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. Immunohistochemistry; Masson, Sirius red, and TUNEL staining; follicle counting; estrous cycle analysis; and Western blotting were used for validating the protective effect of I3C against ovarian senescence. Human granulosa-like tumor cell line and primary granulosa cells were used for in vitro assay. The results indicated that I3C inhibited ovarian fibrosis and apoptosis while increasing the number of primordial follicles. Mechanistic studies have shown that I3C promoted the nuclear translocation of nuclear factor-erythroid 2-related factor (Nrf2) and upregulated the expression of heme oxygenase 1 (HO-1). Additionally, I3C increased cell viability and decreased lactate dehydrogenase, malondialdehyde, reactive oxygen species and JC-1 levels. Furthermore, the antioxidant effect of I3C was found to be dependent on the activation of Nrf2 and HO-1, as demonstrated by the disappearance of the effect upon inhibition of Nrf2 expression. In conclusion, I3C can alleviate the ovarian damage caused by aging and may be a protective agent to delay ovarian aging.

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. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds. RESULTS: These two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model. CONCLUSIONS: This study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.

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. As a result, this nutraceutical microemulsion was easily formed without the need of co-surfactants, and it appeared light yellow, transparent, good flowability with a particle size of 152 ± 0.73 nm, with a sustained drug release manner of 56.45 ± 0.36% over 24 h and a great stability within the harsh intestinal environment. It enhanced intestinal drug uptake and transportation over human intestinal epithelial Caco-2 cells, and drastically elevated the oral peptide bioavailability of 4.1-fold higher than that of OPBP-1 solution. Meanwhile, the mechanism of these dietary droplets permeated over the intestinal enterocytic membrane was found via clathrin and caveolae-mediated endocytic pathways. From the in vivo studies, the microemulsion facilitated the infiltration of CD8+ T lymphocytes in tumors, with increased interferon-γ (IFN-γ) secretion. Thus, it manifested a promising immune anti-tumor effect and significantly inhibited the growth of murine colonic carcinoma (CT26). Furthermore, it was found that the fish oil could induce ferroptosis in tumor cells and exhibited synergistic effect with OPBP-1 for cancer immunotherapy. In conclusion, this fish oil-based formulation demonstrated great potential for oral delivery of peptides with its natural property in reactive oxygen species (ROS)-related ferroptosis of tumor cells, which provides a great platform for functional green oral delivery system in cancer immunotherapy.

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. A chimeric peptide Pal-DMPOP was designed by conjugating the double mutation of CD47/SIRPα blocking peptide (DMP) with the truncation of PD-1/PD-L1 blocking peptide OPBP-1(8-12) and was modified by a palmitic acid tail. Pal-DMPOP can significantly enhance macrophage-mediated phagocytosis of tumor cells and activate primary T cells to secret IFN-γ in vitro. Due to its superior hydrolysis-resistant activity as well as tumor tissue and lymph node targeting properties, Pal-DMPOP elicited stronger anti-tumor potency than Pal-DMP or OPBP-1(8-12) in immune-competent MC38 tumor-bearing mice. The in vivo anti-tumor activity was further validated in the colorectal CT26 tumor model. Furthermore, Pal-DMPOP mobilized macrophage and T-cell anti-tumor responses with minimal toxicity. Overall, the first bispecific CD47/SIRPα and PD-1/PD-L1 dual-blockade chimeric peptide was designed and exhibited synergistic anti-tumor efficacy via CD8+ T cell activation and macrophage-mediated immune response. The strategy could pave the way for designing effective therapeutic agents for 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. METHODS: In the present study, we hypothesized that integration of single-cell transcriptomic sequencing and large microarray sequencing of ESCC biopsies would reveal the key cell subtypes and therapeutic targets that determine the prognostic and tumorigenesis of ESCC. We characterized the gene expression profiles, gene sets enrichment, and the TME landscape of a microarray cohort including 84 ESCC tumors and their paired peritumor samples. We integrated single-cell transcriptomic sequencing and bulk microarray sequencing of ESCC to reveal key cell subtypes and druggable targets that determine the prognostic and tumorigenesis of ESCC. We then designed and screened a blocking peptide targeting Chemokine C-C motif ligand 18 (CCL18) derived from tumor associated macrophages and validated its potency by MTT assay. The antitumor activity of CCL18 blocking peptide was validated in vivo by using 4-nitroquinoline-1-oxide (4-NQO) induced spontaneous ESCC mouse model. RESULTS: Comparative gene expression and cell-cell interaction analyses revealed dysregulated chemokine and cytokine pathways during ESCC carcinogenesis. TME deconvolution and cell interaction analyses allow us to identify the chemokine CCL18 secreted by tumor associated macrophages could promote tumor cell proliferation via JAK2/STAT3 signaling pathway and lead to poor prognosis of ESCC. The peptide Pep3 could inhibit the proliferation of EC-109 cells promoted by CCL18 and significantly restrain the tumor progression in 4-NQO-induced spontaneous ESCC mouse model. CONCLUSIONS: For the first time, we discovered and validated that CCL18 blockade could significantly prevent ESCC progression. Our study revealed the comprehensive cell-cell interaction network in the TME of ESCC and provided novel therapeutic targets and strategies to ESCC treatment.

