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Background: Cutaneous lupus erythematosus (CLE) and dermatomyositis (DM) are autoimmune diseases that present with a wide variety of cutaneous manifestations. In both cases, first-line therapy includes topical corticosteroids. Patients may present with more widespread disease requiring systemic treatments, including corticosteroids, traditional immunosuppressants, or antimalarials. Due to their complex nature, both CLE and DM remain difficult to treat and continue to cause significant distress to patients. Objective: To summarize the most recent literature on the safety and efficacy of novel treatment modalities for CLE and DM. Methods: A literature search was conducted on PubMed using search terms \"(dermatomyositis) AND (treatment)\" and \"(cutaneous lupus) AND (treatment)\". Additional search terms included specific names of biologic agents, phosphodiesterase inhibitors (apremilast), and JAK inhibitors. Results: JAK inhibitors, PDE-4 inhibitors, and biologics have shown promise in reducing cutaneous symptoms of both CLE and DM, including reduction in SLE Disease Activity Index 2000 (SLEDAI-2K), Cutaneous Lupus Erythematosus Disease Area and Severity Index (CLASI), British Isles Lupus Assessment Group (BILAG), Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI), and Disease Activity Score (DAS). Conclusion: While there have been recent advancements in the treatment for CLE and DM, further research and clinical trials are required to better elucidate which therapy is best for individual patients. Keywords: biologics, cutaneous lupus erythematosus, dermatomyositis, JAK inhibitors, PDE-4 inhibitors

Deep vein thrombosis (DVT), caused by alterations in venous homeostasis, is the third most common cause of cardiovascular mortality, however, key molecular determinants in venous thrombosis have not been fully elucidated. Several lines of evidence indicate that DVT occurs at the intersection of dysregulated inflammation and coagulation. The enzyme ectonucleoside tri(di)phosphohydrolase (ENTPD1, also known as CD39) is a vascular ecto-apyrase on the surface of leukocytes and the endothelium that inhibits intravascular inflammation and thrombosis by hydrolysis of phosphodiester bonds from nucleotides released by activated cells. Here, we evaluated the contribution of CD39 to venous thrombosis in a restricted-flow model of murine inferior vena cava stenosis. CD39 deficiency conferred a greater than 2-fold increase in venous thrombogenesis, characterized by increased leukocyte engagement, neutrophil extracellular trap formation, fibrin, and local activation of tissue factor in the thrombotic milieu. This venous thrombogenesis was orchestrated by increased phosphorylation of the p65 subunit of NF-kB, activation of the NLR family pyrin domain-containing 3 (NLRP3) inflammasome, and IL-1ß release in CD39-deficient mice. Substantiating these findings, an IL-1ß-neutralizing antibody or the IL-1 receptor inhibitor anakinra attenuated the thrombosis risk in CD39-deficient mice. These data demonstrate that IL-1ß is a key accelerant of venous thrombo-inflammation, which can be suppressed by CD39. CD39 inhibits in vivo crosstalk between inflammation and coagulation pathways and is a critical vascular checkpoint in venous thrombosis.

Deep vein thrombosis (DVT), caused by alterations in venous homeostasis, is the third most common cause of cardiovascular mortality, however, key molecular determinants in venous thrombosis have not been fully elucidated. Several lines of evidence indicate that DVT occurs at the intersection of dysregulated inflammation and coagulation. The enzyme ectonucleoside tri(di)phosphohydrolase (ENTPD1, also known as CD39) is a vascular ecto-apyrase on the surface of leukocytes and the endothelium that inhibits intravascular inflammation and thrombosis by hydrolysis of phosphodiester bonds from nucleotides released by activated cells. Here, we evaluated the contribution of CD39 to venous thrombosis in a restricted-flow model of murine inferior vena cava stenosis. CD39 deficiency conferred a greater than 2-fold increase in venous thrombogenesis, characterized by increased leukocyte engagement, neutrophil extracellular trap formation, fibrin, and local activation of tissue factor in the thrombotic milieu. This venous thrombogenesis was orchestrated by increased phosphorylation of the p65 subunit of NF-[kappa]B, activation of the NLR family pyrin domain-containing 3 (NLRP3) inflammasome, and IL-1[beta] release in CD39-deficient mice. Substantiating these findings, an IL-1[beta]-neutralizing antibody or the IL-1 receptor inhibitor anakinra attenuated the thrombosis risk in CD39- deficient mice. These data demonstrate that IL-1[beta] is a key accelerant of venous thrombo-inflammation, which can be suppressed by CD39. CD39 inhibits in vivo crosstalk between inflammation and coagulation pathways and is a critical vascular checkpoint in venous thrombosis.

