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Pharmacokinetic parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time course data enable the physio-biological interpretation of tissue angiogenesis. This study aims to develop machine learning approaches for cervical carcinoma prediction based on pharmacokinetic parameters. The performance of individual parameters was assessed in terms of their efficacy in differentiating cancerous tissue from normal cervix tissue. The effect of combining parameters was evaluated using the following two approaches the first approach was based on support vector machines (SVMs) to combine the parameters from one pharmacokinetic model or across several models; the second approach was based on a novel method called APITL (artificial pharmacokinetic images for transfer learning), which was designed to fully utilize the comprehensive pharmacokinetic information acquired from DCE-MRI data. A "winner-takes-all" strategy was employed to consolidate the slice-wise prediction into subject-wise prediction. Experiments were carried out with a dataset comprising 36 patients with cervical cancer and 17 healthy subjects. The results demonstrated that parameter Ve, representing volume fraction of the extracellular extravascular space (EES), attained high discriminative power regardless of the pharmacokinetic model used for estimation. An approximately 10% improvement in the accuracy was achieved with the SVM approach. The APITL method further outperformed SVM and attained a subject-wise prediction accuracy of 94.3%. Our experiment demonstrated that APITL could predict cervical carcinoma with high accuracy and had potential in clinical applications. Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. [https://www.selleckchem.com/products/adavivint.html Adavivint] The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications. INTRODUCTION Placental viral infections are associated with fetal inflammation and adverse pregnancy outcomes. However, there have been limited studies on how placental macrophages in the villous and adjacent fetal umbilical endothelial cells respond to a viral insult. This study aimed to evaluate the communication between Hofbauer cells (HBCs) and human umbilical vein endothelial cells (HUVECs) during a viral infection. METHODS HBCs were either uninfected or infected with the γ-herpesvirus, MHV-68, and the conditioned medium (CM) collected. HUVECs were exposed to HBC CM and the levels of the pro-neutrophilic response markers IL-8; E-selectin; intercellular adhesion molecule 1 (ICAM-1); and vascular adhesion molecule 1 (VCAM-1) measured by ELISA and qPCR. The role of HBC-derived IL-1β was investigated using an IL-1β blocking antibody (Ab) or IL-1 receptor antagonist (IL-1Ra). RESULTS MHV-68 infection of HBCs induced a significant increase in IL-1β secretion. CM from infected HBCs induced HUVEC expression of IL-8, E-selectin, VCAM-1, ICAM-1 mRNA, and secretion of IL-8. The HUVEC response to the CM of MHV-infected HBCs was inhibited by a neutralizing IL-1β Ab and by IL-1Ra. DISCUSSION Virally-induced HBC IL-1β activates HUVECs to generate a pro-neutrophilic response. This novel cell-cell communication pathway may play an important role in the genesis of fetal inflammation associated with placental viral infection. INTRODUCTION Our aim was to assess placental function by diffusion-weighted magnetic resonance imaging (MRI) using intravoxel incoherent motion (IVIM) analysis in uncomplicated pregnancies and pregnancies complicated by placental dysfunction. METHODS 31 normal pregnancies and 9 pregnancies complicated by placental dysfunction (birthweight&nbsp;≤&nbsp;-2SD and histological signs of placental vascular malperfusion) were retrieved from our placental MRI research database. MRI was performed at gestational weeks 20.1-40.6 in a 1.5&nbsp;T system using 10&nbsp;b-values (0-1000&nbsp;s/mm2). Regions of interest were drawn covering the entire placenta in five transverse slices. Diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) were estimated by IVIM analysis. RESULTS In normal pregnancies, placental f decreased linearly with gestational age (r&nbsp;=&nbsp;-0.522, p&nbsp;=&nbsp;0.002) being 26.2% at week 20 and 18.8% at week 40. D and D* were 1.57&nbsp;±&nbsp;0.03 and 31.7&nbsp;±&nbsp;3.1&nbsp;mm2/s (mean&nbsp;±&nbsp;SD), respectively, and they were not correlated with gestational age. In complicated pregnancies, f was significantly reduced (mean Z-score&nbsp;=&nbsp;-1.16; p&nbsp;=&nbsp;0.02) when compared to the group of normal pregnancies, whereas D and D* did not differ significantly between groups. Subgroup analysis demonstrated that f was predominantly reduced in dysfunctional placentas characterized by fetal vascular malperfusion (mean Z-score&nbsp;=&nbsp;-2.11, p&nbsp; less then &nbsp;0.001) rather than maternal vascular malperfusion (mean Z-score&nbsp;=&nbsp;-0.40, p&nbsp;=&nbsp;0.42). In addition, f was negatively correlated with uterine artery pulsatility index (r&nbsp;=&nbsp;-0.396, p&nbsp;=&nbsp;0.01). DISCUSSION Among parameters obtained by the IVIM analysis, only f revealed significant differences between the normal and the dysfunctional placentas. Subgroup analysis suggests that placental f may be able to discriminate non-invasively between different histological types of vascular malperfusion.