Maternal And Neonatal Outcomes In Non-Diabetic Large For Gestational Age Vs. Appropriate For Gestational Age Births: A Prospective Comparative Study

Large for Gestational Age (LGA) and Appropriate for Gestational Age (AGA) are terms used to describe the weight of a newborn to their gestational age. LGA is defined as a birth weight greater than the 90th percentile for gestational age (ACOG,2020b).1 The WHO definition of AGA refers to a fetus or newborn whose birthweight falls between the 10th and 90th percentile for their gestational age.2 Diabetes, especially gestational diabetes, is a known risk factor for LGA, but the impact of LGA in non-diabetic pregnancies has been less well-studied. This study aims to explore and compare maternal and neonatal outcomes in non-diabetic pregnancies that result in LGA versus AGA births. The findings could offer insights into how LGA affects maternal health, the course of pregnancy, and the immediate neonatal period in the absence of diabetes

Placental Growth Factor Levels in Populations with High Versus Low Risk for Cardiovascular Disease and Stressful Physiological Environments such as Microgravity: A Pilot Study

This pilot study compared placental growth factor (PIGF) levels in populations with high versus low risk for cardiovascular disease. Previous experiments from our laboratory (Sundaresan et al. 2005, 2009) revealed that the angiogenic factor PIGF was up regulated in modeled microgravity conditions in human lymphocytes leading to possible atherogenesis and pathogenesis in microgravity. Since the findings came from microgravity analog experiments, there is a strong link to its usefulness in the microgravity field as a biomarker. It is important to understand, that these findings came from both studies on expression levels of this cardiovascular marker in human lymphocytes in microgravity (in vitro microgravity analog), and a follow up gene expression study in hind limb suspended mice (in vivo microgravity analog). The relevance is enhanced because in life on earth, PIGF is an inflammatory biomarker for cardiovascular disease. Studies on the levels of PIGF would help to reduce the risk and prevention of heart failures in astronauts. If we can use this marker to predict and reduce the risk of cardiac events in astronauts and pilots, it would significantly help aerospace medicine operations. The investigations here confirmed that in a cardiovascular stressed population such as coronary artery disease (CAD) and acute coronary syndrome (ACS) patients, PIGF could be overexpressed. We desired to re-evaluate this marker in patients with cardiovascular disease in our own study. PIGF is a marker of inflammation and a predictor of short-term and long-term adverse outcome in ACS. In addition, elevated PIGF levels may be associated with increased risk for CAD.PIGF levels were determined in thirty-one patients undergoing cardiovascular catheterization for reasons other than ACS and in thirty-three low-risk asymptomatic subjects. Additional data on traditional cardiovascular risk factors for both populations were also compiled and compared. We found that PIGF levels were significantly higher in the high-risk population as compared to low-risk population. Also we were able to ascertain that PIGF levels were inversely correlated with HDL-cholesterol but directly correlated with the triglyceride levels. With further validation, PIGF may prove a useful addition to the armamentarium of noninvasive biomarkers for cardiovascular disease including a new area of stressful physiological conditions such as microgravity

IP-10 response to RD1 antigens might be a useful biomarker for monitoring tuberculosis therapy

Background There is an urgent need of prognosis markers for tuberculosis (TB) to improve treatment strategies. The results of several studies show that the Interferon (IFN)-γ-specific response to the TB antigens of the QuantiFERON TB Gold (QFT-IT antigens) decreases after successful TB therapy. The objective of this study was to evaluate whether there are factors other than IFN-γ [such as IFN-γ inducible protein (IP)-10 which has also been associated with TB] in response to QFT-IT antigens that can be used as biomarkers for monitoring TB treatment. Methods In this exploratory study we assessed the changes in IP-10 secretion in response to QFT-IT antigens and RD1 peptides selected by computational analysis in 17 patients with active TB at the time of diagnosis and after 6 months of treatment. The IFN-γ response to QFT-IT antigens and RD1 selected peptides was evaluated as a control. A non-parametric Wilcoxon signed-rank test for paired comparisons was used to compare the continuous variables at the time of diagnosis and at therapy completion. A Chi-square test was used to compare proportions. Results We did not observe significant IP-10 changes in whole blood from either NIL or QFT-IT antigen tubes, after 1-day stimulation, between baseline and therapy completion (p = 0.08 and p = 0.7 respectively). Conversely, the level of IP-10 release to RD1 selected peptides was significantly different (p = 0.006). Similar results were obtained when we detected the IFN-γ in response to the QFT-IT antigens (p = 0.06) and RD1 selected peptides (p = 0.0003). The proportion of the IP-10 responders to the QFT-IT antigens did not significantly change between baseline and therapy completion (p = 0.6), whereas it significantly changed in response to RD1 selected peptides (p = 0.002). The proportion of IFN-γ responders between baseline and therapy completion was not significant for QFT-IT antigens (p = 0.2), whereas it was significant for the RD1 selected peptides (p = 0.002), confirming previous observations. Conclusions Our preliminary study provides an interesting hypothesis: IP-10 response to RD1 selected peptides (similar to IFN-γ) might be a useful biomarker for monitoring therapy efficacy in patients with active TB. However, further studies in larger cohorts are needed to confirm the consistency of these study results

