Structural Determination and Hierarchical Evolution of Transition Metal Clusters Based on an Improved Self-Adaptive Differential Evolution with Neighborhood Search Algorithm.

Determining the optimal structures and clarifying the corresponding hierarchical evolution of transition metal clusters are of fundamental importance for their applications. The global optimization of clusters containing a large number of atoms, however, is a vastly challenging task encountered in many fields of physics and chemistry. In this work, a high-efficiency self-adaptive differential evolution with neighborhood search (SaNSDE) algorithm, which introduced an optimized cross-operation and an improved Basin Hopping module, was employed to search the lowest-energy structures of CoN, PtN, and FeN (N = 3-200) clusters. The performance of the SaNSDE algorithm was first evaluated by comparing our results with the parallel results collected in the Cambridge Cluster Database (CCD). Subsequently, different analytical methods were introduced to investigate the structural and energetic properties of these clusters systematically, and special attention was paid to elucidating the structural evolution with cluster size by exploring their overall shape, atomic arrangement, structural similarity, and growth pattern. By comparison with those results listed in the CCD, 13 lower-energy structures of FeN clusters were discovered. Moreover, our results reveal that the clusters of three metals had different magic numbers with superior stable structures, most of which possessed high symmetry. The structural evolution of Co, Pt, and Fe clusters could be, respectively, considered as predominantly closed-shell icosahedral, Marks decahedral, and disordered icosahedral-ring growth. Further, the formation of shell structures was discovered, and the clusters with hcp-, fcc-, and bcc-like configurations were ascertained. Nevertheless, the growth of the clusters was not simply atom-to-atom piling up on a given cluster despite gradual saturation of the coordination number toward its bulk limit. Our work identifies the general growth trends for such a wide region of cluster sizes, which would be unbearably expensive in first-principles calculations, and advances the development of global optimization algorithms for the structural prediction of clusters.

An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters.

Global optimization of multicomponent cluster structures is considerably time-consuming due to the existence of a vast number of isomers. In this work, we proposed an improved self-adaptive differential evolution with the neighborhood search (SaNSDE) algorithm and applied it to the global optimization of bimetallic cluster structures. The cross operation was optimized, and an improved basin hopping module was introduced to enhance the searching efficiency of SaNSDE optimization. Taking (PtNi)N (N = 38 or 55) bimetallic clusters as examples, their structures were predicted by using this algorithm. The traditional SaNSDE algorithm was carried out for comparison with the improved SaNSDE algorithm. For all the optimized clusters, the excess energy and the second difference of the energy were calculated to examine their relative stabilities. Meanwhile, the bond order parameters were adopted to quantitatively characterize the cluster structures. The results reveal that the improved SaNSDE algorithm possessed significantly higher searching capability and faster convergence speed than the traditional SaNSDE algorithm. Furthermore, the lowest-energy configurations of (PtNi)38 clusters could be classified as the truncated octahedral and disordered structures. In contrast, all the optimal (PtNi)55 clusters were approximately icosahedral. Our work fully demonstrates the high efficiency of the improved algorithm and advances the development of global optimization algorithms and the structural prediction of multicomponent clusters.

Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey.

