Association between dietary inflammation index and cataract: a population-based study from NHANES 2005-2008.

Importance: Various studies have widely explored the association between index of dietary inflammation (DII) and occurrence of diseases. Accumulating evidence have revealed that a lower DII seems to be protective against a variety of diseases. Nevertheless, the association between DII and age-related cataract remains unclear. Objective: To investigate the correlation between DII and age-related cataract in a representative sample of the American population. Design setting and participants: This cross-sectional population-based study comprised 6,395 participants from the National Health and Nutrition Examination Survey (NHANES) conducted in cycles from 2005 to 2008. DII was calculated using dietary recall information, with higher scores indicating greater inflammatory potential of the diet. Age-related cataract was evaluated using cataract surgery as a surrogate measure. Covariates included sociodemographic factors, lifestyle factors, physical measures, and comorbidities. Logistic regression models were employed to assess the association between DII and cataract. The presence of a non-linear relationship was examined using restricted cubic spline analysis. Subgroup analysis was conducted to explore potential interaction effects. Data analysis was performed from September 1 to December 30, 2022. Main outcomes and measures: Age-related cataract assessed through cataract surgery information obtained from a self-reported questionnaire. Results: A total of 6,395 participants were included, with a mean (standard deviation, SD) age of 48.7 (15.3) years. Of these, 3,115 (48.7%) were male, 3,333 (52.1%) were non-Hispanic white, and 683 (10.7%) had cataract. The mean (SD) DII was -4.78 (1.74). After adjusting for all included covariates, DII showed a positive association with cataract, both as a continuous variable (odds ratio (OR): 1.054, 95% confidence interval (CI): 1.007-1.103, p = 0.023) and in quartiles, with the highest quartile compared to the lowest (OR: 1.555, 95% CI: 1.233-1.967, p < 0.001). Restricted cubic spline analysis revealed no evidence of a non-linear relationship (p for non-linearity 0.085). Subgroup analysis indicated no interaction effects among the studied covariates. Conclusions and relevance: These findings suggest that a pro-inflammatory diet serves as a risk factor for the occurrence of cataracts.

Exenatide reduces atrial fibrillation susceptibility by inhibiting hKv1.5 and hNav1.5 channels.

Exenatide, a promising cardioprotective agent, protects against cardiac structural remodeling and diastolic dysfunction. Combined blockade of sodium and potassium channels is valuable for managing atrial fibrillation (AF). Here, we explored whether exenatide displayed anti-AF effects by inhibiting human Kv1.5 and Nav1.5 channels. We used the whole-cell patch-clamp technique to investigate the effects of exenatide on hKv1.5 and hNav1.5 channels expressed in human embryonic kidney 293 cells and studied the effects of exenatide on action potential (AP) and other cardiac ionic currents in rat atrial myocytes. Additionally, an electrical mapping system was used to explore the effects of exenatide on electrical properties and AF activity in isolated rat hearts. Finally, a rat AF model, established using acetylcholine and calcium chloride, was employed to evaluate the anti-AF potential of exenatide in rats. Exenatide reversibly suppressed IKv1.5 with IC50 of 3.08 µM, preferentially blocked hKv1.5 channel in its closed state, and positively shifted the voltage-dependent activation curve. Exenatide also reversibly inhibited INav1.5 with IC50 of 3.30 µM, negatively shifted the voltage-dependent inactivation curve, and slowed its recovery from inactivation with significant use-dependency at 5 and 10 Hz. Furthermore, exenatide prolonged AP duration and suppressed the sustained K+ current (Iss) and transient outward K+ current (Ito), but without inhibition of L-type Ca2+ current (ICa,L) in rat atrial myocytes. Exenatide prevented AF incidence and duration in rat hearts and rats. These findings demonstrate that exenatide inhibits IKv1.5 and INav1.5in vitro and reduces AF susceptibility in isolated rat hearts and rats.

CT Image-Based Radiomic Analysis for Detecting PD-L1 Expression Status in Bladder Cancer Patients.

