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Background: Breast cancer is the most diagnosed cancer in women. Its pathogenesis includes several pathways in cancer proliferation, apoptosis, and metastasis. Some clinical data have indicated the association between coffee consumption and decreased cancer risk. However, little data is available on the effect of coffee on breast cancer cells in vitro and in vivo., Methods: In our study, we assessed the effect of Turkish coffee and Fridamycin-H on different pathways in breast cancer, including apoptosis, proliferation, and oxidative stress. A human breast cancer cell line (MCF-7) was treated for 48 h with either coffee extract (5% or 10 v/v) or Fridamycin-H (10 ng/ml). Ehrlich solid tumors were induced in mice for in vivo modeling of breast cancer. Mice with Ehrlich solid tumors were treated orally with coffee extract in drinking water at a final concentration (v/v) of either 3%, 5%, or 10% daily for 21 days. Protein expression levels of Caspase-8 were determined in both in vitro and in vivo models using ELISA assay. Moreover, P-glycoprotein and peroxisome proliferator-activated receptor gamma (PPAR-γ) protein expression levels were analyzed in the in vitro model. β-catenin protein expression was analyzed in tumor sections using immunohistochemical analysis. In addition, malondialdehyde (MDA) serum levels were analyzed using colorimetry., Results: Both coffee extract and Fridamycin-H significantly increased Caspase-8, P-glycoprotein, and PPAR-γ protein levels in MCF-7 cells. Consistently, all doses of in vivo coffee treatment induced a significant increase in Caspase-8 and necrotic zones and a significant decrease in β- catenin, MDA, tumor volume, tumor weight, and viable tumor cell density., Conclusion: These findings suggest that coffee extract and Fridamycin-H warrant further exploration as potential therapies for breast cancer., (© 2024. The Author(s).)

Hepatocellular carcinoma (HCC) has risen as the villain of cancer-related death globally, with a usual cruel forecasting. Sorafenib was officially approved by the FDA as first-line treatment for advanced HCC. Despite the brilliant promise revealed in research, actual clinical results are limited due to the widespread appearance of drug resistance. The tumor microenvironment (TME) has been correlated to pharmacological resistance, implying that existing cellular level strategies may be insufficient to improve therapy success. The role of autophagy in cancer is a two-edged sword. On one hand, autophagy permits malignant cells to overcome stress, such as hypoxic TME and therapy-induced starvation. Autophagy, on the other hand, plays an important role in damage suppression, which can reduce carcinogenesis. As a result, controlling autophagy is certainly a viable technique in cancer therapy. The goal of this study was to investigate at the impact of autophagy manipulation with sorafenib therapy by analyzing autophagy induction and inhibition to sorafenib monotherapy in rats with HCC. Western blot, ELISA, immunohistochemistry, flow cytometry, and quantitative-PCR were used to investigate autophagy, apoptosis, and the cell cycle. Routine biochemical and pathological testing was performed. Ultracellular features and autophagic entities were observed using a transmission electron microscope (TEM). Both regimens demonstrated significant reductions in chemotherapeutic resistance and hepatoprotective effects. According to the findings, both autophagic inhibitors and inducers are attractive candidates for combating sorafenib-induced resistance in HCC., (© 2024. The Author(s).)

As a critical cause of human dysfunctionality, hepatic failure leads to approximately two million deaths per year and is on the rise. Considering multiple inflammatory, oxidative, and apoptotic mechanisms behind hepatotoxicity, it urges the need for finding novel multi-targeting agents. Curcumin is a phenolic compound with anti-inflammatory, antioxidant, and anti-apoptotic roles. Curcumin possesses auspicious health benefits and protects against several diseases with exceptional safety and tolerability. This review focused on the hepatoprotective mechanisms of curcumin. The need to develop novel delivery systems of curcumin (e.g., nanoparticles, self-micro emulsifying, lipid-based colloids, solid lipid nanoparticles, cyclodextrin inclusion, phospholipid complexes, and nanoemulsions) is also considered., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: MiRNA-146a and miRNA-223 are key epigenetic regulators of toll-like receptor 4 (TLR4)/tumor necrosis factor-receptor-associated factor 6 (TRAF6)/NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome pathway, which is involved in diabetic nephropathy (DN) pathogenesis. The currently available oral anti-diabetic treatments have been insufficient to halt DN development and progression. Therefore, this work aimed to assess the renoprotective effect of the natural compound 6-gingerol (GR) either alone or in combination with metformin (MET) in high-fat diet/streptozotocin-induced DN in rats. The proposed molecular mechanisms were also investigated., Methods: Oral gavage of 6-gingerol (100 mg/kg) and metformin (300 mg/kg) were administered to rats daily for eight weeks. MiRNA-146a, miRNA-223, TLR4, TRAF6, nuclear factor-kappa B (NF-κB) (p65), NLRP3, caspase-1, and hypoxia-inducible factor-1 alpha (HIF-1α) mRNA expressions were measured using real-time PCR. ELISA was used to measure TLR4, TRAF6, NLRP3, caspase-1, tumor necrosis factor-alpha (TNF-α), and interleukin-1-beta (IL-1β) renal tissue levels. Renal tissue histopathology and immunohistochemical examination of fibronectin and NF-κB (p65) were performed., Results: 6-Gingerol treatment significantly reduced kidney tissue damage and fibrosis. 6-Gingerol up-regulated miRNA-146a and miRNA-223 and reduced TLR4, TRAF6, NF-κB (p65), NLRP3, caspase-1, TNF-α, IL-1β, HIF-1α and fibronectin renal expressions. 6-Gingerol improved lipid profile and renal functions, attenuated renal hypertrophy, increased reduced glutathione, and decreased blood glucose and malondialdehyde levels. 6-Gingerol and metformin combination showed superior renoprotective effects than either alone., Conclusion: 6-Gingerol demonstrated a key protective role in DN by induction of miRNA-146a and miRNA-223 expression and inhibition of TLR4/TRAF6/NLRP3 inflammasome signaling. 6-Gingerol, a safe, affordable, and abundant natural compound, holds promise for use as an adjuvant therapy with metformin in diabetic patients to attenuate renal damage and stop the progression of DN., (© 2024. The Author(s).)