BACKGROUND: In the development of artificial intelligence in ophthalmology, the ophthalmic AI-related recognition issues are prominent, but there is a lack of research into people's familiarity with and their attitudes toward ophthalmic AI. This survey aims to assess medical workers' and other professional technicians' familiarity with, attitudes toward, and concerns about AI in ophthalmology. METHODS: This is a cross-sectional study design study. An electronic questionnaire was designed through the app Questionnaire Star, and was sent to respondents through WeChat, China's version of Facebook or WhatsApp. The participation was voluntary and anonymous. The questionnaire consisted of four parts, namely the respondents' background, their basic understanding of AI, their attitudes toward AI, and their concerns about AI. A total of 562 respondents were counted, with 562 valid questionnaires returned. The results of the questionnaires are displayed in an Excel 2003 form. RESULTS: There were 291 medical workers and 271 other professional technicians completed the questionnaire. About 1/3 of the respondents understood AI and ophthalmic AI. The percentages of people who understood ophthalmic AI among medical workers and other professional technicians were about 42.6 % and 15.6 %, respectively. About 66.0 % of the respondents thought that AI in ophthalmology would partly replace doctors, about 59.07 % having a relatively high acceptance level of ophthalmic AI. Meanwhile, among those with AI in ophthalmology application experiences (30.6 %), above 70 % of respondents held a full acceptance attitude toward AI in ophthalmology. The respondents expressed medical ethics concerns about AI in ophthalmology. And among the respondents who understood AI in ophthalmology, almost all the people said that there was a need to increase the study of medical ethics issues in the ophthalmic AI field. CONCLUSIONS: The survey results revealed that the medical workers had a higher understanding level of AI in ophthalmology than other professional technicians, making it necessary to popularize ophthalmic AI education among other professional technicians. Most of the respondents did not have any experience in ophthalmic AI but generally had a relatively high acceptance level of AI in ophthalmology, and there was a need to strengthen research into medical ethics issues.

Recurrence of moderate to severe ulcerative colitis after fecal microbiota transplantation treatment and the efficacy of re-FMT: a case series.

BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD), the pathogenesis of which is complicated, and it is difficult to treat. In recent years, the emerging fecal microbiota transplantation (FMT) has shown good effects in UC treatment and is therefore accepted by increasing numbers of patients. Our hospital has carried out FMT since 2017, and has achieved good results in UC treatment. We have found in our clinical work that the efficacy of re-FMT after recurrence decreased. This is difference from reported literatures. In order to attract clinical attention, here we selected typical cases for analysis. METHODS: Among all UC patients who received FMT in our hospital, 12 patients with moderate to severe UC were selected. They all received multiple FMT and were followed up for 52 weeks. Besides, none of them had other underlying diseases. Colonoscopy images of patients were presentated, SCCAI and UCDAI were used assess the effect of FMT. RESULTS: On the whole, FMT has a significant effect on moderate to severe UC. Of the 12 patients, 11 (91.7%) achieved a clinical response, 9 (75.0%) achieved clinical remission, and only one patient did not respond to FMT treatment. However, 6 patients relapsed within 52 weeks after remission, with a recurrence rate of 54.5%. Four of the six relapsed patients received FMT again, but the efficacy of FMT after relapse was significantly lower than that of the initial FMT. Fortunately, compared to before the initial FMT treatment, the severity of the disease after relapse was significantly reduced. CONCLUSION: FMT has a good effect on the relief of moderate to severe UC. However, the effect of FMT treatment after relapse is reduced. For patients who relapse after remission, the efficacy of FMT reapplication requires more experiments to verify.

Changes in vessel density of the patients with narrow antenior chamber after an acute intraocular pressure elevation observed by OCT angiography.

BACKGROUND: Although the pathogenesis of glaucoma is not fully understood,an elevated intraocular pressure (IOP) is a major factor contributing to its development and progression. The aim of this study was to investigate the changes in the vessel densities of the macula and optic nerve head (ONH) after an acute elevation in the intraocular pressure (IOP) observed using optical coherence tomography angiography (OCTA). METHODS: This was a prospective comparative study of subjects with narrow anterior chamber angles who underwent laser peripheral iridotomies (LPIs). The IOP was measured before and one hour after the LPI. The retinal vessel densities of the macula and ONH were measured using OCTA at the baseline and one hour after the LPI. RESULTS: A total of 64 eyes of 51 individuals were enrolled in this study, and 58 eyes of 43 individuals finally completed the study with a mean IOP rise of 10.5 ± 7.6 mmHg after the LPI. Based on the magnitude of the rise in the IOP, we divided the subjects into three groups: group A = IOP rise ≤10 mmHg, group B = 10 mmHg < IOP rise ≤20 mmHg, and group C = IOP rise > 20 mmHg. The vessel density did not differ after the acute IOP elevation in either the macular region or papillary region in group A or group B (p > 0.05), but there was a significant difference in group C (p < 0.05). However, when the subjects were not separated into groups, the vessel densities of the ONH and macular region did not differ between the measurements obtained at the baseline and one hour after the LPI (p > 0.05). The correlation existed in peripapillary and macular vessel density (p < 0.05). CONCLUSION: In these subjects with narrow antenior chamber, an acute mild or moderate IOP elevation for one hour after the LPI did not affect the vessel density in the macula or ONH, as examined using OCTA. However, when the IOP rise was greater than 20 mmHg, the macular and papillary vessel density decreased significantly.

