Objective To screen immune-related long non-coding RNAs(LncRNAs)in the TCGA database pancreatic cancer dataset and construct a prognostic risk assessment model with immune-related LncRNAs to explore prognosis-related potential molecular mechanisms.Methods RNA-seq data of 171 pancreatic cancer samples and corresponding clinical information were obtained by The Cancer Genome Atlas(TCGA)database,and two classical immune-related gene datasets(GO0006955/IMMUNE RESPONSE and GO0002376/IMMUNE SYSTERM PROCESS)and gene annotation information were used to identify immune-related LncRNAs.The immune-related LncRNAs associated with pancreatic cancer prognosis were used for univariate and multivariate Cox analyses to establish a model for the assessment of pancreatic cancer prognostic risk based on immune-associated LncRNAs.This risk model was used for survival analysis,clinical correlation analysis,immune cell infiltration analysis,pathway enrichment analysis,and prognostic column line plot modeling.Results We screened 119 immune-related LncRNAs in pancreatic cancer,and five immune-related LncRNAs(AC064836.3,LINC00941,ZNF236-DT,TMEM161B-AS1 and AC068580.2)were identified for the development of pancreatic cancer prognostic risk assessment model.According to the prognostic risk assessment model,pancreatic cancer patients were divided into low-risk group(n=86)and high-risk group(n=85).Compared with the low-risk group,the high-risk group showed a significant negative enrichment trend for immune-related signaling pathways,the 5-year overall survival of pancreatic cancer patients was significantly increased in the low-risk group compared with the high-risk group.The expression of low-risk immune-related LncRNAs(AC064836.3,ZNF236-DT and TMEM161B-AS1)gradually decreased with increasing clinical stage of pancreatic cancer patients.Patient age(P=0.031,risk ratio and 95％CI:1.025/1.002-1.048)and prognostic risk score(P＜0.001,risk ratio and 95％ confidence interval 1.801/1.465-2.215)could be used as independent prognostic risk factors for overall survival in pancreatic cancer.In addition,the prognostic risk assessment model had better predictive efficiency(area under the curve=0.695)compared with the disease predictive ability of common clinical characteristics.Steroid biosynthesis,pentose phosphate pathway,intercellular linkage,cytoskeletal rearrangement and other pathways related to energy metabolism and invasive migration of pancreatic cancer cells were significantly activated in the high-risk group.Meanwhile,pancreatic cancer patients in the high-risk group had lower levels of naive B cells,plasma cells and neutrophils with anti-tumor activity,but their macrophage infiltration levels were significantly higher than those in the low-risk group.Conclusion The prognostic risk assessment model constructed based on five immune-related LncRNAs can effectively predict the survival status,clinical characteristics,molecular pathways,and immune cell infiltration differences of pancreatic cancer patients.Meanwhile,relying on this model,the prognosis of pancreatic cancer patients can be prospectively predicted,which enhances the usefulness of this risk prediction model.

Objective To screen immune-related long non-coding RNAs(LncRNAs)in the TCGA database pancreatic cancer dataset and construct a prognostic risk assessment model with immune-related LncRNAs to explore prognosis-related potential molecular mechanisms.Methods RNA-seq data of 171 pancreatic cancer samples and corresponding clinical information were obtained by The Cancer Genome Atlas(TCGA)database,and two classical immune-related gene datasets(GO0006955/IMMUNE RESPONSE and GO0002376/IMMUNE SYSTERM PROCESS)and gene annotation information were used to identify immune-related LncRNAs.The immune-related LncRNAs associated with pancreatic cancer prognosis were used for univariate and multivariate Cox analyses to establish a model for the assessment of pancreatic cancer prognostic risk based on immune-associated LncRNAs.This risk model was used for survival analysis,clinical correlation analysis,immune cell infiltration analysis,pathway enrichment analysis,and prognostic column line plot modeling.Results We screened 119 immune-related LncRNAs in pancreatic cancer,and five immune-related LncRNAs(AC064836.3,LINC00941,ZNF236-DT,TMEM161B-AS1 and AC068580.2)were identified for the development of pancreatic cancer prognostic risk assessment model.According to the prognostic risk assessment model,pancreatic cancer patients were divided into low-risk group(n=86)and high-risk group(n=85).Compared with the low-risk group,the high-risk group showed a significant negative enrichment trend for immune-related signaling pathways,the 5-year overall survival of pancreatic cancer patients was significantly increased in the low-risk group compared with the high-risk group.The expression of low-risk immune-related LncRNAs(AC064836.3,ZNF236-DT and TMEM161B-AS1)gradually decreased with increasing clinical stage of pancreatic cancer patients.Patient age(P=0.031,risk ratio and 95％CI:1.025/1.002-1.048)and prognostic risk score(P＜0.001,risk ratio and 95％ confidence interval 1.801/1.465-2.215)could be used as independent prognostic risk factors for overall survival in pancreatic cancer.In addition,the prognostic risk assessment model had better predictive efficiency(area under the curve=0.695)compared with the disease predictive ability of common clinical characteristics.Steroid biosynthesis,pentose phosphate pathway,intercellular linkage,cytoskeletal rearrangement and other pathways related to energy metabolism and invasive migration of pancreatic cancer cells were significantly activated in the high-risk group.Meanwhile,pancreatic cancer patients in the high-risk group had lower levels of naive B cells,plasma cells and neutrophils with anti-tumor activity,but their macrophage infiltration levels were significantly higher than those in the low-risk group.Conclusion The prognostic risk assessment model constructed based on five immune-related LncRNAs can effectively predict the survival status,clinical characteristics,molecular pathways,and immune cell infiltration differences of pancreatic cancer patients.Meanwhile,relying on this model,the prognosis of pancreatic cancer patients can be prospectively predicted,which enhances the usefulness of this risk prediction model.

