Sustainable soil rehabilitation with multiple network structures of layered double hydroxide beads: Immobilization of heavy metals, fertilizer release, and water retention.

The remediation of heavy metal-contaminated soils necessitated a holistic approach that encompassed water and fertilizer conservation alongside soil property restoration. This study introduced the synthesis of (poly)acrylamide-layered double hydroxide gel spheres (PAM-LDH beads), which were designed to simultaneously immobilize heavy metals, control the release of fertilizers, and enhance soil water retention. Laboratory soil experiments under diverse conditions highlighted the superior performance of PAM-LDH beads in the immobilization of hexavalent chromium (Cr(VI)). The layered double hydroxide (LDH) component was identified as the key player in Cr(VI) immobilization, with anion exchange being the predominant mechanism. Notably, the encapsulated urea within the beads was released independently of environmental influences, governed by a concentration gradient across the beads surface. This release process was characterized by an initial phase of absorptive swelling followed by a diffusive phase. The impact on plant growth was assessed, revealing that PAM-LDH beads significantly curtailed Cr(VI) accumulation and alleviated its phytotoxic effects. Changes in the carbon (C) and nitrogen (N) content of the plants suggested that the urea encapsulated within the beads served as a nutrient source, contributing to soil fertility. Moreover, the water-holding capacity and soil-water characteristic curves of PAM-LDH beads suggested that these superabsorbent beads could delay soil water evaporation. The observed shifts in microbial community structure provided evidence for the enhancement of soil carbon and nitrogen cycles, indicative of improved soil properties.

Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study.

OBJECTIVE: To investigate the value of the pre-operative amide proton transfer-weighted (APTw) MRI to assess the prognostic factors in rectal adenocarcinoma (RA). METHODS: This prospective study ran from January 2022 to September 2023 and consecutively enrolled participants with RA who underwent pre-operative MRI and radical surgery. The APTw signal intensity (SI) values of RA with various tumor (T), node (N) stages, perineural invasion (PNI), and tumor grade were compared by Mann-Whitney U-test or t-test. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the APTw SI values. RESULTS: A total of 51 participants were enrolled (mean age, 58 years ± 10 [standard deviation], 26 men). There were 24 in the T1-T2 stage and 9 with positive PNI. The APTw SI max, 99th, and 95th values were significantly higher in T3-T4 stage tumor than in T1-T2; the median (interquartile range) (M (IQR)) was (4.0% (3.6-4.9%) vs 3.4% (2.9- 4.3%), p = 0.017), (3.7% (3.2-4.1%) vs 3.2% (2.8-3.8%), p = 0.013), and (3.3% (2.8-3.8%) vs 2.9% (2.3-3.5%), p = 0.033), respectively. These indicators also differed significantly between the PNI groups, with the M (IQR) (4.5% (3.6-5.7%) vs 3.7% (3.2-4.2%), p = 0.017), (4.1% (3.4-4.8%) vs 3.3% (3.0-3.9%), p = 0.022), and (3.7% (2.7-4.2%) vs 2.9% (2.6-3.5%), p = 0.045), respectively. CONCLUSION: Pre-operative APTw MRI has potential value in the assessment of T-staging and PNI determination in RA. CLINICAL RELEVANCE STATEMENT: Pre-operative amide proton transfer-weighted MRI provides a quantitative method for noninvasive assessment of T-staging and PNI in RA aiding in precision treatment planning. KEY POINTS: The efficacy of APTw MRI in RA needs further investigation. T3-T4 stage and PNI positive APTw signal intensities were higher than T1-T2 and non-PNI, respectively. APTw MRI provides a quantitative method for assessment of T staging and PNI in RA.

Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for identifying the presence of breast disease. However, accurate segmentation of breast tumor is a challenging task, often necessitating the development of complex networks. To strike an optimal tradeoff between computational costs and segmentation performance, we propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. Specifically, the hybrid network consists of a encoder-decoder architecture by stacking convolution and deconvolution layers. Effective 3D transformer layers are then implemented after the encoder subnetworks, to capture global dependencies between the bottleneck features. To improve the efficiency of hybrid network, two parallel encoder sub-networks are designed for the decoder and the transformer layers, respectively. To further enhance the discriminative capability of hybrid network, a prototype learning guided prediction module is proposed, where the category-specified prototypical features are calculated through online clustering. All learned prototypical features are finally combined with the features from decoder for tumor mask prediction. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network achieves superior performance than the state-of-the-art (SOTA) methods, while maintaining balance between segmentation accuracy and computation cost. Moreover, we demonstrate that automatically generated tumor masks can be effectively applied to identify HER2-positive subtype from HER2-negative subtype with the similar accuracy to the analysis based on manual tumor segmentation. The source code is available at https://github.com/ZhouL-lab/ PLHN.

Effect of Interval Between Neoadjuvant Chemotherapy and Surgery on Oncological Outcomes in Poor Responders With Locally Advanced Breast Cancer.

PURPOSE: The interval between neoadjuvant chemotherapy (NAC) and surgery for locally advanced breast cancer (LABC) remains controversial. At the same time, the prognostic effect of delayed surgery in patients with poor responses is currently unclear. METHODS: Data was collected from patients who had poor responses to NAC and underwent modified radical surgery from January 2013 to December 2018. The interval from completion of NAC to surgery was divided into two groups: a longer (greater than four weeks) or shorter (four weeks or less) interval. The associations of these interval groups with overall survival (OS) and recurrence-free survival (RFS) were evaluated by multivariable Cox models adjusting for the existing prognostic factors. Propensity score matching (PSM) was used to minimize election bias. RESULTS: A total of 1,229 patients (mean age, 47.2 ± 8.9 years; median follow-up duration, 32.67 [6.57-52.63] months) were included. The 5-year OS rates were 73.2% and 60.8% in the shorter (n = 171) and longer interval group (n = 1,058), respectively, while the 3-year RFS rates were 80.8% and 71.7%, respectively. In multivariate Cox analysis, the longer interval was associated with an increased risk of mortality (hazard ratio [HR], 1.43; 95% confidence interval [CI], 1.01-2.02; p = 0.046) and recurrence (HR, 1.50; 95% CI, 1.12-1.99; p = 0.006). There was an interaction between the molecular subtype and the surgery interval for OS (pinteraction = 0.014) and RFS (pinteraction = 0.027). After PSM, no significant difference in OS (p = 0.180) and RFS (p = 0.069) was observed between the two groups. CONCLUSION: Among LABC patients with a poor response, those with a longer interval between NAC and surgery had worse OS and RFS. The results indicate that these patients should receive modified radical surgery timely, which may in turn improve their prognosis.

VNN1 Gene Expression and Polymorphisms Associated with Chicken Carcass Traits.

This study aimed to investigate the association between hepatic VNN1 expression and carcass traits in Mahuang chickens as well as to identify polymorphisms in the upstream and downstream regions of VNN1 that could potentially be associated with these carcass traits. The study revealed that VNN1 expression levels in liver correlated with various carcass traits such as dressed weight, eviscerated weight, and abdominal fat weight. A total of 39 polymorphic sites were identified, among which 23 were found to be associated with 15 different carcass traits. These polymorphic sites were organized into three distinct haplotype blocks, with BLOCK2 and BLOCK3 being associated with various eviscerated weight percentages, thigh weight, breast muscle weight, wing weight, and other traits. The study underscores the significant role of VNN1 in influencing the carcass traits of Mahuang chickens and sheds light on the genetic foundations of these traits. The findings provide valuable insights that could inform breeding strategies aimed at optimizing traits relevant to market demands and slaughtering efficiency.

ALKBH5 regulates chicken adipogenesis by mediating LCAT mRNA stability depending on m6A modification.

