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ObjectivesTo evaluate the epidemiological characteristics of myopia among school-aged children before, during, and after the coronavirus disease (COVID-19) pandemic.MethodsA total of 848,697 students aged 6–15 years from 786 primary and secondary schools in Shenzhen, China, were randomly selected as research subjects. We conducted annual myopia screenings from 2019 to 2022. 2019 was considered before the COVID-19 pandemic, 2020 as during the pandemic, and 2021 and 2022 as after the pandemic. Demographic characteristics, visual acuity, and spherical equivalent refraction (SE) were collected.ResultsDuring the 4-year follow-up period, the uncorrected visual acuity (UCVA) of the study subjects progressed following a trend of −0.18 ± 0.30D (−0.17 ± 0.29D for boys, −0.21 ± 0.32Dfor girls) (p

BackgroundAs one of several refractive surgeries, Implant Collamer Lens (ICL) surgery offers stable biocompatibility and consistent, high-quality visual outcomes. ICL has become an effective complement to corneal refractive surgery, gradually becoming one of the mainstream methods for correcting refractive errors. This study employs bibliometric methods to analyze research on ICL surgery to understand the progress, hotspots, and potential future trends in this field.MethodsThis study performed a bibliometric analysis of all ICL-related articles collected from the Web of Science Core Collection database between January 1st, 1996, and December 31st, 2023. The CiteSpace 6.2.R4 tool, Excel and the Web of Science website were used to analyze data by country, institution, keywords, and clusters of keywords. Additionally, an in-depth interpretation and analysis were conducted on the field’s high-impact articles.ResultsSince the first clinical application report of ICL, there have been a total of 875 studies. The number of papers published annually has shown an overall increasing trend. Studies published from China are the most numerous, accounting for 29.14% (n = 255) of the total. Among the institutions, Fudan University and Kitasato University both have published more than 50 papers, with Kitasato University having the highest H-index of 26. The journals with the top 10 publication volumes are all specialized in ophthalmology. The burst keywords since the introduction of ICL surgery have been “intraocular lens,” “refractive surgery,” and “cataract surgery.” The current burst keywords include “visual quality,” “vector analysis,” “axial length,” etc. The results of keyword clustering included ICL, pIOL, high myopia, axial length, optical quality, refractive surgery, ICL implantation, and pupil size. In the High-impact Articles, it was found that the high-impact articles predominantly focus on the safety, efficacy, and predictability of ICL surgery.ConclusionResearch on ICL has grown since its clinical introduction, with the advent of the central hole ICL sparking a surge in recent hotspots, particularly in China. Current hotpots in the field of ICL surgery are “visual quality,” “ICL implantation,” “vector analysis,” “axial length,” “evo ICL,” “ICL v4c,” and “ICL.” ICL surgery research trends have evolved from implantation techniques to biological parameters associated with ICL surgery and the benefits of new ICL designs.

Cataracts are a leading cause of blindness worldwide, making accurate diagnosis and effective surgical planning critical. However, grading the severity of the lens nucleus is challenging because deep learning (DL) models pretrained using ImageNet perform poorly when applied directly to medical data due to the limited availability of labeled medical images and high interclass similarity. Self-supervised pretraining offers a solution by circumventing the need for cost-intensive data annotations and bridging domain disparities. In this study, to address the challenges of intelligent grading, we proposed a hybrid model called nuclear cataract mask encoder network (NCME-Net), which utilizes self-supervised pretraining for the four-class analysis of nuclear cataract severity. A total of 792 images of nuclear cataracts were categorized into the training set (533 images), the validation set (139 images), and the test set (100 images). NCME-Net achieved a diagnostic accuracy of 91.0 % on the test set, a 5.0 % improvement over the best-performing DL model (ResNet50). Experimental results demonstrate NCME-Net's ability to distinguish between cataract severities, particularly in scenarios with limited samples, making it a valuable tool for intelligently diagnosing cataracts. In addition, the effect of different self-supervised tasks on the model's ability to capture the intrinsic structure of the data was studied. Findings indicate that image restoration tasks significantly enhance semantic information extraction.

BackgroundDespite reports suggesting a link between obesity and keratoconus, the causal relationship is not fully understood.MethodsWe used genome-wide association study (GWAS) data from public databases for a two-sample Mendelian randomization analysis to investigate the causal link between body mass index (BMI) and keratoconus. The primary method was inverse variance weighted (IVW), complemented by different analytical techniques and sensitivity analyses to ensure result robustness. A meta-analysis was also performed to bolster the findings’ reliability.ResultsOur study identified a significant causal relationship between BMI and keratoconus. Out of 20 Mendelian randomization (MR) analyses conducted, 9 showed heterogeneity or pleiotropy. Among the 11 analyses that met all three MR assumptions, 4 demonstrated a significant causal difference between BMI and keratoconus, while the remaining 7 showed a positive trend but were not statistically significant. Meta-analysis confirmed a significant causal relationship between BMI and keratoconus.ConclusionThere is a significant causal relationship between BMI and keratoconus, suggesting that obesity may be a risk factor for keratoconus.

[Introduction] The offshore wind power industry is among the most pioneering and strategic emerging sectors in the global development of new energy sources. The research focuses on the inclined helical pile-group foundation, which is currently applied for offshore wind turbines, and systematically studies its load-bearing characteristics. It is of great significance to accurately understand the load-bearing characteristics for the subsequent popularization of this promising new type of foundation and the overall development of the offshore wind power industry. [Method] The effects of various variables, including pile number, pile spacing and inclination angle, on the bearing characteristics of inclined helical pile groups were systematically studied by finite element software, and the change trends of the pile group effect coefficient and bearing capacity with these factors were obtained under various working conditions. [Result] The results show an approximately 15% improvement in the load carrying capacity of inclined helical pile groups compared to monopoles. Additionally, the efficiency coefficient of pile groups increases with a larger pile number. The load carrying capacity of pile-group foundations is positively correlated with pile spacing, as smaller pile spacing leads to a more significant superposition of central soil pressure. The variation in ultimate load carrying capacity remains within about 4% across the value range of pile spacing. Larger inclination angles enhance the compressive load carrying capacity of inclined helical pile-group foundations, causing an increase in their compression efficiency coefficient. Compressive bearing is found to be effective at an inclination angle of 8°. When subjected to pulling action, the upper soil of inclined helical pile groups experiences less disturbance, the foundations' behavior is more influenced by the pile spacing, and the extreme depth of vertical displacement of the soil at the center of the pile groups gradually decreases with the increase of pile spacing. Conversely, under compressive action, the inclined helical pile-groups exert an influence on the soil in a larger extent, and the interaction effect among piles becomes more stable, without indicating obvious correlation between the extreme depth of vertical displacement of the soil at the center of the pile groups and the pile spacing and inclination angle. [Conclusion] The research results serve to provide certain research approaches and means for the construction of offshore wind power helical pile-group foundations in China, and hold certain reference significance for the dimensional design and bearing capacity evaluation of inclined helical pile-group foundations, demonstrating their value in scientific research and engineering applications.

Purpose: We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees. Methods: The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models. The diagnostic results were compared with those of the VGG16 and ResNet50 classification models. The leading evaluation indicators were sensitivity, specificity, F1 score, area under the receiver operating characteristic (ROC) curve area under curve (AUC), 95% confidence interval, kappa value, and accuracy. The ROC curves of the ten grading models were also compared. Results: We used 1199 color fundus photographs to evaluate the myopic maculopathy grading models. The size of the EfficientNet-B0 myopic maculopathy grading model was 15.6 MB, and it had the highest kappa value (88.32%) and accuracy (83.58%). The model's sensitivities to diagnose tessellated fundus (TF), diffuse chorioretinal atrophy (DCA), patchy chorioretinal atrophy (PCA), and macular atrophy (MA) were 96.86%, 75.98%, 64.67%, and 88.75%, respectively. The specificity was above 93%, and the AUCs were 0.992, 0.960, 0.964, and 0.989, respectively. Conclusion: The EfficientNet models were used to design grading diagnostic models for myopic maculopathy. Based on the collected fundus images, the models could diagnose a healthy fundus and four types of myopic maculopathy. The models might help ophthalmologists to make preliminary diagnoses of different degrees of myopic maculopathy.

The clinical efficacy of adrenergic β-receptor (β-AR) blockers in significantly stabilizing atherosclerotic plaques has been extensively supported by evidence-based medical research; however, the underlying mechanism remains unclear. Recent findings have highlighted the impact of lipid-induced aberrant polarization of macrophages during normal inflammatory-repair and regenerative processes on atherosclerosis formation and progression. In this review, we explore the relationship between macrophage polarization and atherosclerosis, as well as the influence of β-AR blockers on macrophage polarization. Based on the robust evidence supporting the use of β-AR blockers for treating atherosclerosis, we propose that their main mechanism involves inhibiting monocyte-derived macrophage differentiation towards an M2-like phenotype.

BackgroundOphthalmopathy occurring in childhood can easily lead to irreversible visual impairment, and therefore a great deal of clinical and fundamental researches have been conducted in pediatric ophthalmopathy. However, a few studies have been performed to analyze such large amounts of research using bibliometric methods. This study intended to apply bibliometric methods to analyze the research hotspots and trends in pediatric ophthalmopathy, providing a basis for clinical practice and scientific research to improve children's eye health.MethodsPublications related to pediatric ophthalmopathy were searched and identified in the Web of Science Core Collection (WoSCC) database. Bibliometric and visualized analysis was performed using the WoSCC analysis system and CiteSpace.6.2.6 software, and high-impact publications were analyzed.ResultsThis study included a total of 7,177 publications from 162 countries and regions. Of these, 2,269 from the United States and 1,298 from China. The centrality and H-index were highest in the United States at 0.27 and 66, respectively. The University of London and Harvard University had the highest H-index at 37. Freedman,Sharon F published 55 publications, with the highest H-index at 19. The emerging burst keyword in 2020–2023 was “eye tracking,” and the burst keywords in 2021–2023 were “choroidal thickness,” “pediatric ophthalmology,” “impact” and “childhood glaucoma.” Retinopathy of prematurity, myopia, retinoblastoma and uveitis in juvenile idiopathic arthritis were the main topics in the high-impact publications, with clinical studies in the majority, especially in retinopathy of prematurity.ConclusionEye health in children is a research hotspot, with the United States publishing the largest number of papers and having the greatest influence in research on pediatric ophthalmopathy, and China coming in second. The University of London and Stanford University had the greatest influence. Freedman, Sharon F was the most influential author. Furthermore, “choroidal thickness,” “pediatric ophthalmology,” “impact,” “childhood glaucoma” and “eye tracking”are the latest hotspots in the field of pediatric ophthalmopathy. These hotspots represent hot diseases, hot technologies and holistic concepts, which are exactly the research trends in the field of pediatric ophthalmopathy, providing guidance and grounds for clinical practice and scientific research on children's eye health.

AimConventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images.MethodsThis research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology.ResultsThe ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%.ConclusionAmong various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.

Thyroid-associated ophthalmopathy (TAO), also referred to as Graves’ ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.

This study aimed to investigate altered static and dynamic functional network connectivity (FNC) and its correlation with clinical symptoms in patients with knee osteoarthritis (KOA). One hundred and fifty-nine patients with KOA and 73 age- and gender-matched healthy subjects (HS) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluations. Group independent component analysis (GICA) was applied, and seven resting-state networks were identified. Patients with KOA had decreased static FNC within the default mode network (DM), visual network (VS), and cerebellar network (CB) and increased static FNC between the subcortical network (SC) and VS (p < 0.05, FDR corrected). Four reoccurring FNC states were identified using k-means clustering analysis. Although abnormalities in dynamic FNCs of KOA patients have been found using the common window size (22 TR, 44 s), but the results of the clustering analysis were inconsistent when using different window sizes, suggesting dynamic FNCs might be an unstable method to compare brain function between KOA patients and HS. These recent findings illustrate that patients with KOA have a wide range of abnormalities in the static and dynamic FNCs, which provided a reference for the identification of potential central nervous therapeutic targets for KOA treatment and might shed light on the other musculoskeletal pain neuroimaging studies.

BackgroundThyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients’ appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO.MethodThe study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.ResultsThe proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.ConclusionThe ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.

