In vitro screening methods of novel immune checkpoint inhibitors related to T cell infiltration and anti-PD-1 resistance.

Immune checkpoint blockade-based cancer immunotherapy is an effective tool for cancer treatment. PD-1/PD-L1 blockade, however, is limited by a low response rate and adaptive resistance. A growing body of studies has shown that the high stromal content dense with extracellular matrix plays a significant role in immune checkpoint blockade resistance as well as T cell exclusion.

Indole-3-carbinol ameliorates ovarian damage in female old mice through Nrf2/HO-1 pathway activation.

Ovarian aging leads to infertility and birth defects. We aimed to clarify the role of Indole-3-carbinol (I3C) in resistance to oxidative stress, apoptosis, and fibrosis in ovarian aging. I3C was administered via intraperitoneal injection for 3 weeks in young or old mice.

An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery.

Background: Colorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM.

Methods: This study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM.

Fish oil-based microemulsion can efficiently deliver oral peptide blocking PD-1/PD-L1 and simultaneously induce ferroptosis for cancer immunotherapy.

Peptide immune checkpoint inhibitors in cancer immunotherapy have attracted great attention recently, but oral delivery of these peptides remains a huge challenge due to the harsh gastrointestinal environment, large molecular size, high hydrophilic, and poor transmembrane permeability. Here, for the first time, a fish oil-based microemulsion was developed for oral delivery of programmed death-1/programmed cell death-ligand 1 (PD-1/PD-L1) blocking model peptide, OPBP-1. The delivery system was characterized, in vitro and in vivo studies were conducted to evaluate its overall implication.

Design of a novel chimeric peptide via dual blockade of CD47/SIRPα and PD-1/PD-L1 for cancer immunotherapy.

Although immune checkpoint inhibition has been shown to effectively activate antitumor immunity in various tumor types, only a small subset of patients can benefit from PD-1/PD-L1 blockade. CD47 expressed on tumor cells protects them from phagocytosis through interaction with SIRPα on macrophages, while PD-L1 dampens T cell-mediated tumor killing. Therefore, dual targeting PD-L1 and CD47 may improve the efficacy of cancer immunotherapy.

Integrative analysis of bulk and single-cell gene expression profiles to identify tumor-associated macrophage-derived CCL18 as a therapeutic target of esophageal squamous cell carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC) is a common gastrointestinal malignancy with poor patient prognosis. Current treatment for ESCC, including immunotherapy, is only beneficial for a small subset of patients. Better characterization of the tumor microenvironment (TME) and the development of novel therapeutic targets are urgently needed.

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Purpose: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.

Methods: DeepRetina uses the improved Xception65 to extract and learn the characteristics of retinal layers. The Xception65-extracted feature maps are inputted to an atrous spatial pyramid pooling module to obtain multiscale feature information.

FecalNet: Automated detection of visible components in human feces using deep learning.

Purpose: To automate the detection and identification of visible components in feces for early diagnosis of gastrointestinal diseases, we propose FecalNet, a method using multiple deep neural networks.

Methods: FecalNet uses the ResNet152 residual network to extract and learn the characteristics of visible components in fecal microscopic images, acquire feature maps in combination with the feature pyramid network, apply the full convolutional network to classify and locate the fecal components, and implement the improved focal loss function to reoptimize the classification results. This allowed the complete automation of the detection and identification of the visible components in feces.

Inspection of visible components in urine based on deep learning.

Purpose: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial microscopy is subjective and time consuming, and various previous detection algorithms lack the adequate accuracy.