Fluorine-18 prostate-specific membrane antigen-1007-avid indeterminate bone lesions in prostate cancer: clinical and PET/CT features to predict outcomes and prognosis

AIM

To determine clinical and fluorine-18 prostate-specific membrane antigen-1007 (18F-PSMA-1007) integrated positron-emission tomography (PET)/computed tomography (CT) features that could be used to interpret indeterminate bone lesions (IBLs) and assess the prognosis of prostate cancer (PCa) in patients with IBLs.

MATERIALS AND METHODS

Consecutive patients who underwent PSMA PET/CT were analysed retrospectively. IBLs were identified as benign or malignant based on follow-up imaging and clinical management. Lesion- and patient-based assessments were performed to define features predictive of bone lesion results and determine clinical risk. Patients' prognosis was analysed based on clinical characteristics, including prostate-specific antigen (PSA) and alkaline phosphatase (ALP), respectively.

RESULTS

A total of 105 patients (mean age ± SD, 72.1 ± 8 years) were evaluated and 158 IBLs were identified. Fifty-three (33.5%), 36 (22.8%), and 69 (43.7%) IBLs were benign, malignant, and equivocal, respectively. Variables including location, maximum standard uptake value (SUVmax), and lymph node metastases (LNM) were related to the benignancy or malignancy of IBLs (p=0.046, p<0.001 and p<0.001, respectively). Regression analysis indicated that LNM, SUVmax, and location of IBLs could be predictors of lesion interpretation (p<0.001, p=0.002 and p=0.035). Patients with benign IBLs experienced the most considerable decreases in PSA and ALP levels.

CONCLUSIONS

LNM, SUVmax, and location may contribute to IBL interpretation. A rapid decrease in PSA and ALP levels might suggest a better prognosis for patients with benign IBLs.

The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma

AIM

To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma.

MATERIALS AND METHODS

The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values.

RESULTS

Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z = 2.624; p=0.007, z = 2.679).

CONCLUSION

The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.

Efficacy of rTMS in treating functional impairment in post-stroke patients: a systematic review and meta-analysis

BACKGROUND

Most stroke patients suffer from an imbalance in blood supply, which causes severe brain damage leading to functional deficits in motor, sensory, swallowing, cognitive, emotional, and speech functions. Repetitive transcranial magnetic stimulation (rTMS) is thought to restore functions impaired during the stroke process and improve the quality of life of stroke patients. However, the efficacy of rTMS in treating post-stroke function impairment varies significantly. Therefore, we conducted a meta-analysis of the number of patients with effective rTMS in treating post-stroke dysfunction.

METHODS

The PubMed, Embase, and Cochrane Library databases were searched. Screening and full-text review were performed by three investigators. Single-group rate meta-analysis was performed on the extracted data using a random variable model. Then subgroup analyses were performed at the levels of stroke acuity (acute, chronic, or subacute); post-stroke symptoms (including upper and lower limb motor function, dysphagia, depression, aphasia); rTMS stimulation site (affected side, unaffected side); and whether or not it was a combination therapy.

RESULTS

We obtained 8955 search records, and finally 33 studies (2682 patients) were included in the meta-analysis. The overall analysis found that effective strength (ES) of rTMS was 0.53. In addition, we found that the ES of rTMS from acute/subacute/chronic post-stroke was 0.69, 0.45, and 0.52. We also found that the ES of rTMS using high-frequency stimulation was 0.56, while the ES of rTMS using low-frequency stimulation was 0.53. From post-stroke symptoms, we found that the ES of rTMS in sensory aspects, upper limb functional aspects, swallowing function, and aphasia was 0.50, 0.52, 0.51, and 0.54. And from the site of rTMS stimulation, we found that the ES of rTMS applied to the affected side was 0.51, while the ES applied to the unaffected side was 0.54. What's more, we found that the ES of rTMS applied alone was 0.53, while the ES of rTMS applied in conjunction with other therapeutic modalities was 0.53.

CONCLUSIONS

By comparing the results of the data, we recommend rTMS as a treatment option for rehabilitation of functional impairment in patients after stroke. We also recommend that rehabilitation physicians or clinicians use combination therapy as one of the options for patients.

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Factors associated with early failure of the femoral neck system (FNS) in patients with femoral neck fractures

BACKGROUND

Femoral neck system (FNS) is a new type of internal fixation system which has been widely used for treating femoral neck fractures (FNFs).Compared with other internal fixation methods, FNS is minimally invasive and stable, and often achieves satisfactory short-term efficacy.Early failure of FNS (EFFNS) is not uncommon, however, there are few literatures and reports on factors associated with EFFNS.This study aimed to survey the prevalence and risk factors of EFFNS.

METHODS

We retrospectively analysed 62 patients with FNFs and underwent FNS fixation between 2019 and 2021. Demographic data, clinical characteristics, radiographic features and treatment process were described. Multifactor logistic regression analysis was used to analyse the different influencing factors.

RESULTS

Out of the 62 FNFs patients, 10 patients (16.1%) developed EFFNS, including 6 cases of severe femoral neck shortening, 2 cases of screw-out, 1 case of avascular necrosis of the femoral head and 1 case of nonunion. In the failure group, all patients were younger than 65 years old, which was significantly higher than 59.6% in the healing group (P = 0.012). There were no significant differences in sex(P = 0.490), BMI (P = 0.709), injured side (P = 0.312), injury mechanism (P = 0.617), reduction method(P = 0.570),femoral neck-shaft angle(P = 0.545), Pauwels classification (P = 0.564) and Garden classification (P = 0.195). Moreover, we not found that Garden classification (P = 0.464) and age (P = 0.128) were statistically significant risk factors for EFFNS at multivariate analysis.

CONCLUSION

In this study, sex, BMI, injury side, injury mechanism, reduction method, Pauwels angle, femoral neck-shift angle, Pauwels classification and Garden classification were excluded as EFFNS risk factors. Moreover, our study demonstrated that age and Garden classification were not significant risk factors at multivariate analysis.

TRIAL REGISTRATION

ChiCTR, ChiCTR2100051360. Registered on 21 September, 2021. https://www.chictr.org.cn/index.aspx .