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. This information is then recovered to capture clearer retinal layer boundaries in the encoder-decoder module, thus completing retinal layer auto-segmentation of the retinal optical coherence tomography (OCT) images. Results: We validated this method using a retinal OCT image database containing 280 volumes (40 B-scans per volume) to demonstrate its effectiveness. The results showed that the method exhibits excellent performance in terms of the mean intersection over union and sensitivity (Se), which are as high as 90.41 and 92.15%, respectively. The intersection over union and Se values of the nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, outer limiting membrane, photoreceptor inner segment, photoreceptor outer segment, and pigment epithelium layer were found to be above 88%. Conclusions: DeepRetina can automate the segmentation of retinal layers and has great potential for the early diagnosis of fundus retinal diseases. In addition, our approach will provide a segmentation model framework for other types of tissues and cells in clinical practice. Translational Relevance: Automating the segmentation of retinal layers can help effectively diagnose and monitor clinical retinal diseases. In addition, it requires only a small amount of manual segmentation, significantly improving work efficiency.

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. RESULTS: We validated this method using a fecal database of 1,122 patients. The results indicated a mean average precision (mAP) of 92.16% and an average recall (AR) of 93.56%. The average precision (AP) and AR of erythrocyte, leukocyte, intestinal mucosal epithelial cells, hookworm eggs, ascarid eggs, and whipworm eggs were 92.82% and 93.38%, 93.99% and 96.11%, 90.71% and 92.41%, 89.95% and 93.88%, 96.90% and 91.21%, and 88.61% and 94.37%, respectively. The average times required by the GPU and the CPU to analyze a fecal microscopic image are approximately 0.14 and 1.02 s, respectively. CONCLUSION: FecalNet can automate the detection and identification of visible components in feces. It also provides a detection and identification framework for detecting several other types of cells in clinical practice.

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. In this study, a method is proposed for the analysis of urinary particles based on deep learning. METHODS: We used seven cellular components (i.e., erythrocytes, leukocytes, epithelial, low-transitional epithelium, casts, crystal, and squamous epithelial cells) in the microscopic imaging of urine as the detection targets. After the extraction of features using Resnet50, feature maps of different sizes are obtained in the last few layers of the feature pyramid net (FPN). The feature maps are then input into the classification subnetwork and regression subnetwork for classification and localization respectively, and detection results are obtained. First, we introduce the basic model (RetinaNet) to detect the cellular components in urinary particles, and the features of the objects can then be extracted more effectively by replacing different basic networks. Lastly, the effects of different weight initialization methods and different anchor scales on the performance of the model are investigated. RESULTS: We obtained the optimal network structure based on the adjustment of the loss functional parameters, thereby achieving the best results in the test set of urinary particles. The experimental data yielded an accuracy of 88.65% with a processing time of only 0.2 s for each image on a GeForce GTX 1080 graphics processing unit (GPU). Our results demonstrate that this method cannot only achieve the speed of the first-stage target detector, but also the accuracy of the two-stage target algorithm in the analysis of urinary particles. CONCLUSION: This study developed new automated analysis urinary particles based on deep learning, and this method is expected to be used for the automated analysis and detection of urinary particles. Moreover, our approach will be useful for the detection of other cells in the clinical setting.