Platinum-based targeted chemotherapies and reversal of cisplatin resistance in non-small cell lung cancer (NSCLC).

Lung cancer is the one of the most prevalent cancer in the world. It kills more people from cancer than any other cause and is especially common in underdeveloped nations. With 1.2 million instances, it is also the most prevalent cancer in men worldwide, making about 16.7% of the total cancer burden. Surgery is the main form of curative treatment for early-stage lung cancer. However, the majority of patients had incurable advanced non-small cell lung cancer (NSCLC) recurrence after curative purpose surgery, which is indicative of the aggressiveness of the illness and the dismal outlook. The gold standard of treatment for NSCLC patients includes drug targeting of specific mutated genes drive in development of lung cancer. Furthermore, patients with advanced NSCLC and those with early-stage illness needing adjuvant therapy should use cisplatin as it is the more active platinum drug. So, this review encompasses the non-small cell lung cancer microenvironment, treatment approaches, and use of cisplatin as a first-line regimen for NSCLC, its mechanism of action, cisplatin resistance in NSCLC and also the prevention strategies to revert the drug resistance.

Development, optimization and characterization of cisplatin loaded cubosomes for human lung carcinoma.

OBJECTIVES: This study aimed to develop, optimize and evaluate glyceryl monooleate (GMO) based cubosomes as a drug delivery system containing cisplatin for treatment of human lung carcinoma. SIGNIFICANCE: The significance of this research was to successfully incorporate slightly water soluble and potent anticancer drug (cisplatin) into cubosomes, which provide slow and sustained release of drug for longer period of time. METHODS: The delivery system was developed through top-down approach by melting GMO and poloxamer 407 (P407) at 70 °C and then drop-wise addition of warm deionized water (70 °C) containing cisplatin. The formulation then exposed to probe sonicator for about 2 min. A randomized regular two level full factorial design with help of Design Expert was used for optimization of blank cubosomal formulations. Cisplatin loaded cubosomes were then subjected to physico-chemical characterization. RESULTS: The characterization of the formulation revealed that it had a sufficient surface charge of -9.56 ± 1.33 mV, 168.25 ± 5.73 nm particle size, and 60.64 ± 0.11% encapsulation efficiency. The in vitro release of cisplatin from the cubosomes at pH 7.4 was observed to be sustained, with 94.5% of the drug being released in 30 h. In contrast, 99% of cisplatin was released from the drug solution in just 1.5 h. In vitro cytotoxicity assay was conducted on the human lung carcinoma NCI-H226 cell line, the cytotoxicity of cisplatin-loaded cubosomes was relative to that of pure cisplatin solution, while blank (without cisplatin) cubosomes were nontoxic. CONCLUSIONS: The obtained results demonstrated the successful development of cubosomes for sustained delivery of cisplatin.

Cubosomes were prepared, optimized, and evaluated for cisplatin delivery.A randomized regular two level full factorial design was constructed to optimize blank cubosomes.Blank cubosomes consisted of GMO as the lipid and P407 as an emulsifying agent.In vitro release studies demonstrated sustained release of cisplatin from cubosomes at pH 7.4.Cytotoxicity assay on human lung carcinoma cell line NCI-H226 showed similar cytotoxicity between cisplatin-loaded cubosomes and pure cisplatin solution while blank cubosomes exhibited no toxicity.

Silica-Based Superhydrophobic and Superoleophilic Cotton Fabric with Enhanced Self-Cleaning Properties for Oil-Water Separation and Methylene Blue Degradation.

