Non-invasive Method Research Articles

Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population

BACKGROUND Hepatitis C virus (HCV) infection progresses through various phases, starting with inflammation and ending with hepatocellular carcinoma. There are several invasive and non-invasive methods to diagnose chronic HCV infection. The invasive methods have their benefits but are linked to morbidity and complications. Thus, it is important to analyze the potential of non-invasive methods as an alternative. Shear wave elastography (SWE) is a non-invasive imaging tool widely validated in clinical and research studies as a surrogate marker of liver fibrosis. Liver fibrosis determination by invasive liver biopsy and non-invasive SWE agree closely in clinical studies and therefore both are gold standards. AIM To analyzed the diagnostic efficacy of non-invasive indices [serum fibronectin, aspartate aminotransferase to platelet ratio index (APRI), alanine aminotransferase ratio (AAR), and fibrosis-4 (FIB-4)] in relation to SWE. We have used an Artificial Intelligence method to predict the severity of liver fibrosis and uncover the complex relationship between non-invasive indices and fibrosis severity. METHODS We have conducted a hospital-based study considering 100 untreated patients detected as HCV positive using a quantitative Real-Time Polymerase Chain Reaction assay. We performed statistical and probabilistic analyses to determine the relationship between non-invasive indices and the severity of fibrosis. We also used standard diagnostic methods to measure the diagnostic accuracy for all the subjects. RESULTS The results of our study showed that fibronectin is a highly accurate diagnostic tool for predicting fibrosis stages (mild, moderate, and severe). This was based on its sensitivity (100%, 92.2%, 96.2%), specificity (96%, 100%, 98.6%), Youden’s index (0.960, 0.922, 0.948), area under receiver operating characteristic curve (0.999, 0.993, 0.922), and Likelihood test (LR+ > 10 and LR- < 0.1). Additionally, our Bayesian Network analysis revealed that fibronectin (> 200), AAR (> 1), APRI (> 3), and FIB-4 (> 4) were all strongly associated with patients who had severe fibrosis, with a 100% probability. CONCLUSION We have found a strong correlation between fibronectin and liver fibrosis progression in HCV patients. Additionally, we observed that the severity of liver fibrosis increases with an increase in the non-invasive indices that we investigated.

Giant Cell Arteritis: Can Simple Ultrasound Examination Prevent Complex Consequences?

Giant cell arteritis (GCA) is a rare disease of the arteries, occurring mainly in the elderly. Although the involvement of temporal arteries can be mostly symptomatic, the occlusion of ophthalmic arteries has severe consequences. GCA affecting temporal arteries is an emergency requiring quick commencement of treatment with glucocorticoids due to the serious consequences of neglect—blindness. According to the new guidelines released by EULAR, ultrasound is the tool of choice in support of the clinical diagnosis of giant cell arteritis, replacing temporal artery biopsy (TAB), as it is a sensitive and non-invasive method that is widely available. The main limitation is that the reliability of this imaging is based on the technical expertise of ultrasonographers. However, performing imaging should not delay commencing the treatment. In this work, we present ultrasound images from a case report of a 74-year-old female patient where difficulties in establishing a diagnosis led to vision loss in both eyes. In this example, we describe the ultrasound findings in giant cell arteritis, emphasizing its usefulness in supporting a diagnosis of GCA.

Comparison of Stool Antigen and PCR-Based Diagnostics for Helicobacter pylori Infection and Associated Gut Dysbiosis in Wad-Medani City, Sudan

Background: The development of stomach illnesses has been linked, in part, to Helicobacter pylori (H. pylori) infection; in clinical practice, accurate identification of Helicobacter pylori infection is crucial, H. pylori infection was associated with a decrease in microbial diversity and an increase in pathogenic taxa, indicating its role in gut dysbiosis. Objectives: We tested a noninvasive method using immunochromatography to detect the presence of the H. pylori antigen in stool specimens. PCR was used to validate the presence of H. pylori by identifying the bacteria's ureC gene in the DNA extracted from stool specimens. Additionally, gut microbiome changes were assessed, understanding the link between H. pylori infection and gut dysbiosis is vital for public health interventions. Method: A total of 100 stool samples obtained from dyspeptic patients (46 men and 54 women, average age 46.1) were assessed using these methods. The results were then compared between the PCR-based technique and the SAT technique. Results: The results showed that 35 (35%) out of 100 samples were positive by the PCR-based technique, compared to 33 (33%) positive by the SAT technique and The PCR method demonstrated a sensitivity of 95% and specificity of 92% in detecting H. pylori. Beta diversity was assessed using Bray-Curtis dissimilarity, revealing significant variations in microbial composition among samples. Conclusion: These data suggest that the techniques used in this study are valuable for studying the molecular epidemiology of H. pylori infection in dyspeptic patients. Stool, as a non-invasive sample, has the potential to be a good replacement for the detection of H. pylori. Additionally, we found that infection with this bacterium contributes to gastric microbial dysbiosis, while no statistically significant differences were observed.

Noninvasive Method Using Mechanical Extensometer for the Estimation of Intracranial Compliance by Repeated Measures Agreement Analysis.

Intracranial compliance refers to the relationship between changes in volume and the resultant changes in intracranial pressure (ICP). This study aimed to assess the agreement of a noninvasive ICP waveform device for the estimation of compliance compared with invasive ICP monitoring employing three distinct methods. We conducted a retrospective analysis of ICP waveform morphology recorded through both invasive (external ventricular drain) and noninvasive (mechanical extensometer) methods in adult patients with acute brain injury admitted to a neurointensive care unit between August 2021 and August 2022. Compliance was calculated as the amplitude of the fundamental component of cerebral arterial blood volume (estimated with concurrent Transcranial Doppler [TCD] recordings), divided by the amplitude of the fundamental component of the invasive and noninvasive ICP waveforms. Subsequently, we assessed the agreement between invasive and noninvasive intracranial compliance by repeated measures correlation coefficient analysis using three methods: TCD-derived, P2/P1 ratio, and time-to-peak (TTP). A linear mixed-effects model was used to compute the concordance correlation coefficient, total deviation index, and coefficient of individual agreement. Coverage probability plot was calculated to estimate the percent of observations within different cut points for each of the three methods. A total of 21 patients were identified for this study. Repeated measures correlation analysis showed a strong correlation (R = 0.982, 95% confidence interval [0.980-0.984], p < 0.0001) between log-transformed noninvasive and invasive compliance. Agreement statistics for TCD, P2/P1 ratio, and TTP indicated that although the concordance correlation coefficient was highest for log(TCD) values, TTP and P2:P1 ratio measures had better agreement with total deviation index and coverage probability plot analyses. Repeated measures correlations suggest that ICP waveform analyses may offer a more accurate estimate of compliance than TCD-derived methods for noninvasive ICP monitoring. Further validations studies are warranted to confidently establish this method as an indicator of intracranial compliance.

Low-Level Tragus Stimulation Attenuates Blood Pressure in Young Individuals With Hypertension: Results From a Small-Scale Single-Blind Controlled Randomized Clinical Trial.

