Prevalence and influencing factors of malnutrition in stroke patients with bulbar paralysis: a cross-sectional study in China.

Background: Although malnutrition has been shown to influence the clinical outcomes of Stroke Patients with Bulbar Paralysis (SPBP), the prevalence and influencing factors have yet to be uncovered. Objective: This study aims to assess the current prevalence and factors associated with malnutrition in SPBP. Methods: A multicenter cross-sectional investigation was conducted among SPBP in China from 2019 to 2021. Information was collected on basic information, health condition, diagnosis, treatment, neurological function, activities of daily living, swallowing function, and nutritional status. A multivariable logistic regression model was used to identify the factors that influenced nutritional status. ROC analysis was used to assess the predictive value of each independent influencing factor and the logit model. Results: In total, 774 SPBP were enrolled, and the prevalence of malnutrition was 60.59%. Pulmonary infection [aOR:2.849, 95%CI: (1.426, 5.691)], hemoglobin [aOR: 0.932, 95%CI: (0.875, 0.982)], serum albumin [aOR: 0.904, 95%CI: (0.871, 0.938)], total protein [aOR: 0.891, 95%CI: (0.819, 0.969)], prealbumin [aOR: 0.962, 95%CI: (0.932, 0.993)], and National Institute of Health Stroke Scale (NIHSS) scores [aOR: 1.228, 95%CI: (1.054, 1.431)] were independent factors associated with malnutrition in SPBP. ROC analysis revealed that the logit model had the best predictive value [area under the curve: 0.874, 95% CI: (0.812, 0.936); specificity: 83.4%; sensitivity: 79.3%; p < 0.05]. Subgroup analysis showed that the nutritional status in dysphagic SPBP was additionally influenced by swallowing function and nutrition support mode. Conclusion: The prevalence of malnutrition in SPBP was 60.59%. Pulmonary infection, hemoglobin level, and NIHSS score were the independent factors associated with malnutrition. Swallowing function and nutrition support mode were the factors associated with malnutrition in dysphagic SPBP.

Analysis of risk factors and development of predictive model for malnutrition in patients with traumatic brain injury.

Malnutrition is a highly prevalent complication in patients with traumatic brain injury (TBI), and it is closely related to the prognosis of patients. Accurate identification of patients at high risk of malnutrition is essential. Therefore, we analyzed the risk factors of malnutrition in patients with TBI and developed a model to predict the risk of malnutrition. A retrospective collection of 345 patients with TBI, and they were divided into malnutrition and comparison groups according to the occurrence of malnutrition. Univariate correlation and multifactor logistic regression analyses were performed to determine patients' malnutrition risk factors. We used univariate and logistic regression (forward stepwise method) analyses to identify significant predictors associated with malnutrition in patients with TBI and developed a predictive model for malnutrition prediction. The model's discrimination, calibration, and clinical utility were evaluated using the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). A total of 216 patients (62.6%) developed malnutrition. Multifactorial logistic regression analysis showed that pulmonary infection, urinary tract infection, dysphagia, application of NGT, GCS score ≤ 8, and low ADL score were independent risk factors for malnutrition in patients with TBI (P < 0.05). The area under the curve of the model was 0.947. Calibration plots showed good discrimination of model calibration. DCA showed that the column line plot models were all clinically meaningful when nutritional interventions were performed over a considerable range of threshold probabilities (0-0.98). Malnutrition is widespread in patients with TBI, and the nomogram is a good predictor of whether patients develop malnutrition.

Excavating important nodes in complex networks based on the heat conduction model.

Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node's neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

How stroke-related dysphagia relates to quality of life: the mediating role of nutritional status and psychological disorders, and the moderating effect of enteral nutrition mode.

