Brain tumor classification using fine-tuned transfer learning models on magnetic resonance imaging (MRI) images.

Objective: Brain tumors are a leading global cause of mortality, often leading to reduced life expectancy and challenging recovery. Early detection significantly improves survival rates. This paper introduces an efficient deep learning model to expedite brain tumor detection through timely and accurate identification using magnetic resonance imaging images. Methods: Our approach leverages deep transfer learning with six transfer learning algorithms: VGG16, ResNet50, MobileNetV2, DenseNet201, EfficientNetB3, and InceptionV3. We optimize data preprocessing, upsample data through augmentation, and train the models using two optimizers: Adam and AdaMax. We perform three experiments with binary and multi-class datasets, fine-tuning parameters to reduce overfitting. Model effectiveness is analyzed using various performance scores with and without cross-validation. Results: With smaller datasets, the models achieve 100% accuracy in both training and testing without cross-validation. After applying cross-validation, the framework records an outstanding accuracy of 99.96% with a receiver operating characteristic of 100% on average across five tests. For larger datasets, accuracy ranges from 96.34% to 98.20% across different models. The methodology also demonstrates a small computation time, contributing to its reliability and speed. Conclusion: The study establishes a new standard for brain tumor classification, surpassing existing methods in accuracy and efficiency. Our deep learning approach, incorporating advanced transfer learning algorithms and optimized data processing, provides a robust and rapid solution for brain tumor detection.

The long non-coding RNA NEAT1 contributes to aberrant STAT3 signaling in pancreatic cancer and is regulated by a metalloprotease-disintegrin ADAM8/miR-181a-5p axis.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and several studies demonstrate that STAT3 has critical roles throughout the course of PDAC pathogenesis. METHODS: TCGA, microarray, and immunohistochemistry data from a PDAC cohort were used for clinical analyses. Panc89 cells with ADAM8 knockout, re-expression of ADAM8 mutants, and Panc1 cells overexpressing ADAM8 were generated. Gene expression analyses of ADAM8, STAT3, long non-coding (lnc) RNA NEAT1, miR-181a-5p and ICAM1 were performed by quantitative PCR. Subcellular fractionation quantified NEAT1 expression in cytoplasm and nucleus of PDAC cell lines. Cell proliferation, scratch, and invasion assays were performed to detect growth rate, migration and invasion capabilities of cells. Gain and loss of function experiments were carried out to investigate the biological effects of lncRNA NEAT1 and miR-181a-5p on PDAC cells and downstream genes. Dual-luciferase reporter gene assay determined interaction and binding sites of miR-181a-5p in lncRNA NEAT1. Pull down assays, RNA binding protein immunoprecipitation (RIP), and ubiquitination assays explored the molecular interaction between lncRNA NEAT1 and STAT3. RESULTS: High ADAM8 expression causes aberrant STAT3 signaling in PDAC cells and is positively correlated with NEAT1 expression. NEAT1 binding to STAT3 was confirmed and prevents STAT3 degradation in the proteasome as increased degradation of STAT3 was observed in ADAM8 knockout cells and cells treated with bortezomib. Furthermore, miRNA-181a-5p regulates NEAT1 expression by direct binding to the NEAT1 promoter. CONCLUSION: ADAM8 regulates intracellular STAT3 levels via miR-181a-5p and NEAT1 in pancreatic cancer.

