Enabling robust blue circularly polarized organic afterglow through self-confining isolated chiral chromophore.

Creating circularly polarized organic afterglow system with elevated triplet energy levels, suppressed non-radiative transitions, and effective chirality, which are three critical prerequisites for achieving blue circularly polarized afterglow, has posed a formidable challenge. Herein, a straightforward approach is unveiled to attain blue circularly polarized afterglow materials by covalently self-confining isolated chiral chromophore within polymer matrix. The formation of robust hydrogen bonds within the polymer matrix confers a distinctly isolated and stabilized molecular state of chiral chromophores, endowing a blue emission band at 414 nm, lifetime of 3.0 s, and luminescent dissymmetry factor of ~ 10-2. Utilizing the synergistic afterglow and chirality energy transfer, full-color circularly polarized afterglow systems are endowed by doping colorful fluorescent molecules into designed blue polymers, empowering versatile applications. This work paves the way for the streamlined design of blue circularly polarized afterglow materials, expanding the horizons of circularly polarized afterglow materials into various domains.

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection.

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma.

Background: The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis. Methods: In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method. Results: Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates. Conclusions: These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.

Demonstration of magnetic resonance Z-spectral imaging for fatty acid characterization of bone marrow at 3 T.

Magnetic resonance Z-spectral imaging (ZSI) has emerged as a new approach to measure fat fraction (FF). However, its feasibility for fat spectral imaging remains to be elucidated. In this study, a single-slice ZSI sequence dedicated to fat spectral imaging was designed, and its capability for fatty acid characterization was investigated on peanut oil samples, a multiple-vial fat-water phantom with varied oil volumes, and vertebral body marrow in healthy volunteers and osteoporosis patients at 3 T. The peanut oil spectrum was also recorded with a 400-MHz NMR spectrometer. A Gaussian-Lorentzian sum model was used to resolve water and six fat signals of the pure oil sample or four fat signals of the fat-water phantom or vertebral bone marrow from Z spectra. Fat peak amplitudes were normalized to the total peak amplitude of water and all fat signals. Normalized fat peak amplitudes and FF were quantified and compared among vials of the fat-water phantom or between healthy volunteers and osteoporosis patients. An unpaired student's t-test and Pearson's correlation were conducted, with p less than 0.05 considered statistically significant. The results showed that the peanut oil spectra measured with the ZSI technique were in line with respective NMR spectra, with amplitudes of the six fat signal peaks significantly correlated between the two methods (y = x + 0.001, r = 0.996, p < 0.001 under a repetition time of 1.6 s; and y = 1.026x - 0.003, r = 0.996, p < 0.001 under a repetition time of 3.1 s). Moreover, ZSI-measured FF exhibited a significant correlation with prepared oil volumes (y = 0.876x + 1.290, r = 0.996, p < 0.001). The osteoporosis patients showed significantly higher normalized fat peak amplitudes and FF in the L4 vertebral body marrow than the healthy volunteers (all p < 0.01). In summary, the designed ZSI sequence is feasible for fatty acid characterization, and has the potential to facilitate the diagnosis and evaluation of diseases associated with fat alterations at 3 T.

Fast and robust pulsed chemical exchange saturation transfer (CEST) MRI using a quasi-steady-state (QUASS) algorithm at 3 T.

