Ultrasound-driven facile fabrication of Pd doped SnO2 hierarchical superstructures: Structural, growth mechanism, dermatoglyphics, and anti-cancer activity

This research introduces a novel method that leverages Spirulina extract (S.E) as a bio-surfactant in the ultrasound-assisted synthesis (UAS) of Pd3+ (0.25-10 mol%) doped tin oxide (SnO2) self-assembled superstructures. Nanotechnology has witnessed significant advancements in recent years, driven by the exploration of novel synthesis methods and the development of advanced nanomaterials tailored for specific applications. Metal oxide nanoparticles, particularly SnO2, have garnered considerable attention due to their versatile properties and potential applications in various fields, including gas sensing, catalysis, and biomedical engineering. The study explores how varying influential parameters like S.E concentration, sonication time, pH, and sonication power can influence the resulting superstructures' morphology, size, and shape. A theoretical model for forming different hierarchical superstructures (HS) is proposed. X-ray diffraction (XRD) analysis confirms the crystalline tetragonal rutile phase of the SnO2:Pd HS. Raman spectroscopy reveals a red shift in the A1g mode, indicating phonon confinement due to various defects in the SnO2 structure. Further characterization using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) provides insights into particle size, surface morphology, elemental composition, and binding energy. The study also demonstrates the application of optimized SnO2:3Pd HS in developing latent fingerprints (LFPs) on different surfaces using a simple powder dusting (PD) method, with the fingerprints (FPs) visualized under normal light. A mathematical model developed in Python-based software is used to analyze various features of the developed FPs, including pore properties such as number, position, inter-spacing, area, and shape. Additionally, an in vitro MTT assay shows concentration-dependent anticancer activity of SnO2:3Pd nanoparticles (NPs) on MCF7 cell lines, highlighting their potential as a promising cancer treatment option. Overall, the study suggests that the optimized HS can serve as multifunctional platforms for biomedical and dermatoglyphics applications, demonstrating the versatility and potential of the synthesized materials.

A physics-informed deep learning framework for modeling of coronary in-stent restenosis

Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- β (TGF- β ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment.

Computational modeling of in-stent restenosis: Pharmacokinetic and pharmacodynamic evaluation

Persistence of the pathology of in-stent restenosis even with the advent of drug-eluting stents warrants the development of highly resolved in silico models. These computational models assist in gaining insights into the transient biochemical and cellular mechanisms involved and thereby optimize the stent implantation parameters. Within this work, an already established fully-coupled Lagrangian finite element framework for modeling the restenotic growth is enhanced with the incorporation of endothelium-mediated effects and pharmacological influences of rapamycin-based drugs embedded in the polymeric layers of the current generation drug-eluting stents. The continuum mechanical description of growth is further justified in the context of thermodynamic consistency. Qualitative inferences are drawn from the model developed herein regarding the efficacy of the level of drug embedment within the struts as well as the release profiles adopted. The framework is then intended to serve as a tool for clinicians to tune the interventional procedures patient-specifically.

In-vivo assessment of vascular injury for the prediction of in-stent restenosis

BACKGROUND

Despite optimizations of coronary stenting technology, a residual risk of in-stent restenosis (ISR) remains. Vessel wall injury has important impact on the development of ISR. While injury can be assessed in histology, there is no injury score available to be used in clinical practice.

METHODS

Seven rats underwent abdominal aorta stent implantation. At 4 weeks after implantation, animals were euthanized, and strut indentation, defined as the impression of the strut into the vessel wall, as well as neointimal growth were assessed. Established histological injury scores were assessed to confirm associations between indentation and vessel wall injury. In addition, stent strut indentation was assessed by optical coherence tomography (OCT) in an exemplary clinical case.

RESULTS

Stent strut indentation was associated with vessel wall injury in histology. Furthermore, indentation was positively correlated with neointimal thickness, both in the per-strut analysis (r = 0.5579) and in the per-section analysis (r = 0.8620; both p ≤ 0.001). In a clinical case, indentation quantification in OCT was feasible, enabling assessment of injury in vivo.

CONCLUSION

Assessing stent strut indentation enables periprocedural assessment of stent-induced damage in vivo and therefore allows for optimization of stent implantation. The assessment of stent strut indentation might become a valuable tool in clinical practice.

Exploration of NMI-MsCl mediated amide bond formation for the synthesis of novel 3,5-substituted-1,2,4-oxadiazole derivatives: synthesis, evaluation of anti-inflammatory activity and molecular docking studies

We herein report the facile synthesis of a series of 3,5-substituted-1,2,4-oxadiazole derivatives 9a-e and 10a-e in good to excellent yields by employing NMI-MsCl mediated amide bond formation reaction. The anti-inflammatory potential of the newly synthesized compounds were evaluated by anti-denaturation assay using diclofenac sodium as the reference drug. The compounds 9a and 9d demonstrated promising activity profile when compared to the reference standard. The SAR and molecular docking studies were also carried out for obtaining more details about the profound activity profile of the synthesized molecules. The synthesized compounds were docked against two target proteins TGF-β and IL-1 by AutoDock vina and Auto Dock 4.2.