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.

Deep vein thrombosis (DVT), caused by alterations in venous homeostasis is the third most common cause of cardiovascular mortality; however, key molecular determinants in venous thrombosis have not been fully elucidated. Several lines of evidence indicate that DVT occurs at the intersection of dysregulated inflammation and coagulation. The enzyme ectonucleoside tri(di)phosphohydrolase (ENTPD1, also known as CD39) is a vascular ecto-apyrase on the surface of leukocytes and the endothelium that inhibits intravascular inflammation and thrombosis by hydrolysis of phosphodiester bonds from nucleotides released by activated cells. Here, we evaluated the contribution of CD39 to venous thrombosis in a restricted-flow model of murine inferior vena cava stenosis. CD39-deficiency conferred a >2-fold increase in venous thrombogenesis, characterized by increased leukocyte engagement, neutrophil extracellular trap formation, fibrin, and local activation of tissue factor in the thrombotic milieu. This was orchestrated by increased phosphorylation of the p65 subunit of NFκB, activation of the NLRP3 inflammasome, and interleukin-1β (IL-1β) release in CD39-deficient mice. Substantiating these findings, an IL-1β-neutralizing antibody attenuated the thrombosis risk in CD39-deficient mice. These data demonstrate that IL-1β is a key accelerant of venous thrombo-inflammation, which can be suppressed by CD39. CD39 inhibits in vivo crosstalk between inflammation and coagulation pathways, and is a critical vascular checkpoint in venous thrombosis.

Advances in quantum computing increasingly threaten the security and privacy of data protected by current cryptosystems, particularly those relying on public-key cryptography. In response, the international cybersecurity community has prioritized the implementation of Post-Quantum Cryptography (PQC), a new cryptographic standard designed to resist quantum attacks while operating on classical computers. The National Institute of Standards and Technology (NIST) has already standardized several PQC algorithms and plans to deprecate classical asymmetric schemes, such as RSA and ECDSA, by 2035. Despite this urgency, PQC adoption remains slow, often due to limited developer expertise. Application Programming Interfaces (APIs) are intended to bridge this gap, yet prior research on classical security APIs demonstrates that poor usability of cryptographic APIs can lead developers to introduce vulnerabilities during implementation of the applications, a risk amplified by the novelty and complexity of PQC. To date, the usability of PQC APIs has not been systematically studied. This research presents an empirical evaluation of the usability of the PQC APIs, observing how developers interact with APIs and documentation during software development tasks. The study identifies cognitive factors that influence the developer's performance when working with PQC primitives with minimal onboarding. The findings highlight opportunities across the PQC ecosystem to improve developer-facing guidance, terminology alignment, and workflow examples to better support non-specialists.

