Biophysical Markers Research Articles

The correlation between first-trimester serum biochemical and biophysical markers and skin microvascular reactivity assessed by laser speckle contrast imaging

The correlation between first-trimester serum biochemical and biophysical markers and skin microvascular reactivity assessed by laser speckle contrast imaging

Prevention of Pregnancy Complications Using a Multimodal Lifestyle, Screening, and Medical Model.

Prevention of pregnancy complications related to the "great obstetrical syndromes" (preeclampsia, fetal growth restriction, spontaneous preterm labor, and stillbirth) is a global research and clinical management priority. These syndromes share many common pathophysiological mechanisms that may contribute to altered placental development and function. The resulting adverse pregnancy outcomes are associated with increased maternal and perinatal morbidity and mortality and increased post-partum risk of cardiometabolic disease. Maternal nutritional and environmental factors are known to play a significant role in altering bidirectional communication between fetal-derived trophoblast cells and maternal decidual cells and contribute to abnormal placentation. As a result, lifestyle-based interventions have increasingly been recommended before, during, and after pregnancy, in order to reduce maternal and perinatal morbidity and mortality and decrease long-term risk. Antenatal screening strategies have been developed following extensive studies in diverse populations. Multivariate preeclampsia screening using a combination of maternal, biophysical, and serum biochemical markers is recommended at 11-14 weeks' gestation and can be performed at the same time as the first-trimester ultrasound and blood tests. Women identified as high-risk can be offered prophylactic low dose aspirin and monitored with angiogenic factor assessment from 22 weeks' gestation, in combination with clinical assessment, serum biochemistry, and ultrasound. Lifestyle factors can be reassessed during counseling related to antenatal screening interventions. The integration of lifestyle interventions, pregnancy screening, and medical management represents a conceptual advance in pregnancy care that has the potential to significantly reduce pregnancy complications and associated later life cardiometabolic adverse outcomes.

Feto-maternal indicators of cardiac dysfunction as a justification for the cardiac origins for pre-eclampsia.

While the pathophysiology of pre-eclampsia has been postulated as being secondary to placental dysfunction, a cardiac origin has more recently been proposed. Although an association between fetal congenital cardiovascular disease and pre-eclampsia has been demonstrated, no precise pathophysiologic mechanism for this association has been described. This review highlights the current biophysical (including echocardiography and Doppler indices) and biochemical (including proteomic, metabolomic and genetic/transcriptomic) markers of cardiac dysfunction that have been investigated in maternal and fetal cardiac disease and their overlap with predictors of pre-eclampsia. Common pathways of inflammatory and anti-angiogenesis imbalance, endothelial damage, and oxidative stress have been demonstrated in both cardiovascular disease and pre-eclampsia and further investigation into these pathways could help to elucidate the common pathophysiologic mechanisms linking these disorders.

Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.

Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13+6 weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia. This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation. We ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively). Our models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes.

Prospective Validation of First-Trimester Screening for Preterm Preeclampsia in Nulliparous Women (PREDICTION Study).

Fetal Medicine Foundation (FMF) studies suggest that preterm preeclampsia can be predicted in the first trimester by combining biophysical, biochemical, and ultrasound markers and prevented using aspirin. We aimed to evaluate the FMF preterm preeclampsia screening test in nulliparous women. We conducted a prospective multicenter cohort study of nulliparous women recruited at 11 to 14 weeks. Maternal characteristics, mean arterial blood pressure, PAPP-A (pregnancy-associated plasma protein A), PlGF (placental growth factor) in maternal blood, and uterine artery pulsatility index were collected at recruitment. The risk of preterm preeclampsia was calculated by a third party blinded to pregnancy outcomes. Receiver operating characteristic curves were used to estimate the detection rate (sensitivity) and the false-positive rate (1-specificity) for preterm (<37 weeks) and for early-onset (<34 weeks) preeclampsia according to the FMF screening test and according to the American College of Obstetricians and Gynecologists criteria. We recruited 7554 participants including 7325 (97%) who remained eligible after 20 weeks of which 65 (0.9%) developed preterm preeclampsia, and 22 (0.3%) developed early-onset preeclampsia. Using the FMF algorithm (cutoff of ≥1 in 110 for preterm preeclampsia), the detection rate was 63.1% for preterm preeclampsia and 77.3% for early-onset preeclampsia at a false-positive rate of 15.8%. Using the American College of Obstetricians and Gynecologists criteria, the equivalent detection rates would have been 61.5% and 59.1%, respectively, for a false-positive rate of 34.3%. The first-trimester FMF preeclampsia screening test predicts two-thirds of preterm preeclampsia and three-quarters of early-onset preeclampsia in nulliparous women, with a false-positive rate of ≈16%. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02189148.