Fangchinoline inhibits metastasis and reduces inflammation-induced epithelial-mesenchymal transition by targeting the FOXM1-ADAM17 axis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. Efforts have been focused on developing new anti-HCC agents and understanding their pharmacology. However, few agents have been able to effectively combat tumor growth and invasiveness due to the rapid progression of HCC. In this study, we discovered that fangchinoline (FAN), a bisbenzylisoquinoline alkaloid derived from Stephania tetrandra S. Moore, effectively inhibited the migration, invasion, and epithelial-mesenchymal transition (EMT) of HCC cells. FAN treatment also led to the suppression of IL6 and IL1ß release, as well as the expression of inflammation-related proteins such as COX-2 and iNOS, and the activation of the NF-κB pathway, thereby reducing inflammation-related EMT. Additionally, FAN directly bound to forkhead box protein M1 (FOXM1), resulting in decreased levels of FOXM1 proteins and disruption of the FOXM1-ADAM17 axis. Our in vivo findings confirmed that FAN effectively hindered the growth and lung metastasis of HCCLM3-xenograft tumors. Importantly, the upregulation of FOXM1 in HCC tissue suggested that targeting FOXM1 inhibition with FAN or its inhibitors could be a promising therapeutic approach for HCC. Overall, this study elucidated the anti-tumor effects and potential pharmacological mechanisms of FAN, and proposed that targeting FOXM1 inhibition may be an effective therapeutic strategy for HCC with potential clinical applications.

Expression of HMGB1, TGF-ß1, BIRC3, ADAM17, CDKN1A, and FTO in Relation to Left Ventricular Remodeling in Patients Six Months after the First Myocardial Infarction: A Prospective Study.

Background: After myocardial infarction (MI), adverse left ventricular (LV) remodeling may occur. This is followed by LV hypertrophy and eventually heart failure. The remodeling process is complex and goes through multiple phases. The aim of this study was to investigate the expression of HMGB1, TGF-ß1, BIRC3, ADAM17, CDKN1A, and FTO, each involved in a specific step of LV remodeling, in association with the change in the echocardiographic parameters of LV structure and function used to assess the LV remodeling process in the peripheral blood mononuclear cells (PBMCs) of patients six months after the first MI. The expression of selected genes was also determined in PBMCs of controls. Methods: The study group consisted of 99 MI patients, who were prospectively followed-up for 6 months, and 25 controls. Cardiac parameters, measured via conventional 2D echocardiography, were evaluated at two time points: 3-5 days and 6 months after MI. The mRNA expression six-months-post-MI was detected using TaqMan® technology (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). Results:HMGB1 mRNA was significantly higher in patients with adverse LV remodeling six-months-post-MI than in patients without adverse LV remodeling (p = 0.04). HMGB1 mRNA was significantly upregulated in patients with dilated LV end-diastolic diameter (LVEDD) (p = 0.03); dilated LV end-diastolic volume index (LVEDVi) (p = 0.03); severely dilated LV end-systolic volume index (LVESVi) (p = 0.006); impaired LV ejection fraction (LVEF) (p = 0.01); and LV enlargement (p = 0.03). It was also significantly upregulated in PBMCs from patients compared to controls (p = 0.005). TGF-ß1 and BIRC3 mRNA were significantly lower in patients compared to controls (p = 0.02 and p = 0.05, respectively). Conclusions: Our results suggest that HMGB1 is involved in adverse LV remodeling six-months-post-MI, even on the mRNA level. Further research and validation are needed.

TRPV4 Mediates Alveolar Epithelial Barrier Integrity and Induces ADAM10-Driven E-Cadherin Shedding.

Transient receptor potential vanilloid 4 (TRPV4) channels have been associated with numerous pulmonary pathologies, including hypertension, asthma, and acute lung injury. However, their role in the alveolar epithelium remains unclear. We performed impedance-based resistance measurements in primary differentiated alveolar epithelial type I (AT1) cells from wild-type (WT) and TRPV4-deficient (TRPV4-/-) C57/BL6J mice to detect changes in AT1 barrier integrity upon TRPV4 activation. Both pharmacological (GSK1016790A) and a low pH-driven activation of TRPV4 were quantified, and the downstream effects on adherens junctions were assessed through the Western blotting of epithelial cadherin (E-cadherin) protein levels. Importantly, a drop in pH caused a rapid decrease in AT1 barrier resistance and increased the formation of a ~35 kDa E-cadherin C-terminal fragment, with both effects significantly reduced in TRPV4-/- AT1 cells. Similarly, the pharmacological activation of TRPV4 in AT1 cells triggered an immediate transient loss of barrier resistance and the formation of the same E-cadherin fragment, which was again diminished by TRPV4 deficiency. Moreover, TRPV4-mediated E-cadherin cleavage was significantly reduced by GI254023X, an antagonist of a disintegrin and metalloprotease 10 (ADAM10). Our results confirm the role of TRPV4 in regulating alveolar epithelial barrier permeability and provide insight into a novel signaling pathway by which TRPV4-induced Ca2+ influx stimulates metalloprotease-driven ectodomain shedding.

