Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.
An increase in temperature of up to 2 °C occurs when the amount of CO2 reaches a range of 450 ppm. The permanent use of mineral oil is closely related to CO2 emissions. Maintaining the sustainability of fossil fuels and eliminating and reducing CO2 emissions is possible through carbon capture and storage (CCS) processes. One of the best ways to maintain CCS is hydrate-based gas separation. Selected type T1-5 (0.01 mol % sodium dodecyl sulphate (SDS) + 5.60 mol % tetrahydrofuran (THF), with the help of this silica gel promotion was strongly stimulated. A pressure of 36.5 bar of CO2 is needed in H2O to investigate the CO2 hydrate formation. Therefore, ethylene glycol monoethyl ether (EGME at 0.10 mol %) along with SDS (0.01 mol %) labeled as T1A-2 was used as an alternative to THF at the comparable working parameters in which CO2 uptake of 5.45 mmol of CO2/g of H2O was obtained. Additionally, it was found that with an increase in tetra-n-butyl ammonium bromide (TBAB) supplementation of CO2, the hydrate and operating capacity of the process increased. When the bed height was reduced from 3 cm to 2 cm with 0.1 mol % TBAB and 0.01% SDS (labelled as T3-2) in fixed bed reactor (FBR), the outcomes demonstrated a slight expansion in gas supply to 1.54 mmol of CO2/g of H2O at working states of 283 K and 70 bar. The gas selectivity experiment by using the high-pressure volume analysis through hydrate formation was performed in which the highest CO2 uptake for the employment of silica contacts with water in fuel gas mixture was observed in the non-IGCC conditions. Thus, two types of reactor configurations are being proposed for changing the process from batch to continuous with the employment of macroporous silica contacts with new consolidated promoters to improve the formation of CO2 hydrate in the IGCC conditions. Later, much work should be possible on this with an assortment of promoters and specific performance parameters. It was reported in previous work that the repeatability of equilibrium moisture content and gas uptake attained for the sample prepared by the highest rates of stirring was the greatest with the CIs of ±0.34 wt % and ±0.19 mmol of CO2/g of H2O respectively. This was due to the amount of water occluded inside silica gel pores was not an issue or in other words, vigorous stirring increased the spreadability. The variation of pore size to improve the process can be considered for future work.
The growing prevalence of new toxins in the environment continues to cause widespread concerns. Pharmaceuticals, organic pollutants, heavy metal ions, endocrine-disrupting substances, microorganisms, and others are examples of persistent organic chemicals whose effects are unknown because they have recently entered the environment and are displaying up in wastewater treatment facilities. Pharmaceutical pollutants in discharged wastewater have become a danger to animals, marine species, humans, and the environment. Although their presence in drinking water has generated significant concerns, little is known about their destiny and environmental effects. As a result, there is a rising need for selective, sensitive, quick, easy-to-handle, and low-cost early monitoring detection systems. This study aims to deliver an overview of a low-cost carbon-based composite to detect and remove pharmaceutical components from wastewater using the literature reviews and bibliometric analysis technique from 1970 to 2021 based on the web of science (WoS) database. Various pollutants in water and soil were reviewed, and different methods were introduced to detect pharmaceutical pollutants. The advantages and drawbacks of varying carbon-based materials for sensing and removing pharmaceutical wastes were also introduced. Finally, the available techniques for wastewater treatment, challenges and future perspectives on the recent progress were highlighted. The suggestions in this article will facilitate the development of novel on-site methods for removing emerging pollutants from pharmaceutical effluents and commercial enterprises.
Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning's contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures -InceptionV3, ResNet50, and VGG19-on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.
Hyperglycemia is a condition with high glucose levels that may result in dyslipidemia. In severe cases, this alteration may lead to diabetic retinopathy. Numerous drugs have been approved by officials to treat these conditions, but usage of any synthetic drugs in the long term will result in unavoidable side effects such as kidney failure. Therefore, more emphasis is being placed on natural ingredients due to their bioavailability and absence of side effects. In regards to this claim, promising results have been witnessed in the usage of Ipomoea batatas (I. batatas) in treating the hyperglycemic and dyslipidemic condition. Thus, the aim of this paper is to conduct an overview of the reported effects of I. batatas focusing on in vitro and in vivo trials in reducing high glucose levels and regulating the dyslipidemic condition. A comprehensive literature search was performed using Scopus, Web of Science, Springer Nature, and PubMed databases to identify the potential articles on particular topics. The search query was accomplished based on the Boolean operators involving keywords such as (1) Beneficial effect OR healing OR intervention AND (2) sweet potato OR Ipomoea batatas OR traditional herb AND (3) blood glucose OR LDL OR lipid OR cholesterol OR dyslipidemia. Only articles published from 2011 onwards were selected for further analysis. This review includes the (1) method of intervention and the outcome (2) signaling mechanism involved (3) underlying mechanism of action, and the possible side effects observed based on the phytoconstiuents isolated. The comprehensive literature search retrieved a total of 2491 articles using the appropriate keywords. However, on the basis of the inclusion and exclusion criteria, only 23 articles were chosen for further review. The results from these articles indicate that I. batatas has proven to be effective in treating the hyperglycemic condition and is able to regulate dyslipidemia. Therefore, this systematic review summarizes the signaling mechanism, mechanism of action, and phytoconstituents responsible for those activities of I. batatas in treating hyperglycemic based on the in vitro and in vivo study.
