Blood magnesium level and risk of hepatocellular carcinoma in a prospective liver cirrhosis cohort.

BACKGROUND: Higher magnesium intake was linked to lower risk of hepatocellular carcinoma (HCC). However, the relationship between blood magnesium level and HCC has not been fully characterized, especially among liver cirrhosis patients who are at higher risk for HCC. METHODS: In the Mass General Brigham Biobank, we developed a new prospective cohort of 1,460 liver cirrhosis patients without liver cancer history using the validated International Classification of Diseases codes. We used Cox proportional hazard models to generate hazard ratios (HRs) with 95% confidence intervals (CIs) for incident HCC, and used generalized estimating equations to compare changes in liver biomarkers according to baseline blood magnesium, adjusting for age, sex, race, lifestyles, body mass index, type 2 diabetes, model for end-stage liver disease score, and hepatitis infection. RESULTS: During a median follow-up period of 4.26 years, 109 developed HCC. Magnesium deficiency (<1.70mg/dL; N = 158) was associated with a higher risk of HCC (HR =1.88; 95%CI:1.10-3.22) compared to magnesium sufficiency. This association remained robust in the 1-year lag analysis (HR=2.19; 95%CI:1.12-4.29) and in sensitivity analysis excluding patients with alcoholic liver disease (HR=2.26; 95%CI:1.16-4.42). Magnesium in the lowest quartile was associated with a faster increase in alanine transaminase (ß=3.74; 95%CI:0.51-6.97), direct bilirubin (ß=0.18; 95%CI:0.01-0.35), and total bilirubin (ß=0.20; 95%CI:0.02-0.37), compared to the highest quartile. CONCLUSIONS: Lower blood magnesium level is associated with higher HCC risk and unfavorable liver biomarkers changes. IMPACT: If confirmed, our findings may potentially enable better identification of high-risk patients for HCC and inform better management strategies for liver cirrhosis.

Pre-diagnostic plasma inflammatory proteins and risk of hepatocellular carcinoma in three population-based cohort studies from the United States and the United Kingdom.

Previous studies suggest a role for inflammation in hepatocarcinogenesis. However, no study has comprehensively evaluated associations between circulating inflammatory proteins and risk of hepatocellular carcinoma (HCC) among the general population. We conducted a nested case-control study in the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) with 56 pairs of incident HCC cases and controls. External validation was performed in the UK Biobank (34 HCC cases and 48,471 non-HCC controls). Inflammatory protein levels were measured in pre-diagnostic plasma using the Olink® Inflammation Panel. We used conditional logistic regression to calculate multivariable odds ratios (ORs) with 95% confidence intervals (CIs) for associations between a 1-standard deviation (SD) increase in biomarker levels and HCC risk, considering a statistically significant threshold of false discovery rate (FDR)-adjusted p < .05. In the NHS/HPFS, among 70 analyzed proteins with call rates >80%, 15 proteins had significant associations with HCC risk (pFDR < .05). Two proteins (stem cell factor, OR per SD = 0.31, 95% CI = 0.16-0.58; tumor necrosis factor superfamily member 12, OR per SD = 0.51, 95% CI = 0.31-0.85) were inversely associated whereas 13 proteins were positively associated with risk of HCC; positive ORs per SD ranged from 1.73 for interleukin (IL)-10 to 2.35 for C-C motif chemokine-19. A total of 11 proteins were further replicated in the UK Biobank. Seven of the eight selected positively associated proteins also showed positive associations with HCC risk by enzyme-linked immunosorbent assay, with ORs ranging from 1.56 for IL-10 to 2.72 for hepatocyte growth factor. More studies are warranted to further investigate the roles of these observed inflammatory proteins in HCC etiology, early detection, risk stratification, and disease treatment.

The dietary inflammatory index and cardiometabolic parameters in US firefighters.

