Single color virtual H&E staining with In-and-Out Net.

Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics.

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

Portable Mid-Infrared Spectroscopy Combined with Chemometrics to Diagnose Fibromyalgia and Other Rheumatologic Syndromes Using Rapid Volumetric Absorptive Microsampling.

The diagnostic criteria for fibromyalgia (FM) have relied heavily on subjective reports of experienced symptoms coupled with examination-based evidence of diffuse tenderness due to the lack of reliable biomarkers. Rheumatic disorders that are common causes of chronic pain such as rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain are frequently found to be comorbid with FM. As a result, this can make the diagnosis of FM more challenging. We aim to develop a reliable classification algorithm using unique spectral profiles of portable FT-MIR that can be used as a real-time point-of-care device for the screening of FM. A novel volumetric absorptive microsampling (VAMS) technique ensured sample volume accuracies and minimized the variation introduced due to hematocrit-based bias. Blood samples from 337 subjects with different disorders (179 FM, 158 non-FM) collected with VAMS were analyzed. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. The OPLS-DA algorithm enabled the classification of the spectra into their corresponding classes with 84% accuracy, 83% sensitivity, and 85% specificity. The OPLS-DA regression plot indicated that spectral regions associated with amide bands and amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases.

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics.

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Rapid Biomarker-Based Diagnosis of Fibromyalgia Syndrome and Related Rheumatologic Disorders by Portable FT-IR Spectroscopic Techniques.

Fibromyalgia syndrome (FM), one of the most common illnesses that cause chronic widespread pain, continues to present significant diagnostic challenges. The objective of this study was to develop a rapid vibrational biomarker-based method for diagnosing fibromyalgia syndrome and related rheumatologic disorders (systemic lupus erythematosus (SLE), osteoarthritis (OA) and rheumatoid arthritis (RA)) through portable FT-IR techniques. Bloodspot samples were collected from patients diagnosed with FM (n = 122) and related rheumatologic disorders (n = 70), including SLE (n = 17), RA (n = 43), and OA (n = 10), and stored in conventional protein saver bloodspot cards. The blood samples were prepared by four different methods (blood aliquots, protein-precipitated extraction, and non-washed and water-washed semi-permeable membrane filtration extractions), and spectral data were collected with a portable FT-IR spectrometer. Pattern recognition analysis, OPLS-DA, was able to identify the signature profile and classify the spectra into corresponding classes (Rcv > 0.93) with excellent sensitivity and specificity. Peptide backbones and aromatic amino acids were predominant for the differentiation and might serve as candidate biomarkers for syndromes such as FM. This research evaluated the feasibility of portable FT-IR combined with chemometrics as an accurate and high-throughput tool for distinct spectral signatures of biomarkers related to the human syndrome (FM), which could allow for real-time and in-clinic diagnostics of FM.

Psychosocial and quality-of-life factors associated with depigmentation therapy for vitiligo.

BACKGROUND: There is a paucity of data examining the psychosocial factors relevant to depigmentation therapy, an irreversible treatment for vitiligo. This study explores patients' perspective and experience while undergoing depigmentation therapy and quality-of-life effects of such therapy. METHODS: An online instrument assessing the impact of depigmentation therapy on various psychosocial variables and including the validated Dermatology Life Quality Index (DLQI) were administered to two groups of participants with vitiligo: (1) those who are currently undergoing or have completed depigmentation therapy and (2) those with vitiligo who have not undergone depigmentation therapy but had considered it. Data were collected on psychosocial factors such as length of time until depigmentation therapy was offered, duration, financial burden, level of satisfaction, impact on life activities, and challenges faced during and after depigmentation therapy. DLQI scores were also measured. RESULTS: Thirty-five vitiligo patients who did not undergo depigmentation and 42 patients who did undergo depigmentation therapy were included in the study. Baseline characteristics were comparable between groups. Mean DLQI was higher for patients who did not undergo depigmentation than for those who underwent depigmentation (10.2 versus 5.3, p = 0.002), indicating worse quality-of-life in those not depigmenting. Patients who underwent depigmentation reported significantly less discomfort in various social situations after undergoing depigmentation therapy compared to how they felt before undergoing therapy and reported significantly less discomfort in these situations than patients who did not undergo depigmentation therapy. CONCLUSIONS: Despite potential challenges, depigmentation therapy appears to augment quality-of-life across various domains in individuals with vitiligo.

Understanding the primary health care experiences of individuals who are homeless in non-traditional clinic settings.

BACKGROUND: Despite the widespread implementation of Health Care for the Homeless programs that focus on comprehensive, integrated delivery systems of health care for people experiencing homelessness, engaging and retaining people experiencing homelessness in primary care remains a challenge. Few studies have looked at the primary care delivery model in non-traditional health care settings to understand the facilitators and barriers to engagement in care. The objective of our study was to explore the clinic encounters of individuals experiencing homelessness receiving care at two different sites served under a single Health Care for the Homeless program. METHODS: Semi-structured interviews were conducted with people experiencing homelessness for an explorative qualitative study. We used convenience sampling to recruit participants who were engaged in primary care at one of two sites: a shelter clinic, n = 16, and a mobile clinic located in a church, n = 15. We then used an iterative, thematic approach to identify emergent themes and further mapped these onto the Capability-Opportunity-Motivation model. RESULTS: Care accessibility, quality and integration were themes that were often identified by participants as being important facilitators to care. Psychological capability and capacity became important barriers to care in instances when patients had issues with memory or difficulty with perceiving psychological safety in healthcare settings. Motivation for engaging and continuing in care often came from a team of health care providers using shared decision-making with the patient to facilitate change. CONCLUSION: To optimize health care for people experiencing homelessness, clinical interventions should: (1) utilize shared-decision making during the visit, (2) foster a sense of trust, compassion, and acceptance, (3) emphasize continuity of care, including consistent providers and staff, and (4) integrate social services into Health Care for the Homeless sites.

Assessing Treatment Delays for Vitiligo Patients: A Retrospective Chart Review.

Many barriers to care exist for vitiligo patients that can impact patients' quality of life. Because early treatment of vitiligo is more efficacious, we investigated the factors associated with delay to treatment via a retrospective chart review of 102 consecutive patients attending an academic outpatient clinic over a 36-month time frame. Demographic information, clinical characteristics of vitiligo, and treatment details were obtained via a standardized questionnaire given to all patients with vitiligo. Our findings emphasize the need to investigate barriers to care to reduce health disparities among individuals with vitiligo.

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician. AIM: The aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis). APPROACH: Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative. RESULTS: Deep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images. CONCLUSIONS: Combining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.

High-throughput and high-dimensional single-cell analysis of antigen-specific CD8+ T cells.

Although critical to T cell function, antigen specificity is often omitted in high-throughput multiomics-based T cell profiling due to technical challenges. We describe a high-dimensional, tetramer-associated T cell antigen receptor (TCR) sequencing (TetTCR-SeqHD) method to simultaneously profile cognate antigen specificities, TCR sequences, targeted gene expression and surface-protein expression from tens of thousands of single cells. Using human polyclonal CD8+ T cells with known antigen specificity and TCR sequences, we demonstrate over 98% precision for detecting the correct antigen specificity. We also evaluate gene expression and phenotypic differences among antigen-specific CD8+ T cells and characterize phenotype signatures of influenza- and Epstein-Barr virus-specific CD8+ T cells that are unique to their pathogen targets. Moreover, with the high-throughput capacity of profiling hundreds of antigens simultaneously, we apply TetTCR-SeqHD to identify antigens that preferentially enrich cognate CD8+ T cells in patients with type 1 diabetes compared to healthy controls and discover a TCR that cross-reacts with diabetes-related and microbiome antigens. TetTCR-SeqHD is a powerful approach for profiling T cell responses in humans and mice.

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging.

