Intelligent Disease Prediagnosis Only Based on Symptoms.
Ontology highlight
ABSTRACT:
SUBMITTER: Luo F
PROVIDER: S-EPMC8352683 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Age at onset of Huntington disease, an inherited neurodegenerative disorder, is influenced by the size of the disease-causing CAG trinucleotide repeat expansion in HTT and by genetic modifier loci on chromosomes 8 and 15. Stratifying by modifier genotype, we have examined putamen volume, total motor score (TMS), and symbol digit modalities test (SDMT) scores, both at study entry and longitudinally, in normal controls and CAG-expansion carriers who were enrolled prior to the emergence of manifest HD in the PREDICT-HD study. The modifiers, which included onset-hastening and onset-delaying alleles on chromosome 15 and an onset-hastening allele on chromosome 8, revealed no major effect in controls but distinct patterns of modification in prediagnosis HD subjects. Putamen volume at study entry showed evidence of reciprocal modification by the chromosome 15 alleles, but the rate of loss of putamen volume was modified only by the deleterious chromosome 15 allele. By contrast, both alleles modified the rate of change of the SDMT score, but neither had an effect on the TMS. The influence of the chromosome 8 modifier was evident only in the rate of TMS increase. The data indicate that (1) modification of pathogenesis can occur early in the prediagnosis phase, (2) the modifier loci act in genetic interaction with the HD mutation rather than through independent additive effects, and (3) HD subclinical phenotypes are differentially influenced by each modifier, implying distinct effects in different cells or tissues. Together, these findings indicate the potential benefit of using genetic modifier strategies for dissecting the prediagnosis pathogenic process in HD.
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Project description:IntroductionGenetic associations for endophenotypes of Alzheimer's disease (AD) in cognitive stages preceding AD have not been thoroughly evaluated.MethodsWe conducted genome-wide association studies for AD-related endophenotypes including hippocampal volume, logical memory scores, and cerebrospinal fluid Aβ42 and total/phosphorylated tau in cognitively normal (CN), mild cognitive impairment, and AD dementia subjects from the Alzheimer's Disease Neuroimaging Initiative study.ResultsIn CN subjects, study-wide significant (P < 8.3 × 10-9) loci were identified for total tau near SRRM4 and C14orf79 and for hippocampal volume near MTUS1. In mild cognitive impairment subjects, study-wide significant association was found with single nucleotide polymorphisms (SNPs) near ZNF804B for logical memory test of delayed recall scores. We found consistent expression patterns of C14orf40 and MTUS1 in carriers with risk alleles of expression SNPs and in brains of AD patients, compared with in the noncarriers and in brains of controls.DiscussionOur findings for AD-related brain changes before AD provide insight about early AD-related biological processes.
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Project description:BACKGROUND:The authors hypothesized that epigenetic changes may help to clarify the underlying biologic mechanism linking aspirin use to breast cancer prognosis. To the authors' knowledge, this is the first epidemiologic study to examine whether global methylation and/or tumor promoter methylation of breast cancer-related genes interact with aspirin use to impact mortality after breast cancer. METHODS:Prediagnosis aspirin use was assessed through in-person interviews within a population-based cohort of 1508 women diagnosed with a first primary breast cancer in 1996 and 1997. Global methylation in peripheral blood was assessed by long interspersed elements-1 (LINE-1) and the luminometric methylation assay. Promoter methylation of 13 breast cancer-related genes was measured in tumor by methylation-specific polymerase chain reaction and the MethyLight assay. Vital status was determined by the National Death Index through December 31, 2014 (N = 202/476 breast cancer-specific/all-cause deaths identified among 1266 women with any methylation assessment and complete aspirin data). Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs, and the likelihood ratio test was used to evaluate multiplicative interactions. RESULTS:All-cause mortality was elevated among aspirin users who had methylated promotor of BRCA1 (HR, 1.67; 95% CI, 1.26-2.22), but not among those with unmethylated promoter of BRCA1 (HR, 0.99; 95% CI, 0.67-1.45; P for interaction ?.05). Decreased breast cancer-specific mortality was observed among aspirin users who had unmethylated promotor of BRCA1 and PR and global hypermethylation of LINE-1 (HR, 0.60, 0.78, and 0.63, respectively; P for interaction ?.05), although the 95% CIs included the null. CONCLUSIONS:The current study suggests that the LINE-1 global methylation and promoter methylation of BRCA1 and PR in tumor may interact with aspirin use to influence mortality after breast cancer.
