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.

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.

Analysis of chloroplast genome structure and phylogeny of the traditional medicinal of Ardisia crispa (Myrsinaceae).

Ardisia crispa(Myrsinaceae) is an ethnomedicine with horticultural and important medicinal values. Its morphology is complex, and its identification is difficult. We analyse the chloroplast genome characteristics and phylogenetic position of A. crispa to provide basic research data for the identification of A. crispa species and resource conservation. This study assemble and annotate the chloroplast genome of A. crispa and to compare it with the chloroplast genome within Ardisia. The A. crispa chloroplast genome is 156,785 bp in length, with a typical quadripartite structure containing 131 genes, including 86 protein-coding genes, 37 tRNA genes, and 8 rRNA genes; a total of 59 SSRs sites were identified, and the codon preference of this chloroplast genome is greater in A/U than in G/C, and leucine is the amino acid with the highest frequency of use. The chloroplast genomes of the nine Ardisia species are conserved in gene content and number, with more stable boundaries and less variation. In the phylogenetic tree, A. crispa is clustered on a branch with A. crispa var dielsii, and is closely related to A. mamillata and A. pedalis. In this study, we constructed and analyzed the chloroplast genome structure of A. crispa, and conducted phylogenetic analysis using the whole chloroplast genome sequence data of Ardisia plants, which is of great significance in understanding the genetic basis of A. crispa and adaptive evolution in Ardisia plants, and this will lay the foundation for the future research on A. crispa resource conservation and species identification.

Global research of artificial intelligence in eyelid diseases: A bibliometric analysis.

Purpose: To generate an overview of global research on artificial intelligence (AI) in eyelid diseases using a bibliometric approach. Methods: All publications related to AI in eyelid diseases from 1900 to 2023 were retrieved from the Web of Science (WoS) Core Collection database. After manual screening, 98 publications published between 2000 and 2023 were finally included. We analyzed the annual trend of publication and citation count, productivity and co-authorship of countries/territories and institutions, research domain, source journal, co-occurrence and evolution of the keywords and co-citation and clustering of the references, using the analytic tool of the WoS, VOSviewer, Wordcloud Python package and CiteSpace. Results: By analyzing a total of 98 relevant publications, we detected that this field had continuously developed over the past two decades and had entered a phase of rapid development in the last three years. Among these countries/territories and institutions contributing to this field, China was the most productive country and had the most institutions with high productivity, while USA was the most active in collaborating with others. The most popular research domains was Ophthalmology and the most productive journals were Ocular Surface. The co-occurrence network of keywords could be classified into 3 clusters respectively concerned about blepharoptosis, meibomian gland dysfunction and blepharospasm. The evolution of research hotspots is from clinical features to clinical scenarios and from image processing to deep learning. In the clustering analysis of co-cited reference network, cluster "0# deep learning" was the largest and latest, and cluster "#5 meibomian glands visibility assessment" existed for the longest time. Conclusions: Although the research of AI in eyelid diseases has rapidly developed in the last three years, there are still gaps in this area. Our findings provide researchers with a better understanding of the development of the field and a reference for future research directions.

Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms.

Background: The assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis. Methods: The LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability. Results: In testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision. Conclusion: To our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases.

Towards automated animal density estimation with acoustic spatial capture-recapture.

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually, but identifying target species calls in recordings is non-trivial. Machine learning (ML) techniques can do detection quickly but may miss calls and produce false positives, i.e., misidentify calls from other sources as being from the target species. While abundance estimation methods can address the former issue effectively, methods to deal with false positives are under-investigated. We propose an acoustic spatial capture-recapture (ASCR) method that deals with false positives by treating species identity as a latent variable. Individual-level outputs from ML techniques are treated as random variables whose distributions depend on the latent identity. This gives rise to a mixture model likelihood that we maximize to estimate call density. We compare our method to existing methods by applying it to an ASCR survey of frogs and simulated acoustic surveys of gibbons based on real gibbon acoustic data. Estimates from our method are closer to ASCR applied to the dataset without false positives than those from a widely used false positive "correction factor" method. Simulations show our method to have bias close to zero and accurate coverage probabilities and to perform substantially better than ASCR without accounting for false positives.

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.

Tuning zinc content in wollastonite bioceramic endowing outstanding angiogenic and antibacterial functions beneficial for orbital reconstruction.