BACKGROUND: Previous studies have demonstrated the role of N6-methyladenosine (m6A) RNA methylation in various biological processes, our research is the first to elucidate its specific impact on LCAT mRNA stability and adipogenesis in poultry. RESULTS: The 6 100-day-old female chickens were categorized into high (n = 3) and low-fat chickens (n = 3) based on their abdominal fat ratios, and their abdominal fat tissues were processed for MeRIP-seq and RNA-seq. An integrated analysis of MeRIP-seq and RNA-seq omics data revealed 16 differentially expressed genes associated with to differential m6A modifications. Among them, ELOVL fatty acid elongase 2 (ELOVL2), pyruvate dehydrogenase kinase 4 (PDK4), fatty acid binding protein 9 (PMP2), fatty acid binding protein 1 (FABP1), lysosomal associated membrane protein 3 (LAMP3), lecithin-cholesterol acyltransferase (LCAT) and solute carrier family 2 member 1 (SLC2A1) have ever been reported to be associated with adipogenesis. Interestingly, LCAT was down-regulated and expressed along with decreased levels of mRNA methylation methylation in the low-fat group. Mechanistically, the highly expressed ALKBH5 gene regulates LCAT RNA demethylation and affects LCAT mRNA stability. In addition, LCAT inhibits preadipocyte proliferation and promotes preadipocyte differentiation, and plays a key role in adipogenesis. CONCLUSIONS: In conclusion, ALKBH5 mediates RNA stability of LCAT through demethylation and affects chicken adipogenesis. This study provides a theoretical basis for further understanding of RNA methylation regulation in chicken adipogenesis.

Genomics and transcriptomics reveal ß-carotene synthesis mechanism in Dunaliella salina.

Dunaliella salina is by far the most salt-tolerant organism and contains many active substances, including ß-carotene, glycerol, proteins, and vitamins, using in the production of dried biomass or cell extracts for the biofuels, pharmaceutical formulations, food additives, and fine chemicals, especially ß-carotene. We report a high-quality genome sequence of D. Salina FACHB435, which has a 472 Mb genome size, with a contig N50 of 458 Kb. A total of 30,752 protein-coding genes were predicted. The annotation results evaluated by BUSCO was shown that completeness was 91.0% and replication was 53.1%. The fragments were 6.3% and the deletions were 2.6%. Phylogenomic and comparative genomic analyses revealed that A. thaliana diverged from Volvocales about 448 million years ago, then Volvocales C. eustigma, D. salina, and other species diverged about 250 million years ago. High light could promote the accumulation of ß-carotene in D. salina at a 13 d stage of culture. The enrichment of DEGs in KEGG, it notes that the predicted up-regulated genes of carotenoid metabolic pathway include DsCrtB, DsPDS, DsZ-ISO, DsZDS, DsCRTISO, DsLUT5, DsCrtL-B, and DsCCD8, while the predicted down-regulated genes include DsCrtF, and DsLUT1. The four genes that were both up-regulated and down-regulated were DsZEP, DsCrtR-b, DsCruA/P and DsCrtZ 4. The research results can provide scientific basis for the industrialization practice of D. salina.

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma.

Background and objective: Spatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence suggests that the spatial distance between tumor cells and lymphocytes also influences the immune responses, but the distance analysis based on Hematoxylin and Eosin (H&E) -stained whole-slide images (WSIs) remains insufficient. To address this issue, we aim to explore the relationship between distance and prognosis prediction of patients with LUAD in this study. Methods: We recruited patients with resectable LUAD from three independent cohorts in this multi-center study. We proposed a simple but effective deep learning-driven workflow to automatically segment different cell types in the tumor region using the HoVer-Net model, and quantified the spatial distance (DIST) between tumor cells and lymphocytes based on H&E-stained WSIs. The association of DIST with disease-free survival (DFS) was explored in the discovery set (D1, n = 276) and the two validation sets (V1, n = 139; V2, n = 115). Results: In multivariable analysis, the low DIST group was associated with significantly better DFS in the discovery set (D1, HR, 0.61; 95 % CI, 0.40-0.94; p = 0.027) and the two validation sets (V1, HR, 0.54; 95 % CI, 0.32-0.91; p = 0.022; V2, HR, 0.44; 95 % CI, 0.24-0.81; p = 0.009). By integrating the DIST with clinicopathological factors, the integrated model (full model) had better discrimination for DFS in the discovery set (C-index, D1, 0.745 vs. 0.723) and the two validation sets (V1, 0.621 vs. 0.596; V2, 0.671 vs. 0.650). Furthermore, the computerized DIST was associated with immune phenotypes such as immune-desert and inflamed phenotypes. Conclusions: The integration of DIST with clinicopathological factors could improve the stratification performance of patients with resectable LUAD, was beneficial for the prognosis prediction of LUAD patients, and was also expected to assist physicians in individualized treatment.