Effect of composition and architecture on the thermodynamic behavior of AuCu nanoparticles.

The chemical and physical properties of nanomaterials ultimately rely on their crystal structures, chemical compositions and distributions. In this paper, a series of AuCu bimetallic nanoparticles with well-defined architectures and variable compositions has been addressed to explore their thermal stability and thermally driven behavior by molecular dynamics simulations. By combination of energy and Lindemann criteria, the solid-liquid transition and its critical temperature were accurately identified. Meanwhile, atomic diffusion, bond order, and particle morphology were examined to shed light on thermodynamic evolution of the particles. Our results reveal that composition-dependent melting point of AuCu nanoparticles significantly departs from the Vegard's law prediction. Especially, chemically disordered (ordered) alloy nanoparticles exhibited markedly low (high) melting points in comparison with their unary counterparts, which should be attributed to enhancing (decreasing) atomic diffusivity in alloys. Furthermore, core-shell structures and heterostructures demonstrated a mode transition between the ordinary melting and the two-stage melting with varying Au content. AuCu alloyed nanoparticles presented the evolution tendency of chemical ordering from disorder to order before melting and then to disorder during melting. Additionally, as the temperature increases, the shape transformation was observed in AuCu nanoparticles with heterostructure or L10 structure owing to the difference in thermal expansion coefficients of elements and/or of crystalline orientations. Our findings advance the fundamental understanding on thermodynamic behavior and stability of metallic nanoparticles, offering theoretical insights for design and application of nanosized particles with tunable properties.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.

AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Bibliometric analysis of hotspots and trends of global myopia research.

AIM: To gain insights into the global research hotspots and trends of myopia. METHODS: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS: A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION: Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

The optimal atropine concentration for myopia control in Chinese children: a systematic review and network Meta-analysis.

AIM: To figure out whether various atropine dosages may slow the progression of myopia in Chinese kids and teenagers and to determine the optimal atropine concentration for effectively slowing the progression of myopia. METHODS: A systematic search was conducted across the Cochrane Library, PubMed, Web of Science, EMBASE, CNKI, CBM, VIP, and Wanfang database, encompassing literature on slowing progression of myopia with varying atropine concentrations from database inception to January 17, 2024. Data extraction and quality assessment were performed, and a network Meta-analysis was executed using Stata version 14.0 Software. Results were visually represented through graphs. RESULTS: Fourteen papers comprising 2475 cases were included; five different concentrations of atropine solution were used. The network Meta-analysis, along with the surface under the cumulative ranking curve (SUCRA), showed that 1% atropine (100%)>0.05% atropine (74.9%) >0.025% atropine (51.6%)>0.02% atropine (47.9%)>0.01% atropine (25.6%)>control in refraction change and 1% atropine (98.7%)>0.05% atropine (70.4%)>0.02% atropine (61.4%)>0.025% atropine (42%)>0.01% atropine (27.4%)>control in axial length (AL) change. CONCLUSION: In Chinese children and teenagers, the five various concentrations of atropine can reduce the progression of myopia. Although the network Meta-analysis showed that 1% atropine is the best one for controlling refraction and AL change, there is a high incidence of adverse effects with the use of 1% atropine. Therefore, we suggest that 0.05% atropine is optimal for Chinese children to slow myopia progression.

XRCC1 codon 280 polymorphism and susceptibility to lung cancer: a meta-analysis of the literatures.

