Milled flaxseed-added diets ameliorated hepatic inflammation by reducing gene expression of TLR4/NF-κB pathway and altered gut microbiota in STZ-induced type 1 diabetic mice

Flaxseed has displayed the potential beneficial as functional foods.However,most studies focused on effects of flaxseed extracts or ingredients in flaxseed.Besides,few studies showed that flaxseed extracts contributed to anti-type 1 diabetes(T1D),yet the underlying mechanism is still unknown.In the present study,16.7% of milled flaxseed(MF)-added diet was given to diabetic mice induced by streptozocin for 6 weeks.The results showed that MF feeding 1)slightly decreased blood glucose levels and improved the ability of glucose tolerance by oral glucose tolerance test,2)decreased liver tumor necrosis factor-αlevels and increased liver glycogen levels with significance via down-regulating TLR4/NF-κB pathways,3)and significantly altered some beneficial bacteria in gut microbiota.In conclusion,the present study showed that milled flaxseed showed the potential on anti-T1D through anti-inflammation via TLR4/NF-κB and altering the gut microbiota in STZ-induced diabetic mice.

Detection and analysis of T cell subsets, T cell cytokine and immunoglobulin in patients with oral cancer before and after operation

Objective:To investigate the changes of T cell subsets, T cell cytokine and immunoglobulin in patients with oral cancer before and after operation.Methods:A total of 40 cases of oral cancer treated in our hospital from January 2017 to December 2017 were selected as the observation group, and 40 healthy volunteers in our hospital were selected as the control group. The levels of T cell subsets, T cell cytokine and immunoglobulin in the observation group before and after operation were detected and compared with those in the control group.Results:The CD3+, CD4+ and CD4+/ CD8+ of the observation group before operation were lower than those of the control group, and CD8+ was higher than that of the control group, the differences were statistically significant. The CD3+, CD4+ and CD4+/CD8+ after operation in the observation group were (64.98 ± 3.88)%, (42.40 ± 3.53)% and (1.56 ± 0.41) respectively, which were all higher than those before the operation, but still lower than those in the control group. The CD8+ of the observation group was (27.25 ± 3.54)% lower than that before operation, but it was still higher than that of the control group. In the observation group, the levels of INF-γ+, CD4+/INF-γ+, IL-2+, and CD4+/IL-2+ before operation were lower in the observation group than those in the control group. In the observation group, INF-γ+, CD4+/INF-γ+, IL-2+, and CD4+/IL-2+ after surgery were (0.15±0.06)%, (0.07±0.03)%, (0.47±0.13)%, (0.16 ± 0.11)%, were higher than those before surgery, but still lower than those in the control group. The IgA and IgG before and after the surgery in the observation group were (2.01±0.13) g/L, (1.81±0.12) g/L, (10.53±3.12) g/L, and (8.41±2.01) g/L, respectively, compared with the control group, the difference was statistically significant. There was no significant change in IgA, IgG and IgM in the observation group before and after operation.Conclusion:Surgery can effectively improve the T lymphocyte subsets and T cell cytokine levels in oral cancer patients, and has a positive effect on improving immune function.

Different Regional Patterns in Gray Matter-based Age Prediction

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as living habit.

Retraction Note to:Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

The authors have retracted this article.After publication we found an error in the implementation code that resulted in data leakage in the age-prediction model training process.We have redesigned the prediction model and tested the mode with an extended dataset(around 2000 subjects,in contrast to the 600 subjects in this article).

Biological Subtypes Bridge Diagnoses and Biomarkers:A Novel Research Track for Mental Disorders

Neuroimaging has shown promise for diagnosing depression,but these diagnostic systems have not performed as well when tested in large multi-site datasets.Recently,a paper published in Nature Medicine[1]has made major progress and provided good cues about how to understand mental disorders,not limited to depression.It is well known that mental disorders are associated with numerous personal and societal costs.According

High-fat diet-induced adipose tissue expansion occurs prior to insulin resistance in C57BL/6J mice

Background::To date,there is only scare evidence characterizing the temporal features and progression of metabolic dysfunction in high-fat diet(HFD)-fed obese mice.Hence,its specific pathogenesis remains unclear.Methods::Sixty 6-week-old male C57BL/6J mice were randomly divided into HFD and control diet(CD)groups and sacrificed at 1,5,9,13,17,and 21 weeks,respectively.At weekly intervals,intraperitoneal glucose tolerance testing(IPGTT)and intraperitoneal insulin tolerance testing(IPITT)were performed in both groups.A detailed time course in HFD-fed mice was investigated by evaluating the initiation of glucose homeostasis impairment,dyslipidemia,systemic insulin sensitivity,monocyte chemoattractant protein-1(MCP-1)levels,epididymal white adipose tissue(eWAT)expansion,macrophage content changes,proinflammatory(M1)/anti-inflammatory(M2)macrophage imbalance,lipid accumulation in the liver,andβ-cell morphometry in the pancreas.Results::In the HFD group,progressive weight gain and impairments in glucose metabolism(elevated fasting blood glucose and area under the curve(AUC)of IPGTT)were observed from the 3rd week,and a significantly elevated AUC of IPITT was first detected after week 7 of HFD feeding.As for dyslipidemia,after 9 weeks of feeding,the low-density lipoprotein cholesterol level and total cholesterol level in HFD group were significantly higher than those in the CD group(all P<0.05),whereas no significant differences were shown in triglyceride level.Adipocyte size increased significantly in the HFD group in the 1st week,a phenotypic switch in eWAT from anti-inflammatory(M2)to pro-inflammatory(M1)macrophages was observed in the 5th week,and the metabolic inflammation was distinct in eWAT in the 9th week.Additionally,liver steatosis was considerably obvious at the 17th week and pancreaticβ-cell morphometry did not change during 21 weeks of HFD feeding.Conclusion::The eWAT expansion was detected early in HFD-induced obese mice,which occurred prior to obvious insulin resistance.

Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].

Data-driven multimodal fusion:approaches and applications in psychiatric research

In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion methods.These methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders.However,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data.This is where data-driven multi-modal fusion techniques come into play.By combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed.In this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component analysis.The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain disorders.After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical translation.Overall,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.