Prosthetic eye is indispensable as filler after enucleation in patients with anophthalmia, whereas there are still many complications including postoperative infection and eye socket depression or extrusion during the conventional artificial eye material applications. Some Ca-silicate biomaterials showed superior bioactivity but their biological stability in vivo limit the biomedical application as long-term or permanent implants. Herein we aimed to understand the physicochemical and potential biological responses of zinc doping in wollastonite bioceramic used for orbital implants. The wollastonite powders with different zinc dopant contents (CSi-Znx) could be fabricated as porous implants with strut or curve surface pore geometries (cubic, IWP) via ceramic stereolithography. The experimental results indicated that, by increasing zinc-substituting-Ca ratio (up to 9%), the sintering and mechanical properties could be significantly enhanced, and meanwhile the bio-dissolution in vitro and biodegradability in vivo were thoroughly inhibited. In particular, an appreciable angiogenic activity and expected antibacterial efficacy (over 90 %) were synergistically achieved at 9 mol% Zn dopant. In the back-embedding and enucleation and implantation model experiments in rabbits, the superior continuous angiogenesis was corroborated from the 2D/3D fibrovascular reconstruction in the IWP-pore CSi-Zn9 and CSi-Zn13.5 groups within very short time stages. Totally, the present silicate-based bioceramic via selective Zn doping could produce outstanding structural stability and bifunctional biological responses which is especially valuable for developing the next-generation implants with vascular insertion and fixation in orbital reconstruction prothesis.

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.].

Double Balloon Combined with Oxytocin in Labor Induction: Analysis of Multivariate Factors Affecting the Efficacy of Cervical Ripening.

Objective: Labor induction during the late trimester of pregnancy is a common option of terminating pregnancy by inducing uterine contractions through medication or cervical mechanical dilation. However, there are few researches on the factors influencing the effectiveness of cervical ripening balloon combined with oxytocin in inducing labor. To explore factors affecting the efficacy of cervical ripening double balloon combined with oxytocin in labor induction. Methods: Using a convenient sampling method, this study retrospectively collected the clinical data of 230 pregnant women who underwent cervical ripening double balloon combined with oxytocin for labor induction in our hospital from September 2021 to August 2022. The included subjects were divided into a vaginal delivery group (n = 180) and a cesarean section group (n = 50) based on the delivery mode for comparing relevant indicators between the two groups. Results: The presence of acute chorioamnionitis (OR = 1.456, 95% CI: 1.257-2.112), fetal distress (OR = 1.371, 95% CI: 1.331-2.633), and the placement of cervical ripening balloon catheter for >12h (OR = 1.563, 95% CI: 1.231-3.263) were risk factors for successful application of cervical ripening double balloon combined with oxytocin for labor induction in pregnant women; while multi-gravidity (OR = 0.736, 95% CI: 0.455-0.875) was a protective factor. In addition, evaluation of the predictive value revealed that acute chorioamnionitis, fetal distress, the placement of cervical ripening balloon catheter for >12h, and gravidity all had certain predictive value for the failure of cervical ripening double balloon combined with oxytocin for labor induction, with the highest predictive value found through joint predictive (AUC: 0.931, 95% CI: 0.714-0.811). Conclusion: Cervical ripening double balloon combined with oxytocin for labor induction may have a high success rate in multigravida. Acute chorioamnionitis, fetal distress, and prolonged placement of the balloon may have a negative impact on the success rate of cervical ripening double balloon combined with oxytocin for labor induction.

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.

Investigation on the Potential Functions of ZmEPF/EPFL Family Members in Response to Abiotic Stress in Maize.

Maize is an important crop used for food, feed, and fuel. Abiotic stress is an important factor affecting maize yield. The EPF/EPFL gene family encodes class-specific secretory proteins that play an important role in the response to abiotic stress in plants. In order to explore and utilize the EPF/EPFL family in maize, the family members were systematically identified, and their chromosomal localization, physicochemical properties, cis-acting element prediction in promoters, phylogenetic tree construction, and expression pattern analysis were carried out using bioinformatics techniques. A total of 18 ZmEPF/EPFL proteins were identified in maize, which are mostly alkaline and a small portion acidic. Subcellular localization results showed that ZmEPF6, ZmEPF12, and ZmEPFL2 are localized in the nucleus and cytoplasm. Analysis of cis-acting elements revealed that members of the ZmEPF/EPFL family contain regulatory elements such as light response, anoxic, low temperature, and hormone response regulatory elements. RT-qPCR results showed that these family members are indeed responding to cold stress and hormone treatments. These results of this study provide a theoretical basis for improving the abiotic stress resistance of maize in future research.

Automated interpretation of retinal vein occlusion based on fundus fluorescein angiography images using deep learning: A retrospective, multi-center study.

