Sorption of Cd(II) and Pb(II) ions by nanolimestone from underground water samples from Tehama region of Saudi Arabia

333-340Powdered nano limestone (NLS) has been investigated as an in-expensive adsorbent for removal of heavy toxic metals such as cadmium and lead from aqueous solutions. Batch experiments has been carried out, the favorable pH for maximum metals adsorption is found to be 6.8 for both. The surface area has increased in case of NLS up to 6.2 m2/g. The adsorption capacity calculated by Langmuir equation is found to be 75.1 mg/g for Cd (II) and 68.4 for Pb (II) ions at pH 6.8. The adsorption capacity has increased with temperature and the kinetics followed a First-order rate equation for both. The enthalpy change (ΔH0) is 25.4 J mol−1 for Cd (II) and 20.8 J mol−1 for Pb (II), while entropy change (ΔS0) is 41.6 J K−1 mol−1 for Cd (II) and 38.7 J K−1 mol−1 Pb (II), which indicate that adsorption process is endothermic and spontaneous in nature. About 25 collected samples of groundwater has been tested and found to be contaminated with cadmium and lead elements with different rates, with using NLS as adsorbent able to remove both metals from the samples. All of the results suggested that the NLS is excellent nano-adsorbents for cadmium and lead contaminated water samples

Breast cancer diagnosis using an efficient CAD system based on multiple classifiers

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples

Breast cancer detection using deep convolutional neural networks and support vector machines

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions

A framework for breast cancer classification using Multi-DCNNs

Background: Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep convolutional neural networks (DCNN) are currently the main tool used for image analysis and classification purposes. There are several DCNN architectures among them AlexNet, GoogleNet, and residual networks (ResNet). Method: This paper presents a new computer-aided diagnosis (CAD) system based on feature extraction and classification using DL techniques to help radiologists to classify breast cancer lesions in mammograms. This is performed by four different experiments to determine the optimum approach. The first one consists of end-to-end pre-trained fine-tuned DCNN networks. In the second one, the deep features of the DCNNs are extracted and fed to a support vector machine (SVM) classifier with different kernel functions. The third experiment performs deep features fusion to demonstrate that combining deep features will enhance the accuracy of the SVM classifiers. Finally, in the fourth experiment, principal component analysis (PCA) is introduced to reduce the large feature vector produced in feature fusion and to decrease the computational cost. The experiments are performed on two datasets (1) the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) and (2) the mammographic image analysis society digital mammogram database (MIAS). Results and Conclusions: The accuracy achieved using deep features fusion for both datasets proved to be the highest compared to the state-of-the-art CAD systems. Conversely, when applying the PCA on the feature fusion sets, the accuracy did not improve; however, the computational cost decreased as the execution time decreased

Toxoplasma gondii and Neospora caninum Antibodies in Dogs and Cats from Egypt and Risk Factor Analysis.

BACKGROUND Toxoplasma gondii and Neospora caninum are major protozoan parasites of worldwide distribution and significance in veterinary medicine and, for T. gondii, in public health. Cats and dogs, as final hosts for T. gondii and N. caninum, respectively, have a key function in environmental contamination with oocysts and, thus, in parasite transmission. Very little is known about the prevalence of T. gondii infections in dogs and cats in Egypt, and even less about the prevalence of N. caninum in the same hosts. METHODS In the current study, 223 serum samples of both dogs (n = 172) and cats (n = 51) were investigated for specific antibodies to T. gondii and N. caninum using commercially available ELISAs. A risk factor analysis was conducted to identify factors associated with seropositivity. RESULTS & DISCUSSION Exposure to T. gondii was reported in 23.3% of the dogs and in 9.8% of the cats, respectively. In addition, N. caninum-specific antibodies were recorded in 5.8% of dogs and in 3.4% of cats. A mixed infection was found in two dogs (1.2%) and in one cat (2%). Antibodies to T. gondii in dogs were significantly more frequent in dogs aged 3 years or more and in male German Shepherds. As this breed is often used as watchdogs and was the most sampled breed in Alexandria governorate, the purpose "watchdog" (compared to "stray" or "companion"), the male sex, and the governorate "Alexandria" also had a significantly higher seroprevalence for T. gondii. No factors associated with antibodies to N. caninum could be identified in dogs, and no significant factors were determined in cats for either T. gondii or N. caninum infection. Our study substantially adds to the knowledge of T. gondii infection in dogs and cats and presents data on N. caninum infection in cats for the first and in dogs in Egypt for the second time

