Survival after Trastuzumab Therapy in Patients with Locally Advanced or Metastatic HER2-Positive Gastric or Gastroesophageal Junction Cancer: A Meta-Analysis of Randomized Controlled Trials.

Background: In the Trastuzumab for Gastric Cancer study, it was found that trastuzumab combined with doublet chemotherapy (fluoropyrimidine and platinum) was the gold-standard treatment for gastroesophageal adenocarcinoma (GEA) that was locally advanced, unresectable, or metastatic (HER2+). Materials and Methods: We performed a meta-analysis of randomized phase II/III studies testing trastuzumab in combination or alone. Results: This meta-analysis's findings involved 2048 patients in total. The treatment arm and hormone receptor status were used to stratify the combined HR. Overall, the PFS (Random model) HR [0.80] and 95% confidence intervals (CI) [0.68-0.95] were significantly higher for regimens containing trastuzumab, fluoropyrimidine, and platinum compared to regimens containing fluoropyrimidine and platinum. Conclusions: The results of this meta-analysis provide additional support for trastuzumab's use in treating HER2-positive GEA, particularly in cases where the disease lacks a HER2+ receptor.

Knowledge, Attitude, and the Practice of Community Pharmacists to Dispense Opioid-Related and Opioid-Containing Medication in Eastern Province, Saudi Arabia.

Objectives: This study was done to evaluate the knowledge of community pharmacists-working in Saudi Arabia-regarding over-the-counter opioid-related and opioid-containing medications. Materials and Methods: A quantitative cross-sectional study was conducted using an online questionnaire that was distributed through social media and taken to the community pharmacists in person. A total of 101 responders were reached in a period of around 3 weeks. Results: Most of the pharmacists working in community pharmacies in Saudi Arabia are non-Saudi (85.15%), males (88.12%), and the majority are holders of bachelor's degrees (85.15%). More than half of the participants spend a considerably sufficient time with the patient 6-10 minutes (60.4%). The availability of a private counseling room in community pharmacies is significantly low (21.78%). Only (69.41%) of participants always counsel the patient before dispensing opioid-related medications. Most of the participants (84.76%) believe that all over-the-counter opioid-related and opioid-containing medications should be restricted to avoid drug abuse. Conclusion: A little neglect from pharmacists' side regarding dispensing such medications was observed; thus, there is a significant need to raise their awareness regarding over-the-counter opioid abuse.

A transposon-derived gene family regulates heading date in rice.

As a consequence of transposon domestication, transposon-derived proteins often acquire important biological functions. However, there have been limited studies on transposon-derived proteins in rice, and a systematic analysis of transposon-derived genes is lacking. Here, for the first time, we conducted a comprehensive analysis of the DDE_Tnp_4 (DDE) gene family, which originated from transposons but lost their transpositional ability and acquired new gene functions in Oryza species. A total of 58 DDE family genes, categorized into six groups, were identified in Oryza species, including 13 OsDDE genes in Oryza sativa ssp. japonica. Our analysis indicates that gene duplication events were not the primary mechanism behind the expansion of OsDDE genes in rice. Promoter cis-element analysis combined with haplotype analysis confirmed that OsDDEs regulate the heading date in rice. Specifically, OsDDE9 is a nuclear-localized protein expressed ubiquitously, which promotes heading date by regulating the expression of Ghd7 and Ehd1 under both short-day and long-day conditions. Single-nucleotide polymorphism (SNP) variations in the OsDDE9 promoter leads to changes in promoter activity, resulting in variations in heading dates. This study provides valuable insights into the molecular function and mechanism of the OsDDE genes.

Isolation of multiple electrocardiogram artifacts using independent vector analysis.

Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove. Independent component analysis (ICA) is a well-known technique of blind source separation (BSS) and is extensively used in literature for ECG artifact elimination. In this article, the independent vector analysis (IVA) is used for artifact removal in the ECG data. This technique takes advantage of both the canonical correlation analysis (CCA) and the ICA due to the utilization of second-order and high order statistics for un-mixing of the recorded mixed data. The utilization of recorded signals along with their delayed versions makes the IVA-based technique more practical. The proposed technique is evaluated on real and simulated ECG signals and it shows that the proposed technique outperforms the CCA and ICA because it removes the artifacts while altering the ECG signals minimally.

Identification of Small Inhibitors for Human Metadherin, an Oncoprotein, through in silico Approach.

AIMS: Cancer is a disease that takes lives of thousands of people each year. There are more than 100 different types of cancers known to man. This fatal disease is one of the leading causes of death today. BACKGROUND: Astrocyte elevated gene-1(AEG-1)/Metadherin (MTDH) activates multiple oncogenic signaling pathways and leads to different types of cancers. MTDH interacting with staphylococcal nuclease domain containing 1(SND1) supports the survival and growth of mammary epithelial cells under oncogenic conditions. OBJECTIVE: Silencing MTDH or SND1 individually or disrupting their interaction compromises the tumorigenic potential of tumor-initiating cells. The aim of our present study was to investigate novel interactions of staphylococcal nuclease domain containing 1 (SND1) binding domain of AEG-1/MTDH with different lead compounds through molecular docking approach using MOE software. METHODS: Molecular docking was done by docking the ChemBridge database against important residues of MTDH involved in interaction with SND1. After docking the whole ChemBridge database, the top 200 interactive compounds were selected based on docking scores. After applying Lipinski's rule, all the remaining chosen compounds were studied on the basis of binding affinity, binding energy, docking score and protein-ligand interactions. Finally, 10 compounds showing multiple interactions with different amino acid residues were selected as the top interacting compounds. RESULTS: Three compounds were selected for simulation studies after testing these compounds using topkat toxicity and ADMET studies. The simulation study indicated that compound 32538601 is a lead compound for inhibiting MTDH-SND1 complex formation. CONCLUSION: These novels, potent inhibitors of MTDH-SND1 complex can ultimately help us in controlling cancer up to some extent.

Mechanistic Investigation of the Androgen Receptor DNA-Binding Domain and Modulation via Direct Interactions with DNA Abasic Sites: Understanding the Mechanisms Involved in Castration-Resistant Prostate Cancer.

The androgen receptor (AR) is an important drug target in prostate cancer and a driver of castration-resistant prostate cancer (CRPC). A significant challenge in designing effective drugs lies in targeting constitutively active AR variants and, most importantly, nearly all AR variants lacking the ligand-binding domain (LBD). Recent findings show that an AR's constitutive activity may occur in the presence of somatic DNA mutations within non-coding regions, but the role of these mutations remains elusive. The discovery of new drugs targeting CRPC is hampered by the limited molecular understanding of how AR binds mutated DNA sequences, frequently observed in prostate cancer, and how mutations within the protein and DNA regulate AR-DNA interactions. Using atomistic molecular dynamics (MD) simulations and quantum mechanical calculations, we focused our efforts on (i) rationalising the role of several activating DBD mutations linked to prostate cancer, and (ii) DBD interactions in the presence of abasic DNA lesions, which frequently occur in CRPC. Our results elucidate the role of mutations within DBD through their modulation of the intrinsic dynamics of the DBD-DNA ternary complex. Furthermore, our results indicate that the DNA apurinic lesions occurring in the androgen-responsive element (ARE) enhance direct AR-DNA interactions and stabilise the DBD homodimerisation interface. Moreover, our results strongly suggest that those abasic lesions may form reversible covalent crosslinks between DNA and lysine residues of an AR via a Schiff base. In addition to providing an atomistic model explaining how protein mutations within the AR DNA-binding domain affect AR dimerisation and AR-DNA interactions, our findings provide insight into how somatic mutations occurring in DNA non-coding regions may activate ARs. These mutations are frequently observed in prostate cancer and may contribute to disease progression by enhancing direct AR-DNA interactions.

Synergy of 1-MCP and hypobaric treatments prevent fermented flavour and improve consumers' acceptability of 'Shughri' pear.

Development of fermented flavour during storage reduces acceptability of Shughri pear. Therefore, the current study was designed to investigate the combined effect of 1-Methylcyclopropene (1-MCP) and hypobaric treatment on stability of Shughri pear during 120 days of storage. Fruit were treated individually or combinedly with 25, 50, and 75 kilo pascal hypobaric treatments for 4 h and 1-MCP (0.3 µLL-1 and 0.6 µLL-1) for 24 h, whereas control received no treatment. The pears were stored for 120 days at (0 ± 1 °C, 85 ± 5% RH), and were evaluated after every 30 days. After cold storage, pears were shifted to simulated retail conditions (20 ± 3 °C, 65 ± 5% RH). The combination of 25 kPa + 0.6 µLL-1 1-MCP significantly (P ≤ 0.05) delayed fruit ripening, reduced Alcohol dehydrogenase (ADH), and Pyruvate decarboxylase (PDC) activities, maintained the quality, and led to higher consumers' acceptability of the pear followed by 50 kPa + 0.6 µLL-1 and 25 kPa + 0.3 µLL-1. The control fruit were marketable for a week after storage with relatively less acceptability due to fermented flavour compared to treated fruit, marketable for more than two weeks. Among all the treatments, the synergy of 1-MCP and hypobaric treatment 25 kPa + 0.6 µLL-1 1-MCP improved the postharvest storage life and quality parameters, preventing development of fermented flavour in the pears. The experiment was conducted on pilot scale, for commercial application, the results of this study should be validated on large scale.