RATIONALE AND OBJECTIVES: The role of Programmed death-ligand 1 (PD-L1) expression is crucial in guiding immunotherapy selection. This study aims to develop and evaluate a radiomic model, leveraging Computed Tomography (CT) imaging, with the objective of predicting PD-L1 expression status in patients afflicted with bladder cancer. MATERIALS AND METHODS: The study encompassed 183 subjects diagnosed with histologically confirmed bladder cancer, among which the PD-L1(+) cohort constituted 60.1% of the total population. Stratified random sampling was utilized at a 7:3 ratio. We employed five diverse machine learning algorithms-Decision Tree, Random Forest, Linear Support Vector Classification, Support Vector Machine, and Logistic Regression-to establish radiomic models on the training dataset. These models endeavored to predict PD-L1 expression status premised on radiomic features derived from region-of-interest segmentation. Subsequent to this, the predictive performance of these models was examined on a validation set employing the receiver operating characteristic (ROC) curve. The DeLong test was utilized to contrast ROC curves, thereby pinpointing the model with superior predictive accuracy. RESULTS: 16 features were chosen for the model construction. All five models revealed strong performance in the training set (AUC, 0.920-1) and commendable predictive ability in the validation set (AUC, 0.753-0.766). As per the DeLong test, no statistically significant disparities were observed among any of the models (P > 0.05) in the validation set. Additional verification through the calibration curve and decision curve analysis indicated that the Logistic Regression model exhibited extraordinary precision and practicality. CONCLUSION: Our machine learning model, grounded on radiomic features, demonstrated its proficiency in accurately distinguishing bladder cancer patients with high PD-L1 expression. Future research, incorporating more exhaustive datasets, could potentially augment the predictive efficiency of radiomic algorithms, thereby advancing their clinical utility.

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.

PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation.

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.

Recent Advances in Nanomedicine for Ocular Fundus Neovascularization Disease Management.

As an indispensable part of the human sensory system, visual acuity may be impaired and even develop into irreversible blindness due to various ocular pathologies. Among ocular diseases, fundus neovascularization diseases (FNDs) are prominent etiologies of visual impairment worldwide. Intravitreal injection of anti-vascular endothelial growth factor drugs remains the primary therapy but is hurdled by common complications and incomplete potency. To renovate the current therapeutic modalities, nanomedicine emerged as the times required, which is endowed with advanced capabilities, able to fulfill the effective ocular fundus drug delivery and achieve precise drug release control, thus further improving the therapeutic effect. This review provides a comprehensive summary of advances in nanomedicine for FND management from state-of-the-art studies. First, the current therapeutic modalities for FNDs are thoroughly introduced, focusing on the key challenges of ocular fundus drug delivery. Second, nanocarriers are comprehensively reviewed for ocular posterior drug delivery based on the nanostructures: polymer-based nanocarriers, lipid-based nanocarriers, and inorganic nanoparticles. Thirdly, the characteristics of the fundus microenvironment, their pathological changes during FNDs, and corresponding strategies for constructing smart nanocarriers are elaborated. Furthermore, the challenges and prospects of nanomedicine for FND management are thoroughly discussed.

rWTC-MBTA Vaccine Induces Potent Adaptive Immune Responses Against Glioblastomas via Dynamic Activation of Dendritic Cells.

Despite strides in immunotherapy, glioblastoma multiforme (GBM) remains challenging due to low inherent immunogenicity and suppressive tumor microenvironment. Converting "cold" GBMs to "hot" is crucial for immune activation and improved outcomes. This study comprehensively characterized a therapeutic vaccination strategy for preclinical GBM models. The vaccine consists of Mannan-BAM-anchored irradiated whole tumor cells, Toll-like receptor ligands [lipoteichoic acid (LTA), polyinosinic-polycytidylic acid (Poly (I:C)), and resiquimod (R-848)], and anti-CD40 agonistic antibody (rWTC-MBTA). Intracranial GBM models (GL261, SB28 cells) are used to evaluate the vaccine efficacy. A substantial number of vaccinated mice exhibited complete regression of GBM tumors in a T-cell-dependent manner, with no significant toxicity. Long-term tumor-specific immune memory is confirmed upon tumor rechallenge. In the vaccine-draining lymph nodes of the SB28 model, rWTC-MBTA vaccination triggered a major rise in conventional dendritic cell type 1 (cDC1) 12 h post-treatment, followed by an increase in conventional dendritic cell type 2 (cDC2), monocyte-derived dendritic cell (moDC), and plasmacytoid dendritic cell (pDC) on Day 5 and Day 13. Enhanced cytotoxicity of CD4+ and CD8+ T cells in vaccinated mice is verified in co-culture with tumor cells. Analyses of immunosuppressive signals (T-cell exhaustion, myeloid-derived suppressor cells (MDSC), M2 macrophages) in the GBM microenvironment suggest potential combinations with other immunotherapies for enhanced efficacy. In conclusion, the authors findings demonstrate that rWTC-MBTA induces potent and long-term adaptive immune responses against GBM.

A novel injectable thermo/photo dual-crosslinking hydrogel based on modified chitosan for fast sealing open globe injury.

Open globe injuries (OGIs) demand immediate attention to prevent further complications and improve vision prognosis. Herein, we developed a thermo/photo dual-crosslinking injectable hydrogel, HBC_m_Arg, for rapidly sealing OGIs in emergency ophthalmic cases. HBC_m_Arg was prepared with arginine and methacrylic anhydride modified hydroxybutyl chitosan (HBC). HBC_m_Arg was initially in liquid form at 25 °C, enabling easy injection at the injury site. After reaching the ocular surface temperature, it underwent reversible heat-induced gelation to achieve in situ transformation. Further, HBC_m_Arg was capable of rapid photocrosslinking under UV light, forming a dual network structure to bolster mechanical strength, thereby facilitating effective OGI closure. Biocompatibility assessments, including in vitro studies with three ocular cell types and in vivo experiments on rabbit eyes, confirmed the safety profile of HBC_m_Arg. Ex vivo and in vivo burst pressure tests demonstrated the hydrogel's ability to promptly restore intraocular pressure and withstand elevated pressures, underscoring its potential for OGI stabilization. Additionally, the suitable degradation of HBC_m_Arg within ocular tissues, coupled with its stability in ex vivo assessments, presented a delicate balance between stability and biodegradability. In conclusion, HBC_m_Arg holds promise for improving emergency ophthalmic care by providing a rapid, effective, and safe way to seal OGIs in critical situations.

Radiomics Features on Enhanced Computed Tomography Predict FOXP3 Expression and Clinical Prognosis in Patients with Head and Neck Squamous Cell Carcinoma.

Forkhead box P3 (FOXP3) has been identified as a novel molecular marker in various types of cancer. The present study assessed the expression of FOXP3 in patients with head and neck squamous cell carcinoma (HNSCC) and its potential as a clinical prognostic indicator, and developed a radiomics model based on enhanced computed tomography (CT) imaging. Data from 483 patients with HNSCC were downloaded from the Cancer Genome Atlas for FOXP3 prognostic analysis and enhanced CT images from 139 patients included in the Cancer Imaging Archives, which were subjected to the maximum relevance and minimum redundancy and recursive feature elimination algorithms for radiomics feature extraction and processing. Logistic regression was used to build a model for predicting FOXP3 expression. A prognostic scoring system for radiomics score (RS), FOXP3, and patient clinicopathological factors was established to predict patient survival. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration curve and decision curve analysis (DCA) were used to evaluate model performance. Furthermore, the relationship between FOXP3 and the immune microenvironment, as well as the association between RS and immune checkpoint-related genes, was analyzed. Results of analysis revealed that patients with HNSCC and high FOXP3 mRNA expression exhibited better overall survival. Immune infiltration analysis revealed that FOXP3 had a positive correlation with CD4 + and CD8 + T cells and other immune cells. The 8 best radiomics features were selected to construct the radiomics model. In the FOXP3 expression prediction model, the AUC values were 0.707 and 0.702 for the training and validation sets, respectively. Additionally, the calibration curve and DCA demonstrated the positive diagnostic utility of the model. RS was correlated with immune checkpoint-related genes such as ICOS, CTLA4, and PDCD1. A predictive nomogram was established, the AUCs were 0.87, 0.787, and 0.801 at 12, 24, and 36 months, respectively, and DCA demonstrated the high clinical applicability of the nomogram. The enhanced CT radiomics model can predict expression of FOXP3 and prognosis in patients with HNSCC. As such, FOXP3 may be used as a novel prognostic marker to improve individualized clinical diagnosis and treatment decisions.

Artificial intelligence-assisted management of retinal detachment from ultra-widefield fundus images based on weakly-supervised approach.

