Integrated management of groundwater quantity, physicochemical properties, and microbial quality in West Nile delta using a new MATLAB code and geographic information system mapping

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.

Study on the interaction between erythrosine B and the cardiac drug amiodarone using fluorescence, scattering, and absorbance spectra and their analytical application

In an acidic buffered solution, erythrosine B can react with amiodarone to form an association complex, which not only generates great enhancement in resonance Rayleigh scattering (RRS) spectrum of erythrosine B at 346.5 nm but also results in quenching of fluorescence spectra of erythrosine B at λemission = 550.4 nm/λexcitation = 528.5 nm. In addition, the formed erythrosine B-amiodarone complex produces a new absorbance peak at 555 nm. The spectral characteristics of the RRS, absorbance, and fluorescence spectra, as well as the optimum analytical conditions, were studied and investigated. As a result, new spectroscopic methods were developed to determine amiodarone by utilizing erythrosine B as a probe. Moreover, the ICH guidelines were used to validate the developed RRS, photometric, and fluorimetric methods. The enhancements in the absorbance and the RRS intensity and the decrease in the fluorescence intensity of the used probe were proportional to the concentration of amiodarone in ranges of 2.5-20.0, 0.2-2.5, and 0.25-1.75 μg/mL, respectively. Furthermore, limit of detection values were 0.52 ng/mL for the spectrophotometric method, 0.051 μg/mL for the RRS method, and 0.075 μg/mL for the fluorimetric method. Moreover, with good recoveries, the developed spectroscopic procedures were applied to analyze amiodarone in its commercial tablets.

White blood cells classification using multi-fold pre-processing and optimized CNN model

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling

This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems.

Contrast-enhanced MRI synthesis using dense-dilated residual convolutions based 3D network toward elimination of gadolinium in neuro-oncology

Recent studies have raised broad safety and health concerns about using of gadolinium contrast agents during magnetic resonance imaging (MRI) to enhance identification of active tumors. In this paper, we developed a deep learning-based method for three-dimensional (3D) contrast-enhanced T1-weighted (T1) image synthesis from contrast-free image(s). The MR images of 1251 patients with glioma from the RSNA-ASNR-MICCAI BraTS Challenge 2021 dataset were used in this study. A 3D dense-dilated residual U-Net (DD-Res U-Net) was developed for contrast-enhanced T1 image synthesis from contrast-free image(s). The model was trained on a randomly split training set (n = 800) using a customized loss function and validated on a validation set (n = 200) to improve its generalizability. The generated images were quantitatively assessed against the ground-truth on a test set (n = 251) using the mean absolute error (MAE), mean-squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized mutual information (NMI), and Hausdorff distance (HDD) metrics. We also performed a qualitative visual similarity assessment between the synthetic and ground-truth images. The effectiveness of the proposed model was compared with a 3D U-Net baseline model and existing deep learning-based methods in the literature. Our proposed DD-Res U-Net model achieved promising performance for contrast-enhanced T1 synthesis in both quantitative metrics and perceptual evaluation on the test set (n = 251). Analysis of results on the whole brain region showed a PSNR (in dB) of 29.882 ± 5.924, a SSIM of 0.901 ± 0.071, a MAE of 0.018 ± 0.013, a MSE of 0.002 ± 0.002, a HDD of 2.329 ± 9.623, and a NMI of 1.352 ± 0.091 when using only T1 as input; and a PSNR (in dB) of 30.284 ± 4.934, a SSIM of 0.915 ± 0.063, a MAE of 0.017 ± 0.013, a MSE of 0.001 ± 0.002, a HDD of 1.323 ± 3.551, and a NMI of 1.364 ± 0.089 when combining T1 with other MRI sequences. Compared to the U-Net baseline model, our model revealed superior performance. Our model demonstrated excellent capability in generating synthetic contrast-enhanced T1 images from contrast-free MR image(s) of the whole brain region when using multiple contrast-free images as input. Without incorporating tumor mask information during network training, its performance was inferior in the tumor regions compared to the whole brain which requires further improvements to replace the gadolinium administration in neuro-oncology.

