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.

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.

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.

Visualizing viscoelastic waves in materials and tissues through noninvasive imaging is valuable for analyzing their mechanical properties and detecting internal anomalies. However, traditional elastography techniques have been limited by a maximum wave frequency below 1-10 kHz, which hampers temporal and spatial resolution. Here, we introduce an optical coherence elastography technique that overcomes the limitation by extending the frequency range to MHz. Our system can measure the stiffness of hard materials including bones and extract viscoelastic shear moduli for polymers and hydrogels in conventionally inaccessible ranges between 100 Hz and 1 MHz. The dispersion of Rayleigh surface waves across the ultrawide band allowed us to profile depth-dependent shear modulus in cartilages ex vivo and human skin in vivo with sub-mm anatomical resolution. This technique holds immense potential as a noninvasive measurement tool for material sciences, tissue engineering, and medical diagnostics.

A decline in the fertility rate has been observed worldwide, which hampers social development severely. Given the impacts of COVID-19 on individuals and society, it is of great significance to investigate the fertility intention of reproductive couples under COVID-19. The convenience sampling method was used to obtain our study sample. The self-administered questionnaire included the following components: sociodemographic characteristics (age, residence, education, occupation, characteristics of the couples, and annual household income), reproductive history (parity, number of children, child gender, and duration of preparing pregnancy), and attitudes toward COVID-19, was distributed online via an applet of WeChat. The results showed that among 4,133 valid questionnaires, 1,091 had fertility intention before COVID-19, whereas 3,042 did not, indicating a fertility intention rate of 26.4% among participating couples. Of the 1,091 couples who had fertility intention before COVID-19, 520 (47.7%) were affected by the outbreak, whereas 571 (52.3%) were not. By multivariable logistic regression analysis, we further found that couples living in Hubei Province, the epicenter in China (OR 2.20, 95% CI 1.35–3.60), and couples who prepared for pregnancy longer before COVID-19 (OR 1.19, 95% CI 1.06–1.33) were more likely to change their fertility intention under the pandemic. In addition, most of the participants reported their fertility intention was affected by the inconvenience of seeking medical service under COVID-19. Therefore, more forms of medical services to provide convenience for patients might be effective ways to reverse the declined fertility intention rate in facing COVID-19.