Search

Lymphoma is the sixth most prevalent cancer globally. Non‐Hodgkin's lymphomas are the majority group of lymphomas, with B cells accounting for approximately 95% of these lymphomas. A key feature of B‐cell lymphoma is the functional perturbations of essential biological pathways caused by genetic aberrations. These lead to atypical gene expression, providing cells with a selective growth advantage. Molecular analysis reveals that each lymphoma subtype has unique molecular mutations, which pose challenges in disease management and treatment. Substantial efforts over the last decade have led to the integration of this information into clinical applications, resulting in crucial insights into clinical diagnosis and targeted therapies. However, with the growing need for more effective medication development, we anticipate a deeper understanding of signaling pathways and their interactions to emerge. This review aims to demonstrate how the BCR, specific signaling pathways like PI3K/AKT/mTOR, NF‐kB, and JAK/STAT are diverse in common types of B‐cell lymphoma. Furthermore, it offers a detailed examination of each pathway and a synopsis of the approved or in‐development targeted therapies. In conclusion, finding the activated signaling pathways is crucial for developing effective treatment plans to improve the prognosis of patients with relapsed or refractory lymphoma. Trial Registration: ClinicalTrials.gov identifier: NCT02180724, NCT02029443, NCT02477696, NCT03836261, NCT02343120, NCT04440059, NCT01882803, NCT01258998, NCT01742988, NCT02055820, NCT02285062, NCT01855750, NCT03422679, NCT01897571 [ABSTRACT FROM AUTHOR]

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. [ABSTRACT FROM AUTHOR]

The deep beam in load transfer is very important as well as difficult to design due to its shear stress problems. Accurate estimation of shear stress would help engineers to get a safer design. One of the major obstacles in building an accurate prediction model is optimising the input variables. Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. The feature-section algorithm based on the combination of genetic algorithm and information theory (GAITH) was used to select the most important input combinations and introduce them into the prediction models. Four models were used in this study: locally weighted linear regression (LWLR) based on the radial basis kernel function, multiple linear regression (MLR), extreme learning machine (ELM), and random forest (RF). The study found that all applied models were significantly improved by the presence of the GAITH algorithm, except for the MLR model. The LWLR-GAITH model showed 29.15% to 47.88% higher performance accuracy in terms of root mean square error (RMSE) than the other hybrid models during the test phase. Moreover, the results of the standard models (without using the GAITH algorithm) proved the superiority of the LWLR model in reducing the RMSE by 34.51%, 55.17%, and 35.35% compared to RF, MLR, and ELM, respectively. Thus, the inclusion of the LWLR model with GAITH has demonstrated a reliable and applicable computer aid for modelling shear strength in deep beams. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration ET o is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly mean ET o t over Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regression MLR , random forest RF , and extreme learning machine ELM . Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such as MLR and RF. Moreover, among the 10 statistical measures, the uncertainty at 95% U 95 indicator showed an excellent ability to select the best and most efficient forecast model. The superiority of ELM in the prediction of mean monthly ET o over MLR and RF approaches is illustrated in the reduction of the U 95 parameter to 49.02% and 34.07% for RF and MLR models, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate the ET o using the simplest and cheapest meteorological and geographical variables with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it stands behind the reasons for increasing the mortality rates of fish and aquatic organisms. The accurate and more reliable prediction of TDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths. Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have been applied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For the USGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and the rest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, the hourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from 12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on different parameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy of adopted models, for instance, not limited, correlation of determination ( R 2 ), mean absolute relative error (MAE), and uncertainty at 95% ( U 95 ). The obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG in comparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R 2 of 0.986 (0.986), MAE of 0.316 (0.441), and U 95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported as R 2 of 0.991 (0.991), MAE of 0.338 (0.396), and U 95 of 0.832 (0.837). In addition, ELM's training process computational time is stated to be much shorter than that of SVM. The results also showed that the temperature parameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model (ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute to the basic knowledge of environmental considerations. [ABSTRACT FROM AUTHOR]

