Astragalus polysaccharide ameliorates CD8+ T cell dysfunction through STAT3/Gal-3/LAG3 pathway in inflammation-induced colorectal cancer.

Chronic inflammation can promote cancer development as observed in inflammation-induced colorectal cancer (CRC). However, the poor treatment outcomes emphasize the need for effective treatment. Astragalus polysaccharide (APS), a vital component of the natural drug Astragalus, has anti-tumor effects by inhibiting cancer cell proliferation and enhancing immune function. In this study, we found that APS effectively suppressed CRC development through activating CD8+ T cells and reversing its inhibitory state in the tumor microenvironment (TME) of AOM/DSS inflammation-induced CRC mice. Network pharmacology and clinical databases suggested that the STAT3/ Galectin-3(Gal-3)/LAG3 pathway might be APS's potential target for treating CRC and associated with CD8+ T cell dysfunction. In vivo experiments showed that APS significantly reduced phosphorylated STAT3 and Gal-3 levels in tumor cells, as well as LAG3 in CD8+ T cells. Co-culture experiments with MC38 and CD8+ T cells demonstrated that APS decreased the expression of co-inhibitory receptor LAG3 in CD8+ T cells by targeting STAT3/Gal-3 in MC38 cells. Mechanism investigations revealed that APS specifically improved CD8+ T cell function through modulation of the STAT3/Gal-3/LAG3 pathway to inhibit CRC development, providing insights for future clinical development of natural anti-tumor drugs and immunotherapies as a novel strategy combined with immune checkpoint inhibitors (ICIs).

A simple route to functionalized porous carbon foams from carbon nanodots for metal-free pseudocapacitors.

The development of potent pseudocapacitive charge storage materials has emerged as an effective solution for closing the gap between high-energy density batteries and high-power density and long-lasting electrical double-layer capacitors. Sulfonyl compounds are ideal candidates owing to their rapid and reversible redox reactions. However, structural instability and low electrical conductivity hinder their practical application as electrode materials. This work addresses these challenges using a fast and clean laser process to interconnect sulfonated carbon nanodots into functionalized porous carbon frameworks. In this bottom-up approach, the resulting laser-converted three-dimensional (3D) turbostratic carbon foams serve as high-surface-area, conductive scaffolds for redox-active sulfonyl groups. This design enables efficient faradaic processes using pendant sulfonyl groups, leading to a high specific capacitance of 157.6 F g-1 due to the fast reversible redox reactions of sulfonyl moieties. Even at 20 A g-1, the capacitance remained at 78.4% due to the uniform distribution of redox-active sites on the graphitic domains. Additionally, the 3D-tsSC300 electrode showed remarkable cycling stability of >15 000 cycles. The dominant capacitive processes and kinetics were analysed using extensive electrochemical characterizations. Furthermore, we successfully used 3D-tsSC300 in flexible solid-state supercapacitors, achieving a high specific capacitance of up to 17.4 mF cm-2 and retaining 91.6% of the initial capacitance after 20 000 cycles of charge and discharge coupled with 90° bending tests. Additionally, an as-assembled flexible all-solid-state symmetric supercapacitor exhibits a high energy density of 12.6 mW h cm-3 at a high power density of 766.2 W cm-3, both normalized by the volumes of the full device, which is comparable or better than state-of-the-art commercial pseudocapacitors and hybrid capacitors. The integrated supercapacitor provides a wide potential window of 2.0 V using a serial circuit, showing great promise for metal-free energy storage devices.

The ACE2 activator diminazene aceturate ameliorates colitis by repairing the gut-vascular barrier in mice.