Chemical exchange saturation transfer (CEST) has emerged as a powerful technique to image dilute labile protons. However, its measurement depends on the RF saturation duration (Tsat) and relaxation delay (Trec). Although the recently developed quasi-steady-state (QUASS) solution can reconstruct equilibrium CEST effects under continuous-wave RF saturation, it does not apply to pulsed-CEST MRI on clinical scanners with restricted hardware or specific absorption rate limits. This study proposed a QUASS algorithm for pulsed-CEST MRI and evaluated its performance in muscle CEST measurement. An approximated expression of a steady-state pulsed-CEST signal was incorporated in the off-resonance spin-lock model, from which the QUASS pulsed-CEST effect was derived. Numerical simulation, creatine phantom, and healthy volunteer scans were conducted at 3 T. The CEST effect was quantified with asymmetry analysis in the simulation and phantom experiments. CEST effects of creatine, amide proton transfer, phosphocreatine, and combined magnetization transfer and nuclear Overhauser effects were isolated from a multi-pool Lorentzian model in muscles. Apparent and QUASS CEST measurements were compared under different Tsat/Trec and duty cycles. Paired Student's t-test was employed with P < 0.05 as statistically significant. The simulation, phantom, and human studies showed the strong impact of Tsat/Trec on apparent CEST measurements, which were significantly smaller than the corresponding QUASS CEST measures, especially under short Tsat/Trec times. In comparison, the QUASS algorithm mitigates such impact and enables accurate CEST measurements under short Tsat/Trec times. In conclusion, the QUASS algorithm can accelerate robust pulsed-CEST MRI, promising the efficient detection and evaluation of muscle diseases in clinical settings.

Fatty Acids Composition of the Sacroiliac Joint in Axial Spondyloarthritis: Analysis Using 3.0 T Chemical Shift-Encoded MRI.

BACKGROUND: Axial spondyloarthritis (axSpA) is a group of inflammatory diseases that may lead to ankylosis of the sacroiliac joint and spine. Fat lesion in the sacroiliac joint is an important feature in diagnosis and disease progression of axSpA. However, whether there is alteration of fatty acids (FAs) composition has not been investigated using MRI. PURPOSE: To investigate bone marrow FA composition of the sacroiliac joint in patients with axSpA compared to controls. STUDY TYPE: Prospective. SUBJECTS: Eighty five participants (mean age, 32.3 ± 6.1 years): 48 axSpA (25 male, 23 female) and 37 non-SpA controls (18 male, 19 female). FIELD STRENGTH/SEQUENCE: 3.0 T/Two multiple gradient-echo chemical shift-encoded (CSE) MRI which differed only in echo times (TEs) were scanned consecutively. ASSESSMENT: Axial multi-echo CSE MRI was performed in the sacroiliac joints in vivo. Regions of interest (ROIs) were manually placed on subchondral bone with and without fat lesion in axSpA patients, and on subchondral bone without fat lesion in controls. FA composition was computed within the ROIs using a nonlinear least square method from literature. STATISTICAL TESTS: Intergroup comparisons were performed using t tests. RESULTS: In axSpA, male patients had significantly higher monounsaturated FA compared to controls in areas with fat lesion in the sacrum (+12%) and in the ilium (+9%), and in areas without fat lesion in the sacrum (+10%). Significantly lower polyunsaturated FAs were found in areas with fat lesion in the sacrum (-10%) and ilium (-11%), and lower saturated FAs were found in areas without fat lesion in the sacrum (-6%). In female, patients with axSpA had significantly higher saturated FAs in areas with fat lesion in the ilium (+7%) in comparison to controls. DATA CONCLUSION: FA composition of the sacroiliac joint alters in patients with axSpA, and it can be detected using CSE MRI based analysis.

High-efficiency 3D black-blood thoracic aorta imaging with patch-based low-rank tensor reconstruction.

Background: Three-dimensional (3D) black-blood (BB) vessel wall imaging is a promising noninvasive imaging technique for assessing thoracic aortic diseases. We aimed to develop and evaluate a fast thoracic aorta vessel wall imaging method with patch-based low-rank tensor (Pt-LRT) reconstruction using the 3D-modulated variable flip angle fast-spin echo (vFA-FSE) sequence. Methods: The Pt-LRT technique adopts a low-rank tensor image model with regularization to explore the local low-rankness and nonlocal redundancies of the images to assess the thoracic aorta vessel wall. It uses high-order tensors to capture correlations between data in multiple dimensions and reconstructs images from highly undersampled data. For this study, 12 healthy participants and 2 patients with thoracic aortic diseases were evaluated at 3T magnetic resonance (MR). The reconstruction results were compared to the traditional generalized autocalibrating partially parallel acquisitions (GRAPPA) and ℓ1-SPIRiT reconstruction to assess the feasibility of the proposed framework. Quantitative analyses of the vessel wall thickness (VWT), internal diameter (ID), lumen area (LA), and contrast-to-noise ratio (CNR) between the lumen and vessel wall were performed on all healthy participants. Results: Results demonstrated no significant differences between the GRAPPA and the proposed Pt-LRT in VWT, ID, or LA of the aorta (P<0.05). A higher mean CNR was attained with 3D patch-based low-rank tensor reconstruction than with ℓ1-SPIRiT reconstruction (49.4±10.8 vs. 38.9±8.2). Conclusions: The proposed 3D BB thoracic aorta vessel wall imaging method can reduce the scan time and produce an image quality that is in good agreement with the conventional GRAPPA acquisition, which takes approximately more than 8 min. This study also shows that the proposed Pt-LRT method substantially improves the visualization and sharpness of the vessel wall and the definition of the tissue boundary compared to the imaging obtained with ℓ1-SPIRiT.