Effects of high-dose L-carnitine supplementation on diaphragmatic function in patients with respiratory failure: A randomized clinical trial

ABSTRACTObjectives Evaluation of diaphragmatic function by bedside ultrasound provides information on the degree of disability and the response to treatment intervention in respiratory failure patients with associated respiratory muscle fatigue. This study aimed to assess the impact of high and low-dose L-carnitine supplementation on diaphragmatic muscle function.Methods This was a prospective, randomized, controlled clinical trial (trial registration number NCT05322447), for which approval was obtained from our institutional ethics committee (R80/2021). Participating patients were randomly assigned to two groups of 30 patients each. In the low-dose group, L-carnitine was administered at a dose of 6 g/day. The high-dose group received an intravenous infusion of 18 g/day of L-carnitine. On days 0, 3, and 7, diaphragmatic function was assessed by ultrasound, and serum levels of L-carnitine were measured.Results Both diaphragmatic excursion (DE) and diaphragmatic thickening fraction (DTf) measurements were positively correlated with serum L-carnitine levels (+r = 0.58 and +r = 0.61, respectively; p< 0.001). High-dose L-carnitine independently influenced the DE only, both in an unadjusted model (p = 0.04) and after adjustment for age and sex (p = 0.02). However, it had no significant effect on DTf, either before (p = 0.25) or after (p = 0.17) adjustment.Conclusion Serum levels of L-carnitine are positively correlated with the two measures of diaphragmatic function (DE and DTf). Moreover, high-dose L-carnitine supplementation had rapid and significant positive effects on DE. This improvement indirectly enhanced patient outcomes and resulted in shorter stays in the Intensive Care Unit and hospital

Antibiofilm and Anti-Quorum-Sensing Activities of Novel Pyrazole and Pyrazolo[1,5-<i>a</i>]pyrimidine Derivatives as Carbonic Anhydrase I and II Inhibitors: Design, Synthesis, Radiosterilization, and Molecular Docking Studies

Nowadays, searching for new anti-infective agents with diverse mechanisms of action has become necessary. In this study, 16 pyrazole and pyrazolo[1,5-a]pyrimidine derivatives were synthesized and assessed for their preliminary antibacterial and antibiofilm activities. All these derivatives were initially screened for their antibacterial activity against six clinically isolated multidrug resistance by agar well-diffusion and broth microdilution methods. The initial screening presented significant antibacterial activity with a bactericidal effect for five compounds, namely 3a, 5a, 6, 9a, and 10a, compared with Erythromycin and Amikacin. These five derivatives were further evaluated for their antibiofilm activity against both S. aureus and P. aeruginosa, which showed strong biofilm-forming activity at their MICs by >60%. The SEM analysis confirmed the biofilm disruption in the presence of these derivatives. Furthermore, anti-QS activity was observed for the five hybrids at their sub-MICs, as indicated by the visible halo zone. In addition, the presence of the most active derivatives reduces the violacein production by CV026, confirming that these compounds yielded anti-QS activity. Furthermore, these compounds showed strong inhibitory action against human carbonic anhydrase (hCA-I and hCA-II) isoforms with IC50 values ranging between 92.34 and 168.84 nM and between 73.2 and 161.22 nM, respectively. Finally, radiosterilization, ADMET, and a docking simulation were performed