Pseudomonas aeruginosa is an opportunistic pathogen that frequently causes severe bloodstream and respiratory infections, yet the interactions between this bacterium and the innate immune system remain poorly investigated. In this study we identified transthyretin, the transporter protein of thyroid hormone and retinol, as a novel binding partner of the bacterium. We show that transthyretin binds to lipopolysaccharide via lipid A. Transthyretin binding induces the agglutination of transthyretin-bacteria complexes and a reduction in bacterial viability. Mapping studies reveal that the N-terminal region of transthyretin mediates bacterial interaction, and a synthetic peptide derived from this domain exhibits potent bactericidal activity against a broad collection of P. aeruginosa isolates as well as other Gram-negative bacteria by disrupting membrane integrity. These findings identify transthyretin as an endogenous antimicrobial factor and uncover cryptic antimicrobial activity within its N-terminal region. Beyond extending the functional repertoire of transthyretin, these results suggest a novel role for this protein in innate defense.
Kristen J. Davis, Kerri-Lynn Sheahan, Connor S. Murphy
et al.
Abstract Type III secretion systems (T3SS) play critical roles in disease for many Gram-negative pathogens. For Yersinia pseudotuberculosis ( Yptb ), the machinery delivers the Yop bacterial effectors into host cells, misregulating cytoskeletal components and sabotaging immune defenses. Although the structural details of T3SS assembly have been extensively studied, the intimate interactions between T3SS components and the target host plasma membrane are poorly characterized. A previous RNA interference (RNAi) study identified the GPI-linked surface protein CD59 as a critical host component that supports T3SS translocation pore formation and effector movement into the host cytosol. We show here that both depletion and increased levels of CD59 reduced Yptb T3SS pore formation and Yop translocation into host target cells. Enzymatic removal of all GPI-linked surface proteins from wild-type cells prior to Yptb infection had no effect on T3SS function, indicating that neither CD59 nor other GPI-linked proteins were receptors for the T3SS machinery. Consistent with its importance in supporting the T3SS function, efficient bacterial effector-mediated focal adhesion disassembly was dependent on CD59. Depletion of CD59 interfered with the rearrangement of the lipid raft marker GM1 that occurs after Yptb adhesion to host cells, indicating that CD59 supports lipid microdomain dynamics. Consistent with a role in modulating lipid composition, the loss of CD59 resulted in the accumulation of phosphatidylcholine lipids and alterations in both fatty acid chain lengths and fatty acid saturation levels. This work points to a role for CD59 in supporting efficient T3SS function by maintaining plasma membrane dynamics necessary to support efficient T3SS function.
Leveraging multiple partially labeled datasets to train a model for multiple retinal disease screening reduces the reliance on fully annotated datasets, but remains challenging due to significant domain shifts across training datasets from various medical sites, and the label absent issue for partial classes. To solve these challenges, we propose PSScreen, a novel Partially Supervised multiple retinal disease Screening model. Our PSScreen consists of two streams and one learns deterministic features and the other learns probabilistic features via uncertainty injection. Then, we leverage the textual guidance to decouple two types of features into disease-wise features and align them via feature distillation to boost the domain generalization ability. Meanwhile, we employ pseudo label consistency between two streams to address the label absent issue and introduce a self-distillation to transfer task-relevant semantics about known classes from the deterministic to the probabilistic stream to further enhance the detection performances. Experiments show that our PSScreen significantly enhances the detection performances on six retinal diseases and the normal state averagely and achieves state-of-the-art results on both in-domain and out-of-domain datasets. Codes are available at https://github.com/boyiZheng99/PSScreen.
Accurate healthcare prediction is essential for improving patient outcomes. Existing work primarily leverages advanced frameworks like attention or graph networks to capture the intricate collaborative (CO) signals in electronic health records. However, prediction for rare diseases remains challenging due to limited co-occurrence and inadequately tailored approaches. To address this issue, this paper proposes UDC, a novel method that unveils discrete clues to bridge consistent textual knowledge and CO signals within a unified semantic space, thereby enriching the representation semantics of rare diseases. Specifically, we focus on addressing two key sub-problems: (1) acquiring distinguishable discrete encodings for precise disease representation and (2) achieving semantic alignment between textual knowledge and the CO signals at the code level. For the first sub-problem, we refine the standard vector quantized process to include condition awareness. Additionally, we develop an advanced contrastive approach in the decoding stage, leveraging synthetic and mixed-domain targets as hard negatives to enrich the perceptibility of the reconstructed representation for downstream tasks. For the second sub-problem, we introduce a novel codebook update strategy using co-teacher distillation. This approach facilitates bidirectional supervision between textual knowledge and CO signals, thereby aligning semantically equivalent information in a shared discrete latent space. Extensive experiments on three datasets demonstrate our superiority.
In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeqDenoise algorithm combines CNN and RNN to effectively remove the sequencing noise, and improves the signal-to-noise ratio by 9.4 dB. We screened 17 key features by feature engineering, and constructed an integrated learning model to predict disease-causing genes with 94.3% accuracy. We successfully identified 57 new candidate disease-causing genes in a cardiovascular disease cohort validation, and detected 3 missed variants in clinical applications. The method significantly outperforms existing tools and provides strong support for accurate diagnosis of genetic diseases.
We propose an epidemic model for the spread of vector-borne diseases. The model, which is built extending the classical susceptible-infected-susceptible model, accounts for two populations -- humans and vectors -- and for cross-contagion between the two species, whereby humans become infected upon interaction with carrier vectors, and vectors become carriers after interaction with infected humans. We formulate the model as a system of ordinary differential equations and leverage monotone systems theory to rigorously characterize the epidemic dynamics. Specifically, we characterize the global asymptotic behavior of the disease, determining conditions for quick eradication of the disease (i.e., for which all trajectories converge to a disease-free equilibrium), or convergence to a (unique) endemic equilibrium. Then, we incorporate two control actions: namely, vector control and incentives to adopt protection measures. Using the derived mathematical tools, we assess the impact of these two control actions and determine the optimal control policy.
Guilherme S. Arruda, Katie Morris, Augusto Martins
et al.
The recent development of drugs able to mitigate neurodegenerative diseases has created an urgent need for novel diagnostics. Although biomarker detection directly in patients' blood is now possible, low-cost point-of-care tests remain a challenge, because relevant biomarkers, especially amyloid-β(A$β$) peptides, are small, they occur at very low concentrations, and detecting a single marker is insufficient. Here, we introduce an optical biosensor based on a nanopillar structure that employs a gold nanoparticle amplification strategy. The sensor is able to detect 20 pg/ml of A$β$42 and A$β$40 in undiluted serum, which is the clinically required level. We also show the detection of the A$β$42 and A$β$40 peptides in the same channel, which is highly relevant for assessing disease progress and opens a route towards multiplexing. Alongside their simplicity and portability, these nanotechnology innovations make a major contribution to the ability to detect and monitor the progression of neurodegenerative diseases such as Alzheimer's.
BackgroundColon adenocarcinoma (COAD) is the most frequently occurring type of colon cancer. Cancer-associated fibroblasts (CAFs) are pivotal in facilitating tumor growth and metastasis; however, their specific role in COAD is not yet fully understood. This research utilizes single-cell RNA sequencing (scRNA-seq) to identify and validate gene markers linked to the malignancy of CAFs.MethodsScRNA-seq data was downloaded from a database and subjected to quality control, dimensionality reduction, clustering, cell annotation, cell communication analysis, and enrichment analysis, specifically focusing on fibroblasts in tumor tissues compared to normal tissues. Fibroblast subsets were isolated, dimensionally reduced, and clustered, then combined with copy number variation (CNV) inference and pseudotime trajectory analysis to identify genes related to malignancy. A Cox regression model was constructed based on these genes, incorporating LASSO analysis, nomogram construction, and validation.Subsequently, we established two FNDC5-knockdown cell lines and utilized colony formation and transwell assays to investigate the impact of FNDC5 on cellular biological behaviors.ResultsUsing scRNA-seq data, we analyzed 8,911 cells from normal and tumor samples, identifying six distinct cell types. Cell communication analysis highlighted interactions between these cell types mediated by ligands and receptors. CNV analysis classified CAFs into three groups based on malignancy levels. Pseudo-time analysis identified 622 pseudotime-related genes and generated a forest plot using univariate Cox regression. Lasso regression identified the independent prognostic gene FNDC5, which was visualized in a nomogram. Kaplan-Meier survival analysis confirmed the prognostic value of FNDC5, showing associations with T stage and distant metastasis. In vitro experiment results demonstrated a strong association between FNDC5 expression levels and the proliferative, migratory, and invasive abilities of colon cancer cells.ConclusionWe developed a risk model for genes related to the malignancy of CAFs and identified FNDC5 as a potential therapeutic target for COAD.
The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transformation has given rise to concerns about the fairness of AI, which disproportionately affects certain groups, particularly patients from underprivileged populations. Recently, a number of methods and large-scale datasets have been proposed to address group performance disparities. Although these methods have shown effectiveness in disease classification tasks, they may fall short in ensuring fair prediction of disease progression, mainly because of limited longitudinal data with diverse demographics available for training a robust and equitable prediction model. In this paper, we introduce TransFair to enhance demographic fairness in progression prediction for ocular diseases. TransFair aims to transfer a fairness-enhanced disease classification model to the task of progression prediction with fairness preserved. Specifically, we train a fair EfficientNet, termed FairEN, equipped with a fairness-aware attention mechanism using extensive data for ocular disease classification. Subsequently, this fair classification model is adapted to a fair progression prediction model through knowledge distillation, which aims to minimize the latent feature distances between the classification and progression prediction models. We evaluate FairEN and TransFair for fairness-enhanced ocular disease classification and progression prediction using both two-dimensional (2D) and 3D retinal images. Extensive experiments and comparisons with models with and without considering fairness learning show that TransFair effectively enhances demographic equity in predicting ocular disease progression.
Hysteroscopy enables direct visualization of morphological changes in the endometrium, serving as an important means for screening, diagnosing, and treating intrauterine lesions. Accurate identification of the benign or malignant nature of diseases is crucial. However, the complexity and variability of uterine morphology increase the difficulty of identification, leading to missed diagnoses and misdiagnoses, often requiring the expertise of experienced gynecologists and pathologists. Here, we provide the video and image dataset of hysteroscopic examinations conducted at Beijing Chaoyang Hospital, Capital Medical University (named the HS-CMU dataset), recording videos of 175 patients undergoing hysteroscopic surgery to explore the uterine cavity. These data were obtained using corresponding supporting software. From these videos, 3385 high-quality images from 8 categories were selected to form the HS-CMU dataset. These images were annotated by two experienced obstetricians and gynecologists using lableme software. We hope that this dataset can be used as an auxiliary tool for the diagnosis of intrauterine benign and malignant diseases.
Abnormalities in retinal fundus images may indicate certain pathologies such as diabetic retinopathy, hypertension, stroke, glaucoma, retinal macular edema, venous occlusion, and atherosclerosis, making the study and analysis of retinal images of great significance. In conventional medicine, the diagnosis of retina-related diseases relies on a physician's subjective assessment of the retinal fundus images, which is a time-consuming process and the accuracy is highly dependent on the physician's subjective experience. To this end, this paper proposes a fast, objective, and accurate method for the diagnosis of diseases related to retinal fundus images. This method is a multiclassification study of normal samples and 13 categories of disease samples on the STARE database, with a test set accuracy of 99.96%. Compared with other studies, our method achieved the highest accuracy. This study innovatively propose Segmentation-based Vascular Enhancement(SVE). After comparing the classification performances of the deep learning models of SVE images, original images and Smooth Grad-CAM ++ images, we extracted the deep learning features and traditional features of the SVE images and input them into nine meta learners for classification. The results shows that our proposed UNet-SVE-VGG-MLP model has the optimal performance for classifying diseases related to retinal fundus images on the STARE database, with a overall accuracy of 99.96% and a weighted AUC of 99.98% for the 14 categories on test dataset. This method can be used to realize rapid, objective, and accurate classification and diagnosis of retinal fundus image related diseases.
Decline of immunity is a phenomenon characterized by immunocompromised host and plays a crucial role in the epidemiology of emerging infectious diseases (EIDs) such as COVID-19. In this paper, we propose an age-structured model with vaccination and reinfection of immune individuals. We prove that the disease-free equilibrium of the model undergoes backward and forward transcritical bifurcations at the critical value of the basic reproduction number for different values of parameters. We illustrate the results by numerical computations, and also find that the endemic equilibrium exhibits a saddle-node bifurcation on the extended branch of the forward transcritical bifurcation. These results allow us to understand the interplay between the decline of immunity and EIDs, and are able to provide strategies for mitigating the impact of EIDs on global health.
Rita Szoke-Kovacs, Rita Szoke-Kovacs, Sophie Khakoo
et al.
CD1 isoforms are MHC class I-like molecules that present lipid-antigens to T cells and have been associated with a variety of immune responses. The lipid repertoire bound and presented by the four CD1 isoforms may be influenced by factors such as the cellular lipidome, subcellular microenvironment, and the properties of the binding pocket. In this study, by shotgun mass spectrometry, we performed a comprehensive lipidomic analysis of soluble CD1 molecules. We identified 1040 lipids, of which 293 were present in all isoforms. Comparative analysis revealed that the isoforms bind almost any cellular lipid.CD1a and CD1c closely mirrored the cellular lipidome, while CD1b and CD1d showed a preference for sphingolipids. Each CD1 isoform was found to have unique lipid species, suggesting some distinct roles in lipid presentation and immune responses. These findings contribute to our understanding of the role of CD1 system in immunity and could have implications for the development of lipid-based therapeutics.
Marina Amaral de Avila Machado, Sonja Gandhi-Banga, Sophie Gallo
et al.
Enhanced Passive Safety Surveillance (EPSS) was conducted for quadrivalent inactivated split-virion influenza vaccines (IIV4) in Germany (high dose [HD]) and Finland (standard dose [SD]) for the northern hemisphere (NH) 2022/23 influenza season. The primary objective was to assess adverse events following immunization (AEFI) occurring ≤7 days post-vaccination. In each country, the EPSS was conducted at the beginning of the NH influenza season. Exposure information was documented using vaccination cards (VC), and AEFI were reported via an electronic data collection system or telephone. AEFI were assessed by seriousness and age group (Finland only). The vaccinee reporting rate (RR) was calculated as the number of vaccinees reporting ≥ 1 AEFI divided by the total vaccinees. In Germany, among 1041 vaccinees, there were 31 AEFI (ten vaccinees) during follow-up, including one serious AEFI. Of 16 AEFI (six vaccinees) with reported time of onset, 15 occurred ≤7 days post-vaccination (RR 0.58%, 95% confidence interval [CI] 0.21, 1.25), which was lower than the 2021/22 season (RR 1.88%, 95% CI: 1.10, 3.00). In Finland, among 1001 vaccinees, there were 142 AEFI (51 vaccinees) during follow-up, none of which were serious. Of 133 AEFI (48 vaccinees) with time of onset reported, all occurred ≤7 days post-vaccination (RR 4.80%, 95% CI: 3.56, 6.31), which was similar to the 2021/22 season (RR 4.90%, 95% CI: 3.65, 6.43). The EPSS for HD-IIV4 and for SD-IIV4 in the 2022/23 influenza season did not suggest any clinically relevant changes in safety beyond what is known/expected for IIV4s.
Mario A. Alarcón‐Sánchez, Julieta S. Becerra‐Ruiz, Celia Guerrero‐Velázquez
et al.
Abstract Objective This systematic review aimed to investigate the role of the C‐X3‐C motif ligand 1/chemokine receptor 1 C‐X3‐C motif (CX3CL1/CX3CR1) axis in the pathogenesis of periodontitis. Furthermore, as a secondary objective, we determine whether the CX3CL1/CX3CR1 axis could be considered complementary to clinical parameters to distinguish between periodontitis and rheumatoid arthritis (RA) and/or systemically healthy subjects. Methods The protocol used for this review was registered in OSF (10.17605/OSF.IO/KU8FJ). This study was designed following Preferred Reporting Items for Systematic Review and Meta‐Analysis guidelines. Records were identified using different search engines (PubMed/MEDLINE, Scopus, Science Direct, and Web of Science) from August 10, 2006, to September 15, 2023. The observational studies on human subjects diagnosed with periodontitis and RA and/or systemically healthy were selected to analyze CX3CL1 and CX3CR1 biomarkers. The methodological validity of the selected articles was assessed using NIH. Results Six articles were included. Biological samples (gingival crevicular fluid [GCF], saliva, gingival tissue biopsies, serum) from 379 subjects (n = 275 exposure group and n = 104 control group) were analyzed. Higher CX3CL1 and CX3CR1 chemokine levels were found in subjects with periodontitis and RA compared with periodontal and systemically healthy subjects. Conclusion Very few studies highlight the role of the CX3CL1/CX3CR1 axis in the pathogenesis of periodontitis; however, increased levels of these chemokines are observed in different biological samples (GCF, gingival tissue, saliva, and serum) from subjects with periodontitis and RA compared with their healthy controls. Future studies should focus on long‐term follow‐up of subjects and monitoring changes in cytokine levels before and after periodontal therapy to deduce an appropriate interval in health and disease conditions.
IntroductionThe complex and unresolved pathogenesis of schizophrenia has posed significant challenges to its diagnosis and treatment. While recent research has established a clear association between immune function and schizophrenia, the causal relationship between the two remains elusive.MethodsWe employed a bidirectional two-sample Mendelian randomization approach to investigate the causal relationship between schizophrenia and 731 immune cell traits by utilizing public GWAS data. We further validated the causal relationship between schizophrenia and six types of white cell measures.ResultsWe found the overall causal effects of schizophrenia on immune cell traits were significantly higher than the reverse ones (0.011 ± 0.049 vs 0.001 ± 0.016, p < 0.001), implying that disease may lead to an increase in immune cells by itself. We also identified four immune cell traits that may increase the risk of schizophrenia: CD11c+ monocyte %monocyte (odds ratio (OR): 1.06, 95% confidence interval (CI): 1.03~1.09, FDR = 0.027), CD11c+ CD62L- monocyte %monocyte (OR:1.06, 95% CI: 1.03~1.09, FDR = 0.027), CD25 on IgD+ CD38- naive B cell (OR:1.03, 95% CI:1.01~1.06, FDR = 0.042), and CD86 on monocyte (OR = 1.04, 95% CI:1.01~1.06, FDR = 0.042). However, we did not detect any significant causal effects of schizophrenia on immune cell traits. Using the white blood cell traits data, we identified that schizophrenia increases the lymphocyte counts (OR:1.03, 95%CI: 1.01-1.04, FDR = 0.007), total white blood cell counts (OR:1.02, 95%CI: 1.01-1.04, FDR = 0.021) and monocyte counts (OR:1.02, 95%CI: 1.00-1.03, FDR = 0.034). The lymphocyte counts were nominally associated with the risk of schizophrenia (OR:1.08,95%CI:1.01-1.16, P=0.019).DiscussionOur study found that the causal relationship between schizophrenia and the immune system is complex, enhancing our understanding of the role of immune regulation in the development of this disorder. These findings offer new insights for exploring diagnostic and therapeutic options for schizophrenia.
Zulqarnain Rashid, Amos A Folarin, Yatharth Ranjan
et al.
Digital Biomarkers and remote patient monitoring can provide valuable and timely insights into how a patient is coping with their condition (disease progression, treatment response, etc.), complementing treatment in traditional healthcare settings.Smartphones with embedded and connected sensors have immense potential for improving healthcare through various apps and mHealth (mobile health) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka, to support scalability, extensibility, security, privacy and quality of data. It provides support for study design and set-up, active (eg PROMs) and passive (eg. phone sensors, wearable devices and IoT) remote data collection capabilities with feature generation (eg. behavioural, environmental and physiological markers). The backend enables secure data transmission, and scalable solutions for data storage, management and data access. The platform has successfully collected longitudinal data for various cohorts in a number of disease areas including Multiple Sclerosis, Depression, Epilepsy, ADHD, Alzheimer, Autism and Lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base provides a modern open-source, community-driven solution for remote monitoring, data collection, and digital phenotyping of physical and mental health diseases. Clinicians can use digital biomarkers to augment their decision making for the prevention, personalisation and early intervention of disease.
The importance of indoor human mobility in the transmission dynamics of respiratory infectious diseases has been acknowledged. Previous studies have predominantly addressed a single type of mobility behavior such as queueing and a series of behaviors under specific scenarios. However, these studies ignore the abstraction of mobility behavior in various scenes and the critical examination of how these abstracted behaviors impact disease propagation. To address these problems, this study considers people's mobility behaviors in a general scenario, abstracting them into two main categories: crowding behavior, related to the spatial aspect, and stopping behavior, related to the temporal aspect. Accordingly, this study investigates their impacts on disease spreading and the impact of individual spatio-temporal distribution resulting from these mobility behaviors on epidemic transmission. First, a point of interest (POI) method is introduced to quantify the crowding-related spatial POI factors (i.e., the number of crowdings and the distance between crowdings) and stopping-related temporal POI factors (i.e., the number of stoppings and the duration of each stopping). Besides, a personal space determined with Voronoi diagrams is used to construct the individual spatio-temporal distribution factor. Second, two indicators (i.e., the daily number of new cases and the average exposure risk of people) are applied to quantify epidemic transmission. These indicators are derived from a fundamental model which accurately predicts disease transmission between moving individuals. Third, a set of 200 indoor scenarios is constructed and simulated to help determine variable values. Concurrently, the influences and underlying mechanisms of these behavioral factors on disease transmission are examined using structural equation modeling and causal inference modeling......