A study on table tennis landing point detection algorithm based on spatial domain information

To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach is based on spatial domain information and reduces the dependency on vision acquisition equipment. This method incorporates several steps: employing dynamic color thresholding to determine the centroid coordinates of all objects in the video frames, utilizing target area thresholding and spatial Euclidean distance to eliminate interference balls and noise, optimizing the total number of video frames through keyframe extraction to reduce the number of operations for object recognition and landing point detection, and employing the four-frame difference slope method and polygonal area determination to detect the landing point and area of the target object, thereby obtaining precise coordinates and their corresponding areas. Experimental results on the above method on the Jetson Nano development board show that the dynamic color thresholding method achieves a detection speed of 45.3 fps. The keyframe extraction method correctly identifies the landing point frames with an accuracy rate exceeding 93.3%. In terms of drop point detection, the proposed method achieves 78.5% overall accuracy in detecting table tennis ball drop points while ensuring real-time detection. These experiments validate that the proposed method has the ability to detect table tennis ball drop points in real time and accurately in low frame rate vision acquisition devices and real environments.

Improving the visualisation of perforator arteries for anterolateral thigh flaps harvest in CT angiography via sublingual glyceryl trinitrate

AIM

To investigate the improvement of image quality and visualisation of the anterolateral thigh (ALT) flap perforators on computed tomography angiography (CTA) after administration of sublingual glyceryl trinitrate (GTN).

MATERIALS AND METHODS

Sixty patients with oral lesions received thigh CTA examinations were divided randomly into two groups after administration of sublingual GTN (GTN group) or without administration of sublingual GTN (non-GTN group). Two radiologists calculated the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and assessed the image quality of each vessel. Besides, the grade of thigh artery, the lumen diameter of deep femoral artery, lateral circumflex femoral artery (LCFA), the descending branch of LCFA and its proximal and distal perforators, and the number and type of visible perforators were evaluated quantitatively.

RESULTS

The SNR and CNR were not significantly different between the two groups (p>0.05). The image quality of CTA in the GTN group was significantly better than that in the non-GTN group (p<0.01). The lumen diameters of the deep femoral artery, LCFA, the descending branch of LCFA and its perforators were significantly larger in the GTN group than those in the non-GTN group (p<0.01). Compared with the non-GTN group, the number of visible perforators and the number of visible septocutaneous perforators were significantly more in the GTN group, and the qualitative grade of visible perforators was significantly higher (p<0.001).

CONCLUSIONS

The administration of sublingual GTN in preoperative thigh CTA can improve the image quality and visualisation of perforator vessels, thus could help surgeons to select the optimum ALT flaps.

Biomaterials / bioinks and extrusion bioprinting

Bioinks are formulations of biomaterials and living cells, sometimes with growth factors or other biomolecules, while extrusion bioprinting is an emerging technique to apply or deposit these bioinks or biomaterial solutions to create three-dimensional (3D) constructs with architectures and mechanical/biological properties that mimic those of native human tissue or organs. Printed constructs have found wide applications in tissue engineering for repairing or treating tissue/organ injuries, as well as in vitro tissue modelling for testing or validating newly developed therapeutics and vaccines prior to their use in humans. Successful printing of constructs and their subsequent applications rely on the properties of the formulated bioinks, including the rheological, mechanical, and biological properties, as well as the printing process. This article critically reviews the latest developments in bioinks and biomaterial solutions for extrusion bioprinting, focusing on bioink synthesis and characterization, as well as the influence of bioink properties on the printing process. Key issues and challenges are also discussed along with recommendations for future research.

Predicting circRNA-drug resistance associations based on a multimodal graph representation learning framework

Circular RNA (circRNA) is a class of noncoding RNA that is highly conserved and exhibit exceptional stability. Due to its function as a microRNA sponge, circRNA has gained significant attention as an essential biomarker and potential drug target in the pathogenesis of several cancers. Although many circRNAs have been identified to play a role in cancer resistance, traditional methods are time-consuming and expensive. In this context, computational methods offer a promising way to facilitate the discovery process. However, most existing prediction models focus on the association between circRNAs and drug resistance, without considering the corresponding disease-related information in the circRNA-drug resistance association. Incorporating disease-related information into the prediction of circRNA-drug resistance associations could potentially improve the efficiency and speed of discovering and developing circRNA-targeting drugs. We propose a computational framework, named GraphCDD, for predicting the association between circRNA and drug resistance. Our model utilizes data from three sources, namely circRNA, disease, and drug, to construct three similarity networks that represent the features of circRNA, disease, and drug, respectively. We utilize a multimodal graph neural network to acquire efficient representations of circRNAs, diseases, and drugs by integrating various types of information, and establish a predictive model. The experimental results have validated the effectiveness of our model and provided a promising method in predicting potential associations between circRNA and drug resistance. The source code and dataset of GraphCDD can be found at https://github.com/Ziqiang-Liu/GraphCDD.

[A method for sensitivity analysis of deviation factor for geometric correction of cone-beam CT system]

OBJECTIVE

To propose a sensitivity test method for geometric correction position deviation of cone-beam CT systems.

METHODS

We proposed the definition of center deviation and its derivation. We analyzed the influence of the variation of the three-dimensional spatial center of the steel ball point, the projection center and the size of the steel ball point on the deviation of geometric parameters and the reconstructed image results by calculating the geometric correction parameters based on the Noo analytical method using the FDK reconstruction algorithm for image reconstruction.

RESULTS

The radius of the steel ball point was within 3 mm. The deviation of the center of the calibration parameter was within the order of magnitude and negligible. A 10% Gaussian perturbation of a single pixel in the 3D spatial coordinates of the steel ball point produced a deviation of about 3 pixel sizes, while the same Gaussian perturbation of the 2D projection coordinates of the steel ball point produced a deviation of about 2 pixel sizes.

CONCLUSION

The geometric correction is more sensitive to the deviation generated by the three-dimensional spatial coordinates of the steel ball point with limited sensitivity to the deviation generated by the two-dimensional projection coordinates of the steel ball point. The deviation sensitivity of a small diameter steel ball point can be ignored.

Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods

Background

Diagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC.

Methods

Two gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) associated with NPC were identified by gene expression differential analysis. Subsequently, significant DEGs were identified by a RF algorithm. ANN were used to construct a diagnostic model for NPC. The performance of the diagnostic model was evaluated by area under the curve (AUC) values using a validation set. Lasso-Cox regression examined gene signatures associated with prognosis. Overall survival (OS) and disease-free survival (DFS) prediction models were constructed and validated from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database.

Results

A total of 582 DEGs associated with NPC were identified, and 14 significant genes were identified by the RF algorithm. A diagnostic model for NPC was successfully constructed using ANN, and the validity of the model was confirmed on the training set AUC =0.947 [95% confidence interval (CI): 0.911-0.969] and the validation set AUC =0.864 (95% CI: 0.828-0.901). The 24-gene signatures associated with prognosis were identified by Lasso-Cox regression, and prediction models for OS and DFS of NPC were constructed on the training set. Finally, the ability of the model was validated on the validation set.

Conclusions

Several potential gene signatures associated with NPC were identified, and a high-performance predictive model for early diagnosis of NPC and a prognostic prediction model with robust performance were successfully developed. The results of this study provide valuable references for early diagnosis, screening, treatment and molecular mechanism research of NPC in the future.

Sphingosine-1-phosphate derived from PRP-Exos promotes angiogenesis in diabetic wound healing via the S1PR1/AKT/FN1 signalling pathway

Background

Sphingosine-1-phosphate (S1P), a key regulator of vascular homeostasis and angiogenesis, is enriched in exosomes derived from platelet-rich plasma (PRP-Exos). However, the potential role of PRP-Exos-S1P in diabetic wound healing remains unclear. In this study, we investigated the underlying mechanism of PRP-Exos-S1P in diabetic angiogenesis and wound repair.

Methods

Exosomes were isolated from PRP by ultracentrifugation and analysed by transmission electron microscopy, nanoparticle tracking analysis and western blotting. The concentration of S1P derived from PRP-Exos was measured by enzyme-linked immunosorbent assay. The expression level of S1P receptor1-3 (S1PR1-3) in diabetic skin was analysed by Q-PCR. Bioinformatics analysis and proteomic sequencing were conducted to explore the possible signalling pathway mediated by PRP-Exos-S1P. A diabetic mouse model was used to evaluate the effect of PRP-Exos on wound healing. Immunofluorescence for cluster of differentiation 31 (CD31) was used to assess angiogenesis in a diabetic wound model.

Results

In vitro, PRP-Exos significantly promoted cell proliferation, migration and tube formation. Furthermore, PRP-Exos accelerated the process of diabetic angiogenesis and wound closure in vivo. S1P derived from PRP-Exos was present at a high level, and S1PR1 expression was significantly elevated compared with S1PR2 and S1PR3 in the skin of diabetic patients and animals. However, cell migration and tube formation were not promoted by PRP-Exos-S1P in human umbilical vein endothelial cells treated with shS1PR1. In the diabetic mouse model, inhibition of S1PR1 expression at wounding sites decreased the formation of new blood vessels and delayed the process of wound closure. Bioinformatics analysis and proteomics indicated that fibronectin 1 (FN1) was closely related to S1PR1 due to its colocalization in the endothelial cells of human skin. Further study supported that FN1 plays an important role in the PRP-Exos-S1P-mediated S1PR1/protein kinase B signalling pathway.

Conclusions

PRP-Exos-S1P promotes angiogenesis in diabetic wound healing via the S1PR1/protein kinase B/FN1 signalling pathway. Our findings provide a preliminary theoretical foundation for the treatment of diabetic foot ulcers using PRP-Exos in the future.

Predicting Potential Drug-Disease Associations Based on Hypergraph Learning with Subgraph Matching

The search for potential drug-disease associations (DDA) can speed up drug development cycles, reduce costly wasted resources, and accelerate disease treatment by repurposing existing drugs that can control further disease progression. As technologies such as deep learning continue to mature, many researchers tend to use emerging technologies to predict potential DDA. The performance of DDA prediction is still challenging and there is some space for improvement due to issues such as the small number of existing associations and possible noise in the data. To better predict DDA, we propose a computational approach based on hypergraph learning with subgraph matching (HGDDA). In particular, HGDDA first extracts feature subgraph information in the validated drug-disease association network and proposes a negative sampling strategy based on similarity network to reduce the data imbalance. Second, the hypergraph Unet module is used by extracting Finally, the potential DDA is predicted by designing a hypergraph combination module to convolution and pooling the two constructed hypergraphs separately, and calculating the difference information between the subgraphs using cosine similarity for node matching. The performance of HGDDA is verified under two standard datasets by 10-fold cross-validation (10-CV), and the results outperform existing drug-disease prediction methods. In addition, to validate the overall utility of the model, the top 10 drugs for the specific disease are predicted through the case study and validated using the CTD database.

Risk factors analysis and establishment of predictive nomogram of extranodal B-cell lymphoma of mucosal-associated lymphoid tissue

PURPOSE

The role of radiation therapy in mucosa-associated lymphoid tissue (MALT) lymphoma is poorly defined. The objective of this study was to explore the factors associated with the performance of radiotherapy and to assess its prognostic impact in patients with MALT lymphoma.

PATIENTS AND METHODS

Patients with MALT lymphoma diagnosed between 1992 and 2017 were identified in the US Surveillance, Epidemiology, and End Results database (SEER). Factors associated with the delivery of radiotherapy were assessed by chi-square test. Overall survival (OS) and lymphoma-specific survival (LSS) were compared between patients with and without radiotherapy, using Cox proportional hazard regression models, in patients with early stage as well as those with advanced stage.

RESULTS

Of the 10,344 patients identified with a diagnosis of MALT lymphoma, 33.6% had received radiotherapy; this rate was 38.9% for stage I/II patients and 12.0% for stage III/IV patients, respectively. Older patients and those who already received primary surgery or chemotherapy had a significantly lower rate of receiving radiotherapy, regardless of lymphoma stage. After univariate and multivariate analysis, radiotherapy was associated with improved OS and LSS in patients with stage I/II (HR=0.71 [0.65-0.78]) and (HR=0.66 [0.59-0.74]), respectively, but not in patients with stage III/IV (HR=1.01 [0.80-1.26]) and (HR=0.93 [0.67-1.29]). The nomogram built from the significant prognostic factors associated with overall survival of stage I/II patients had a good concordance (C-index=0.749±0.002).

CONCLUSION

This cohort study shows that radiotherapy is significantly associated with a better prognosis in patients with early but not advanced MALT lymphoma. Prospective studies are needed to confirm the prognostic impact of radiotherapy in patients with MALT lymphoma.

Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer

AIM

To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

MATERIALS AND METHODS

Forty ICI-P patients and 101 patients without ICI-P were divided randomly into the training (n=113) and test (n=28) sets. The convolution neural network (CNN) algorithm was used to extract the CT-based radiological features of predictable ICI-P and calculated the CT score of each patient. A nomogram model to predict the risk of ICI-P was developed by logistic regression.

RESULTS

CT score was calculated from five radiological features extracted by the residual neural network-50-V2 with feature pyramid networks. Four predictors of ICI-P in the nomogram model included a clinical feature (pre-existing lung diseases), two serum markers (absolute lymphocyte count and lactate dehydrogenase), and a CT score. The area under curve of the nomogram model in the training (0.910 versus 0.871 versus 0.778) and test (0.900 versus 0.856 versus 0.869) sets was better than the radiological and clinical models. The nomogram model showed good consistency and better clinical practicability.

CONCLUSION

The nomogram model that combined CT-based radiological factors and clinical factors can be used as a new non-invasive tool for the early prediction of ICI-P in lung cancer patients after immunotherapy with low cost and low manual input.

Canagliflozin promotes osteoblastic MC3T3-E1 differentiation via AMPK/RUNX2 and improves bone microarchitecture in type 2 diabetic mice

Individuals with type 2 diabetes mellitus (T2DM) have an increased risk of bone metabolic disorders and bone fracture due to disease progression and clinical treatment. The effect of sodium-glucose cotransporter 2 (SGLT2) inhibitors, now greatly prescribed for the treatment of T2DM, on bone metabolism is not clear. This study aimed to explore the possible influence of bone metabolic disorder and the underlying mechanism through a comparison of three different SGLT2 inhibitors (canagliflozin, dapagliflozin, and empagliflozin) in the treatment of type 2 diabetic mice. For the in vivo experiments, four groups (DM, DM+Cana, DM+Dapa, and DM+Empa) were established using micro-CT to detect the bone microarchitecture and bone-related parameters. The study results indicated that canagliflozin, but not dapagliflozin or empagliflozin, increased bone mineral density (p<0.05) and improved bone microarchitecture in type 2 diabetic mice. Furthermore, canagliflozin promoted osteoblast differentiation at a concentration of 5 μM under high glucose concentration (HG). Phosphorylated adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) α (Thr172) has been confirmed to activate run-related transcription factor-2 (RUNX2) to perform this function. This effect can be partially reversed by the AMPK inhibitor dorsomorphin (compound C) and strengthened by the AMPK activator acadesine (AICAR) in vitro. The level trend of RUNX2 and p-AMPK in vivo were consistent with those in vitro. This study suggested that canagliflozin played a beneficial role in bone metabolism in type 2 diabetic mice compared with dapagliflozin and empagliflozin. It provides some theoretical support for the chosen drugs, especially for patients with osteoporosis or a high risk of fracture.

Acute improvement in the attention network with repetitive transcranial magnetic stimulation in Parkinson's disease

PURPOSE

To investigate the effect of two weeks of repetitive transcranial magnetic stimulation (rTMS) on the attention network in Parkinson's disease (PD) patients.

MATERIALS AND METHODS

Sixty PD patients were randomly divided into equal-sized active- and sham-rTMS groups. Executive function was assessed by neuropsychological tests including the Trail-Making Test (TMT), word fluency test, digit span, Wisconsin Card Sorting Test (WCST) and Stroop test. The attention network was evaluated by the attention network test (ANT). rTMS (5 Hz) was applied over the left dorsolateral prefrontal cortex (DLPFC) in the active-rTMS group, and the sham-rTMS group underwent sham stimulation, both for two weeks. All tests were performed before and after rTMS.

RESULTS

After active rTMS, nonparametric analysis revealed significant improvements in categories completed (CC) (p < 0.001) in the WCST and reaction times (RTs) in part 3 (p = 0.002) and the Stroop interference effect (SIE) (p < 0.001) in the Stroop test. Regarding the ANT, the RTs of the executive control network were significantly reduced (p < 0.001). There was no significant change after sham rTMS.

CONCLUSIONS

In the short term, in PD patients, rTMS improved the executive control network involved in resolving conflicting information. However, it showed milder effects on neuropsychological test outcomes assessing executive function, which may involve different neuromechanisms.Implications for rehabilitationCognitive impairment is common in patients with Parkinson's disease (PD), and it is related to functional disability and reduced quality of life.Attention is a main component of the cognitive system, and attention deficits are responsible for disability.This study demonstrates that rTMS is beneficial for cognitive rehabilitation in PD, as patients showed improved performance on the attention network test and neuropsychological tests.

DHNLDA: A Novel Deep Hierarchical Network Based Method for Predicting lncRNA-Disease Associations

Recent studies have found that lncRNA (long non-coding RNA) in ncRNA (non-coding RNA) is not only involved in many biological processes, but also abnormally expressed in many complex diseases. Identification of lncRNA-disease associations accurately is of great significance for understanding the function of lncRNA and disease mechanism. In this paper, a deep learning framework consisting of stacked autoencoder(SAE), multi-scale ResNet and stacked ensemble module, named DHNLDA, was constructed to predict lncRNA-disease associations, which integrates multiple biological data sources and constructing feature matrices. Among them, the biological data including the similarity and the interaction of lncRNAs, diseases and miRNAs are integrated. The feature matrices are obtained by node2vec embedding and feature extraction respectively. Then, the SAE and the multi-scale ResNet are used to learn the complementary information between nodes, and the high-level features of node attributes are obtained. Finally, the fusion of high-level feature is input into the stacked ensemble module to obtain the prediction results of lncRNA-disease associations. The experimental results of five-fold cross-validation show that the AUC of DHNLDA reaches 0.975 better than the existing methods. Case studies of stomach cancer, breast cancer and lung cancer have shown the great ability of DHNLDA to discover the potential lncRNA-disease associations.

