Performance of three artificial intelligence (AI)-based large language models in standardized testing; implications for AI-assisted dental education.

INTRODUCTION: The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long-term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT-4 and GPT-3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in-service examination questions posed by the American Academy of Periodontology (AAP). METHODS: Under a comparative cross-sectional study design, a corpus of 1312 questions from the annual in-service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi-square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub-analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions. RESULTS: ChatGPT-4 (total average: 79.57%) outperformed all human control groups as well as GPT-3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT-3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first- (63.48% ± 31.67) and second-year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third-year residents (69.06% ± 30.45). CONCLUSIONS: Within the confines of this analysis, ChatGPT-4 exhibited a robust capability in answering AAP in-service exam questions in terms of accuracy and reliability while Gemini and ChatGPT-3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image-based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT-4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study.

Basal knowledge in the field of pediatric nephrology and its enhancement following specific training of ChatGPT-4 "omni" and Gemini 1.5 Flash.

BACKGROUND: We aimed to evaluate the baseline performance and improvement of ChatGPT-4 "omni" (ChatGPT-4o) and Gemini 1.5 Flash (Gemini 1.5) in answering multiple-choice questions related to pediatric nephrology after specific training. METHODS: Using questions from the "Educational Review" articles published by Pediatric Nephrology between January 2014 and April 2024, the models were tested both before and after specific training with Portable Data Format (PDF) and text (TXT) file formats of the Educational Review articles removing the last page containing the correct answers using a Python script. The number of correct answers was recorded. RESULTS: Before training, ChatGPT-4o correctly answered 75.2% of the 1395 questions, outperforming Gemini 1.5, which answered 64.9% correctly (p < 0.001). After training with PDF files, ChatGPT-4o's accuracy increased to 77.8%, while Gemini 1.5 improved significantly to 84.7% (p < 0.001). Training with TXT files showed similar results, with ChatGPT-4o maintaining 77.8% accuracy and Gemini 1.5 further improving to 87.6% (p < 0.001). CONCLUSIONS: The study highlights that while ChatGPT-4o has strong baseline performance, specific training does not significantly enhance its accuracy. Conversely, Gemini 1.5, despite its lower initial performance, shows substantial improvement with training, particularly with TXT files. These findings suggest Gemini 1.5's superior ability to store and retrieve information, making it potentially more effective in clinical applications, albeit with a dependency on additional data for optimal performance.

Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison.

PURPOSE: The aim of this study was to define the capability of ChatGPT-4 and Google Gemini in analyzing detailed glaucoma case descriptions and suggesting an accurate surgical plan. METHODS: Retrospective analysis of 60 medical records of surgical glaucoma was divided into "ordinary" (n = 40) and "challenging" (n = 20) scenarios. Case descriptions were entered into ChatGPT and Bard's interfaces with the question "What kind of surgery would you perform?" and repeated three times to analyze the answers' consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results. RESULTS: ChatGPT surgical choice was consistent with those of glaucoma specialists in 35/60 cases (58%), compared to 19/60 (32%) of Gemini (p = 0.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In "challenging" cases, ChatGPT agreed with specialists in 9/20 choices (45%), outperforming Google Gemini performances (4/20, 20%). Overall, GQS scores were 3.5 ± 1.2 and 2.1 ± 1.5 for ChatGPT and Gemini (p = 0.002). This difference was even more marked if focusing only on "challenging" cases (1.5 ± 1.4 vs. 3.0 ± 1.5, p = 0.001). CONCLUSION: ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers.

Comparative analysis of ChatGPT, Gemini and emergency medicine specialist in ESI triage assessment.