Next-generation open radio access networks (O-RAN) continuously stream tens of key performance indicators (KPIs) together with raw in-phase/quadrature (IQ) samples, yielding ultra-high-dimensional, non-stationary time series that overwhelm conventional transformer architectures. We introduce a reservoir-augmented masked autoencoding transformer (RA-MAT). This time series foundation model employs echo state network (ESN) computing with masked autoencoding to satisfy the stringent latency, energy efficiency, and scalability requirements of 6G O-RAN testing. A fixed, randomly initialized ESN rapidly projects each temporal patch into a rich dynamical embedding without backpropagation through time, converting the quadratic self-attention bottleneck into a lightweight linear operation. These embeddings drive a patch-wise masked autoencoder that reconstructs 30% randomly masked patches, compelling the encoder to capture both local dynamics and long-range structure from unlabeled data. After self-supervised pre-training, RA-MAT is fine-tuned with a shallow task head while keeping the reservoir and most transformer layers frozen, enabling low-footprint adaptation to diverse downstream tasks such as O-RAN KPI forecasting. In a comprehensive O-RAN KPI case study, RA-MAT achieved sub-0.06 mean squared error (MSE) on several continuous and discrete KPIs. This work positions RA-MAT as a practical pathway toward real-time, foundation-level analytics in future 6G networks.

Federated Learning (FL) has emerged as a powerful paradigm for collaborative model training while keeping client data decentralized and private. However, it is vulnerable to Data Reconstruction Attacks (DRA) such as \"LoKI\" and \"Robbing the Fed\", where malicious models sent from the server to the client can reconstruct sensitive user data. To counter this, we introduce DRArmor, a novel defense mechanism that integrates Explainable AI with targeted detection and mitigation strategies for DRA. Unlike existing defenses that focus on the entire model, DRArmor identifies and addresses the root cause (i.e., malicious layers within the model that send gradients with malicious intent) by analyzing their contribution to the output and detecting inconsistencies in gradient values. Once these malicious layers are identified, DRArmor applies defense techniques such as noise injection, pixelation, and pruning to these layers rather than the whole model, minimizing the attack surface and preserving client data privacy. We evaluate DRArmor's performance against the advanced LoKI attack across diverse datasets, including MNIST, CIFAR-10, CIFAR-100, and ImageNet, in a 200-client FL setup. Our results demonstrate DRArmor's effectiveness in mitigating data leakage, achieving high True Positive and True Negative Rates of 0.910 and 0.890, respectively. Additionally, DRArmor maintains an average accuracy of 87%, effectively protecting client privacy without compromising model performance. Compared to existing defense mechanisms, DRArmor reduces the data leakage rate by 62.5% with datasets containing 500 samples per client.

In this thesis, we consider phased waveguide arrays for wavelength division multi-plexer applications fabricated on InP substrates. A typical phased array component consists of different sections for power splitting, phase shifting and power combination. The analysis of these structures requires both efficient mode solvers and propagation techniques. We have therefore conducted a study of phased waveguide array structures utilizing finite difference procedures for modal analysis and the beam propagation method for the examination of the phase shifts in the waveguide array and in multimode interference sections. Our analytical and numerical procedures include a non-iterative method for the determination of complex propagation constants in planar multilayer waveguide structures and a generalization of a recently proposed one-dimensional asymmetric boundary condition to two-dimensional transverse index profiles. We also examine the possibility of using multimode interference waveguide sections as phase shifters in a phased waveguide array and finally consider an iterative method for optimizing the structure of phased arrays.

G protein-coupled receptors （GPCRs） are involved in all human physiological systems where they are responsible for transducing extracellular signals into cells. GPCRs signal in response to a diverse array of stimuli including light, hormones, and lipids, where these signals affect downstream cascades to impact both health and disease states. Yet, despite their importance as therapeutic tar- gets, detailed molecular structures of only 30 GPCRs have been determined to date. A key challenge to their structure determination is adequate protein expression. Here we report the quantification of protein expression in an insect cell expression system for all 826 human GPCRs using two different fusion constructs. Expression char- acteristics are analyzed in aggregate and among each of the five distinct subfamilies. These data can be used to identify trends related to GPCR expression between dif- ferent fusion constructs and between different GPCR families, and to prioritize lead candidates for future structure determination feasibility.