Flexible model-based non-negative matrix factorization with application to mutational signatures.

Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. Our novel estimation procedure is based on the expectation-maximization (EM) algorithm and regression in the log-linear quasi-Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework in a large simulation study where we compare to state of the art methods, and show results for three data sets of somatic mutation counts from patients with cancer in the breast, Liver and urinary tract.

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification.

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorization based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high-quality phase spectral and spatial signatures via consecutive factorizations. After validating PSNMF with synthetic data sets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nanomineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, but data sets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis.

Single-cell dissection reveals the role of DNA damage response patterns in tumor microenvironment components contributing to colorectal cancer progression and immunotherapy.

Colorectal cancer (CRC) is one of the leading malignant cancers. DNA damage response (DDR), referring to the molecular process of DNA damage, is emerging as a promising field in targeted cancer therapy. However, the engagement of DDR in the remodeling of the tumor microenvironment is rarely studied. In this study, by sequential nonnegative matrix factorization (NMF) algorithm, pseudotime analysis, cell-cell interaction analysis, and SCENIC analysis, we have shown that DDR genes demonstrate various patterns among different cell types in CRC TME (tumor microenvironment), especially in epithelial cells, cancer-associated fibroblasts, CD8+ T cells, tumor-associated macrophages, which enhance the intensity of intercellular communication and transcription factor activation. Furthermore, based on the newly identified DDR-related TME signatures, cell subtypes including MNAT+CD8+T_cells-C5, POLR2E+Mac-C10, HMGB2+Epi-C4, HMGB1+Mac-C11, PER1+Mac-C5, PER1+CD8+T_cells-C1, POLR2A+Mac-C1, TDG+Epi-C5, TDG+CD8+T_cells-C8 are determined as critical prognostic factors for CRC patients and predictors of immune checkpoint blockade (ICB) therapy efficacy in two public CRC cohorts, TCGA-COAD and GSE39582. Our novel and systematic analysis on the level of the single-cell analysis has revealed the unique role of DDR in remodeling CRC TME for the first time, facilitating the prediction of prognosis and guidance of personalized ICB regimens in CRC.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM.

BACKGROUND: Anoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays. RESULTS: NMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion. CONCLUSION: This pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

Impact of fed-batch process intensification on the productivity and product quality of two CHO cell lines expressing unique novel molecular format proteins.

While monospecific antibodies have long been the foundational offering of protein therapeutics, recent advancements in antibody engineering have allowed for the development of far more complex antibody structures. Novel molecular format (NMF) proteins, such as bispecific antibodies (BsAbs), are structures capable of multispecific binding, allowing for expanded therapeutic functionality. As demand for NMF proteins continues to rise, biomanufacturers face the challenge of increasing bioreactor process productivity while simultaneously maintaining consistent product quality. This challenge is exacerbated when producing structurally complex proteins with asymmetric modalities, as seen in NMFs. In this study, the impact of a high inoculation density (HID) fed-batch process on the productivity and product quality attributes of two CHO cell lines expressing unique NMFs, a monospecific antibody with an Fc-fusion protein and a bispecific antibody, compared to low inoculation density (LID) platform fed-batch processes was evaluated. It was observed that an intensified platform fed-batch process increased product concentrations by 33 and 109% for the two uniquely structured complex proteins in a shorter culture duration while maintaining similar product quality attributes to traditional fed-batch processes.

Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease.

INTRODUCTION: Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. METHODS: We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes. RESULTS: We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. DISCUSSION: Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. HIGHLIGHTS: NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.

Inferring Drug Set and Identifying the Mechanism of Drugs for PC3.

Drug repurposing is a strategy for discovering new applications of existing drugs for use in various diseases. Despite the use of structured networks in drug research, it is still unclear how drugs interact with one another or with genes. Prostate adenocarcinoma is the second leading cause of cancer mortality in the United States, with an estimated incidence of 288,300 new cases and 34,700 deaths in 2023. In our study, we used integrative information from genes, pathways, and drugs for machine learning methods such as clustering, feature selection, and enrichment pathway analysis. We investigated how drugs affect drugs and how drugs affect genes in human pancreatic cancer cell lines that were derived from bone metastases of grade IV prostate cancer. Finally, we identified significant drug interactions within or between clusters, such as estradiol-rosiglitazone, estradiol-diclofenac, troglitazone-rosiglitazone, celecoxib-rofecoxib, celecoxib-diclofenac, and sodium phenylbutyrate-valproic acid.

Robust Multi-Dimensional Time Series Forecasting.

Large-scale and high-dimensional time series data are widely generated in modern applications such as intelligent transportation and environmental monitoring. However, such data contains much noise, outliers, and missing values due to interference during measurement or transmission. Directly forecasting such types of data (i.e., anomalous data) can be extremely challenging. The traditional method to deal with anomalies is to cut out the time series with anomalous value entries or replace the data. Both methods may lose important knowledge from the original data. In this paper, we propose a multidimensional time series forecasting framework that can better handle anomalous values: the robust temporal nonnegative matrix factorization forecasting model (RTNMFFM) for multi-dimensional time series. RTNMFFM integrates the autoregressive regularizer into nonnegative matrix factorization (NMF) with the application of the L2,1 norm in NMF. This approach improves robustness and alleviates overfitting compared to standard methods. In addition, to improve the accuracy of model forecasts on severely missing data, we propose a periodic smoothing penalty that keeps the sparse time slices as close as possible to the time slice with high confidence. Finally, we train the model using the alternating gradient descent algorithm. Numerous experiments demonstrate that RTNMFFM provides better robustness and better prediction accuracy.

High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.

AIMS/HYPOTHESIS: Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways. METHODS: We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance. RESULTS: We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes. CONCLUSIONS/INTERPRETATION: Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.

MDSR-NMF: Multiple deconstruction single reconstruction deep neural network model for non-negative matrix factorization.

Dimension reduction is one of the most sought-after strategies to cope with high-dimensional ever-expanding datasets. To address this, a novel deep-learning architecture has been designed with multiple deconstruction and single reconstruction layers for non-negative matrix factorization aimed at low-rank approximation. This design ensures that the reconstructed input matrix has a unique pair of factor matrices. The two-stage approach, namely, pretraining and stacking, aids in the robustness of the architecture. The sigmoid function has been adjusted in such a way that fulfils the non-negativity criteria and also helps to alleviate the data-loss problem. Xavier initialization technique aids in the solution of the exploding or vanishing gradient problem. The objective function involves regularizer that ensures the best possible approximation of the input matrix. The superior performance of MDSR-NMF, over six well-known dimension reduction methods, has been demonstrated extensively using five datasets for classification and clustering. Computational complexity and convergence analysis have also been presented to establish the model.

Restoring natural upper limb movement through a wrist prosthetic module for partial hand amputees.

BACKGROUND: Most partial hand amputees experience limited wrist movement. The limited rotational wrist movement deteriorates natural upper limb system related to hand use and the usability of the prosthetic hand, which may cause secondary damage to the musculoskeletal system due to overuse of the upper limb affected by repetitive compensatory movement patterns. Nevertheless, partial hand prosthetics, in common, have only been proposed without rotational wrist movement because patients have various hand shapes, and a prosthetic hand should be attached to a narrow space. METHODS: We hypothesized that partial hand amputees, when using a prosthetic hand with a wrist rotation module, would achieve natural upper limb movement muscle synergy and motion analysis comparable to a control group. To validate the proposed prototype design with the wrist rotation module and verify our hypothesis, we compared a control group with partial hand amputees wearing hand prostheses, both with and without the wrist rotation module prototype. The study contained muscle synergy analysis through non-negative matrix factorization (NMF) using surface electromyography (sEMG) and motion analyses employing a motion capture system during the reach-to-grasp task. Additionally, we assessed the usability of the prototype design for partial hand amputees using the Jebsen-Taylor hand function test (JHFT). RESULTS: The results showed that the number of muscle synergies identified through NMF remained consistent at 3 for both the control group and amputees using a hand prosthesis with a wrist rotation module. In the motion analysis, a statistically significant difference was observed between the control group and the prosthetic hand without the wrist rotation module, indicating the presence of compensatory movements when utilizing a prosthetic hand lacking this module. Furthermore, among the amputees, the JHFT demonstrated a greater improvement in total score when using the prosthetic hand equipped with a wrist rotation module compared to the prosthetic hand without this module. CONCLUSION: In conclusion, integrating a wrist rotation module in prosthetic hand designs for partial hand amputees restores natural upper limb movement patterns, reduces compensatory movements, and prevent the secondary musculoskeletal. This highlights the importance of this module in enhancing overall functionality and quality of life.

Block-Active ADMM to Minimize NMF with Bregman Divergences.

Over the last ten years, there has been a significant interest in employing nonnegative matrix factorization (NMF) to reduce dimensionality to enable a more efficient clustering analysis in machine learning. This technique has been applied in various image processing applications within the fields of computer vision and sensor-based systems. Many algorithms exist to solve the NMF problem. Among these algorithms, the alternating direction method of multipliers (ADMM) and its variants are one of the most popular methods used in practice. In this paper, we propose a block-active ADMM method to minimize the NMF problem with general Bregman divergences. The subproblems in the ADMM are solved iteratively by a block-coordinate-descent-type (BCD-type) method. In particular, each block is chosen directly based on the stationary condition. As a result, we are able to use much fewer auxiliary variables and the proposed algorithm converges faster than the previously proposed algorithms. From the theoretical point of view, the proposed algorithm is proved to converge to a stationary point sublinearly. We also conduct a series of numerical experiments to demonstrate the superiority of the proposed algorithm.

Discovering Low-Dimensional Descriptions of Multineuronal Dependencies.

Coordinated activity in neural populations is crucial for information processing. Shedding light on the multivariate dependencies that shape multineuronal responses is important to understand neural codes. However, existing approaches based on pairwise linear correlations are inadequate at capturing complicated interaction patterns and miss features that shape aspects of the population function. Copula-based approaches address these shortcomings by extracting the dependence structures in the joint probability distribution of population responses. In this study, we aimed to dissect neural dependencies with a C-Vine copula approach coupled with normalizing flows for estimating copula densities. While this approach allows for more flexibility compared to fitting parametric copulas, drawing insights on the significance of these dependencies from large sets of copula densities is challenging. To alleviate this challenge, we used a weighted non-negative matrix factorization procedure to leverage shared latent features in neural population dependencies. We validated the method on simulated data and applied it on copulas we extracted from recordings of neurons in the mouse visual cortex as well as in the macaque motor cortex. Our findings reveal that neural dependencies occupy low-dimensional subspaces, but distinct modules are synergistically combined to give rise to diverse interaction patterns that may serve the population function.

Biomarkers in asthma in the context of atopic dermatitis in young children.

BACKGROUND: Diverse pathways stemming from a history of atopic dermatitis (AD) might modulate different biomarkers associated with the development of asthma. Biomarkers associated with AD and asthma separately have been investigated, but none have characterized a combined AD+asthma phenotype. We investigated the clinical and molecular characteristics associated with an AD+asthma phenotype compared with AD, asthma and controls. METHODS: From a prospective birth cohort and the outpatient allergy clinic, we included four groups of 6-12-year-old children: (1) healthy controls (2) previous, current, or present AD without asthma, (3) previous, current, or present AD and current asthma and (4) current asthma without AD. We performed clinical examinations and interviews and measured serum IgE, natural moisturizing factors (NMF), and plasma cytokine levels. RESULTS: We found an increased number of IgE sensitizations in AD+asthma, prominent after stratifying for food allergens (p < .05). Pro-Th2 cytokines CCL18, TSLP, and Eotaxin-3 were elevated in AD+asthma, though not significantly higher than asthma, and elevated in asthma compared with controls. NMF levels were decreased in AD compared with asthma and control groups (p = .019, p < .001, respectively). NMF levels correlated negatively to sensitization (p = .026), though nonsignificant with only the patient groups. CONCLUSION: Our results indicate that Th2 cytokines and increased number of sensitizations are associated with AD + asthma phenotypes compared with AD alone and that skin barrier impairment as well as decreased airway epithelial integrity may play a role in sensitization and immune modulation. Our findings suggest candidate biomarkers that should be further explored for their functional roles and prognostic potential.

Machine learning revealed molecular classification of colorectal cancer with negative lymph node metastasis.

Purpose: Accurate preoperative staging directly affects the treatment decision of patients with rectal cancer. However, our understanding of the immune subclasses of CRC without lymph node metastasis is still incomplete.Materials and methods: Here, we first analyzed the subclasses of CRC without lymph node metastasis on the Cancer Genome Atlas (TCGA) and verified its stability in the GSE39582 dataset. Four immune subclasses (C1-C4) were identified and verified by non-negative matrix factorization (NMF) of gene expression profiles. Then, ICI scores of six genes were constructed to characterize subclasses.Results: There were significant differences in metabolic and progression-associated signatures, immune characteristics, and clinical characteristics among subclasses. C3 represented a good prognosis with high TMB. C4 showed unique immune characteristics. We believe that C3 is the initial stage of CRC. After the C1 and C2 stages, it progresses to the C4 stage, and finally, lymph node metastasis occurs.Conclusions: This work may help to provide a basis for immunotherapy decision-making in early CRC and may guide personalized methods of cancer immunotherapy.

Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site.

Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO2, and O3) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and non-normalized data sets. We identify four factors with different temporal trends and properties for both normalized and non-normalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.

Prognostic implication of an energy metabolism-related 11-gene signature in lung cancer.

Energy metabolism therapy has gradually shown its potential in the treatment of tumor patients, but it has significant selectivity, thus distinguishing energy subtypes of lung adenocarcinoma (LUAD) is necessary to identify patients who may benefit from energy metabolism interference therapy. Gene expression data downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, molecular subtypes were selected using NMF algorithm, prognostic differentially expressed genes (DEGs) were identified with DESeq. 2 and survival package, Lasso and cox regression analysis were used to Construct of Risk Signature. The relationship between molecular subtypes and prognosis as well as clinical characteristics were evaluated. Univariate and multivariate COX regression were used to analyze the correlation between the signature and patient prognosis. Based on 592 energy metabolism-related genes, 430 LUAD samples were divided into three subtypes, of which C2 has the worst prognosis, and 942 prognostic DEGs were identified. 11-gene prognostic risk signature was constructed. Compared with the traditional clinical features of T, N, and age, this 11-gene signature performs better in predicting the risk of LUAD prognosis. At the same time, it is an independent risk factor for patient prognosis. The signature showed strong robustness in different cohorts. Compared with other published signatures, 11-gene signatures have strong clinical applicability and accuracy. The predictive signature will enable patients with LUAD to be more accurately managed in clinical practice.

Construction and validation of molecular subtypes of coronary artery disease based on ferroptosis-related genes.

BACKGROUND: This study aims to construct a reliable diagnostic model for coronary artery disease (CAD) patients and explore its potential mechanism by consensus molecular subtypes of ferroptosis-related genes. METHODS: GSE12288 and GSE20680 were downloaded from Gene Expression Omnibus database. CAD patients were divided into different molecular subtypes according to the expression level of ferroptosis-related genes. Then, the distribution of differentially expressed genes, functional annotations and immune infiltration cells between the two subtypes were compared. Finally, a prognostic model of ferroptosis-related genes in CAD was constructed and verified. RESULTS: Two different molecular subtypes of CAD were obtained according to the expression level of ferroptosis-related genes. Then, a total of 1944 differentially expressed genes (DEGs) were found, among which, 236 genes were up-regulated and 1708 genes were down-regulated. In addition, 43 DEGs were ferroptosis-related genes. Functional enrichment analysis showed that these DEGs between two subtypes of CAD were mainly enriched in immune-related pathways and processes, such as T cell receptor, mTOR, NOD-like receptor and Toll-like receptor signaling pathways. We also found that 21 immune cells were significantly changed between two subtypes of CAD. The LASSO method was performed to identify and construct the 16 ferroptosis-related genes-based diagnostic signature. Diagnostic efficiency of diagnostic signature measured by AUC in the training set and validation cohort was 0.971 and 0.899, respectively. CONCLUSIONS: This study contributes to a more comprehensive understanding of the mechanism of ferroptosis-related genes in CAD.

Confocal Raman spectroscopy is suitable to assess hair cleansing-derived skin dryness on human scalp.

BACKGROUND: The purpose of this pilot study was to provide information about the washout-dependent depletion of important skin components in the horny layer of the scalp. They were taken as markers for scalp drying effects of cosmetic cleansing products and were measured directly in vivo. METHOD: In vivo confocal Raman spectroscopy was used to measure the depletion of the total natural moisturizing factor (total NMF) and some of its components (urea and lactic acid) as well as a fraction of stratum corneum lipids, after repeated washing with a standard shampoo on the human scalp. RESULTS: The measurements showed a reduction in the amount of NMF and lipids of the stratum corneum caused by repeated shampooing. CONCLUSION: Confocal Raman spectroscopy is an innovative technology that can be used successfully in vivo on the hairy scalp. The loss of the most important skin components caused by hair washing can be quantified directly with this technology. The method is valuable to support the development cosmetic cleansing products, as it is suitable to directly compare the effects of different product candidates on the human scalp in a most realistic way.