Misalignment between natural light rhythm and modern life activities induces disruption of the circadian rhythm. It is mainly evident that light at night (LAN) interferes with the human endocrine ...system and contributes to the increasing rates of obesity and lipid metabolic disease. Maintaining hepatointestinal circadian homeostasis is vital for improving lipid homeostasis. Melatonin is a chronobiotic substance that plays a main role in stabilizing bodily rhythm and has shown beneficial effects in protecting against obesity. Based on the dual effect of circadian rhythm regulation and antiobesity, we tested the effect of melatonin in mice under constant light exposure. Exposure to 24-h constant light (LL) increased weight and insulin resistance compared with those of the control group (12-h light-12-h dark cycle, LD), and simultaneous supplementation in the melatonin group (LLM) ameliorated this phenotype. Constant light exposure disturbed the expression pattern of a series of transcripts, including lipid metabolism, circadian regulation and nuclear receptors in the liver. Melatonin also showed beneficial effects in improving lipid metabolism and circadian rhythm homeostasis. Furthermore, the LL group had increased absorption and digestion of lipids in the intestine as evidenced by the elevated influx of lipids in the duodenum and decrease in the efflux of lipids in the jejunum. More interestingly, melatonin ameliorated the gut microbiota dysbiosis and improved lipid efflux from the intestine. Thus, these findings offer a novel clue regarding the obesity-promoting effect attributed to LAN and suggest a possibility for obesity therapy by melatonin in which melatonin could ameliorate rhythm disorder and intestinal dysbiosis.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Hyperuricemia is a central risk factor for gout and increases the risk for other chronic diseases, including cardiometabolic disease, kidney disease, and hypertension. Overproduction of urate is one ...of the main reasons for hyperuricemia, and dietary factors including seafoods, meats, and drinking are contributed to the development of it. However, the lack of a suitable animal model for urate metabolism is one of the main reasons for the delay and limitations of hyperuricemia research. Combining evolutionary biological studies and clinical studies, we conclude that chicken is a preferred animal model for hyperuricemia. Thus, we provided chickens a high-protein diet (HPD) to evaluate the changes in the serum urate levels in chickens. In our study, the HPD increased the serum urate level and maintained it at a long-term high level in chickens. Long-term high serum urate levels induced an abnormal chicken claw morphology and the precipitation of monosodium urate (MSU) in joint synovial fluid. In addition, a long-term HPD also decreased the glomerular filtration rate and induced mild renal injury. Most importantly, allopurinol and probenecid displayed the positive effects in decreasing serum urate and then attenuated hyperuricemia in chicken model. These findings provide a novel model for hyperuricemia and a new opportunity to further investigate the effects of long-term hyperuricemia on other metabolic diseases.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Environmental chemicals, which are known to impact offspring health, have become a public concern. Constitutive activated receptor (CAR) is activated by various environmental chemicals and ...participates in xenobiotic metabolism. Here, we described the effects of maternal exposure to the CAR-specific ligand 1,4-bis2-(3,5-dichloropyridyloxy) benzene (TCPOBOP, TC) on offspring health outcomes. Maternal TC exposure exhibited a stronger inhibition of body weight in 3-week-old and 8-week-old first-generation (F1) offspring female mice compared to controls. Further, maternal TC exposure obtained a strong increase in hepatic drug-metabolizing enzyme expression in 3-week-old female mice that persisted into 8-week-old adulthood. Interestingly, we observed distorted intestinal morphological features in 8-week-old F1 female mice in the TC-exposed group. Moreover, maternal TC exposure triggered a loss of intestinal barrier integrity by reducing the expression of intestinal tight junction proteins. Accordingly, maternal exposure to TC down-regulated serum triglyceride levels as well as decreased the expression of intestinal lipid uptake and transport marker genes. Mechanistically, maternal TC exposure activated the intestinal inflammatory response and disrupted the antioxidant system in the offspring female mice, thereby impeding the intestinal absorption of nutrients and seriously threatening offspring health. Altogether, these findings highlight that the effects of maternal TC exposure on offspring toxicity could not be ignored.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing ...decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments. Keywords: E-commerce reviews, Sentiment analysis, BERT, MixMatch, Focal loss
Epidemiological studies have indicated that obesity is an independent risk factor for colitis and that a high-fat diet (HFD) increases the deterioration of colitis-related indicators in mice. ...Melatonin has multiple anti-inflammatory effects, including inhibiting tumor growth and regulating immune defense. However, the mechanism of its activity in ameliorating obesity-promoted colitis is still unclear. This study explored the possibility that melatonin has beneficial functions in HFD-induced dextran sodium sulfate (DSS)-induced colitis in mice. Here, we revealed that HFD-promoted obesity accelerated DSS-induced colitis, while melatonin intervention improved colitis. Melatonin significantly alleviated inflammation by increasing anti-inflammatory cytokine release and reducing the levels of proinflammatory cytokines in HFD- and DSS-treated mice. Furthermore, melatonin expressed antioxidant activities and reversed intestinal barrier integrity, resulting in improved colitis in DSS-treated obese mice. We also found that melatonin could reduce the ability of inflammatory cells to utilize fatty acids and decrease the growth-promoting effect of lipids by inhibiting autophagy. Taken together, our study indicates that the inhibitory effect of melatonin on autophagy weakens the lipid-mediated prosurvival advantage, which suggests that melatonin-targeted autophagy may provide an opportunity to prevent colitis in obese individuals.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
With the development of society, human beings are paying increasing attention to the pollution of the ecological environment. Traditional instrumental analyses are costly and inconvenient, and thus ...it is necessary to seek a detection method that integrates high efficiency, sensitivity and convenience. In recent years, metal-organic frameworks (MOFs) have been developed and utilized in the field of detection, especially the MOFs containing group IIIA elements, which have achieved certain research results in the field of environmental pollutant detection due to their excellent stability. In this study, we summarize the latest results on the application of IIIA-based MOFs (Al(
iii
)/In(
iii
) MOFs) for the detection of representative pollutants and briefly discuss their potential development to provide a direction for the in-depth exploration of this field.