ISLMI:Predicting lncRNA-miRNA Interactions Based on Information Injection and Second-Order Graph Convolution Network

Studies have shown that IncRNA-miRNA interactions can affect cellular expression at the level of gene molecules through a variety of regulatory mechanisms and have important effects on the biological activities of living organisms. Several biomolecular network-based approaches have been proposed to accelerate the identification of lncRNA-miRNA interactions. However, most of the methods cannot fully utilize the structural and topological information of the lncRNA-miRNA interaction network. In this article, we proposed a new method, ISLMI, a prediction model based on information injection and second order graph convolution network(SOGCN). The model calculated the sequence similarity and Gaussian interaction profile kernel similarity between lncRNA and miRNA, fused them to enhance the intrinsic interaction between the nodes, using SOGCN to learn second-order representations of similarity matrix information. At the same time, multiple feature representations obtain using different graph embedding methods were also injected into the second-order graph representation. Finally, matrix complementation was used to increase the model accuracy. The model combined the advantages of different methods and achieved reliable performance in 5-fold cross-validation, significantly improved the performance of predicting lncRNA-miRNA interactions. In addition, our model successfully confirmed the superiority of ISLMI by comparing it with several other model algorithm.

GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs

MOTIVATION

CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs.

RESULTS

We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA-disease associations. Firstly, the circRNA-circRNA similarity network and disease-disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA-disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA-disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.

MRI-based radiomics analysis in differentiating solid non-small-cell from small-cell lung carcinoma: a pilot study

AIM

To investigate the ability of a T2-weighted (W) magnetic resonance imaging (MRI)-based radiomics signature to differentiate solid non-small-cell lung carcinoma (NSCLC) from small-cell lung carcinoma (SCLC).

MATERIALS AND METHODS

The present retrospective study enrolled 152 eligible patients (NSCLC = 125, SCLC = 27). All patients underwent MRI using a 3 T scanner and radiomics features were extracted from T2W MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression model was used to identify the optimal radiomics features for the construction of a radiomics model to differentiate solid NSCLC from SCLC. Threefold cross validation repeated 10 times was used for model training and evaluation. The conventional MRI morphology features of the lesions were also evaluated. The performance of the conventional MRI morphological features, and the radiomics signature model and nomogram model (combining radiomics signature with conventional MRI morphological features) was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Five optimal features were chosen to build a radiomics signature. There was no significant difference in age, gender, and the largest diameter. The radiomics signature and conventional MRI morphological features (only pleural indentation and lymph node enlargement) were independent predictive factors for differentiating solid NSCLC from SCLC. The area under the ROC curves (AUCs) for MRI morphological features, and the radiomics model, and nomogram model was 0.69, 0.85, and 0.90 (ROC), respectively.

CONCLUSIONS

The T2W MRI-based radiomics signature is a potential non-invasive approach for distinguishing solid NSCLC from SCLC.

Predicting RBP Binding Sites of RNA With High-Order Encoding Features and CNN-BLSTM Hybrid Model

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.

Effectiveness of aquatic exercise in lower limb osteoarthritis: a meta-analysis of randomized controlled trials

The objective of this study was to evaluate the short-term and follow-up effectiveness of aquatic training on the health status of lower limb osteoarthritis. Randomized controlled trials (RCTs) on related topics were systematically searched in PubMed, Embase, Web of Science, the Cochrane Library, Physiotherapy Evidence Database (PEDro), the China National Knowledge Infrastructure and Wanfang databases from inception to January 2021. RevMan 5.3 was used for statistical analysis, and the standardized mean difference (SMD) was used to present pooled effect sizes. As a result, 19 RCTs (1592 patients) were included. Compared with unsupervised home exercise or usual care (land-based training excluded), aquatic training showed short-term pain relief (SMD, -0.54; 95% CI, -0.81 to -0.28), physical function improvement (SMD, -0.64; 95% CI, -1.00 to -0.28), stiffness reduction (SMD, -0.40; 95% CI, -0.79 to -0.01) and improved function in sport and recreation (SMD, -0.30; 95% CI, -0.59 to -0.02). Analyses restricted to patients with knee osteoarthritis only also confirmed the positive effects of aquatic training on most dimensions excluding physical function. At medium-term follow-ups, improvements in physical function and function in sport and recreation were observed. No significant difference was observed between arms in the above four outcomes at long-term follow-ups. All studies reported no major adverse event with relation to aquatic training, and the minor adverse events were not common. It is concluded that aquatic training likely has short-term benefits on pain, physical function, stiffness and sport ability in lower limb osteoarthritis patients, but these positive effects may not last long.

[Dynamic changes and influencing factors of HIV-1 DNA load in HIV-1 infected individuals under antiretroviral therapy]

Objective: To analyze the dynamic changes and influencing factors of HIV-1 DNA load in HIV-1 infected individuals under antiretroviral therapy (ART) in Dehong Dai and Jingpo autonomous prefecture, Yunnan province, and provide information support for the clinical use of HIV-1 DNA quantitative detection. Methods: The HIV infection cases in recent infection cohort from Dehong Center for Disease Control and Prevention during 2009-2018 were selected as study subjects. The dynamic curve of HIV-1 DNA load varrying with time was generated and logistic regression analysis was conducted to identify the risk factors for HIV-1 load in the recent follow up after ART and statistical analysis was performed by using SPSS 17.0. Results: Among the 113 HIV infection cases detected from the recent infection cohort, the recent HIV infection rate were 49.6%(56/113) males, sexual transmission cases and drug injection transmission cases accounted for 53.1% (60/113), 80.5% (91/113) and 19.5% (22/113), respectively. The dynamic changes curve showed that HIV-1 DNA load was relatively high (>800 copies /10⁶ PBMCs) before ART, and droped rapidly (<400 copies /10⁶ PBMCs) after ART for 1 year. However, HIV-1 DNA load decreased insignificantly from the second year of ART, and remained to be 269 copies/10⁶ PBMCs after ART for 6 years. Univariable logistic regression analysis indicated that OR (95%CI) of CD8, CD4/CD8 and HIV-1 DNA load were 1.00 (1.00-1.00), 0.30 (0.09-1.05) and 1.01 (1.00-1.01), respectively. Multivariable logistic regression analysis showed that OR value of HIV-1 DNA load base was 1.00 (1.00-1.01). Conclusions: HIV-1 DNA load decreased significantly in the first year of ART, then remained stable for years. HIV-1 DNA load base was the key factor associated with the decrease of HIV-1 DNA load, the lower the HIV-1 DNA load base, the lower HIV-1 DNA load. Therefore, earlier ART can contribute to the decrease of HIV-1 DNA load.

