Background:Temporomandibular joint (TMJ) problems refer to a group of heterogeneous pain and dysfunction conditions involving the mastication, reducing life quality of the patients. Arthrocentesis is a simple and less invasive surgical method for the treatment of inflammatory-degenerative disease than other conservative procedures and better than arthroscope. This clinical study aimed at evaluating the benefit of arthrocentesis with injection of hyaluronic acid in the management of inflammatory-degenerative disease of the TMJ. Material and methods: Eighty consecutive patients were enrolled in this study with pain symptoms of TMJ, insufficient masticatory efficiency during function and limitation of mouth opening; they were assessed with

Five novel nickel, iron, cobalt, copper, and mercury complexes were synthesized from tetraazamacrocyclic Schiff base ligand (L), which were derived from 3-(4-(dimethyl amino) benzylidene) pentane-2,4-dione and 1,2- diaminocyclohexane in a 2:2 molar ratio. Many physico-chemical and spectroscopic techniques, including melting point, 1HNMR, 13CNMR, elemental analysis, molar conductance, magnetic susceptibility, UV-Vis, FT-IR, and thermogravimetric analysis (TGA), were used to characterize the Schiff base ligand and all metal complexes. The octahedral geometry of all the complexes [MLCl2] is confirmed by spectroscopic analyses. All substances' biological properties, such as their in vitro antioxidant activity or level of free radical scavenging

Platinum nanoparticles (PtNPs) exhibit promising biomedical properties, but concerns about biocompatibility and synthesis-related toxicity remain. This study aimed to develop eco-friendly PtNPs using aqueous broccoli extract as a natural reducing and stabilizing agent, and to assess their multifunctional biomedical potential. PtNPs were synthesized through sonochemical reduction of K₂PtCl₆ in broccoli extract, followed by purification and comprehensive physicochemical characterization. UV–Vis confirmed nanoparticle formation at 253 nm, while XRD and FTIR analyses verified the crystalline FCC structure and phytochemical capping. TEM revealed mainly spherical PtNPs with an average core size of 14.83 ± 7.67 nm. Conversely, DLS showe

The Mannich base ligand was synthesized in an ethanol medium through a condensation reaction of 2-mercaptobenzimidazole and ciprofloxacin at room temperature. Subsequently, several metal complexes of this ligand were prepared. To characterize both the base ligand and the metal complexes, various techniques were employed, including elemental analysis, FT-IR spectroscopy, UV-Vis spectroscopy, molar conductivity measurements, magnetic moment determination, and melting point analysis. The results were shown that the metal complexes formed have the formula [Cr(L)2Cl2] Cl.H2O and [Rh(L)2(H2O)2] Cl3.H2O, where L= mannich base ligand. Based on spectroscopic analytical, coordination with metal ions involves the 'N' donor atom of mannich base

composition,depiction,antibacterial,antioxidant,and cytotoxicity activities studies of a new nano-sized binuclear metal(||) schiff base complexes

The 3D electro-Fenton technique is, due to its high efficiency, one of the technologies suggested to eliminate organic pollutants in wastewater. The type of particle electrode used in the 3D electro-Fenton process is one of the most crucial variables because of its effect on the formation of reactive species and the source of iron ions. The electrolytic cell in the current study consisted of graphite as an anode, carbon fiber (CF) modified with graphene as a cathode, and iron foam particles as a third electrode. A response surface methodology (RSM) approach was used to optimize the 3D electro-Fenton process. The RSM results revealed that the quadratic model has a high R2 of 99.05 %. At 4 g L-1 iron foam particles, time of 5 h, and

CUPPER(||)AND MERCURY (||)Complexes WITH SCHIFF BASE LIGAND FROM BENZIDIN WITH ISATIN AND BENZOIN:SYNTHESIS,SPECTRAL CHARACTERIZATION, THERMAL STUDIES AND BIOLOGICAL ACTIVITIES

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre