Clinical Performance of the Line Immunoassay and Digital Liquid Chip Method for Detecting Autoimmune Liver Disease Autoantibodies.

CONTEXT.­: The identification of autoantibodies associated with autoimmune liver disease (ALD) is crucial for diagnosis and management. Various laboratory methods have been introduced to detect autoantibody profiles. However, the variable performance of these assays may create challenges for clinicians and patients. OBJECTIVE.­: To investigate the concordance rates and diagnostic performance of 2 commercially available assays, line immunoassay (LIA) and digital liquid chip method (DLCM), in patients with ALD. DESIGN.­: A total of 291 serum samples were collected, consisting of 180 sera from patients with ALD and 111 sera from controls. The samples were detected through LIA and DLCM. The agreement and diagnostic performance of each assay were analyzed. RESULTS.­: There was substantial to almost perfect agreement among prevalent autoantibodies (anti-mitochondrial antibody M2, antibodies against gp210, Sp100, and Ro52). Nevertheless, the Cohen κ coefficient of some uncommon autoantibodies (anti-LKM-1, anti-LC-1, and anti-SLA/LP) between the 2 methods was not ideal. LIA showed slightly better sensitivity, accuracy, and negative predictive value, while DLCM exhibited slightly higher specificity and positive predictive value. CONCLUSIONS.­: LIA and DLCM demonstrated comparable performance for the detection of common ALD-related autoantibodies. LIA seemed to be more sensitive, while DLCM displayed more specificity. However, standardization of ALD autoantibody detection still faces challenges between these diverse detection systems. Comprehensive interlaboratory validation is essential to mitigate potential misunderstanding and confusion among patients and clinicians.

A population-based study of Helicobacter pylori: Does asymptomatic infection mean no gastroscopic lesions?

OBJECTIVES: We determined the common clinical characteristics of patients infected with Helicobacter pylori (H. pylori) and investigated the relationship between H. pylori infection, and clinical symptoms, and gastroscopic manifestations. Our focus was specifically on the clinical manifestations in asymptomatic patients. METHODS: We obtained the physical examination data of patients who underwent the 14C urea breath test between January 2018 and December 2020 at our Hospital. Basic demographic data, questionnaire data on clinical symptoms, and clinical examination data of the patients were also collected, and the correlation analysis was performed. RESULTS: A total of 2863 participants were included in the study. The overall H. pylori infection rate was 26.30%. The clinical symptoms between H. pylori-positive patients and H. pylori-negative patients did not differ significantly (P > .05). However, H. pylori-positive patients exhibited more severe gastroscopic manifestations (P < .001). The 14C urea breath test disintegrations per minute (DPM) values in H. pylori-positive patients correlated with their serum pepsinogen and gastrin-17 levels. With an increase in the DPM value, more combinations of clinical symptoms appeared in the patients. Among H. pylori-positive patients, DPM levels in asymptomatic patients were lower than those in symptomatic patients (P < .001). However, gastroscopic manifestations did not vary significantly between asymptomatic and symptomatic patients (P > .05). CONCLUSION: Patients infected with H. pylori showed no specific gastrointestinal symptoms. Patients with asymptomatic infection showed lower DPM levels, but their gastroscopic manifestations were similar to those of patients with symptomatic infection, and their lesions were more severe than H. pylori-negative people.

Multivariable identification based MPC for closed-loop glucose regulation subject to individual variability.

The controller is important for the artificial pancreas to guide insulin infusion in diabetic therapy. However, the inter- and intra-individual variability and time delay of glucose metabolism bring challenges to control glucose within a normal range. In this study, a multivariable identification based model predictive control (mi-MPC) is developed to overcome the above challenges. Firstly, an integrated glucose-insulin model is established to describe insulin absorption, glucose-insulin interaction under meal disturbance, and glucose transport. On this basis, an observable glucose-insulin dynamic model is formed, in which the individual parameters and disturbances can be identified by designing a particle filtering estimator. Next, embedded with the identified glucose-insulin dynamic model, a mi-MPC method is proposed. In this controller, plasma glucose concentration (PGC), an important variable and indicator of glucose regulation, is estimated and controlled directly. Finally, the method was tested on 30 in-silico subjects produced by the UVa/Padova simulator. The results show that the mi-MPC method including the model, individual identification, and the controller can regulate glucose with the mean value of 7.45 mmol/L without meal announcement.

Endoscopic ultrasound-guided intraportal injection of autologous bone marrow in patients with decompensated liver cirrhosis: A case series.

BACKGROUND: Recently, stem cell therapy has been extensively studied as a promising treatment for decompensated liver cirrhosis (DLC). Technological advances in endoscopic ultrasonography (EUS) have facilitated EUS-guided portal vein (PV) access, through which stem cells can be precisely infused. AIM: To investigate the feasibility and safety of fresh autologous bone marrow injection into the PV under EUS guidance in patients with DLC. METHODS: Five patients with DLC were enrolled in this study after they provided written informed consent. EUS-guided intraportal bone marrow injection with a 22G FNA needle was performed using a transgastric, transhepatic approach. Several parameters were assessed before and after the procedure for a follow-up period of 12 mo. RESULTS: Four males and one female with a mean age of 51 years old participated in this study. All patients had hepatitis B virus-related DLC. EUS-guided intraportal bone marrow injection was performed in all patients successfully without any complications such as hemorrhage. The clinical outcomes of the patients revealed improvements in clinical symptoms, serum albumin, ascites, and Child-Pugh scores throughout the 12-mo follow-up. CONCLUSION: The use of EUS-guided fine needle injection for intraportal delivery of bone marrow was feasible and safe and appeared effective in patients with DLC. This treatment may thus be a safe, effective, non-radioactive, and minimally invasive treatment for DLC.

Assessment of the reliability and quality of breast cancer related videos on TikTok and Bilibili: cross-sectional study in China.

Background: As the most common malignant tumor in the world, breast cancer also brings a huge disease burden to China. Ordinary people are increasingly inclined to use the Internet, especially video social platforms, as a source of health information. Educating the public to obtain correct information is important to reduce the incidence of breast cancer and improve the prognosis. However, the quality and reliability of breast cancer-related video content have not been fully studied. Objective: This study aims to evaluate the quality of the information of breast cancer-related videos on TikTok and Bilibili video sharing platforms and factors related to video quality. Methods: We collected the top 100 videos about breast cancer on TikTok and Bilibili, respectively. Categorize videos according to video source and video content. Video quality and reliability were assessed using Global Quality Score (GQS) and modified DISCERN (mDISCERN) tools. We also analyzed the correlation between video quality and video likes, comments, saves, and shares. Results: Although the quality and reliability of Bilibili's breast cancer videos were higher than TikTok (p = 0.002 and p = 0.001, respectively), the video quality of both video sharing platforms was not satisfactory, with a median GQS scores of 2.00 and 3.00 and mDISCERN scores of 1.00 and 2.00, respectively. In general, the quality and reliability of videos released by medical practitioners were higher than those of non-medical practitioners, and the quality and reliability of videos covering disease-related knowledge were higher than those of news reports (all p < 0.001). Among medical practitioners, the quality of videos uploaded by doctors in breast disease was significantly lower than that of doctors in other areas (p < 0.05). There was a significant positive correlation between video quality and duration (r = 0.240, p < 0.001), a weak negative correlation between video quality and likes (r = 0.191, p < 0.01), video quality and comments (r = 0.256, p < 0.001), video reliability and likes (r = 0.198, p < 0.001), video reliability and comments (r = 0.243, p < 0.01). Conclusion: Our study shows that the quality and reliability of breast cancer-related videos on TikTok and Bilibili are poor, and the overall quality is unsatisfactory. But videos uploaded by medical practitioners covering disease knowledge, prevention and treatment are of higher quality. Medical practitioners are encouraged to publish more high-quality videos, while video social platforms should formulate relevant policies to censor and supervise health education videos, so as to enable the public to obtain reliable health information.

