Influence of Different Ions on Pulse Electrodeposition of CaCO3 Scales in the Simulated Seawater

In the circulating water system of coastal power plants, various kinds of ions have a great influence on the formation and growth of CaCO3 scales. This paper focuses on investigating the influence of existing ions on the pulse electrodeposition behaviors of CaCO3 scales. Different concentrations of ions, such as Fe3+, Mg2+, PO43- and SiO32-, are introduced to simulate the actual seawater environment, and their influence on the CaCO3 scale deposition behaviors is assessed by linear sweep voltammetry, chronoamperometry, and electrochemical impedance spectroscopy tests. The surface coverage of the CaCO3 scale layer is evaluated through the residual current density and polarization resistance values, while the crystal structure and surface compactness of the layer are confirmed by the scanning electron microscope and X-ray diffractometer tests. Results indicate that high concentrations of Mg2+, Fe3+, and PO43- ions have the most significant inhibitory effect on the pulse electrodeposition of CaCO3 scales, among which the inhibition effect of Mg2+ ions is mainly reflected in the change of crystal morphology of CaCO3, that is, the crystallization growth process is inhibited. The inhibition effect of PO43- ions is mainly reflected in the gradually reduced coverage and density of CaCO3 crystals on the electrode surface, suggesting that the crystallization nucleation process is inhibited, while Fe3+ ions have a certain inhibition effect on both the crystallization nucleation and growth processes. Furthermore, lower concentrations of SiO32- ions also display a significant inhibition effect on the crystallization nucleation and growth process, and the inhibition effect weakens with increased concentration. This study provides a theoretical basis for exploring the removal of ions in the industrial water softening field.

Cellulose nanocrystal-infused polymer hydrogel imbued with ferric-manganese oxide nanoparticles for efficient antinomy removal

Antimony is a highly poisonous pollutant that needs to be removed from water to ensured safety. In this work, we have fabricated a novel adsorbent, the ferric-manganese oxide (FeMnOx) nanoparticles embedded cellulose nanocrystal-based polymer hydrogel (FeMnOx @CNC-g-PAA/qP4VP, denoted as FMO@CPqP), specifically engineered for the remediation of antimony-laden water. Comprehensive evaluations have been conducted to investigate the efficacy of the FMO@CPqP hydrogel in removal of antimony from water. The hydrogel exhibits superior affinity for antimony, with maximum adsorption capacities of 276.1 mg/g for Sb(III) and 286.8 mg/g for Sb(V). The adsorptive dynamics, governed by the kinetics and isotherm analyses, elucidate that the immobilization of both Sb(III) and Sb(V) is facilitated through a homogeneous and monolayer chemisorption mechanism. The hydrogel has a three-dimensional interconnected porous structure and exhibits good swelling behavior, which facilitates the rapid absorption of antimony ions by this high surface area hydrogel into the channels. Furthermore, various effects, including the oxidation and inner-sphere coordination mediated by FeMnOx NPs and the electrostatic attractions of the quaternized P4VP chains, promote the immobilization of antimony species. Owing to its high removal efficiency, stability and reusability, the FMO@CPqP hydrogel emerges as an exemplary candidate for the removal of antimony contaminants in water treatment processes.

Emerging trends of invasive yeast infections and azole resistance in Beijing intensive care units

BACKGROUND

Invasive fungal infections pose a substantial threat to patients in healthcare settings globally. Recent changes in the prevalence of fungal species and challenges in conducting reference antifungal susceptibility testing emphasize the importance of monitoring fungi and their antifungal resistance.

METHODS

A two-phase surveillance project was conducted in Beijing, China, involving 37 centres across 12 districts, from January 2012 to December 2013 and from January 2016 to December 2017.

