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Abstract Although a sequential work-up for deep vein thrombosis has reached agreement worldwide, the mysterious nature of DVT following fractures brings challenges to early diagnosis and intervention. The objective of the present study was to develop and validate a nomogram for predicting preoperative DVT risk in patients with thoracolumbar fractures using readily available clinical data. Of the 1350 patients, 930 were randomly assigned to the training cohort. A prediction model was established and visualized as a nomogram based on eight predictors related to preoperative DVT. The performance of the model was tested by the receiver operating characteristic curve, Hosmer–Lemeshow test, calibration curve, and decision curve analysis. We further verified the model in the validation cohort. The AUCs of the prediction model were 0.876 and 0.853 in training and validation cohorts, respectively. The Hosmer–Lemeshow test demonstrated good fitness in the training set (X2 = 5.913, P = 0.749) and the validation set (X2 = 9.460, P = 0.396). Calibration and decision curve analyses performed well in training and validation sets. In short, we developed a prediction model for preoperative DVT risk in patients with thoracolumbar fractures and verified its accuracy and clinical utility.

Abstract Protein-encoding circular RNAs (circRNAs) are newly identified RNA molecules characterized by intense interaction with translating ribosome. Emerging evidence has implicated physiological and pathological significance of these non-canonical RNAs, yet a large body of them remains unidentified. Due to limited tools at hand, we developed CircProPlus, an automated computational pipeline for de novo detection of translated circRNAs. In comparison to previously established CircPro, CircProPlus adjusts the overall workflow and integrates more robust implements for achieving easier accessibility, higher flexibility and productivity. In present study, we tested the performance of CircProPlus when using different circRNA-detecting implements (i.e., CIRI2, CirComPara2) in the evaluation of coding ability of circRNAs. Results showed that CirComPara2, a state-of-the-art algorithm, consistently outperformed CIRI2 when coupled with CircProPlus in testing real data collected from different RNA libraries and species, which highlighted its potency in data mining of circRNAs with protein-coding potential.

Abstract With the popularity of the media cloud computing industry, individuals and organizations outsource image computation and storage to the media cloud server to reduce the storage burden. Media images usually contain a large amount of private information. To prevent disclosure of privacy of the image owners, media images are encrypted before uploading to the server. However, this operation will greatly limit the utilization of the image for the user, such as content-based image retrieval. We propose an efficient similarity query algorithm with access control based on Bkd-tree in this paper, in which a searchable encryption scheme is designed for similarity image retrieval, and the encrypted image is used to extract image features by a pre-trained CNN model. The Bkd-tree is utilized to generate an index tree for the image features to speed up retrieval and make it faster than linear indexing. Finally, the security performances of the proposed scheme is analyzed and the performance of this scheme is evaluated by experiments. The results show that the security of the image content and image features can be ensured, and it has a shorter retrieval time and higher retrieval efficiency.

An intelligent tool wear prediction model has been proposed for the micro-grinding tool, optimized using a genetic algorithm (GA) based BP neural network. The GA-BP prediction model is applied with in-situ tool wear detection to obtain training set data and combines cluster analysis to divide the tool wear stages. To represent the uncertainty in wear characteristics, the loss of cross-sectional area of the micro-grinding tool has been selected as an index to evaluate tool wear loss. The K-means clustering algorithm is used to cluster and analyze the tool wear stages under different process parameters. The GA-BP neural network includes five neurons in the input layer: rotating speed, feed rate, cutting depth, grinding length, and the initial cross-sectional area of the tool. The output layer neuron predicts the loss of the tool's cross-sectional area. To validate the method, a series of micro-grinding experiments were performed under different parameters for the micro-groove array of monocrystalline silicon. The loss of the tool's cross-sectional area was measured by a self-made visual inspection system, providing learning samples for the prediction model. The predicted results of the GA-BP neural network model were compared with the traditional Gaussian process regression method. The results show that the GA-BP neural network model can correctly predict tool wear loss and identify wear stages under different process parameters and grinding lengths. It has higher prediction accuracy during the self-learning process, with an average error of 5% .