Improved spatial resolution using focal modulation microscopy with a Tai Chi aperture.

Focal modulation microscopy (FMM) has gained significant interest in biological imaging. However, the spatial resolution and penetration depth limit the imaging quality of FMM due to the strong scattering background. Here, we introduce FMM with a Tai Chi aperture (TCFMM) based on diffraction theory to improve the spatial resolution. The results show that the transverse resolution is improved by 61.60% and 41.37% in two orthogonal directions, and the axial resolution is improved by 29.67%, compared with confocal microscopy (CM). The signal background ratio (SBR) of TCFMM is increased by 23.26% compared with CM and remains nearly the same compared with FMM using D-shape apertures (DFMM). These improvements in spatial resolution and SBR indicate that TCFMM has potential in deep tissue imaging.

Classification of breast density categories based on SE-Attention neural networks.

BACKGROUND AND OBJECTIVE: Breast density (BD) is an independent predictor of breast cancer risk factor. The automatic classification of BD has yet to resolve. In this paper, we propose an improved convolutional neural network (CNN) framework that integrates innovative SE-Attention mechanism to learn discriminative features, aiming for automatic BD classification in mammography. METHODS: A new benchmarking dataset was constructed from 18157 BD images, manually segmented into 4 levels based on Breast Imaging and Reporting Data System (BI-RADS): A (fatty), B (fibro-glandular), C (heterogeneously dense) and D (extremely dense). The proposed method consists of three main phases: (i) data enhancement and normalization of breast images (ii) SE-Attention training for feature re-calibration and fusion to better classify density and (iii) designing the auxiliary loss. We adopt an attention approach where SE-Attention mechanism is used to learn the density features, which is different from previous works. RESULTS: Experimental results demonstrate that the proposed framework obtains higher classification accuracy than the original network, such as Inception-V4, ResNeXt, DenseNet, increasing the performance from 89.97% to 92.17%, 89.64% to 91.57%, 89.20% to 91.79% respectively. Among them, improved Inception-V4 possesses the highest accuracy meanwhile DenseNet improves in the largest extent, both the original and improved methods are more effective than other state-of-the-art image descriptors regarding classification. CONCLUSIONS: We insist that our method will help radiologists provide reliable BD diagnostic services at the expert level, allowing them to focus on patients who are really in need.

Channel Prediction Based on BP Neural Network for Backscatter Communication Networks.

Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric ß to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.

Intelligent Management of Chemical Warehouses with RFID Systems.

At present, most chemical warehouses rely on human management, which is a time-consuming and laborious process. Therefore, it is very meaningful to use radio frequency identification (RFID) systems for the intelligent management of chemicals. Detecting the remaining amount of chemicals is an important process in the management of a chemical warehouse. It helps managers find the chemicals that are going to run out and replenish them in time. However, in a traditional chemical warehouse, managers usually inspect each chemical on the shelf in turn manually, which is a waste of time and labor. Although some solutions using RFID technology have been proposed, they are expensive and difficult to deploy in a real environment. In order to solve this problem, we propose an intelligent system called the RF-Detector in this paper, which combines robotics and RFID technology. An RFID reader and an antenna are installed on the robot, which achieves automatic scanning of the chemicals. The RF-Detector can achieve two functions: One function is to detect the remaining amount of chemicals using the changes in received signal strength indication (RSSI) and read rate, and the other is to locate chemicals using the phase curve, so that managers can quickly find the chemicals with an insufficient amount remaining. In this paper we implement the RF-Detector and evaluate its performance. The experimental results show that the RF-Detector achieves about 93% detection accuracy and 92% positioning accuracy for chemicals.

