Photoacoustic Flow System for the Detection of Ovarian Circulating Tumor Cells Utilizing Copper Sulfide Nanoparticles

High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions

Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level. We highlight and discuss the current trends in continual flow high-throughput methods for analyzing single cells, such as advanced flow cytometry techniques and inductively coupled plasma mass spectrometry methods, as well as their intersection in the form of mass cytometry. This review further discusses the challenges and opportunities with current single-cell analysis approaches and provides proposed directions for innovation in the high-throughput analysis of nanoparticle-cell interactions.

Photoacoustic and absorption spectroscopy imaging analysis of human blood

Photoacoustic and absorption spectroscopy imaging are safe and non-invasive molecular quantification techniques, which do not utilize ionizing radiation and allow for repeated probing of samples without them being contaminated or damaged. Here we assessed the potential of these techniques for measuring biochemical parameters. We investigated the statistical association between 31 time and frequency domain features derived from photoacoustic and absorption spectroscopy signals and 19 biochemical blood parameters. We found that photoacoustic and absorption spectroscopy imaging features are significantly correlated with 14 and 17 individual biochemical parameters, respectively. Moreover, some of the biochemical blood parameters can be accurately predicted based on photoacoustic and absorption spectroscopy imaging features by polynomial regression. In particular, the levels of uric acid and albumin can be accurately explained by a combination of photoacoustic and absorption spectroscopy imaging features (adjusted R-squared > 0.75), while creatinine levels can be accurately explained by the features of the photoacoustic system (adjusted R-squared > 0.80). We identified a number of imaging features that inform on the biochemical blood parameters and can be potentially useful in clinical diagnosis. We also demonstrated that linear and non-linear combinations of photoacoustic and absorption spectroscopy imaging features can accurately predict some of the biochemical blood parameters. These results demonstrate that photoacoustic and absorption spectroscopy imaging systems show promise for future applications in clinical practice.

The sound of blood: photoacoustic imaging in blood analysis

Blood analysis is a ubiquitous and critical aspect of modern medicine. Analyzing blood samples requires invasive techniques, various testing systems, and samples are limited to relatively small volumes. Photoacoustic imaging (PAI) is a novel imaging modality that utilizes non-ionizing energy that shows promise as an alternative to current methods. This paper seeks to review current applications of PAI in blood analysis for clinical use. Furthermore, we discuss obstacles to implementation and future directions to overcome these challenges. Firstly, we discuss three applications to cellular analysis of blood: sickle cell, bacteria, and circulating tumor cell detection. We then discuss applications to the analysis of blood plasma, including glucose detection and anticoagulation quantification. As such, we hope this article will serve as inspiration for PAI's potential application in blood analysis and prompt further studies to ultimately implement PAI into clinical practice.

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

The Application of Nanoparticle-Based Imaging and Phototherapy for Female Reproductive Organs Diseases

Various female reproductive disorders affect millions of women worldwide and bring many troubles to women's daily life. Let alone, gynecological cancer (such as ovarian cancer and cervical cancer) is a severe threat to most women's lives. Endometriosis, pelvic inflammatory disease, and other chronic diseases-induced pain have significantly harmed women's physical and mental health. Despite recent advances in the female reproductive field, the existing challenges are still enormous such as personalization of disease, difficulty in diagnosing early cancers, antibiotic resistance in infectious diseases, etc. To confront such challenges, nanoparticle-based imaging tools and phototherapies that offer minimally invasive detection and treatment of reproductive tract-associated pathologies are indispensable and innovative. Of late, several clinical trials have also been conducted using nanoparticles for the early detection of female reproductive tract infections and cancers, targeted drug delivery, and cellular therapeutics. However, these nanoparticle trials are still nascent due to the body's delicate and complex female reproductive system. The present review comprehensively focuses on emerging nanoparticle-based imaging and phototherapies applications, which hold enormous promise for improved early diagnosis and effective treatments of various female reproductive organ diseases.

Nanotechnology-integrated ovarian cancer metastasis therapy: Insights from the metastatic mechanisms into administration routes and therapy strategies

Ovarian cancer is a kind of malignant tumour which locates in the pelvic cavity without typical clinical symptoms in the early stages. Most patients are diagnosed in the late stage while about 60 % of them have suffered from the cancer cells spreading in the abdominal cavity. The high recurrence rate and mortality seriously damage the reproductive needs and health of women. Although recent advances in therapeutic regimes and other adjuvant therapies improved the overall survival of ovarian cancer, overcoming metastasis has still been a challenge and is necessary for achieving cure of ovarian cancer. To present potential targets and new strategies for curbing the occurrence of ovarian metastasis and the treatment of ovarian cancer after metastasis, the first section of this paper explained the metastatic mechanisms of ovarian cancer comprehensively. Nanomedicine, not limited to drug delivery, offers opportunities for metastatic ovarian cancer therapy. The second section of this paper emphasized the advantages of various administration routes of nanodrugs in metastatic ovarian cancer therapy. Furthermore, the third section of this paper focused on advances in nanotechnology-integrated strategies for targeting metastatic ovarian cancer based on the metastatic mechanisms of ovarian cancer. Finally, the challenges and prospects of nanotherapeutics for ovarian cancer metastasis therapy were evaluated. In general, the greatest emphasis on using nanotechnology-based strategies provides avenues for improving metastatic ovarian cancer outcomes in the future.

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System

With the development of theories and technologies in medical imaging, most of the tumors can be detected in the early stage. However, the nature of ovarian cysts lacks accurate judgement, leading to that many patients with benign nodules still need Fine Needle Aspiration (FNA) biopsies or surgeries, increasing the physical pain and mental pressure of patients as well as unnecessary medical health care costs. Therefore, we present an image diagnosis system for classifying the ovarian cysts in color ultrasound images, which novelly applies the image features fused by both high-level features from deep learning network and low-level features from texture descriptor. Firstly, the ultrasound images are enhanced to improve the quality of training data set and the rotation invariant uniform local binary pattern (ULBP) features are extracted from each of the images as the low-level texture features. Then the high-level deep features extracted by the fine-tuned GoogLeNet neural network and the low-level ULBP features are normalized and cascaded as one fusion feature that can represent both the semantic context and the texture patterns distributed in the image. Finally, the fusion features are input to the Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign". The high-level features extracted by the deep neural network from the medical ultrasound image can reflect the visual features of the lesion region, while the low-level texture features can describe the edges, direction and distribution of intensities. Experimental results indicate that the combination of the two types of features can describe the differences between the lesion regions and other regions, and the differences between lesions regions of malignant and benign ovarian cysts.