Superhydrophobic textiles with multifunctional characteristics are highly desired and have attracted tremendous research attention. This research employs a simple dip-coating method to obtain a fluorine-free silica-based superhydrophobic and superoleophilic cotton fabric. Pristine cotton fabric is coated with SiO2 nanoparticles and octadecylamine. SiO2 nanoparticles are anchored on the cotton fabric to increase surface roughness, and octadecyl amine lowers the surface energy, turning the hydrophilic cotton fabric into superhydrophobic. The designed cotton fabric exhibits a water contact angle of 159° and a sliding angle of 7°. The prepared cotton fabric is characterized by attenuated total reflectance-fourier transform infrared spectroscopy, X-ray diffraction, atomic force microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. In addition, the coated fabric reveals excellent features, including mechanical and chemical stability, superhydrophobicity, superoleophilicity, and the self-cleaning ability. SiO2 nanoparticles and octadecylamine-coated cotton fabric demonstrate exceptional oil-water separation and wastewater remediation performance by degrading the methylene blue solution up to 89% under visible light. The oil-water separation ability is tested against five different oils with more than 90% separation efficiency. This strategy has the advantages of low-cost precursors, a simple and scalable coating method, enhanced superhydrophobicity and superoleophilicity, self-cleaning ability, efficient oil-water separation, and exceptional wastewater remediation performance.

Hegemony of inflammation in atherosclerosis and coronary artery disease.

Inflammation drives coronary artery disease and atherosclerosis implications. Lipoprotein entry, retention, and oxidative modification cause endothelial damage, triggering innate and adaptive immune responses. Recruited immune cells orchestrate the early atherosclerotic lesions by releasing proinflammatory cytokines, expediting the foam cell formation, intraplaque haemorrhage, secretion of matrix-degrading enzymes, and lesion progression, eventually promoting coronary artery syndrome via various inflammatory cascades. In addition, soluble mediators disrupt the dynamic anti- and prothrombotic balance maintained by endothelial cells and pave the way for coronary artery disease such as angina pectoris. Recent studies have established a relationship between elevated levels of inflammatory markers, including C-reactive protein (CRP), interleukins (IL-6, IL-1ß), and tumour necrosis factor-alpha (TNF-α) with the severity of CAD and the possibility of future cardiovascular events. High-sensitivity C-reactive protein (hs-CRP) is a marker for assessing systemic inflammation and predicting the risk of developing CAD based on its peak plasma levels. Hence, understanding cross-talk interactions of inflammation, atherogenesis, and CAD is highly warranted to recalculate the risk factors that activate and propagate arterial lesions and devise therapeutic strategies accordingly. Cholesterol-inflammation lowering agents (statins), monoclonal antibodies targeting IL-1 and IL-6 (canakinumab and tocilizumab), disease-modifying antirheumatic drugs (methotrexate), sodium-glucose transport protein-2 (SGLT2) inhibitors, colchicine and xanthene oxidase inhibitor (allopurinol) have shown promising results in reducing inflammation, regressing atherogenic plaque and modifying the course of CAD. Here, we review the complex interplay between inflammatory, endothelial, smooth muscle and foam cells. Moreover, the putative role of inflammation in atherosclerotic CAD, underlying mechanisms and potential therapeutic implications are also discussed herein.

A dual-mode fluorescence and colorimetric sensing platform for efficient detection of ofloxacin in aquatic products using iron alkoxide nanozyme.

Trace detection of ofloxacin (OFL) with high sensitivity, reliability, and visual clarity is challenging. To address this, a novel dual-modal aptasensor with fluorescence-colorimetric capabilities was designed that exploit the target-induced release of 3,3',5,5'-tetramethylbenzidine (TMB) molecules from aptamer-gated mesoporous silica nanoparticles (MSNs), the oxidase-like activity of iron alkoxide (IA) nanozyme, and the fluorescence attributes of core-shell upconversion nanoparticles. Therefore, the study reports a dual mode detection, with a fluorescence detection range for OFL spanning from 0.1 µg/kg to 1000 µg/kg (and a detection limit of 0.048 µg/kg). Additionally, the colorimetric method offered a linear detection range of 0.3 µg/kg to 1000 µg/kg, with a detection limit of 0.165 µg/kg. The proposed biosensor had been successfully applied to the determination of OFL content in real samples with satisfactory recoveries (78.24-96.14 %).

Development and characterization of letrozole-encapsulated polymeric beads for sustained release.