In Bangladesh, sweet potato holds the fourth position as a crucial carbohydrate source, trailing rice, wheat, and potato. However, locally grown sweet potato varieties often display limited stability and yield. To tackle this challenge, diverse selection methods and statistical models were utilized to pinpoint sweet potato genotypes showcasing both stability and superior yield and quality traits. In the initial two years, multiple selection methods were employed to narrow down the collections based on preferences for yield and its contributing traits. Subsequently, a multi-environment trial (MET) was conducted in the following year to pinpoint superior and stable genotypes with desirable yield and quality characteristics. An integrated approach involving the Multi-Trait Genotype Ideotype Distance Index (MGIDI), Factor Analysis and Ideotype-Design (FAI-BLUP), and Smith-Hazel Index (SH) led to the identification of 71 superior sweet potato genotypes out of a total of 351 in the initial growing season. In the subsequent season, the MGIDI selection index was applied to the 71 genotypes, resulting in the selection of 11 top-performing genotypes. This selection process was complemented by a detailed analysis of the strengths and weaknesses of the selected genotypes. In the MET, the mixed effect model, specifically the linear mixed model (LMM), identified significant genotypic and genotype-environment interaction (GEI) variances. This points to elevated heritability and selection accuracy, ultimately boosting the model's reliability. By combining the strengths of LMM and additive main effects and multiplicative interaction (AMMI), the best linear unbiased prediction (BLUP) index identified H20 as the top-performing genotype for marketable root yield (MRY), H37 for dry weight of root (DW), H8 for beta carotene (BC) and H41 for vitamin c (VC). These genotypes surpassed the overall average in the WAAS index. For simultaneous stability and high performance, the WAASBY index selected H37 for MRY, H6 for DW, H61 for BC, and H3 for VC. Finally, genotypes H3 and H20 were selected using multi-trait stability index (MTSI), as they possessed high performance and stability. Based on the selection sense, the objective has been achieved with regards to the trait MRW, which serves as a major criterion for a superior variety of sweet potato., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Zakaria Alam reports financial support, administrative support, and equipment, drugs, or supplies were provided by 10.13039/501100005867Bangladesh Agricultural Research Institute, Bangladesh. The International Potato Centre (CIP) in Lima, Peru, arranged the article processing charge (APC) for this manuscript., (© 2024 The Author(s).)

Introduction: Bangladesh has 22 million adult users of smokeless tobacco (ST). The prevalence among women is higher (24.8%). Health-related quality of life outcome (HRQoL) for ST use is little known. We investigated the association between HRQoL and daily ST use among adult women in Bangladesh., Methods: Using multi-stage design, a cross-sectional survey was conducted. Adult women (randomly selected) were surveyed from 4 purposively selected divisions (Dhaka, Chittagong, Khulna and Rangpur). Female ST users and non-users were compared using HRQoL scores. Self-perceived Visual Analogue Scale (EQ-VAS) values and HRQoL scores were modelled to examine their association with ST use., Results: A total of 2610 women (1149 users and 1461 non-users) were surveyed. The proportion reported any type of problem in all health dimensions was significantly higher among female ST users than non-users (mobility: 43.3% vs 19.5%, self-care: 29.6% vs 11.9%, usual activities: 48.7% vs 21.8%, pain or discomfort: 69.8% vs 40.6%, and anxiety or depression: 61.3% vs 37.5%). The average HRQoL scores were 0.79 (95% CI: 0.78-0.81) and 0.90 (95% CI: 0.89-0.90) for users and non-users, respectively. Moreover, EQ-VAS average values were significantly higher for non-users [80.7 (95% CI: 79.9-81.6) vs 70.27 (95% CI: 69.2-71.2)]. Controlling the sociodemographics, ST use significantly reduced the HRQoL score by an average of 0.15 points. The EQ-VAS values on average decreased by 0.04 points for ST use., Conclusions: ST use is significantly associated with the HRQoL of females in Bangladesh. Considering the higher prevalence of ST, especially among women, HRQoL hazards need to be communicated for awareness building., Competing Interests: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported., (© 2024 Huque R. et al.)

