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Swimming is a widely enjoyed physical activity that provides various health benefits, such as improved cardiovascular fitness, enhanced muscle strength, and reduced stress levels. Nevertheless, swimming also involves exposure to different water environments, including chlorinated pools, seawater, and freshwater lakes. Each of these environments possesses distinct chemical and microbial properties that can uniquely affect the skin microbiome. Consequently, comprehending the significance of the skin microbiome in swimming is essential. What we know: Studies have found that exposure to chlorinated pool water reduces microbial diversity on the skin, as it acts as a disinfectant, killing both harmful and beneficial bacteria, which can lead to an imbalance in the skin microbiome. This imbalance may increase the risk of skin conditions like dermatitis and infections (Puce et al ., 2022). Ocean water contains a diverse range of marine bacteria, thereby enhancing the diversity of the skin microbiome. The ocean water simultaneously removes resident skin bacteria while depositing ocean-borne bacteria onto the skin (Nielsen et al ., 2019). The predominating phyla Actinobacteria , Firmicutes , and Proteobacteria on the skin changed after swimming when compared to before swimming tends to decrease, whereas Bacteroidetes  tends to increase. As time passed, the bacterial community composition trended towards baseline (Nielsen et al ., 2019). The quantity of Vibrio spp.  found on human skin was over ten times higher than that in the ocean water sample (which was only 0.032%), indicating that Vibrio spp.  has a particular affinity for adhering to human skin (Nielsen et al ., 2019). Industry impact & potential: Research shows that males are more prone to acquiring infections from Vibrio vulnificus and Aeromonas spp.  following water exposure. Future research could provide valuable insights into the factors contributing to these infections and explore potential differences in the skin microbiome between males and females after such exposure (Nielsen et al ., 2019). Formulations such as post and pre-swim cleansers and moisturizers should be designed to aid in microbiome recovery while also protecting the skin from chlorine and salt damage. Our solution: Sequential, is a company focusing on microbiome studies. We carry out various services from clinical testing to helping with formulations. We have at home testing kits that will allow you to discover the state of your skin microbiome. Through our Skin Health Tracker app, we can give you tips on how you can improve your skin and the microbiome.  Reference: Nielsen MC, Jiang SC. Alterations of the human skin microbiome after ocean water  exposure. Mar Pollut Bull. 2019 Aug;145:595-603. doi: 10.1016/j.marpolbul.2019.06.047. Epub 2019 Jul 2. PMID: 31590829; PMCID: PMC8061468. Puce L, Hampton-Marcell J, Trabelsi K, Ammar A, Chtourou H, Boulares A, Marinelli L, Mori  L, Cotellessa F, Currà A, Trompetto C, Bragazzi NL. Swimming and the human microbiome at the intersection of sports, clinical, and environmental sciences: A scoping review of the literature. Front Microbiol. 2022 Aug 3;13:984867. doi: 10.3389/fmicb.2022.984867. PMID: 35992695; PMCID: PMC9382026.

The scalp microbiome plays a crucial yet often overlooked role in the development and treatment of alopecia. Studies have shed light on how rebalancing these microbes can significantly enhance the efficacy of treatments for hair loss, offering new hope for patients. What We Know: Cutibacterium spp.  and Staphylococcus spp . constitute about 90% of healthy scalp microbiomes, with Corynebacterium spp., Streptococcus spp., Acinetobacter spp . and Prevotella spp . making up the remaining 10% (Jo et al., 2022) . Alopecia patients’ scalp microbiomes exhibit increased C. acnes , Stenotrophomonas geniculata, Wallemia  and Eurotium , as well as reduced Malassezia, when compared to healthy individuals. Therefore, it is likely that an imbalance in scalp microbiota may contribute to alopecia (Zhang et al., 2024) . Industry Impact and Potential: Platelet-rich plasma (PRP) has proven effective in treating alopecia, but its impact on the scalp microbiome was previously unexplored. A recent study revealed that PRP treatment rebalances the scalp microbiome, specifically increasing Cutibacterium  levels while decreasing Staphylococcus  and Lawsonella  levels (Zhang et al., 2024) . Cutibacterium  plays a vital role in maintaining skin homeostasis and is crucial for lipid regulation, follicular niche competition, immune regulation and mitigating oxidative stress. Furthermore, the balance between Cutibacterium and Staphylococcus  is important for regulating immune response. Reduction in Lawsonella  suggests decreased scalp sebum production following treatment. This is relevant to alopecia treatment, as imbalances in sebum production can exacerbate hair loss by contributing to inflammation and follicle damage (Zhang et al., 2024) .  Lactic acid bacteria (LAB), Limosilactobacillus fermentum  LM1020 and its heat-treated version HT-LM1020, can help promote hair growth on human scalp tissue and dermal papilla cells. These bacteria work with other ingredients to fight hair loss by boosting cell growth and regulating the expression of proteins important for cell division (Bae et al., 2024) . AMOREPACIFIC patented a composition that uses extracellular follicles derived from LAB to prevent hair loss, stimulate hair growth and support overall hair health. These extracellular follicles (cellular components or secretions released by the bacteria) represent a promising advancement in alopecia treatment, offering potential benefits for both hair and scalp health. Our Solution: With a database of over 20,000 microbiome samples and 4,000 ingredients, and a global network of more than 10,000 testing participants, Sequential offers comprehensive services to evaluate product impacts and formulations. Our customisable microbiome studies provide real-life context testing, and our formulation support ensures products maintain biome integrity, making us the ideal partner for your product development and efficacy needs. References: Bae, W.-Y., Jung, W.-H., Shin, S.L., Kim, T.-R., Sohn, M., Suk, J., Jung, I., Lee, Y.I. & Lee, J.H. (2024) Heat-treated Limosilactobacillus fermentum LM1020 with menthol, salicylic acid, and panthenol promotes hair growth and regulates hair scalp microbiome balance in androgenetic alopecia: A double-blind, randomized and placebo-controlled clinical trial. Journal of Cosmetic Dermatology . n/a (n/a). doi:10.1111/jocd.16357. Jo, H., Kim, S.Y., Kang, B.H., Baek, C., Kwon, J.E., Jeang, J.W., Heo, Y.M., Kim, H.-B., Heo, C.Y., Kang, S.M., Shin, B.H., Nam, D.Y., Lee, Y.-G., Kang, S.C. & Lee, D.-G. (2022) Staphylococcus epidermidis Cicaria, a Novel Strain Derived from the Human Microbiome, and Its Efficacy as a Treatment for Hair Loss. Molecules . 27 (16). doi:10.3390/molecules27165136. Zhang, Q., Wang, Y., Ran, C., Zhou, Y., Zhao, Z., Xu, T., Hou, H. & Lu, Y. (2024) Characterization of distinct microbiota associated with androgenetic alopecia patients treated and untreated with platelet‐rich plasma (PRP). Animal Models and Experimental Medicine . 7 (2), 106–113. doi:10.1002/ame2.12414.