Introduction: Dietary choices play a crucial role in influencing systemic inflammation and the eventual development of cardiovascular diseases (CVD). The Dietary Inflammatory Index (DII®) is a novel tool designed to assess the inflammatory potential of one's diet. Firefighting, which is characterized by high-stress environments and elevated CVD risk, represents an interesting context for exploring the dietary inflammatory-CVD connection. Aim: This study aims to investigate the associations between Energy-adjusted Dietary Inflammatory Index (E-DII™) scores and cardiometabolic risk parameters among US firefighters. Methods: The study analyzed 413 participants from the Indianapolis Fire Department who took part in a Federal Emergency Management Agency (FEMA)-sponsored Mediterranean diet intervention trial. Thorough medical evaluations, encompassing physical examinations, standard laboratory tests, resting electrocardiograms, and submaximal treadmill exercise testing, were carried out. Participants also completed a detailed food frequency questionnaire to evaluate dietary patterns, and E-DII scores were subsequently computed based on the gathered information. Results: Participants had a mean body mass index (BMI) of 30.0 ± 4.5 kg/m2 and an average body fat percentage of 28.1 ± 6.6%. Regression analyses, adjusted for sex, BMI, maximal oxygen consumption (VO2 max), max metabolic equivalents (METS), age, and body fat percentage, revealed significant associations between high vs. low E-DII scores and total cholesterol (ß = 10.37, p = 0.04). When comparing low Vs median E-DII scores there is an increase in glucose (ß = 0.91, p = 0.72) and total cholesterol (ß = 5.51, p = 0.26). Conclusion: Our findings support an association between higher E-DII scores and increasing adiposity, as well as worse lipid profiles.

Association of Ultra-processed Food and Unprocessed or Minimally Processed Food Consumption With Bowel Habits Among U.S. Adults.

BACKGROUND & AIMS: Ultra-processed foods (UPFs) may have a negative impact on bowel habits. We aimed to assess the association between UPF and unprocessed or minimally processed food (MPF) intake and bowel habits among adults in the United States (U.S.). METHODS: We performed a cross-sectional study using data from the National Health and Nutrition Examination Survey (2005-2010). We used two 24-hour dietary recalls and, based on the Nova classification, calculated intakes of UPFs and MPFs. Constipation and diarrhea were defined using the Bristol Stool Form Scale and stool frequency. We performed survey-weighted logistic regression and substitution analysis to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Among 12,716 U.S. adults, there were 1290 cases of constipation and 1067 cases of diarrhea. Median UPF and MPF intakes were 26.5% and 66.2% of total grams per day, respectively. Greater UPF consumption (in % gram/d) was associated with higher odds of constipation (adjusted OR [aORQ4 vs Q1], 2.20; 95% CI, 1.76-2.74) (Ptrend < .001) but not diarrhea (aORQ4 vs Q1, 0.82; 95% CI, 0.62-1.09) (Ptrend = .12). Increased MPF consumption was associated with lower odds of constipation (aORQ4 vs Q1, 0.46; 95% CI, 0.370-0.57) (Ptrend < .001). Associations with constipation were attenuated after adjusting for diet quality (aORQ4 vs Q1, UPF, 1.53; MPF, 0.69). Substituting 10% of UPF intake with an equivalent proportion of MPFs was associated with lower odds of constipation (aOR, 0.90; 95% CI, 0.87-0.93). CONCLUSIONS: UPF intake was associated with higher odds of constipation, whereas the odds were lower with greater MPF consumption. The effect of food processing on bowel habits was independent of diet quality.

From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning.

The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis's 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model's classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model's performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model's generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.

Dietary inflammatory index after liver transplantation: Associated effects and long-term outcomes.

BACKGROUND & AIMS: We examined the dietary inflammatory potential in patients who underwent liver transplantation (LTx), associated factors and its relationship with clinical outcomes ten years after the initial evaluation. METHODS: Dietary Inflammatory Index (DII®) scores were generated from data derived from the 24-h recall in 108 patients. RESULTS: Patients with higher DII scores (highest tertile), indicating a pro-inflammatory diet, had significantly higher serum LDL cholesterol (108.0 vs 78.2 mg/dL, p = <0.01) at the initial evaluation. However, DII scores did not significantly predict the occurrence of clinical outcomes after ten years of follow-up. Patient age was predictive of neoplasia (OR:1.05 95% CI:1.00-1.11; p = 0.03). Higher BMI at the initial evaluation was associated with steatosis (OR:1.51; 95% CI:1.29-1.77; p < 0.01), and smoking history was associated with the occurrence of cardiovascular events (OR:7.71; 95% CI:1.53-38.79; p = 0.01). CONCLUSIONS: A pro-inflammatory diet was associated with higher serum LDL cholesterol in the initial evaluation but may not be strongly related to clinical outcomes during long-term follow-up.

Pre-diagnostic plasma proteomics profile for hepatocellular carcinoma.

OBJECTIVE: Proteomics may discover pathophysiological changes related to hepatocellular carcinoma (HCC), an aggressive and lethal type of cancer with low sensitivity for early-stage diagnosis. DESIGN: We measured 1,305 pre-diagnostic (median 12.7 years) SOMAscan proteins from 54 pairs of healthy individuals who subsequently developed HCC and matched non-HCC controls from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). Candidate proteins were validated in the independent, prospective UK Biobank Pharma Proteomics Project (UKB-PPP). RESULTS: In NHS/HPFS, we identified 56 elevated proteins in HCC with absolute fold-change >1.2 and Wald test P < .05 in conditional logistic regression analysis. Ingenuity Pathway Analysis identified enrichment of pathways associated with cell viability, adhesion, proteolysis, apoptosis, and inflammatory response. Four proteins, chitinase-3-like protein 1, growth/differentiation factor 15, interleukin-1 receptor antagonist protein, and E-selectin, showed strong positive associations with HCC and were thus validated by ELISA (odds ratio ranged 2.48-14.7, all P < .05) in the NHS/HPFS and by Olink platform (hazard ratio ranged 1.90-3.93, all P < .05) in the UKB-PPP. Adding these four proteins to a logistic regression model of traditional HCC risk factors increased the area under the curve (AUC) from 0.67 to 0.87 in the NHS/HPFS. Consistently, model AUC was 0.88 for HCC risk prediction in the UKB-PPP. CONCLUSION: However, the limited number of HCC cases in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

Association of a dietary inflammatory index with cardiometabolic, endocrine, liver, renal and bones biomarkers: cross-sectional analysis of the UK Biobank study.

BACKGROUND AND AIMS: Research into the relationship between an Energy-adjusted Diet-Inflammatory Index (E-DII) and a wider health-related biomarkers profile is limited. Much of the existing evidence centers on traditional metabolic biomarkers in populations with chronic diseases, with scarce data on healthy individuals. Thus, this study aims to investigate the association between an E-DII score and 30 biomarkers spanning metabolic health, endocrine, bone health, liver function, cardiovascular, and renal functions, in healthy individuals. METHODS AND RESULTS: 66,978 healthy UK Biobank participants, the overall mean age was 55.3 (7.9) years were included in this cross-sectional study. E-DII scores, based on 18 food parameters, were categorised as anti-inflammatory (E-DII < -1), neutral (-1 to 1), and pro-inflammatory (>1). Regression analyses, adjusted for confounding factors, were conducted to investigate the association of 30 biomarkers with E-DII. Compared to those with an anti-inflammatory diet, individuals with a pro-inflammatory diet had increased levels of 16 biomarkers, including six cardiometabolic, five liver, and four renal markers. The concentration difference ranged from 0.27 SD for creatinine to 0.03 SD for total cholesterol. Conversely, those on a pro-inflammatory diet had decreased concentrations in six biomarkers, including two for endocrine and cardiometabolic. The association range varied from -0.04 for IGF-1 to -0.23 for SHBG. CONCLUSION: This study highlighted that a pro-inflammatory diet was associated with an adverse profile of biomarkers linked to cardiometabolic health, endocrine, liver function, and renal health.

Associations between sleep duration, sleep disturbance and cardiovascular disease biomarkers among adults in the United States.

BACKGROUND: Sleep problems are associated with abnormal cardiovascular biomarkers and an increased risk of cardiovascular diseases (CVDs). However, studies investigating associations between sleep problems and CVD biomarkers have reported conflicting findings. This study examined the associations between sleep problems and CVD biomarkers in the United States. METHODS: Data were from the National Health and Nutrition Examination Survey (NHANES) (2007-2018) and analyses were restricted to adults ≥ 20 years (n = 23,749). CVD biomarkers [C-reactive Protein (CRP), low-density lipoproteins, high-density lipoproteins (HDL), triglycerides, insulin, glycosylated hemoglobin (HbA1c), and fasting blood glucose] were categorized as abnormal or normal using standardized cut-off points. Sleep problems were assessed by sleep duration (short [≤ 6 h], long [≥ 9 h], and recommended [> 6 to < 9 h) and self-reported sleep disturbance (yes, no). Multivariable logistic regression models explored the associations between sleep duration, sleep disturbance, and CVD biomarkers adjusting for sociodemographic characteristics and lifestyle behaviors. RESULTS: The mean sleep duration was 7.1 ± 1.5 h and 25.1% of participants reported sleep disturbances. Compared to participants with the recommended sleep duration, those with short sleep duration had higher odds of abnormal levels of HDL (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI] = 1.05-1.39), CRP (aOR = 3.08, 95% CI = 1.18-8.05), HbA1c (aOR = 1.25, 95% CI = 1.05-1.49), and insulin (aOR = 1.24, 95% CI = 1.03-1.51). Long sleep duration was associated with increased odds of abnormal CRP (aOR = 6.12, 95% CI = 2.19-17.15), HbA1c (aOR = 1.54, 95% CI = 1.09-2.17), and blood glucose levels (aOR = 1.45, 95% CI = 1.07-1.95). Sleep disturbance predicted abnormal triglyceride (aOR = 1.18, 95% CI = 1.01-1.37) and blood glucose levels (aOR = 1.24, 95% CI = 1.04-1.49). CONCLUSION: Short and long sleep durations were positively associated with abnormal CRP, HDL, HbA1c, blood glucose, and insulin levels, while sleep disturbance was associated with abnormal triglyceride and blood glucose levels. Since sleep is a modifiable factor, adopting healthy sleeping habits may create a balanced metabolism and reduce the risk of developing a CVD. Our study may provide insights into the relationship between sleep duration, sleep disturbance, and CVD risk.

GmCYB5-4 inhibit SMV proliferation by targeting P3 protein.

Soybean mosaic virus (SMV) is a potyvirus found worldwide in soybean (Glycine max). GmCYB5-4 is a strong candidate interactor of P3. In this study, we comprehensively analyzed the GmCYB5 family in soybeans, including its distribution on chromosomes, promoter analysis, conserved motifs, phylogenetic analysis, and expression patterns. We cloned the full-length GmCYB5-4 and examined its interaction with P3 in yeast, which was later confirmed using bimolecular fluorescence complementation (BiFc). We silenced GmCYB5-4 using a bean pottle mosaic viris (BPMV) based system to generate SilCYB5-4 tissues, which surprisingly knocked down four isoforms of GmCYB5s for functional characterization. SilCYB5-4 plants were challenged with the SC3 strain to determine its involvement in SMV infection. Silencing GmCYB5-4 increased SMV accumulation, indicating that GmCYB5-4 inhibited SMV proliferation. However, further experiments are needed to elucidate the mechanism underlying the involvement of GmCYB5-4 in SMV infection.

Spectral intelligent detection for aflatoxin B1 via contrastive learning based on Siamese network.

Aflatoxins, harmful substances found in peanuts, corn, and their derivatives, pose significant health risks. Addressing this, the presented research introduces an innovative MSGhostDNN model, merging contrastive learning with multi-scale convolutional networks for precise aflatoxin detection. The method significantly enhances feature discrimination, achieving an impressive 97.87% detection accuracy with a pre-trained model. By applying Grad-CAM, it further refines the model to identify key wavelengths, particularly 416 nm, and focuses on 40 key wavelengths for optimal performance with 97.46% accuracy. The study also incorporates a task dimensionality reduction approach for continuous learning, allowing effective ongoing aflatoxin spectrum monitoring in peanuts and corn. This approach not only boosts aflatoxin detection efficiency but also sets a precedent for rapid online detection of similar toxins, offering a promising solution to mitigate the health risks associated with aflatoxin exposure.

The Development and Evaluation of a Literature-Based Dietary Index for Gut Microbiota.

