Folic acid supplementation appears to improve cognitive function and reduce blood levels of Aβ-related biomarkers in MCI. Larger-scale double-blind placebo-controlled randomized trials of longer duration are needed.
Background:
Folate and vitamin B12 are well-known as essential nutrients that play key
roles in the normal functions of the brain. Inflammatory processes play at least some role in the pathology
of AD. Effective nutritional intervention approaches for improving cognitive deficits that reduce the
peripheral inflammatory cytokine levels have garnered special attention.
Objective:
The present study aimed to determine whether supplementation with folic acid and vitamin
B12, alone and in combination improves cognitive performance via reducing levels of peripheral inflammatory
cytokines.
Methods:
240 participants with MCI were randomly assigned in equal proportion to four treatment
groups: folic acid alone, vitamin B12 alone, folic acid plus vitamin B12 or control without treatment daily
for 6 months. Cognition was measured with WAIS-RC. The levels of inflammatory cytokines were
measured using ELISA. Changes in cognitive function or blood biomarkers were analyzed by repeatedmeasure
analysis of variance or mixed-effects models. This trial has been registered with trial number
ChiCTR-ROC-16008305.
Results:
Compared with control group, the folic acid plus vitamin B12 group had significantly greater
improvements in serum folate, homocysteine, vitamin B12 and IL-6, TNF-α, MCP-1. The folic acid plus
vitamin B12 supplementation significantly changed the Full Scale IQ (effect size d = 0.169; P = 0.024),
verbal IQ (effect size d = 0.146; P = 0.033), Information (d = 0.172; P = 0.019) and Digit Span (d =
0.187; P = 0.009) scores. Post hoc Turkey tests found that folic acid and vitamin B12 supplementation
was significantly more effective than folic acid alone for all endpoints.
Conclusions:
The combination of oral folic acid plus vitamin B12 in MCI elderly for six months can significantly
improve cognitive performance and reduce the levels of inflammatory cytokines in human
peripheral blood. The combination of folic acid and vitamin B12 was significantly superior to either folic
acid or vitamin B12 alone.
The 3D CNN-based classification algorithm is a promising tool for the diagnosis of pre-invasive and invasive lung cancer and for the treatment choice decision.
Background:
Inflammation plays a significant role in the pathophysiology of cognitive impairment
in previous studies. Neutrophil-lymphocyte ratio (NLR) is a reliable measure of systemic inflammation.
Objective:
The aim of this study was to investigate the association between NLR and mild cognitive
impairment (MCI), and further to explore the diagnostic potential of the inflammatory markers NLR for
the diagnosis of MCI in elderly Chinese individuals.
Methods:
186 MCI subjects and 153 subjects with normal cognitive function were evaluated consecutively
in this study. Neutrophil (NEUT) count and Lymphocyte (LYM) count were measured in fasting
blood samples. The NLR was calculated by dividing the absolute NEUT count by the absolute LYM
count. Multivariable logistic regression was used to evaluate the potential association between NLR and
MCI. NLR for predicting MCI was analyzed using Receiver Operating Characteristic (ROC) curve
analysis.
Results:
The NLR of MCI group was significantly higher than that of subjects with normal cognitive
function (2.39 ± 0.55 vs. 1.94 ± 0.51, P < 0.001). Logistic regression analysis showed that higher NLR
was an independent risk factor for MCI (OR: 4.549, 95% CI: 2.623-7.889, P < 0.001). ROC analysis
suggested that the optimum NLR cut-off point for MCI was 2.07 with 73.66% sensitivity, 69.28% specificity,
74.48% Positive Predictive Values (PPV) and 68.36% negative predictive values (NPV). Subjects
with NLR ≥ 2.07 showed higher risk relative to NLR < 2.07 (OR: 5.933, 95% CI: 3.467-10.155, P <
0.001).
Conclusion:
The elevated NLR is significantly associated with increased risk of MCI. In particular,
NLR level higher than the threshold of 2.07 was significantly associated with the probability of MCI.
Background
Substantial evidence indicates that dysbiosis of the gut microbial community is associated with colorectal neoplasia. This review aims to systematically summarise the microbial markers associated with colorectal neoplasia and to assess their predictive performance.
Methods
A comprehensive literature search of MEDLINE and EMBASE databases was performed to identify eligible studies. Observational studies exploring the associations between microbial biomarkers and colorectal neoplasia were included. We also included prediction studies that constructed models using microbial markers to predict CRC and adenomas. Risk of bias for included observational and prediction studies was assessed.
Results
Forty-five studies were included to assess the associations between microbial markers and colorectal neoplasia. Nine faecal microbiotas (i.e., Fusobacterium, Enterococcus, Porphyromonas, Salmonella, Pseudomonas, Peptostreptococcus, Actinomyces, Bifidobacterium and Roseburia), two oral pathogens (i.e., Treponema denticola and Prevotella intermedia) and serum antibody levels response to Streptococcus gallolyticus subspecies gallolyticus were found to be consistently associated with colorectal neoplasia. Thirty studies reported prediction models using microbial markers, and 83.3% of these models had acceptable-to-good discrimination (AUROC > 0.75). The results of predictive performance were promising, but most of the studies were limited to small number of cases (range: 9–485 cases) and lack of independent external validation (76.7%).
Conclusions
This review provides insight into the evidence supporting the association between different types of microbial species and their predictive value for colorectal neoplasia. Prediction models developed from case-control studies require further external validation in high-quality prospective studies. Further studies should assess the feasibility and impact of incorporating microbial biomarkers in CRC screening programme.
In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to evaluate the final image classification accuracy. Experimental results show that our proposed relief-SVM-RFE algorithm can achieve significant improvements for feature selection in image classification.
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