Intermediate-weighted MRI with fat suppression (IW-FS): diagnostic performance for bone marrow edema and erosion detection in axial spondyloarthritis.

BACKGROUND: Bone marrow edema (BME) and erosion of the sacroiliac joint are both key lesions for diagnosing axial spondyloarthritis (axSpA) on magnetic resonance imaging (MRI). PURPOSE: To qualitatively and quantitatively compare intermediate-weighted MRI with fat suppression (IW-FS) with T2-weighted short tau inversion recovery (T2-STIR) in assessment of sacroiliac BME and erosion in axSpA. MATERIAL AND METHODS: Patients aged 18-60 years with axSpA were prospectively enrolled. All patients underwent a 3.0-T MRI examination of the sacroiliac joints. Para-coronal IW-FS, T2-STIR, and T1-weighted (T1W) images were acquired. BME and erosion were scored by two readers in consensus on IW-FS and STIR using a modified Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system. Consensus scores on T1WI were used as the reference for erosion. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for BME. RESULTS: In total, 49 patients (mean age=33.4 ± 7.6 years) were included. More patients were scored as having BME on T2-STIR (36 vs. 29, P = 0.016). SPARCC-BME score on IW-FS was lower than that acquired on T2-STIR (mean, 11.5 vs. 14.7, P = 0.002). SNR and CNR of BME were both lower on IW-FS than on T2-STIR (mean SNR, 118 vs. 218, P < 0.001; mean CNR, 44 vs. 137, P < 0.001). The sensitivity of erosion detection was higher on IW-FS (83%) than on T2-STIR (54%, P = 0.006). CONCLUSION: IW-FS is not sufficient for BME detection using T2-STIR as the reference standard in patients with axSpA. IW-FS has a much higher sensitivity than T2-STIR for erosion detection in the sacroiliac joint.

Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging.

OBJECTIVE: To achieve efficient segmentation of human supraclavicular adipose tissue (sclavAT) using high-resolution T2-weighted magnetic resonance images. METHODS: High-resolution 1.0 mm isotropic 3D T2-weighted images covering human supraclavicular area were acquired in transverse or coronary plane from 29 volunteers using a 3.0 T MRI scanner. There were typically 144/288 slices for the transverse/coronary scans for each subject, which amounts to a total of 6816 images in 29 volunteers. A U-NET network was trained to segment the supraclavicular adipose tissue (sclavAT). The performance of the automatic segmentation method was evaluated by comparing the output results with the manual labels using the quantitative indices of dice similarity coefficient (DSC), precision rate (PR), and recall rate (RR). The auto-segmented images were used to calculate the sclavAT volumes and registered to the MR fat fraction (FF) images to quantify the fat component of the sclavAT area. The relationship between body mass index (BMI), the volume and FF of sclavAT area was evaluated for all subjects. RESULTS: The DSC, PR and RR of the automatic sclavAT segmentation method on the testing datasets were 0.920 ± 0.048, 0.915 ± 0.070 and 0.930 ± 0.058. The volume and the mean FF of sclavAT were both found to be strongly correlated to BMI, with the correlation coefficient of 0.703 and 0.625 (p < 0.05), respectively. The averaged computation time of the automatic segmentation method was approximately 0.06 s per slice, compared to more than 5 min for manual labeling. CONCLUSION: The present study demonstrates that the proposed automatic segmentation method using U-Net network is able to identify human sclavAT efficiently and accurately.