Physical and biophysical markers of assessment in medical training: A scoping review of the literature

Purpose Assessment in medical education has changed over time to measure the evolving skills required of current medical practice. Physical and biophysical markers of assessment attempt to use technology to gain insight into medical trainees’ knowledge, skills, and attitudes. The authors conducted a scoping review to map the literature on the use of physical and biophysical markers of assessment in medical training. Materials and methods The authors searched seven databases on 1 August 2022, for publications that utilized physical or biophysical markers in the assessment of medical trainees (medical students, residents, fellows, and synonymous terms used in other countries). Physical or biophysical markers included: heart rate and heart rate variability, visual tracking and attention, pupillometry, hand motion analysis, skin conductivity, salivary cortisol, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The authors mapped the relevant literature using Bloom’s taxonomy of knowledge, skills, and attitudes and extracted additional data including study design, study environment, and novice vs. expert differentiation from February to June 2023. Results Of 6,069 unique articles, 443 met inclusion criteria. The majority of studies assessed trainees using heart rate variability (n = 160, 36%) followed by visual attention (n = 143, 32%), hand motion analysis (n = 67, 15%), salivary cortisol (n = 67, 15%), fMRI (n = 29, 7%), skin conductivity (n = 26, 6%), fNIRs (n = 19, 4%), and pupillometry (n = 16, 4%). The majority of studies (n = 167, 38%) analyzed non-technical skills, followed by studies that analyzed technical skills (n = 155, 35%), knowledge (n = 114, 26%), and attitudinal skills (n = 61, 14%). 169 studies (38%) attempted to use physical or biophysical markers to differentiate between novice and expert. Conclusion This review provides a comprehensive description of the current use of physical and biophysical markers in medical education training, including the current technology and skills assessed. Additionally, while physical and biophysical markers have the potential to augment current assessment in medical education, there remains significant gaps in research surrounding reliability, validity, cost, practicality, and educational impact of implementing these markers of assessment.

Performance of the first-trimester Fetal Medicine Foundation competing risks model for preeclampsia prediction: an external validation study in Brazil

Background: The current version of the Fetal Medicine Foundation competing risks model for preeclampsia prediction has not been previously validated in Brazil.Objectives: (1) To validate the Fetal Medicine Foundation combined algorithm for the prediction of preterm preeclampsia in the Brazilian population and (2) to describe the accuracy and calibration of the Fetal Medicine Foundation algorithm when considering the prophylactic use of aspirin, by clinical criteria.Study design: Cohort study, including consecutive singleton pregnancies undergoing preeclampsia screening at 11-14 weeks, examining maternal characteristics, medical history, and biophysical markers between October 2010 and December 2018 in a university hospital in Brazil. Risks were calculated using the 2018 version of the algorithm available on the Fetal Medicine Foundation website, and cases were classified as low- or high-risk using cut-off of 1/100 to evaluate predictive performance. Expected and observed cases with PE according to the FMF estimated risk range (≥1 in 10; 1 in 11 to 1 in 50; 1 in 51 to 1 in 100; 1 in 101 to 1 in 150; and <1 in 150) were compared. After identifying high-risk pregnant women who used aspirin, the treatment effect of 62% reduction in preterm preeclampsia identified in the ASPRE trial was used to evaluate the predictive performance adjusted for the effect of aspirin. The number of potentially unpreventable cases in the group without aspirin use was estimated.Results: Among 2,749 pregnancies, preterm preeclampsia occurred in 84 (3.1%). With a risk cut-off of 1/100, the screen-positive rate was 25.8%. The detection rate was 71.4%, with a false positive rate of 24.4%. The area under the curve was 0.818 (95% confidence interval 0.773 to 0.863). In the risk range ≥1/10, there is an agreement between the number of expected and observed cases, and in the other ranges, the predicted risk was lower than the observed rates. Accounting for the effect of aspirin resulted in an increase in detection rate and positive predictive values and a slight decrease in the false positive rate. With 27 cases of preterm preeclampsia in the high-risk group without aspirin use, we estimate that 16 of these cases of preterm preeclampsia would have been avoided if this group had received prophylaxis.Conclusions: In a high prevalence setting, the Fetal Medicine Foundation algorithm can identify women who are more likely to develop preterm preeclampsia. Not accounting for the effect of aspirin underestimates the screening performance.

“Patchiness” in mechanical stiffness across a tumor as an early-stage marker for malignancy

Mechanical phenotyping of tumors, either at an individual cell level or tumor cell population level is gaining traction as a diagnostic tool. However, the extent of diagnostic and prognostic information that can be gained through these measurements is still unclear. In this work, we focus on the heterogeneity in mechanical properties of cells obtained from a single source such as a tissue or tumor as a potential novel biomarker. We believe that this heterogeneity is a conventionally overlooked source of information in mechanical phenotyping data. We use mechanics-based in-silico models of cell-cell interactions and cell population dynamics within 3D environments to probe how heterogeneity in cell mechanics drives tissue and tumor dynamics. Our simulations show that the initial heterogeneity in the mechanical properties of individual cells and the arrangement of these heterogenous sub-populations within the environment can dictate overall cell population dynamics and cause a shift towards the growth of malignant cell phenotypes within healthy tissue environments. The overall heterogeneity in the cellular mechanotype and their spatial distributions is quantified by a “patchiness” index, which is the ratio of the global to local heterogeneity in cell populations. We observe that there exists a threshold value of the patchiness index beyond which an overall healthy population of cells will show a steady shift towards a more malignant phenotype. Based on these results, we propose that the “patchiness” of a tumor or tissue sample, can be an early indicator for malignant transformation and cancer occurrence in benign tumors or healthy tissues. Additionally, we suggest that tissue patchiness, measured either by biochemical or biophysical markers, can become an important metric in predicting tissue health and disease likelihood just as landscape patchiness is an important metric in ecology.

Incidence and first trimester risk factors of stillbirth in Indonesia.

To determine the incidence and the risk factors of stillbirth from maternal biophysical, ultrasound, and biochemical markers at 11-13weeks of gestation in the Indonesian population. This was a retrospective cohort study of pregnant women for first-trimester preeclampsia screening at 11-13weeks of gestation in some clinics and hospital in Jakarta. Maternal characteristics and history, mean arterial pressure (MAP) measurement, uterine artery pulsatility index (UtA-PI) ultrasound, maternal ophthalmic peak ratio (Oph-PR) Doppler, and placental growth factor (PlGF) serum were collected during the visit. Stillbirth was classified into placental dysfunction-related when it occurred with preeclampsia or birth weight <10th percentile and non-placental dysfunction-related. Bivariate and multivariate logistic regression analyses were employed to determine the risk factors associated with stillbirth. Of 1,643 eligible participants, 13 (0.79 %) stillbirth cases were reported. More than half of the stillbirths (7) were placental dysfunction-related. After adjusted with maternal age, body mass index (BMI), and parity status, chronic hypertension (aOR (adjusted odds ratio)) 24.41, 95 % CI {confidence interval} 5.93-100.43), previous pregnancy with preeclampsia (aOR 15.79, 95 % CI 4.42-56.41), MAP >101.85 (aOR 26.67, 95 % CI 8.26-86.06), UtA-PI >1.90 (aOR 10.68, 95 % CI 2.34-48.58, and PlGF <28.77 pg/mL (aOR 18.60, 95 % CI 5.59-61.92) were associated with stillbirth. The incidence of stillbirth in the population is comparable to studies conducted in developed countries. Most routine variables assessed at the 11-13weeks combined screening for preeclampsia are associated with the risk of stillbirth.

Probing underlying biophysical mechanisms of electrical properties change by pathogenesis at the microscopic cellular level

The electrical properties of human tissue have significant potential as biophysical markers in clinical applications, as they can indicate biochemical/biophysical changes occurring at the cellular and extracellular levels. Magnetic resonance electrical properties tomography (MR-EPT) provides a noninvasive approach for extracting pixel-wise electrical properties by processing the B1 field mapping data. However, our current understanding of the biophysical mechanisms underlying changes in electrical properties at the microscopic cellular level during pathogenesis remains incomplete. In this study, an inhomogeneous liver model was developed to establish a linear correlation between fat fraction and electrical properties. We further fit the correlation of liver phantoms with different fat fractions of 0%, 1%, 9%, 17%, 25%, 30%, and 50% (R2 &amp;gt; 0.93). In addition, an inhomogeneous liver phantom was fabricated and measured through MR-EPT at 128 MHz (3 T). The outcomes of this research have the potential to bridge the gap between microscopic lesions and pixel-wise MR-EPT images, offering a feasible method for extracting electrical properties through fat quantification techniques like MRI-Dixon technique.

High-throughput single-cell assay for precise measurement of the intrinsic mechanical properties and shape characteristics of red blood cells.

The intrinsic physical and mechanical properties of red blood cells (RBCs), including their geometric and rheological characteristics, can undergo changes in various circulatory and metabolic diseases. However, clinical diagnosis using RBC biophysical phenotypes remains impractical due to the unique biconcave shape, remarkable deformability, and high heterogeneity within different subpopulations. Here, we combine the hydrodynamic mechanisms of fluid-cell interactions in micro circular tubes with a machine learning method to develop a relatively high-throughput microfluidic technology that can accurately measure the shear modulus of the membrane, viscosity, surface area, and volume of individual RBCs. The present method can detect the subtle changes of mechanical properties in various RBC components at continuum scales in response to different doses of cytoskeletal drugs. We also investigate the correlation between glycosylated hemoglobin and RBC mechanical properties. Our study develops a methodology that combines microfluidic technology and machine learning to explore the material properties of cells based on fluid-cell interactions. This approach holds promise in offering novel label-free single-cell-assay-based biophysical markers for RBCs, thereby enhancing the potential for more robust disease diagnosis.

Spectral graph modeling reveals global slowing of neurophysiological network transmission in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While various neuroimaging studies have revealed functional network abnormalities in patients with AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. In order to answer these questions, we employed a spectral graph‐theory based model (SGM) to identify abnormal biophysical markers of neuronal activity in AD.MethodSGM is an analytic model that describes how the long‐range fiber projections in the brain mediate and couple the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled models, the SGM is linear, available in closed‐form, and parameterized by a small set of global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well characterized clinical population of patients with AD (N = 88) and a cohort of age‐matched healthy controls (N = 88). We estimated SGM parameters that best captured the regional magnetoencephalography (MEG) frequency spectra of patients with AD and age‐matched controls.ResultSGM was able to capture the regional MEG frequency spectra of patients with AD and healthy controls, specifically, the abnormal slowing of neural oscillations in AD (Fig 1A). Patients with AD have significantly elevated long‐range excitatory neuronal time constant (tG) compared to controls (p &lt; 0.001). Time constant tG is also the most important feature for accurate classification of patients with AD from controls (Area Under Curve = 0.85; Fig 1B). Lastly, higher tG is negatively correlated with global cognitive decline score (p = 0.02; Fig 1C).ConclusionThese results indicate that a global impairment in the long‐range excitatory neuronal activity is associated with abnormal spectral signatures and cognitive deficits in AD. Intriguingly, our work is able to recapitulate the spatial and spectral patterns of AD‐related functional activity without introducing any spatial heterogeneity whatsoever; indeed, the SGM model is entirely global and spatially‐invariant. This raises the possibility that a global increase in the long‐range excitatory time constant might be a sufficient factor underlying observed spatiotemporal alterations of neuronal activity in AD. Our findings provide new insights into potential mechanistic links between abnormal neural oscillations and their cellular correlates in AD.

Quantification of biophysical markers of inflammation using Spatial Light Interference Microscopy (SLIM) in a repeated social stress disorder murine model

Quantification of biophysical markers of inflammation using Spatial Light Interference Microscopy (SLIM) in a repeated social stress disorder murine model

The value of some indices of heart rate variability in the diagnosis of fetal growth retardation

Fetal growth retardation (FGR) is one of the most serious problems in current perinatology. This pathology leads not only to the deterioration of perinatal outcomes but also significantly increases morbidity during adult life. FGR is known to be featured by its delayed “neurological maturation”. This is manifested in a decrease in autonomic nervous regulation according to the fetal heart rate variability (HRV) scores and indices. The objective: to study HRV in fetuses with normal biometric indices compared to similar parameters of growth retarded fetuses. Materials and methods. The 47 pregnant women who had fetuses with normal biometric indices (I group) and 33 patients with fetal growth retardation (II group) were included in the study. The investigation of the fetal HRV was performed by processing the R–R intervals obtained via non-invasive electrocardiogram (ECG) and involved the following indices: STV (short term variations), LTV (long term variations), AC/DC (acceleration capacity/deceleration capacity), TP (total power), SI (stress index), SDNN (standard deviation of normal to normal intervals), EnRE (entropy of the time series), D2 (correlation dimension of the time series), Z (temporary irreversibility) і FL (measure of fuzzy logic, integrated measure of HRV). Results. The obtained data indicated that the indices of fetal HRV were reduced in the patients with FGR. These indices have utility in screening programs for the detection of fetal intrauterine nutrition and growth disturbances. The results of the study allow us to believe that the ability to accelerate heart rate patterns is partly preserved even during FGR. This may indicate a very ancient phylogenetic nature of the reaction of the heart system of the fetus to its motile activity. However, decelerations according to the DC were typical for FGR. A non-invasive fetal ECG could become a useful supplementary method in the current methods’ spectrum used in perinatal medicine. Conclusions. Therefore, the development of algorithms for the diagnosis of FGR requires the inclusion of the following HRV indices: STV, LTV, DC, TP, SI, SDNN, D2, Z, and FL. These biophysical markers of FGR can be used as a screening program in conditions of lack of access to high-quality ultrasound diagnostics or be a valuable addition to existing perinatal strategies.

Morphometric and Nanomechanical Screening of Peripheral Blood Cells with Atomic Force Microscopy for Label-Free Assessment of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis.

Neurodegenerative disorders (NDDs) are complex, multifactorial disorders with significant social and economic impact in today's society. NDDs are predicted to become the second-most common cause of death in the next few decades due to an increase in life expectancy but also to a lack of early diagnosis and mainly symptomatic treatment. Despite recent advances in diagnostic and therapeutic methods, there are yet no reliable biomarkers identifying the complex pathways contributing to these pathologies. The development of new approaches for early diagnosis and new therapies, together with the identification of non-invasive and more cost-effective diagnostic biomarkers, is one of the main trends in NDD biomedical research. Here we summarize data on peripheral biomarkers, biofluids (cerebrospinal fluid and blood plasma), and peripheral blood cells (platelets (PLTs) and red blood cells (RBCs)), reported so far for the three most common NDDs-Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). PLTs and RBCs, beyond their primary physiological functions, are increasingly recognized as valuable sources of biomarkers for NDDs. Special attention is given to the morphological and nanomechanical signatures of PLTs and RBCs as biophysical markers for the three pathologies. Modifications of the surface nanostructure and morphometric and nanomechanical signatures of PLTs and RBCs from patients with AD, PD, and ALS have been revealed by atomic force microscopy (AFM). AFM is currently experiencing rapid and widespread adoption in biomedicine and clinical medicine, in particular for early diagnostics of various medical conditions. AFM is a unique instrument without an analog, allowing the generation of three-dimensional cell images with extremely high spatial resolution at near-atomic scale, which are complemented by insights into the mechanical properties of cells and subcellular structures. Data demonstrate that AFM can distinguish between the three pathologies and the normal, healthy state. The specific PLT and RBC signatures can serve as biomarkers in combination with the currently used diagnostic tools. We highlight the strong correlation of the morphological and nanomechanical signatures between RBCs and PLTs in PD, ALS, and AD.

PO1_09. Evaluation of biophysical markers associated with arterial hypertension in women belonging to the 1978/79 cohort in the city of Ribeirão Preto/SP

PO1_09. Evaluation of biophysical markers associated with arterial hypertension in women belonging to the 1978/79 cohort in the city of Ribeirão Preto/SP

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance- This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.

Biomarkers at 6 weeks' gestation in the prediction of early miscarriage in pregnancy following assisted reproductive technology.

Miscarriage is a major concern in early pregnancy among women having conceived with assisted reproductive treatments. This study aimed to examine potential miscarriage-related biophysical and biochemical markers at 6 weeks' gestation among women with confirmed clinical pregnancy following in vitro fertilization (IVF)/embryo transfer (ET) and evaluate the performance of a model combining maternal factors, biophysical and biochemical markers at 6 weeks' gestation in the prediction of first trimester miscarriage among singleton pregnancies following IVF/ET. A prospective cohort study was conducted in a teaching hospital between December 2017 and January 2020 including women who conceived through IVF/ET. Maternal mean arterial pressure, ultrasound markers including mean gestational sac diameter, fetal heart activity, crown rump length and mean uterine artery pulsatility index (mUTPI) and biochemical biomarkers including maternal serum soluble fms-like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF), kisspeptin and glycodelin-A were measured at 6 weeks' gestation. Logistic regression analysis was carried out to determine significant predictors of miscarriage prior to 13 weeks' gestation and performance of screening was estimated by receiver-operating characteristics curve analysis. Among 169 included pregnancies, 145 (85.8%) pregnancies progressed to beyond 13 weeks' gestation and had live births whereas 24 (14.2%) pregnancies resulted in a miscarriage during the first trimester. In the miscarriage group, compared to the live birth group, maternal age, body mass index, and mean arterial pressure were significantly increased; mean gestational sac diameter, crown rump length, mUTPI, serum sFlt-1, glycodelin-A, and the rate of positive fetal heart activity were significantly decreased, while no significant differences were detected in PlGF and kisspeptin. Significant prediction for miscarriage before 13 weeks' gestation was provided by maternal age, fetal heart activity, mUTPI, and serum glycodelin-A. The combination of maternal age, ultrasound (fetal heart activity and mUTPI), and biochemical (glycodelin-A) markers achieved the highest area under the curve (AUC: 0.918, 95% CI 0.866-0.955), with estimated detection rates of 54.2% and 70.8% for miscarriage before 13 weeks' gestation, at fixed false positive rates of 5% and 10%, respectively. A combination of maternal age, fetal heart activity, mUTPI, and serum glycodelin-A at 6 weeks' gestation could effectively identify IVF/ET pregnancies at risk of first trimester miscarriage.

Signatures of obstructions and expansions in the arterial frequency response

Background and Objective: The blood pressure and flow waveforms carry valuable information about the condition of the cardiovascular system and a patient's health. Waveform analysis in health and pathological conditions can be performed in the time or frequency domains; the information to be emphasised defines the use of either domain. However, physicians are more familiar with the time domain, and the changes in the waveforms due to cardiovascular diseases and ageing are better characterised in such domain. On the other hand, the analysis of the vascular and geometrical variables determining the signatures in the frequency response of local vascular anomalies, such as aneurysms and stenoses, has not been thoroughly explored. This paper aims to characterise the signatures of obstructions (stenoses) and expansions (aneurysms) in the frequency response of tapered arteries. Methods: The first step in our methodology was to incorporate the viscous response of the arterial wall into a one-dimensional elastic formulation that solves the governing equations in the frequency domain. As a second step, we imposed a volumetric flow excitation in arteries simulating the aorta with increasing geometry complexity: from straight to tapered arteries with local expansions or obstructions; and we assessed the frequency response. Results: We found that the obstructions and expansions cause characteristic signatures in an artery's frequency response that are distinguishable from a health condition. The signatures of obstruction and expansions differ; the obstructions increase the magnitude of fundamental frequency and work as a close boundary condition. On the other hand, the expansions diminish the fundamental frequency and work as an open boundary condition. Furthermore, such signatures correlate to the distance between the artery's inlet and the anomaly's starting point and have the potential to pinpoint abnormalities non-invasively. Conclusions: We found that the obstructions and expansions cause characteristic signatures in an artery's frequency response that have the potential to detect and follow up on the development of vascular abnormalities. For the latter purpose, constant monitoring may be required; despite this not being a common clinical practice, the new wearable technology offers the possibility of continuous monitoring of biophysical markers such as the pressure waveform.

Extended combined screening for preeclampsia prediction in women with diabetes mellitus

BACKGROUND:Early screening for preeclampsia has shown high efficiency for low-risk groups, however, the presence of systemic vascular disease in patients with diabetes mellitus complicates their use and requires the development of additional approaches to predicting preeclampsia in this group of patients.&#x0D; AIM:The aim of this study was to evaluate the effectiveness of early prediction of preeclampsia with extended combined screening in patients with pregestational types of diabetes mellitus.&#x0D; MATERIALS AND METHODS:This study included 75 pregnant women: 40 patients with type 1 diabetes mellitus, and 35 patients with type 2 diabetes mellitus. To determine the risk of further preeclampsia development, we evaluated biochemical, biophysical and anamnestic factors, along with the serum levels of placental growth factor, soluble fms-like tyrosine kinase 1, and soluble endoglin at 11+0to 13+6gestational weeks. The main outcome assessed was the development of preeclampsia.&#x0D; RESULTS:In patients with further development of preeclampsia (35% in type 1 and 40 % in type 2 diabetic women), we observed higher soluble fms-like tyrosine kinase 1 / placental growth factor ratios, as well as elevated serum soluble endoglin (type 1 diabetes mellitus) and soluble fms-like tyrosine kinase 1 (type 2 diabetes mellitus) levels. Isolated determination of placental growth factor showed no significant differences in the levels of this angiogenic factor in preeclampsia. A multivariate predictive model for preeclampsia demonstrated high prognostic parameters: for patients with type 1 diabetes mellitus, area under the curve was 0.96 (95% confidence interval 0.931.00), with positive predictive value 76.7% and negative predictive value 90.1%; for patients with type 2 diabetes mellitus, area under the curve was 0.94 (95% confidence interval 0.861.00), with positive predictive value 88.5% and negative predictive value 97.1%.&#x0D; CONCLUSIONS:Specific biochemical and biophysical markers of preeclampsia combined with maternal risk factors and assessment of carbohydrate metabolism can increase the predictive value of early screening studies for preeclampsia in women with pregestational diabetes mellitus.