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The presence of phosphate in water has become a worldwide problem because of improving eutrophication and decreasing the quality of water. In this work, phosphate () removal from water using activated carbon was studied and main process parameters such as initial phosphate concentration (C0), adsorbent dosage, and pH of solution have been optimized to obtain maximum removal. Central composite design in response surface methodology (RSM) package has been used to perform the experimental design according to RSM analysis, the phosphate removal model proved to be highly significant with very low probability value (<0.0001). Based on the developed predictive model, the optimum conditions were 0.53 (g/50 mL) adsorbent dosage, pH 4, andC0 = 11.62 (mg/L) for having 95.41% of phosphate removal. This optimum predicted result was investigated by performing the corresponding experiment, and it was observed that the experiment and model result were fitted well. Kinetic data were analyzed using pseudo-first-order, pseudo-second-order, and intraparticle diffusion equations. According to the results, adsorption of phosphate onto activated carbon is an effective approach and economical alternative process in comparison with common applications. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT), as a large language model (LLM), for diagnosing glaucoma using the Ocular Hypertension Treatment Study (OHTS) dataset, and comparing the diagnostic capability of ChatGPT 3.5 and ChatGPT 4.0., Design: Prospective data collection study., Participants: A total of 3170 eyes of 1585 subjects from the OHTS were included in this study., Methods: We selected demographic, clinical, ocular, visual field, optic nerve head photo, and history of disease parameters of each participant and developed case reports by converting tabular data into textual format based on information from both eyes of all subjects. We then developed a procedure using the application programming interface of ChatGPT, a LLM-based chatbot, to automatically input prompts into a chat box. This was followed by querying 2 different generations of ChatGPT (versions 3.5 and 4.0) regarding the underlying diagnosis of each subject. We then evaluated the output responses based on several objective metrics., Main Outcome Measures: Area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1 score., Results: Chat Generative Pre-Trained Transformer 3.5 achieved AUC of 0.74, accuracy of 66%, specificity of 64%, sensitivity of 85%, and F1 score of 0.72. Chat Generative Pre-Trained Transformer 4.0 obtained AUC of 0.76, accuracy of 87%, specificity of 90%, sensitivity of 61%, and F1 score of 0.92., Conclusions: The accuracy of ChatGPT 4.0 in diagnosing glaucoma based on input data from OHTS was promising. The overall accuracy of ChatGPT 4.0 was higher than ChatGPT 3.5. However, ChatGPT 3.5 was found to be more sensitive than ChatGPT 4.0. In its current forms, ChatGPT may serve as a useful tool in exploring disease status of ocular hypertensive eyes when specific data are available for analysis. In the future, leveraging LLMs with multimodal capabilities, allowing for integration of imaging and diagnostic testing as part of the analyses, could further enhance diagnostic capabilities and enhance diagnostic accuracy., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (© 2024 by the American Academy of Ophthalmology.)

Background: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs)., Objective: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers., Methods: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease-related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field., Results: The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F 1 -score of 0.85 based on the RenD data set., Conclusions: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines., (©Hina Raja, Asim Munawar, Nikolaos Mylonas, Mohammad Delsoz, Yeganeh Madadi, Muhammad Elahi, Amr Hassan, Hashem Abu Serhan, Onur Inam, Luis Hernandez, Hao Chen, Sang Tran, Wuqaas Munir, Alaa Abd-Alrazaq, Siamak Yousefi. Originally published in JMIR Formative Research (https://formative.jmir.org), 22.03.2024.)