The aim of the study was to develop and evaluate a novel dietary index for gut microbiota (DI-GM) that captures dietary composition related to gut microbiota profiles. We conducted a literature review of longitudinal studies on the association of diet with gut microbiota in adult populations and extracted those dietary components with evidence of beneficial or unfavorable effects. Dietary recall data from the National Health and Nutrition Examination Survey (NHANES, 2005-2010, n = 3812) were used to compute the DI-GM, and associations with biomarkers of gut microbiota diversity (urinary enterodiol and enterolactone) were examined using linear regression. From a review of 106 articles, 14 foods or nutrients were identified as components of the DI-GM, including fermented dairy, chickpeas, soybean, whole grains, fiber, cranberries, avocados, broccoli, coffee, and green tea as beneficial components, and red meat, processed meat, refined grains, and high-fat diet (≥40% of energy from fat) as unfavorable components. Each component was scored 0 or 1 based on sex-specific median intakes, and scores were summed to develop the overall DI-GM score. In the NHANES, DI-GM scores ranged from 0-13 with a mean of 4.8 (SE = 0.04). Positive associations between DI-GM and urinary enterodiol and enterolactone were observed. The association of the novel DI-GM with markers of gut microbiota diversity demonstrates the potential utility of this index for gut health-related studies.

Identifying a group of factors predicting cognitive impairment among older adults.

BACKGROUND: Cognitive impairment has multiple risk factors spanning several domains, but few studies have evaluated risk factor clusters. We aimed to identify naturally occurring clusters of risk factors of poor cognition among middle-aged and older adults and evaluate associations between measures of cognition and these risk factor clusters. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) III (training dataset, n = 4074) and the NHANES 2011-2014 (validation dataset, n = 2510). Risk factors were selected based on the literature. We used both traditional logistic models and support vector machine methods to construct a composite score of risk factor clusters. We evaluated associations between the risk score and cognitive performance using the logistic model by estimating odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Using the training dataset, we developed a composite risk score that predicted undiagnosed cognitive decline based on ten selected predictive risk factors including age, waist circumference, healthy eating index, race, education, income, physical activity, diabetes, hypercholesterolemia, and annual visit to dentist. The risk score was significantly associated with poor cognitive performance both in the training dataset (OR Tertile 3 verse tertile 1 = 8.15, 95% CI: 5.36-12.4) and validation dataset (OR Tertile 3 verse tertile 1 = 4.31, 95% CI: 2.62-7.08). The area under the receiver operating characteristics curve for the predictive model was 0.74 and 0.77 for crude model and model adjusted for age, sex, and race. CONCLUSION: The model based on selected risk factors may be used to identify high risk individuals with cognitive impairment.

Bacillus sp. alone or combined with salicylic acid inhibited Trichoderma spp. infection on harvested white Hypsizygus marmoreus.

Introduction: White Hypsizygus marmoreus is a popular edible mushroom. It is rich in nutrition and flavor but vulnerable to fungal disease, resulting in nutrient loss and aging. Methods: In this study, the pathogenic fungus Trichoderma spp. BBP-6 and its antagonist Bacillus sp. 1-23 were isolated and identified. The negative effects caused by this pathogen were judged by detecting a series of changes in the infected white H. marmoreus. The effects of Bacillus sp. 1-23 on Trichoderma spp. BBP-6 and the infected white H. marmoreus were detected. The effect of Bacillus sp. 1-23 treatment combined with salicylic acid (SA) was also considered. Results: The results showed that Trichoderma spp. BBP-6 could affect the activities of antioxidant enzymes PAL, POD, CAT, SOD, GR, PPO, and APX to interfere with the stability of the white H. marmoreus antioxidant enzyme system and cause the mushroom severe browning and nutrition loss, as well as general quality deterioration. Bacillus sp. 1-23 could produce chitinase and chitosanase enzymes to inhibit Trichoderma spp. BBP-6 directly. SA reinforced this inhibitory. Bacillus sp. 1-23 alone or combined with SA could help white H. marmoreus from the Trichoderma spp. BBP-6 infection to effectively maintain nutrients, restore and stabilize the antioxidant system, and reduce the production of malondialdehyde, superoxide anion and hydrogen peroxide. Discussion: Thus, such treatments could be considered potential methods to alleviate damage from disease and extend the shelf life of white H. marmoreus.

Association between clusters of antibodies against periodontal microorganisms and Alzheimer disease mortality: Evidence from a nationally representative survey in the USA.