FINDINGS

We found that the proportion of Candida albicans in intensive care units (ICUs) during 2016-2017 exhibited a significant decline compared with the 2012-2013 period, although it remained the most predominant pathogen. In contrast, the prevalence of Nakaseomyces glabratus (formerly Candida glabrata) and Candida tropicalis notably increased during the two-phase surveillance. The high prevalence of C. tropicalis and its resistance to azole drugs posed a serious threat to patients in ICUs. The pathogens causing invasive fungal infections in Beijing were relatively sensitive to echinocandins. While C. albicans continued to exhibit susceptibility to azoles, the resistance and growth rates of C. tropicalis towards azoles were particularly prominent. Concerns were raised due to the emergence of multiple, short-term isolates of Clavispora lusitaniae and Candida parapsilosis complex in neonatal ICUs, given their similarity in antifungal susceptibilities. Such occurrences point towards the potential for transmission and persisting presence of these pathogens within the ICU environment.

CONCLUSIONS

Our study complements existing data on the epidemiology of invasive fungal infections. It is imperative to exercise cautious medication management for ICU patients in Beijing, paying particular attention to azole resistance in C. tropicalis.

Chloride Ions-Responsive Intelligent Coatings for the Active Protection of Degradable Biomedical Mg Alloys

In this work, the hydroxyapatite (HA) microspheres are utilized as carriers for 8-hydroxyquinoline (8-HQ) inhibitors with a sodium alginate-silver nitrate layer (Ag-SA) added to confer chloride-responsive properties. These 8-HQ@Ag-SA-HA microspheres are subsequently integrated into poly(lactic acid) (PLA) coatings to produce biocompatible coatings. The resulting 8-HQ@Ag-SA-HA microsphere exhibits a spherical structure with a diameter of 3.16 μm. Thermogravimetric analysis indicates that the encapsulated 8-HQ inhibitors are approximately 11.83 wt %. Furthermore, the incorporation of these microspheres fills the micropores within the PLA coating, leading to a denser coating surface, enhanced wettability (contact angle value = 88°), and improved adhesion strength, thereby reinforcing the physical barrier effect. Corrosion tests reveal that the coatings exhibit increased resistance to corrosion in simulated body fluid (SBF) solutions. The released 8-HQ inhibitors in response to chloride ions form a protective layer of Mg(HQ)2, providing the coatings with self-healing properties and ensuring their durability in the SBF environment. Additionally, the cell test demonstrates a significant presence of MG-63 cells, accompanied by a low hemolysis rate of 3.81%, confirming the exceptional biocompatibility of the coatings. These findings offer valuable insights into the development of stimuli-responsive biocompatible coatings for effectively protecting Mg alloys.

ASG-YOLOv5: Improved YOLOv5 unmanned aerial vehicle remote sensing aerial images scenario for small object detection based on attention and spatial gating

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git.

Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion

Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method based on attention mechanism and multi-scale feature fusion (AM-MSFF). By combining the advantages of attention mechanism and multi-scale feature fusion, this method significantly improves the accuracy of pest recognition. Firstly, we introduce the relation-aware global attention (RGA) module to adaptively adjust the feature weights of each position, thereby focusing more on the regions relevant to pests and reducing the background interference. Then, we propose the multi-scale feature fusion (MSFF) module to fuse feature maps from different scales, which better captures the subtle differences and the overall shape features in pest images. Moreover, we introduce generalized-mean pooling (GeMP) to more accurately extract feature information from pest images and better distinguish different pest categories. In terms of the loss function, this study proposes an improved focal loss (FL), known as balanced focal loss (BFL), as a replacement for cross-entropy loss. This improvement aims to address the common issue of class imbalance in pest datasets, thereby enhancing the recognition accuracy of pest identification models. To evaluate the performance of the AM-MSFF model, we conduct experiments on two publicly available pest datasets (IP102 and D0). Extensive experiments demonstrate that our proposed AM-MSFF outperforms most state-of-the-art methods. On the IP102 dataset, the accuracy reaches 72.64%, while on the D0 dataset, it reaches 99.05%.