Diabetes mellitus is a chronic condition marked by elevated blood glucose levels due to abnormal insulin production or insulin resistance, leading to complications of the heart, kidneys, eyes, blood vessels and nerves. Type 2 diabetes specifically is associated with skin issues like chronic foot ulcers and increased infections, potentially due to disruptions in the skin microbiome. What We Know: In 2021, the global prevalence of diabetes among adults was estimated at 537 million and is projected to rise to 783 million by 2045 (International Diabetes Federation, 2021).  Diabetic foot ulcers (DFUs) are defined as “Ulcers in the foot of individuals with diabetes, often accompanied by lower limb neuropathy and/or peripheral arterial disease.” DFU can also be defined as a chronic skin disease linked to altered bacterial diversity and instability in wound microbiota (Zhang et al., 2023).  The total lifetime risk of DFU complications for patients with diabetes (type 1 or 2) is 25%. These wounds are slow to heal, difficult to treat and vulnerable to infection (Gardiner et al., 2017; Packer, Ali & Manna, 2024). Bacterial infection is the most common cause of delayed healing in DFUs and the lack of appropriate diagnostic tools makes it difficult to determine if the bacteria in the DFU are due to changes in the original colonised bacteria or an external infection. Few studies have investigated the transition of bacterial flora from healthy skin to diabetic skin to DFU skin (Zhang et al., 2023).  Industry Impact and Potential: While some bacterial species can hinder wound healing and lead to chronic wounds, others can accelerate healing and prevent pathogen colonisation. Significant differences in skin microbial composition exist between diabetic and non-diabetic patients, with diabetic wounds showing increased levels of Staphylococcus, Aerococcus, Porphyromonadaceae  and Proteobacteria , and decreased levels of Streptococcus, Lachnospiraceae  and Acinetobacter  (Zhang et al., 2023).  There are changes in the bacterial colony structure of DFU skin compared to healthy or diabetic skin without ulcers. In DFU skin, Staphylococcus , Enhydrobacter  and Corynebacterium_1  are significantly reduced, while Escherichia coli  and Pseudomonas  are increased (Zhang et al., 2023).  Thus, the difference between healthy skin and diabetic skin with or without ulcers lies in the balance between normal and pathogenic microbiota. Subsequently, altering the microbiota composition of wounds may help the treatment of DFU (Zhang et al., 2023).  Our Solution: Sequential offers customisable end-to-end Microbiome Testing for your research needs, like investigating the influence of the skin microbiome on diabetic skin and wound healing. Sequential offers real-world testing scenarios, and additionally formulation support to develop products that maintain microbiome stability for the skin, as well as for the oral, scalp and vaginal microbiomes.  References: Gardiner, M., Vicaretti, M., Sparks, J., Bansal, S., Bush, S., Liu, M., Darling, A., Harry, E. & Burke, C.M. (2017) A longitudinal study of the diabetic skin and wound microbiome. PeerJ. 5, e3543. doi:10.7717/peerj.3543. International Diabetes Federation (2021) IDF Diabetes Atlas. 2021. IDF Diabetes Atlas. https://diabetesatlas.org/ [Accessed: 5 July 2024]. Packer, C.F., Ali, S.A. & Manna, B. (2024) Diabetic Ulcer. In: StatPearls. Treasure Island (FL), StatPearls Publishing. p. http://www.ncbi.nlm.nih.gov/books/NBK499887/ . Zhang, X.-N., Wu, C.-Y., Wu, Z.-W., Xu, L.-X., Jiang, F.-T. & Chen, H.-W. (2023) Association Between the Diabetic Foot Ulcer and the Bacterial Colony of the Skin Based on 16S rRNA Gene Sequencing: An Observational Study. Clinical, Cosmetic and Investigational Dermatology. 16, 2801–2812. doi:10.2147/CCID.S425922.

