Deformation-induced grain boundary segregation in a powder-metallurgy ultrafine-grained MoNbTaTiV refractory high-entropy alloy.

A powder-metallurgy MoNbTaTiV refractory high-entropy alloy synthesized by mechanical alloying (MA) and spark plasma sintering was subjected to hot deformations at different temperatures and strain rates. The microstructural morphologies were characterized, and component element segregation was elucidated. With grain refinement and lattice strain increase, the large inhomogeneous milled powder became refined and homogeneous after the MA. Component element segregation was observed at relatively low deformation temperatures and high strain rates. As the deformation temperature increased and the strain rate decreased, the segregation gradually disappeared, which was attributed to dislocation movement.

Biocompatible Nano-Cocrystal Engineering for Targeted Herbicide Delivery: Enhancing Efficacy through Stimuli-Responsive Release and Reduced Environmental Losses.

In addressing the critical challenges posed by the misuse and inefficiency of traditional pesticides, we introduce a Nano-Cocrystal material composed of the herbicide clopyralid and coformer phenazine. Developed through synergistic supramolecular self-assembly and mechanochemical nanotechnology, this Nano-Cocrystal significantly enhances pesticide performance. It exhibits a marked improvement in stability, with reductions in hygroscopicity and volatility by approximately 38%. Moreover, it intelligently modulates release according to environmental factors, such as temperature, pH, and soil inorganic salts, demonstrating decreased solubility by up to four times and improved wettability and adhesion on leaf surfaces. Importantly, the herbicidal activity surpasses that of pure clopyralid, increasing suppression rates of Medicago sativa L. and Oxalis corniculata L. by up to 27% at the highest dosage. This Nano-Cocrystal also shows enhanced crop safety and reduced genotoxicity compared to conventional formulations. Offering a blend of simplicity, cost-effectiveness, and robust stability, our findings contribute a sustainable solution to agricultural practices, favoring the safety of nontarget organisms.

Design and Computational Validation of γ-Ray Shielding Effectiveness in Heavy Metal/Rare Earth Oxide-Natural Rubber Composites.

This study involved the preparation of natural rubber-based composites incorporating varying proportions of heavy metals and rare earth oxides (Sm2O3, Ta2O5, and Bi2O3). The investigation analyzed several parameters of the samples, including mass attenuation coefficients (general, photoelectric absorption, and scattering), linear attenuation coefficients (µ), half-value layers (HVLs), tenth-value layers (TVLs), mean free paths (MFPs), and radiation protection efficiencies (RPEs), utilizing the Monte Carlo simulation software Geant4 and the WinXCom database across a gamma-ray energy spectrum of 40-150 keV. The study also compared the computational discrepancies among these measurements. Compared to rubber composites doped with single-component fillers, multi-component mixed shielding materials significantly mitigate the shielding deficiencies observed with single-component materials, thereby broadening the γ-ray energy spectrum for which the composites provide effective shielding. Subsequently, the simulation outcomes were juxtaposed with experimental data derived from a 133Ba (80 keV) γ-source. The findings reveal that the simulated results align closely with the experimental observations. When compared to the WinXCom database, the Geant4 software demonstrates superior accuracy in deriving radiation shielding parameters and notably enhances experimental efficiency.

Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients.

OBJECTIVE: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images. METHODS: A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA). RESULTS: BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction. CONCLUSION: This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

The association between non-HDL cholesterol and high-grade pancreatic neuroendocrine neoplasms.

