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oncology Oncology

The Exciting Potential for ChatGPT in Obstetrics and Gynecology.

In American journal of obstetrics and gynecology

Natural Language Processing (NLP) - the branch of artificial intelligence (AI) concerned with the interaction between computers and human language - has advanced markedly in recent years with the introduction of sophisticated deep learning models. Improved performance in NLP tasks such as text and speech processing have fueled impressive demonstrations of these models' capabilities. Perhaps no demonstration has been more impactful to date than with the introduction of the publicly available online chatbot "ChatGPT" in November 2022 by OpenAI which is based on an NLP model known as a GPT (Generative Pretrained Transformer). Through a series of questions posed by the authors about Obstetrics and Gynecology to ChatGPT as prompts, we evaluated the model's ability to handle clinical related queries. Its answers demonstrate that in its current form, ChatGPT can be valuable for users who want preliminary information about virtually any topic in the field. As its educational role is being defined, we must recognize its limitations. While answers were generally eloquent, informed and lacked a significant degree of mistakes or misinformation, we also observed evidence of its weaknesses. A significant drawback is that the data on which the model has been trained are apparently not readily updated. The model assessed here seems to not reliably (if at all) source data after 2021. Users of ChatGPT who expect data to be more up to date need to be aware of this drawback. Inability to cite sources or truly understand what the user is asking suggests it has the capability to mislead. Responsible use of models like ChatGPT will be important in ensuring that they work to help but not harm users seeking information in Obstetrics and Gynecology.

Grünebaum Amos, Chervenak Joseph, Pollet Susan L, Katz Adi, Chervenak Frank A

2023-Mar-14

AI, ChatGPT, artificial Intelligence, cesarean, chatbots, ethics, gynecology, home birth, informed consent, maternal-fetal medicine, obstetrics, oncology, preeclampsia, prematurity, preterm birth, progesterone, reproductive medicine, short cervix, vaginal progesterone

Ophthalmology Ophthalmology

Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images.

In Ophthalmology. Retina

PURPOSE : To create and validate code-free automated deep learning models (autoML) for diabetic retinopathy (DR) classification from handheld retinal images.

DESIGN : Prospective development and validation of autoML models for DR image classification.

PARTICIPANTS : 17,829 de-identified retinal images from 3,566 eyes with diabetes acquired using handheld retinal cameras in a community-based DR screening program.

METHODS : AutoML models were generated based on previously acquired 5-field (macula-centered, disc-centered, superior, inferior, temporal macula) handheld retinal images. Each individual image was labeled using the International DR and diabetic macular edema (DME) classification scale by four certified graders at a centralized reading center under oversight by a senior retina specialist. Images for model development were split 8-1-1 for training, optimization, and testing to detect referable DR [(refDR), defined as moderate nonproliferative DR or worse or any level of DME]. Internal validation was performed using a published image set from the same patient population (N=450 images from 225 eyes). External validation was performed using a publicly available retinal imaging dataset from the Asia Pacific Tele-Ophthalmology Society (N=3,662 images).

MAIN OUTCOME MEASURES : Area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, (SN, SP, PPV, NPV, respectively) accuracy, and F1 scores.

RESULTS : RefDR was present in 17.3%, 39.1% and 48.0% of the training set, internal and external validation sets respectively. The model's AUPRC was 0.995 with a precision and recall of 97% using a score threshold of 0.5. Internal validation showed SN, SP, PPV, NPV, accuracy and F1 scores were 0.96 (95% CI:0.884-0.99), 0.98 (95% CI:0.937-0.995), 0.96 (95% CI:0.884-0.99), 0.98 (95% CI:0.937-0.995), 0.97 and 0.96, respectively. External validation showed SN, SP, PPV, NPV, accuracy and F1 scores were 0.94 (95% CI:0.929-0.951), 0.97 (95% CI:0.957-0.974), 0.96 (95% CI:0.952-0.971), 0.95 (95% CI:0.935-0.956), 0.97 and 0.96, respectively.

CONCLUSIONS : This study demonstrates the accuracy and feasibility of code-free autoML models for identifying refDR developed using handheld retinal imaging in a community-based screening program. Potentially, the use of autoML may increase access to machine learning models that may be adapted for specific programs that are guided by the clinical need to rapidly address disparities in healthcare delivery.

Jacoba Cris Martin P, Doan Duy, Salongccay Recivall P, Aquino Lizzie Anne C, Silva Joseph Paolo Y, Salva Claude Michael G, Zhang Dean, Alog Glenn P, Zhang Kexin, Locaylocay Kaye B, Saunar Aileen V, Ashraf Mohamed, Sun Jennifer K, Peto Tunde, Aiello Lloyd P, Silva Paolo S

2023-Mar-14

Artificial Intelligence, Automated Machine Learning, Diabetic Retinopathy, Handheld devices, Retinal Imaging, Screening

General General

PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features.