FLCMC: Federated Learning Approach for Chinese Medicinal Text Classification.

Addressing the privacy protection and data sharing issues in Chinese medical texts, this paper introduces a federated learning approach named FLCMC for Chinese medical text classification. The paper first discusses the data heterogeneity issue in federated language modeling. Then, it proposes two perturbed federated learning algorithms, FedPA and FedPAP, based on the self-attention mechanism. In these algorithms, the self-attention mechanism is incorporated within the model aggregation module, while a perturbation term, which measures the differences between the client and the server, is added to the local update module along with a customized PAdam optimizer. Secondly, to enable a fair comparison of algorithms' performance, existing federated algorithms are improved by integrating a customized Adam optimizer. Through experiments, this paper first conducts experimental analyses on hyperparameters, data heterogeneity, and validity on synthetic datasets, which proves that the proposed federated learning algorithm has significant advantages in classification performance and convergence stability when dealing with heterogeneous data. Then, the algorithm is applied to Chinese medical text datasets to verify its effectiveness on real datasets. The comparative analysis of algorithm performance and communication efficiency shows that the algorithm exhibits strong generalization ability on deep learning models for Chinese medical texts. As for the synthetic dataset, upon comparing with comparison algorithms FedAvg, FedProx, FedAtt, and their improved versions, the experimental results show that for data with general heterogeneity, both FedPA and FedPAP show significantly more accurate and stable convergence behavior. On the real Chinese medical dataset of doctor-patient conversations, IMCS-V2, with logistic regression and long short-term memory network as training models, the experiment results show that in comparison to the above three comparison algorithms and their improved versions, FedPA and FedPAP both possess the best accuracy performance and display significantly more stable and accurate convergence behavior, proving that the method in this paper has better classification effects for Chinese medical texts.

Downregulation of PIK3IP1/TrIP on T cells is controlled by TCR signal strength, PKC and metalloprotease-mediated cleavage.

The protein known as PI3K-interacting protein (PIK3IP1), or transmembrane inhibitor of PI3K (TrIP), is highly expressed by T cells and can modulate PI3K activity in these cells. Several studies have also revealed that TrIP is rapidly downregulated following T cell activation. However, it is unclear as to how this downregulation is controlled. Using a novel monoclonal antibody that robustly stains cell-surface TrIP, we demonstrate that TrIP is lost from the surface of activated T cells in a manner dependent on the strength of signaling through the T cell receptor (TCR) and specific downstream signaling pathways, in particular classical PKC isoforms. TrIP expression returns by 24 hours after stimulation, suggesting that it may play a role in resetting TCR signaling at later time points. We also provide evidence that ADAM family proteases are required for both constitutive and stimulation-induced downregulation of TrIP in T cells. Finally, by expressing truncated forms of TrIP in cells, we identify the region in the extracellular stalk domain of TrIP that is targeted for proteolytic cleavage.

Modulation of ADAM17 Levels by Pestiviruses Is Species-Specific.

