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Abstract Previous studies disclosed that a high thyroid stimulating hormone level is an independent risk factor for diabetes peripheral neuropathy (DPN) in subclinical hypothyroidism (SCH) patients with type 2 diabetes mellitus (T2DM). However, whether thyroid metabolism has an effect on DPN in euthyroid T2DM patients remains unknown. The aim of this study was to identify the association between thyroid function and DPN in euthyroid T2DM patients. A set of 580 euthyroid T2DM patients was enrolled in the current study and stratified into DPN and Non-DPN groups. Mann–Whitney U test was performed to analyze the continuous variables of biochemical and thyroid metabolism indicators, and the Chi-square test was used to compare the categorical variables. Spearman correlation analysis was performed to analyze the relationship between clinical indicators and free thyroxine (FT4). By using the logistic regression analysis, the prevalence of DPN in different thyroid function indicators were evaluated. T2DM patients with DPN had obviously lower levels of aspartate aminotransferase (AST), alpha-hydroxybutyric dehydrogenase (α-HBDH), superoxide dismutase (SOD), calcium (Ca), creatinine (Cr), uric acid (UA), retinol binding protein (RBP), total protein (TP), albumin (ALB), alanine aminotransferase (ALT) and FT4 than the T2DM patients without DPN (P

Background: MRI segmentation offers crucial insights for automatic analysis. Although deep learning-based segmentation methods have attained cutting-edge performance, their efficacy heavily relies on vast sets of meticulously annotated data. Methods: In this study, we propose a novel semi-supervised MRI segmentation model that is able to explore unlabeled data in multiple aspects based on various semi-supervised learning technologies. Results: We compared the performance of our proposed method with other deep learning-based methods on 2 public datasets, and the results demonstrated that we have achieved Dice scores of 90.3% and 89.4% on the LA and ACDC datasets, respectively. Conclusions: We explored the synergy of various semi-supervised learning technologies for MRI segmentation, and our investigation will inspire research that focuses on designing MRI segmentation models.

Nitrogen (N) application rates and planting density are key factors affecting grain yield and quality. In this experiment, four varieties, Huijing 753 (HJ 753), Huijing 855 (HJ 855), Huijing 866 (HJ 866), and Dangjing 8 (DJ 8), were used to investigate their effects on the yield and quality of japonica rice in Anhui, which has a lower soil N supply capacity. The current study set four levels of N application N1, N2, N3, and N4 (120, 180, 240, and 300) kg ha−1 and three planting densities D1, D2, and D3 (high-density: 45 × 104; medium-density: 37.5 × 104; low-density: 32.2 × 104) hills ha−1 for a total of 12 treatments. The effects of N application and planting density on rice yield, composition, processing quality, appearance quality, and cooking quality were analyzed. The results showed that with the increase in N application rates and planting density, the effective panicle number per plant and grain number per panicle of each rice variety increased, while HJ 753, HJ 855, and HJ 866 reached the highest yield under the N3D1 treatment and then gradually declined, and DJ 8 had the highest yield in the N4D1 treatment. In addition, the processing quality of various varieties can be improved by increasing the N application rate and planting density, but the appearance quality will deteriorate, the amylose content will also decrease, and gel consistency will also be lower. Therefore, under medium-planting density conditions, N at a rate of 240 kg ha−1 was the best for HJ 753, HJ 855, and HJ 866, and using 300 kg ha−1 for DJ 8 resulted in high grain yields and superior rice grain quality. In the next step, physiological, biochemical, and genotypic analyses of these three japonica rice varieties were carried out to provide a scientific basis and technical support for accelerating the breeding of high-yield japonica rice varieties.

The long overhanging distance of hard roofs and long-collapse steps induces a large area of suspension on the working face in underground coal mines, resulting in excessive pressure and deformation on the surrounding rocks of the adjacent roadway in the work face, which seriously threatens the safety of coal mining operations. In this study, in order to study the hydraulic fracturing effects on hard roofs, numerical simulation and in situ tests were conducted. The analysis and comparison of fracturing effects under different hydraulic fracturing parameters were carried out, and the reasonable hydraulic fracturing parameters of the hydraulic weakening of hard roofs were designed accordingly. Based on designed hydraulic fracturing, industrial tests were conducted in the field while stress and deformation were recorded. The results show that hydraulic fracturing could effectively reduce the pressure of the hard roof. Hydraulic fracturing effectively destroyed the cantilever beam structure above the coal pillar, reduced the stress concentration, and moderated mineral pressure at the working face. The proposed methods and obtained results provide theoretical and technical support for the treatment of underground mines with hard roofs.

Directional hydraulic fracturing (DHF) is popular with hydraulic fracturing operations in coal mining to create cave-hard roofs, in which radial initial notches are created around open borehole walls before injecting high-pressurized fluid. Despite extensive field application of DHF, the three-dimensional irregular hydraulic fracture (HF) geometry in DHF remains unclear, and the HF re-orientation mechanism requires comprehensive understanding. Here, we experimentally examined factors affecting HF re-orientation in DHF in transparent gelatin samples with a self-developed experimental device. We found that it is the ratio between the differential stress and gelatin elastic moduls that determines HF re-orientation rather than the absolute magnitudes of these two factors. Both shear failure and tensile failure occur during HF re-orientation. The HF tends to propagate asymmetrically, and the step-like HF geometry is likely to form in gelatin samples with low elastic moduli and under high differential stresses. HF re-orientation is not necessarily a near-borehole effect, and HFs can propagate along the notch direction for longer distances in stiffer gelatin samples under relatively low or moderate differential stresses. Finally, recommendations are provided for the effective utilization of DHF at coal mine sites.

