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.

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.

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.

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 (AU)

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.

Artificial intelligence assisted pterygium diagnosis: current status and perspectives.

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

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.

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.

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+.

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.

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.

[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.

Effectiveness, safety, and acceptability of postplacental insertion of GyneFix postpartum intrauterine device among women undergoing cesarean section: A multicenter prospective cohort study in China.

OBJECTIVES: To assess the effectiveness, safety, and acceptability of postplacental insertion of GyneFix postpartum intrauterine device (PPIUD) in women undergoing cesarean section (C-section). STUDY DESIGN: We conducted a prospective cohort study at 14 hospitals in four eastern coastal provinces of China between September 2017 and November 2020. A total of 470 women who underwent C-section and consented to the postplacental insertion of GyneFix PPIUD were enrolled, and 400 completed the 12-month follow-up. Participants were interviewed in the wards after delivery and followed up at 42 days, and months 3, 6, and 12 after delivery. We used Pearl Index (PI) to measure the rate of contraceptive failure, life-table method to measure the rate of PPIUD discontinuation, including IUD expulsion, and Cox regression model to explore the risk factors associated with discontinuation of the device. RESULTS: Nine pregnancies were detected during the first year after GyneFix PPIUD insertion: seven were due to device expulsion and two occurred with PPIUD in situ. The PIs for overall 1-year pregnancy rate and pregnancies with IUD in situ were 2.3 (95% CI: 1.1-4.4) and 0.5 (95% CI: 0.1-1.9), respectively. The 6- and 12-month cumulative expulsion rates for PPIUD expulsion were 6.3% and 7.6%, respectively. The overall 1-year continuation rate was 86.6% (95% CI: 83.3-89.8). We did not identify any patient with insertion failure, uterine perforation, pelvic infection, or excess bleeding due to GyneFix PPIUD insertion. Women's age, education, occupation, previous history of C-section, parity, and breastfeeding were not associated with removal of GyneFix PPIUD in the first year of use. CONCLUSIONS: Postplacental insertion of GyneFix PPIUD is effective, safe, and acceptable for women undergoing C-section. Expulsion is the most common reason for GyneFix PPIUD discontinuation and pregnancy. The expulsion rate for GyneFix PPIUD is lower than that for framed IUDs, but more evidence is needed for a firm verdict.

Thermally Activated Microstructural Evolution of PtIrCu Alloyed Nanorings: Insights from Molecular Dynamics Simulations.

Nanoalloys have attracted extensive interest from the research and industrial community due to their unique properties. In this work, the thermally activated microstructural evolution and resultant collapse of PtIrCu nanorings were investigated using molecular dynamics simulations. Three PtIrCu nanorings with a fixed outer radius and varied inner radii were addressed to investigate the size effects on their thermal and shape stabilities. The shape factor was introduced to monitor their shape changes, and a common neighbor analysis was employed to characterize the local structures of atoms. The results reveal that both the thermal and shape stabilities of these nanorings can be remarkably improved by decreasing the inner radius. Furthermore, they all experienced the evolutionary process from ring to pie and spherelike morphologies, finally resulting in structural collapse. The stacking faults were observed in these rings during the heating process. Our work sheds light on the fundamental understanding of alloyed nanorings subjected to heating, hence offering a theoretical foundation for their syntheses and applications.

Primary diffuse large B-cell lymphoma of orbit: A population-based analysis.

Objective: Primary orbital lymphoma (POL) accounts for an essential part of adult orbital malignancies. Nevertheless, it remains a relatively rare lymphoid malignancy, accounting for <1% of all non-Hodgkin's lymphoma (NHL) cases. Orbital diffuse large B-cell lymphoma (DLBCL) is one of the most prevalent subtypes of POL that confers the worst outcomes. The prognostic determinants of orbital DLBCL remain unknown. Therefore, a retrospective analysis was conducted by investigating the Surveillance, Epidemiology, and End Results (SEER) database for independent predictive factors for the prognosis of orbital DLBCL. Materials and methods: Using the SEER program, we acquired patient data including demographics, clinical characteristics, and treatment strategies. Our cohort included cases of primary orbital DLBCL diagnosed from 2000 to 2017. We conducted Kaplan-Meier analyses to visualize the overall survival (OS) and cause-specific survival (CSS). The Cox proportional hazard regression models were applied to assess the effects of these prognostic factors on OS and CSS. Results: The present cohort included 332 patients with orbital DLBCL. Age was the most impacted variable by orbital DLBCL. Three independent prognostic variables of orbital DLBCL were identified on diagnosis: advanced age, no radiation treatment, and late-stage (Stage IV). Moreover, patients who underwent chemotherapy demonstrated a greater OS when compared with those who did not. In orbital DLBCL, being unmarried was also a poor prognostic factor. Conclusion: The current study is the largest population-based case series of orbital DLBCL. The age at the time of diagnosis, marital status, absence of chemotherapy or radiotherapy, and tumor stage were all found to be correlated with worse prognosis.

Thermally activated microstructural evolution of metallic heterophase nanoparticles: insights from molecular dynamics simulations.

A crystal phase is a key factor to determine the physical and chemical properties of crystalline materials. As a new class of nanoscale structures, heterophase nanoparticles, which assemble conventional and unconventional phases, exhibit exceptional properties in comparison with their single-phase counterparts. In this work, we explored the thermodynamic stability of Au, Co, and AuCo heterophase nanoparticles with fcc and hcp phases by using molecular dynamics simulations. These heterostructured nanoparticles were continuously heated to examine their thermally activated structural evolutions. Au and Co single-phase nanoparticles were also considered for comparison. The results show that the phase transition between fcc and hcp is absent in these heterophase nanoparticles despite the existence of an unconventional phase. Although the melting of Au and Co heterophase nanoparticles is homogeneous, AuCo heterophase nanoparticles show heterogeneous melting, i.e., the Au fcc domain firstly melts, followed by the melting of the Co hcp domain, exhibiting a typical two-stage melting characteristic and resulting in the existence of a solid-core/liquid-shell structure within a considerable temperature region. Furthermore, the mutual diffusion of atoms between fcc and hcp domains is observed in the Au and Co heterophase nanoparticles. However, the unidirectional diffusion from the Au domain to the Co domain is found in the AuCo heterophase nanoparticles prior to their overall melting. This study deepens the fundamental understanding of the thermodynamic evolution of metallic heterogeneous nanoparticles and provides mechanistic and quantitative guidance for the rational design and applications of nanoscale multiphase heterostructures.

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.

Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning.

Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs. Methods: A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium. The intelligent diagnostic results were compared with those of the expert diagnosis. Indicators including accuracy, sensitivity, specificity, kappa value, the area under the receiver operating characteristic curve (AUC), as well as 95% confidence interval (CI) and F1-score were evaluated. Results: The accuracy rate of the intelligent diagnosis system on the 470 testing photographs was 94.68%; the diagnostic consistency was high; the kappa values of the three groups were all above 85%. Additionally, the AUC values approached 100% in group 1 and 95% in the other two groups. The best results generated from the proposed system for sensitivity, specificity, and F1-scores were 100, 99.64, and 99.74% in group 1; 90.06, 97.32, and 92.49% in group 2; and 92.73, 95.56, and 89.47% in group 3, respectively. Conclusion: The intelligent pterygium diagnosis system based on deep learning can not only judge the presence of pterygium but also classify the severity of pterygium. This study is expected to provide a new screening tool for pterygium and benefit patients from areas lacking medical resources.