Analysis of the efficacy of hyperbaric oxygen therapy for disorders of consciousness: A retrospective cohort study.

OBJECTIVE: To analyze the efficacy and associated factors affecting the prognosis in patients with disturbance of consciousness after hyperbaric oxygen (HBO) treatment. METHODS: A retrospective study was carried out on patients with disorders of consciousness (DOC) receiving HBO treatment from January to January 2022 in the Second Department of Rehabilitation Medicine of the Second Hospital of Hebei Medical University, China. RESULTS: HBO therapy improved the Glasgow Coma Scale (GCS) and Chinese Nanjing Persistent Vegetative State Scale (CNPVSS), as well as the clinical efficacy in patients with DOC. The comparison of GCS and CNPVSS scores in patients with DOC before and after HBO treatment was all statistically significant, with 325 patients (67.1%) showing effective results and 159 patients (32.9%) having unchanged outcomes. Univariate analysis indicated that there were statistically significant differences in age, HBO intervention time, HBO treatment times, pre-treatment GCS score, and etiology and underlying diseases between the good and poor prognoses groups. Multivariate regression analysis showed that HBO intervention time ≤7 days, HBO treatment ＞ times, high GCS score before HBO treatment, and brain trauma were independent influencing factors in achieving a good prognosis for patients with DOC. Low pre-treatment GCS scores were an independent risk factor for a poor prognosis in patients with brain trauma while being male, late HBO intervention time, fewer HBO treatment times, and low pre-treatment GCS scores were independent risk factors for a poor prognosis in patients with DOC after a stroke. Being ≥50 years of age, late HBO intervention time, and low pre-treatment GCS scores were independent risk factors for a poor prognosis in patients with DOC after hypoxic-ischaemic encephalopathy. CONCLUSION: HBO therapy can improve the GCS, CNPVSS scores and clinical efficacy in patients with DOC, and the timing of HBO intervention ≤7 days, times of HBO treatment, high pre-treatment GCS score, and brain trauma were the independent influencing factors of good prognosis in patients with DOC.

Delayed postoperative spontaneous spinal epidural hematoma: Case based review.

INTRODUCTION AND IMPORTANCE: Postoperative spontaneous spinal epidural hematoma (SSEDH) is a rare complication in clinical practice. Despite its rarity, SSEDH is a critical emergency situation associated with neurological deficits, and improper or delayed management may lead to severe consequences. Therefore, surgical operators should familiarize themselves with SSEDH and give it more attention. CASE PRESENTATION: This study describes the case of an elderly woman diagnosed with a left unilateral femoral neck fracture, severe osteoporosis, and multi-segmental vertebral compression fracture. Following artificial femoral head replacement surgery, the patient developed postoperative SSEDH. Subsequently, the patient underwent surgical removal of the posterior epidural hematoma and spinal cord decompression. The postoperative recovery was favorable, with normal muscle strength and tension in both lower limbs. A 4-year follow-up showed no complications. CLINICAL DISCUSSION: The occurrence of SSEDH during the perioperative period of non-spinal surgeries is relatively uncommon. However, SSEDH is a neurosurgical emergency associated with neurological deficits, and prompt surgical intervention is crucial for successful treatment. CONCLUSION: Clinicians should enhance their knowledge of SSEDH and remain vigilant towards this condition. Literature review highlights the significance of factors such as aging in the development of SSEDH following non-spinal surgeries in the perioperative period.

A statistical symptomatic evaluation on SAPHO syndrome from 56 cases of confirmed diagnosis and 352 cases of non-SAPHO involvement.

OBJECTIVE: To report a statistical evaluation of symptomatology based on 56 cases of SAPHO syndrome and 352 non-SAPHO involvement cases, to propose a symptomatic scoring system in consideration of early warning for SAPHO syndrome. METHODS: A cohort comprising 56 subjects diagnosed with SAPHO syndrome was reported, as well as 352 non-SAPHO involvement cases, including their chief complaints, skin manifestations, radiological findings, and laboratory tests. We systematically reviewed previous published five representative huge cohorts from different countries to conclude several specific features of SAPHO by comparing with our case series. The score of each specific index is based on respective incidence and comparison of two cohorts was performed. RESULT: In terms of complaint rates, all subjects of two cohorts suffered from osseous pain, which appeared in the anterior chest wall, spine, and limb which were calculated. In respect to dermatological lesions, SAPHO patients suffered from severe acne, and other patients (82.14%) accompanied with palmoplantar pustulosis. Having received radiological examinations, most SAPHO subjects rather than non-SAPHO involvement cases showed abnormal osteoarticular lesions under CT scanning and more detailed information under whole-body bone scintigraphy. Differences also emerged in elevation of inflammation values and rheumatic markers like HLA-B27. Based on our cases and huge cohorts documented, the early warning standard is set to be 5 scores. CONCLUSIONS: SAPHO syndrome case series with 56 subjects were reported and an accumulative scoring system for the early reminder on SAPHO syndrome was proposed. The threshold of this system is set to be 5 points. Key Points • Fifty-six patients diagnosed by SAPHO syndrome with detailed symptoms and radiological findings were reported. • Comparison was made between the 56 SAPHO patients and 352 non-SAPHO involvement cases. • An accumulative scoring system for the early reminder on SAPHO syndrome was proposed and the threshold of this system is set to be five points.

CellT-Net: A Composite Transformer Method for 2-D Cell Instance Segmentation.

Cell instance segmentation (CIS) via light microscopy and artificial intelligence (AI) is essential to cell and gene therapy-based health care management, which offers the hope of revolutionary health care. An effective CIS method can help clinicians to diagnose neurological disorders and quantify how well these deadly disorders respond to treatment. To address the CIS task challenged by dataset characteristics such as irregular morphology, variation in sizes, cell adhesion, and obscure contours, we propose a novel deep learning model named CellT-Net to actualize effective cell instance segmentation. In particular, the Swin transformer (Swin-T) is used as the basic model to construct the CellT-Net backbone, as the self-attention mechanism can adaptively focus on useful image regions while suppressing irrelevant background information. Moreover, CellT-Net incorporating Swin-T constructs a hierarchical representation and generates multi-scale feature maps that are suitable for detecting and segmenting cells at different scales. A novel composite style named cross-level composition (CLC) is proposed to build composite connections between identical Swin-T models in the CellT-Net backbone and generate more representational features. The earth mover's distance (EMD) loss and binary cross entropy loss are used to train CellT-Net and actualize the precise segmentation of overlapped cells. The LiveCELL and Sartorius datasets are utilized to validate the model effectiveness, and the results demonstrate that CellT-Net can achieve better model performance for dealing with the challenges arising from the characteristics of cell datasets than state-of-the-art models.

Construction of a hepatocellular carcinoma high-risk population rating scale and independent predictors' assessment.

BACKGROUND: With increasing mortality and incidence, hepatocellular carcinoma (HCC) has become a major public health problem. The early diagnosis of HCC can improve its prognosis. The aim of this study was to identify potential risk factors related to HCC development and to establish a high-risk population rating scale. METHODS: A total of 853 patients with chronic hepatitis B (CHB) were enrolled in this study, including 403 patients with HCC as the case group and others as the control group. Their demographic and clinical characteristics were compared and the independent risk factors for HCC were assessed. Then, the optimal cutoff levels of these factors were analyzed by the receiver operating characteristic (ROC) method. A high-risk population rating scale was constructed based on the factors and then evaluated in the modeling population. RESULTS: The factors that presented statistically significant differences between the two groups included age, smoking, alcohol abuse, body mass index, triglyceride, high‒density lipoprotein cholesterol, aspartate transaminase, alanine transaminase, fasting plasma glucose, creatinine and uric acid. The ROC curve showed that the cutoff score for the HCC high risk population was 5 (AUC=0.74, P<0.001) and the Hosmer‒Lemeshow analysis showed that the fitting effect of this rating scale was good (P = 0.294). CONCLUSIONS: The integration of these factors can contribute to a prognostic score for the risk of HCC development, which offered certain clinical practicability.

Development and evaluation of a CRISPR-Cas13a system-based diagnostic for hepatitis E virus.