Self-powered biosensing platform for Highly sensitive detection of soluble CD44 protein

In this study, a self-powered biosensor based on an enzymatic biofuel cell was proposed for the first time for the ultrasensitive detection of soluble CD44 protein. The as-prepared biosensor was composed of the co-exist aptamer and glucose oxidase bioanode and bilirubin oxidase modified biocathode. Initially, the electron transfer from bioanode to biocathode was hindered due to the presence of the aptamer with high insulation, generating a low open-circuit voltage (EOCV). Once the target CD44 protein was present, it was recognized and captured by the aptamer at the bioanode, thus the interaction between the target CD44 protein and the immobilized aptamer caused the structural change at the surface of the electrode, which facilitated the transfer of electrons. The EOCV showed a good linear relationship with the logarithm of the CD44 protein concentrations in the range of 0.5-1000 ng mL-1 and the detection limit was 0.052 ng mL-1 (S/N = 3). The sensing platform showed excellent anti-interference performance and outstanding stability that maintained over 97% of original EOCV after 15 days. In addition, the relative standard deviation (1.40-1.96%) and recovery (100.23-101.31%) obtained from detecting CD44 protein in real-life blood samples without special pre-treatment indicated that the constructed biosensor had great potential for early cancer diagnosis.

Cellulose nanocrystal-based polymer hydrogel embedded with iron oxide nanorods for efficient arsenic removal

A cellulose nanocrystal (CNC) polymer hydrogel containing magnetic iron oxide nanorods (Fe3O4NRs) was prepared for As(III) removal in water. Systematic studies on the performance of these prepared CNC-based composite hydrogels for the removal of As(III) have been undertaken. The maximum adsorption capacity of the CNC-g-PAA/qP4VP (CPqP) hydrogel was 241.3 mg/g. After introduction of Fe3O4NRs in the hydrogel, the maximum adsorption capacity of the resulting Fe3O4NRs@CNC-g-PAA/qP4VP (FN@CPqP) hydrogel was further improved to 263.0 mg/g. The high adsorption performance can be attributed to the facts that the 3D interconnected porous network of the hydrogel allows As species to easily enter into the hydrogel, the quaternized P4VP chains provides more adsorption sites, Fe3O4NRs uniformly distributed in the internal cavity of the hydrogel significantly reduces the nanoparticle aggregation. The adsorption kinetics indicated that the adsorption of arsenic by the hydrogel was mainly chemisorption. The isotherm analysis revealed that the adsorption of arsenic by the hydrogel was principally monolayer adsorption on a homogeneous surface. Moreover, the as-prepared CNC-based polymer hydrogels exhibited good stability and reusability with negligible performance loss after five adsorption-desorption cycles. The novel FN@CPqP hydrogel demonstrates great potential as a cost-effective adsorbent for the removal of arsenic contaminants from wastewater.

Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas

AIM

To enhance the prediction of mutation status of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter, which are crucial for glioma prognostication and therapeutic decision-making, via sub-regional radiomics analysis based on multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A retrospective study was conducted on 401 participants with adult-type diffuse gliomas. Employing the K-means algorithm, tumours were clustered into two to four subregions. Sub-regional radiomics features were extracted and selected using the Mann-Whitney U-test, Pearson correlation analysis, and least absolute shrinkage and selection operator, forming the basis for predictive models. The performance of model combinations of different sub-regional features and classifiers (including logistic regression, support vector machines, K-nearest neighbour, light gradient boosting machine, and multilayer perceptron) was evaluated using an external test set.

RESULTS

The models demonstrated high predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.918 to 0.994 in the training set for IDH mutation prediction and from 0.758 to 0.939 for TERT promoter mutation prediction. In the external test sets, the two-cluster radiomics features and the logistic regression model yielded the highest prediction for IDH mutation, resulting in an AUC of 0.905. Additionally, the most effective predictive performance with an AUC of 0.803 was achieved using the four-cluster radiomics features and the support vector machine model, specifically for TERT promoter mutation prediction.

CONCLUSION

The present study underscores the potential of sub-regional radiomics analysis in predicting IDH and TERT promoter mutations in glioma patients. These models have the capacity to refine preoperative glioma diagnosis and contribute to personalised therapeutic interventions for patients.

Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning

In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance detection in today's digital age, where the absence of training examples for specific models poses significant challenges. This task necessitates models with robust generalization abilities to discern target-related, transferable stance features within training data. Recent advances in prompt-based learning have showcased notable efficacy in few-shot text classification. Such methods typically employ a uniform prompt pattern across all instances, yet they overlook the intricate relationship between prompts and instances, thereby failing to sufficiently direct the model towards learning task-relevant knowledge and information. This paper argues for the critical need to dynamically enhance the relevance between specific instances and prompts. Thus, we introduce a stance detection model underpinned by a gated multilayer perceptron (gMLP) and a prompt learning strategy, which is tailored for zero-shot stance detection scenarios. Specifically, the gMLP is utilized to capture semantic features of instances, coupled with a control gate mechanism to modulate the influence of the gate on prompt tokens based on the semantic context of each instance, thereby dynamically reinforcing the instance-prompt connection. Moreover, we integrate contrastive learning to empower the model with more discriminative feature representations. Experimental evaluations on the VAST and SEM16 benchmark datasets substantiate our method's effectiveness, yielding a 1.3% improvement over the JointCL model on the VAST dataset.

Study on the construction of nomogram prediction model for prognostic assessment of heart failure patients based on serological markers and echocardiography

OBJECTIVE

We aimed to construct a nomogram prediction model for prognostic assessment of patients with heart failure (HF) based on serological markers and echocardiography.

PATIENTS AND METHODS

A total of 200 HF patients admitted to the Second Affiliated Hospital of Nanchang University from January 2018 to January 2020 were selected as the research objects. According to the New York Heart Association (NYHA) cardiac function classification, they were divided into 3 groups, including 65 cases of grade II, 97 cases of grade III, and 38 cases of grade IV. Three groups of echocardiographic parameters were compared [including left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD), left ventricular end-systolic diameter (LVESD), left ventricular end-systolic volume (LVESV)], differences in serum markers brain natriuretic peptide (BNP), soluble growth-stimulating expression gene 2 (sST2) and the Modified Early Warning Score (MEWS). The patients were divided into two groups according to their clinical outcomes during the follow-up period, including 52 cases in the death group and 148 cases in the survival group. The clinical data of the two groups were compared, and multi-factor logistic regression analysis was performed to screen out the independent risk factors affecting the patient's death. A nomogram model of the patient's mortality risk was constructed based on the independent risk factors. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the discrimination and accuracy of the nomogram model.

RESULTS

As the cardiac function class of elderly chronic heart failure (CHF) patients increases, LVEDD, LVESD, sST2, and MEWS increase and LVEF decreases (p<0.05). Multifactor analysis results showed that LVEF, LVEDD, sST2, and MEWS were independent factors affecting the clinical outcome of patients. The AUCs predicted using LVEF, LVEDD, sST2, and MEWS alone were 0.738, 0.775, 0.717, 0.831, and 0.768, respectively. There is a certain degree of discrimination, and the model has extremely high accuracy.

CONCLUSIONS

MEWS, LVEDD, and sST2 increase as the NYHA cardiac function grade of HF patients increases and LVEF decreases, which can reflect the severity of the disease to a certain extent. Additionally, the nomogram model established based on this has a high predictive value for the long-term prognosis of patients and can formulate effective intervention measures for quantitative values.

Clinicopathological features and prognostic factors of salivary gland myoepithelial carcinoma: institutional experience of 42 cases

Myoepithelial carcinoma (MECA) is a rare type of carcinoma for which the clinicopathological features and prognostic factors have not yet been fully clarified. A retrospective study of 42 patients diagnosed with salivary gland MECA was performed, focusing on the clinicopathological features and prognostic factors. Of the 42 patients, 20 died of cancer, 20 lived without tumour, one lived with distant metastasis, and one was lost to follow-up. Overall, 69.0% had tumour recurrence, 16.7% had cervical nodal metastasis, and 21.4% had distant metastasis. The 5-year overall survival rate was 70.2%. Kaplan-Meier analysis revealed that patients with pathological positive lymph nodes (pN+), multiple recurrences of tumour, and higher histological grade had worse overall survival. Multivariate Cox analysis indicated pN+ and higher histological grade to be independent predictors of decreased survival. The 5-year overall survival rate in the pN0 group was 87.5%, while that in the pN+ group was 28.6%. In conclusion, myoepithelial carcinoma can be defined as a tumour with a high incidence of recurrence and poor prognosis, especially in pN+ patients. Pathological positive lymph nodes and histological grade may serve as predictors of survival.