Proton density fat fraction (PDFF) maps aid fat metaplasia evaluation in the sacroiliac joints in additional to T1WI: Improved diagnostic accuracy in axial spondyloarthritis.

PURPOSE: To evaluate the added value of qualitative and quantitative fat metaplasia analysis using proton-density fat fraction (PDFF) map in additional to T1-weighted imaging (T1WI) of the sacroiliac joints (SIJ) for diagnosis of axial spondyloarthritis (axSpA). METHOD: Patients aged 18-45 years with axSpA were enrolled. Non-SpA patients and healthy volunteers were included as controls. All participants underwent 3.0T MRI of the SIJs including semi-coronal T1WI and semi-coronal chemical-shift encoded MRI sequence for generating PDFF map. Each joint was divided into four quadrants for analysis. Two independent readers scored fat metaplasia on T1WI alone or with additional PDFF map and measured PDFF values in different reading sessions. Using clinical diagnosis as the reference, diagnostic accuracy of visual scores and PDFF measurements was evaluated by area under the receiver operating characteristic curve (AUC). Inter-reader agreement was evaluated by the intra-class correlation coefficient (ICC). RESULTS: Forty-nine patients with axSpA and thirty-six controls were included. Qualitative fat metaplasia scores using additional PDFF map performed better than using T1WI alone (AUC: Reader 1, 0.847 vs 0.795, p = 0.082; Reader 2, 0.785 vs 0.719, p = 0.048). AUCs of quantitative analysis using number of quadrants with PDFF value ≥75 % were higher than qualitative analysis using T1WI alone (Reader 1, 0.863 vs 0.795, p = 0.046; Reader 2, 0.823 vs 0.785, p = 0.011). ICCs were 0.854 to 0.922 for qualitative analysis and 0.935 for quantitative analysis. CONCLUSIONS: Additional PDFF map can increase the diagnostic accuracy for axSpA by qualitative and quantitative fat metaplasia analysis, in comparison to using T1WI alone.

Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning.

OBJECTIVE: To propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images. MATERIALS AND METHODS: Adipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat-water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images). RESULTS: The automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets. CONCLUSION: The proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.

An iterative reconstruction method for sparse-projection data for low-dose CT.

Reducing X-ray radiation is beneficial for reducing the risk of cancer in patients. There are two main approaches for achieving this goal namely, one is to reduce the X-ray current, and another is to apply sparse-view protocols to do image scanning and projections. However, these techniques usually lead to degradation of the reconstructed image quality, resulting in excessive noise and severe edge artifacts, which seriously affect the diagnosis result. In order to overcome such limitation, this study proposes and tests an algorithm based on guided kernel filtering. The algorithm combines the characteristics of anisotropic edges between adjacent image voxels, expresses the relevant weights with an exponential function, and adjusts the weights adaptively through local gray gradients to better preserve the image structure while suppressing noise information. Experiments show that the proposed method can effectively suppress noise and preserve the image structure. Comparing with similar algorithms, the proposed algorithm greatly improves the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) of the reconstructed image. The proposed algorithm has the best effect in quantitative analysis, which verifies the effectiveness of the proposed method and good image reconstruction performance. Overall, this study demonstrates that the proposed method can reduce the number of projections required for repeated CT scans and has potential for medical applications in reducing radiation doses.

T2-Weighted Whole-Brain Intracranial Vessel Wall Imaging at 3 Tesla With Cerebrospinal Fluid Suppression.