The study aimed to prepare and characterize biodegradable sustained-release beads of letrozole (LTZ) for treating cancerous disease. The ionotropic gelation method was used for the preparation and calcium chloride (CaCl2) was used as a gelating agent, while chitosan (CTS) and sodium alginate (NaAlg) as biodegradable polymeric matrices in the blend hydrogel beads. The beads were characterized for their size, surface morphology, drug entrapment efficiency, drug-polymer interaction and crystallinity using different analytic techniques, including optical microscopy, Scanning Electron Microscopy (SEM), UV-spectroscopy, Fourier-transform Infrared Spectroscopy (FTIR), Thermo gravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and X-ray Diffraction Analysis (XRD) respectively. In vitro swelling studies were also applied to observe the response of these polymeric networks against different pH (at 1.2, 6.8 and 7.4 pH). The results from TGA and DSC exhibited that the components in the formulation possess better thermal stability. The XRD of polymeric networks displays a minor crystalline and significant amorphous nature. The SEM micrographs revealed that polymeric networks have uneven surfaces and grooves. Better swelling and in vitro outcomes were achieved at a high pH (6.8,7.4), which endorsed the pH-responsive characteristics of the prepared beads. Hence, beads based on chitosan and sodium alginate were successfully synthesized and can be used for the controlled release of letrozole.

Dual activity of indolin-2-ones containing an arylidene motif: DNA and BSA interaction.

Applying a multistep approach, novel indolin-2-ones (IND) that possess an arylidene motif have been synthesized. Eight compounds were chosen for different biological tests (antimicrobial and cytotoxicity). IND containing 2-thienyl (4h) fragment have been found to exhibit good antimicrobial activity against B. cereus. Molecules that have 3-aminophenyl (4d) or 2-pyridyl (4g) groups have shown the best antifungal activities against all tested fungi. These compounds have also been noticed as promising pharmaceuticals against MCF-7 cancer cell lines. Experimental outcomes from the investigation of the interaction of 4d with DNA implied its moderate binding to DNA (KSV = 1.35 × 104 and 3.05 × 104 M-1 for EB and Hoechst binder, respectively). However, considerably stronger binding of 4d to BSA has been evidenced (Ka = 6.1 × 106 M-1). In summary, IND that contains m-aminobenzylidene fragment (4d) exhibits a good dual biological activity making it a promising candidate for further investigation in the drug discovery sector.

Wnt signaling pathway inhibitor promotes mesenchymal stem cells differentiation into cardiac progenitor cells in vitro and improves cardiomyopathy in vivo.

BACKGROUND: Cardiovascular diseases particularly myocardial infarction (MI) are the leading cause of mortality and morbidity around the globe. As cardiac tissue possesses very limited regeneration potential, therefore use of a potent small molecule, inhibitor Wnt production-4 (IWP-4) for stem cell differentiation into cardiomyocytes could be a promising approach for cardiac regeneration. Wnt pathway inhibitors may help stem cells in their fate determination towards cardiomyogenic lineage and provide better homing and survival of cells in vivo. Mesenchymal stem cells (MSCs) derived from the human umbilical cord have the potential to regenerate cardiac tissue, as they are easy to isolate and possess multilineage differentiation capability. IWP-4 may promote the differentiation of MSCs into the cardiac lineage. AIM: To evaluate the cardiac differentiation ability of IWP-4 and its subsequent in vivo effects. METHODS: Umbilical cord tissue of human origin was utilized to isolate the MSCs which were characterized by their morphology, immunophenotyping of surface markers specific to MSCs, as well as by tri-lineage differentiation capability. Cytotoxicity analysis was performed to identify the optimal concentration of IWP-4. MSCs were treated with 5 µM IWP-4 at two different time intervals. Differentiation of MSCs into cardiomyocytes was evaluated at DNA and protein levels. The MI rat model was developed. IWP-4 treated as well as untreated MSCs were implanted in the MI model, then the cardiac function was analyzed via echocardiography. MSCs were labeled with 1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate (DiI) dye for tracking, while the regeneration of infarcted myocardium was examined by histology and immunohistochemistry. RESULTS: MSCs were isolated and characterized. Cytotoxicity analysis showed that IWP-4 was non-cytotoxic at 5 µM concentration. Cardiac specific gene and protein expression analyses exhibited more remarkable results in fourteen days treated group that was eventually selected for in vivo transplantation. Cardiac function was restored in the IWP-4 treated group in comparison to the MI group. Immunohistochemical analysis confirmed the homing of pre-differentiated MSCs that were labeled with DiI cell labeling dye. Histological analysis confirmed the significant reduction in fibrotic area, and improved left ventricular wall thickness in IWP-4 treated MSC group. CONCLUSION: Treatment of MSCs with IWP-4 inhibits Wnt pathway and promotes cardiac differentiation. These pre-conditioned MSCs transplanted in vivo improved cardiac function by cell homing, survival, and differentiation at the infarcted region, increased left ventricular wall thickness, and reduced infarct size.

