Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

Introduction

Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF.

Methods

In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors.

Results

White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF.

Conclusion

The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34's deep representation learning and UNet's efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

Liver Cancer Algorithm: A novel bio-inspired optimizer

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.

Exposure time as an influencing factor among rheumatoid arthritis patients subjected to traditional Siwan therapy

Rheumatoid arthritis (RA) is a long-term autoimmune disease characterized by intra- and extra-articular manifestations. Sand therapy is traditionally indicated for RA, chronic pain, skin diseases, and musculoskeletal disorders. Many places in the world use sand therapy, including Siwa, which is a famous place in Egypt. This study investigated the exposure time to Siwan traditional therapy as a factor influencing central sensitization, pain severity, pain threshold, and kinesiophobia in RA by measuring the central sensory inventory (CSI), visual analogue scale, pressure algometer, and TAMPA kinesiophobia scale, respectively. Twenty-four patients with RA were recruited from 6 traditional healing centers, 24 RA patients were recruited and randomly assigned to 2 equal groups (GI and GII). The first received Siwan traditional therapy for 3 days, while the second received the same program for 5 days. The results revealed a significant difference in CSI between pre- and posttreatment within the GII (P = .038). The Tampa Scale score improved significantly in both groups (P = .004 and P = .014, respectively). Pain severity and pain threshold at all sites showed significant posttreatment improvements in the GII. Significant posttreatment changes were only found for GI in terms of pain severity and the most painful joint (P = .010 and P = .035, respectively). Significant changes were observed in kinesiophobia, pain severity, and pain threshold in the most painful joint 3 and 5 days after Siwan traditional therapy. Despite the nonsignificant differences in all parameters between the 2 groups, all the measured parameters produced favorable results after 5 days of treatment, suggesting the need for a long-term effect investigation.

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Background

The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification.

Materials and Methods

Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance.

Results

The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.

Conclusion

Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo∧2 = 0.5548, Q_lto∧2 = 0.5278, R∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo∧2 = 0.7033, Q_lto∧2 = 0.6827, Q_lmo∧2 = 0.6305, R∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria's upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4's stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Dynamic Coati Optimization Algorithm for Biomedical Classification Tasks

Medical datasets are primarily made up of numerous pointless and redundant elements in a collection of patient records. None of these characteristics are necessary for a medical decision-making process. Conversely, a large amount of data leads to increased dimensionality and decreased classifier performance in terms of machine learning. Numerous approaches have recently been put out to address this issue, and the results indicate that feature selection can be a successful remedy. To meet the various needs of input patterns, medical diagnostic tasks typically involve learning a suitable categorization model. The k-Nearest Neighbors algorithm (kNN) classifier's classification performance is typically decreased by the input variables' abundance of irrelevant features. To simplify the kNN classifier, essential attributes of the input variables have been searched using the feature selection approach. This paper presents the Coati Optimization Algorithm (DCOA) in a dynamic form as a feature selection technique where each iteration of the optimization process involves the introduction of a different feature. We enhance the exploration and exploitation capability of DCOA by employing dynamic opposing candidate solutions. The most impressive feature of DCOA is that it does not require any preparatory parameter fine-tuning to the most popular metaheuristic algorithms. The CEC'22 test suite and nine medical datasets with various dimension sizes were used to evaluate the performance of the original COA and the proposed dynamic version. The statistical results were validated using the Bonferroni-Dunn test and Kendall's W test and showed the superiority of DCOA over seven well-known metaheuristic algorithms with an overall accuracy of 89.7%, a feature selection of 24%, a sensitivity of 93.35% a specificity of 96.81%, and a precision of 93.90%.

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN's proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%.

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet's architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.

Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%).

Effect of compressive therapy on sensorimotor function of the more affected upper extremity in chronic stroke patients: A randomized clinical trial

BACKGROUND

Common upper extremity (UE) physical impairments after stroke include paresis, abnormal muscle tone, and somatosensory affection. This study evaluated the effect of passive somatosensory stimulation using compressive therapy on sensorimotor function of the more affected UE in chronic stroke patients.

