Circulating vitamin levels mediate the causal relationship between gut microbiota and cholecystitis: a two-step bidirectional Mendelian randomization study

BackgroundThe relationship between gut microbiota and the occurrence of cholecystitis remains unclear. Existing research lacks a clear understanding of how circulating vitamin levels modulate this relationship. Therefore, our study aims to investigate whether circulating vitamin levels mediate the causal relationship between gut microbiota and cholecystitis using a two-step bidirectional Mendelian randomization approach.MethodsIn this study, we initially employed Linkage Disequilibrium Score Regression (LDSC) analysis to assess the genetic correlation of five circulating vitamin level genome-wide association study (GWAS) summary datasets, thereby avoiding potential sample overlap. Subsequently, we conducted a two-step analysis to investigate the causal effects between gut microbiota and cholecystitis. In the second step, we explored the causal relationship between circulating vitamin levels and cholecystitis and identified the mediating role of vitamin D. The primary method used for causal analysis was the inverse variance-weighted approach. We performed additional sensitivity analyses to ensure result robustness, including the cML-MA method and reverse Mendelian randomization (MR) analysis.ResultsAn increment of one standard deviation in RuminococcaceaeUCG003 was associated with a 25% increased risk of cholecystitis (OR = 1.25, 95%CI = 1.01–1.54, p = 0.04), along with a 3% decrease in 25-hydroxyvitamin D levels (OR = 0.97, 95%CI = 0.944–0.998, p = 0.04). However, following the rigorous Bonferroni correction, every one standard deviation decrease in circulating vitamin D levels was associated with a 33% increased risk of cholecystitis (OR = 0.67, 95%CI = 0.49–0.90, p = 0.008, Padjust = 0.04). Thus, the potential link between gut microbiota and cholecystitis risk might be mediated by circulating vitamin D levels (proportion mediated = 5.5%). Sensitivity analyses provided no evidence of pleiotropy.ConclusionOur study results suggest that an elevated abundance of specific gut microbiota is associated with an increased susceptibility to cholecystitis, with the causal relationship being mediated by circulating vitamin D levels. Further large-scale randomized controlled trials are necessary to validate the causal effects of gut microbiota on cholecystitis risk. This study provides novel insights into cholecystitis prevention through the regulation of gut microbiota

Systematic Fusion of Multi-Source Cognitive Networks With Graph Learning - A Study on Fronto-Parietal Network

Cognitive tasks induce fluctuations in the functional connectivity between brain regions which constitute cognitive networks in the human brain. Although several cognitive networks have been identified, consensus still cannot be achieved on the precise borders and distribution of involved brain regions for each network, due to the multifarious use of diverse brain atlases in different studies. To address the problem, the current study proposed a novel approach to generate a fused cognitive network with the optimal performance in discriminating cognitive states by using graph learning, following the synthesization of one cognitive network defined by different brain atlases, and the construction of a hierarchical framework comprised of one main version and other supplementary versions of the specific cognitive network. As a result, the proposed method demonstrated better results compared with other machine learning methods for recognizing cognitive states, which was revealed by analyzing an fMRI dataset related to the mental arithmetic task. Our findings suggest that the fused cognitive network provides the potential to develop new mind decoding approaches

Detecting depression using an ensemble classifier based on Quality of Life scales

Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research, an ensemble binary classifier is proposed to analyse health survey data against ground truth from the SF-20 Quality of Life scales. The classifier aims to improve the performance of machine learning techniques on large datasets and identify depressed cases based on associations between items on the QoL scale and mental illness by increasing predictive performance. On the experimental evaluation on the National Health and Nutrition Examination Survey (NHANES), the classifier demonstrated an F1 score of 0.976 in the prediction, without any incorrectly identified depression instances. Only about 4% of instances had been mistakenly classified into depressed cases, with a significant accuracy of 95.4% comparing to the result from PHQ-9 mental screen inventory. The presented ensemble binary classifier performed comparably better than each baseline algorithm in all measures and all experiments. We trained the ensemble model on the processed NHANES dataset, tested and evaluated the results of its performance against mental screen inventory and discussed the comparable predictions. Finally, we provided future research directions

Role of melatonin in enhancing arbuscular mycorrhizal symbiosis and mitigating cold stress in perennial ryegrass (Lolium perenne L.)

