Potential Applications Research Articles

3D printing of hydrogel composite systems: Recent advances in technology for tissue engineering.

Three-dimensional (3D) printing of hydrogels is now an attractive area of research due to its capability to fabricate intricate, complex and highly customizable scaffold structures that can support cell adhesion and promote cell infiltration for tissue engineering. However, pure hydrogels alone lack the necessary mechanical stability and are too easily degraded to be used as printing ink. To overcome this problem, significant progress has been made in the 3D printing of hydrogel composites with improved mechanical performance and biofunctionality. Herein, we provide a brief overview of existing hydrogel composite 3D printing techniques including laser based-3D printing, nozzle based-3D printing, and inkjet printer based-3D printing systems. Based on the type of additives, we will discuss four main hydrogel composite systems in this review: polymer- or hydrogel-hydrogel composites, particle-reinforced hydrogel composites, fiber-reinforced hydrogel composites, and anisotropic filler-reinforced hydrogel composites. Additionally, several emerging potential applications of hydrogel composites in the field of tissue engineering and their accompanying challenges are discussed in parallel.

Effects of Topology Optimization in Multimaterial 3D Bioprinting of Soft Actuators

Recently, there has been a proliferation of soft robots and actuators that exhibit improved capabilities and adaptability through three-dimensional (3D) bioprinting. Flexibility and shape recovery attributes of stimuli-responsive polymers as the main components in the production of these dynamic structures enable soft manipulations in fragile environments, with potential applications in biomedical and food sectors. Topology optimization (TO), when used in conjunction with 3D bioprinting with optimal design features, offers new capabilities for efficient performance in compliant mechanisms. In this paper, multimaterial TO analysis is used to improve and control the bending performance of a bioprinted soft actuator with electrolytic stimulation. The multimaterial actuator performance is evaluated by the amplitude and rate of bending motion and compared with the single material printed actuator. The results demonstrated the efficacy of multimaterial 3D bioprinting optimization for the rate of actuation and bending.

Recent Advances in the Synthesis of Antioxidant Derivatives: Pharmacological Insights for Neurological Disorders.

Neurological disorders, characterized by oxidative stress (OS) and inflammation, have become a major global health concern. Redox reactions play a vital role in regulating the balance of the neuronal microenvironment. Specifically, the imbalance leads to a significant weakening of the organism's natural defensive mechanisms. This, in turn, causes the development of harmful oxidative stress, which plays a crucial role in the onset and progression of neurodegenerative dis-eases. The quest for effective therapeutic agents has led to significant advancements in the syn-thesis of antioxidant derivatives. This review provides a comprehensive overview of the recent developments in the use of novel antioxidant compounds with potential pharmacological applica-tions in the management of neurological disorders. The discussed compounds encompass a di-verse range of chemical structures, including polyphenols, vitamins, flavonoids, and hybrid mole-cules, highlighting their varied mechanisms of action. This review also focuses on the mechanism of oxidative stress in developing neurodegenerative disease. The neuroprotective effects of these antioxidant derivatives are explored in the context of specific neurological disorders, including Alzheimer's disease, Parkinson's disease, and Huntington's disease. The ultimate goal is to pro-vide effective treatments for these debilitating conditions and improve the quality of life for pa-tients.

Cu-Co Bimetallic Catalyst-based Electrochemical Sensing Platform for Determination of Bisoprolol in Clinical Samples.