Surface curvature-induced oriented assembly of sushi-like Janus therapeutic nanoplatform for combined chemodynamic therapy

BACKGROUND

Chemodynamic therapy (CDT) based on Fenton/Fenton-like reaction has emerged as a promising cancer treatment strategy. Yet, the strong anti-oxidation property of tumor microenvironment (TME) caused by endogenous glutathione (GSH) still severely impedes the effectiveness of CDT. Traditional CDT nanoplatforms based on core@shell structure possess inherent interference of different subunits, thus hindering the overall therapeutic efficiency. Consequently, it is urgent to construct a novel structure with isolated functional units and GSH depletion capability to achieve desirable combined CDT therapeutic efficiency.

RESULTS

Herein, a surface curvature-induced oriented assembly strategy is proposed to synthesize a sushi-like novel Janus therapeutic nanoplatform which is composed of two functional units, a FeOOH nanospindle serving as CDT subunit and a mSiO2 nanorod serving as drug-loading subunit. The FeOOH CDT subunit is half covered by mSiO2 nanorod along its long axis, forming sushi-like structure. The FeOOH nanospindle is about 400 nm in length and 50 nm in diameter, and the mSiO2 nanorod is about 550 nm in length and 100 nm in diameter. The length and diameter of mSiO2 subunit can be tuned in a wide range while maintaining the sushi-like Janus structure, which is attributed to a Gibbs-free-energy-dominating surface curvature-induced oriented assembly process. In this Janus therapeutic nanoplatform, Fe3+ of FeOOH is firstly reduced to Fe2+ by endogenous GSH, the as-generated Fe2+ then effectively catalyzes overexpressed H2O2 in TME into highly lethal ·OH to achieve efficient CDT. The doxorubicin (DOX) loaded in the mSiO2 subunit can be released to achieve combined chemotherapy. Taking advantage of Fe3+-related GSH depletion, Fe2+-related enhanced ·OH generation, and DOX-induced chemotherapy, the as-synthesized nanoplatform possesses excellent therapeutic efficiency, in vitro eliminating efficiency of tumor cells is as high as ~ 87%. In vivo experiments also show the efficient inhibition of tumor, verifying the synthesized sushi-like Janus nanoparticles as a promising therapeutic nanoplatform.

CONCLUSIONS

In general, our work provides a successful paradigm of constructing novel therapeutic nanoplatform to achieve efficient tumor inhibition.

Efficient estimation of population variance of a sensitive variable using a new scrambling response model

This study introduces a pioneering scrambling response model tailored for handling sensitive variables. Subsequently, a generalized estimator for variance estimation, relying on two auxiliary information sources, is developed following this novel model. Analytical expressions for bias, mean square error, and minimum mean square error are meticulously derived up to the first order of approximation, shedding light on the estimator's statistical performance. Comprehensive simulation experiments and empirical analysis unveil compelling results. The proposed generalized estimator, operating under both scrambling response models, consistently exhibits minimal mean square error, surpassing existing estimation techniques. Furthermore, this study evaluates the level of privacy protection afforded to respondents using this model, employing a robust framework of simulations and empirical studies.

Quad-port MIMO antenna with high isolation characteristics for sub 6-GHz 5G NR communication

A four-port MIMO antenna with high isolation is presented. The antenna is primarily envisioned to cover the n48 band of Frequency Range-1 (FR-1) with TDD duplex mode. The engineered antenna has electrical dimensions of 90 × 90 × 1.57 mm3. The size miniaturization of a single antenna unit is achieved through an optimized placement of slots and extended arms. The quad-antennas are then placed orthogonally to achieve antenna diversity. The antenna resonates at 3.56 GHz and 5.28 GHz having 2:1 VSWR fractional bandwidth of 1.82% and 2.12%. The proposed resonator provides 88.34% and 79.28% efficiency at lower and upper bands, respectively. The antenna is an exceptional radiator regarding MIMO diversity performance owing to high inter-element isolation. The values of envelope correlation coefficient < 0.05, channel capacity loss is nearly 0.1 bits/sec/Hz, and total active reflection coefficient is - 24.26. The full ground plane profile aids in high directivity and cross-pol isolation. The antenna exhibits a gain of 4.2 dBi and 2.8 dBi, respectively, justifying intended application requirements. There is a good coherence between simulation and experimental results. The self-decoupled antenna poses its application in 5G and WLAN Communication Applications.

Prevalence of Multidrug-Resistant and ESBL-Producing Bacterial Pathogens in Patients with Chronic Wound Infections and Spinal Cord Injury Admitted to a Tertiary Care Rehabilitation Hospital

A pressure ulcer is defined as a skin lesion of ischemic origin, a condition that contributes to morbidity and mortality in patients with spinal cord injuries. The most common complication of ulcers is a bacterial infection. Antimicrobial therapy should be selected with caution for spinal cord injury patients since they have a high risk of developing multidrug-resistant (MDR) infections. The aim of this study was to determine the prevalence of different bacterial pathogens in patients with pressure ulcers admitted with spinal cord injuries. This was a retrospective single-center study that included adult patients aged 18 years and above, admitted with chronic pressure wounds after a spinal cord injury requiring hospitalization between 2015 and 2021. A total of 203 spinal cord injury patients with pressure ulcers were included in the study. Ulcers were commonly infected by Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, and they were mostly located in the sacral and gluteal areas. More than half of the bacteria isolated from patients were sensitive to commonly tested antibiotics, while 10% were either MDR- or pan-drug-resistant organisms. Of the MDR bacterial isolates, 25.61% were methicillin-resistant S. aureus, and 17.73% were extended-spectrum beta-lactamase Enterobacteriaceae. The most prevalent bacteria in pressure ulcers of spinal cord injury patients were S. aureus. Other antibiotic-resistant organisms were also isolated from the wounds.

Mechanistic Study on Steric Activity Interplay of Olefin/Polar Monomers for Industrially Selective Late Transition Metal Catalytic Reactions

A significant issue in developing metal-catalyzed plastic polymer materials is obtaining distinctive catalytic characteristics to compete with current plastics in industrial commodities. We performed first-principle DFT calculations on the key insertion steps for industrially important monomers, vinyl fluoride (VF) and 3,3,3-trifluoropropene (TFP), to explain how the ligand substitution patterns affect the complex's polymerization behaviors. Our results indicate that the favorable 2,1-insertion of TFP is caused by less deformation in the catalyst moiety of the complexes in contrast to the 1,2-insertion mode. In contrast to the VF monomer, the additional interaction between the fluorine atoms of 3,3,3-trifluoropropene and the carbons of the catalyst ligands also contributed to favor the 2,1-insertion. It was found that the regioselectivity of the monomer was predominated by the progressive alteration of the catalytic geometry caused by small dihedral angles that were developed after the ligand-monomer interaction. Based on the distribution of the 1,2- and 2,1-insertion products, the activity and selectivity were influenced by the steric environment surrounding the palladium center; thus, an increased steric bulk visibly improved the selectivity of the bulkier polar monomer (TFP) during the copolymerization mechanism. In contrast, better activity was maintained through a sterically less hindered Pd metal center; the calculated moderate energy barriers showed that a catalyst with less steric hindrance might provide an opportunity for a wide range of prospective industrial applications.

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck

PURPOSE

In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics.

METHODS

A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices.

RESULTS

The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for theD m a x ${D_{max}}$ andD m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net.

CONCLUSION

All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.

Bio-organic fertilizers promote yield, chemical composition, and antioxidant and antimicrobial activities of essential oil in fennel (Foeniculum vulgare) seeds

The aromatic fennel plant (Foeniculum vulgare Miller) is cultivated worldwide due to its high nutritional and medicinal values. The aim of the current study was to determine the effect of the application of bio-organic fertilization (BOF), farmyard manure (FM) or poultry manure (PM), either individually or combined with Lactobacillus plantarum (LP) and/or Lactococcus lactis (LL) on the yield, chemical composition, and antioxidative and antimicrobial activities of fennel seed essential oil (FSEO). In general, PM + LP + LL and FM + LP + LL showed the best results compared to any of the applications of BOF. Among the seventeen identified FSEO components, trans-anethole (78.90 and 91.4%), fenchone (3.35 and 10.10%), limonene (2.94 and 8.62%), and estragole (0.50 and 4.29%) were highly abundant in PM + LP + LL and FM + LP + LL, respectively. In addition, PM + LP + LL and FM + LP + LL exhibited the lowest half-maximal inhibitory concentration (IC50) values of 8.11 and 9.01 μg mL-1, respectively, compared to L-ascorbic acid (IC50 = 35.90 μg mL-1). We also observed a significant (P > 0.05) difference in the free radical scavenging activity of FSEO in the triple treatments. The in vitro study using FSEO obtained from PM + LP + LL or FM + LP + LL showed the largest inhibition zones against all tested Gram positive and Gram negative bacterial strains as well as pathogenic fungi. This suggests that the triple application has suppressive effects against a wide range of foodborne bacterial and fungal pathogens. This study provides the first in-depth analysis of Egyptian fennel seeds processed utilizing BOF treatments, yielding high-quality FSEO that could be used in industrial applications.