Efficacy and Safety of Preoperative Transcatheter Rectal Arterial Chemoembolisation in Patients with Locally Advanced Rectal Cancer: Results from a Prospective, Phase II PCAR Trial

AIMS

The PCAR study aimed to assess the efficacy and safety of preoperative transcatheter rectal arterial chemoembolisation (TRACE) in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This was a single-centre, prospective, phase II trial conducted in China. Eligible patients were adults aged 18 years and older with histologically confirmed stage II or III rectal carcinoma and an Eastern Cooperative Oncology Group performance status of 0-1. Patients received TRACE with oxaliplatin, followed by radiotherapy with a cumulative dose of 45 Gy (1.8 Gy/time/day, five times a week for 5 weeks) and received oral S1 capsules twice daily (7 days a week for 4 weeks). Patients underwent total mesorectal excision 4-8 weeks after the completion of chemoradiotherapy, followed by mFOLFOX6 or CAPOX regimens for 4-6 months. The hypothesis of this study was that adding TRACE to preoperative neoadjuvant chemoradiotherapy would improve tumour regression and prognosis. The primary end point was the pathological complete response rate; secondary end points included the major pathological response rate, anal preservation rate, 5-year disease-free survival (DFS), 5-year overall survival and treatment-related adverse events.

RESULTS

In total, 111 LARC patients received TRACE and subsequent scheduled treatment plans. The pathological complete response and major pathological response rates were 20.72% and 48.65%, respectively. The 5-year DFS and 5-year overall survival were 61.89% (95% confidence interval 51.45-74.45) and 74.80% (95% confidence interval 65.05-86.01), respectively. Grade 3-4 toxicities were reported in 29 patients (26.13%). The postoperative complication rate was 21.62%, without serious surgical complications. Multivariate Cox regression analysis showed that ypN stage (hazard ratio = 4.242, 95% confidence interval 2.101-8.564, P = 0.00017) and perineural invasion (hazard ratio = 2.319, 95% confidence interval 1.058-5.084, P = 0.0487) were independent risk factors associated with DFS, whereas ypN stage (hazard ratio = 3.164, 95% confidence interval 1.347-7.432, P = 0.0101), perineural invasion (hazard ratio = 4.118, 95% confidence interval 1.664-10.188, P = 0.0134) and serum carbohydrate antigen 199 (CA199; hazard ratio = 4.142, 95% confidence interval 1.290-13.306, P = 0.0344) were independent predictors for overall survival.

CONCLUSION

The current study provides evidence that adding TRACE to neoadjuvant chemoradiotherapy can improve the pathological remission rate in LARC patients with acceptable toxicity. Given its promising effectiveness and safe profile, incorporating TRACE into the standard treatment strategy for patients with LARC should be considered.

Clinical characteristics, outcomes and risk factors for mortality in hospitalized diabetes and chronic kidney disease patients after COVID-19 infection following widespread vaccination

BACKGROUND

COVID-19 poses a significant threat to patients with comorbidities, such as diabetes and chronic kidney disease (CKD). China experienced a nationwide COVID-19 endemic from December 2022 to January 2023, which is the first occurrence of such an outbreak following China's widespread administration of COVID-19 vaccinations.

METHODS

A total of 338 patients with diabetes and CKD combined with COVID-19 infection between December 7, 2022 and January 31, 2023 were included in this study. The end follow-up date was February 10, 2023. Univariate analysis and multivariate Cox analysis were used to analyze risk factors for death.

RESULTS

During the 50-day median follow-up period, 90 patients in the study cohort died, for a mortality rate of 26.63%. The median age of the study cohort was 74 years, with a male predominance of 74%. During hospitalization, 21% of patients had incident AKI, 17% of patients experienced stroke, and 40% of patients experienced respiratory failure. Cox proportional hazard regression showed that older age, a diagnosis of severe or critically severe COVID-19 infection, incident AKI and respiratory failure, higher level of average values of fasting glucose during hospitalization, UA, and total bilirubin were independent risk factors for death in our multivariate model.