Hypertension is a significant risk factor for cardiovascular and chronic kidney diseases. Its management in young people remains limited. Device-based therapies, such as low-level tragus stimulation (LL-TS), a noninvasive method that reduces sympathetic activity, have recently been explored for resistant hypertension. This trial involved patients with Grade 1 hypertension with no other medical history. LL-TS (20 Hz, 1 mA, 1 h/day) was applied for 3 months on the tragus (Intervention group [IG]) or earlobe (Control group [CG]). Blood pressure and outcomes were assessed at the first, second, and third months. Among 40 patients, 21 were in IG and 19 in CG. Baseline systolic blood pressure was similar between IG (142.62±8.18 mm Hg) and CG (143.00±8.61 mm Hg), P=0.89. Post-LL-TS, systolic blood pressure showed significant reductions in IG compared with CG at the first (IG: 134.47±5.95 mm Hg, CG: 141.28±6.78 mm Hg, P=0.002), second (IG: 132.50±7.51 mm Hg, CG: 140.62±7.15 mm Hg, P=0.001), and third months (IG: 128.81±7.13 mm Hg, CG: 136.51±7.96 mm Hg, P=0.003). diastolic blood pressure also differed significantly: first month (IG: 85.34±5.81 mm Hg, CG: 89.74±6.32 mm Hg, P=0.03), second month (IG: 82.12±5.22 mm Hg, CG: 88.57±7.11 mm Hg, P=0.002), and third month (IG: 80.71±5.96 mm Hg, CG: 87.55±5.26 mm Hg, P=0.001). Heart rate was unchanged (P>0.05). Only 0.01% of IG subjects reported site irritation, with no serious adverse events. LL-TS led to significant blood pressure reductions in young patients with essential hypertension. Further larger trials are needed to confirm the safety and efficacy of LL-TS. URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR2000038448.

Exploring Metabolic Aberrations after Intracerebral Hemorrhage In Vivo with Deuterium Metabolic Spectroscopy Imaging.

Aberrations in metabolism after intracerebral hemorrhage (ICH), particularly lactate metabolism, play a crucial role in the pathophysiology and patient outcome. To date, the evaluation of metabolism relies heavily on invasive methods such as microdialysis, restricting a comprehensive understanding of the metabolic mechanisms associated with ICH. This study proposes a noninvasive metabolic imaging method based on 2H magnetic resonance spectroscopy and imaging (2H-MRS/MRSI) to detect metabolic changes after ICH in vivo. To overcome the low-sensitivity limitation of 2H, we designed a new 1H-2H double-resonance coil with 2H-channel active detuning and proposed chemical shift imaging based on the balanced steady-state free precession method (CSI-bSSFP). Compared with the volume coil, the signal-to-noise ratio (SNR) of the new coil was increased by 4.5 times. In addition, the SNR of CSI-bSSFP was 1.5 times higher than that of conventional CSI. These two technologies were applied to measure lactate metabolic flux at different phases of ICH. The results show a higher lactate concentration in ICH rats than in control rats, which is in line with the increased expression of lactate dehydrogenase measured via immunohistochemistry staining (AUCLac_area/Glc_area: control, 0.08 ± 0.02 vs ICH-3d, 0.39 ± 0.05 vs ICH-7d, 0.18 ± 0.02, P < 0.01; H-score: control, 126.4 ± 5.03 vs ICH-3d, 168.4 ± 5.71 vs ICH-7d,133.6 ± 7.70, P < 0.05). A higher lactate signal also appeared near the ICH region than in normal brain tissue. In conclusion, 2H-MRS/MRSI shows potential as a useful method for in vivo metabolic imaging and noninvasive assessment of ICH.

Development of a PCR-based method to identify fetal sex during IVF cycles.

One of the most recognizable cases of preimplantation genetic diagnosis (PGD) is X-linked diseases. Diagnosis of fetal sex is essential for couples who are known to be at risk of some X-linked disorders. The objective of this study was to discriminate between female (XX) and male (XY) embryos by detecting sex chromosomes-specific sequences in spent culture medium and comparing these results to PGD/CGH array results. It may open new window for the development of a non-invasive PGD method. 120 Embryo's spent media from Day 3 and Day 5 embryos were collected. Modified phenol-chloroform solution was used for DNA extraction from spent media. Sex determination was performed using SRY, TSPY and AMELOGENIN evaluation through quantitative polymerase chain reaction (q-PCR) method. IBM SPSS and MedCalc were used for statistical analyses to compare sex determination of embryos by spent medium with PGD/CGH array results. Culture time was demonstrated to increase the DNA amount among day 5 embryos culture medium samples. Non-invasive PGD by means of spent culture medium gave a sensitivity, specificity, positive predictive value and negative predictive value of 100% for sex determination. Results of sex determination using spent medium by q-PCR were consistent with the results of PGD/CGH array. Improvements in cell-free DNA extraction and PCR amplification procedures provide us an effective method to perform a PGD test without biopsy in the future, especially about X-linked diseases.