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC. MATERIALS AND METHODS: Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined. RESULTS: The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer-Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56). CONCLUSION: This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients' survival, utilizing intratumoral and peritumoral radiomics features.

Trajectory patterns and cumulative burden of CEA during follow-up with non-small cell lung cancer outcomes: A retrospective longitudinal cohort study.

BACKGROUND: Previous studies of non-small cell lung cancer (NSCLC) focused on CEA measured at a single time point, ignoring serial CEA measurements. METHODS: This retrospective cohort included 2959 patients underwent surgery for stage I-III NSCLC. CEA trajectory patterns and long-term cumulative CEA burden were evaluated using the latent class growth mixture model. RESULTS: Four CEA trajectory groups were identified, named as low-stable, decreasing, early-rising and later-rising. Compared with the low-stable group, the adjusted hazard ratios associated with death were 1.27, 4.50, and 3.68 for the other groups. Cumulative CEA burden were positively associated with the risk of death in patients not belonging to the low-stable group. The 5-year overall survival (OS) rates decreased from 62.3% to 33.0% for the first and fourth quantile groups of cumulative CEA burden. Jointly, patients with decreasing CEA trajectory could be further divided into the decreasing & low and decreasing & high group, with 5-year OS rates to be 77.9% and 47.1%. Patients with rising CEA trajectory and high cumulative CEA were found to be more likely to develop bone metastasis. CONCLUSIONS: Longitudinal trajectory patterns and long-term cumulative burden of CEA were independent prognostic factors of NSCLC. We recommend CEA in postoperative surveillance of NSCLC.

No prognostic impact of staging bone scan in patients with stage IA non-small cell lung cancer.

OBJECTIVE: To investigate the survival benefit of preoperative bone scan in asymptomatic patients with early-stage non-small cell lung cancer (NSCLC). METHODS: This retrospective study included patients with radical resection for stage T1N0M0 NSCLC between March 2013 and December 2018. During postoperative follow-up, we monitored patient survival and the development of bone metastasis. We compared overall survival, bone metastasis-free survival, and recurrence-free survival in patients with or without preoperative bone scan. Propensity score matching and inverse probability of treatment weighting were used to minimize election bias. RESULTS: A total of 868 patients (58.19 ± 9.69 years; 415 men) were included in the study. Of 87.7% (761 of 868) underwent preoperative bone scan. In the multivariable analyses, bone scan did not improve overall survival (hazard ratio [HR] 1.49; 95% confidence intervals [CI] 0.91-2.42; p = 0.113), bone metastasis-free survival (HR 1.18; 95% CI 0.73-1.90; p = 0.551), and recurrence-free survival (HR 0.89; 95% CI 0.58-1.39; p = 0.618). Similar results were obtained after propensity score matching (overall survival [HR 1.28; 95% CI 0.74-2.23; p = 0.379], bone metastasis-free survival [HR 1.00; 95% CI 0.58-1.72; p = 0.997], and recurrence-free survival [HR 0.76; 95% CI 0.46-1.24; p = 0.270]) and inverse probability of treatment weighting. CONCLUSION: There were no significant differences in overall survival, bone metastasis-free survival, and recurrence-free survival between asymptomatic patients with clinical stage IA NSCLC with or without preoperative bone scan.

Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer.

BACKGROUND: Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated. METHODS: We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out. RESULTS: According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells. CONCLUSIONS: Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

ZNF326 as a potential prognostic and predictive biomarker in stage II colorectal cancer.