Patients with chronic obstructive pulmonary disease (COPD) commonly exhibit muscle atrophy and dysfunction due to a reduction in muscle mass; and protein supplements such as chicken egg whites have been reported to improve muscle mass. The aim of this study was to evaluate the impact of physical exercise and egg white supplementation on the muscle mass of COPD patients. An experimental study was conducted among stable COPD patients at Universitas Sumatra Utara Hospital Medan, Indonesia, between August and October 2022. The patients were divided into two groups, control and interventional groups, with each patient subjected to a pre- and post-muscle mass assessment. All the patients performed respiratory endurance and upper extremity muscle strength training three times/week for a total of 12 weeks. In addition, the patients in the intervention group were also given egg white supplementation (10 eggs/day) during the period of intervention in addition to the physical training. The Wilcoxon and Mann-Whitney tests were performed to identify the significance of the difference between pre- and post-intervention and between the control and intervention groups, respectively. A total of 38 COPD patients were included in the study, 19 from each group. Our data suggested no significant difference in muscle mass of the patients in the control group before and after 12 weeks of physical exercise (pre-intervention 27.37±4.54% and post-intervention 27.68±4.5% with p =0.174). However, there was a significant muscle mass increment of patients in the intervention group upon 12 weeks of physical training and egg white supplementation (pre-intervention 27.18±4.15%, post-intervention 29.95±3.76%, p <0.001). A significant difference in muscle mass was observed between patients in the control and the intervention groups ( p =0.046) after the intervention. The study highlights that physical exercise in combination with egg white supplementation may serve as potential and effective non-pharmacological treatment for muscle mass restoration in COPD patients as compared to physical exercise alone., Competing Interests: All the authors declare that there are no conflicts of interest., (© 2024 by the authors.)

To address the problem of increased antimicrobial resistance, we developed a glycoconjugate vaccine comprised of O-polysaccharides (OPS) of the four most prevalent serotypes of Klebsiella pneumoniae (KP) linked to recombinant flagellin types A and B (rFlaA and rFlaB) of Pseudomonas aeruginosa (PA). Flagellin is the major subunit of the flagellar filament. Flagella A and B, essential virulence factors for PA, are glycosylated with different glycans. We previously reported that while both rFlaA and rFlaB were highly immunogenic, only the rFlaB antisera reduced PA motility and protected mice from lethal PA infection in a mouse model of thermal injury. Since recombinant flagellin is not glycosylated, we examined the possibility that the glycan on native FlaA (nFlaA) might be critical to functional immune responses. We compared the ability of nFlaA to that of native, deglycosylated FlaA (dnFlaA) to induce functionally active antisera. O glycan was removed from nFlaA with trifluoromethanesulfonic acid. Despite the similar high-titered anti-FlaA antibody levels elicited by nFlaA, rFlaA, and dnFlaA, only the nFlaA antisera inhibited PA motility and protected mice following lethal intraperitoneal bacterial challenge. Both the protective efficacy and carrier protein function of nFlaA were retained when conjugated to KP O1 OPS. We conclude that unlike the case with FlaB O glycan, the FlaA glycan is an important epitope for the induction of functionally active anti-FlaA antibodies., Competing Interests: A.S.C., S.M.T., and R.S. have an issued patent on the Klebsiella/Pseudomonas glycoconjugate vaccine.

Dementia is a common neurodegenerative disorder connected to damage to nerve cells in the brain. Although some conventional drugs are available for dementia treatments and are still sanctified for dementia patients, their short- and long-term side effects and other limitations make treating patients more challenging. The authors aimed to explain novel options for treating dementia with natural products and unravel some clinically proven natural products. This article systematically reviewed recent studies that have investigated the role of natural products and their bioactive compounds for dementia. PubMed Central, Scopus, and Google Scholar databases of articles were collected, and abstracts were reviewed for relevance to the subject matter.In this review, we provide mechanistic insights of clinically validated natural products, including like- Yokukansan, Souvenaid, BDW, Hupergene, Bacopa monnier , Omega-3, Tramiprostate and Palmitoylethanolamide with which have therapeutic efficacy against dementia in the management of dementia. As shown by studies, certain natural ingredients could be used to treat and prevent dementia. We strongly believe that the medicinal plants and phytoconstituents alone or in combination with other compounds would be effective treatments against dementia with lesser side effects as compared to currently available treatments. Moreover, these products should be studied further in order to develop novel dementia medications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Skin cancer, a prevalent malignancy worldwide, poses significant health concerns owing to its increasing incidence. Autophagy, a natural cellular process, is a pivotal event in skin cancer and has advantageous and detrimental effects. This duality has prompted extensive investigations into medical interventions targeting autophagy modulation for their substantial therapeutic potential. This systematic review aimed to investigate the relationship between skin cancer and autophagy and the contribution and mechanism of autophagy modulators in skin cancer. We outlined the effectiveness and safety of targeting autophagy as a promising therapeutic strategy for the treatment of skin cancer. This comprehensive review identified a diverse array of autophagy modulators with promising potential for the treatment of skin cancer. Each of these compounds demonstrates efficacy through distinct physiological mechanisms that have been elucidated in detail. Interestingly, findings from a literature search indicated that none of the natural, synthetic, or semisynthetic compounds exhibited notable adverse effects in either human or animal models. Consequently, this review offers novel mechanistic and therapeutic perspectives on the targeted modulation of autophagy in skin cancer., (© 2024 The Authors.)

