BackgroundMonkeypox is a globally spreading disease, representing a significant threat to human life in all countries of the world, and Saudi Arabia is no exception. This study aimed to assess the knowledge of the general adult population in Riyadh, Saudi Arabia about monkeypox.MethodologyThis cross-sectional study was conducted in Riyadh, Saudi Arabia among a sample of adults (aged over 18 years) who attended the outpatient clinics of the First Cluster primary healthcare centers, King Saud Medical City, and Ministry of Health in November 2022. A self-administered Arabic questionnaire, including sociodemographic characteristics of the participants, beliefs regarding monkeypox, and knowledge assessment about monkeypox through 23 multiple-choice questions, was utilized.ResultsThe study included 375 participants, of whom 270 (72%) were aged between 26 and 45 years, and 195 (52%) were males. Overall, 258 (68.8%) participants had insufficient knowledge about monkeypox. Multivariate logistic regression analysis revealed that participants aged over 45 years were almost at a three-fold risk of having an insufficient level of knowledge about the disease compared to those aged between 18 and 25 years (adjusted odds ratio (AOR) = 3.03, 95% confidence interval (CI) = 1.07-8.69, p = 0.037). University/higher educated participants were at 61% lower risk of having an insufficient level of knowledge about the disease compared to those with an education level of below secondary school . Compared to housewives/non-working participants, governmental employees were at a significantly lower risk of having insufficient knowledge about monkeypox .ConclusionsKnowledge of the general population in Riyadh, Saudi Arabia regarding monkeypox is insufficient, particularly regarding the fact that a monkeypox vaccine is available in Saudi Arabia and monkeypox is not a new infection that appeared in 2022. Therefore, there is a need to implement educational sessions at primary care centers and outpatient clinics of hospitals about the disease. Monkeypox is a rare zoonotic disease caused by the monkeypox disease virus (MPXV), a member of the genus Orthopoxvirus, which belongs to the same family of virus that causes smallpox . MonkeypMonkeypox has an incubation period of 5-21 days. Its main transmission route is via droplets and contact with skin lesions or bodily materials and fluids contaminated with the virus ,5. HowevCertain behaviors that require a high level of human-human contact pose a potential risk of transmitting the virus, including sleeping in the same room or bed, living in the same household, or drinking or eating from the same dish . SurprisHuman monkeypox disease was first discovered in the Democratic Republic of Congo in 1970 in a 12-year-old child. Although the disease was limited to West Africa, recently, new epidemics have been discovered in the United States, central Africa, and outside the African continent ,9. As moTo date, there is no available vaccine or treatment for monkeypox as the Modified Vaccinia Ankara-Bavarian Nordic (MVA-BN) and tecovirimat drugs are still unavailable on a wide scale, despite being approved . In addiIn Saudi Arabia, the first Monkeypox case was discovered in a person who came from Europe in July 2022 in Riyadh . This stStudy design and settingThis cross-sectional study was conducted in Riyadh, which is the largest city and capital of Saudi Arabia, with a population of about 7.6 million people according to the 2019 census .Study participants and eligibility criteriaStudy participants were the general population who attended the outpatient clinics of the First Cluster primary healthcare centers (PHCCs), King Saud Medical City (KSMC), and the Ministry of Health (MOH) in Riyadh (N = 45 PHCCs). Participants included patients, caregivers, healthcare workers, administrators, and any person within the zone of PHCCs. Adults (aged over 18 years) of both genders and all nationalities were considered for inclusion in the study. Mentally disabled adults, those with severe physical problems, or those aged over 65 years were excluded. The period of data collection for the study (November 2022) constituted the target population.Sample size estimationThe sample size was estimated with the assumption that the total population of adults attending outpatient clinics of the First Cluster PHCCs, KSMC in Riyadh in one month was more than 20,000 . The prevalence of good knowledge level regarding monkeypox was 48% in a recent study from Saudi Arabia . ConsideData collection tool and procedureA two-stage sampling technique was adopted to select the required sample size. In the first stage, five PHCCs were selected using a simple random technique. In the second stage, the sample was equally distributed over the five selected PHCCs. Thus, almost 75 patients were selected from each PHCC through a systematic random sample  to select 10 patients from each center daily. Hence, about eight working days were needed for each center to select the required sample.A self-administered Arabic questionnaire was utilized in this study. It consisted of three main parts, namely, sociodemographic characteristics of the patients , beliefs regarding monkeypox , and assessment of the knowledge about monkeypox through 23 multiple-choice questions with “Yes,” “No,” and “I don’t know” responses. It was validated in a previous Saudi study , and adoThe researchers visited the involved PHCCs after obtaining approval from the MOH. The questionnaires were distributed to selected patients in these centers while they were waiting for their physician appointments after explaining the study goals. No names were disclosed to ensure confidentiality. Filled questionnaires were collected on the same day from each center. One or two centers were visited each day according to the circumstances.Ethical considerationsEthical approval for the study was obtained from the Institutional Review Board of KSMC . Before their involvement in the study, study aims were explained to all participants and informed consent was obtained verbally. Confidentiality was maintained throughout the study.Data analysisSPSS software version 28.0  was used for data entry and analysis. Descriptive statistics  and bivariate analytic statistics were computed using the chi-square test. Variables significant at the p <0.05 level were further analyzed by multiple regression analysis to determine the significant predictors of knowledge of monkeypox. P-values <0.05 were considered statistically significant.The study included 375 participants out of the targeted 377 with a response rate of 99.5%. A total of 401 participants’ data were collected; however, due to incomplete questionnaires, 26 were excluded. Thus, a total of 375 questionnaires were included with a response rate of 99.5%. Overall, 270 (72%) participants were aged between 26 and 45 years. Slightly more than half of the participants  were males. The majority were Saudis  and living in urban areas . Overall, 230 (61.3%) participants were married, and 239 (63.8%) had a university/higher education. Governmental employees represented 160 (42.6%) participants while 103 (27.5%) were housewives/not working. In total, 153 (42.3%) participants had a monthly income ranging between 3,000 and 10,999 Saudi riyals  participants had insufficient knowledge about monkeypox, as illustrated in Figure Participants aged over 45 years had a higher rate of insufficient knowledge about monkeypox compared to those aged between 18 and 25 years  (p = 0.030). University-educated/postgraduate participants had a higher rate of sufficient knowledge about monkeypox compared to those with an educational level below high school  (p = 0.006). Regarding participants’ jobs, the highest rate of sufficient knowledge was observed among students , whereas the lowest rate was observed among private sector employees  (p = 0.014). Family income of the participants was significantly associated with their level of knowledge about monkeypox as 31 (46.3%) of those whose income ranged between 11,000 and 15,999 Saudi riyals/month compared to 39 (25.5%) participants whose income ranged between 3,000 and 10,999 Saudi riyals/month had sufficient knowledge about monkeypox (p = 0.008)  = 3.03, 95% confidence interval (CI) = 1.07-8.69, p = 0.037). University/higher educated participants were at 61% lower risk of having an insufficient level of knowledge about the disease compared to those with an education level of below secondary school . Compared to housewives/non-working participants, governmental employees were at a significantly lower risk of having insufficient knowledge about monkeypox . Family income was not significantly associated with the level of knowledge about monkeypox  expressed insufficient knowledge about monkeypox. Other studies have also reported insufficient knowledge about monkeypox among the general population. In a recent population-based Saudi study, 52% of the participants expressed inadequate knowledge . In the In this study, insufficient knowledge about monkeypox was higher among older participants (aged over 45 years) while higher-educated participants were less likely to have insufficient knowledge about the disease. Additionally, governmental employees were less likely to have insufficient knowledge compared to housewives/non-working participants. In accordance with our study, a similar study from Saudi Arabia showed that age, educational level, job status, and being a healthcare worker were among the determinants of the level of knowledge of the general population about monkeypox . Among UThere are some limitations of this study. The study was conducted among people attending one healthcare facility in Riyadh (First Cluster PHCCs), which could impact the ability to generalize its findings to other facilities. Moreover, the cross-sectional design is considered another limitation as the dependent and independent variables were investigated at the same time. Despite these limitations, the study is among the few to investigate knowledge of the general population about this important health threat in Saudi Arabia which could have public health importance from a prevention point of view.Generally, knowledge of the general population about monkeypox in Riyadh, Saudi Arabia is insufficient, particularly regarding the facts that a smallpox vaccine is available that can be used for monkeypox disease, a monkeypox vaccine is available in Saudi Arabia, no specific treatment is available for monkeypox, and it is not a new disease that appeared in 2022. Older people, those with low education levels, and those not working/housewives were more likely to have insufficient knowledge about the disease. Almost half of the participants thought that monkeypox would affect social and economic life as the COVID-19 pandemic, and about a fourth believed that the disease is a conspiracy or bioterrorism. Therefore, there is a need to implement educational sessions at primary care centers and outpatient clinics of hospitals about the disease. Further nationwide studies are needed to explore the situation throughout the country.


Cells constituting a multicellular organism die in a variety of ways throughout life, and most of them die via apoptosis under normal conditions. The occurrence of apoptosis is especially prevalent during development and in tissues with a high cellular turnover rate, such as the thymus and bone marrow. Interestingly, although the number of apoptotic cells produced daily is known to be innumerable in a healthy adult human body, apoptotic cells are rarely observed. This absence is due to the existence of a cellular process called efferocytosis that efficiently clears apoptotic cells. Studies over the past decades have focused on how phagocytes are able to remove apoptotic cells specifically, swiftly, and continuously, resulting in defined molecular and cellular events. In this review, we will discuss the current understanding of the clearance of apoptotic cells at the molecular level. Vast numbers of cells die every day in a healthy adult and are cleared away in a process called efferocytosis in which ‘phagocyte’ cells engulf and degrade the remains. Daeho Park and colleagues at Gwangju Institute of Science and Technology in South Korea review the current understanding of efferocytosis at the molecular level. Research over several decades has discovered many of the molecules involved in the multiple stages of efferocytosis, which include finding, recognizing, engulfing, and degrading a wide variety of materials, but significant details remain to be uncovered. Defects in efferocytosis are implicated in many diseases, including autoimmune conditions, atherosclerosis, airway inflammation, and colitis. A better understanding of the multistep molecular processes of efferocytosis could open new avenues toward treatments for a variety of immunological and metabolic diseases. Through apoptosis, multicellular organisms can achieve developmental integrity, maintain tissue homeostasis, and protect themselves from the spread of infection2. However, inducing cell death of unwanted cells alone cannot accomplish these tasks. The dead cells must eventually be removed, as they can induce inflammation themselves. Removal of apoptotic cells, known as efferocytosis, is the final step of apoptosis3. It is known that hundreds of billions of cells undergo apoptosis in a healthy human body daily. However, apoptotic cells are rarely observed, even in tissues with a high cellular turnover rate4. For example, although most developing thymocytes in the thymus undergo apoptosis, apoptotic thymocytes are seldom seen in the thymus because they are swiftly and continuously removed6.Apoptosis is an essential process that occurs in multicellular organisms to eliminate unwanted cells, such as superfluous cells produced during development, aged cells that have lost their function, cells that can develop into cancer, damaged cells, and cells infected with viruses or bacteria, throughout life7. Phagocytosis is the process of taking in extracellular particles larger than 0.5 µm in diameter by cells called phagocytes. In immunology, the term specifically refers to the ingestion and removal of bacteria and other pathogens within membrane-bound vesicles called phagosomes8. The common usage of the term phagocytosis for both pathogens and apoptotic cells may be confusing to individuals outside the field, as the immune response induced after phagocytosis of pathogens differs completely from the response after the engulfment of apoptotic cells. Pathogen phagocytosis often triggers proinflammatory responses, while engulfment of apoptotic cells generally induces anti-inflammatory responses or is immunologically silent10. Therefore, to avoid confusion, the term efferocytosis, which means "taking to the grave" or “burying,” was proposed instead of phagocytosis of apoptotic cells and is now used in the field3. Efferocytosis can be divided into four steps: recruitment of phagocytes to apoptotic cells, recognition of apoptotic cells by phagocytes, internalization of apoptotic cells into phagocytes, and degradation of apoptotic cells in phagocytes11. In this review, we will discuss the molecular mechanisms underlying each of these steps in efferocytosis. In addition, calcium signaling holds considerable importance in a wide range of cellular processes, and efferocytosis is no different. In the later part of this review, our focus will be on calcium signaling during efferocytosis and elucidating its molecular-level influence on each individual step of efferocytosis, which has not been previously reviewed.Efferocytosis is generally referred to as the phagocytosis of apoptotic cells because it involves the ingestion of extracellular substances13. Apoptosis occurs in various sites, especially in areas with a high cellular turnover rate, such as the thymus, bone marrow, mammary gland, and spleen. In the thymus, 95% of thymocytes undergo apoptosis during T-cell development, with only a small number of cells making it through key checkpoints6. In the bone marrow, a subset of excess immature B cells survives through a selection process during B cell development14. The mammary gland is also a site where apoptosis is actively occurring. After lactation, mammary epithelial cells undergo apoptosis16. Aged cells in need of replacement also undergo apoptosis. For example, neutrophils, whose lifespan is approximately 24 h, undergo apoptosis after one day. There are ~3000 to 7000 neutrophils in 1 µl of blood, and therefore, at least 18 billion neutrophils undergo apoptosis in an adult body every day19.Our body is made up of ~37 trillion cells, and it is estimated that ~300 billion cells undergo apoptosis every day to eliminate superfluous, aged, and damaged cells, which ultimately maintain homeostasis. This is an astonishing number, representing 1% of the cells that make up our body20. The primary function of professional phagocytes is to remove apoptotic cells or exogenous particles such as bacteria. Macrophages and immature dendritic cells are examples of professional phagocytes that are specifically designed for phagocytosis and have the ability to quickly and continuously phagocytose targets21. In contrast, nonprofessional phagocytes, such as fibroblasts and epithelial cells, possess the ability to perform phagocytosis, but it is not their primary role24. Most cells in tissues or organs possess the ability to perform phagocytosis, although they are inferior to professional phagocytes in terms of rate and capacity. Fibroblasts and epithelial cells are examples of nonprofessional phagocytes25. Specialized phagocytes are a recently classified type of phagocytes whose primary role is not phagocytosis, but they have other functions similar to those of nonprofessional phagocytes, and they routinely remove apoptotic cells with phagocytosis, similar to professional phagocytes26. Retinal pigment epithelial (RPE) and Sertoli cells, as specialized phagocytes, remove the shed outer segments of photoreceptors in the retina and phagocytose apoptotic germ cells and the residual body of the sperm during spermatogenesis, respectively31. Additionally, astrocytes can be categorized as specialized phagocytes because they are phagocytic, mediate synapse elimination, and support neighboring neurons33.Phagocytes are a type of cell that defends the body by engulfing extracellular particles such as bacteria and dead or dying cells. There are two types of phagocytes, professional and nonprofessional, which are classified based on their level of effectiveness in phagocytosis34. These apoptotic cells are generally cleared in four steps , sphingosine 1-phosphate (S1P), lysophosphatidylcholine (LPC), and nucleotides (ATP and UTP) have attracted the most attention. LPC is the first find-me signal discovered, and along with S1P, it is one of the lipid find-me signals39. Although S1P was previously known to be released from apoptotic cells, its role in inducing phagocyte chemotaxis in efferocytosis was not clear40. Gude et al. reported that during apoptosis, sphingosine kinase 1 (SphK1) upregulates and increases the release of S1P from apoptotic cells. They also demonstrated that purified S1P induces phagocyte migration39. S1P receptors may be involved in phagocyte migration. However, the expression of several S1P receptors (1–5) in phagocytes makes it difficult to determine which receptor is responsible for phagocyte migration in efferocytosis. Moreover, similar to LPC, the concentration of S1P required to induce phagocyte migration is much higher than that found in apoptotic supernatants41. Further investigations are needed to determine whether S1P acts as a find-me signal in vivo.S1P is another lipid find-me signal43. Studies have shown that CX3CR1 receptors on phagocytes sense fractalkine and induce phagocyte migration toward apoptotic cells. However, fractalkine is limited in tissue distribution, as it is only expressed in a few cell types.The protein fractalkine is found in the cell membrane and is released from apoptotic B cells and neurons. During apoptosis, cells form membrane blebs, which are released in the form of microparticles. Fractalkine-associated microparticles released from apoptotic cells are known to induce monocyte chemotaxis toward apoptotic B cells44. Nucleotides are known to be released from both apoptotic and necrotic cells, but much smaller amounts of nucleotides are released from apoptotic cells in a regulated manner. It was shown that nucleotides released from apoptotic cells induced phagocyte chemotaxis in vitro and in vivo, and co-treatment of nucleotides with apyrase, an ATP diphosphohydrolase, impaired phagocyte chemotaxis and efferocytosis in vivo44. Although various purinergic receptors are known to recognize nucleotides, it was reported that ATP and UTP were recognized by the P2Y2 receptor, leading to phagocyte chemotaxis. The regulated-manner release of nucleotides from apoptotic cells is controlled by Panx1. During apoptosis, Caspase-3 and −7 cleave the C-terminal tail of Panx1 and generate an open conformation of Panx1, causing the release of nucleotides45.Nucleotides such as ATP and UTP also serve as find-me signals during efferocytosis47. Further investigation is needed to determine whether they are secreted only from specific types of apoptotic cells. In addition, it will be interesting to investigate whether various find-me signals exhibit synergistic effects on phagocyte chemotaxis.There are various other find-me signals, such as ribosomal protein S19 and EMAP II, that have not been mentioned48. In this section, molecules involved in the second step of efferocytosis will be discussed.Phagocytes have another task to distinguish cells to be phagocytosed from cells not to be phagocytosed, even though phagocytes are recruited into the proximity of apoptotic cells by find-me signals. This recognition is achieved by the interaction between ligands on apoptotic cells, called eat-me signals, and their receptors on phagocytes. Apoptotic cells expose various ligands on the cell surface that are not expressed in live cells. These ligands on apoptotic cells are sensed by receptors on phagocytes indirectly by bridging molecules or directly Fig. 48. In th4. These include phosphatidylserine (PS), oxLDL, calreticulin, ICAM3, C1q, and Annexin I. Among them, PS has attracted the most attention due to its universal exposure to apoptotic cells and the drastic inhibitory effect on efferocytosis upon masking it50. Some phospholipids, such as PS and phosphatidylethanolamine, exclusively exist in the inner leaflet of the plasma membrane in live cells. This asymmetric distribution of phospholipids between the lipid bilayer is maintained by phospholipid flippases that translocate specific phospholipids, such as PS, from the outer leaflet to the inner leaflet of the plasma membrane51. The asymmetric distribution is disrupted through the inactivation of flippases and activation of scramblases during apoptosis, exposing PS on the cell surface52. It was reported that ATP11, a flippase, was cleaved, leading to its inactivation, whereas Xkr8, a scramblase, was also cleaved by caspases, leading to its activation during apoptosis, which eventually exposed PS on the surface of apoptotic cells54.Various eat-me signals present on apoptotic cells have been reported thus far55. It was shown that even viable cells could be engulfed when they expressed PS53. In contrast, in some biological processes, such as platelet activation, live cells express PS56, but these cells are not engulfed. Additionally, forced PS exposure on live cells through a mutant of TMEM16F, a calcium-dependent scramblase, failed to phagocytose live cells55. One possible explanation for the failure of engulfment of PS-exposed live cells is that PS-exposed live cells possess signals not to be engulfed, called don't eat-me signals57. In efferocytosis, phagocytes must remove only apoptotic cells without phagocytosing healthy and normal cells even though they expose PS on the cell surface. This specificity is achieved by the balance of the two signals, the eat-me and don’t eat-me signals. Cells that should not be engulfed may possess both signals, but cells that should be engulfed present only eat-me signals, resulting in efferocytosis. Don’t eat-me signals include CD31, CD47, CD24, PD-L1, and MHC I57. A molecular mechanism by which the signals prevent phagocytosis appears to be shared. Upon binding of don’t eat-me signals to their receptors, phosphatases such as SHP-1 and SHP-2 are activated and dephosphorylate myosin II63. Overall, the presence of eat-me signals on target cells are required for phagocytes to engulf them.It seems that PS exposure on the cell surface is sufficient to induce phagocytosis of the target in some cases, but it is insufficient for phagocytosis of the target in other cases48. Due to the importance of PS as a ligand on apoptotic cells in efferocytosis, this section will mainly discuss engulfment receptors that recognize PS. The engulfment receptors that recognize PS can be divided into two types based on how they recognize PS. One group recognizes PS directly, also called PS receptors, while the other group recognizes PS indirectly by bridging molecules11. This mode of recognition of apoptotic cells, indirectly by bridging molecules or direct recognition of apoptotic cells, also applies to other engulfment receptors that do not sense PS. For example, LRP1 indirectly recognizes apoptotic cells via calreticulin, whereas CD14 binds to ICAM3 exposed to apoptotic cells67. It has been reported that Tim-1, Tim-3, Tim-4, BAI1, Stabilin-2, and RAGE are engulfment receptors that directly recognize PS73, whereas members of the TAM family, integrins such as αVβ3 and αVβ5, and CD36 are engulfment receptors that indirectly recognize PS on apoptotic cells by bridging molecules77. CD36 is also known to directly bind to oxidized PS or oxidized LDL78. TSP-1, Gas6, protein S, and MFG-E8 function as linkers connecting PS with CD36, TAM members, and integrins, respectively80. In particular, Gas6 and protein S are well characterized. Gas6 binds to Mertk and Axl among the TAM family members and has a high binding affinity to Axl, while protein S binds to Mertk and Tyro381. Engulfment receptors for eat-me signals are pro-phagocytic and promote efferocytosis upon binding of ligands to engulfment receptors by the mechanism described in the next section. In contrast, don’t eat-me signals engage anti-phagocytic receptors that prevent the engulfment of apoptotic cells, and several such receptors have also been identified57. SIRPα and Siglec-10 are representative receptors for the don’t eat-me signals CD47 and CD24 as ligands, respectively82. It was shown that SIRPα recruited SHP-1 upon CD47 binding, causing dephosphorylation of myosin II and, thus, disruption of contractile force to ingest apoptotic cells. Additionally, PD-1 and LILRB1 are also known as anti-phagocytic receptors for the don't eat-me signal84.As mentioned above, a number of receptors expressed on phagocytes sense the ligands on apoptotic cells. These are collectively called engulfment receptors and include lectins, LRP1, CD14, scavenger receptors such as CD68 and SR-A, DD1α, and receptors recognizing PS85. Its activation results in actin polymerization, which is required for the internalization of apoptotic cells , acting as a bipartite GEF together with Dock180  for Rac1. BAI1 senses PS on apoptotic cells through the thrombospondin type 1 repeats of the extracellular domain, which leads to the activation of the Elmo1-Dock180-Rac1 signal module, causing actin cytoskeletal rearrangement71. Another PS receptor, Stabilin-2, is also known to interact with Gulp and activate Rac1 to promote efferocytosis89. In contrast, the signaling pathway downstream of Tim-4 remains elusive. Since Tim-4 without the cytoplasmic tail is still able to promote efferocytosis, it was considered a tethering receptor without direct signaling90. Tim-4 secured apoptotic cells on phagocytes, and then other engulfment receptors, such as integrin, generated signals to ingest the apoptotic cells, called two-step engulfment92. In addition, it was shown that tethering receptors could promote efferocytosis without biochemical interaction with their co-receptors, but Tim-4 biochemically interacted with its co-receptors, such as Mertk95.A pivotal molecule for the internalization of apoptotic cells in efferocytosis is Rac1, a member of the Rho family GTPase97. In this process, LC3-associated phagocytosis (LAP) matures phagosomes more efficiently than non-LAP, leading to the more rapid degradation of apoptotic cells  increases in phagocytes. Simultaneously, Ucp2, a protein that leaks MMP, is upregulated, which dissipates protons across the inner membrane of mitochondria and lowers MMP. Thus, Ucp2 maintains the appropriate MMP in phagocytes, enabling them to continuously engulf other apoptotic cells during efferocytosis102. Continuous efferocytosis is also enhanced by arginine derived from apoptotic cells, which is metabolized to putrescine that activates Rac1 by stabilizing Dbl mRNA. Thus, phagocytes that engulf the first apoptotic cell can more efficiently ingest the subsequent apoptotic cells due to increased Rac1 activation103.Since efferocytosis involves one cell engulfing another, the intracellular contents, such as carbohydrates, lipids, proteins, and nucleotides in phagocytes, are doubled in the final stage of efferocytosis, which can be a heavy burden for phagocytes. Thus, phagocytes release some of these contents and reprogram their metabolism to maintain appropriate levels of intracellular contents105. Furthermore, it was recently reported that efferocytosis increased glycolysis while decreasing oxidative phosphorylation, resulting in lactate release and upregulated IL-10 expression. Increases in the levels of Slc2al, Sgk1, and Slc16a1, which are involved in glucose transport and lactate release during efferocytosis, contribute to these results106.Reprogramming of energy metabolism appears to affect the anti-inflammatory response as well. Apoptotic cell-derived methionine and fatty acids facilitate the production of anti-inflammatory cytokines such as IL-10, PGE2, and TGF-β1108. Depletion of either intracellular or extracellular calcium completely abrogates efferocytosis. Extracellular calcium is necessary for the recognition of apoptotic cells by phagocytes because a number of PS binding proteins, including PS receptors and bridging molecules, require calcium for their binding to PS110. Additionally, extracellular calcium acts as a provider, elevating intracellular calcium levels during efferocytosis107. Thus, the interruption of proteins involved in calcium influx impairs efferocytosis114. Studies have shown that Undertaker in Drosophila and Junctophilin in C. elegans, which link calcium channels at the plasma membrane to those of the endoplasmic reticulum, are required for efferocytosis. Disruption of Stim1 and Orai1, which are necessary for store-operated calcium entry (SOCE), also impairs efferocytosis112. Recent studies have reported that Mertk activates the PLCγ1-IP3R axis, causing the release of calcium from the ER and inducing the Orai1-Stim1 interaction and SOCE, leading to the elevation of calcium levels in phagocytes during efferocytosis107. Furthermore, Orai1, a calcium release-activated calcium channel mediating SOCE, is upregulated through the attenuation of Crbn-mediated ubiquitination, contributing to the elevation of intracellular calcium during efferocytosis (Fig. 114.Calcium is essential for various cellular processes, including efficient and continuous efferocytosis. Efferocytosis requires calcium both inside and outside phagocytes113. However, a better understanding of the role of intracellular calcium in efferocytosis requires further exploration.The elevation of calcium levels in phagocytes during efferocytosis is also the result of decreased calcium sequestration by mitochondria. A previous study reported that upregulation of Drp-1 during efferocytosis increases mitochondrial fission, which prevents MCU-mediated mitochondrial calcium uptake and thus increases the intracellular calcium level. Drp-1-mediated mitochondrial fission also facilitates the continuous removal of apoptotic cells by regulating vesicular trafficking and phagolysosomal degradation Fig. . Therefo115, and swift and continuous efferocytosis is considered the final stage of this process. Research over the past several decades has discovered a variety of molecules involved in the multistep process of efferocytosis, including finding, recognizing, internalizing, and degrading apoptotic cells by phagocytes. Despite these long-standing efforts, several crucial questions about efferocytosis remain unanswered. In particular, the post-efferocytosis responses resulting from the different types of phagocytes and their efferocytosis receptors in tissues need to be further investigated. Additionally, since apoptotic cell degradation in phagocytes and the effects of their metabolites on phagocytes and neighboring cells have not been fully explored, further studies are needed.Apoptosis was discovered approximately half a century agoEfferocytosis following apoptosis is an integral part of maintaining tissue homeostasis in multicellular organisms and is closely related to overall health. Indeed, defects in efferocytosis have been shown to be causative factors in numerous pathologies, including autoimmune diseases, atherosclerosis, airway inflammation, colitis, and others. Consequently, a better understanding of the multistep process of efferocytosis will provide new insights into the treatment of multiple immunological and metabolic diseases.


Freshwater shortage and inadequate nutrient management are the two major challenges for sustainable wheat production in arid agro-ecosystems. Relatively little is known about the positive roles of the application methods for the combination of salicylic acid (SA) and plant nutrients in sustaining wheat production under arid climatic conditions. A two-year field study was undertaken to assess the impact of seven treatments for the integrated application of SA, macronutrients, and micronutrients on the morpho-physiological traits, yield, and irrigation water use efficiency (IWUE) of wheat subjected to full (FL) and limited (LM) irrigation regimes. The results showed that the LM regime caused a significant reduction in different plant growth traits, relative water content, chlorophyll pigments, yield components, and yield, while a significant increase was observed in IWUE. The sole application of SA or co-application with micronutrients through soil did not significantly affect the studied traits under the FL regime, while they achieved some improvement over untreated plants under the LM regime. Based on the different multivariate analyses, the soil and foliar applications for the combinations of SA and micronutrients, as well as a foliar application for the combinations of SA, macronutrients, and micronutrients were identified as an efficient option for mitigating the negative impacts of water deficit stress and enhancing the growth and production of wheat under normal conditions. In conclusion, the results obtained herein indicated that the co-application of SA and macro- and micronutrients is an effective option to greatly enhance and improve the growth and production of wheat crops in water-scarce countries of arid regions, such as Saudi Arabia, while an appropriate application method for this combination was required for positive effects. Water scarcity is one of the significant environmental issues that restrict food crop production and affect all factors related to ensuring global food security. Unfortunately, several studies have predicted that water scarcity will increase due to continued human activity and increased emissions leading to global warming and climate change, particularly in arid and semiarid regions ,2. It isTriticum aestivum L.) is one of the most important cereal crops produced and consumed worldwide. It provides approximately 20% of the protein and daily calories for 4.5 billion people around the world. It is grown on a total area of approximately 219 million ha, with a total production of over 760 million tons in 2020 [Bread wheat  to sink (grains), regulation of stomatal movement and energy production, stimulation of adventitious roots and cell division, maintenance of membrane structures, cell turgidity, osmotic adjustment, root cell membrane integrity, and chloroplast structure and functions, avoidance of the osmotic stress induced by environmental stresses, and act as an activator and cofactor of hundreds of metalloenzymes in plants, which eventually help plants in alleviating various abiotic stresses ,18,19,20When plants are exposed to water deficit stress, several physiological processes are disrupted by increasing the production of ROS, which leads to oxidative stress. This oxidative stress results in inhibiting chlorophyll biosynthesis, ion homeostasis, hormonal balance, photosynthetic activity, DNA damage, and imbalances in nutrient and water relations ,29,30. T−1) increased the production of wheat under drought stress conditions. Munsif et al. [Previous studies have also reported that the integration of SA with plant nutrients can achieve good results in the response of plants to abiotic stresses. For instance, Yavas and Unay  found thf et al.  also fouHowever, the effect of the combined application of SA and plant nutrients through the soil on wheat performance under arid conditions has yet to be studied. This study hypothesized that the application methods of combinations between SA and plant nutrients may play an important role in enhancing the performance of wheat under normal and stress conditions. Therefore, the overall goal of this study was to identify the appropriate application methods for the combinations between SA, macronutrients and micronutrients to achieve maximum growth, yield, and IWUE for wheat under sufficient and limited irrigation water supply conditions in an arid agro-ecosystem. p ≤ 0.05 or 0.01 for all growth parameters in each growing season and the combined two seasons, except for plant height (PH) in the second season and the combined two seasons and tiller number (TN) in the two seasons (p ≤ 0.01 by the interaction IR × T. Seasons (S) had an insignificant effect on all growth parameters, except shoot dry weight (SDW). The interaction IR × S, T × S, and IR × T × S showed insignificant effects on all growth parameters at p ≤ 0.05 or 0.01, except TN, green leaf number (GLN), leaf area index (LAI), and shoot fresh weight (SFW), which were significantly affected by IR × S interaction  and different combination treatments between SA, macronutrients and micronutrients (T), were significant at  seasons . All groeraction . Averaged over the two seasons, the limited irrigation regime (LM) hampered PH, TN, GLN, LAI, SFW, and SDW by 12.2%, 21.5%, 43.6%, 47.9%, 35.7%, and 32.1%, respectively, when compared with the full irrigation regime (FL) . HoweverThe responses of growth parameters to different T under each IR regime over both growing seasons are shown in The IR regime, T, and S had a significant main effect on all physiological parameters, namely relative water content (RWC), chlorophyll a (Chla), chlorophyll b (Chlb), and total chlorophyll content (Chlt) in each growing season as well as the combined two seasons . AdditioIrrespective of T, averaged over both seasons, the LM regime significantly reduced RWC, Cha, Chb, and Cht by 22.4%, 39.8%, 40.8%, and 41.1%, respectively, as compared to the FL regime . RegardiThe responses of physiological parameters to different T under each irrigation regime are presented in The F-values of the ANOVA analysis showed that all yield components and irrigation water use efficiency (IWUE) were significantly affected by the IR regime, T, and their interaction, with the exception of spike length (SL), in which the T and IR × T interaction had non-significant effects on this parameter . The S sAveraged over both seasons, the LM regime significantly reduced SL, GWS, GNPS, thousand grain weight (TGW), grain yield per ha (GY), biological yield per ha (BY), and HI by 16.2%, 38.5%, 26.5%, 16.5%, 41.8%, 29.1%, and 18.1%, respectively, while it increased irrigation water use efficiency (IWUE) by 14.1%, when compared with the FL regime . RegardlThe responses of yield components, yield, and IWUE to the exogenous applications of combination treatments under each irrigation regime are shown in In The associations between the 16 studied parameters and 7 combination treatments under each irrigation regime were evaluated via a principal component analysis (PCA) scatter plot  and heatIn this study, all studied parameters were significantly affected by irrigation regimes  and the The current study showed that the combined application of SA, macronutrients, and micronutrients through soil and foliar spray methods improved the performance of wheat under the FL regime as well as having the ability to ameliorate the negative impacts of water deficit stress on morpho-physiological traits and the production of wheat under LM regimes . SeveralIn this study, the response of different morpho-physiological and yield parameters of wheat to the co-application of SA, macronutrients, and micronutrients varied according to the irrigation regimes . Under tThe results of this study also found that the co-application of SA, macronutrients,- and micronutrients through foliar sprays only (T7) is a more efficient strategy for enhancing the performance of wheat crops under the FL regime than under the LM regime, as shown with the heatmap clustering for different combination treatments under the FL and LM conditions . This fi2 from stomatal apertures to the carboxylation sites by Rubisco and the export of carbohydrates from source to sink [It is observed from this study that yield, yield components, and IWUE were significantly and strongly correlated with each other and with the different morpho-physiological parameters under both irrigation regimes . This re to sink ,62. It a to sink . The mac to sink ,64. K pl to sink ,15,22. ATwo field experiments were conducted from December to April in two successive seasons of 2020/2021 and 2021/2022 at the Agricultural Research Farm of the Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia  to investigate the effect of the exogenous co-application of SA, macronutrients, and micronutrients through soil and/or foliar spray methods on the morpho-physiological traits, yield attributes, and IWUE of wheat under FL and LM irrigation regimes. The soil texture of the research farm is classified as a sandy loam , and it is climatically categorized as typical arid climatic conditions. The monthly averages of the meteorological data at the research farm during both growing winter seasons are shown in 2O5 ha−1 in the form of calcium superphosphate (17% P2O5). Then, seeds of the spring wheat cultivar Summit were planted manually in ten rows, each 4 m long and 20 cm apart from the adjacent row, at a seeding rate of 150 kg ha−1. The seeds were planted on December 8th and 1st in the first and second seasons, respectively. Other nutrients, potassium (K) and nitrogen (N), were applied at the rate of 60 kg K2O and 180 kg N ha−1 in the form of potassium sulfate (50% K2O) and urea (46% N), respectively. Potassium and nitrogen fertilizers were applied at two and three equal doses, respectively. The first and second doses of both fertilizers were applied at the seedling and stem elongation stages, while the third dose of N was applied at booting stage. Other agronomic practices, such as the control of weeds and disease, were done in a timely manner.The soil was ploughed twice, leveled, and divided into plots (4 m × 2 m each). Phosphorus fertilizer was applied during the seedbed preparation at the rate of 90 kg PThe experiments were accomplished in a randomized complete block design with a split plot arrangement and replicated three times. The two irrigation regimes (FL and LM regimes) were assigned to the main plot, while the different combination treatments were randomly distributed in subplots. The first irrigation regime represents non-water-stressed plants , while the second one represents water-stressed plants (50% ETc). The different combination treatments included a possible combination between the application methods for the co-application of SA, macronutrients (NPK), and micronutrients (Zn and Mn). These treatments included control (T1), the soil application of SA (T2), soil application of SA and micronutrients (T3), soil and foliar application of SA and micronutrients (T4), soil application of SA and foliar application of macronutrients and micronutrients (T5), foliar application of SA (T6), and foliar application of SA, macronutrients, and micronutrients (T7).Based on the reference evapotranspiration rate (ETo) and crop coefficient (Kc), the amount of irrigation water required (ETc) for the FL regime was calculated using the following Equation:3 ha−1 for the first and second seasons, respectively. The LM regime received 50% of the irrigation volume of the FL regime. The irrigation water was applied using the modified surface irrigation method, as outlined by El-Hendawy et al. [The ETo was determined based on the modified Penman–Monteith equation , while ty et al. . The irr−1, respectively. SA, Zn and Mn were applied in the form of HOC6H4COOH, ZnSO4.7H2O, and MnSO4.3H2O, respectively. The foliar application treatments were applied twice at 40 and 60 days after sowing at the concentrations of 0.5% for Mn, 1% for N, P, K, and Zn, and 2.0 mM for SA. Salicylic acid was dissolved in absolute ethanol, while the other compounds were dissolved in distilled water to make a stock. Then, the appropriate quantities of this stock were gently added to the distilled water to make spray solutions with the required concentrations. The various combinations of SA solution and nutrient elements containing 0.1% Tween-20 were sprayed directly onto the leaves of the plants to the point of runoff using a back-mounted pressurized sprayer (16 L) with a T-jet nozzle that was calibrated to deliver 15 mL s-1 at a pressure of 207 kPa. The soil application of SA and micronutrients were applied 30 days after sowing. The SA, Zn, and Mn were applied at a rate of 3, 20, and 15 kg haTen plants were randomly harvested from each plot 100 days after sowing to record SFW, PH, TN, and GLN. Then, the surface area of all green leaf blades was measured using an area meter  to record the leaf area per plant, which was used to calculate the leaf area index (LAI) according to Equation (2). All parts of the ten plants were oven-dried at 80 °C to a constant weight and then weighed to obtain the SDW.2 from each leaf, TW is the turgid weight of leaf samples after being rehydrated in distilled water for 24 h in the darkroom at 25 °C, and DW is the dry weight of leaf samples after being dried at 80 °C for 72 h.A second leaf of five randomly selected plants was taken to record the RWC using the following equation :(3)RWC  from each plot were harvested manually, sun-dried, and then weighed to determine BY in kg per 3.0 m2 before being converted to ton ha−1. All spikes of the harvested area were threshed and the grains were collected, cleaned, dried, and weighed to determine GY in kg per 3.0 m2 and then converted to ton ha−1. The HI, which is the ratio of GY and BY, was calculated using Equation (7), while the IWUE, which is the ratio of GY and ETc, was calculated using Equation (8) [After the plants reached maturity on 14th April in both seasons, fifty randomly selected spikes from each plot were collected to determine SL, GWS, NGPS, and TGW. To determine BY and GY, five inner rows of 3 m (3.0 mtion (8) : (7)HI  were applied and performed using a computer software program XLSTAT statistical package . A heatmap was performed using heatmap packages in R statistical software (version 4.2.2). The Pearson’s correlation, PCA, and heatmap were made based on the pooled data of the two seasons. All figures were plotted using SigmaPlot 14 software program .An analysis of variance (ANOVA) was performed according to the split plot in a randomized complete block design by using the SAS statistical software 9.3 for Windows . The mean values of treatments were compared using Duncan’s test at a The results of this study confirmed that it is difficult to grow wheat under deficit irrigation in arid climatic conditions without a significant decrease in its growth and production. Although the LM regime improved the IWUE, it markedly reduced the different morpho-physiological traits, which ultimately lead to a wheat yield and yield components reduction as compared to the FL regime. Nevertheless, the co-application of SA, macro nutrients, and micronutrients through soil and foliar spray methods effectively mitigated the negative impacts of the LM regime, mainly by improving plant growth and physiological attributes. It also enhanced the performance of wheat under the FL regime. Therefore, strong and positive correlations among most studied parameters were observed under both irrigation regimes. In conclusion, combinations of osmolytes compounds, macronutrients, and micronutrients that are exogenously applied through foliar and soil methods could be recommended as an effective strategy to augment the growth and production of wheat crops under both full and deficit irrigation regimes in arid agro-ecosystems.


Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5. Firstly, to mitigate the influence of the similarity between bearing defects and non-defective regions on the detection performance, gamma transformation is introduced in the preprocessing stage of the model to adjust the image’s grayscale and contrast. Secondly, to better capture the details and semantic information of the defects, this approach incorporates the ResC2Net model with a residual-like structure during the feature-extraction stage, enabling more nonlinear transformations and channel interaction operations so as to enhance the model’s perception and representation capabilities of the defect targets. Additionally, PConv convolution is added in the feature fusion part to increase the network depth and better capture the detailed information of defects while maintaining time complexity. The experimental results demonstrate that the GRP-YOLOv5 model achieves a mAP@0.5 of 93.5%, a mAP@0.5:0.95 of 52.7%, and has a model size of 25 MB. Compared to other experimental models, GRP-YOLOv5 exhibits excellent performance in bearing defect detection accuracy. However, the model’s FPS (frames per second) performance is not satisfactory. Despite its small size of 25 MB, the processing speed is relatively slow, which may have some impact on real-time or high-throughput applications. This limitation should be considered in future research and in the optimization efforts to improve the overall performance of the model. Bearings are important components in chemical equipment that support rotating shafts. If the bearing performance is poor, it can lead to imbalance in the rotating parts, causing equipment vibration, increased noise, and even equipment failure, shutdown, or damage, thereby affecting the stability and reliability of the equipment . AdditioWith technological advancements, modern detection methods such as computer vision and artificial intelligence have been gradually applied to the surface-defect detection of bearings. These methods offer advantages such as high intelligence, accuracy, and efficiency. Both two-stage object detection algorithms like R-CNN , Fast R-In the preprocessing stage, a gamma transformation preprocessing technique is introduced to perform a nonlinear transformation of the image’s grayscale values, thus enhancing the image’s contrast and brightness. This process eliminates the similarity between bearing defects and non-defective regions, reducing the occurrence of false positives and false negatives.In the backbone network, a novel ResC2Net residual-like network structure is proposed to enhance the network’s feature representation and attention enhancement capabilities, further improving the detection performance for small bearing defects.In the model’s neck, a PConv local convolution operation is added to improve the accuracy of defect edge detection without increasing the computational complexity.Therefore, this paper proposes an accurate and real-time bearing defect detection algorithm based on the single-stage YOLOv5 model. The specific contributions of this paper are as follows:Defect detection methods have been a long-standing research area of interest in both engineering and academia. Traditional defect-detection methods encompass technologies such as signal processing, image processing, feature extraction, and machine learning. Zhao et al.  providedIt can be observed that traditional detection methods generally suffer from issues such as low efficiency, low accuracy, and significant human intervention. Moreover, they fail to meet the requirements of modern industrial production for efficient and accurate detection.Currently, methods based on convolutional neural networks (CNNs) have been widely applied in the field of bearing defect detection. These methods leverage their powerful feature extraction and representation capabilities for high-dimensional data, and have achieved success in various detection tasks . Zhao etAlthough deep learning-based object detection algorithms have achieved some results in bearing defect detection, issues such as false positives, false negatives, and detection challenges with overlapping and small defects remain.As a consumable component, bearings are prone to various damage during production and transportation, resulting in a wide range of defect types. This diversity makes it challenging to capture the details and deep semantic information of defects during the detection process. To address this issue, this paper proposes an improved bearing defect detection model. The model first employs gamma transformation to adjust the brightness levels of the pixels within the bearing defect detection region, thereby reducing the similarity between defect target areas and the background. Additionally, this paper introduces a network model called ResC2Net, which is based on the C2f , and ResThe experimental data used for training the network model in this paper are from a chemical equipment bearing defects dataset. It includes various common bearing defects such as grooves, abrasions, and scratches. To enhance the model’s generalization ability and robustness, random scaling, image flipping, and Mosaic Augmentation techniques were applied to augment the original data, thereby expanding the dataset and increasing its diversity. Data augmentation techniques not only effectively prevent overfitting, improve the model’s generalization ability, and stability, but they also help the model better understand the manifestation of bearing defects in different scenarios, thereby facilitating better recognition and detection of these defects, as well as improving the industrial production efficiency and quality.I is the pixel value in the original image, c is a constant used to scale the transformed pixel values within the possible range of the pixel values.Furthermore, this paper employs gamma transformation as an automatic technique through which to adjust the brightness and contrast of the images, thereby reducing background noise in the defect images and enhancing the model’s robustness. Gamma transformation is a nonlinear transformation used to adjust the brightness and contrast of images. It is based on the nonlinear characteristics of human visual perceptions of brightness, and it achieves brightness adjustment by altering the pixel value mapping relationship in the image. The transformation formula is given as follows:In this paper, when applying gamma transformation, the pixel values of the image are first normalized to the range of 0 to 1 by dividing each pixel value by the maximum possible value. Then, the normalized pixel values are raised to the power of the gamma value, which makes darker pixel values brighter and brighter pixel values darker. Finally, the transformed pixel values were rescaled to the original range of pixel values by multiplying them by the maximum possible value. From the observation of In the backbone network, the conventional YOLOv5 network model utilizes CSPDarknet53 for feature extraction. In CSPDarknet53, the C3 structure serves as a module for constructing the feature extraction layers. It simply concatenates the feature maps processed by bottleneck modules with the original input feature map. However, this straightforward fusion approach may not fully capture the correlations among the different feature maps regarding the defects, resulting in suboptimal fusion effects. Additionally, the bottleneck structure in the C3 module is formed by sequentially stacking convolutional layers, which limits the receptive field of each layer’s feature map and hinders the handling of multi-scale defect targets. Therefore, we propose a ResC2Net structure, as shown in In response to the challenge of insufficiently fused defect information, the C2f structure is introduced as a module aimed at enhancing feature representation and boosting network performance. The C2f structure partitions the input feature map into two segments, processes them through two branches, and then merges the outcomes of these branches so as to amplify feature representation and to facilitate the flow of information across diverse branches, thereby bolstering the fusion effect of the bearing defect features.Additionally, to overcome the limitation of confined receptive fields, the Res2Net structure was introduced. The Res2Net module can inherently assimilate information from all of the preceding subsets of the feature maps; it can also extract the varying scales and dimensions of receptive fields, as well as enhance the efficacy of feature extraction through parallel connections and information amalgamation. By substituting grouped convolutions for the The ResC2Net design effectively harnesses the benefits of C2f and Res2Net to adeptly capture the features at different scales and semantic strata. The C2f structure employs bottleneck blocks for feature extraction, encompassing multiple convolutional layers and activation functions, to amplify the network’s capacity for nonlinear expression. However, the bottleneck structure, being composed of stacked convolutional layers, restricts the receptive fields of the feature maps in each layer. As a remedy, this paper substitutes the bottleneck structure with the Res2Net configuration, enhancing each layer’s feature map receptive fields through Given the ability of the ResC2Net module to extract more comprehensive feature representations through interactions with varying scales of convolutional kernels and channels, it excels in capturing defects of diverse dimensions and intricacies. This architectural approach equips the model to perform adeptly across distinct defect types and severities, thus substantiating its robust generalization capabilities.The overall structure flow of ResC2Net involves the input tensor passing through a The output tensors In the neck network, we adopted the Path Aggregation Network (PAN)  for featM5 is adjusted in the channel number using a N2 with The input image size in the study is During the PConv local convolution, only a portion of the input channels undergo spatial feature extraction via convolution; meanwhile, the remaining channels are left unchanged. In the training process, the input feature map is divided into two parts: the convolutional output obtained from the partial convolution operation on the front portion of channels and the remaining portion of the channels. These two parts of the channels are then concatenated along the channel dimension to form the final output feature map.The loss function is used to measure the difference between the predicted results and the ground truth labels, thus guiding the training process of the model. The loss function used in the model is defined as Equation , where tHere, The confidence loss measures the difference between the predicted box confidence and the ground truth labels. The classification loss measures the difference between the predicted class probabilities and the ground truth labels. Both the confidence loss and the classification loss are calculated using the binary cross-entropy loss function, as expressed in Equations  and 5),,5), respN is the number of predicted boxes, and Here, The regression loss measures the difference between the predicted boxes and the ground truth boxes. The regression loss is calculated using the quations –9):(6)C:(6)CCIOUc is the normalization factor of the diagonal length of the target box. v is a correction factor used to reduce the overlap differences between the large and small objects. Here, The loss function serves as the objective function for the optimization algorithm. By minimizing the loss function, the model’s parameters are adjusted to better fit the training data, thereby improving the model’s performance.In this paper, the defect detection data for the bearings in the chemical plant equipment before usage were collected. The dataset comprises a total of 6543 images , which wThe performance of the model in this paper was evaluated using two metrics: detection accuracy and model size. In terms of detection accuracy, metrics such as Precision measures the proportion of true positives among the detected targets and can be calculated as follows:R, and n represents the number of classes.In this paper, in addition to assessing detection accuracy, various metrics such as Model Size, All experiments in this study were conducted on a Windows 10 operating system. The training process utilized a GPU device, specifically a Tesla V100-SXM2-16GB with a memory capacity of 16130 MiB. The experiments were conducted using Python version 3.8, with the PyTorch 1.8.0 framework and CUDA version 10.2.89. The model was trained for 200 iterations, with a batch size of 16. During the training process, the SGD optimizer with a momentum of 0.937 was employed for optimization. The learning rate was adjusted periodically with a Warm-Up strategy, where the initial learning rate was set to 0.01 and the learning rate decay weight was set to 0.0005. In the Warm-Up phase, the learning rate was updated using a one-dimensional linear interpolation for each iteration until it reached 0.002. Additionally, a Cosine Annealing learning rate decay method was utilized to automatically adjust the learning rate.To investigate the contributions of the improved modules in the algorithm, ablation experiments were conducted in this study. Independent experiments were performed on the dataset to evaluate the three proposed improvements while keeping the experimental conditions consistent, including the devices used, training hyperparameters, and iteration counts. These experiments aimed to reveal the roles of the different components in the object detection task, as well as provided valuable insights into the performance enhancement and improvement of the object detection algorithm. The experimental results are presented in F-Score, which considers both precision and recall, demonstrated that introducing gamma transformation, the ResC2Net module, and PConv convolution individually resulted in F-Score improvements of 0.2%, 1.1%, and 0.4%, respectively, when compared to the YOLOv5s model. For the mAP@0.5 metric, introducing gamma transformation, the ResC2Net module, and PConv convolution separately led to enhancements of 0.8%, 1.9%, and 0.9%, respectively, when compared to the YOLOv5s model. However, when considering the broader range of mAP@0.5:0.95, the introduction of gamma transformation caused a reduction of 0.1%, while the incorporation of the ResC2Net module and PConv convolution resulted in increments of 1.6% and 0.8%, respectively. These experimental findings underscore the effectiveness of these three enhancement methods. Notably, introducing gamma transformation alone resulted in decreased Precision and mAP@0.5:0.95 values, while the introduction of PConv convolution alone led to reduced Precision. Therefore, in future work, further research into the influencing factors of gamma transformation and PConv convolution is warranted, along with attempts to optimize these methods to enhance their stability in various scenarios. It is worth noting that simultaneously incorporating gamma transformation, the ResC2Net structure, and PConv convolution into the YOLOv5 model resulted in a 2.2% improvement in recall, a 3.7% improvement in precision, a 2.8% improvement in mAP@0.5, a 2.1% improvement in mAP@0.5:0.95, and a 2.9% improvement in F-Score. These outcomes further validate the stabilizing and performance-enhancing effects of introducing gamma transformation and these two modules to the model.In this study, a series of improvements were applied to the YOLOv5 model, including the incorporation of gamma transformation, the ResC2Net module, and PConv convolution. Based on the ablation experimental results  at different thresholds , and recall were used to assess the performance of the GRP-YOLOv5 model, which were then compared with the traditional YOLOv5 model.The results are shown in The experimental results demonstrate that the GRP-YOLOv5 model outperforms the traditional YOLOv5 model in terms of precision, mAP@0.5, and mAP@0.5:0.95 for groove, scratch, and dent defect detection. This indicates that the GRP-YOLOv5 model exhibits a higher accuracy and overall performance in localizing and classifying defects. However, in terms of recall, the traditional YOLOv5 model performs better than the GRP-YOLOv5 model for groove and scratch defects. Analysis revealed that the GRP-YOLOv5 model shows a significant increase in recall for scratch defects compared to the YOLOv5 model. The diversity and varying difficulty of defect categories can result in differences in shape, texture, and color, thereby making scratch defects more easily detectable while the other two categories may pose more challenges. These factors can affect the model’s discrimination ability and recall for different targets.This experiment aimed to evaluate the performance of the GRP-YOLOv5 model on the test set for different defect categories, including groove, scratch, and dent defects. The test results are presented in The test set prediction results are shown in In the The experimental results show that our GRP-YOLOv5s model achieves outstanding performance—with mAP@0.5 and mAP@0.5:0.95 scores of 93.5% and 52.7%, respectively—when compared to the commonly used object detection algorithms, thus surpassing them. Additionally, the GRP-YOLOv5s model demonstrates superiority in terms of model size and GFLOPs, with values of 25.17 MB and 16.0 GFLOPs, respectively. However, the FPS of the GRP-YOLOv5s is relatively lower at 68.5 f/s. In comparison to defect detection algorithms, GRP-YOLOv5 performs the best with an mAP score of 93.5%, which is 11.3% higher than Improved Faster R-CNN’s 82.2% and 3.8% higher than Improved YOLOxs’s 89.7%. For the mAP@0.5:0.95 metric, GRP-YOLOv5 also outperforms other algorithms, achieving 52.7%, indicating its high accuracy even under stricter confidence thresholds. The GRP-YOLOv5 model boasts the smallest model size with only 25.17 MB. Moreover, in terms of computational complexity (GFLOPs), GRP-YOLOv5 excels, with only 16.0 GFLOPs, compared to the 941.0 GFLOPs required for Improved Faster R-CNN and the 26.7 GFLOPs required for Improved YOLOxs. Therefore, GRP-YOLOv5 exhibits a higher computational efficiency. Regarding the FPS f/s metric, GRP-YOLOv5 achieves 68.5 f/s, which is higher than the 18.3 f/s of Improved Faster R-CNN and the 50.4 f/s of Improved YOLOxs.This study aims to address the challenges in the field of bearing defect detection by proposing an enhanced YOLOv5 network architecture for defect detection. The algorithm introduces gamma transformation, a ResC2Net residual-like structure, and PConv local convolution to adjust image grayscale and contrast, thus enhancing the model’s perception and representation of target objects while resolving issues of information loss in bearing defect detection. The experimental results demonstrate that the GRP-YOLOv5 algorithm achieves remarkable performance with mAP@0.5 and mAP@0.5:0.95 scores of 93.5% and 52.7%, respectively, thus surpassing other detection models. Furthermore, the GRP-YOLOv5 model excels in terms of model size and GFLOPs, which were measured at 25.17 MB and 16.0 GFLOPs, respectively. However, the FPS of GRP-YOLOv5s was relatively lower at 68.5 f/s. The overall research outcomes highlighted significant performance advantages of the GRP-YOLOv5 algorithm in object detection tasks, particularly in it showcasing impressive results under high-precision requirements. In future work, we will continue optimizing the model to enhance its frame rate performance in real-time scenarios. Additionally, we plan to validate the model’s generalization capability on different datasets, and to explore various hardware acceleration techniques in order to achieve more efficient inference speeds.


Nearly half of the deep-fried street foods contained medium to high SFAs content (1.7 g/100 g–24.3 g/100 g), while the MUFAs were also high (32.0–44.4%). The Chi-square test of association showed that the type of preparation methods (low or high fat) used was significantly associated (p < 0.05) with the number of SFAs. These findings provide valuable information about fat composition in local street foods for the Malaysian Food Composition Database and highlight the urgency to improve nutritional composition.Despite growing evidence of increased saturated and trans fat contents in street foods, little is known about their fatty acid (FA) compositions. This study aimed to analyse the saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and trans fatty acids (TFAs) content of 70 selected and most commonly available street foods in Malaysia. The street foods were categorised into main meals, snacks, and desserts. TFAs were not detected in any of the street foods. Descriptively, all three categories mainly contained SFAs, followed by MUFAs, and PUFAs. However, the one-way ANOVA testing showed that the differences between each category were insignificant ( Globalisation has resulted in a nutrition transition towards high-fat, sugar, and salty foods in low- and middle-income countries (LMICs), including Malaysia, an upper-middle-income country . Street The presence of TFAs and SFAs may be influenced by the use of TFAs- or SFAs-rich ingredients, such as butter  and partKnowledge of the fatty acids (FAs) composition in foods is crucial because the high consumption of SFAs ,20 and TDocumentation of FA composition in Malaysian street foods is still lacking compared to the well-documented FA composition in street foods from other countries ,9,10,11.In this cross-sectional study, data collection was conducted in two phases: (1) a survey of street foods and (2) sampling and analysis of FA composition in selected street foods. In Phase 1 of the study, a survey was conducted in all states to identify the top 15 most frequently available street foods for each food category in every state. In Phase 2 of the study, food sampling of 70 street foods (5 foods × 14 states) was carried out to analyse the FA composition. However, among the 70 analysed street foods, 14 similar foods were sampled from more than one state, and 25 different foods were sampled from only one state. In order to achieve the aim of this study, the FA contents of the 14 similar foods were presented as average values from the respective states. Thus, this study presented the FA composition for 39 street foods. This study is an extension of previous research undertaken to determine the most frequently available street food in all states of Malaysia while assessing the sodium levels . FurtherPhase 1 involved a field survey of locally available street foods in 13 states and one Federal Territory of Kuala Lumpur in Malaysia. The definition of street food was adopted from the Food and Agriculture Organization (FAO) : “Ready-Main meal: food commonly eaten during main mealtimes, i.e., breakfast, lunch, and dinner;Snack: savoury food eaten between the main mealtimes, i.e., morning tea and afternoon tea;Dessert: sweet food eaten at the end of a main meal or as part of the main meal.A survey form was used to record information such as the name of street food, state, district, category of street food , and preparation method of the street food. The criteria for main meals, snacks, and desserts in this study are based on the description by the International Scientific Committee  as follon = 4234) of the street foods were snacks, while 37% (n = 3887) and 23% (n = 2399) were the main meals and dessert categories, respectively. The top 15 most frequently available street foods for each category in all states were subsequently identified and recorded.A total of 10,520 street foods were surveyed, in which 40% (kerepek (only found in one state) was analysed based on two kerepek purchases from stall 1 and stall 2 within the respective state. The purchased food samples were transported in an ice box and stored in the freezer at −20 °C to delay food spoilage before further analysis.The selection of food for sampling in each state was conducted based on the top 15 most frequently available street foods for each category previously identified in Phase 1  as well as the following criteria: (1) use of high-fat preparation method, (2) use of high-fat ingredients, and (3) availability of the street food during the sampling . Based on the selection, five street foods (regardless of the food categories) from every state were selected for food sampling, according to a method used by Tee et al. . Thus, 7The preparation of street food samples was conducted in the food analysis laboratory. Each purchased food sample was weighed with the packaging using a top pan balance . Then, the food was removed from the packaging, weighed, and placed on a plate or bowl. The inedible portions of the food samples, such as bones, were removed, and the sample was reweighed. The second sample of the same food purchased from a different location was also prepared using the same method. The two samples were then homogenised in a food processor, scooped into an airtight container, and subsequently stored in the freezer at −20 °C for further analysis. Prior to the FA composition analysis, the determination of fat content using the homogenised samples was first carried out through the Soxhlet method . The extMethanol (99.9%), chloroform, potassium chloride (0.88%), and sodium sulphate were used during the extraction of FAs. Meanwhile, toluene, 2% sulphuric acid in methanol, sodium chloride (5%), heptane, and sodium bicarbonate (2%) were used during the esterification of FAs. The Reference Standard 37 Component FAME Mix  was used to analyse FAs using the Gas chromatography flame ionization detection (GC-FID) method. All reagents and standards were prepared with deionised water, except for the 2% sulphuric acid in methanol (2 mL in 100 mL methanol).Approximately 1.5 g (±0.1 g) of the homogenised sample was placed into a glass bottle. Next, 10 mL of methanol and 20 mL of chloroform were added to the glass bottle. The glass bottle was then vortexed and left to sediment for a few minutes. The solvent extracts were then filtered into a Schott bottle by leaving a small portion of the sample in the glass bottle to retain any food debris. The extraction process was repeated an additional two times. After the extraction, 20 mL of 0.88% potassium chloride was added to the Schott bottle. The bottle was then shaken to allow the separation into two layers, and the upper layer was discarded. Similarly, 10 mL of 0.88% potassium chloride and 10 mL methanol mixture (ratio 1:1) were added into the same Schott bottle and shaken to allow separation, and the upper layer was discarded. The solvent extract was filtered through sodium sulphate and poured into a new glass bottle to remove moisture. The extracts were then concentrated under nitrogen gas below 60 °C.The extract containing the FAs underwent an esterification process to form fatty acid methyl esters (FAMEs). During this procedure, the sample dried with nitrogen gas was mixed with 1 mL of toluene and 2 mL of 2% sulphuric acid in methanol in the same glass bottle. The glass bottle was incubated overnight at 50–55 °C. After incubation, the following steps were conducted and repeated twice: (1) 5 mL of 5% sodium chloride in water was added to the glass bottle, and (2) 5 mL of heptane was added, shaken slightly, and left for a few minutes. The upper layer (heptane) was then transferred into a new glass bottle and combined with the subsequent upper layers (heptane) from the repeated process. Then, 4 mL of 2% sodium bicarbonate in water was added to the extract. The sample mixture was shaken and left to separate. The upper layer was filtered through sodium sulphate and placed in a new glass bottle. The 1 mL of the filtered n-heptane layer was pipetted from the glass bottle directly into the vials. Then, a 10× dilution using n-heptane was performed by mixing 100 μL of the sample and 900 μL of n-heptane in a 2 mL vial.Blanks for each batch of samples were run through the entire procedure to measure any contributions to residue from reagents. GC-FID analysis was carried out using a gas chromatograph  equipped with a ZB-FAME GC Column (60 m × 0.25 mm × 0.2 µm) and a flame ionisation detector (FID). The chromatographic conditions were as follows: initial column temperature 100 °C; injection temperature 250 °C; detector temperature 280 °C; and run time 64 min. The flow rate of the carrier gas (nitrogen) was set at 20 mL/min.In this method, 1 μL of the methyl esters (extract) solution was injected with the reference standard mixture into the GC-FID. The reference standard mixture was analysed under the same operating conditions as those employed for the sample, and the retention times were measured, along with the eluted methyl ester from the reference standard mixture were identified. The limit of detection (LOD) for all FAs was 0.01 mg/100 g.The quantification and identification of FAs present in the food sample were conducted using the peak area normalization method. Graphs showing the logarithm of the retention distance of each peak were constructed. All peaks in the sample chromatogram (including those that were not within the 37 FAME retention time) were integrated. The entire sample component was assumed to be represented on the chromatogram. Hence, the total area under the peaks represents 100% of the constituents. As for the result, only 37 components were reported based on the reference standards.The analytical results of SFAs, MUFAs, PUFAs, and TFAs in each sample were expressed as a percentage (%) based on total fats in the respective samples, similar to the MyFCD. For the purpose of classifying the street food samples based on the low, medium, or high content of SFAs, the amount of SFAs was also expressed as g/100 g of food. The percentage of SFAs based on total FAs was converted into grams of SFAs based on 100 g of food using the following calculation:There is no standardised classification for high SFAs, MUFAs, PUFAs, and TFAs in ready-to-eat dishes like street foods in Malaysia. However, the United Kingdom’s Traffic Light Labelling Scheme  has a clBased on the latest Malaysian Dietary Guidelines (MDG) , the prep < 0.05.A descriptive test was used to determine the total frequency of every street food surveyed for each state, the frequency of the street foods based on food categories, the preparation methods, and the type of street foods. Following the analysis of FA composition, a descriptive test was also used to determine the average and standard deviation of each FA for the selected street foods. Inferential test such as the one-way ANOVA was used to compare the average SFAs, MUFAs, and PUFAs between food categories. If the food categories had unequal sample sizes and both one-way ANOVA and homogeneity of variance were significant, a Games-Howell posthoc test was performed to identify the specific differences between the three food categories. The one-way ANOVA was also used to compare each FAs within each food category. The Chi-square test of association was used to determine the association between the SFAs content (g/100 g food) and the preparation method used for each street food. All the descriptive and statistical tests were conducted using IBM  Statistical Package for Social Sciences (SPSS) version 25.0. The significance level for all conducted statistical analyses was set at A total of 70 street foods  were analysed for their FA composition, as displayed in p > 0.05) from one to another. Within each food category, all three food categories were composed mainly of SFAs, followed by MUFAs, and the least was in PUFAs, although the findings were not significantly different (p > 0.05). None of the analysed street foods contained any amount of TFAs. cakoi (24.3 ± 3.6 g/100 g) contained the highest SFAs content, while chicken rice (1.2 ± 0.3 g/100 g) had the lowest SFAs content. Based on the classification of SFAs content in 100 g of food sample, cakoi, kerepek, french fries, chicken nuggets, and donuts. This was followed by other preparation methods, such as pan-frying (21.6%), steaming (13.5%), stir-frying (8.1%), grilling (8.1%), stewing (2.7%), and baking (2.7%). Meanwhile, only 5.1% of the 39 street foods contained low SFAs content (1.2–1.4 g/100 g). The foods with low SFAs content were those prepared by stir-frying (fried vermicelli) and steaming (chicken rice).Most of the analysed main meals (85.7%) and all the snacks and desserts contained medium to high SFAs content, ranging from 1.7–24.3 g/100 g. According to the classification , 56.4% okuih jelurut had the least. The majority (46.9%) of the street foods were prepared using deep-fried methods such as curry puff, fried chicken with cheese, banana fritters, kuih keria, kerepek, fried popiah, chicken nuggets, fried fish ball and donuts. This was followed by street foods that were pan-fried (21.9%), stir-fried (12.5%), steamed (9.4%), grilled (6.3%), and stewed (3.1%). The remaining 7 street foods contained MUFAs ranging from 7.0% to 27.5%. The MUFAs content for all 39 street foods, from highest to lowest amount, is shown in There were 32 street foods that contained the highest MUFAs content (32.0–44.4%), mostly found in snacks (43.8%), main meals (40.6%), and desserts (15.6%). Compared to the other street foods, fried vermicelli contained the most MUFAs, while Apam balik was reported to contain the highest amount of PUFAs, while kuih jelurut had the least amount of PUFAs. The PUFAs content for all 39 street foods, from highest to lowest amount, is shown in Surprisingly, only 7 street foods had the highest amount of PUFAs (30.0–38.8%), mainly found in snacks (57.1%), desserts (28.6%), and main meals (14.3%). Approximately 42.9% were pan-fried, 28.6% were grilled (28.6%), and 14.3% were deep-fried and baked, respectively. The remaining 32 street foods contained PUFAs ranging from 4.5% to 24.6%. p < 0.001) and strongly associated (Φ = 0.59) with the classification of the preparation methods used. This means that foods prepared using a healthier method were more likely to contain low to medium SFAs contents than foods prepared less healthily.vanaspati, a vegetable ghee rich in TFAs [vanaspati, other sources of TFAs, such as partially hydrogenated margarine and shortenings, are commonly used in street foods, such as roti canai, curry puff, chicken burgers, and donuts [This study found that the gas chromatography did not detect TFAs in any of the local street foods, which is not in agreement with the findings from other countries in the Asian ,6,8,12,  in TFAs  that is  in TFAs . Accordi in TFAs , palm oi in TFAs . Besidesd donuts . Howeverd donuts . Notablyd donuts . Other td donuts . A recend donuts  found thd donuts  also foupateuri (a fried pastry). In this study, all street foods under the dessert category were sweet; meanwhile, all street foods under the snack category were of the savoury type. Despite SFAs being the highest in desserts and MUFAs and PUFAs being the highest in snacks, SFAs were the most dominant FA present in all three food categories. This finding supports the evidence from a study [In this study, desserts contained the most SFAs, whereas snacks had the most MUFAs and PUFAs. These findings were in line with street food studies in Kazakhstan  and Mold a study  reportin a study ,47,48, w a study  of contr a study , as well a study . As stre a study ,51,52, t a study ,54. Cakoi ranked first among the 39 street foods in terms of SFAs content and is classified as a high-SFA food. Cakoi is a long, golden-brown deep-fried strip of dough that originated in China but is now a popular snack in Malaysia. On the contrary, chicken rice contains the least SFAs and is classified as a low-SFA food. This is most likely due to the difference in preparation methods, as cakoi is deep-fried, whereas steaming and roasting methods are mainly used to prepare the rice and chicken in the chicken rice. Cakoi, in the present study, is deep-fried in palm oil, which explains the high SFAs content, considering that palm oil has approximately 50% SFAs [cakoi, kerepek, french fries, chicken nuggets, and donuts) may also be linked to the use of palm oil. Thus, street food vendors should be encouraged to strain the excess oil off the fried food, and consumers should be reminded to control their consumption of deep-fried street foods, which is also in accordance with the MDG 2020 [The SFAs, MUFAs, and PUFAs content between the street foods varied considerably, indicating possible differences in the preparation methods or ingredients used . Cakoi r50% SFAs ,57,58. O50% SFAs  found thMDG 2020 . roti jala), glutinous rice with rendang, noodles with gravy, nasi lemak, and nasi lemak with fried chicken and desserts, such as kuih seri muka and kuih jelurut, are all coconut milk-based dishes. Traditionally, coconut milk is used extensively in Malaysian main dishes and desserts (except for kuih jelurut) [roti jala), the rendang and glutinous rice, and the pandan custard layer of kuih seri muka [nasi lemak is also cooked with coconut milk [Kuih jelurut, a local steamed delicacy among the Brunei ethnicities [satay, also known as meat skewers, is often skewered with meat fats to add more flavour. As expected, street foods with added cheese, such as fried chicken, banana fritters, and apam balik with cheese, had more SFAs than the regular versions.It is important to note that street foods not prepared by deep-frying also contain medium to high SFAs, possibly due to the usage of vegetable- and animal-based fats. In terms of ingredients, the main meals, such as net crepes (jelurut) , such aseri muka . The ricnut milk . Kuih jenicities  in Sabahnicities . As the nicities , this manicities  found thnicities . Chickencakoi, kerepek, french fries, chicken nuggets, fried popiah, curry puff, fried chicken, fried chicken with cheese, keropok lekor, fried sausage and fried fish ball, and deep-fried desserts, such as donuts, kuih keria, banana fritters with cheese, and banana fritters. Besides palm oil, nuts and seeds are also good sources of MUFAs, contributing almost 50% to the FA profile [roti canai, might be due to the lentils gravy or dhal, as these two are commonly served together. In contrast, nasi lemak and apam balik might be good sources of MUFAs due to the use of peanuts that predominantly contain MUFAs [kerepek or deep-fried chips in this study also contained peanuts, which contribute additional MUFAs to this deep-fried snack. Chicken burgers also contain high MUFAs content, which may be linked to the chicken patty used. Laskowski et al. [kuey teow, fried noodles, noodles with gravy, fried rice, and chicken rice, were also among the high-MUFA-containing foods. The low MUFA (7%) content in kuih jelurut was possibly due to the lack of MUFA-rich ingredients used in this sweet dessert, as the main ingredients [gula apong, and coconut milk. The rich source of MUFAs reported in fried vermicelli should be made known to the consumers, as this main meal is one of the most commonly consumed ready-to-eat dishes among adults in Malaysia [The majority (82.1%) of the street foods in this study contained high MUFAs content. The MUFAs content is likely from the use of palm oil used to deep-fry most of the foods and other MUFA-rich ingredients. Although palm oil contains a high percentage of SFAs, it is also rich in MUFAs, with a nearly equal amount of SFAs ,68. Henc profile ,67,68. Tin MUFAs . Likewisi et al.  reportedredients  (p. 148)Malaysia . Apam balik and apam balik with cheese were the two street foods with the most amount of PUFAs. In a normal set of apam balik, this sweet pancake-like dessert is filled with nuts, sugar, and corn. The PUFAs content may be due to the use of PUFA-rich ingredients such as nuts. Therefore, apam balik could be considered a good source of PUFAs and may be beneficial to health, as frequent consumption of nuts has been demonstrated to have an association with lowering the risk of developing diet-related chronic diseases such as CVD [h as CVD ,74. HoweThis study showed that deep-fried street foods tended to contain higher SFAs content than foods prepared using healthier methods, such as steaming, baking, stewing, grilling, pan-frying, and stir-frying. Deep-frying is the process of immersing food in hot oil between 130 °C and 190 °C . SeveralThis study demonstrated that SFAs content predominated in all three categories of street foods—main meals, snacks, and desserts, followed by MUFAs and PUFAs. Desserts contained the most SFAs, whereas snacks contained the most MUFAs and PUFAs. The majority of street foods that contained medium to high SFAs were deep-fried dishes, coconut milk-based dishes, and those that contained butter, processed meats, animal fats, or cheese. Meanwhile, street foods with low amounts of SFAs were prepared by steaming and stir-frying. Street foods that contained nuts had the highest content of MUFAs and PUFAs. Similarly, deep-fried street foods were also high in MUFAs. The local street foods contained no TFAs, possibly due to the limited use of partially hydrogenated fats such as ghee. The street foods that were prepared by deep-frying mostly contained high SFAs content, whereas, among the street foods that were prepared using healthier methods, most contained low SFAs content. Apart from updating the MyFCD database, these findings may encourage consumers to limit their consumption of coconut milk-based foods, deep-fried foods, and processed foods, as well as recognise several local street foods that may potentially increase their intake of unsaturated fats. Improving the nutritional composition of local street foods is imperative to creating a healthier food environment and reducing the development of diet-related chronic diseases. Educating street food vendors could be one of the ways to achieve this. Given that no amount of TFAs was detected in any of the street foods, especially those that are deep-fried, future studies could focus on the factors that may influence the TFAs content in foods, such as the frying temperature, duration of frying, usage of TFA-rich ingredients, and the practice of reusing oils.