Symptoms experienced after transcatheter arterial chemoembolization in patients with primary liver cancer: A network analysis.

Objective: This study aimed to establish a symptom network for patients with primary liver cancer posttranscatheter arterial chemoembolization (TACE), identifying core and bridge symptoms. The goal is to provide a foundation for precise and comprehensive nursing interventions. Methods: A total of 1207 post-TACE patients were included using a consecutive sampling method. Data collection involved a general information questionnaire, the Anderson Symptom Assessment Scale, and a primary liver cancer-specific symptom module. The symptom network was constructed using the R language. Results: In the overall network, distress exhibited the highest strength (rs = 1.31) and betweenness (rb = 62). Fatigue had the greatest closeness (rc = 0.0043), while nausea and vomiting (r = 0.76 ± 0.02) had the highest marginal weights. Nausea had the highest bridge strength (rbs = 5.263). In the first-time TACE-treated symptom network, sadness (rbs = 5.673) showed the highest bridge strength, whereas in the non-first-time symptom network, fever (rbs = 3.061) had the highest bridge strength. Conclusions: Distress serves as a core symptom, and nausea acts as a bridge symptom after TACE treatment in liver cancer patients. Interventions targeting bridge symptoms should be tailored based on the number of treatments, enhancing the quality of symptom management.

Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Development and Validation of a Self-Aligning Knee Exoskeleton With Hip Rotation Capability.

The self-aligning capability of an exoskeleton is important to ensure wearing comfort, and the delicate motion ability of the exoskeleton is essential for motion assistance. Designing a self-aligning exoskeleton that offers improved wearing comfort and enhanced motion-assistance functions remains a challenge. This paper proposes a novel spatial self-aligning mechanism for a knee exoskeleton to enable simultaneous assistance in the flexion and extension (FE) of the knee joint and the internal and external rotation (IER) of the hip joint. Additionally, considering the misalignment of the human-robot joint axes, a kinematic model of the knee exoskeleton is established and analyzed to demonstrate the kinematic compatibility of the exoskeleton. Furthermore, a global torque manipulability (GTM) index is proposed to evaluate the effects of dimensional parameters on the exoskeleton's performance, and then the knee exoskeleton is optimized according to the GTM index. Finally, experiments are conducted to validate the performance of the proposed exoskeleton. The experimental results show that during knee FE and hip IER, the proposed exoskeleton exhibits lower interaction forces and torques than existing exoskeletons.

Prognostic Value of Elevated Plasma Galectin-3 for Renal Adverse Events in Dialysis Patients: A Systematic Review and Meta-Analysis.

Context: In prognostic research, Galectin-3 (Gal-3) has gained recognition in renal fibrosis and nephrosis for its characteristics of promoting inflammation and fibrosis. High levels of Gal-3 may function as a predictor of adverse outcomes for patients with end-stage renal disease (ESRD). Objective: The review intended to systematically examine the significance of Gal-3 in the forecast of adverse outcomes for dialysis patients, using a method of evidence-based medicine. Design: The research team performed a systematic narrative review and meta-analysis by searching the Excerpta Medica Database (EMBASE) and the PubMed, Cochrane, and Web of Science databases for pertinent studies published before June 1, 2022. The search contained both meshes and free terms, such as Galectin 3, Gal-3, renal dialysis, hemodialysis, peritoneal dialysis, HD, and PD. Setting: The review took place at First People's Hospital of Linping District in Hangzhou, China. Outcome Measures: The research team used the Newcastle-Ottawa Scale (NOS) for assessment of the quality of the included research. The team created two reports to assess the value of Gal-3 in prediction of risk: (1) one for studies using continuous variables and (2) one for studies using categorical variables, dividing patients into high- and low-level Gal-3 groups with a cut-off value of Gal-3, being Gal-3 < 10.5 ng/mL for the lower tertile, and Gal-3 ≥ 13.4 ng/mL for the higher tertile. The team performed the meta-analysis using Stata 15.0, analyzed publication bias using Egger's test and directly showed it in a funnel plot. Results: The search found 1061 publications, with eight studies with 5194 participants being included in the current review. For the continuous variables, Gal-3 was associated with all-cause risk of death-Hazard ratio (HR) 1.06, 95%CI 1.01-1.12, and P = .024-and cardiovascular (CV) events-HR 1.13, 95%CI 1.07-1.203, and P = .000, but no significant correlation existed between Gal-3 and risk of CV mortality-HR 1.07, 95%CI 0.99-1.16, and P = .091. For the categorical variables, a high level of Gal-3 was correlated with a high risk of dying, from all causes-HR 2.05, 95%CI 1.50-2.80, and P = .000. Conclusions: Clinicians can use Gal-3 as a standalone forecaster of all-cause mortality and CV events for hemodialysis patients because correlates with these outcomes. Further research is necessary to determine its predictive value for CV mortality. Investigators need to perform further research with a large sample size on the predictive value of Gal-3 for dialysis patients, particularly PD patients, from a variety of ethnic backgrounds to improve the precise treatment for high-risk patients.

A cross-modal deep metric learning model for disease diagnosis based on chest x-ray images.

The emergence of unknown diseases is often with few or no samples available. Zero-shot learning and few-shot learning have promising applications in medical image analysis. In this paper, we propose a Cross-Modal Deep Metric Learning Generalized Zero-Shot Learning (CM-DML-GZSL) model. The proposed network consists of a visual feature extractor, a fixed semantic feature extractor, and a deep regression module. The network belongs to a two-stream network for multiple modalities. In a multi-label setting, each sample contains a small number of positive labels and a large number of negative labels on average. This positive-negative imbalance dominates the optimization procedure and may prevent the establishment of an effective correspondence between visual features and semantic vectors during training, resulting in a low degree of accuracy. A novel weighted focused Euclidean distance metric loss is introduced in this regard. This loss not only can dynamically increase the weight of hard samples and decrease the weight of simple samples, but it can also promote the connection between samples and semantic vectors corresponding to their positive labels, which helps mitigate bias in predicting unseen classes in the generalized zero-shot learning setting. The weighted focused Euclidean distance metric loss function can dynamically adjust sample weights, enabling zero-shot multi-label learning for chest X-ray diagnosis, as experimental results on large publicly available datasets demonstrate.

A Deep Learning Method for Pneumonia Detection Based on Fuzzy Non-Maximum Suppression.

Pneumonia is one of the largest causes of death in the world. Deep learning techniques can assist doctors to detect the areas of pneumonia in the chest X-rays images. However, existing methods lack sufficient consideration for the large variation scale and the blurred boundary of the pneumonia area. Here, we present a deep learning method based on Retinanet for pneumonia detection. Firstly, we introduce Res2Net into Retinanet to get the multi-scale feature of pneumonia. Then, we proposed a novel predicted boxes fusion algorithm, named Fuzzy Non-Maximum Suppression (FNMS), which gets a more robust predicted box by fusing the overlapping detection boxes. Finally, we get the performance outperforms than existing methods by integrating two models with different backbones. We report the experimental result in the single model case and the model ensemble case. In the single model case, RetinaNet with FNMS algorithm and Res2Net backbone is better than RetinaNet and other models. In the model ensemble case, the final score of predicted boxes that fused by the FNMS algorithm is better than NMS, Soft-NMS, and weighted boxes fusion. Experimental results on the pneumonia detection dataset verify the superiority of the FNMS algorithm and the proposed method in the pneumonia detection task.

Association between tea consumption and glucose metabolism and insulin secretion in the Shanghai High-risk Diabetic Screen (SHiDS) study.

INTRODUCTION: The relationship between tea consumption and glucose metabolism remains controversial. This study investigated the associations of tea consumption with impaired glucose regulation, insulin secretion and sensitivity in Shanghai High-risk Diabetic Screen project. RESEARCH DESIGN AND METHODS: A total of 2337 Chinese subjects were enrolled in the study from 2014 to 2019. Each participant conducted a 75 g oral glucose tolerance test (OGTT) with five-point glucose and insulin level examined. They also completed a nurse-administered standard questionnaire including tea, coffee, and alcohol consumption, smoking habit, physical activity, education, sleep quality, etc. RESULTS: The result showed that tea consumption was positively associated with plasma glucose levels during OGTT after adjusting for confounder (Ps <0.05) and was associated with worsening glucose tolerance (OR 1.21, 95% CI 1.01-1.44; p=0.034). Strong tea consumption or long-term tea intake (>10 years) had an increased risk of glucose intolerance (all p<0.05). These associations did not vary in participants drinking green tea. In addition, insulin secretion indexes were decreased 7.0%-13.0% in tea consumption group. Logistic regression analysis showed that tea consumption was independently associated with lower insulin secretion (homeostasis model assessment of ß-cell function (HOMA-ß) (OR 0.81, 95% CI 0.68-0.97; p=0.021); Stumvoll first-phase index (OR 0.81, 95% CI 0.68-0.97; p=0.020)) in a fully adjusted model. Green tea consumption showed a negative association with insulin secretion (HOMA-ß (OR 0.77, 95% CI 0.62-0.96; p=0.019)). CONCLUSIONS: Tea intake is associated with an increased risk of glucose intolerance in a large high-risk diabetic Chinese population. Habitual tea consumption subjects might have lower pancreatic ß-cell function.

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary clinical diagnosis. However, the majority of contemporary efforts exclusively focus on regression from input to binary labels, ignoring the relationship between visual features and semantic vectors of labels. In addition, there is an imbalance in data amount between diseases, which frequently causes intelligent diagnostic systems to make erroneous disease predictions. Therefore, we aim to improve the accuracy of the multi-label classification of chest X-ray images. Chest X-ray14 pictures were utilized as the multi-label dataset for the experiments in this study. By fine-tuning the ConvNeXt network, we got visual vectors, which we combined with semantic vectors encoded by BioBert to map the two different forms of features into a common metric space and made semantic vectors the prototype of each class in metric space. The metric relationship between images and labels is then considered from the image level and disease category level, respectively, and a new dual-weighted metric loss function is proposed. Finally, the average AUC score achieved in the experiment reached 0.826, and our model outperformed the comparison models.

Identification of potential crosstalk genes and mechanisms between periodontitis and diabetic nephropathy through bioinformatic analysis.

Periodontitis and diabetic nephropathy are significant public health concerns globally and are closely related with each other. This study aimed to identify potential crosstalk genes, pathways, and mechanisms associated with the interaction between periodontitis and diabetic nephropathy. Expression profiles of periodontitis and diabetic nephropathy were retrieved from the Gene expression omnibus gene expression omnibus database, and differentially expressed genes (DEGs) were screened, followed by identification of co-expressed differential genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. A protein-protein interaction network was constructed via STRING website, and key crosstalk genes were selected using Cytoscape. Subsequent gene ontology and KEGG analyses were conducted for the key genes, and a validation dataset was obtained from the gene expression omnibus database for differential gene validation. The TRRUST website was employed to identify transcription factors (TFs) associated with the key crosstalk genes between periodontitis and diabetic nephropathy, followed by differential analysis of TFs. A total of 17 crosstalk genes were obtained. Among them, SAMSN1, BCL2A1, interleukin-19, IL1B, RGS1, CXCL3, CCR1, CXCR4, CXCL1, and PTGS2 were identified as key crosstalk genes between periodontitis and diabetic nephropathy. Additionally, 16 key TFs were discovered. This bioinformatic analysis revealed potential crosstalk genes between periodontitis and diabetic nephropathy. The identified key genes participate in signaling pathways, including cytokine signaling and chemokine signaling transduction, which might collectively influence these 2 diseases. These genes may serve as potential biomarkers guiding future research in this field.