MD-MLI: Prediction of miRNA-lncRNA Interaction by Using Multiple Features and Hierarchical Deep Learning

Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequence-derived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model's good generalization ability.

The Evaluation of Gait and Balance for Patients with Early Diabetic Peripheral Neuropathy: A Cross-Sectional Study

Objective

Falls often occur in patients with diabetic neuropathy due to biomechanical alternation. The implication of diabetic peripheral neuropathy (DPN) on gait and balance remains poorly understood.

Methods

A total of 11 dynamic gait, balance and electrophysiological parameters were evaluated in 176 participants. The biomechanical parameters were compared between groups.

Results

Stride length and stride velocity were significantly lower in all subgroups of DPN compared with healthy subjects (p<0.05). Stance phase and double support phase were significantly higher, but swing phase were significantly lower across all subgroups of DPN than healthy subjects (p<0.05). Under eyes-open standing, the ML and AP range parameters of CoM sway, ankle sway and hip sway, CoM sway index, ankle swing index in both subclinical and confirmed DPN patients were all significantly higher compared to healthy subjects (p<0.05). Under eyes-closed standing, AP range parameters of CoM sway in subclinical DPN and confirmed DPN patients were significantly higher than healthy subjects (p<0.05). The hip sway areas in diabetics were significantly higher compared to healthy subjects (p<0.05).

Conclusion

The abnormal biomechanical parameters existed in the early stages of patients with DPN. The static balance under eyes-open and eye-closed condition is maintained by ankle joint compensation strategy and hip joint protection strategy. An early evaluation and better risk management is essential for diabetic patients with a history of more than 5 years even without DPN clinical symptoms and signs.

Clinical Trial Registration Number

No. ChiCTR1800019179, www.chictr.org.cn.

RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision

Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.

Development of a novel combined nomogram model integrating Rad-score, age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization

Background

Liver cancer is affecting more and more people's health. Transcatheter arterial chemoembolization (TACE) has become a routine treatment option, but the prognosis of patients is not optimistic. Effectively prediction of prognosis can provide clinicians with an objective basis for patient prognosis and timely adjustment of treatment strategies, thus improving the quality of patient survival. However, the current prediction methods have some limitations. Therefore, this study aims to develop a radiomics nomogram for predicting survival after TACE in patients with advanced hepatocellular carcinoma (HCC).

Methods

Seventy advanced HCC patients treated with TACE were enrolled from January 2013 to July 2019. Clinical information included age, sex, and Eastern Cooperative Oncology Group (ECOG) score. Overall survival (OS) was confirmed by postoperative follow-up. Radiomics features were extracted using 3D Slicer (version 4.11.20210226) software, then obtain radiomics signature and calculate radiomics score (Rad-score) for each patient. Univariate and multivariate Cox regression were used to analyze the baseline clinical data of patients and establish clinical models. The obtained radiomics signature was incorporated into the clinical model to establish the radiomics nomogram. The predictive performance and calibration ability of the model were assessed by the area under the receiver operating characteristic (ROC) curve (AUC), C-index, and calibration curve.

Results

Three significant features were selected from 851 radiomics features by the least absolute shrinkage and selection operator (LASSO) Cox regression model to construct the radiomics signature, and were significantly correlated with overall survival (P<0.001). Rad-score, age, and ECOG score were combined to construct a radiomics nomogram. The AUC, sensitivity, and specificity of the radiomics nomogram were 0.801 (95% CI: 0.693–0.909), 0.822 (95% CI: 0.674–0.915), and 0.720 (95% CI: 0.674–0.915), respectively. The C-index of the radiomics nomogram was 0.700 (95% CI: 0.547–0.853). Calibration curves showed better agreement between the predicted and actual probabilities in the radiomics nomogram among the 3 features.

Conclusions

The Rad-score was a strong risk predictor of survival after TACE for HCC patients. The radiomics nomogram might be improved the predictive efficacy of survival after TACE and it may also provide assistance to physicians in making treatment decisions.

Predicting miRNA-disease associations using an ensemble learning framework with resampling method

MOTIVATION

Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and treatment of human diseases. As the biological experiments are time-consuming and labor-intensive, developing effective computational methods has become indispensable to identify associations between miRNAs and diseases.

RESULTS

We present an Ensemble learning framework with Resampling method for MiRNA-Disease Association (ERMDA) prediction to discover potential disease-related miRNAs. Firstly, the resampling strategy is proposed for building multiple different balanced training subsets to address the challenge of sample imbalance within the database. Then, ERMDA extracts miRNA and disease feature representations by integrating miRNA-miRNA similarities, disease-disease similarities and experimentally verified miRNA-disease association information. Next, the feature selection approach is applied to reduce the redundant information and increase the diversity among these subsets. Lastly, ERMDA constructs an individual learner on each subset to yield primitive outcomes, and the soft voting method is introduced for making the final decision based on the prediction results of individual learners. A series of experimental results demonstrates that ERMDA outperforms other state-of-the-art methods on both balanced and unbalanced testing sets. Besides, case studies conducted on the three human diseases further confirm the ERMDA's prediction capability for identifying potential disease-related miRNAs. In conclusion, these experimental results demonstrate that our method can serve as an effective and reliable tool for researchers to explore the regulatory role of miRNAs in complex diseases.

Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an important issue in drug safety. ADRs are health threats caused by medication. Identifying ADRs in a timely manner can reduce harm to patients and can also assist doctors in the rational use of drugs. Many studies have investigated potential ADRs based on social media due to the openness and timeliness of this resource; however, they have ignored the fine-grained emotional expression in social media text. In addition, the benchmark datasets from social media are usually small, which can result in the problem of over-fitting. In this paper, we propose the Adversarial Neural Network with Sentiment-aware Attention (ANNSA) model, which enhances the sentimental element in social media and improves the performance of neural networks via data augmentation. Specifically, a sentiment-aware attention mechanism is proposed to extract the word-level sentiment features associated with sentiment words and learn task-related information by optimizing a task-specific loss. For low-resource datasets, we use an adversarial training approach to generate perturbations of the word embeddings via an implicit regularization technique. ANNSA was tested on three social media ADR detection datasets, namely, Twitter, TwiMed (Twitter) and CADEC. The experimental results indicated the ability to achieve F1 values of 48.84%, 64.18% and 83.06%, respectively, comparable to the best results reported for state-of-the-art methods. Our study demonstrates that sentiment words are highly correlated with ADRs and that word-level sentiment features can assist in detecting ADRs from social media datasets.

Optimization of cold chain distribution path of fresh agricultural products under carbon tax mechanism: A case study in China

According to the problem of large amount of carbon emissions during the cold chain distribution process, a cold chain distribution route optimization method for fresh agricultural products under the carbon tax mechanism was proposed. Firstly, with the goal of minimizing carbon emission cost and comprehensive cost, quantitative analysis of carbon tax mechanism is introduced, considering the demand quantity, demand time and unloading time constraints, a mathematical model of the problem is established. In addition, an improved quantum bacterial foraging optimization algorithm is put forward, which uses the bacterial optimization algorithm information update strategy to maintain group memory, and uses the carbon tax cost as the decision variable of the improved algorithm. Through experimental simulation, comparative analysis of the shortest distribution path, uninitialized pheromone bacterial foraging optimization algorithm and quantum bacterial foraging optimization algorithm on the last selected study model, the method proposed in this thesis can effectively optimize the distribution path, reduce carbon tax cost and comprehensive cost.

POS0754 DEVELOPMENT OF A RISK PREDICTION MODEL FOR VENOUS THROMBOEMBOLISM IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: THE SLE-VTE SCORE

Background:

Patients with systemic lupus erythematosus (SLE) have a substantially increased risk of venous thromboembolism (VTE). An individual VTE risk assessment is important to ensure that all patients are assessed and given adequate thromboprophylaxis.

Objectives:

We conducted this study to develop a risk score for VTE in patients with SLE.

Methods:

Patients with SLE who participated in the Chinese SLE Treatment and Research group were enrolled in this study. Patient baseline information and clinical laboratory indicators were obtained, and VTE events were recorded every 3-6 months during follow-up visits. The risk prediction model was created and internally validated using the bootstrap methods, and a scoring system was established (Figure 1).

Figure 1.

Flow chart of study design.

Results:

Out of 4,502 patients included in this study, 135 had a VTE event. After univariate analysis and Lasso regression, the following 11 variables were identified and included in the risk prediction model: male sex, age, BMI ≥25 kg/m2, hyperlipidemia, hypoalbuminemia, hsCRP>3 mg/L, renal involvement, nervous system involvement, anti-β2-glycoprotein I antibody positivity, lupus anticoagulant positivity, and no use of hydroxychloroquine. The AUC for the SLE-VTE score (Table 1) was 0.947 (95% CI, 0.9249-0.9694). The SLE-VTE score’s sensitivity and specificity with the optimal cutoff value of 13 were 0.919 and 0.881, respectively. The SLE-VTE score was superior to the GAPSS system in predicting the risk of VTE in patients with SLE (AUC= 0.947 vs. 0.680, P< 0.001; integrated discrimination improvement (IDI)= 0.6652, P< 0.001; net reclassification improvement (NRI)= 0.6652, P< 0.001).

Table 1.

Final multivariable analysis for venous thromboembolism risk in patients with SLE

β coefficientsOdds ratio* (95% CI)P-valuePoints in scoring systemMale0.6211.86(0.953-3.503)0.0612Age at study entry(≥50)0.8372.308(1.339-3.915)0.0023BMI02(kg/m20.7922.209(1.333-3.627)0.0023Hyperlipemia0.8382.313(1.246-4.166)0.0063Hypoalbuminemia2.1638.697(5.185-14.794)< 0.0017hsCRP>3 mg/L1.4524.272(2.618-6.968)< 0.0015Anti β2GPI1.0132.754(1.543-4.853)0.0013LA1.5594.752(2.799-8.072)< 0.0015Nervous system2.38210.832(6.163-18.998)< 0.0018Lupus nephritis0.8352.305(1.414-3.756)0.0013No use of hydroxychloroquine1.7715.876(3.722-9.401)< 0.0016

BMI: body mass index; hsCRP: Hypersensitive c-reactive protein; ACL: anticardiolipin, antiβ2GPI: anti-β2-glycoprotein I, LA: lupus anticoagulantm;

Values in bold are statistically significant at p <0.05.

Conclusion:

Various factors are related to the occurrence of VTE in patients with SLE. The proposed SLE-VTE risk score can accurately predict the risk of VTE and help identify SLE patients with a high risk of VTE who may benefit from thromboprophylaxis.

References:

[1]Ramirez GA, Efthymiou M, Isenberg DA, Cohen H. Under crossfire: thromboembolic risk in systemic lupus erythematosus. Rheumatology. 2018;58:940-952.

[2]Chung WS, Lin CL, Chang SN, Lu CC, Kao CH. Systemic lupus erythematosus increases the risks of deep vein thrombosis and pulmonary embolism: a nationwide cohort study. J Thromb Haemost. 2014;12:452-458.

[3]Liew NC, Alemany GV, Angchaisuksiri P, et al. Asian venous thromboembolism guidelines: updated recommendations for the prevention of venous thromboembolism. Int Angiol. 2017;36:1.

[4]Savino S, Giovanni S, Veronica M, Dario R, Khamashta MA, Laura BM. GAPSS: the Global Anti-Phospholipid Syndrome Score. Rheumatology. 2013:8.

[5]Konstantinides SV, Meyer G, Becattini C, et al. 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS). Eur Heart J. 2020;41:543-603.

[6]Moghadamyeghaneh Z, Hanna MH, Carmichael JC, Nguyen NT, Stamos MJ. A Nationwide Analysis of Postoperative Deep Vein Thrombosis and Pulmonary Embolism in Colon and Rectal Surgery. J Gastrointest Surg. 2014;18:2169-2177.

Disclosure of Interests:

None declared

Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO/ISUP classification of clear cell renal cell carcinoma

AIM

To investigate the effect of radiomics obtained from different virtual monochromatic images (VMIs) in dual-energy spectral computed tomography (CT) on the World Health Organization/International Association for Urological Pathology (WHO/ISUP) classification of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

A retrospective study of 99 ccRCC patients who underwent contrast-enhanced dual-energy CT was undertaken. ccRCC was confirmed at surgery or biopsy and graded according to the WHO/ISUP pathological grading criteria as low grade (n=68, grade I and II) or high grade (n=31, grade III and IV). Radiomics risk scores (RRSs) for differentiating high and low grades of ccRCC were constructed from 11 sets of VMI in (40-140 keV, 10 keV interval) the cortical phase. Receiver operating characteristic (ROC) curves were drawn and the area under the curves (AUCs) was calculated to evaluate the discriminatory power of RRS for each VMI. The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of each model and the decision curve was used to analyse its net benefit to patients.

RESULTS

The AUC values for distinguishing low-from high-grade ccRCC with RRS of 40-140 keV VMIs were all >0.920. The Hosmer-Lemeshow test showed that the p-values of RRS of VMIs were >0.05, suggesting good fits. In the decision curve analysis, RRS from the 40-140 keV VMIs had similar decision curves and provided better net benefits than considering all patients either as high-grade or low-grade.

CONCLUSIONS

The RRS obtained from multiple VMIs in dual-energy spectral CT have high diagnostic efficiencies for distinguishing between low- and high-grade ccRCC with no significant differences between different VMIs.

The Relationship Between Metabolic Parameters, Age, and Thyroid Status: A Cross-Sectional Study-Based National Survey of Iodine Nutrition, Thyroid Disease

Aim

The relationship between thyroid status and metabolic factors was investigated as well as iodine nutrition in the general population and among the elderly population.

Methods

We performed a cross-sectional survey of 2483 subjects to assess the status of national iodine nutrition and incidence of thyroid disease. The general and elderly populations were divided into normal thyroid function (NTF) and subclinical hypothyroidism (SCH) groups. The anthropometric parameters and biochemical indicators were then analyzed.

Results

Overall, 327 participants were diagnosed with thyroid diseases, 73 (22.32%) of whom were 65 years or older. For the general population, compared with the NTF group, individuals in the SCH group were older, presented with higher systolic blood pressure, fasting blood glucose, serum cholesterol, and urinary iodine concentration (all p<0.05) but lower blood uric acid (UA) (p<0.05). Linear regression analysis further revealed that age and triglyceride (TG) serum levels positively correlated with serum thyroid-stimulating hormone (TSH) levels. Multiple regression analyses revealed that age, TG, waist circumference (WC), and body mass index were independent predictors for abnormal TSH serum levels. For the elderly population, compared with NTF group, individuals in the SCH group were substantially older (p<0.05) but presented with lower UA (p<0.05). Pearson linear regression analysis revealed a positive correlation between age (p=0.003) as well as TG levels (p<0.001) and serum TSH levels. In contrast, WC (p=0.003) was negatively related to TSH serum levels. Further multiple regression analysis revealed that age, TG, WC and heart rate were independent predictors of blood TSH.

Conclusion

The large-scale national study of iodine nutrition, thyroid disease has shown a vital relationship between metabolic indicators and serum TSH levels. Age and metabolic diseases increase the likelihood of developing thyroid diseases, both the general population and among the elderly.

Extending the Anion Channelrhodopsin-Based Toolbox for Plant Optogenetics

Optogenetics was developed in the field of neuroscience and is most commonly using light-sensitive rhodopsins to control the neural activities. Lately, we have expanded this technique into plant science by co-expression of a chloroplast-targeted β-carotene dioxygenase and an improved anion channelrhodopsin GtACR1 from the green alga Guillardia theta. The growth of Nicotiana tabacum pollen tube can then be manipulated by localized green light illumination. To extend the application of analogous optogenetic tools in the pollen tube system, we engineered another two ACRs, GtACR2, and ZipACR, which have different action spectra, light sensitivity and kinetic features, and characterized them in Xenopus laevis oocytes, Nicotiana benthamiana leaves and N. tabacum pollen tubes. We found that the similar molecular engineering method used to improve GtACR1 also enhanced GtACR2 and ZipACR performance in Xenopus laevis oocytes. The ZipACR1 performed in N. benthamiana mesophyll cells and N. tabacum pollen tubes with faster kinetics and reduced light sensitivity, allowing for optogenetic control of anion fluxes with better temporal resolution. The reduced light sensitivity would potentially facilitate future application in plants, grown under low ambient white light, combined with an optogenetic manipulation triggered by stronger green light.

The efficacy of repetitive transcranial magnetic stimulation on emotional processing in apathetic patients with Parkinson's disease: A Placebo-controlled ERP study

BACKGROUND

Few studies have examined the effect of rTMS on impaired emotional processing in apathetic Parkinson's disease (PD) patients, which impelled us to use a combination of repetitive transcranial magnetic stimulation (rTMS) with event-related potentials (ERPs) to gain insight into the underlying mechanisms.

METHODS

Apathetic and non-apathetic patients diagnosed with PD performed a facial emotion categorization task, and ERP data were collected before and after HF-rTMS or sham stimulation over the right dorsolateral prefrontal cortex (DLPFC). We observed the behavioural results and early components of P100 and N170.

RESULTS

After rTMS, accuracy (ACC) was higher for negative expressions, and reaction times (RTs) were shorter for all expressions in both apathetic and non-apathetic patients. P100 amplitudes significantly increased except for negative expressions in the right hemisphere, and hemisphere lateralization was reversed in apathetic patients. P100 amplitudes increased for all expressions bilaterally in non-apathetic patients. N170 amplitudes in apathetic patients increased, and hemisphere lateralization was normalized. Neither the apathetic sham group nor the non-apathetic cohort of patients showed changes in P100 or N170 lateralization.

LIMITATIONS

The effects of rTMS on PD patients relative to normal subjects should be compared and observed over a long period of time.

CONCLUSIONS

HF-rTMS over the right DLPFC is beneficial for emotional processing in both apathetic and non-apathetic PD patients but shows different sensitivities. The effect of rTMS on emotional processing impairments in apathetic PD patients is profound. Treatment with rTMS may be an important, novel nonpharmaceutical approach for apathy in PD patients.