Bisoprolol (BIS) is a selective beta-blocker. It has been successfully used to treat hypertension and angina pectoris. An overdose of BIS can lead to serious complications. An overdose is a medical emergency that requires immediate medical attention to overcome the adverse effects of the overdose. Hence, sensitive, reliable, and cost-effective methods are required for the determination of BIS. In this work, a new electrochemical sensing platform based on a bimetallic catalyst was developed for the determination of BIS. The Cu-Co nanocatalyst was easily synthesized by galvanic displacement onto a carbon paste electrode (CPE). Then, field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), and cyclic voltammetry (CV) were utilized for the characterization of the Cu-Co catalyst. The galvanic displacement of Cu metal significantly affected the electro-catalytic behavior of the Cu-Co catalyst and the Cu-Co/CPE electrode displayed a very sensitive and accurate response towards BIS. Under optimized conditions, the response was linear in the 3 to 120 μM concentration range, sensitivity of 631.1 μA mM-1 and a detection limit of as low as 0.4 μM using cyclic voltammetry. The simple proposed method was also successfully employed in the analysis of BIS in biological and pharmaceutical samples. The advantages of Cu-Co/CPE are its fast and simple manufacturing and the possibility of a repeated surface regeneration of the sensing platform, as well as its application for the detection of BIS in tablets and biological samples, making Cu-Co significant promise for use in clinical diagnostics. Besides, the synthesized catalysts showed excellent reusability and stability. The presence of Cu metal due to galvanic displacement increased the sensitivity. These findings suggest that the new nanocatalyst has potential applications in sensors and electronics.

Naturally Occurring Cholinesterase Inhibitors from Plants, Fungi, Algae, and Animals: A Review of the Most Effective Inhibitors Reported in 2012-2022.

Since the development of the "cholinergic hypothesis" as an important therapeutic approach in the treatment of Alzheimer's disease (AD), the scientific community has made a remarkable effort to discover new and effective molecules with the ability to inhibit the enzyme acetylcholinesterase (AChE). The natural function of this enzyme is to catalyze the hydrolysis of the neurotransmitter acetylcholine in the brain. Thus, its inhibition increases the levels of this neurochemical and improves the cholinergic functions in patients with AD alleviating the symptoms of this neurological disorder. In recent years, attention has also been focused on the role of another enzyme, butyrylcholinesterase (BChE), mainly in the advanced stages of AD, transforming this enzyme into another target of interest in the search for new anticholinesterase agents. Over the past decades, Nature has proven to be a rich source of bioactive compounds relevant to the discovery of new molecules with potential applications in AD therapy. Bioprospecting of new cholinesterase inhibitors among natural products has led to the discovery of an important number of new AChE and BChE inhibitors that became potential lead compounds for the development of anti-AD drugs. This review summarizes a total of 260 active compounds from 142 studies which correspond to the most relevant (IC50 ≤ 15 μM) research work published during 2012-2022 on plant-derived anticholinesterase compounds, as well as several potent inhibitors obtained from other sources like fungi, algae, and animals.

Antioxidant properties and L-asparaginase activities of endophytic fungi from Cymbidium orchids.

This study profiled the various endophytic fungi isolated from the orchid Cymbidium sp. and their L-asparaginase production and antioxidant potential. The L-asparaginase production was first screened through qualitative plate screening then quantified by the Nesslerization method. The antioxidant potential was quantified via the 2,2-diphenyl-1-picrylhydrazyl assay. A total of 30 endophytic fungi were isolated and all fungal isolates exhibited various degrees of radical scavenging activities (45.28% to 76.4%). Isolate Lasiodiplodia theobromae (C11) had the highest antioxidant capacity, represented by the lowest IC50 value (5.75 mg/mL) and highest ascorbic acid equivalent antioxidant capacity value (12.17 mg/g). Additionally, 16 isolates produced L-asparaginase (53.33%), which includes primarily species of Fusarium proliferatum, Fusarium fujikuroi, Fusarium incarnatum, and Fusarium oxysporum. A new isolate has also been discovered from Cymbidium orchid, Buergenerula spartinae (C28), which showed the highest L-asparaginase activity (1.736 unit/mL). These findings supported the postulation that medicinal species of Orchidaceae such as Cymbidium sp. harbor endophytes that are producers of L-asparaginase and antioxidants with various potential applications.

PSMA PET/MR is a New Imaging Option for Identifying Glioma Recurrence and Predicting Prognosis.

