Analysis of international publication trends in artificial intelligence in skin cancer.

Bibliometric methods were used to analyze publications on artificial intelligence in skin cancer from 2010 to 2022, aiming to explore current publication trends and future directions. A comprehensive search using four terms - "artificial intelligence," "machine learning," "deep learning," and "skin cancer" was performed in the Web of Science database for original English-language publications on artificial intelligence in skin cancer from 2010 to 2022. We visually analyzed publication, citation, and coupling information, focusing on authors, countries and regions, publishing journals, institutions, and core keywords. The analysis of 989 publications revealed a consistent year-on-year increase in publications from 2010 to 2022 (0.51% vs. 33.57%). The USA, India, and China emerged as the leading contributors. IEEE Access was identified as the most prolific journal in this area. Key journals and influential authors were highlighted. Examination of the top 10 most cited publications highlights the significant potential of AI in oncology. Co-citation network analysis identified four primary categories of classical literature on AI in skin tumors. Keyword analysis indicated that "melanoma," "classification," and "deep learning" were the most prevalent keywords, suggesting that deep learning for melanoma diagnosis and grading is the current research focus. The term "pigmented skin lesions" showed the strongest burst and longest duration, while "texture" was the latest emerging keyword. AI represents a rapidly growing area of research in skin cancer with the potential to significantly improve skin cancer management. Future research is likely to focus on machine learning and deep learning technologies for screening and diagnostic purposes.

Multidimensional quantitative characterization of periocular morphology: distinguishing esotropia from epicanthus by deep learning network.

Background: Prominent epicanthus could not only diminish the eyes' aesthetics but may be deceptive for its typical appearance of pseudo-esotropia. This study aims to apply a deep learning model to characterize the periocular morphology for preliminary identification. Methods: This prospective study consecutively included 300 subjects visiting the ophthalmology department in a tertiary referral hospital. Children aged 7-18 years with simple epicanthus or concomitant esotropia and healthy volunteers who were age- and gender-matched were eligible for inclusion. Multiple metrics were extracted automatically and manually from facial images to characterize the periocular morphology and binocular symmetry. The dice coefficient (Dice), intraclass correlation coefficient (ICC), and Bland-Altman biases were calculated to evaluate their consistency. The receiver operating characteristic (ROC) curve determined the cut-off values of symmetry indexes (SIs) for distinguishing concomitant esotropia subjects from epicanthus ones. Results: The Dice for eyelid and cornea segmentation were 0.949 and 0.944, respectively. The ICCs of the two measurements ranged from 0.898 to 0.983. Biases ranged from 0.16 to 0.74 mm. The periocular morphology of epicanthus eyes was significantly different from the normal ones, including palpebral fissure width (21.41±1.53 vs. 24.45±1.82 mm; P<0.01), and palpebral fissure height (8.91±1.37 vs. 9.60±1.25 mm; P<0.01). The ROC analysis yielded an area under the curve of 0.971 [95% confidence interval (CI): 0.950-0.991] with SI for distinguishing esotropia subjects. Its optimal cut-off value was 1.296 with 0.920 sensitivity and 0.910 specificity. Conclusions: Our study established a standard deep learning system for characterizing the periocular morphology of epicanthus and esotropia eyes with great accuracy. This objective method could be generalized to other periocular morphological assessments for clinical care.

Role of B cells in intratumoral MBTA immunotherapy of murine pheochromocytoma model.

Immunotherapy represents a revolutionary advancement in cancer treatment, which has traditionally focused on T cells; however, the role of B cells in cancer immunotherapy has gained interest because of their role in antigen presentation, antibody production, and cytokine release. In this study, we examined the role of B cells in previously developed intratumoral MBTA therapy (mannan-BAM, TLR ligands, and anti-CD40 antibody) in murine models of MTT pheochromocytoma. The results indicated that B cells significantly enhance the success of MBTA therapy, with wild-type mice exhibiting a lower tumor incidence and smaller tumors compared with B cell-deficient mice. Increased IL-6 and TNF-alpha levels indicated severe inflammation and a potential cytokine storm in B cell-deficient mice. Neutralization of TNF-alpha ameliorated these complications but resulted in increased tumor recurrence. The results highlight the important role of B cells in enhancing the immune response and maintaining immune homeostasis during MBTA therapy. Our findings offer new insights into improving therapeutic outcomes.