Vehicle exhaust is one of the major organic sources in urban areas. Old taxis equipped with failed three-way catalysts (TWCs) have been regarded as “super emitters”. Compressed natural gas (CNG) is a regular substitution fuel for gasoline in taxis. The relative effect of fuel substitution and TWC failure has not been thoroughly investigated. In this work, vehicle exhausts from gasoline and CNG taxis with optimally functioning and malfunctioning TWCs are sampled by Tenax TA tubes and then analyzed by a comprehensive two-dimensional gas chromatography-mass spectrometer (GC×GC–MS). A total of 216 organics are quantified, including 80 volatile organic compounds (VOCs) and 132 intermediate volatility organic compounds (IVOCs). Failure of TWC introduces super emitters with 30 – 70 times emission factors (EFs), 60 – 112 times ozone formation potentials (OFPs), and 34 – 92 times secondary organic aerosols (SOAs) more than normal vehicles. Specifically, for the taxi with failed TWC, the total organic EF of CNG is 16 times that of gasoline, indicating that the failure of TWC exceeds the emission reduction achieved by CNG-gasoline substitution. A significant but unbalanced reduction of ozone and SOA is observed after TWC, whereas a notable “enrichment” in IVOCs was observed. Naphthalene is a typical IVOC component strongly associated with CNG-gasoline substitution and TWC failure, which is lacking in current VOC measurement. We especially emphasize that there is an urgent need to scrap vehicles with failed TWCs in order to significantly reduce air pollution.

Introduction: This study investigates the characteristics of sediment disturbance caused by impeller rotation in reservoirs of inland rivers with high sediment content in China. A scaled experimental model, reflecting typical environmental conditions of inland water reservoirs in Northwest China, was established in Lanzhou, Gansu Province, following the principle of similarity.Methods: The study integrates numerical simulations using Ansys Fluent software and corroborates the findings through hydraulic experiments. Computational Fluid Dynamics (CFD) and the κ–ε Realizable model were employed to simulate the solid-liquid mixing process, which was verified against the experimental model.Results: The results indicate that increasing the impeller velocity from 2 rad/s to 8 rad/s, while submerged at a depth of 1000 mm in the flow field, enhances the rate of bottom sediment suspension. Furthermore, the rate of suspended sediment discharge from the model outlet increased with inflow velocity ranging from 0.1 m/s to 0.8 m/s. A decrease in the impeller’s submersion depth from 600 mm to 1200 mm was found to reduce the maximum disturbance radius affecting the bottom sediment.Discussion: The reliability of the simulation was confirmed by comparing the software results with experimental data. This study provides insights into the mechanisms of sediment-laden flow disturbance in the reservoir areas of inland rivers in China and lays the groundwork for more comprehensive investigations into sediment discharge in these environments.

ABSTRACTMultiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method’s performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies.

Delayed luminescence (DL) refers to the photon-induced ultra-weak luminescence emitted by samples after the light source is switched off. As a noninvasive method for health monitoring and disease diagnosis, DL has attracted increasing attention. The further development of this technology is valuable for the study of complex biological processes, such as different growth stages. If such studies were to be conducted in humans, large numbers of subjects of all ages would need to be recruited, and individual differences would be inevitable. The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has a short population lifespan, and the growth phases can be monitored within dozens of hours. Therefore, S. cerevisiae is an ideal model organism for research. In this study, we investigated the physiological characteristics and DL emission of S. cerevisiae during growth in glucose-based media and entry into stationary phase, and the results showed that DL kinetic curves of yeast cells in the growing phase were obviously separated from those of stationary phase cells. Moreover, the metabolic and physiological characteristics of the yeast cell population were discussed using the DL emission parameters I0, τ and γ. We also discussed the possibility of assessing entropy using DL emission parameters. Our research demonstrates the potential of this technology to be used in wider applications.