Monkeypox genome mutation analysis using a timeseries model based on long short-term memory

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.

Rational Design of Trimetallic Sulfide Electrodes for Alkaline Water Electrolysis with Ampere-Level Current Density

Electrochemical water splitting is considered an environmentally friendly approach to hydrogen generation. However, it is difficult to achieve high current density and stability. Herein, we design an amorphous/crystalline heterostructure electrode based on trimetallic sulfide over nickel mesh substrate (NiFeMoS/NM), which only needs low overpotentials of 352 mV, 249 mV, and 360 mV to achieve an anodic oxygen evolution reaction (OER) current density of 1 A cm-2 in 1 M KOH, strong alkaline electrolyte (7.6 M KOH), and alkaline-simulated seawater, respectively. More importantly, it also shows superior stability with negligible decay after continuous work for 120 h at 1 A cm-2 in the strong alkaline electrolyte. The excellent OER performance of the as-obtained electrode can be attributed to the strong electronic interactions between different metal atoms, abundant amorphous/crystalline hetero-interfaces, and 3D porous nickel mesh structure. Finally, we coupled NiFeMoS/NM as both the anode and cathode in the anion exchange membrane electrolyzer, which can achieve low cell voltage and high stability at ampere-level current density, demonstrating the great potential of practicability.

Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features

Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.

Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel

Objective

Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models.

Methods

In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while attaining remarkable accuracy. The system's core is built upon a stacked ensemble voting classifier model, which amalgamates three distinct machine learning models, all harmoniously integrated with the KNN Imputer to address the issue of missing values.

Results

The model put forth attains an accuracy of 99.41%, precision of 97.63%, recall of 95.96%, and an F1 score of 96.76% when incorporating the KNN imputation method. The investigation conducts a comparative analysis, contrasting the performance of this model with seven alternative machine learning algorithms in two scenarios: one where missing values are eliminated, and another employing KNN imputation. This study offers validation of the effectiveness of the proposed model in comparison to current state-of-the-art methodologies.

Conclusions

This research delves into the challenge of handling missing data in the dataset utilized for cervical cancer detection. The findings have the potential to assist healthcare professionals in achieving early detection and enhancing the quality of care provided to individuals affected by cervical cancer.

In-silico Investigations of quinine and quinidine as potential Inhibitors of AKR1B1 and AKR1B10: Functional and structural characterization

The aberrant expression of aldo keto reductases (AKR1B1 & AKR1B10) has been extensively studied in different types of cancer especially the colon cancer but a very few studies have yet been reported regarding the discovery of inhibitors for the treatment of colon cancer by targeting these isozymes. Therefore, there is a need of selective inhibitors of both targets for the eradication of colon cancer. Currently, the study is focused on the exploration of two quinolone compounds i.e., (S)-(6-Methoxyquinolin-4-yl)[(1S,2R,4S,5R)-5-vinylquinuclidin-2-yl]methanol (Quinidine) and (R)-(6-Methoxyquinolin-4-yl)[(1S,2S,4S,5R)-5-vinylquinuclidin-2-yl]methanol (Quinine) as the potential inhibitors of AKR1B1 and AKR1B10 via detailed in-silico approach. The structural properties including vibrational frequencies, dipole moment, polarizability and the optimization energies were estimated using density functional theory (DFT) calculations; where both compounds were found chemically reactive. After that, the optimized structures were used for the molecular docking studies and here quinidine was found more selective towards AKR1B1 and quinine exhibited maximum inhibition of AKR1B10. The results of molecular docking studies were validated by molecular dynamics simulations which provided the deep insight of stability of protein ligand complex. At the end, the ADMET properties were determined to demonstrate the druglikeness properties of both selected compounds. These findings suggested further exploration of both compounds at molecular level using different in-vivo and in-vitro approaches that will lead to the designing of potential inhibitor of AKR1B1/AKR1B10 for curing colon cancer and related malignancies.