Objective:This study evaluates the performance of chemiluminescence assay, which is designed to detect Hepatitis D Virus (HDV) Immunoglobulin G (IgG) antibodies.Methods:A comparative analysis was conducted among chemiluminescence anti-HDV IgG reagent, the magnetic particle-based domestic reagent A and domestic reagent B, and the Robo Gene HDV RNA kit, using 1909 HBsAg-positive plasma samples. This comparison aimed to delineate clinical specificity and detection accuracy. The anti-HDV IgG reagent precision was assessed at three different concentration levels following the Clinical Laboratory Standards Institute EP5-A2 guidelines. The specificity of the assay was validated using 200 HAV IgM positive, 545 HBsAg-positive but anti-HDV IgG-negative, 350 anti HCV positive plasma samples and 200 healthy human blood samples. Additionally, a concordance study was conducted with 545 HBsAg-positive and 37 anti-HDV IgG-positive plasma samples, comparing the anti-HDV IgG reagent against reagent A.Results:1 909 HBsAg-positive plasma samples were tested using 3 anti HDV IgG reagent and 1 HDV RNA reagent, 19 samples were identified as anti-HDV IgG-positive. The anti-HDV IgG demonstrated superior accuracy and specificity. The assay exhibited excellent precision, with intra-assay coefficient of variation (CV) values ranging from 1.57% to 4.30%, and inter-assay CV values between 1.71% and 4.67% for detecting samples at high, medium, and low concentration levels. Concordance with Reagent A showed consistent results in both positive and negative detections.Conclusion:In this study, the anti-HDV IgG reagent (chemiluminescence method) displayed outstanding specificity in detecting clinical samples and exhibited a high conformity rate with commercialized reagents, making it potentially suitable for screening anti-HDV IgG in HBsAg-positive samples.

Objective:This study aims to evaluate the quality and explore the preliminary clinical applications of a domestically developed hepatitis D virus nucleic acid quantification reagent (abbreviated as"domestic HDV RNA reagent").Methods:The sensitivity and accuracy of the reagent were evaluated in accordance with the WHO HDV RNA international standard, employing the Bio-Rad CFX Opus 96 real-time fluorescence quantitative PCR analysis system. Serial dilutions of pseudo-viruses or cell culture-derived virus were used to determine the linear range of the domestic HDV RNA reagent. Specificity was assessed using positive samples of HAV, HBV, HCV infection, and HEV national reference materials. Precision was evaluated with samples at both high and low concentrations. In a comparative analysis, 30 HDV IgG positive samples were tested using both the domestic HDV RNA reagent and the RoboGene HDV RNA kit based on the ABI 7500 FAST DX system. The Pearson correlation coefficient (r) was used to examine the correlation between the two reagents.Results:The domestic HDV RNA reagent demonstrated a high sensitivity of up to 6 IU/ml, consistent with that of the comparator reagent. The calibration curve for WHO HDV RNA standards had a slope of -3.286, with an amplification efficiency of 101.6%. The linear detection range spanned from 10 to 10 8 IU/ml for eight HDV genotypes. The domestic HDV RNA reagent exhibited exceptional specificity, without cross-reactivity observed with HAV, HBV, HCV, or HEV. Accuracy assessments at five concentration levels met the required standards, with intra-assay precision coefficient of variation ( CV) ranging from 1.20% to 4.20%, and inter-assay precision CV from 1.20% to 7.90%. The detection results for HDV IgG positive samples were highly correlated with the comparator reagent ( r=0.984, P<0.001), achieving a diagnostic accuracy of 100% compared to sequencing results. Conclusion:In this study, the domestic HDV RNA reagent possesses excellent specificity, accuracy, precision, and a broad linear range, attaining a sensitivity level on par with international reagents of the same type.

Cardiovascular diseases is the leading cause of threat to human life and health worldwide. Early risk assessment, timely diagnosis, and prognosis evaluation are critical to the treatment of cardiovascular diseases. Currently, the evaluation of diagnosis and prognosis of cardiovascular diseases mainly relies on imaging examinations such as coronary CT and coronary angiography, which are expensive, time-consuming, partly invasive, and require high professional competence of the operator, making it difficult to promote in the community or in areas where medical resources are scarce. The fundus microcirculation is a part of the human microcirculation and has similar embryological origins and physiopathological features to cardiovascular circulation. Several studies have revealed fundus imaging biomarkers associated with cardiovascular diseases, and developed and validated intelligent diagnosis and treatment models for cardiovascular diseases based on fundus imaging data. Fundus imaging is expected to be an important adjunct to cardiovascular disease diagnosis and treatment given its noninvasive and convenient nature. The purpose of this review is to summarize the current research status, challenges, and future prospects of the application of artificial intelligence based on multimodal fundus imaging data in cardiovascular disease diagnosis and treatment.