Background: Although stroke-related dysphagia has been shown to influence quality of life (QOL), the underlying mechanisms have yet to be uncovered. Objective: This study aims to investigate the mediating role of nutritional status and psychological disorders in the relationship between stroke-related dysphagia and QOL in stroke patients and explore the moderating effect of enteral nutrition mode. Methods: In 2022, A questionnaire survey using stratified random sampling was conducted on 5,322 stroke patients with dysphagia, including Functional Oral Intake Scale (FOIS), Swallowing Quality of Life Questionnaire, Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder-7 (GAD-7) to assess dysphagia, QOL and psychological disorders, respectively, for each participant. Records of serum albumin, Hemoglobin, Total serum protein, serum prealbumin and Body mass index were enrolled to assess nutritional status. Results: FOIS demonstrated a significant positive predictive effect on QOL. Nutritional status and psychological disorders (PHQ-9 and GAD-7) mediated the relationship between FOIS and QOL. Nutritional status-psychological disorders showed a chain mediation effect in the relationship between FOIS and QOL. The moderating effect of enteral nutrition mode was observed. Conclusion: The mediating role of nutritional status and psychological disorders with moderating effect of enteral nutrition mode in the relationship between dysphagia and QOL in stroke patients was found.

Effect of Intermittent Oro-Esophageal Tube Feeding in Bulbar Palsy After Ischemic Stroke: A Randomized Controlled Study.

BACKGROUND: Nasogastric tube feeding (NG) has been widely used in patients with bulbar palsy after ischemic stroke but is associated with a significant risk of complications including malnutrition and pneumonia. Intermittent oro-esophageal tube feeding (IOE) can help alleviate these concerns. This study explored the clinical effect of IOE versus NG on nutritional status, swallowing function, stroke-associated pneumonia, and depression in patients with bulbar palsy after ischemic stroke. METHODS: This randomized controlled study included 148 patients with bulbar palsy after ischemic stroke who underwent routine treatment and swallowing rehabilitation training in the Department of Rehabilitation Medicine between July 2017 and July 2019 in China. The participants were randomly divided into the IOE group (n=74) and NG group (n=74) with IOE and NG as nutritional supports, respectively. The primary outcome was nutritional status including (1) body mass index (kg/m2), (2) serum ALB (albumin, g/L), and (3) PA (prealbumin, mg/L). The secondary outcomes were (1) swallowing function including (i) Functional Oral Intake Scale (FOIS) and (ii) Penetration-Aspiration Scale, (2) pneumonia, (3) depression, and (4) adverse events. Statistical analyses for continuous outcomes were performed using t test, Mann-Whitney U test and Wilcoxon signed-rank test and categorical variables using χ2 test. SPSS 21.0 was used for all analysis. RESULTS: There were no significant baseline differences between the 2 groups. After the treatment, the IOE group demonstrated significantly better results compared with the NG group in ALB ([32.71±0.94] versus [32.28±0.81] g/L; P=0.003), PA ([278.15±13.81] versus [270.31±15.08] mg/L; P=0.001], body mass index ([19.77±1.03] versus [19.41±0.98] kg/m2; P=0.002], FOIS (P<0.001), Penetration-Aspiration Scale (P<0.001), stroke-associated pneumonia ([1, 4.05%] versus [26, 35.14%]; P<0.001), depression ([1, 1.35%] versus [44, 59.46%]; P<0.001) and overall less adverse events (reflux, fever, discomfort in the throat; P<0.001). CONCLUSIONS: In patients with dysphagia with bulbar palsy after ischemic stroke who received routine treatment and swallowing rehabilitation training, IOE is safer and more conducive to the improvement of nutritional status, swallowing function, stroke-associated pneumonia, and depression than NG. REGISTRATION: URL: https://www.chictr.org.cn; Unique identifier: ChiCTR-INC-17011741.

Predicting potential microbe-disease associations based on multi-source features and deep learning.

Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.

Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks.

BACKGROUND: More and more evidence showed that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human sophisticated diseases. Therefore, predicting human lncRNA-disease associations is a challenging and urgently task in bioinformatics to research of human sophisticated diseases. RESULTS: In the work, a global network-based computational framework called as LRWRHLDA were proposed which is a universal network-based method. Firstly, four isomorphic networks include lncRNA similarity network, disease similarity network, gene similarity network and miRNA similarity network were constructed. And then, six heterogeneous networks include known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations network were applied to design a multi-layer network. Finally, the Laplace normalized random walk with restart algorithm in this global network is suggested to predict the relationship between lncRNAs and diseases. CONCLUSIONS: The ten-fold cross validation is used to evaluate the performance of LRWRHLDA. As a result, LRWRHLDA achieves an AUC of 0.98402, which is higher than other compared methods. Furthermore, LRWRHLDA can predict isolated disease-related lnRNA (isolated lnRNA related disease). The results for colorectal cancer, lung adenocarcinoma, stomach cancer and breast cancer have been verified by other researches. The case studies indicated that our method is effective.

[Acupuncture adjuvant treatment for dysphagia in patients with Parkinson's disease: a randomized controlled trial].

OBJECTIVE: To observe the effect of acupuncture on swallowing function and nutritional status of patients with Parkinson's disease (PD) dysphagia. METHODS: A total of 56 patients with PD dysphagia were randomly divided into an observation group and a control group, 28 cases in each one. Both groups were given conventional treatment and swallowing function rehabilitation training.On the basis, the observation group was treated with acupuncture (filiform needling and tongue picking acupuncture). The acupoints of filiform needling were Lianquan (CV 23), Shanglianquan (Extra), Yifeng (TE 17), etc.; and the tongue picking acupuncture was applied at Jinjin (EX-HN 12), Yuye (EX-HN 13) and posterior pharyngeal wall. Rehabilitation training and acupuncture were performed once a day for 5 consecutive days a week, 2 weeks as a course of treatment, 3 courses in total. The swallowing function [oral transit time (OTT), swallowing response time (SRT), pharyngeal transit time (PTT), laryngeal closure duration (LCD)] and nutritional indexes [body mass index (BMI), serum albumin (ALB), serum prealbumin (PA), hemoglobin (Hb)] in the two groups were evaluated before and after treatment, and the clinical effects were compared. RESULTS: After treatment, the paste and liquid OTT, SRT, PTT, LCD in the two groups were shorter than those before treatment (P<0.05), and the observation group was shorter than the control group (P<0.05). After treatment, the nutritional indexes (BMI, ALB, PA, Hb) in the two groups were higher than those before treatment (P<0.05), and the observation group was higher than the control group (P<0.05). The total effective rate of the observation group was 92.9% (26/28), which was better than 67.9% (19/28) of the control group (P<0.05). CONCLUSION: Acupuncture-assisted rehabilitation training can improve the swallowing function and nutritional status in patients with Parkinson's disease swallowing disorders.

How demographic and clinical characteristics contribute to the recovery of post-stroke dysphagia?

ABSTRACT: According to the analysis to find out how demographic and clinical characteristics influent the dysphagia outcome after stroke, furthermore, giving some insights to clinical treatment.One hundred eighty post-stroke dysphagia (PSD) patients were enrolled in this retrospective study at the stroke rehabilitation department. The outcome measurements are beside water swallow test at discharge and length of stay at hospital. Twenty-five demographic and clinical variables were collected for this study. Logistic regression and multilinear regression were utilized to estimate models to identify the risk and protect predictors of PSD outcome.Mouth-opening degree, drooling severity scale (DSS) level, mini-mental state exam (MMSE) level, Barthel index and Berg balance scale were significant different between recovered and unrecovered group. Type of stroke, MMSE degree, DSS and hemoglobin level shown significant predictive value for PSD outcome in logistic regression. In addition, obstructive sleep apnea (OSA) and DSS degree were important risk factors for PSD outcome. Gender, body mass index, drinking, hypertension, recurrent stroke, water swallow test level on admission, Berg balance scale, DSS and days between onset to admission shown significant predictive value for length of stay of PSD patients.PSD outcome was influenced by type of stroke, MMSE degree, DSS and hemoglobin level significantly and obstructive sleep apnea act as an important risk role for PSD recovery.