Efficacy and Safety of Rituximab in the Treatment of Idiopathic Membranous Nephropathy: A Meta-Analysis.

Background: Primary membranous nephropathy (MN), sometimes referred to as idiopathic membranous nephropathy (IMN), is a kind of MN whose pathogenesis is yet unclear. According to research reports, the incidence of IMN is about 9.8-26.8%, and it is on the rise. Methods: The computer retrieves eight databases to obtain controlled trials at home and abroad on the rituximab (RTX) actions in IMN management. After a rigorous literature quality evaluation, software called RevMan 5.3 was used for data analysis. Results: This meta-analysis finally contained 8 papers. They were all regarded as controlled trials. Six studies reported serum creatinine (standardized mean difference [SMD]: -6.87; 95% CI: -14.09, 0.35; P = 0.062), ALB (SMD: 1.91; 95% CI: -0.31, 4.14; P = 0.092), and adverse reactions (OR: 0.56; 95% CI: 0.36, 0.90; P < 0.01), all of which were significantly higher in the test group than in the control group (OR: 1.37; 95% CI: 1.07, 1.76; P < 0.01). Conclusion: The overall effective rate, serum creatinine, adverse effects, and ALB of this trial indicate that RTX may be beneficial for individuals with IMN, but further high-quality research is required to confirm these findings.

Involvement of JMJ15 in the dynamic change of genome-wide H3K4me3 in response to salt stress.

Post-translational histone modifications play important roles in regulating chromatin structure and transcriptional regulation. Histone 3 lysine 4 trimethylation (H3K4me3) is a prominent histone modification mainly associated with gene activation. Here we showed that a histone demethylase, JMJ15, belonging to KDM5/JARID group, is involved in salt stress response in Arabidopsis thaliana. Jmj15 loss-of-function mutants displayed increased sensitivity to salt stress. Moreover, knockout of JMJ15 impaired the salt responsive gene expression program and affected H3K4me3 levels of many stress-related genes under salt-stressed condition. Importantly, we demonstrated that JMJ15 regulated the expression level of two WRKY transcription factors, WRKY46 and WRKY70, which were negatively involved in abiotic stress tolerance. Furthermore, JMJ15 directly bound to and demethylated H3K4me3 mark in the promoter and coding regions of WRKY46 and WRKY70, thereby repressing these two WRKY gene expression under salt stress. Overall, our study revealed a novel molecular function of the histone demethylase JMJ15 under salt stress in plants.

Serum IL-17A concentration and a IL17RA single nucleotide polymorphism contribute to the risk of autoimmune type 1 diabetes.

AIMS: Interleukin (IL)-17 is associated with autoimmunity. This study aimed to affirm the role of IL-17A, IL-17F and single nucleotide polymorphisms (SNPs) in genes related to them and their receptors in autoimmune type 1 diabetes (T1D) for Chinese population. METHODS: In this study, 130 patients with autoimmune T1D and 140 non-T1D controls were included for analysis. Clinical and biochemical data were collected, and serum levels of IL-17A, IL-17F, IL-6, and high-sensitivity C reactive protein were measured using ELISA. The SNPs rs2275913, rs8193036, rs3819025, rs763780, rs879577, rs4819554, and rs708567 were genotyped using the SNaPshot assay. RESULTS: IL-17A levels were higher in patients with autoimmune T1D than in controls (median [IQR] 28.83[37.38] vs. 16.68[8.10], p < 0.001) and high IL-17A was a risk factor for autoimmune T1D (odds ratio (OR), 1.013; 95% CI, 1.003-1.023; p = 0.013) after adjusting for confounding factors. Linear regression analysis revealed that log10 IL-17A levels were independently associated with fasting C-peptide, IL-6, body mass index, and IL-17F. However, no independent association was found between IL-17F and autoimmune T1D. The GG genotype of SNP rs4819554 in the interleukin 17 receptor A (IL17RA) gene was associated with a decreased risk of autoimmune T1D (OR, 0.458; 95% CI, 0.246-0.852; p = 0.014) after adjusting for other confounders. The IL17RA rs4819554 GG genotype was negatively correlated with serum glutamic acid decarboxylase antibody appearance (p < 0.05). CONCLUSIONS: Increased serum IL-17A, but not IL-17F, is a risk factor for autoimmune T1D. The GG genotype of IL17RA rs4819554 might decrease the risk for autoimmune T1D.

A Composite Hydrogel Containing Mesoporous Silica Nanoparticles Loaded With Artemisia argyi Extract for Improving Chronic Wound Healing.

Chronic wounds are a major health problem with increasing global prevalence, which endangers the physical and mental health of those affected and is a heavy burden to healthcare providers. Artemisia argyi extract (AE) has excellent antibacterial and anti-inflammatory properties. In this research, we developed AE loaded composite hydrogel scaffold based on methacrylate gelatin (GelMA)/methacrylate hyaluronic acid (HAMA) and mesoporous silica nanoparticle (MSN) as sustained-release drug carrier vehicles for the treatment of chronic wounds. The presented GelMA/1%HAMA hydrogel possessed stable rheological properties, suitable mechanical properties, appropriate biodegradability, swelling, sustained-release AE capacity. In vitro antibacterial and cell experiments showed that the GelMA/HAMA/MSN@AE hydrogel had excellent antibacterial activity and biocompatibility and induced macrophages to differentiate into M2 phenotype. In vivo wound healing of rat full-thickness cutaneous wounds further demonstrated that the prepared GelMA/HAMA/MSN@AE hydrogel could significantly promote chronic wound healing by upregulating the expression of IL-4, TGF-ß1, CD31, and α-SMA but downregulating the expression of TNF-α and IFN-γ and promoting M1-M2 macrophages polarization. Altogether, we believe that the GelMA/HAMA/MSN@AE hydrogel will have wide application prospects in healing chronic wounds.

Circular RNAs in stem cells: from basic research to clinical implications.

Circular RNAs (circRNAs) are a special class of endogenous RNAs with a wide variety of pathophysiological functions via diverse mechanisms, including transcription, microRNA (miRNA) sponge, protein sponge/decoy, and translation. Stem cells are pluripotent cells with unique properties of self-renewal and differentiation. Dysregulated circRNAs identified in various stem cell types can affect stem cell self-renewal and differentiation potential by manipulating stemness. However, the emerging roles of circRNAs in stem cells remain largely unknown. This review summarizes the major functions and mechanisms of action of circRNAs in stem cell biology and disease progression. We also highlight circRNA-mediated common pathways in diverse stem cell types and discuss their diagnostic significance with respect to stem cell-based therapy.

Novel ceruloplasmin gene mutation causing aceruloplasminemia with diabetes in a Chinese woman: a case report.

Hereditary aceruloplasminemia (ACP) is a rare adult-onset autosomal recessive disease characterized by a ceruloplasmin (CP) gene mutation and defective or absent CP function. In the present study, we report a case of ACP in a 34-year-old Chinese woman with diabetes, fatigue, anxiety, and progressive membrane loss with low hemoglobin associated with microcytosis. The fasting glucose level was 5.6-7.96 mmol/L. Postprandial blood glucose ranged from 6.8 to 9.6 mmol/L. The Stumvoll first-phase and second-phase insulin secretion disposition indices were very low, and the serum iron content was low, even though transferrin levels were normal. Moreover, the transferrin saturation was low (5%), and the ferritin level was extremely high, above 2,000 µg/L in the patient. Furthermore, her serum CP level was extremely low (<0.0183 g/L). Abdominal computed tomography (CT) examination showed moderate iron overload in the liver. Brain CT also showed a mildly increased density of the bilateral thalami and basal ganglia. Finally, gene analysis showed a rare homozygous mutation (c.146+1G>A) in the CP gene and was diagnosed with ACP. To date, less than 60 family cases of ACP have been reported worldwide, and only two cases of ACP have been reported in China. Here, we report a case of ACP accompanied by diabetes with a novel mutation of the CP gene, which suggests that increased awareness should be highlighted in this disorder as diabetes is an important typical symptom.

High level of complement factor Ba within first prenatal test of gestation increases the risk of subsequent gestational diabetes: a propensity score-matched study.

OBJECTIVE: This study was to assess the alteration of circulating complement factor Ba (CFBa) within 11 to 17 weeks of gestation and its association with subsequent gestational diabetes mellitus (GDM) and its delivery outcome. METHODS: Biochemical parameters and blood samples were collected from 399 pregnant women within 11 to 17 weeks of gestation. At 24 to 28 weeks of gestation, all participants underwent 75-g oral glucose tolerance test and were assigned to GDM group (n = 80) and normal control group (n = 319). Perinatal data were collected after delivery. A propensity score-matched (PSM) analysis was performed to reduce the impact of confounding factors on glucose metabolism during pregnancy between the two groups. RESULTS: Two groups of 74 well-matched patients who maintained balance in terms of baseline characteristics. The levels of CFBa in pregnant women who later developed GDM were significantly higher than those in healthy pregnant women [0.4(0.1-0.8) vs. 0.2(0.2-0.3), p = 0.024]. Logistic regression analysis results confirmed that the level of CFBa was an independent impact factor for the occurrence of GDM (OR = 1.57, 95% CI: 1.118-2.210, p = 0.009). Further grouping according to the median level of CFBa, it was found that the incidence of GDM in category two (>0.23 ng/ml, n = 74) was markedly higher than that in the first category (≤0.23 ng/ml, n = 74) (p = 0.021). CONCLUSIONS: High level of the CFBa within 11 to 17 weeks of gestation increases the risk of subsequent GDM, and maybe a biomarker for predicting GDM.

The role of complement factor H in gestational diabetes mellitus and pregnancy.

BACKGROUND: Complement factor H (CFH) has been found to be associated with insulin resistance. This study assessed the correlation between CFH and other clinical parameters, and determined whether CFH played a role in gestational diabetes mellitus (GDM) and adverse pregnancy outcomes. METHODS: A total of 397 pregnant women were included for analysis in this nested case-control study. Clinical parameters and serum were collected within the 11-17th gestational age at the first prenatal visit. At 24-28 weeks of gestation, a 75 g oral glucose tolerance test was performed and subjects were divided into a GDM (n = 80) and a non-GDM control group (n = 317). The delivery data were also followed. The serum CFH level was assayed by ELISA. RESULTS: CFH was higher in GDM than in non-GDM controls (280.02 [58.60] vs. 264.20 [68.77]; P = 0.014). CFH level was moderately associated with pre-pregnancy body mass index (BMI), BMI and total triglycerides (TG), and slightly associated with gestational age, low density lipoprotein cholesterol (LDL-C), total cholesterol (TC) in GDM and non-GDM (all P <  0.05). Moreover, CFH level was moderately correlated with alkaline phosphatase (ALP) and slightly correlated with age, uric acid (UA) and total bilirubin (TB) in non-GDM (all P <  0.05). After adjustment for clinical confounding factors, BMI, TG, gestational age, ALP, TB, age and UA were independent risk factors for log10 CFH levels (all P <  0.05) in all subjects. In addition, overweight or obese pregnant women, women with hypertriglyceridemia and women in the second trimester had significantly higher CFH levels than normal weight and underweight group (P <  0.001), the non-hypertriglyceridemia group (P <  0.001) and women in the first trimester group (P < 0.05) in all pregnant women respectively. Following binary logistic regression, CFH was not independently associated with GDM and related pregnant outcomes. CONCLUSIONS: The CFH in 11-17th weeks of gestation might be affected by many factors, including BMI, TG, gestational age, ALP, TB, age and UA. CFH was not an independent risk factor for GDM and avderse pregnancy outcomes.

Serum Glycated Albumin Levels Are Affected by Alcohol in Men of the Jinuo Ethnic Group in China.

AIM: To investigate the effects of alcohol on serum glycated albumin (GA) levels in Chinese men. METHODS: A total of 2314 male subjects from the Jinuo ethnic group in China were enrolled. Of these, 986 subjects drank alcohol frequently and 404 subjects did not. Lifestyle information was gathered by using a questionnaire, and measurements of blood pressure, body mass index, blood glucose level, liver function, and kidney function were collected. GA was measured by using an enzymatic method. Frequent drinking was defined as a history of drinking ethanol > 80 g/d within the past two weeks. Nondrinking was defined as no alcohol consumption in the past three months. Subjects with an alcohol intake between 0 and 80 g/d in the past two weeks were included in the drinking-occasionally group. Analysis of variance (ANOVA), correlation analysis, and linear regression were used to evaluate the effects of drinking on serum GA levels. Decision tree regression (DTR) algorithm was used to evaluate the effect of features (variables) on GA levels. RESULTS: We found that male subjects who drank frequently had significantly lower serum GA levels than subjects who did not drink (13.0 ± 1.7 vs. 14.1 ± 3.7, p < 0.05). Spearman's correlation analysis calculated a coefficient of -0.152 between drinking and GA (p < 0.005). Linear regression established that drinking was an independent predictor for GA levels with a standardized regression coefficient of -0.144 (p < 0.05). Decision tree regression showed that the effect of drinking on GA levels (0.0283) is five times higher than that of smoking (0.0057). CONCLUSIONS: Frequent alcohol consumption could result in decreased GA levels in men of the Jinuo ethnic group in China.

Evaluation of Psychosocial Pathways to Family Adaptation of Chinese Patients With Liver Cancer Using the McCubbin's Family Resilience Model.

Background and Aim: With the prolonged survival time of patients with liver cancer, these families may face tremendous pressure and development dilemmas that can easily lead to family adaptation crises. Correspondingly, family adaptation crises adversely affect the quality of life of patients and family members. Basing on McCubbin's resilience model of family stress, adjustment, and adaptation, and considering the key factors affecting family resilience based on a review of literature, this study involved a construction of a family adaptation influencing factors model in Chinese liver cancer patients, which was then verified and revised. Methods: This cross-sectional study was conducted between August and December 2020. Using convenience sampling, we selected 265 liver cancer families from the liver tumor center of a teaching hospital affiliated with a university in Shanghai, China. Data from 252 patients with liver cancer and their caregivers were used to identify the factors and pathways associated with family adaptation. The relationships were modeled using structural equations. Results: A total of 265 liver cancer families participated in the survey, and 252 valid questionnaires were returned, with a response rate of 95.09%. The pathway regression coefficients of six factors (family burden, individual resilience, family problem-solving and coping, inner family support, outer family social support, and family function) in the model were found to be statistically significant (P < 0.05), indicating that all of them were significantly associated with family adaptation. Among them, inner family support, outer family social support, and family function were direct influencing factors, while the others were indirect. The path coefficients of the total effect of the determinants on family adaptation were as follows (from largest to smallest): individual resilience (0.562), family function (0.483), outer family social support (0.345), family burden (-0.300), inner family support (0.293), family problem-solving and coping (0.127). Conclusions: Our findings suggest that clinical nurses should not only pay particular attention to direct influencing factors, develop strategies to strengthen the overall family function, encourage patients and caregivers to utilize inner family and outer family social support, but should also consider indirect influence factors, focus on the vital role of the individual, and promote patients' and caregivers' personal and family coping ability.

A Hybrid Ensemble Algorithm Combining AdaBoost and Genetic Algorithm for Cancer Classification with Gene Expression Data.

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM, and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.