Recurrence in Eyelid Sebaceous Carcinoma: A Multicentric Study of 418 Patients.

Purpose: Local recurrence predicts dismal prognosis in eyelid sebaceous carcinoma (SC). Recurrence predictors vary across studies. Accurate recurrence estimation is essential for individualized therapy in eyelid SC. This study aims to identify recurrence predictors and develop a nomogram for personalized prediction in eyelid SC. Methods: We conducted a multicenter retrospective cohort study. Chart reviews were performed in 418 consecutive patients with eyelid SC. All patients were followed up after their initial surgery. Multivariate Cox regression was used to explore the independent predictors of recurrence. A nomogram for recurrence prediction was developed and validated with bootstrap resampling. The predictive accuracy and discriminative ability were compared with the Tumor, Node, Metastasis (TNM) staging system. Results: Over a median of 60-month follow-up, 167 patients (40%) had local recurrence. The median time from diagnosis to recurrence was 14 months. The 1-year cumulative recurrence rate was 18%. Diagnostic delay (hazard ratio [HR] = 1.01, 95% confidence interval [CI] = 1.00-1.01, P = 0.001), orbital involvement (HR = 4.47, 95% CI = 3.04-6.58, P < 0.001), Ki67 (HR = 1.01, 95% CI = 1.00-1.02, P = 0.008) and initial surgery of Mohs micrographic surgery with intraoperative frozen section control (HR = 0.53, 95% CI = 0.35-0.80, P = 0.003) were independent influencing factors of recurrence. A nomogram integrating these four factors combined with pagetoid spread displayed satisfactory discriminative ability (C-index = 0.80-0.83; area under the curve [AUC] = 0.82-0.84), which compared favorably than TNM staging (all P < 0.05). Conclusions: The recurrence rate is high in eyelid SC. Early detection and primary resection with Mohs micrographic surgery are recommended in controlling recurrence. Patients with orbital involvement, high Ki67 expression, and pagetoid spread may require adjuvant measures. This nomogram offers more accurate recurrence estimates, aiding in therapeutic decision making.

Erratum to 'B4GALNT1 promotes hepatocellular carcinoma stemness and progression via integrin α2ß1-mediated FAK and AKT activation' (JHEP Reports 5 [2023] 100903).

[This corrects the article DOI: 10.1016/j.jhepr.2023.100903.].

Integrative genomic analysis reveals cancer-associated mutations in patients with ophthalmic tumors.

OBJECTIVE: Apart from the role of the retinoblastoma gene, the genomic events associated with poor outcomes in patients with ophthalmic tumors are poorly understood. METHODS: We retrospectively analyzed 48 patients with six types of ophthalmic tumors. We searched for high-frequency mutated genes and susceptibility genes in these patients using combined exome and transcriptome analysis. RESULTS: We identified four clearly causative genes (TP53, PTCH1, SMO, BAP1). Susceptibility gene analysis identified hotspot genes, including RUNX1, APC, IDH2, and BRCA2, and high-frequency gene analysis identified several genes, including TP53, TTN, and MUC16. Transcriptome analysis identified 5868 differentially expressed genes, of which TOP2A and ZWINT were upregulated in all samples, while CFD, ELANE, HBA1, and HBB were downregulated. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that the phosphoinositide 3-kinase (PI3K)-Akt and Transcriptional misregulation in cancer signaling pathways may be involved in ophthalmic tumorigenesis. CONCLUSIONS: TP53 is clearly involved in ophthalmic tumorigenesis, especially in basal cell carcinoma, and the PI3K-Akt signaling pathway may be an essential pathway involved in ophthalmic tumorigenesis. RUNX1, SMO, TOP2A, and ZWINT are also highly likely to be involved in ophthalmic tumorigenesis, but further functional experiments are needed to verify the mechanisms of these genes in regulating tumorigenesis.

System-wide identification of novel de-ubiquitination targets for USP10 in gastric cancer metastasis through multi-omics screening.

OBJECTIVE: Ubiquitin-specific peptidase 10 (USP10), a typical de-ubiquitinase, has been found to play a double-edged role in human cancers. Previously, we reported that the expression of USP10 was negatively correlated with the depth of gastric wall invasion, lymph node metastasis, and prognosis in gastric cancer (GC) patients. However, it remains unclear whether USP10 can regulate the metastasis of GC cells through its de-ubiquitination function. METHODS: In this study, proteome, ubiquitinome, and transcriptome analyses were conducted to comprehensively identify novel de-ubiquitination targets for USP10 in GC cells. Subsequently, a series of validation experiments, including in vitro cell culture studies, in vivo metastatic tumor models, and clinical sample analyses, were performed to elucidate the regulatory mechanism of USP10 and its de-ubiquitination targets in GC metastasis. RESULTS: After overexpression of USP10 in GC cells, 146 proteins, 489 ubiquitin sites, and 61 mRNAs exhibited differential expression. By integrating the results of multi-omics, we ultimately screened 9 potential substrates of USP10, including TNFRSF10B, SLC2A3, CD44, CSTF2, RPS27, TPD52, GPS1, RNF185, and MED16. Among them, TNFRSF10B was further verified as a direct de-ubiquitination target for USP10 by Co-IP and protein stabilization assays. The dysregulation of USP10 or TNFRSF10B affected the migration and invasion of GC cells in vitro and in vivo models. Molecular mechanism studies showed that USP10 inhibited the epithelial-mesenchymal transition (EMT) process by increasing the stability of TNFRSF10B protein, thereby regulating the migration and invasion of GC cells. Finally, the retrospective clinical sample studies demonstrated that the downregulation of TNFRSF10B expression was associated with poor survival among 4 of 7 GC cohorts, and the expression of TNFRSF10B protein was significantly negatively correlated with the incidence of distant metastasis, diffuse type, and poorly cohesive carcinoma. CONCLUSIONS: Our study established a high-throughput strategy for screening de-ubiquitination targets for USP10 and further confirmed that inhibiting the ubiquitination of TNFRSF10B might be a promising therapeutic strategy for GC metastasis.

Automatic Measurement and Comparison of Normal Eyelid Contour by Age and Gender Using Image-Based Deep Learning.

Purpose: This study aimed to propose a fully automatic eyelid measurement system and compare the contours of both the upper and lower eyelids of normal individuals according to age and gender. Design: Prospective study. Participants: Five hundred and forty healthy Chinese aged 0 to 79 years in a tertiary hospital were included. Methods: Facial images in the primary gazing position were used to train and test the proposed automatic system for eye recognition and eye segmentation. According to the 10-millimeter diameter circular marker, measurements were transformed from pixel sizes into factual distances. Main Outcome Measures: Midpupil lid distances (MPLDs) every 15° of all participants were automatically measured in both genders (30 males and 30 females in each age group) by the proposed deep learning (DL)-based system. Intraclass correlation coefficients (ICCs) were performed to assess the agreement between the automatic and manual margin reflex distances (MRDs). The eyelid contour, eyelid asymmetry, and palpebral fissure obliquity were analyzed using MPLD, temporal-versus-nasal MPLD ratio, and the angle between the inner and outer canthi, respectively. Results: The measurement of MRDs by the automatic system excellently agreed with that of the expert, with ICCs ranging from 0.863 to 0.886. As the age of the participants increased, the values of MPLDs reached a peak in those in their 20s or 30s and then gradually decreased at all angles. The temporal sector showed greater changes in MPLDs than the nasal sector, and the changes were more significant in females than in males. The maximum value of palpebral fissure obliquity appeared before 10 years in both genders and remained relatively stable after the 20s (P > 0.05). Conclusions: The proposed DL-based eyelid analysis system allowed automatic, accurate, and comprehensive measurement of the eyelid contour. The refinement of eyelid shape quantification could be beneficial for future objective assessment preocular and postocular plastic surgery. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

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.

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.

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.

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.

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.

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.

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.

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.

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.