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Abstract The association between vitamin A and single cardiometabolic diseases has been extensively studied, but the relationship between dietary vitamin A intake and the risk of cardiometabolic multimorbidity (CMM) has not been studied. Therefore, the present study was conducted to explore the association with CMM risk by analyzing different sources of vitamin A. This study utilized 13,603 subjects aged ≥ 18 years from 1997 to 2015 from the China Health and Nutrition Survey (CHNS). Dietary intake was calculated from 3 consecutive 24-h dietary recalls combined with a house hold food inventory. CMM is defined as the development of at least two cardiometabolic diseases. After a median follow-up of 9.0 years, there were 1050 new cases of CMM. The risk of CMM was significantly lower in those with higher vitamin A intake (Q1 vs Q5 HR 0.66, 95% CI 0.54–0.81). β-carotene (Q1 vs Q5 HR 0.82, 95% CI 0.66–1.02) and retinol (Q1 vs Q5 HR 0.59, 95% CI 0.48–0.73) intake had a similarly negative correlation. Using restricted cubic spline found an L-shaped relationship between retinol intake and CMM (p non-linear

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms. This leads to a relatively singular training approach for the models, and furthermore, some networks can only perform segmentation for specific diseases. In this paper, we propose a causal relationship-based generative medical image segmentation model named GU-Net. We integrate a counterfactual attention mechanism combined with CBAM into the decoder of U-Net as a generative network, and then combine it with a GAN network where the discriminator is used for backpropagation. This enables alternate optimization and training between the generative network and discriminator, enhancing the expressive and learning capabilities of the network model to output prediction segmentation results closer to the ground truth. Additionally, the interaction and transmission of information help the network model capture richer feature representations, extract more accurate features, reduce overfitting, and improve model stability and robustness through feedback mechanisms. Experimental results demonstrate that our proposed GU-Net network achieves better segmentation performance not only in cases with abundant data samples and relatively simple segmentation targets or high contrast between the target and background regions but also in scenarios with limited data samples and challenging segmentation tasks. Comparing with existing U-Net networks with attention mechanisms, GU-Net consistently improves Dice scores by 1.19%, 2.93%, 5.01%, and 5.50% on ISIC 2016, ISIC 2017, ISIC 2018, and Gland Segmentation datasets, respectively.

Abstract Objectives With the world’s population increasing in age, there has been a significant rise in the prevalence of cognitive impairment and dementia among individuals. This study aims to investigate the association between grandparenting and cognitive function among middle-aged and older Chinese using data from 2011 to 2018 China Health and Retirement Longitudinal Study (CHARLS). Additionally, the study seeks to explore the potential mediating effect of intergenerational support from children on this relationship, using data from the CHARLS 2011 database. Methods 5254 participants were recruited at the baseline survey in CHARLS 2011. Subsequently, a follow-up survey was conducted over 8 years, from CHARLS 2011 to 2018, with 1472 individuals completing the follow-up survey. The CHARLS included surveys on grandparenting and cognitive assessments. Grandparenting was categorized as yes and no. The assessment of cognitive function involved the evaluation of episodic memory and mental intactness. The present study used cross-sectional and longitudinal analyses to examine the relationship between grandparenting and cognitive function. The bootstrap method assessed the mediating effect of children’s intergenerational support. Results The results of both cross-sectional and longitudinal studies indicated a positive association between grandparenting and cognitive function in middle-aged and older Chinese (B = 0.138, p

Objectives Incomplete combustion of solid fuel and exposure to secondhand smoke (SHS) are the primary causes of indoor air pollution (IAP), potentially leading to detrimental effects on individual mental health. However, current evidence regarding the association between IAP and depression remains inconclusive. This study aims to systematically investigate the evidence regarding the association between IAP and the risk of depression.Design Systematic review and meta-analysis of cohort studies.Data sources Two independent reviewers searched PubMed, the Cochrane Library, Web of Science and EMBASE for available studies published up to 13 January 2024.Eligibility criteria We included all cohort studies published in English that aimed to explore the relationship between IAP from solid fuel use and SHS exposure and the risk of depression.Data extraction and synthesis Two independent reviewers extracted data and assessed the risk of bias. The association between IAP and depression was calculated using pooled relative risk (RR) with 95% CIs. Heterogeneity was assessed using the I2 value, and the effect estimates were pooled using fixed-effects or random-effects models depending on the results of homogeneity analysis.Results We included 12 articles with data from 61 217 participants. The overall findings demonstrated a significant association between IAP exposure and depression (RR=1.22, 95% CI: 1.13 to 1.31), although with substantial heterogeneity (I2=75%). Subgroup analyses based on pollutant type revealed that IAP from solid fuel use was associated with a higher risk of depression (RR=1.20, 95% CI: 1.13 to 1.26; I2=62%; 5 studies, 36 768 participants) than that from SHS exposure (RR=1.11, 95% CI: 0.87 to 1.41; I2=80%; 7 studies, 24 449 participants). In terms of fuel use, the use of solid fuel for cooking (RR: 1.23, 95% CI: 1.16 to 1.31; I2=58%; 4 studies, 34 044 participants) and heating (RR 1.15, 95% CI: 1.04 to 1.27; I2=65%; 3 studies, 24 874 participants) was associated with increased depression risk.Conclusions The findings from this systematic review and meta-analysis of cohort studies indicated an association between exposure to IAP and depression.PROSPERO registration number CRD42022383285.

IntroductionPrevious studies have found that diet’s inflammatory potential is related to various diseases. However, little is known about its relationship with gallstones. The present study aims to investigate the relationship between dietary inflammatory index (DII) and gallstones.MethodsData were obtained from the 2003–2020 National Health and Nutrition Examination Survey (NHANES). We used the nearest neighbor propensity score matching (PSM) with a ratio of 1:1 to reduce selection bias. Logistic regression models estimated the association between DII and gallstones. The non-linear relationship was explored with restricted cubic splines (RCS). BMI subgroup stratification was performed to explore further the connection between DII and gallstones in different populations.Results10,779 participants were included. Before and after PSM, gallstone group individuals had higher DII scores than non-gallstone group individuals (p

Abstract Objective: This systematic review aimed to investigate the association between dietary inflammatory potential and liver cancer to provide evidence regarding scientific dietary health education. Design: Systematic review and meta-analysis. Setting: A comprehensive literature review was conducted to identify case–control or cohort studies that involved dietary inflammation index (DII)/empirical dietary inflammation pattern (EDIP) and liver cancer in PubMed, EMBASE, Cochrane, and Web of Science databases. Using a combination of DII/EDIP and liver cancer as the search terms, the associations between DII/EDIP and liver cancer were then assessed. Participants: Three case–control studies and two cohort studies were brought into the meta-analysis, with 225 713 enrolled participants. Results: Meta-analysis of categorical variables showed that DII/EDIP in the highest category increased the risk of liver cancer compared to DII/EDIP in the lowest category (relative risk (RR) = 2·35; 95 % CI 1·77, 3·13; P = 0·000) and with low heterogeneity across studies (I2 = 40·8 %, P = 0·119). Meta-analysis of continuous variables showed that significant positive association between liver cancer and DII/EDIP scores (RR = 1·24; 95 % CI 1·09, 1·40; P = 0·001), and no heterogeneity (I² = 0·0 %, P = 0·471). Stratified according to the study design, there was a significant positive association between liver cancer and DII/EDIP scores in both cohort studies (RR = 2·16; 95 % CI 1·51, 3·07; P = 0·000) and case–control studies (RR = 2·75; 95 % CI 1·71, 4·41; P = 0·000). Conclusion: The higher the DII/EDIP score, the higher the risk of liver cancer. This finding may have prominent implications for the general population.

Background and aimsAmnestic mild cognitive impairment (aMCI) is the most common subtype of MCI, which carries a significantly high risk of transitioning to Alzheimer’s disease. Recently, increasing attention has been given to remnant cholesterol (RC), a non-traditional and previously overlooked risk factor. The aim of this study was to explore the association between plasma RC levels and aMCI.MethodsData were obtained from Brain Health Cognitive Management Team in Wuhan (https://hbtcm.66nao.com/admin/). A total of 1,007 community-dwelling elders were recruited for this project. Based on ten tools including general demographic data, cognitive screening and some exclusion scales, these participants were divided into the aMCI (n = 401) and normal cognitive groups (n = 606). Physical examinations were conducted on all participants, with clinical indicators such as blood pressure, blood sugar, and blood lipids collected.ResultsThe aMCI group had significantly higher RC levels compared to the normal cognitive group (0.64 ± 0.431 vs. 0.52 ± 0.447 mmol/L, p

BackgroundDepression is the most common psychiatric disorder among older adults. Despite the effectiveness of pharmacological and psychological therapies, many patients with late-life depression (LLD) are unable to access timely treatment. Telecare has been shown to be effective in addressing patients' psychosocial issues, while its effectiveness in serving patients with LLD remains unclear. ObjectiveThis study aimed to evaluate the effectiveness of telecare in reducing depression and anxiety symptoms and improving quality of life (QoL) in patients with LLD. MethodsDatabases including the Cochrane Library, Web of Science, PubMed, Embase, and EBSCO were searched for randomized controlled trials (RCTs) evaluating the effectiveness of telecare for LLD from database establishment to December 28, 2022. ResultsA total of 12 RCTs involving 1663 participants were identified in this study. The meta-analysis showed that (1) telecare significantly reduced depressive symptoms in patients with LLD compared to those in usual care (UC; standardized mean difference [SMD]=–0.46, 95% CI –0.53 to –0.38; P

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation.