Rare missense variants in Tropomyosin-4 (TPM4) are associated with platelet dysfunction, cytoskeletal defects, and excessive bleeding

Background: A significant challenge is faced for the genetic diagnosis of inherited platelet disorders in which candidate genetic variants can be found in more than 100 bleeding, thrombotic, and platelet disorder genes, especially within families in which there are both normal and low platelet counts. Genetic variants of unknown clinical significance (VUS) are found in a significant proportion of such patients in which functional studies are required to prove pathogenicity. Objective: To identify the genetic cause in patients with a suspected platelet disorder and subsequently perform a detailed functional analysis of the candidate genetic variants found. Methods: Genetic and functional studies were undertaken in three patients in two unrelated families with a suspected platelet disorder and excessive bleeding. A targeted gene panel of previously known bleeding and platelet genes was used to identify plausible genetic variants. Deep platelet phenotyping was performed using platelet spreading analysis, transmission electron microscopy, immunofluorescence, and platelet function testing using lumiaggregometry and flow cytometry. Results: We report rare conserved missense variants (p.R182C and p.A183V) in TPM4 encoding tromomyosin-4 in 3 patients. Deep platelet phenotyping studies revealed similar platelet function defects across the 3 patients including reduced platelet secretion, and aggregation and spreading defects suggesting that TPM4 missense variants impact platelet function and show a disordered pattern of tropomyosin staining. Conclusions: Genetic and functional TPM4 defects are reported making TPM4 a diagnostic grade tier 1 gene and highlights the importance of including TPM4 in diagnostic genetic screening for patients with significant bleeding and undiagnosed platelet disorders, particularly for those with a normal platelet count

A half-metallic A- and B-site-ordered quadruple perovskite oxide CaCu₃Fe₂Re₂O₁₂ with large magnetization and a high transition temperature

Strong correlation between spins and conduction electrons is key in spintronic materials and devices. A few ferro- or ferrimagnetic transition metal oxides such as La 1-x Sr x MnO 3, Fe 3 O 4, CrO 2 and Sr 2 FeMoO 6 have spin-polarized conduction electrons at room temperature, but it is difficult to find other spin-polarized oxides with high Curie temperatures (well above room temperature) and large magnetizations for spintronics applications. Here we show that an A- and B-site-ordered quadruple perovskite oxide, CaCu 3 Fe 2 Re 2 O 12, has spin-polarized conduction electrons and is ferrimagnetic up to 560 K. The couplings between the three magnetic cations lead to the high Curie temperature, a large saturation magnetization of 8.7a μ B and a half-metallic electronic structure, in which only minority-spin bands cross the Fermi level, producing highly spin-polarized conduction electrons. Spin polarization is confirmed by an observed low-field magnetoresistance effect in a polycrystalline sample. Optimization of CaCu 3 Fe 2 Re 2 O 12 and related quadruple perovskite phases is expected to produce a new family of useful spintronic materials.</p

Vibration based real time brake health monitoring system – A machine learning approach

Abstract In an automobile, the brake system is the most important control component which ensures the safety of both passengers and vehicles. The continuous application of the brake causes the system gets faulty due to reasons like wear, mechanical fade, oil leak, etc., These faults or failures need to be monitored using proper monitoring techniques in order to avoid the incidents that may lead to accidents. Thus, the continuous monitoring of the brake system is very much essential for the safety of the vehicle. In this study, an experimental investigation was carried out for monitoring the brake system using vibration signals. An experimental setup which resembles the brake system was fabricated. The vibration signals were acquired under various brake condition such as good and faulty. From the acquired vibration signals, the features were extracted using statistical feature extraction techniques and feature selection was carried out. The selected features were then classifieds using a set of tree family classifiers such as random forest, random tree, LMT and decision tree. The classification accuracy of all the algorithms was compared and discussed.</jats:p