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Ophthalmology Ophthalmology

Association between vitamin D and myopia in adolescents and young adults: Evidence of national cross-sectional study.

In European journal of ophthalmology

PURPOSE : Studies have indicated that the observed association between vitamin D and myopia was confounded by time spent outdoors. This study aimed to elucidate this association using a national cross-sectional dataset.

METHODS : Participants with 12 to 25 years who participated in non-cycloplegic vision exam from National Health and Nutrition Examination Survey (NHANES) 2001 to 2008 were included in the present study. Myopia was defined as spherical equivalent of any eyes ≤ -0.5 diopters (D).

RESULTS : 7,657 participants were included. The weighted proportion of emmetropes, mild myopia, moderate myopia, and high myopia were 45.5%, 39.1%, 11.6%, and 3.8%, respectively. After adjusting for age, gender, ethnicity, TV/computer usage, and stratified by education attainment, every 10 nmol/L increment of serum 25(OH)D concentration was associated with a reduced risk of myopia (odds ratio [OR] = 0.96, 95% confidence interval [95%CI] 0.93-0.99 for any myopia; OR = 0.96, 95%CI 0.93-1.00 for mild myopia; OR = 0.99, 95%CI 0.97-1.01 for moderate myopia; OR = 0.89, 95%CI 0.84-0.95 for high myopia). Serum 25(OH)D level was closely correlated with time spent outdoors. After categorizing time spent outdoors into quarters (low, low-medium, medium-high, and high), every 1 quarter increment of time spent outdoors was associated with 2.49 nmol/L higher serum 25(OH)D concentration. After adjusting for time spent outdoors, serum 25(OH)D level did not show significant association with myopia (OR = 1.01, 95%CI 0.94-1.06 for 10 nmol/L increment).

CONCLUSIONS : The association between high serum vitamin D and reduced risk of myopia is confounded by longer time spent outdoors. Evidence from the present study does not support that there is a direct association between serum vitamin D level with myopia.

Zhang Rui-Heng, Yang Qiong, Dong Li, Li Yi-Fan, Zhou Wen-Da, Wu Hao-Tian, Li He-Yan, Shao Lei, Zhang Chuan, Wang Ya-Xing, Wei Wen Bin

2023-Mar-03

National Health and Nutrition Examination Survey, myopia, vitamin D

Internal Medicine Internal Medicine

A Computational Approach in the Diagnostic Process of COVID-19: The Missing Link between the Laboratory and Emergency Department.

In Frontiers in bioscience (Landmark edition)

BACKGROUND : The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the right evaluation of viral infection. According to the Centers for Disease Control and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in respiratory samples is the gold standard for confirming the disease. However, it has practical limitations as time-consuming procedures and a high rate of false-negative results. We aim to assess the accuracy of COVID-19 classifiers based on Arificial Intelligence (AI) and statistical classification methods adapted on blood tests and other information routinely collected at the Emergency Departments (EDs).

METHODS : Patients admitted to the ED of Careggi Hospital from April 7th-30th 2020 with pre-specified features of suspected COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19 likely/unlikely case, based on clinical features and bedside imaging support. Considering the limits of each method to identify a case of COVID-19, further evaluation was performed after an independent clinical review of 30-day follow-up data. Using this as a gold standard, several classifiers were implemented: Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor (K-NN), Naive Bayes (NB).

RESULTS : Most of the classifiers show a ROC >0.80 on both internal and external validation samples but the best results are obtained applying RF, LR and NN. The performance from the external validation sustains the proof of concept to use such mathematical models fast, robust and efficient for a first identification of COVID-19 positive patients. These tools may constitute both a bedside support while waiting for RT-PCR results, and a tool to point to a deeper investigation, by identifying which patients are more likely to develop into positive cases within 7 days.

CONCLUSIONS : Considering the obtained results and with a rapidly changing virus, we believe that data processing automated procedures may provide a valid support to the physicians facing the decision to classify a patient as a COVID-19 case or not.

Lanzilao Luisa, Mariniello Antonella, Polenzani Bianca, Aldinucci Alessandra, Nazerian Peiman, Prota Alessio, Grifoni Stefano, Tonietti Barbara, Neri Chiara, Turco Livia, Fanelli Alessandra, Amedei Amedeo, Stanghellini Elena

2023-Feb-22

COVID-19, automated classifiers, diagnosis, laboratory medicine, machine learning, “physicians gestalt”

General General

Research of intelligent reasoning system of Arabidopsis thaliana phenotype based on automated multi-task machine learning.

In Frontiers in plant science

Traditional machine learning in plant phenotyping research requires the assistance of professional data scientists and domain experts to adjust the structure and hy-perparameters tuning of neural network models with much human intervention, making the model training and deployment ineffective. In this paper, the automated machine learning method is researched to construct a multi-task learning model for Arabidopsis thaliana genotype classification, leaf number, and leaf area regression tasks. The experimental results show that the genotype classification task's accuracy and recall achieved 98.78%, precision reached 98.83%, and classification F 1 value reached 98.79%, as well as the R 2 of leaf number regression task and leaf area regression task reached 0.9925 and 0.9997 respectively. The experimental results demonstrated that the multi-task automated machine learning model can combine the benefits of multi-task learning and automated machine learning, which achieved more bias information from related tasks and improved the overall classification and prediction effect. Additionally, the model can be created automatically and has a high degree of generalization for better phenotype reasoning. In addition, the trained model and system can be deployed on cloud platforms for convenient application.

Yuan Peisen, Xu Shuning, Zhai Zhaoyu, Xu Huanliang

2023

Arabidopsis thaliana, automated machine learning, cloud deployment, multi-task learning, plant phenotype reasoning

Ophthalmology Ophthalmology

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.

In Frontiers in ophthalmology

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

Ma Da, Pasquale Louis R, Girard Michaël J A, Leung Christopher K S, Jia Yali, Sarunic Marinko V, Sappington Rebecca M, Chan Kevin C

2023

artificial intelligence, deep learning, glaucoma, optical coherence tomography, reverse translation, transfer learning, visual field

General General

In-vivo processing of nanoassemblies: a neglected framework for recycling to bypass nanotoxicological therapeutics.

In Toxicology research

The proof-of-concept of nanomaterials (NMs) in the fields of imaging, diagnosis, treatment, and theranostics shows the importance in biopharmaceuticals development due to structural orientation, on-targeting, and long-term stability. However, biotransformation of NMs and their modified form in human body via recyclable techniques are not explored owing to tiny structures and cytotoxic effects. Recycling of NMs offers advantages of dose reduction, re-utilization of the administered therapeutics providing secondary release, and decrease in nanotoxicity in human body. Therefore, approaches like in-vivo re-processing and bio-recycling are essential to overcome nanocargo system-associated toxicities such as hepatotoxicity, nephrotoxicity, neurotoxicity, and lung toxicity. After 3-5 stages of recycling process of some NMs of gold, lipid, iron oxide, polymer, silver, and graphene in spleen, kidney, and Kupffer's cells retain biological efficiency in the body. Thus, substantial attention towards recyclability and reusability of NMs for sustainable development necessitates further advancement in healthcare for effective therapy. This review article outlines biotransformation of engineered NMs as a valuable source of drug carriers and biocatalyst with critical strategies like pH modification, flocculation, or magnetization for recovery of NMs in the body. Furthermore, this article summarizes the challenges of recycled NMs and advances in integrated technologies such as artificial intelligence, machine learning, in-silico assay, etc. Therefore, potential contribution of NM's life-cycle in the recovery of nanosystems for futuristic developments require consideration in site-specific delivery, reduction of dose, remodeling in breast cancer therapy, wound healing action, antibacterial effect, and for bioremediation to develop ideal nanotherapeutics.

Kantak Maithili, Shende Pravin

2023-Feb

biotransformation, graphene, iron oxide nanoparticles, nanomaterials, polymeric nanoparticles, recycling

Radiology Radiology

Bone age recognition based on mask R-CNN using xception regression model.

In Frontiers in physiology

Background and Objective: Bone age detection plays an important role in medical care, sports, judicial expertise and other fields. Traditional bone age identification and detection is according to manual interpretation of X-ray images of hand bone by doctors. This method is subjective and requires experience, and has certain errors. Computer-aided detection can effectually enhance the validity of medical diagnosis, especially with the fast development of machine learning and neural network, the method of bone age recognition using machine learning has gradually become the focus of research, which has the advantages of simple data pretreatment, good robustness and high recognition accuracy. Methods: In this paper, the hand bone segmentation network based on Mask R-CNN was proposed to segment the hand bone area, and the segmented hand bone region was directly input into the regression network for bone age evaluation. The regression network is using an enhancd network Xception of InceptionV3. After the output of Xception, the convolutional block attention module is connected to refine the feature mapping from channel and space to obtain more effective features. Results: According to the experimental results, the hand bone segmentation network model based on Mask R-CNN can segment the hand bone region and eliminate the interference of redundant background information. The average Dice coefficient on the verification set is 0.976. The mean absolute error of predicting bone age on our data set was only 4.97 months, which exceeded the accuracy of most other bone age assessment methods. Conclusion: Experiments show that the accuracy of bone age assessment can be enhancd by using the Mask R-CNN-based hand bone segmentation network and the Xception bone age regression network to form a model, which can be well applied to actual clinical bone age assessment.

Liu Zhi-Qiang, Hu Zi-Jian, Wu Tian-Qiong, Ye Geng-Xin, Tang Yu-Liang, Zeng Zi-Hua, Ouyang Zhong-Min, Li Yuan-Zhe

2023

Xception, bone age assessment, deep learning, hand bone X-ray images, mask R-CNN