Anticancer properties of sulforaphane: current insights at the molecular level.

Sulforaphane (SFN) is an isothiocyanate with multiple biomedical applications. Sulforaphane can be extracted from the plants of the genus Brassica. However, broccoli sprouts are the chief source of sulforaphane and are 20 to 50 times richer than mature broccoli as they contain 1,153 mg/100 g. SFN is a secondary metabolite that is produced as a result of the hydrolysis of glucoraphanin (a glucosinolate) by the enzyme myrosinase. This review paper aims to summarize and understand the mechanisms behind the anticancer potential of sulforaphane. The data was collected by searching PubMed/MedLine, Scopus, Web of Science, and Google Scholar. This paper concludes that sulforaphane provides cancer protection through the alteration of various epigenetic and non-epigenetic pathways. It is a potent anticancer phytochemical that is safe to consume with minimal side effects. However, there is still a need for further research regarding SFN and the development of a standard dose.

Comparison of Si-GA-PLS and Si-CARS-PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system.

BACKGROUND: The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si-PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. RESULTS: The collected spectral data were divided into 19 sub-interval selections totaling 246 variables, yielding the lowest root mean square error of cross-validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC ), prediction (RP ), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si-GA-PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si-CARS-PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. CONCLUSION: The build model's prediction ability was amended in the order PLS < Si-PLS < CARS-PLS when full spectroscopic data were used and Si-PLS < Si-GA-PLS < Si-CARS-PLS when interval selection was performed with the Si-PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

Stimuli-responsive SERS biosensor for ultrasensitive tetracycline sensing using EDTA-driven PEI@CaCO3 microcapsule and CS@FeMMs.

In this work, a stimuli-responsive SERS biosensor was fabricated for tetracycline (TTC) by "signal-on" strategy using (EDTA)-driven polyethyleneimine grafted calcium carbonate (PEI@CaCO3) microcapsule and chitosan-Fe magnetic microbeads (CS@FeMMs). Initially, aptamer conjugated magnetic-bead CS@FeMMs@Apt with superparamagnetism and excellent biocompatibility was employed as capture probe, which facilitated the rapid and easy magnetic separation. Subsequently, the PEI cross-linked layer and aptamer network layer were constructed onto the outer layer of CaCO3@4-ATP microcapsule to form sensing probes (PEI@CaCO3@4-ATP@Apt) via the layer-by-layer assembly method. In the presence of TTC, a sandwich SERS-assay was exploited by aptamer recognition induced target-bridged strategy. When the solution of EDTA was added, the core layer of CaCO3 would be dissolved quickly, destroying the microcapsule to release 4-ATP. The released 4-ATP could be quantitatively monitored by dripping the supernatant onto the AuNTs@PDMS SERS platform, resulting in a strong Raman "signal-on". Under the optimal conditions, a good linear relationship was established with a correlation coefficient (R2) of 0.9938 and a LOD of 0.03 ng/mL. Additionally, the application capacity of the biosensor to detect TTC was also affirmed in food matrixes, and the results were consistent with the standard ELISA method (P > 0.05). Hence, this SERS biosensor affords extensive application prospects for TTC detection with multiple merits such as high sensitivity, environment friendliness, and high stability.

