Synthesis and Antibacterial Activity of Isatin Schiff Base Derivative with 3-Aminoacetophenone and its Ni(II), Co(II) Transition Metals Complexes

The (E)-3-(3-acetylphenylimino) indolin-2-one (Bidentate) ligand type [HL], has been prepared from Isatin and 3-aminoacetophenone in the presence of KOH. In general, the ligand contains oxygen (O) and nitrogen (N) donor atoms. The reaction of Isatin and 3-aminoacetophenon was carried out in ethanol by condensation reaction at 80°C with reflux for 4 h, to form [HL] ligand type. This ligand has been used to prepare NiII and CoII complexes in the ratio of 1:1 metal-ligand. All compounds have been characterized by spectroscopic methods (Fourier transform infrared and ultravioletvisible), C.H.N, thin-layer chromatography, mass spectrum, X-ray diffraction, magnetic moment, conductivity measurements and milting point, the synthesized ligand and its metal complexes have been tested for their antibacterial activity against Staphylococcus aureus and Bacillus subtilis using agar disc diffusion method. The ligand and its complexes showed significant activities against S. aureus and B. subtilis. Our study revealed the formation of four coordinate square planar complexes around NiII and CoII metal ions

Synthesis and Characterization of Iron(II), Cobalt(II), Nickel(II), Copper(II), and Zinc(II) Complexes Using Diphenylmethyl Xanthate Ligand

Potassium Diphenylmethyl Xanthate and its monomeric complexes were synthesized at room temperature under inert gas and stirring condition. The ligand and its complexes of the general formula [M(L)2] (where M= FeII, CoII, NiII , CuII, ZnII and CdII) were characterized by spectroscopic methods (IR, UV-Vis, 1H-, 13C-, DEPT-, HQMC- and COSY-NMR), elemental analysis, metal content, magnetic susceptibility  measurement and molar conductance. These studies revealed the formation of four coordinate complexes

Synthesis and Characterization of Sodium Diphenylcarbamodithioate Ligand [L] and its Cobalt, Nickel, and Copper Complexes

A correlation of the infrared spectra of thiocarbonyl derivatives based on the literature data has been carried out. Assignments have also been made in some new systems. Sodium Diphenylcarbamodithioate ligand and its monomeric complexes were synthesized at room temperature and stirring condition. The ligand and its complexes of the general formula [M(L)2] (where M= Co+2, Ni+2  and Cu+2) were characterized by spectroscopic methods (IR and ultraviolet-visible), elemental analysis (C.H.N. and S) metal content, magnetic susceptibility measurement, and biological activity (an antibacterial activity of the complex was studied by agar disc diffusion method and minimum inhibitory concentration strain against Staphylococcus aureus and Bacillus subtilis). The complex exhibited significant activities against S. aureus and B. subtilis, thin-layer chromatography, mass spectrometry, X-ray powder diffraction, and molar conductance. Our study revealed the formation of four-coordinate square planar complexes around Coп, Niп, and Cuп metal ions

Synthesis and characterization of benzil crown cyclic Schiff base ligand and its metal complexes

ABSTRACT. The reaction between benzil and hexamethylenediamine formed a new ligand [L], [(1Z,3Z)-2,3-diphenyl-5,6,7,8,9,10-hexahydro-1,4-diazecine], of the type [N2], was synthesized by the condensation reaction through Schiff base reaction between benzil and hexamethylenediamine. The new Schiff base ligand reacts with Mnп, Niп and Coп metal ions to give the complexes with the general formula: [M(L)Cl2]. The elemental investigations have been used to analyze the ligand and its complexes by CHN, FT-IR, UV-Vis, TLC, mass spectrum, melting point with the study of biological activity to the formed compounds. From the data obtained, the proposed molecular structure adopts square planar structure about the metal ions. The study reveals the formation of a new ligand type [N2] and it's metal ion complexes with square planar structures metal:ligand ratio of 1:1, which can be employed in a variety of fields such as medicine and industry.     KEY WORDS: Benzil, Hexamethylenediamine, Metal complexes, Square planar structure   Bull. Chem. Soc. Ethiop. 2022, 36(4), 791-799.                                                                DOI: https://dx.doi.org/10.4314/bcse.v36i4.6                                                       &nbsp

