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Abstract Background Neuropsychiatric manifestations, such as cognitive impairment, are relatively prevalent in systemic sclerosis (SSc) patients. This study aimed to investigate the resting state (RS) functional alternations of SSc patients and the potential influenced factors. Methods Forty-four SSc patients (mean age, 46.3 ± 11.4 years; 40 females) and 19 age and sex comparable healthy volunteers (mean age, 42.6 ± 11.3 years; 16 females) were recruited and underwent RS functional MR imaging (fMRI) and neuropsychological assessments. Functional segregation analysis was performed to calculate the amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo). Functional integration analysis was conducted using group independent component analysis to calculate intra-network and inter-network functional connectivity (FC). The fMRI measurements were compared between SSc patients and healthy volunteers using voxel-based pairwise two-sample t-tests. The correlations between clinical characteristics and fMRI measurements were also analyzed. Results Compared to healthy volunteers, SSc patients exhibited significantly decreased ALFF and increased ReHo (all P 30; inter-network FC: P

Small animal PET/MRI is an integrated imaging device that combines the high-performance molecular imaging function of PET with the excellent soft tissue contrast function of MRI，offering unique advantages and significant application value in drug development and evaluation，pharmacology，and mechanistic studies of diseases. To verify the feasibility and accuracy of this device in quantitative analysis，this study evaluated the linear detection range，stability，and quantitative performance of the Bruker BioSpec 94/30 USR + PET insert for 18F. High-resolution MRI images were utilized to remove interference from factors such as the gallbladder and blood vessels，ensuring accurate in vivo quantification in animals. The results demonstrate that the Bruker BioSpec 94/30 USR + PET insert small animal PET/MRI system exhibits excellent linearity (R2=0.9992) within the activity range of 1-1500 μCi，good reproducibility，and accurate in vivo quantitative performance. This provides reliable data for subsequent studies utilizing the Bruker BioSpec 94/30 USR + PET insert for in vivo visualization，distribution，metabolism，and pharmacodynamic evaluation of nuclear tracers.

Abstract Background Gut microbial composition and its metabolites are crucial for livestock production performance. Metabolite profiles from autopsied biospecimens provide vital information on the basic mechanisms that affect the overall health and production traits in livestock animals. However, the role of the host breed in the gut microbiome and fecal metabolome of commercial pigs remains unclear. In this work, differences in microbiota composition among three commercial pig breeds Duroc, Yorkshire, and Landrace were measured by 16S rRNA gene sequencing. Fecal metabolite compositions of the three pig breeds were detected using untargeted metabolomics. Results There were significant differences in the gut microbiomes of the three species, indicating that host breed affects the diversity and structure of gut microbiota. Several breed-associated microorganisms were identified at different taxonomic levels. Notely, most microbial taxa were annotated as Lactobacillacea , Muribaculaceae, and Oscillospiraceae. Several bacteria, including Lactobacillus, Subdoligranulum, Faecalibacterium, Oscillospira, Oscillospiraceae_UCG-002, and Christensenellaceae_R-7_group, could be considered as biomarkers for improving the backfat thickness (BF) for commercial pigs. Additionally, KEGG analysis of gut microbiota further revealed that arginine biosynthesis, pyruvate metabolism, and fatty acid biosynthesis varied greatly among pig breeds. Multiple gut bacterial metabolites (e.g., spermidine, estradiol, and palmitic acid) were identified as breed-associated. Mediation analysis ultimately revealed the cross-talk among gut microbiota, metabolites, and BF thickness, proclaiming that the microbial and metabolic biomarkers identified in this study could be used as biomarkers for improving BF phenotype. Conclusions This work provides vital insights into breed effects on gut microbiota and metabolite compositions of commercial pigs and uncovers potential biomarkers that are significant for pig breed improvement.

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification’s accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

Abstract Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Abstract Background The pathological results obtained from endoscopic forceps biopsy (EFB) do not always align with the findings of postoperative endoscopic submucosal dissection (ESD). Furthermore, as ESD becomes more widespread, the number of noncurative endoscopic cases increases; thus, an accurate preoperative diagnosis and an appropriate treatment method are crucial. The purpose of this study was to explore the risk factors for postoperative pathological upgrading and noncurative resection and to gather experience in clinical and pathological diagnosis. Methods From March 2016 to November 2023, 292 ESD specimens were collected from 262 patients with gastric mucosal lesions. Clinicopathological information, the coincidence rate of pathological diagnosis between EFB and ESD specimens, and risk factors related to noncurative resection were analyzed retrospectively. Results The overall upgraded pathological diagnosis rate between EFB and ESD was 26.4%. The independent predictors for the upgraded group included proximal stomach lesions, lesion size > 2 cm, surface ulceration, and surface nodules. Twenty of the 235 early gastric cancer (EGC) patients underwent noncurative ESD resection. Multivariate analysis showed that undifferentiated carcinoma and tumor infiltration into the submucosa were significantly associated with noncurative resection. Conclusion Biopsy cannot fully represent the lesions of gastric intraepithelial neoplasia (GIN). When a suspected epithelial dysplasia is suspected, a careful endoscopic examination should be conducted to evaluate the lesion site, size, and surface characteristics to ensure an accurate diagnosis. Noncurative endoscopic resection is associated with undifferentiated carcinoma and submucosal infiltration. Clinicians must be familiar with these predictive factors for noncurative resection and select the appropriate treatment for their patients.

