Circulating tissue factor-positive procoagulant microparticles in patients with type 1 diabetes

Aim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF(+)PS(+)MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF(+)PS(+)MPs and PS(+)MPs (TF(+)PS(+)MPs/PS(+)MPs). Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS(+)MPs, TF(+)PS(+)MPs and TF(+)PS(+)MPs/PS(+)MPs were elevated in patients with T1DM (PS(+)MPs: 1078.5 +/- 158.08 vs 686.84 +/- 122.04/mu L, P &lt;0.001; TF(+)PS(+)MPs: 202.10 +/- 47.47 vs 108.33 +/- 29.42/mu L, P &lt;0.001; and TF(+)PS(+)MPs/PS(+)MPs: 0.16 +/- 0.04 vs 0.19 +/- 0.05, P = 0.004), mostly derived from platelet, lymphocytes and endothelial cells. In the subgroup analysis, the counts of total and platelet TF(+)PS(+)MPs were increased in patients with diabetic retinopathy (DR) and with higher HbA1c, respectively. Conclusion: Circulating TF(+)PS(+)MPs and those derived from platelet, lymphocytes and endothelial cells were elevated in patients with T1DM.De tre första författarna delar förstaförfattarskapet.</p

Preliminary Evidence of Sex Differences in Cortical Thickness Following Acute Mild Traumatic Brain Injury

The main objective of this study was to evaluate sex differences in cortical thickness after acute mild traumatic brain injury (mTBI) and its associations with clinical outcomes. Thirty-two patients with mTBI at acute phase (2.4 ± 1.3 days post-injury) and 30 healthy controls were enrolled. All the participants underwent comprehensive neurocognitive assessments and MRI to assess cortical thickness. Significant sex differences were determined by using variance analysis of factorial design. Relations between the cortical thickness and clinical assessments were measured with the Spearman Correlation. Results revealed that patients with mTBI had significantly reduced cortical thickness in the left entorhinal cortex while increased cortical thickness in the left precuneus cortex and right lateral occipital cortex, compared with healthy controls. The interaction effect of the group × sex on cortical thickness was significant. Female patients had significant thicker cortical thickness in the left caudal anterior cingulate cortex (ACC) than male patients and had higher scores on Posttraumatic stress disorder Checklist—Civilian Version (PCL-C). Spearman correlational analysis showed a significantly positive correlations between the cortical thickness of the left caudal ACC and PCL-C ratings in female patients. Sex differences in cortical thickness support its potential as a neuroimaging phenotype for investigating the differences in clinical profiles of mild TBI between women and men

Sex Differences in Abnormal Intrinsic Functional Connectivity After Acute Mild Traumatic Brain Injury

Mild traumatic brain injury (TBI) is considered to induce abnormal intrinsic functional connectivity within resting-state networks (RSNs). The objective of this study was to estimate the role of sex in intrinsic functional connectivity after acute mild TBI. We recruited a cohort of 54 patients (27 males and 27 females with mild TBI within 7 days post-injury) from the emergency department (ED) and 34 age-, education-matched healthy controls (HCs; 17 males and 17 females). On the clinical scales, there were no statistically significant differences between males and females in either control group or mild TBI group. To detect whether there was abnormal sex difference on functional connectivity in RSNs, we performed independent component analysis (ICA) and a dual regression approach to investigate the between-subject voxel-wise comparisons of functional connectivity within seven selected RSNs. Compared to female patients, male patients showed increased intrinsic functional connectivity in motor network, ventral stream network, executive function network, cerebellum network and decreased connectivity in visual network. Further analysis demonstrated a positive correlation between the functional connectivity in executive function network and insomnia severity index (ISI) scores in male patients (r = 0.515, P = 0.006). The abnormality of the functional connectivity of RSNs in acute mild TBI showed the possibility of brain recombination after trauma, mainly concerning male-specific

Structural Remodeling Of Vagal Afferent Innervation Of Aortic Arch And Nucleus Ambiguus (Na) Projections To Cardiac Ganglia In A Transgenic Mouse Model Of Type 1 Diabetes (Ove26)