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Project description:Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.
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Project description:Huntington's disease (HD) is a devastating neurodegenerative disorder caused by the expansion of CAG repeats in the huntingtin (HTT) gene. Recent advances in gene editing technologies, such as CRISPR/CasRx, have opened new avenues for therapeutic interventions. In this study, we explored the efficacy of CRISPR/CasRx, which can specifically and accurately digest single-stranded RNA and down-regulate the expression of related genes, in targeting the HTT mRNA and its potential as a treatment strategy for HD. Our results showed that CRISPR/CasRx could significantly down-regulate HTT mRNA in different models, including human embryonic kidney (HEK) 293T cells, HD140Q-knockin (HD 140Q-KI) mice at various disease stages, and Huntingtin knockin (HD-KI) pigs, and lead to a subsequent decrease in the expression of mutant Huntingtin (mHTT) protein. Moreover, this intervention could significantly ameliorate the neurological symptoms in HD 140Q-KI mice and HD-KI pigs. These findings highlight the effectiveness of the RNA-targeting CRISPR/CasRx as a potential therapeutic strategy for HD. Furthermore, the success of this approach provides valuable insights and novel avenues for the treatment of other genetic disorders caused by gene mutations.
2024-12-01 | GSE248873 | GEO
Project description:Using intelligent agriculture is an important way for the industry to achieve high-quality development. To improve the accuracy of the identification of crop diseases under conditions of limited computing resources, such as in mobile and edge computing, we propose an improved lightweight MobileNetV2 crop disease identification model. In this study, MobileNetV2 is used as the backbone network for the application of an improved Bottleneck structure. First, the number of operation channels is reduced using point-by-point convolution, the number of parameters of the model is reduced, and the re-parameterized multilayer perceptron (RepMLP) module is introduced; the latter can capture long-distance dependencies between features and obtain local a priori information to enhance the global perception of the model. Second, the efficient channel-attention mechanism is added to adjust the image-feature channel weights so as to improve the recognition accuracy of the model, and the Hardswish activation function is introduced instead of the ReLU6 activation function to further improve performance. The final experimental results show that the improved MobilNetV2 model achieves 99.53% accuracy in the PlantVillage crop disease dataset, which is 0.3% higher than the original model, and the number of covariates is only 0.9M, which is 59% less than the original model. Also, the inference speed is improved by 8.5% over the original model. The crop disease identification method proposed in this article provides a reference for deployment and application on edge and mobile devices.
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Project description:Background: The associations between telomere length and cancer risk are equivocal, and none have examined the association between prediagnosis leukocyte telomere length (LTL) and the risk of developing ovarian cancer.Methods: We prospectively measured LTL collected from 442 ovarian cancer cases and 727 controls in the Nurses' Health Studies and the Northern Sweden Health and Disease Study. Cases were matched to one or two controls on age, menopausal status, and date of blood collection. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using conditional logistic regression.Results: LTL was measured a median of 9.5 years before ovarian cancer diagnosis among cases. We observed a decreased risk of ovarian cancer with longer LTL. In multivariable models, women in the top quartile of LTL had an OR for ovarian cancer of 0.67 (95% CI, 0.46-0.97) compared with those in the bottom quartile. Inverse associations were stronger for nonserous cases (ORquartile 4 vs. quartile 1 of LTL = 0.55, 95% CI, 0.33-0.94) and rapidly fatal cases (i.e., cases who died within 3 years of diagnosis; ORquartile 4 vs. quartile 1 of LTL = 0.55, 95% CI, 0.32-0.95).Conclusions: Our prospective findings suggest that longer circulating LTL may be associated with a lower ovarian cancer risk, especially for nonserous and rapidly fatal cases. The evaluation of LTL in relation to ovarian cancer risk by tumor subtypes is warranted in larger prospective studies.Impact: Prediagnosis LTL may reflect an early event in the ovarian cancer development and could serve as a biomarker to predict future risk. Cancer Epidemiol Biomarkers Prev; 26(3); 339-45. ©2017 AACR.
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Project description:Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.
| S-EPMC7959643 | biostudies-literature