SIGNIFICANCE: Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications. AIM: Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. APPROACH: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue. RESULTS: The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes. CONCLUSION: These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Effect of Layperson-Delivered, Empathy-Focused Program of Telephone Calls on Loneliness, Depression, and Anxiety Among Adults During the COVID-19 Pandemic: A Randomized Clinical Trial.

Importance: Loneliness is a risk factor for many clinical conditions, but there are few effective interventions deployable at scale. Objective: To determine whether a layperson-delivered, empathy-focused program of telephone calls could rapidly improve loneliness, depression, and anxiety in at-risk adults. Design, Setting, and Participants: From July 6 to September 24, 2020, we recruited and followed up 240 adults who were assigned to receive calls (intervention group) or no calls (control group) via block randomization. Loneliness, depression, and anxiety were measured using validated scales at enrollment and after 4 weeks. Intention-to-treat analyses were conducted. Meals on Wheels Central Texas (MOWCTX) clients received calls in their homes or wherever they might have been when the call was received. The study included MOWCTX clients who fit their service criteria, including being homebound and expressing a need for food. A total of 296 participants were screened, of whom 240 were randomized to intervention or control. Interventions: Sixteen callers, aged 17 to 23 years, were briefly trained in empathetic conversational techniques. Each called 6 to 9 participants over 4 weeks daily for the first 5 days, after which clients could choose to drop down to fewer calls but no less than 2 calls a week. Main Outcomes and Measures: Primary outcome was loneliness (3-item UCLA Loneliness Scale, range 3-9; and 6-item De Jong Giervald Loneliness [De Jong] Scale, range 0-6). Secondary outcomes were depression (Personal Health Questionnaire for Depression), anxiety (Generalized Anxiety Disorder scale), and self-rated health (Short Form Health Survey Questionnaire). Results: The 240 participants were aged 27 to 101 years, with 63% aged at least 65 years (n = 149 of 232), 56% living alone (n = 135 of 240), 79% women (n = 190 of 240), 39% Black or African American (n = 94 of 240), and 22% Hispanic or Latino (n = 52 of 240), and all reported at least 1 chronic condition. Of 240 participants enrolled, 13 were lost to follow-up in the intervention arm and 1 in the control arm. Postassessment differences between intervention and control after 4 weeks showed an improvement of 1.1 on the UCLA Loneliness Scale (95% CI, 0.5-1.7; P < .001; Cohen d of 0.48), and improvement of 0.32 on De Jong (95% CI, -0.20 to 0.81; P = .06; Cohen d, 0.17) for loneliness; an improvement of 1.5 on the Personal Health Questionnaire for Depression (95% CI, 0.22-2.7; P < .001; Cohen d, 0.31) for depression; and an improvement of 1.8 on the Generalized Anxiety Disorder scale (95% CI, 0.44 to 3.2; P < .001; Cohen d, 0.35) for anxiety. General physical health on the Short Form Health Questionnaire Survey showed no change, but mental health improved by 2.6 (95% CI, 0.81 to 4.4; P = .003; Cohen d of 0.46). Conclusions and Relevance: A layperson-delivered, empathy-oriented telephone call program reduced loneliness, depression, and anxiety compared with the control group and improved the general mental health of participants within 4 weeks. Future research can determine whether effects on depression and anxiety can be extended to maximize clinical relevance. Trial Registration: ClinicalTrials.gov Identifier: NCT04595708.

Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening.

A key challenge in melanoma diagnosis is the large number of unnecessary biopsies on benign nevi, which requires significant amounts of time and money. To reduce unnecessary biopsies while still accurately detecting melanoma lesions, we propose using Raman spectroscopy as a non-invasive, fast, and inexpensive method for generating a "second opinion" for lesions being considered for biopsy. We collected in vivo Raman spectral data in the clinical skin screening setting from 52 patients, including 53 pigmented lesions and 7 melanomas. All lesions underwent biopsies based on clinical evaluation. Principal component analysis and logistic regression models with leave one lesion out cross validation were applied to classify melanoma and pigmented lesions for biopsy recommendations. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.903 and a specificity of 58.5% at perfect sensitivity. The number needed to treat for melanoma could have been decreased from 8.6 (60/7) to 4.1 (29/7). This study in a clinical skin screening setting shows the potential of Raman spectroscopy for reducing unnecessary skin biopsies with in vivo Raman data and is a significant step toward the application of Raman spectroscopy for melanoma screening in the clinic.

