Brain structure ages-A new biomarker for multi-disease classification.

Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.

FedSPL: federated self-paced learning for privacy-preserving disease diagnosis.

The growing expansion of data availability in medical fields could help improve the performance of machine learning methods. However, with healthcare data, using multi-institutional datasets is challenging due to privacy and security concerns. Therefore, privacy-preserving machine learning methods are required. Thus, we use a federated learning model to train a shared global model, which is a central server that does not contain private data, and all clients maintain the sensitive data in their own institutions. The scattered training data are connected to improve model performance, while preserving data privacy. However, in the federated training procedure, data errors or noise can reduce learning performance. Therefore, we introduce the self-paced learning, which can effectively select high-confidence samples and drop high noisy samples to improve the performances of the training model and reduce the risk of data privacy leakage. We propose the federated self-paced learning (FedSPL), which combines the advantage of federated learning and self-paced learning. The proposed FedSPL model was evaluated on gene expression data distributed across different institutions where the privacy concerns must be considered. The results demonstrate that the proposed FedSPL model is secure, i.e. it does not expose the original record to other parties, and the computational overhead during training is acceptable. Compared with learning methods based on the local data of all parties, the proposed model can significantly improve the predicted F1-score by approximately 4.3%. We believe that the proposed method has the potential to benefit clinicians in gene selections and disease prognosis.

EXPERT: transfer learning-enabled context-aware microbial community classification.

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection.

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

Validation of GPT-4 for clinical event classification: A comparative analysis with ICD codes and human reviewers.

BACKGROUND AND AIM: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre-trained Transformer 4 (GPT-4) for this task and comparison with conventional methods remains unexplored. METHODS: We evaluated the performance of the GPT-4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code-based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers. RESULTS: GPT-4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT-4's accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT-4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT-4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8-9 h each. CONCLUSION: Our study indicates GPT-4 offers a reliable, cost-efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high-performance AI models in clinical data processing.

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

BACKGROUND: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease. OBJECTIVE: The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet. METHOD: The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO). METHOD: The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.

Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.

Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-Decoder Cascaded Network (DEDCNet) model can precisely segment diseased areas from the leaf images to differentiate and classify multiple diseases. On the other hand, the model training depends on the appropriate selection of hyperparameters. Also, this network structure has weak robustness with different parameters. Hence, in this manuscript, an Optimized DEDCNet (ODEDCNet) model is proposed for improved leaf disease image segmentation. To choose the best DEDCNet hyperparameters, a brand-new Dingo Optimization Algorithm (DOA) is included in this model. The DOA depends on the foraging nature of dingoes, which comprises exploration and exploitation phases. In exploration, it attains many predictable decisions in the search area, whereas exploitation enables exploring the best decisions in a provided area. The segmentation accuracy is used as the fitness value of each dingo for hyperparameter selection. By configuring the chosen hyperparameters, the DEDCNet is trained to segment the leaf disease regions. The segmented images are further given to the pre-trained Convolutional Neural Networks (CNNs) followed by the Support Vector Machine (SVM) for classifying leaf diseases. ODEDCNet performs exceptionally well on the PlantVillage and Betel Leaf Image datasets, attaining an astounding 97.33% accuracy on the former and 97.42% accuracy on the latter. Both datasets achieve noteworthy recall, F-score, Dice coefficient, and precision values: the Betel Leaf Image dataset shows values of 97.4%, 97.29%, 97.35%, and 0.9897; the PlantVillage dataset shows values of 97.5%, 97.42%, 97.46%, and 0.9901, all completed in remarkably short processing times of 0.07 and 0.06 seconds, respectively. The achieved outcomes are evaluated with the contemporary optimization algorithms using the considered datasets to comprehend the efficiency of DOA.

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.

One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN's parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.

Drug-Resistant Epithelial Ovarian Cancer: Current and Future Perspectives.

Epithelial ovarian cancer (EOC) is a complex disease with diverse histological subtypes, which, based on the aggressiveness and course of disease progression, have recently been broadly grouped into type I (low-grade serous, endometrioid, clear cell, and mucinous) and type II (high-grade serous, high-grade endometrioid, and undifferentiated carcinomas) categories. Despite substantial differences in pathogenesis, genetics, prognosis, and treatment response, clinical diagnosis and management of EOC remain similar across the subtypes. Debulking surgery combined with platinum-taxol-based chemotherapy serves as the initial treatment for High Grade Serous Ovarian Carcinoma (HGSOC), the most prevalent one, and for other subtypes, but most patients exhibit intrinsic or acquired resistance and recur in short duration. Targeted therapies, such as anti-angiogenics (e.g., bevacizumab) and PARP inhibitors (for BRCA-mutated cancers), offer some success, but therapy resistance, through various mechanisms, poses a significant challenge. This comprehensive chapter delves into emerging strategies to address these challenges, highlighting factors like aberrant miRNAs, metabolism, apoptosis evasion, cancer stem cells, and autophagy, which play pivotal roles in mediating resistance and disease relapse in EOC. Beyond standard treatments, the focus of this study extends to alternate targeted agents, including immunotherapies like checkpoint inhibitors, CAR T cells, and vaccines, as well as inhibitors targeting key oncogenic pathways in EOC. Additionally, this chapter covers disease classification, diagnosis, resistance pathways, standard treatments, and clinical data on various emerging approaches, and advocates for a nuanced and personalized approach tailored to individual subtypes and resistance mechanisms, aiming to enhance therapeutic outcomes across the spectrum of EOC subtypes.

Deep learning in systems medicine.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

Taxonomic reclassification of Kaposi Sarcoma identifies disease entities with distinct immunopathogenesis.

BACKGROUND: The taxonomy of Kaposi Sarcoma (KS) is based on a classification system focused on the description of clinicopathological features of KS in geographically and clinically diverse populations. The classification includes classic, endemic, epidemic/HIV associated and iatrogenic KS, and KS in men who have sex with men (MSM). We assessed the medical relevance of the current classification of KS and sought clinically useful improvements in KS taxonomy. METHODS: We reviewed the demographic and clinicopathological features of 676 patients with KS, who were referred to the national centre for HIV oncology at Chelsea Westminster hospital between 2000 and 2021. RESULTS: Demographic differences between the different subtypes of KS exist as tautological findings of the current classification system. However, no definitive differences in clinicopathological, virological or immunological parameters at presentation could be demonstrated between the classic, endemic or MSM KS patients. Reclassifying patients as either immunosuppressed or non-immunosuppressed, showed that the immunosuppressed group had a significantly higher proportion of adverse disease features at presentation including visceral disease and extensive oral involvement, classified together as advanced disease (chi2 P = 0.0012*) and disseminated skin involvement (chi2 P < 0.0001*). Immunosuppressed patients had lower CD4 counts, higher CD8 counts and a trend towards higher HHV8 levels compared to non-immunosuppressed patients, however overall survival and disease specific (KS) survival was similar across groups. CONCLUSION: The current system of KS classification does not reflect meaningful differences in clinicopathological presentation or disease pathogenesis. Reclassification of patients based on the presence or absence of immunosuppression is a more clinically meaningful system that may influence therapeutic approaches to KS.

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.

INTRODUCTION/AIMS: Needle impedance-electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2-mdx muscular dystrophy and wild-type (WT) mouse skeletal muscle. METHODS: iEMG data were obtained from quadriceps of D2-mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampled across the frequency range using three different approaches. Random forest-based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross-validation procedure. RESULTS: Data from 20 D2-mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy. DISCUSSION: We have demonstrated an ML-based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM-EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.

New daily persistent headache: A systematic review and meta-analysis.

OBJECTIVE: To perform a systematic review and meta-analysis of the epidemiology, precipitants, phenotype, comorbidities, pathophysiology, treatment, and prognosis of primary new daily persistent headache. METHODS: We searched PubMed/Medline, EMBASE, Cochrane, and clinicaltrials.gov until 31 December 2022. We included original research studies with any design with at least five participants with new daily persistent headache. We assessed risk of bias using National Institutes of Health Quality Assessment Tools. We used random-effects meta-analysis where suitable to calculate pooled estimates of proportions. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis compliant study is registered with PROSPERO (registration number CRD42022383561). RESULTS: Forty-six studies met inclusion criteria, predominantly case series, including 2155 patients. In 67% (95% CI 57-77) of cases new daily persistent headache has a chronic migraine phenotype, however new daily persistent headache has been found to be less likely than chronic migraine to be associated with a family history of headache, have fewer associated migrainous symptoms, be less vulnerable to medication overuse, and respond less well to injectable and neuromodulatory treatments. CONCLUSIONS: New daily persistent headache is a well described, recognisable disorder, which requires further research into its pathophysiology and treatment. There is a lack of high-quality evidence and, until this exists, we recommend continuing to consider new daily persistent headache a distinct disorder.

Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model.

Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification due to some different and complex leaf diseases. Also, the disease's adhesion and dimension can overlap, causing partial under-segmentation. Therefore, a novel robust Deep Encoder-Decoder Cascaded Network (DEDCNet) model is proposed in this manuscript for leaf image segmentation that precisely segments the diseased leaf spots and differentiates similar diseases. This model is comprised of an Infected Spot Recognition Network and an Infected Spot Segmentation Network. Initially, ISRN is designed by integrating cascaded CNN with a Feature Pyramid Pooling layer to identify the infected leaf spot and avoid an impact of background details. After that, the ISSN developed using an encoder-decoder network, which uses a multi-scale dilated convolution kernel to precisely segment the infected leaf spot. Moreover, the resultant leaf segments are provided to the pre-learned CNN models to learn texture features followed by the SVM algorithm to categorize leaf disease classes. The ODEDCNet delivers exceptional performance on both the Betel Leaf Image and PlantVillage datasets. On the Betel Leaf Image dataset, it achieves an accuracy of 94.89%, with high precision (94.35%), recall (94.77%), and F-score (94.56%), while maintaining low under-segmentation (6.2%) and over-segmentation rates (2.8%). It also achieves a remarkable Dice coefficient of 0.9822, all in just 0.10 seconds. On the PlantVillage dataset, the ODEDCNet outperforms other existing models with an accuracy of 96.5%, demonstrating high precision (96.61%), recall (96.5%), and F-score (96.56%). It excels in reducing under-segmentation to just 3.12% and over-segmentation to 2.56%. Furthermore, it achieves a Dice coefficient of 0.9834 in a mere 0.09 seconds. It evident for the greater efficiency on both segmentation and categorization of leaf diseases contrasted with the existing models.

MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.

BACKGROUND: Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration. RESULTS: We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. CONCLUSIONS: Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.

Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning.

Citrus has become a pivotal industry for the rapid development of agriculture and increasing farmers' incomes in the main production areas of southern China. Knowing how to diagnose and control citrus huanglongbing has always been a challenge for fruit farmers. To promptly recognize the diagnosis of citrus huanglongbing, a new classification model of citrus huanglongbing was established based on MobileNetV2 with a convolutional block attention module (CBAM-MobileNetV2) and transfer learning. First, the convolution features were extracted using convolution modules to capture high-level object-based information. Second, an attention module was utilized to capture interesting semantic information. Third, the convolution module and attention module were combined to fuse these two types of information. Last, a new fully connected layer and a softmax layer were established. The collected 751 citrus huanglongbing images, with sizes of 3648 × 2736, were divided into early, middle, and late leaf images with different disease degrees, and were enhanced to 6008 leaf images with sizes of 512 × 512, including 2360 early citrus huanglongbing images, 2024 middle citrus huanglongbing images, and 1624 late citrus huanglongbing images. In total, 80% and 20% of the collected citrus huanglongbing images were assigned to the training set and the test set, respectively. The effects of different transfer learning methods, different model training effects, and initial learning rates on model performance were analyzed. The results show that with the same model and initial learning rate, the transfer learning method of parameter fine tuning was obviously better than the transfer learning method of parameter freezing, and that the recognition accuracy of the test set improved by 1.02~13.6%. The recognition accuracy of the citrus huanglongbing image recognition model based on CBAM-MobileNetV2 and transfer learning was 98.75% at an initial learning rate of 0.001, and the loss value was 0.0748. The accuracy rates of the MobileNetV2, Xception, and InceptionV3 network models were 98.14%, 96.96%, and 97.55%, respectively, and the effect was not as significant as that of CBAM-MobileNetV2. Therefore, based on CBAM-MobileNetV2 and transfer learning, an image recognition model of citrus huanglongbing images with high recognition accuracy could be constructed.

Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT.

Macular pathologies can cause significant vision loss. Optical coherence tomography (OCT) images of the retina can assist ophthalmologists in diagnosing macular diseases. Traditional deep learning networks for retinal disease classification cannot extract discriminative features under strong noise conditions in OCT images. To address this issue, we propose a multi-scale-denoising residual convolutional network (MS-DRCN) for classifying retinal diseases. Specifically, the MS-DRCN includes a soft-denoising block (SDB), a multi-scale context block (MCB), and a feature fusion block (FFB). The SDB can determine the threshold for soft thresholding automatically, which removes speckle noise features efficiently. The MCB is designed to capture multi-scale context information and strengthen extracted features. The FFB is dedicated to integrating high-resolution and low-resolution features to precisely identify variable lesion areas. Our approach achieved classification accuracies of 96.4% and 96.5% on the OCT2017 and OCT-C4 public datasets, respectively, outperforming other classification methods. To evaluate the robustness of our method, we introduced Gaussian noise and speckle noise with varying PSNRs into the test set of the OCT2017 dataset. The results of our anti-noise experiments demonstrate that our approach exhibits superior robustness compared with other methods, yielding accuracy improvements ranging from 0.6% to 2.9% when compared with ResNet under various PSNR noise conditions.

Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity.

BACKGROUND: Accurate, detailed, and standardized phenotypic descriptions are essential to support diagnostic interpretation of genetic variants and to discover new diseases. The Human Phenotype Ontology (HPO), extensively used in rare disease research, provides a rich collection of vocabulary with standardized phenotypic descriptions in a hierarchical structure. However, to date, the use of HPO has not yet been widely implemented in the field of inborn errors of immunity (IEIs), mainly due to a lack of comprehensive IEI-related terms. OBJECTIVES: We sought to systematically review available terms in HPO for the depiction of IEIs, to expand HPO, yielding more comprehensive sets of terms, and to reannotate IEIs with HPO terms to provide accurate, standardized phenotypic descriptions. METHODS: We initiated a collaboration involving expert clinicians, geneticists, researchers working on IEIs, and bioinformaticians. Multiple branches of the HPO tree were restructured and extended on the basis of expert review. Our ontology-guided machine learning coupled with a 2-tier expert review was applied to reannotate defined subgroups of IEIs. RESULTS: We revised and expanded 4 main branches of the HPO tree. Here, we reannotated 73 diseases from 4 International Union of Immunological Societies-defined IEI disease subgroups with HPO terms. We achieved a 4.7-fold increase in the number of phenotypic terms per disease. Given the new HPO annotations, we demonstrated improved ability to computationally match selected IEI cases to their known diagnosis, and improved phenotype-driven disease classification. CONCLUSIONS: Our targeted expansion and reannotation presents enhanced precision of disease annotation, will enable superior HPO-based IEI characterization, and hence benefit both IEI diagnostic and research activities.

Using a novel convolutional neural network for plant pests detection and disease classification.

