Perceived age and perceived health among a Chinese cohort: Does it mean the same thing?

Messaraa, Cyril; Richard, Thibaud J. C.; Walsh, Melissa; Doyle, Leah; O'Connor, C.; Robertson, Nicola J.; Mansfield, Anna; Hurley, Sarah; Mavon, Alain; Grenz, Annika

doi:10.1111/ics.12647

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Some skin features were specific to age perception (eg, eye bags, eye lid sagging, wrinkles), while others were rather related to health perception, such as pigmented spots, acne, scars, or skin hydration. 123 …”

Section: Emotional Balance/stressmentioning

confidence: 99%

The Skin Interactome: A Holistic “Genome-Microbiome-Exposome” Approach to Understand and Modulate Skin Health and Aging

Khmaladze¹,

Leonardi²,

Fabre³

et al. 2020

CCID

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Higher demands on skin care cosmetic products for strong performance drive intense research to understand the mechanisms of skin aging and design strategies to improve overall skin health. Today we know that our needs and influencers of skin health and skin aging change throughout our life journey due to both extrinsic factors, such as environmental factors and lifestyle factors, as well as our intrinsic factors. Furthermore, we need to consider our microflora, a collection of micro-organisms such as bacteria, viruses, and fungi, which is a living ecosystem in our gut and on our skin, that can have a major impact on our health. Here, we are viewing a holistic approach to understand the collective effect of the key influencers of skin health and skin aging both reviewing how each of them impact the skin, but more importantly to identify molecular conjunction pathways of these different factors in order to get a better understanding of the integrated “genome-microbiome-exposome” effect. For this purpose and in order to translate molecularly the impact of the key influencers of skin health and skin aging, we built a digital model based on system biology using different bioinformatics tools. This model is considering both the positive and negative impact of our genome (genes, age/gender), exposome: external (sun, pollution, climate) and lifestyle factors (sleep, stress, exercise, nutrition, skin care routine), as well as the role of our skin microbiome, and allowed us in a first application to evaluate the effect of the genome in the synthesis of collagen in the skin and the determination of a suitable target for boosting pro-collagen synthesis. In conclusion, we have, through our digital holistic approach, defined the skin interactome concept, as an advanced tool to better understand the molecular genesis of skin aging and further develop a strategy to balance the influence of the exposome and microbiome to protect, prevent, and delay the appearance of skin aging signs and preserve good skin health condition. In addition, this model will aid in identifying and optimizing skin treatment options based on external triggers, as well as helping to design optimal treatments modulating the intrinsic pathways.

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Section: Emotional Balance/stressmentioning

confidence: 99%

The Skin Interactome: A Holistic “Genome-Microbiome-Exposome” Approach to Understand and Modulate Skin Health and Aging

Khmaladze¹,

Leonardi²,

Fabre³

et al. 2020

CCID

Self Cite

View full text Add to dashboard Cite

show abstract

“…In these circumstances, parents are obligated to be assessed their perception of their children's OHRQoL [16]. It was found that those persons belonging to higher age groups tend to perceive their own or their family health in a significantly more positive fashion than members of younger adult age groups [17,18]. This study is the first that specifically examined the relationship between the maternal age group with the OHRQoL of their children.…”

Section: Discussionmentioning

confidence: 99%

Modelling the Maternal Oral Health Knowledge, Age Group, Social-Economic Status, and Oral Health-Related Quality of Life in Stunting Children

Indriyanti

Nainggolan

Sundari

et al. 2021

ijsmr

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The main themes are two main health problems affecting children under five in Indonesia, namely nutrition and oral health. Lack of nutrition in children can also affect their general health, and so does their oral health, leading to their quality of life. The study aimed to analyse the relationship between maternal oral health knowledge, maternal age group, social-economic status with the oral health-related of life in stunting children. This type of analytical research used a survey method on 86 mothers aged 2-5 years in one of 15 villages designated by the mayor of Bandung as a stunting locus. Maternal oral health knowledge, social-economic status, and oral health-related quality of life were assessed using a set of questionnaires that have been pre-tested to non-participant mothers. The hypotheses of the conceptual model were tested using structural equation modelling-partial least squares. The results showed that 16.7% of the variance in OHRQoL was explained by maternal oral health knowledge and the maternal age group. Social-economic status has an indirect relationship to OHRQoL by predicting the maternal oral health knowledge 10.6%. The path coefficient between maternal age group and OHRQoL was the strongest (b = -0.350, P = 0.000), followed by SES and maternal oral health knowledge (b = 0.325, P = 0.04) and to OHRQoL (b = 0.215, P=0.02). The overall predictive power of the model was 10.6%. This result indicated maternal oral health knowledge, social-economic status, and maternal age group related to children's oral health quality of life.

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“…Recently, different applications of artificial intelligence (AI), such as computer vision and machine learning algorithms, have been utilized in the cosmetics industry for the automated detection and analysis of image-based data, mostly as a face skin diagnostic tool [17,18,19]. In those assessments, the AI was validated by parallel assessments conducted by expert dermatologists, who normally use validated scales for skin ageing assessment.…”

Section: Hair Assembly Properties and Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features

Daniels¹,

Tamburić²,

Benini

et al. 2021

Intern J of Cosmetic Sci

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Objective The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The second objective was to explore and compare the performance of human assessment with that obtained from artificial intelligence (AI) algorithms. Methods Machine learning was applied to a data set of hair tress images (virgin and bleached), both untreated and treated with a shampoo and conditioner set, aimed at increasing hair volume whilst improving alignment and reducing the flyway of the hair. The automatic quantification of the following hair image features was conducted: local and global hair volumes and hair alignment. These features were assessed at three time points: t0 (no treatment), t1 (two treatments) and t2 (three treatments). Classifier tests were applied to test the accuracy of the machine learning. A sensory test (paired comparison of t0 vs t2) and an online front image‐based survey (paired comparison of t0 vs t1, t1 vs t2, t0 vs t2) were conducted to compare human assessment with that of the algorithms. Results The automatic image analysis identified changes to hair volume and alignment which enabled the successful application of the classification tests, especially when the hair images were grouped into untreated and treated groups. The human assessment of hair presented in pairs confirmed the automatic image analysis. The image assessment for both virgin hair and bleached only partially agreed with the analysis of the subset of images used in the online survey. One hypothesis is that treatments changed somewhat the shape of the hair tress, with the effect being more pronounced in bleached hair. This made human assessment of flat images more challenging than when viewed directly in 3D. Overall, the bleached hair exhibited effects of higher magnitude than the virgin hair. Conclusions This study illustrated the capacity of artificial intelligence for hair image detection and classification, and for image analysis of hair assembly features following treatments. The human assessment partially confirmed the image analysis and highlighted the challenges imposed by the presentation mode.

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Perceived age and perceived health among a Chinese cohort: Does it mean the same thing?

Cited by 6 publications

References 23 publications

The Skin Interactome: A Holistic “Genome-Microbiome-Exposome” Approach to Understand and Modulate Skin Health and Aging

The Skin Interactome: A Holistic “Genome-Microbiome-Exposome” Approach to Understand and Modulate Skin Health and Aging

Modelling the Maternal Oral Health Knowledge, Age Group, Social-Economic Status, and Oral Health-Related Quality of Life in Stunting Children

Artificial Intelligence in hair research: A proof‐of‐concept study on evaluating hair assembly features

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