PURPOSE: High-density lipoprotein cholesterol (HDL-c) plays an important role in tumorigenesis in several endocrine-related cancers. Few studies have shown the effect of non-HDL-c in malignant tumors. The present study aimed to identify the association between non-HDL-c and high-grade pancreatic neuroendocrine neoplasms (PNENs). METHODS: A total of 197 PNEN patients who underwent surgery were analyzed retrospectively. Clinical and histopathological features, such as patients' age and sex, tumor location and size, tumor grade, the level of serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c) and fasting plasma-glucose levels were obtained. Non-HDL-c was calculated as total cholesterol - HDL-c. The relationships between those features and high-grade PNENs were identified using logistic regression analysis. RESULTS: Among the 197 patients with PNENs, a lower HDL-c level was more common seen in patients with poorly differentiated PNENs than in those with well-differentiated PNENs (P < 0.05). The non-HDL-c/HDL-c ratio was greater in patients with poorly differentiated PNENs than in those with well-differentiated PNENs (P < 0.01). Similarly, a greater proportion of patients with a non-HDL-c/HDL-c ratio larger than 5 was found in patients with poorly differentiated PNENs than in those with well-differentiation PNENs (P < 0.01). Multivariate logistic analysis showed that the non-HDL-c/HDL-c ratio was positively associated with poorly differentiated PNENs (odds ratio (OR) = 1.45, 95% conference interval (CI):1.13-1.87). Similarly, the risk of poorly differentiated PNENs increased significantly in patients with a non-HDL-c/HDL-c greater than 5 (OR = 14.13, 95%CI: 2.98-66.89). The risk of high-grade PNENs increased in patients with a high non-HDL-c/HDL-c ratio (OR = 1.27, 95% CI: 1.04-1.55), and the risk also increased markedly when the ratio was greater than 5 (OR = 5.00, 95%CI: 1.28-19.49). CONCLUSIONS: A high ratio of non-HDL-c/HDL-c was associated with high-grade PNENs or poorly differentiated PNENs.

Identification of ASF1A and HJURP by global H3-H4 histone chaperone analysis as a prognostic two-gene model in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignancy with poor prognosis. Abnormal expression of H3-H4 histone chaperones has been identified in many cancers and holds promise as a biomarker for diagnosis and prognosis. However, systemic analysis of H3-H4 histone chaperones in HCC is still lacking. Here, we investigated the expression of 19 known H3-H4 histone chaperones in HCC. Integrated analysis of multiple public databases indicated that these chaperones are highly expressed in HCC tumor tissues, which was further verified by immunohistochemistry (IHC) staining in offline samples. Additionally, survival analysis suggested that HCC patients with upregulated H3-H4 histone chaperones have poor prognosis. Using LASSO and Cox regression, we constructed a two-gene model (ASF1A, HJURP) that accurately predicts prognosis in ICGC-LIRI and GEO HCC data, which was further validated in HCC tissue microarrays with follow-up information. GSEA revealed that HCCs in the high-risk group were associated with enhanced cell cycle progression and DNA replication. Intriguingly, HCCs in the high-risk group exhibited increased immune infiltration and sensitivity to immune checkpoint therapy (ICT). In summary, H3-H4 histone chaperones play a critical role in HCC progression, and the two-gene (ASF1A, HJURP) risk model is effective for predicting survival outcomes and sensitivity to immunotherapy for HCC patients.

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Purpose: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

Single-cell transcriptomics reveals ferrimagnetic vortex iron oxide nanoring-mediated mild magnetic hyperthermia exerts antitumor effects by alleviating macrophage suppression in breast cancer.

The potential of Ferrimagnetic vortex iron oxide nanoring-mediated mild magnetic hyperthermia (FVIO-MHT) in solid tumor therapy has been demonstrated. However, the impact of FVIO-MHT on the tumor microenvironment (TME) remains unclear. This study utilized single-cell transcriptome sequencing to examine the alterations in the TME in response to FVIO-MHT in breast cancer. The results revealed the cellular composition within the tumor microenvironment (TME) was primarily modified due to a decrease in tumor cells and an increased infiltration of myeloid cells. Subsequently, an enhancement in active oxygen (ROS) metabolism was observed, indicating oxidative damage to tumor cells. Interestingly, FVIO-MHT reprogrammed the macrophages' phenotypes, as evidenced by alterations in the transcriptome characteristics associated with both classic and alternative activated phenotypes. And an elevated level of ROS generation and oxidative phosphorylation suggested that activated phagocytosis and inflammation occurred in macrophages. Additionally, cell-cell communication analysis revealed that FVIO-MHT attenuated the suppression between tumor cells and macrophages by inhibiting phagocytic checkpoint and macrophage migration inhibitory factor signaling pathways. Inhibition of B2m, an anti-phagocytosis checkpoint, could promote macrophage-mediated phagocytosis and significantly inhibit tumor growth. These data emphasize FVIO-MHT may promote the antitumor capabilities of macrophages by alleviating the suppression between tumor cells and macrophages.

