A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection.

BACKGROUND: Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response. METHODS: In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics. RESULTS: Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions. CONCLUSIONS: The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Histological, chemical and gene expression differences between western redcedar seedlings resistant and susceptible to cedar leaf blight.

Introduction: Western redcedar (Thuja plicata) is an important species in the Cupressaceae both at economic and cultural levels in the Pacific Northwest of North America. In adult trees, the species produces one of the most weathering-resistant heartwoods among conifers, making it one of the preferred species for outdoor applications. However, young T. plicata plants are susceptible to infection with cedar leaf blight (Didymascella thujina), an important foliar pathogen that can be devastating in nurseries and small-spaced plantations. Despite that, variability in the resistance against D. thujina in T. plicata has been documented, and such variability can be used to breed T. plicata for resistance against the pathogen. Objective: This investigation aimed to discern the phenotypic and gene expression differences between resistant and susceptible T. plicata seedlings to shed light on the potential constitutive resistance mechanisms against cedar leaf blight in western redcedar. Methods: The study consisted of two parts. First, the histological differences between four resistant and four susceptible families that were never infected with the pathogen were investigated. And second, the differences between one resistant and one susceptible family that were infected and not infected with the pathogen were analyzed at the chemical (C, N, mineral nutrients, lignin, fiber, starch, and terpenes) and gene expression (RNA-Seq) levels. Results: The histological part showed that T. plicata seedlings resistant to D. thujina had constitutively thicker cuticles and lower stomatal densities than susceptible plants. The chemical analyses revealed that, regardless of their infection status, resistant plants had higher foliar concentrations of sabinene and α-thujene, and higher levels of expression of transcripts that code for leucine-rich repeat receptor-like protein kinases and for bark storage proteins. Conclusion: The data collected in this study shows that constitutive differences at the phenotypic (histological and chemical) and gene expression level exist between T. plicata seedlings susceptible and resistant to D. thujina. Such differences have potential use for marker-assisted selection and breeding for resistance against cedar leaf blight in western redcedar in the future.

Variability in the unregulated opioid market in the context of extreme rates of overdose.

BACKGROUND: Drug checking uses analytical chemistry technologies to report on the composition of drugs from the unregulated market to reduce substance use-related risks, while additionally allowing for monitoring and reporting of the supply. In the context of an overdose crisis linked to fentanyl, we used drug checking data to examine variability within the illicit opioid supply. METHODS: In this time-series analysis, data was collected from a drug checking service in Victoria, Canada from November 2020 to July 2021. Drugs reported as opioids by participants of the service (N = 454) were analyzed to determine sample composition and paper spray mass spectroscopy was used to quantify low-concentration actives. Interquartile and statistical process control (SPC) analysis, namely standard deviation control charts, were used to examine the degree of variability among samples. RESULTS: Fentanyl was found in 96% of samples reported to be opioids, with a median concentration of 9%. Concentrations varied significantly, with a standard deviation of 7% for fentanyl and where nearly 20% of data points fell outside the control limits. Over half of the samples contained an additional and unexpected active, most commonly etizolam (43% of samples). Etizolam also showed a large level of variability, uncorrelated to that of fentanyl. CONCLUSIONS: Based on our chemical quantification and SPC analysis, a high degree of variability was found in opioid samples from the unregulated market in both the drugs detected and the concentrations of those drugs. This demonstrated the opioid crisis to be less attributable to a bad batch of drugs but rather the general variability found in the unregulated market.