Melatonin is a biomolecule that affects plant development and is involved in protecting plants from environmental stress. However, the mechanisms of melatonin’s impact on arbuscular mycorrhizal (AM) symbiosis and cold tolerance in plants are still unclear. In this research, AM fungi inoculation and exogenous melatonin (MT) were applied to perennial ryegrass (Lolium perenne L.) seedlings alone or in combination to investigate their effect on cold tolerance. The study was conducted in two parts. The initial trial examined two variables, AM inoculation, and cold stress, to investigate the involvement of the AM fungus Rhizophagus irregularis in endogenous melatonin accumulation and the transcriptional levels of its synthesis genes in the root system of perennial ryegrass under cold stress. The subsequent trial was designed as a three-factor analysis, encompassing AM inoculation, cold stress, and melatonin application, to explore the effects of exogenous melatonin application on plant growth, AM symbiosis, antioxidant activity, and protective molecules in perennial ryegrass subjected to cold stress. The results of the study showed that compared to non-mycorrhizal (NM) plants, cold stress promoted an increase in the accumulation of melatonin in the AM-colonized counterparts. Acetylserotonin methyltransferase (ASMT) catalyzed the final enzymatic reaction in melatonin production. Melatonin accumulation was associated with the level of expression of the genes, LpASMT1 and LpASMT3. Treatment with melatonin can improve the colonization of AM fungi in plants. Simultaneous utilization of AM inoculation and melatonin treatment enhanced the growth, antioxidant activity, and phenylalanine ammonia-lyase (PAL) activity, while simultaneously reducing polyphenol oxidase (PPO) activity and altering osmotic regulation in the roots. These effects are expected to aid in the mitigation of cold stress in Lolium perenne. Overall, melatonin treatment would help Lolium perenne to improve growth by promoting AM symbiosis, improving the accumulation of protective molecules, and triggering in antioxidant activity under cold stress

Prognostic values of ALDOB expression and 18F-FDG PET/CT in hepatocellular carcinoma

PurposeThe glycolytic enzyme fructose 1,6-bisphosphate aldolase B (ALDOB) is aberrantly expressed and impacts the prognosis in hepatocellular carcinoma (HCC). Hepatic ALDOB loss leads to paradoxical upregulation of glucose metabolism, favoring hepatocellular carcinogenesis. Nevertheless, the relationship between ALDOB expression and 18F-fluorodeoxyglucose (18F-FDG) uptake, and their effects on HCC prognosis remain unclear. We evaluated whether ALDOB expression is associated with 18F-FDG uptake and their impacts on HCC prognosis prediction.MethodsChanges in ALDOB expression levels and the prognostic values in HCC were analyzed using data from The Cancer Genome Atlas (TCGA) database. Ultimately, 34 patients with HCC who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) preoperatively were enrolled in this retrospective study. ALDOB expression was determined using immunohistochemistry (IHC) staining, and the maximum standardized uptake value (SUVmax) of HCC was calculated from the 18F-FDG PET/CT scans. The relationship between ALDOB expression and SUVmax was examined, and their impacts on overall survival were evaluated using Cox proportional hazards models and Kaplan–Meier survival analysis. ALDOB overexpression in HUH7 and 7721 cells was used to analyze its role in tumor metabolism.ResultsAccording to TCGA database, the ALDOB mRNA level was downregulated in HCC compared to normal tissue, and significantly shortened overall survival in HCC patients. ALDOB protein expression was similarly decreased in IHC findings in HCC than that in adjacent normal tissues (P<0.05) and was significantly associated with tumor size (P<0.001), high tumor-node-metastasis stage (P=0.022), and elevated SUVmax (P=0.009). ALDOB expression in HCC was inversely correlated with SUVmax (r=-0.454; P=0.012), and the optimal SUVmax cutoff value for predicting its expression was 4.15. Prognostically, low ALDOB expression or SUVmax ≥3.9 indicated shorter overall survival time in HCC. Moreover, COX regression analysis suggested high SUVmax as an independent prognostic risk factor for HCC (P=0.036). HCC patients with negative ALDOB expression and positive SUVmax (≥3.9) were correlated with worse prognosis. ALDOB overexpression in HCC cells significantly decreases 18F-FDG uptake and lactate production.ConclusionSUVmax in HCC patients is inversely correlated with ALDOB expression, and 18F-FDG PET/CT may be useful for ALDOB status prediction. The combined use of ALDOB expression and 18F-FDG PET/CT data can provide additional information on disease prognosis in HCC patients

Discovery of potential biomarkers for osteoporosis using LC/GC−MS metabolomic methods

PurposeFor early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC−MS in a Chinese elderly population to find possible diagnostic biomarkers.MethodsA total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC−MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways.ResultsWe screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women.ConclusionThese findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals