Non-invasive Method Research Articles

Hepatic Biotransformation of Renal Clearable Gold Nanoparticles for Noninvasive Detection of Liver Glutathione Level via Urinalysis

AbstractRenal clearable nanoparticles have been drawing much attention as they can avoid prolonged accumulation in the body by efficiently clearing through the kidneys. While much effort has been made to understand their interactions within the kidneys, it remains unclear whether their transport could be influenced by other organs, such as the liver, which plays a crucial role in metabolizing and eliminating both endogenous and exogenous substances through various biotransformation processes. Here, by utilizing renal clearable IRDye800CW conjugated gold nanocluster (800CW4‐GS18‐Au25) as a model, we found that although 800CW4‐GS18‐Au25 strongly resisted serum‐protein binding and exhibited minimal accumulation in the liver, its surface was still gradually modified by hepatic glutathione‐mediated biotransformation when passing through the liver, resulting in the dissociation of IRDye800CW from Au25 and biotransformation‐generated fingerprint message of 800CW4‐GS18‐Au25 in urine, which allowed us to facilely quantify its urinary biotransformation index (UBI) via urine chromatography analysis. Moreover, we observed the linear correlation between UBI and hepatic glutathione concentration, offering us a noninvasive method for quantitative detection of liver glutathione level through a simple urine test. Our discoveries would broaden the fundamental understanding of in vivo transport of nanoparticles and advance the development of urinary probes for noninvasive biodetection.

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images.

One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in vivo imaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific haemodynamics models. KEY POINTS: This study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error. Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks. Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty. Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii.

Correlation of left atrial strain with invasively measured left ventricular end-diastolic pressure; determining LA strain cut-off value.

Left atrium longitudinal strain (LAS) with speckle tracking method has been proposed as a non-invasive method for the assessment of left ventricular filling pressure and diastolic dysfunction. This study aimed to investigate left atrial strain compared to invasively measured left ventricular filling pressure. All Patients candidates for coronary angiography were consecutively recruited. LAS measured by transthoracic echocardiography. Left ventricular end-diastolic pressure (LVEDP) pressure was invasively measured. Current echocardiographic modalities for diastolic function evaluated. A total of 125 people were included. 45 patients had preserved ejection fraction (EF ≥ 50%) and 85 patients had reduced EF (EF < 50%) and compared two groups. LVEDP was significantly higher in reduced EF compared to preserved EF (p-value < 0.001). LA-reservoir and LA-booster strains were significantly lower in patients with reduced EF compared to preserved EF (p-value = 0.008, mean Reservoir = 16.4% ± 6.4, mean Reservoir = 19.5% ± 5.6, respectively) and (p-value = 0.009, mean Booster = 9.09% ± 4.0, mean Booster =11. 9% ± 4.3, respectively). LA 4ch-reservoir strain <14.4%, and LA 2ch-reservoir strain <14.1% were related to LVEDP≥20 mmHg (sensitivity 63.5% and specificity 75%) (sensitivity 77.9% and specificity of 60%) respectively. LAS is significantly lower in patients with elevated LVEDP (≥ 20mmHg). LAS is significantly lower in patients with reduced EF. Both LA-reservoir and LA-booster strains have a significant relation to predicting LVFP but LA-reservoir strain is more accurate. The mean LA-reservoir strain less than 12.4% is associated with LVEDP ≥ 20mmHg.

New Biomarkers in Liver Fibrosis: A Pass through the Quicksand?

Chronic liver diseases (CLD) stem from various causes and lead to a gradual progression that ultimately may result in fibrosis and eventually cirrhosis. This process is typically prolonged and asymptomatic, characterized by the complex interplay among various cell types, signaling pathways, extracellular matrix components, and immune responses. With the prevalence of CLD increasing, diagnoses are often delayed, which leads to poor prognoses and in some cases, the need for liver transplants. Consequently, there is an urgent need for the development of novel, non-invasive methods for the diagnosis and monitoring of CLD. In this context, serum biomarkers—safer, repeatable, and more acceptable alternatives to tissue biopsies—are attracting significant research interest, although their clinical implementation is not yet widespread. This review summarizes the latest advancements in serum biomarkers for detecting hepatic fibrogenesis and advocates for concerted efforts to consolidate current knowledge, thereby providing patients with early, effective, and accessible diagnoses that facilitate personalized therapeutic strategies.

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer

PurposeA practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.MethodsA total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.ResultsThe area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514–1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741–1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.ConclusionThis model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Positive feed-forward regulation of nitrate uptake by rice roots and its molecular mechanism

To investigate the positive feed-forward regulatory mechanism of nitrate uptake by rice, its responses to various light and carbohydrates were compared. In order to measure nitrate uptake in real time, the non-invasive method was used. The results showed that net nitrate uptake increased in the light and decreased in the dark, and finally reached a steady state after about 5 h. Based on it, carbohydrates effects could be investigated without considering light effects. After sucrose addition for 2 h, net nitrate uptake increased by about 80% without a lag, while glucose, fructose and raffinose had a slight effect with a lag and other sugars had no effect. It provided an evidence that sucrose was a positive feed-forward signal molecule of nitrate uptake by rice roots. To further analyze the effect of sucrose on the expression of high affinity nitrate transporter genes OsNRT2.1, OsNRT2.2, OsNRT2.3a and OsNRT2.3b, qRT-PCR was used to further verify after treated with 10 mM sucrose. The results revealed that these genes expression was immediately up-regulated, which indicated that these genes were post transcriptionally regulated. Further, 15N exchange dynamics analyzed N transport. It is benefit for increasing nitrate uptake by rice and improving its yield.

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC). This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated. A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features. The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER. This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management. Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.

Comparison of continuous temperature measurement methods in the intensive care unit: standard bladder catheter measurements versus non-invasive transcutaneous sensors.

The purpose of this study was to compare a wearable system for body core temperature measurement versus bladder and tympanic thermometers in an intensive care setting. The question was, if continuous non-invasive sensors in the intensive care unit represent an alternative to current standard methods of invasive continuous bladder temperature measurement methods?Between May and September 2023, a comparative investigation involving 112 patients was conducted in a 20-bed surgical intensive care unit to assess various temperature probes, including those placed in the tympanic tube, bladder, and skin. To achieve this, a wireless non-invasive sensor system provided by greenTEG AG, Switzerland, was affixed to different body locations (clavicular and lateral chest) of each catheterized patient (equipped with a temperature probe) admitted to the intensive care unit. Furthermore, tympanic temperatures were recorded at specified intervals. The measurement duration ranged from a minimum of six hours to a maximum of six days, resulting in the analysis of a total of 355 simultaneous temperature measurements.In this study, a wearable temperature measurement system attached to two different body sites revealed a consistent negative bias compared to bladder temperature. In addition, the measurements were particularly influenced by body constitution. The tested system in all patients showed a mean absolute error (MAE) of 0.45°C for the lateral chest and 0.50°C for the clavicular position. Tympanic measurements had a mean absolute error of 0.35°C. In patients with body mass index (BMI) ≥ 25 the MAE increased to 0.5°C for the lateral chest and 0.56°C for the clavicular position. In contrast, the tympanic measurement had a reduced MAE of 0.32°C, which is well below this threshold when compared to bladder measurements.In conclusion the investigated system did not meet the clinically relevant acceptance criteria and showed low precision in correctly identifying fever episodes compared to invasive temperature probes, however its main advantage lies in its continuity and non-invasiveness. This makes it a potential alternative to intermittent tympanic measurement devices. In this study we were able to show, that in at least one subset of patients, the non-invasive and continuous device demonstrated a precision comparable to tympanic measurements.The accuracy of all non-invasive methods was lower than in previous studies, suggesting that the use of bladder temperature as reference and user related variations may have introduced additional errors.

Current and Emerging Approaches for Primary Prevention of Coronary Artery Disease Using Cardiac Computed Tomography.

To summarize the current use of cardiac computed tomography (CT) technologies as well as their pertinent evidence in regards to prevention of coronary artery disease (CAD). Cardiac CTA has now become a main non-invasive method for the evaluation of symptomatic CAD. In addition to coronary calcium score, other CT technologies such as atherosclerotic plaque analysis, fractional flow reserve estimation by CT, pericoronary fat attenuation, and endothelial wall shear stress have emerged. Whether the use of CT modalities can enhance risk prediction and prevention in CAD has not been fully answered. We discuss the evidence for coronary artery calcium scoring and coronary CT angiography in primary prevention and the current barriers to their use. We attempt to delineate what can be done to expand use and what studies are needed to broaden adoption in the future. We also examine the potential roles of emerging CT technologies. Finally, we describe potential clinical approaches to prevention that would incorporate cardiac CT technologies.

Rapid and non-invasive diagnosis of hyperkalemia in patients with systolic myocardial failure using a model based on machine learning algorithms

ABSTRACT Background: Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection. Methods: The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results Results: In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively Conclusions: A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Human Brain Deuterium Metabolic Imaging at 7 T: Impact of Different [6,6'-2H2]Glucose Doses.

Deuterium metabolic imaging (DMI) is an innovative, noninvasive metabolic MR imaging method conducted after administration of 2H-labeled substrates. DMI after [6,6'-2H2]glucose consumption has been used to investigate brain metabolic processes, but the impact of different [6,6'-2H2]glucose doses on DMI brain data is not well known. To investigate three different [6,6'-2H2]glucose doses for DMI in the human brain at 7 T. Prospective. Six healthy participants (age: 28 ± 8 years, male/female: 3/3). 7 T, 3D 2H free-induction-decay (FID)-magnetic resonance spectroscopic imaging (MRSI) sequence. Three subjects received two different doses (0.25 g/kg, 0.50 g/kg or 0.75 g/kg body weight) of [6,6'-2H2]glucose on two occasions and underwent consecutive 2H-MRSI scans for 120 minutes. Blood was sampled every 10 minutes during the scan, to determine plasma glucose levels and plasma 2H-Glucose atom percent excess (APE) (part-1). Three subjects underwent the same protocol once after receiving 0.50 g/kg [6,6'-2H2]glucose (part-2). Mean plasma 2H-Glucose APE and glucose plasma concentrations were compared using one-way ANOVA. Brain 2H-Glc and brain 2H-Glx (part-1) were analyzed with a two-level Linear Mixed Model. In part-2, a General Linear Model was used to compare brain metabolite signals. Statistical significance was set at P < 0.05. Between 60 and 100 minutes after ingesting [6,6'-2H2]glucose, plasma 2H-Glc APE did not differ between 0.50 g/kg and 0.75 g/kg doses (P = 0.961), but was significantly lower for 0.25 g/kg. Time and doses significantly affected brain 2H-Glucose levels (estimate ± standard error [SE]: 0.89 ± 0.01, 1.09 ± 0.01, and 1.27 ± 0.01, for 0.25 g/kg, 0.50 g/kg, and 0.75 g/kg, respectively) and brain 2H-Glutamate/Glutamine levels (estimate ± SE: 1.91 ± 0.03, 2.27 ± 0.03, and 2.46 ± 0.03, for 0.25 g/kg, 0.50 g/kg, and 0.75 g/kg, respectively). Plasma 2H-Glc APE, brain 2H-Glc, and brain 2H-Glx levels were comparable among subjects receiving 0.50 g/kg [6,6'-2H2]glucose. Brain 2H-Glucose and brain 2H-Glutamate/Glutamine showed to be [6,6'-2H2]glucose dose dependent. A dose of 0.50 g/kg demonstrated comparable, and well-detectable, 2H-Glucose and 2H-Glutamate/Glutamine signals in the brain. 1 TECHNICAL EFFICACY: Stage 2.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

The Effect of In-Ear and Behind-Ear Transcutaneous Auricular Vagus Nerve Stimulation on Autonomic Function: A Randomized, Single-Blind, Sham-Controlled Study

Background/Objectives: Transcutaneous auricular vagus nerve stimulation (TaVNS) is a non-invasive method of electrical stimulation used to autonomic neuromodulation. Position and form of the electrodes are important for the effectiveness of autonomic modulation. This study was aimed to investigate the effect of TaVNS in-ear and behind-ear on autonomic variables. Methods: A total of 76 healthy participants (male: 40, female: 36) were randomized into four groups as in-ear TaVNS, behind-ear TaVNS, in-ear sham, and behind-ear sham. The TaVNS protocol included bilateral auricular stimulation for 20 min, 25 hertz frequency, a pulse width of 250 μs, and a suprathreshold current (0.13–50 mA). Heart rate (HR), systolic and diastolic blood pressure (SBP and DBP), and heart rate variability (HRV) were measured baseline and after stimulation. The parameters RMSSD (root mean square of consecutive differences between normal heartbeats), LF power (low-frequency), and HF power (high-frequency) were assessed in the HRV analysis. Results: HR decreased in the in-ear TaVNS after intervention (p &lt; 0.05), but did not change in behind-ear TaVNS and sham groups compared to baseline (p &gt; 0.05). SBP and DBP decreased and RMSSD increased in the in-ear and behind-ear TaVNS groups (p &lt; 0.05), but did not change in sham groups compared to baseline (p &gt; 0.05). There was no significant difference in LF and HF power after TaVNS compared to baseline in all groups (p &gt; 0.05). SBP was lower and RMSSD was higher in-ear TaVNS than behind-ear TaVNS after intervention (p &lt; 0.05). Conclusions: In-ear TaVNS appears to be more effective than behind-ear TaVNS in modulating SBP and RMSSD, but this needs to be studied in larger populations.

Preliminary investigations into human neurofluid transport using multiple novel non-contrast MRI methods.

We discuss two potential non-invasive MRI methods to study phenomena related to subarachnoid cerebrospinal fluid (CSF) motion and perivascular fluid transport, and their association with sleep and aging. We apply diffusion-based intravoxel incoherent motion (IVIM) imaging to evaluate pseudodiffusion coefficient, D*, or CSF movement across large spaces like the subarachnoid space (SAS). We also performed perfusion-based multi-echo, Hadamard encoded arterial spin labeling (ASL) to evaluate whole brain cortical cerebral blood flow (CBF) and trans-endothelial exchange (Tex) of water from the vasculature into the perivascular space and parenchyma. Both methods were used in young adults (N = 9, 6 F, 23 ± 3 years old) in the setting of sleep and sleep deprivation. To study aging, 10 older adults (6 F, 67 ± 3 years old) were imaged after a night of normal sleep and compared with the young adults. D* in SAS was significantly (p < 0.05) reduced with sleep deprivation (0.016 ± 0.001 mm2/s) compared to normal sleep (0.018 ± 0.001 mm2/s) and marginally reduced with aging (0.017 ± 0.001 mm2/s, p = 0.029). Cortical CBF and Tex were unchanged with sleep deprivation but significantly lower in older adults (37 ± 3 ml/100 g/min, 578 ± 61 ms) than in young adults (42 ± 2 ml/100 g/min, 696 ± 62 ms). IVIM was sensitive to sleep physiology and aging, and multi-echo, multi-delay ASL was sensitive to aging.

Systematic cross-species comparison of prefrontal cortex functional networks targeted via transcranial magnetic stimulation

Abstract Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that safely modulates neural activity in vivo. Its precision in targeting specific brain networks makes TMS invaluable in diverse clinical applications. For example, TMS is used to treat depression by targeting prefrontal brain networks and their connection to other brain regions. Despite its widespread use, however, the underlying neural mechanisms of TMS are not completely understood. Non-human primates (NHPs) offer an ideal model to study TMS mechanisms through invasive electrophysiological recordings. As such, bridging the gap between NHP experiments and human applications is imperative to ensure translational relevance. Here, we systematically compare the TMS-targeted functional networks in the prefrontal cortex in humans and NHPs. We combine TMS electric field modeling in humans and macaques with resting-state functional magnetic resonance imaging (fMRI) data to compare the functional networks targeted via TMS across species. Distinct stimulation zones in macaque and human models arose, each exhibiting differences in impacted networks (macaque: Frontoparietal Network, Somatomotor Network; human: Frontoparietal Network, Default Network). We identified differences in brain gyrification and functional organization across species as the underlying cause of found network differences. The TMS-network profiles we identified will allow researchers to establish consistency in network stimulation across species, aiding in the translational efforts to develop improved TMS functional network targeting approaches.

Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space

Abstract The article explores the usability of the geodiscussion administrative panel, an online tool aiding Public Participation Geographic Information Systems. This tool was specifically designed and developed for distribution in a Software as a service model, offering geodiscussion users the flexibility to leverage the tool without the need for substantial investments in expensive infrastructure or complex deployments. The research primarily centers on the geodiscussion administrative panel, a key component empowering organizers of public consultations in spatial planning to efficiently create and manage consultation processes. In contrast to previous studies that focused on the usability for consultation participants, this research uniquely targets individuals responsible for organizing and overseeing the consultation processes. A diverse array of usability research methods were employed, ranging from the creation of personas and conducting direct interviews based on test scenarios to non-invasive methods for recording user activities. The study involved 682 participants who had prior experience with public consultations across various public administration units and representatives from consulting firms. Given that the usability studies were an integral part of the geodiscussion administrative panel’s development process, the research team has the opportunity to influence the further implementation of the applied functionalities. This implies that insights gained from the research can actively shape and enhance the tool’s features as it progresses in its development cycle.

Capture and analysis of MDMB-4en-PINACA and ADB-4en-PINACA in exhaled breath using functionalized cotton fiber: A novel approach to noninvasive drug abuse detection

Breath analysis has emerged as a promising noninvasive method for detecting drug abuse, offering potential advantages over traditional blood or urine samples. However, the capture and analysis of trace analytes in exhaled breath (EB) remains a challenge for practical applications. In this study, we explore the utilization of poly (methacrylic anhydride-co-ethylene glycol dimethacrylate)-functionalized cotton fiber (poly (MAA-co-EGDMA)-functionalized cotton fiber) for the capture of MDMB-4en-PINACA and ADB-4en-PINACA, two popular synthetic cannabinoids, from exhaled breath. Capture efficiencies of 92 % and 88 % for MDMB-4en-PINACA and ADB-4en-PINACA were achieved demonstrating robust performance across batches and excellent stability. Animal experiments were conducted and exhaled breath samples were collected and analyzed. Shorter half-lives were shown for MDMB-4en-PINACA and ADB-4en-PINACA in EB than in plasma. The detection window for MDMB-4en-PINACA was 210 min, while 150 min for ADB-4en-PINACA. This pilot study presents a novel approach for the capture of MDMB-4en-PINACA and ADB-4en-PINACA in exhaled breath using functionalized cotton fiber. The demonstrated efficacy in capturing MDMB-4en-PINACA and ADB-4en-PINACA highlights the potential of this low-cost and stable platform for noninvasive drug abuse detection. By offering a viable alternative to invasive methods, our findings demonstrate a new noninvasive method of drug abuse detection using exhaled breath holds great promise for enhancing public health efforts to combat drug abuse.

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning

Background and ObjectivesInstantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the “gold standard” of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR can be approximated by iFRCT, which is calculated based on noninvasive coronary CT angiography (CTA) images and computational fluid dynamics (CFD). However, the existing methods for calculating iFRCT fail to accurately simulate the resting state of the coronary artery, resulting in low computational accuracy. Furthermore, the use of CFD technology limits its computational efficiency, making it difficult to meet clinical application needs. The role of coronary microcirculatory resistance compensation suggests that microcirculatory resistance can be adaptively reduced to compensate for increases in coronary stenotic resistance, thereby maintaining stable myocardial perfusion in the resting state. It is therefore necessary to consider this compensation mechanism to establish a high-fidelity microcirculation resistance model in the resting state in line with human physiology, and so to achieve accurate calculation of iFRCT. MethodsIn this study we successfully collected clinical data, such as FFR, in 205 stenotic vessels from 186 patients with coronary heart disease. A neural network model was established to predict coronary artery stenosis resistance. Based on the compensation mechanism of coronary microcirculation resistance, an iterative solution algorithm for microcirculation resistance in the resting state was developed. Combining the two methods, a simplified single-branch model combining coronary stenosis and microcirculation resistance was established, and the noninvasive and rapid numerical calculation of iFRCT was performed. ResultsThe results showed that the mean squared error (MSE) between the pressure drop predicted by the neural network value for the coronary artery stenosis model and the ground truth in the test set was 0.053 %, and correlation analysis proved that there was a good correlation between them (r = 0.99, p < 0.001). With reference to clinical diagnosis of myocardial ischaemia (using FFR as the gold standard), the diagnostic accuracy of the iFRCT calculation model for the 205 cases was 88.29 % (r = 0.71, p < 0.001), and the total calculation time was < 8 s. ConclusionsThe results of this study demonstrate the utility of a simplified single-branch model in an iFRCT calculation method based on haemodynamics and deep learning, which is important for noninvasive and rapid diagnosis of myocardial ischaemia.

Myocardial Work by Speckle Tracking Echocardiography in Heart Transplant Recipients: Association between Global Work Efficiency and Coronary Allograft Vasculopathy

Coronary allograft vasculopathy (CAV) is a leading cause of morbidity and mortality following heart transplantation. CAV is often diagnosed in later stages or during routine screening in asymptomatic subjects. Myocardial work (MW), calculated using left-ventricular global longitudinal strain (LV-GLS) and systemic blood pressure, may be associated with the presence of CAV and outperform conventional echocardiographic parameters. In this retrospective observational study, heart transplant recipients undergoing regular follow-up at our Institution between May 2022 and September 2023 were enrolled. All included patients underwent speckle tracking echocardiography, including MW indices. CAV was classified according to invasive coronary angiography or computed tomography performed within 12 months of index echocardiography. We collected all available clinical and echocardiographic parameters and evaluated potential association with CAV. CAV was detected in 29/93 (31%) patients (CAV+). Of the MW indices, mean global work efficiency (GWE) was 90±6%, and was significantly lower in CAV+ than CAV- subjects (86±7% vs 91±4%, p<0,001). GWE (OR 0.86; CI 0.77-0.94, p=0.002), E/e’ ratio (OR 1.27; CI 1.08-1.52, p=0.006) and LVEF (OR 0.90; CI 0.81-0.98, p=0.017) were independently associated with the presence of CAV. GWE (GWE vs LV-GLS, delta AUC 0.154, p=0.047) and the proposed model (GWE+E/e’ vs LV-GLS, delta AUC 0.198, p=0.004) were significantly superior in stratifying incremental risk for CAV compared to LV-GLS. In conclusion GWE was observed to be independently associated with the presence of CAV. MW could represent a novel non-invasive screening method for CAV in heart transplant recipients. Larger and prospective studies are needed to confirm this hypothesis.

Smart Response Biomaterials for Pain Management.

The intricate nature of pain classification and mechanism constantly affects the recovery of diseases and the well-being of patients. Key medical challenges persist in devising effective pain management strategies. Therefore, a comprehensive review of relevant methods and research advancements in pain management is conducted. This overview covers the main categorization of pain and its developmental mechanism, followed by a review of pertinent research and techniques for managing pain. These techniques include commonly prescribed medications, invasive procedures, and noninvasive physical therapy methods used in rehabilitation medicine. Additionally, for the first time, a systematic summary of the utilization of responsive biomaterials in pain management is provided, encompassing their response to physical stimuli such as ultrasound, magnetic fields, electric fields, light, and temperature, as well as changes in the physiological environment like reactive oxygen species (ROS) and pH. Even though the application of responsive biomaterials in pain management remains limited and at a fundamental level, recent years have seen the examination and debate of relevant research findings. These profound discussions aim to provide trends and directions for future research in pain management.