Alleviating vascular barrier injury improves colitis. Angiotensin converting enzyme 2/angiotensin 1-7/Mas receptor (ACE2/Ang1-7/MasR) axis-related drugs have various biological properties, such as inhibition of inflammation and fibrosis, but their role in improving the gut-vascular barrier (GVB) has rarely been reported. This study aims to investigate the effects of diminazene aceturate (DIZE), an ACE2 activator, on vascular barrier damage in colitis. Mice were randomly divided into three groups: control, dextran sulfate sodium salt (DSS), and DIZE+DSS. Mice in the DSS group drank DSS for 8 days starting on day 4. Mice in the DIZE+DSS group were pregavaged with DIZE for 3 days and then drank DSS for 8 days while continuing to be gavaged with DIZE for 4 days. Mice were euthanized and samples were collected on the last day. Injury to colonic structure and colonic microvasculature was assessed by visual observation and appropriate staining. DSS-induced colonic and microvascular pathological damage in mice was substantially reversed by DIZE treatment. Molecular pathways were investigated by Western blot, quantitative real-time polymerase chain reaction (qRT-PCR), and enzyme linked immunosorbent assay (ELISA). DSS treatment upregulated angiotensin converting enzyme (ACE), angiotensin type 1 receptor (AT1R) protein, pro-inflammatory cytokines and inhibited tight junction-related protein expression. DIZE treatment activated ACE2/MasR protein expression and reversed epithelial barrier damage and inflammatory infiltration during DSS injury. In addition, DIZE treatment inhibited vascular endothelial growth factor A/vascular endothelial growth factor receptor 2/proto-oncogene tyrosine-protein kinase Src (VEGFA/VEGFR2/Src) pathway activation and restored vascular adhesion-linker protein vascular endothelial cadherin (VE-cadherin) expression during DSS injury. In conclusion, DIZE treatment ameliorated colitis, which was associated with balancing the two axes of the renin-angiotensin system (RAS) and repairing the GVB injury.

Mitigating Membership Inference in Deep Learning Applications with High Dimensional Genomic Data.

The use of deep learning techniques in medical applications holds great promises for advancing health care. However, there are growing privacy concerns regarding what information about individual data contributors (i.e., patients in the training set) these deep models may reveal when shared with external users. In this work, we first investigate the membership privacy risks in sharing deep learning models for cancer genomics tasks, and then study the applicability of privacy-protecting strategies for mitigating these privacy risks.

MSC-ACE2 Ameliorates Streptococcus uberis-Induced Inflammatory Injury in Mammary Epithelial Cells by Upregulating the IL-10/STAT3/SOCS3 Pathway.

In the dairy industry, Streptococcus uberis (S. uberis) is one of the most important pathogenic bacteria associated with mastitis in milk-producing cows, causing vast economic loss. To date, the only real effective method of treating and preventing streptococcal mastitis is antimicrobial therapy. In many inflammatory diseases, mesenchymal stem cells (MSCs) and angiotensin-converting enzyme 2 (ACE2) play an anti-inflammatory and anti-injurious role. Accordingly, we hypothesized that MSCs overexpressing ACE2 (MSC-ACE2) would ameliorate the inflammatory injury caused by S. uberis in mammary epithelial cells more efficiently than MSC alone. By activating the transcription 3/suppressor of cytokine signaling 3 (IL-10/STAT3/SOCS3) signaling pathway, MSC-ACE2 inhibited the NF-κB, MAPKs, apoptosis, and pyroptosis passways. Moreover, MSC-ACE2 overturned the downregulation of Occludin, Zonula occludens 1 (ZO-1), and Claudin-3 expression levels caused by S. uberis, suggesting that MSC-ACE2 promotes the repair of the blood-milk barrier. MSC-ACE2 demonstrated greater effectiveness than MSC alone, as expected. Based on these results, MSC-ACE2 effectively inhibits EpH4-Ev cell's inflammatory responses induced by S. uberis, and would be an effective therapeutic tool for treating streptococcal mastitis.

Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction.

We introduce the CPFNN (Correlation Pre-Filtering Neural Network) for biological age prediction based on blood DNA methylation data. The model is built on 20,000 top correlated DNA methylation features and trained by 1810 healthy samples from GEO database. The input data format and the instructions for parser and CPFNN model are detailed in this chapter. Followed by two potential uses, age acceleration detection and unknown age prediction are discussed.

