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Amini P, Okeme JO. Tear Fluid as a Matrix for Biomonitoring Environmental and Chemical Exposures. Curr Environ Health Rep 2024; 11:340-355. [PMID: 38967858 DOI: 10.1007/s40572-024-00454-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2024] [Indexed: 07/06/2024]
Abstract
PURPOSE Exposures to hazardous chemicals have been linked to many detrimental health effects and it is therefore critical to have effective biomonitoring methods to better evaluate key environmental exposures that increase the risk of chronic disease and death. Traditional biomonitoring utilizing blood and urine is limited due to the specialized skills and invasiveness of collecting these fluid samples. This systematic review focuses on tear fluid, which is largely under-researched, as a promising complementary matrix to the traditional fluids used for biomonitoring. The objective is to evaluate the practicability of using human tear fluid for biomonitoring environmental exposures, highlighting potential pitfalls and opportunities. RECENT FINDING Tear fluid biomonitoring represents a promising method for assessing exposures because it can be collected with minimal invasiveness and tears contain exposure markers from both the external and internal environments. Tear fluid uniquely interfaces with the external environment at the air-tear interface, providing a surface for airborne chemicals to diffuse into the ocular environment and interact with biomolecules. Tear fluid also contains molecules from the internal environment that have travelled from the blood to tears by crossing the blood-tear barrier. This review demonstrates that tear fluid can be used to identify hazardous chemicals from the external environment and differentiate exposure groups.
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Affiliation(s)
- Parshawn Amini
- Department of Chemistry & Chemical Biology, McMaster University, Hamilton, ON, Ontario, L8S 4L8, Canada
| | - Joseph O Okeme
- Department of Chemistry & Chemical Biology, McMaster University, Hamilton, ON, Ontario, L8S 4L8, Canada.
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Wu SN, Qin DY, Zhu L, Guo SJ, Li X, Huang CH, Hu J, Liu Z. Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning. Cancer Sci 2024. [PMID: 38992984 DOI: 10.1111/cas.16276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/13/2024] Open
Abstract
Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.
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Affiliation(s)
- Shi-Nan Wu
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Dan-Yi Qin
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Linfangzi Zhu
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Shu-Jia Guo
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Xiang Li
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Cai-Hong Huang
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Jiaoyue Hu
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Department of Ophthalmology, Xiang'an Hospital of Xiamen University, Xiamen, Fujian, China
| | - Zuguo Liu
- Xiamen University Affiliated Xiamen Eye Center, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Department of Ophthalmology, Xiang'an Hospital of Xiamen University, Xiamen, Fujian, China
- Department of Ophthalmology, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
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Chidi-Egboka NC, Jalbert I, Wagner P, Golebiowski B. Blinking and normal ocular surface in school-aged children and the effects of age and screen time. Br J Ophthalmol 2023; 107:1613-1620. [PMID: 36002237 DOI: 10.1136/bjo-2022-321645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/09/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Baseline ocular surface characteristics in children require investigation. This study characterised blinking and relationships with ocular symptoms, tear film and digital device use. METHODS 45 children aged 6-15 years (56% female) participated in a cross-sectional study. Ocular surface symptoms (Instant Ocular Symptoms Survey, Dry Eye Questionnaire 5, Symptoms Assessment in Dry Eye, Ocular Surface Disease Index, Ocular Comfort Index and Numerical Rating Scale) and clinical indices (lipid layer thickness, tear secretion and stability, meibomian gland) were assessed. Blink rate and interblink interval were measured in situ using a wearable eye-tracking headset (Pupil Labs GmbH, Germany). Associations between blinking, ocular surface, age, and digital device use (bivariate and partial correlations) and between automated and manually counted blink rate (Bland & Altman) were examined. RESULTS Mean blink rate and interblink interval were 20.5±10.5 blinks/min and 2.9±1.9 s during conversation. There was no difference between automated and manual blink rate (p=0.78) and no relationship between blinking and digital device use, age or sex. Mean group symptoms were within normal range and not associated with clinical measurements including blinking. Greater tear volume was associated with a faster blink rate (r=0.46, p=0.001) and shorter interblink interval (r=-0.36, p=0.02). Older age was associated with improved tear volume (r=0.37, p=0.01) and stability (r=0.38, p=0.01). CONCLUSIONS Blinking characterised in situ was not impacted by age or habitual digital device use. A faster blink rate was associated with greater tear volume but not symptoms. Improved tear function was found with age suggesting that the ocular surface continues to develop through childhood.
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Affiliation(s)
- Ngozi Charity Chidi-Egboka
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Isabelle Jalbert
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Peter Wagner
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Blanka Golebiowski
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
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Zhao H, Shao Y, Wu SN, Tang LY, Zhang Q, Zhao C, Chen XY, Fu J. Characteristic of red eye related diseases of Han and Uygur population in Urumchi compared with Shanghai, China. Int J Ophthalmol 2022; 15:1165-1173. [PMID: 35919311 DOI: 10.18240/ijo.2022.07.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 03/17/2022] [Indexed: 11/23/2022] Open
Abstract
AIM To study the characteristics, relative distribution and to compare causes of red eye in ophthalmic clinics in Urumchi and Shanghai, China. METHODS Data on continuous cases of red-eye patients admitted to the Ophthalmology Center of Xinhua Hospital Affiliated to Shanghai Jiao Tong University and the First Affiliated Hospital of Xinjiang Medical University were collected between November 2018 and September 2019. Demographic data, the incidence of red eye and related disease distribution of all cases were obtained. The independent t-test method was used for age comparison, while the Chi-square test was used to compare classified data information. RESULTS The information on 335 and 415 patients with red eyes in Shanghai and Urumchi were collected, respectively. The main causes of red eye were conjunctival disease and dry eye. The age of female patients with red eyes was significantly higher than that of males, and the proportion of female patients with dry eyes was also higher. Red-eye-related diseases occurred more frequently in patients over 46 years old than in those under 18, and dry eye was more common with increasing age. The incidence of infectious conjunctivitis in Urumchi was significantly higher than that in Shanghai, and allergic conjunctivitis occurred more frequently in spring, summer, or autumn than in winter (all P<0.05). CONCLUSION Significant differences exist in the distribution of red-eye-related diseases in Urumchi and Shanghai regions of China, and distribution varies with age and season, the latter being an important feature of allergic conjunctivitis.
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Affiliation(s)
- Hui Zhao
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Jiangxi Province Ocular Disease Clinical Research Center, Nanchang 330006, Jiangxi Province, China
| | - Shi-Nan Wu
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Jiangxi Province Ocular Disease Clinical Research Center, Nanchang 330006, Jiangxi Province, China
| | - Li-Ying Tang
- Eye Institute of Xiamen University, Xiamen 361102, Fujian Province, China
| | - Qi Zhang
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Chen Zhao
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Xue-Yi Chen
- The First Affiliated Hospital of Xin Jiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China
| | - Jing Fu
- The First Affiliated Hospital of Xin Jiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China
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