An automatic corneal subbasal nerve registration system using FFT and phase correlation techniques for an accurate DPN diagnosis

yesConfocal microscopy is employed as a fast and non-invasive way to capture a sequence of images from different layers and membranes of the cornea. The captured images are used to extract useful and helpful clinical information for early diagnosis of corneal diseases such as, Diabetic Peripheral Neuropathy (DPN). In this paper, an automatic corneal subbasal nerve registration system is proposed. The main aim of the proposed system is to produce a new informative corneal image that contains structural and functional information. In addition a colour coded corneal image map is produced by overlaying a sequence of Cornea Confocal Microscopy (CCM) images that differ in their displacement, illumination, scaling, and rotation to each other. An automatic image registration method is proposed based on combining the advantages of Fast Fourier Transform (FFT) and phase correlation techniques. The proposed registration algorithm searches for the best common features between a number of sequenced CCM images in the frequency domain to produce the formative image map. In this generated image map, each colour represents the severity level of a specific clinical feature that can be used to give ophthalmologists a clear and precise representation of the extracted clinical features from each nerve in the image map. Moreover, successful implementation of the proposed system and the availability of the required datasets opens the door for other interesting ideas; for instance, it can be used to give ophthalmologists a summarized and objective description about a diabetic patient’s health status using a sequence of CCM images that have been captured from different imaging devices and/or at different time

A Fast and Accurate Iris Localization Technique for Healthcare Security System

yesIn the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out

A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms

yesn this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches

Fundus-DeepNet: Multi-Label Deep Learning Classification System for Enhanced Detection of Multiple Ocular Diseases through Data Fusion of Fundus Images

YesDetecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e.g., left and right eyes). The study initiates with a comprehensive image pre-processing procedure, including circular border cropping, image resizing, contrast enhancement, noise removal, and data augmentation. Subsequently, discriminative deep feature representations are extracted using multiple deep learning blocks, namely the High-Resolution Network (HRNet) and Attention Block, which serve as feature descriptors. The SENet Block is then applied to further enhance the quality and robustness of feature representations from a pair of fundus images, ultimately consolidating them into a single feature representation. Finally, a sophisticated classification model, known as a Discriminative Restricted Boltzmann Machine (DRBM), is employed. By incorporating a Softmax layer, this DRBM is adept at generating a probability distribution that specifically identifies eight different ocular diseases. Extensive experiments were conducted on the challenging Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition (OIA-ODIR) dataset, comprising diverse fundus images depicting eight different ocular diseases. The Fundus-DeepNet system demonstrated F1-scores, Kappa scores, AUC, and final scores of 88.56%, 88.92%, 99.76%, and 92.41% in the off-site test set, and 89.13%, 88.98%, 99.86%, and 92.66% in the on-site test set.In summary, the Fundus-DeepNet system exhibits outstanding proficiency in accurately detecting multiple ocular diseases, offering a promising solution for early diagnosis and treatment in ophthalmology.European Union under the REFRESH – Research Excellence for Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Program Just Transition. The Ministry of Education, Youth, and Sports of the Czech Republic - Technical University of Ostrava, Czechia under Grants SP2023/039 and SP2023/042

N,N'-Bis(di-phenyl-meth-yl)benzene-1,4-di-amine.

The complete molecule of the title compound, C32H28N2, is generated by crystallographic inversion symmetry. The dihedral angles between the central aromatic ring and the pendant adjacent rings are 61.37 (16) and 74.20 (14). The N— H group does not participate in hydrogen bonds and there are no aromatic – stacking interactions in the crystal

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting