Corrigendum: Detection of Microbial 16S rRNA Gene in the Blood of Patients With Parkinson's Disease

Qian, Yiwei; Yang, Xiaodong; Xu, S. B.; Wu, Chunyan; Qin, Nan; Chen, Shengdi; Xiao, Qin

doi:10.3389/fnagi.2019.00004

Cited by 4 publications

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“…Brain age has a range of potential applications for the clinical assessment of individual patients at various stages of health and disease, including the support of diagnosis, prognosis, and treatment decisions ( Figure 3 ). Brain age studies are being conducted in a wide range of clinical populations including neurological conditions such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) [ 1 , 14 , 15 , 25 , 37 , [56] , [57] , [58] , [59] ], traumatic brain injury [ 60 , 61 ], epilepsy [ 18 , 19 , 62 ], multiple sclerosis [ 37 , 54 , 63 ], and stroke [ 64 , 65 ], as well as psychiatric disorders such as schizophrenia [ 10 , 12 , 69 , 20 , 26 , 37 , 38 , 45 , [66] , [67] , [68] ], including clinical high-risk for psychosis (CHR) and first-episode psychosis (FEP), bipolar disorder [ 37 , 38 , 66 , 67 , 70 ], major depressive disorder (MDD) [ 6 , 20 , 32 , 37 , 71 , 72 ], borderline personality disorder [20] , autism spectrum disorder [ 8 , 37 , 73 ], and attention deficit hyperactivity disorder [37] . The results of the clinical studies are summarised in Tables 1 and 2 .…”

Section: Five Promising Clinical Applicationsmentioning

confidence: 99%

“…2019 [63] Multiple sclerosis 76 21-49 +4.40 * Egorova et al. 2019 [65] Stroke 135 >18 +3.87 * Savjani et al. 2017 [61] Traumatic brain injury 92 22-57 +5.4/+3.6/+9.8 Cole et al.…”

Section: Five Promising Clinical Applicationsmentioning

confidence: 99%

“… [76] , it is plausible that brain ageing may be a “global phenomenon”, where different initial brain abnormalities manifest as similar changes downstream. As more large-scale longitudinal studies are being conducted, we gain greater understanding of dynamic ageing patterns [ 15 , 18 , 25 , 26 , 45 , 54 , 56 , 63 , 65 ], but further investigations are needed to examine brain age during the course of various diseases. These will help establish when abnormal brain ages start being noticeable and how they develop over time ( Section 4.3 ), also on the regional level ( Section 4.2 ).…”

Section: Five Promising Clinical Applicationsmentioning

confidence: 99%

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Machine learning for brain age prediction: Introduction to methods and clinical applications

et al. 2021

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The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.

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Section: Five Promising Clinical Applicationsmentioning

confidence: 99%

“…2019 [63] Multiple sclerosis 76 21-49 +4.40 * Egorova et al. 2019 [65] Stroke 135 >18 +3.87 * Savjani et al. 2017 [61] Traumatic brain injury 92 22-57 +5.4/+3.6/+9.8 Cole et al.…”

Section: Five Promising Clinical Applicationsmentioning

confidence: 99%

Section: Five Promising Clinical Applicationsmentioning

confidence: 99%

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Machine learning for brain age prediction: Introduction to methods and clinical applications

et al. 2021

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“…Overproduction of free radicals which can also induce oxidative stress leads to the damage to lipids, nucleic acid, and other biomolecules [1,2]. Microbial infections can also damage biomolecules, induce oxidative stress, and are involved in pathogenesis of various diseases [3], such as periodontal diseases, inflammation, Parkinson's disease [4,5].…”

Section: Introductionmentioning

confidence: 99%

Geno-Protective, Free Radical Scavenging and Antimicrobial Potential of Hyptis suaveolens Methanolic Fraction: An In-Vitro Study

Iqbal

Dukhyil

Khan

2021

JPRI

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Aims: Hyptis suaveolens (L.) Poit, is one of the natural herbs with several medicinal properties. However, many medicinal aspects of this herb still need to be explored. Therefore, our aim was to examine the antioxidant, antimicrobial properties and genoprotective effect of H. suaveolens methanolic extracts (HSME) of seed, stem, and root. Study design: extraction and therapeutic aspects of H. suaveolens. Place and Duration of Study: 1) Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al-Majmaah and 2) Clinical Biochemistry & Natural Product research laboratory, Department of Biosciences, Integral University, lucknow between 2018-2020. Methodology: HSME were extracted through soxhlet extractor and further analyzed for TPC, antioxidant activity through DPPH and FRAP assay followed by antimicrobial potential through zone of inhibition and MIC/MBC assay. We also examined the genoprotective properties of HSME on oxidative DNA damage. Results: Our results showed that TPC (180±5 mg GAE/g dw), DPPH scavenging activity (IC50 value = 72±0.45 µg/ml) and FRAP value (1.443±0.02 µM ferrous ion/mg extract) was highest in HSME seeds followed by root and stem. The results also illustrated that the antimicrobial activity of HSME (seed and stem) against five bacterial strain were found very effective than root part. Moreover, genoprotective effect of HSME seeds (80±3 % retention) was better than stem (41±2 %) and root (32±2 %) extract. Conclusion: The study revealed that HSME seed extract showed potential bioactivities might be due to presence of high TPC and can be used to treat diseases related with oxidative stress or microbial infections.

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“…We thank Dr. Zhang for his interest in our study 1 and for the valuable feedback. 2 Dr. Zhang correctly remarks that individuals in the same household typically share similar diets, highlighting our decision to enroll patient spouses as controls whenever possible. Control selection for case-control studies involves inherent trade-offs between comparability and efficiency, 3 and we accordingly made substantial effort to control for potential confounders beyond simply selection strategy.…”

mentioning

confidence: 99%

Comment on “Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson's Disease”

Zhang

2020

Movement Disorders

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I would like to comment on the recently published article titled "Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson's Disease" in Movement Disorders. Cirstea et al 1 analyzed the correlation between intestinal symptoms, intestinal flora, and serum metabolomics in patients with Parkinson's disease (PD) based on large samples and came up with novel findings. The researchers evaluated 300 participants in detail, including daily dietary intake and intestinal complications. However, I have some concerns. First, it was declared in the Results section that p-cresol (HMDB0001858) and phenylacetylglutamine (HMDB0006344) were both significantly positively correlated with constipation severity among patients (Fig. 3E). Nevertheless, the result of phenylacetylglutamine cannot be found in Figure 3E, and there are 2 plots of different correlations between p-cresol and constipation severity among patients with PD. Second, as shown in the Discussion section, nearly half of controls (43 of 103) were spouses of the PD patients. In other words, within the cohort composed of pairs of couples, the diet-driven heterogeneity in gut microbiota between the patient and the control might be negligible because couples share more of their gut microbiota than individuals from different households. 2 However, an individual's dietary pattern might be a confounding factor for analysis of gut microbiota for the rest of the 147 patients and the other healthy controls. Notably, several studies 2-4 assessing the relationship between gut microbiota and PD included merely couples-based populations to reduce the interfering effect of different dietary patterns. Therefore, we hope that the authors will make a further subgroup analysis to validate their findings in different patient cohorts. To sum up, the present study is interesting and inspiring research in which more detailed information should be analyzed and available to readers so as to be more helpful in a deep understanding of intestinal symptoms, intestinal flora, and serum metabolomics in PD patients.

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Corrigendum: Detection of Microbial 16S rRNA Gene in the Blood of Patients With Parkinson's Disease

Cited by 4 publications

References 0 publications

Machine learning for brain age prediction: Introduction to methods and clinical applications

Machine learning for brain age prediction: Introduction to methods and clinical applications

Geno-Protective, Free Radical Scavenging and Antimicrobial Potential of Hyptis suaveolens Methanolic Fraction: An In-Vitro Study

Comment on “Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson's Disease”

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