Background: Retinal detachment (RD) is a common sight-threatening condition in the emergency department. Early postural intervention based on detachment regions can improve visual prognosis. Methods: We developed a weakly supervised model with 24,208 ultra-widefield fundus images to localize and coarsely outline the anatomical RD regions. The customized preoperative postural guidance was generated for patients accordingly. The localization performance was then compared with the baseline model and an ophthalmologist according to the reference standard established by the retina experts. Results: In the 48-partition lesion detection, our proposed model reached an 86.42% (95% confidence interval (CI): 85.81-87.01%) precision and an 83.27% (95%CI: 82.62-83.90%) recall with an average precision (PA) of 0.9132. In contrast, the baseline model achieved a 92.67% (95%CI: 92.11-93.19%) precision and limited recall of 68.07% (95%CI: 67.25-68.88%). Our holistic lesion localization performance was comparable to the ophthalmologist's 89.16% (95%CI: 88.75-89.55%) precision and 83.38% (95%CI: 82.91-83.84%) recall. As to the performance of four-zone anatomical localization, compared with the ground truth, the un-weighted Cohen's κ coefficients were 0.710(95%CI: 0.659-0.761) and 0.753(95%CI: 0.702-0.804) for the weakly-supervised model and the general ophthalmologist, respectively. Conclusion: The proposed weakly-supervised deep learning model showed outstanding performance comparable to that of the general ophthalmologist in localizing and outlining the RD regions. Hopefully, it would greatly facilitate managing RD patients, especially for medical referral and patient education.

An antibacterial and anti-oxidant hydrogel containing hyperbranched poly-l-lysine and tea polyphenols accelerates healing of infected wound.

Both bacteria-infection and excessive inflammation delay the wound healing process and even create non-healing wound, thus it is highly desirable to endow the wound dressing with bactericidal and anti-oxidation properties. Herein an antibacterial and antioxidation hydrogel based on Carbomer 940 (CBM) and hydroxypropyl methyl cellulose (HPMC) loaded with tea polyphenols (TP) and hyperbranched poly-l-lysine (HBPL) was designed and fabricated. The hydrogel killed 99.9 % of methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli) at 107 CFU mL-1, and showed strong antioxidation against H2O2 and 2,2-di(4-tert-octylphenyl)-1-picryl-hydrazyl (DPPH) radicals without noticeable cytotoxicity in vitro. The CBM/HPMC/HBPL/TP hydrogel significantly shortened the inflammatory period of the MRSA-infected full-thickness skin wound of rats in vivo, with 2 orders of lower MRSA colonies compared with the blank control, and promoted the wound closure especially at the earlier stage. The inflammation was suppressed and the vascularization was promoted significantly as well, resulting in reduced pro-inflammatory factors including interleukin-6 (IL-6), interleukin-1ß (IL-1ß) and tumor necrosis factor-α (TNF-α), and increased anti-inflammatory factors such as interleukin-4 (IL-4) and interleukin-10 (IL-10).

Ultrasound-enhanced nano catalyst with ferroptosis-apoptosis combined anticancer strategy for metastatic uveal melanoma.

Uveal melanoma is the most common primary ocular tumor owing to its highly invasive and metastatic characteristics. Currently, standard clinical treatment has an unsatisfied curative effect due to the lack of an effective approach to inhibit the tumor metastasis. Therefore, it is necessary to develop a new strategy that can both restraint local tumors and suppress the ocular tumor metastasis. Herein, we developed ultrasound-responsive nanoparticles (FeP NPs) that can both hinder the growth of in situ ocular tumor and prevent the tumor metastasis through the ferroptosis-apoptosis combined-anticancer strategy. The FeP NPs were assembling by stimulating gallic acid-Fe (III) and paclitaxel, then could be internalized into tumor cells under the cooperative effect of ultrasound, which further activates the intracellular Fenton reaction and generates high reactive oxygen species levels, ultimately leading to mitochondrial damage, lipid per-oxidation, and apoptosis. The FeP NPs can efficiently inhibit the tumor growth in an orthotopic uveal melanoma model. More importantly, the level of the promoting-metastatic factor nerve growth factor receptor (NGFR) secreted by cancer cells is significantly reduced, further limits cancer metastasis to the cervical lymph node and finally inhibits lung metastasis of uveal melanoma. We believe that these designed ultrasound-enhanced nanoparticles possess potential clinical application for preventing the regeneration and metastasis of uveal melanoma.