Breast Cancer Dataset, Classification and Detection Using Deep Learning.

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

Chronic Myeloid Leukaemia In A 14-Year-Old Child: An Unusual Case Report.

Chronic myeloid leukaemia is a myeloproliferative stem cell disorder characterized by dysregulated production and proliferation of myeloid cells. Hallmark of the disease is the reciprocal translocation between chromosome 9 and 22 giving rise to an abnormal chromosome known as Philadelphia chromosome. Approximately 15% of all leukaemias are chronic myeloid leukaemia with a slight male predominance. This is a case of a 14-year-old boy with no premorbid presented with 4 months history of fatigue and shortness of breath on exertion. On examination patient was vitally stable. On general physical examination there was pallor and sternal tenderness. On abdominal examination spleen was palpable with a size of about 8cm. Respiratory, cardiovascular and musculoskeletal examination was unremarkable. Complete blood picture showed leukocytosis, low haemoglobin and normal platelets. Erythrocyte sedimentation rate was 65 mm/hr. Liver function tests, Renal function tests, Serum electrolytes, Urine routine examination and c-reactive protein were normal. Chest x-ray and Electrocardiography was normal. Peripheral blood smear showed neutrophils 56% with 3% lymphocytes, 1% blasts and retic count of 0.5%. Bone marrow biopsy was suggestive of chronic myeloid leukaemia which was confirmed by FISH and Cytogenetic studies.

Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique's higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC's compressive and flexural strengths, respectively.

Steel Fiber-Reinforced Concrete: A Systematic Review of the Research Progress and Knowledge Mapping.

This study performed a scientometric-based examination of the literature on steel fiber-reinforced concrete (SFRC) to identify its key elements. Typical review papers are limited in their capacity to link distinct segments of the literature in an organized and systematic method. The most challenging aspects of current research are knowledge mapping, co-occurrence, and co-citation. The Scopus search engine was used to search for and obtain the data required to meet the goals of the study. During the data evaluation, the relevant publication sources, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas vigorously involved in SFRC studies were recognized. The VOSviewer software tool was used to evaluate the literature data from 9562 relevant papers, which included citation, abstract, bibliographic, keywords, funding, and other information. Furthermore, the applications and constraints related to the usage of SFRC in the construction sector were examined, as well as potential solutions to these constraints. It was determined that only 17 publication sources (journals/conferences) had published at least 100 articles on SFRC up to June 2022. Additionally, the mostly employed keywords by authors in SFRC research include steel fibers, fiber-reinforced concrete, concrete, steel fiber-reinforced concrete, and reinforced concrete. The assessment of authors revealed that 39 authors had published at least 30 articles. Moreover, China, the United States, and India were found to be the most active and participating countries based on publications on SFRC research. This study can assist academics in building collaborative initiatives and communicating new ideas and techniques because of the quantitative and graphical depiction of participating nations and researchers.

New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete.

Recently, artificial intelligence (AI) approaches have gained the attention of researchers in the civil engineering field for estimating the mechanical characteristics of concrete to save the effort, time, and cost of researchers. Consequently, the current research focuses on assessing steel-fiber-reinforced concrete (SFRC) in terms of flexural strength (FS) prediction by employing delicate AI techniques as well as to predict the raw material interaction that is still a research gap. In this study, the FS of SFRC is estimated by deploying supervised machine learning (ML) techniques, such as DT-Gradient Boosting, DT-XG Boost, DT-AdaBoost, and DT-Bagging. In addition to that, the performance model is also evaluated by using R2, root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the k-fold cross-validation method is also applied to validate the model's performance. It is observed that DT-Bagging with an R2 value of 0.95 is superior to DT-XG Boost, DT-Gradient Boosting, and DT-AdaBoost. Lesser error MAE and RMSE and higher R2 values for the DT-Bagging model show the enhanced performance of the model compared to the other ensembled approaches. Considerable conservation of time, effort, and cost can be made by applying ML techniques to predict concrete properties. The evaluation of the outcome depicts that the estimated results of DT-Bagging are closer to the experimental results, indicating the accurate estimation of SFRC flexural strength. It is further revealed from the SHapley Additive exPlanations (SHAP) study that the volumetric content of steel fiber highly and positively influences the FS of SFRC.

