Adequate nutritional support is crucial in preventing complications and improving outcomes in critically ill patients. Extracorporeal membrane oxygenation (ECMO) is a mode of supportive care for patients with respiratory and/or cardiac failure. ECMO patients frequently exhibit a hypermetabolic state characterized by protein catabolism and insulin resistance, which can lead to malnutrition. Nutritional therapy is a vital component of intensive care, but its optimal administration for ECMO patients is unknown. This case report aims to provide insights into effective nutritional management for critically ill patients undergoing ECMO therapy. The patient was a 72-year-old male with a history of gastric and lung cancer who underwent a lobectomy complicated by bronchopleural fistula, postoperative bleeding, pneumonia, and acute respiratory distress syndrome (ARDS). The patient's nutritional status was assessed indicating a high risk of malnutrition, using the modified Nutrition Risk in the Critically Ill (mNUTRIC) Score. Nutritional support was administered based on the recommendations of European Society for Clinical Nutrition and Metabolism (ESPEN) and the American Society for Parenteral and Enteral Nutrition (ASPEN), with energy requirements set at 25–30 kcal/kg/d and protein requirements set at 1.2–2.0 g/kg/day. The patient received parenteral nutrition until the enteral nutrition target amount was reached, with zinc supplements for wound healing. The study highlights the need for further research on proactive and effective nutritional support for ECMO patients to improve compliance and prognosis.
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Patients with colorectal cancer may experience symptoms such as diarrhea, nausea, and anorexia, during surgery and chemotherapy, which can increase the risk of malnutrition. In addition, dietary habits play a key role in the onset of colorectal cancer; therefore, it is necessary to improve dietary habits to prevent recurrence during treatment after diagnosis. In this study, a clinical nutritionist conducted 4 interviews for patients diagnosed with colorectal cancer and scheduled for colectomy: before surgery, after surgery, 1st chemotherapy, and 2nd chemotherapy, and provided nutrition care for each treatment course to determine its effects on nutrition status and disease prognosis. Significant weight loss but no decrease in muscle mass was observed during treatment. Body fat mass, although not statistically significant, showed a decreasing tendency. The percentage of people who responded ‘yes’ to the below items increased after compared to before receiving nutrition education: ‘I eat meat or eggs more than 5 times a week,’ ‘I eat seafood at least three times a week,’ ‘I eat vegetables at every meal,’ ‘I eat fruits every day,’ and ‘I eat milk or dairy products every day.’ These results indicate that the patients changed their dietary habit from a monotonous eating pattern to a pattern of consuming various food groups after receiving nutrition education. These results suggest that continuous nutrition care by clinical dietitians, according to the patient’s treatment process, can help improve the patient's nutritional status and establish healthy eating habits.
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The present study sought to examine the association between an infant’s anthropometric outcomes with maternal Dietary Inflammatory Index (DII) and Alternate Healthy Eating Index for Pregnancy (AHEI-P) scores during the third trimester of pregnancy. This prospective cohort study was applying 130 pregnant women, at the pregnancy training center in west Tehran, Iran (November 2020 to July 2021). The maternal dietary intake, and body mass index (BMI), and social economic level were evaluated. The data about birth weight, birth height, head circumference, and, gestational age at birth were extracted from each child’s health records. The ultimate sample included 122 (93.8%) pairs of women/newborn children. The participants, mean age was 28.13 ± 4.66 years with gestational age between 28 to 40 weeks and the mean of BMI was 24.62 ± 3.51. Our outcomes, after adjustment for confounding factors, suggested that those newborn infants in the highest quartile of maternal DII score had a significantly lower weight (p < 0.001) and height (p = 0.05), in comparison to those in the lowest quartile, but not head circumference (p = 0.18). Moreover, after adjustment for confounding factors, results suggested that those newborn infants in the First quartile of maternal AHEI-P score had a significantly lower weight (p = 0.018) and, in comparison to those in the higher quartile. It appears that newborn infants with lower maternal DII and higher AHEI-P scores may have a better anthropometric outcome. Further longitudinal and in-depth qualitative and quantitative studies, with a longer-term follow-up, is warranted to support the integrity of our outcomes.
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Hemodialysis (HD) patients face a common problem of malnutrition due to poor appetite. This study aims to verify the appetite alteration model for malnutrition in HD patients through quantitative data and the International Classification of Functioning, Disability, and Health (ICF) framework. This study uses the Mixed Method-Grounded Theory (MM-GT) method to explore various factors and processes affecting malnutrition in HD patients, create a suitable treatment model, and validate it systematically by combining qualitative and quantitative data and procedures. The demographics and medical histories of 14 patients were collected. Based on the theory, the research design is based on expansion and confirmation sequence. The usefulness and cut-off points of the creatinine index (CI) guidelines for malnutrition in HD patients were linked to significant categories of GT and the domain of ICF. The retrospective CIs for 3 months revealed patients with 3 different levels of appetite status at nutrition assessment and 2 levels of uremic removal. In the same way, different levels of dry mouth, functional support, self-efficacy, and self-management were analyzed. Poor appetite, degree of dryness, and degree of taste change negatively affected CI, while self-management, uremic removal, functional support, and self-efficacy positively affected CI. This study identified and validated the essential components of appetite alteration in HD patients. These MM-GT methods can guide the selection of outcome measurements and facilitate the perspective of a holistic approach to self-management and intervention.
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The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40–69 years from the Korea National Health and Nutrition Examination Survey (2013–2018). We set MetS (3–5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = −2.0545] and saturated fatty acid [β = −2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.
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Hepatic encephalopathy (HE) associated with liver failure is accompanied by hyperammonemia, severe inflammation, depression, anxiety, and memory deficits as well as liver injury. Recent studies have focused on the liver-brain-inflammation axis to identify a therapeutic solution for patients with HE. Lipocalin-2 is an inflammation-related glycoprotein that is secreted by various organs and is involved in cellular mechanisms including iron homeostasis, glucose metabolism, cell death, neurite outgrowth, and neurogenesis. In this study, we investigated that the roles of lipocalin-2 both in the brain cortex of mice with HE and in Neuro-2a (N2A) cells. We detected elevated levels of lipocalin-2 both in the plasma and liver in a bile duct ligation mouse model of HE. We confirmed changes in cytokine expression, such as interleukin-1β, cyclooxygenase 2 expression, and iron metabolism related to gene expression through AKT-mediated signaling both in the brain cortex of mice with HE and N2A cells. Our data showed negative effects of hepatic lipocalin-2 on cell survival, iron homeostasis, and neurite outgrowth in N2A cells. Thus, we suggest that regulation of lipocalin-2 in the brain in HE may be a critical therapeutic approach to alleviate neuropathological problems focused on the liver-brain axis.
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