Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Recording of Age-Related Changes on Murine and Human Brain Slices.

Preparation of brain slices for electrophysiological and imaging experiments has been developed several decades ago, and the method is still widely used due to its simplicity and advantages over other techniques. It can be easily combined with other well established and recently developed methods as immunohistochemistry and morphological analysis or opto- and chemogenetics. Several aspects of this technique are covered by a plethora of excellent and detailed review papers, in which one can gain a deep insight of variations in it. In this chapter, I briefly describe the solutions, equipment, and preparation techniques routinely used in our laboratory. I also aim to present how certain "old school" brain slice lab devices can be made in a cost-efficient way. These devices can be easily adapted for the special needs of the experiments. I also aim to present some differences in the preparatory techniques of acutely isolated human brain tissue.

Analysis of ChatGPT Responses to Ophthalmic Cases: Can ChatGPT Think like an Ophthalmologist?

Objective: Large language models such as ChatGPT have demonstrated significant potential in question-answering within ophthalmology, but there is a paucity of literature evaluating its ability to generate clinical assessments and discussions. The objectives of this study were to (1) assess the accuracy of assessment and plans generated by ChatGPT and (2) evaluate ophthalmologists' abilities to distinguish between responses generated by clinicians versus ChatGPT. Design: Cross-sectional mixed-methods study. Subjects: Sixteen ophthalmologists from a single academic center, of which 10 were board-eligible and 6 were board-certified, were recruited to participate in this study. Methods: Prompt engineering was used to ensure ChatGPT output discussions in the style of the ophthalmologist author of the Medical College of Wisconsin Ophthalmic Case Studies. Cases where ChatGPT accurately identified the primary diagnoses were included and then paired. Masked human-generated and ChatGPT-generated discussions were sent to participating ophthalmologists to identify the author of the discussions. Response confidence was assessed using a 5-point Likert scale score, and subjective feedback was manually reviewed. Main Outcome Measures: Accuracy of ophthalmologist identification of discussion author, as well as subjective perceptions of human-generated versus ChatGPT-generated discussions. Results: Overall, ChatGPT correctly identified the primary diagnosis in 15 of 17 (88.2%) cases. Two cases were excluded from the paired comparison due to hallucinations or fabrications of nonuser-provided data. Ophthalmologists correctly identified the author in 77.9% ± 26.6% of the 13 included cases, with a mean Likert scale confidence rating of 3.6 ± 1.0. No significant differences in performance or confidence were found between board-certified and board-eligible ophthalmologists. Subjectively, ophthalmologists found that discussions written by ChatGPT tended to have more generic responses, irrelevant information, hallucinated more frequently, and had distinct syntactic patterns (all P < 0.01). Conclusions: Large language models have the potential to synthesize clinical data and generate ophthalmic discussions. While these findings have exciting implications for artificial intelligence-assisted health care delivery, more rigorous real-world evaluation of these models is necessary before clinical deployment. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Applicability Domain for Trustable Predictions.

In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), understanding and correctly applying the concept of the applicability domain (AD) has emerged as an essential part. This chapter begins with an introduction and background on the critical area of AD. It dives into the definition and different methodologies associated with the applicability domain, laying a solid foundation for further exploration. A detailed examination of AD's role within the framework of AI and ML is undertaken, supported by in-depth theoretical foundations. The paper then proceeds to delineate the various measures of AD in AI and ML, offering insights into methods like DA index (κ, γ, δ), class probability estimation, and techniques involving local vicinity, boosting, classification neural networks, and subgroup discovery (SGD), among others. We also discussed a series of AD methods employed in Quantitative Structure-Activity Relationship (QSAR) studies. Lastly, the diverse applications of AD are addressed, underlining its widespread influence across different sectors. This chapter is intended to offer a thorough understanding of AD and its applications, particularly in AI and ML, leading to more informed research and decision-making in these fields as a good amount of literature already exists regarding AD of QSAR modeling.