Micro-Botox is a specialised technique involving the injecting of diluted botulinum toxin into the skin. It is a frequently performed procedure to improve facial skin tone, texture, fine wrinkles, and enlarged pores. Unlike traditional Botox, which targets deeper facial muscles, Micro-Botox is administered more superficially and in a more diluted form. This allows for a more uniform and subtle rejuvenation effect without significantly affecting facial expressions.  What we know: ▫️Botulinum toxin type A (BoNT-A) is widely recognized for its use as a neuromodulator in treating facial lines, correcting facial asymmetry, and creating a lifting effect in the lower face by administering via multiple injections into the superficial fibres of facial muscles ( Fabi , et al., 2023). ▫️Micro-botox has demonstrated effectiveness in enhancing the skin's sheen and texture, reducing sweat and sebum production, and minimising enlarged pores. It works by shrinking sebaceous glands, which in turn tightens the skin envelope (Salem et al., 2023).  ▫️Sebum provides a nutrient-rich environment for Cutibacterium acnes. By reducing sebum production, Micro-botox can decrease the availability of nutrients for Cutibacterium acnes bacteria, potentially reducing their population (Rho et al., 2021). ▫️The combined use of Micro-Botox and hyaluronic acid has been found to enhance skin hydration and improve the dermal barrier function ( Kim , 2021).  ▫️Micro-Botox is effective for facial rejuvenation, lifting the mid to lower face, and reducing fine wrinkles in the forehead and cheek areas, particularly in younger individuals. It also works for neck rejuvenation, especially in older individuals ( Iranmanesh  et al., 2022).  Industry impact & potential: ▫️The demand for Micro-Botox procedures continues to increase, driven by its effectiveness in addressing various skin concerns. ▫️There's potential for combining Micro-Botox with other skincare to enhance overall skin health and appearance. ▫️There is very little research that has been done on Micro-Botox and its impact on the skin microbiome. Hence, further research is needed to understand how Micro-Botox procedures impact the skin microbiome. Our solution: Sequential, specialises in microbiome analysis and we use advanced testing technologies and longitudinal study designs, to analyse changes in microbial diversity and composition pre and post treatments. By correlating these microbiome shifts with clinical outcomes such as improvements in skin texture or pore size, we aim to uncover new insights into the cosmetic procedure's impact on skin health at a microbial level. Reference: Fabi SG, Park JY, Goldie K, Wu W. Microtoxin for Improving Pore Size, Skin Laxity, Sebum  Control, and Scars: A Roundtable on Integrating Intradermal Botulinum Toxin Type A Microdoses Into Clinical Practice. Aesthet Surg J. 2023 Aug 17;43(9):1015-1024. doi: 10.1093/asj/sjad044. PMID: 36857534; PMCID: PMC10481112. Iranmanesh B, Khalili M, Mohammadi S, Amiri R, Aflatoonian M. Employing microbotox  technique for facial rejuvenation and face-lift. J Cosmet Dermatol. 2022 Oct;21(10):4160-4170. doi: 10.1111/jocd.14768. Epub 2022 Jan 22. PMID: 35064633. Kim JS. Fine Wrinkle Treatment and Hydration on the Facial Dermis Using HydroToxin  Mixture of MicroBotox and MicroHyaluronic Acid. Aesthet Surg J. 2021 May 18;41(6):NP538-NP549. doi: 10.1093/asj/sjaa231. PMID: 32779694; PMCID: PMC8240748. Rho NK, Gil YC. Botulinum Neurotoxin Type A in the Treatment of Facial Seborrhea and  Acne: Evidence and a Proposed Mechanism. Toxins (Basel). 2021 Nov 19;13(11):817. doi: 10.3390/toxins13110817. PMID: 34822601; PMCID: PMC8626011. Salem RM, Salah SAE, Ibrahim SE. Microbotox injection versus its topical application  following microneedling in the treatment of wide facial pores: A split face comparative study. J Cosmet Dermatol. 2023 Apr;22(4):1249-1255. doi: 10.1111/jocd.15590. Epub 2023 Jan 6. PMID: 36606384.

Snail mucin is the secretion produced by various species of snails, and it has recently gained attention for its potential benefits in skincare and cosmetic applications. Emerging studies indicate that its unique composition provides intense hydration and skin regeneration and positively influences the skin's microbiome, which is the delicate ecosystem of microorganisms on the skin's surface. What we know: Snail mucin is a complex mixture of proteins, glycoproteins, and other bioactive compounds such as allantoin, glycolic acid, and antibacterial peptides, providing benefits such as moisturizing, wound healing, and anti-inflammatory effects (Zhu et al ., 2024). Snail mucin contains natural antimicrobial peptides that selectively inhibits the growth of pathogenic bacteria while promoting the growth of beneficial bacteria, thereby maintaining a balanced skin microbiome (McDermott et al ., 2021).  Researchers have found that snail mucin demonstrates antibacterial effects against Pseudomonas aeruginosa and Staphylococcus aureus known to cause infections (Aflatooni et al ., 2023). Snail mucin is abundant in hyaluronic acid and glycolic acid, which improve skin hydration and barrier function (Yongeun et al ., 2022). A healthy skin barrier is essential for maintaining a stable microbiome, as it protects against external pathogens and prevents moisture loss. In-vitro studies had shown that Snail mucin had significantly improved the dermal density, skin elasticity, and wrinkles (Singh et al ., 2024). Cryptomphalus Aspersa  snail mucin boosts keratinocytes and fibroblasts proliferation, migration, and adhesion protein expression, potentially aiding scar healing, and thereby promoting a stable microbiome environment (Singh et at ., 2024). Industry impact & potential: The growing demand for snail mucin products and the need for research into its potential uses are driving an expanding economic market. The increasing demand for snail mucin creates pressure on collection methods, highlighting the critical need for ethical habitats for their collection (Singh et al ., 2024). As snail mucin is an animal-derived product it can lead to sustainability concerns. Therefore, more sustainable alternatives are much needed, such as synthetic or lab-grown mucin.    Our solution: Sequential, specializes in skin health solutions, features a state-of-the-art testing facility where we analyze the skin microbiome. We use advanced technology to test various skincare ingredients to better understand its impact on the skin microbiome. By conducting these tests, we aim to provide insights into the efficacy and sustainability of ingredients in skincare products.   Reference: Aflatooni S, Boby A, Natarelli N, Albers S. Snails and Skin: A Systematic Review on the  Effects of Snail-based Products on Skin Health. Journal of Integrative Dermatology . Published online October 31, 2023. McDermott M, Cerullo AR, Parziale J, Achrak E, Sultana S, Ferd J, Samad S, Deng W,  Braunschweig AB, Holford M. Advancing Discovery of Snail Mucins Function and Application. Front Bioeng Biotechnol. 2021 Oct 11;9:734023. doi: 10.3389/fbioe.2021.734023. PMID: 34708024; PMCID: PMC8542881. Singh N,  Brown AN,  Gold MH.  Snail extract for skin: A review of uses, projections, and  limitations. J Cosmet Dermatol .  2024; 23: 1113-1121. doi: 10.1111/jocd.16269 Yongeun Kim, Woo-Jin Sim, Jeong-seok Lee, Tae-Gyu Lim, Snail mucin is a functional food  ingredient for skin, Journal of Functional Foods, Volume 92, 2022, 105053, ISSN 1756-4646, https://doi.org/10.1016/j.jff.2022.105053 . Zhu K, Zhang Z, Li G, Sun J, Gu T, Ain NU, Zhang X, Li D. Extraction, structure,  pharmacological activities and applications of polysaccharides and proteins isolated from snail mucus. Int J Biol Macromol. 2024 Feb;258(Pt 1):128878. doi: 10.1016/j.ijbiomac.2023.128878. Epub 2023 Dec 21. PMID: 38141709.

