Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening.

BACKGROUND: To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment. METHODS: A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening. RESULTS: The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening. CONCLUSIONS: In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.

Efficacy and safety of super pulse CO2 laser-assisted punctoplasty with canalicular curettage in primary canaliculitis.

The objective of this study was to evaluate the efficacy and safety of super pulse CO2 laser-assisted punctoplasty with canalicular curettage in primary canaliculitis. In this retrospective serial case study, the clinical data of 26 patients who underwent super pulse CO2 laser-assisted punctoplasty for the treatment of canaliculitis were collected from January 2020 to May 2022. The clinical presentation, intraoperative and microbiologic findings, surgical pain severity, postoperative outcome, and complications were studied. Of the 26 patients, most were females (female:male 20:6), with a mean age of 60.1 ± 16.1 years (range, 19-93). Mucopurulent discharge (96.2%), eyelid redness and swelling (53.8%), and epiphora (38.5%) were the most common presentations. During the surgery, concretions were present in 73.1% (19/26) of the patients. The surgical pain severity scores ranged from 1 to 5, according to the visual analog scale, with a mean score of 3.2 ± 0.8. This procedure resulted in complete resolution in 22 (84.6%) patients and significant improvement in 2 (7.7%) patients, and 2 (7.7%) patients required additional lacrimal surgery with a mean follow-up time of 10.9 ± 3.7 months. The surgical procedure of super pulse CO2 laser-assisted punctoplasty followed by curettage appears to be a safe, effective, minimally invasive, and well-tolerated treatment for primary canaliculitis.

Gene Network Analysis of Alzheimer's Disease Based on Network and Statistical Methods.

Gene network associated with Alzheimer's disease (AD) is constructed from multiple data sources by considering gene co-expression and other factors. The AD gene network is divided into modules by Cluster one, Markov Clustering (MCL), Community Clustering (Glay) and Molecular Complex Detection (MCODE). Then these division methods are evaluated by network structure entropy, and optimal division method, MCODE. Through functional enrichment analysis, the functional module is identified. Furthermore, we use network topology properties to predict essential genes. In addition, the logical regression algorithm under Bayesian framework is used to predict essential genes of AD. Based on network pharmacology, four kinds of AD's herb-active compounds-active compound targets network and AD common core network are visualized, then the better herbs and herb compounds of AD are selected through enrichment analysis.

Neointimal hyperplasia in the inferior vena cava of adenine-induced chronic kidney disease rats with aortocaval fistulas.

BACKGROUND: The failure of autologous arteriovenous fistulas (AVFs) occurs primarily due to stenosis in the anastomotic site, which is mainly related to the development of neointimal hyperplasia (NIH). Therefore, we conducted a study to establish a novel approach to create aortocaval fistulas (ACFs) in adenine-induced (AD) chronic kidney disease (CKD) rats to study the NIH in the inferior vena cava. METHODS: Ten adult female rats received a 0.75% adenine-rich diet for 4 weeks to induce CKD and underwent ACF surgery. Ten healthy rats served as controls. A 5-10-mm segment of a vein immediately adjacent to that the portion of the vein used for creating the fistula was surgically removed at the time of creating the fistula, and reconstruction of the failed fistula from the same patient was used as controls. ACF was assessed using duplex scans and histopathological analyses. RESULTS: At the end of the experiment, AD rats showed higher serum creatinine and urea nitrogen than those of vehicle-treated rats. Remarkable histological changes in kidney tissues demonstrated successful CKD models. Sections of the ACF in AD rats and veins removed at the time of the reconstruction of the failed fistula of the patient demonstrated that the eccentric neointima formation is irregularly thickened, with several small vessels within a more cellular region of the neointima. Immunohistochemistry demonstrated the presence of myofibroblasts, contractile smooth muscle cells and macrophages within the neointima. CONCLUSIONS: Our rat models with ACFs showed typical features of NIH in the formation of fistula stenosis, which can resemble clinical findings in uremic patients.

TP-GCL: graph contrastive learning from the tensor perspective.

Graph Neural Networks (GNNs) have demonstrated significant potential as powerful tools for handling graph data in various fields. However, traditional GNNs often encounter limitations in information capture and generalization when dealing with complex and high-order graph structures. Concurrently, the sparse labeling phenomenon in graph data poses challenges in practical applications. To address these issues, we propose a novel graph contrastive learning method, TP-GCL, based on a tensor perspective. The objective is to overcome the limitations of traditional GNNs in modeling complex structures and addressing the issue of sparse labels. Firstly, we transform ordinary graphs into hypergraphs through clique expansion and employ high-order adjacency tensors to represent hypergraphs, aiming to comprehensively capture their complex structural information. Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized hypergraph. This process effectively extracts crucial structural features from graph data. Experimental results demonstrate that TP-GCL achieves significant performance improvements compared to baseline methods across multiple public datasets, particularly showcasing enhanced generalization capabilities and effectiveness in handling complex graph structures and sparse labeled data.