Multi-scale consistent self-training network for semi-supervised orbital tumor segmentation.

PURPOSE: Segmentation of orbital tumors in CT images is of great significance for orbital tumor diagnosis, which is one of the most prevalent diseases of the eye. However, the large variety of tumor sizes and shapes makes the segmentation task very challenging, especially when the available annotation data is limited. METHODS: To this end, in this paper, we propose a multi-scale consistent self-training network (MSCINet) for semi-supervised orbital tumor segmentation. Specifically, we exploit the semantic-invariance features by enforcing the consistency between the predictions of different scales of the same image to make the model more robust to size variation. Moreover, we incorporate a new self-training strategy, which adopts iterative training with an uncertainty filtering mechanism to filter the pseudo-labels generated by the model, to eliminate the accumulation of pseudo-label error predictions and increase the generalization of the model. RESULTS: For evaluation, we have built two datasets, the orbital tumor binary segmentation dataset (Orbtum-B) and the orbital multi-organ segmentation dataset (Orbtum-M). Experimental results on these two datasets show that our proposed method can both achieve state-of-the-art performance. In our datasets, there are a total of 55 patients containing 602 2D images. CONCLUSION: In this paper, we develop a new semi-supervised segmentation method for orbital tumors, which is designed for the characteristics of orbital tumors and exhibits excellent performance compared to previous semi-supervised algorithms.

A tough, antibacterial and antioxidant hydrogel dressing accelerates wound healing and suppresses hypertrophic scar formation in infected wounds.

Wound management is an important issue that places enormous pressure on the physical and mental health of patients, especially in cases of infection, where the increased inflammatory response could lead to severe hypertrophic scars (HSs). In this study, a hydrogel dressing was developed by combining the high strength and toughness, swelling resistance, antibacterial and antioxidant capabilities. The hydrogel matrix was composed of a double network of polyvinyl alcohol (PVA) and agarose with excellent mechanical properties. Hyperbranched polylysine (HBPL), a highly effective antibacterial cationic polymer, and tannic acid (TA), a strong antioxidant molecule, were added to the hydrogel as functional components. Examination of antibacterial and antioxidant properties of the hydrogel confirmed the full play of the efficacy of HBPL and TA. In the in vivo studies of methicillin-resistant Staphylococcus aureus (MRSA) infection, the hydrogel had shown obvious promotion of wound healing, and more profoundly, significant suppression of scar formation. Due to the common raw materials and simple preparation methods, this hydrogel can be mass produced and used for accelerating wound healing while preventing HSs in infected wounds.

Application of artificial intelligence in oculoplastics.

Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.

Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study.

BACKGROUND: Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical settings, as well as its reasoning, have not been explored in depth. OBJECTIVE: This study aimed to evaluate ChatGPT's diagnostic performance and inference abilities for retinal vascular diseases in a non-English clinical environment. METHODS: In this cross-sectional study, we collected 1226 fundus fluorescein angiography reports and corresponding diagnoses written in Chinese and tested ChatGPT with 4 prompting strategies (direct diagnosis or diagnosis with a step-by-step reasoning process and in Chinese or English). RESULTS: Compared with ChatGPT using Chinese prompts for direct diagnosis that achieved an F1-score of 70.47%, ChatGPT using English prompts for direct diagnosis achieved the best diagnostic performance (80.05%), which was inferior to ophthalmologists (89.35%) but close to ophthalmologist interns (82.69%). As for its inference abilities, although ChatGPT can derive a reasoning process with a low error rate (0.4 per report) for both Chinese and English prompts, ophthalmologists identified that the latter brought more reasoning steps with less incompleteness (44.31%), misinformation (1.96%), and hallucinations (0.59%) (all P<.001). Also, analysis of the robustness of ChatGPT with different language prompts indicated significant differences in the recall (P=.03) and F1-score (P=.04) between Chinese and English prompts. In short, when prompted in English, ChatGPT exhibited enhanced diagnostic and inference capabilities for retinal vascular disease classification based on Chinese fundus fluorescein angiography reports. CONCLUSIONS: ChatGPT can serve as a helpful medical assistant to provide diagnosis in non-English clinical environments, but there are still performance gaps, language disparities, and errors compared to professionals, which demonstrate the potential limitations and the need to continually explore more robust large language models in ophthalmology practice.