Abstract The hormone receptor (HR) status plays a significant role in breast cancer, serving as the primary guide for treatment decisions and closely correlating with prognosis. This study aims to investigate the predictive value of radiomics analysis in long-axis and short-axis ultrasound planes for distinguishing between HR-positive and HR-negative breast cancers. A cohort of 505 patients from two hospitals was stratified into discovery (Institute 1, 416 patients) and validation (Institute 2, 89 patients) cohorts. A comprehensive set of 788 ultrasound radiomics features was extracted from both long-axis and short-axis ultrasound planes, respectively. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, distinct models were constructed for the long-axis and short-axis data. Subsequently, radiomics scores (Rad-scores) were computed for each patient. Additionally, a combined model was formulated by integrating data from long-axis and short-axis Rad-scores along with clinical factors. The diagnostic efficacy of all models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The long-axis and short-axis models, consisting of 11 features and 15 features, respectively, were established, yielding AUCs of 0.743 and 0.751 in the discovery cohort, and 0.795 and 0.744 in the validation cohort. The calculated long-axis and short-axis Rad-scores exhibited significant differences between HR-positive and HR-negative groups across all cohorts (all p

Abstract Background This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. Methods In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. Results According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P 0.05). Conclusions The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

Background: Pirfenidone (PFD) is commonly applied for antifibrotic treatment in patients with idiopathic pulmonary fibrosis but has rarely been studied in cases with connective tissue disease-associated interstitial lung diseases (CTD-ILDs). Objectives: We aimed to examine the efficacy of PFD in patients with CTD-ILD based on real-world data. Design: A retrospective cohort study. Methods: This study assessed the clinical features of CTD-ILD patients with or without a 6-month PFD treatment. A linear mixed effects model was employed to evaluate the effectiveness of PFD in alleviating lung function changes. Differences in response to PFD were analyzed based on CTD subtype, imaging classification, and pattern of pulmonary function at baseline. Results: A total of 289 patients with CTD-ILD were included, with 155 (53.6%) receiving PFD treatment and the remaining constituting the control group. Patients with the usual interstitial pneumonia (UIP) pattern were more likely to receive PFD treatment, and a relatively lower proportion of cases in the PFD group received immunosuppressive therapies compared to the control group ( p

Abstract Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer‐association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer‐associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor‐microbe‐immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer‐associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow‐up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

IntroductionAnaerobic–anoxic–oxic (A2/O) and deoxidation ditch (DOD) processes are being increasingly preferred owing to their effectiveness in treating various wastes in wastewater treatment plants (WWTPs). Considering seasonal variations is crucial in optimizing treatment processes, ensuring compliance with regulations, and maintaining the overall efficiency and effectiveness of WWTPs. This study aimed to determine the influence of seasonality on nitrogen removing microbes and functional genes within A2/O and DOD processes in the humid Wuhan and semi-arid Xi’an cities, China.MethodsThe physicochemical parameters of water quality were determined, and molecular and bioinformatic analyses of the bacterial community and nitrogen metabolism functional genes in the two different treatment processes of two WWTPs were performed over four seasons.Results and discussionOur analyses revealed a significant difference in all physicochemical parameters across all experimental groups (p

ObjectivesThe diagnosis of intracranial atherosclerotic stenosis (ICAS) is of great significance for the prevention of stroke. Deep learning (DL)-based artificial intelligence techniques may aid in the diagnosis. The study aimed to identify ICAS in the middle cerebral artery (MCA) based on a modified DL model.MethodsThis retrospective study included two datasets. Dataset1 consisted of 3,068 transcranial Doppler (TCD) images of the MCA from 1,729 patients, which were assessed as normal or stenosis by three physicians with varying levels of experience, in conjunction with other medical imaging data. The data were used to improve and train the VGG16 models. Dataset2 consisted of TCD images of 90 people who underwent physical examination, which were used to verify the robustness of the model and compare the consistency between the model and human physicians.ResultsThe accuracy, precision, specificity, sensitivity, and area under curve (AUC) of the best model VGG16 + Squeeze-and-Excitation (SE) + skip connection (SC) on dataset1 reached 85.67 ± 0.43(%),87.23 ± 1.17(%),87.73 ± 1.47(%),83.60 ± 1.60(%), and 0.857 ± 0.004, while those of dataset2 were 93.70 ± 2.80(%),62.65 ± 11.27(%),93.00 ± 3.11(%),100.00 ± 0.00(%), and 0.965 ± 0.016. The kappa coefficient showed that it reached the recognition level of senior doctors.ConclusionThe improved DL model has a good diagnostic effect for MCV stenosis in TCD images and is expected to help in ICAS screening.

BackgroundCoronary heart disease (CHD) is one of the leading causes of mortality. The current study aims to assess the association between C-reactive protein-triglyceride glucose index (CTI) and the risk of prevalent CHD and to evaluate the usefulness of CTI to refine the identification of prevalent CHD.Methods19,451 subjects from the National Health and Nutrition Examination Survey 1999–2010 were enrolled. CHD was ascertained according to the questionnaire.ResultsThe prevalent of CHD was 6.23%. After adjusting for conventional cardiovascular risk factors, each SD increase of CTI could cast a 1.357 times risk of CHD. In quartile analysis, the top quartile had a 1.807 times risk of CHD than the bottom quartile. Smooth curving fitting displayed that the association was linear in the entire range of CTI. Subgroup analysis revealed that the association was robust among several common subpopulations but stronger in subjects aged

BackgroundDespite emerging evidence linking alterations in gut microbiota to childhood obesity, the metabolic mechanisms linking gut microbiota to the lipid profile during childhood obesity and weight loss remain poorly understood.MethodologyIn this study, children with obesity were treated with lifestyle weight loss therapy. Metagenomics association studies and serum untargeted lipidomics analyses were performed in children with obesity and healthy controls before and after weight loss.Main findingsWe identified alterations in gut microbiota associated with childhood obesity, as well as variations in circulating metabolite concentrations. Children with obesity showed significant decreases in the levels of s-Rothia_kristinae and s-Enterobacter_roggenkampii, alongsige elevated levels of s-Clostridiales_bacterium_Marseille-P5551. Following weight loss, the levels of s-Streptococcus_infantarius and s-Leuconostoc_citreum increased by factors of 3.354 and 1.505, respectively, in comparison to their pre-weight loss levels. Correlation analyses indicated a significant positive relationship between ChE(2:0) levels and both with s-Lachnospiraceae_bacterium_TF09-5 and fasting glucose levels. CoQ8 levels were significantly negatively correlated with s-Rothia_kristinae and HOMA-IR.ConclusionWe linked altered gut microbiota and serum lipid levels in children with obesity to clinical indicators, indicating a potential impact on glucose metabolism via lipids. This study contributes to understanding the mechanistic relationship between altered gut microbiota and childhood obesity and weight loss, suggesting gut microbiome as a promising target for intervention.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=178971, ChiCTR2300072179.