Glioma is characterized by a high recurrence rate, while the results of the traditional imaging methods (including magnetic resonance imaging, MRI) to distinguish recurrence from treatment-related changes (TRCs) are poor. Prostate-specific membrane antigen (PSMA) (US10815200B2, Deutsches Krebsforschungszentrum, German Cancer Research Center) is a type II transmembrane glycoprotein overexpressed in glioma vascular endothelium, and it is a promising target for imaging and therapy. The study aimed to assess the performance of PSMA positron emission tomography/ magnetic resonance (PET/MR) for diagnosing recurrence and predicting prognosis in glioma patients. Patients suspected of glioma recurrence who underwent 18F-PSMA-1007 PET/MR were prospectively enrolled. Eight metabolic parameters and fifteen texture features of the lesion were extracted from PSMA PET/MR. The ability of PSMA PET/MR to diagnose glioma recurrence was investigated and compared with conventional MRI. The diagnostic agreement was assessed using Cohen κ scores and the predictive parameters of PSMA PET/MR were obtained. Kaplan-Meier method and Cox proportional hazard model were used to analyze recurrence- free survival (RFS) and overall survival (OS). Finally, the expression of PSMA was analyzed by immunohistochemistry (IHC). Nineteen patients with a mean age of 48.11±15.72 were assessed. The maximum tumorto- parotid ratio (TPRmax) and texture features extracted from PET and T1-weighted contrast enhancement (T1-CE) MR showed differences between recurrence and TRCs (all p <0.05). PSMA PET/MR and conventional MRI exhibited comparable power in diagnosing recurrence with specificity and PPV of 100%. The interobserver concordance was fair between the two modalities (κ = 0.542, p = 0.072). The optimal cutoffs of metabolic parameters, including standardized uptake value (SUV, SUVmax, SUVmean, and SUVpeak) and TPRmax for predicting recurrence were 3.35, 1.73, 1.99, and 0.17 respectively, with the area under the curve (AUC) ranging from 0.767 to 0.817 (all p <0.05). In grade 4 glioblastoma (GBM) patients, SUVmax, SUVmean, SUVpeak, TBRmax, TBRmean, and TPRmax showed improved performance of AUC (0.833-0.867, p <0.05). Patients with SUVmax, SUVmean, or SUVpeak more than the cutoff value had significantly shorter RFS (all p <0.05). In addition, patients with SUVmean, SUVpeak, or TPRmax more than the cutoff value had significantly shorter OS (all p <0.05). PSMA expression of glioma vascular endothelium was observed in ten (10/11, 90.9%) patients with moderate-to-high levels in all GBM cases (n = 6/6, 100%). This primitive study shows multiparameter PSMA PET/MR to be useful in identifying glioma (especially GBM) recurrence by providing excellent tumor background comparison, tumor heterogeneity, recurrence prediction and prognosis information, although it did not improve the diagnostic performance compared to conventional MRI. Further and larger studies are required to define its potential clinical application in this setting.

From Simple to Complex Scenes: Learning Robust Feature Representations for Accurate Human Parsing.

Human parsing has attracted considerable research interest due to its broad potential applications in the computer vision community. In this paper, we explore several useful properties, including high-resolution representation, auxiliary guidance, and model robustness, which collectively contribute to a novel method for accurate human parsing in both simple and complex scenes. Starting from simple scenes: we propose the boundary-aware hybrid resolution network (BHRN), an advanced human parsing network. BHRN utilizes deconvolutional layers and multi-scale supervision to generate rich high-resolution representations. Additionally, it includes an edge perceiving branch designed to enhance the fineness of part boundaries. Building on BHRN, we construct a dual-task mutual learning (DTML) framework. It not only provides implicit guidance to assist the parser by incorporating boundary features, but also explicitly maintains the high-order consistency between the parsing prediction and the ground truth. Toward complex scenes: we develop a domain transform method to enhance the model robustness. By transforming the input space from the spatial domain to the polar harmonic Fourier moment domain, the mapping relationship to the output semantic space is highly stable. This transformation yields robust representations for both clean and corrupted data. When evaluated on standard benchmark datasets, our method achieves superior performance compared to state-of-the-art human parsing methods. Furthermore, our domain transform strategy significantly improves the robustness of DTML dramatically in most complex scenes.

Thermophysical properties of Molten FLiNaK: A moment tensor potential approach

Fluoride salts demonstrate significant potential for applications in next-generation nuclear reactors, necessitating a comprehensive understanding of their thermophysical properties for technological advancements. Experimental measurement of these properties poses challenges, due to factors such as high temperatures, impurity control, and corrosion. Consequently, precise computational modeling methods become essential for predicting the thermophysical properties of molten salts. In this work, we performed molecular dynamics (MD) simulations of several thermophysical properties of the eutectic salt mixture LiF-NaF-KF (FLiNaK) melt, including density, self-diffusion coefficients, viscosity, and thermal conductivity. We demonstrated the successful application of moment tensor potentials (MTP) as an accurate model for interatomic interactions in FLiNaK. Our results on thermophysical properties calculations exhibit strong agreement with experimental data. An important aspect of our methodology is the incorporation of an active learning scheme, which enables the generation of a robust and accurate potential, while maintaining a moderate-sized training dataset.

Robust correlated magnetic moments in end-modified graphene nanoribbons

We conduct a theoretical examination of the electronic and magnetic characteristics of end-modified 7-atom wide armchair graphene nanoribbons (AGNRs). Our investigation is performed within the framework of a single-band Hubbard model, beyond a mean-field approximation. First, we carry out a comprehensive comparison of various approaches for accommodating di-hydrogenation configurations at the AGNR ends. We demonstrate that the application of an on-site potential to the modified carbon atom, coupled with the addition of an electron, replicates phenomena such as the experimentally observed reduction of the bulk-states (BS) gap. These results for the density of states (DOS) and electronic densities align closely with those obtained through a method explicitly designed to account for the orbital properties of hydrogen atoms. Furthermore, our study enables a clear differentiation between magnetic moments already described in a mean-field (MF) approach, which are spatially confined to the same sites as the topological end-states (ES), and correlation-induced magnetic moments, which exhibit localization along all edges of the AGNRs. Notably, we show the robustness of these correlation-induced magnetic moments relative to end modifications, within the scope of the method we employ.

The Imidazacridine Derivative LPSF/AC-05 Induces Apoptosis, Cell Cycle Arrest, and Topoisomerase II Inhibition in Breast Cancer, Leukemia, and Lymphoma.

Cancer is the major cause of morbidity and mortality worldwide. Current treatments for both solid and hematological tumors are associated with severe adverse effects and drug resistance, necessitating the development of novel selective antineoplastic drugs. The present study describes the antitumor activity of the imidazacridine derivative 5-acridin-9-ylmethylidene-2-thioxoimidazolidin-4-one (LPSF/AC05) in breast cancer, leuke-mia, and lymphoma cells. Cytotoxicity assays were performed in PBMC and in breast cancer, leukemia, and lymphoma cell lines using the MTT method. Changes in cell cycle progression and apoptosis were assessed using flow cytometry. Moreover, topoisomerase II inhibition as-says were performed. LPSF/AC05 exhibited cytotoxicity in six of the nine cell lines tested. The best results for leukemia and lymphoma were observed in the Toledo, Jurkat, and Raji cell lines (IC50 = 27.18, 31.04, and 33.36 M, respectively). For breast cancer, the best re-sults were observed in the triple-negative cell line MDA-MB-231 (IC50 = 27.54 μM). The compound showed excellent selectivity, with no toxicity to normal human cells (IC50 > 100M; selectivity index > 3). Cell death was primarily induced by apoptosis in all cell lines. Furthermore, LPSF/AC05 treatmentinduced cell cycle arrest at the G0/G1 phase in leuke-mia/lymphoma and at the G2/M phase in breast cancer. Finally, topoisomerase II was inhibited. These results indicate the potential ap-plication of LPSF/AC05 in cancer therapy.