The improved electronic conductivity and ion diffusion efficiency of TiO2‐based anode materials have been extensively studied by introducing oxygen vacancies or creating amorphous structure. There has been little exploration of the synergistic effects by combining these two modification strategies into one TiO2‐based matrix. In addition, the structure–activity relationship and energy storage mechanism involved remain to be understood. Herein, a facile one‐step coreduction method is reported to successfully produce the oxygen vacancy‐doped amorphous TiO2 nanoparticles. The oxygen vacancy‐doped amorphous TiO2 anode exhibits significantly enhanced electrochemical activity and high‐rate stability (up to 87 mAh g−1 over 10 000 discharge/charge cycles at a current rate of 100 C). This outstanding electrochemical performance is attributable to the synergistic effects of amorphous structure and oxygen vacancies. Density functional theory calculations reveal the enhanced electronic conductivity and thermodynamically favorable lithium insertion architecture due to the introduction of oxygen vacancy and the construction of the amorphous skeleton. Dynamic analysis indicates that the lithium‐storage mechanism is a hybrid of surface capacitive storage and enhanced diffusion‐controlled ion insertion. This work opens up new pathways in developing novel anode materials for efficient energy storage from the wide spectrum of metal oxides.

ABSTRACTUnlike alphabet-based languages such as English, the Chinese language has no specifying word boundaries. Segmentation, particularly for the Chinese language, is a fundamental step towards Chinese text processing, information retrieval, and knowledge discovery. In the geoscience domain, most existing Chinese word segmentation tools/models require a prespecified dictionary and a large amount of relevant training corpus, and the segmentation accuracies drop significantly when processing out-domain situations using these same methods. To address this issue, a purely unsupervised and generic two-stage architecture (named BERTCWS) for domain-specific Chinese word segmentation is proposed. We first design an incidence matrix termed the ‘character combination tightness’ to calculate the closeness between characters. Then, BERTCWS recognizes geoscience terms based on a Bidirectional Encoder Representations from Transformers(BERT)-based segmenter, and multi-granular segmentation is generated by setting different thresholds. Finally, the discriminator is constructed to validate the correctness of the segmented words. Our numerical study demonstrates that BERTCWS can identify both general-domain terms and geoscience-domain terms. Additionally, multi-granular segmentation could be applied to offer a set of potential geoscience terms of various lengths.

The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility.

Background: The impact of dominant ventricular morphology on Fontan patient outcomes remain controversial. This study evaluates long-term results of right ventricle (RV) dominance versus left ventricle (LV) dominance in Fontan circulation without hypoplastic left heart syndrome (HLHS). Methods: We retrospectively examined 323 Fontan operations from our center. To minimize pre- and intra-Fontan heterogeneity, 42 dominant RV patients were matched with 42 dominant LV patients using propensity score matching, allowing for a comparative analysis of outcomes between groups. Results: The mean follow-up was 8.0 ± 4.6 years for matched RV dominant and 6.5 ± 4.7 years for matched LV dominant group (p > 0.05), showing no significant difference. The cumulative incidence of moderate or greater atrioventricular valve regurgitation was also comparable between the two groups (p > 0.05). Similarly, 10-year freedom from death or transplantation following the Fontan operation was 84% ± 7% in the matched dominant RV group, similar to 81% ± 7% in the matched dominant LV group (p > 0.05). The 10-year freedom from Fontan failure was 78% ± 8% in the matched dominant RV group, also similar to 75% ± 8% in the matched dominant LV group (p >0.05). Multivariate analysis did not identify RV dominance as a risk factor for Fontan failure (p > 0.05). Conclusions: In the pre- and intra-Fontan context, RV dominance demonstrated similar and comparable long-term outcomes compared to LV dominance in non-HLHS Fontan circulation.