Overexpression of angiotensin-converting enzyme 2 contributes to the amelioration of Streptococcus uberis-induced inflammatory injury in mammary epithelial cells.

Streptococcus uberis (S. uberis) is an environmentally important pathogenic bacterium and is the main pathogenic microorganism responsible for mastitis, which causes significant economic losses worldwide. Currently, there is no particularly effective treatment other than antibiotic therapy. Angiotensin-converting enzyme 2 (ACE2) plays an anti-inflammatory as well as an anti-injury role in numerous inflammatory diseases. Therefore, this study aimed to assess the hypothesis that S. uberis-induced mammary epithelial cells injury associated with ACE2, angiotensin II (Ang II) as well as angiotensin 1-7 (Ang-(1-7)) imbalance and that overexpression of ACE2 can repair S. uberis-induced mammary epithelial cells injury. We observed that the expression level of ACE2 was significantly downregulated after treatment of EpH4-Ev cells with S. uberis. Next, this assay verified the role of ACE2 in S. uberis-induced inflammatory injury in EpH4-Ev cells by overexpressing the ACE2 gene as well as its silencing. The results showed that overexpression of the ACE2 gene significantly activated the interleukin-10/signal transducer and activator of transcription 3/suppressors-of-cytokine-signaling 3 (IL-10/STAT3/SOCS3) pathway, thereby inhibiting the nuclear factor-κB (NF-κB) as well as pyroptosis pathways. Furthermore, overexpression of the ACE2 gene reversed the downregulation of zonula occludens 1 (ZO-1), Occludin, Claudin-1, and Claudin-2 caused by S. uberis, suggesting that ACE2 could promote to repair the blood-milk barrier. However, siRNA silencing of the ACE2 gene produced the opposite effect. These results suggest that ACE2 ameliorates S. uberis-induced mammary epithelial cells injury. AVAILABILITY OF DATA: All data generated or analyzed during this study are included within the article and its additional information file.

Multiple Color Emission of Mechanoluminescence and Photoluminescence from SrZnSO: Bi3+ for Multimode Anticounterfeiting.

Mechanoluminescence materials that emit light under mechanical stimulation have attracted widespread attention in sensing, anticounterfeiting, and imaging applications. In this study, a series of Sr1-xBixZnSO (0.001 ≤ x ≤ 0.1) samples was synthesized by the method of high temperature solid-state reaction. It is worth noting that the distortion degree of the SrO3S3 octahedron was increased with increasing Bi3+ concentration, and the color manipulated Sr1-xBixZnSO which can emit different photoluminescence (blue to dark blue and finally red) and mechanoluminescence (orange to red) colors is obtained. Moreover, the deep traps can stably store and provide electronic supplements in shallow traps released under mechanical stimulation. Therefore, devices made of SrZnSO:Bi3+ phosphor and polydimethylsiloxane (PDMS) can be used as thermo-mechano-opto three-mode anticounterfeiting. The ML intensity is linear to the external load and can be utilized for stress sensing or imaging.

Age Prediction by DNA Methylation in Neural Networks.

Aging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples. This leads to overfitting and a poor generalization of neural networks. We propose Correlation Pre-Filtered Neural Network (CPFNN) that uses Spearman Correlation to pre-filter the input features before feeding them into neural networks. We compare CPFNN with the statistical regressions (i.e., Horvath's and Hannum's formulas), the neural networks with LASSO regularization and elastic net regularization, and the Dropout Neural Networks. CPFNN outperforms these models by at least 1 year in term of Mean Absolute Error (MAE), with a MAE of 2.7 years. We also test for association between the epigenetic age with Schizophrenia and Down Syndrome ( p=0.024 and , respectively). We discover that for a large number of candidate features, such as genome-wide DNA methylation data, a key factor in improving prediction accuracy is to appropriately weight features that are highly correlated with the outcome of interest.