CT-Measured Angulation Between the Frontal Bone and Bony Nasolacrimal Duct: Variations in Obstructed and Healthy Lacrimal Ducts.

PURPOSE: The aim of this study was to analyze the characteristics of CT-measured intersection angle (FB-BNLD) between the frontal bone and bony nasolacrimal duct and to provide suggestions for treating primary acquired nasolacrimal duct obstruction (PANDO) patients in West China. METHODS: Three hundred and nine participants' CT were, respectively, evaluated with RadiAnt DICOM Viewer. We defined the FB-BNLD angle >0° as the anterior type and the FB-BNLD angle ≤0° as the posterior type. RESULTS: The mean FB-BNLD was -2.52° (95% CI, -3.16° to -1.88°) across all participants, of whom 37.2% were of the anterior type and 62.8% of the posterior type. Approximately 65.0% of the female patients had a posterior FB-BNLD type, and 54.2% of the male patients had an anterior FB-BNLD type (p = .002). Posterior FB-BNLD was the dominant type in the PANDO and control groups (p = .011), and the angle of FB-BNLD was statistically different in both groups (PANDO group, -2.54° to -0.71°; control group, -4.42° to -2.67°; p < .001). Among the male participants, the type of FB-BNLD differed between the two groups (p = .036), with differences in the angle of FB-BNLD (PANDO group, 0.59° to 5.13°; control group, -4.08° to 1.89°; p = .034). There was no difference in the type of FB-BNLD in female participants between the two groups (p = .051). CONCLUSION: The present study revealed individual differences in the type of FB-BNLD, with anterior-type majority in males and posterior-type dominance in females. Evaluating the FB-BNLD type on CT can provide a fast method for knowing the nasolacrimal duct condition during planning for lacrimal manipulation.

Traditional Chinese Medicine studies for Alzheimer's disease via network pharmacology based on entropy and random walk.

Alzheimer's disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI (China National Knowledge Internet) are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients' scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD.

LGNN: a novel linear graph neural network algorithm.

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural characteristics of graph data, and the convolution process has a large amount of calculation. It is necessary to use multi-channel or deep neural network structure to achieve the goal of modeling the high-order structural characteristics of the network. Therefore, this paper proposes a linear graph neural network framework [Linear Graph Neural Network (LGNN)] with superior performance. The model first preprocesses the input graph, and uses symmetric normalization and feature normalization to remove deviations in the structure and features. Then, by designing a high-order adjacency matrix propagation mechanism, LGNN enables nodes to iteratively aggregate and learn the feature information of high-order neighbors. After obtaining the node representation of the network structure, LGNN uses a simple linear mapping to maintain computational efficiency and obtain the final node representation. The experimental results show that the performance of the LGNN algorithm in some tasks is slightly worse than that of the existing mainstream graph neural network algorithms, but it shows or exceeds the machine learning performance of the existing algorithms in most graph neural network performance evaluation tasks, especially on sparse networks.

Quality of Vision in Eyes with Chronic Dacryocystitis After Endoscopic Dacryocystorhinostomy.

Purpose: To assess the quality of vision (QOV) of patients with chronic dacryocystitis and explore the impact of endoscopic dacryocystorhinostomy (En-DCR) on patients' QOV, with the aim of increasing clinicians' attention to the potential QOV changes in patients with chronic dacryocystitis. Patients and Methods: Chronic dacryocystitis patients who attended the Department of Oculoplastics, Zhongshan Ophthalmic Center, Sun Yat-sen University from January 2022 to June 2022 and had En-DCR were included. The QOV was evaluated preoperatively and postoperatively using the Quick-contrast sensitivity function test (qCSF) and the OPD ScanIII comprehensive visual quality analyser, and the contrast sensitivity function (CSF), higher-order aberrations (HOAs), and modulation transfer function (MTF) data were recorded. Results: Thirty-eight patients with chronic dacryocystitis were included. QOV analysis of those with monocular chronic dacryocystitis revealed that HOAs were significantly higher in affected eyes than in contralateral eyes (P < 0.05), and CSF and MTF were significantly lower in affected eyes than in contralateral eyes (P < 0.05). One month after En-DCR, patients' HOAs, CSF, and MTF were significantly improved (P < 0.05), and there was no statistically significant difference in the effect of intraoperative combined silicone intubation versus no intubation on QOV (P > 0.05). Conclusion: Chronic dacryocystitis can significantly affect the QOV of patients. Successful En-DCR significantly improves the CSF, HOAs and MTF, which can effectively improve the QOV.

