Journal of Biophotonics最新文献

Cervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful treatment and extending patient survival. Fluorescence spectroscopy stands out as a highly sensitive method for identifying biochemical alterations associated with cancer and numerous other pathological conditions. In our study, empirical mode decomposition (EMD) and Grassmann manifold (GM) learning are explored for reliable cancer detection using fluorescence spectral signals collected from 110 subjects representing various categories of the human cervix. Initially, EMD is used to decompose the signal into several multi-feature intrinsic mode functions (IMFs) on a spectral scale. Each IMF demonstrates uniqueness by capturing the inherent frequency characteristics within the signal, thus facilitating the extraction of signal features. The GM representation of IMFs is employed for investigating the non-linear subspace structure within spectral signals, which is subsequently followed by a low-rank representation to transform and analyze the spectral signals. The GM allows for the extraction of relevant information, reduction of dimensionality, and exploration of complex relationships within data, ultimately contributing to improved diagnosis. Mutual information is further used for feature selection to reduce the number of features and hence the computational cost. When the selected features were employed for classification, the Random Forest (RF) classifier attained a high five-fold validation accuracy of 99% and exhibited a minimal standard deviation of 0.02. Other state-of-the-art machine learning classifiers were also used and compared with the RF model.

A 2D nano-scale multipurpose diamond-shaped ring resonator photonic crystal (PhC) biosensor with 15 × 11 circular silicon (Si) rods in a triangular lattice arrangement is proposed in this article. The distinct feature of the suggested biosensor is the design of a nano-cavity with two different rod radii. The designed biosensor is intended for identifying diabetic blood samples, malaria, and chikungunya virus. The proposed biosensor achieves a high quality factor (QF) of 8080, a high sensitivity (S) of 1108 nm/RIU with a low detection limit (DL) of 9.02 × 10⁻⁶ RIU, towards breast cancer analysis. The QF, S, and DL of the biosensor for the diabetic blood sample are 6963, 1087 nm/RIU, and 1.01 × 10⁻⁵ RIU respectively. The highest QF and sensitivity of the biosensor are 8561 and 1121.6 nm/RIU respectively, towards malarial detection. A high QF of 9129.4 and a high sensitivity of 1166.9 is achieved for infected plasma towards chikungunya virus detection.

Endometrial cancer and uterine leiomyomas (fibroid) are common uterine pathologies that require early diagnosis to improve a patient's symptoms and increase the success rate of interventional procedures. In this work, we report on near-infrared spectroscopy (NIRS) spectral features of uterine cancer and fibroids from 69 surgical specimens obtained from 24 patients following hysterectomies. Normal uterus, cancer, and fibroid tissue were identified by NIR spectral contrast parameters based on the differences in spectrum morphology. Using the significant optical features and spectral principal components, a classification model was able to classify uterus tissue with a prediction accuracy higher than 70%, identifying cancer specimens with 70% sensitivity and 93% specificity, and fibroid samples with 86% sensitivity and 83% specificity. These results demonstrated NIRS mapping has promise as a complementary method for gynecologic imaging.

Recently, the beauty market demands easier and more accurate skin diagnosis methods that can diagnose skin in a non-contact manner. In this study, we proposed a noncontact skin diagnosis method to evaluate personal skin information through mechanical matching of simulated optical skin reflection spectrum based on the Kubelka-Munk 2-layer model with actually measured skin reflection spectrum. For the validation of this skin information, correlation analysis between the spectrum matched skin information and the actually measured skin information was performed. Results confirmed that the spectrum matched skin information (melanin, hemoglobin, and dermis thickness) had a strong correlation with the actually measured skin information in terms of statistical significance.

The lack of high-throughput, label-free, and intelligent recognition models for assessing cell death hinders the broad application of cell death analysis in chemotherapy for lung cancer. We propose an intelligent quantitative detection technique for cell deaths. Using high-throughput quantitative phase imaging flow cytometry to capture numerous label-free images and employing convolutional neural networks (CNN) to characterize the heterogeneity and quantitative detection of cell death. We revealed the heterogeneity of cell death through morphology features and achieved interpretability analysis of the CNN using clustering. Finally, the classification reliability of the CNN was validated by extracting features from classified cells. This method, compared with biochemical methods, showed a correlation of 0.92 and 0.91 with autophagy detection (Pearson and Cosine Similarity), and an average error of 12.52% with apoptosis detection. Our approach has the potential to become a valuable tool for studying cell death mechanisms and offers a new perspective for cancer treatment.

Helicobacter pylori ( H. pylori ) is a bacterium that infects the stomach and can lead to conditions like peptic ulcers, chronic gastric inflammation, and stomach cancer. A key feature of H. pylori is urease, an enzyme that breaks down urea into ammonium carbonate. Various methods exist for diagnosing H. pylori infections, including breath tests. This study expands on previous research that focused on ammonia detection by introducing a novel approach using photoacoustic spectroscopy to measure ethylene and carbon dioxide levels in the breath of infected individuals. Our results show significant differences in gas concentrations between H. pylori -infected individuals and healthy controls. Ethylene concentrations were 113.64% higher, and carbon dioxide levels were 433.47% higher in infected participants, suggesting that both gases may serve as biomarkers for H. pylori detection.

Mild cognitive impairment (MCI) is primarily characterized by a gradual decline in cognitive function, where early detection and intervention are crucial to preventing Alzheimer's disease progression. This study integrates upper limb multimodal tasks (ULMTs) with functional near-infrared spectroscopy (fNIRS) to assess cognitive and motor functions in MCI patients. Thirty-seven elderly participants were categorized into healthy control (HC) and MCI groups. The experiment consisted of resting state, numerical cognitive task (NCT), motor task (MT), and ULMT phases. fNIRS measured hemodynamic responses in the prefrontal and motor cortices, while an upper limb trainer recorded motor data. Results showed weaker cortical responses in the MCI group during rest and reduced motor cortex activation during NCT. Both groups displayed increased cortical activity during ULMT compared to NCT but reduced motor performance compared to MT. These findings demonstrate the potential of ULMTs combined with fNIRS for early MCI assessment and intervention.