Purpose: Fundus fluorescein angiography (FFA) is the gold standard for retinal vein occlusion (RVO) diagnosis. This study aims to develop a deep learning-based system to diagnose and classify RVO using FFA images, addressing the challenges of time-consuming and variable interpretations by ophthalmologists. Methods: 4028 FFA images of 467 eyes from 463 patients were collected and annotated. Three convolutional neural networks (CNN) models (ResNet50, VGG19, InceptionV3) were trained to generate the label of image quality, eye, location, phase, lesions, diagnosis, and macular involvement. The performance of the models was evaluated by accuracy, precision, recall, F-1 score, the area under the curve, confusion matrix, human-machine comparison, and Clinical validation on three external data sets. Results: The InceptionV3 model outperformed ResNet50 and VGG19 in labeling and interpreting FFA images for RVO diagnosis, achieving 77.63%-96.45% accuracy for basic information labels and 81.72%-96.45% for RVO-relevant labels. The comparison between the best CNN and ophthalmologists showed up to 19% accuracy improvement with the inceptionV3. Conclusion: This study developed a deep learning model capable of automatically multi-label and multi-classification of FFA images for RVO diagnosis. The proposed system is anticipated to serve as a new tool for diagnosing RVO in places short of medical resources.

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.

C176-loaded and phosphatidylserine-modified nanoparticles treat retinal neovascularization by promoting M2 macrophage polarization.

Retinal neovascularization (RNV), a typical pathological manifestation involved in most neovascular diseases, causes retinal detachment, vision loss, and ultimately irreversible blindness. Repeated intravitreal injections of anti-VEGF drugs were developed against RNV, with limitations of incomplete responses and adverse effects. Therefore, a new treatment with a better curative effect and more prolonged dosage is demanding. Here, we induced macrophage polarization to anti-inflammatory M2 phenotype by inhibiting cGAS-STING signaling with an antagonist C176, appreciating the role of cGAS-STING signaling in the retina in pro-inflammatory M1 polarization. C176-loaded and phosphatidylserine-modified dendritic mesoporous silica nanoparticles were constructed and examined by a single intravitreal injection. The biosafe nanoparticles were phagocytosed by retinal macrophages through a phosphatidylserine-mediated "eat me" signal, which persistently release C176 to suppress STING signaling and thereby promote macrophage M2 polarization specifically. A single dosage can effectively alleviate pathological angiogenesis phenotypes in murine oxygen-induced retinopathy models. In conclusion, these C176-loaded nanoparticles with enhanced cell uptake and long-lasting STING inhibition effects might serve as a promising way for treating RNV.

An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis.

Infectious keratitis is among the major causes of global blindness. Anterior segment optical coherence tomography (AS-OCT) images allow the characterizing of cross-sectional structures in the cornea with keratitis thus revealing the severity of inflammation, and can also provide 360-degree information on anterior chambers. The development of image analysis methods for such cases, particularly deep learning methods, requires a large number of annotated images, but to date, there is no such open-access AS-OCT image repository. For this reason, this work provides a dataset containing a total of 1168 AS-OCT images of patients with keratitis, including 768 full-frame images (6 patients). Each image has associated segmentation labels for lesions and cornea, and also labels of iris for full-frame images. This study provides a great opportunity to advance the field of image analysis on AS-OCT images in both two-dimensional (2D) and three-dimensional (3D) and would aid in the development of artificial intelligence-based keratitis management.

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

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

Molten Alkali-Assisted Formation of Silicate Gels and Its Application for Preparing Zeolites.

Fly ash was used as raw material to prepare zeolites through silicate gels, assisted by the hydrothermal method. The silicate gels could be effectively formed in a few minutes in a molten alkali environment. The zeolites could be prepared by using these silicate gels through the hydrothermal method, which realizes the transformation from useless materials to highly valuable materials. The obtained zeolites were applied to the removal of ammonium in water, achieving the highvalue utilization of fly ash. The synthesized zeolites were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrum (EDS), thermogravimetric (TG), and Fourier transform infrared (FTIR) spectroscopy. The study on the adsorption and removal of ammonium in water shows that the adsorption of ammonium is more in line with pseudo first-order kinetics, and the adsorption mainly occurs in the first 20 min. The adsorption can reach equilibrium in 30 min, and the maximum adsorption capacity can reach 49.1 mg/g. The adsorption capacity of ammonium has the best performance at pH = 5. Furthermore, within a certain range, an increase in temperature is beneficial for the removal of ammonium.

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.

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.

We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.