The X-ray repair cross-complementation group 1 (XRCC1) protein plays an important role in base excision repair, and the genetic polymorphisms in the XRCC1 gene influence its function. XRCC1 codon 280 polymorphism is an Arg-His change in the XRCC1 gene. Many studies were published to investigate the association between XRCC1 codon 280 polymorphism and risk of lung cancer, but the results were inconsistent. We performed a meta-analysis of 16 studies with a total of 18,660 subjects (8,736 cases and 9,924 controls). The pooled odds ratios (OR) and corresponding 95 % confidence intervals (95 % CI) for the gene-disease association were calculated. Overall, there was a significant association between XRCC1 codon 280 polymorphism and increased risk of lung cancer (HisHis vs. ArgArg: OR = 1.53, 95 % CI 1.08-2.16, P = 0.016; HisHis vs. ArgArg/ArgHis: OR = 1.55, 95 % CI 1.10-2.19, P = 0.012). However, subgroup analysis by race failed to confirm the obvious association in Europeans and Asians. Therefore, there is a significant association between XRCC1 codon 280 polymorphism and increased risk of lung cancer. More studies with a large sample are needed to further evaluate the possible race-specific effect in the association above.

Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).

With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.

[Emission Characteristics and Risk Assessment of Gas Pollutants in Typical Pesticide Manufacturing Enterprises].

In order to explore the characteristics of exhaust gas emissions, environmental impact, and human health risks in the pesticide manufacturing industry, two typical pesticide manufacturing enterprises were selected as the research objects, and samples were collected and analyzed for all exhaust pipes of each enterprise. The following results were noted:there were certain differences in the pollutants produced by different enterprises due to different products and production links. The main pollutants in enterprise A were ammonia and VOCs. The concentration of ammonia in enterprise A ranged from 0 to 847.83 mg·m-3, and the concentration of VOCs ranged from 4.21 to 91.68 mg·m-3. The main pollutants in enterprise B were VOCs, and the concentration of VOCs ranged from 3.37 to 197.30 mg·m-3. The ozone formation potential (OFP) ranged from 1.96 to 107.24 mg·m-3. Substances that required further attention in terms of ozone formation potential:enterprise A mainly included ethanol, methanol, toluene, xylene, and other substances; enterprise B mainly included 1, 1-dichloroethylene, 1, 2-dichloroethane, toluene, methylal, and other substances. The secondary organic aerosol formation potential (SOAFP) ranged from 0.94 to 74.72 mg·m-3. The main contributors to the secondary organic aerosol formation potential were aromatic hydrocarbons and oxygen-containing organics. In addition, ammonia also required additional attention. The odorous substances in pesticide enterprises were more complex, and there were differences in the exhaust pipes of different enterprises and different production links of the same enterprise. There were certain health risks in the gas pollutants of pesticide enterprises. The main carcinogens were 1, 2-dichloroethane, trichloroethylene, tetrachloroethylene, methyl chloride, and benzene. In addition, pyridine and hexachloroethane had certain non-carcinogenic risks in pesticide production enterprises.

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.

AIM: To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend. METHODS: On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms. RESULTS: Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification. CONCLUSION: The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.

Autophagy in graves' ophthalmopathy.

Graves' ophthalmopathy (GO) is an inflammatory autoimmune disease that affects the eyes. It can significantly alter the quality of life in patients because of its distinctive pathological appearance and the effect on vision. To date, the exact pathological mechanism of GO has not been explicitly discovered. However, several studies have associated autophagy with this disease. Autophagy is a catabolic process that helps maintain homeostasis in all organisms by protecting the cells and tissues from various endogenous and exogenous stress factors. Based on our results, patients affected with GO have comparatively elevated levels of autophagy, which critically affects the pathological mechanism of the GO. In this review, we have summarized the autophagy mechanism in the pathogenesis of GO.

Analysis of retinal arteriolar and venular parameters in primary open angle glaucoma.