The association between jaundice and poorly differentiated pancreatic neuroendocrine neoplasms (Ki67 index > 55.0%).

BACKGROUND: Jaundice occurs in some pancreatic disease. However, its occurrences and role in pancreatic neuroendocrine neoplasms (PNENs) has not been well studied. In this study we showed the association between jaundice and the risk of high grade and poorly differentiated PNENs. METHODS: Ninety-three patients with head-neck PNENs were included. Poorly differentiated pancreatic neuroendocrine neoplasms were defined by a ki67 index > 55.0%. Logistic regression was used to show the association between demographic information, clinical signs and symptoms and the risk of poorly differentiated tumors. A nomogram model was developed to predict poorly differentiated tumor. RESULTS: Eight of 93 PNEN patients (8.6%) had jaundice. The age and ki67 index in patients with jaundice were significantly higher than those patients without jaundice. All jaundice occurred in patients with grade 3 PNENs. Mutivariable regression analysis showed that age (odds ratio(OR) = 1.10, 95% confidence interval (CI):1.02-1.19), tumor size (OR = 1.42, 95%CI:1.01-2.00) and jaundice (OR = 14.98, 95%CI: 1.22-184.09) were associated with the risk of poorly differentiated PNENs. The age and size combination showed a good performance in predicting poorly differentiated PNENs (area under the curve (AUC) = 0.81, 95% CI: 0.71-0.90). The addition of jaundice further improved the age- and size-based model (AUC = 0.86, 95% CI: 0.78-0.91). A nomogram was developed based on age, tumor size and jaundice. CONCLUSION: Our data showed that jaundice was associated with the risk of high grade PNENs and poorly differentiated PNENs.

Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). METHODS: A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). RESULTS: Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. CONCLUSION: Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram.

Enhancing Mechanical Properties: Exploring the Effect of Annealing Temperature on Wire Arc Additively Manufactured High-Strength Steel.

This study investigates the mechanical properties of exceptionally high-strength steel produced by wire and arc additive manufacturing (WAAM), using the 304 stainless steel wire and the low carbon wire (LCS). The study found that annealing treatment can enhance the steel's mechanical properties. The microstructure in the LCS layer changed from ferrite to bainite and then to a mixture of austenite, pearlite, and bainite with increasing annealing temperature. In contrast, the SS layer retained its martensitic structure, albeit with altered lath sizes. The annealing treatment also improved the orientation of the grains in the steel. The optimal annealing temperature observed for the steel was 900 ℃, which resulted in a maximum tensile strength of 1176 MPa along the Y direction and 1255 MPa along the Z direction. Despite the superior mechanical properties, the LCS layer still exhibited failure during tensile testing due to its lower hardness. The study suggests that annealing treatment can be a useful technique for enhancing the mechanical properties of high-strength steel in WAAM applications.

Association of Plasma Lipids with White Matter Hyperintensities in Patients with Acute Ischemic Stroke.

Purpose: White matter hyperintensities (WMH) are the common marker of cerebral small vessel disease (CSVD). Dyslipidemia plays a notable role in the pathogenesis of CSVD. However, the relationship between dyslipidemia and WMH is poorly elucidated. This study aims to investigate the association between serum lipid fractions and WMH in patients with acute ischemic stroke (AIS). Patients and Methods: A total of 901 patients with AIS were included in this study. The burden of WMH, including deep white matter hyperintensities (DWMH), periventricular white matter hyperintensities (PVWMH), and total WMH load, were evaluated on magnetic resonance imaging (MRI) by the Fazekas scale. All the WMH burden were set as dichotomous variables. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c) were collected. The association of serum lipid fractions with WMH burden was analyzed using univariate and multivariate logistic regression models. Results: The average age of the participants was 67.6±11.6 years, and 584 cases (64.8%) were male. About 33.5% (n = 302) patients were smoker, and 23.5% (n = 212) patients had a history of alcohol consumption. The proportion of previous diabetes, ischemic cardiomyopathy and hypertension was 39.0% (n = 351), 21.2% (n = 191) and 75.9% (n = 684), respectively. The average of serum HDL-c, TC, TG, LDL-c levels for all participants were 1.26 ± 0.28 mmol/l, 4.54 ± 1.06 mmol/l, 1.67 ± 1.09 mmol/l, 3.08 ± 0.94 mmol/l. There were no statistical associations between HDL-c, TG, TC, LDL-c and each type of WMH burden (P > 0.05) in multivariate logistic regression analysis. Similar findings were found in subgroup analysis based on gender classification. Conclusion: Serum lipid levels were not associated with the presence of any type of WMH in patients with AIS.