Diabetes-induced structural changes of vagal aortic afferent and cardiac efferent axons are not well understood. FVB control and OVE26 diabetic mice at different ages received injections of the tracer tetramethylrhodamine dextran (TMR-D) into the nodose ganglion to label vagal aortic afferents (at 3 and 6 months), or DiI injections into the nucleus ambiguus to label vagal cardiac efferents (at 3, 6, and 9 months). The aortic arch and atria were examined by using confocal microscopy. In the aortic arch, TMR-D labeled large and small vagal afferent axons (axonsL and axonsS) that formed different types of terminals: axonsL produced large flower-sprays (flower-spraysL) and end-nets (end-netsL), whereas axonsS produced small flower-sprays (flower-spraysS) and end-nets (end-netsS). In the atria, DiI-labeled vagal efferent axons formed basket endings around ganglion principle neurons (PNs). The vagal afferents, PNs and vagal cardiac efferents in diabetic mice were compared with age-matched control mice. We found (P \u3c 0.05) that: 1) the size of axonsL, flower-spraysL, flower-spraysS and end-netsS were reduced at 6 and 9 months; 2) the size of cardiac ganglia and the somatic area of the PNs were decreased, and the PN density in cardiac ganglia was increased at all ages and the PN nuclei/soma area ratio was increased at 9 months; and 3) the percentage of DiI-labeled axons-innervated PNs was decreased at all ages. Furthermore, the number of synaptic-like terminal varicosities around PNs was decreased. Compared with 3 months, more advanced diabetes at 9 months further reduced the number of varicosities/PN. In addition to these changes, swollen axons and terminals, as well as leaky-like DiI-labeled terminals, were observed in long-term diabetic mice (6 and 9 months of age). Taken together, our data show that chronic diabetes induces a significant structural atrophy of vagal aortic afferent and cardiac efferent axons and terminals. Although different morphologies of vagal afferent terminals in the aortic arch may serve as substrates for the future investigation of aortic depressor afferent physiology, structural remodeling of vagal afferents and efferents provides a foundation for further analysis of diabetesinduced impairment of cardiac autonomic regulation. J. Comp. © 2010 Wiley-Liss, Inc

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time

Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality

The Garbage Enzyme with Chinese Hoenylocust Fruits Showed Better Properties and Application than When Using the Garbage Enzyme Alone

Garbage enzyme (GE) is a vinegar or alcohol product derived from fermenting fresh kitchen waste, such as vegetable and fruit residues (peels, cuttings and bits), sugar (brown sugar, jaggery or molasses sugar) and water. Chinese honeylocust fruits (Gleditsia sinensis) have been used in China for at least 2000 years as a detergent. The aim of the study was to investigate the properties and application of Chinese honeylocust garbage enzyme (CHGE), which is produced when equal amounts of Chinese honeylocust fruits and fresh wastes are mixed. The results showed that CHGE had lesser microbial communities and lower surface tension than GE. CHGE also had higher viscosity, foam stability and emulsion stability than GE. Compared with GE, CHGE induced higher enzymatic amylase, cellulase, lipase and protease activities. CHGE had stronger detergency than GE and a 100× dilution of CHGE could significantly remove pesticide residues after a 30 min soaking treatment. The study showed that as a biological detergent, CHGE is safer and more environmentally friendly than GE and has remarkable washing and cleaning power. The preparation method of the detergent is simple: it can be prepared at home using fruit and vegetable waste, which is beneficial to the secondary utilization of waste and the reduction of pollution to the environment and damage to human health

Identifying and mapping individual medicinal plant Lamiophlomis rotata at high elevations by using unmanned aerial vehicles and deep learning

Abstract Background The identification and enumeration of medicinal plants at high elevations is an important part of accurate yield calculations. However, the current assessment of medicinal plant reserves continues to rely on field sampling surveys, which are cumbersome and time-consuming. Recently, unmanned aerial vehicle (UAV) remote sensing and deep learning (DL) have provided ultrahigh-resolution imagery and high-accuracy object recognition techniques, respectively, providing an excellent opportunity to improve the current manual surveying of plants. However, accurate segmentation of individual plants from drone images remains a significant challenge due to the large variation in size, geometry, and distribution of medicinal plants. Results In this study, we proposed a new pipeline for wild medicinal plant detection and yield assessment based on UAV and DL that was specifically designed for detecting wild medicinal plants in an orthomosaic. We used a drone to collect panoramic images of Lamioplomis rotata Kudo (LR) in high-altitude areas. Then, we annotated and cropped these images into equally sized sub-images and used a DL model Mask R-CNN for object detection and segmentation of LR. Finally, on the basis of the segmentation results, we accurately counted the number and yield of LRs. The results showed that the Mask R-CNN model based on the ResNet-101 backbone network was superior to ResNet-50 in all evaluation indicators. The average identification precision of LR by Mask R-CNN based on the ResNet-101 backbone network was 89.34%, while that of ResNet-50 was 88.32%. The cross-validation results showed that the average accuracy of ResNet-101 was 78.73%, while that of ResNet-50 was 71.25%. According to the orthomosaic, the average number and yield of LR in the two sample sites were 19,376 plants and 57.93 kg and 19,129 plants and 73.5 kg respectively. Conclusions The combination of DL and UAV remote sensing reveals significant promise in medicinal plant detection, counting, and yield prediction, which will benefit the monitoring of their populations for conservation assessment and management, among other applications