METHODS

Forty chronic stroke patients were enrolled in this study. They were randomized into 2 groups: Gr1 and Gr2. Three patients dropped out leaving us with a total of 37 patients completing the study. Gr1 received UE motor program for the more affected UE along with sham electrical stimulation while Gr2 had the same UE motor program along with passive somatosensory stimulation. The session duration in both groups was 85 min. Gr1 and Gr2 received a total of 36 sessions for 6 successive weeks. UE function in Gr1 and Gr2 was examined, before and after treatment using Box and Block test (BBT) and Perdue Pegboard test (PPBT) as measures of motor of both the more affected and less affected UE while the Nottingham sensory assessment (NSA) scale was used as a measure of sensory function of the more affected UE.

RESULTS

There were significant improvements in motor and sensory function of the more affected UE compared to the less affected UE in both groups, measured by the BBT, PPBT, and NSA scales post-treatment (P < .05). However, the comparison between both groups regarding improvement revealed no significant change (P > .05).

CONCLUSION

Upper extremity motor and passive somatosensory stimulation techniques are effective in improving sensorimotor function of the more affected UE, but none of them had the advantage over the other, in terms of improving motor and sensory function in chronic stroke patients.

Therapeutic Efficacy of Transcutaneous Electrical Nerve Stimulation Acupoints on Motor and Neural Recovery of the Affected Upper Extremity in Chronic Stroke: A Sham-Controlled Randomized Clinical Trial

Inability to use the affected upper extremity (UE) in daily activities is a common complaint in stroke patients. The somatosensory system (central and peripheral) is essential for brain reorganization and plasticity. Neuromuscular electrical stimulation is considered an effective modality for improving UE function in stroke patients. The aim of the current study was to determine the therapeutic effects of transcutaneous electrical nerve stimulation (TENS) acupoints on cortical activity and the motor function of the affected UE in chronic stroke patients. Forty male and female patients diagnosed with stroke agreed to join the study. They were randomly assigned to group 1 (G1) and group 2 (G2). G1 received task-specific training (TST) and sham electrical stimulation while G2 received TST in addition to TENS acupoints. Session duration was 80 min. Both groups received 18 sessions for 6 successive weeks, 3 sessions per week. Evaluation was carried out before and after completion of the treatment program. Outcome measures used were the Fugl-Meyer Assessment of the upper extremity (FMA-UE) and the box and block test (BBT) as measures of the motor function of the affected UE. Brain activity of the motor area (C3) in the ipsilesional hemisphere was measured using a quantitative electroencephalogram (QEEG). The measured parameter was peak frequency. It was noted that the motor function of the affected UE improved significantly post-treatment in both groups, while no significant change was reported in the FMA-UE and BBT scores post-treatment in either G1 or G2. On the other hand, the activity of the motor area C3 improved significantly in G2 only, post-treatment, while G1 showed no significant improvement. There was also significant improvement in the activity of the motor area (C3) in G2 compared to G1 post-treatment. The results of the current study indicate that TST only or combined with TENS acupoints can be considered an effective method for improving motor function of the affected UE in chronic stroke patients, both being equally effective. However, TST combined with TENS acupoints proved better in improving brain plasticity in chronic stroke patients.

Effect of orange polarized light on post burn pediatric scar: a single blind randomized clinical trial

[Purpose] This study was carried out to investigate the effect of orange filtered polarized polychromatic light on post burn pediatric scar. [Participants and Methods] Thirty children with post burn scar in wrist and hands participated in this study. They were between 3 to 7 years old, having hypertrophic burn scar ≥2 months post healing, free from concomitant skin disease and keloids. They were randomly assigned into two groups. The control group (A) (n=15) received Scar Standard Management (SSM) protocol and the study group (B) (n=15) received SSM protocol along with 15 min/area polarized light with medical range filter followed by 15 min/area orange filtered polarized light. All children received the study protocol once a day, 3 times/week for one month. Scar assessment was done before and after the study protocol by using Vancouver Scar Scale (VSS). [Results] All participated children were analyzed. Comparison of post treatment results between groups revealed significant improvement of post burn scar for both groups with significant difference in favor to the study group. [Conclusion] Ultimately it was revealed that Orange filtered polarized light has a special and beneficial effect on decreasing post burn pediatric scar.