INTRODUCTION: The term Artificial Intelligence (AI) was first coined in the 1960s and has made significant progress up to the present day. During this period, numerous AI applications have been developed. GPT-4 and Gemini are two of the best-known of these AI models. As a triage system The Emergency Severity Index (ESI) is currently one of the most commonly used for effective patient triage in the emergency department. The aim of this study is to evaluate the performance of GPT-4, Gemini, and emergency medicine specialists in ESI triage against each other; furthermore, it aims to contribute to the literature on the usability of these AI programs in emergency department triage. METHODS: Our study was conducted between February 1, 2024, and February 29, 2024, among emergency medicine specialists in Turkey, as well as with GPT-4 and Gemini. Ten emergency medicine specialists were included in our study but as a limitation the emergency medicine specialists participating in the study do not frequently use the ESI triage model in daily practice. In the first phase of our study, 100 case examples related to adult or trauma patients were extracted from the sample and training cases found in the ESI Implementation Handbook. In the second phase of our study, the provided responses were categorized into three groups: correct triage, over-triage, and under-triage. In the third phase of our study, the questions were categorized according to the correct triage responses. RESULTS: In the results of our study, a statistically significant difference was found between the three groups in terms of correct triage, over-triage, and under-triage (p < 0.001). GPT-4 was found to have the highest correct triage rate with an average of 70.60 (±3.74), while Gemini had the highest over-triage rate with an average of 35.2 (±2.93) (p < 0.001). The highest under-triage rate was observed in emergency medicine specialists (32.90 (±11.83)). In the ESI 1-2 class, Gemini had a correct triage rate of 87.77%, GPT-4 had 85.11%, and emergency medicine specialists had 49.33%. CONCLUSION: In conclusion, our study shows that both GPT-4 and Gemini can accurately triage critical and urgent patients in ESI 1&2 groups at a high rate. Furthermore, GPT-4 has been more successful in ESI triage for all patients. These results suggest that GPT-4 and Gemini could assist in accurate ESI triage of patients in emergency departments.

The accuracy of Gemini, GPT-4, and GPT-4o in ECG analysis: A comparison with cardiologists and emergency medicine specialists.

INTRODUCTION: GPT-4, GPT-4o and Gemini advanced, which are among the well-known large language models (LLMs), have the capability to recognize and interpret visual data. When the literature is examined, there are a very limited number of studies examining the ECG performance of GPT-4. However, there is no study in the literature examining the success of Gemini and GPT-4o in ECG evaluation. The aim of our study is to evaluate the performance of GPT-4, GPT-4o, and Gemini in ECG evaluation, assess their usability in the medical field, and compare their accuracy rates in ECG interpretation with those of cardiologists and emergency medicine specialists. METHODS: The study was conducted from May 14, 2024, to June 3, 2024. The book "150 ECG Cases" served as a reference, containing two sections: daily routine ECGs and more challenging ECGs. For this study, two emergency medicine specialists selected 20 ECG cases from each section, totaling 40 cases. In the next stage, the questions were evaluated by emergency medicine specialists and cardiologists. In the subsequent phase, a diagnostic question was entered daily into GPT-4, GPT-4o, and Gemini Advanced on separate chat interfaces. In the final phase, the responses provided by cardiologists, emergency medicine specialists, GPT-4, GPT-4o, and Gemini Advanced were statistically evaluated across three categories: routine daily ECGs, more challenging ECGs, and the total number of ECGs. RESULTS: Cardiologists outperformed GPT-4, GPT-4o, and Gemini Advanced in all three groups. Emergency medicine specialists performed better than GPT-4o in routine daily ECG questions and total ECG questions (p = 0.003 and p = 0.042, respectively). When comparing GPT-4o with Gemini Advanced and GPT-4, GPT-4o performed better in total ECG questions (p = 0.027 and p < 0.001, respectively). In routine daily ECG questions, GPT-4o also outperformed Gemini Advanced (p = 0.004). Weak agreement was observed in the responses given by GPT-4 (p < 0.001, Fleiss Kappa = 0.265) and Gemini Advanced (p < 0.001, Fleiss Kappa = 0.347), while moderate agreement was observed in the responses given by GPT-4o (p < 0.001, Fleiss Kappa = 0.514). CONCLUSION: While GPT-4o shows promise, especially in more challenging ECG questions, and may have potential as an assistant for ECG evaluation, its performance in routine and overall assessments still lags behind human specialists. The limited accuracy and consistency of GPT-4 and Gemini suggest that their current use in clinical ECG interpretation is risky.