BACKGROUND: Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. METHODOLOGY: An enhanced convolutional neural network (CNN) along with long short-term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre-trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM-CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. RESULTS: The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. CONCLUSION: The presented model attained 99.2% accuracy compared to the cutting-edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre-trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

Non-Hodgkin Lymphoma - Nodal and Extranodal: 20-Year Comparative Mortality, Survival & Biologic Behavior Analysis by Age, Sex, Race, Stage, Cell Morphology/Histology, Cohort Entry Time-Period and Disease Duration: A Systematic Review of 384,651 Total NHL Cases Including 261,144 Nodal and 123,507 Extranodal Cases for Diagnosis Years 1975-2016: (SEER*Stat 8.3.6).

During the past 5 decades, there have been reports of increases in the incidence and mortality rates of non-Hodgkin lymphoma (NHL) in the United States and globally. The ability to address the epidemiologic diversity, prognosis and treatment of NHL depends on the use of an accurate and consistent classification system. Historically, uniform treatment for NHL has been hampered by the lack of a systematic taxonomy of non-Hodgkin lymphoma. Before 1982, there were 6 competing classification schemes with contending terminologies for NHL: the Rappaport, Lukes-Collins, Kiel, World Health Organization, British, and Dorfman systems without consensus as to which system is most satisfactory regarding clinical relevance, scientific accuracy and reproducibility and presenting a difficult task for abstractors of incidence information. In 1982, the National Cancer Institute sponsored a workshop1 that developed a working formulation designed to: 1) provide clinicians with prognostic information for the various types of NHLs, and 2) provide a common language that might be used to compare clinical trials from various treatment centers around the world. Studies imply that prognosis is dependent on tumor stage and histology rather than the primary localization per se.2 This study utilizes the National Cancer Institute PDQ adaptation of the World Health Organization's (WHO) updated REAL (Revised European American Lymphoma) classification3 of lymphoproliferative diseases, and the SEER*Stat 8.3.6 database (released Aug 8, 2019) for diagnosis years 1975-2016. In this article, we make use of 40 years of data to examine patterns of incidence, survival and mortality, and selected cell bio-behavioral characteristics of NHL in the United States. OBJECTIVE: -To update trends in incidence and prevalence in the United States of non-Hodgkin lymphoma, examine, compare and contrast short and long-term patterns of survival and mortality, and consider the outcome impacts of anatomic location of NHL nodal and extranodal subdivisions, utilizing selected ICD-O-3 histologic oncotypes stratified by age, sex, race/ethnicity, stage, cell behavioral morphology and histologic typology, cohort entry time-period and disease duration, employing the statistical database of the National Cancer Institute SEER*Stat 8.3.6 program for diagnosis years 1975-2016.4 Methods.- A retrospective, population-based cohort study using nationally representative data from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program to evaluate 384,651 NHL cases for diagnosis years 1975-2016 comparing multiple variables of age, sex, race, stage, cell behavioral morphology, cohort entry time-period, disease duration and histologic oncotype. Relative survival statistics were analyzed in two cohorts: 1975-1995 and 1996-2016. Survival statistics were derived from SEER*Stat Database: Incidence - SEER 9 Regs Research Data, November 2018 Submission (1975-2016) released April 2019, based on the November 2018 submission. RESULTS: - Incidence rates, relative frequency distributions, survival and mortality by age, sex, stage and cell behavioral morphology, of adult nodal (N) and extranodal (EN) NHL in 2 entrant time-periods as recorded in the SEER Program of the National Cancer Institute for diagnosis years 1975-2016 (SEER Stat 8.3.6) are summarized. Shifts in trends over time are identified, and the findings are correlated with prognosis, including short and long-term observed (actual), expected and relative survival, median observed and relative survival, mortality rates and excess death rates per 1000 people. CONCLUSIONS: - Trends in SEER incidence, prevalence, survival and mortality by age, sex, race, stage, cell behavioral morphology, cohort entry time-period, relative frequency and percent distribution, were examined to provide a current epidemiologic and medical-actuarial risk assessment framework for nodal (N) and extranodal (EN) non-Hodgkin's lymphoma in the 1975-2016 timeframe.