To overcome the biofouling challenge which faces membrane water treatment processed, the novel superhydrophobic carbon nanomaterials impregnated on/powder activated carbon (CNMs/PAC) was utilized to successfully design prepare an antimicrobial membrane. The research was conducted following a systematic statistical design of experiments technique considering various parameters of composite membrane fabrication. The impact of these parameters of composite membrane on Staphylococcus aureus growth was investigated. The bacteria growth was analyzed through spectrophotometer and SEM. The effect of CNMs' hydrophobicity on the bacterial colonies revealed a decrease in the abundance of bacterial colonies and an alteration in structure with increasing the hydrophobicity. The results revealed that the optimum preparative conditions for carbon loading CNMs/PAC was 363.04 mg with a polymer concentration of 22.64 g/100 g, and a casting knife thickness of 133.91 μm. These conditions have resulted in decreasing the number of bacteria colonies to about 7.56 CFU. Our results provided a strong evidence on the antibacterial effect and consequently on the antibiofouling potential of CNMs/PAC in membrane. [ABSTRACT FROM AUTHOR]

Carbon nanofibers (CNFs) were synthesized through nickel ion (Ni2+) impregnation of powdered activated carbon (PAC). Chemical Vapor Deposition (CVD) using acetylene gas, in the presence of hydrogen gas, was employed for the synthesis process. Various percentages (1, 3, 5, and 7 wt. %) of Ni2+ catalysts were used in the impregnation of Ni2+ into PAC. Field Emission Scanning Electron Microscope (FESEM), Fourier Transform Infrared (FTIR) Spectroscopy, Energy Dispersive X-Ray Analyzer (EDX), Transmission Electron Microscopy (TEM), Thermal Gravimetric Analysis (TGA), zeta potential, and Brunauer, Emmett, and Teller (BET) were utilized for the characterization of the novel composite, which possessed micro and nanodimensions. FESEM and TEM images revealed that the carbonaceous structure of the nanomaterials was fibrous instead of tubular with average width varying from 100 to 200 nanometers. The PAC surface area increased from 101 m2/g to 837 m2/g after the growth of CNF. TGA combustion temperature range was within 400°C and 570°C, while the average zeta potential of the nanocomposite materials was −24.9 mV, indicating its moderate dispersive nature in water. [ABSTRACT FROM AUTHOR]

This work presents the experimental and modeling process for mercury ions removal from water using functionalized multi‐walled carbon nanotube as adsorbent. The modeling procedure has been carried out using nonlinear autoregressive network with an exogenous input (NARX) neural network modeling technique is used for modeling the adsorbent's adsorption capacity using different parameters based on experimental data. The effect of different parameters including mercury ions concentration, pH, amount of adsorbent dosage, and contact time is studied. Three kinetics models such as intraparticle diffusion, pseudo first‐order, and pseudo second order are applied using the experimental and predicted outputs, the pseudo second order was the best to describe. A sensitivity study is conducted using different parameters. Various indicators are applied to examine the accuracy and efficiency of the NARX model such are mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error, relative error (RE), and coefficient of determination (R2). The value of the maximum RE was 3.49%, the R2 was 0.9998, and the MSE was 4.28 × 10−6. Based on the used indicators, the NARX model was capable to predict the adsorbent's adsorption capacity by comparing the NARX model outputs to the experimental results. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Temporomandibular disorders (TMDs) pose diagnostic challenges, and selecting appropriate imaging modalities is crucial for accurate assessment. This study aimed to compare the diagnostic accuracy and efficacy of ultrasonography (US) and magnetic resonance imaging (MRI) in identifying TMDs.A comprehensive meta‐analysis was conducted, including studies that compared US and MRI for TMJ disorder assessments. Fixed‐effects models were utilized to calculate pooled odds ratios (ORs) and relative risks (RRs) with 95% confidence intervals (CIs). Heterogeneity was assessed using the chi‐squared test and I2 statistic. Newcastle–Ottawa scale was used to assess the methodological quality of the studies included.Six studies were included, involving a total of 281 participants. The meta‐analysis demonstrated that MRI was statistically somewhat better than US in identifying TMJ disorders. The summary OR was 0.64 (95% CI: 0.46–0.90), and the summary RR was 0.80 (95% CI: 0.68–0.95). Heterogeneity among the studies was low (χ2 = 2.73, df = 5, p = .74; I2 = 0%). Demographic variables revealed variations in sample size, gender ratio and mean age across the studies.This meta‐analysis provides evidence that MRI may be more effective than US in diagnosing TMDs. However, the study is limited by the small number of included studies and variations in demographic variables and study designs. Future research with larger samples and standardised protocols is essential to confirm and strengthen these findings. Understanding the diagnostic accuracy of MRI and US for TMJ disorders will aid clinicians in making informed decisions for effective TMJ disorder assessments and patient management. [ABSTRACT FROM AUTHOR]