Salisediminibacterium halotolerans gen. nov., sp. nov., a halophilic bacterium from soda lake sediment.

An orange-pigmented, Gram-reaction-positive, non-spore-forming, halophilic, alkali-tolerant rod, designated strain halo-2(T), was isolated from sediment of Xiarinaoer soda lake, in China's Inner Mongolia Autonomous Region. Strain halo-2(T) grew in a complex medium with 3-30 % (w/v) NaCl and at pH 5-10. The cell-wall peptidoglycan contained meso-diaminopimelic acid and the major respiratory isoprenoid quinone was MK-7. The predominant cellular fatty acids were anteiso-C(15 : 0) (43.6 %), anteiso-C(17 : 0) (14.8 %) and iso-C(15 : 0) (6.8 %) and the polar lipids consisted of diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylglycerol. The genomic DNA G+C content of the novel strain was 48.2 mol%. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain halo-2(T) was most closely related to Bacillus agaradhaerens DSM 8721(T) (93.9 % sequence similarity). However, strain halo-2(T) could be clearly differentiated from its closest phylogenetic relatives on the basis of several phenotypic, genotypic and chemotaxonomic characteristics. Strain halo-2(T) therefore represents a novel species in a new genus for which the name Salisediminibacterium halotolerans gen. nov., sp. nov. is proposed. The type strain of the type species is halo-2(T) (= CGMCC 1.7654(T) = NBRC 104935(T)).

Risk factors associated with hepatic osteopathy in HBV related cirrhosis measured by liver stiffness: An Observational study.

To investigate the differences in bone mineral density between patients with liver cirrhosis and healthy control, and to analyze the risk factors of hepatic osteoporosis in patients with HBV related liver cirrhosis.A total of 189 patients with liver cirrhosis and 207 health controls were enrolled. The bone mineral density of lumbar spine and femoral neck was examined by dual energy X-ray absorptiometry. -2.0

A Novel Method for Drug Screen to Regulate G Protein-Coupled Receptors in the Metabolic Network of Alzheimer's Disease.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder and the pathogenesis of AD is poorly understood. G protein-coupled receptors (GPCRs) are involved in numerous key AD pathways and play a key role in the pathology of AD. To fully understand the pathogenesis of AD and design novel drug therapeutics, analyzing the connection between AD and GPCRs is of great importance. In this paper, we firstly build and analyze the AD-related pathway by consulting the KEGG pathway of AD and a mass of literature and collect 25 AD-related GPCRs for drug discovery. Then the ILbind and AutoDock Vina tools are integrated to find out potential drugs related to AD. According to the analysis of DUD-E dataset, we select five drugs, that is, Acarbose (ACR), Carvedilol (CVD), Digoxin (DGX), NADH (NAI), and Telmisartan (TLS), by sorting the ILbind scores (≥0.73). Then depending on their AutoDock Vina scores and pocket position information, the binding patterns of these five drugs are obtained. We analyze the regulation function of GPCRs in the metabolic network of AD based on the drug screen results, which may be helpful for the study of the off-target effect and the side effect of drugs.

On the Adjacent Eccentric Distance Sum Index of Graphs.

For a given graph G, ε(v) and deg(v) denote the eccentricity and the degree of the vertex v in G, respectively. The adjacent eccentric distance sum index of a graph G is defined as [Formula in text], where [Formula in text] is the sum of all distances from the vertex v. In this paper we derive some bounds for the adjacent eccentric distance sum index in terms of some graph parameters, such as independence number, covering number, vertex connectivity, chromatic number, diameter and some other graph topological indices.

Uncovering the Molecular Mechanism of Actions between Pharmaceuticals and Proteins on the AD Network.

This study begins with constructing the mini metabolic networks (MMNs) of beta amyloid (Aß) and acetylcholine (ACh) which stimulate the Alzheimer's Disease (AD). Then we generate the AD network by incorporating MMNs of Aß and ACh, and other MMNs of stimuli of AD. The panel of proteins contains 49 enzymes/receptors on the AD network which have the 3D-structure in PDB. The panel of drugs is formed by 5 AD drugs and 5 AD nutraceutical drugs, and 20 non-AD drugs. All of these complexes formed by these 30 drugs and 49 proteins are transformed into dyadic arrays. Utilizing the prior knowledge learned from the drug panel, we propose a statistical classification (dry-lab). According to the wet-lab for the complex of amiloride and insulin degrading enzyme, and the complex of amiloride and neutral endopeptidase, we are confident that this dry-lab is reliable. As the consequences of the dry-lab, we discover many interesting implications. Especially, we show that possible causes of Tacrine, donepezil, galantamine and huperzine A cannot improve the level of ACh which is against to their original design purpose but they still prevent AD to be worse as Aß deposition appeared. On the other hand, we recommend Miglitol and Atenolol as the safe and potent drugs to improve the level of ACh before Aß deposition appearing. Moreover, some nutrients such as NADH and Vitamin E should be controlled because they may harm health if being used in wrong way and wrong time. Anyway, the insights shown in this study are valuable to be developed further.

