Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells

Microfluidic impedance cytometry with flat-end cylindrical electrodes for accurate and fast analysis of marine microalgae

Marine microalgae play an increasingly significant role in addressing the issues of environmental monitoring and disease treatment, making the analysis of marine microalgae at the single-cell level an essential technique. For this, we put forward accurate and fast microfluidic impedance cytometry to analyze microalgal cells by assembling two cylindrical electrodes and microchannels to form a three-dimensional detection zone. Firstly, we established a mathematical model of microalgal cell detection based on Maxwell's mixture theory and numerically investigated the effects of the electrode gap, microalgal positions, and ion concentrations of the solution on detection to optimize detection conditions. Secondly, 80 μm stainless steel wires were used to construct flat-ended cylindrical electrodes and were then inserted into two collinear channels fabricated using standard photolithography techniques to form a spatially uniform electric field to promote the detection throughput and sensitivity. Thirdly, based on the validation of this method, we measured the impedance of living Euglena and Haematococcus pluvialis to study parametric influences, including ion concentration, cell density and electrode gap. The throughput of this method was also investigated, which reached 1800 cells per s in the detection of Haematococcus pluvialis. Fourthly, we analyzed live and dead Euglena to prove the ability of this method to detect the physiological status of cells and obtained impedances of 124.3 Ω and 31.0 Ω with proportions of 15.9% and 84.1%, respectively. Finally, this method was engineered for the analysis of marine microalgae, measuring living Euglena with an impedance of 159.61 Ω accounting for 3.9%, dead Euglena with an impedance of 36.43 Ω accounting for 10.1% and Oocystis sp. with an impedance of 55.00 Ω accounting for about 81.0%. This method could provide a reliable tool to analyze marine microalgae for monitoring the marine environment and treatment of diseases owing to its outstanding advantages of low cost, high throughput and high corrosion resistance.

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.

Multi-wavelength multi-direction laser light scattering for cell characterization using machine learning-based methods

Cell identification and analysis play a crucial role in many biology- and health-related applications. The internal and surface structures of a cell are complex and many of the features are sub-micron in scale. Well-resolved images of these features cannot be obtained using optical microscopy. Previous studies have reported that the single-cell angular laser-light scattering patterns (ALSP) can be used for label-free cell identification and analysis. The ALSP can be affected by cell properties and the wavelength of the probing laser. Two cell properties, cell surface roughness and the number of mitochondria, are investigated in this study. The effects of probing laser wavelengths (blue, green, and red) and the directions of scattered light collection (forward, side, and backward) are studied to determine the optimum conditions for distinguishing the two cell properties. Machine learning (ML) analysis has been applied to ALSP obtained from numerical simulations. The results of ML analysis show that the backward scattering is the best direction for characterizing the surface roughness, while the forward scattering is the best direction for differentiating the number of mitochondria. The laser light having red or green wavelength is found to perform better than that having the blue wavelength in differentiating the surface roughness and the number of mitochondria. This study provides important insights into the effects of probing laser wavelength on gaining information about cells from their ALSP.

Differentiating single cervical cells by mitochondrial fluorescence imaging and deep learning-based label-free light scattering with multi-modal static cytometry

Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light-sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two-dimensional (2D) light scattering (LS) patterns of single cervical cells. Three feature extraction methods are used to extract multi-modal features based on different data characteristics. Analysis and classification of morphological and textural features demonstrate the potential of intracellular mitochondria in cervical cancer cell classification. The deep learning method is used to automatically extract deep features of label-free LS patterns, and an accuracy of 76.16% for the classification of the above two kinds of cervical cells is obtained, which is higher than the other two single modes (BF and FL). Our multi-modal static cytometry uses a variety of feature extraction and analysis methods to provide the mitochondria as promising internal biomarkers for cervical cancer diagnosis, and to show the promise of label-free, automatic classification of early cervical cancer with deep learning-based 2D light scattering.

Identification of Novel Tumor Microenvironment Regulating Factor That Facilitates Tumor Immune Infiltration in Cervical Cancer

Cervical cancer is one of the most common gynecologic malignancies and one of the leading causes of cancer-related deaths in women worldwide. There are more than 30 categories of human papillomavirus infections in the genital tract. The recently discovered immune checkpoint suppression is a potential approach to improve clinical outcomes in these patients by altering immune cell function. However, many questions remain unanswered in terms of this method. For example, the proportion of responders is limited and the exact mechanism of action is uncertain. The tumor microenvironment (TME) has long been regarded as having nonnegligible influence on effectiveness of immunotherapy. The programmed cell death protein 1 (PD-1) pathway has received much attention due to its involvement in activating T-cell immune checkpoint responses. Since tumor cells may evade immune detection and become highly resistant to conventional treatments, anti-PD-1/PD-L1 antibodies are preferred as a kind of cancer treatment and many have just been licensed. To provide a theoretical basis for the development of new therapies, investigating the effect of tumor microenvironment on the prognosis of cervical cancer is necessary. In this work, immunological scores obtained from the ESTIMATE algorithm were used to differentiate between patients with high and low immune cell infiltration. We identified 11 immunologically significant differentially expressed genes (DEGs). For example, CXCR3 was found to be an important factor in CD8⁺ T cell recruitment and tumor immunological infiltration in cervical cancer. These results may lead to novel directions of understanding complex interactions between cancer cells and the tumor microenvironment, as well as new treatment options for cervical cancer.