Beni-Suef University

The local tube cladding utilizes mechanics of deformation at the interface of the punch and the tube. More specifically, the punch shape influences the material flow and the pressure at the interface. This study investigates the cladding of AA6060/SS304 bimetallic tubes using a spherical tipped punch of varying diameter at a constant rate of 5 mm/min. Different from previous studies, where axial feed rate seemed to take precedence, in this study the punch diameter was isolated as the primary variable, and the effects on deformation stability, bonding mechanics, and surface finish, were documented.Two sets of experiments were performed using a spherical tipped punch of 65 mm and 69 mm diameter. Under the studied conditions, cladding was successfully completed using the 65 mm diameter punch with a maximum forming load of 44 kN. The 69 mm diameter punch, however, achieved a maximum forming load of 88 kN, showing unstable deformation with flange rupture, material back flow, and thinning. Although forming was not successful, the larger diameter punch increased the maximum interfacial shear load from 5.35 kN to 21.46 kN and decreased the surface roughness from 0.7133 µm to 0.6325 µm. The 65 mm punch produced cladded tubes with a greater average hardness value of 52.06 HV compared to 51.89 HV of the 69 mm punch. Overall, under the investigated forming conditions, changing the punch diameter from 65 mm to 69 mm resulted in clear differences in forming load, deformation stability, maximum interfacial shear load, and surface roughness.

The paper introduces a powerful, data-driven framework for predicting the electrochemical behavior of Polyindole (PIn)-based supercapacitor electrodes through the integration of traditional machine learning (ML) techniques with modern deep learning (DL) architectures. Motivation behind this study comes from the necessity for reliable and accurate models that would enable quick and efficient prediction of the behavior of the conducting polymer electrodes and thus minimize expenses in terms of time, effort, and resources spent on experimental tests needed for characterization of their energy storage capabilities. PIn was prepared through potentiostatic electrochemical polymerization of indole on a graphite sheet. The deposition time was carefully varied to control film growth and active mass loading, and the fabricated electrodes were characterized using cyclic voltammetry (CV) within a potential window from −0.2 to 1.2 V at scan rates ranging from 5 to 75 mV/s. These measurements were then used to assemble a large dataset of over 30,000 experimental points, and provide this dataset to all five ML models Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ridge Regression (RR) and four DL networks Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a hybrid network using RNN-LSTM. The performance of the model was determined by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The findings indicate that KNN was best in a high-rate situation (R2 = 0.9387, MAE = 0.000289, RMSE = 0.000644 when 5 mV/s), whereas ANN was more robust because it sustained a high predictive accuracy (R2 > 0.9) at all scan rates. Comprehensively, the proposed framework reflects the complementary efficiency of both classical and DL algorithms and enables the application of hybrid deep architectures for reliable predictive modeling of conducting-polymer Supercapacitor (SC) electrodes.

Thrombosis is built, not merely catalyzed. Across arterial and venous disease, thrombus formation depends on a succession of protein–protein interactions (PPIs) that coordinate platelet capture and activation, thromboinflammatory amplification, and assembly of membrane-bound coagulation complexes. Although active-site inhibitors and receptor antagonists have transformed antithrombotic therapy, clinical benefit is often constrained by bleeding because many targets are indispensable for everyday hemostasis when inhibited systemically. This review advances a “thrombus assembly” framework that reframes drug discovery around disrupting interfaces that organize pathologic clot growth in a context-dependent manner shaped by shear, surfaces, local cofactors, and transient complex formation. We highlight translational proof that interface blockade works in humans, focusing on VWF A1–GPIbα inhibition and GPVI-directed strategies as exemplars of lesion- and shear-dependent antiplatelet therapy. We then survey platelet adhesion and activation PPIs (VWF–GPIb, collagen–GPVI, and αIIbβ3–ligand interactions), thromboinflammatory interfaces (including P-selectin–PSGL-1 and CLEC-2–podoplanin), and opportunities to target coagulation complex assembly and thrombin exosites without directly inhibiting catalytic active sites. A modality-focused section links interface class to therapeutic format—antibodies/nanobodies, aptamers with antidotes, peptides and macrocycles, and small-molecule PPI inhibitors—and summarizes trade-offs in reversibility, half-life, manufacturability, and immunogenicity. Finally, we discuss assays that preserve interface biology (whole-blood flow systems, microfluidics, thrombin generation, and clot mechanics) and propose clinical positioning and trial endpoints for context-gated mechanisms. By targeting the contacts that assemble and stabilize thrombi, PPI disruption offers a pragmatic route toward more selective, potentially bleeding-sparing antithrombotic therapy and a roadmap for next-generation pipeline development.