IntroductionEarly oral feeding (EOF) has been shown to improve postoperative recovery for many surgeries. However, surgeons are still skeptical about EOF after gastric cancer surgery due to possible side effects. This updated systematic review and meta-analysis aimed to investigate the efficacy and safety of EOF in patients after gastric cancer surgery.MethodsRandomized controlled trials (RCTs) investigating EOF in patients after gastric cancer surgery were searched in the databases of PubMed, Embase, Clinicaltrials.gov, and Cochrane from 2005 to 2023, and an updated meta-analysis was performed using RevMan 5.4 software.ResultsThe results of 11 RCTs involving 1,352 patients were included and scrutinized in this analysis. Hospital days [weighted mean difference (WMD), −1.72; 95% confidence interval (CI), −2.14 to −1.30; p

The magnesium oxysulfate (MOS) cement has problems of short setting time, poor water resistance, and volume instability. This paper investigates the potential of steel slag (SS) to modify the properties and microstructure of the MOS cement prepared by magnesium desulfurization byproducts (MDBs). The results showed that SS delayed the setting time, restrained the volume shrinkage, and improved the water resistance of the MOS cement. The hydration products of the MOS cement were the fibrous 318 phase (3 Mg(OH)2•MgSO4•8H2O), lamellar Mg(OH)2, and MgCO3. The addition of SS increased the pH and decreased the overall hydration reaction rate and the amount of Mg(OH)2 in the MOS cement. SS reacted with Mg(OH)2 at the late stage to form an amorphous M–S–H gel, which increased the gel pore volume and the density of the cement. In conclusion, SS enhanced the application potential of MOS cement, through improving its macroscopic properties.

Abstract Background To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. Methods A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases’ characteristics were conducted. Results The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. Conclusions The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Abstract Integration of molecular data with histologic, radiologic, and clinical features is imperative for accurate diagnosis of pediatric central nervous system (CNS) tumors. Whole transcriptome RNA sequencing (RNAseq), a genome-wide and non-targeted approach, allows for the detection of novel or rare oncogenic fusion events that contribute to the tumorigenesis of a substantial portion of pediatric low- and high-grade glial and glioneuronal tumors. We present two cases of pediatric glioneuronal tumors occurring in the occipital region with a CLIP2::MET fusion detected by RNAseq. Chromosomal microarray studies revealed copy number alterations involving chromosomes 1, 7, and 22 in both tumors, with Case 2 having an interstitial deletion breakpoint in the CLIP2 gene. By methylation profiling, neither tumor had a match result, but both clustered with the low-grade glial/glioneuronal tumors in the UMAP. Histologically, in both instances, our cases displayed characteristics of a low-grade tumor, notably the absence of mitotic activity, low Ki-67 labeling index and the lack of necrosis and microvascular proliferation. Glial and neuronal markers were positive for both tumors. Clinically, both patients achieved clinical stability post-tumor resection and remain under regular surveillance imaging without adjuvant therapy at the last follow-up, 6 months and 3 years, respectively. This is the first case report demonstrating the presence of a CLIP2::MET fusion in two pediatric low-grade glioneuronal tumors (GNT). Conservative clinical management may be considered for patients with GNT and CLIP2:MET fusion in the context of histologically low-grade features.

This study demonstrated an effective method to enhance the dimensional stability of straw-based biocomposites with modified lignosulfonate as a binder. The ultraviolet (UV) light-curable nanosol was prepared by adding 3-(trimethoxysilyl)propyl methacrylate (MEMO) as sol–gel precursor into polyvinyl alcohol (PVA) solution. The MEMO/PVA coatings were generated using 2-hydroxy-2-methyl-1-phenylpropan-1-one (Darocur 1173) as radical photo-initiator and chitosan (CS) as additive, on straw-based biocomposites via UV-curing process. The effects of the crucial steps, such as the UV-curing process, hydrolysis time, Darocur 1173 dosage, and CS dosage on the dimensional stability of straw-based biocomposites, were evaluated. The optimum preparation parameters, obtained using the Box–Behnken design, were 31.9 min hydrolysis time, 4.5% Darocur 1173 dosage, and 2.7% CS dosage. Moisture resistance of minimum TS of CS-MEMO/PVA-coated straw-based biocomposites resulted in ~23.1% reduction in dimensional stability without significant decline in the mechanical properties when compared with those without UV curing. Moreover, the glossy spherical particles underwent arrangement in a fish-scale shape with scales closely linked with each other and no agglomeration occurred in CS-MEMO/PVA hybrid film. The CS promoted the cross-linking of MEMO/PVA coating on the biocomposite surface. The resulting biocomposites can be directly applied to public humid-environment applications such as bath furniture and bathroom partitions.

To study the seasonal change rules of water quality indicators and water chemical control mechanisms in the nearby city river Xinbian River, in Suzhou City, Anhui Province, China, nine points were selected and periodically sampled for 12 months. Totally 108 water samples were collected, and basic physical–chemical indicators and routine ions (e.g., Ca2+, Mg2+, Na+, K+, SO42−, Cl−, F−, NO3−, CO32−, and HCO3−) were measured. Piper diagram, Gibbs diagram, and ion ratio method were used. The result shows that the seasonal and upstream-to-downstream variations in pH were less varied, whereas the fluctuation in dissolved oxygen was large. Four ions, namely, SO42-, F−, Cl−, and Na+ generally first decreased overall and then changed from spring to winter. The maximum contents of SO42- (406.03 mg/L), Cl− (250.22 mg/L), and Na+ (269.99 mg/L) in single water samples appeared at the S9 sampling point (Suzhou control gate) in March 2020. The dominant hydrochemical types in summer were Na–Ca–HCO3 and Na–Mg–SO4, while Na–Mg–SO4 and Na–Mg–HCO3 were the main hydrochemical types in the other seasons. The control factors of water chemical composition vary according to the season. However, rock weathering (e.g., silicate dissolution) is the dominant control factor of water chemistry in the studied river section. HIGHLIGHTS The annual water quality indicators of a proposed surface water source in a severely water-deficient city were tested.; The seasonal change characteristics of water quality indicators and their control mechanisms were revealed.; Both natural and human factors that affect water quality have been comprehensively analyzed.;