While the harmful effects of smoking on overall health are widely recognised, its impact on the oral microbiome is still not fully understood, despite its significant health implications. This gap highlights the importance of delving deeper into the complex interplay between smoking and alterations in oral microbial communities.  What We Know: Smoking unleashes toxic compounds into the oral cavity, fostering unstable bacterial growth in biofilms, elevating saliva acidity, depleting oxygen levels, altering bacterial attachment, promoting antibiotic resistance, and compromising immune responses (Mohammed et al., 2024) . Smokers exhibit distinctive oral microbiome profiles, featuring reduced bacterial diversity and specific increases in inflammation and disease-associated bacteria. Notably, smokers display lower levels of Neisseria, Porphyromonas , and Capnocytophaga , while showing higher levels of Actinomyces, Veillonella, Streptococcus , and anaerobic bacteria (Wu et al., 2016) . Research comparing cigarette and smokeless tobacco users highlights a more diverse oral bacterial community among tobacco users, characterised by higher Firmicutes  and lower Proteobacteria  abundance. Notably, tobacco users may harbour opportunistic pathogens like Neisseria subflava, Bulleidia moorei , and Porphyromonas endodontalis   (Chattopadhyay et al., 2024) . Geographic and ethnic variations further underscore the complexity of smoking's impact on oral microbiomes. A study on an American smoking population showed reduced levels of Proteobacteria  and genera involved in carbohydrate, energy, and xenobiotic metabolism, supporting the hypothesis that smoking depletes oxygen levels in the oral cavity. Meanwhile, research on a Puerto Rican smoking group showed that Proteobacteria , associated with cardiovascular and metabolic conditions, were highly enriched in smokers, as did a study on a Qatari population (Mohammed et al., 2024) . Industry Impact and Potential: While smoking's detrimental effects are widely acknowledged, further investigation into its impact on the oral microbiome is essential. Comprehensive, age-specific studies are pivotal to elucidate smoking's influence on oral microbiota and its role in disease progression (Mohammed et al., 2024) .   Addressing these knowledge gaps is crucial to understanding smoking's intricate interplay with oral microbiome dysbiosis and chronic mouth diseases, laying the groundwork for more targeted prevention and treatment strategies. Preserving oral microbiome integrity is one clear path forward (Mohammed et al., 2024) . Our Solution: At Sequential, we specialise in oral microbiome analysis and product development, pioneering microbiome-friendly solutions. Leveraging our expertise, we stand poised to collaborate with your company in crafting innovative products that nurture a healthy oral microbiome and enhance microbiota diversity for consumers. References: Chattopadhyay, S., Malayil, L., Chopyk, J., Smyth, E., Kulkarni, P., Raspanti, G., Thomas, S.B., Sapkota, A., Mongodin, E.F. & Sapkota, A.R. (2024) Oral microbiome dysbiosis among cigarette smokers and smokeless tobacco users compared to non-users. Scientific Reports . 14 (1), 10394. doi:10.1038/s41598-024-60730-2. Mohammed, L.I., Zakaria, Z.Z., Benslimane, F.M. & Al-Asmakh, M. (2024) Exploring the role of oral microbiome dysbiosis in cardiometabolic syndrome and smoking. Experimental Lung Research . 50 (1), 65–84. doi:10.1080/01902148.2024.2331185. Wu, J., Peters, B.A., Dominianni, C., Zhang, Y., Pei, Z., Yang, L., Ma, Y., Purdue, M.P., Jacobs, E.J., Gapstur, S.M., Li, H., Alekseyenko, A.V., Hayes, R.B. & Ahn, J. (2016) Cigarette smoking and the oral microbiome in a large study of American adults. The ISME Journal . 10 (10), 2435–2446. doi:10.1038/ismej.2016.37.

The underarm (axillary) microbiome plays a crucial role in body odour production. Although deodorants and fragranced cosmetic products are designed to prevent perspiration and malodour, their impact on these microbial communities has not been extensively studied. What We Know: The human armpit harbours a dense and diverse bacterial community, with recent studies revealing significant variation in armpit bacteria among individuals, more so than in other body areas. This variation is partly due to the use of personal hygiene products, especially deodorants and antiperspirants (Urban et al., 2016). Armpit bacterial communities primarily consist of Corynebacterium, Staphylococcus, Betaproteobacteria, Clostridiales, Lactobacillus, Propionibacterium and Streptococcus species. However, there is significant variability in these communities across different individuals (Urban et al., 2016). Research shows that deodorants and antiperspirants affect the diversity and composition of armpit bacterial communities. Despite these findings, more research is needed to understand the full impact of these products on human health (Chang & Wang, 2023). The high variability of armpit bacterial communities contrasts with the more stable bacterial compositions found in other skin areas, likely due to less frequent use of hygiene products there. Although this variability might be influenced by random factors or the presence of dead bacteria, studies indicate that the common armpit bacteria are among the most metabolically active and contribute significantly to body odour (Urban et al., 2016). Industry Impact and Potential: The broader health implications of antiperspirant and deodorant use are not well studied. While some have suggested a potential link between these products and breast cancer incidence or age of diagnosis, evidence supporting this association is inconsistent and not conclusive (McGrath, 2003; Hardefeldt, Edirimanne & Eslick, 2013). Nonetheless, due to these growing concerns about potential health risks and additionally the harmful effects of deodorants on the environment, researchers are seeking more sustainable alternatives (Chang & Wang, 2023). Additionally, exploring how hygiene products influence the axillary microbiome offers valuable insights into how human behaviour affects microbial communities, especially since over 90% of adults in the US use antiperspirants or deodorants regularly (Benohanian, 2001). We have been fortunate enough to work with pioneers in this field, like Arcaea - who developed their novel odour-preventative technology, based on the armpit microbiome in 2023. Our Solution: Sequential is a leading expert in comprehensive, end-to-end Microbiome Product Testing and Formulation. Our specialised and customisable services empower businesses to innovate microbiome-friendly products confidently, ensuring their effectiveness and compatibility for a healthier microbiome. Let us support your development efforts, particularly in facial, oral, scalp, and vaginal microbiome research and production formulation. References: Benohanian, A. (2001) Antiperspirants and deodorants. Clinics in Dermatology. 19 (4), 398–405. doi:10.1016/S0738-081X(01)00192-4. Chang, Y. & Wang, X. (2023) Sweat and odor in sportswear – A review. iScience. 26 (7). doi:10.1016/j.isci.2023.107067. Hardefeldt, P.J., Edirimanne, S. & Eslick, G.D. (2013) Deodorant Use and Breast Cancer Risk. Epidemiology. 24 (1), 172. doi:10.1097/EDE.0b013e3182781684. McGrath, K.G. (2003) An earlier age of breast cancer diagnosis related to more frequent use of antiperspirants/deodorants and underarm shaving. European Journal of Cancer Prevention. 12 (6), 479. Urban, J., Fergus, D.J., Savage, A.M., Ehlers, M., Menninger, H.L., Dunn, R.R. & Horvath, J.E. (2016) The effect of habitual and experimental antiperspirant and deodorant product use on the armpit microbiome. PeerJ. 4, e1605. doi:10.7717/peerj.1605.