CONCLUSIONS

These findings highlight the critical importance of identifying and managing comorbid risk factors for COVID-19, especially among the elderly, in order to optimize clinical outcomes, even after COVID-19 vaccination.

Research on the inhibitory properties and mechanism of carboxymethyl cellulose-modified sulfur quantum dots towards calcium sulfate and calcium carbonate

An eco-friendly antimicrobial sulfur quantum dot scale inhibitor (CMC-SQDs) synthesized using carboxymethyl cellulose (CMC) showed strong inhibition of calcium sulfate (CaSO4) at a concentration just below 1 mg/L, with an inhibition efficiency exceeding 99 %. However, the precise interaction process between CMC-SQDs and CaSO4 remains unclear. This article investigates the effectiveness of SQDs in inhibiting the formation of CaSO4 and calcium carbonate (CaCO3) scales. Through static scale inhibition tests, molecular dynamics simulations, and quantum chemical calculations, the study aims to elucidate the different impacts of CMC-SQDs on CaSO4 and CaCO3 scale formation. The research focuses on understanding the relationship between the structural activity of CMC-SQDs and their scale-inhibiting performance and delving into the underlying mechanisms of scale inhibition. The findings describe the role of SQDs in a water-based solution, acting as persistent "nanodusts" that interact with calcium (Ca2+) ions and sulfate ions. CMC forms complexes with Ca2+ ions, and the presence of SQDs enhances the van der Waals force, indirectly increasing the resistance of associated ions and the binding energy on the surface of precipitated gypsum. Conversely, SQDs exhibit weak surface stability and have minimal binding energy when interacting with calcite, leading to limited occupation of available adsorption sites.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

FSN: Joint Entity and Relation Extraction Based on Filter Separator Network

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

Nanogap engineering of 3D nanoraspberries into 2D plasmonic nanoclusters toward improved SERS performance

3D raspberry-like core/satellite nanostructures were prepared by controlled surface functionalization of silica spheres using crosslinked poly(4-vinylpyridine) (P4VP) chains with known binding affinity for gold nanoparticles (AuNPs). The 3D SiO2-g-P(4VP-co-DVB)/AuNP nanoraspberries can be further transformed into 2D plasmonic nanoclusters by etching the silica core with hydrofluoric acid (HF). After the transformation, the interparticle distance between the AuNPs dramatically reduced from a 10 nm scale to sub 2 nm. Owing to the strong electromagnetic field generated by the plasmonic coupling between AuNPs in very close proximity, the established P(4VP-co-DVB)/AuNP nanoclusters provided strong and undisturbed Raman signals as a SERS substrate. In addition, benefiting from the stabilizing effect of the crosslinked P(4VP-co-DVB) network, the prepared SERS substrate has the advantages of good uniformity, stability and reproducibility, as well as strong SERS enhancement, endowing it with great potential for rapid and efficient SERS detection.

Epicardial and pericoronary adipose tissue and coronary plaque burden in patients with Cushing's syndrome: a propensity score-matched study

PURPOSE

To assess coronary inflammation by measuring the volume and density of the epicardial adipose tissue (EAT), perivascular fat attenuation index (FAI) and coronary plaque burden in patients with Cushing's syndrome (CS) based on coronary computed tomography angiography (CCTA).

METHODS

This study included 29 patients with CS and 58 matched patients without CS who underwent CCTA. The EAT volume, EAT density, FAI and coronary plaque burden were measured. The high-risk plaque (HRP) was also evaluated. CS duration from diagnosis, 24-h urinary free cortisol (UFC), and abdominal visceral adipose tissue volume (VAT) of CS patients were recorded.