OBJECTIVES: Hepatitis E virus (HEV) is the leading cause of acute viral hepatitis worldwide. HEV RNA detection is the gold standard for HEV infection diagnosis and PCR methods are commonly used but are usually time-consuming and expensive, resulting in low detection efficiency and coverage, especially in low-income areas. Here, we developed a simpler and more accessible HEV RNA detection method based on CRISPR-Cas13a system. METHODS: A total of 265 samples of different types and sources, including 89 positive samples and 176 negative samples, were enrolled for evaluations. The sensitivity and specificity of the Cas13a-crRNA detection system were evaluated. The World Health Organization reference panel for HEV genotypes was used to evaluate the capability for detecting different HEV genotypes. The validity of the assay was compared with RT-qPCR. RESULTS: The 95 % limits of detection (LOD) of Cas13a-crRNA-based fluorescence assay and strip assay were 12.5 and 200 IU/mL, respectively. They did not show cross-reactivity with samples positive for hepatitis A virus, hepatitis B virus, hepatitis C virus, coxsackievirus A16, rotavirus, enterovirus 71, norovirus or enteropathic Escherichia coli. Different HEV genotypes (HEV1-4) can be detected by the assay. Compared to RT-qPCR, the positive predictive agreements of Cas13a-crRNA-based fluorescence and strip assay were 98.9 % (95 % CI: 93.9-99.8 %) and 91.0 % (95 % CI: 83.3-95.4 %), respectively. The negative predictive agreements were both 100 % (95 % CI: 97.8-100 %). CONCLUSIONS: In conclusion, we established a rapid and convenient HEV RNA detection method with good sensitivity and specificity based on CRISPR-Cas13a system, providing a new option for HEV infection diagnosis.

Establishment and Characterization of the First Chinese National Standard for Nucleic Acid Amplification Technology Assays for Hepatitis E Virus Nucleic Acid Detection.

The detection of hepatitis E virus (HEV) RNA is the gold standard for HEV infection diagnosis. In order to address the quality control requirements for HEV RNA detection kits within China, we aimed to establish the first Chinese national standard for HEV RNA detection through a collaborative study. The candidate standard was quantified using digital PCR (dPCR). A total of five laboratories were invited to determine the estimated mean value of this national standard relative to the World Health Organization International Standard (WHO IS). Additionally, four commercial kits were used to assess the applicability of the candidate standard. The stability was determined by freeze-thaw cycles and storage at 37 °C, 25 °C and 4 °C. The estimated mean value of this national standard relative to the WHO IS was 5.67 log10 IU/mL. Two out of the four commercial kits can detect as low as the estimated limit of detection (LOD). The degradation rates of samples in the stability study ranged from 4% to 19%. In conclusion, we have established the first Chinese national standard for HEV nucleic acid detection against WHO IS, which can be employed to evaluate the quality of HEV RNA detection kits.

C2BNet: A Deep Learning Architecture with Coupled Composite Backbone for Parasitic EGG Detection in Microscopic Images.

Internet of Medical Things (IoMT) enabled by artificial intelligence (AI) technologies can facilitate automatic diagnosis and management of chronic diseases (e.g., intestinal parasitic infection) based on two-dimensional (2D) microscopic images. To improve the model performance of object detection challenged by microscopic image characteristics (e.g., focus failure, motion blur, and whether zoomed or not), we propose Coupled Composite Backbone Network (C2BNet) to execute the parasitic egg detection task using 2D microscopic images. In particular, the C2BNet backbone adopts a two-path structure-based backbone and leverages model heterogeneity to learn object features from different perspectives. A novel feature composition style is proposed to flow the feature within the coupled composite backbone, and ensure mutual enhancement of feature representation ability among the different paths of the backbone. To further improve the accuracy of the detection results, we propose Multiscale Weighted Box Fusion (WBF) to fuse the location and confidence scores of all bounding boxes predicted from the multiscale feature maps, and iteratively refine the box coordinates to form the final prediction. Experimental results on Chula-ParasiteEgg-11 dataset demonstrate that the C2BNet not only performs satisfactorily compared with state-of-the-art methods, but also can focus more on learning detailed morphology features and abundant semantic features, resulting in more precise detection for parasitic eggs located in the 2D microscopic image.

Pentraxin 3 deficiency ameliorates the severity of osteoarthritis and alleviates inflammation.