Background: Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction of brain tumors has significant challenges. Purpose: In this thesis, we highlight EAV-UNet, a system designed to accurately detect lesion regions. Optimizing feature extraction, utilizing automatic segmentation techniques to detect anomalous regions, and strengthening the structure. We prioritize the segmentation problem of lesion regions, especially in cases where the margins of the tumor are more hazy. Methods: The VGG-19 network structure is incorporated into the coding stage of the U-Net, resulting in a deeper network structure, and an attention mechanism module is introduced to augment the feature information. Additionally, an edge detection module is added to the encoder to extract edge information in the image, which is then passed to the decoder to aid in reconstructing the original image. Our method uses the VGG-19 in place of the U-Net encoder. To strengthen feature details, we integrate a CBAM (Channel and Spatial Attention Mechanism) module into the decoder to enhance it. To extract vital edge details from the data, we incorporate an edge recognition section into the encoder. Results: All evaluation metrics show major improvements with our recommended EAV-UNet technique, which is based on a thorough analysis of experimental data. Specifically, for low contrast and blurry lesion edge images, the EAV-Unet method consistently produces forecasts that are very similar to the initial images. This technique reduced the Hausdorff distance to 1.82, achieved an F1 score of 96.1%, and attained a precision of 93.2% on Dataset 1. It obtained an F1 score of 76.8%, a Precision of 85.3%, and a Hausdorff distance reduction to 1.31 on Dataset 2. Dataset 3 displayed a Hausdorff distance cut in 2.30, an F1 score of 86.9%, and Precision of 95.3%. Conclusions: We conducted extensive segmentation experiments using various datasets related to brain tumors. We refined the network architecture by employing smaller convolutional kernels in our strategy. To further improve segmentation accuracy, we integrated attention modules and an edge enhancement module to reinforce edge information and boost attention scores.

Abstract Background MUC18 is a glycoprotein highly expressed on the surface of melanoma and other cancers which promotes tumor progression and metastasis. However, its mechanism of action and suitability as a therapeutic target are unknown. Methods A monoclonal antibody (mAb) (JM1-24-3) was generated from metastatic melanoma tumor live cell immunization, and high-throughput screening identified MUC18 as the target. Results Analysis of molecular interactions between MUC18 and JM1-24-3 revealed that the downstream signaling events depended on binding of the mAb to a conformational epitope on the extracellular domain of MUC18. JM1-24-3 inhibited melanoma cell proliferation, migration and invasion in vitro and reduced tumor growth and metastasis in vivo. Conclusion These results confirm that MUC18 is mechanistically important in melanoma growth and metastasis, suggest that the MUC18 epitope identified is a promising therapeutic target, and that the JM1-24-3 mAb may serve as the basis for a potential therapeutic agent.

BackgroundExcessive self-disclosure online may risk the reputations, mental health problems, and professional lives of nursing students. This study investigated nursing students' usage of social media, their attitudes towards social media, mental health problems and self-disclosures, and the relationships of these variables.MethodsA cross-sectional study was conducted online (n = 1054) with questionnaires of Fear of Missing Out (FoMO), Social Media Fatigue (SMF), Students' Uses and Views of Social Media (SUVSM) and self-disclosure in social media which included self-information shown on social media and information viewed by others.ResultsAlthough most of them held positive attitudes towards social media, 17.4% of the participants acknowledged that they had posted inappropriate contents online and 37.6% witnessed improper posts from schoolmates or teachers online. SMF was affected by familiar with relevant regulations on the social media usage (β = -.10, p < .001), FoMO (β = .41, p < .001), and SUVSM (β = .17, p < .001). Additionally, nearly 1/3 participants reported their net-friends could view following information: gender, age, occupation, education level and location. Self- disclosure in social media was positively influenced by education (β = .10, p < .001), sharing moments or Weibo, etc. (β = .009, P = 0.009), time spent on social media daily (β = .11, p < .001), accepting stranger's "friend request" (β = .06, P = 0.047), FoMO (β = .14, p < .001) and SMF (β = .19, p < .001). Furthermore, effect of SUVSM on self-disclosure in social media was mediated by FoMO and SMF.ConclusionInappropriate contents are posted and witnessed by appreciable proportions of nursing students. Positive attitude towards social media may strengthen FoMO and SMF, which may increase self-disclosure in social media in turn.

Abstract Engineering and evaluation of synthetic routes for generating valuable compounds require accurate and cost-effective de novo synthesis of genetic pathways. Here, we present an economical and streamlined de novo DNA synthesis approach for engineering a synthetic pathway with microchip-synthesized oligonucleotides (oligo). The process integrates entire oligo pool amplification, error-removal, and assembly of long DNA molecules. We utilized this method to construct a functional lycopene biosynthetic pathway (11.9 kb encoding 10 genes) in Escherichia coli using a highly error-prone microchip-synthesized oligo pool (479 oligos) without pre-purification, and the error-frequency was reduced from 14.25/kb to 0.53/kb. This low-equipment-dependent and cost-effective method can be widely applied for rapid synthesis of biosynthetic pathways in general molecular biology laboratories.

SUMOylation and ubiquitination are two essential post translational modifications (PTMs) involved in the regulation of important biological processes in eukaryotic cells. Identification of ubiquitin (Ub) and small ubiquitin-related modifier (SUMO)-conjugated lysine residues in proteins is critical for understanding the role of ubiquitination and SUMOylation, but remains experimentally challenging. We have developed a powerful in vitro Ub/SUMO assay using a novel high density peptide array incorporated within a microfluidic device that allows rapid identification of ubiquitination and SUMOylation sites on target proteins. We performed the assay with a panel of human proteins and a microbial effector with known target sites for Ub or SUMO modifications, and determined that 80% of these proteins were modified by Ub or specific SUMO isoforms at the sites previously determined using conventional methods. Our results confirm the specificity for both SUMO isoform and individual target proteins at the peptide level. In summary, this microfluidic high density peptide array approach is a rapid screening assay to determine sites of Ub and SUMO modification of target substrates, which will provide new insights into the composition, selectivity and specificity of these PTM target sites.

A thermo-tolerant NADP(H)-preferring xylose pathway was constructed in Kluyveromyces marxianus for ethanol production with xylose at elevated temperatures (Zhang et al., 2015 [25]). Ethanol production yield and efficiency was enhanced by pathway engineering in the engineered strains. The constructed strain, YZJ088, has the ability to co-ferment glucose and xylose for ethanol and xylitol production, which is a critical step toward enabling economic biofuel production from lignocellulosic biomass. This study contains the fermentation results of strains using the metabolic pathway engineering procedure. The ethanol-producing abilities of various yeast strains under various conditions were compared, and strain YZJ088 showed the highest production and fastest productivity at elevated temperatures. The YZJ088 xylose fermentation results indicate that it fermented well with xylose at either low or high inoculum size. When fermented with an initial cell concentration of OD600=15 at 37 °C, YZJ088 consumed 200 g/L xylose and produced 60.07 g/L ethanol; when the initial cell concentration was OD600=1 at 37 °C, YZJ088 consumed 98.96 g/L xylose and produced 33.55 g/L ethanol with a productivity of 0.47 g/L/h. When fermented with 100 g/L xylose at 42 °C, YZJ088 produced 30.99 g/L ethanol with a productivity of 0.65 g/L/h, which was higher than that produced at 37 °C.

Phage display technology has been widely used for antibody affinity maturation for decades. The limited library sequence diversity together with excessive redundancy and labour-consuming procedure for candidate identification are two major obstacles to widespread adoption of this technology. We hereby describe a novel library generation and screening approach to address the problems. The approach started with the targeted diversification of multiple complementarity determining regions (CDRs) of a humanized anti-ErbB2 antibody, HuA21, with a small perturbation mutagenesis strategy. A combination of three degenerate codons, NWG, NWC, and NSG, were chosen for amino acid saturation mutagenesis without introducing cysteine and stop residues. In total, 7,749 degenerate oligonucleotides were synthesized on two microchips and released to construct five single-chain antibody fragment (scFv) gene libraries with 4 x 10(6) DNA sequences. Deep sequencing of the unselected and selected phage libraries using the Illumina platform allowed for an in-depth evaluation of the enrichment landscapes in CDR sequences and amino acid substitutions. Potent candidates were identified according to their high frequencies using NGS analysis, by-passing the need for the primary screening of target-binding clones. Furthermore, a subsequent library by recombination of the 10 most abundant variants from four CDRs was constructed and screened, and a mutant with 158-fold increased affinity (Kd = 25.5 pM) was obtained. These results suggest the potential application of the developed methodology for optimizing the binding properties of other antibodies and biomolecules.