Salsuginibacillus halophilus sp. nov., a halophilic bacterium isolated from a soda lake.

A Gram-stain-positive, rod-shaped, endospore-forming, halophilic, alkalitolerant bacterium, designated halo-1(T), was isolated from sediment of Xiarinaoer soda lake, located in the Inner Mongolia Autonomous Region of China. Strain halo-1(T) grew in the presence of 9-30 % (w/v) NaCl (optimum 19 %) and at pH 5-10 (optimum pH 9). The cell-wall peptidoglycan contained meso-diaminopimelic acid and the major respiratory isoprenoid quinone was MK-7. The predominant cellular fatty acids of the isolate were anteiso-C(15 : 0) (58.35 %), anteiso-C(17 : 0) (12.89 %) and C(16 : 0) (6.52 %). The polar lipids contained diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, glycolipid and a phospholipid of unknown structure. The DNA G+C content of the strain was 46.4 mol%. On the basis of 16S rRNA gene sequence similarity, strain halo-1(T) showed the highest similarity (93.9 %) to Salsuginibacillus kocurii CH9d(T). Strain halo-1(T) could be clearly differentiated from its closest phylogenetic relative on the basis of several phenotypic, genotypic and chemotaxonomic features. Therefore, strain halo-1(T) represents a novel species, for which the name Salsuginibacillus halophilus sp. nov. is proposed, with the type strain halo-1(T) (=CGMCC 1.7653(T) =NBRC 104934(T)).

Cloacibacterium rupense sp. nov., isolated from freshwater lake sediment.

A Gram-negative, yellow-pigmented bacterium, designated strain R2A-16(T), was isolated from sediment of Rupa Lake in Nepal and analysed using a polyphasic taxonomic approach. Phylogenetic analyses based on 16S rRNA gene sequences showed that strain R2A-16(T) is affiliated to the genus Cloacibacterium of the family Flavobacteriaceae; 16S rRNA gene sequence similarity between strain R2A-16(T) and Cloacibacterium normanense CCUG 46293(T) was 98.07 %. The isolate contained iso-C(15 : 0) (35.6 %) as the major fatty acid and menaquinone MK-6 as the predominant respiratory quinone. The G+C content of the genomic DNA was 33.3 mol%. On the basis of its phenotypic properties and phylogenetic distinctiveness, strain R2A-16(T) represents a novel species of the genus Cloacibacterium, for which the name Cloacibacterium rupense sp. nov. is proposed; the type strain is R2A-16(T) (=CGMCC 1.7656(T) =NBRC 104931(T)).

Halolactibacillus alkaliphilus sp. nov., a moderately alkaliphilic and halophilic bacterium isolated from a soda lake in Inner Mongolia, China, and emended description of the genus Halolactibacillus.

A moderately alkaliphilic and halophilic bacterium was isolated from sediment of Xiarinaoer soda lake located in the Inner Mongolia municipality. This bacterium, designated strain H-5(T), was a facultative anaerobe, Gram-positive, rod-shaped and non-motile. Strain H-5(T) grew in complex medium with 0.5-30% (w/v) NaCl and at pH 7.5-13. The cell wall peptidoglycan contained meso-diaminopimelic acid. The major isoprenoid quinones found in this strain were MK-9H(4) and MK-9H(2), and the major cellular fatty acids were C(16:0) and anteiso-C(13:0). The DNA G+C content of strain H-5(T) was 38.3 mol%. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain H-5(T) was located in the genus Halolactibacillus. The 16S rRNA gene sequence similarities between strain H-5(T) and the type strains of the two recognized species of the genus Halolactibacillus were 98.6 and 98.0%. The DNA-DNA relatedness values between strain H-5(T) and the two type strains were 19 and 5%. Based on the phenotypic and chemotaxonomic data, the phylogenetic analysis and genomic distinctiveness, strain H-5(T) is considered to represent a novel species of the genus Halolactibacillus, for which the name Halolactibacillus alkaliphilus is proposed. The type strain is H-5(T) (=CGMCC AS 1.6843(T)=NBRC 103919(T)).