Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review.

Background: The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text: This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions: Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Total Chemical Synthesis of Aggregation-Prone Disulfide-Rich Starfish Peptides.

A relaxin-like gonad-stimulating peptide (RGP), Aso-RGP, featuring six cysteine residues, was identified in the Crown-of-Thorns Starfish (COTS, Acanthaster cf. solaris) and initially produced through recombinant yeast expression. This method yielded a single-chain peptide with an uncleaved C-peptide (His Tag) and suboptimal purity. Our objective was to chemically synthesize Aso-RGP in its mature form, comprising two chains (A and B) and three disulfide bridges, omitting the C-peptide. Furthermore, we aimed to synthesize a newly identified relaxin-like peptide, Aso-RLP2, from COTS, which had not been previously synthesized. This paper reports the first total chemical synthesis of Aso-RGP and Aso-RLP2. Aso-RGP synthesis proceeded without major issues, whereas the A-chain of Aso-RLP2, in its reduced and unfolded state with two free thiols, presented considerable challenges. These were initially marked by "messy" RP-HPLC profiles, typically indicative of synthesis failure. Surprisingly, oxidizing the A-chain significantly improved the RP-HPLC profile, revealing the main issue was not synthesis failure but the peptide's aggregation tendency, which initially obscured analysis. This discovery highlights the critical need to account for aggregation in peptide synthesis and analysis. Ultimately, our efforts led to the successful synthesis of both peptides with purities exceeding 95 %.

Development of a synthetic relaxin-3/INSL5 chimeric peptide ligand for NanoBiT complementation binding assays.

INSL5 and relaxin-3 are relaxin family peptides with important roles in gut and brain function, respectively. They mediate their actions through the class A GPCRs RXFP4 and RXFP3. RXFP4 has been proposed to be a therapeutic target for colon motility disorders whereas RXFP3 targeting could be effective for neurological conditions such as anxiety. Validation of these targets has been limited by the lack of specific ligands and the availability of robust ligand-binding assays for their development. In this study, we have utilized NanoBiT complementation to develop a SmBiT-conjugated tracer for use with LgBiT-fused RXFP3 and RXFP4. The low affinity between LgBiT:SmBiT should result in a low non-specific luminescence signal and enable the quantification of binding without the tedious separation of non-bound ligands. We used solid-phase peptide synthesis to produce a SmBiT-labelled RXFP3/4 agonist, R3/I5, where SmBiT was conjugated to the B-chain N-terminus via a PEG12 linker. Both SmBiT-R3/I5 and R3/I5 were synthesized and purified in high purity and yield. Stable HEK293T cell lines expressing LgBiT-RXFP3 and LgBiT-RXFP4 were produced and demonstrated normal signaling in response to the synthetic R3/I5 peptide. Binding was first characterized in whole-cell binding kinetic assays validating that the SmBiT-R3/I5 bound to both cell lines with nanomolar affinity with minimal non-specific binding without bound and free SmBiT-R3/I5 separation. We then optimized membrane binding assays, demonstrating easy and robust analysis of both saturation and competition binding from frozen membranes. These assays therefore provide an appropriate rigorous binding assay for the high-throughput analysis of RXFP3 and RXFP4 ligands.

Developing insulin-like peptide 5-based antagonists for the G protein-coupled receptor, RXFP4.

Human insulin-like peptide 5 (INSL5) is a gut hormone produced by colonic L-cells, and its biological functions are mediated by Relaxin Family Peptide Receptor 4 (RXFP4). Our preliminary data indicated that RXFP4 agonists are potential drug leads for the treatment of constipation. More recently, we designed and developed a novel RXFP4 antagonist, A13-nR that was shown to block agonist-induced activity in cells and animal models. We showed that A13-nR was able to block agonist-induced increases in colon motility in mice of both genders that express the receptor, RXFP4. Our data also showed that colorectal propulsion induced by intracolonic administration of short-chain fatty acids was antagonized by A13-nR. Therefore, A13-nR is an important research tool and potential drug lead for the treatment of colon motility disorders, such as bacterial diarrhea. However, A13-nR acted as a partial agonist at high concentrations in vitro and demonstrated modest antagonist potency (∼35 nM). Consequently, the primary objective of this study is to pinpoint novel modifications to A13-nR that eliminate partial agonist effects while preserving or augmenting antagonist potency. In this work, we detail the creation of a series of A13-nR-modified analogues, among which analogues 3, 4, and 6 demonstrated significantly improved RXFP4 affinity (∼3 nM) with reduced partial agonist activity, enhanced antagonist potency (∼10 nM) and maximum agonist inhibition (∼80 %) when compared with A13-nR. These compounds have potential as candidates for further preclinical evaluations, marking a significant stride toward innovative therapeutics for colon motility disorders.

A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality.

Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.

Challenges of artificial intelligence in medicine and dermatology.

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Exploring large language model for next generation of artificial intelligence in ophthalmology.

In recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs' potential in the next generation of AI in ophthalmology. The results encompass a diverse range of studies in the field of ophthalmology, highlighting the versatile applications of LLMs. Subfields encompass general ophthalmology, retinal diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results show LLMs' competence in generating informative and contextually relevant responses, potentially reducing diagnostic errors and improving patient outcomes. Overall, this study highlights LLMs' promising role in shaping AI's future in ophthalmology. By leveraging AI, ophthalmologists can access a wealth of information, enhance diagnostic accuracy, and provide better patient care. Despite challenges, continued AI advancements and ongoing research will pave the way for the next generation of AI-assisted ophthalmic practices.

Further Developments towards a Minimal Potent Derivative of Human Relaxin-2.