To improve the shock resistance of personal protective equipment and reduce casualties due to shock wave accidents, this study prepared four types of carbon fiber/polymethacrylimide (PMI) foam sandwich panels with different face/back layer thicknesses and core layer densities and subjected them to quasi-static compression, low-speed impact, high-speed impact, and non-destructive tests. The mechanical properties and energy absorption capacities of the impact-resistant panels, featuring ceramic/ultra-high molecular-weight polyethylene (UHMWPE) and carbon fiber/PMI foam structures, were evaluated and compared, and the feasibility of using the latter as a raw material for personal impact-resistant equipment was also evaluated. For the PMI sandwich panel with a constant total thickness, increasing the core layer density and face/back layer thickness enhanced the energy absorption capacity, and increased the peak stress of the face layer. Under a constant strain, the energy absorption value of all specimens increased with increasing impact speed. When a 10 kg hammer impacted the specimen surface at a speed of 1.5 m/s, the foam sandwich panels retained better integrity than the ceramic/UHMWPE panel. The results showed that the carbon fiber/PMI foam sandwich panels were suitable for applications that require the flexible movement of the wearer under shock waves, and provide an experimental basis for designing impact-resistant equipment with low weight, high strength, and high energy absorption capacities.

Abstract Aim This study aims to explore the risk factors for perioperative acute heart failure in older patients with hip fracture and establish a nomogram prediction model. Methods The present study was a retrospective study. From January 2020 to December 2021, patients who underwent surgical treatment for hip fracture at the Third Hospital of Hebei Medical University were included. Heart failure was confirmed by discharge diagnosis or medical records. The samples were randomly divided into modeling and validation cohorts in a ratio of 7:3. Relevant demographic and clinic data of patients were collected. IBM SPSS Statistics 26.0 performed univariate and multivariate logistic regression analysis, to obtain the risk factors of acute heart failure. The R software was used to construct the nomogram prediction model. Results A total of 751 older patients with hip fracture were enrolled in this study, of which 138 patients (18.37%, 138/751) developed acute heart failure. Heart failure was confirmed by discharge diagnosis or medical records. Respiratory disease (odd ratio 7.68; 95% confidence interval 3.82–15.43; value of P 0.001), history of heart disease (chronic heart failure excluded) (odd ratio 2.21, 95% confidence interval 1.18–4.12; value of P 0.010), ASA ≥ 3 (odd ratio 14.46, 95% confidence interval 7.78–26.87; value of P 0.001), and preoperative waiting time ≤ 2 days (odd ratio 3.32, 95% confidence interval 1.33–8.30; value of P 0.010) were independent risk factors of perioperative acute heart failure in older patients with hip fracture. The area under the curve (AUC) of the prediction model based on these factors was calculated to be 0.877 (95% confidence interval 0.836–0.918). The sensitivity and specificity were 82.8% and 80.9%, respectively, and the fitting degree of the model was good. In the internal validation group, the AUC was 0.910, and the 95% confidence interval was 0.869–0.950. Conclusions Several risk factors are identified for acute heart failure in older patients, based on which pragmatic nomogram prediction model is developed, facilitating detection of patients at risk early.

Heart failure has become a public health problem that we cannot avoid choosing to face in today's context. In the case of heart failure, pathological cardiac hypertrophy plays a major role because of its condition of absolute increase in ventricular mass under various stresses. Ferroptosis, it could be defined as regulatory mechanisms that regulate cell death in the absence of apoptosis in iron-dependent cells. This paper introduces various new research findings on the use of different regulatory mechanisms of cellular ferroptosis for the treatment of heart failure and cardiac hypertrophy, providing new therapeutic targets and research directions for clinical treatment. The role and mechanism of ferroptosis in the field of heart failure has been increasingly demonstrated, and the relationship between cardiac hypertrophy, which is one of the causes of heart failure, is also an area of research that we should focus on. In addition, the latest applications and progress of inducers and inhibitors of ferroptosis are reported in this paper, updating the breakthroughs in their fields.

