may be a potential risk factor for CVD and DKD in middle-aged and elderly diabetic adults.