Combination of mesenchymal stem cells and three-dimensional collagen scaffold preserves ventricular remodeling in rat myocardial infarction model.

BACKGROUND: Cardiovascular diseases are the major cause of mortality worldwide. Regeneration of the damaged myocardium remains a challenge due to mechanical constraints and limited healing ability of the adult heart tissue. Cardiac tissue engineering using biomaterial scaffolds combined with stem cells and bioactive molecules could be a highly promising approach for cardiac repair. Use of biomaterials can provide suitable microenvironment to the cells and can solve cell engraftment problems associated with cell transplantation alone. Mesenchymal stem cells (MSCs) are potential candidates in cardiac tissue engineering because of their multilineage differentiation potential and ease of isolation. Use of DNA methyl transferase inhibitor, such as zebularine, in combination with three-dimensional (3D) scaffold can promote efficient MSC differentiation into cardiac lineage, as epigenetic modifications play a fundamental role in determining cell fate and lineage specific gene expression. AIM: To investigate the role of collagen scaffold and zebularine in the differentiation of rat bone marrow (BM)-MSCs and their subsequent in vivo effects. METHODS: MSCs were isolated from rat BM and characterized morphologically, immunophenotypically and by multilineage differentiation potential. MSCs were seeded in collagen scaffold and treated with 3 µmol/L zebularine in three different ways. Cytotoxicity analysis was done and cardiac differentiation was analyzed at the gene and protein levels. Treated and untreated MSC-seeded scaffolds were transplanted in the rat myocardial infarction (MI) model and cardiac function was assessed by echocardiography. Cell tracking was performed by DiI dye labeling, while regeneration and neovascularization were evaluated by histological and immunohistochemical analysis, res pectively. RESULTS: MSCs were successfully isolated and seeded in collagen scaffold. Cytotoxicity analysis revealed that zebularine was not cytotoxic in any of the treatment groups. Cardiac differentiation analysis showed more pronounced results in the type 3 treatment group which was subsequently chosen for the transplantation in the in vivo MI model. Significant improvement in cardiac function was observed in the zebularine treated MSC-seeded scaffold group as compared to the MI control. Histological analysis also showed reduction in fibrotic scar, improvement in left ventricular wall thickness and preservation of ventricular remodeling in the zebularine treated MSC-seeded scaffold group. Immunohistochemical analysis revealed significant expression of cardiac proteins in DiI labeled transplanted cells and a significant increase in the number of blood vessels in the zebularine treated MSC-seeded collagen scaffold transplanted group. CONCLUSION: Combination of 3D collagen scaffold and zebularine treatment enhances cardiac differentiation potential of MSCs, improves cell engraftment at the infarcted region, reduces infarct size and improves cardiac function.

Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R2 value with fewer errors, producing higher precision than other models with lesser R2 values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength.

Cubosomes: Design, Development, and Tumor-Targeted Drug Delivery Applications.

Because of the extraordinary advancements in biomedical nanotechnology over the last few decades, traditional drug delivery systems have been transformed into smart drug delivery systems that respond to stimuli. These well-defined nanoplatforms can boost therapeutic targeting efficacy while reducing the side effects/toxicities of payloads, which are crucial variables for enhancing patient compliance by responding to specific internal or external triggers. Cubosomes are lipid-based nano systems that are analogous to well-known vesicular systems, such as lipo- and niosomes. They could be used as part of a unique drug delivery system that includes hydro-, lipo-, and amphiphilic drug molecules. In this review, we critically analyze the relevant literature on cubosomesregarding theories of cubosomeself-assembly, composition, and manufacturing methods, with an emphasis on tumor-targeted drug delivery applications. Due to the bioadhesive and -compatible nature of cubosome dispersion, this review also focuses on a variety of drug delivery applications, including oral, ophthalmic and transdermal.