Genetic diversity of multidrug resistant Staphylococcus aureus isolated from clinical and non clinical samples in Egypt

In recent years, the increasing incidence of diseases caused by Staphylococcus aureus (S. aureus) has been noted in the university hospitals of El-Sharkia and Assuit governorates - Egypt. Therefore, we studied the genetic relatedness of multidrug resistant S. aureus isolates from different sources in the above mentioned governorates. One hundred and fifty six S. aureus isolates were divided into 5 different groups, 1 non clinical isolates from different food products and 4 different clinical isolates of human and animal sources in the 2 different governorates. Epidemiological characteristics of 156 S. aureus isolates were determined by phenotypic methods including quantitative antibiogram typing and biofilm production. Genetic typing of 35 multidrug resistant (MDR) isolates (7 from each group) based on 16S rRNA gene sequence, virulence and antimicrobial resistance gene profiles was done. The genetic relatedness of the highest virulent strain from each group was detected based on different single locus sequence typing and multi-locus sequence typing (MLST). S. aureus strains isolated from different sources and geographical areas showed high diversity. The genetic typing revealed different sequence types and different sequences of coa and spa genes. S. aureus isolates were found highly diverse in Egypt.

Characterization of Methicillin Resistant Staphylococcus aureus isolated from human and animal samples in Egypt

Staphylococcus aureus (S. aureus) has been one of the most problematic pathogens. Methicillin Resistant S. aureus (MRSA) has emerged as a major concern for both human and animal. Antibiotic resistance genes dissemination might be possible between human and animal bacteria. The aim of this study is to show phenotypic and genotypic diversity of human and animal MRSA isolates. Antibiogram typing and biofilm production were used as a primary phenotypic typing tool for the characterization of (40) animal and (38) human MRSA isolates. Genetic typing based on sequencing of 16S rRNA gene and virulence gene profiles were done. Antimicrobial resistance profiles of the animal isolates showed little evidence of widespread of resistance, although this was seen in many human isolates. The biofilm production was detected in higher percentage among animal isolates. Based on the genetic typing and multiple antibiotic resistance (MAR) index, the majority of animal isolates clustered into lineages that were not found in human isolates. Animal and human MRSA isolates showed diversity in antibiotic resistance and virulence gene profiles may be due to host adaptation or chances for contamination between the two hosts were not present in our study.

Ternary metal complexes of guaifenesin drug: Synthesis, spectroscopic characterization and in vitro anticancer activity of the metal complexes

The coordination behavior of a series of transition metal ions named Cr(III), Fe(III), Mn(II), Co(II), Ni(II), Cu(II), Zn(II) and Cd(II) with a mono negative tridentate guaifenesin ligand (GFS) (OOO donation sites) and 1,10-phenanthroline (Phen) is reported. The metal complexes are characterized based on elemental analyses, IR, (1)H NMR, solid reflectance, magnetic moment, molar conductance, UV-vis spectral studies, mass spectroscopy, ESR, XRD and thermal analysis (TG and DTG). The ternary metal complexes were found to have the formulae of [M(GFS)(Phen)Cl]Cl·nH2O (M=Cr(III) (n=1) and Fe(III) (n=0)), [M(GFS)(Phen)Cl]·nH2O (M=Mn(II) (n=0), Zn(II) (n=0) and Cu(II) (n=3)) and [M(GFS)(Phen)(H2O)]Cl·nH2O (M=Co(II) (n=0), Ni(II) (n=0) and Cd(II) (n=4)). All the chelates are found to have octahedral geometrical structures. The ligand and its ternary chelates are subjected to thermal analyses (TG and DTG). The GFS ligand, in comparison to its ternary metal complexes also was screened for their antibacterial activity on gram positive bacteria (Bacillus subtilis and Staphylococcus aureus), gram negative bacteria (Escherichia coli and Neisseria gonorrhoeae) and for in vitro antifungal activity against (Candida albicans). The activity data show that the metal complexes have antibacterial and antifungal activity more than the parent GFS ligand. The complexes were also screened for its in vitro anticancer activity against the Breast cell line (MFC7) and the results obtained show that they exhibit a considerable anticancer activity.

Synthesis of some 1.3.4-thiadiazoles of possible antimicrobial activity

Different new esters of 5-aryl-1,3,4-thiadiazol-2-ylcarbazic acid and dithiocarbazic acid were prepared. In addition, the synthesis of 2-(N,N'-dialkylcarboxy)hydrazino-5-aryl-1,3,4-thiadiazoles is described. IR and NMR data of the new compounds are discussed. Some of these compounds showed a reasonable activity against Gram positive bacteria and fungi.