Mechanism analysis and application of multi-dimensional single potential well stochastic resonance system

Currently, the focus of stochastic resonance (SR) research is primarily on bistable systems, and classical bistable SR systems have the problems of low dimension, inconvenient parameter adjustment, and high threshold for SR effects. In this paper, based on the classic bistable system, a class of multi-dimensional single potential well SR systems without transition threshold is proposed and applied to signal processing. Firstly, the mechanism of double potential well SR and single potential well SR is studied. On this basis, a kind of multi-dimensional single potential well SR system is defined and its theoretical conditions are analyzed. Then a specific four-dimensional single potential well SR system is constructed, and the dynamics of the system is analyzed. The gain range and the approximate relationship between the system input and the four-dimensional output are derived. Finally, the four-dimensional single potential well SR system is applied to the processing of various signals. The experimental results show that the constructed system has good noise reduction and feature amplification effects on noisy signals through the advantages of multi-dimensional output, and can be used to highlight the fault feature frequency in bearing fault signals.

Electromagnetic Characteristic Analysis of a Negative-Salient Hybrid Permanent Magnet Motor for Electric Vehicles

Negative-salient permanent magnet motors (NSPMMs) have a potential application in the field of electric vehicles (EVs) due to their wide constant-power speed range and high efficiency. However, the researches of NSPMMs used for EVs and the relationships between the design parameters of the motor and drive performances of the EV are still absent. First, the operating characteristics of permanent magnet drive motors are investigated. Second, the relationships between the distribution of the driving cycles of the EV and the design parameters of the motor are summarized. Then, a negative-salient hybrid permanent magnet motor with excellent performances in the full-speed range and remedies for its negative impact are proposed. The electromagnetic performances of the proposed topology are compared with the conventional motor by finite element method and the performance evaluation of the electrical driving system is carried out. Finally, a prototype is manufactured and tested. The simulation and experimental results show that the proposed motor exhibits favorable characteristics such as low torque ripple, wide constant-power speed range, high efficiency in a wide speed range and low rare-earth consumption. The results verify the feasibility of the proposed topology applied to EVs.

Can Glycerol Carbonate be Synthesized Without a Catalyst?

Abstract: Biodiesel and oleo-chemical industries have been producing huge quantities of glycerol as a by-product. Value-added products can be synthesized from glycerol through different chemical and enzymatic reactions, such as oxidation, carbonylation, reforming, acetalyzation, etherification, dehydration, hydrogenolysis, hydrolysis, esterification, and transesterification. Glycerol is a low-cost polyol that can be converted into glycerol carbonate, which has potential applications in polymer and biobased non-isocyanate polyurethanes industries (Bio-NIPUs). The present contribution is the first of its kind to report on the synthesis of glycerol carbonate via catalyst and solvent-free transesterification of glycerol with dimethyl carbonate under conventional as well as microwave heating. Additionally, a comparative study of conventional and microwave-assisted transesterification was performed. Under conventional heating, 78% glycerol carbonate is obtained at 120oC in 36 hours, whereas, using microwaves, 92% of glycerol carbonate can be achieved in 30 minutes. Presently, biomass-based heterogeneous materials are used in catalysis due to their importance within the context of sustainability. In line with this, in this work, a series of green catalysts, namely, molecular sieves (MS, 4Å), Hβ- Zeolite, Montmorillonite K-10 clay, activated carbon prepared from the shell of groundnut (Arachis hypogaea), and biochar from sawdust pyrolysis were successfully employed. Glycerol carbonate was thoroughly characterized by 1H and 13C NMR, FT-IR and MS. The method described here is facile and green since the utilization of bioresource (glycerol) for the production of glycerol carbonate is performed under microwave.

Anaerobic process intensification via electro-methanogenesis − Advancing biogas upgradation through CO2 utilization

Augmenting anaerobic processes with electrochemically hybridized strategies i.e., electro-methanogenesis holds promise for biogas upgradation with additional in situ CO2 utilization. The study investigated the effect of applied potential (AP) and closed circuit (CC) conditions on electro-methanogenic bioreactors operating at two temperatures (30 °C and 48 °C) using stainless-steel electrodes. Eight different reactor conditions, including open circuit (OC) and control (C) without electrodes, were compared. Results indicate that mesophilic conditions (48 °C) yielded higher biogas and methane production efficiency compared to ambient conditions (30 °C). Enhanced biogas production was observed across various electrode assembly conditions, with the highest production in the AP bioreactor, followed by CC, OC, and control (C). The combined electrochemical and temperature conditions significantly increased biogas calorific values, showing a 1.6–2.08-fold improvement and contributing to enhanced metabolic performance. R8/EMAP/48 condition demonstrated the highest me- equivalent utilization, with methane being the largest electron sink (748.7 me- equivalent). The application of in situ or external potentials enhanced bacterial metabolic activity, promoting mediated interspecies electron transfer (MIET) via soluble redox-active molecules like quinones and NADH, thereby overcoming the thermodynamic limitations of conventional anaerobic digestion. This study presents an effective method to boost methane (CH4) production and address the limitations in methanogenesis by leveraging in situ electron flux.

Self-powered electromodulated liquid chromatography with preliminary charging of porous graphitic carbon

Electromodulated liquid chromatography (EMLC) allows controlling the retention times of some ions on a carbon sorbent due to the application of electric potential. A simplified two-electrode setup was proposed for EMLC with standard columns. Instead of simultaneous application of constant potential with chromatographic measurements the possibility of working in zero-current mode after preliminary polarization has been shown. Potentiostatic charging of HypercarbTM under constant potential and the following measurement of open circuit potential (OCP) was suggested before recording the chromatogram. OCP appeared to be a more reliable parameter than the applied potential to control the sorbent state and its influence on the chromatographic parameters. Linear dependence of retention time on OCP was obtained as a proof of concept. The anodic charging of the sorbent resulted in the highest stability of the sorption parameters in self-powered regime: the retention of analytes in the absence of external potential is preserved for 3–4 h of chromatographic experiment.

Album of animated drawings as an innovative means of teaching algebra in the digital environment of bilingual students

Introduction. Digitalization of education has opened up new opportunities for the development and application of the didactic potential of digital technologies in teaching algebra in the context of bilingualism. This determined the purpose of this work, which is to create a methodological system for teaching algebra to bilingual students and an album of animated drawings in a digital environment. Scientific and pedagogical research focuses on the problem of mathematical training of schoolchildren in the context of bilingualism. From the point of view of improving the quality of education, it is advisable to rely on visual-figurative and associative thinking developed by bilingual students. Materials and methods. The experimental study involved 50 students of the seventh grade of gymnasium No. 5 in the city of Kyzyl, Republic of Tyva (Russian Federation). The methods of comparative analysis of scientific, pedagogical and methodological sources, empirical and statistical methods (Student's t-test) are applied. Results. A methodological system for teaching algebra to bilingual students using an album of animated drawings in a digital environment has been developed. An album of animated drawings is presented, implemented in the form of an electronic training course in the electronic learning system of Tuvan State University and the results of its application in teaching algebra to bilingual students. As a result of the experiment, it was revealed that in the absence of statistically significant differences in the control and experimental groups at the time of the initial control, since tэмп = 2,00 &lt; tкр = 2,01, during the final control, tэмп = 2,80 &gt; tкр = 2,01 at the significance level p &lt; 0,05. This indicates that the compared groups are significantly different and the experimental hypothesis about the effectiveness of using an album of animated drawings as an innovative means of teaching seventh grade algebra in the digital environment of bilingual students is confirmed. Conclusion. The use of an album of animated drawings as an innovative means of teaching algebra in the digital environment of bilingual students of the seventh grade of the Republic of Tyva increases the effectiveness of learning by increasing the clarity of algebraic concepts and statements, reducing the amount of non-essential calculations, as well as the use of mechanisms for rapid assessment of learning outcomes using a developed system of bilingual prompts. The proposed learning tool forms and develops an experimental research style of learning.

Rethinking Pretraining as a Bridge From ANNs to SNNs.

Spiking neural networks (SNNs) are known as typical kinds of brain-inspired models with their unique features of rich neuronal dynamics, diverse coding schemes, and low power consumption properties. How to obtain a high-accuracy model has always been the main challenge in the field of SNN. Currently, there are two mainstream methods, i.e., obtaining a converted SNN through converting a well-trained artificial NN (ANN) to its SNN counterpart or training an SNN directly. However, the inference time of a converted SNN is too long, while SNN training is generally very costly and inefficient. In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism. We believe that the proposed paradigm is a more efficient pipeline for training SNNs. The pipeline includes pipe-S for static data transfer tasks and pipe-D for dynamic data transfer tasks. State-of-the-art (SOTA) results are obtained in a large-scale event-driven dataset ES-ImageNet. For training acceleration, we achieve the same (or higher) best accuracy as similar leaky-integrate-and-fire (LIF)-SNNs using 1/8 training time on ImageNet-1K and 1/2 training time on ES-ImageNet and also provide a time-accuracy benchmark for a new dataset ES-UCF101. These experimental results reveal the similarity of the functions of parameters between ANNs and SNNs and also demonstrate various potential applications of this SNN training pipeline.

Treatment with Exosomes Derived from Mesenchymal Stem Cells: A New Window of Healing Science in Regenerative Medicine.

Many studies have been conducted on the potential applications of mesenchymal stem cells (MSCs) over recent years due to their growing importance in regenerative medicine. Exosomes are considered cargos capable of transporting proteins, peptides, lipids, mRNAs, and growth factors. MSCsderived exosomes are also involved in the prevention or treatment of a variety of diseases, including cardiovascular diseases, neurological diseases, skin disorders, lung diseases, osteoarthritis, damaged tissue repair, and other diseases. This review attempted to summarize the importance of employing MSCs in regenerative medicine by gathering and evaluating information from current literature. The role of MSCs and the potential applications of MSCs-derived exosomes have also been discussed.

An Insight on Skin Cancer About Different Targets With Update on Clinical Trials and Investigational Drugs.

Cancer is a diverse disease caused by transcriptional changes involving genetic and epigenetic features that influence a huge variety of genes and proteins. Skin cancer is a potentially fatal disease that affects equally men and women globally and is characterized by many molecular changes. Despite the availability of various improved approaches for detecting and treating skin cancer, it continues to be the leading cause of death throughout society. This review highlights a general overview of skin cancer, with an emphasis on epidemiology, types, risk factors, pathological and targeted facets, biomarkers and molecular markers, immunotherapy, and clinical updates of investigational drugs associated with skin cancer. The skin cancer challenges are acknowledged throughout this study, and the potential application of novel biomarkers of skin cancer formation, progression, metastasis, and prognosis is explored. Although the mechanism of skin carcinogenesis is currently poorly understood, multiple articles have shown that genetic and molecular changes are involved. Furthermore, several skin cancer risk factors are now recognized, allowing for efficient skin cancer prevention. There have been considerable improvements in the field of targeted treatment, and future research into additional targets will expand patients' therapeutic choices. In comparison to earlier articles on the same issue, this review focused on molecular and genetic factors and examined various skin cancer-related factors in depth.