Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis.

Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women’s health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. ABSTRACT Gardnerella is the primary pathogenic bacterial genus present in the polymicrobial condition known as bacterial vaginosis (BV). Despite BV’s high prevalence and associated chronic and acute women’s health impacts, the Gardnerella pangenome is largely uncharacterized at both the genetic and functional metabolic levels. Here, we used genome-scale metabolic models to characterize in silico the Gardnerella pangenome metabolic content. We also assessed the metabolic functional capacity in a BV-positive cervicovaginal fluid context. The metabolic capacity varied widely across the pangenome, with 38.15% of all reactions being core to the genus, compared to 49.60% of reactions identified as being unique to a smaller subset of species. We identified 57 essential genes across the pangenome via in silico gene essentiality screens within two simulated vaginal metabolic environments. Four genes, gpsA, fas, suhB, and psd, were identified as core essential genes critical for the metabolic function of all analyzed bacterial species of the Gardnerella genus. Further understanding these core essential metabolic functions could inform novel therapeutic strategies to treat BV. Machine learning applied to simulated metabolic network flux distributions showed limited clustering based on the sample isolation source, which further supports the presence of extensive core metabolic functionality across this genus. These data represent the first metabolic modeling of the Gardnerella pangenome and illustrate strain-specific interactions with the vaginal metabolic environment across the pangenome. IMPORTANCE Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women’s health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. In this paper, we use metabolic models, which are a computational representation of the possible functional metabolism of an organism, to investigate metabolic conservation, gene essentiality, and pathway utilization across 110 Gardnerella strains. These models allow us to investigate in silico how strains may differ with respect to their metabolic interactions with the vaginal-host environment.

Metabolic Network Models of the Gardnerella Pangenome Identify Key Interactions with the Vaginal Environment

Klebsiella pneumoniae is an opportunistic pathogen, which concerns public health systems worldwide, as multiple antibiotic‐resistant strains are frequent. One of its pathogenicity factors is the Type VI Secretion System (T6SS), a macromolecular complex assembled through the bacterial membranes. T6SS injects effector proteins inside target cells. Such effectors confer competitive advantages or modulate the target cell signaling and metabolism to favor bacterial infection. The VgrG protein is a T6SS core component. It may present a variable C‐terminal domain carrying an additional effector function. Kp52.145 genome encodes three VgrG proteins, one of them with a C‐terminal extension (VgrG4‐CTD). VgrG4‐CTD is 138 amino acids long, does not contain domains of known function, but is conserved in some Klebsiella, and non‐Klebsiella species. To get insights into its function, recombinant VgrG4‐CTD was used in pulldown experiments to capture ligands from macrophages and lung epithelial cells. A total of 254 proteins were identified: most of them are ribosomal proteins. Cytoskeleton‐associated and proteins involved in the phagosome maturation pathway were also identified. We further showed that VgrG4‐CTD binds actin and induces actin remodeling in macrophages. This study presents novel clues on the role of K. pneumoniae T6SS in pathogenesis.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/prot.26344

The C‐terminal extension of VgrG4 from Klebsiella pneumoniae remodels host cell microfilaments

Eat, prey, love: Pathogen-mediated subversion of lysosomal biology.

To analyze the hotspots and trends in artificial intelligence (AI) research in the field of cataracts. 

pdf/hotspots-and-trends-of-artificial-intelligence-in-the-field-4liksmnrkt.pdf

Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis

Background: Cataract is the leading cause of blindness worldwide. In order to achieve large-scale cataract screening and remarkable performance, several studies have applied artificial intelligence (AI) to cataract detection based on fundus images. However, the fundus images they used are original from normal optical circumstances, which is less impractical due to the existence of poor-quality fundus images for inappropriate optical conditions in actual scenarios. Furthermore, these poor-quality images are easily mistaken as cataracts because both show fuzzy imaging characteristics, which may decline the performance of cataract detection. Therefore, we aimed to develop and validate an antiinterference AI model for rapid and efficient diagnosis based on fundus images. Materials and Methods: The datasets (including both cataract and noncataract labels) were derived from the Chinese PLA general hospital. The antiinterference AI model consisted of two AI submodules, a quality recognition model for cataract labeling and a convolutional neural networks-based model for cataract classification. The quality recognition model was performed to distinguish poor-quality images from normal-quality images and further generate the pseudo labels related to image quality for noncataract. Through this, the original binary-class label (cataract and noncataract) was adjusted to three categories (cataract, noncataract with normal-quality images, and noncataract with poor-quality images), which could be used to guide the model to distinguish cataract from suspected cataract fundus images. In the cataract classification stage, the convolutional-neural-network-based model was proposed to classify cataracts based on the label of the previous stage. The performance of the model was internally validated and externally tested in real-world settings, and the evaluation indicators included area under the receiver operating curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Results: In the internal and external validation, the antiinterference AI model showed robust performance in cataract diagnosis (three classifications with AUCs >91%, ACCs >84%, SENs >71%, and SPEs >89%). Compared with the model that was trained on the binary-class label, the antiinterference cataract model improved its performance by 10%. Conclusion: We proposed an efficient antiinterference AI model for cataract diagnosis, which could achieve accurate cataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Introduction.Shigella flexneri is an intracellular bacterial pathogen that utilizes a type III secretion apparatus to inject effector proteins into host cells.Hypothesis/Gap Statement. The T3SS effector IpaH4.5 is important for the virulence of Shigella.Aim. This study aimed to elucidate the molecular mechanism and host target of the IpaH4.5 as well as its roles in S. flexneri infection.Methodology. The GAP assay was used to identify substrate Rab GTPases of IpaH4.5. A coimmunoprecipitation assay was applied to identify the interaction of Rab GTPases with IpaH4.5. A confocal microscopy analysis was used to assess the effects of IpaH4.5 on mannose 6-phosphate receptor (MPR) trafficking. To identify the effects of IpaH4.5 GAP activity on the activity of lysosomal cathepsin B, the Magic Red-RR assay was used. Finally, the intracellular persistence assay was used to identify IpaH4.5 GAP activity in S. flexneri intracellular growth.Results. We found that the effector IpaH4.5 disrupts MPR trafficking and lysosomal function, thereby counteracting host lysosomal degradation. IpaH4.5 harbours TBC-like dual-finger motifs and exhibits potent RabGAP activities towards Rab31. IpaH4.5 disrupts the transport of the cation-dependent mannose 6-phosphate receptor (CD-MPR) from the Golgi to the endosome by targeting Rab31, thereby attenuating lysosomal function. As a result, the intracellular persistence of S. flexneri requires IpaH4.5 TBC-like GAP activity to mediate bacterial escape from host lysosome-mediated elimination.Conclusion. We identified an unknown function of IpaH4.5 and its potential role in S. flexneri infection.

Shigella escapes lysosomal degradation through inactivation of Rab31 by IpaH4.5.

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Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Shigella escapes lysosomal degradation through inactivation of Rab31 by IpaH4.5.