Diabetes risk reduction diet and risk of liver cancer and chronic liver disease mortality: A prospective cohort study.

BACKGROUND: We aimed to prospectively evaluate the association between a diabetes risk reduction diet (DRRD) score and the risk of liver cancer development and chronic liver disease-specific mortality. METHODS: We included 98,786 postmenopausal women from the Women's Health Initiative-Observational Study and the usual diet arm of the Diet Modification trial. The DRRD score was derived from eight factors: high intakes of dietary fiber, coffee, nuts, polyunsaturated fatty acids, low intakes of red and processed meat, foods with high glycemic index, sugar-sweetened beverages (SSBs), and trans fat based on a validated Food-Frequency Questionnaire administered at baseline (1993-1998). Multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for liver cancer incidence and chronic liver disease mortality were estimated using Cox proportional hazards regression models. RESULTS AND CONCLUSION: After a median follow-up of 22.0 years, 216 incident liver cancer cases and 153 chronic liver disease deaths were confirmed. A higher DRRD score was significantly associated with a reduced risk of developing liver cancer (HRTertile 3 vs. Tertile 1 = 0.69; 95% CI: 0.49-0.97; Ptrend = 0.03) and chronic liver disease mortality (HRT3 vs. T1 = 0.54; 95% CI: 0.35-0.82; Ptrend = 0.003). We further found inverse associations with dietary fiber and coffee, and positive associations with dietary glycemic index, SSBs, and trans fat. A higher DRRD score was associated with reduced risk of developing liver cancer and chronic liver disease mortality among postmenopausal women.

Genome-wide analysis of the soybean eEF gene family and its involvement in virus resistance.

Eukaryotic elongation factors (eEFs) are protein factors that mediate the extension of peptide chain, among which eukaryotic elongation factor 1 alpha (eEF1A) is one of the most abundant protein synthesis factors. Previously we showed that the P3 protein of Soybean mosaic virus (SMV), one of the most destructive and successful viral pathogens of soybean, targets a component of the soybean translation elongation complex to facilitate its pathogenesis. Here, we conducted a systematic analyses of the soybean eEF (GmeEF) gene family in soybean and examinedits role in virus resistance. In this study, GmeEF family members were identified and characterized based on sequence analysis. The 42 members, which were unevenly distributed across the 15 chromosomes, were renamed according to their chromosomal locations. The GmeEF members were further divided into 12 subgroups based on conserved motif, gene structure, and phylogenetic analyses. Analysis of the promoter regions showed conspicuous presence of myelocytomatosis (MYC) and ethylene-responsive (ERE) cis-acting elements, which are typically involved in drought and phytohormone response, respectively, and thereby in plant stress response signaling. Transcriptome data showed that the expression of 15 GmeEF gene family members changed significantly in response to SMV infection. To further examine EF1A function in pathogen response, three different Arabidopsis mutants carrying T-DNA insertions in orthologous genes were analyzed for their response to Turnip crinkle virus (TCV) and Cucumber mosaic virus (CMV). Results showed that there was no difference in viral response between the mutants and the wild type plants. This study provides a systematic analysis of the GmeEF gene family through analysis of expression patterns and predicted protein features. Our results lay a foundation for understanding the role of eEF gene in soybean anti-viral response.

Ultra-processed products and risk of liver cancer: A prospective cohort study.

BACKGROUND & AIMS: Several studies have shown positive associations between ultra-processed foods and drinks and cancer risk. However, evidence remains limited for liver cancer. We aimed to evaluate the associations between ultra-processed foods and drinks and liver cancer risk. METHODS: We included 73,119 participants (22,431 Whites, 47,837 Blacks, 2851 other race) from the Southern Community Cohort Study. Ultra-processed products were defined based on the Nova classification using data from a validated food frequency questionnaire and calculated as percentage of daily foods by weight. Incident liver cancer and vital status were ascertained via linkages to state cancer registries and the National Death Index as of December 31, 2019. RESULTS: With a median of 13.9 year's follow-up, we documented 453 incident liver cancer cases. Participants with higher intake of ultra-processed foods had an elevated risk of liver cancer (hazard ratios [HR] Tertile 3 vs. tertile 1 1.69, 95% confidence intervals [CI]: 1.28-2.22; Ptrend<0.001). The subclasses of ultra-processed foods, such as ultra-processed grains and fried potatoes (HR T3 vs. T1 1.29, 95% CI: 1.01-1.65; Ptrend = 0.03), processed protein foods (HR T3 vs. T1 1.49, 95% CI: 1.14-1.94; Ptrend = 0.007) and mixed dishes (HR T3 vs. T1 1.39, 95% CI: 1.09-1.77; Ptrend = 0.01), were positively associated with liver cancer risk. No significant association was found for ultra-processed drinks (HR T3 vs. T1 0.85, 95% CI: 0.67-1.07; Ptrend = 0.16). DISCUSSION: In a prospective cohort with predominantly low-income Southern US adults, we found certain ultra-processed foods were associated with a higher risk of liver cancer. Further studies are needed to confirm our findings.

Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging.

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models-AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2-are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Prediagnostic plasma proteomics profile for hepatocellular carcinoma.

OBJECTIVE: Proteomics may discover pathophysiological changes related to hepatocellular carcinoma, an aggressive and lethal type of cancer with low sensitivity for early stage diagnosis. DESIGN: We measured 1305 prediagnostic (median = 12.7 years) SomaScan proteins from 54 pairs of healthy individuals who subsequently developed hepatocellular carcinoma and matched non-hepatocellular carcinoma control individuals 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 and HPFS, we identified 56 elevated proteins in hepatocellular carcinoma with an absolute fold change of more than 1.2 and a Wald test P value less than .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 hepatocellular carcinoma and were thus validated by enzyme-linked immunosorbent assay (odds ratio = 2.48-14.7, all P < .05) in the NHS and HPFS and by Olink platform (hazard ratio = 1.90-3.93, all P < .05) in the UKB-PPP. Adding these 4 proteins to a logistic regression model of traditional hepatocellular carcinoma risk factors increased the area under the curve from 0.67 to 0.87 in the NHS and HPFS. Consistently, model area under the curve was 0.88 for hepatocellular carcinoma risk prediction in the UKB-PPP. CONCLUSION: However, the limited number of hepatocellular carcinoma patients in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

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.