Normal Individuals Research Articles

Are Chiari malformation and basilar invagination associated with jugular foramen stenosis?

To compare the diameter of the jugular foramen (JF) between normal individuals and those with the diagnosis of craniovertebral junction abnormalities, such as Chiari malformation type I (CMI) and/or basilar invagination (BI). This cross-sectional analytical study measured the diameter of right and left JF in magnetic resonance imaging of patients that were split into four groups: 68 with combined CMI and BI (CMI+BI), 42 with isolated BI, 45 with isolated CMI, and 102 control cases. The t test determined sex differences, while univariate ANOVA with Tukey's post-hoc test assessed JF differences between groups. Considering all combinations of laterality and sex, CMI+BI had JF narrower than their respective controls. The JF was bilaterally narrower in BI than the control, only in females. Regardless of laterality, CMI had wider JF than the CMI+BI in males, and it did not differ from the control. In the females of CMI, the right JF did not differ from the control, and it was wider than the CMI+BI; while the left JF was narrower than the control, and it did not differ from the CMI+BI. JF narrowing is associated with CMI and BI when they are found combined in the same patient. When CMI or BI are detected separately, they do not imply evident changes in JF morphometry. It is expected that these findings could bring empiric substrate to support theories that claims a possible role of intracranial pressure disturbances as a triggering factor for CMI.

Co-Occurrence of Apathy and Impulse Control Disorders in Parkinson Disease: Variation across Multiple Measures.

To determine if the co-occurrence of apathy and impulse control disorders (ICDs) in Parkinson disease is dependent on instrument selection and assess the concurrent validity of three motivation measures by examining interrelationships between them. Ninety-seven cognitively normal individuals with idiopathic Parkinson disease (PD) completed the Questionnaire for Impulsive-Compulsive Disorders in Parkinson Disease-Rating Scale (QUIP-RS) and three apathy measures: the Apathy Scale, Lille Apathy Rating Scale, and Item 4 of the Movement Disorder Society-Unified Parkinson Disease Rating Scale. Fifty (51.5%) participants were classified as apathetic on at least one measure, and only four individuals (4.3%) obtained clinically elevated scores on all three measures. The co-occurrence of apathy and ICD varied across measures. We observed a co-occurrence of apathy and ICDs in PD patients with each apathy instrument; however, limited concurrent validity exists across measures. This is important for future investigations into shared pathophysiology and the design of future clinical trials aimed at improving the early detection and treatment of these debilitating syndromes.

Postural effects on blood pressure, intraocular pressure, and ocular perfusion pressure in patients with spinal cord injury

Background Reduced ocular perfusion pressure (OPP) from hypotension and consistent OPP variability due to blood pressure (BP) fluctuations are predisposing factors for glaucoma. Low resting BP and orthostatic hypotension (OH) in spinal cord injury (SCI) patients may increase the risk of glaucoma post-injury. This study investigated BP, intraocular pressure (IOP), and OPP changes between supine and sitting positions in SCI and normal individuals. Methods Twenty SCI cases (high paraplegia, T1–T6 n = 6; low paraplegia, T7–L3 n = 14) and matched controls (1:1) were included. Systolic and diastolic BP (SBP and DBP) were measured digitally, and IOP with rebound tonometry. Measurements were taken one minute apart in both positions. Mean arterial pressure (MAP) was calculated, and OPP was determined using position-specific formulas. Results No SCI subjects exhibited OH. Both groups experienced significant BP increment (P < 0.05) from supine to sitting. SBP increased by 4.4 ± 8.4 mmHg (SCI) and 3.6 ± 6.2 mmHg (normal), while DBP increased by 4.2 ± 5.1 mmHg (SCI) and 6.7 ± 5.5 mmHg (normal). IOP and OPP decreased significantly (P < 0.05) after postural change in both groups, yet differences between them were not significant. Analysis by injury level revealed lower parameter values in high paraplegia than in low paraplegia, with the latter group showing a more significant reduction in OPP after postural change. Conclusion Postural changes differently affect BP, IOP, and OPP in SCI compared to normal individuals, with variations based on the level of SCI. While not directly assessing glaucoma, the study offers insights into ocular hemodynamics in SCI compared to normals.

A comparative study on cutaneous temperature difference between body and foot among patients of diabetic neuropathy and normal individuals

Background: As diabetic neuropathy progresses, the pain subsides and eventually disappears, but a sensory deficit persists and a motor deficit may develop. Loss of protective sensation and distal sensory polyneuropathy are major risk factors for foot ulceration and falls due to small and large nerve fiber dysfunction, which predispose to lower extremity amputation. Aims and Objectives: We did a comparative study on the cutaneous temperature difference between the body and foot among patients of diabetic neuropathy and patients of diabetes without neuropathy to establish the role of pedal temperature monitoring in preventing complications of diabetic neuropathy. Materials and Methods: The study was done at Maharani Laxmi Bal Medical College, Jhansi, between April 2021 and November 2022, including two groups of patients (Group A: 100 patients with diabetes without neuropathy and Group B: 100 patients with diabetes as well as with neuropathy). Results: In our study, in group A (diabetic patients without neuropathy), 9% were in 18–30 years, 14% in 31–40 years, 19% in 41–50, 29% in 51–60, and 29% in &gt;60 years. In group B (diabetic patients with neuropathy), 2% were in 31–40 years, 16% in 41–50, 35% in 51–60, and in 46% in &gt;60 years. In our study, the mean foot temperature in group A (diabetic without neuropathy) was 97.35±0.625 and in group B (diabetic patients with neuropathy) was 99.19±1.375. There was a significant difference between the groups on the basis of mean changes in foot temperature (P≥0.01). Conclusion: Based on the above study, we came to the conclusion that foot temperature is significantly higher in diabetic patients with neuropathy. An infrared thermometer can be used as a home-based tool as well as in an outpatient department setting to avoid the dreaded complications of diabetic neuropathy or at least to delay the progression of the same.

Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network

The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

The usefulness of ultrasonography for the early diagnosis of polyneuropathy in patients with type II diabetes mellitus

Background: Diabetic neuropathies, affecting over 50% of diabetes patients, present varied forms of polyneuropathy impacting somatic and autonomic nerves. Clinical assessments, laboratory findings, and nerve conduction studies (NCSs) aid in precise diagnosis, while recent technological advances highlight ultrasonography (US) as a promising, cost-effective diagnostic tool. Aims and Objectives: This study aims to assess the efficacy of US in early polyneuropathy diagnosis in type II diabetes, exploring its potential as an alternative to NCS. Materials and Methods: In this prospective observational study, we enrolled 30 adult patients with type II non-controlled diabetes and 30 age-matched controls, comprising 15 controlled diabetic patients and 15 normal individuals. The comprehensive assessments encompassed history-taking for diabetes duration, hypertension, and diabetic complications, along with clinical examinations and investigations involving nerve electrophysiology and US on one upper limb based on patient complaints. Results: Significant increases in the cross-sectional area (CSA) of the median nerve were observed in diabetic peripheral neuropathy groups compared to controls, with a P&lt;0.001. Similarly, at the level behind the medial epicondyle, the ulnar nerve CSA exhibited a significant increase in the diabetic peripheral neuropathy group (11.7 mm2) compared to controls (6.7 mm2) with a P&lt;0.002. In addition, a significant difference in cross-sectional values was found in the median nerve proximal to the carpal tunnel between the two groups (P&lt;0.05). In contrast, no significant differences were observed at other sites. Regarding the relationship between sonography and NCSs, NCSs showed higher sensitivity, but sonography demonstrated comparable specificity in evaluating diabetic peripheral neuropathy. Conclusion: Peripheral nerve US in the upper limb can be a valuable diagnostic tool for identifying and diagnosing diabetic peripheral neuropathy.

491. Higher Availability of the α7 Nicotinic Acetylcholine Receptor in the Brains of Older, Cognitively Normal Individuals

491. Higher Availability of the α7 Nicotinic Acetylcholine Receptor in the Brains of Older, Cognitively Normal Individuals

Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness.

Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.

Detection of Cardiovascular Diseases from ECG Images Using Machine Learning and Deep Learning Methods

Abstract: Cardiovascular diseases (CVDs) are the leading cause of death globally, emphasizing the importance of early prediction and classification for saving lives. Electrocardiogram (ECG) is a widely used, cost-effective, and noninvasive tool for measuring heart electrical activity, crucial for detecting CVDs. This study leverages deep learning techniques to predict four major cardiac abnormalities—abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal individuals—using a public dataset of ECG images from cardiac patients. The research explores transfer learning with pretrained deep neural networks such as SqueezeNet and AlexNet, as well as introduces a novel convolutional neural network (CNN) architecture for cardiac abnormality prediction. Additionally, pretrained models and the new CNN architecture are utilized as feature extraction tools for traditional machine learning algorithms including support vector machine, K-nearest neighbors, decision tree, random forest, and Naïve Bayes. Experimental results showcase the superiority of the proposed CNN model over existing works, achieving 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Furthermore, when the proposed CNN model is utilized for feature extraction, it achieves the highest score of 99.79% using the Naïve Bayes algorithm.

Prediction of Unilateral Vocal Cord Paralysis Patients Through Machine Learning Analysis of Acoustic Parameters: A Preliminary Study

Background and Objectives The purpose of this study is to evaluate value of diagnostic tool for vocal cord palsy utilizing artificial intelligence without laryngoscopeMaterials and Method A dataset consisting of recordings from patients with unilateral vocal cord paralysis (n=54) as well as normal individuals (n=163). The dataset included prolonged pronunciations of the vowels /ah/, /u/, /i/, and vocal cord data from paralyzed patients. Various acoustic parameters such as Mel-frequency cepstral coefficients, jitter, shimmer, harmonics-to-noise ratio, and fundamental frequency statistics were analyzed. The classification of vocal cord paralysis encompassed paralysis status, paralysis degree, and paralysis location. The deep learning model employed the leave-one-out method, and the feature set with the highest performance was selected using the following methods.Results Vocal Cord Paralysis Classifier: The classifier accurately distinguished normal voice from vocal cord paralysis, achieving an accuracy and F1 score of 1.0. Paralysis Location Classifier: The classifier accurately differentiated between median and paramedian vocal cord paralysis, achieving an accuracy and micro F1 score of 1.0. Breathiness Degree Classifier: The classifier achieved an accuracy of 0.795 and a mean absolute error of 0.2857 in distinguishing different degrees of breathiness.Conclusion Although the small sample size raises concerns of potential overfitting, this preliminary study highlights distinctive acoustic features in cases of unilateral vocal fold paralysis compared to those of normal individuals. These findings suggest the feasibility of determining the presence, degree, and location of paralysis through the utilization of acoustic parameters. Further research is warranted to validate and expand upon these results.

Comparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Images

The spine is composed of pieces of bone called vertebrae that lie between the skull and the tailbone. Various medical conditions can affect the spine. In this study, two types of degenerative diseases, scoliosis, and spondylolisthesis, were studied. Deep AI architectures have recently enabled further disease diagnosis innovation using medical images. Various traditional and deep learning studies use medical images for disease diagnosis in the literature. This study aims to classify spine X-ray images according to three possible conditions (Normal, Scoliosis, and Spondylolisthesis) and to exploit the potential of these X-ray images to detect possible diseases occurring in the spine. The performance of deep learning models and optimization algorithms used in this process was evaluated. The study uses a data set created and/or analyzed during an existing study. This data set consists of images that belong to three different classes: scoliosis, spondylolisthesis, or x-ray images of normal (i.e. healthy) individuals. A total of 338 spine X-ray images, 188 scoliosis images, 79 spondylolisthesis images, and 71 normal images. Six different deep-learning architectures have been used in the study. These architectures are Alexnet, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, and EfficientNet-bo. While working on these deep architectures, each model has been evaluated using different optimization algorithms. These optimization algorithms are RmsProp, SGDM, and Adam. According to the classification processes, the deep learning model with the highest accuracy value was Alexnet, and the optimization algorithm used with it, Sgdm (99.01%), and the training time lasted 38 seconds. According to the classification processes, the deep learning model with the fastest completion time (30 seconds) was Alexnet and the optimization algorithm used with it was RmsProp. An accuracy rate of 98.02% has been obtained in the training of this model.

Chemerin Levels in Individuals with Type 2 Diabetes and a Normal Weight versus Individuals with Type 2 Diabetes and Obesity: An Observational, Cross-Sectional Study

Chemerin acts as both a chemotactic agent and an adipokine that undergoes proteolytic cleavage, converting inactive precursors into their active forms before being subsequently inactivated. Elevated chemerin levels are linked to obesity and type 2 diabetes mellitus (T2D). This study aimed to elucidate the effects of T2D and obesity on chemerin levels by comparing plasma samples from individuals with a normal weight and T2D (BMI &lt; 25; NWD group n = 22) with those from individuals who are overweight or obese and have T2D (BMI ≥ 25; OWD group n = 39). The total chemerin levels were similar in the NWD and OWD groups, suggesting that T2D may equalize the chemerin levels irrespective of obesity status. The cleavage of chemerin has been previously linked to myocardial infarction and stroke in NWD, with potential implications for inflammation and mortality. OWD plasma exhibited lower levels of cleaved chemerin than the NWD group, suggesting less inflammation in the OWD group. Here, we showed that the interaction between obesity and T2D leads to an equalization in the total chemerin levels. The cleaved chemerin levels and the associated inflammatory state, however, differ significantly, underscoring the complex relationship between chemerin, T2D, and obesity.

Determination and Correlation of Finger Print Pattern and Blood Grouping in Diabetes Mellitus: An Analytical Study

BACKGROUND: To Determine and correlate between fingerprint patterns and blood group in type II diabetes mellitus. RESULTS: One hundred individuals with type II diabetes mellitus and One hundred normal individuals as controls were selected for study. Fingerprints were obtained by dactyloscopy. Various fingerprint patterns like arches, whorls, loops were studied and correlated with the respective blood group based on ABO system among the volunteer controls and diabetics. After obtaining results, the analysis was made using Chi square test and One way ANOVA which showed, the most predominant fingerprint pattern among the 100 diabetic volunteers is “Arch pattern” and the most predominant blood group was “O” positive among the diabetic volunteers. Also correlating both finger print and blood group “Whorl pattern” with “B” blood group showed higher predominance among the healthy volunteers.  CONCLUSIONS: This is the first study correlating finger print pattern with blood grouping in subjects of Diabetes mellitus. Our study findings showed that O positive blood group with arch pattern type of finger print was the most common findings among diabetes. Thus correlating fingerprint pattern and blood grouping act as a early predictor tool for diagnosing Type II Diabetes mellitus.

Identifying the Trends of Urinary microRNAs within Extracellular Vesicles for Esophageal Cancer

Background: The advancement of multidisciplinary treatment has increased the need to develop tests to monitor tumor burden during treatment. We herein analyzed urinary microRNAs within extracellular vesicles from patients with esophageal squamous cell carcinoma (ESCC) and normal individuals using a microarray. Methods: Patients with advanced ESCC who underwent esophagectomy (A), endoscopic submucosal resection (ESD) (B), and healthy donors (C) were included. Based on microRNA expression among the groups (Analysis 1), microRNAs with significant differences between groups A and C were selected (Analysis 2). Of these candidates, microRNAs in which the change between A and C was consistent with the change between B and C were selected for downstream analysis (Analysis 3). Finally, microRNA expression was validated in patients with recurrence from A (exploratory analysis). Results: For analysis 1, 205 microRNAs were selected. For Analyses 2 and 3, the changes in 18 microRNAs were consistent with changes in tumor burden as determined by clinical imaging and pathological findings. The AUC for the detection of ESCC using 18 microRNAs was 0.72. In exploratory analysis, three of eighteen microRNAs exhibited a concordant trend with recurrence. Conclusions: The current study identified the urinary microRNAs which were significantly expressed in ESCC patients. Validation study is warranted to evaluate whether these microRNAs could reflect tumor burden during multidisciplinary treatment for ESCC.

A non-linear relationship between blood pressure and mild cognitive impairment in elderly individuals: A cohort study based on the Chinese longitudinal healthy longevity survey (CLHLS).

Hypertension is an established risk factor for mild cognitive impairment (MCI) in elderly individuals. Nevertheless, the impact of different levels of blood pressure on the progression of MCI remains uncertain. This study aims to investigate the non-linear relationship between blood pressure and MCI in the elderly and detect the critical blood pressure threshold, thus, improving blood pressure management for individuals at high risk of MCI. Data was obtained from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) cohort. We chose normal cognitive elderly individuals who entered the cohort in 2014 for a 5-year follow-up to observe the progression of MCI. Subsequently, we utilized the Cox regression model to identify risk factors for MCI and conducted a Cox-based restricted cubic spline regression (RCS) model to examine the non-linear relationship between systolic blood pressure (SBP) and diastolic blood pressure (DBP) with MCI, determining the critical blood pressure threshold for MCI progression. In the elderly population, female (HR = 1.489, 95% CI: 1.017-2.180), lacking of exercise in the past (HR = 1.714, 95% CI: 1.108-2.653), preferring animal fats (HR = 2.340, 95% CI: 1.348-4.061), increased age (HR = 1.061, 95% CI: 1.038-1.084), increased SBP (HR = 1.036, 95% CI: 1.024-1.048), and increased DBP (HR = 1.056, 95% CI: 1.031-1.081) were associated with MCI progression. After adjusting factors such as gender, exercise, preferred types of fats, and age, both SBP (P non-linear < 0.001) and DBP (P non-linear < 0.001) in elderly individuals exhibited a non-linear association with MCI. The risk of MCI rose when SBP exceeded 135mmHg and DBP was in the range of 80-88mmHg. However, when DBP exceeded 88mmHg, there was a declining trend in MCI progression, although the HR remained above 1. The identified critical blood pressure management threshold for MCI was 135/80mmHg. In this study, we discovered that risk factors affecting the progression of MCI in elderly individuals comprise gender (female), preferring to use animal fat, lack of exercise in the past, increased age, increased SBP, and increased DBP. Additionally, a non-linear relationship between blood pressure levels and MCI progression was confirmed, with the critical blood pressure management threshold for MCI onset falling within the prehypertensive range.

Application of Deep Learning for the Detection of Genetic Variations: Its Implementation in Classifying Alzheimer's Disease

Deep learning emerges as a promising technique, utilizing nonlinear transformations for feature extraction from high-dimensional datasets. However, its application encounters challenges in genome-wide association studies (GWAS) dealing with high-dimensional genomic data. This study introduces an innovative three-step method termed SWAT-CNN for the identification of genetic variants. This approach employs deep learning to pinpoint phenotype-related single nucleotide polymorphisms (SNPs), facilitating the development of precise disease classification models. In the first step, the entire genome undergoes division into non overlapping fragments of an optimal size. Subsequently, convolutional neural network (CNN) analysis is conducted on each fragment to identify phenotype-associated segments. The second step, employs a Sliding Window Association Test (SWAT), where CNN is utilized on the selected fragments to compute phenotype influence scores (PIS) and detect phenotype-associated SNPs based on these scores. The third step involves running CNN on all identified SNPs to construct a comprehensive classification model. Validation of the proposed approach utilized GWAS data from the Alzheimer’s disease Neuroimaging Initiative (ADNI), encompassing 981 subjects, including cognitively normal older adults (CN) and individuals with Alzheimer's disease (AD). Notably, the method successfully identified the widely recognized APOE region as the most significant genetic locus for AD. The resulting classification model exhibited an area under the curve (AUC) of 0.82, demonstrating compatibility with traditional machine learning approaches such as random forest and XGBoost. SWAT-CNN, as a groundbreaking deep learning-based genome-wide methodology, not only identified AD-associated SNPs but also presented a robust classification model for Alzheimer's disease, suggesting potential applications across diverse biomedical domains.

Examining a Preclinical Alzheimer's Cognitive Composite for Telehealth Administration for Reliability Between In-Person and Remote Cognitive Testing with Neuroimaging Biomarkers.

The preclinical Alzheimer's cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline. At the outset of the COVID-19 pandemic, cognitive testing was increasingly performed remotely by telephone or video administration. It is desirable to have a harmonized composite measurement derived from both in-person and remote assessments for identifying cognitive changes and to examine its relationship with common neuroimaging biomarkers. We defined a telehealth compatible PACC (tPACC) and examined its relationship with neuroimaging biomarkers related to neurodegeneration, brain function and perfusion, white matter integrity, and amyloid-β. We examined 648 participants' neuroimaging and in-person and remote cognitive testing data from the Wake Forest Alzheimer's Disease Research Center's Clinical Core cohort (observational study) to calculate a modified PACC (PACC5-RAVLT) score and tPACC scores (in-person and remote). We performed Spearman/intraclass correlation coefficient (ICC) analyses for reliability of tPACC scores and linear regression models to evaluate associations between tPACC and neuroimaging. Bland-Altman plots for agreement were constructed across cognitively normal and impaired (mild cognitive impairment and dementia) participants. There was a significant positive relationship between tPACCin-person and PACC5-RAVLT (Overall group: r2 = 0.94, N = 648), and tPACCin-person and tPACCremote (validation subgroup: ICC = 0.82, n = 53). Overall, tPACC showed significant associations with brain thickness/volume, gray matter perfusion, white matter free water, and amyloid-β deposition. There is a good agreement between tPACCand PACC5-RAVLTfor cognitively normal and impaired individuals. The tPACC is associated with common neuroimaging markers of Alzheimer's disease.

Distribution of Normative Percentiles of Liver Stiffness Measurement Using Ultrasound Shear Wave Elastography in an Adult Asian Indian Population

Abstract Objective The aim of this study was to determine the normative percentiles for liver stiffness measurement (LSM) using shear wave elastography in an adult Asian Indian population as part of the preventive radiology initiative of the Indian Radiological and Imaging Association (IRIA). Methods LSMs were ascertained by two-dimensional (2D) shear wave elastography using the Mindray Resona series of ultrasound machines. The image quality was assessed using the motion stability index (M-STB) and reliability (RLB) map. Ten acquisitions were documented, and an interquartile range-to-median (IQR/M) ratio ≤30% kilopascal (kPa) units was considered a good-quality measurement. A subgroup of the study population without comorbidities was chosen to derive the normative percentile distribution of LSM using a generalized least squares multivariable fractional polynomial regression model that adjusted for sex and body mass index (BMI). The effectiveness of the estimated percentiles was assessed on the entire study population using the greater than 90th percentile value of the LSM as the cutoff for abnormality. Results The study included 852 people who underwent shear wave elastography from June 2022 to July 2023. The magnitude of compensated advanced chronic liver disease (cACLD) and clinically significant portal hypertension (CSPH) was 6.81% (95% confidence interval [CI]: 5.30–8.7) and 4.91% (95% CI: 3.67–6.60), respectively. The normative percentiles were estimated from 282 persons without associated comorbidity and risk factors. The mean age (standard deviation [SD]) of the normal individuals was 40.90 ± 12.92 years, and 210 (71.47%) were males. The mean age (SD) of the 570 persons excluded from the normative percentiles analysis was 47.94 (12.49) years and 72.11% were males. The sex- and BMI-adjusted age-specific 90th percentiles of LSM were 8.76, 8.78, 8.96, 8.97, 9.25, and 9.45 kPa for 18 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 to 70 years, respectively. Conclusion The sex- and BMI-adjusted age-specific 90th percentiles for LSM using shear wave elastography in Asian Indian adults are almost similar to the greater than 9 kPa cutoff proposed by the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement guidelines to discriminate cACLD and CSPH from normal individuals.

Evaluation of the relationship between left atrial stiffness, left ventricular stiffness, and left atrioventricular coupling index in type 2 diabetes patients: a speckle tracking echocardiography study

BackgroundCardiovascular complications are a leading cause of mortality and disability in individuals with diabetes mellitus (DM). Moreover, DM can directly impact the structure and function of cardiac muscle. We conducted a study to evaluate cardiac stiffness in DM patients in both the left atrium (LA) and left ventricle (LV), as well as to assess the impact of DM on the synchronization of the LA and LV, particularly within the Vietnamese population, utilizing speckle tracking echocardiography (STE).MethodsWe studied 111 research subjects divided into two groups comprising 52 patients with DM and 59 healthy individuals. All the subjects provided relevant clinical information, and echocardiography was performed to assess the indices of LA stiffness, LV stiffness, and left atrioventricular coupling index (LACI).ResultsOur study indicated that DM patients exhibited greater LA and LV stiffness than control patients. The LACI (%) in the DM group was also greater than that in the control group (17.12% ± 6.72% vs. 12.28% ± 3.96%, respectively; p &amp;lt; 0.001). The LACI was positively correlated with the LA and LV stiffness indices. Decreased levels of LV GLS, adjusted for age, sex, blood pressure, and BMI, have emerged as identified risk factors for DM.ConclusionsLA stiffness, LV stiffness, and the LACI are greater in DM patients than in normal individuals.

Prevalence and plasma exosome-derive microRNA diagnostic biomarker screening of adolescent idiopathic scoliosis in Yunnan Province, China

BackgroundIdiopathic scoliosis significantly affects the physical and mental health of children and adolescents, with varying prevalence rates in different regions. The occurrence of idiopathic scoliosis is associated with genetic regulation and biochemical factors, but the changes in exosome-derived miRNA profiles among idiopathic scoliosis patients remain unclear. This study aimed to determine the prevalence of idiopathic scoliosis in Yunnan Province, China, and identify key exosome-derived miRNAs in idiopathic scoliosis through a cohort study.MethodsFrom January 2018 to December 2020, a cross-sectional study on idiopathic scoliosis in children and adolescents was conducted in Yunnan Province. A total of 84,460 students from 13 cities and counties in Yunnan Province participated in a scoliosis screening program, with ages ranging from 7 to 19 years. After confirmation through screening and imaging results, patients with severe idiopathic scoliosis and normal control individuals were selected using propensity matching. Subsequently, plasma exosome-derived miRNA sequencing and RT-qPCR validation were performed separately. Based on the validation results, diagnostic performance analysis and target gene prediction were conducted for differential plasma exosome-derived miRNAs.ResultsThe overall prevalence of idiopathic scoliosis in children and adolescents in Yunnan Province was 1.10%, with a prevalence of 0.87% in males and 1.32% in females. The peak prevalence was observed at age 13. Among patients diagnosed with idiopathic scoliosis, approximately 12.8% had severe cases, and there were more cases of double curvature than of single curvature, with thoracolumbar curvature being the most common in the single-curvature group. Sequencing of plasma exosome-derived miRNAs associated with idiopathic scoliosis revealed 56 upregulated and 153 downregulated miRNAs. Further validation analysis confirmed that hsa-miR-27a-5p, hsa-miR-539-5p, and hsa-miR-1246 have potential diagnostic value.ConclusionsWe gained insights into the epidemiological characteristics of idiopathic scoliosis in Yunnan Province and conducted further analysis of plasma exosome-derived miRNA changes in patients with severe idiopathic scoliosis. This study has provided new insights for the prevention and diagnosis of idiopathic scoliosis, paving the way for exploring clinical biomarkers and molecular regulatory mechanisms. However, further validation and elucidation of the detailed biological mechanisms underlying these findings will be required in the future.