In Methods (San Diego, Calif.)

Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PRED (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.

Harini K, Kihara Daisuke, Michael Gromiha M

2023-Mar-14

binding free energy, contact potentials, protein–DNA complex, structure-based features

Ophthalmology Ophthalmology

Clinical analysis of eye movement-based data in the medical diagnosis of amblyopia.

In Methods (San Diego, Calif.)

Amblyopia is an abnormal visual processing-induced developmental disorder of the central nervous system that affects static and dynamic vision, as well as binocular visual function. Currently, changes in static vision in one eye are the gold standard for amblyopia diagnosis. However, there have been few comprehensive analyses of changes in dynamic vision, especially eye movement, among children with amblyopia. Here, we proposed an optimization scheme involving a video eye tracker combined with an "artificial eye" for comprehensive examination of eye movement in children with amblyopia; we sought to improve the diagnostic criteria for amblyopia and provide theoretical support for practical treatment. The resulting eye movement data were used to construct a deep learning approach for diagnostic and predictive applications. Through efforts to manage the uncooperativeness of children with strabismus who could not complete the eye movement assessment, this study quantitatively and objectively assessed the clinical implications of eye movement characteristics in children with amblyopia. Our results indicated that an amblyopic eye is always in a state of adjustment, and thus is not "lazy." Additionally, we found that the eye movement parameters of amblyopic eyes and eyes with normal vision are significantly different. Finally, we identified eye movement parameters that can be used to supplement and optimize the diagnostic criteria for amblyopia, providing a diagnostic basis for evaluation of binocular visual function.

Fan Yunwei, Li Li, Chu Ping, Wu Qian, Wang Yuan, Cao WenHong, Li Ningdong

2023-Mar-14

Amblyopia, Deep learning, Eye movement data, Saccade, Self-attention

General General

An iterative neural processing sequence orchestrates feeding.

In Neuron ; h5-index 148.0

Feeding requires sophisticated orchestration of neural processes to satiate appetite in natural, capricious settings. However, the complementary roles of discrete neural populations in orchestrating distinct behaviors and motivations throughout the feeding process are largely unknown. Here, we delineate the behavioral repertoire of mice by developing a machine-learning-assisted behavior tracking system and show that feeding is fragmented and divergent motivations for food consumption or environment exploration compete throughout the feeding process. An iterative activation sequence of agouti-related peptide (AgRP)-expressing neurons in arcuate (ARC) nucleus, GABAergic neurons in the lateral hypothalamus (LH), and in dorsal raphe (DR) orchestrate the preparation, initiation, and maintenance of feeding segments, respectively, via the resolution of motivational conflicts. The iterative neural processing sequence underlying the competition of divergent motivations further suggests a general rule for optimizing goal-directed behaviors.

Liu Qingqing, Yang Xing, Luo Moxuan, Su Junying, Zhong Jinling, Li Xiaofen, Chan Rosa H M, Wang Liping

2023-Mar-07

AgRP neuron, appetite, behavior identification, dorsal raphe, eating disorder, feeding initiation, feeding maintenance, food consumption, lateral hypothalamus, motivation competition

General General

Identifying constitutive and context-specific molecular-tension-sensitive protein recruitment within focal adhesions.

In Developmental cell ; h5-index 87.0

Mechanosensitive processes often rely on adhesion structures to strengthen, or mature, in response to applied loads. However, a limited understanding of how the molecular tensions that are experienced by a particular protein affect the recruitment of other proteins represents a major obstacle in the way of deciphering molecular mechanisms that underlie mechanosensitive processes. Here, we describe an imaging-based technique, termed fluorescence-tension co-localization (FTC), for studying molecular-tension-sensitive protein recruitment inside cells. Guided by discrete time Markov chain simulations of protein recruitment, we integrate immunofluorescence labeling, molecular tension sensors, and machine learning to determine the sensitivity, specificity, and context dependence of molecular-tension-sensitive protein recruitment. The application of FTC to the mechanical linker protein vinculin in mouse embryonic fibroblasts reveals constitutive and context-specific molecular-tension-sensitive protein recruitment that varies with adhesion maturation. FTC overcomes limitations associated with the alteration of numerous proteins during the manipulation of cell contractility, providing molecularly specific insights into tension-sensitive protein recruitment.

Tao Arnold, LaCroix Andrew S, Shoyer T Curtis, Venkatraman Vidya, Xu Karen L, Feiger Bradley, Hoffman Brenton D

2023-Mar-10

FRET, adhesion, biosensor, co-localization, cytoskeleton, mechanobiology, protein recruitment, tension