Breast cancer is the most common invasive cancer diagnosed among women. A cancer vaccine has been recognized as a form of immunotherapy with a prominent position in the prevention and treatment of breast cancer. The majority of current breast cancer vaccination strategies aim to stimulate antitumor T-cell responses of the HER2/neu oncogene, which is abnormally expressed in breast cancer cells. However, the role of the B-cell humoral response is often underappreciated in the cancer vaccine design. We have advanced this idea by elucidating the role of B-cells in cancer vaccination by designing a chimeric antigenic peptide possessing both cytotoxic T lymphocytes (GP2) and B-cell (P4) peptide epitopes derived from HER2/neu. The chimeric peptide (GP2-P4) was further conjugated to a carrier protein (KLH), forming a KLH-GP2-P4 conjugate. The immunogenicity of KLH-GP2-P4 was compared with KLH-GP2 (lacking the B-cell epitope) in BALB/c mice. Mice immunized with KLH-GP2-P4 elicited more potent antigen-specific neutralizing antibodies against syngeneic TUBO cells (cancer cell line overexpressing HER2/neu) that was governed by a balanced Th1/Th2 polarization in comparison to KLH-GP2. Subsequently, these immune responses led to greater inhibition of tumor growth and longer survival in TUBO tumor-bearing mice in both prophylactic and therapeutic challenge experiments. Overall, our data demonstrated that the B-cell epitope has a profound effect in orchestrating an efficacious antitumor immunity. Thus, a multi-epitope peptide vaccine encompassing cytotoxic T-lymphocytes, T-helper and B-cell epitopes represents a promising strategy in developing cancer vaccines with a preventive and therapeutic modality for the effective management of breast cancer.
Herein, we report recent developments in order to explore chitin and chitosan derivatives for energy-related applications. This review summarizes an introduction to common polysaccharides such as cellulose, chitin or chitosan, and their connection with carbon nanomaterials (CNMs), such as bio-nanocomposites. Furthermore, we present their structural analysis followed by the fabrication of graphene-based nanocomposites. In addition, we demonstrate the role of these chitin- and chitosan-derived nanocomposites for energetic applications, including biosensors, batteries, fuel cells, supercapacitors and solar cell systems. Finally, current limitations and future application perspectives are entailed as well. This study establishes the impact of chitin- and chitosan-generated nanomaterials for potential, unexplored industrial applications.
Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
Medical equipment highly contributes to the effectiveness of healthcare services quality. Generally, healthcare institutions experience malfunctioning and unavailability of medical equipment that affects the healthcare services delivery to the public. The problems are frequently due to a deficiency in managing and maintaining the medical equipment condition by the responsible party. The assessment of the medical equipment condition is an important activity during the maintenance and management of the equipment life cycle to increase availability, performance, and safety. The study aimed to perform a systematic review in extracting and categorising the input parameters applied in assessing the medical equipment condition. A systematic searching was undertaken in several databases, including Web of Science, Scopus, PubMed, Science Direct, IEEE Xplore, Emerald, Springer, Medline, and Dimensions, from 2000 to 2020. The searching processes were conducted in January 2020. A total of 16 articles were included in this study by adopting Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The review managed to classify eight categories of medical equipment reliability attributes, namely equipment features, function, maintenance requirement, performance, risk and safety, availability and readiness, utilisation, and cost. Applying the eight attributes extracted from computerised asset maintenance management system will assist the clinical engineers in assessing the reliability of medical equipment utilised in healthcare institution. The reliability assessment done in these eight attributes will aid clinical engineers in executing a strategic maintenance action, which can increase the equipment's availability, upkeep the performance, optimise the resources, and eventually contributes in providing effective healthcare service to the community. Finally, the recommendations for future works are presented at the end of this study.