This paper focuses on summarizing the Al(
iii
)/In(
iii
)-MOFs for the detection of representative pollutants. The potential scope of development of the MOFs is briefly discussed to provide directions for in-depth exploration of the field.
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Polymer 1 has efficient photocatalytic performances to selectively degrade methylene blue (MB) among the dyes methylene orange and Rhodamine B. A mechanistic investigation toward ...better discussion of the degradation process of MB was conducted by LC-MS methods.
Metal-organic frameworks (MOFs) have brought as splendid class of multidimensional materials which can be explored as photocatalytic materials to degrade dyes present in waste-water discharge. Herein, a new MOF namely Cu3(L)2(4,4′-bipy) (1) (H3L = 1,3,5-tris(4-carbonylphenyloxy)benzene and4,4′-bipy = 4,4′-bipyridine), has been prepared and characterized. It was revealed that 1 features a three-dimensional (3,8)-connected framework with point symbol of (42.6)2(44.610.75.88.9) topology. The current photocatalysis results indicated that 1 has efficient photocatalytic performances to selectively degrade methylene blue (MB) when compared to the dyes methylene orange and Rhodamine B. A mechanistic investigation toward better discussion of the degradation process of MB was conducted by LC-MS methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
AbstractObjectiveTo synthesise results of mental health outcomes in cohorts before and during the covid-19 pandemic.DesignSystematic review.Data sourcesMedline, PsycINFO, CINAHL, Embase, Web of ...Science, China National Knowledge Infrastructure, Wanfang, medRxiv, and Open Science Framework Preprints.Eligibility criteria for selecting studiesStudies comparing general mental health, anxiety symptoms, or depression symptoms assessed from 1 January 2020 or later with outcomes collected from 1 January 2018 to 31 December 2019 in any population, and comprising ≥90% of the same participants before and during the covid-19 pandemic or using statistical methods to account for missing data. Restricted maximum likelihood random effects meta-analyses (worse covid-19 outcomes representing positive change) were performed. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist for Prevalence Studies.ResultsAs of 11 April 2022, 94 411 unique titles and abstracts including 137 unique studies from 134 cohorts were reviewed. Most of the studies were from high income (n=105, 77%) or upper middle income (n=28, 20%) countries. Among general population studies, no changes were found for general mental health (standardised mean difference (SMD)change 0.11, 95% confidence interval −0.00 to 0.22) or anxiety symptoms (0.05, −0.04 to 0.13), but depression symptoms worsened minimally (0.12, 0.01 to 0.24). Among women or female participants, general mental health (0.22, 0.08 to 0.35), anxiety symptoms (0.20, 0.12 to 0.29), and depression symptoms (0.22, 0.05 to 0.40) worsened by minimal to small amounts. In 27 other analyses across outcome domains among subgroups other than women or female participants, five analyses suggested that symptoms worsened by minimal or small amounts, and two suggested minimal or small improvements. No other subgroup experienced changes across all outcome domains. In three studies with data from March to April 2020 and late 2020, symptoms were unchanged from pre-covid-19 levels at both assessments or increased initially then returned to pre-covid-19 levels. Substantial heterogeneity and risk of bias were present across analyses.ConclusionsHigh risk of bias in many studies and substantial heterogeneity suggest caution in interpreting results. Nonetheless, most symptom change estimates for general mental health, anxiety symptoms, and depression symptoms were close to zero and not statistically significant, and significant changes were of minimal to small magnitudes. Small negative changes occurred for women or female participants in all domains. The authors will update the results of this systematic review as more evidence accrues, with study results posted online (https://www.depressd.ca/covid-19-mental-health).Review registrationPROSPERO CRD42020179703.
Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing ...decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT’s high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.
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