Superpixel Raman spectroscopy for rapid skin cancer margin assessment.

Spontaneous Raman micro-spectroscopy has been demonstrated great potential in delineating tumor margins; however, it is limited by slow acquisition speed. We describe a superpixel acquisition approach that can expedite acquisition between ~×100 and ×10 000, as compared to point-by-point scanning by trading off spatial resolution. We present the first demonstration of superpixel acquisition on rapid discrimination of basal cell carcinoma tumor from eight patients undergoing Mohs micrographic surgery. Results have been demonstrated high discriminant power for tumor vs normal skin based on the biochemical differences between nucleus, collagen, keratin and ceramide. We further perform raster-scanned superpixel Raman imaging on positive and negative margin samples. Our results indicate superpixel acquisition can facilitate the use of Raman microspectroscopy as a rapid and specific tool for tumor margin assessment.

Efficacy and patient opinion of wet-wrap dressings using 0.1% triamcinolone acetonide ointment vs cream in the treatment of pediatric atopic dermatitis: A randomized split-body control study.

BACKGROUND: Wet wraps can be an effective means of improving atopic dermatitis (AD). Little research has been done regarding the comparative efficacy of topical steroid vehicles and patient preference. OBJECTIVE: This study aimed to compare the efficacy of 0.1% triamcinolone acetonide ointment vs cream used with wet wraps in pediatric patients with AD and to explore patient preference/opinion. METHODS: We performed a small, randomized, investigator-blind prospective study of 39 pediatric patients experiencing symmetric, bilateral AD flares. Patients were instructed to apply a topical steroid cream to one extremity and apply the same topical steroid in an ointment vehicle to the other extremity using the wet-wrap technique once or twice daily for 3 to 5 consecutive days. Patients were evaluated at a follow-up visit. RESULTS: Comparison of the change in Investigator's Global Assessment scores disclosed no significant difference between efficacy ratings of cream (mean difference = 0.72) and ointment (mean difference = 0.59) when used with wet wraps (P = 0.22). Although patients found the ointment more difficult to apply, they were more likely to prefer ointments for future prescriptions (P < 0.01). CONCLUSION: Patient preference of corticosteroid vehicle is what should ultimately drive treatment. In this small study, we found no difference in efficacy between triamcinolone acetonide wet wraps with cream vs ointment. Dermatologists should select the vehicle of the patient's choice as it may increase satisfaction with treatment.

Biophysical basis of skin cancer margin assessment using Raman spectroscopy.

Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.

Raman biophysical markers in skin cancer diagnosis.

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.

Raman active components of skin cancer.

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

Excimer Light Treatment for Idiopathic Guttate Hypomelanosis: A Pilot Study.

BACKGROUND: Idiopathic guttate hypomelanosis (IGH) is a commonly acquired benign leukoderma characterized by multiple discrete, hypo- or depigmented macules often on extremities that can be aesthetically undesirable for patients. This is the first study using excimer laser for treatment. OBJECTIVE: To determine the effectiveness of excimer laser for repigmentation of idiopathic guttate hypomelanosis. METHODS AND MATERIALS: In this longitudinal, split-body controlled, single-blinded pilot study, 6 patients were treated with excimer laser for 12 weeks using the vitiligo protocol. Effectiveness was graded by the blinded observer scale through photographic comparisons at the end of the study. Participants also graded their progress at intervals during the study. A descriptive trend analysis and an ANOVA model were used to determine outcomes. RESULTS: Lesions that received the excimer treatment had significantly higher repigmentation by the end of the study compared with baseline and untreated lesions. CONCLUSION: Excimer laser treatments are already considered to be a safe modality for a variety of skin conditions. This study suggests that excimer is an effective treatment option with acceptable cosmetic outcomes for IGH.