Effects of maternal pre-pregnancy overweight on macrosomia in women with gestational diabetes mellitus / 中华围产医学杂志

Objective To explore the relationship between maternal pre-pregnancy body mass index (BMI) and neonatal birth weight in women with gestational diabetes mellitus (GDM). Methods From the pregnant women who received prenatal care and delivered at the Department of Obstetrics and Gynecology of Peking University First Hospital between May 1, 2012 and November 1, 2013, 550 GDM women aged 20-49 years and with single gestation were enrolled in this study. According to the pre-pregnancy BMI, the GDM women were divided into overweight group (BMI ≥ 24.0, n=145) and non-overweight group (BMI < 24.0, n=405). Gestational weight gain, glucose level of 75 g oral glucose tolerance test, glucose control, delivery mode and neonatal birth weight were compared between the two groups. The influencing factors for macrosomia and the relationship between maternal pre-pregnancy BMI and neonatal birth weight were analyzed. Independent sample t-test, Chi-square test, multivariate Logistic regression and Pearson correlation analysis were used for statistical analysis. Receiver operating characteristic (ROC) curve was used to determine the optimal threshold of pre-pregnancy BMI to predict macrosomia. Results Compared with the data of non-overweight group, fasting glucose level [(5.1±0.5) vs (5.3±0.5) mmol/L, t=-4.599], 1 h glucose level [(9.4±1.7) vs (9.8±1.6) mmol/L, t= - 2.742], proportion of poor glucose control [20.5% (83/405) vs 33.1% (48/145), χ2=8.281], proportion of cesarean delivery [37.8% (153/405) vs 55.2% (80/145), χ2=13.160], neonatal birth weight [(3 306±424) vs (3 476±545) g, t=-3.374], and ratio of macrosomia [5.4% (22/405) vs 16.6% (24/145), χ2=16.291] were all higher in overweight group (all P < 0.01). The mean gestational weight gain per week in overweight group was significantly lower than in non-overweight group [(336±123) vs (402±131) g, t=5.136, P < 0.01]. Pre-pregnancy overweight (OR=4.009, 95%CI:2.039-7.881), gestational weight gain per week (OR=1.003, 95%CI:1.001-1.005) and fasting glucose level (OR=2.285, 95%CI: 1.326-3.938) were the influencing factors for macrosomia (all P < 0.01). Pre-pregnancy BMI of GDM women was positively related with neonatal birth weight (r=0.179, P < 0.01). Pre-pregnancy BMI ≥ 22.8 was defined as the optimal threshold to predict macrosomia (ROC area under curve=0.691). Conclusions Maternal pre-pregnancy overweight is a significant risk factor for macrosomia in women with GDM. GDM women with pre-pregnancy overweight should control glucose level and weight gain during pregnancy in order to reduce the risk of macrosomia.

An effective way of J wave separation based on multilayer NMF.

J wave is getting more and more important in the clinical diagnosis as a new index of the electrocardiogram (ECG) of ventricular bipolar, but its signal often mixed in normal ST segment, using the traditional electrocardiograph, and diagnosed by experience cannot meet the practical requirements. Therefore, a new method of multilayer nonnegative matrix factorization (NMF) in this paper is put forward, taking the hump shape J wave, for example, which can extract the original J wave signal from the ST segment and analyze the accuracy of extraction, showing the characteristics of hump shape J wave from the aspects of frequency domain, power spectrum, and spectral type, providing the basis for clinical diagnosis and increasing the reliability of the diagnosis of J wave.

Gestational weight gain and risk of gestational diabetes mellitus among Chinese women / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Gestational diabetes mellitus (GDM) is a common complication during pregnancy, and gestational weight gain is one of the major and modifiable risk factors. This study aims to estimate the relationship between the rate of gestational weight gain before diagnosis of GDM and the subsequent risk of GDM.</p><p><b>METHODS</b>A case-control study was conducted with 90 GDM cases and 165 women in the control group from May 2012 to August 2012 at Peking University First Affiliated Hospital. GDM was diagnosed according to the standards issued by the Ministry of Health of China in 2011. The plasma glucose levels, weights, and covariate data of the women were obtained based on medical records. Univariate analysis and unconditional Logistic regression model were used to estimate the associations.</p><p><b>RESULTS</b>After adjusting for age at delivery, parity, and pre-pregnancy body mass index, the risk of GDM increased with increasing rates of gestational weight gain. Compared with the lower rate of gestational weight gain (less than 0.28 kg per week), a rate of weight gain of 0.28 kg per week or more was associated with increased risk of GDM (odds ratio: 2.03; 95% confidence interval: 1.15 to 3.59). The association between the rate of gestational weight gain and GDM was primarily attributed to the increased weight gain in the first trimester.</p><p><b>CONCLUSION</b>High rates of gestational weight gain, particular during early pregnancy, may increase a woman's risk of GDM.</p>

The relationship between dietary fat intake and risk of colorectal cancer: evidence from the combined analysis of 13 case-control studies.

The objective of this study was to examine the effects of the intake of dietary fat upon colorectal cancer risk in a combined analysis of data from 13 case-control studies previously conducted in populations with differing colorectal cancer rates and dietary practices. Original data records for 5,287 cases of colorectal cancer and 10,470 controls were combined. Logistic regression analysis was used to estimate odds ratios (OR) for intakes of total energy, total fat and its components, and cholesterol. Positive associations with energy intake were observed for 11 of the 13 studies. However, there was little, if any, evidence of any energy-independent effect of either total fat with ORs of 1.00, 0.95, 1.01, 1.02, and 0.92 for quintiles of residuals of total fat intake (P trend = 0.67) or for saturated fat with ORs of 1.00, 1.08, 1.06, 1.21, and 1.06 (P trend = 0.39). The analysis suggests that, among these case-control studies, there is no energy-independent association between dietary fat intake and risk of colorectal cancer. It also suggests that simple substitution of fat by other sources of calories is unlikely to reduce meaningfully the risk of colorectal cancer.

Dietary intake of fiber and decreased risk of cancers of the colon and rectum: evidence from the combined analysis of 13 case-control studies.

BACKGROUND: Colorectal cancer is a major public health problem in both North America and western Europe, and incidence and mortality rates are rapidly increasing in many previously low-risk countries. It has been hypothesized that increased intakes of fiber, vitamin C, and beta carotene could decrease the risk of colorectal cancer. PURPOSE: The objective of this study was to examine the effects of fiber, vitamin C, and beta-carotene intakes on colorectal cancer risk in a combined analysis of data from 13 case-control studies previously conducted in populations with differing colorectal cancer rates and dietary practices. The study was designed to estimate risks in the pooled data, to test the consistency of the associations across the studies, and to examine interactions of the effects of the nutrients with cancer site, sex, and age. METHODS: Original data records for 5287 case subjects with colorectal cancer and 10,470 control subjects without disease were combined. Logistic regression analysis was used to estimate relative risks and confidence intervals for intakes of fiber, vitamin C, and beta carotene, with the effects of study, sex, and age group being adjusted by stratification. RESULTS: Risk decreased as fiber intake increased; relative risks were 0.79, 0.69, 0.63, and 0.53 for the four highest quintiles of intake compared with the lowest quintile (trend, P < .0001). The inverse association with fiber is seen in 12 of the 13 studies and is similar in magnitude for left- and right-sided colon and rectal cancers, for men and for women, and for different age groups. In contrast, after adjustment for fiber intake, only weak inverse associations are seen for the intakes of vitamin C and beta carotene. CONCLUSION: This analysis provides substantive evidence that intake of fiber-rich foods is inversely related to risk of cancers of both the colon and rectum. IMPLICATIONS: If causality is assumed, we estimate that risk of colorectal cancer in the U.S. population could be reduced about 31% (50,000 cases annually) by an average increase in fiber intake from food sources of about 13 g/d, corresponding to an average increase of about 70%.