Cisplatin is a highly effective chemotherapeutic agent used to treat various solid tumors; however, its clinical utility is limited by dose-dependent nephrotoxicity. Perampanel, an AMPA-receptor antagonist FDA-approved anti-seizure drug, has recently shown inhibitory effects on oxidative stress and inflammasome-mediated pyroptosis in neurological damage models. The current work examined the possible renoprotective benefits and clarified the underlying molecular signaling modified by perampanel in a cisplatin-renal injury model. Male Wistar rats were used to investigate the effect of perampanel (1 2 mg/kg/day, for 14 days) against renal injury induced by cisplatin (10 mg/kg, on the 9th day), followed by morphological, histopathological, immunohistochemical (IHC), and biochemical estimations. The administration of perampanel to cisplatin-injected rats maintained the kidney-to-body weight ratio and renal function in a dose-dependent manner. Besides, there was a great improvement in the histological features compared to the cisplatin group. IHC analysis revealed the efficient inhibitory impact of perampanel against cisplatin-induced upregulation of NF-κB p65, NLRP3, and caspase-1 expressions. Consequently, the activation of interleukin (IL)-18 and -1β inflammatory cytokines was interrupted, and their renal levels were not elevated. Eventually, the pyroptosis effector protein, gasdermin D (GSDMD), upregulation was impeded. Inflammasome inhibition by perampanel was accompanied by downregulation of the promoter signaling NF-κB p65/TNF-α, enhancement of sirtuin 3/FOXO3 antioxidant signaling alongside upregulated Nrf-2 mRNA expression and antioxidant proteins, as well as maintained balance of Bax/Bcl-2; pro-/anti-apoptotic; genes. Collectively, perampanel could attenuate cisplatin-induced renal injury through its inhibitory influence on NF-κB p65/TNF-α and NLRP3-mediated pyroptosis, in addition to enhancement of antioxidant defense and controlling apoptosis.

Core-binding factor (CBF) leukemias, including inv(16) AML, involve RUNX1/CBFβ chromosomal rearrangements that generate oncogenic fusion proteins. In inv(16) AML, the CBFβ–SMMHC fusion (CBFB–MYH11) dominantly perturbs RUNX1 by sequestering it in aberrant, high-affinity complexes. Structural studies reveal that CBFβ–SMMHC binds the RUNX1 Runt domain with higher affinity than wild-type CBFβ, aided by a second RUNX1-binding site in its SMMHC tail. This altered interface underlies the fusion’s dominant-negative disruption of RUNX1 target-gene regulation. Chemical probes have been developed to disrupt this interface; notably, the bivalent inhibitor AI-10-49 selectively binds CBFβ–SMMHC, displacing RUNX1 and restoring RUNX1 transcriptional function. AI-10-49 delays leukemia progression in murine inv(16) models and induces apoptosis in human inv(16) AML cells. Mechanistically, uncoupling RUNX1 from CBFβ–SMMHC liberates RUNX1 to repress oncogenic programs: for example, RUNX1 rebinds distal MYC enhancers and recruits polycomb factors (RING1B) in place of SWI/SNF (BRG1) to silence MYC, triggering leukemia cell apoptosis. These chromatin and transcriptional consequences underscore how CBFβ–SMMHC sustains leukemic transcriptional programs. Importantly, combining CBFβ–SMMHC inhibitors with BET bromodomain inhibitors synergistically eradicates inv(16) leukemia in preclinical models. Together, these insights into the structural basis and functional role of the CBFβ–SMMHC–RUNX1 interface highlight protein–protein interaction disruption as a promising translational strategy in core-binding factor leukemia therapy.