BACKGROUND: Alzheimer disease (AD) has been linked with periodontal microorganisms such as Porphyromonas gingivalis in observational and mechanistic studies. IgG antibodies against periodontal microorganisms which are markers of past and current periodontal infection have been correlated with cognitive impairment. We examined associations between empirically derived groups of 19 IgG antibodies against periodontal microorganisms and AD mortality. METHODS: Individuals participating in the Third National Health and Nutrition Examination Survey (NHANES III) with complete data on IgG titers were followed up between 1988 and December 31, 2019. The outcome was AD mortality, and the main exposures were IgG antibodies against periodontal microorganisms classified into four mutually exclusive groups using cluster analysis. Survey-weighted Cox proportional hazard models were used to evaluate adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for the relationship between clusters and AD mortality. RESULTS: With up to 21 years of follow-up, 160 AD-related deaths were documented. In the multivariable-adjusted model, AD mortality overall was not associated with the Red-Green (aHR 1.18; 95% CI, 0.46-3.07), Yellow-Orange (aHR 1.36; 95% CI, 0.58-3.19), Orange-Blue (aHR 0.63; 95%, CI, 0.33-1.21), and the Orange-Red (aHR 0.79; 95% CI, 0.37-1.70) when the upper tertiles were compared to the bottom tertiles. However, the subgroup of middle-aged individuals in the highest tertile of the Red-Green cluster, but not older individuals, had a 13% higher risk of AD mortality (aHR 1.13; 95% CI, 1.02-1.26) compared with those in the bottom tertile. CONCLUSION: Clusters of IgG antibodies against periodontal microorganisms did not predict AD mortality in this study.

Higher ultra-processed food intake is associated with adverse liver outcomes: a prospective cohort study of UK Biobank participants.

BACKGROUND: Ultra-processed food (UPF) intake has been positively associated with obesity and diabetes. The relationship between UPF intake and liver health has been scarcely studied. OBJECTIVES: We aimed to evaluate the association of UPF intake with risk of adverse liver outcomes including nonalcoholic fatty liver disease (NAFLD), liver fibrosis/cirrhosis, liver cancer, severe liver disease, and serum biomarkers of liver health. METHODS: A total of 173,889 participants aged 40 to 69 y from the UK Biobank were included. UPF intake was defined using 24-h dietary recalls and NOVA classification. Liver outcome data were obtained from cancer registry, in-hospital records, and death registries. Serum biomarkers were measured at baseline. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between UPF and adverse liver outcomes adjusting for demographics, lifestyle factors, body mass index, and diabetes. We used multinomial logistic regression to evaluate associations between UPF and liver function biomarkers. RESULTS: After a median follow-up of 8.9 y, we documented 1108 NAFLD, 350 liver fibrosis/cirrhosis, 134 liver cancer, and 550 severe liver disease cases. Higher UPF intake was associated with increased risk of NAFLD (HRQuartile 4 vs. Quartile 1: 1.43; 95% CI: 1.21, 1.70; Ptrend < 0.001), liver fibrosis/cirrhosis (HR: 1.18; 95% CI: 0.87, 1.59; Ptrend = 0.009), and severe liver disease (HR: 1.50; 95% CI: 1.19, 1.90; Ptrend < 0.001) but not with liver cancer (HR: 1.00; 95% CI: 0.63, 1.58; Ptrend = 0.88). Higher UPF intake was associated with elevated levels of C-reactive protein, alkaline phosphatase, aspartate aminotransferase, γ-glutamyltransferase, and triglycerides and lower cholesterols (all Ptrend < 0.001). CONCLUSIONS: Higher UPF intake is associated with an increased risk of NAFLD, liver fibrosis and cirrhosis, and severe liver disease and adverse levels of multiple clinical biomarkers, suggesting the potential importance of reducing UPF intake to improve liver health.

Multi-Domain Rapid Enhancement Networks for Underwater Images.

Images captured during marine engineering operations suffer from color distortion and low contrast. Underwater image enhancement helps to alleviate these problems. Many deep learning models can infer multi-source data, where images with different perspectives exist from multiple sources. To this end, we propose a multichannel deep convolutional neural network (MDCNN) linked to a VGG that can target multi-source (multi-domain) underwater image enhancement. The designed MDCNN feeds data from different domains into separate channels and implements parameters by linking VGGs, which improves the domain adaptation of the model. In addition, to optimize performance, multi-domain image perception loss functions, multilabel soft edge loss for specific image enhancement tasks, pixel-level loss, and external monitoring loss for edge sharpness preprocessing are proposed. These loss functions are set to effectively enhance the structural and textural similarity of underwater images. A series of qualitative and quantitative experiments demonstrate that our model is superior to the state-of-the-art Shallow UWnet in terms of UIQM, and the performance evaluation conducted on different datasets increased by 0.11 on average.