Nanostructured Lipid Carrier-Based Codelivery of Raloxifene and Naringin: Formulation, Optimization, In Vitro, Ex Vivo, In Vivo Assessment, and Acute Toxicity Studies

This work aimed to develop dual drug-loaded nanostructured lipid carriers of raloxifene and naringin (RLX/NRG NLCs) for breast cancer. RLX/NRG NLCs were prepared using Compritol 888 ATO and oleic acid using a hot homogenization-sonication method and optimized using central composite design (CCD). The optimized RLX/NRG NLCs were characterized and evaluated using multiple technological means. The optimized RLX/NRG NLCs exhibited a particle size of 137.12 nm, polydispersity index (PDI) of 0.266, zeta potential (ZP) of 25.9 mV, and entrapment efficiency (EE) of 91.05% (raloxifene) and 85.07% (naringin), respectively. In vitro release (81 ± 2.2% from RLX/NRG NLCs and 31 ± 1.9% from the RLX/NRG suspension for RLX and 93 ± 1.5% from RLX/NRG NLCs and 38 ± 2.01% from the RLX/NRG suspension for NRG within 24 h). Concurrently, an ex vivo permeation study exhibited nearly 2.3 and 2.1-fold improvement in the permeability profiles of RLX and NRG from RLX/NRG NLCs vis-à-vis the RLX/NRG suspension. The depth of permeation was proved with CLSM images which revealed significant permeation of the drug from the RLX/NRG NLCs formulation, 3.5-fold across the intestine, as compared with the RLX/NRG suspension. An in vitro DPPH antioxidant study displayed a better antioxidant potential of RLX/NRG in comparison to RLX and NRG alone due to the synergistic antioxidant effect of RLX and NRG. An acute toxicity study in Wistar rats showed the safety profile of the prepared nanoformulations and their excipients. Our findings shed new light on how poorly soluble and poorly permeable medicines can be codelivered using NLCs in an oral nanoformulation to improve their medicinal performance.

Use of artificial intelligence in activating the role of Saudi universities in joint scientific research between university teachers and students

Scientific research in Saudi Arabia’s universities has undergone significant changes in recent years with the speed of higher education expansion and the opening of new universities. Artificial intelligence (AI) can be applied to existing data analysis processes to enhance pattern recognition and to support advanced data analysis. This study aimed to investigate the obstacles to activating the role of university instructors and students in joint scientific research. The study also aimed to evaluate joint scientific research between university teachers and students in universities, as well as the mechanisms for activating joint scientific research among male and female students in health and humanities science within Saudi universities, to enhance creation and invention achievements. To determine the obstacles to activating scientific research roles between students and tutors in Saudi universities using AI, a simple random sampling technique was adopted for this study. A well-structured questionnaire was administered to 250 respondents affiliated with universities in Saudi Arabia. The data collected were statistically analyzed with the aid of the Statistical Package for Social Science (SPSS) version 20. The results of this study revealed that the objectives of joint scientific research between university teachers and students in universities have a significant positive predictor of obstacles to activating the role of teachers in joint scientific research with students in Saudi universities. The study also showed that there was a statistically significant correlation (p value = 0.00) between each of the variables.

New heat-treated vs electropolished nickel-titanium instruments used in root canal treatment: Influence of autoclave sterilization on surface roughness

Nickel-titanium (NiTi) instruments used to treat root canal infections are affected by autoclave sterilization in various ways. The aim of this study was to compare the effect of autoclave sterilization on two NiTi rotary instruments that undergo different manufacturing treatments: The electro-polished Race and the heat-treated Race Evo, using scanning electron microscope analysis. In this in-vitro study, Race and Race-Evo instruments were subjected to a number of autoclaving cycles (0, 1, 3, 5, and 10). Scanning electron microscopy images were obtained at 3 mm from the tip of the file at 450x and 1000x magnifications. Surface roughness parameters were measured using ImageJ software. The results showed that autoclave sterilization caused a significant decrease in conventional NiTi Race surface roughness. While in Race Evo, surface roughness increased following the first autoclaving cycle. After 10 autoclaving cycles, surface roughness significantly decreased for both Race and Race Evo files.