Can artificial intelligence models serve as patient information consultants in orthodontics?

BACKGROUND: To evaluate the accuracy, reliability, quality, and readability of responses generated by ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot in relation to orthodontic clear aligners. METHODS: Frequently asked questions by patients/laypersons about clear aligners on websites were identified using the Google search tool and these questions were posed to ChatGPT-3.5, ChatGPT-4, Gemini, and Copilot AI models. Responses were assessed using a five-point Likert scale for accuracy, the modified DISCERN scale for reliability, the Global Quality Scale (GQS) for quality, and the Flesch Reading Ease Score (FRES) for readability. RESULTS: ChatGPT-4 responses had the highest mean Likert score (4.5 ± 0.61), followed by Copilot (4.35 ± 0.81), ChatGPT-3.5 (4.15 ± 0.75) and Gemini (4.1 ± 0.72). The difference between the Likert scores of the chatbot models was not statistically significant (p > 0.05). Copilot had a significantly higher modified DISCERN and GQS score compared to both Gemini, ChatGPT-4 and ChatGPT-3.5 (p < 0.05). Gemini's modified DISCERN and GQS score was statistically higher than ChatGPT-3.5 (p < 0.05). Gemini also had a significantly higher FRES compared to both ChatGPT-4, Copilot and ChatGPT-3.5 (p < 0.05). The mean FRES was 38.39 ± 11.56 for ChatGPT-3.5, 43.88 ± 10.13 for ChatGPT-4 and 41.72 ± 10.74 for Copilot, indicating that the responses were difficult to read according to the reading level. The mean FRES for Gemini is 54.12 ± 10.27, indicating that Gemini's responses are more readable than other chatbots. CONCLUSIONS: All chatbot models provided generally accurate, moderate reliable and moderate to good quality answers to questions about the clear aligners. Furthermore, the readability of the responses was difficult. ChatGPT, Gemini and Copilot have significant potential as patient information tools in orthodontics, however, to be fully effective they need to be supplemented with more evidence-based information and improved readability.

How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini.

Applications of large language models (LLMs) in the healthcare field have shown promising results in processing and summarizing multidisciplinary information. This study evaluated the ability of three publicly available LLMs (GPT-3.5, GPT-4, and Google Gemini-then called Bard) to answer 60 multiple-choice questions (29 sourced from public databases, 31 newly formulated by experienced breast radiologists) about different aspects of breast cancer care: treatment and prognosis, diagnostic and interventional techniques, imaging interpretation, and pathology. Overall, the rate of correct answers significantly differed among LLMs (p = 0.010): the best performance was achieved by GPT-4 (95%, 57/60) followed by GPT-3.5 (90%, 54/60) and Google Gemini (80%, 48/60). Across all LLMs, no significant differences were observed in the rates of correct replies to questions sourced from public databases and newly formulated ones (p ≥ 0.593). These results highlight the potential benefits of LLMs in breast cancer care, which will need to be further refined through in-context training.

Lipid Giant Vesicles Engulf Living Bacteria Triggered by Minor Enhancement in Membrane Fluidity.

Antibacterial amphiphiles normally kill bacteria by destroying the bacterial membrane. Whether and how antibacterial amphiphiles alter normal cell membrane and lead to subsequent effects on pathogen invasion into cells have been scarcely promulgated. Herein, by taking four antibacterial gemini amphiphiles with different spacer groups to modulate cell-mimic phospholipid giant unilamellar vesicles (GUVs), bacteria adhesion on the modified GUVs surface and bacteria engulfment process by the GUVs are clearly captured by confocal laser scanning microscopy. Further characterization shows that the enhanced cationic surface charge of GUVs by the amphiphiles determines the bacteria adhesion amount, while the involvement of amphiphile in GUVs results in looser molecular arrangement and concomitant higher fluidity in the bilayer membranes, facilitating the bacteria intruding into GUVs. This study sheds new light on the effect of amphiphiles on membrane bilayer and the concurrent effect on pathogen invasion into cell mimics and broadens the nonprotein-mediated endocytosis pathway for live bacteria.

Assessment of artificial intelligence applications in responding to dental trauma.

BACKGROUND: This study assessed the consistency and accuracy of responses provided by two artificial intelligence (AI) applications, ChatGPT and Google Bard (Gemini), to questions related to dental trauma. MATERIALS AND METHODS: Based on the International Association of Dental Traumatology guidelines, 25 dichotomous (yes/no) questions were posed to ChatGPT and Google Bard over 10 days. The responses were recorded and compared with the correct answers. Statistical analyses, including Fleiss kappa, were conducted to determine the agreement and consistency of the responses. RESULTS: Analysis of 4500 responses revealed that both applications provided correct answers to 57.5% of the questions. Google Bard demonstrated a moderate level of agreement, with varying rates of incorrect answers and referrals to physicians. CONCLUSIONS: Although ChatGPT and Google Bard are potential knowledge resources, their consistency and accuracy in responding to dental trauma queries remain limited. Further research involving specially trained AI models in endodontics is warranted to assess their suitability for clinical use.

Study on the Synthesis, Surface Activity, and Self-Assembly Behavior of Anionic Non-Ionic Gemini Surfactants.

The use of surfactants in oil recovery can effectively improve crude oil recovery rate. Due to the enhanced salt and temperature resistance of surfactant molecules by non-ionic chain segments, anionic groups have good emulsifying stability. Currently, there are many studies on anionic non-ionic surfactants for oil recovery in China, but there is relatively little systematic research on introducing EOs into hydrophobic alkyl chains, especially on their self-assembly behavior. This article proposes a simple and effective synthesis method, using 3-aminopropane sulfonic acid, fatty alcohol polyoxyethylene ether, and epichlorohydrin as raw materials, to insert EO into hydrophobic alkyl chains and synthesize a series of new anionic non-ionic Gemini surfactants (CnEO-5, n = 8, 12, 16). The surface activity, thermodynamic properties, and self-assembly behavior of these surfactants were systematically studied through surface tension, conductivity, steady-state fluorescence probes, transmission electron microscopy, and molecular dynamics simulations. The surface tension test results show that CnEO-5 has high surface activity and is higher than traditional single chain surfactants and structurally similar anionic non-ionic Gemini surfactants. Additionally, thermodynamic parameters (e.g., ΔG°mic ΔH°mic ΔS°mic et al. indicate that CnEO-5 molecules are exothermic and spontaneous during the micellization process. DLS, p-values, and TEM results indicate that anionic non-ionic Gemini surfactants with shorter hydrophobic chains (such as C8EO-5) tend to form larger vesicles in aqueous solutions, which are formed in a tail to tail and staggered manner; Negative non-ionic Gemini surfactants with longer hydrophobic chains (such as C12EO-5, C16EO-5) tend to form small micelles. The test results indicate that CnEO-5 anionic non-ionic Gemini surfactants have certain application prospects in improving crude oil recovery.

Assessing the Efficacy of Large Language Models in Health Literacy: A Comprehensive Cross-Sectional Study.

Enhanced health literacy in children has been empirically linked to better health outcomes over the long term; however, few interventions have been shown to improve health literacy. In this context, we investigate whether large language models (LLMs) can serve as a medium to improve health literacy in children. We tested pediatric conditions using 26 different prompts in ChatGPT-3.5, ChatGPT-4, Microsoft Bing, and Google Bard (now known as Google Gemini). The primary outcome measurement was the reading grade level (RGL) of output as assessed by Gunning Fog, Flesch-Kincaid Grade Level, Automated Readability Index, and Coleman-Liau indices. Word counts were also assessed. Across all models, output for basic prompts such as "Explain" and "What is (are)," were at, or exceeded, the tenth-grade RGL. When prompts were specified to explain conditions from the first- to twelfth-grade level, we found that LLMs had varying abilities to tailor responses based on grade level. ChatGPT-3.5 provided responses that ranged from the seventh-grade to college freshmen RGL while ChatGPT-4 outputted responses from the tenth-grade to the college senior RGL. Microsoft Bing provided responses from the ninth- to eleventh-grade RGL while Google Bard provided responses from the seventh- to tenth-grade RGL. LLMs face challenges in crafting outputs below a sixth-grade RGL. However, their capability to modify outputs above this threshold, provides a potential mechanism for adolescents to explore, understand, and engage with information regarding their health conditions, spanning from simple to complex terms. Future studies are needed to verify the accuracy and efficacy of these tools.

Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

Highly Interfacial Active Gemini Surfactants as Simple and Versatile Emulsifiers for Stabilizing, Lubricating and Structuring Liquids.

To date, locking the shape of liquids into non-equilibrium states usually relies on jamming nanoparticle surfactants at an oil/water interface. Here we show that a synthetic water-soluble zwitterionic Gemini surfactant can serve as an alternative to nanoparticle surfactants for stabilizing, structuring and additionally lubricating liquids. By having a high binding energy comparable to amphiphilic nanoparticles at the paraffin oil/water interface, the surfactant can attain near-zero interfacial tensions and ultrahigh surface coverages after spontaneous adsorption. Owing to the strong association between neighboring surfactant molecules, closely packed monolayers with high mechanical elasticity can be generated at the oil/water interface, thus allowing the surfactant to produce not only ultra-stable emulsions but also structured liquids with various geometries by using extrusion printing and 3D printing techniques. By undergoing tribochemical reactions at its sulfonic terminus, the surfactant can endow the resultant emulsions with favorable lubricity even under high load-bearing conditions. Our study may provide new insights into creating complex liquid devices and new-generation lubricants capable of combining the characteristics of both liquid and solid lubricants.

Selective and Efficient CO2 Electroreduction to Formate on Copper Electrodes Modified by Cationic Gemini Surfactants.

Electroreduction of CO2 into valuable chemicals and fuels is a promising strategy to mitigate energy and environmental problems. However, it usually suffers from unsatisfactory selectivity for a single product and inadequate electrochemical stability. Herein, we report the first work to use cationic Gemini surfactants as modifiers to boost CO2 electroreduction to formate. The selectivity, activity and stability of the catalysts can be all significantly enhanced by Gemini surfactant modification. The Faradaic efficiency (FE) of formate could reach up to 96 %, and the energy efficiency (EE) could achieve 71 % over the Gemini surfactants modified Cu electrode. In addition, the Gemini surfactants modified commercial Bi2 O3 nanosheets also showed an excellent catalytic performance, and the FE of formate reached 91 % with a current density of 510 mA cm-2 using the flow cell. Detailed studies demonstrated that the double quaternary ammonium cations and alkyl chains of the Gemini surfactants played a crucial role in boosting electroreduction CO2 , which can not only stabilize the key intermediate HCOO* but also provide an easy access for CO2 . These observations could shine light on the rational design of organic modifiers for promoted CO2 electroreduction.

End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini.

Background: End-of-life care (EOLC) is a critical aspect of healthcare, yet accessing reliable information remains challenging, particularly in culturally diverse contexts like India. Objective: This study investigates the potential of artificial intelligence (AI) in addressing the informational gap by analyzing patient information leaflets (PILs) generated by AI chatbots on EOLC. Methodology: Using a comparative research design, PILs generated by ChatGPT and Google Gemini were evaluated for readability, sentiment, accuracy, completeness, and suitability. Readability was assessed using established metrics, sentiment analysis determined emotional tone, accuracy, and completeness were rated by subject experts, and suitability was evaluated using the Patient Education Materials Assessment Tool (PEMAT). Results: Google Gemini PILs exhibited superior readability and actionability compared to ChatGPT PILs. Both conveyed positive sentiments and high levels of accuracy and completeness, with Google Gemini PILs showing slightly lower accuracy scores. Conclusion: The findings highlight the promising role of AI in enhancing patient education in EOLC, with implications for improving care outcomes and promoting informed decision-making in diverse cultural settings. Ongoing refinement and innovation in AI-driven patient education strategies are needed to ensure compassionate and culturally sensitive EOLC. How to cite this article: Gondode PG, Khanna P, Sharma P, Duggal S, Garg N. End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini. Indian J Crit Care Med 2024;28(6):561-568.

