Noninvasive Method For Early Detection Research Articles

Brainwaves and Soundwaves: A Deep Learning Approach to Alzheimer’s Detection

Cognitive abilities, and the capacity to perform everyday tasks. Early diagnosis is crucial in managing the disease effectively, but it remains a challenge. In recent years, deep learning has shown promise in aiding medical diagnoses, particularly through the analysis of complex data. This project explores a new approach to detecting Alzheimer’s by combining brain MRI scans and speech spectrograms. Using deep learning models, this research examines how well these two different types of data can identify Alzheimer’s, both individually and together. By integrating these datasets using the Keras Functional API, the goal is to enhance diagnostic accuracy, offering a potentially more reliable and non-invasive method for early detection. The hope is that this research will contribute valuable insights to the fight against Alzheimer’s, helping to improve early diagnosis and, ultimately, patient care.

DNA methylation urine test in the diagnosis of upper tract urothelial carcinoma: a systematic review and meta-analysis.

The DNA methylation urine test, a non-invasive early detection method for upper tract urothelial carcinoma (UTUC), is currently in full swing. This study aimed to systematically assess its diagnostic performance on UTUC. PubMed, Scopus, Embase, and Cochrane were our main databases when searching articles published from January 2000 to December 2023. Sensitivity and specificity were study primary endpoints. I2 was used to evaluated heterogeneity, meanwhile subgroup and meta-regression analyses were adopted to investigated the source of heterogeneity. Sensitivity analysis was performed to evaluate the result robustness, while Deeks' funnel plot asymmetry test was for the publication bias. Nine studies with 1326 patients were included. The pooled sensitivity was 0.89 (95% confidence interval (CI), 0.83-0.93) and specificity were 0.91 (95% CI, 0.83-0.96). The area under the receiver operating characteristic curve was 0.96 (95% CI, 0.93-0.97). Substantial heterogeneity was found during the data synthesis, whereas the pooled results remained robust in the sensitivity analysis. None of the potential covariates-urine sample collection method, population, country, methylation test method, or tumor grade-could account for the heterogeneity. DNA methylation urine test is a promising method with high efficiency for UTUC early detection. Nevertheless, owing to the significant heterogeneity, more well-organized studies are warranted to further explore its diagnostic efficiency and application context.

Exosomal miRNAs in prenatal diagnosis: Recent advances.

Exosomes, small membranous microvesicles released by cells, contain a range of bioactive molecules, including proteins and miRNAs, which play critical roles in intercellular communication and physiological and pathological processes. Current research suggests that exosomal miRNAs could serve as valuable biomarkers for prenatal diseases, offering a noninvasive method for early detection and monitoring. Studies linking exosomal miRNAs to various birth defects, including fetal growth restriction, urinary tract malformations, cardiovascular system malformations, and hereditary diseases like Down syndrome, were discussed. However, there are some conflicting study findings due to different exosome separation methods. Here, we also discussed exosome separation methods, emphasizing the importance of method selection based on specific purposes and sample types. Further studies are needed to standardize isolation techniques, understand the specific mechanisms underlying exosomal miRNA function, and develop reliable noninvasive prenatal diagnostic indicators. Overall, exosomal miRNAs show promise as potential biomarkers for prenatal diagnosis, but further research is necessary to validate their clinical utility.

Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

A novel, noninvasive, multimodal screening test for the early detection of precancerous lesions and colorectal cancers using an artificial intelligence–based algorithm.

3627 Background: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger populations. Detection of early-stage CRC and its precursor lesions, such as advanced adenomas (AA), is crucial for successful treatment and reduces CRC-related mortality. Although non-invasive methods for early detection are available and increase screening compliance, their performance with respect to detection of advanced adenomas and early-stage cancers is limited. Here, we describe a novel and non-invasive stool-based approach combining diagnostic biomarkers with an algorithm generated by machine learning/artificial intelligence (ML/AI) resulting in significantly improved diagnostic performance for the detection of not only CRC, but especially precancerous lesions like AA. Methods: Data were generated from a combined cohort of stool samples collected at 10 clinical sites in Europe (COLOFUTURE study) and 21 clinical sites in the US (eAArly DETECT study). The evaluable study cohort consists of 690 subjects, including 78 CRC, 146 AA, 147 with non-advanced adenoma (AD) and 319 normal colonoscopy negative control (NC) subjects (49% female, 51% male, average age 61.8 years). Results were compared with colonoscopy and pathology findings to determine sensitivity and specificity for detection of early-stage CRC and AA vs. AD and NC. Nucleic acids were extracted from stabilized stool samples using a silica bead-based extraction method. Expression of mRNA biomarkers was analyzed utilizing Real-Time PCR. Human hemoglobin was quantified using FIT. The Emerge Quantitative Evolution ML/AI platform was leveraged to develop classifiers capable of distinguishing CRC and AA from AD and NC. Results: Applying the combined approach of non-invasive diagnostic testing with an AI/ML generated algorithm, the sensitivity for detection of CRC overall was 92.3%. In addition, this method enabled AA detection with a sensitivity of 82.2%. Specificity turned out to be 90.1 % (AD+NC). Conclusions: This innovative, non-invasive, multimodal screening strategy based on self-collected stool samples which combines mRNA expression patterns and FIT analysis with an AI/ML-generated algorithm represents a substantial improvement in the effectiveness of non-invasive CRC screening. Such improvements in usability and performance leading to reliable detection of early-stage CRC and precursor lesions, such as advanced adenomas, are required for wide-spread availability and adaption of non-invasive screening tests to accepted clinically relevant usage and to prevent the development of CRC effectively.

Potential non-invasive detection of lesions in broiler femur heads: application of the DXA imaging system.

Leg health is a significant economic and welfare concern for the poultry industry. Current methods of detection rely on visual assessment of the legs and gait scores and bone scoring during necropsy for full characterization. Additionally, the current scoring of femurs only examines the external surface of the femoral head. Through the use of the dual-energy X-ray absorptiometry (DXA) imaging system, we show the presence of a necrotic region in the femurs that would otherwise be considered healthy based on the current evaluation procedures. Importantly, these lesions were present in almost 60% (22 of 37) of femurs that scored normal for femoral head necrosis (FHN). Additionally, these femurs showed greater bone mineral content (BMC) relative to weight compared to their counterparts with no lucent lesions (6.95% ± 0.20% vs. 6.26% ± 0.25; p = 0.038). Identification of these lesions presents both a challenge and an opportunity. These subclinical lesions are likely to be missed in routine scoring procedures for FHN and can inadvertently impact the characterization of the disease and genetic selection programs. Furthermore, this imaging system can be used for in vivo, ex vivo, and embryonic (egg) studies and, therefore, constitutes a potential non-invasive method for early detection of bone lesions in chickens and other avian species.

DNA methylation markers in esophageal cancer.

Esophageal cancer (EC) is a prevalent malignancy characterized by a low 5-year survival rate, primarily attributed to delayed diagnosis and limited therapeutic options. Currently, early detection of EC heavily relies on endoscopy and pathological examination, which pose challenges due to their invasiveness and high costs, leading to low patient compliance. The detection of DNA methylation offers a non-endoscopic, cost-effective, and secure approach that holds promising prospects for early EC detection. To identify improved methylation markers for early EC detection, we conducted a comprehensive review of relevant literature, summarized the performance of DNA methylation markers based on different input samples and analytical methods in EC early detection and screening. This review reveals that blood cell free DNA methylation-based method is an effective non-invasive method for early detection of EC, although there is still a need to improve its sensitivity and specificity. Another highly sensitive and specific non-endoscopic approach for early detection of EC is the esophageal exfoliated cells based-DNA methylation analysis. However, while there are substantial studies in esophageal adenocarcinoma, further more validation is required in esophageal squamous cell carcinoma. In conclusion, DNA methylation detection holds significant potential as an early detection and screening technology for EC.

An Innovative Non-Invasive Method for Early Detection and Monitoring of Acute Compartment Syndrome.

Background: Acute compartment syndrome is a major surgical emergency with complex pathophysiology and a highly unpredictable pattern of evolution. We hypothesized that the onset of acute compartment syndrome of the leg or forearm is associated with variations in the surface temperature of the distal segment (foot or hand) with a distinct pattern, which acts as an early warning sign. Materials and Methods: We developed a monitoring device that consists of two thermic sensors attached to a modular limb splint, which continuously measure the temperature difference between the proximal and distal regions of the limb (i.e., arm-hand, thigh-foot). Firstly, we investigated both the arm-hand and thigh-foot temperature gradients of hospitalized patients' healthy limbs (43 patients, 56 upper limbs, 64 lower limbs) in order to establish a baseline. Secondly, we examined the correlation between the thermic gradients and intracompartmental pressure values in compartment syndrome limbs (20 patients, 6 upper limbs, 14 lower limbs). Results: For the control group, the mean values for the normal limb thermic gradients were -0.17 °C for the upper limbs. and 0.03 °C for the lower limbs. In the impending compartment syndrome group (defined by intracompartmental pressure values), the mean index was -0.38 °C. In the fully developed compartment syndrome group, the mean value was 4.11 °C. Discussions: Analysis was performed using the ANOVA one-way statistical method. This showed significant differences between the compartment syndrome group and the impending and control groups. A decreasing trend in the thermic gradient in patients with impending compartment syndrome compared with the control group was noted. Conclusions: The thermic gradient of limbs presenting signs of impending compartment syndrome decreases as a result of the increased temperature of the distal segment. This pattern can be used as an early diagnostic method for acute compartment syndrome. This technique is non-invasive and bears no risk to the patient, allowing facile continuous monitoring during immobilization.

Investigating FGFR2 gene as a blood-based epigenetic biomarker in gastric cancer.

Gastric adenocarcinoma is a prevalent form of cancer that often remains undetected in its early stages due to the lack of specific symptoms. This delayed diagnosis leads to poor clinical outcomes, underscoring the need for an effective and non-invasive method for early detection. Recent advances in cancer epigenetics have led to the identification of biomarkers that have the potential to revolutionize the early detection and monitoring of this disease. One such promising biomarker is the methylation of the FGFR2 promoter. This study aims to measure the methylation levels of a specific CpG site in the FGFR2 promoter gene in DNA extracted from blood leukocytes from patients with intestinal metaplasia, gastric cancer, and healthy control. The CpG site of the FGFR2 gene promoter was identified in its control region. Methylation alteration of the selected FGFR2 CpG site was determined through the (methylation-sensitive restriction enzyme) MSRE-qPCR. Genomic DNA was extracted from one hundred twenty-five participants. The normal group had mean methylation levels of 93.23 ± 4.929%, while the IM group had a level of 69.85 ± 27.15%. In GC patients, the levels varied, with 25.96 ± 18.98% in the intestinal type and 28.30 ± 16.07% in the diffuse type. The methylation levels in the IM and GC patients were significantly lower than those in the normal control group. However, no significant difference was observed between the methylation status of the intestinal type of GC and the diffuse type. The Receiver operating characteristic (ROC) curve analysis showed that FGFR2 CpG methylation levels in GC patients compared to normal controls had a high sensitivity of 100% and specificity of 100%, with a cut-off of < 74.25%; when GC patients were compared to IM patients, the sensitivity was 85%, and the specificity was 80%, with a cut-off < 44.45%. The potential of the FGFR2 methylation status as a non-invasive biomarker lies in its ability to be detected in blood leukocytes, which makes it a promising tool for the early detection of intestinal metaplasia and gastric cancer. This could significantly improve the detection and management of these gastric conditions.

A deep learning framework for the early detection of multi-retinal diseases.

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model's performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study's contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

Platelet membrane-derived biomimetic microbubbles with enhanced targeting ability for the early detection of myocardial ischemia-reperfusion injury

Myocardial ischemia-reperfusion injury (MIRI) is a widely recognized cardiovascular disease that significantly impacts the prognosis of patients undergoing myocardial infarction recanalization. This condition can be fatal and involves complex pathophysiological mechanisms. Early diagnosis of MIRI is crucial to minimize myocardial damage and reducing mortality. Based on the inherent relationship between platelets and MIRI, we developed biomimetic microbubbles coated with platelet membrane (MB-pla) for early identification of MIRI. The MB-pla were prepared through a recombination process involving platelet membrane obtained from rat whole blood and phospholipids, blended in appropriate proportions. By coating the microbubbles with platelet membrane, MB-pla acquired various adhesion molecules, thereby gaining the capability to selectively adhere to damaged endothelial cells in the context of MIRI. In vitro experiments demonstrated that MB-pla exhibited remarkable targeting characteristics, particularly toward type IV collagen and human umbilical vein endothelial cells that had been injured through hypoxia/reoxygenation procedures. In a rat model of MIRI, the signal intensity produced by MB-pla was notably higher than that of control microbubbles. These findings were consistent with results obtained from fluorescence imaging of isolated hearts and immunofluorescence staining of tissue sections. In conclusion, MB-pla has great potential as a non-invasive early detection method for MIRI. Furthermore, this approach can potentially find application in other conditions involving endothelial injury in the future.

Myocardial Injury in Rats Exposed to High-Intensity Exercise Evaluated by 2-D Speckle Tracking Imaging.

The aim of the work described here was to evaluate strain and morphological change of the left ventricle in Sprague Dawley (SD) rats at different exercise intensities by 2-D speckle tracking imaging (STI). Seventy-two 8-wk-old SD rats were divided into four groups on the basis of exercise intensity: sedentary (SED), low-intensity running, medium-intensity running (MIR) and high-intensity running (HIR). Each group was further sub-divided into three groups of different exercise lengths: 1, 4 and 8 wk. The structural measurements of the left ventricle and left ventricular ejection fraction (LVEF) were obtained by echocardiography. Systolic peak values of global longitudinal, circumferential and radial strains (GLS, GCS and GRS) were obtained. Histopathological results of the cross-sectional area (CSA) of myocardial cells, collagen volume fraction (CVF) of the myocardium and perivascular collagen area (PVCA) were also observed. Structural measurements of the left ventricle and LVEF did not change with different exercise intensities or lengths. GLS of the HIR8 wk sub-group was significantly lower than those of the SED8 wk and MIR8 wk sub-groups. Conversely, the GLS and GCS of the HIR8 wk sub-group were lower than those of the HIR1 wk and HIR4 wk sub-groups. Histopathologically, the CSA of myocardial cells significantly increased across all HIR sub-groups and the MIR4 wk and MIR8 wk sub-groups. CVFendo and PVCA were also significantly increased in the HIR4 wk and HIR8 wk sub-groups. The HIR8 wk group also had regional swelling and ill-defined boundaries of myocardial cells. Prolonged, high-intensity exercise may lead to exercise-induced injury of the myocardium. Two-dimensional STI can be used as a non-invasive early detection method for exercise-induced injury of myocardial function, compared with LVEF.

Breast Self-Examination: An Early Familiar Diagnostic Test of Breast Cancer

Background: Among female patients’ breast cancer are a growing threat for over a century. In women, breast cancer is the most common malignancy universally such as 154 of 185 nations. Breast self-examination (BSE) is a simple, very low-cost, non-invasive early detection method used to detect early breast cancer, which involves the woman herself looking at and feeling for any change in their breast as early as possible, which yields a better survival rate. BSE should be done for all women older than 20 years. This study intended to determine knowledge, attitude, and practice (KAP) of breast cancer among medical and non-medical undergraduate students. Material &amp; Methods: A descriptive cross‑sectional study was conducted from October to November 2019 to assess the knowledge, attitude, and practice of BSE. The target population consisted of undergraduate female medical and non‑medical students. A total of 154 (N=154) undergraduate female students participated in this study. Verbal consent was taken before recruiting the study population. Completed data forms were reviewed, edited, and processed for computer data entry. The data analysis was performed using Statistical Package for the Social Sciences (SPSS) Version 25.0. Results: Among the study population (N=154) with an early familiar diagnosis of breast cancer ”breast self-examination, the mean age of them was 20.434±4.38. Of seventy-eight respondents (n=78), breast cancer screening test was heard by around half of the medical students (42, 53.8%) &amp; of seventy-six non-medical respondents (n=76), around three-fifth of them (46, 60.5%) did not hear about breast cancer screening test. There was no significant relationship between them (p=0.394). Forty medical students (40, 51.3% and twenty-five non-medical students (25, 32.9%) had no idea, twenty-four medical students (24, 30.8%) &amp; forty-three (43, 56.6%) non-medical students had monthly BSE done. Conclusion: Initial diagnosis and treatment of breast cancer have a definite survival benefit. So early detection is an issue of necessity to be uplifted. The results of the present study suggested that knowledge regarding breast cancer, its risk factors, sign symptoms, prevention measures, and performance of BSE is insufficient.

Abstract 3333: Exosomal non-coding RNAs as a biomarker to improve early ovarian cancer detection

Abstract Purpose: There is no consensus on screening for ovarian cancer in high-risk women because no effective biomarker or screening method exists, especially for early-stage cancer patients. The available methods, such as monitoring of CA125 levels, transvaginal ultrasound, and combination of pelvic examination are associated with high false-positive results, leading to additional unnecessary imaging tests, emotional burdens, and risk of invasive surgery for women without cancer. An effective, reliable, and non-invasive method for early detection of ovarian cancer is thus critically needed. In this research, weThus, we aim to identify a distinctive signature of exosomal small RNAs to serve as a novel biomarker for detecting early-stage ovarian cancer in high-risk population. Methods: Women of all ages and ethnical backgrounds were included in this study. Blood samples were collected in the clinic from women with germline BRCA mutations. A total of 30 patients with ovarian cancer before surgery, and 30 age-matched (mean age = 55) healthy women as controls were used. Among the 30 thirty cases, 11 patients had early (stage I) ovarian cancer. Identification of differential exosome small non-coding RNA copy numbers in plasma samples from BRCA1 mutation carriers with and without ovarian cancer was performed by next-generation RNA-Sseq. We randomly split 60 samples into two groups: training set (20 cancer, 20 normal) and , testing set (10 cancer, 10 normal). We apply 10-fold cross-validation to determine hyperparameter of five machine learning models and use the ensemble learning method to evaluate the predictive value of the small RNAs in the exosome by integrating the five machine learning models. Results: The ensemble learning model achieved the testing accuracy of 85% with the sensitivity of 70% and the specificity of 100% by using small RNA and CA125 abundance level as input features. While uUsing CA125 alone, the testing accuracy equals to 90% with the sensitivity of 80% and the specificity of 100%. For the 11 eleven early-stage patient samples, our ensemble learning model made 2 false negative predictions with a total accuracy of 95.1% (sensitivity=81.8%, specificity=100%) while using CA125 alone had five 5 false negative predictions with a total accuracy of 87.8% (sensitivity=54.5%, specificity=100%). All 30 control samples had been correctly classified. Conclusions: We found that ensemble learning model using combining both exosome small RNAs and CA125 will not improve the prediction accuracy for late-stage cancer patients compared to CA125 alone but will significantly improve the prediction accuracy for early-stage patients. Our research findings may indicate a two-pronged strategy in assessing ovarian cancer risks using existing CA125 test followed by small RNA features to detect early-stage ovarian cancer. We are now collecting a larger number of early-stage cancer patient samples for validation. Citation Format: Yuliang Cao, Cun Han, Jianting Sheng, Samuel Mok, Stephen T.C. Wong. Exosomal non-coding RNAs as a biomarker to improve early ovarian cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3333.

Electrodermal activity and heart rate variability for detection of peripheral abnormalities in type 2 diabetes: A review.

Modern medicine exhibits an upward trend towards non-invasive methods for early detection of disease and long-term monitoring of patients' health. Diabetes mellitus and its complications are a promising area for implementation of new medical diagnostic devices. One of the most serious complications of diabetes is diabetic foot ulcer. The main causes responsible for diabetic foot ulcer are ischemia caused by peripheral artery disease and diabetic neuropathy caused by polyol pathway-induced oxidative stress. Autonomic neuropathy impairs function of sweat glands, which can be measured by electrodermal activity. On the other hand, autonomic neuropathy leads to changes in heart rate variability, which is used to assess autonomic regulation of the sinoatrial node. Both methods are enough sensitive to detect pathological changes caused by autonomic neuropathy and are promising screening methods for early diagnosis of diabetic neuropathy, which could prevent the onset of diabetic ulcer.

Application of Dual-Source CT Using Iterative Reconstruction Technology Combined with CT-FFR Parameters in the Diagnosis of Coronary Heart Disease.

In order to achieve early detection of myocardial ischemia and improve the diagnosis of coronary heart disease (CHD), it is necessary to find a convenient, non-invasive and effective examination method. This study aimed to explore the application value of dual-source computed tomography (CT) by using advanced modeled iterative reconstruction (ADMIRE) combined with computed tomography-fractional flow reserve (CT-FFR) technique in CHD, which provides imaging basis for early diagnosis of CHD and myocardial ischemia. Seventy-five CHD patients were examined by coronary computed tomography angiography (CCTA). Their CCTA images were reconstructed by iterative algorithm in ADMIRE 1-5 using post-processing workstation. The standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of proximal vascular images in right coronary artery (RCA), left main coronary artery (LM), left anterior descending artery (LAD) and left circumflex artery (LCX) were analyzed and compared. Invasive coronary angiography (ICA) was adopted in patients with ≥50% stenosis of coronary artery diameter. Taking ICA as the gold-standard method to accurately assess coronary arterial stenosis degree, the diagnostic efficiency of dual-source CT to diagnose coronary artery stenosis was analyzed. Some patients were subjected to myocardial CT perfusion (CTP) scanning and CT-FFR analysis, which facilitated analyzing the correlation and consistency between the diagnostic results of CTP and analysis results of CT-FFR. There was no statistical difference in the CT values of RCA, LM, LAD and LCX in groups with different ADMIRE reconstruction intensities (p > 0.05). But the noise, SNR and CNR interval were different among the iterative intensity groups (p < 0.05). Kappa consistency analysis was used to analyze the subjective evaluation results of image quality under different iterative reconstruction grades. The independent sample t-test performed on the subjective scores revealed that the scores on images were the best at ADMIRE 4. CCTA has sensitivity, specificity, positive predictive value, and negative predictive value of 91.52%, 97.59%, 97.98%, and 96.42% for identifying coronary artery stenosis, respectively. The diagnostic efficacy of CT-FFR, the Kappa analysis of myocardial CTP and CT-FFR results, which yields a Kappa value of 0.830 (p < 0.05). Spearman correlation analysis was used to statistically analyze the results of myocardial CTP and CT-FFR (correlation coefficient r = 0.774, p < 0.05). Diagnostic sensitivity and specificity were 93% and 95%, respectively. The dual-source CT using ADMIRE iterative algorithm has the best display of coronary vessels and higher image quality when the intensity is 4, and CT-FFR can be used as a non-invasive method for early detection of myocardial ischemia, which is worthy of clinical application.

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

MicroRNA Profiling from Tears as a Potential Non-invasive Method for Early Detection of Diabetic Retinopathy.

Diabetic retinopathy (DR) is a vascular complication of diabetes that can lead to partial or complete loss of vision. Early detection and treatment of DR can prevent blindness. Regular clinical examination is recommended for DR diagnosis; however, it is not always possible or feasible due to limited resources, expertise, time, and infrastructure. Several clinical and molecular biomarkers are proposed for the prediction of DR including microRNAs. MicroRNAs are a class of small non-coding RNAs that are found in biofluids and can be measured using reliable and sensitive methods. The most commonly used biofluid for microRNA profiling is plasma or serum; however, tear fluid (tears) is also demonstrated to contain microRNAs. MicroRNAs isolated from tears present a non-invasive source for DR detection. Different methods of microRNA profiling are available including digital PCR-based methods that can detect up to a single copy of microRNA in the biofluids. Here, we describe microRNA isolation from tears using manual method as well as using a high-throughput automated platform followed by microRNA profiling using digital PCR system.

Heart Sound Classification based on Residual Shrinkage Networks.

Heart sound classification is one of the non-invasive methods for early detection of the cardiovascular diseases (CVDs), the leading cause for deaths. In recent years, Computer Audition (CA) technology has become increasingly sophisticated, auxiliary diagnosis technology of heart disease based on CA has become a popular research area. This paper proposes a deep Convolutional Neural Network (CNN) model for heart sound classification. To improve the classification accuracy of heart sound, we design a classification algorithm combining classical Residual Network (ResNet) and Long Short-Term Memory (LSTM). The model performance is evaluated in the PhysioNet/CinC Challenges 2016 datasets using a 2D time-frequency feature. We extract the four features from different filter-bank coefficients, including Filterbank (Fbank), Mel-Frequency Spectral Coefficients (MFSCs), and Mel-Frequency Cepstral Coefficients (MFCCs). The experimental results show the MFSCs feature outperforms the other features in the proposed CNN model. The proposed model performs well on the test set, particularly the F1 score of 84.3 % - the accuracy of 84.4 %, the sensitivity of 84.3 %, and the specificity of 85.6 %. Compared with the classical ResNet model, an accuracy of 4.9 % improvement is observed in the proposed model.

Pro-inflammatory and (Epi-)genetic markers in saliva for disease risk in childhood obesity

Background and aimChildhood obesity is an emerging problem often leading to earlier onset of non-communicable diseases in later life. Biomarkers to identify individual risk scores are insufficient in routine clinical practice, which is related to the need for easily sampled, non-invasive survey methods in children. We aimed to investigate and strengthen possible pro-inflammatory markers and epigenetic risk factors in saliva of obese children compared to lean controls. Methods and results19 overweight/obese (OC, 10.1 ± 1.9 years, BMI 27.7 ± 3.2 kg/m2) and 19 lean control children (CC, 9.7 ± 2.5 years, BMI 16.4 ± 1.8 kg/m2) participated in this explorative pilot study. Anthropometric measures, saliva and cheek swab samples were taken. Saliva profiles were examined for acute phase proteins (CRP and neopterin) and pro-inflammatory cytokines (IL-17a/IL-1β/IL-6). Cheek swabs were analyzed to investigate DNA methylation differences with subsequent hierarchical cluster and principal component analyses (PCA). Saliva analysis showed significant increased CRP concentrations in OC compared to CC (p < 0.001). There were no significant differences, but high intra-individual values in neopterin, IL-17a, IL-1β and IL-6. An unsupervised PCA of CpG loci with high variance (σ/σmax > 0.2) clearly separated OC and CC according to their methylation pattern. Furthermore, a supervised approach revealed 7125 significantly differentially methylated loci, whose corresponding genes were significantly enriched for genes playing roles in e.g., cellular signalling, cytoskeleton organization and cell motility. ConclusionsCRP and methylation status determinations in saliva are suitable as non-invasive methods for early detection of risks for non-communicable diseases in children/adolescents and might be a useful supplementary approach in the routine clinical practice/monitoring.