Abstract Nonlinear optical processing of ambient natural light is highly desired for computational imaging and sensing. Strong optical nonlinear response under weak broadband incoherent light is essential for this purpose. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than achievable in most optical nonlinear materials. We fabricated a 10,000-pixel array of optoelectronic neurons, and experimentally demonstrated an intelligent imaging system that instantly attenuates intense glares while retaining the weaker-intensity objects captured by a cellphone camera. This intelligent glare-reduction is important for various imaging applications, including autonomous driving, machine vision, and security cameras. The rapid nonlinear processing of incoherent broadband light might also find applications in optical computing, where nonlinear activation functions for ambient light conditions are highly sought.

A coupled model of micro-segregation and macro-heat transfer was established to study the solute micro-segregation behaviour of bainite rail steel in the continuous casting process. The effects of solute elements, effective cooling rate of the mushy zone (Ceff), and MnS precipitation on the solute micro-segregation and characteristic solidification temperatures were analysed. The results show that Ceff gradually decreases from the surface of the bloom to the centre as the solidification of the molten steel progresses, and the maximum effective cooling rate is in the corner of the bloom. During the solidification of bainite rail steel, the micro-segregation degree of P and S is more serious, followed by C, Si, Mo, etc. At the later stage of solidification, the precipitation of MnS leads to a decrease in the micro-segregation degree of S and Mn. Carbon mainly affects the solute micro-segregation behaviour by changing the solidification mode of molten steel, while the initial S and Mn contents in molten steel have no obvious effect on the micro-segregation degree of C, Si, Cr, Ni, Mo, and P. Ceff has different effects on the micro-segregation behaviors of different solute elements. An increase in C, Mn, and S content and in Ceff will advance the MnS precipitation time and lead to an increase in the amount of MnS precipitation. Increasing the initial C, Mn, and S content in molten steel leads to a decrease in both ZST and ZDT.

Objective: To develop a ultrasound images based dual-channel deep learning model to achieve accurate early diagnosis of thyroid nodules less than 1 cm. Methods: A dual-channel deep learning model called thyroid nodule transformer network (TNT-Net) was proposed. The model has two input channels for transverse and longitudinal ultrasound images of thyroid nodules, respectively. A total of 9649 nodules from 8455 patients across five hospitals were retrospectively collected. The data were divided into a training set (8453 nodules, 7369 patients), an internal test set (565 nodules, 512 patients), and an external test set (631 nodules, 574 patients). Results: TNT-Net achieved an area under the curve (AUC) of 0.953 (95 % confidence interval (CI): 0.934, 0.969) on the internal test set and 0.941 (95 % CI: 0.921, 0.957) on the external test set, significantly outperforming traditional deep convolutional neural network models and single-channel swin transformer model, whose AUCs ranged from 0.800 (95 % CI: 0.759, 0.837) to 0.856 (95 % CI: 0.819, 0.881). Furthermore, feature heatmap visualization showed that TNT-Net could extract richer and more energetic malignant nodule patterns. Conclusion: The proposed TNT-Net model significantly improved the recognition capability for thyroid nodules with size less than 1 cm. This model has the potential to reduce overdiagnosis and overtreatment of such nodules, providing essential support for precise management of thyroid nodules while complementing fine-needle aspiration biopsy.

Background and aim Pyogenic liver abscess (PLA) is a devastating and potentially life-threatening disease globally, with Klebsiella pneumoniae liver abscess (KPLA) being the most prevalent in Asia. This study aims to develop an effective and comprehensive nomogram combining clinical and radiomics features for early prediction of KPLA.Methods 255 patients with PLA from 2013 to 2023 were enrolled and randomly divided into the training and validation cohorts at a 7:3 ratio. The differences between the two cohorts of patients were assessed via univariate analysis. The radiomics features were extracted from imaging data from enhanced CT of liver abscesses. The optimal radiomics features were filtered using the independent sample t-test and least absolute shrinkage and selection operator, and a radiomics score (Rad-score) was calculated by weighting their respective coefficients. Clinically independent predictors were identified from the clinical data and combined with the Rad-score to develop a nomogram by multivariate logistic regression. The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve.Results The nomogram incorporated four clinical features of diabetes mellitus, cryptogenic liver abscess, C-reactive protein level, and splenomegaly, and the Rad-score that was constructed based on seven optimal radiomics features. It had an AUC of 0.929 (95% CI, 0.894-0.964) and 0.923 (95% CI, 0.864-0.981) in the training and validation cohorts, respectively. The calibration and decision curves showed that the nomogram had good agreement and clinical applicability.Conclusions The clinical-radiomics nomogram performed well in predicting KPLA, hopefully serving as a reference for early diagnosis of KPLA.

Background The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis.Objective This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer.Methods A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model.Results During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model.Conclusions The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

A sudden angle change between the Y-shaped diversion channel and the inlet tank of the circular pump station often leads to boundary layer separation and large vortex formation. These flow patterns can cause significant sediment deposition in sediment-laden rivers, reducing pump efficiency. A mixture model is introduced to describe water-sediment two-phase flows. Simulation results analyze vortex characteristics and formation mechanisms, and evaluate potential sediment deposition areas. Without rectification, vortex areas cause significant sediment migration towards the sidewalls of the Y-shaped diversion channel and pump inlet. Diversion measures like sills and pressure plates are introduced to reduce flow vortices. Results show that sills generally optimize flow structures. Sills in the main Y-shaped diversion channel outperform those in other channels in terms of optimization. Pressure plates also optimize flow structures. Vortex structure and position are closely related to the distance between the pressure plate and the circular intake tank. Optimized measures with pressure plates significantly reduce the flow deviation angle and improve flow velocity uniformity. Numerical simulation accuracy is validated through experiments. Results reveal the formation mechanism of adverse vortices and their sedimentation effects, which are detrimental to pump stations. The proposed rectification scheme provides theoretical support for optimizing pump station operation.

BackgroundChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. ObjectiveThe aim of this study was to evaluate ChatGPT’s competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. MethodsWe developed a model termed PanelGPT to assess ChatGPT’s competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. ResultsBy tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. ConclusionsChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.

BackgroundCarbapenem-resistant Gram-negative organism (CRO) infection is a critical clinical disease with high mortality rates. The 30-day mortality rate following antibiotic treatment serves as a benchmark for assessing the quality of care. Colistin sulfate is currently considered the last resort therapy against infections caused by CRO. Nevertheless, there is a scarcity of reliable tools for personalized prognosis of CRO infections. This study aimed to develop and validate a nomogram to predict the 30-day all-cause mortality in patients with CRO infection who underwent colistin sulfate treatment.MethodsA prediction model was developed and preliminarily validated using CRO-infected patients treated with colistin sulfate at Tongji Hospital in Wuhan, China, who were hospitalized between May 2018 and May 2023, forming the study cohort. Patients admitted to Xianning Central Hospital in Xianning, China, between May 2018 and May 2023 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of 30-day all-cause mortality. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and the calibration curve were used to evaluate model performance. The decision curve analysis (DCA) was used to assess the model clinical utility.ResultsA total of 170 patients in the study cohort and 65 patients in the external validation cohort were included. Factors such as age, duration of combination therapy, nasogastric tube placement, history of previous surgery, presence of polymicrobial infections, and occurrence of septic shock were independently associated with 30-day all-cause mortality and were used to construct the nomogram. The AUC of the nomogram constructed from the above six factors was 0.888 in the training set. The Hosmer-Lemeshow test showed that the model was a good fit (p = 0.944). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram.ConclusionA nomogram was developed and validated to predict the occurrence of 30-day all-cause mortality in patients with CRO infection treated with colistin sulfate. This nomogram offers healthcare providers a precise and efficient means for early prediction, treatment management, and patient notification in cases of CRO infection treated with colistin sulfate.

The aerobic granular sludge(AGS) technology draw scientific researchers attention, and more and more scientific research focuses on it, due to its superior advantages, such as good settling performance, high biological phase, high toxicity resistance and multiple biological effects. With the rapid development of AGS technology, a considerable amount of residual AGS will be produced, and dehydration is the biggest bottleneck of sludge reduction. This study investigated the dewatering process and method of residual AGS cultured by continuous flow experiment. Experiments were conducted using centrifugal dewatering technology with a dosing scheme to analyze the granular sludge dewatering process, and investigate the release process of EPS component in AGS dewatering. Our results implied the specific resistance of AGS has a very low value ((1.82 ± 0.03) × 109 m/kg) and it was not obvious for the conditioning effect of chemical conditioner on AGS dewatering. However, the moisture content can be reduced to 63.5% after dewatering with the presence of inorganic substances. The addition of drinking water treatment plant sludge (Alum sludge) can improve the efficiency of the dewatering of AGS. A possible dewatering process of AGS dewatering was proposed which was divided into two stages: First, a considerable amount of free water in the sludge was quickly removed under the action of gravity without pressure filtration. Second, the bound water release required cooperation between applying centrifugal or pressing force to grind granular cells and separate protein-like substances with the inorganic matter inside the granular sludge. The possible mechanism of AGS dewatering and hypothesis dewatering process are useful to optimize the AGS dewatering process.

Abstract Pelvic organ prolapse (POP) is one of the most common pelvic floor dysfunction disorders worldwide. The weakening of pelvic connective tissues initiated by excessive collagen degradation is a leading cause of POP. However, the patches currently used in the clinic trigger an unfavorable inflammatory response, which often leads to implantation failure and the inability to simultaneously reverse progressive collagen degradation. Therefore, to overcome the present challenges, a new strategy is applied by introducing puerarin (Pue) into poly(l‐lactic acid) (PLLA) using electrospinning technology. PLLA improves the mechanical properties of the patch, while Pue offers intrinsic anti‐inflammatory and pro‐collagen synthesis effects. The results show that Pue is released from PLLA@Pue in a sustained manner for more than 20 days, with a total release rate exceeding 80%. The PLLA@Pue electrospun patches also show good biocompatibility and low cytotoxicity. The excellent anti‐inflammatory and pro‐collagen synthesis properties of the PLLA@Pue patch are demonstrated both in vitro in H2O2‐stimulated mouse fibroblasts and in vivo in rat abdominal wall muscle defects. Therefore, it is believed that this multifunctional electrospun patch integrating anti‐inflammatory and pro‐collagen synthesis properties can overcome the limitations of traditional patches and has great prospects for efficient pelvic floor reconstruction.

Abstract Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Abstract The elevation in the optic nerve sheath (ONS) pressure (ONSP) due to microgravity-induced headward fluid shift is the primary hypothesized contributor to SANS. This longitudinal study aims to quantify the axial plane of the optic nerve subarachnoid space area (ONSSA), which is filled with cerebrospinal fluid (CSF) and expands with elevated ONSP during and after head-down tilt (HDT) bed rest (BR). 36 healthy male volunteers (72 eyes) underwent a 90-day strict 6° HDT BR. Without obtaining the pre-HDT data, measurements were performed on days 30, 60, and 90 during HDT and at 6 recovery time points extended to 180-days (R + 180) in a supine position. Portable B-scan ultrasound was performed using the 12 MHz linear array probe binocularly. The measurements of the ONS and the calculation of the ONSSA were performed with ImageJ 1.51 analysis software by two experienced observers in a masked manner. Compared to R + 180, the ONSSA on HDT30, HDT60, and HDT90 exhibited a consistently significant distention of 0.44 mm2 (95% CI: 0.13 to 0.76 mm2, P = 0.001), 0.45 mm2 (95% CI: 0.15 to 0.75 mm2, P = 0.001), and 0.46 mm2 (95% CI: 0.15 to 0.76 mm2, P

Abstract Background For complete revascularization, patients with diffuse coronary artery disease should have a coronary endarterectomy and a coronary artery bypass graft (CE-CABG). Sadly, CE can lead to a lack of endothelium, which raises the risk of thrombotic events. Even though daily dual antiplatelet therapies (DAPT) have been shown to reduce thrombotic events, the risk of perioperative thrombotic events is high during the high-risk period after CE-CABG, and there is no consistent protocol to bridge DAPT. This trial aims to compare safety and efficacy between tirofiban and heparin as DAPT bridging strategies after CE-CABG. Methods In phase I, 266 patients undergoing CE-CABG will be randomly assigned to tirofiban and heparin treatment groups to compare the two treatments in terms of the primary safety endpoint, chest tube drainage in the first 24 h. If the phase I trial shows tirofiban non-inferiority, phase II will commence, in which an additional 464 patients will be randomly assigned. All 730 patients will be studied to compare major cardiovascular and cerebrovascular events (MACCEs) between the groups in the first 30 days after surgery. Discussion Given the possible benefits of tirofiban administration after CE-CABG, this trial has the potential to advance the field of adult coronary heart surgery. Trial registration chictr.org.cn, ChiCTR2200055697. Registered 6 January 2022. https://www.chictr.org.cn/com/25/showproj.aspx?proj=149451 . Current version: 20,220,620.

This essay aims to comparatively study the microscopic morphology and grinding properties of brazed diamonds with amorphous and crystalline filler alloy in the case of rare-earth Nd-doped modified Ni-based filler alloy. The results show that at the same content of Nd, Cr7C3 is mainly generated at the brazing interface of crystalline samples. In contrast, Cr27C6 is mainly generated at the brazing interface of amorphous samples. Amorphous solder material achieves a robust chemical metallurgical union, producing a dense and homogeneous carbide layer. The bonding strength between amorphous solder material and diamond is further enhanced. The addition of rare earth element Nd contributes greatly to the forming ability of amorphous filler alloy. Nd atoms in the amorphous solder material readily bond with Ni atoms, reducing the chemical corrosion of Ni atoms on diamonds and resulting in a lower degree of graphitization. When the amount of Nd added is 1.0 wt%, the morphology of amorphous filler alloy brazing diamond is more complete than the effect of the crystalline state, the degree of thermal damage is lower, and the surface of amorphous filler alloy brazing is smoother. Frictional wear experiments show that the amorphous filler alloy holds diamond better than the crystalline filler alloy, and the diamond specimens prepared with the amorphous filler alloy demonstrate a significantly superior grinding performance in comparison to the diamond specimens prepared with the crystalline filler alloy.

Abstract Backgrounds The purpose of this study is to investigate the relationship between clinical characteristics and cervical lymph node metastasis (LNM) in patients with thyroid carcinoma, as well as estimate the preoperative diagnosis values of ultrasound (US) and contrast enhanced computed tomography (CECT) examinations on the neck for detection of cervical LNM in thyroid carcinoma. Methods A retrospective analysis of 3 026 patients with surgically proven thyroid carcinoma was conducted. Patients’ clinical characteristics, including gender, age, tumor size, bilateral lesions, multifocality, adenomatous nodules, Hashimoto’s thyroiditis (HT), and extrathyroidal extension, were collected to explore their association with cervical LNM in thyroid carcinoma. Preoperative assessments for central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM) were conducted through US and CECT. The diagnostic value of US, CECT and US combined with CECT for detection of LNM located in various cervical compartments was estimated based on the pathological results. Results The risk of cervical LNM was higher in thyroid cancer patients who were male, age 10 mm, bilateral lesions, and extrathyroidal extension, while multifocality, adenomatous nodules and HT had no significant effect on LNM. US, CECT and US combined with CECT all had a higher sensitivity to LLNM (93.1%, 57.8%, 95.4%) than to CLNM (32.3%, 29.0%, 43.4%). US and CECT had a high specificity to both CLNM and LLNM (94.3–97.8%). Conclusion Preoperative clinical characteristics and imaging examinations on patients with thyroid carcinoma are crucial to the evaluation of cervical lymph nodes and conducive to individualizing surgical treatments by clinicians. US combined with CECT are superior to single US or CECT alone in detection of CLNM and LLNM.

Shallow floodplains play a crucial role in river basins by providing essential ecological, hydrological, and geomorphic functions. During floods, intricate hydrodynamic conditions arise as flow exits and re-enters the river channel, interacting with the shallow vegetation. The influence and mechanism of shoal vegetation on channel hydrodynamics, bed topography, and sediment transport remain poorly understood. This study employs numerical simulations to address this gap, focusing on the Xiaolangdi–Taochengpu river section downstream of the Yellow River. Sinusoidal-derived curves are applied to represent the meandering river channel to simulate the river’s evolutionary process at a true scale. The study simulated the conditions of bare and vegetated shallow areas using rigid water-supported vegetation with the same diameter but varying spacing. The riverbed substrate was composed of non-cohesive sand and gravel. The analysis examined alterations in in-channel sediments, bed morphology, and bed heterogeneity in relation to variations in vegetation density. Findings indicated a positive correlation between vegetation density and bed heterogeneity, implying that the ecological complexity of river habitats can be enhanced under natural hydrological conditions in shallow plain vegetation and riparian diffuse flow. Therefore, for biological river restoration, vegetation planting in shallow plain regions can provide greater effectiveness.

The flexible operation of carbon capture units is crucial for the economic performance of coal-fired power plants equipped with CO2 capture systems. This paper aims to investigate the impact of electricity, CO2, and fuel prices on the economic operation of such plants. A novel economic optimization model is proposed, integrating a static model of the carbon capture system with a particle swarm optimization algorithm. A new concept, the CO2 boundary price, is introduced as a key metric for determining the operating conditions of CO2 capture units. The CO2 boundary price rises when the power load decreases due to the decline in power generation efficiency, and it also increases with rising fuel prices, as the cost of steam for CO2 capture increases. Additionally, when the objective is to meet power load demand, CO2 prices have a great influence on the operation of CO2 capture units, assuming fixed coal and electricity prices. However, when the primary goal is to maximize plant profitability, the system’s operational conditions are strongly influenced by the relative prices of electricity and CO2. The proposed optimization model and the uncovered price-effect mechanisms provide valuable insights into the economic operation of carbon capture power plants.

This paper presents a novel low-carbon binder formulated from fly ash (FA), ground granulated blast furnace slag, steel slag, and desulfurization gypsum as a quaternary solid waste-based material. It specifically examines the influence of FA content on the mechanical properties and hydration reactions of the quaternary solid waste-based binder. The mortar test results indicate that the optimal FA content is 10%, which yields a 28-day compressive strength 11.28% higher than that of the control group without FA. The spherical particles of fly ash reduce the overall water demand and provide a “lubricating” effect to the paste due to their continuous gradation, improving the fluidity of the slag-steel slag-gypsum cementitious materials. The micro test results indicate that fly ash has minimal effect on the early hydration products and process of the solid waste-based cementitious materials, but after 7 days, it continuously dissolves silicon-oxygen tetrahedrons or aluminum-oxygen tetrahedrons, consuming Ca2+ and OH− in the system. After 28 days, the amount of ettringite and C-(A)-S-H gel generated increases significantly. The pozzolanic activity of fly ash is mainly stimulated by the Ca(OH)2 from steel slag in the later hydration stage. Additionally, spherical fly ash particles can fill the voids in the hardened paste, reducing the formation of cracks and weak zones, and thereby contributing to a denser overall structure of the hydrated binder. The findings of this paper provide data support for the development of low-carbon cement-free binders using fly ash in conjunction with metallurgical slags, thereby contributing to the low-carbon advancement of the construction materials industry.

Longitudinal ordinary rebars are discontinuous at the joints of precast concrete segmental bridges (PCSBs), which can open under tensile stresses induced by bending moments. This will lead to durability issues with the joints. To improve the bending properties of PCSBs, a novel joint type featuring continuous rebars was proposed in the present study. Three specimens were subjected to testing to examine the bending behavior of PCSBs using this new joint design compared to traditional joints. The crack propagation, structural deformation, joint opening width, strain in rebars, failure mode, stiffness, and flexural capacity were comprehensively investigated. The test results reveal that the continuous rebars effectively transferred bending normal stress between joints, ensuring full development of cracks in each beam segment. Effective control of joint opening width was also observed. Compared to traditional joints, the new joints exhibited a 29% to 33% increase in cracking load and a 32% increase in ultimate load. Failure in the continuous rebar joints was characterized by partial anchorage failure of the rebars along with localized concrete crushing in the top compression zone. Based on the test results, a computational method was proposed for calculating the cracking strength and flexural capacity of the new joints.

Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.

Access to urban greenspace is a fundamental requirement in providing critical ecosystem services, improving health and well-being across all ages, fostering social cohesion, and addressing prevalent health disparities in an increasingly urbanised society. Access refers to the availability of urban greenspace, while accessibility indicates the ease of reaching and enjoying these greenspaces and the quality of greenspace services. However, quantitative studies to interpret such difference between accessibility and access are limited. To contribute to this gap, this study developed a Spatial Delta Model (SDM) to quantify the difference between accessibility and access to greenspace and assess its spatial characteristics. The study examines the block-level access, accessibility, and their difference in Perth, Australia, using the SDM with a series of high-resolution greenspace and socio-economic spatial data. Access was calculated as the total greenspace near residential blocks and accessibility was derived using a modified Gaussian two-step floating catchment area (MG2SFCA) approach. Once they were quantified, a set of residential, morphological, and greenspace related factors were utilised to explain the spatial patterns of the difference between accessibility and access using a machine learning geographical detector model. The findings on the measure of the greenspace usability and the user experience further contribute to develop a green city classification system (GCCS), which is useful to informing urban planning and greenspace management.

This research investigates the efficacy of two cladding techniques, namely soaking in alkali-activated fly ash-slag (AAFS) slurry (AS) and two-step pelletization using AAFS powder (AP), in improving the mechanical and leaching properties of phosphogypsum-based cold-bonded aggregates (PCBAs). Findings demonstrate that both AS and AP claddings effectively decrease water absorption from ∼7.5% to

Background The programmed cell death protein-1 (PD-1)/programmed death receptor ligand 1 (PD-L1) axis critically facilitates cancer cells immune evasion. Antibody therapeutics targeting the PD-1/PD-L1 axis have shown remarkable efficacy in various tumors. Immuno-positron emission tomography (ImmunoPET) imaging of PD-L1 expression may help reshape solid tumors immunotherapy landscape.Methods By immunizing an alpaca with recombinant human PD-L1, three clones of the variable domain of the heavy chain of heavy-chain only antibody (VHH) were screened, and RW102 with high binding affinity was selected for further studies. ABDRW102, a VHH derivative, was further engineered by fusing RW102 with the albumin binder ABD035. Based on the two targeting vectors, four PD-L1-specific tracers ([68Ga]Ga-NOTA-RW102, [68Ga]Ga-NOTA-ABDRW102, [64Cu]Cu-NOTA-ABDRW102, and [89Zr]Zr-DFO-ABDRW102) with different circulation times were developed. The diagnostic efficacies were thoroughly evaluated in preclinical solid tumor models, followed by a first-in-human translational investigation of [68Ga]Ga-NOTA-RW102 in patients with non-small cell lung cancer (NSCLC).Results While RW102 has a high binding affinity to PD-L1 with an excellent KD value of 15.29 pM, ABDRW102 simultaneously binds to human PD-L1 and human serum albumin with an excellent KD value of 3.71 pM and 3.38 pM, respectively. Radiotracers derived from RW102 and ABDRW102 have different in vivo circulation times. In preclinical studies, [68Ga]Ga-NOTA-RW102 immunoPET imaging allowed same-day annotation of differential PD-L1 expression with specificity, while [64Cu]Cu-NOTA-ABDRW102 and [89Zr]Zr-DFO-ABDRW102 enabled longitudinal visualization of PD-L1. More importantly, a pilot clinical trial shows the safety and diagnostic value of [68Ga]Ga-NOTA-RW102 immunoPET imaging in patients with NSCLCs and its potential to predict immune-related adverse effects following PD-L1-targeted immunotherapies.Conclusions We developed and validated a series of PD-L1-targeted tracers. Initial preclinical and clinical evidence indicates that immunoPET imaging with [68Ga]Ga-NOTA-RW102 holds promise in visualizing differential PD-L1 expression, selecting patients for PD-L1-targeted immunotherapies, and monitoring immune-related adverse effects in patients receiving PD-L1-targeted treatments.Trial registration number NCT06165874.