Silicon carbide fiber-reinforced silicon carbide matrix ceramic composites (SiCf/SiC) are extensively utilized in high-temperature resistant materials in the aerospace industry. This study investigated the influence of stacking structure on the performance of SiCf/SiC laminated composite plates with film cooling. Initially, the thermal conductivity of cross-piled SiCf/SiC composites was determined using the laser flash analysis (LFA) method and differential scanning calorimetry (DSC) method. Subsequently, a representative volume element (RVE) model that reflected the stacking structure was established. The anisotropic thermal conductivity of the unidirectional SiCf/SiC layer was calculated using numerical methods and experimental results. Finally, numerical simulations were carried out to assess the film cooling effectiveness of various stacking sequences and layers. The results showed that the thermal conductivity values predicted by the RVE model for the laminated composite aligned well with the experimental results, and the unidirectional SiCf/SiC composite thermal conductivities at different temperatures were obtained. The stacking sequence impacted the temperature distribution near the film hole, with the [0-90-0] structure exhibiting a more pronounced effect on film cooling performance compared with the [0-90] and [0-90-90-0] structures. The performance of the film cooling in the laminated SiCf/SiC composites was consistent across all stacking layers [0-90]1, [0-90]2, and [0-90]3. The maximum difference in overall cooling efficiency was 1.7% between [0-90-0]1 and [0-90]1 and [0-90-90-0]1

This paper examines the Wasserstein metric between the empirical probability measure of n discrete random variables and a continuous uniform measure in the d-dimensional ball, providing an asymptotic estimation of their expectations as n approaches infinity. Furthermore, we investigate this problem within a mixed process framework, where n discrete random variables are generated by the Poisson process.

The architecture of aeroengine air-cooling system channels is notably intricate, with impingement heat transfer standing out as a critical process amidst the complexity. This study delves into the casing annular cavity’s structure, examining the influence of flow and structural parameters on heat transfer characteristics. Experimental findings have revealed a trend towards more uniform temperature distributions across the impingement target plate as the relative impingement distance (H/d) increases, under a constant impingement Reynolds number (Rej). Notably, an impingement angle (β) of 90° yields optimal heat transfer effects on the target surface. Furthermore, a higher impingement hole consistency (Is) correlates with a lower and more uniform temperature distribution across the target plate. Additionally, escalating the crossflow ratio (mc/m) results in a decline in the temperature at the impingement stagnation point and a corresponding rise in the heat transfer coefficient.

Retinal blood vessels are the only directly observed blood vessels in the body; changes in them can help effective assess the occurrence and development of ocular and systemic diseases. The specificity and efficiency of retinal vessel quantification technology has improved with the advancement of retinal imaging technologies and artificial intelligence (AI) algorithms; it has garnered attention in clinical research and applications for the diagnosis and treatment of common eye and related systemic diseases. A few articles have reviewed this topic; however, a summary of recent research progress in the field is still needed. This article aimed to provide a comprehensive review of the research and applications of retinal vessel quantification technology in ocular and systemic diseases, which could update clinicians and researchers on the recent progress in this field.

ObjectiveThis study aimed to evaluate the value of 18F-FDG PET/CT radiomics in predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis.MethodsThe PubMed, Embase, Cochrane Library, Web of Science, and CNKI databases were searched from the earliest available date to June 30, 2023. The meta-analysis was performed using the Stata 15.0 software. The methodological quality and risk of bias of included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score criteria. The possible causes of heterogeneity were analyzed by meta-regression.ResultsA total of 17 studies involving 3763 non-small cell lung cancer patients were finally included. We analyzed 17 training cohorts and 10 validation cohorts independently. Within the training cohort, the application of 18F-FDG PET/CT radiomics in predicting EGFR mutations in NSCLC demonstrated a sensitivity of 0.76 (95% CI: 0.70-0.81) and a specificity of 0.78 (95% CI: 0.74-0.82), accompanied by a positive likelihood ratio of 3.5 (95% CI:3.0-4.2), a negative likelihood ratio of 0.31 (95% CI: 0.24-0.39), a diagnostic odds ratio of 11.0 (95% CI: 8.0-16.0), and an area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87). In the validation cohort, the values included a sensitivity of 0.76 (95% CI: 0.67-0.83), a specificity of 0.75 (95% CI: 0.68-0.80), a positive likelihood ratio of 3.0 (95% CI:2.4-3.8), a negative likelihood ratio of 0.32 (95% CI: 0.24-0.44), a diagnostic odds ratio of 9 (95% CI: 6-15), and an AUC of 0.82 (95% CI: 0.78-0.85). The average Radiomics Quality Score (RQS) across studies was 10.47 ± 4.72. Meta-regression analysis identifies the application of deep learning and regions as sources of heterogeneity.Conclusion18F-FDG PET/CT radiomics may be useful in predicting mutation status of the EGFR gene in non-small cell lung cancer.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42022385364.

Pterygium and subconjunctival hemorrhage are two common types of ocular surface diseases that can cause distress and anxiety in patients. In this study, 2855 ocular surface images were collected in four categories: normal ocular surface, subconjunctival hemorrhage, pterygium to be observed, and pterygium requiring surgery. We propose a diagnostic classification model for ocular surface diseases, dual-branch network reinforced by PFM block (DBPF-Net), which adopts the conformer model with two-branch architectural properties as the backbone of a four-way classification model for ocular surface diseases. In addition, we propose a block composed of a patch merging layer and a FReLU layer (PFM block) for extracting spatial structure features to further strengthen the feature extraction capability of the model. In practice, only the ocular surface images need to be input into the model to discriminate automatically between the disease categories. We also trained the VGG16, ResNet50, EfficientNetB7, and Conformer models, and evaluated and analyzed the results of all models on the test set. The main evaluation indicators were sensitivity, specificity, F1-score, area under the receiver operating characteristics curve (AUC), kappa coefficient, and accuracy. The accuracy and kappa coefficient of the proposed diagnostic model in several experiments were averaged at 0.9789 and 0.9681, respectively. The sensitivity, specificity, F1-score, and AUC were, respectively, 0.9723, 0.9836, 0.9688, and 0.9869 for diagnosing pterygium to be observed, and, respectively, 0.9210, 0.9905, 0.9292, and 0.9776 for diagnosing pterygium requiring surgery. The proposed method has high clinical reference value for recognizing these four types of ocular surface images.

Background: Virtual reality (VR), widely used in the medical field, may affect future medical training and treatment. Therefore, this study examined VR’s potential uses and research directions in medicine. Methods: Citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate VR in medicine in articles published between 1 January 2012 and 31 December 2023. These data were analyzed using CiteSpace 6.2. R2 software. Present limitations and future opportunities were summarized based on the data. Results: A total of 2143 related publications from 86 countries and regions were analyzed. The country with the highest number of publications is the USA, with 461 articles. The University of London has the most publications among institutions, with 43 articles. The burst keywords represent the research frontier from 2020 to 2023, such as “task analysis”, “deep learning”, and “machine learning”. Conclusion: The number of publications on VR applications in the medical field has been steadily increasing year by year. The USA is the leading country in this area, while the University of London stands out as the most published, and most influential institution. Currently, there is a strong focus on integrating VR and AI to address complex issues such as medical education and training, rehabilitation, and surgical navigation. Looking ahead, the future trend involves integrating VR, augmented reality (AR), and mixed reality (MR) with the Internet of Things (IoT), wireless sensor networks (WSNs), big data analysis (BDA), and cloud computing (CC) technologies to develop intelligent healthcare systems within hospitals or medical centers.

The silicon carbide fiber-reinforced silicon carbide matrix (SiC/SiC), ceramic matrix composite (CMC) and nickel-based superalloy GH4169 can be utilized in high-temperature applications due to their high-temperature performance. The SiC/SiC composites are commonly used in turbine outer rings, where they encounter friction and wear against the turbine blades. This high-speed rubbing occurs frequently in aircraft engines and steam turbines. To investigate the tribological behavior of these materials, rubbing experiments were conducted between the SiC/SiC and the GH4169 superalloy. The experiments involved varying the blade tip speeds ranging from 100 m/s to 350 m/s and incursion rates from 5 μm/s to 50 μm/s at room temperature. Additionally, experiments were conducted at high temperatures to compare the tribological behavior under ambient conditions. The results indicated that the GH4169 superalloy exhibited abrasive furrow wear during rubbing at both room temperature and high temperature. Furthermore, at elevated temperatures, some of the GH4169 superalloy adhered to the surface of the SiC/SiC. The analysis of the experiments conducted at ambient temperatures revealed that the friction coefficient increased with higher blade tip velocities (100~350 m/s). However, the coefficient was lower at high temperatures compared to room temperature. Furthermore, significant temperature increases were observed during rubbing at room temperature, whereas minimal temperature changes were detected on the rubbing surface at high temperatures.

The intermediate particle in the neutral current deeply inelastic scattering process can be either a virtual photon or a Z0 boson. In this paper we calculate contributions from the weak interaction in the semi-inclusive deeply inelastic scattering process. Semi-inclusive implies that a jet in the current region is also measured in addition to the scattered electron. Jet can be a good candidate to study the three-dimensional structure of the nucleon in the semi-inclusive deeply inelastic scattering process. In the vector boson-nucleon collinear frame, the transverse momentum of the jet is the same to that of the quark in the nucleon under the leading order approximation. As a result, one can define the intrinsic asymmetry which reveals the imbalance of the transverse momentum of the incident quark to explore the transverse momentum dependent parton distribution functions (TMD PDFs). With the Gaussian ansatz for the TMD PDFs, we restudy the transverse momentum dependent behavior of the intrinsic asymmetry. We find that the asymmetry tends to a linear function with respect to k⊥ when Δu,d2 approach Δ′u,d2.

ObjectiveThe purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes.Materials and methodsThe data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman’s correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model).ResultsThis study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set.ConclusionAge, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.

In order to improve the film cooling efficiency of the cooling gas on the hot side wall of the plate, the flow field, temperature field and the average film cooling efficiency of the cylindrical hole, rectangular hole and waveform hole near the wall were compared and analyzed by numerical calculation method. Compared with cylindrical holes, the average spanwise film cooling efficiency of rectangular hole is improved by 100%, and the average spanwise film cooling efficiency of waveform hole is improved by 130%. In addition, the effects of the amplitude A of the waveform hole and the initial phase ϕ on the cooling efficiency are compared and analyzed. The results show that: Within the scope of this study, when the blowing ratio M = 1, lifting the initial phase ϕ of the waveform hole can effectively improve the film cooling efficiency of the cooling gas on the wall. When the amplitude A of the waveform hole is raised, the film cooling efficiency of the cooling gas on the wall will first increase and then decrease, reaching the maximum at the amplitude A = 0.4D. At low blowing ratio, the geometric structure of waveform hole has little influence on the cooling effect of wall surface. Under high blowing ratio, when the geometric structure of waveform hole is changed, the influence of secondary flow on the average spanwise cooling efficiency of wall surface is great.

Abstract Background Iruplinalkib (WX-0593) is an anaplastic lymphoma kinase (ALK)/c-ros oncogene 1 (ROS1) tyrosine kinase inhibitor. Here we reported the single-arm, phase II study (INTELLECT) results of the efficacy and safety of iruplinalkib for ALK-positive crizotinib-resistant advanced non-small cell lung cancer (NSCLC) patients. Methods ALK-positive crizotinib-resistant advanced NSCLC patients aged ≥18 years, with Eastern Cooperative Oncology Group performance status of 0–2 were eligible. Patients received iruplinalkib 180 mg orally once daily for a 21-day cycle with a 7-day lead-in phase at 60 mg orally once daily. The primary endpoint was the independent review committee (IRC)-assessed objective response rate (ORR). Results From August 7, 2019, to October 30, 2020, 146 patients were included. As of the data cut-off date on November 30, 2021, the median follow-up time was 18.2 months (95% confidence interval [CI] 16.8–18.8). IRC-assessed ORR and disease control rate (DCR) were 69.9% (95% CI 61.7–77.2%) and 96.6% (95% CI 92.2–98.9%), respectively. Investigator-assessed ORR and DCR were 63.0% (95% CI 54.6–70.8%) and 94.5% (95% CI 89.5–97.6%), respectively. Investigator-assessed median duration of response and progression-free survival (the same as median time to progression) were 13.2 months (95% CI 10.4–17.7) and 14.5 months (95% CI 11.7–20.0), respectively. Corresponding IRC-assessed results were 14.4 months (95% CI 13.1–not evaluable [NE]), 19.8 months (95% CI 14.5–NE), and NE (95% CI 14.5–NE), respectively. Investigator-assessed intracranial ORRs were 46% (41/90, 95% CI 35–56%) in patients with central nervous system metastases and 64% (27/42, 95% CI 48–78%) in patients with measurable intracranial lesions. Overall survival data were immature. Treatment-related adverse events (TRAEs) occurred in 136/146 (93.2%) patients. The most common TRAEs were aspartate aminotransferase increased (63 [43.2%]), alanine aminotransferase increased (54 [37.0%]), and blood creatine phosphokinase increased (51 [34.9%]). Dose interruption, reduction, and discontinuation due to TRAEs occurred in 21 (14.4%), 16 (11.0%), and four (2.7%) patients, respectively. Conclusions In this study, iruplinalkib (WX-0593) demonstrated favorable efficacy and manageable safety profiles in patients with ALK-positive crizotinib-resistant advanced NSCLC. Iruplinalkib could be a new treatment option for this patient population. Trial registration Center for Drug Evaluation of National Medical Products Administration of China: CTR20190789, registered on April 28, 2019; ClinicalTrials.gov: NCT04641754, registered on November 24, 2020.

Abstract Background Thyroid-associated ophthalmopathy (TAO) is an autoimmune disorder. It has discriminable appearance. This study was conducted to dig the clinical significance of demographic characteristics and ophthalmologic diagram features in TAO diagnosis and stage/severity evaluation. Results We included 320 males and 633 females, with an average age of 41.75 ± 13.75. A majority of TAO patients had hyperthyroidism, and most of them were in the inactive stage and at the moderate level. The thyroid function type, stage and severity were closely associated with hypopsia, eyelid congestion, conjunctival congestion, corneal ulcer, ocular motility disorder, best corrected visual acuity, and extraocular muscle thickening. Using these features, we established different logistic regression models to predict thyroid function subtypes, abnormal thyroid function, stage, and severity, in which the AUC of the ROC curve and accuracies were satisfactory. Conclusion Together, TAO subtype, stage and severity can be diagnosed by auxiliary references including demographic factors, symptoms from complains, and image features. These non-invasive indices can be applied in a timely manner in clinical estimating TAO status.

Purpose: Recently, the proportion of patients with high myopia has shown a continuous growing trend, more toward the younger age groups. This study aimed to predict the changes in spherical equivalent refraction (SER) and axial length (AL) in children using machine learning methods. Methods: This study is a retrospective study. The cooperative ophthalmology hospital of this study collected data on 179 sets of childhood myopia examinations. The data collected included AL and SER from grades 1 to 6. This study used the six machine learning models to predict AL and SER based on the data. Six evaluation indicators were used to evaluate the prediction results of the models. Results: For predicting SER in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the multilayer perceptron (MLP) algorithm, MLP algorithm, orthogonal matching pursuit (OMP) algorithm, OMP algorithm, and OMP algorithm, respectively. The R2 of the five models were 0.8997, 0.7839, 0.7177, 0.5118, and 0.1758, respectively. For predicting AL in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the Extra Tree (ET) algorithm, MLP algorithm, kernel ridge (KR) algorithm, KR algorithm, and MLP algorithm, respectively. The R2 of the five models were 0.7546, 0.5456, 0.8755, 0.9072, and 0.8534, respectively. Conclusion: Therefore, in predicting SER, the OMP model performed better than the other models in most experiments. In predicting AL, the KR and MLP models were better than the other models in most experiments.

Objective: Levodopa (L-DOPA) is the primary treatment for Parkinson’s disease (PD). Nevertheless, the underlying mechanism of its action is not entirely learned. This study aims to probe the action of L-DOPA on NLR pyrin domain containing 3 (NLRP3) inflammasome activation and tyrosine hydroxylase (TH) levels in the striatum (STR) and substantia nigra (SN) of mice with PD symptoms. Methods: PD was simulated by administering 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP; 25 mg/kg/d) to induce mice, followed by L-DOPA (8 mg/kg/d) treatment. The behavioral performance of the mice was assessed using the pole test, balance beam, and rotarod test. After euthanasia with 120 mg/kg sodium pentobarbital, STR and SN were collected for evaluation of protein level of TH, NLR pyrin domain containing 3 (NLRP3), ASC and Cleaved caspase-1 using Western blot and mRNA levels of TH, inflammatory factors IL-1β and IL-18 using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: Treatment with L-DOPA significantly ameliorated the behavioral deficits caused by MPTP in mice with PD symptoms. L-DOPA administration resulted in reduced levels of apoptosis-associated speck-like protein containing a CARD (caspase recruitment domain) (ASC), NLRP3, and Cleaved caspase-1 protein levels, and decreased mRNA levels of IL-1β and IL-18 in the STR and SN. L-DOPA increased the TH mRNA and TH protein levels, while suppressing NLRP3 inflammasome activation in the STR and SN of mice with PD symptoms. Conclusions: L-DOPA improves the behavioral deficits in mice with PD symptoms possibly by suppressing NLRP3 inflammasome activation and increasing TH levels in the STR and SN TH levels. These findings provide further perceptions into the property of L-DOPA in PD.

ObjectiveThis study aims to develop and evaluate a non-imaging clinical data-based nomogram for predicting the risk of vision-threatening diabetic retinopathy (VTDR) in diabetes mellitus type 2 (T2DM) patients.MethodsBased on the baseline data of the Guangdong Shaoguan Diabetes Cohort Study conducted by the Zhongshan Ophthalmic Center (ZOC) in 2019, 2294 complete data of T2DM patients were randomly divided into a training set (n=1605) and a testing set (n=689). Independent risk factors were selected through univariate and multivariate logistic regression analysis on the training dataset, and a nomogram was constructed for predicting the risk of VTDR in T2DM patients. The model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) in the training and testing datasets to assess discrimination, and Hosmer-Lemeshow test and calibration curves to assess calibration.ResultsThe results of the multivariate logistic regression analysis showed that Age (OR = 0.954, 95% CI: 0.940-0.969, p = 0.000), BMI (OR = 0.942, 95% CI: 0.902-0.984, p = 0.007), systolic blood pressure (SBP) (OR =1.014, 95% CI: 1.007-1.022, p = 0.000), diabetes duration (10-15y: OR =3.126, 95% CI: 2.087-4.682, p = 0.000; >15y: OR =3.750, 95% CI: 2.362-5.954, p = 0.000), and glycated hemoglobin (HbA1C) (OR = 1.325, 95% CI: 1.221-1.438, p = 0.000) were independent risk factors for T2DM patients with VTDR. A nomogram was constructed using these variables. The model discrimination results showed an AUC of 0.7193 for the training set and 0.6897 for the testing set. The Hosmer-Lemeshow test results showed a high consistency between the predicted and observed probabilities for both the training set (Chi-square=2.2029, P=0.9742) and the testing set (Chi-square=7.6628, P=0.4671).ConclusionThe introduction of Age, BMI, SBP, Duration, and HbA1C as variables helps to stratify the risk of T2DM patients with VTDR.

Abstract Background A high-risk prevention strategy is an effective way to fight against human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS). The China AIDS Fund for Non-Governmental Organizations (CAFNGO) was established in 2015 to help social organizations intervene to protect high-risk populations in 176 cities. This study aimed to evaluate the role of social organizations in high-risk population interventions against HIV/AIDS. Methods This study was based on the CAFNGO program from 2016 to 2020. The collected data included the number and types of social organizations participating in high-risk group interventions and the amount of funds obtained by these organizations each year. We explored the factors influencing the number of newly diagnosed AIDS cases using a spatial econometric model. Furthermore, we evaluated the effectiveness of intervention activities by comparing the percentages of the individuals who initially tested positive, and the individuals who took the confirmatory test, as well as those who retested positive and underwent the treatment. Results Overall, from 2016 to 2020, the number of social organizations involved in interventions to protect HIV/AIDS high-risk populations increased from 441 to 532, and the invested fund increased from $3.98 to $10.58 million. The number of newly diagnosed cases decreased from 9128 to 8546 during the same period. Although the number of cities with overall spatial correlations decreased, the spatial agglomeration effect persisted in the large cities. City-wise, the number of social organizations (direct effect 19.13), the permanent resident population (direct effect 0.12), GDP per capita (direct effect 17.58; indirect effect − 15.38), and passenger turnover volume (direct effect 5.50; indirect effect − 8.64) were the major factors influencing new positive cases confirmed through the testing interventions performed by the social organizations. The initial positive test rates among high-risk populations were below 5.5%, the retesting rates among those who initially tested positive were above 60%, and the treatment rates among diagnosed cases were above 70%. Conclusions The spatial effect of social organizations participating in interventions targeting high-risk populations funded by CAFNGO is statistically significant. Nevertheless, despite the achievements of these social organizations in tracking new cases and encouraging treatment, a series of measures should be taken to further optimize the use of CAFNGO. Working data should be updated from social organizations to CAFNGO more frequently by establishing a data monitoring system to help better track newly diagnosed AIDS cases. Multichannel financing should be expanded as well.

A detection method for the determination of 20 kinds of cephalosporins in animal derived food was established with multiple stable isotope internal standards by liquid chromatography-tandem mass spectrometry. Cephalosporins were extracted from samples with methanol, and purified with octadecylsilane sorbent. An Ascentis C8 column was used for analyzing with 0.1% formic acid aqueous solution and acetonitrile as mobile phases. Analysis of cephalosporins were respectively performed on the positive and negative scan mode by multiple reactions monitoring. Results showed that the linear range of the methods was 5~100 μg/L for 17 kinds of cephalosporins with highly responsive, and 25~500 μg/L for 3 kinds of cephalosporins with lowly responsive. The determination coefficients of the linear equations ranged from 0.9913 to 0.9998. The limit of detection for 20 kinds of cephalosporins ranged from 1.0 to 20.0 μg/kg, and the limit of quantification ranged from 3.0 to 60.0 μg/kg in animal derived sample. The average recovery rate of 20 cephalosporins ranged from 78.08% to 115.47%, and the relative standard deviation ranged from 3.07% to 12.44%. The validated method was simple and accurate. It was suitable for the determination of 20 cephalosporins in animal food.

Abstract Accurate and quantitative investigation of the physical structure and fractal geometry of coal has important theoretical and practical significance for coal bed methane (CBM) development and the prevention of dynamic disasters such as coal and gas outbursts. This study investigates the pore structure and fractal characteristics of soft and hard coals using nitrogen and carbon dioxide (N2/CO2) adsorption. Coal samples from Pingdingshan Mine in Henan province of China were collected and pulverized to the required size (0.20–0.25 mm). N2/CO2 adsorption tests were performed to evaluate the specific surface area (SSA), pore size distribution (PSD), and pore volume (PV) using Braunuer-Emmett-Teller (BET), Barrett-Joyner-Halenda (BJH), and Density Functional Theory (DFT). The pore structure was characterized based on the theory of fractal dimensions. The results unveiled that the strength of coal has a significant influence on pore structure and fractal dimensions. There are significant differences in SSA and PV between both coals. The BJH-PV and BET-SSA obtained by N2-adsorption for soft coal are 0.029–0.032 cm3/g and 3.523–4.783 m2/g. While the values of PV and SSA obtained by CO2-adsorption are 0.037–0.039 cm3/g and 106.016–111.870 m2/g. Soft coal shows greater SSA and PV than hard coal, which is consistent with the adsorption capacity ( $${V}_{\mathrm{L}}$$ V L ). The fractal dimensions of soft and hard coal are respectively different. The Ding coal exhibits larger D 1 and smaller D 2, and the reverse for the Wu coal seam is observed. The greater the value of D 1 (complexity of pore surface) of soft coal is, the larger the pore surface roughness and gas adsorption capacity is. The results enable us to conclude that the characterization of pores and fractal dimensions of soft and hard coals is different, tending to different adsorption/desorption characteristics. In this regard, the results provide a reference for formulating corresponding coal and gas outburst prevention and control measures.

Abstract Fragmentation functions are important quantities in describing the hadronization process in high energy reactions. They can induce various azimuthal modulations which can be measured to reveal correlations between transverse momenta and polarizations. Without introducing initial state uncertainties, electron positron annihilation process is known as an ideal process to investigate the fragmentation functions. In this paper, therefore, we calculate the hadron pair production in the semi-inclusive electron positron annihilation process $$e^+ + e^- \rightarrow h_1+h_2 + {\bar{q}} +X$$ e + + e - → h 1 + h 2 + q ¯ + X at twist-4 to study dihadron fragmentation functions. Here $${\bar{q}}$$ q ¯ denotes an antiquark that corresponds to a jet of hadrons in experiments. Together with single hadron fragmentation functions, dihadron fragmentation functions can provide additional ways to extract nucleon parton distribution functions from the semi-inclusive deeply inelastic scattering experiments with two detected final state hadrons. We calculate the differential cross section of the hadron pair production semi-inclusive electron positron annihilation process at twist-4 level. The calculation is carried out by using the collinear expansion method. We also calculate azimuthal asymmetries in terms of dihadron fragmentation functions. Contributions from four-quark correlator are also taken into account. Both the electromagnetic and weak interactions are considered in this paper.

Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations.Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011–2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R “bibliometrix” package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts.Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021–2022).Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.

Our understanding of the parton-level structure in the nucleon comes predominantly from the electron deeply inelastic scattering measurements with very high precision. It deserves further study. We therefore calculate the neutral current jet production semi-inclusive deeply inelastic scattering process in this paper. Neutral current implies that interactions can be mediated by the photon, Z0 boson and their interference. The initial electron is assumed to be polarized and then scattered off by a target particle with spin 1. After obtaining the differential cross section, we calculate sets of quantities, including structure functions, azimuthal asymmetries, parity-violating asymmetries and charge asymmetries. They can be measured in the experiments. We find that these quantities are expressed in terms of the parton distribution functions and the electroweak coefficients. These coefficients are different from that in the γ⁎N collinear frame. As a result, our calculation provides a set of new quantities for determining the electroweak couplings as well as the parton distribution functions in the deeply inelastic scattering. It is helpful to test the universality of the parton distribution functions or to understand the hadronic weak interactions and strong interactions simultaneously.

PurposeTo propose a five-category model for the automatic detection of myopic macular lesions to help grassroots medical institutions conduct preliminary screening of myopic macular lesions from limited number of color fundus images.MethodsFirst, 1,750 fundus images of non-myopic retinal lesions and four categories of pathological myopic maculopathy were collected, graded, and labeled. Subsequently, three five-classification models based on Vision Outlooker for Visual Recognition (VOLO), EfficientNetV2, and ResNet50 for detecting myopic maculopathy were trained with data-augmented images, and the diagnostic results of the different trained models were compared and analyzed. The main evaluation metrics were sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), area under the curve (AUC), kappa and accuracy, and receiver operating characteristic curve (ROC).ResultsThe diagnostic accuracy of the VOLO-D2 model was 96.60% with a kappa value of 95.60%. All indicators used for the diagnosis of myopia-free macular degeneration were 100%. The sensitivity, NPV, specificity, and PPV for diagnosis of leopard fundus were 96.43, 98.33, 100, and 100%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of diffuse chorioretinal atrophy were 96.88, 98.59, 93.94, and 99.29%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of patchy chorioretinal atrophy were 92.31, 99.26, 97.30, and 97.81%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of macular atrophy were 100, 98.10, 84.21, and 100%, respectively.ConclusionThe VOLO-D2 model accurately identified myopia-free macular lesions and four pathological myopia-related macular lesions with high sensitivity and specificity. It can be used in screening pathological myopic macular lesions and can help ophthalmologists and primary medical institution providers complete the initial screening diagnosis of patients.

Over binary-input memoryless symmetric (BMS) channels, the performance of polar codes under successive cancellation list (SCL) decoding can approach maximum likelihood (ML) algorithm when the list size L is greater than or equal to 2MF, where MF, known as mixing factor of code, represents the number of information bits before the last frozen bit. Recently, Yao et al. showed the upper bound of the mixing factor of decreasing monomial codes with length n=2m and rate R≤12 when m is an odd number; moreover, this bound is reachable. Herein, we obtain an achievable upper bound in the case of an even number. Further, we propose a new decoding hard-decision rule beyond the last frozen bit of polar codes under BMS channels.

The use of Mn-rich layered cathodes in Na-based batteries is hindered by inadequate cycling reversibility and sluggish anionic redox kinetics. Here, the authors report a strategy to stabilize the structure and promote anionic redox via configurational entropy and ion-diffusion structural tuning.

In order to ensure the accuracy of test results, the uncertainty determination of cephalosporins in chicken by high performance liquid chromatography-tandem mass spectrometry isotope internal standard method was evaluated. The source of uncertainty was evaluated and the mathematical model was established. Through quantitative analysis of each component of uncertainty, the uncertainty of each component was calculated. Finally, standard uncertainty was composited. When the content of cephalosporins in the sample was 250 μg/kg, the expanded uncertainty was 43.60~50.10 μg/kg. The results showed that uncertainty mainly derived from the measuring instruments (0.0762), repeatability (0.0337), analytical instruments (0.0250) and the addition of internal standards (0.0224). Among them the measurement instrument contributed the most to the uncertainty. It provided a reference basis for uncertainty evaluation of determination of cephalosporins by high performance liquid chromatography-tandem mass spectrometry isotope internal standard method.

Tear meniscus height (TMH) is an important reference parameter in the diagnosis of dry eye disease. However, most traditional methods of measuring TMH are manual or semi-automatic, which causes the measurement of TMH to be prone to the influence of subjective factors, time consuming, and laborious. To solve these problems, a segmentation algorithm based on deep learning and image processing was proposed to realize the automatic measurement of TMH. To accurately segment the tear meniscus region, the segmentation algorithm designed in this study is based on the DeepLabv3 architecture and combines the partial structure of the ResNet50, GoogleNet, and FCN networks for further improvements. A total of 305 ocular surface images were used in this study, which were divided into training and testing sets. The training set was used to train the network model, and the testing set was used to evaluate the model performance. In the experiment, for tear meniscus segmentation, the average intersection over union was 0.896, the dice coefficient was 0.884, and the sensitivity was 0.877. For the central ring of corneal projection ring segmentation, the average intersection over union was 0.932, the dice coefficient was 0.926, and the sensitivity was 0.947. According to the evaluation index comparison, the segmentation model used in this study was superior to the existing model. Finally, the measurement outcome of TMH of the testing set using the proposed method was compared with manual measurement results. All measurement results were directly compared via linear regression; the regression line was y0.98x−0.02, and the overall correlation coefficient was r20.94. Thus, the proposed method for measuring TMH in this paper is highly consistent with manual measurement and can realize the automatic measurement of TMH and assist clinicians in the diagnosis of dry eye disease.

SiC fiber-reinforced silicon matrix (SiCf/SiC) composites are of significant interest for the aircraft engine. The thermal conductive behaviors of [0-90-0]s SiCf/SiC composites along in-plane and out-of-plane (thickness) directions were reported in this paper. The thermal conductivity of the SiCf/SiC composite was tested by using the steady-state measuring apparatus. Then, the thermal conductivity of the SiC matrix was predicted by finite element analysis (FEA). The representative volume element (RVE) models of SiCf/SiC composites were built, and FEA was used to investigate thermal conductive behaviors. It was found that the thermal conductivity along the fiber radial direction was greater than it was in the axial direction in the SiCf/SiC composite and that the thermal conductivity of the out-of-plane direction is lower than that of the in-plane one. The thermal conductivity of the SiCf/SiC composite in the in-plane direction at room temperature was 31.1 Wm−1K−1 and 28.5 Wm−1K−1, and the thermal conductivity in the thickness direction was 24.9 Wm−1K−1. The experimental thermal conductivity showed a good agreement with the thermal conductivity of FEA.

BackgroundPosner-Schlossman syndrome (PSS) is a relatively rare cause of chronic secondary open-angle glaucoma (OAG), but the exact cause is unknown. This study aimed to determine potential risk factors for OAG secondary to PSS and to provide a basis for early intervention in the development of PSS.MethodsThis was a retrospective case-control study. Nine cases diagnosed with PSS and seven cases diagnosed with OAG secondary to PSS were selected and their aqueous humor assays at the first occurrence of PSS were collected. Clinical characteristics including age, sex, disease duration, eye laterality, baseline visual acuity, maximum IOP, corneal endothelial cell density, visual field, retinal nerve fiber layer thickness, cup-to-disk ratio, keratic precipitates, anterior chamber inflammation, and aqueous humor cytokine assay results were compared between the two groups.ResultsThe cytomegalovirus (CMV) positivity was 55.60% in patients with PSS and 100% in patients with OAG secondary to PSS. Corneal endothelial cell density was lower in patients with CMV-positive PSS (p = 0.0116). Concentrations of basic fibroblast growth factor (bFGF), interleukin (IL)-6, and vascular cell adhesion molecule (VCAM) in patients with PSS and IL-8, IL-6, and VCAM in patients with OAG secondary to PSS were higher than standard reference values; and IL-8 concentration was significantly higher in patients with OAG secondary to PSS (p = 0.0229). There were significant positive correlations between IL-8 and IL-6, IL-6 and VCAM (p = 0.0304, p = 0.0172) and a significant negative correlation between bFGF and vascular endothelial growth factor (VEGF) (p = 0.0497). Simultaneous increase of IL-8 and IL-6 concentration levels could be used as a cytokine indicator to predict secondary OAG in patients with PSS (p = 0.0095).ConclusionSimultaneous increase of IL-8 and IL-6 concentrations may be an important cause of accelerated secondary OAG in patients with PSS, with IL-8 playing a more critical role. IL-8 and IL-6 may be more reliable cytokine markers for predicting secondary OAG in PSS, However, the high possibility of secondary OAG in patients with CMV-positive PSS should not be ignored. Regulation of IL-8 and IL-6 levels may be a new strategy of preventing OAG secondary to PSS.

Calcium sulfonate complex greases (CSCG) have proven to be a sustainable alternative to lithium complex greases, which still require appropriate additives to deliver lubrication performance benefits under extreme working conditions such as heavy load, high speed, and high temperature. The anti-wear and friction reducing properties of CSCG enhanced by two-dimensional MoS2 nanosheets (2D MoS2) with a narrow lateral size and thickness distributions were evaluated by a four-ball tribometer. The results showed that the CSCG with 0.6 wt.% 2D MoS2 performs best, with a 56.4% decrease in average friction coefficient (AFC), 16.5% reduction in wear scar diameter (WSD), 14.3% decrease in surface roughness, and a 59.4% reduction in average wear depth. Combining SEM-EDS images, Raman, and X-ray photoelectron spectra, it is illustrated that the physical transferred film and tribo-chemical film consisting of MoS2, Fe2O3, FeSO4, CaCO3, CaO, and MoO3 were generated on the worn surface, which improves the lubrication performance of CSCG considerably.

PurposeTo assess the value of an automated classification model for dry and wet macular degeneration based on the ConvNeXT model.MethodsA total of 672 fundus images of normal, dry, and wet macular degeneration were collected from the Affiliated Eye Hospital of Nanjing Medical University and the fundus images of dry macular degeneration were expanded. The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models. A total of 289 fundus images were used to test the models, and the classification results of the models on different datasets were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), accuracy, and kappa.ResultsUsing 289 fundus images, three-category models trained on the original and expanded datasets were assessed. The ConvNeXT model trained on the expanded dataset was the most effective, with a diagnostic accuracy of 96.89%, kappa value of 94.99%, and high diagnostic consistency. The sensitivity, specificity, F1-score, and AUC values for normal fundus images were 100.00, 99.41, 99.59, and 99.80%, respectively. The sensitivity, specificity, F1-score, and AUC values for dry macular degeneration diagnosis were 87.50, 98.76, 90.32, and 97.10%, respectively. The sensitivity, specificity, F1-score, and AUC values for wet macular degeneration diagnosis were 97.52, 97.02, 96.72, and 99.10%, respectively.ConclusionThe ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.

BackgroundArtificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI’s general applications and research frontiers in the detection and gradation of DR.MethodsCitation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software.ResultsOverall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled “automated detection” was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning.ConclusionInitial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.

In recent years, the incidence of myopia has increased at an alarming rate among children and adolescents in China. The exploration of an effective prevention and control method for myopia is in urgent need. With the development of information technology in the past decade, artificial intelligence with the Internet of Things technology (AIoT) is characterized by strong computing power, advanced algorithm, continuous monitoring, and accurate prediction of long-term progression. Therefore, big data and artificial intelligence technology have the potential to be applied to data mining of myopia etiology and prediction of myopia occurrence and development. More recently, there has been a growing recognition that myopia study involving AIoT needs to undergo a rigorous evaluation to demonstrate robust results.

Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.

PurposeTo assess the value of automatic disc-fovea angle (DFA) measurement using the DeepLabv3+ segmentation model.MethodsA total of 682 normal fundus image datasets were collected from the Eye Hospital of Nanjing Medical University. The following parts of the images were labeled and subsequently reviewed by ophthalmologists: optic disc center, macular center, optic disc area, and virtual macular area. A total of 477 normal fundus images were used to train DeepLabv3+, U-Net, and PSPNet model, which were used to obtain the optic disc area and virtual macular area. Then, the coordinates of the optic disc center and macular center were obstained by using the minimum outer circle technique. Finally the DFA was calculated.ResultsIn this study, 205 normal fundus images were used to test the model. The experimental results showed that the errors in automatic DFA measurement using DeepLabv3+, U-Net, and PSPNet segmentation models were 0.76°, 1.4°, and 2.12°, respectively. The mean intersection over union (MIoU), mean pixel accuracy (MPA), average error in the center of the optic disc, and average error in the center of the virtual macula obstained by using DeepLabv3+ model was 94.77%, 97.32%, 10.94 pixels, and 13.44 pixels, respectively. The automatic DFA measurement using DeepLabv3+ got the less error than the errors that using the other segmentation models. Therefore, the DeepLabv3+ segmentation model was finally chosen to measure DFA automatically.ConclusionsThe DeepLabv3+ segmentation model -based automatic segmentation techniques can produce accurate and rapid DFA measurements.

Abstract Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan–Meier plot, time‐dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five‐gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five‐gene signature using The Cancer Genome Atlas—Liver Hepatocellular Carcinoma (TCGA‐LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five‐gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA‐LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high‐risk group and histidine metabolism in the low‐risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real‐time PCR validation. In brief, a five‐gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five‐gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.