Application of real time shear wave elastography to the differential diagnosis of secondary parathyroid hyperplasia and adenoma.

We aimed to explore the value of ultrasonic elastic imaging in the diagnosis of parathyroid hyperplasia and adenoma in patients with secondary hyperparathyroidism and provide more evidence for clinical treatment. Forty patients who were on dialysis and underwent parathyroid surgery were selected All patients underwent routine ultrasound, ultrasound elasticity examination and blood biochemical examination before surgery, including calcium, phosphorus, parathyroid hormone (PTH), etc. According to postoperative results, adenoma group and hyperplasia group were divided into 2 groups. Receiver operating characteristic curve was drawn to evaluate the diagnostic efficacy and combined diagnostic efficacy of each index. The PTH levels significantly differed between the adenoma and hyperplasia groups (P < .001). The volume and blood flow grades significantly differed between the adenoma and hyperplasia groups (P < .001) The minimum of the adenoma group was 14.62 ±â€…6.79 kPa, mean was 19.42 ±â€…6.29 kPa, and maximum was 24.25 ±â€…6.35 kPa which were significantly different from those in the hyperplasia group (P < .05). The combinations of more than 6 indicators in the diagnosis of parathyroid adenoma resulted in an area under the curve of 0.892 (P < .001), and the sensitivity and specificity were 78.9% and 97.4%, respectively. Shear wave elastography can be used as an effective tool to distinguish secondary parathyroid hyperplasia from adenoma. When combined with PTH, conventional ultrasound blood flow grading and volume measurement, it has higher diagnostic efficacy.

Testbed as a RegUlatory Science Tool (TRUST): A Testbed Design for Evaluating 5G-Enabled Medical Devices.

The fifth-generation (5G) cellular communication technology introduces technical advances that can expand medical device access to connectivity services. However, assessing the safety and effectiveness of emerging 5G-enabled medical devices is challenging as relevant evaluation methods have not yet been established. In this paper, we propose a design model for 5G testbed as a regulatory science tool (TRUST) for assessing 5G connectivity enablers of medical device functions. Specifically, we first identify application specific testing needs and general testing protocols. Next, we outline the selection and customization of key system components to create a 5G testbed. A TRUST demonstration is documented through a realistic 5G testbed implementation along with the deployment of a custom-built example use-case for 5G-enabled medical extended reality (MXR). Detailed configurations, example collected data, and implementation challenges are presented. The openness of the TRUST design model allows a TRUST testbed to be easily extended and customized to incorporate available resources and address the evaluation needs of different stakeholders.

Roles of GFAT and PFK genes in energy metabolism of brown planthopper, Nilaparvata lugens.

Glutamine:fructose-6-phosphate aminotransferases (GFATs) and phosphofructokinase (PFKs) are the principal rate-limiting enzymes involved in hexosamine biosynthesis pathway (HBP) and glycolysis pathway, respectively. In this study, the NlGFAT and NlPFK were knocked down through RNA interference (RNAi) in Nilaparvata lugens, the notorious brown planthopper (BPH), and the changes in energy metabolism were determined. Knockdown of either NlGFAT or NlPFK substantially reduced gene expression related to trehalose, glucose, and glycogen metabolism pathways. Moreover, trehalose content rose significantly at 72 h after dsGFAT injection, and glycogen content increased significantly at 48 h after injection. Glucose content remained unchanged throughout the experiment. Conversely, dsPFK injection did not significantly alter trehalose, but caused an extreme increase in glucose and glycogen content at 72 h after injection. The Knockdown of NlGFAT or NlPFK significantly downregulated the genes in the glycolytic pathway, as well as caused a considerable and significant decrease in pyruvate kinase (PK) activity after 48 h and 72 h of inhibition. After dsGFAT injection, most of genes in TCA cycle pathway were upregulated, but after dsNlPFK injection, they were downregulated. Correspondingly, ATP content substantially increased at 48 h after NlGFAT knockdown but decreased to an extreme extent by 72 h. In contrast, ATP content decreased significantly after NlPFK was knocked down and returned. The results have suggested the knockdown of either NlGFAT or NlPFK resulted in metabolism disorders in BPHs, highlighting the difference in the impact of those two enzyme genes on energy metabolism. Given their influence on BPHs energy metabolism, developing enzyme inhibitors or activators may provide a biological control for BPHs.

