Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.Comment: 14 page

Protective role of curcumin in disease progression from non-alcoholic fatty liver disease to hepatocellular carcinoma: a meta-analysis

Background: Pathological progression from non-alcoholic fatty liver disease (NAFLD) to liver fibrosis (LF) to hepatocellular carcinoma (HCC) is a common dynamic state in many patients. Curcumin, a dietary supplement derived from the turmeric family, is expected to specifically inhibit the development of this progression. However, there is a lack of convincing evidence.Methods: The studies published until June 2023 were searched in PubMed, Web of Science, Embase, and the Cochrane Library databases. The SYstematic Review Center for Laboratory animal Experimentation (SYRCLE) approach was used to evaluate the certainty of evidence. StataSE (version 15.1) and Origin 2021 software programs were used to analyze the critical indicators.Results: Fifty-two studies involving 792 animals were included, and three disease models were reported. Curcumin demonstrates a significant improvement in key indicators across the stages of NAFLD, liver fibrosis, and HCC. We conducted a detailed analysis of common inflammatory markers IL-1β, IL-6, and TNF-α, which traverse the entire disease process. The research results reveal that curcumin effectively hinders disease progression at each stage by suppressing inflammation. Curcumin exerted hepatoprotective effects in the dose range from 100 to 400 mg/kg and treatment duration from 4 to 10 weeks. The mechanistic analysis reveals that curcumin primarily exerts its hepatoprotective effects by modulating multiple signaling pathways, including TLR4/NF-κB, Keap1/Nrf2, Bax/Bcl-2/Caspase 3, and TGF-β/Smad3.Conclusion: In summary, curcumin has shown promising therapeutic effects during the overall progression of NAFLD–LF–HCC. It inhibited the pathological progression by synergistic mechanisms related to multiple pathways, including anti-inflammatory, antioxidant, and apoptosis regulation

CMTM6 shapes antitumor T cell response through modulating protein expression of CD58 and PD-L1

The dysregulated expression of immune checkpoint molecules enables cancer cells to evade immune destruction. While blockade of inhibitory immune checkpoints like PD-L1 forms the basis of current cancer immunotherapies, a deficiency in costimulatory signals can render these therapies futile. CD58, a costimulatory ligand, plays a crucial role in antitumor immune responses, but the mechanisms controlling its expression remain unclear. Using two systematic approaches, we reveal that CMTM6 positively regulates CD58 expression. Notably, CMTM6 interacts with both CD58 and PD-L1, maintaining the expression of these two immune checkpoint ligands with opposing functions. Functionally, the presence of CMTM6 and CD58 on tumor cells significantly affects T cell-tumor interactions and response to PD-L1-PD-1 blockade. Collectively, these findings provide fundamental insights into CD58 regulation, uncover a shared regulator of stimulatory and inhibitory immune checkpoints, and highlight the importance of tumor-intrinsic CMTM6 and CD58 expression in antitumor immune responses

STC1 competitively binding βPIX enhances melanoma progression via YAP nuclear translocation and M2 macrophage recruitment through the YAP/CCL2/VEGFA/AKT feedback loop

This study investigates the role of Stanniocalcin-1 (STC1) in melanoma progression, with a focus on its impact on metastasis, angiogenesis, and immune evasion. Systematic bioinformatics analysis revealed the potential influence of STC1 dysregulation on prognosis, immune cell infiltration, response to immune therapy, and cellular functions. In vitro assays were conducted to assess the proliferation, invasion, migration, and angiogenesis capabilities of A375 cells. In vivo experiments utilizing C57BL/6 J mice established a lung metastasis model using B16-F10 cells to evaluate macrophage infiltration and M2 polarization. A Transwell co-culture system was employed to explore the crosstalk between melanoma and macrophages. Molecular interactions among STC1, YAP, βPIX, and CCL2 are investigated using mass spectrometry, Co-Immunoprecipitation, Dual-Luciferase Reporter Assay, and Chromatin Immunoprecipitation experiments. STC1 was found to enhance lung metastasis by promoting the recruitment and polarization of M2 macrophages, thereby fostering an immunosuppressive microenvironment. Mechanistically, STC1 competes with YAP for binding to βPIX within the KER domain in melanoma cells, leading to YAP activation and subsequent CCL2 upregulation. CCL2-induced M2 macrophages secrete VEGFA, which enhances tumor vascularization and increases STC1 expression via the AKT signaling pathway in melanoma cells, establishing a pro-metastatic feedback loop. Notably, STC1-induced YAP activation increases PD-L1 expression, promoting immune evasion. Silencing STC1 enhances the efficacy of PD-1 immune checkpoint therapy in mice. This research elucidates STC1's role in melanoma metastasis and its complex interactions with tumor-associated macrophages, proposing STC1 as a potential therapeutic target for countering melanoma metastasis and augmenting the efficacy of PD-1 immunotherapy

Association between promoter methylation of <i>DAPK</i> gene and HNSCC: A meta-analysis

