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Abstract Colorectal cancer (CRC) poses a significant burden on both the healthcare systems as well as individuals. The high mortality rate of CRC may be attributed to its metastatic potential, heterogeneity, and delayed diagnosis. CircRNAs are an essential class of regulatory RNAs that play significant roles in cancers. This study aimed to detect the expression status of circ-SMARCA5, circ-NOL10, circ-LDLRAD3, and circ-RHOT1 in patients with CRC. This study included 50 CRC patients, 30 individuals with colorectal diseases (non-cancer), and 20 healthy volunteers. By using real-time PCR, the relative expression of circ-SMARCA5, circ-NOL10, circ-LDLRAD3, and circ-RHOT1 was determined in the collected blood samples. In addition, ECLIA was used to quantify carcinoembryonic antigen (CEA) level. All circRNAs expression and CEA levels were significantly up-regulated in cancer patients (CRC, colon, rectum) as compared to healthy controls, except circ-SMARCA5. Moreover, there was a significant up-regulation of circRNAs in most non-cancer patients (UC, polyp, piles). Insignificant upregulation was observed in circRNAs and CEA when comparing cancer with non-cancer patients. No correlations were found between the studied parameters and most clinicopathological characteristics of cancer and non-cancer patients. Circ-SMARCA5, circ-NOL10, circ-LDLRAD3, and circ-RHOT1 were differentially expressed in patients with CRC as well as in non-cancer patients. Circ-SMARCA5 and circ-NOL10 may act as tumor suppressors, while circ-LDLRAD3 and circ-RHOT1 may be oncogenes. Circ-SMARCA5, circ-NOL10, circ-LDLRAD3, and circ-RHOT1 could be promising markers for the early detection of CRC.

Azithromycin (AM) is one of the prescribed drugs in pandemic medication treatment which has paid great attention. We developed in this study a simply modified carbon paste electrode (CPE) to detect AM using poly-threonine (PT). PT or similar polymers are used as carriers to enhance the delivery and effectiveness of AM. The work was characterised via Cyclic Voltammetry (CV), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM). We take into consideration the effects of pH, scan rate, accumulation time, interference, and calibration curve.A very sensitive response to the oxidation of 1.0 mM from AM in phosphate buffer solution (PBS) over a pH range of 5.0 to 10.0 was observed using the developed poly-threonine carbon paste electrode (PTCPE). The impact of different AM concentrations was investigated resulting in a detection limit of 0.32 µM and a quantification limit of 1.07 µM at PBS (pH 7.4). Finally, the recently used electrode realized acceptable sensitivity and consistency for AM detection in pharmaceutical drugs. [ABSTRACT FROM AUTHOR]

The recent investigation targets to use adapted carbon paste (CP) with copper nanoparticles (CuNs) operating in a phosphate buffer (PBS) medium with a pH range of 5.0–8.0, to synthesize a novel, susceptible, and simple electrochemical sensor for the detection of one of the most important drugs, vitamin B6. Copper (Cu) is one of the most three common essential trace elements found in the bodies of both humans and animals, along with iron and zinc for all crucial physiological and biochemical functions. Its properties, which are assessed using a variety of methods including scanning electron microscopy (SEM), cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS), have also drawn a lot of attention recently. We considered the effects of pH, buffer, scan rate, interference, and calibration curve. The susceptible electrode's linear calibration curve encompassed concentration values between 8.88 and 1000.0 µM. The calculated limits of detection and quantification were 32.12 and 107.0 µM, respectively. Furthermore, this method was established in real human urine samples and drug validation which have been shown satisfactory results for vitamin B6 detection. [ABSTRACT FROM AUTHOR]

The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g., 128 × 128 ) to the largest size patches ( 512 × 512 ). These levels are then fused by random forest (RF) to estimate the severity grade of NMIBC. The optimal patch sizes and other model hyperparameters are determined using a grid search algorithm. For each patch size, the proposed system has been trained on 32K patches (comprising 16K low-grade and 16K high-grade samples) and subsequently tested on 8K patches (consisting of 4K low-grade and 4K high-grade samples), all annotated by two pathologists. Incorporating light and efficient processing, defining new benchmarks in the application of AI to histopathology, the ShuffleNet-based AI system achieved notable metrics on the testing data, including 94.25% ± 0.70% accuracy, 94.47% ± 0.93% sensitivity, 94.03% ± 0.95% specificity, and a 94.29% ± 0.70% F1-score. These results highlight its superior performance over traditional models like ResNet-18. The proposed system's robustness in accurately grading pathology demonstrates its potential as an advanced AI tool for diagnosing human diseases in the domain of digital pathology., (© 2024. The Author(s).)

Groundwater is an excellent alternative to freshwater for drinking, irrigation, and developing arid regions. Agricultural, commercial, industrial, residential, and municipal activities may affect groundwater quantity and quality. Therefore, we aimed to use advanced methods/techniques to monitor the piezometric levels and collect groundwater samples to test their physicochemical and biological characteristics. Our results using software programs showed two main types of groundwater: the most prevalent was the Na-Cl type, which accounts for 94% of the groundwater samples, whereas the Mg-Cl type was found in 6% of samples only. In general, the hydraulic gradient values, ranging from medium to low, could be attributed to the slow movement of groundwater. Salinity distribution in groundwater maps varied between 238 and 1350 mg L -1 . Although lower salinity values were observed in northwestern wells, higher values were recorded in southern ones. The collected seventeen water samples exhibited brackish characteristics and were subjected to microbial growth monitoring. Sample WD12 had the lowest total bacterial count (TBC) of 4.8 ± 0.9 colony forming unit (CFU mg L -1 ), while WD14 had the highest TBC (7.5 ± 0.5 CFU mg L -1 ). None of the tested water samples, however, contained pathogenic microorganisms. In conclusion, the current simulation models for groundwater drawdown of the Quaternary aquifer system predict a considerable drawdown of water levels over the next 10, 20, and 30 years with the continuous development of the region., (© 2024. The Author(s).)

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney's shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels., (© 2022. The Author(s).)

Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34-82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F[Formula: see text] score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of [Formula: see text], 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.