Plants are remarkable in their ability to adapt to changing environments, with receptor-like kinases (RLKs) playing a pivotal role in perceiving and transmitting environmental cues into cellular responses. Despite extensive research on RLKs from the plant kingdom, the function and activity of many kinases, i.e., their substrates or “clients”, remain uncharted. To validate a novel client prediction workflow and learn more about an important RLK, this study focuses on P2K1 (DORN1), which acts as a receptor for extracellular ATP (eATP), playing a crucial role in plant stress resistance and immunity. We designed a Kinase-Client (KiC) assay library of 225 synthetic peptides, incorporating previously identified P2K phosphorylated peptides and novel predictions from a deep-learning phosphorylation site prediction model (MUsite) and a trained hidden Markov model (HMM) based tool, HMMER. Screening the library against purified P2K1 cytosolic domain (CD), we identified 46 putative substrates, including 34 novel clients, 27 of which may be novel peptides, not previously identified experimentally. Gene Ontology (GO) analysis among phosphopeptide candidates revealed proteins associated with important biological processes in metabolism, structure development, and response to stress, as well as molecular functions of kinase activity, catalytic activity, and transferase activity. We offer selection criteria for efficient further in vivo experiments to confirm these discoveries. This approach not only expands our knowledge of P2K1’s substrates and functions but also highlights effective prediction algorithms for identifying additional potential substrates. Overall, the results support use of the KiC assay as a valuable tool in unraveling the complexities of plant phosphorylation and provide a foundation for predicting the phosphorylation landscape of plant species based on peptide library results.

Since the approval for the treatment of melanoma in 2014, immune checkpoint inhibitors (ICIs) have revolutionized the therapy pattern across various malignancies. Coinciding with their frequent usage, their adverse effects, including fever, cannot be neglected. In the context of cancer diseases and cancer treatments, fever of unknown origin (FUO), which has long posed a challenge for clinicians in terms of diagnosis and management, brings forth new connotation and significance. In this paper review, we present the concept of ICIs-associated FUO, consider activated immune system and elevated cytokines as common mechanisms by which ICIs induce fever and various immune-related adverse events (irAEs), summarize and compare the primary etiologies of ICI-associated FUO, and compare it with conventional types of FUO.

Asthma is a heterogeneous respiratory disease characterized by airway inflammation occurring in the bronchus. Direct delivery of targeted treatments to the bronchus for asthma has the potential to optimize therapeutic efficacy. Inhaled corticosteroids are cornerstone therapy for persistent asthma, however, there remain challenges in designing effective formulations for the inhaled route and minimizing long-term corticosteroid side effects. Here, a nanogel delivery system with good bio-adhesion based on alginate crosslinked with zinc ions was designed. The prepared alginate nanogel particles (ALG-NPs) effectively delivered icariin (ICA) which possesses anti-inflammatory and antioxidant activities, to the bronchus through ultrasonic aerosol inhalation, thereby ameliorating asthmatic symptoms and attenuating bronchial inflammation. The ALG-NPs exhibited favorable biological safety and could be deposited in the bronchus to control the release of ICA, enabling targeted treatment of asthma. In addition, ICA loaded ALG-NPs significantly reduced airway inflammation by modulating key targets associated with asthm (Th1 transcription factor and Th2 transcription factor) while also inducing eosinophilic apoptosis by regulating the Bax/Bcl-2 ratio expression. This study suggests that the ultrasonic aerosol inhalation of ICA loaded ALG-NPs was a promising strategy of treating asthma.

The utilization of solid waste as a resource is a beneficial approach to achieve pollution reduction and carbon reduction simultaneously. In this paper, we developed a quaternary solid waste-based cementitious materials (SWCMs) that can be used as a substitute for cement by utilizing four types of solid waste, namely ground granulated blast furnace slag (GGBS), steel slag (SS), ammonia-soda residue (ASR) and desulfurization gypsum (DG). The performance optimization and carbon emissions of SWCMs are investigated by response surface methodology and emission factor calculations. The results showed that a second-order polynomial model can accurately predict the compressive strength of mortar specimens of SWCMs, with prediction accuracies of 96.78 % and 87.17 % for compressive strengths at 3 days and 28 days, respectively. In terms of raw materials, DG content positively correlates with the compressive strength of the mortar containing SWCMs, moreover, ratios of GGBS to ASR of less than two or more than eight are beneficial. In addition, the production process of each ton of SWCMs emits 71.51 kg CO2, which is only 10 % of the production process of ordinary Portland cement. Overall, this work elucidates the influence of raw materials on the mechanical properties of quaternary SWCMs and quantifies their carbon reduction effects as a substitute for traditional cement, advancing the investigation and application of SWCMs in the realm of low-carbon materials.

Abstract Background Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules. Methods This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists. Results The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P

Abstract Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.