Clinically, neuropathic pain treatment remains a challenging issue because the major therapy, centred around pharmacological intervention, is not satisfactory enough to patient by reason of low effectiveness and more adverse reaction. Therefore, it is still necessary to find more effective and safe therapy to ameliorate neuropathic pain. The purpose of this study was to explore the antinociceptive effect of Echinacoside (ECH), an active compound of Cistanche deserticola Ma, on peripheral neuropathic pain induced by chronic constriction injury (CCI) in mice, and to demonstrate its potential mechanism in vivo and vitro. In the present study, results showed that intraperitoneal administration of ECH (50, 100, and 200 mg/kg) could alleviate mechanical allodynia, cold allodynia and thermal hyperalgesia via behavioural test. Moreover, the structure and function of injured sciatic nerve by CCI were taken a turn for the better to a certain extent after ECH treatment using histopathological and electrophysiological test. Furthermore, ECH repressed the expression of the P2X7R and FKN and reduced the expression and release of the IL-1β, IL-6 and TNF-α. Besides, ECH could decrease Ca2+ influx and Cats efflux and inhibit phosphorylation of p38MAPK. To sum up, the present study illustrated that ECH could alleviate peripheral neuropathic pain by inhibiting microglia overactivation and inflammation through P2X7R/FKN/CX3CR1 signalling pathway in spinal cord. This study would provide a new perspective and strategy for the pharmacological treatment on neuropathic pain.

ABSTRACTVibrio cholerae utilizes the Type VI secretion system (T6SS) to gain an advantage in interbacterial competition by delivering anti-prokaryotic effectors in a contact-dependent manner. However, the impact of T6SS and its secreted effectors on physiological behavior remains poorly understood. In this study, we present Tle1Vc, a phospholipase effector in atypical pathogenic V. cholerae E1 that is secreted by T6SS via its interaction with VgrG1E1. Tle1Vc contains a DUF2235 domain and belongs to the Tle1 (type VI lipase effector) family. Bacterial toxicity assays, lipase activity assays and site-directed mutagenesis revealed that Tle1Vc possessed phospholipase A1 activity and phospholipase A2 activity, and that Tle1Vc-induced toxicity required a serine residue (S356) and two aspartic acid residues (D417 and D496). Cells intoxication with Tle1Vc lead to membrane depolarization and alter membrane permeability. Tli1tox-, a cognate immunity protein, directly interacts with Tle1Vc to neutralize its toxicity. Moreover, Tle1Vc can kill multiple microorganisms by T6SS and promote in vivo fitness of V. cholerae through mediating antibacterial activity. Tle1Vc induces bacterial motility by increasing the expression of flagellar-related genes independently of functional T6SS and the tit-for-tat (TFT) response, where Pseudomonas aeruginosa uses its T6SS-H1 cluster to counterattack other offensive attackers. Our study also demonstrated that the physical puncture of E1 T6SS can induce a moderate TFT response, which is essential to the Tle1Vc-mediated strong TFT response, maximizing effector functions. Overall, our study characterized the antibacterial mechanism of phospholipase effector Tle1Vc and its multiple physiological significance.

BackgroundThere is a lack of evidence guiding the surgical timing selection in pulmonary atresia with ventricular septal defect. This study aims to compare the long-term outcomes of different initial rehabilitative surgical ages in patients with pulmonary atresia with ventricular septal defect (PAVSD).MethodsFrom January 2011 to December 2020, a total of 101 PAVSD patients undergoing the initial rehabilitative surgery at our center were retrospectively reviewed. Receiver-operator characteristics curve analysis was used to identify the cutoff age of 6.4 months and therefore to classify the patients into two groups. Competing risk models were used to identify risk factors associated with complete repair. The probability of survival and complete repair were compared between the two groups using the Kaplan-Meier curve and cumulative incidence curve, respectively.ResultsThe median duration of follow-up was 72.76 months. There were similar ΔMcGoon ratio and ΔNakata index between the two groups. Multivariate analysis showed that age ≤6.4 months (hazard ratio (HR) = 2.728; 95% confidence interval (CI):1.122–6.637; p = 0.027) and right ventricle-to-pulmonary artery connection (HR = 4.196; 95% CI = 1.782–9.883; p = 0.001) were associated with increased probability of complete repair. The cumulative incidence curve showed that the estimated complete repair rates were 64% ± 8% after 3 years and 69% ± 8%% after 5 years in the younger group, significantly higher than 28% ± 6% after 3 years and 33% ± 6% after 5 years in the elder group (p 6.4 months, PAVSD patients at the age ≤6.4 months had an equal pulmonary vasculature development, a similar probability of survival but an improved probability of complete repair.