BACKGROUND: T2-weighted (T2w) intracranial vessel wall imaging (IVWI) provides good contrast to differentiate intracranial vasculopathies and discriminate various important plaque components. However, the strong cerebrospinal fluid (CSF) signal in T2w images interferes with depicting the intracranial vessel wall. In this study, we propose a T2-prepared sequence for whole-brain IVWI at 3T with CSF suppression. METHODS: A preparation module that combines T2 preparation and inversion recovery (T2IR) was used to suppress the CSF signal and was incorporated into the commercial three-dimensional (3D) turbo spin echo sequence-Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE). This new technique (hereafter called T2IR-SPACE) was evaluated on nine healthy volunteers and compared with two other commonly used 3D T2-weighted sequences: T2w-SPACE and FLAIR-SPACE (FLAIR: fluid-attenuated inversion recovery). The signal-to-noise ratios (SNRs) of the vessel wall (VW) and CSF and contrast-to-noise ratios (CNRs) between them were measured and compared among these three T2-weighted sequences. Subjective wall visualization of the three T2-weighted sequences was scored blindly and independently by two radiologists using a four-point scale followed by inter-rater reproducibility analysis. A pilot study of four stroke patients was performed to preliminarily evaluate the diagnostic value of this new sequence, which was compared with two conventional T2-weighted sequences. RESULTS: T2IR-SPACE had the highest CNR (11.01 ± 6.75) compared with FLAIR-SPACE (4.49 ± 3.15; p < 0.001) and T2w-SPACE (-56.16 ± 18.58; p < 0.001). The subjective wall visualization score of T2IR-SPACE was higher than those of FLAIR-SPACE and T2w-SPACE (T2IR-SPACE: 2.35 ± 0.59; FLAIR-SPACE: 0.52 ± 0.54; T2w-SPACE: 1.67 ± 0.58); the two radiologists' scores showed excellent agreement (ICC = 0.883). CONCLUSION: The T2IR preparation module markedly suppressed the CSF signal without much SNR loss of the other tissues (i.e., vessel wall, white matter, and gray matter) compared with the IR pulse. Our results suggest that T2IR-SPACE is a potential alternative T2-weighted sequence for assessing intracranial vascular diseases.

Joint intracranial and carotid vessel wall imaging in 5 minutes using compressed sensing accelerated DANTE-SPACE.

OBJECTIVES: To compare visualization of joint intracranial and carotid vessel walls between 5× compressed sensing accelerated three-dimensional DANTE-SPACE sequence (CS VWI) acquired in 5 min and the same sequence accelerated by 2.7× parallel imaging (PI VWI) which takes 9-10 min currently. METHODS: Following institutional review board approval and informed consent, 28 subjects including 20 stroke patients underwent PI and CS VWI examinations with an acquired spatial resolution of isotropic 0.55 mm and joint coverage of intracranial and carotid arteries. Quantitative wall thickness measurements of CS VWI and PI VWI were compared on healthy volunteers and patients with wall thickening respectively. Subjective wall visualizations of the two VWI methods on patients were scored by two radiologists blindly and independently using a 4-point scale followed by inter-rater reproducibility analysis. RESULTS: Linear regression analysis of wall thickness measurements showed excellent agreement between CS VWI and PI VWI in both healthy volunteers (r = 0.99) and stroke patients with wall thickening (r = 0.99). Subjective wall visualization score of CS VWI was slightly lower than PI VWI (3.13 ± 0.41 vs. 3.31 ± 0.79) but still had good diagnostic quality (> 3 based on a 4-point scale). The two radiologists' scores agreed excellently, evidenced by the intraclass correlation coefficient (ICC) values being higher than 0.75 (p < 0.001). CONCLUSIONS: Compressed sensing expedients joint intracranial and carotid VWI acquired at an isotropic resolution of 0.55 mm in 5 min without compromising quantitative vessel wall thickness measurement or diagnostic wall visualization. KEY POINTS: • CS VWI facilitates comprehensive visualization of intracranial and carotid vessel walls at an acquired isotropic resolution of 0.55 mm in a single 5-min scan. • CS VWI affords comparable vessel wall visualization and morphology measurement as PI VWI with a shortened acquisition time by 45%. • CS VWI alleviates the intensive trade-off between imaging resolution and scan time, and benefits the scan efficiency, motion robustness, and patient tolerance of high-resolution joint intracranial and carotid VWI.

Maternal pre-pregnancy risk factors for miscarriage from a prevention perspective: a cohort study in China.

OBJECTIVE: To assess the relationship of the pre-pregnancy modifiable risks with miscarriage. STUDY DESIGN: We randomly selected 51 communities or villages from January 2013 to December 2014 in Anhui, China. We calculated incidence rate ratios (RR) and 95% confidence intervals (CIs) for each risk factor for miscarriage. RESULTS: The incidence rate of miscarriage was 7.45%, and pre-embryonic loss account for 12.66%, embryonic loss account for 38%, and fetal losses account for 49.34% of all loss. In multivariate analysis, women with hypertension (RR=2.272, 95% CI=1.27-4.04), women had a family history of abortion in their mother (RR=1.96, 95% CI=1.22-3.14) prior to pregnancy had significantly higher adjusted risk ratio for miscarriage. Obese, overweight, and underweight prior to pregnancy were about 2.01 (95% CI=1.1-3.68), 1.71 (95% CI=1.04-2.81), and 2.05 (95% CI=1.3-3.23) times more likely to end in miscarriage compared with normal weight. Some physical examination indicators, for example pH value of leucorrhea ≥4.5 (RR=2.13, 95% CI=1.48-3.07), red blood cell count <5 10(12)/L (RR=1.52, 95% CI=1.02-2.26), and positive LgG antibodies to human cytomegalovirus (RR=1.45, 95% CI=1.02-3.14) prior to pregnancy had good prediction effect on miscarriage. We also found remarkable differences on risk factors between non-fetal losses and fetal losses. CONCLUSIONS: Our results suggest that these modifiable risks should be included into pre-conception counseling as important risk factors for screening high-risk population and reducing the rate of spontaneous abortion.

[A competency model of rural general practitioners: theory construction and empirical study].

OBJECTIVE: To perform theory construction and empirical study of the competency model of rural general practitioners. METHODS: Through literature study, job analysis, interviews, and expert team discussion, the questionnaire of rural general practitioners competency was constructed. A total of 1458 rural general practitioners were surveyed by the questionnaire in 6 central provinces. The common factors were constructed using the principal component method of exploratory factor analysis and confirmatory factor analysis. The influence of the competency characteristics on the working performance was analyzed using regression equation analysis. RESULTS: The Cronbach 's alpha coefficient of the questionnaire was 0.974. The model consisted of 9 dimensions and 59 items. The 9 competency dimensions included basic public health service ability, basic clinical skills, system analysis capability, information management capability, communication and cooperation ability, occupational moral ability, non-medical professional knowledge, personal traits and psychological adaptability. The rate of explained cumulative total variance was 76.855%. The model fitting index were Χ(2)/df 1.88, GFI=0.94, NFI=0.96, NNFI=0.98, PNFI=0.91, RMSEA=0.068, CFI=0.97, IFI=0.97, RFI=0.96, suggesting good model fitting. Regression analysis showed that the competency characteristics had a significant effect on job performance. CONCLUSIONS: The rural general practitioners competency model provides reference for rural doctor training, rural order directional cultivation of medical students, and competency performance management of the rural general practitioners.

Intracellular Ca2+ regulates spike encoding at cortical GABAergic neurons and cerebellar Purkinje cells differently.

Spike encoding at GABAergic neurons plays an important role in maintaining the homeostasis of brain functions for well-organized behaviors. The rise of intracellular Ca2+ in GABAergic neurons causes synaptic plasticity. It is not clear how intracellular Ca2+ influences their spike encoding. We have investigated this issue at GFP-labeled GABAergic cortical neurons and cerebellar Purkinje cells by whole-cell recording in mouse brain slices. Our results show that an elevation of intracellular Ca2+ by infusing adenophostin-A lowers spike encoding at GABAergic cortical neurons and enhances encoding ability at cerebellar Purkinje cells. These differential effects of cytoplasmic Ca2+ on spike encoding are mechanistically associated with Ca2+-induced changes in the refractory periods and threshold potentials of sequential spikes, as well as with various expression ratios of CaM-KII to calcineurin in GABAergic cortical neurons and cerebellar Purkinje cells.