The increasing pattern of colorectal cancer (CRC) in specific geographic region, compounded by interaction of multifactorial determinants, showed the tendency to cluster. The review aimed to identify and synthesize available evidence on clustering patterns of CRC incidence, specifically related to the associated determinants. Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and EBSCOHost. The approach for identification of the final articles follows PRISMA guidelines. Selected full-text articles were published between 2016 and 2021 of English language and spatial studies focusing on CRC cluster identification. Articles of systematic reviews, conference proceedings, book chapters, and reports were excluded. Of the final 12 articles, data on the spatial statistics used and associated factors were extracted. Identified factors linked with CRC cluster were further classified into ecology (health care accessibility, urbanicity, dirty streets, tree coverage), biology (age, sex, ethnicity, overweight and obesity, daily consumption of milk and fruit), and social determinants (median income level, smoking status, health cost, employment status, housing violations, and domestic violence). Future spatial studies that incorporate physical environment related to CRC cluster and the potential interaction between the ecology, biology and social determinants are warranted to provide more insights to the complex mechanism of CRC cluster pattern.
Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.
A research study on morphometrics of Kalophrynus palmatissimus (commonly known as Lowland Grainy Frog) at Ayer Hitam Forest Reserve (AHFR), Selangor and Pasoh Forest Reserve (PFR), Negeri Sembilan was carried out from 12 November 2016 to 13 September 2017. The study was to examine data on the morphometric traits of K. palmatissimus at the two forest reserves. 15 morphometric traits of K. palmatissimus that were taken by using vernier calipers. Frog surveys were done by using 15 and 18 nocturnal 400 m transect lines with an interval distance of 20 m at AHFR and PFR, respectively. The GPS coordinates for all frog samples were recorded to ensure the precise geographic location. In addition, five climatic data were recorded. The results showed that most morphometric traits in AHFR (n = 34) and PFR (n = 31) were positively correlated with each other. On the other hand, climatic factor, which was soil pH, had a significant positive influence on most of the morphometric traits (p .05). Later, it was found that the snout-vent length of K. palmatissimus at AHFR was slightly larger than at PFR (AHFR: μ = 37.00 mm, SE = 1.16 c.f. PFR: μ = 30.29 mm, SE = 1.07). It showed that there were variations in morphometric traits of K. palmatissimus at AHFR and PFR. From PCA analysis, morphometric traits are grouped into two components for AHFR and PFR, respectively. In AHFR, head length, eye diameter, head width, internarial distance, interorbital distance, forearm length, tibia length, foot length, and thigh length were strongly correlated, while snout length and eye-nostril distance were strongly correlated. In PFR, eye diameter, head width, internarial distance, interorbital distance, foot length, and thigh length were strongly correlated, though snout length and eye-nostril distance were strongly correlated, hence, suggested that all morphometric traits grow simultaneously in K. palmatissimus with eye-nostril distance (EN), and snout length (SL) growing almost simultaneously at AHFR (r = .91) and PFR (r = .97). There is still a lack of available information regarding the distribution and morphometric studies of K. palmatissimus in Malaysia, especially at AHFR and PFR. This study showed 15 different morphometric traits of K. palmatisssimus between AHFR and PFR, with K. palmatissimus at AHFR were found to be slightly larger than at PFR.
The study aims to create a composite risk index of CVD among adolescents and examine the influence of demographic, socioeconomic and lifestyle-related risk factors on the composite risk index of biological CVD risk factors among adolescents in Malaysia. A Malaysian adolescent cohort of 1320 adolescents were assessed at 13, 15 and 17 years. Seven biological CVD risk factors with moderate correlation were identified, standardized and averaged to form a composite CVD risk index. Generalised estimating equation using longitudinal linear regression was used to examine the effects of changes in adolescent lifestyle-related risk factors on the composite CVD risk index over time. From the ages 13 to 17 years, physical fitness (β = - 0.001, 90% CI = - 0.003, 0.00002) and BMI (β = 0.051, 95% CI = 0.042, 0.060) were significant predictors of attaining high scores of CVD risk. Female (β = 0.118, 95% CI = 0.040, 0.197), Chinese (β = 0.122, 95% CI = 0.006, 0.239), Indians (β = - 0.114, 95% CI = - 0.216, - 0.012) and adolescents from rural schools (β = 0.066, 95% CI = - 0.005, 0.136) were also found to be considerably significant. A more robust and gender-specific intervention programme focusing on healthy lifestyle (including achieving ideal BMI and improving physical fitness) need to be implemented among school-going adolescents.
Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has attracted public attention. The gold standard for diagnosing COVID-19 is reverse transcription-quantitative polymerase chain reaction (RT-qPCR). However, RT-qPCR can only be performed in centralized laboratories due to the requirement for advanced laboratory equipment and qualified workers. In the last decade, clustered regularly interspaced short palindromic repeats (CRISPR) technology has shown considerable promise in the development of rapid, highly sensitive, and specific molecular diagnostic methods that do not require complicated instrumentation. During the current COVID-19 pandemic, there has been growing interest in using CRISPR-based diagnostic techniques to develop rapid and accurate assays for detecting SARS-CoV-2. In this work, we review and summarize reverse-transcription loop-mediated isothermal amplification (RT-LAMP) CRISPR-based diagnostic techniques for detecting SARS-CoV-2.
Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.
Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.
We report a viable method to produce nanocrystalline graphene films on polycrystalline nickel (Ni) with enhanced N doping at low temperatures by a cold-wall plasma-assisted chemical vapor deposition (CVD) method. The growth of nanocrystalline graphene films was carried out in a benzene/ammonia/argon (C6H6/NH3/Ar) system, in which the temperature of the substrate heated by Joule heating can be further lowered to 100 °C to achieve a low sheet resistance of 3.3 kΩ sq-1 at a high optical transmittance of 97.2%. The morphological, structural, and electrical properties and the chemical compositions of the obtained N-doped nanocrystalline graphene films can be tailored by controlling the growth parameters. An increase in the concentration of atomic N from 1.42 to 11.28 atomic percent (at.%) is expected due to the synergetic effects of a high NH3/Ar ratio and plasma power. The possible growth mechanism of nanocrystalline graphene films is also discussed to understand the basic chemical reactions that occur at such low temperatures with the presence of plasma as well as the formation of pyridinic-N- and pyrrolic-N-dominated nanocrystalline graphene. The realization of nanocrystalline graphene films with enhanced N doping at 100 °C may open great potential in developing future transparent nanodevices.
Curcumin is one of the main polyphenolic compounds in the turmeric rhizome. It possesses antioxidant, anti-inflammatory, anti-cancer, anti-arthritis, anti-asthmatic, anti-microbial, anti-viral and anti-fungal properties. This review aims to provide an overview of the potential health benefits of curcumin to treat female reproductive disorders, including polycystic ovary syndrome (PCOS), ovarian failure and endometriosis. Comprehensive information on curcumin was retrieved from electronic databases, which were MEDLINE via EBSCOhost, Scopus and Google Scholar. The available evidence showed that curcumin reduced the high level of androgen in PCOS. Studies in rodents suggest that curcumin resulted in the disappearance of cysts and the appearance of healthy follicles and corpora lutea. Furthermore, animal studies showed curcumin improved the overall function of the ovary in ovarian diseases and reversed the disturbance in oxidative stress parameters. Meanwhile, in vitro and in vivo studies reported the positive effects of curcumin in alleviating endometriosis through anti-inflammatory, anti-proliferative, anti-angiogenic and pro-apoptotic mechanisms. Thus, curcumin possesses various effects on PCOS, ovarian diseases and endometriosis. Some studies found considerable therapeutic effects, whereas others found no effect. However, none of the investigations found curcumin to be harmful. Curcumin clinical trials in endometriosis and ovarian illness are still scarce; thus, future studies need to be conducted to confirm the safety and efficacy of curcumin before it could be offered as a complementary therapy agent.
Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely to resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic has changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.
Palm oil clinker (POC) aggregates is a viable alternative to the naturally occurring sand and gravel in the manufacturing of concrete. The usage of POC aggregates assists in the reduction of solid waste and preserves the consumption of natural resources. Although researchers investigated the mechanical response of POC-containing concrete, limited research is available for its torsional behavior. In general, the torsional strength depends on the tensile strength of concrete. This research investigates the compressive, tensile, and torsional response of concrete with various ratios of POC-aggregates. Five batches of concrete were casted with POC-aggregate replacing granite at ratios of 0, 20, 40, 60, and 100%. The selection for the mixture proportions for the various batches was based on the design of experiments (DOE) methodology. The hard density, compressive strength, splitting tensile strength, and flexural strength of concrete with a 100% replacement of granite with POC-aggregates reduced by 8.80, 37.25, 30.94, and 14.31%, respectively. Furthermore, a reduction in initial and ultimate torque was observed. While cracks increased with the increase in POC-aggregates. Finally, the cracking of concrete subjected to torsional loads was monitored and characterized by acoustic emissions (AE). The results illustrate a sudden rise in AE activities during the initiation of cracks and as the ultimate cracks were developed. This was accompanied by a sudden drop in the torque/twist curve.