AIM: To measure the retinal vessels of primary open angle glaucoma (POAG) patients on spectral domain optical coherence tomography (SD-OCT) with a full-width at half-maximum (FWHM) algorithm to better explore their structural changes in the pathogenesis of POAG. METHODS: In this retrospective case-control study, the right eyes of 32 patients with POAG and 30 healthy individuals were routinely selected. Images of the supratemporal and infratemporal retinal vessels in the B zones were obtained by SD-OCT, and the edges of the vessels were identified by the FWHM method. The internal and external diameters, wall thickness (WT), wall cross-sectional area (WCSA) and wall-to-lumen ratio (WLR) of the blood vessels were studied. RESULTS: Compared with the healthy control group, the POAG group showed a significantly reduced retinal arteriolar outer diameter (RAOD), retinal arteriolar lumen diameter (RALD) and WSCA in the supratemporal (124.22±12.42 vs 138.32±10.73 µm, 96.09±11.09 vs 108.53±9.89 µm, and 4762.02±913.51 vs 5785.75±1148.28 µm2, respectively, all P<0.05) and infratemporal regions (125.01±15.55 vs 141.57±10.77 µm, 96.27±13.29 vs 110.83±10.99 µm, and 4925.56±1302.88 vs 6087.78±1061.55 µm2, all P<0.05). The arteriolar WT and WLR were not significantly different between the POAG and control groups, nor were the retinal venular outer diameter (RVOD), retinal venular lumen diameter (RVLD) or venular WT in the supratemporal or infratemporal region. There was a positive correlation between the arteriolar parameters and visual function. CONCLUSION: In POAG, narrowing of the supratemporal and infratemporal arterioles and a significant reduction in the WSCA is observed, while the arteriolar WT and WLR do not change. Among the venular parameters, the external diameter, internal diameter, WT, WLR, and WSCA of the venules are not affected.

Modified Hongyu Decoction promotes wound healing by activating the VEGF/PI3K/Akt signaling pathway.

Wound healing is a considerable problem for clinicians. Ever greater attention has been paid to the role of Chinese herbal monomers and compounds on wound healing. This study aims to elucidate the wound healing mechanism of Modified Hongyu Decoction (MHD) in vivo and in vitro. MHD wound healing activity in vivo was evaluated using an excision rat model. H and E staining, Masson's staining and immunofluorescence of wound tissue on days 7 and 14 were performed to evaluate the efficacy of MHD on wound healing. Subsequently, human umbilical vein endothelial cells (HUVECs) were used to evaluate wound healing characteristics in vitro. Cell Counting Kit-8 (CCK-8) and scratch assays were conducted to assess the effects of MHD on the proliferation and migration of HUVECs. The involvement of the VEGF/PI3K/Akt signaling pathway was assessed by western blotting. The rats in the MHD group displayed more neovascularization and collagen fibers. Western blotting of wound tissue showed that VEGF, PI3K, p-Akt and p-eNOS expression were significantly increased (p<0.05) in the MHD group. Cell Counting Kit-8 and scratch assays demonstrated that MHD promoted HUVECs proliferation and migration. MHD treatment significantly increased VEGF, PI3K, p-Akt and p-eNOS expression in HUVECs (p<0.05), which was inhibited by LY294002. Both in vivo and in vitro data indicated that MHD promotes wound healing by regulating the VEGF/PI3K/Akt signaling pathway.

Evaluation of a novel deep learning based screening system for pathologic myopia.