Upon host cell infection, viruses modulate their host cells to better suit their needs, including the downregulation of virus entry receptors. ADAM17, a cell surface sheddase, is an essential factor for infection of bovine cells with several pestiviruses. To assess the effect of pestivirus infection on ADAM17, the amounts of cellular ADAM17 and its presence at the cell surface were determined. Mature ADAM17 levels were reduced upon infection with a cytopathic pestivirus bovis (bovine viral diarrhea virus, cpBVDV), pestivirus suis (classical swine fever virus, CSFV) or pestivirus giraffae (strain giraffe), but not negatively affected by pestivirus L (Linda virus, LindaV). A comparable reduction of ADAM17 surface levels, which represents the bioactive form, could be observed in the presence of E2 of BVDV and CSFV, but not LindaV or atypical porcine pestivirus (pestivirus scrofae) E2. Superinfection exclusion in BVDV infection is caused by at least two proteins, glycoprotein E2 and protease/helicase NS3. To evaluate whether the lowered ADAM17 levels could be involved in superinfection exclusion, persistently CSFV- or LindaV-infected cells were challenged with different pestiviruses. Persistently LindaV-infected cells were significantly more susceptible to cpBVDV infection than persistently CSFV-infected cells, whilst the other pestiviruses tested were not or only hardly able to infect the persistently infected cells. These results provide evidence of a pestivirus species-specific effect on ADAM17 levels and hints at the possibility of its involvement in superinfection exclusion.

Fully human monoclonal antibody targeting the cysteine-rich substrate-interacting region of ADAM17 on cancer cells.

ADAM17 sheds EGFR/erbB ligands and triggers oncogenic pathways that lead to the progression of solid tumors. We targeted the ADAM17 disintegrin and cysteine rich domain region (D+C) to generate a panel of single-chain antibody fragments (scFvs) that selectively bind to the D or C domains of ADAM17, but not of ADAM10 or ADAM19. From the panel, we selected one scFv, referred to as C12, based on its high binding affinity towards the target, and re-formatted it to a full IgG for further studies. High-resolution cryo-electron microscopy studies documented that the mAb binds to the ADAM17 C-domain that in ADAM proteases, notably ADAM10 and ADAM17, is known to impart substrate-specificity. The C12 mAb significantly inhibited EGFR phosphorylation in cancer cell lines by hindering the cleavage of EGFR ligands tethered to the cell surface. This inhibition provides a mechanism for potential anti-tumor effects, and indeed C12 diminished the viability of a variety of EGFR-expressing cancer cell lines. Cell-based ELISA studies revealed that C12 preferentially bound to activated ADAM17 present on tumor cells, as compared to the autoinhibited ADAM17 that is the predominant form on HEK293 and other non-tumor cells. C12 also exhibited tumor growth inhibition in an ovarian cancer xenograft mouse model. Consistent with its selective tumor cell binding in vitro, radioimmuno PET (positron emission tomography) imaging with 89Zr-DFO-C12 in mouse xenograft models confirmed tumoral accumulation of the C12 mAb.

MHC class I polypeptide-related sequence B shedding modulates pancreatic tumor immunity via the activation of NKG2DLow T cells.

Natural killer group 2 member D ligands (NKG2DLs) are expressed as stress response proteins in cancer cells. NKG2DLs induce immune cell activation or tumor escape responses, depending on their expression. Human pancreatic cancer cells, PANC-1, express membrane MHC class I polypeptide-related sequence A/B (mMICA/B), whereas soluble MICB (sMICB) is detected in the culture supernatant. We hypothesized that sMICB saturates NKG2D in NKG2DLow T cells and inhibits the activation signal from mMICB to NKG2D. Knockdown of MICB by siRNA reduced sMICB level, downregulated mMICB expression, maintained NKG2DLow T cell activation, and inhibited NKG2DHigh T cell activation. To maintain mMICB expression and downregulate sMICB expression, we inhibited a disintegrin and metalloproteinase (ADAM), a metalloproteinase that sheds MICB. Subsequently, the shedding of MICB was prevented using ADAM17 inhibitors, and the activation of NKG2DLow T cells was maintained. In vivo xenograft model revealed that NKG2DHigh T cells have superior anti-tumor activity. These results elucidate the mechanism of immune escape via sMICB and show potential for the activation of NKG2DLow T cells within the tumor microenvironment.

Proinflammatory microglial activation impairs in vitro cortical tissue repair via zinc-dependent ADAM17 cleavage of the CSF-1 receptor.

Infection and subsequent inflammatory processes negatively impact prognosis in individuals with traumatic brain injury (TBI). Tissue repair following TBI is tightly regulated by microglia, promoting or, importantly, preventing astrocyte-mediated repair processes, depending on the activation state of the neuroimmune cells. This study investigated the poorly understood mechanism linking proinflammatory microglia activation and astrocyte-mediated tissue repair using an in vitro mechanical injury model in mixed cortical cultures of rat neurons and glia. We hypothesized that proinflammatory activation disrupts the microglial response to colony-stimulating factor 1 (CSF-1), which stimulates microglia migration and proliferation, both essential for astrocyte-mediated tissue repair. Following mechanical damage, cultures were treated with lipopolysaccharide (LPS) and interferon-gamma (IFNγ) to induce a proinflammatory state. Immunocytochemical and biochemical analyses were used to evaluate glial repair. Proinflammatory activation dramatically impeded wound closure, reducing microglial levels via upregulation of the zinc-dependent disintegrin and metalloprotease 17 (ADAM17), leading to the cleavage of the CSF-1 receptor (CSF-1R). Indeed, pharmacological inhibition of ADAM17 effectively promoted wound closure during inflammation. Moreover, zinc chelation prevented ADAM17-mediated cleavage of CSF-1R and induced the release of trophic factors, dramatically improving tissue recovery. Our findings strongly identify ADAM17 as a primary regulator of CSF-1R-mediated signaling and establish a mechanism defining the association between pro-inflammatory microglial activation and tissue repair following injury.

The metalloproteinase ADAM10 sheds angiotensin-converting enzyme (ACE) from the pulmonary endothelium as a soluble, functionally active convertase.

The renin-angiotensin-aldosterone system (RAAS) plays a critical role in the regulation of blood pressure and fluid balance, with angiotensin-converting enzyme (ACE) being a key transmembrane enzyme that converts angiotensin I to angiotensin II. Hence, ACE activity is an important drug target in cardiovascular pathologies such as hypertension. Our study demonstrates that human pulmonary microvascular endothelial cells (HPMECs) are an important source of proteolytically released ACE. The proteolytic release of transmembrane proteins, a process known as ectodomain shedding, is facilitated by membrane proteases called sheddases. By knockout and inhibition studies, we identified ADAM10 (A disintegrin and metalloprotease 10) as a primary sheddase responsible for ACE release in HEK293 cells. The function of ADAM10 as primary, constitutive sheddase of ACE was confirmed in HPMECs. Moreover, we demonstrated the physiological relevance of ADAM10 for ACE shedding in ex vivo precision cut lung slices (PCLS) from human and mouse lungs. Notably, ADAM17 activity is not directly involved in ACE shedding but indirectly by regulating ACE mRNA and protein levels, leading to increased ADAM10-mediated ACE shedding. Importantly, soluble ACE generated by shedding is enzymatically active and can thereby participate in systemic RAAS functions. Taken together, our findings highlight the critical role of ADAM10 (directly) and ADAM17 (indirectly) in ACE shedding and RAAS modulation.

ADAM10 modulates the efficacy of T-cell-mediated therapy in solid tumors.

T-cell-mediated therapeutic strategies are the most potent effectors of cancer immunotherapy. However, an essential barrier to this therapy in solid tumors is disrupting the anti-cancer immune response, cancer-immunity cycle, T-cell priming, trafficking and T-cell cytotoxic capacity. Thus, reinforcing the anti-cancer immune response is needed to improve the effectiveness of T-cell-mediated therapy. Tumor-associated protease ADAM10, endothelial cells (ECs) and cytotoxic CD8+ T cells engage in complex communication via adhesion, transmigration and chemotactic mechanisms to facilitate an anti-cancer immune response. The precise impact of ADAM10 on the intricate mechanisms underlying these interactions remains unclear. This paper broadly explores how ADAM10, through different routes, influences the efficacy of T-cell-mediated therapy. ADAM10 cleaves CD8+ T-cell-targeting genes and impacts their expression and specificity. In addition, ADAM10 mediates the interactions of adhesion molecules with T cells and influences CD8+ T-cell activity and trafficking. Thus, understanding the role of ADAM10 in these events may lead to innovative strategies for advancing T-cell-mediated therapies.

First Principles Study of Electronic and Optical Properties of Al-P Co-Doped ZnO in the Presence of Zn Vacancies.

The BP neural network optimized by the Adam algorithm was used to predict the defect formation energy of Al-P co-doped ZnO systems with different concentrations of P replacing O under the presence of different concentrations of VZn. It was found that the easily formed AlZnPo-1VZn, AlZnPO-2VZn, and AlZn2PO-1VZn systems. The first principles of density function were used to study the geometric, electronic, and optical properties of each system. The simulation results show that the bandgap values of the three systems have decreased relative to the intrinsic ZnO, among which AlZnPO-1VZn and AlZnPO-2VZn is still a p-type conductive system, AlZnPO-2VZn has the highest conductivity. From the analysis of reflectivity, absorption rate, and light transmittance, AlZn2PO-1VZn has the most relatively excellent optical properties, followed by AlznPo-2VZn.

ADAM8 promotes alcoholic liver fibrosis through the MAPK signaling pathway.

The effect and molecular regulatory mechanism of A Disintegrin and Metalloproteinase 8 (ADAM8) were explored in alcoholic liver fibrosis (ALF). C57BL/6N male mice were randomly divided into control, alcohol, and ADAM8-sgRNA3 plasmid groups. The control group received control liquid diet, while the alcohol and ADAM8-sgRNA3 plasmid groups were given alcohol liquid feed diet combined with ethanol gavage treatment for 8 weeks to induce ALF modeling. In addition, the ADAM8-sgRNA3 plasmid group was injected with the effective ADAM8-sgRNA3 plasmid, while the alcohol and control group mice were injected with an equivalent amount of physiological saline. LX-2 human hepatic stellate cells were divided into control, alcohol, si-ADAM8-2, and si-ADAM8-NC groups and induced for 48 h for model establishment in vitro. Serological detection, pathological staining, Western blotting, qRT-PCR and CCK8 assay were performed for experiments. Compared with the alcohol group, ADAM8 mRNA, protein and, positive area rate, serological indicators, pathological changes, and the expression of liver fibrosis marker and MAPK signaling pathway-related factors in the ADAM8-sgRNA3 plasmid group significantly decreased in vivo. Compared with the alcohol group, ADAM8 mRNA and protein expression, cell viability, and the expression of liver fibrosis markers and MAPK signaling pathway-related factors (p-ERK1/2, PCNA, Bcl-2, p-c-Jun, TGFß1, p-p38 MAPK and HSP27) reduced significantly in the si-ADAM8-2 group. Therefore, ADAM8 promotes ALF through the MAPK signaling pathway, a promising target for treating ALF.

Development of a Proteomic Workflow for the Identification of Heparan Sulphate Proteoglycan-Binding Substrates of ADAM17.

Ectodomain shedding, which is the proteolytic release of transmembrane proteins from the cell surface, is crucial for cell-to-cell communication and other biological processes. The metalloproteinase ADAM17 mediates ectodomain shedding of over 50 transmembrane proteins ranging from cytokines and growth factors, such as TNF and EGFR ligands, to signalling receptors and adhesion molecules. Yet, the ADAM17 sheddome is only partly defined and biological functions of the protease have not been fully characterized. Some ADAM17 substrates (e.g., HB-EGF) are known to bind to heparan sulphate proteoglycans (HSPG), and we hypothesised that such substrates would be under-represented in traditional secretome analyses, due to their binding to cell surface or pericellular HSPGs. Thus, to identify novel HSPG-binding ADAM17 substrates, we developed a proteomic workflow that involves addition of heparin to solubilize HSPG-binding proteins from the cell layer, thereby allowing their mass spectrometry detection by heparin-treated secretome (HEP-SEC) analysis. Applying this methodology to murine embryonic fibroblasts stimulated with an ADAM17 activator enabled us to identify 47 transmembrane proteins that were shed in response to ADAM17 activation. This included known HSPG-binding ADAM17 substrates (i.e., HB-EGF, CX3CL1) and 14 novel HSPG-binding putative ADAM17 substrates. Two of these, MHC-I and IL1RL1, were validated as ADAM17 substrates by immunoblotting.