Artifact Management for Cerebral Near-Infrared Spectroscopy Signals: A Systematic Scoping Review

Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust “gold-standard” method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

A pilot study: Examining cytoskeleton gene expression profiles in Pakistani children with autism spectrum disorder.

Finding effective pharmacological interventions to address the complex array of neurodevelopmental disorders is currently an urgent imperative within the scientific community as these conditions present significant challenges for patients and their families, often impacting cognitive, emotional, and social development. In this study, we aimed to explore non-invasive method to diagnose autism spectrum disorders (ASD) within Pakistan children population and to identify clinical drugs for its treatment. The current report outlines a comprehensive bidirectional investigation showcasing the successful utilization of saliva samples to quantify the expression patterns of profilins (PFN1, 2, and 3); and ERM (ezrin, radixin, and moesin) proteins; and additionally moesin pseudogene 1 and moesin pseudogene 1 antisense (MSNP1AS). Subsequently, these expression profiles were employed to forecast interactions between drugs and genes in children diagnosed with ASD. This study sought to delve into the intricate gene expression profiles using qualitative polymerase chain reaction of profilin isoforms (PFN1, 2, and 3) and ERM genes extracted from saliva samples obtained from children diagnosed with ASD. Through this analysis, we aimed to elucidate potential molecular mechanisms underlying ASD pathogenesis, shedding light on novel biomarkers and therapeutic targets for this complex neurological condition. (n = 22). Subsequently, we implemented a diagnostic model utilizing sparse partial least squares discriminant analysis (sPLS-DA) to predict drugs against our genes of interest. Furthermore, connectivity maps were developed to illustrate the predicted associations of 24 drugs with the genes expression. Our study results showed varied expression profile of cytoskeleton linked genes. Similarly, sPLS-DA model precisely predicted drug to genes response. Sixteen of the examined drugs had significant positive correlations with the expression of the targeted genes whereas eight of the predicted drugs had shown negative correlations. Here we report the role of cytoskeleton linked genes (PFN and ERM) in co-relation to ASD. Furthermore, variable yet significant quantitative expression of these genes successfully predicted drug-gene interactions as shown with the help of connectivity maps that can be used to support the clinical use of these drugs to treat individuals with ASD in future studies.

Utility of photoplethysmography in detecting elevated blood glucose among non-diabetics.

This study aimed to evaluate a technique of using photoplethysmography (PPG) for detecting elevated blood glucose in individuals. This is a prospective, cross-sectional study in which 500 healthy volunteers were recruited at a tertiary hospital in Singapore from October 2021 to February 2023. Capillary glucose was measured concurrently with PPG signals acquired using the wrist-worn Actxa Tracker (Spark + Series 2) and the In-Ear Prototype model SVT, which were worn for a duration of 8 min. Participants with a capillary blood test reading ≤11.1 mmol/dL had to consume a standard glucose tolerance drink and return 1 h later for a second capillary blood test. Two hundred and forty-four features were subsequently extracted from the PPG signals. Of the 500 volunteers, 17 were excluded because of incomplete records. This led to a total of 483 participants' records being included in the final analysis. For predicting elevated capillary blood glucose level, demographics alone achieved an area under the curve (AUC) of 0.75. When wearable features derived from PPG were combined with demographics, AUC improved significantly to 0.82 (P = 0.0001). This study shows that a non-invasive method of assessing diabetes mellitus risk using PPG combined with demographics is a viable option to provide a cheaper and more accessible modality for population-wide diabetes mellitus risk assessment.

Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography-ion mobility spectrometry.

To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-invasive diabetes-monitoring method. Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10-15 minutes, were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles. Statistical analysis revealed moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose levels. Machine learning models showed high accuracy in classifying glycaemic states, with the best performance achieved by a two-class PLS-DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2-butanone, methanol, ethanol, 2-propanol and 2-pentanone. This study shows the potential of breath VOCs to accurately classify glycaemic states in individuals with T1D. While key biomarkers such as isoprene, acetone and 2-butanone were identified, the analysis emphasizes the importance of using overall VOC patterns rather than individual compounds, which can be markers for multiple conditions. Machine learning models leveraging these patterns achieved high accuracy, sensitivity and specificity. These findings suggest that breath analysis using GC-IMS could be a viable non-invasive method for monitoring glycaemic states and managing diabetes.

Pre- and post-skeletal muscle biopsy quantitative magnetic resonance imaging reveals correlations with histopathological findings.

Quantitative muscle magnetic resonance imaging (MRI) is a promising non-invasive method in the diagnostic workup as well as follow-up of neuromuscular disorders. The aim of this study was to correlate quantitative MRI (qMRI) parameters to histopathological changes in skeletal muscle tissue and thus to verify the data from our pilot study. Twenty-six patients (eight females, 46.4 ± 15.1 years) were examined within 72 h before and within 24 h after a skeletal muscle biopsy using quantitative muscle MRI. Post-biopsy MRI was employed to pinpoint the exact localization of the biopsy. qMRI parameters including fat fraction, water T2 relaxation time and diffusion metrics including fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity were extracted from the localization of the biopsy and correlated with histopathological findings. Additionally, three different segmentation masks were applied to the qMRI dataset, to evaluate whether the whole muscle represents the exact biopsy location. Fat fraction and water T2 relaxation time in qMRI correlated significantly with the fat fraction in the muscle biopsy and histopathological inflammatory markers. Fractional anisotropy correlated with the quantity of type 2 fibres, whilst mean diffusivity correlated with p62. No differences were found using different segmentation masks in qMRI. In this follow-up study, the results from our previous study were verified regarding the correlation of qMRI parameters with histopathological features in muscle biopsies, indicating that qMRI serves as a suitable non-invasive method in the follow-up of patients with neuromuscular disorders. If post-biopsy MRI is not available, whole muscle volume can be used for histopathological correlations.

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Total Reflection (ATR-FTIR) platform was applied to discriminate systemic diseases using saliva, however, the salivary diagnostic application in viral diseases is less explored. The study aimed to identify unique vibrational modes of salivary infrared profiles to detect CHIKV infection using chemometrics and artificial intelligence algorithms. Thus, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with CHIKV (20 µl, 1 X 105 PFU/ml, n = 6) or vehicle (20 µl, n = 7). Saliva and serum samples were collected on day 3 (due to the peak of viremia). CHIKV infection was confirmed by Real-time PCR in the serum of CHIKV-infected mice. The best pattern classification showed a sensitivity of 83%, specificity of 86%, and accuracy of 85% using support vector machine (SVM) algorithms. Our results suggest that the salivary ATR-FTIR platform can discriminate CHIKV infection with the potential to be applied as a non-invasive, sustainable, and cost-effective detection tool for this emerging disease.

Decoding Octopus Skin Mucus: Impact of Aquarium-Maintenance and Senescence on the Proteome Profile of the Common Octopus (Octopus vulgaris)

The common octopus (Octopus vulgaris) is an excellent candidate for aquaculture diversification, due to its biological traits and high market demand. To ensure a high-quality product while maintaining welfare in captive environments, it is crucial to develop non-invasive methods for testing health biomarkers. Proteins found in skin mucus offer a non-invasive approach to monitoring octopus welfare. This study compares the protein profiles in the skin mucus of wild, aquarium-maintained, and senescent specimens to identify welfare biomarkers. A tandem mass tag (TMT) coupled with an Orbitrap Eclipse Tribrid mass spectrometer was used to create a reference dataset from octopus skin mucus, identifying 1496 non-redundant protein groups. Although similar profiles were observed, differences in relative abundances led to the identification of potential biomarkers, including caspase-3-like, protocadherin 4, deleted in malignant brain tumors, thioredoxin, papilin, annexin, cofilin and mucin-4 proteins. Some of these proteins also revealed potential as bioactive peptides. This investigation provides the most extensive analysis of the skin mucus proteome in the common octopus and is the first to explore how aquarium maintenance and senescence alter the mucus proteome. This research highlights the potential of skin mucus protein/peptides as non-invasive monitoring biomarkers in cultured animals.

Clinical diagnostic model for predicting indolent or aggressive lymphoma based on clinical information and ultrasound features of superficial lymph nodes

PurposeThe aim of this study was to develop a diagnostic model for predicting indolent lymphoma or aggressive lymphoma using clinical information and ultrasound characteristics of superficial lymph nodes. MethodPatients with confirmed pathological lymphoma subtypes who had undergone ultrasound and contrast-enhanced ultrasound examinations were enrolled. Clinical and ultrasound imaging features were retrospectively analysed and compared to the pathological results, which were considered the gold standard for diagnosis. Two diagnostic models were developed: a clinical model (Model-C) using clinical data only, and a combined model (Model-US) integrating ultrasound features into the clinical model. The efficacy of these models in differentiating between indolent and aggressive lymphoma was compared. ResultsIn total, 236 consecutive patients were enrolled, including 78 patients with indolent lymphomas and 158 patients with aggressive lymphomas. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of Model-C and Model-US were 0.78 (95 % confidence interval: 0.72–0.84) and 0.87 (95 % confidence interval: 0.82–0.92), respectively (p < 0.001). Model-US was further evaluated for calibration and is presented as a nomogram. ConclusionsThe diagnostic model incorporated clinical and ultrasound characteristics and offered a noninvasive method for assessing lymphoma with good discrimination and calibration.

Characterization and Automatic Discrimination between Predominant Hypoperfusion and Hyperperfusion Stages of NPDR

Background/Objectives: Diabetic retinopathy (DR) is a common diabetes complication that can lead to blindness through vision-threatening complications like clinically significant macular edema and proliferative retinopathy. Identifying eyes at risk of progression using non-invasive methods could help develop targeted therapies to halt diabetic retinal disease progression. Methods: A set of 82 imaging and systemic features was used to characterize the progression of nonproliferative diabetic retinopathy (NPDR). These features include baseline measurements (static features) and those capturing the temporal dynamic behavior of these static features within one year (dynamic features). Interpretable models were trained to distinguish between eyes with Early Treatment Diabetic Retinopathy Study (ETDRS) level 35 and eyes with ETDRS levels 43–47. The data used in this research were collected from 109 diabetic type 2 patients (67.26 ± 2.70 years; diabetes duration 19.6 ± 7.26 years) and acquired over 2 years. Results: The characterization of the data indicates that NPDR progresses from an initial stage of hypoperfusion to a hyperperfusion response. The performance of the classification model using static features achieved an area under the curve (AUC) of the receiver operating characteristics equal to 0.84 ± 0.07, while the model using both static and dynamic features achieved an AUC of 0.91 ± 0.05. Conclusion: NPDR progresses through an initial hypoperfusion stage followed by a hyperperfusion response. Characterizing and automatically identifying this disease progression stage is valuable and necessary. The results indicate that achieving this goal is feasible, paving the way for the improved evaluation of progression risk and the development of better-targeted therapies to prevent vision-threatening complications.

Magnetic Resonance Imaging-Based Monitoring of the Accumulation of Polyethylene Terephthalate Nanoplastics

Polyethylene terephthalate (PET) is one of the most produced plastic materials in the world. The emergence of microplastics and nanoplastics (MPs/NPs) as a significant environmental contaminant has become a matter of increasing concern. While the toxicological effects of PET NPs have been widely researched, there is a lack of methodologies for studying their accumulation. The present study introduces a novel method to monitor the distribution of PET NPs in germinating wheat (Triticum aestivum L.) seeds. This involves the functionalization of superparamagnetic iron oxide nanoparticles (SPIONs) with PET NPs (PET–fSPIONs) coupled with magnetic resonance microimaging (µMRI) to provide insight into their distribution within the seed. The present study has demonstrated that PET–fSPIONs accumulate in specific regions of germinating wheat seeds, including the shoot apical meristem, the radicle, the coleoptile, the plumule, and the scutellum. Furthermore, the accumulation of PET–fSPIONs has been shown to exert a discernible effect on spin–spin relaxation (T2), as observed via MRI and quantitative T2 relaxation time analysis. The accumulation of PET NPs in embryo regions was also confirmed by SEM. Diffusion-weighted magnetic resonance imaging (DW-MRI) and non-invasive chemical shift imaging analyses demonstrated that PET NPs resulted in restricted diffusion within the highlighted areas, as well as an impact on lipid content. Our study reveals that using µMRI with fSPIONs provides a non-invasive method to monitor the biodistribution of PET nanoparticles in wheat seeds. Additionally, it offers valuable insights into the microstructural interactions of PET.

Roles and action mechanisms of NRIP1 in pre-eclampsia.

Pre-eclampsia (PE) is characterized by the onset of hypertension and proteinuria during pregnancy. Here, we aimed to explore the functions of nuclear receptor-interacting protein 1 (NRIP1) in PE mice and human placental JEG-3 cells. We evaluated its effects on JEG-3 cell proliferation, apoptosis, invasion, and inflammatory response and regulation of Wnt/β-catenin pathway. NRIP1 levels in human serum and placental tissues, JEG-3 cells, and mouse models were assessed via quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blotting. JEG-3 cell growth, apoptosis, migration, and invasion were evaluated via 5-ethynyl-2'-deoxyuridine, flow cytometry, and transwell assays. Levels of the inflammatory factors, matrix metalloproteinase (MMP)-2, tumor necrosis factor (TNF)-α, and interleukin (IL)-6, were determined via enzyme-linked immunosorbent assay. Wnt/β-catenin pathway was assessed via western blotting and qRT-PCR. Systolic blood pressure and proteinuria were measured using the non-invasive tail cuff method and Coomassie brilliant blue assay, respectively. TdT-mediated dUTP nick-end labeling assay was used to assess cell apoptosis in the placental tissues of PE mice. NRIP1 levels were upregulated in the serum and placental tissues of patients with PE. In vitro experiments revealed that NRIP1-small interfering RNA (siRNA) increased the cell viability, migration, and invasion and reduced the cell apoptosis compared to the control siRNA. Moreover, NRIP1-siRNA activated the Wnt/β-catenin signaling pathway, as indicated by the increased Wnt3a, β-catenin, p-glycogen synthase kinase-3β, c-Myc, and cyclin D1 levels. Levels of the inflammatory factors, IL-6, TNF-α, and MMP-2, were decreased in the NRIP1-siRNA-treated group. Notably, NRIP1 downregulation improved the PE-like symptoms, inhibited the inflammatory responses, and reduced apoptosis in PE mice. This study revealed the crucial roles of NRIP1 in PE. Our findings revealed that NRIP1 downregulation relieved PE symptoms by inhibiting cell proliferation, migration, and invasion via the Wnt/β-catenin pathway, thus providing a novel candidate for PE treatment.

Ultrasound frequency-controlled microbubble dynamics in brain vessels regulate the enrichment of inflammatory pathways in the blood-brain barrier

Microbubble-enhanced ultrasound provides a noninvasive physical method to locally overcome major obstacles to the accumulation of blood-borne therapeutics in the brain, posed by the blood-brain barrier (BBB). However, due to the highly nonlinear and coupled behavior of microbubble dynamics in brain vessels, the impact of microbubble resonant effects on BBB signaling and function remains undefined. Here, combined theoretical and prospective experimental investigations reveal that microbubble resonant effects in brain capillaries can control the enrichment of inflammatory pathways that are sensitive to wall shear stress and promote differential expression of a range of transcripts in the BBB, supporting the notion that microbubble dynamics exerted mechanical stress can be used to establish molecular, in addition to spatial, therapeutic windows to target brain diseases. Consistent with these findings, a robust increase in cytotoxic T-cell accumulation in brain tumors was observed, demonstrating the functional relevance and potential clinical significance of the observed immuno-mechano-biological responses.