AIM: To evaluate the clinical application value of the artificial intelligence assisted pathologic myopia (PM-AI) diagnosis model based on deep learning. METHODS: A total of 1156 readable color fundus photographs were collected and annotated based on the diagnostic criteria of Meta-pathologic myopia (PM) (2015). The PM-AI system and four eye doctors (retinal specialists 1 and 2, and ophthalmologists 1 and 2) independently evaluated the color fundus photographs to determine whether they were indicative of PM or not and the presence of myopic choroidal neovascularization (mCNV). The performance of identification for PM and mCNV by the PM-AI system and the eye doctors was compared and evaluated via the relevant statistical analysis. RESULTS: For PM identification, the sensitivity of the PM-AI system was 98.17%, which was comparable to specialist 1 (P=0.307), but was higher than specialist 2 and ophthalmologists 1 and 2 (P<0.001). The specificity of the PM-AI system was 93.06%, which was lower than specialists 1 and 2, but was higher than ophthalmologists 1 and 2. The PM-AI system showed the Kappa value of 0.904, while the Kappa values of specialists 1, 2 and ophthalmologists 1, 2 were 0.968, 0.916, 0.772 and 0.730, respectively. For mCNV identification, the AI system showed the sensitivity of 84.06%, which was comparable to specialists 1, 2 and ophthalmologist 2 (P>0.05), and was higher than ophthalmologist 1. The specificity of the PM-AI system was 95.31%, which was lower than specialists 1 and 2, but higher than ophthalmologists 1 and 2. The PM-AI system gave the Kappa value of 0.624, while the Kappa values of specialists 1, 2 and ophthalmologists 1 and 2 were 0.864, 0.732, 0.304 and 0.238, respectively. CONCLUSION: In comparison to the senior ophthalmologists, the PM-AI system based on deep learning exhibits excellent performance in PM and mCNV identification. The effectiveness of PM-AI system is an auxiliary diagnosis tool for clinical screening of PM and mCNV.

Artificial intelligence-aided diagnosis and treatment in the field of optometry.

With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice.

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm.

AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification. METHODS: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established. Each algorithm was combined with four models for experiments on the HMM dataset. Each experiment consisted of 20 active learning rounds, with 100 images selected per round. The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+ outperformed other algorithms. Finally, this study employed six models, including EfficientFormer, to classify HMM. The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+ algorithm to achieve satisfactory classification results with a small dataset. RESULTS: ALFA-Mix+ outperforms other algorithms with an average superiority of 16.6, 14.75, 16.8, and 16.7 rounds in terms of accuracy, sensitivity, specificity, and Kappa value, respectively. This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images. The EfficientFormer achieved the best results with an accuracy, sensitivity, specificity, and Kappa value of 0.8821, 0.8334, 0.9693, and 0.8339, respectively. Therefore, by combining ALFA-Mix+ with EfficientFormer, this study achieved results with an accuracy, sensitivity, specificity, and Kappa value of 0.8964, 0.8643, 0.9721, and 0.8537, respectively. CONCLUSION: The ALFA-Mix+ algorithm reduces the required samples without compromising accuracy. Compared to other algorithms, ALFA-Mix+ outperforms in more rounds of experiments. It effectively selects valuable samples compared to other algorithms. In HMM classification, combining ALFA-Mix+ with EfficientFormer enhances model performance, further demonstrating the effectiveness of ALFA-Mix+.

Deleted in lymphocytic leukemia 2 (DLEU2): a possible biomarker that holds promise for future diagnosis and treatment of cancer.

The mechanism of deleted in lymphocytic leukemia 2 (DLEU2)-long non-coding RNA in tumors has become a major point of interest in recent research related to the occurrence and development of a variety of tumors. Recent studies have shown that the long non-coding RNA DLEU2 (lncRNA-DLEU2) can cause abnormal gene or protein expression by acting on downstream targets in cancers. At present, most lncRNA-DLEU2 play the role of oncogenes in different tumors, which are mostly associated with tumor characteristics, such as proliferation, migration, invasion, and apoptosis. The data thus far show that because lncRNA-DLEU2 plays an important role in most tumors, targeting abnormal lncRNA-DLEU2 may be an effective treatment strategy for early diagnosis and improving the prognosis of patients. In this review, we integrated lncRNA-DLEU2 expression in tumors, its biological functions, molecular mechanisms, and the utility of DLEU2 as an effective diagnostic and prognostic marker of tumors. This study aimed to provide a potential direction for the diagnosis, prognosis, and treatment of tumors using lncRNA-DLEU2 as a biomarker and therapeutic target.