<div><p>Background</p><p>The death-associated protein kinase (<i>DAPK</i>) is a tumor suppressor gene, which is a mediator of cell death of INF-γ–induced apoptosis. Aberrant methylation of <i>DAPK</i> promoter has been reported in patients with head and neck squamous cell carcinoma (HNSCC). However, the results of these studies are inconsistent. Hence, the present study aimed to evaluate the association between the promoter methylation of <i>DAPK</i> gene and HNSCC.</p><p>Methods</p><p>Relevant studies were systematically searched in PubMed, Web of Science, Ovid, and Embase. The association between <i>DAPK</i> promoter methylation and HNSCC was assessed by odds ratio (ORs) and 95% confidence intervals (CI). To evaluate the potential sources of heterogeneity, we conducted the meta-regression analysis and subgroup analysis.</p><p>Results</p><p>Eighteen studies were finally included in the meta-analysis. The frequency of <i>DAPK</i> promoter methylation in patients with HNSCC was 4.09-fold higher than the non-cancerous controls (OR = 3.96, 95%CI = 2.26–6.95). A significant association between <i>DAPK</i> promoter methylation and HNSCC was found among the Asian region and the Non-Asia region (Asian region, OR = 4.43, 95% CI = 2.29–8.58; Non-Asia region, OR = 3.39, 95% CI = 1.18–9.78). In the control source, the significant association between <i>DAPK</i> promoter methylation and HNSCC was seen among the autologous group and the heterogeneous group (autologous group, OR = 2.71, 95% CI = 1.49–4.93; heterogeneous group, OR = 9.50, 95% CI = 2.98–30.27). <i>DAPK</i> promoter methylation was significantly correlated with alcohol status (OR = 1.85, 95% CI = 1.07–3.21).</p><p>Conclusion</p><p>The results of this meta-analysis suggested that aberrant methylation of <i>DAPK</i> promoter was associated with HNSCC.</p></div

Characteristics of studies included in the meta-analysis of <i>DAPK</i> promoter methylation and HNSCC.

<p>Characteristics of studies included in the meta-analysis of <i>DAPK</i> promoter methylation and HNSCC.</p

Forest plots of <i>DAPK</i> promoter methylation associated with clinicopathological features

<p>A: Forest plots of <i>DAPK</i> promoter methylation associated with sex B: Forest plots of <i>DAPK</i> promoter methylation associated with smoking status C: Forest plots of <i>DAPK</i> promoter methylation associated with alcohol status D: Forest plots of <i>DAPK</i> promoter methylation associated with lymph node invasion.</p

Malware on Internet of UAVs Detection Combining String Matching and Fourier Transformation

Advanced persistent threat (APT), with intense penetration, long duration, and high customization, has become one of the most grievous threats to cybersecurity. Furthermore, the design and development of Internet-of-Things (IoT) devices often do not focus on security, leading APT to extend to IoT, such as the Internet of emerging unmanned aerial vehicles (UAVs). Whether malware with attack payload can be successfully implanted into UAVs or not is the key to APT on the Internet of UAVs. APT malware on UAVs establishes communication with the command and control (CC) server to achieve remote control for UAVs-aware information stealing. Existing effective methods detect malware by analyzing malicious behaviors generated during CC communication. However, APT malware usually adopts a low-traffic attack mode, a large amount of normal traffic is mixed in each attack step, to avoid virus checking and killing. Therefore, it is difficult for traditional malware detection methods to discover APT malware on UAVs that carry weak abnormal signals. Fortunately, we found that most APT attacks use domain name system (DNS) to locate CC server of malware for information transmission periodically. This behavior will leave some records in the network flow and DNS logs, which provides us with an opportunity to identify infected internal UAVs and external malicious domain names. This article proposes an APT malware on the Internet of UAVs detection method combining string matching and Fourier transformation based on DNS traffic, which is able to handle encrypted and obfuscated traffic due to packet payloads independence. We preprocessed the collected network traffic by converting DNS timestamps of DNS request to strings and used the trained random forest model to discover APT malware domain names based on features extracted through string-matching-based periodicity detection and Fourier transformation-based periodicity detection. The proposed method has been evaluated on the data set, including part of normal domains from the normal traffic and malicious domains marked by security experts from APT malware traffic. Experimental results have shown that our proposed detection method can achieve the accuracy of 94%, which is better than the periodicity detection algorithm alone. Moreover, the proposed method does not need to set the confidence to filter the periodicity with high confidence.Manuscript received March 12, 2020; revised July 15, 2020 and August 27, 2020; accepted September 28, 2020. Date of publication October 12, 2020; date of current version June 7, 2021. This work was supported in part by the National Key Research and Development Plan under Grant 2016QY04W0800; in part by the National Natural Science Foundation of China under Grant 61902262 and Grant U19A2066; and in part by the National Defense Innovation Special Zone Program of Science and Technology under Grant JG2019055. (Corresponding author: Xiaosong Zhang.) Weina Niu, Jian’an Xiao, and Xiyue Zhang are with the School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: [email protected]; [email protected]; [email protected])

Summary of the subgroup analysisin the meta-analysis of <i>DAPK</i> promoter methylation and HNSCC.

<p>Summary of the subgroup analysisin the meta-analysis of <i>DAPK</i> promoter methylation and HNSCC.</p