Lactoferrin (Lf) is a multifunctional protein that is synthesized endogenously and has various biological roles including immunological regulation, antibacterial, antiviral, and anticancer properties. Recently, research has uncovered Lf’s critical functions in bone remodeling, where it regulates the function of osteoblasts, chondrocytes, osteoclasts, and mesenchymal stem cells. The signaling pathways involved in Lf’s signaling in osteoblasts include (low density lipoprotein receptor-related protein – 1 (LRP-1), transforming growth factor β (TGF-β), and insulin-like growth factor – 1 (IGF-1), which activate downstream pathways such as ERK, PI3K/Akt, and NF-κB. These pathways collectively stimulate osteoblast proliferation, differentiation, and mineralization while inhibiting osteoclast differentiation and activity. Additionally, Lf’s inhibitory effect on nuclear factor kappa B (NF-κB) suppresses the formation and activity of osteoclasts directly. Lf also promotes chondroprogenitor proliferation and differentiation to chondrocytes by activating the mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) and phosphoinositide 3-kinase/protein kinase B(PI3K/Akt)signaling pathways while inhibiting the expression of matrix-degrading enzymes through the suppression of the NF-κB pathway. Lf’s ability to stimulate osteoblast and chondrocyte activity and inhibit osteoclast function accelerates fracture repair, as demonstrated by its effectiveness in animal models of critical-sized long bone defects. Moreover, studies have indicated that Lf can rescue dysregulated bone remodeling in osteoporotic conditions by stimulating bone formation and suppressing bone resorption. These beneficial effects of Lf on bone health have led to its exploration in nutraceutical and pharmaceutical applications. However, due to the large size of Lf, small bioactive peptides are preferred for pharmaceutical applications. These peptides have been shown to promote bone fracture repair and reverse osteoporosis in animal studies, indicating their potential as therapeutic agents for bone-related diseases. Nonetheless, the active concentration of Lf in serum may not be sufficient at the site requiring bone regeneration, necessitating the development of various delivery strategies to enhance Lf’s bioavailability and target its active concentration to the site requiring bone regeneration. This review provides a critical discussion of the issues mentioned above, providing insight into the roles of Lf in bone remodeling and the potential use of Lf as a therapeutic target for bone disorders.

Accurate diabetic retinopathy (DR) grading is crucial for making the proper treatment plan to reduce the damage caused by vision loss. This task is challenging due to the fact that the DR related lesions are often small and subtle in visual differences and intra-class variations. Moreover, relationships between the lesions and the DR levels are complicated. Although many deep learning (DL) DR grading systems have been developed with some success, there are still rooms for grading accuracy improvement. A common issue is that not much medical knowledge was used in these DL DR grading systems. As a result, the grading results are not properly interpreted by ophthalmologists, thus hinder the potential for practical applications. This paper proposes a novel fine-grained attention & knowledge-based collaborative network (FA+KC-Net) to address this concern. The fine-grained attention network dynamically divides the extracted feature maps into smaller patches and effectively captures small image features that are meaningful in the sense of its training from large amount of retinopathy fundus images. The knowledge-based collaborative network extracts a-priori medical knowledge features, i.e., lesions such as the microaneurysms (MAs), soft exudates (SEs), hard exudates (EXs), and hemorrhages (HEs). Finally, decision rules are developed to fuse the DR grading results from the fine-grained network and the knowledge-based collaborative network to make the final grading. Extensive experiments are carried out on four widely-used datasets, the DDR, Messidor, APTOS, and EyePACS to evaluate the efficacy of our method and compare with other state-of-the-art (SOTA) DL models. Simulation results show that proposed FA+KC-Net is accurate and stable, achieves the best performances on the DDR, Messidor, and APTOS datasets.