Congenital absence of the left pericardium: a case report.

BACKGROUND: Congenital absence of the pericardium (CAP) is rare in clinical practice, the symptoms vary among patients, and most doctors do not have enough knowledge of the condition. Most reported CAP cases are incidental findings. Therefore, this case report aimed to present a rare case of left partial CAP that presented with non-specific, possibly cardiac-related symptoms. CASE PRESENTATION: The patient, male, 56 years old, Asian, was admitted on March 2, 2021. The patient complained of occasional dizziness in the past week. The patient was suffering from hyperlipidemia and hypertension (stage 2), both untreated. The patient reported chest pain, palpitations, discomfort in the precordium, and dyspnea in the lateral recumbent position after strenuous activities, all of which started when he was about 15 years old. ECG showed sinus rhythm, 76 bpm, premature ventricular beats, incomplete right bundle branch block, and clockwise rotation of the electrical axis. Most of the ascending aorta could be detected in the parasternal intercostal space 2-4 by transthoracic echocardiography in the left lateral position. Chest computed tomography revealed the absence of pericardium between the aorta and the pulmonary artery, and part of the left lung was extending into the space. No changes in his condition have been reported up to now (March 2023). CONCLUSIONS: CAP should be considered when multiple examinations suggest heart rotation and a large moving range of the heart in the thoracic cavity.

Dynamic changes of resting state functional network following acute ischemic stroke.

Stroke, the second common cause of death in the world, is commonly considered to the well-known phenomenon of diaschisis. After stroke, regions far from the lesion can show altered neural activity. However, the comprehensive treatment recovery mechanism of acute ischemic stroke remains unclear. This study aims to investigate the impact of comprehensive treatment on resting state brain functional connectivity to reveal the therapeutic mechanism through a three time points study design. Twenty-one acute ischemic stroke patients and twenty matched healthy controls (HC) were included. Resting state functional magnetic resonance imaging (fMRI) and clinical evaluations were assessed in three stages: baseline (less than 72 h after stroke onset), post-first month and post-third month. Amplitude of low-frequency fluctuations (ALFF) and Independent component analysis (ICA) were conducted. We found: 1) stroke patients had decreased ALFF in the right cuneus (one of the important parts of the visual network). After three months, ALFF increased to the normal level; 2) the decreased functional connectivity in the right cuneus within the visual network and restored three months after onset. However, the decreased functional connectivity in the right precuneus within the default mode network restored one month after onset; 3) a significant association was found between the clinical scale score change over time and improvement in the cuneus and precuneus functional connectivity. Our results demonstrate the importance of the cuneus and precuneus within the visual network and default mode network in stroke recovery. These findings suggest that the different restored patterns of neural functional networks may contribute to the neurological function recovery. It has potential applications from stroke onset through rehabilitation because different rehabilitation phase corresponds to specific strategies.

Research progress of hydrogels as delivery systems and scaffolds in the treatment of secondary spinal cord injury.

Secondary spinal cord injury (SSCI) is the second stage of spinal cord injury (SCI) and involves vasculature derangement, immune response, inflammatory response, and glial scar formation. Bioactive additives, such as drugs and cells, have been widely used to inhibit the progression of secondary spinal cord injury. However, the delivery and long-term retention of these additives remain a problem to be solved. In recent years, hydrogels have attracted much attention as a popular delivery system for loading cells and drugs for secondary spinal cord injury therapy. After implantation into the site of spinal cord injury, hydrogels can deliver bioactive additives in situ and induce the unidirectional growth of nerve cells as scaffolds. In addition, physical and chemical methods can endow hydrogels with new functions. In this review, we summarize the current state of various hydrogel delivery systems for secondary spinal cord injury treatment. Moreover, functional modifications of these hydrogels for better therapeutic effects are also discussed to provide a comprehensive insight into the application of hydrogels in the treatment of secondary spinal cord injury.