Targeting ferroptosis regulators in lung cancer: Exploring natural products.

Lung cancer remains a formidable global health challenge, necessitating innovative therapeutic strategies for improved efficacy. This review explores the untapped potential of natural products and Traditional Chinese Medicine (TCM) in lung cancer therapy, focusing on targeting ferroptosis regulators. Natural compounds, such as curcumin and resveratrol, exhibit diverse anti-cancer mechanisms, complemented by TCM's holistic approach rooted in a 3500-year history.

Multi-scale window transformer for cervical cytopathology image recognition.

Cervical cancer is a major global health issue, particularly in developing countries where access to healthcare is limited. Early detection of pre-cancerous lesions is crucial for successful treatment and reducing mortality rates. However, traditional screening and diagnostic processes require cytopathology doctors to manually interpret a huge number of cells, which is time-consuming, costly, and prone to human experiences.

Cervical cytopathology image refocusing via multi-scale attention features and domain normalization.

Cervical cytopathology image refocusing is important for addressing the problem of defocus blur in whole slide images. However, most of current deblurring methods are developed for global motion blur instead of local defocus blur and need a lot of supervised re-training for unseen domains. In this paper, we propose a refocusing method for cervical cytopathology images via multi-scale attention features and domain normalization.

STSRNet: Self-Texture Transfer Super-Resolution and Refocusing Network.

Biomedical microscopy images with high-resolution (HR) and axial information can help analysis and diagnosis. However, obtaining such images usually takes more time and economic costs, which makes it impractical in most scenarios. In this paper, we first propose a novel Self-texture Transfer Super-resolution and Refocusing Network (STSRNet) to reconstruct HR multi-focal plane (MFP) images from a single 2D low-resolution (LR) wide field image without relying on scanning or any special devices.

An unsupervised style normalization method for cytopathology images.

Diverse styles of cytopathology images have a negative effect on the generalization ability of automated image analysis algorithms. This article proposes an unsupervised method to normalize cytopathology image styles. We design a two-stage style normalization framework with a style removal module to convert the colorful cytopathology image into a gray-scale image with a color-encoding mask and a domain adversarial style reconstruction module to map them back to a colorful image with user-selected style.

Chemical sectioning fluorescence tomography: high-throughput, high-contrast, multicolor, whole-brain imaging at subcellular resolution.

A thorough neuroanatomical study of the brain architecture is crucial for understanding its cellular compositions, connections, and working mechanisms. However, the fine- and multiscale features of neuron structures make it challenging for microscopic imaging, as it requires high contrast and high throughput simultaneously. Here, we propose chemical sectioning fluorescence tomography (CSFT) to solve this problem.

PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network.

In the cytopathology screening of cervical cancer, high-resolution digital cytopathological slides are critical for the interpretation of lesion cells. However, the acquisition of high-resolution digital slides requires high-end imaging equipment and long scanning time. In the study, we propose a GAN-based progressive multi-supervised super-resolution model called PathSRGAN (pathology super-resolution GAN) to learn the mapping of real low-resolution and high-resolution cytopathological images.

DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.

Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology.

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline.

Large-scale localization of touching somas from 3D images using density-peak clustering.

Background: Soma localization is an important step in computational neuroscience to map neuronal circuits. However, locating somas from large-scale and complicated datasets is challenging. The challenges primarily originate from the dense distribution of somas, the diversity of soma sizes and the inhomogeneity of image contrast.

Factors derived from mouse embryonic stem cells promote self-renewal of goat embryonic stem-like cells.

Goat embryonic stem (ES)-like cells could be isolated from primary materials-inner cell masses (ICMs) and remain undifferentiated for eight passages in a new culture system containing mouse ES cell conditioned medium (ESCCM) and on a feeder layer of mouse embryo fibroblasts (MEFs). However, when cultured in medium without mouse ESCCM, goat ES-like cells could not survive for more than three passages. In addition, no ES-like cells could be obtained when ICMs were cultured on goat embryo fibroblasts or the primary materials-whole goat blastocysts were cultured on MEFs.