Abstract Background Recently, tranexamic acid (TXA) and epsilon aminocaproic acid (EACA) have been applied in total hip arthroplasty (THA). However, doubts in clinicians’ minds about which medicine is more efficient and economical in THA need to be clarified. Therefore, this study compared the efficacy and cost of the intraoperative administration of TXA and EACA per surgery in decreasing perioperative blood transfusion rates in THA. Methods This study enrolled patients who underwent THA between January 2019 to December 2020. A total of 295 patients were retrospectively divided to receive topical combined with intravenous TXA (n = 94), EACA (n = 97) or control (n = 104). The primary endpoints included transfusions, estimated perioperative blood loss, cost per patient and the drop in the haemoglobin and haematocrit levels. Results Patients who received EACA had greater total blood loss, blood transfusion rates, changes in HGB levels and mean cost of blood transfusion per patient (P

Abstract Background Patients with triple-negative breast cancer (TNBC) often have poorer prognosis than those with other subtypes because of its aggressive behaviors. Cancer cells are heterogeneous, and only a few highly metastatic subclones metastasize. Although the majority of subclones may not metastasize, they could contribute by releasing factors that increase the capacity of highly metastatic cells and/or provide a favorable tumor microenvironment (TME). Here, we analyzed the interclonal communication in TNBC which leads to efficient cancer progression, particularly lung metastasis, using the polyclonal murine 4T1 BC model. Methods We isolated two 4T1 subclones, LM.4T1 and HM.4T1 cells with a low and a high metastatic potential, respectively, and examined the effects of LM.4T1 cells on the behaviors of HM.4T1 cells using the cell scratch assay, sphere-forming assay, sphere invasion assay, RT-qPCR, and western blotting in vitro. We also examined the contribution of LM.4T1 cells to the lung metastasis of HM.4T1 cells and TME in vivo. To identify a critical factor which may be responsible for the effects by LM.4T1 cells, we analyzed the data obtained from the GEO database. Results Co-injection of LM.4T1 cells significantly augmented lung metastases by HM.4T1 cells. LM.4T1-derived exosomes promoted the migration and invasion of HM.4T1 cells in vitro, and blocking the secretion of exosome abrogated their effects on HM.4T1 cells. Analyses of data obtained from the GEO database suggested that Wnt7a might be a critical factor responsible for the enhancing effects. In fact, a higher level of Wnt7a was detected in LM.4T1 cells, especially in exosomes, than in HM.4T1 cells, and deletion of Wnt7a in LM.4T1 cells significantly decreased the lung metastasis of HM.4T1 cells. Further, treatment with Wnt7a increased the spheroid formation by HM.4T1 cells via activation of the PI3K/Akt/mTOR signaling pathway. Finally, infiltration of αSMA-positive fibroblasts and angiogenesis was more prominent in tumors of LM.4T1 cells and deletion of Wnt7a in LM.4T1 cells markedly reduced angiogenesis. Conclusions We demonstrated, for the first time, that a low metastatic subclone can enhance lung metastasis of highly metastatic subclone via exosomal Wnt7a and propose Wnt7a as a molecular target to treat TNBC patients.

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

The efficient and precise retrieval of desired information from extensive geological databases is a prominent and pivotal focus within the realm of geological information services. Conventional information retrieval methods primarily rely on keyword matching approaches, which often overlook the contextual and semantic aspects of the keywords, consequently impeding the retrieval system’s ability to accurately comprehend user query requirements. To tackle this challenge, this study proposes an ontology-driven information-retrieval framework for geological data that integrates spatiotemporal and topic associations. The framework encompasses the development of a geological domain ontology, extraction of key information, establishment of a multi-feature association and retrieval framework, and validation through a comprehensive case study. By employing the proposed framework, users are empowered to actively and automatically retrieve pertinent information, simplifying the information access process, mitigating the burden of comprehending information organization and software application models, and ultimately enhancing retrieval efficiency.