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This report focuses on the application of computational molecular filters, applied either pre- or post-screening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agency) and ECHA (European CHemicals Agency). In this challenging scenario, non-test methods employing explainable artificial intelligence based techniques can provide a significant help to derive transparent predictive models whose results can be easily interpreted to assess the developmental toxicity of new chemicals at very early stages. To accomplish this task, we have developed web platforms such as TIRESIA and TISBE.Based on a benchmark dataset, TIRESIA employs an explainable artificial intelligence approach combined with SHAP analysis to unveil the molecular features responsible for calculating the developmental toxicity. Descending from TIRESIA, TISBE employs a larger dataset, an explainable artificial intelligence framework based on a fragment-based fingerprint encoding, a consensus classifier, and a new double top-down applicability domain. We report here some practical examples for getting started with TIRESIA and TISBE.

Generating Artificial Ribozymes Using Sparse Coevolutionary Models.

RNA ribozyme (Walter Engelke, Biologist (London, England) 49:199-203, 2002) datasets typically contain from a few hundred to a few thousand naturally occurring sequences. However, the potential sequence space of RNA is huge. For example, the number of possible RNA sequences of length 150 nucleotides is approximately 1 0 90 , a figure that far surpasses the estimated number of atoms in the known universe, which is around 1 0 80 . This disparity highlights a vast realm of sequence variability that remains unexplored by natural evolution. In this context, generative models emerge as a powerful tool. Learning from existing natural instances, these models can create artificial variants that extend beyond the currently known sequences. In this chapter, we will go through the use of a generative model based on direct coupling analysis (DCA) (Russ et al., Science 369:440-445, 2020; Trinquier et al., Nat Commun 12:5800, 2021; Calvanese et al., Nucleic Acids Res 52(10):5465-5477, 2024) applied to the twister ribozyme RNA family with three key applications: generating artificial twister ribozymes, designing potentially functional mutations of a natural wild type, and predicting mutational effects.

One-class modelling applied to ATR-FTIR spectroscopy for determining document forgeries related to paper aging.

One of the great challenges of document analysis is determining document forgeries. The present work proposes a non-destructive approach to discriminate natural and artificially aged papers using infrared spectroscopy and soft independent modeling by class analogy (SIMCA) algorithms. This is of particular interest in cases of document falsifications made by artificial aging, for this study, SIMCA, and Data-Driven SIMCA (DD-SIMCA) classification models were built using naturally aged paper samples, taken from three time periods: 1st period from 1998 to 2003; 2nd period from 2004 to 2009; and 3rd period from 2010 to 2015. Artificially aged samples (exposed to high temperature or UV radiation) were used as test sets. Promising results in detecting document falsifications related to aging were obtained. Samples artificially aged at high temperature were correctly discriminated from the authentic samples (naturally aged) with 100% accuracy. In contrast, the samples under the photodegradation process showed a lower classification performance, with results above 90%.

Suppressing hydrogen evolution and promoting dendrite free zinc deposition by fluorinated triazine framework towards robust aqueous zinc ion batteries.

Aqueous zinc-ion batteries (AZIBs) have become a research hotspot, but the inevitable zinc dendrites and parasitic reactions in the zinc anode seriously hinder their further development. In this study, three covalent triazine frameworks (DCPY-CTF, CTF-1 and FCTF) have been synthesized and used as artificial protective coatings, in which the fluorinated triazine framework (FCTF) increases the zinc-philic site, thus better promoting dendritic free zinc deposition and inhibiting hydrogen evolution reactions. Excitingly, both experimental results and theoretical calculations indicate that the FCTF interface adjusts the deposition of Zn2+ along the (002) plane, effectively alleviating the formation of zinc dendrites. As expected, Zn@FCTF symmetric cells exhibit cycling stability of over 4000 h (0.25 mA cm-2), meanwhile Zn@FCTF//NHVO full cells provide a high specific capacity of 280 mAh/g at 1.0 A/g, which are superior to those of bare Zn anode. This work provides new insights for suppressing hydrogen evolution and promoting dendrite-free zinc deposition to construct highly stable and reversible AZIBs.

Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning.

Purpose: To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation. Design: Retrospective multicenter study. Participants: Patients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007-2017) or Mayo Clinic Rochester (2015-2023). Methods: Multimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. Machine learning models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating characteristic curve (AUROC). The Wills Eye Hospital cohort was used for training and testing (80% training-20% testing) with fivefold cross validation. The Mayo Clinic cohort provided external validation. Model performance was characterized by AUROC and area under precision-recall curve (AUPRC). Models were interrogated using SHapley Additive exPlanations (SHAP) to identify the features most predictive of conversion from nevus to melanoma. Differences in AUROC and AUPRC between models were tested using 10 000 bootstrap samples with replacement and results. Main Outcome Measures: Area under receiver operating curve and AUPRC for each ML model. Results: There were 2870 nevi included in the study, with conversion to melanoma confirmed in 128 cases. Simple AI Nevus Transformation System (SAINTS; XGBoost) was the top-performing model in the test cohort [pooled AUROC 0.864 (95% confidence interval (CI): 0.864-0.865), pooled AUPRC 0.244 (95% CI: 0.243-0.246)] and in the external validation cohort [pooled AUROC 0.931 (95% CI: 0.930-0.931), pooled AUPRC 0.533 (95% CI: 0.531-0.535)]. Other models also had good discriminative performance: LGBM (test set pooled AUROC 0.831, validation set pooled AUROC 0.815), Random Forest (test set pooled AUROC 0.812, validation set pooled AUROC 0.866), and Extra Tree (test set pooled AUROC 0.826, validation set pooled AUROC 0.915). A model including only nevi with at least 5 years of follow-up demonstrated the best performance in AUPRC (test: pooled 0.592 (95% CI: 0.590-0.594); validation: pooled 0.656 [95% CI: 0.655-0.657]). The top 5 features in SAINTS by SHAP values were: tumor thickness, largest tumor basal diameter, tumor shape, distance to optic nerve, and subretinal fluid extent. Conclusions: We demonstrate accuracy and generalizability of a ML model for predicting choroidal nevus transformation to melanoma based on multimodal imaging. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Maternal and Neonatal Consequences of Early Augmentation of Labor Among Women With Spontaneous Onset of Labor: A National Population-Based Study.

BACKGROUND: While some labor interventions are essential in preventing maternal and neonatal morbidity, there is little evidence to support systematic early augmentation of labor (EAL). Our objective was to assess the association between EAL and cesarean delivery rate, postpartum hemorrhage and adverse neonatal outcomes. METHODS: Population-based study using data from the 2016 French Perinatal Survey. Women with a singleton cephalic fetus, delivering at term after a spontaneous labor were included. "EAL" was defined by artificial rupture of the membranes (AROM) and/or oxytocin within 1 h of admission and/or duration between interventions of less than 1 h. Women without EAL were women without labor augmentation or without EAL. The primary endpoint, cesarean delivery and the secondary endpoints were compared between women with and without EAL using univariate analysis. A multivariable logistic regression was adjusted on the suspected confounders and a propensity score approach was then performed. RESULTS: Among the 7196 women included, 1524 (21.2%) had EAL. Cesarean delivery rates were significantly higher in the EAL group compared with the no EAL group, 8.40% versus 6.15% (p < 0.01). EAL was associated with cesarean delivery in the multivariable analysis aOR 1.45 95% CI [1.15-1.82] and in the cohort matched on the propensity score, OR 1.56 [1.17-2.07]. EAL was not associated with severe postpartum hemorrhage, low 5-min Apgar score, low neonatal cord pH or transfer to NICU. CONCLUSION: EAL is frequent, involving one in five spontaneous laboring women in France. This practice is associated with an increased cesarean delivery risk.

Clinical Characteristics of Children with Acute Post-Streptococcal Glomerulonephritis and Re-Evaluation of Patients with Artificial Intelligence.

Objective: Acute post-streptococcal glomerulonephritis (APSGN) is a common cause of acute glomerulonephritis in children. The condition may present as acute nephritic and/or nephrotic syndrome and rarely as rapidly progressive glomerulonephritis. ChatGPT (OpenAI, San Francisco, California, United States of America) has been developed as a chat robot supported by artificial intelligence (AI). In this study, we evaluated whether AI can be used in the follow-up of patients with APSGN. Methods: The clinical characteristics of patients with APSGN were noted from patient records. Twelve questions about APSGN were directed to ChatGPT 3.5. The accuracy of the answers was evaluated by the researchers. Then, the clinical features of the patients were transferred to ChatGPT 3.5 and the follow-up management of the patients was examined. Results: The study included 11 patients with an average age of 9.08±3.96 years. Eight (72.7%) patients had elevated creatinine and 10 (90.9%) had hematuria and/or proteinuria. Anti-streptolysin O was high in all patients (955±353 IU/mL) and C3 was low in 9 (81.8%) patients (0.56±0.34 g/L). Hypertensive encephalopathy, nephrotic syndrome, and rapidly progressive glomerulonephritis were observed in three patients. Normal creatinine levels were achieved in all patients. Questions assessing the definition, epidemiologic characteristics, pathophysiologic mechanisms, diagnosis, and treatment of APSGN were answered correctly by ChatGPT 3.5. All patients were diagnosed with APSGN, and the treatment steps applied by clinicians were similarly recommended by ChatGPT 3.5. Conclusions: The insights and recommendations offered by ChatGPT for patients with APSGN can be an asset in the care and management of patients. With AI applications, clinicians can review treatment decisions and create more effective treatment plans.

[Strengths, Weaknesses, Opportunities, and Threats Analysis of Integrating ChatGPT Into Nursing Education].

Artificial intelligence (AI) is driving global change, and the implementation of generative AI in higher education is inevitable. AI language models such as the chat generative pre-trained transformer (ChatGPT) hold the potential to revolutionize the delivery of nursing education in the future. Nurse educators play a crucial role in preparing nursing students for a future technology-integrated healthcare system. While the technology has limitations and potential biases, the emergence of ChatGPT presents both opportunities and challenges. It is critical for faculty to be familiar with the capabilities and limitations of this model to foster effective, ethical, and responsible utilization of AI technology while preparing students in advance for the dynamic and rapidly advancing landscape of nursing and healthcare. Therefore, this article was written to present a strengths, weaknesses, opportunities, and threats (SWOT) analysis of integrating ChatGPT into nursing education, providing a guide for implementing ChatGPT in nursing education and offering a well-rounded assessment to help nurse educators make informed decisions.

[Application of Artificial Intelligence Models in Nursing Research].

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.

[A Guide to Network Meta-Analysis Using Generative AI and No-Code Tools].

Network meta-analysis (NMA), an increasingly appealing method of statistical analysis, is superior to traditional analysis methods in terms of being able to compare multiple medical treatment methods in one analysis run. In recent years, the prevalence of NMA in the medical literature has increased significantly, while advances in NMA-related statistical methods and software tools continue to improve the effectiveness of this approach. Various commercial and free statistical software packages, some of which employ generative artificial intelligence (GAI) to generate code, have been developed for NMA, leading to numerous innovative developments. In this paper, the use of generative AI for writing R programming language scripts and the netmeta package for performing NMA are introduced. Also, the web-based tool ShinyNMA is introduced. ShinyNMA allows users to conduct NMA using an intuitive "clickable" interface accessible via a standard web browser, with visual charts employed to present results. In the first section, we detail the netmeta package documentation and use ChatGPT (chat generative pre-trained transformer) to write the R scripts necessary to perform NMA with the netmeta package. In the second section, a user interface is developed using the Shiny package to create a ShinyNMA tool. This tool provides a no-code option for users unfamiliar with programming to conduct NMA statistical analysis and plotting. With appropriate prompts, ChatGPT can produce R scripts capable of performing NMA. Using this approach, an NMA analysis tool is developed that meets the research objectives, and potential applications are demonstrated using sample data. Using generative AI and existing statistical packages or no-code tools is expected to make conducting NMA analysis significantly easier for researchers. Moreover, greater access to results generated by NMA analyses will enable decision-makers to review analysis results intuitively in real-time, enhancing the importance of statistical analysis in medical decision-making. Furthermore, enabling non-specialists to conduct clinically meaningful systematic reviews may be expected to sustainably improve analytical capabilities and produce higher-quality evidence.

Cadmium Minimization in Crops: A Trade-Off With Mineral Nutrients in Safe Breeding.

Cadmium (Cd) contamination poses a threat to global crop safety. To address this issue, researchers mainly focused on the Cd, explored mechanism of accumulation to low-Cd breeding technologies and created several low-Cd varieties over the past decades. However, new challenges have emerged, particularly the yield reduction due to disturbances in mineral nutrient balance. The goals of breeding have been transferred from a primary focus on 'low-Cd crops' to 'low-Cd/nutrient-balanced' crops, which means limiting Cd content while maintaining other nutrient elements like iron (Fe), manganese (Mn) and zinc (Zn) at a proper content, thus to meet the future agricultural demands. Here, on a multielement perspective, we reviewed the mechanisms of Cd and mineral nutrient transport system in crops and summarized the research advances in Cd minimization through artificial mutations, natural variations and genetic engineering. Furthermore, the challenge of disruption of mineral nutrients in low-Cd crops was discussed and two potential approaches designing Cd-mineral nutrient-optimized artificial transporters and pyramiding Cd-mineral nutrient-optimized variations were proposed. Aiming at addressing these challenges, these approaches represent promising advancements in the field and offer potential pathways for future research and development in the creation of safe and high-quality crops.

Artificial Compartments Encapsulating Enzymatic Reactions: Towards the Construction of Artificial Organelles.

Cells have used compartmentalization to implement complex biological processes involving thousands of enzyme cascade reactions. Enzymes are spatially organized into the cellular compartments to carry out specific and efficient reactions in a spatiotemporally controlled manner. These compartments are divided into membrane-bound and membraneless organelles. Mimicking such cellular compartment systems has been a challenge for years. A variety of artificial scaffolds, including liposomes, polymersomes, proteins, nucleic acids, or hybrid materials have been used to construct artificial membrane-bound or membraneless compartments. These artificial compartments may have great potential for applications in biosynthesis, drug delivery, diagnosis and therapeutics, among others. This review first summarizes the typical examples of cellular compartments. In particular, the recent studies on cellular membraneless organelles (biomolecular condensates) are reviewed. We then summarize the recent advances in the construction of artificial compartments using engineered platforms. Finally, we provide our insights into the construction of biomimetic systems and the applications of these systems. This review article provides a timely summary of the relevant perspectives for the future development of artificial compartments, the building blocks for the construction of artificial organelles or cells.

Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.

BACKGROUND: In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges. MAIN TEXT: In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions. CONCLUSION: In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.

Solitary Fibrous Tumor Requiring Abdominal Aortic Vessel Replacement.

Solitary fibrous tumors (SFTs) are mesenchymal tumors, and retroperitoneal occurrence is rare. It has been identified in a variety of soft tissues and organs, such as the pleura, peritoneum, and meninges. In this case, the tumor was in contact with the abdominal aorta, and the invasion was difficult to judge preoperatively. Intraoperatively, it was revealed that the tumor could not be completely removed without aortic replacement. Although SFTs have a generally good prognosis, certain factors, such as tumor incomplete resection, have been reported to increase the risk of recurrence and metastasis. We were able to completely remove the tumor by performing a combined resection of the aorta. The specimens were microscopically disorganized proliferation of spindle-shaped cells. Immunostaining was positive for cluster of differentiation 34 (CD34) and signal transducer and activator of transcription 6 (STAT6). The tumor cells infiltrating into aortic adventitia were observed. This is a valuable case in which artificial blood vessel replacement was able to reduce the risk of recurrence and metastasis due to tumor remnants. We report a rare case of SFT resected with artificial blood vessel replacement.