Opportunities and challenges with CRISPR-Cas mediated homologous recombination based precise editing in plants and animals.

KEY MESSAGE: We summarise recent advancements to achieve higher homologous recombination based gene targeting efficiency in different animals and plants. The genome editing has revolutionized the agriculture and human therapeutic sectors by its ability to create precise, stable and predictable mutations in the genome. It depends upon targeted double-strand breaks induction by the engineered endonucleases, which then gets repaired by highly conserved endogenous DNA repair mechanisms. The repairing could be done either through non-homologous end joining (NHEJ) or homology-directed repair (HDR) pathways. The HDR-based editing can be applied for precise gene targeting such as insertion of a new gene, gene replacement and altering of the regulatory sequence of a gene to control the existing protein expression. However, HDR-mediated editing is considered challenging because of lower efficiency in higher eukaryotes, thus, preventing its widespread application. This article reviews the recent progress of HDR-mediated editing and discusses novel strategies such as cell cycle synchronization, modulation of DNA damage repair factors, engineering of Cas protein favoring HDR and CRISPR-Cas reagents delivery methods to improve efficiency for generating knock-in events in both plants and animals. Further, multiplexing of described methods may be promising towards achieving higher donor template-assisted homologous recombination efficiency at the target locus.

Quantitative Measurement of Drug Release Dynamics within Targeted Organelles Using Förster Resonance Energy Transfer.

Measuring the release dynamics of drug molecules after their delivery to the target organelle is critical to improve therapeutic efficacy and reduce side effects. However, it remains challenging to quantitatively monitor subcellular drug release in real time. To address the knowledge gap, a novel gemini fluorescent surfactant capable of forming mitochondria-targeted and redox-responsive nanocarriers is designed. A quantitative Förster resonance energy transfer (FRET) platform is fabricated using this mitochondria-anchored fluorescent nanocarrier as a FRET donor and fluorescent drugs as a FRET acceptor. The FRET platform enables real-time measurement of drug release from organelle-targeted nanocarriers. Moreover, the obtained drug release dynamics can evaluate the duration of drug release at the subcellular level, which established a new quantitative method for organelle-targeted drug release. This quantitative FRET platform can compensate for the absent assessment of the targeted release performances of nanocarriers, offering in-depth understanding of the drug release behaviors at the subcellular targets.

Preparation of Cubosomes with Improved Colloidal and Structural Stability Using a Gemini Surfactant.

Cubosomes are nanoparticles with bicontinuous cubic internal nanostructures that have been considered for use in drug delivery systems (DDS). However, their low structural stability is a crucial concern for medical applications. Herein, we investigated the use of a gemini surfactant, sodium dilauramidoglutamide lysine (DLGL), which is composed of two monomeric surfactants linked with a spacer to improve the structural stability of cubosomes prepared with phytantriol (PHY). Uniform nanosuspensions comprising a specific mixing ratio of DLGL and PHY in water prepared via ultrasonication were confirmed by using dynamic light scattering. Small-angle X-ray scattering and cryo-transmission electron microscopy revealed the formation of Pn3̅m cubosomes in a range of DLGL/PHY solid ratios between 1 and 3% w/w. By contrast, cubosome formation was not observed at DLGL/PHY solid ratios of 5% w/w or higher, suggesting that excess DLGL interfered with cubosome formation and caused them to transform into small unilamellar vesicles. The addition of phosphate-buffered saline to the nanosuspension caused aggregation when the solid ratio of DLGL/PHY was less than 5% w/w. However, Im3̅m cubosomes were obtained at solid ratios of DLGL/PHY of 6, 7.5, and 10% w/w. The lattice parameters of the Pn3̅m and Im3̅m cubosomes were approximately 7 and 11-13 nm, respectively. The lattice parameters of Im3̅m cubosomes were affected by the concentration of DLGL. Pn3̅m cubosomes were surprisingly stable for 4 weeks at both 25 and 5 °C. In conclusion, DLGL, a gemini surfactant, was found to act as a new stabilizer for PHY cubosomes at specific concentrations. Cubosomes composed of DLGL are stable under low-temperature storage conditions, such as in refrigerators, making them a viable option for heat-sensitive DDS.