BACKGROUND: Osteoarthritis is one of the most common degenerative joint disorders, characterized by articular cartilage breakdown, synovitis, osteophytes generation and subchondral bone sclerosis. Pentraxin 3 (PTX3) is a long pentraxin protein, secreted by immune cells, and PTX3 is identified to play a critical role in inflammation and macrophage polarization. However, the underlying mechanism of PTX3 in osteoarthritis under the circumstance of Ptx3-knockout (KO) mice model is still unknown. METHODS: Murine destabilization of the medial meniscus (DMM) OA model was created in Ptx3-knockout (KO) and wildtype mice, respectively. The degenerative status of cartilage was detected by Safranin O, H&E staining, immunohistochemistry (IHC) and micro-CT. OARSI scoring was employed to assess the proteoglycan of cartilage. Serum inflammatory cytokines were examined by ELISA and systematic macrophage polarization in spleen was analyzed by flow cytometry. RESULTS: Safranin O and H&E staining confirmed that the joint cartilage was mostly with reduced degeneration in both the senior KO mice and the DMM model generated from the KO mice, compared to the WT group. This is also supported by micro-CT examination and OARSI scoring. Immunohistochemistry illustrated an up-regulation of Aggrecan and Collagen 2 and down-regulation of ADAMTS-5 and MMP13 in KO mice in comparison with the WT mice. ELISA indicated a dramatical decrease in the serum levels of TNF-α and IL-6 in KO mice. Polarization of M2-like macrophages was observed in the KO group. CONCLUSION: Pentraxin 3 deficiency significantly ameliorated the severity of osteoarthritis by preventing cartilage degeneration and alleviated systematic inflammation by inducing M2 polarization.

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition.

Background: Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition. Method: Group depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model. Results: Based on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%. Conclusion: Our approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.

Using landscape genomics to assess local adaptation and genomic vulnerability of a perennial herb Tetrastigma hemsleyanum (Vitaceae) in subtropical China.

Understanding adaptive genetic variation of plant populations and their vulnerabilities to climate change are critical to preserve biodiversity and subsequent management interventions. To this end, landscape genomics may represent a cost-efficient approach for investigating molecular signatures underlying local adaptation. Tetrastigma hemsleyanum is, in its native habitat, a widespread perennial herb of warm-temperate evergreen forest in subtropical China. Its ecological and medicinal values constitute a significant revenue for local human populations and ecosystem. Using 30,252 single nucleotide polymorphisms (SNPs) derived from reduced-representation genome sequencing in 156 samples from 24 sites, we conducted a landscape genomics study of the T. hemsleyanum to elucidate its genomic variation across multiple climate gradients and genomic vulnerability to future climate change. Multivariate methods identified that climatic variation explained more genomic variation than that of geographical distance, which implied that local adaptation to heterogeneous environment might represent an important source of genomic variation. Among these climate variables, winter precipitation was the strongest predictor of the contemporary genetic structure. F ST outlier tests and environment association analysis totally identified 275 candidate adaptive SNPs along the genetic and environmental gradients. SNP annotations of these putatively adaptive loci uncovered gene functions associated with modulating flowering time and regulating plant response to abiotic stresses, which have implications for breeding and other special agricultural aims on the basis of these selection signatures. Critically, modelling revealed that the high genomic vulnerability of our focal species via a mismatch between current and future genotype-environment relationships located in central-northern region of the T. hemsleyanum's range, where populations require proactive management efforts such as assistant adaptation to cope with ongoing climate change. Taken together, our results provide robust evidence of local climate adaption for T. hemsleyanum and further deepen our understanding of adaptation basis of herbs in subtropical China.

ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images.

Background: Accurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image. Methods: In particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes. Results: The proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly. Conclusion: Our results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM.

EEGformer: A transformer-based brain activity classification method using EEG signal.

Background: The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain. Method: Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG). Results: The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance. Conclusion: EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.

Preliminary investigation of hepatitis E virus detection by a recombinase polymerase amplification assay combined with a lateral flow strip.

Hepatitis E virus (HEV) is a zoonotic pathogen that is a significant public health problem. Detecting HEV relies mainly on conventional PCR, which is time-consuming and requires sophisticated instruments and trained staff. We aimed to establish a reverse-transcription (RT)-recombinase polymerase amplification (RPA) assay (RT-RPA) combined with a lateral flow strip (LFS; RT-RPA-LFS) to rapidly detect HEV RNA in human and rabbit samples. With the optimal reaction conditions (37°C for 30 min), our assay detected as few as 1.0 × 102 copies/mL of HEV and showed no cross-reactivity with other hepatitis viruses. We tested 28 human samples (4 fecal and 24 serum samples) and 360 rabbit samples (180 fecal and 180 serum samples) with our RT-RPA-LFS assay and compared our assay to an RT-qPCR method. There was no significant difference (p > 0.05) in the test results between the 2 assays. Our RT-RPA-LFS assay detected both HEV3 and HEV4 genotypes. Our rapid, sensitive, and specific RT-RPA-LFS assay for the detection of HEV may provide a useful detection tool for limited-resource areas.

Type 2 diabetes mellitus characteristics affect hepatocellular carcinoma development in chronic hepatitis B patients with cirrhosis.

BACKGROUND: Type 2 diabetes mellitus (T2DM) has been shown to be correlated with hepatocellular carcinoma (HCC) development. However, further investigation is needed to understand how T2DM characteristics affect the prognosis of chronic hepatitis B (CHB) patients. AIM: To assess the effect of T2DM on CHB patients with cirrhosis and to determine the risk factors for HCC development. METHODS: Among the 412 CHB patients with cirrhosis enrolled in this study, there were 196 with T2DM. The patients in the T2DM group were compared to the remaining 216 patients without T2DM (non-T2DM group). Clinical characteristics and outcomes of the two groups were reviewed and compared. RESULTS: T2DM was significantly related to hepatocarcinogenesis in this study (P = 0.002). The presence of T2DM, being male, alcohol abuse status, alpha-fetoprotein > 20 ng/mL, and hepatitis B surface antigen > 2.0 log IU/mL were identified to be risk factors for HCC development in the multivariate analysis. T2DM duration of more than 5 years and treatment with diet control or insulin ± sulfonylurea significantly increased the risk of hepatocarcinogenesis. CONCLUSION: T2DM and its characteristics increase the risk of HCC in CHB patients with cirrhosis. The importance of diabetic control should be emphasized for these patients.

Whole Genome Resequencing Identifies Single-Nucleotide Polymorphism Markers of Growth and Reproduction Traits in Zhedong and Zi Crossbred Geese.

The broodiness traits of domestic geese are a bottleneck that prevents the rapid development of the goose industry. To reduce the broodiness of the Zhedong goose and thus improve it, this study hybridized it with the Zi goose, which has almost no broody behavior. Genome resequencing was performed for the purebred Zhedong goose, as well as the F2 and F3 hybrids. The results showed that the F1 hybrids displayed significant heterosis in growth traits, and their body weight was significantly greater than those of the other groups. The F2 hybrids showed significant heterosis in egg-laying traits, and the number of eggs laid was significantly greater than those of the other groups. A total of 7,979,421 single-nucleotide polymorphisms (SNPs) were obtained, and three SNPs were screened. Molecular docking results showed that SNP11 located in the gene NUDT9 altered the structure and affinity of the binding pocket. The results suggested that SNP11 is an SNP related to goose broodiness. In the future, we will use the cage breeding method to sample the same half-sib families to accurately identify SNP markers of growth and reproductive traits.

MyopiaDETR: End-to-end pathological myopia detection based on transformer using 2D fundus images.

Background: Automated diagnosis of various retinal diseases based on fundus images can serve as an important clinical decision aid for curing vision loss. However, developing such an automated diagnostic solution is challenged by the characteristics of lesion area in 2D fundus images, such as morphology irregularity, imaging angle, and insufficient data. Methods: To overcome those challenges, we propose a novel deep learning model named MyopiaDETR to detect the lesion area of normal myopia (NM), high myopia (HM) and pathological myopia (PM) using 2D fundus images provided by the iChallenge-PM dataset. To solve the challenge of morphology irregularity, we present a novel attentional FPN architecture and generate multi-scale feature maps to a traditional Detection Transformer (DETR) for detecting irregular lesion more accurate. Then, we choose the DETR structure to view the lesion from the perspective of set prediction and capture better global information. Several data augmentation methods are used on the iChallenge-PM dataset to solve the challenge of insufficient data. Results: The experimental results demonstrate that our model achieves excellent localization and classification performance on the iChallenge-PM dataset, reaching AP50 of 86.32%. Conclusion: Our model is effective to detect lesion areas in 2D fundus images. The model not only achieves a significant improvement in capturing small objects, but also a significant improvement in convergence speed during training.

Rational Design of Covalent Bond Engineered Encapsulation Structure toward Efficient, Long-Lived Multicolored Phosphorescent Carbon Dots.

