Pub Date : 2024-07-23DOI: 10.69598/sehs.18.24050004
Tushar Palekar, Rasika Panse Kaluskar
Motion sickness susceptibility depends on the sensitivity of each individual and the ability of the vestibular system to adapt to continued exposure to the stimulus affecting activities of daily living. For this systematic review, data were extracted from PubMed, Pedro, Cochrane, and Google Scholar from 2000 to 2021 publication dates using the following MESH terms: ‘motion sickness’, ‘exercise’, ‘physiotherapy’, and ‘physical therapy’. A total of 41,789 articles were identified from 2 databases, of which 41,767 were excluded, and 18 were saved for secondary screening. After a detailed review of these articles, 7 articles were selected, including RCTs, case studies, and experimental studies. Strong evidence was identified for 2 strategies used, including breathing techniques and vestibular adaptation exercises. Physiotherapy interventions play an important role for individuals with motion sickness by alleviating the symptoms.
{"title":"Physiotherapy interventions for motion sickness: A systematic review","authors":"Tushar Palekar, Rasika Panse Kaluskar","doi":"10.69598/sehs.18.24050004","DOIUrl":"https://doi.org/10.69598/sehs.18.24050004","url":null,"abstract":"Motion sickness susceptibility depends on the sensitivity of each individual and the ability of the vestibular system to adapt to continued exposure to the stimulus affecting activities of daily living. For this systematic review, data were extracted from PubMed, Pedro, Cochrane, and Google Scholar from 2000 to 2021 publication dates using the following MESH terms: ‘motion sickness’, ‘exercise’, ‘physiotherapy’, and ‘physical therapy’. A total of 41,789 articles were identified from 2 databases, of which 41,767 were excluded, and 18 were saved for secondary screening. After a detailed review of these articles, 7 articles were selected, including RCTs, case studies, and experimental studies. Strong evidence was identified for 2 strategies used, including breathing techniques and vestibular adaptation exercises. Physiotherapy interventions play an important role for individuals with motion sickness by alleviating the symptoms.","PeriodicalId":36726,"journal":{"name":"Science, Engineering and Health Studies","volume":"32 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.69598/sehs.18.24020001
Burapa Phatichon, C. Chantrapornchai
This work considers the use of virtual reality (VR) technology to self-teach first aid training. It is known that VR provides realistic experiences to train individuals. We created interactable first aid lessons using the Unity engine and a VR interaction framework, and provided hands-on experience, with tests based on practical exercises. The VR application materials, with the first aid knowledge gathered from many government and hospital websites, consisted of 10 lessons and 7 tests. The lessons were appraised by 14 learners, resulting in a total average satisfaction score of 9.1. The post training first aid knowledge test scores increased by 35% from pre-course level. All learners reported having greater confidence, with their practical test scores improving by an average of 22% after multiple tests, demonstrating that the application could be effectively used for learning and practice purposes.
{"title":"First aid training using virtual reality","authors":"Burapa Phatichon, C. Chantrapornchai","doi":"10.69598/sehs.18.24020001","DOIUrl":"https://doi.org/10.69598/sehs.18.24020001","url":null,"abstract":"This work considers the use of virtual reality (VR) technology to self-teach first aid training. It is known that VR provides realistic experiences to train individuals. We created interactable first aid lessons using the Unity engine and a VR interaction framework, and provided hands-on experience, with tests based on practical exercises. The VR application materials, with the first aid knowledge gathered from many government and hospital websites, consisted of 10 lessons and 7 tests. The lessons were appraised by 14 learners, resulting in a total average satisfaction score of 9.1. The post training first aid knowledge test scores increased by 35% from pre-course level. All learners reported having greater confidence, with their practical test scores improving by an average of 22% after multiple tests, demonstrating that the application could be effectively used for learning and practice purposes.","PeriodicalId":36726,"journal":{"name":"Science, Engineering and Health Studies","volume":"64 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.69598/sehs.18.24050003
M. F. Mastuki, Noryuslina Yusoff, Nur Suraya Zainal Abidin
This study assesses the antimicrobial potential of ethyl acetate and dichloromethane extracts obtained from pomelo, Citrus maxima (C. maxima), flavedo against various food pathogens and spoilage microorganisms. The antimicrobial activities of these extracts were evaluated using the agar disc diffusion method against gram-positive bacteria (Bacillus cereus), Staphylococcus aureus and gram-negative bacteria (Escherichia coli). The results indicated that both extracts demonstrated antibacterial properties against the tested microorganisms. The ethyl acetate extract exhibited significantly higher antibacterial activity against the majority of bacterial strains compared to the dichloromethane extract, particularly against S. aureus and B. cereus. However, dichloromethane extract showed a better effect on E. coli, with the inhibition zone ranging from 8.7 to 11.3 mm. S. aureus displayed the highest sensitivity to ethyl acetate and dichloromethane extracts of pomelo flavedo with inhibition zones ranging from 1.3 to 1.5 mm, respectively. In conclusion, the findings suggest that pomelo extracts have significant potential as natural antimicrobials and can be safely utilized as food preservatives. This highlights the value of pomelo as a potential source of antimicrobial compounds for food safety and preservation purposes.
{"title":"Antimicrobial properties of Citrus maxima flavedo extracts against food pathogens and spoilage microorganisms","authors":"M. F. Mastuki, Noryuslina Yusoff, Nur Suraya Zainal Abidin","doi":"10.69598/sehs.18.24050003","DOIUrl":"https://doi.org/10.69598/sehs.18.24050003","url":null,"abstract":"This study assesses the antimicrobial potential of ethyl acetate and dichloromethane extracts obtained from pomelo, Citrus maxima (C. maxima), flavedo against various food pathogens and spoilage microorganisms. The antimicrobial activities of these extracts were evaluated using the agar disc diffusion method against gram-positive bacteria (Bacillus cereus), Staphylococcus aureus and gram-negative bacteria (Escherichia coli). The results indicated that both extracts demonstrated antibacterial properties against the tested microorganisms. The ethyl acetate extract exhibited significantly higher antibacterial activity against the majority of bacterial strains compared to the dichloromethane extract, particularly against S. aureus and B. cereus. However, dichloromethane extract showed a better effect on E. coli, with the inhibition zone ranging from 8.7 to 11.3 mm. S. aureus displayed the highest sensitivity to ethyl acetate and dichloromethane extracts of pomelo flavedo with inhibition zones ranging from 1.3 to 1.5 mm, respectively. In conclusion, the findings suggest that pomelo extracts have significant potential as natural antimicrobials and can be safely utilized as food preservatives. This highlights the value of pomelo as a potential source of antimicrobial compounds for food safety and preservation purposes.","PeriodicalId":36726,"journal":{"name":"Science, Engineering and Health Studies","volume":"33 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.69598/sehs.18.24050002
Porawan Aumklad, Phuvamin Suriyaamporn, S. Panomsuk, Boonnada Pamornpathomkul, P. Opanasopit
Artificial intelligence (AI) is now applied across various domains in nanomedicine. Self-microemulsifying drug delivery systems (SMEDDS) are isotropic mixtures of active compounds that can produce spontaneous oil-in-water emulsions. SMEDDS can improve the solubility of lipophilic drugs such as progesterone (PG). However, the physicochemical properties of SMEDDS are sensitive to various factors, depending on their components. This study generated a prediction model algorithm for PG-loaded SMEDDS to provide appropriate droplet size (DS), polydispersity index (PDI), zeta potential (ZP), and % drug loading (%DL). Various machine learning algorithms were compared for their accuracy, as reported by root mean square error (RMSE) and coefficient of determination (R2). The selected machine learning algorithms were implemented with an unseen training dataset, and the model performance was re-evaluated. The correlation of each factor was investigated. Self-micro emulsifying (SME) time, cloud point, pH, and viscosity of predicted PG-loaded SMEDDS were evaluated. Results showed that linear regression algorithms gave the highest accuracy and optimal prediction performance with the highest RMSE and R2. All components of PG-loaded SMEDDS correlated with DS, PDI, ZP, and %DL. The physical properties of predicted PG-loaded SMEDDS showed SME time within 39 s, cloud point at around 71.3 °C, pH between 5.53 and 6.10, and viscosity between 10.32 and 14.23 cP. This research outlined the application of a machine learning algorithm to build a prediction model to optimize PG-loaded SMEDDS drug delivery formulations.
{"title":"Artificial intelligence-aided rational design and prediction model for progesterone-loaded self-microemulsifying drug delivery system formulations","authors":"Porawan Aumklad, Phuvamin Suriyaamporn, S. Panomsuk, Boonnada Pamornpathomkul, P. Opanasopit","doi":"10.69598/sehs.18.24050002","DOIUrl":"https://doi.org/10.69598/sehs.18.24050002","url":null,"abstract":"Artificial intelligence (AI) is now applied across various domains in nanomedicine. Self-microemulsifying drug delivery systems (SMEDDS) are isotropic mixtures of active compounds that can produce spontaneous oil-in-water emulsions. SMEDDS can improve the solubility of lipophilic drugs such as progesterone (PG). However, the physicochemical properties of SMEDDS are sensitive to various factors, depending on their components. This study generated a prediction model algorithm for PG-loaded SMEDDS to provide appropriate droplet size (DS), polydispersity index (PDI), zeta potential (ZP), and % drug loading (%DL). Various machine learning algorithms were compared for their accuracy, as reported by root mean square error (RMSE) and coefficient of determination (R2). The selected machine learning algorithms were implemented with an unseen training dataset, and the model performance was re-evaluated. The correlation of each factor was investigated. Self-micro emulsifying (SME) time, cloud point, pH, and viscosity of predicted PG-loaded SMEDDS were evaluated. Results showed that linear regression algorithms gave the highest accuracy and optimal prediction performance with the highest RMSE and R2. All components of PG-loaded SMEDDS correlated with DS, PDI, ZP, and %DL. The physical properties of predicted PG-loaded SMEDDS showed SME time within 39 s, cloud point at around 71.3 °C, pH between 5.53 and 6.10, and viscosity between 10.32 and 14.23 cP. This research outlined the application of a machine learning algorithm to build a prediction model to optimize PG-loaded SMEDDS drug delivery formulations.","PeriodicalId":36726,"journal":{"name":"Science, Engineering and Health Studies","volume":" 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This retrospective survey examines drug use patterns in COVID-19 patients from 2021 to 2022 with 81 participants, who reported 13 symptoms between March and May 2023. Application of the k-means clustering method led to identification of three distinct symptom severities, severe (Cluster I), moderate (Cluster II), and mild (Cluster III), with respective average scores of 3.67±0.87, 3.20±0.98, and 1.87±0.81. In Clusters I and II, myalgia was the most notable symptom, while in Cluster III, sore throat was predominant. On average, individuals in Clusters I–III used 2.00–2.34 types of drugs, with use of a single drug having the highest frequency. Notably, Andrographis paniculata capsules were highly utilized across all clusters (51.85%), while favipiravir was less often used. Furthermore, one in five participants in the combined Clusters I and II employed substantial pharmaceutical interventions for COVID-19 treatment, whereas in Cluster III, this use remained below 10%. This research provides valuable insights into drug use patterns for managing COVID-19. The findings offer crucial information about symptoms from each cluster, tailoring treatment approaches to specific symptom severity clusters as well as overlapping medications.
这项回顾性调查研究了2021年至2022年期间COVID-19患者的药物使用模式，共有81名参与者在2023年3月至5月期间报告了13种症状。应用 k-means 聚类方法确定了三种不同的症状严重程度，即重度（群组 I）、中度（群组 II）和轻度（群组 III），其平均得分分别为 3.67±0.87、3.20±0.98 和 1.87±0.81。在群组 I 和 II 中，肌痛是最显著的症状，而在群组 III 中，喉咙痛是主要症状。组群 I 至组群 III 的患者平均使用 2.00-2.34 种药物，其中使用单一药物的频率最高。值得注意的是，穿心莲胶囊在所有群组中的使用率都很高（51.85%），而法非拉韦的使用率较低。此外，在群组 I 和群组 II 中，每五名参与者中就有一人使用大量药物干预来治疗 COVID-19，而在群组 III 中，这一比例仍低于 10%。这项研究为我们了解治疗 COVID-19 的药物使用模式提供了宝贵的信息。研究结果提供了有关每个群组症状的重要信息，可针对特定症状严重程度群组以及重叠药物定制治疗方法。