Previously, the diagnosis of schizophrenia was empirical since it relied on the patient′s subjective symptoms and the practitioner′s clinical experience. There is an urgent need for objective and reproducible assessment tools to aid its diagnosis. Changes in brain structure and function are closely related to the development of schizophrenia. The retina, a part of the central nervous system, is highly similar to the brain morphologically and neurophysiologically and can be assessed by a non-invasive and convenient fundus examination. There have been studies using optical coherence tomography, fundus photography, optical coherence tomography angiography, and electroretinography to identify significant changes in the structure and function of the fundus in patients with schizophrenia. However, there is still a lack of generalization in these studies. This article summarises and discusses the findings in this area and presents a vision for future studies to explore the application value of fundus in the management of schizophrenia.

Previously, the diagnosis of schizophrenia was empirical since it relied on the patient′s subjective symptoms and the practitioner′s clinical experience. There is an urgent need for objective and reproducible assessment tools to aid its diagnosis. Changes in brain structure and function are closely related to the development of schizophrenia. The retina, a part of the central nervous system, is highly similar to the brain morphologically and neurophysiologically and can be assessed by a non-invasive and convenient fundus examination. There have been studies using optical coherence tomography, fundus photography, optical coherence tomography angiography, and electroretinography to identify significant changes in the structure and function of the fundus in patients with schizophrenia. However, there is still a lack of generalization in these studies. This article summarises and discusses the findings in this area and presents a vision for future studies to explore the application value of fundus in the management of schizophrenia.

Objective:To propose a deep learning network model 2D-PE-GAN to automatically delineate the target area of nasopharyngeal carcinoma and improve the efficiency of target area delineation.Methods:The model adopted the architecture of generative adversarial networks which used a UNet similar structure as the generator, and 2D-PE-block was added after each layer of convolution operation of the generator to improve the accuracy of delineation. The experimental data included CT images from 130 cases of nasopharyngeal carcinoma. The images were preprocessed before model training. In addition, three models of UNet, GAN, and GAN with an attention mechanism were compared, and Dice similarity coefficient, Hausdorff distance, accuracy, Matthews correlation coefficient, Jaccard distance were employed to evaluate network performance.Results:Compared with UNet, GAN and GAN with the attention mechanism, the average Dice similarity coefficient of 2D-PE-GAN network segmentation of CTV was increased by 26%, 4% and 2%. The average Dice similarity coefficient of GTV segmentation was increased by 21%, 4%, 2%, respectively. Compared with the GAN network with the attention mechanism, the parameters and time of 2D-PE-GAN were reduced by 0.16% and 18%, respectively.Conclusions:Compared with the above three networks, 2D-PE-GAN network can increase the segmentation accuracy of nasopharyngeal carcinoma target area delineation. At the same time, compared with the attention mechanism with similar reasons, 2D-PE-GAN network can reduce the occupation of computing resources when the segmentation accuracy is not much different.

Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R= 0.81 and the mean absolute error (MAE) =1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54％ and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

Objective To investigate the effect of nutritional intervention upon the clinical efficacy of chemoradiotherapy in patients diagnosed with esophageal carcinoma. Methods A total of 46 patients who were diagnosed with esophageal cancer in Anhui Cancer Hospital from November 2016 to August 2017 were enrolled in this prospective study. All patients were randomly and evenly divided into the nutritional intervention (NI) and routine treatment (RT) groups. The changes in body mass index (BMI),PG-SGA, serum albumin ( ALB), hemoglobin ( HB), white blood cell ( WBC) and other objective nutritional parameters and the incidence of chemoradiotherapy-induced complications were recorded before and after chemoradiotherapy. Results Prior to chemoradiotherapy,age,sex,BMI,ALB,PLT and clinical staging did not significantly differ between two groups (all P>0. 05).In the NI group,the BMI was (21.52±2. 67) after chemoradiotherapy,significantly higher than (21.13±2. 73) before radiotherapy (P= 0. 000).Moreover,the PG-SGA score after chemoradiotherapy was significantly lower compared with that before chemoradiotherapy (P= 0. 000).In the RT group,the BMI,Hb,ALB,PLT and WBC after chemoradiotherapy were significantly lower than those before radiotherapy, and thePG-SGA score was worse after chemoradiotherapy ( all P<0. 05).In the NI group, the incidence of grade 3 myelosuppression was 4. 34％, significantly lower than 8. 68％ in the RT group ( P= 0. 000 ). Conclusions Patients with esophageal cancer treated with chemoradiotherapy have a high nutritional risk. Nutritional intervention can improve the nutritional status, reduce the incidence of chemoradiotherapy-induced complications,and probably improve the quality of life and clinical prognosis.