Potential applications of Fe3+-activated Sr9Al6O18 nanophosphors for fingerprint detection, oxidative stress, and thrombosis treatment

This study reports on the synthesis of Fe³⁺-activated Sr₉Al₆O₁₈ nanophosphors (SAO:Fe NPs) using a simple solution combustion process, which emits a pale green light and possesses excellent fluorescence properties. An in-situ powder dusting method was utilized to extract unique ridge features of latent fingerprints (LFPs) on various surfaces using ultra-violet 254 nm excitation. The results showed that SAO:Fe NPs possess high contrast, high sensitivity, and no background interference, enabling the observation of LFPs for longer periods. Poroscopy, which is the examination of sweat pores on the skin's papillary ridges, is important in the identification process, and the YOLOv8x program based on deep convolutional neural networks was used to study the features visible in FPs. The potential of SAO:Fe NPs to ameliorate oxidative stress and thrombosis was analyzed. The results showed that SAO:Fe NPs have antioxidant properties by scavenging 2,2-diphenylpicrylhydrazyl (DPPH) and normalized the stress markers in NaNO₂-induced oxidative stress in Red Blood Cells (RBC). In addition, SAO:Fe inhibited platelet aggregation induced by adenosine diphosphate (ADP). Therefore, SAO:Fe NPs may have potential applications in advanced cardiology and forensic sciences. Overall, this study highlights the synthesis and potential applications of SAO:Fe NPs, which can enhance the sensitivity and specificity of fingerprint detection and provide insights into developing novel treatments for oxidative stress and thrombosis.

A multiphysics modeling approach for in-stent restenosis: Theoretical aspects and finite element implementation

Development of in silico models that capture progression of diseases in soft biological tissues are intrinsic in the validation of the hypothesized cellular and molecular mechanisms involved in the respective pathologies. In addition, they also aid in patient-specific adaptation of interventional procedures. In this regard, a fully-coupled high-fidelity Lagrangian finite element framework is proposed within this work which replicates the pathology of in-stent restenosis observed post stent implantation in a coronary artery. Advection-reaction-diffusion equations are set up to track the concentrations of the platelet-derived growth factor, the transforming growth factor-β, the extracellular matrix, and the density of the smooth muscle cells. A continuum mechanical description of volumetric growth involved in the restenotic process, coupled to the evolution of the previously defined vessel wall constituents, is presented. Further, the finite element implementation of the model is discussed, and the behavior of the computational model is investigated via suitable numerical examples. Qualitative validation of the computational model is presented by emulating a stented artery. Patient-specific data are intended to be integrated into the model to predict the risk of in-stent restenosis, and thereby assist in the tuning of stent implantation parameters to mitigate the risk.

Structural, electronic and magnetic properties of Sc3+ doped CoCr2O4 nanoparticles

Experimental and theoretical analyses were combined to reveal the major properties of Co_1−xSc_xCr₂O₄ nanoparticles – a putative candidate for magnetic applications.

Comparative study of the efficacy of gentamicin-coated intramedullary interlocking nail versus regular intramedullary interlocking nail in Gustilo type I and II open tibia fractures

PURPOSE

Open tibia fracture is prone to infection, consequently causing significant morbidity and increasing the hospital stay, occupational loss and onset of chronic osteomyelitis. Intramedullary nailing is one choice for treating tibia shaft fractures. To improve the delivery of antibiotics at the tissue-implant interface, many methods have been proposed as a part of prophylaxis against infection. This study was conducted to study the role of gentamicin-impregnated intramedullary interlocking (IMIL) nail in the prevention of infection in Gustilo type I and II open tibia fractures and to compare the results with regular intramedullary nail.

METHODS

The study included 28 patients with open tibia fractures (Gustilo type 1 or type 2); of them 14 underwent regular IMIL nailing and the other 14 were treated with gentamicin-coated nailing. Randomization was done by alternate allocation of the patients. Follow-up was done postoperatively (day 1), 1 week, 6 weeks, and 6 months for bone union, erythrocyte sedimentation rate (ESR), hemoglobin and C-reactive protein (CRP). Statistical significance was tested using unpaired t-test. A p value less than 0.05 was considered significant.

RESULTS

There were 4 cases of infection in controls (regular IMIL nail) and no infection among patients treated with gentamicin-coated nail during the follow up (X² = 4.66, p = 0.031). At 6 months postoperatively, CRP (p = 0.031), ESR (p = 0.046) and hemoglobin level (p = 0.016) showed significant difference between two groups. The bone healing rate was better with gentamicin-coated nail in comparison to regular IMIL nail at 6 months follow-up (p = 0.016).

CONCLUSION

Gentamicin-coated IMIL nail has a positive role in preventing infection in Gustilo type I and II open tibia fractures.