A retrospective evaluation of side-effects associated with the booster dose of Pfizer-BioNTech/BNT162b2 COVID-19 vaccine among females in Eastern Province, Saudi Arabia.

BACKGROUNDS: The development of several types of vaccines to avert COVID-19 has taken place. Despite several reports of undesirable reactions noted post-COVID-19 vaccine administration, later remains one of the best prevention and management tools in fighting the spread of the virus and its variants and reducing the harshness of this viral attack. The purpose of the current paper was to explore the side-effects experienced by the females in the Eastern Province of Saudi Arabia directly after receiving the booster dose of the Pfizer-BioNTech/BNT162b2 COVID-19 vaccine. METHODS: A descriptive cross-sectional study among adults living in the East-ern Province, Saudi Arabia was applied. A survey link was, distributed through WhatsApp, SMS, or e-mail to community members. Respondent's demographic information was acquired, as well as information about any local and systemic side-effects reported following booster dose of BioNTech/BNT162b2 COVID-19 vaccine. RESULTS: A total of 72.36% (432/597) of the respondents who participated in this study reported at least one side-effect. Pain and redness at the injection site (75.93%), myalgia (71.99%), headache (53.24%), fever (33.56%), and fatigue (43.78%) were the highest frequently stated side-effects. Furthermore, 9.25% of the respondents had to see a physician due to side effects, plus merely four participants were admitted to the hospital. The respondents working in the non-healthcare-related sector had a 1.677-fold more possibility of side effects in comparison with the other respondents (adjusted odds ratio = 1.677; 95% CI = 1.363, 2.064). CONCLUSIONS: All reported side-effects were mild to moderate. These findings might persuade pessimists and refusers to get the COVID-19 vaccine. Myalgia and pain or redness at the site of injection were the most common reported side-effects in our study.

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete.

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.

Prevalence of side-effects associated with the booster dose of Pfizer-BioNTech (BNT162b2) of COVID-19 Vaccine among vaccinated adults in the Eastern province of Saudi Arabia.

Background: Reports of local and systemic side-effects of COVID-19 vaccination may play an important role in public confidence in the acceptance of the COVID-19 vaccine booster dose. Methods: We conducted a retrospective cross-sectional study among adults living in Eastern Province of Saudi Arabia. A link to the survey was distributed to community members via WhatsApp, SMS, or e-mail. Participants' general and demographic information was also collected, as well as information about any local and systemic side-effects reported following vaccination. Results: A total of 370/390 (94.87%) of respondents reported one or more side-effects. Pain or redness at the site of injection (88.92%), fatigue (43.78%), body pain fever (37.84%), and headache (15.95%) were the most commonly reported side-effects. Moreover, 2.43% of the participants reported side-effects needed to see a physician; only four were admitted to the hospital. The non-healthcare respondents (n=273 (97.15%), OR (95% CI) = 5.22 (2.02, 13.48, P <0.001) were more likely to report side-effects compared to the healthcare related respondents (n=36 (85.71%), OR (95% CI) = 0.25 (0.10, 0.70), P=0.013). Conclusion: According to this study, the Pfizer-BioNTech (BNT162b2) COVID-19 vaccine was safe when given to Saudi Arabian adults. All reported side-effects were mild to moderate. The findings will likely persuade vaccine-hesitant individuals and pessimists to accept booster dose of COVID-19 vaccine.

Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique's higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC's compressive and flexural strengths, respectively.

Hypobaric treatment augments the efficacy of 1-MCP in apple fruit.

This research aimed to extend the postharvest shelf life of Royal Gala apple during cold storage and maintain its market value in simulated retail conditions. Apples were treated with hypobaric pressure (50 kPa for 4 h) followed by 1-MCP (0.5 µL L-1, 0.7 µL L-1, and 1.0 µL L-1) treatment for 24 h individually and in combinations, stored at (1 ± 1 °C, 85 ± 3% RH) for 120 d and analyzed for different quality parameters (Peel color, firmness, weight loss, TSS, acidity, ethylene production rate, and respiration rate) at each 30 d interval, followed by a 20 d simulated retail condition at 20 ± 3 °C with 4 d interval. Results indicated that all 1-MCP concentrations were more effective in retaining quality compared to individual hypobaric treatment. However, a synergistic effect was observed by combining 1-MCP with hypobaric treatment. Among the combined treatments, 1.0 µL L-11-MCP + 50 kPa more effectively and significantly retained quality during cold storage. Furthermore, the apples were more juicy, tasty, and attractive in color than others in simulated conditions. Addition of hypobaric treatment to 1-MCP might reduce endogenous ethylene in fruit by outward diffusion and blocking further ethylene synthesis by the action of 1-MCP. However, in-depth study is required for further understanding the phenomena.

Aspergillus violaceofuscus alleviates cadmium and chromium stress in Okra through biochemical modulation.

Endophytic fungi from the Chilli were used to help okra plants exposed to cadmium (Cd) or chromium (Cr) stress. Initially, the strain Ch06 produced higher amounts of indole acetic acid (IAA) (230.5 µg/mL), sugar (130.7 µg/mL), proteins (128.2 µg/mL), phenolics (525.6 µg/mL) and flavonoids (98.4 µg/mL) in Czapek broth supplemented with Cd or Cr. The production of IAA and other metabolites in such a higher concentration suggested that Ch06 might improve plant growth under heavy metal stress. For this reason, an experiment was designed, in which biomass of Ch06 (at 2g/100g of sand) were applied to the okra plants exposed to Cd or Cr stress (at 100 or 500 µg/g). The results exhibited that Ch06 improved the total chlorophyll (36.4±0.2 SPAD), shoot length (22.6±0.2 cm), root length (9.1±0.6 cm), fresh weight (5±0.6 g), dry weight (1.25±0.01 g), sugars (151.6 µg/g), proteins (114.8 µg/g), proline (6.7 µg/g), flavonoids (37.9 µg/g), phenolics (70.7 µg/g), IAA (106.7 µg/g), catalase (0.75 enzyme units/g tissue) and ascorbic acid oxidaze (2.2 enzyme units/g tissue) of the associated okra plants. Similar observations have been recorded in Ch06 associated okra plants under Cd and Cr stress. Also, Ch06 association reduced translocation of Cd (35% and 45%) and Cr (47% and 53%) to the upper parts of the okra plants and thus reduced their toxicity. The internal transcribed spacer (ITS) region amplification of 18S rDNA (ribosomal deoxyribo nucleic acid) exhibited that the potent strain Ch06 was Aspergillus violaceofuscus. The results implied that A. violaceofuscus has the ability to promote host species growth exposed to Cd and Cr. Moreover, it helped the host plants to recover in Cd and Cr polluted soils, hence can be used as biofertilizer.

Steel Fiber-Reinforced Concrete: A Systematic Review of the Research Progress and Knowledge Mapping.

This study performed a scientometric-based examination of the literature on steel fiber-reinforced concrete (SFRC) to identify its key elements. Typical review papers are limited in their capacity to link distinct segments of the literature in an organized and systematic method. The most challenging aspects of current research are knowledge mapping, co-occurrence, and co-citation. The Scopus search engine was used to search for and obtain the data required to meet the goals of the study. During the data evaluation, the relevant publication sources, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas vigorously involved in SFRC studies were recognized. The VOSviewer software tool was used to evaluate the literature data from 9562 relevant papers, which included citation, abstract, bibliographic, keywords, funding, and other information. Furthermore, the applications and constraints related to the usage of SFRC in the construction sector were examined, as well as potential solutions to these constraints. It was determined that only 17 publication sources (journals/conferences) had published at least 100 articles on SFRC up to June 2022. Additionally, the mostly employed keywords by authors in SFRC research include steel fibers, fiber-reinforced concrete, concrete, steel fiber-reinforced concrete, and reinforced concrete. The assessment of authors revealed that 39 authors had published at least 30 articles. Moreover, China, the United States, and India were found to be the most active and participating countries based on publications on SFRC research. This study can assist academics in building collaborative initiatives and communicating new ideas and techniques because of the quantitative and graphical depiction of participating nations and researchers.

Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete.

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study's aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R2), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R2 for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC's CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients.

New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete.

Recently, artificial intelligence (AI) approaches have gained the attention of researchers in the civil engineering field for estimating the mechanical characteristics of concrete to save the effort, time, and cost of researchers. Consequently, the current research focuses on assessing steel-fiber-reinforced concrete (SFRC) in terms of flexural strength (FS) prediction by employing delicate AI techniques as well as to predict the raw material interaction that is still a research gap. In this study, the FS of SFRC is estimated by deploying supervised machine learning (ML) techniques, such as DT-Gradient Boosting, DT-XG Boost, DT-AdaBoost, and DT-Bagging. In addition to that, the performance model is also evaluated by using R2, root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the k-fold cross-validation method is also applied to validate the model's performance. It is observed that DT-Bagging with an R2 value of 0.95 is superior to DT-XG Boost, DT-Gradient Boosting, and DT-AdaBoost. Lesser error MAE and RMSE and higher R2 values for the DT-Bagging model show the enhanced performance of the model compared to the other ensembled approaches. Considerable conservation of time, effort, and cost can be made by applying ML techniques to predict concrete properties. The evaluation of the outcome depicts that the estimated results of DT-Bagging are closer to the experimental results, indicating the accurate estimation of SFRC flexural strength. It is further revealed from the SHapley Additive exPlanations (SHAP) study that the volumetric content of steel fiber highly and positively influences the FS of SFRC.

Analyzing preventive precautions to limit spread of COVID-19.

With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples' sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques.

Interface yield stress (YS) and plastic viscosity (PV) have a significant impact on the pumpability of concrete mixes. This study is based on the application of predictive machine learning (PML) techniques to forecast the rheological properties of fresh concrete. The artificial neural network (NN) and random forest (R-F) PML approaches were introduced to anticipate the PV and YS of concrete. In comparison, the R-F model outperforms the NN model by giving the coefficient of determination (R2) values equal to 0.92 and 0.96 for PV and YS, respectively. In contrast, the model's legitimacy was also verified by applying statistical checks and a k-fold cross validation approach. The mean absolute error, mean square error, and root mean square error values for R-F models by investigating the YS were noted as 30.36 Pa, 1141.76 Pa, and 33.79 Pa, respectively. Similarly, for the PV, these values were noted as 3.52 Pa·s, 16.48 Pa·s, and 4.06 Pa·s, respectively. However, by comparing these values with the NN's model, they were found to be higher, which also gives confirmation of R-F's high precision in terms of predicting the outcomes. A validation approach known as k-fold cross validation was also introduced to authenticate the precision of employed models. Moreover, the influence of the input parameters was also investigated with regard to predictions of PV and YS. The proposed study will be beneficial for the researchers and construction industries in terms of saving time, effort, and cost of a project.

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions.

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC's compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model's performance. Subsequently, the k-fold cross-validation method is executed to validate the model's performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC's compressive strength.

Awareness, knowledge, attitude, perception, and utilization of complementary and alternative medicines (CAMs) in the common population of Dammam, Saudi Arabia.

Background: Complementary and alternative medicine (CAM)/natural products/herbs are frequently used for therapeutic purposes as a homemade remedy in Saudi Arabia. The use of CAMs is rising globally. However, special attention or precaution should be paid when using CAMs along with other drugs. Objective: The current study aims to examine the perception, attitude, knowledge, and awareness of the general population about CAMs and their utilization. Material and Methods: A cross-sectional study was conducted in March 2020 in the Dammam region of Saudi Arabia. The study included both the gender participants of the general population of Dammam, Saudi Arabia. The questionnaire contains the following sections including socio-demographic data, knowledge, attitude, and behavior of participants related to CAMs, and the prevalence of CAMs utilization in Dammam. Results: Participants were closely sex-matched (male: 50.93% and female: 49.07%), consisting of 97.1% Saudis and only 2.9% expats. The response rate was 67%; out of 559 participants, only 375 participants completed the questionnaire. Approximately 53% of participants had some knowledge of CAMs. Mass media (media/web/internet, advertisement, and television (TV), academic education, and family friends/relative represented the main source of the CAMs information (50%, 8.8%, and 15.2%, respectively). Among 77.3% of the participants, one of their family members has used some form of CAMs earlier. 90% of the participants or their family members using CAMs reported that olive oil (95.7%), green tea (89.1%), thyme (85.3%), cinnamon bark (84.8%), star anise (84.5%), and black seeds (78.4%) were the most used CAMs. Participants used CAMs for cold (28.3%), cough (14%), constipation (11.97%), ease of menstruation (10.8%), diarrhea (9.87%), and fever (8.62%). Approximately 75% of participants did not observe any side effects. Only 1% of participants reported that the CAMs are not useful therapeutically whereas the majority of the participants reported them as very useful (32.7%), useful (45.5%), and good (20.7%). Conclusion: Current study demonstrates that there is a positive attitude and perception toward CAMs. It is influenced by their traditional use as well. However, a community awareness program is essential for the adverse effects of CAMs.