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Dysregulation of lung tissue collagen level plays a vital role in understanding how lung diseases progress. However, traditional scoring methods rely on manual histopathological examination introducing subjectivity and inconsistency into the assessment process. These methods are further hampered by inter-observer variability, lack of quantification, and their time-consuming nature. To mitigate these drawbacks, we propose a machine learning-driven framework for automated scoring of lung collagen content. Our study begins with the collection of a lung slide image dataset from adult female mice using second harmonic generation (SHG) microscopy. In our proposed approach, first, we manually extracted features based on the 46 statistical parameters of fibrillar collagen. Subsequently, we pre-processed the images and utilized a pre-trained VGG16 model to uncover hidden features from pre-processed images. We then combined both image and statistical features to train various machine learning and deep neural network models for classification tasks. We employed advanced unsupervised techniques like K-means, principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to conduct thorough image analysis for lung collagen content. Also, the evaluation of the trained models using the collagen data includes both binary and multi-label classification to predict lung cancer in a urethane-induced mouse model. Experimental validation of our proposed approach demonstrates promising results. We obtained an average accuracy of 83% and an area under the receiver operating characteristic curve (ROC AUC) values of 0.96 through the use of a support vector machine (SVM) model for binary categorization tasks. For multi-label classification tasks, to quantify the structural alteration of collagen, we attained an average accuracy of 73% and ROC AUC values of 1.0, 0.38, 0.95, and 0.86 for control, baseline, treatment_1, and treatment_2 groups, respectively. Our findings provide significant potential for enhancing diagnostic accuracy, understanding disease mechanisms, and improving clinical practice using machine learning and deep learning models. 

Abstract Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3‐bromopyruvate (3‐BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor‐associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody‐functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface‐enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC‐MS) analysis. The study reveals the efficacy of 3‐BP in treating pancreatic cancer and identifies drug treatment‐induced lipid species remodeling and associated pathways through bioinformatics analysis. 

Abstract Stem cell‐based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non‐destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping‐based machine learning analysis is reported to distinguish differentiating human adipose‐derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single‐cell resolution. The label‐free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton‐based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix‐assisted laser desorption/ionization‐mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label‐free identification of stem cell differentiation with high spatial and chemical resolution. 

Abstract Liquid interfaces facilitate the organization of nanometer‐scale biomaterials with plasmonic properties suitable for molecular diagnostics. Using hierarchical assemblage of 2D hafnium disulfide nanoplatelets and zero‐dimensional spherical gold nanoparticles, the design of a multifunctional material is reported. When the target analyte is present, the nanocomposites’ self‐assembling pattern changes, altering their plasmonic response. Using monkeypox virus (MPXV) as an example, the findings reveal that adding genomic DNA to the nanocomposite surface increases the agglomeration between gold nanoparticles and decreases the π‐stacking distance between hafnium disulfide nanoplatelets. Further, this self‐assembled nanomaterial is found to have minimal cross‐reactivity toward other pathogens and a limit of detection of 7.6 pg µL⁻¹(i.e., 3.57 × 10⁴copies µL⁻¹) toward MPXV. Overall, this study helped to gain a better understanding of the genomic organization of MPXV to chemically design and develop targeted nucleotides. The study has been validated by UV–vis spectroscopy, X‐ray diffraction, scanning transmission electron microscopy, surface‐enhanced Raman microscopy and electromagnetic simulation studies. To the best knowledge, this is the first study in literature reporting selective molecular detection of MPXV within a few minutes and without the use of any high‐end instrumental techniques like polymerase chain reactions.