Multicolored phosphorescent materials based on carbon dots (CDs) constructed using the same or similar precursors with long lifetimes are conducive to their wide range of practical applications due to the developed compatibility. Herein, a universal method is developed to prepare long-lived multicolored phosphorescent CD-based composites for which heavy-metal doping is not required. The multicolored CDs are encapsulated in silica via silane hydrolysis, which forms many covalent SiOC and SiC bonds; hence, the vibrations and rotations of the luminescent centers on the CD surfaces are hindered. The transformation of SiOC to a more rigid SiC moiety occurs during high-temperature calcination. Furthermore, during calcination, the silica collapses, resulting in more tightly encapsulated CDs. The synergistic effect of these two calcination phenomena produces blue, green, yellow, and red phosphorescence, at wavelengths spanning 465 to 680 nm and with lifetimes of up to 2.11 s. Taking advantage of their superior phosphorescence performances, the CD-based composites are successfully applied to 3D multichannel information storage and encryption.

Cross-sectional study of the quality of randomized control trials on problem-based learning in medical education.

Problem-based learning (PBL) is increasingly being used in medical education globally, but its effectiveness in teaching remains controversial. A randomized controlled trial (RCT) is the method of choice for evaluating its effectiveness. The quality of an RCT has a significant effect on this evaluation, but to date we have not seen an assessment of the quality of RCTs for PBL. Two researchers searched MEDLINE and EMBASE for RCTs addressing PBL in medical education. The overall quality of each report was measured on a 28-point overall quality score (OQS) based on the 2010 revised Comprehensive Standards for Reporting and Testing (CONSORT) Statement. Furthermore, to study the key factors affecting OQS more effectively, a linear regression model of those factors was established using SPSS. After literature screening, 30 RCTs were eventually included and analyzed. The median OQS was 15 (range, 7-20), which meant that half of the items in the revised 2010 CONSORT statement were poorly reported in at least 40% of the RCTs analyzed. The regression model showed that the year of publication of RCTs and the impact factors of the journals in which they were published were the main factors affecting OQS. The overall quality of reporting of RCTs on PBL teaching in medical education was not satisfactory. Some RCTs were subjectively selective in reporting certain items, leading to heterogeneity in quality. It is expected that statisticians will develop new standards more suitable for evaluating RCTs related to teaching research and that editors and peer reviewers will be required to review the relevant RCTs more strictly.

Reproductive Performance of Zi-Goose Promoted by Red Color Illumination.

The color of light affects the reproductive performance of poultry, but it is not clear what efficient illumination strategy could be adopted to improve the reproductive performance of Zi-goose. Red light can increase the average weekly egg production rate, egg production, and qualified production. It can increase the serum GnRH level and decrease the serum PRL, MT, and T4 levels. In our study, red light for 12 h increased the average weekly laying rate, average qualified egg production, and hatching rate of Zi-goose eggs, and increased the serum levels of FSH, LH, P4, E2, MT, T3, and T4. Blue light at 14 h improved the average weekly egg production rate, average egg production, and average qualified egg production, and reduce serum PRL and MT levels to ensure the improvement of reproductive performance of goose. A total of 705,714 overlapping group sequences, 471,145 transcript sequences, and 268,609 single gene sequences were obtained from 18 sequencing samples, with a total length of 323.04, 668.53, and 247.88 M, respectively. About 176,416 unigenes were annotated successfully in six databases, accounting for 65.68% of the total unigenes obtained. 2,106, 2,142, and 8,892 unigenes were identified in the hypothalamus, pituitary gland, and ovary of the birds respectively, with different expressions of light regulation. The hypothalamus, ovary, and pituitary were involved in 279, 327, and 275 KEGG (Kyoto Encyclopedia of Genes and Genomes) metabolic pathways in response to light, respectively. Through further significance analysis and differential discovery rate control, a total of five metabolic pathways were obtained which were closely related to the reproductive hormones of goose. Ten candidate genes related to the reproductive performance of goslings were selected according to the identification results of differentially expressed genes of goslings under red light and white light conditions and the genes involved in metabolic pathways significantly related to the reproductive hormones of goslings. The expression levels of GnRh-1 in the hypothalamus, GnRH-R, FSH ß and LH ß in the pituitary gland, and FSH-R and LH-R candidate genes in the ovary were higher under the 12 h red light treatment than white light. However, the expression levels of VIP, PRL, and PRL-R candidate genes in the hypothalamus, pituitary and ovary were lower under 12 h red light than under 12 h white light.