PurposeThis study aimed to investigate the changes in extracellular space (ECS) in cryptococcal brain granuloma and its pathological mechanism.Materials and methodsThe animal model of cryptococcal brain granuloma was established by injecting 1 × 106 CFU/ml of Cryptococcus neoformans type A suspension into the caudate nucleus of Sprague–Dawley rats with stereotactic technology. The infection in the brain was observed by conventional MRI scanning on days 14, 21, and 28 of modeling. The tracer-based MRI with a gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) as a magnetic tracer was performed on the rats with cryptococcal granuloma and the rats in the control group. The parameters of ECS in each area of cryptococcal brain granuloma were measured. The parameters of ECS in the two groups were compared by independent sample t-test, and the changes in ECS and its mechanism were analyzed.ResultsUp to 28 days of modeling, the success rate of establishing the brain cryptococcal granuloma model with 1 × 106 CFU/ml Cryptococcus neoformans suspension was 60%. In the internal area of cryptococcal granuloma, the effective diffusion coefficient D* was significantly higher than that of the control group (t = 2.76, P < 0.05), and the same trend showed in the volume ratio α (t = 3.71, P < 0.05), the clearance rate constant k (t = 3.137, P < 0.05), and the tracer half-life T1/2 (t = 3.837, P < 0.05). The tortuosity λ decreased compared with the control group (t = −2.70, P < 0.05). At the edge of the cryptococcal granuloma, the D* and α decreased, while the λ increased compared with the control group (D*:t = −6.05, P < 0.05; α: t = −4.988, P < 0.05; λ: t = 6.222, P < 0.05).ConclusionThe internal area of the lesion demonstrated a quicker, broader, and more extended distribution of the tracer, while the edge of the lesion exhibited a slower and narrower distribution. MRI tracer method can monitor morphological and functional changes of ECS in pathological conditions and provide a theoretical basis for the treatment via ECS.

Foxtail millet is commonly used as a food and forage grass. ERECTA (ER) is a receptor-like kinase that can improve plant biomass and stress resistance. The sorghum SbER10_X1 gene was used as a probe to identify ER family genes on the Setaria italica genomes (SiERs), and determine the characteristics of the SiERs family. Herein, the structural features, expression patterns, and thermotolerance of SiERs function were identified by bioinformatics analysis, real-time PCR and transgenesis estimation. Results showed that SiERs had four members: two members were located on chromosome 1 with a total of six copies (SiER1_X1, SiER1_X2, SiER1_X3, SiER1_X4, SiER1_X5, and SiER1_X6), and two were on chromosome 4, namely, SiER4 (SiER4_X1 and SiER4_X2) and SiERL1. Among them, SiER1_X4 and SiER4_X1 were expressed highest in above-ground organs of foxtail millet, and actively responded to treatments with abscisic acid, brassinolide, gibberellin, and indole acetic acid. After overexpression of SiER1_X4 and SiER4_X1 in Arabidopsis, the plant height and biomass of the transgenic Arabidopsis significantly increased. Following high-temperature treatment, transgenic seedlings survived better compared to wild type. Transgenic lines showed higher SOD and POD activities, and expression level of AtHSF1 and AtBl1 genes significantly increased. These results indicated that SiER1_X4 and SiER4_X1 played important regulatory roles in plant growth and thermotolerance. The two genes provide potential targets for conventional breeding or biotechnological intervention to improve the biomass of forage grass and thermotolerance of field crops.

The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance.

Unhindered transportation of substances in the brain extracellular space (ECS) is essential for maintaining brain function. Regulation of transportation is a novel strategy for treating ECS blockage-related brain diseases, but few techniques have been developed to date. In this study, we established a novel approach for accelerating the drainage of brain interstitial fluid (ISF) in the ECS using minimally invasive surgery, in which a branch of the external carotid artery is separated and implanted epidurally (i.e., epidural arterial implantation [EAI]) to promote a pulsation effect on cerebrospinal fluid (CSF) in the frontoparietal region. Tracer-based magnetic resonance imaging was used to evaluate the changes in ISF drainage in rats 7 and 15 days post-EAI. The drainage of the traced ISF from the caudate nucleus to ipsilateral cortex was significantly accelerated by EAI. Significant increases in the volume fraction of the ECS and molecular diffusion rate were demonstrated using the DECS-mapping technique, which may account for the mechanisms underlying the changes in brain ISF. This study provides a novel perspective for encephalopathy treatment via the brain ECS. [ABSTRACT FROM AUTHOR]

Background Hypoparathyroidism is one of the common complications of radical thyroidectomy, but there are few studies on differentiated thyroid cancer. Objective To investigate the influencing factors of hypoparathyroidism in patients with differentiated thyroid carcinoma after radical thyroidectomy. Methods A total of 166 patients with differentiated thyroid carcinoma who underwent radical thyroidectomy from 940 Hospital of PLA Joint Logistics Support Force from June 2018 to December 2020 were included, and their clinical data were collected, including gender, age, body mass index (BMI), tumor site, tumor size, blood supply of lesion, whether there is capsule invasion, surgical method, scope of lymph node dissection, whether there is use of carbon nanoparticles, parathyroid gland resection. Also, this study monitors preoperative and postoperative serum PTH and follow-up monitoring levels of Ca2+. Results Among the 166 patients, the incidence of postoperative hypoparathyroidism was 28.31% (47/166), among which the incidence of temporary hypoparathyroidism was 21.68% (36/166) and the incidence of permanent hypoparathyroidism was 6.63% (11/166). There were statistically significant differences between patients with normal parathyroid function and patients with hypoparathyroidism in gender, age, tumor location, membranous invasion, surgical method, lymph node dissection and the use of carbon nanotube (P