{"title":"Drug use patterns in COVID-19 patients: A retrospective survey 2021–2022","authors":"Phaksachiphon Khanthong, Vadhana Jayathavaj, Sarinrat Jitjum","doi":"10.69598/sehs.18.24050001","DOIUrl":"https://doi.org/10.69598/sehs.18.24050001","url":null,"abstract":"This retrospective survey examines drug use patterns in COVID-19 patients from 2021 to 2022 with 81 participants, who reported 13 symptoms between March and May 2023. Application of the k-means clustering method led to identification of three distinct symptom severities, severe (Cluster I), moderate (Cluster II), and mild (Cluster III), with respective average scores of 3.67±0.87, 3.20±0.98, and 1.87±0.81. In Clusters I and II, myalgia was the most notable symptom, while in Cluster III, sore throat was predominant. On average, individuals in Clusters I–III used 2.00–2.34 types of drugs, with use of a single drug having the highest frequency. Notably, Andrographis paniculata capsules were highly utilized across all clusters (51.85%), while favipiravir was less often used. Furthermore, one in five participants in the combined Clusters I and II employed substantial pharmaceutical interventions for COVID-19 treatment, whereas in Cluster III, this use remained below 10%. This research provides valuable insights into drug use patterns for managing COVID-19. The findings offer crucial information about symptoms from each cluster, tailoring treatment approaches to specific symptom severity clusters as well as overlapping medications.","PeriodicalId":36726,"journal":{"name":"Science, Engineering and Health Studies","volume":" 67","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-12DOI: 10.69598/sehs.18.24030001
Usaraphan Pithanthanakul, V. Rungsardthong, Bang-On Kiatthanakorn, S. Vatanyoopaisarn, B. Thumthanaruk, D. Uttapap, Yulong Ding
The duration of the fragrance is one of the factors that influences a customer’s choice of fabric softeners. Fragrances, a mixture of various aromatic compounds, usually present low solubility and stability in the environment, so they do not last long. Micro/nanoencapsulation technology of fragrances can be used to solve this problem. This research studied the factors influencing the preparation of zein nanoencapsulation with fragrances. Fruity fragrances were encapsulated in zein nanoparticles (PF-ZNs) by the liquid-liquid dispersion method, using Tween 20 as a surfactant. The effects of zein and ethanol concentrations of 0.4%–0.8% and 70%–85%, respectively, homogenized at 15,000 rpm for 5–15 min on zein encapsulation, were investigated. The fruity fragrance was loaded at 30% of the zein content. Increased zein concentration resulted in increased particle size with decreased zeta potential. Particle agglomeration was detected when the ethanol concentration was decreased from 85% to 75%. Compared to using a vacuum concentrator centrifuge, the zein nanoparticles agglomerated less when freeze-dried. The encapsulation efficiency of the fruity fragrance was 39.7%–68.4%, and the yield percentage was 54.5%–72.3% when freeze-drying was used.
{"title":"Factors influencing the properties of zein nanoparticles encapsulated with fragrances prepared by liquid-liquid dispersion","authors":"Usaraphan Pithanthanakul, V. Rungsardthong, Bang-On Kiatthanakorn, S. Vatanyoopaisarn, B. Thumthanaruk, D. Uttapap, Yulong Ding","doi":"10.69598/sehs.18.24030001","DOIUrl":"https://doi.org/10.69598/sehs.18.24030001","url":null,"abstract":"The duration of the fragrance is one of the factors that influences a customer’s choice of fabric softeners. Fragrances, a mixture of various aromatic compounds, usually present low solubility and stability in the environment, so they do not last long. Micro/nanoencapsulation technology of fragrances can be used to solve this problem. This research studied the factors influencing the preparation of zein nanoencapsulation with fragrances. Fruity fragrances were encapsulated in zein nanoparticles (PF-ZNs) by the liquid-liquid dispersion method, using Tween 20 as a surfactant. The effects of zein and ethanol concentrations of 0.4%–0.8% and 70%–85%, respectively, homogenized at 15,000 rpm for 5–15 min on zein encapsulation, were investigated. The fruity fragrance was loaded at 30% of the zein content. Increased zein concentration resulted in increased particle size with decreased zeta potential. Particle agglomeration was detected when the ethanol concentration was decreased from 85% to 75%. Compared to using a vacuum concentrator centrifuge, the zein nanoparticles agglomerated less when freeze-dried. The encapsulation efficiency of the fruity fragrance was 39.7%–68.4%, and the yield percentage was 54.5%–72.3% when freeze-drying was used.","PeriodicalId":36726,"journal":{"name":"Science, Engineering and Health Studies","volume":"29 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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