Malaria remains one of the deadliest diseases of the last century, posing a significant global health challenge. Researchers are continually exploring innovative methods to treat and prevent the disease, with recent studies suggesting that the skin microbiome may play a crucial role in influencing malaria transmission and severity. What We Know: Malaria is caused by the Plasmodium parasite, which is carried by Anopheles mosquitoes, and reproduces inside a human host after a bite. Over 90 countries are affected by malaria, and although the mortality rate has decreased significantly over the last century, the disease remains a major global health challenge (Garcia, 2010). In 2022, there were still 247 million malaria cases and 619,000 deaths worldwide (World Health Organization, 2023). When selecting its blood host, the Anopheles mosquito is largely influenced by human body odour. Therefore, the skin microbiome plays a significant role in this process, as it is responsible for the composition of volatile organic compounds (VOCs), which are a major component of body odour (Verhulst et al., 2010). Skin secretions contain over 500 VOCs, including acids, alcohols, aldehydes, esters and ketones and Anopheles mosquitoes exhibbit electrophysiological and behavioural responses to several of these VOCs. Specific VOCs (butanoic acid, carbon dioxide, lactic acid and propanoic acid) have demonstrated an attractive quality for Anopheles mosquitoes. Meanwhile, other VOCs, including aldehydes (decanal, octanal, nonanal) and ketones (geranylacetone and 6-methyl-5-hepten-2-one) repelled mosquitoes (Showering et al., 2022). Industry Impact and Potential: Research found that Anopheles was attracted to microbial VOCs produced by Staphylococcus and was repelled by those produced by Corynebacterium. Abundance of the latter has been linked to increased levels of hexanoic acid in body odour, which may act as a contextual repellent (Showering et al., 2022). However, further insights into the mechanisms of attractive and repellent microbial VOCs are needed, and could pave the way for developing mosquito repellents with diverse modes of action (Showering et al., 2022). Modifying the human skin microbiome to produce fewer mosquito attractants or to generate repellents has the potential to decrease mosquito bites and prevent the spread of deadly mosquito-borne diseases (Coutinho-Abreu et al., 2023). Our Solution: At Sequential, we specialise in comprehensive Microbiome Product Testing tailored to meet your specific goals in formulating products, such as mosquito repellent. Our expertise and customised services empower businesses to innovate confidently in developing topical solutions. We facilitate microbiome studies to ensure these products are maintain the microbiome, promoting efficacy and compatibility for healthier skin. References: Coutinho-Abreu, I., Jamshidi, O., Raban, R., Atabakhsh, A., Merriman, J., Fischbach, M. & Akbari, O. (2023) Identification of human skin microbiome odorants that manipulate mosquito landing behavior. bioRxiv : the preprint server for biology. doi:10.1101/2023.08.19.553996. Garcia, L.S. (2010) Malaria. Clinics in Laboratory Medicine. 30 (1), 93–129. doi:10.1016/j.cll.2009.10.001. Showering, Martinez, J., Benavente, E., Gezan, S., Jones, R., Oke, C., Tytheridge, S., Pretorius, E., Scott, D., Allen, R., D’Alessandro, U., Lindsay, S., Armour, J., Pickett, J. & Logan, J. (2022) Skin microbiome alters attractiveness to Anopheles mosquitoes. BMC microbiology. 22 (1). doi:10.1186/s12866-022-02502-4. Verhulst, N.O., Andriessen, R., Groenhagen, U., Kiss, G.B., Schulz, S., Takken, W., Loon, J.J.A. van, Schraa, G. & Smallegange, R.C. (2010) Differential Attraction of Malaria Mosquitoes to Volatile Blends Produced by Human Skin Bacteria. PLOS ONE. 5 (12), e15829. doi:10.1371/journal.pone.0015829. World Health Organization (2023) Google-Books-ID: u6UOEQAAQBAJ. World malaria report 2023. World Health Organization.

