Motivation: Predicting gene-disease associations (GDAs) is the problem to determine which gene is associated with a disease. GDA prediction can be framed as a ranking problem where genes are ranked for a query disease, based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e., query entities must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. Results: We developed INDIGENA, an inductive disease-gene association method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses graph embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known gene-disease associations. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over the inductive semantic similarity baseline measures, and reach a performance similar to transductive methods for predicting gene-disease associations while being more general. Availability and Implementation: https://github.com/bio-ontology-research-group/indigena
COVID-19 vaccine-induced protection declines over time. This waning of immunity has been described in modelling as a lower level of protection. This study incorporated fine-scale vaccine waning into modelling to predict the next surge of the Omicron variant of the SARS-CoV-2 virus. In Hong Kong, the Omicron subvariant BA.2 caused a significant epidemic wave between February and April 2022, which triggered high vaccination rates. About half a year later, a second outbreak, dominated by a combination of BA.2, BA.4 and BA.5 subvariants, began to spread. We developed mathematical equations to formulate continuous changes in vaccine boosting and waning based on empirical serological data. These equations were incorporated into a multi-strain discrete-time Susceptible-Exposed-Infectious-Removed model. The daily number of reported cases during the first Omicron outbreak, with daily vaccination rates, the population mobility index and daily average temperature, were used to train the model. The model successfully predicted the size and timing of the second surge and the variant replacement by BA.4/5. It estimated 655,893 cumulative reported cases from June 1, 2022 to 31 October 2022, which was only 2.69% fewer than the observed cumulative number of 674,008. The model projected that increased vaccine protection (by larger vaccine coverage or no vaccine waning) would reduce the size of the second surge of BA.2 infections substantially but would allow more subsequent BA.4/5 infections. Increased vaccine coverage or greater vaccine protection can reduce the infection rate during certain periods when the immune-escape variants co-circulate; however, new immune-escape variants spread more by out-competing the previous strain.
Ananya Joshi, George Khoury, Christodoulas Floudas
Alzheimer's disease is characterized by dangerous amyloid plaques formed by deposits of the protein Beta-Amyloid aggregates in the brain. The specific amino acid sequence that is responsible for the aggregates of Beta-Amyloid is lys-leu-val-phe-phe (KLVFF). KLVFF aggregation inhibitors, which we design in this paper, prevent KLVFF from binding with itself to form oligomers or fibrils (and eventually plaques) that cause neuronal death. Our binder-blocker peptides are designed such that, on one side, they bind strongly to KLVFF, and on the other side, they disrupt critical interactions, thus preventing aggregation. Our methods use optimization techniques and molecular simulations and identify 10 candidate sequences for trial of the 3.2 million possible sequences. This approach for inhibitor identification can be generalized to other diseases characterized by protein aggregation, such as Parkinson's, Huntington's, and prion diseases.
Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing. Machine learning approaches accelerate this process by leveraging biological knowledge represented in ontologies and the structure of knowledge graphs. Still, many existing works overlook ontologies explicitly representing diseases, missing causal and semantic relationships between them. The gene-disease association problem naturally frames itself as a link prediction task, where embedding algorithms directly predict associations by exploring the structure and properties of the knowledge graph. Some works frame it as a node-pair classification task, combining embedding algorithms with traditional machine learning algorithms. This strategy aligns with the logic of a machine learning pipeline. However, the use of negative examples and the lack of validated gene-disease associations to train embedding models may constrain its effectiveness. This work introduces a novel framework for comparing the performance of link prediction versus node-pair classification tasks, analyses the performance of state of the art gene-disease association approaches, and compares the different order-based formalizations of gene-disease association prediction. It also evaluates the impact of the semantic richness through a disease-specific ontology and additional links between ontologies. The framework involves five steps: data splitting, knowledge graph integration, embedding, modeling and prediction, and method evaluation. Results show that enriching the semantic representation of diseases slightly improves performance, while additional links generate a greater impact. Link prediction methods better explore the semantic richness encoded in knowledge graphs. Although node-pair classification methods identify all true positives, link prediction methods outperform overall.
In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed. Our proposed model leverages MTL with four different deep learning models such as 2D CNN, ResNet50, MobileNet and Densenet to extract relevant features from the lung sound recordings. The ICBHI 2017 Respiratory Sound Database was employed in the current study. The MTL for MobileNet model performed better than the other models considered, with an accuracy of74\% for lung sound analysis and 91\% for lung diseases classification. Results of the experimentation demonstrate the efficacy of our approach in classifying both lung sounds and lung diseases concurrently. In this study,using the demographic data of the patients from the database, risk level computation for Chronic Obstructive Pulmonary Disease is also carried out. For this computation, three machine learning algorithms namely Logistic Regression, SVM and Random Forest classifierswere employed. Among these ML algorithms, the Random Forest classifier had the highest accuracy of 92\%.This work helps in considerably reducing the physician's burden of not just diagnosing the pathology but also effectively communicating to the patient about the possible causes or outcomes.
Paridhi Mundra, Manik Sharma, Yashwardhan Chaudhuri
et al.
As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care. There is a need for improved diagnostic methods for immediate medical assessments for optimal patient outcomes. This paper introduces VoxMed, a UI-assisted one-step classifier that uses digital stethoscope recordings to diagnose respiratory diseases. It employs an Audio Spectrogram Transformer(AST) for feature extraction and a 1-D CNN-based architecture to classify respiratory diseases, offering professionals information regarding their patients respiratory health in seconds. We use the ICBHI dataset, which includes stethoscope recordings collected from patients in Greece and Portugal, to classify respiratory diseases. GitHub repository: https://github.com/Sample-User131001/VoxMed
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.
Montoya-Urrego D, Velasco-Castaño JJ, Quintero Velez JC
et al.
Daniela Montoya-Urrego,1 Juan José Velasco-Castaño,1 Juan C Quintero Velez,1– 3 J Natalia Jiménez Quiceno1 1Grupo de Investigación en Microbiología Básica y Aplicada (MICROBA), Escuela de Microbiología, Universidad de Antioquia, Medellín, Colombia; 2Grupo de Investigación Ciencias Veterinarias Centauro, Facultad de Ciencias Agrarias, Universidad de Antioquia, Medellín, Colombia; 3Grupo de Epidemiología, Facultad Nacional de Salud Pública, Universidad de Antioquia, Medellín, ColombiaCorrespondence: J Natalia Jiménez Quiceno, Universidad de Antioquia, Escuela de Microbiología, Calle 67 No. 53-108, Medellín, Antioquia, 050010, Colombia, Tel +57 604 219 54 97 ; +574-219-5481, Email jnatalia.jimenez@udea.edu.coPurpose: The lack of knowledge and the excessive and inappropriate use of antibiotics are some of the causes of bacterial resistance. Hemodialysis patients have a high consumption of antibiotics and are constantly cared by their household contacts. This population circulates between hospital and community and are a model to study knowledge regarding bacterial resistance and antibiotic use in these settings. This study describes the knowledge, attitudes and practices (KAP) about antibiotic use and bacterial resistance in hemodialysis patients and their household contacts in Medellín-Colombia.Patients and Methods: A cross-sectional descriptive study was conducted on hemodialysis patients from a renal unit associated with a hospital in Medellín-Colombia, and their household contacts between May 2019 and March 2020. A KAP instrument was applied to participants during home visits. The KAP regarding antibiotic use were characterized, and a content analysis of open questions was made.Results: A total of 35 hemodialysis patients and 95 of their household contacts were included. Of participants, 83.1% (108/130) did not correctly identify the situations in which antibiotics should be used. Likewise, a gap in knowledge about antibacterial resistance was evidenced thanks to the emerging categories in content analysis. Regarding attitudes, 36.9% (48/130) of the participants discontinued antibiotic treatment when they felt better. Additionally, 43.8% (57/130) agree to keep antibiotics in their home. Finally, it was found that it is usual for pharmacists and family members to recommend or sell antibiotics without prescription; likewise, pharmacies were the most popular place to acquire these medications.Conclusion: This study identified gaps in KAP regarding the use of antibiotics and bacterial resistance in hemodialysis patients and their household contacts. This allows focusing education strategies in this regard, in order to increase awareness about the correct use of antibiotics and the consequences of bacterial resistance and to improve prevention actions in this vulnerable population.Keywords: KAP, antibiotics, bacterial resistance
We numerically study disease dynamics that lead to the disease switching from one host species to another, resulting in diseases gaining the ability to infect, e.g., humans. Unlike previous studies that focused on branching processes starting with the first infected humans, we begin by considering a disease pathogen that initially cannot infect humans. We model the entire process, starting from an infection in the animal population, including mutations that eventually enable the disease to cause an epidemic outbreak in the human population. We use an SIR model on a network consisting of 132 dog and 1320 human nodes, with a single parameter representing the gene of the pathogen. We use numerical large-deviation techniques, specifically the $1/t$ Wang-Landau algorithm, to calculate the potentially very small probability of the host switching event. With this approach we are able to resolve probabilities as small as $10^{-120}$. Additionally the $1/t$ Wang-Landau algorithm allows us to obtain the complete probability density function $P(C)$ of the cumulative fraction $C$ of infected humans, which is an indicator for the severity of the disease in the human population. We also calculate correlations of $C$ with selected quantities $q$ that characterize the outbreak. Due to the application of the rare-event algorithm, this is possible for the entire range of $C$ values.
Gauri Naik, Nandini Narvekar, Dimple Agarwal
et al.
Eye diseases have posed significant challenges for decades, but advancements in technology have opened new avenues for their detection and treatment. Machine learning and deep learning algorithms have become instrumental in this domain, particularly when combined with Optical Coherent Technology (OCT) imaging. We propose a novel method for efficient detection of eye diseases from OCT images. Our technique enables the classification of patients into disease free (normal eyes) or affected by specific conditions such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), or Drusen. In this work, we introduce an end to end web application that utilizes machine learning and deep learning techniques for efficient eye disease prediction. The application allows patients to submit their raw OCT scanned images, which undergo segmentation using a trained custom UNet model. The segmented images are then fed into an ensemble model, comprising InceptionV3 and Xception networks, enhanced with a self attention layer. This self attention approach leverages the feature maps of individual models to achieve improved classification accuracy. The ensemble model's output is aggregated to predict and classify various eye diseases. Extensive experimentation and optimization have been conducted to ensure the application's efficiency and optimal performance. Our results demonstrate the effectiveness of the proposed approach in accurate eye disease prediction. The developed web application holds significant potential for early detection and timely intervention, thereby contributing to improved eye healthcare outcomes.
Minh-Chau Nguyen, Peter Bonnaud, Rayane Dibsy
et al.
Screening of unknown particles, including viruses and nanoparticles, is key in medicine, industry and pollutant determination. However, existing techniques require sample a priori knowledge or modification (e.g. fluorescence). Here we introduce RYtov MIcroscopy for Nanoparticles Identification (RYMINI), a noninvasive and non-destructive optical approach that is combining holographic labelfree 3D tracking and high-sensitivity quantitative phase imaging into a compact optical setup. Dedicated to the characterization of nano-objects in solution, it is compatible with highly demanding environments such as level-3 biological laboratories. Metrological characterization has been performed at the level of each single object on both absorbing and transparent particles as well as on infectious HIV-1, SARS-CoV-2 and extracellular vesicles in solution. We demonstrate the capability of RYMINI to determine the nature, concentration, size, complex refractive index and mass of each single particle. We discuss the application of the method in unknown solution without requiring any knowledge or model of the particles' response. It paves the way to label-free nano-object identification in terra incognita.
The healthcare industry generates enormous amounts of complex clinical data that make the prediction of disease detection a complicated process. In medical informatics, making effective and efficient decisions is very important. Data Mining (DM) techniques are mainly used to identify and extract hidden patterns and interesting knowledge to diagnose and predict diseases in medical datasets. Nowadays, heart disease is considered one of the most important problems in the healthcare field. Therefore, early diagnosis leads to a reduction in deaths. DM techniques have proven highly effective for predicting and diagnosing heart diseases. This work utilizes the classification algorithms with a medical dataset of heart disease; namely, J48, Random Forest, and Naïve Bayes to discover the accuracy of their performance. We also examine the impact of the feature selection method. A comparative and analysis study was performed to determine the best technique using Waikato Environment for Knowledge Analysis (Weka) software, version 3.8.6. The performance of the utilized algorithms was evaluated using standard metrics such as accuracy, sensitivity and specificity. The importance of using classification techniques for heart disease diagnosis has been highlighted. We also reduced the number of attributes in the dataset, which showed a significant improvement in prediction accuracy. The results indicate that the best algorithm for predicting heart disease was Random Forest with an accuracy of 99.24%.
Three or four intramuscular doses of the inactivated human rabies virus vaccines are needed for pre- or post-exposure prophylaxis in humans. This procedure has made a great contribution to prevent human rabies deaths, which bring huge economic burdens in developing countries. Herein, a recombinant adeno-associated virus serotype 9, AAV9-RABVG, harbouring a RABV G gene, was generated to serve as a single dose rabies vaccine candidate. The RABV G protein was stably expressed in the 293T cells infected with AAV9-RABVG. A single dose of 2 × 1011 v.p. of AAV9-RABVG induced robust and long-term positive seroconversions in BALB/c mice with a 100% survival from a lethal RABV challenge. In Cynomolgus Macaques vaccinated with a single dose of 1 × 1013 v.p. of AAV9-RABVG, the titres of rabies VNAs increased remarkably from 2 weeks after immunity, and maintained over 31.525 IU/ml at 52 weeks. More DCs were activated significantly for efficient antigen presentations of RABV G protein, and more B cells were activated to be responsible for antibody responses. Significantly more RABV G specific IFN-γ-secreting CD4+ and CD8+ T cells, and IL-4-secreting CD4+ T cells were activated, and significantly higher levels of IL-2, IFN-γ, IL-4, and IL-10 were secreted to aid immune responses. Overall, the AAV9-RABVG was a single dose rabies vaccine candidate with great promising by inducing robust, long-term humoral responses and both Th1 and Th2 cell-mediated immune responses in mice and non-human primates.
Suipeng Chen,1,* Mengjiao Kuang,1,* Ying Qu,1,2 Shirui Huang,1 Binbin Gong,1 Suzhen Lin,1 Huiyan Wang,1 Guiye Wang,1 Hongqun Tao,1 Jian Yu,1 Zuqin Yang,3 Minghua Jiang,1 Qipeng Xie1 1Department of Laboratory Medicine, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, People’s Republic of China; 2Department of Clinical Laboratory, Wenzhou People’s Hospital, The Third Affiliated Hospital of Shanghai University, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, Zhejiang, 325035, People’s Republic of China; 3Newborn Department of Pediatrics, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qipeng Xie, Department of Laboratory Medicine, The Second Affiliated Hospital & Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, People’s Republic of China, Tel +86 15157787159, Email pandon2002@163.comObjective: Sepsis remains a major cause of neonatal death. To better characterize the inflammatory response during neonatal sepsis, we compared the differences in serum cytokines and chemokines between full-term neonates with sepsis and without infection.Methods: We enrolled 40 full-term neonates with sepsis and 26 full-term neonates without infection as controls between October 2016 and June 2018. Forty cytokines /chemokines in serum were analyzed using the Luminex Bead Immunoassay System.Results: Our results showed that serum IL-6, IL-8, TNF-α, IL-1β, MIF, CXCL13, CXCL1, CXCL2, CXCL5, CXCL6, CXCL16, CCL27, CCL2, CCL8, CCL3, CCL20, CCL23, and CX3CL1 levels were significantly increased in neonates with sepsis compared to those in the control group (all p< 0.05). The levels of serum CCL20, and IL-17 were higher in late-onset sepsis (LOS) than those in early-onset sepsis (EOS) (all p< 0.05). Conversely, serum CXCL16 was lower in LOS than that in EOS (p< 0.05).Conclusion: Our findings revealed that excessive pro-inflammatory cytokines might be involved in neonatal sepsis. In addition, chemokines significantly increased the recruitment of immune cells after infection to participate in the anti-infection defense of neonates, but this could lead to damage.Keywords: neonatal sepsis, cytokines, chemokines
Aim. The use of erythrocyte sedimentation rate (ESR) in coronavirus disease 2019 (COVID-19) to determine disease severity and prognosis is limited. This study aimed to interrogate the diagnostic and prognostic role of ESR compared to other acute-phase reactants. Method. This retrospective cross-sectional study included 493 confirmed and hospitalized adult COVID-19 patients. Pneumonia, radiological severity, oxygen, intensive care requirements, mortality, ESR, and other acute-phase reactant values were recorded. Logistic regression and ROC analysis identified the effect of ESR on mortality and the sensitivity and specificity of the optimal cutoff values of ESR for the prediction of pneumonia, intensive care needs, and mortality and compared these with values for CRP. Results. Of patients, 346 (70.2%) had pneumonia, 98 (19.9%) required intensive care, 183 (37.1%) required oxygen support, and 62 (12.6%) died. ESR data were obtained for 278 patients. Among patients, 80.2% had ESR above 20 mm/h, with a median value of 53 (interquartile range: 49). ESR was higher among those with pneumonia (p<0.001), requiring oxygen (p<0.001), and requiring intensive care (p=0.003) compared to those without these, and in exitus patients (p=0.043) compared to survivors. Logistic regression analysis identified that ESR did not impact mortality. ROC analysis found the AUC, cutoff, sensitivity, and specificity results of ESR for pneumonia were 0.827, 37 mm/h, 77%, and 78%; for intensive care were 0.625, 50 mm/h, 74%, and 52; and for mortality were 0.606, 51 mm/h, 71%, and 49%, respectively. However, ROC analysis values for CRP were superior to ESR for all these categories. Conclusion. ESR increased in COVID-19 patients in the presence of pneumonia and severe disease; however, it was not prognostic. Sensitivity and specificity values for pneumonia, intensive care requirements, and mortality were lower than those for CRP.
We propose a system to visualize the chirality of the protein in brains, which would be helpful to diagnose early neurological degenerative diseases in vivo. These neurological degenerative diseases often occur along with some mark proteins. By nanoparticle instilling and metamaterial technique, the chiral effect of the mark proteins is assumed to be manifest in microwave regime. Therefore, by detecting the transmission of cross-polarization, we could detect the chirality that rotates the microwave polarization angle. We developed a numerical method to simulate the electromagnetic response upon chiral (bi-isotropic) material. Then a numerical experiment was conduct with a numerical head phantom. A map of cross-polarized transmission magnitude can be reached by sweeping the antenna pair. The imaging results matches well with the distribution of chiral materials. It suggests that the proposed method would be capable of in vivo imaging of neurological degenerative disease using microwaves.