RESULTS

The CS group had higher EAT volume (146.9 [115.4, 184.2] vs. 119.6 [69.0, 147.1] mL, P = 0.006), lower EAT density (- 78.79 ± 5.89 vs. - 75.98 ± 6.03 HU, P = 0.042), lower FAI (- 84.0 ± 8.92 vs. - 79.40 ± 10.04 HU, P = 0.038), higher total plaque volume (88.81 [36.26, 522.5] vs. 44.45 [0, 198.16] mL, P = 0.010) and more HRP plaques (7.3% vs. 1.8%, P = 0.026) than the controls. The multivariate analysis suggested that CS itself (β [95% CI], 29.233 [10.436, 48.03], P = 0.014), CS duration (β [95% CI], 0.176 [0.185, 4.242], P = 0.033), and UFC (β [95% CI], 0.197 [1.803, 19.719], P = 0.019) were strongly associated with EAT volume but not EAT density, and EAT volume (β [95% CI] - 0.037[- 0.058, - 0.016], P = 0.001) not CS was strongly associated with EAT density. EAT volume, FAI and plaque burden increased (all P < 0.05) in 6 CS patients with follow-up CCTA. The EAT volume had a moderate correlation with abdominal VAT volume (r = 0.526, P = 0.008) in CS patients.

CONCLUSIONS

Patients with CS have higher EAT volume and coronary plaque burden but less inflammation as detected by EAT density and FAI. The EAT density is associated with EAT volume but not CS itself.

Relationships between intravoxel incoherent motion parameters and expressions of programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) in patients with cervical cancer

AIM

To determine the associations of intravoxel incoherent motion (IVIM) parameters with expression of programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), and evaluate the performance of the combined model established based on IVIM and clinicopathological parameters in predicting PD-L1and PD-1 status of cervical cancer (CC) patients.

MATERIALS AND METHODS

Seventy-eight consecutive CC patients were enrolled prospectively and underwent magnetic resonance imaging (MRI) including IVIM. IVIM quantitative parameters were measured, compared, and correlated with PD-L1 and PD-1 expression. Independent factors related to PD-L1 and PD-1 positivity were identified and were used to establish the combined model. The combined model's diagnostic performance was evaluated using the receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) algorithm was used to explain the contribution of each parameter in the combined model.

RESULTS

The real diffusion coefficient (D) value was significantly lower in the PD-L1-positive group than in the PD-L1-negative group (0.64 ± 0.12 versus 0.72 ± 0.11, p=0.021). The PD-1-positive and PD-1-negative groups showed similar trends (0.63 ± 0.13 versus 0.73 ± 0.09, p=0.003). Parametrial invasion, lymph node status, pathological grade, FIGO (International Federation of Gynecology and Obstetrics) staging, and D values were independently associated with PD-L1 and PD-1expression. A combined model incorporating these parameters showed good discrimination with the sensitivity, specificity of 90.9%, 82.6% for PD-L1, and 93.5%, 72% for PD-1. According to the SHAP value, FIGO staging and pathological grade were the most influential features of the prediction model.

CONCLUSION

IVIM parameters were found to correlate with PD-L1 and PD-1 expression. The combined model, incorporating parametrial invasion, lymph node status, pathological grade, FIGO staging, and D values, showed good discrimination in predicting PD-L1 and PD-1 status, providing the basis for CC immunotherapy.

Channel semantic mutual learning for visible-thermal person re-identification

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval issue aiming to match the same pedestrian between visible and infrared cameras. Thus, the modality discrepancy presents a significant challenge for this task. Most methods employ different networks to extract features that are invariant between modalities. While we propose a novel channel semantic mutual learning network (CSMN), which attributes the difference in semantics between modalities to the difference at the channel level, it optimises the semantic consistency between channels from two perspectives: the local inter-channel semantics and the global inter-modal semantics. Meanwhile, we design a channel-level auto-guided double metric loss (CADM) to learn modality-invariant features and the sample distribution in a fine-grained manner. We conducted experiments on RegDB and SYSU-MM01, and the experimental results validate the superiority of CSMN. Especially on RegDB datasets, CSMN improves the current best performance by 3.43% and 0.5% on the Rank-1 score and mINP value, respectively. The code is available at https://github.com/013zyj/CSMN.