Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method

Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method

Influencing factors of length of stay among repeatedly hospitalized patients with mood disorders: a longitudinal study in China

Abstract Background Patients with mood disorders usually require repeated and prolonged hospitalization, resulting in a heavy burden on healthcare resources. This study aims to identify variables associated with length of stay(LOS) of repeatedly hospitalized patients with mood disorders and to provide information for optimizing psychiatry management and healthcare resource allocation. Methods Electronic medical records (EMRs) of repeatedly hospitalized patients with mood disorders from January 2010 to December 2018 were collected and retrospectively analyzed. Chi-square and t-test were adopted to investigate the differences in characteristics between the two groups of short LOS and long LOS. Generalized estimating equation (GEE) was conducted to investigate potential factors influencing LOS. Results A total of 2,009 repeatedly hospitalized patients with mood disorders were enrolled, of which 797 (39.7%) had a long LOS and 1,212 (60.3%) had a short LOS. Adverse effects of treatment, continuous clinical manifestation, chronic onset type, suicide attempt, comorbidity and use of antidepressants were positively associated with long LOS among all repeatedly hospitalized patients with mood disorders (P < 0.050). For patients with depression, factors associated with long LOS consisted of age, monthly income, adverse effects of treatment, continuous clinical manifestation, suicide attempt and comorbidity (P < 0.050). Whereas, for patients with bipolar disorder (BD), adverse effects of treatment, four or more hospitalizations and use of antidepressants contributed to the long LOS (P < 0.050). Influencing factors of LOS also vary among patients with different effectiveness of treatment. Conclusion The LOS in repeatedly hospitalized patients with mood disorders was influenced by multiple factors. There were discrepancies in the factors affecting LOS in patients with different diagnoses and effectiveness of treatment, and specific factors should be addressed when evaluating the LOS

Primary dysmenorrhoea : a comparative study on Australian and Chinese women

Objective: To explore the extent to which traditional Chinese medicine (TCM) diagnostic categories for primary dysmenorrhoea are useful in describing the clinical presentation of this condition in Australian women in comparison with Chinese women, and therefore the potential usefulness of these categories in guiding TCM treatment of Australian women. Design and setting: A comparative study of 120 Australian and 122 Chinese women aged from 18 to 45 years with primary dysmenorrhoea. Main outcome measures: Modified valid TCM diagnostic protocol. Results: Difference in menstruation and menstrual pain profiles between the two groups of women found in the same study did not translate into differences in the underlying syndrome according to TCM diagnostic categories. The study found that Australian and Chinese women were represented in broadly similar proportions across the defined five diagnostic categories. Conclusion: Some evidence suggests that although the clinical presentation of symptoms in Australian and Chinese women is different, the distribution of women across the diagnostic categories in TCM is similar. Therefore, the TCM protocol used to diagnose primary dysmenorrhoea and guide treatment is unlikely to require adaptation for use with Australian women

End to end multitask joint learning model for osteoperosis classification in CT images

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present

Genome-Wide Identification of Auxin-Responsive GH3 Gene Family in Saccharum and the Expression of ScGH3-1 in Stress Response

Gretchen Hagen3 (GH3), one of the three major auxin-responsive gene families, is involved in hormone homeostasis in vivo by amino acid splicing with the free forms of salicylic acid (SA), jasmonic acid (JA) or indole-3-acetic acid (IAA). Until now, the functions of sugarcane GH3 (SsGH3) family genes in response to biotic stresses have been largely unknown. In this study, we performed a systematic identification of the SsGH3 gene family at the genome level and identified 41 members on 19 chromosomes in the wild sugarcane species, Saccharum spontaneum. Many of these genes were segmentally duplicated and polyploidization was the main contributor to the increased number of SsGH3 members. SsGH3 proteins can be divided into three major categories (SsGH3-I, SsGH3-II, and SsGH3-III) and most SsGH3 genes have relatively conserved exon-intron arrangements and motif compositions. Diverse cis-elements in the promoters of SsGH3 genes were predicted to be essential players in regulating SsGH3 expression patterns. Multiple transcriptome datasets demonstrated that many SsGH3 genes were responsive to biotic and abiotic stresses and possibly had important functions in the stress response. RNA sequencing and RT-qPCR analysis revealed that SsGH3 genes were differentially expressed in sugarcane tissues and under Sporisorium scitamineum stress. In addition, the SsGH3 homolog ScGH3-1 gene (GenBank accession number: OP429459) was cloned from the sugarcane cultivar (Saccharum hybrid) ROC22 and verified to encode a nuclear- and membrane-localization protein. ScGH3-1 was constitutively expressed in all tissues of sugarcane and the highest amount was observed in the stem pith. Interestingly, it was down-regulated after smut pathogen infection but up-regulated after MeJA and SA treatments. Furthermore, transiently overexpressed Nicotiana benthamiana, transduced with the ScGH3-1 gene, showed negative regulation in response to the infection of Ralstonia solanacearum and Fusarium solani var. coeruleum. Finally, a potential model for ScGH3-1-mediated regulation of resistance to pathogen infection in transgenic N. benthamiana plants was proposed. This study lays the foundation for a comprehensive understanding of the sequence characteristics, structural properties, evolutionary relationships, and expression of the GH3 gene family and thus provides a potential genetic resource for sugarcane disease-resistance breeding

Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

Vascular images contain a lot of key information, such as length, diameter and distribution. Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the diagnosis of some abdominal diseases. However automatic segmentation of abdominal vessels is extremely challenging due to the multi-scale nature of vessels, boundary-blurring, low contrast, artifact disturbance and vascular cracks in Maximum Intensity Projection images. In this work, we propose a dual attention guided method where an adaptive adjustment field is applied to deal with multi-scale vessel information, and a channel feature fusion module is used to refine the extraction of thin vessels, reducing the interference and background noise. In particular, we propose a novel structure that accepts multiple sequential images as input, and successfully introduces spatial-temporal features by contextual information. A further IterUnet step is introduced to connect tiny cracks caused using CT scans. Comparing our proposed model with other state-of-the-art models, our model yields better segmentation and achieves an average F1 metric of 0.812

PE-Net: a parallel framework for 3D inferior mesenteric artery segmentation

The structural morphology of mesenteric artery vessels is of significant importance for the diagnosis and treatment of colorectal cancer. However, developing automated vessel segmentation methods for this purpose remains challenging. Existing convolution-based segmentation methods have limitations in capturing long-range dependencies, while transformer-based models require large datasets, making them less suitable for tasks with limited training samples. Moreover, over-segmentation, mis-segmentation, and vessel discontinuity are common challenges in vessel segmentation tasks. To address these issues, we propose a parallel encoding architecture that combines transformers and convolutions to retain the advantages of both approaches. The model effectively learns position deviations and enhances robustness for small-scale datasets. Additionally, we introduce a vessel edge capture module to improve vessel continuity and topology. Extensive experimental results demonstrate the improved performance of our model, with Dice Similarity Coefficient and Average Hausdorff Distance scores of 81.64% and 7.7428, respectively