Sketch-Supervised Histopathology Tumour Segmentation: Dual CNN-Transformer With Global Normalised CAM.

Deep learning methods are frequently used in segmenting histopathology images with high-quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like labelling is more cost-effective and easier to obtain in clinical practice. The coarse annotations provide limited supervision, so employing them directly for segmentation network training remains challenging. We present a sketch-supervised method, called DCTGN-CAM, based on a dual CNN-Transformer network and a modified global normalised class activation map. By modelling global and local tumour features simultaneously, the dual CNN-Transformer network produces accurate patch-based tumour classification probabilities by training only on lightly annotated data. With the global normalised class activation map, more descriptive gradient-based representations of the histopathology images can be obtained, and inference of tumour segmentation can be performed with high accuracy. Additionally, we collect a private skin cancer dataset named BSS, which contains fine and coarse annotations for three types of cancer. To facilitate reproducible performance comparison, experts are also invited to label coarse annotations on the public liver cancer dataset PAIP2019. On the BSS dataset, our DCTGN-CAM segmentation outperforms the state-of-the-art methods and achieves 76.68 % IOU and 86.69 % Dice scores on the sketch-based tumour segmentation task. On the PAIP2019 dataset, our method achieves a Dice gain of 8.37 % compared with U-Net as the baseline network.

Retinogenesis in a Dish: Bibliometric Analysis and Visualization of Retinal Organoids From 2011 to 2022.

Retinal organoid (RO) is the three-dimensional (3D) retinal culture derived from pluripotent or embryonic stem cells which recapitulates organ functions, which was a revolutionary milestone in stem cell technology. The purpose of this study is to explore the hotspots and future directions on ROs, as well as to better understand the fields of greatest research opportunities. Eligible publications related to RO from 2011 to 2022 were acquired from the Web of Science (WoS) Core Collection database. Bibliometric analysis was performed by using software including VOSviewer, CiteSpace, and ArcGIS. A total of 520 articles were included, and the number of annual publications showed a rapid increase with an average rate of 40.86%. The United States published the most articles (241/520, 46.35%) with highest total citation frequencies (5,344). University College London (UK) contributed the largest publication output (40/520, 7.69%) and received highest total citation frequencies. Investigative Ophthalmology & Visual Science was the most productive journal with 129 articles. Majlinda Lako contributed the most research with 32 articles, while Olivier Goureau has the strongest collaboration work. Research could be subdivided into four keyword clusters: "culture and differentiation," "morphogenesis and modeling," "gene therapy," and "transplantation and visual restoration," and evolution of keywords was identified. Last decade has witnessed the huge progress in the field of RO, which is a young and promising research area with extensive and in-depth studies. More attention should be paid to RO-related models and therapies based on specific retinal diseases, especially inherited retinopathies.

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.

Application of toll-like receptors (TLRs) and their agonists in cancer vaccines and immunotherapy.

Toll-like receptors (TLRs) are pattern recognition receptors (PRRs) expressed in various immune cell types and perform multiple purposes and duties involved in the induction of innate and adaptive immunity. Their capability to propagate immunity makes them attractive targets for the expansion of numerous immunotherapeutic approaches targeting cancer. These immunotherapeutic strategies include using TLR ligands/agonists as monotherapy or combined therapeutic strategies. Several TLR agonists have demonstrated significant efficacy in advanced clinical trials. In recent years, multiple reports established the applicability of TLR agonists as adjuvants to chemotherapeutic drugs, radiation, and immunotherapies, including cancer vaccines. Cancer vaccines are a relatively novel approach in the field of cancer immunotherapy and are currently under extensive evaluation for treating different cancers. In the present review, we tried to deliver an inclusive discussion of the significant TLR agonists and discussed their application and challenges to their incorporation into cancer immunotherapy approaches, particularly highlighting the usage of TLR agonists as functional adjuvants to cancer vaccines. Finally, we present the translational potential of rWTC-MBTA vaccination [irradiated whole tumor cells (rWTC) pulsed with phagocytic agonists Mannan-BAM, TLR ligands, and anti-CD40 agonisticAntibody], an autologous cancer vaccine leveraging membrane-bound Mannan-BAM, and the immune-inducing prowess of TLR agonists as a probable immunotherapy in multiple cancer types.