Higher ultra-processed food intake was positively associated with odds of NAFLD in both US adolescents and adults: A national survey.

BACKGROUND: The effect of ultra-processed foods (UPF) on NAFLD remains unclear. Related evidence for adult NAFLD is limited and no study has yet evaluated UPF's impact on NAFLD in adolescence. METHODS: We used data from the National Health and Nutrition Examination Survey (2017-2018) with 806 adolescents and 2734 adults. UPF intake was estimated using dietary data from two 24-hour dietary recalls. NAFLD was defined by transient elastography. Logistic regression was used to estimate the multivariable OR and 95% CI for associations between UPF and NAFLD with survey weight adjustments. RESULTS: The mean UPF intake was 812 g/d in adolescents and 823 g/d in adults. A total of 12.4% of the adolescents and 35.6% of the adults had NAFLD. Higher UPF intake was associated with higher odds of NAFLD in both adolescents (OR Quintile 5 vs. Quartile 1 = 2.34, 95% CI, 1.01, 5.41; ptrend = 0.15) and adults (OR Quintile 5 vs. Quintile 1 = 1.72, 95% CI, 1.01, 2.93; ptrend = 0.002). In adults, ~68% and 71% of the association between UPF intake and NAFLD was mediated by body mass index and waist circumference (all p-values < 0.001), respectively. The results were similar for adolescents but not statistically significant. A higher UPF intake was associated with lower levels of serum albumin and higher levels of C-reactive protein in adults. CONCLUSIONS: Higher UPF intake was linked to higher NAFLD odds in both adolescents and adults, mainly because of elevated body fatness. If confirmed, reducing UPF intake may help prevent NAFLD in both adolescents and adults.

Daytime napping, nighttime sleeping duration, and risk of hepatocellular carcinoma and liver disease-related mortality.

Background & Aims: Sleep duration has been linked to metabolic dysfunction and chronic inflammation, which may contribute to the development of liver cancer and chronic liver disease (CLD). However, little is known about the relationship between sleep or napping duration and hepatocellular carcinoma (HCC) risk and CLD mortality. Methods: We followed 295,837 individuals in the National Institutes of Health-American Association of Retired Persons (NIH-AARP) Diet and Health Study. We examined the associations of nighttime sleep duration and daytime napping duration with risk of HCC incidence and CLD mortality. Cox proportional hazards regression was used to calculate multivariable hazard ratios (HRs) and 95% confidence intervals (95% CIs). Results: A total of 357 incident HCC cases and 578 CLD deaths were identified after a median follow-up time of 15.5 years. After adjusting for confounder factors, we found U-shaped associations of nighttime sleep duration with the incidence of HCC (HR<5 vs. 7-8 h = 2.00, 95% CI: 1.22-3.26 and HR≥9 vs. 7-8 h = 1.63, 95% CI: 1.04-2.65) and CLD mortality (HR<5 vs. 7-8 h = 1.78, 95% CI: 1.18-2.69 and HR≥9 vs. 7-8 h = 1.91, 95% CI: 1.35-2.70). Daytime napping was associated with higher risk of HCC (HR≥1 vs. non-nappers = 1.46, 95% CI: 1.04-2.06) and higher CLD mortality (HR≥1 h vs. non-nappers = 1.54, 95% CI: 1.18-2.01) compared with no napping. Conclusions: We observed U-shaped associations for nighttime sleeping and risk of HCC and CLD mortality. Additionally, longer daytime napping duration was associated with higher risk of HCC and CLD death. Our study suggests that clinical follow up of individuals at risk for liver cancer or living with a liver disease should include information on nighttime and daytime sleep. Impact and implications: Sleep or napping duration may play a role in the development of liver cancer and chronic liver disease, but little is known about the relationship between them. In addition, abnormal sleep patterns in patients with chronic liver disease may further promote the development of liver disease, creating a vicious cycle. Our study suggests that clinical follow up of individuals at risk for liver cancer or living with a liver disease should include information on nighttime and daytime sleep, as they can be potentially important factors in the development and progression of liver disease.