The present study aimed to evaluate the effect of Mangifera indica (mango) on dental caries. The entire plant, including the leaves, fruit, roots, and flowers, has various therapeutic characteristics used for centuries to cure various illnesses. This systematic review aimed to identify an inexpensive, simple, and effective method of preventing and controlling dental caries. The search was performed among the studies written in English, the database of abstracts concentrating on the effects of Mangifera indica (Mango) on dental caries detected in Pubmed, Scopus, Google Scholar, and Central. In total, we find 37 articles. The relevant English language articles published up to August 2022 were collected, screened, and reviewed. Search words contained "Mangifera indica" and "dental caries" or "Streptococcus mutans" or "tooth demineralization." For our systematic review analysis, we included 3 randomized controlled trial studies studying a total of 130 people, of whom 110 were children aged 8 to 14 and 20 were adults aged 20 to 25. These experiments all employed mouthwash containing an extract from Mangifera indica. In conclusion, it has been proven in 2 separate studies that saliva's PH will increase significantly. In addition, a reduction of S. mutants has been observed in another research. Overall, it was concluded that mango extract mouthwash is highly effective in decreasing the bacteria that can cause dental caries. however, we firmly believe that conduction of more detailed in vivo studies regarding Mangifera indica implications in dental caries treatment is essentially needed for further confirmation., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Hepatocellular carcinoma (HCC) is one of the global health concerns. Hepatitis B virus (HBV) is one of the major causes of HCC. Poor clinical outcome of HCC patients is attributed to a small population of cancer cells known as cancer stem cells (CSCs). In this work, we studied the effect of inhibiting the enhancer of zeste homologue 2 (EZH2), a histone methyltransferase known to be overexpressed in CSCs, using tazemetostat (Taz). The effect of Taz was assessed in the HCC cell line (HEPG2) and Hepatitis B virus-transfected HEPG2 (HBV/HEPG2) cells. MTT assay showed a significant decrease in HEPG2 cells viability after 48 h treatment with either 0.5, 1, 4 or 6 μM Taz. HEPG2 and HBV/HEPG2 cells were incubated with either 0.5 or 1 μM Taz for 48 h, and then, the cells and supernatants were collected for protein expression analysis of EZH2, CD13, epithelial cell adhesion molecule (EpCAM) and β-catenin using enzyme-linked immunosorbent assay (ELISA). Taz showed a significant dose-dependent inhibition of EZH2, CD13 and β-catenin in HEPG2 and HBV/HEPG2 cells. Also, EpCAM protein levels were significantly decreased in HBV/HEPG2 but not in HEPG2 cell line alone. Our results indicate that Taz inhibition of EZH2 leads to downregulation of β-catenin signaling and eventually decreased expression of CD13 and EpCAM, which are characteristic for CSCs. The present study suggests that Taz could be a promising treatment for HCC including HBV-induced HCC that might be used in combination with radio/chemotherapy to target CSCs and prevent tumor relapse., (© 2023. The Author(s).)

Soil carbon sequestration programmes are a way of offsetting GHG emissions, however, it requires agricultural landholders to be engaged in such initiatives for carbon offsets to occur. Farmer engagement is low in market-based programmes for soil carbon credits in Australia. We interviewed long-term practitioners (n = 25) of rotational grazing in high-rainfall lands of New South Wales, Australia to understand their current social-ecological system (SES) of soil carbon management (SCM). The aim was to identify those components of the SES that motivate them to manage soil carbon and also influence their potential engagement in soil carbon sequestration programmes. Utilising first-tier and second-tier concepts from Ostrom's SES framework, the interview data were coded and identified a total of 51 features that characterised the farmers' SES of SCM. Network analysis of farmer interview data revealed that the current SES of SCM has low connectivity among the SES features (30%). In four workshops with interviewed farmers (n = 2) and invited service providers (n = 2) the 51 features were reviewed and participants decided on the positioning and the interactions between features that were considered to influence SCM into a causal loop diagram. Post-workshop, 10 feedback loops were identified that revealed the different and common perspectives of farmers and service providers on SCM in a consolidated causal loop diagram. Defining the SES relationships for SCM can identify the challenges and needs of stakeholders, particularly farmers, which can then be addressed to achieve local, national and international objectives, such as SCM co-benefits, GHG reduction, carbon sequestration targets and SDGs., (© 2023. The Author(s).)