Human relaxin-2 (H2 relaxin) is a peptide hormone with potent vasodilatory and anti-fibrotic effects, which is of interest for the treatment of heart failure and fibrosis. H2 relaxin binds to the Relaxin Family Peptide Receptor 1 (RXFP1). Native H2 relaxin is a two-chain, three-disulfide-bond-containing peptide, which is unstable in human serum and difficult to synthesize efficiently. In 2016, our group developed B7-33, a single-chain peptide derived from the B-chain of H2 relaxin. B7-33 demonstrated poor affinity and potency in HEK cells overexpressing RXFP1; however, it displayed equivalent potency to H2 relaxin in fibroblasts natively expressing RXFP1, where it also demonstrated the anti-fibrotic effects of the native hormone. B7-33 reversed organ fibrosis in numerous pre-clinical animal studies. Here, we detail our efforts towards a minimal H2 relaxin scaffold and attempts to improve scaffold activity through Aib substitution and hydrocarbon stapling to re-create the peptide helicity present in the native H2 relaxin.

Development of Novel High-Affinity Antagonists for the Relaxin Family Peptide Receptor 1.

H2 relaxin is a peptide hormone that exerts its biological actions through the G protein-coupled receptor, RXFP1. The numerous important biological functions of H2 relaxin, including potent renal, vasodilatory, cardioprotective, and anti-fibrotic actions, have resulted in considerable interest in its use as a therapeutic for various cardiovascular diseases and other fibrotic indications. Interestingly though, H2 relaxin and RXFP1 have been shown to be overexpressed in prostate cancer, allowing for the downregulation or blocking of relaxin/RXFP1 to decrease prostate tumor growth. These findings suggest the application of an RXFP1 antagonist for the treatment of prostate cancer. However, these therapeutically relevant actions are still poorly understood and have been hindered by the lack of a high-affinity antagonist. In this study, we chemically synthesized three novel H2 relaxin analogues that have complex insulin-like structures with two chains (A and B) and three disulfide bridges. We report here the structure-activity relationship studies on H2 relaxin that resulted in the development of a novel high-affinity RXFP1 antagonist, H2 B-R13HR (∼40 nM), that has only one extra methylene group in the side chain of arginine 13 in the B-chain (ArgB13) of H2 relaxin. Most notably, the synthetic peptide was shown to be active in a mouse model of prostate tumor growth in vivo where it inhibited relaxin-mediated tumor growth. Our compound H2 B-R13HR will be an important research tool to understand relaxin actions through RXFP1 and may be a potential lead compound for the treatment of prostate cancer.

Total Chemical Synthesis of Palmitoyl-Conjugated Insulin.

Commercially available insulins are manufactured by recombinant methods for the treatment of diabetes. Long-acting insulin drugs (e.g., detemir and degludec) are obtained by fatty acid conjugation at LysB29 ε-amine of insulin via acid-amide coupling. There are three amine groups in insulin, and they all react with fatty acids in alkaline conditions. Due to the lack of selectivity, such conjugation reactions produce non-desired byproducts. We designed and chemically synthesized a novel thiol-insulin scaffold (CysB29-insulin II), by replacing the LysB29 residue in insulin with the CysB29 residue. Then, we conjugated a fatty acid moiety (palmitic acid, C16) to CysB29-insulin II by a highly efficient and selective thiol-maleimide conjugation reaction. We obtained the target peptide (palmitoyl-insulin) rapidly within 5 min without significant byproducts. The palmitoyl-insulin is shown to be structurally similar to insulin and biologically active both in vitro and in vivo. Importantly, unlike native insulin, palmitoyl-insulin is slow and long-acting.

A Lipidated Single-B-Chain Derivative of Relaxin Exhibits Improved In Vitro Serum Stability without Altering Activity.

Human relaxin-2 (H2 relaxin) is therapeutically very important due to its strong anti-fibrotic, vasodilatory, and cardioprotective effects. Therefore, relaxin's receptor, relaxin family peptide receptor 1 (RXFP1), is a potential target for the treatment of fibrosis and related disorders, including heart failure. H2 relaxin has a complex two-chain structure (A and B) and three disulfide bridges. Our laboratory has recently developed B7-33 peptide, a single-chain agonist based on the B-chain of H2 relaxin. However, the peptide B7-33 has a short circulation time in vitro in serum (t1/2 = ~6 min). In this study, we report structure-activity relationship studies on B7-33 utilizing different fatty-acid conjugations at different positions. We have shown that by fatty-acid conjugation with an appropriate spacer length, the in vitro half-life of B7-33 can be increased from 6 min to 60 min. In the future, the lead lipidated molecule will be studied in animal models to measure its PK/PD properties, which will lead to their pre-clinical applications.

Engineering of a Biologically Active Insulin Dimer.

The growing epidemic of diabetes means that there is a need for therapies that are more efficacious, safe, and convenient. Here, we report the efficient synthesis of a novel disulfide dimer of human insulin tethered at the N-terminus of its B-chain through placement of a cysteine residue. The resulting peptide was shown to bind to both the insulin receptor isoform B and insulin-like growth factor-1 receptor with comparable affinity to native insulin. In in vivo insulin tolerance tests, the dimer was equipotent to Actrapid insulin and possessed a sustained duration of action greater than that of Actrapid and Glargine. While the secondary structure of our dimeric insulin was similar to that of insulin, it was more resistant to proteolysis. More importantly, our analogue was produced in quantitative yield from a monomeric thiol insulin scaffold. Our results suggest that this dimer has significant potential to address the clinical needs in the treatment of diabetes.