Science, Engineering and Health Studies最新文献

Physiotherapy interventions for motion sickness: A systematic review 晕动病的物理治疗干预：系统回顾

Science, Engineering and Health Studies Pub Date : 2024-07-23 DOI: 10.69598/sehs.18.24050004

Tushar Palekar, Rasika Panse Kaluskar

引用次数: 0

First aid training using virtual reality 利用虚拟现实技术进行急救培训

Science, Engineering and Health Studies Pub Date : 2024-07-23 DOI: 10.69598/sehs.18.24020001

Burapa Phatichon, C. Chantrapornchai

引用次数: 0

Antimicrobial properties of Citrus maxima flavedo extracts against food pathogens and spoilage microorganisms 柑橘黄酮提取物对食品病原体和腐败微生物的抗菌特性

Science, Engineering and Health Studies Pub Date : 2024-07-23 DOI: 10.69598/sehs.18.24050003

M. F. Mastuki, Noryuslina Yusoff, Nur Suraya Zainal Abidin

{"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}

引用次数: 0

Artificial intelligence-aided rational design and prediction model for progesterone-loaded self-microemulsifying drug delivery system formulations 人工智能辅助黄体酮自微乳化给药系统配方的合理设计与预测模型

Science, Engineering and Health Studies Pub Date : 2024-07-18 DOI: 10.69598/sehs.18.24050002

Porawan Aumklad, Phuvamin Suriyaamporn, S. Panomsuk, Boonnada Pamornpathomkul, P. Opanasopit

{"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}

引用次数: 0

Drug use patterns in COVID-19 patients: A retrospective survey 2021–2022 COVID-19 患者的用药模式：回顾性调查 2021-2022

Science, Engineering and Health Studies Pub Date : 2024-07-18 DOI: 10.69598/sehs.18.24050001

Phaksachiphon Khanthong, Vadhana Jayathavaj, Sarinrat Jitjum

{"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}

引用次数: 0

Factors influencing the properties of zein nanoparticles encapsulated with fragrances prepared by liquid-liquid dispersion 影响通过液-液分散法制备的包裹香料的玉米蛋白纳米粒子特性的因素

Science, Engineering and Health Studies Pub Date : 2024-07-12 DOI: 10.69598/sehs.18.24030001

Usaraphan Pithanthanakul, V. Rungsardthong, Bang-On Kiatthanakorn, S. Vatanyoopaisarn, B. Thumthanaruk, D. Uttapap, Yulong Ding

{"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}

引用次数: 0