Background: Adjuvant chemotherapy is considered for stage II colorectal cancer (CRC) patients with poor prognostic risk factors. However, current stratification algorithms are still insufficient to identify high-risk patients. Methods: We conducted a screening strategy to define ZNF326 based on quantitative proteomics in 11 paired CRC patients selected by a nested case-control design, and tested the association between ZNF326 expression level with the prognosis of stage II CRC patients and the benefit from adjuvant chemotherapy in public datasets; further investigation was conducted through subgroup analyses. Results: We found that low ZNF326 expression was significantly associated with a lower 5-year overall survival (OS) rate among stage II patients in both the discovery [P=0.008; hazard ratio (HR): 3.13, 95% confidence interval (CI): 1.29-7.58] and validation (P=0.025; HR: 1.98, 95% CI: 1.08-3.65) cohorts. In the Cox multivariable analysis, low ZNF326 expression was both associated with shorter OS after adjustment for age, sex, and adjuvant chemotherapy in the discovery and validation data sets. Subgroup analyses yielded largely similar results. In a pooled database, the rate of 5-year OS was higher among stage II ZNF326-high tumors who were treated with adjuvant chemotherapy than it was among those who were not treated with adjuvant chemotherapy (P=0.011; HR: 0.28, 95% CI: 0.10-0.80). Conclusions: ZNF326 has the potential to be used in clinical practice for risk classification. ZNF326-low expression level identified a subgroup of patients with high-risk stage II CRC who appeared to less benefit from adjuvant chemotherapy.

SC-Net: Symmetrical conical network for colorectal pathology image segmentation.

BACKGROUND AND OBJECTIVE: Image segmentation of histopathology of colorectal cancer is a core task of computer aided medical image diagnosis system. Existing convolutional neural networks generally extract multi-scale information in linear flow structures by inserting multi-branch modules, which is difficult to extract heterogeneous semantic information under multi-level and different receptive field and tough to establish context dependency among different receptive field features. METHODS: To address these issues, we propose a symmetric spiral progressive feature fusion encoder-decoder network called the Symmetric Conical Network (SC-Net). First, we design a Multi-scale Feature Extraction Block (MFEB) matching with the Symmetric Conical Network to obtain multi-branch heterogeneous semantic information under different receptive fields, so as to enrich the diversity of extracted feature information. The encoder is composed of MFEB through spiral and multi-branch arrangement to enhance context dependence between different information flow. Secondly, the information loss of contour, color and others in high-level semantic information through causally stacking MFEB, the Feature Mapping Layer (FML) is designed to map low-level features to high-level semantic features along the down-sampling branch and solve the problem of insufficient global feature extraction in deep levels. RESULTS: The SC-Net was evaluated on our self-constructed colorectal cancer dataset, a publicly available breast cancer dataset and a polyp dataset. The results revealed that the mDice of segmentation reached 0.8611, 0.7259 and 0.7144. We compare our model with the state-of-art semantic segmentation UNet++, PSPNet, Attention U-Net, R2U-Net and other advanced segmentation networks. The experimental results demonstrate that we achieve the most advanced performance. CONCLUSIONS: The results indicate that the proposed SC-Net excels in segmenting H&E stained pathology images, effectively preserving morphological features and spatial information even in scenarios with weak texture, poor contrast, and variations in appearance.

Measuring and classifying IP usage scenarios: a continuous neural trees approach.

Understanding user behavior via IP addresses is a crucial measure towards numerous pragmatic IP-based applications, including online content delivery, fraud prevention, marketing intelligence, and others. While profiling IP addresses through methods like IP geolocation and anomaly detection has been thoroughly studied, the function of an IP address-e.g., whether it pertains to a private enterprise network or a home broadband-remains underexplored. In this work, we initiate the first attempt to address the IP usage scenario classification problem. We collect data consisting of IP addresses from four large-scale regions. A novel continuous neural tree-based ensemble model is proposed to learn IP assignment rules and complex feature interactions. We conduct extensive experiments to evaluate our model in terms of classification accuracy and generalizability. Our results demonstrate that the proposed model is capable of efficiently uncovering significant higher-order feature interactions that enhance IP usage scenario classification, while also possessing the ability to generalize from the source region to the target one.

A nomogram for predicting liver metastasis in patients with gastric gastrointestinal stromal tumor.

BACKGROUND: Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM. METHODS: Patient data diagnosed as having gastric GIST between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training cohort and internal validation cohort in a 7:3 ratio. For external validation, retrospective data collection was performed on patients diagnosed as having gastric GIST at Yunnan Cancer Center (YNCC) between January 2015 and May 2023. Univariate and multivariate logistic regression analyses were used to identify independent risk factors associated with LIM in patients with gastric GIST. An individualized LIM nomogram specific for gastric GIST was formulated based on the multivariate logistic model; its discriminative performance, calibration, and clinical utility were evaluated. RESULTS: In the SEER database, a cohort of 2341 patients with gastric GIST was analyzed, of which 173 cases (7.39%) were found to have LIM; 239 patients with gastric GIST from the YNCC database were included, of which 25 (10.46%) had LIM. Multivariate analysis showed tumor size, tumor site, and sex were independent risk factors for LIM (P < .05). The nomogram based on the basic clinical characteristics of tumor size, tumor site, sex, and age demonstrated significant discrimination, with an area under the curve of 0.753 (95% CI, 0.692-0.814) and 0.836 (95% CI, 0.743-0.930) in the internal and external validation cohort, respectively. The Hosmer-Lemeshow test showed that the nomogram was well calibrated, whereas the decision curve analysis and the clinical impact plot demonstrated its clinical utility. CONCLUSION: Tumor size, tumor subsite, and sex were significantly correlated with the risk of LIM in gastric GIST. The nomogram for patients with GIST can effectively predict the individualized risk of LIM and contribute to the planning and decision making related to metastasis management in clinical practice.

Constrained Origami Artificial Muscle-Driven Robotic Manipulator Capable of Coordinating Twisting and Grasping Motions for Object Manipulation.

Grasping and twisting motions are vital when manipulating objects due to their fundamental role in enabling precision, adaptability, and effective interaction. However, few studies in soft robotics exploiting artificial muscles have achieved object manipulation in situ through the coordination of twisting and grasping motions akin to our forearm and hand's capabilities. Especially, when using the same artificial muscle module to achieve these two motions will greatly simplify the manufacturing and control complexity. Here, we introduce identical origami artificial muscle modules (OAMMs) subjected to distinct end constraints into the design of the robotic manipulator, allowing it to achieve independent grasping and twisting motions to achieve effective, precise object manipulation. Applying different end constraints to the identical OAMMs yields distinct motions at their ends, where utilizing a fixed end and a sliding end realizes pure translation, while opting for a fixed end and a rotating end enables pure rotation. The differentially constrained OAMMs then serve as soft actuators for the manipulator's torsional mechanism and grasping mechanism to accomplish independent, controllable twisting and grasping motions. The coordination of twisting and grasping motions finally enables the manipulator to complete various tasks, including installing a light bubble, pouring the water from a lidded bottle into a cup, and sorting and stacking puzzle blocks. Our study pioneers the utilization of OAMMs for precise and versatile object manipulation through the coordination of independent twisting and grasping motions.

Evaluating Childhood Overweight- and Obesity-Related Food Marketing Policies in China Using the Food-Environment Policy Index (Food-EPI).

OBJECTIVE: Addressing the increasing global health issue of childhood obesity, exacerbated by pervasive food marketing, this study critically evaluated China's food marketing policies in comparison with international best practices, aiming to uncover policy content and implementation gaps and inform policy enhancement strategies. METHOD: Three key indicators were utilized from the Healthy Food-Environment Policy Index (Food-EPI)'s food promotion domain. A panel of experts (n = 13) from academic institutions, China Centers for Disease Control and Prevention, and the food industry assessed the Chinese government's policy scores and implementation levels concerning food marketing. Benchmarked against international best practices using the Food-EPI process, this evaluation encompassed context analysis, data collection, evidence-based policy action, government validation, policy rating, scoring, and results translation for government and stakeholders. The three chosen indicators specifically addressed childhood overweight- and obesity-related food marketing in broadcast media (Indicator 1), non-broadcast media (Indicator 2), and child gathering settings (Indicator 3). RESULTS: Specifically, Indicator 1, the Single Food Marketing Indicator Score was measured at 2.31 ± 0.38, with an accompanying Food Marketing Policy Implementation Percentage of 46.2%, and Low Implementation Level. For non-broadcast mediums (Indicator 2), these metrics were gauged at 1.77 ± 0.27, 35.4%, and Low Implementation Level, respectively. In child gathering settings (Indicator 3), for efforts curbing unhealthy food promotion, a score of 2.77 ± 0.27, an implementation percentage of 55.4%, and Medium Implementation Level was obtained. Cumulatively, the overarching efficacy of food marketing policy enforcement was determined to be suboptimal, with the consolidated figures being Total Food Marketing Score as 2.28 ± 0.97, Total Food Marketing Policy Implementation Percentage as 45.6%, and Total Food Marketing Policy Implementation Level as Low. CONCLUSION: Like many countries, China's food marketing policies and implementation have room for improvement when compared to international best practices. Recommendations include emphasizing nutritional legislation, fostering stakeholder collaboration, bolstering public health campaigns, and leveraging technology for stringent enforcement.

Different Dimensions of the Home Food Environment May Be Associated with the Body Mass Index of Older Adults: A Cross-Sectional Survey Conducted in Beijing, China.

OBJECTIVE: The objective of this study was to evaluate the home food environment of the elderly in Beijing and analyze its association with the body mass index (BMI) of the elderly, as well as to provide recommendations for improving the home food environment for the elderly. METHODS: This study was conducted in Beijing, China, in 2019. The participants were 1764 elderly individuals aged 65 to 80, recruited from 12 communities through a multistage stratified random sampling method. The study involved the use of questionnaire surveys to gather data on participants' demographics, the availability of various foods in their households, and their living conditions. Socioeconomic status (SES) was evaluated based on their educational level, occupation, and income level. Height and weight measurements were taken to calculate BMI. We conducted both univariate analysis and multiple linear regression analysis to evaluate the relationship between the home food environment and BMI. RESULTS: A total of 1800 questionnaires were distributed, of which 1775 were retrieved, resulting in a questionnaire recovery rate of 98.6%. Among these, 1764 questionnaires were deemed valid, corresponding to a questionnaire validity rate of 99.4%. The participants had a mean age of 69.7 ± 4.3 years old, over 40% of whom were overweight or obese. In terms of low-energy/high-nutrient-density foods, the most readily available items were fresh vegetables (95.6%), followed by coarse grains (94.1%), fresh fruits (90.4%), and dairy products (83.6%). Among high-energy/low-nutrient-density foods, preserved foods were the most available (51.9%), followed by salted snacks (40.6%), sugary beverages (28.2%), and fried foods (9.4%). Approximately 7.3% of participants lived alone. Elderly individuals with higher SES had a lower BMI compared to those with medium to low SES (25.9 vs. 26.5, 25.9 vs. 26.4, p < 0.05). Those living alone had a higher BMI than those who did not (27.2 vs. 26.2, p = 0.001). After controlling for potential confounding variables, older adults with high SES exhibited a BMI reduction of 0.356 kg/m2 (p = 0.001), whereas those living alone exhibited an increase in BMI of 1.155 kg/m2 (p < 0.001). The presence of preserved foods at home was linked to a BMI increase of 0.442 kg/m2 (p = 0.008). CONCLUSION: This study underscores the significant impact of family SES, living conditions, and the availability of preserved foods on the BMI of elderly individuals.

Comparison of the penetration depth in mouse brain in vivo through 3PF imaging using AIE nanoparticle labeling and THG imaging within the 1700 nm window.

3-Photon microscopy (3PM) excited at the 1700 nm window features a smaller tissue attenuation and hence a larger penetration depth in brain imaging compared with other excitation wavelengths in vivo. While the comparison of the penetration depth quantified by effective attenuation length le with other excitation wavelengths have been extensively investigated, comparison within the 1700 nm window has never been demonstrated. This is mainly due to the lack of a proper excitation laser source and characterization of the in vivo emission properties of fluorescent labels within this window. Herein, we demonstrate detailed measurements and comparison of le through the 3-photon imaging of the mouse brain in vivo, at different excitation wavelengths (1600 nm, 1700 nm, and 1800 nm). 3PF imaging and in vivo spectrum measurements were performed using AIE nanoparticle labeling. Our results show that le derived from both 3PF imaging and THG imaging is the largest at 1700 nm, indicating that it enables the deepest penetration in brain imaging in vivo.