Sensitive skin (SS), also known as cutaneous sensory syndrome, is characterised by abnormal hypersensitivity to various stimuli, leading to symptoms such as itching, irritation, redness, dryness, and sensations of tightness, stinging, and pain. While many factors contribute to SS, research is increasingly focusing on the role of the skin microbiome in this condition. What We Know: SS can affect individuals with both normal and disrupted skin barriers. Symptoms suggest the involvement of cutaneous nerve endings, which research has confirmed. Additionally, epidermal cells, sensory proteins, skin barrier disruption, and immune mechanisms may play roles. Some studies indicate that an impaired skin barrier and drier skin, associated with increased mast cell degranulation, underlie SS (Seite & Misery, 2018). Commensal bacteria reside in the epidermis, where pain and itch receptors (nociceptors and proprioceptors) are located. The skin microbiota influences mast cell movement, location, and development in the skin, and can directly stimulate pain and itch receptors. Therefore, it is suggested that bacteria may contribute to SS development, but more research is needed to fully elucidate this link​ (Seite & Misery, 2018). Studies have shown that the facial microbiome of individuals with SS differs from those with normal skin. Increased levels of Actinomyces, Microbotryomycetes, Dermabacter, Chryseobacterium, Rhodotorula, Peptoniphilus, Cutibacterium, Corynebacterium , and Staphylococcus  were observed in SS. Notably,  Dermabacter hominis  was also more prevalent in the SS group, while Streptococcus  strains and Acidimicrobiia  levels were decreased when compared to the controls​ (Lu, Cheng & Shi, 2024). Industry Impact and Potential: Skincare product formulations designed to manage inflammation and support the skin barrier and microbiota diversity are emerging. These advanced formulas often include prebiotics, which can stimulate or inhibit bacterial growth (Seite & Misery, 2018). Specifically, new moisturiser formulations that include prebiotic ingredients that act directly on the skin microbiota are promising. This is because bacterial growth is sensitive to free water, and while traditional moisturisers improve surface hydration by binding water to skin cells, they do not increase free water levels. Non-pathogenic bacterial extracts may also be added to effectively support skin health by modulating the microbiota and reducing inflammation (Seite & Misery, 2018). Our Solution: Sequential offers comprehensive services to evaluate product impacts and formulations, leveraging a vast database of over 20,000 microbiome samples and 4,000 ingredients, and a global network of over 10,000 testing participants. Our team of experts will help your business develop innovative skincare solutions for sensitive skin that work with the microbiome to achieve optimal skin health. References: Lu, Y.-N., Cheng, L. & Shi, X.-M. (2024) Correlation between the facial skin microbiome and sensitive skin using the 2bRAD-M technique. International Journal of Cosmetic Science. doi:10.1111/ics.12941. Seite, S. & Misery, L. (2018) Skin sensitivity and skin microbiota: Is there a link? Experimental Dermatology. 27 (9), 1061–1064. doi:10.1111/exd.13686.

Rosemary oil has become increasingly popular in the hair care cosmetics industry, praised for its potential to improve scalp health and promote hair growth. Subsequent research has focused on examining the impact of rosemary oil products on the scalp microbiome and the treatment of various scalp conditions. What We Know: Rosmarinus officinalis, commonly known as rosemary, is a familiar aromatic household plant characterised by needle-like leaves. This medicinal plant is also renowned for its various beneficial properties, including cardiovascular health, nervous disorders treatment and notably, benefits for hair and scalp health through its ability to enhance microcapillary perfusion (Begum et al., 2023). Rosemary is also known to possess anti-inflammatory properties due to its rich content of phenolic phytoconstituents (Hashem et al., 2024). Moreover, the chemical composition of the plant encompasses essential oils containing primary constituents such as camphene, camphor, cineol and borneol. Additionally, it is known to harbour abundant flavonoids, bitter principles, tannins and terpenoids, alongside amino acids, steroids, glycosides, volatile oils and vitamins (Begum et al., 2023). Industry Impact and Potential: A study found that rosemary oil was as clinically effective as 2% minoxidil for treating androgenetic alopecia. Additionally, participants using rosemary oil experienced less scalp itching compared to those using minoxidil (Panahi et al., 2015). Hair lotion with 1% methanolic extract of R. officinalis administered in a study on mice demonstrated significant hair growth promoting activity, when compared to the control of 2% minoxidil hair lotion (Begum et al., 2023). An additional study combined rosemary with neem (Azadirachta indica) to create hair gel and leave-in products to treat dandruff. The products were successful, proving more effective than ketoconazole (a conventional antifungal agent) at managing Malassezia furfur, a dandruff-causing fungus, and Trichophyton rubrum, which is also associated with scalp disorders. Their products showed strong anti-inflammatory activity and also proved more effective than minoxidil in promoting hair growth (Hashem et al., 2024). Research like this suggests the potential of medicinal herbs like rosemary as natural and cost-effective ingredients to explore for targeting the scalp microbiome to treat diverse scalp conditions, thereby enhancing overall scalp and hair health (Hashem et al., 2024). Our Solution: With a database of 20,000 microbiome samples and 4,000 ingredients and a global network of 10,000 testing participants, Sequential provides tailored solutions for custom microbiome studies and product formulation. Considering rosemary oil-based scalp health products, our commitment to creating microbiome-safe and friendly formulations ensures the preservation of biome integrity. References: Begum, A., S, S., N, A.K. & Ali, S.S. (2023) Evaluation of Herbal Hair Lotion loaded with Rosemary for Possible Hair Growth in C57BL/6 Mice. Advanced Biomedical Research. 12, 60. doi:10.4103/abr.abr_306_21. Hashem, M.M., Attia, D., Hashem, Y.A., Hendy, M.S., AbdelBasset, S., Adel, F. & Salama, M.M. (2024) Rosemary and neem: an insight into their combined anti-dandruff and anti-hair loss efficacy. Scientific Reports. 14 (1), 7780. doi:10.1038/s41598-024-57838-w. Panahi, Y., Taghizadeh, M., Marzony, E.T. & Sahebkar, A. (2015) Rosemary oil vs minoxidil 2% for the treatment of androgenetic alopecia: a randomized comparative trial. Skinmed. 13 (1), 15–21.

Although the ageing process is complex and individualised, research highlights the significant role of the skin microbiome in skin ageing. Various topical ingredients show promise in supporting the microbiome. What We Know: The skin microbiome is known to play a significant role in barrier function. A characteristic feature of ageing skin is the decline in barrier function, causing decreased moisture retention, increased vulnerability and a decrease in overall skin integrity (Woo & Kim, 2024). Research has also identified significant changes in the skin microbiota of elderly people, marked by a decrease in Cutibacterium and an increase in Corynebacterium and Proteobacteria. Specifically, studies have linked an increased abundance of Corynebacterium species to a higher incidence of erythrasma in the elderly (Salemi et al., 2022). These findings highlight a potential connection between age-related changes in skin microbiota and the occurrence of specific skin conditions, emphasising the importance of microbiota composition in maintaining skin health and understanding disease manifestation in older populations (Woo & Kim, 2024). Industry Impact and Potential: The pursuit of healthy, resilient skin has resulted in innovative therapies. Notably, specific moisturisers, antioxidant-rich products, probiotics, prebiotics, postbiotics and effective UV protection show promise for strengthening the ageing skin barrier and addressing dysbiosis (Woo & Kim, 2024). Topical antioxidant ingredients (including α-tocopherol (free vitamin E), vitamin C, ferulic acid, resveratrol and niacinamide) have the ability to strengthen impaired skin barriers and protect the skin against oxidative stress. In addition, UV-protecting products also offer benefit to the skin microbiome, but their properties are enhanced with the addition of barrier-enforcing lipid formulations (i.e., ceramide-containing sunscreens) and antioxidants (i.e., sunscreens containing pre-tocopheryl) (Woo & Kim, 2024). Probiotics, prebiotics and postbiotics have shown efficacy in enhancing stratum corneum hydration, reducing wrinkle depth and offering photoprotective properties, ultimately supporting skin barrier health (Woo & Kim, 2024). Topical Epidermidibacterium Keratini EPI-7 ferment filtrate applied twice daily for three weeks caused significant improvement in skin hydration, elasticity and dermal density. Furthermore, an increase in beneficial commensal microorganisms like Cutibacterium, Corynebacterium, Staphylococcus, Streptococcus, Clostridium, Lawsonella, Rothia, Lactobacillus and Prevotella was also observed (Kim et al., 2023). Our Solution: Sequential specialises in comprehensive microbiome product testing, customised to align with your unique anti-aging product development and formulation goals. With our expert guidance and tailored services, we empower businesses to pioneer innovative strategies for creating anti-aging solutions. References: Kim, J., Lee, Y.I., Mun, S., Jeong, J., Lee, D.-G., Kim, M., Jo, H., Lee, S., Han, K. & Lee, J.H. (2023) Efficacy and Safety of Epidermidibacterium Keratini EPI-7 Derived Postbiotics in Skin Aging: A Prospective Clinical Study. International Journal of Molecular Sciences. 24 (5). doi:10.3390/ijms24054634. Salemi, S.Z., Memar, M.Y., Kafil, H.S., Sadeghi, J., Ghadim, H.H., Alamdari, H.A., Nezhadi, J. & Ghotaslou, R. (2022) The Prevalence and Antibiotics Susceptibility Patterns of Corynebacterium minutissimum Isolates from Skin Lesions of Patients with Suspected Erythrasma from Tabriz, Iran M. Adnan (ed.). Canadian Journal of Infectious Diseases and Medical Microbiology. 2022, 4016173. doi:10.1155/2022/4016173. Woo, Y.R. & Kim, H.S. (2024) Interaction between the microbiota and the skin barrier in aging skin: a comprehensive review. Frontiers in Physiology. 15, 1322205. doi:10.3389/fphys.2024.1322205.

The skin microbiome, a complex ecosystem of bacteria, fungi, viruses, and other microorganisms living on our skin, plays a crucial role in maintaining skin health (Berg et al., 2020). The microbiome acts as a protective barrier, helps in wound healing, and regulates the immune system. An imbalance in the microbiome can result in various skin conditions, such as acne, eczema, and psoriasis. Historically, traditional skincare formulations have often taken a one-size-fits-all approach, which may not be effective for everyone due to individual differences in skin microbiomes. As a result, new approaches are being adopted within the industry to facilitate the transition to better researched solutions. With growing demand for unique formulations, and diagnostic tools, the industry has opened its arms to new technologies that can facilitate research within the space. Artificial Intelligence (AI) has become a central player in transforming our understanding and treatment of the skin microbiome, leading to innovative solutions in product development and clinical research (Sun et al., 2023). AI and machine learning is now being adopted on a global scale in various industries as a way of redefining workflows and increasing efficiency. This article will outline how AI can be applied to microbiome research, evaluating its potential uses as well as constraints. As this technology continues to develop, AI powered insights will likely play an important role in microbiome research and intervention in the future. What is Artificial Intelligence? Artificial intelligence (AI) “refers to the ability of any machines which can stimulate the intelligence of higher organisms” (Bhardwaj et al., 2022). AI is a branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, understanding natural language, and even mimicking human interactions. AI encompasses a wide range of technologies and methodologies, such as: Machine learning: where algorithms improve through experience Deep learning: which involves neural networks with many layers Artificial neural networks (ANN): computing systems inspired by biological neural networks, designed to recognize patterns and solve problems through a layered architecture of interconnected nodes or "neurons" that process information and learn from data Natural language processing: which allows machines to understand and respond to human language Computer vision: enabling machines to interpret and make decisions based on visual inputs​ At its core, AI operates by processing large amounts of data, identifying correlations and patterns, and making predictions or decisions based on this analysis. This capability has led to AI's integration into various fields, including healthcare and wellness. For example, AI is used in medical diagnostics to analyse medical images, predict disease outcomes, drug discovery, and even radiography (Al-Antari, 2023). The development of AI continues to advance, pushing the boundaries of what machines can achieve and transforming numerous aspects of different industries. AI’s application in microbiome work AI and classical machine learning methodologies have been used in microbiome studies for more than a decade, with research articles highlighting its uses as far back as 2013. Microbiome research at its core is driven by large amounts of data centred around different types of sequencing technologies: Amplicon sequencing - 16S, 18S, ITS gene sequencing for taxonomic identification Metagenomics - deep sequencing to characterise the collective genomes of microorganisms, and infer function Metatranscriptomics - RNA sequencing on multiple organisms (human, bacteria, fungi, viruses etc) to understand gene activity across organisms Metaproteomics - Assess protein expression to characterise multiple organisms (human, bacteria, fungi, viruses etc) Metabolomics - Sequence small molecule production/consumption on multiple organisms (human, bacteria, fungi, viruses etc) The results from these bioinformatic tools can be used effectively as inputs for AI models. AI excels with large and complex datasets and can manage data gaps that usually pose problems for traditional statistics. By employing AI and machine learning, we can efficiently process the huge amounts of data generated by microbiome testing. AI models use embedded feature selection to identify the most relevant data features during training, eliminating the need to analyse the entire dataset each time. Potential applications for AI in skin microbiome testing Microbiome Profiling and Diversity Analysis Disease Biomarker Discovery Microbiome-Host Interaction Analysis Skin Microbiome-Based Therapeutics Development Personalised Skincare and Treatment Optimization Microbiome-Based Product Development and Formulation Optimization Longitudinal Monitoring and Predictive Modeling Study 1: Microbiome Profiling and Neural Networks Recent advances in high-throughput sequencing technologies have made microbiome profiles publicly accessible, revealing distinct profiles for healthy and diseased individuals and suggesting their potential as diagnostic tools. However, the complexity of metagenomic data poses challenges for current machine learning models. To address this, the study proposes MetaNN, a neural network framework that uses a new data augmentation technique to reduce overfitting. MetaNN significantly improves classification accuracy for both synthetic and real metagenomic data, outperforming existing models and paving the way for personalised treatments for microbiome-related diseases (Lo & Marculescu, 2019). Study 2: AI and Longitudinal Data The study investigated how the human microbiome changes dynamically over time due to factors like diet and medical interventions. It introduced 'phyLoSTM,' a deep learning framework that combined Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) to extract features and analyse temporal dependencies in longitudinal microbiome data along with environmental factors for disease prediction. The framework also managed variable time points and balanced weights between imbalanced cases and controls. Testing on 100 simulated datasets and two real longitudinal studies demonstrated that phyLoSTM achieved higher predictive accuracy, with AUC improvements of 5% in simulated studies and significant gains in real studies compared to Random Forest, enhancing the prediction of disease outcomes from microbiome data (Sharma & Shu, 2021). Limitations While it is undeniable that AI presents a promising future when it comes to advancing the workflows in microbiome testing, several notable limitations need to be addressed before AI models can reach their full potential in the field. Interpretability: “but why?” Transitioning from input to output in AI systems is straightforward, but in healthcare understanding the "why" behind decisions is crucial. AI, deep learning especially, often struggles with transparency, making it difficult to interpret how conclusions are reached. This lack of interpretability can lead to legal issues and the possibility of unknown factors influencing decisions. Without insight into the model's logic, trust in its output is limited. There's also the risk of confusing correlation with causation, meaning that just because data points are correlated, it doesn't imply one causes the other. Proper study design and longitudinal data are essential to provide context and distinguish between cases and controls. Efforts are underway to improve AI interpretability by identifying the importance of different predictors within models. Incorporating prior knowledge into model creation can help guide the AI, adding constraints, and enhancing performance. This approach not only improves the AI's accuracy but also makes its decision-making process more transparent and trustworthy. Data quality: “garbage in, garbage out” While the method used in AI is important, it's equally crucial to examine how the data is structured to ensure it is relevant and useful. Blindly trusting data produced by AI, especially in personal care and healthcare, can lead to significant problems. The quality of AI outputs heavily depends on the quality and quantity of input data.In the context of the microbiome, gathering suitable datasets can be challenging: the field is still evolving, and much remains unexplained. The field is still evolving, and much remains unexplained. Additionally, microbiome data is highly complex and influenced by numerous contextual factors. We need comprehensive datasets to train AI models effectively, and without them, the application of AI in microbiome research will be limited. Thus, it's vital to continue uncovering the intricacies of the microbiome to enhance the effectiveness of AI models in this area. Conclusion Moving forward, AI is poised to become increasingly prevalent in microbiome research, significantly simplifying the processes for bioinformaticians. By automating the analysis of complex datasets and enhancing the precision of predictive models, AI will streamline workflows, reducing the time and effort required for data interpretation and hypothesis generation. This technological advancement not only accelerates the pace of discovery but also enables more personalised and effective interventions, heralding a new era in microbiome research where scientists can leverage AI to unlock deeper insights and drive innovation in health and wellness. References Al-Antari MA. Artificial Intelligence for Medical Diagnostics-Existing and Future AI Technology! Diagnostics (Basel). 2023 Feb 12;13(4):688. doi: 10.3390/diagnostics13040688. PMID: 36832175; PMCID: PMC9955430. Berg G, Rybakova D, Fischer D, Cernava T, Vergès MC, Charles T, Chen X, Cocolin L, Eversole K, Corral GH, Kazou M, Kinkel L, Lange L, Lima N, Loy A, Macklin JA, Maguin E, Mauchline T, McClure R, Mitter B, Ryan M, Sarand I, Smidt H, Schelkle B, Roume H, Kiran GS, Selvin J, Souza RSC, van Overbeek L, Singh BK, Wagner M, Walsh A, Sessitsch A, Schloter M. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020 Jun 30;8(1):103. doi: 10.1186/s40168-020-00875-0. Erratum in: Microbiome. 2020 Aug 20;8(1):119. doi: 10.1186/s40168-020-00905-x. PMID: 32605663; PMCID: PMC7329523. Lo C, Marculescu R. MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks. BMC Bioinformatics. 2019 Jun 20;20(Suppl 12):314. doi: 10.1186/s12859-019-2833-2. PMID: 31216991; PMCID: PMC6584521. Sharma D, Xu W. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Bioinformatics. 2021 Nov 5;37(21):3707-3714. doi: 10.1093/bioinformatics/btab482. PMID: 34213529. Sun T, Niu X, He Q, Chen F, Qi RQ. Artificial Intelligence in microbiomes analysis: A review of applications in dermatology. Front Microbiol. 2023 Feb 1;14:1112010. doi: 10.3389/fmicb.2023.1112010. PMID: 36819026; PMCID: PMC9929457.