Evaluation of the antiviral potential of gemini surfactants against influenza virus H1N1.

Influenza A virus (IAV) affects human health worldwide as a high-risk disease. It can neither be easily controlled by current vaccines and nor be treated by conventional drugs. Gemini surfactants (GS) have shown several properties including antiviral activity. In this study, the antiviral capacity of some GS compounds with different levels of hydrophobicity was examined. The 50% cytotoxic (CC50) and non-cytotoxic (NCTC) concentrations of the compounds were determined by MTT method. The NCTCs, the same as effective concentrations (EC50s), were tested for the antiviral capacity against IAV in different combination treatments for 1 h incubation on MDCK cells. The HA and MTT assays were used to evaluate the virus titer and cell viabilities, respectively. The hemolytic activity of the compounds was also assessed using an HA inhibition assay. To evaluate the apoptotic effect of GS compounds, Annexin V-PI kit was used. The HA titers decreased between 1-6.5 logs, 1-4.5 logs, and 1-5.5 logs in simultaneous, pre- and post-penetration combination treatments, respectively. The cell viability values in all combination treatments were favorable. The HI assay indicated the hemolytic potential of GSs and their physical interaction with viral HA. The apoptosis test results highlighted anti-apoptotic capacity of the GS compounds alone and in the presence of influenza virus especially for the hydrophobic ones. Gemini surfactants were generally more efficacious in simultaneous treatment. Their antiviral potential may be attributed to their physical interaction with viral membrane or HA glycoprotein that disrupts viral particle or blocks viral entry to the cell and inhibits its propagation.

Synthesis, Properties, and Biomedical Application of Dicationic Gemini Surfactants with Dodecane Spacer and Carbamate Fragments.

A synthesis procedure and aggregation properties of a new homologous series of dicationic gemini surfactants with a dodecane spacer and two carbamate fragments (N,N'-dialkyl-N,N'-bis(2-(ethylcarbamoyloxy)ethyl)-N,N'-dimethyldodecan-1,6-diammonium dibromide, n-12-n(Et), where n = 10, 12, 14) were comprehensively described. The critical micelle concentrations of gemini surfactants were obtained using tensiometry, conductometry, spectrophotometry, and fluorimetry. The thermodynamic parameters of adsorption and micellization, i.e., maximum surface excess (Гmax), the surface area per surfactant molecule (Amin), degree of counterion binding (ß), and Gibbs free energy of micellization (∆Gmic), were calculated. Functional activity of the surfactants, including the solubilizing capacity toward Orange OT and indomethacin, incorporation into the lipid bilayer, minimum inhibitory concentration, and minimum bactericidal and fungicidal concentrations, was determined. Synthesized gemini surfactants were further used for the modification of liposomes dual-loaded with α-tocopherol and donepezil hydrochloride for intranasal treatment of Alzheimer's disease. The obtained liposomes have high stability (more than 5 months), a significant positive charge (approximately + 40 mV), and a high degree of encapsulation efficiency toward rhodamine B, α-tocopherol, and donepezil hydrochloride. Korsmeyer-Peppas, Higuchi, and first-order kinetic models were used to process the in vitro release curves of donepezil hydrochloride. Intranasal administration of liposomes loaded with α-tocopherol and donepezil hydrochloride for 21 days prevented memory impairment and decreased the number of Aß plaques by 37.6%, 40.5%, and 72.6% in the entorhinal cortex, DG, and CA1 areas of the hippocampus of the brain of transgenic mice with Alzheimer's disease model (APP/PS1) compared with untreated animals.