Associations of SEMA7A, SEMA4D, ADAMTS10, and ADAM8 with KRAS, NRAS, BRAF, PIK3CA, and AKT Gene Mutations, Microsatellite Instability Status, and Cytokine Expression in Colorectal Cancer Tissue.

Semaphorins (SEMAs), ADAM, and ADAMTS family members are implicated in various cancer progression events within the tumor microenvironment across different cancers. In this study, we aimed to evaluate the expression of SEMA7A, SEMA4D, ADAM8, and ADAMTS10 in colorectal cancer (CRC) in relation to the mutational landscape of KRAS, NRAS, BRAF, PIK3CA, and AKT genes, microsatellite instability (MSI) status, and clinicopathological features. We also examined the associations between the expression of these proteins and selected cytokines, chemokines, and growth factors, assessed using a multiplex assay. Protein concentrations were quantified using ELISA in CRC tumors and tumor-free surgical margin tissue homogenates. Gene mutations were evaluated via RT-PCR, and MSI status was determined using immunohistochemistry (IHC). GSEA and statistical analyses were performed using R Studio. We observed a significantly elevated expression of SEMA7A in BRAF-mutant CRC tumors and an overexpression of ADAM8 in KRAS 12/13-mutant tumors. The expression of ADAMTS10 was decreased in PIK3CA-mutant CRC tumors. No significant differences in the expression of the examined proteins were observed based on MSI status. The SEMA7A and SEMA4D expressions were correlated with the expression of numerous cytokines associated with various immune processes. The potential immunomodulatory functions of these molecules and their suitability as therapeutic targets require further investigation.

Inhibition of ADAM17 attenuates high glucose-induced angiogenesis and inflammation in endothelial cells partly through down-regulation of GRO-α/CXCR2 expression: implications in peritoneal dialysis.

BACKGROUND: Angiogenesis and inflammation are key events leading to peritoneal morphologic alteration and ultrafiltration failure in patients undergoing peritoneal dialysis (PD). The current study aims to explore the role of ADAM17 in the angiogenetic and inflammatory responses of endothelial cells. METHODS: Human umbilical vein endothelial cells (HUVECs) were cultured and treated with a high glucose-containing medium. In parallel experiments, the expression of ADAM17 in HUVECs was inhibited by SiRNA interference. The mRNA and protein expression of ADAM17, GRO-α and CXCR2 were assessed by qPCR and Western blotting, respectively. The concentrations of GRO-α, VEGF, IL-6 and TNF-α in the cellular supernatants were determined by ELISA. Tube formation and migration of HUVECs were evaluated by Matrigel and transwell migration apparatus. RESULTS: High glucose increased the expression of ADAM17, CXCR2 and GRO-α in cultured HUVECs. RNA silencing of ADAM17 abolished high glucose-mediated increase of GRO-α and CXCR2, which were accompanied by reduced secretion of VEGF, IL-6, TNF-α, as well as tube formation and cell migration in HUVECs. CONCLUSIONS: Inhibition of ADAM17 ameliorates high glucose-induced angiogenic and inflammatory responses in endothelial cells partly through down-regulation of GRO-α/CXCR2 expression.

Enhancing liver tumor segmentation with UNet-ResNet: Leveraging ResNet's power.

BACKGROUND: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning. OBJECTIVE: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet's deep residual learning to address training issues in deep neural networks. At the same time, U-Net's structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training. METHODS: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation. RESULTS: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation. CONCLUSION: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.