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R 2 ), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid attack, the dataset produced through testing methods was utilized. The test results indicated that the CS loss of the cement mortar might be reduced by utilizing glass powder. For maximum resistance to acidic conditions, the optimum proportion of glass powder was noted to be 10% as cement, which restricted the CS loss to 5.54%, and 15% as a sand replacement, which restricted the CS loss to 4.48%, compared to the same mix poured in plain water. The built ML models also agreed well with the test findings and could be utilized to calculate the CS of cementitious composites incorporating glass powder after the acid attack. On the basis of the R2 value (random forest: 0.97 and bagging regressor: 0.96), the variance between tests and forecasted results, and errors assessment, it was found that the performance of both the bagging regressor and random forest models was similarly accurate., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Alfaiad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: People are treating their neuropathic pain with several approved and licensed pharmacological drugs. But due to having existing limitations like low efficacy with some side effects, there needs to be a more effective alternative and complementary therapeutic options., Purpose: s: The study was designed to discuss the mechanistic role of several clinically proven natural products that have been shown to play a significant role against different nerve pain or neuropathic pain., Method: ology: Information for this review article was salvaged using several accessible searching databases like SciVerse Scopus ® (Elsevier Properties S. A, USA), Web of Science® (Thomson Reuters, USA), and PubMed® (U.S. National Library of Medicine, USA) considering some search items like - nerve pain, natural products in pain/nerve pain management, clinically proven natural products in pain management, pain-reducing agents and so on., Result: Our study reported the therapeutic efficacy of natural products and their possible mechanism against neuropathic pain in the human body. Natural products widely used to treat neuropathic pain include comfrey root extract ointment, lavender oil, Rose Oil, aromatic essential oil, ginger oil, vitex agnus-castus, peganum oil, and ajwain 10%. Some common pathways are involved in pain relief through sensory stimulation, enzymatic, anti-inflammatory, and pain-related receptor regulation., Conclusion: The present study suggests that the mentioned natural products can be an appropriate choice for the treatment and management of neuropathic pain., Competing Interests: The authors declare no conflict of interest.We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (© 2023 Published by Elsevier Ltd.)

Background: and Purposes: The terminology "immune boost-up" was the talk of the topic in this Covid-19 pandemic. A significant number of the people took initiative to increase the body's defense capacity through boosting up immunity worldwide. Considering this, the study was designed to explain the natural products, vitamins and mineral that were proved by clinical trail as immunity enhancer., Methods: Information was retrieved from SciVerse Scopus ® (Elsevier Properties S. A, USA), Web of Science® (Thomson Reuters, USA), and PubMed based on immunity, nutrients, natural products in boosting up immunity, minerals and vitamins in boosting up immunity, and immune booster agents., Result: A well-defined immune cells response provide a-well functioning defense system for the human physiological system. Cells of the immune system must require adequate stimulation so that these cells can prepare themselves competent enough to fight against any unintended onslaught. Several pharmacologically active medicinal plants and plants derived probiotics or micronutrients have played a pivotal role in enhancing the immune boost-up process. Their role has been well established from the previous study. Immune stimulating cells, especially cells of acquired immunity are closely associated with the immune-boosting up process because all the immunological reactions and mechanisms are mediated through these cells., Conclusion: This article highlighted the mechanism of action of different natural products, vitamins and mineral in boosting up the immunity of the human body and strengthening the body's defense system. Therefore, it is recommended that until the specific immune-boosting drugs are available in pharma markets, anyone can consider the mentioned products as dietary supplements to boost up the immunity., Competing Interests: 9The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

Hepatocellular carcinoma (HCC) is a frequent primary aggressive cancer, a crucial cause of cancer-related mortality globally. Simvastatin is a well-known safe cholesterol-lowering medication that has been recently shown to suppress cancer progression. Apoptosis is a well-organized and controlled cellular process that happens both physiologically and pathologically leading to executing cell death. Apoptosis is frequently downregulated in cancer cells. In the present study, we aimed to test the effect of simvastatin on HCC progression. HCC was induced in experimental rats by means of diethylnitrose amine (DEN) and thioacetamide (TAA) injections. Gross examination and liver index along with biochemical analysis of hepatic function were evaluated. Serum alpha-feto protein (AFP) concentration was measured by ELISA. Histopathological examination was used for assessing necroinflammatory scores and fibrosis degree. Apoptosis was assessed using immunohistochemistry (IHC) and quantitative PCR (qPCR). Simvastatin was found to induce apoptosis successfully in HCC and improve liver fibrosis, overall hepatic function, and necroinflammatory score. Simvastatin, therefore, may be a potential adjunctive therapeutic option in clinical settings of treating HCC., (© 2022. The Author(s).)

Background and Objectives: Obesity has become a global health issue, more precisely, a pandemic throughout the present world due to its high prevalence in the recent era. Increased risk of morbidity and mortality in obese patients can be attributed to its association with the development of different life-threatening conditions. Plants are considered one of the most important sources of bioactive molecules which are used against a wide range of health disorders. This systematic review explores the efficacy as well as the safety profile of commonly used medicinal plants in the management of obesity that may help people to maintain a healthy weight., Methods: This review is based on comprehensive literature searches from PubMed, Science Direct, Scopus, and Google Scholar databases using the keywords- "plants in obesity", "plants used in weight reduction" or keywords that are similar to those. Medicinal plants which have been clinically proven for their anti-obesity effect have only been selected for this study and attempts to investigate beneficial effects and adverse effects along with their mechanism of action have also been taken in this review., Results: A significant reduction of weight in both human and other animals are exhibited by the extracts of Phaseolus vulgaris, green coffee, Yerba Mate, green tea, Gynostemma pentaphyllum, and the combination of Cissus quadrangularis/Irvingia gabonensis. All of those plant extracts seemed to work on different physiological pathways and none of those extracts showed any notable adverse effects in human or animal models., Conclusion: Our review suggests that the discussed medicinal plants are effective in reducing the weight of obese patients without causing notable adverse reactions. Although further study is necessary to confirm their exact molecular mechanism and safety in human use., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

Doxorubicin (DOX) is a widely used powerful anthracycline for treatment of many varieties of malignancies; however its cumulative and dose-dependent cardio-toxicity has been limited its clinical use. In the current study, in vivo and in vitro (neonatal rat's cardiomyocytes) experiments were conducted to identify the impact of nifuroxazide (NIFU) on DOX-induced cardiomyopathy, vascular injury, and hemato-toxcity and plot the underlying regulatory mechanisms. Cardiovascular injury was induced in vivo by I.P. injection of an overall dose of DOX (21 mg/kg) administered (3.5 mg/kg) twice weekly for 21 days. NIFU (10 and 30 mg/kg) was administered orally once daily for 21 days, 1 week after DOX injection initiation. In vivo experiments confirmed NIFU to restore blood cells counts and hemoglobin concentration. Moreover, NIFU normalized the myocardial functional status as confirmed by ECG examination and myocardial injury markers; CK-MB, LDH, and AST. NIFU restored the balance between TAC and both of ROS and MDA and down-regulated the protein expression of TLR4, NF-kB, TXNIP, NLR-family pyrin domain containing 3 (NLRP3), caspase-1, IL-1β, and GSDMD-N terminal, with inhibition of the up-stream of NLRP3 and the down-stream DOX-induced pyroptosis. The in vitro assay confirmed well preserved cardiomyocytes' architecture, amelioration of NLRP3/IL-1 β-mediated cell pyroptosis, enhanced cell viability, and improved spontaneous beating. Moreover, NIFU normalized the disturbed aortic oxidant-antioxidant balance; enhanced eNOS- mediated endothelial relaxation, and down regulated IL-1β expression. Thus, NIFU may be proposed to serve as a cardioprotective agent to attenuate DOX-induced cardio-toxicity and vascular injury., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches' results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Amin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study's aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

Due to the increasing prices of cement and its harmful effect on the environment, the use of cement has become highly unsustainable in concrete. There is a considerable need for promoting the use of cement replacement materials. This study investigates the effect of variable percentages of metakaolin (MK) on the mechanical and durability performance of concrete. Kaolin clay (KC) was used in the current research to prepare the MK by the calcination process; it was ground in a ball mill to its maximum achievable fineness value of 2550 m 2 /Kg. Four replacement levels of MK, i.e., 5%, 10%, 15%, and 20% by weight of cement, in addition to control samples, at a constant water-to-cement (w/c) ratio of 0.55 were used. For evaluating the mechanical and durability performance, 27 cubes (6 in. × 6 in. × 6 in.) and 6 cylinders (3.875 in. diameter, 2 in. height) were cast for each mix. These samples were tested for compressive strength under standard conditions and in an acidic environment, in addition to being subjected to water permeability, sorptivity, and water absorption tests. Chemical analysis revealed that MK could be used as pozzolana as per the American Society for Testing and Materials (ASTM C 618:2003). The results demonstrated an increased compressive strength of concrete owing to an increased percentage of MK in the mix with aging. In particular, the concrete having 20% MK after curing under standard conditions exhibited 33.43% higher compressive strength at 90 days as compared to similarly aged control concrete. However, with increasing MK, the workability of concrete decreased drastically. After being subjected to an acid attack (immersing concrete cubes in 2% sulfuric acid solution), the samples exhibited a significant decrease in compressive strength at 90 days in comparison to those without acid attack at the same age. The density of acid attack increased with increasing MK with a maximum corresponding to 5% MK concrete. The current findings suggest that the local MK has the potential to produce good-quality concrete in a normal environment.

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 (R 2 ) for the BR model was 0.95, whereas for SVM and MLP, the R 2 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.

The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods—namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models—based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP.

In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc'). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc' of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete's fc'. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete's fc' but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter's weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc' with R 2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc' the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete's fc' least. ML algorithms estimate MK concrete's mechanical characteristics to promote sustainability., Competing Interests: The authors declare no conflict of interest.

Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R 2 , RMSE, and a comparison of regression slopes. It was observed that the GEP model with 100 chromosomes, a head size of 10, and five genes resulted in an optimum GEP model, as apparent from its high R 2 value of 0.80 and 0.70 in the TR and TS phase, respectively. However, the ANN model performed better than the GEP model, as evident from its higher R 2 value of 0.94 and 0.88 in the TR and TS phase, respectively. Similarly, lower values of RMSE and MAE were observed for the ANN model in comparison to the GEP model. The regression slope analysis revealed that the predicted values obtained from the ANN model were in good agreement with the experimental values, as shown by its higher R 2 value (0.89) compared with that of the GEP model (R 2 = 0.80). Subsequently, parametric analysis of the ANN model revealed that the addition of pozzolanic materials enhanced the compressive strength of the ternary blended concrete samples. Additionally, we observed that the compressive strength of the ternary blended concrete samples increased rapidly within the first 28 days of casting.

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the n th estimators to achieve a robust R 2 . Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R 2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R 2 ), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

The utilization of carbon-fiber-reinforced polymer (CFRP) composites as strengthening materials for structural components has become quite famous over the last couple of decades. The present experimental study was carried out to examine the effect of varied widths of externally bonded CFRP on the debonding strain of CFRP and the failure mode of plain concrete beams. Twelve plain concrete prims measuring 100 mm × 100 mm × 500 mm were cast and tested under identical loading conditions. The twelve specimens include two control prisms, i.e., without CFRP strips, and the remaining ten prisms were reinforced with CFRP strips with widths of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm, respectively, i.e., two prisms in each group. Four-point loading flexural testing was carried out, and the resulting data are presented in the form of peak load vs. midpoint displacement, load vs. concrete strain, and load vs. CFRP strain. The peak load was directly recorded from the testing machine, while the midpoint deflection was recorded through the linear variable differential transducer (LVDT) installed at the midpoint. To measure the strain, two separate strain gauges were installed at the bottom of each concrete prism, i.e., one on the concrete surface and the other on the surface of the CFRP strip. The results of this study indicate that the debonding strain is a function of CFRP strip width and that the failure patterns of beams are significantly affected by the CFRP reinforcement ratio.

The safety and economy of an infrastructure project depends on the material and design equations used to simulate the performance of a particular member. A variety of materials can be used in conjunction to achieve a composite action, such as a hollow steel section filled with concrete, which can be successfully utilized in the form of an axially loaded member. This study aims to model the ultimate compressive strength (P u ) of concrete-filled hollow steel sections (CFSS) by formulating a mathematical expression using gene expression programming (GEP). A total of 149 datapoints were obtained from the literature, considering ten input parameters, including the outer diameter of steel tube (D), wall thickness of steel tube, compressive strength of concrete (f c '), elastic modulus of concrete (E c ), yield strength of steel (f v ), elastic modulus of steel (E s ), length of the column (L), confinement factor (ζ), ratio of D to thickness of column, and the ratio of length to D of column. The performance of the developed models was assessed using coefficient of regression R 2 , root mean squared error RMSE, mean absolute error MAE and comparison of regression slopes. It was found that the optimal GEP Model T3, having number of chromosomes N c = 100, head size H s = 8 and number of genes N g = 3, outperformed all the other models. For this particular model, R 2 overall equaled 0.99, RMSE values were 133.4 and 162.2, and MAE = 92.4 and 108.7, for training (TR) and testing (TS) phases, respectively. Similarly, the comparison of regression slopes analysis revealed that the Model T3 exhibited the highest R 2 of 0.99 with m = 1, in both the TR and TS stages, respectively. Finally, parametric analysis showed that the P u of composite steel columns increased linearly with the value of D, t and f y .

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (N c ), genes (N g ) and, the head size (H s ) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R 2 , RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the N c = 100, H s = 8 and N g = 3. This model exhibits an R 2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R 2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.

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 (R 2 ), 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 R 2 , 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 R 2 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.

The outcomes of acute coronary syndromes (ACS) vary internationally, given regional differences in patient co-morbidities, access to health care, interventional procedures and adherence to guideline-based management practices. This study aimed to identify the predictors of mortality from a large ACS registry of patients admitted to a tertiary care cardiac centre in Dhaka, Bangladesh. This was a hospital record based retrospective cross sectional observational study that included all patients presenting with ACS to Ibrahim Cardiac Hospital and Research Institute, Dhaka, Bangladesh from January 2013 to December 2013. Data were collected from cardiac catheterization laboratory database and hospital discharge records. Statistical analysis was done using Statistical Package for Social Sciences (SPSS) version 16.0. A p value <0.05 was considered statistically significant. Ethical approval was obtained by Institutional Review Board of the hospital. A total of 1914 ACS patients were studied: 39.8% presented with ST-elevation myocardial infarction (STEMI), 39.7% with non-ST-elevation myocardial infarction (NSTEMI) and 20.5% with unstable angina (UA). There were 146 in-hospital deaths (7.6%). Mortality was highest among STEMI patients (10.5%), followed by NSTEMI (8.1%) and UA (1.03%). The mean age of expired patients was significantly higher than that of those who survived (64.82±12.14 years vs. 57.32±11.99 years; p<0.001). Male patients were 71.4%, with no significant gender differences observed between expired and surviving groups. Age >50 years {odds ratio (OR) 2.56, p=0.005}, chronic kidney disease (CKD) (OR 2.1, p<0.001), shock (OR 16.82, p<0.001), left ventricular failure (LVF) (OR 2.43, p<0.001) and STEMI (OR 1.92, p=0.002) were independent predictors of mortality among ACS patients. Although diabetes per se was not associated with mortality (OR 1.3; 95% CI=0.89-1.91; p=0.169), uncontrolled diabetes defined as HbA1c levels ≥7.5% had significant risk of mortality (OR 51.4, p<0.001). ACS patients who did not undergo angiography (OR 16.4; p<0.001) or PCI (OR 18.9; p<0.001) had greater risk of mortality. ACS patients complicated with shock, LVF, uncontrolled diabetes and CKD had increased risk of in-hospital mortality. Improved outcomes may be likely with prompt angiography and PCI during index admission. This study is a preliminary initiative, and prospective multi-centre registries with nation-wide involvement are warranted.

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 (R 2 ), 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 R 2 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.

In order to forecast the axial load-carrying capacity of concrete-filled steel tubular (CFST) columns using principal component analysis (PCA), this work compares hybrid models of artificial neural networks (ANNs) and meta-heuristic optimization algorithms (MOAs). In order to create hybrid ANN models, a dataset of 149 experimental tests was initially gathered from the accessible literature. Eight PCA-based hybrid ANNs were created using eight MOAs, including artificial bee colony, ant lion optimization, biogeography-based optimization, differential evolution, genetic algorithm, grey wolf optimizer, moth flame optimization and particle swarm optimization. The created ANNs' performance was then assessed. With R 2 ranges between 0.7094 and 0.9667 in the training phase and between 0.6883 and 0.9634 in the testing phase, we discovered that the accuracy of the built hybrid models was good. Based on the outcomes of the experiments, the generated ANN-GWO (hybrid model of ANN and grey wolf optimizer) produced the most accurate predictions in the training and testing phases, respectively, with R 2 = 0.9667 and 0.9634. The created ANN-GWO may be utilised as a substitute tool to estimate the load-carrying capacity of CFST columns in civil engineering projects according to the experimental findings.

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 R 2 , 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 R 2 value of 0.95 is superior to DT-XG Boost, DT-Gradient Boosting, and DT-AdaBoost. Lesser error MAE and RMSE and higher R 2 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.

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.

A scientometric-based assessment of the literature on geopolymers was conducted in this study to determine its critical aspects. Typical review studies are restricted in their capability to link disparate segments of the literature in a systematic and exact way. Knowledge mapping, co-citation, and co-occurrence are very difficult components of creative research. This study adopted an advanced strategy of data mining, data processing and analysis, visualization and presentation, and interpretation of the bibliographic data on geopolymers. The Scopus database was used to search for and retrieve the data needed to complete the study's objectives. The relevant sources of publications, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas actively engaged in the research of geopolymers are recognized during the data assessment. The VOSviewer (VOS: visualization of similarities) software application was employed to analyze the literature data comprising citation, bibliographic, abstract, keywords, funding, and other information from 7468 relevant publications. In addition, the applications and restrictions associated with the use of geopolymers in the construction sector are discussed, as well as possible solutions to overcome these restrictions. The scientometric analysis revealed that the leading publication source (journal) in terms of articles and citations is "Construction and building materials"; the mostly employed keywords are geopolymer, fly ash, and compressive strength; and the top active and contributing countries based on publications are China, India, and Australia. Because of the quantitative and graphical representation of participating nations and researchers, this study can help academics to create collaborative efforts and exchange creative ideas and approaches. In addition, this study concluded that the large-scale usage of geopolymer concrete is constrained by factors such as curing regime, activator solution scarcity and expense, efflorescence, and alkali-silica reaction. However, embracing the potential solutions outlined in this study might assist in boosting the building industry's adoption of geopolymer concrete.

The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R 2 (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R 2 of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL.