Ultrahigh Heat/Fire-Resistant, Mechanically Robust, and Closed-Loop Chemical Recyclable Polycarbonate Enabled by Facile Bond Dissociation Energy Modulation.

Plastics serve as an essential foundation in contemporary society. Nevertheless, meeting the rigorous performance demands in advanced applications and addressing their end-of-life disposal are two critical challenges that persist. Here, an innovative and facile method is introduced for the design and scalable production of polycarbonate, a key engineering plastic, simultaneously achieving high performance and closed-loop chemical recyclability. The bisphenol framework of polycarbonate is strategically adjusted from the low-bond-dissociation-energy bisphenol A to high-bond-dissociation-energy 4,4'-dihydroxydiphenyl, in combination with the incorporation of polysiloxane segments. As expected, the enhanced bond dissociation energy endows the polycarbonate with an extremely high glow-wire flammability index surpassing 1025 °C, a 0.8 mm UL-94 V-0 rating, a high LOI value of 39.2%, and more than 50% reduction of heat and smoke release. Furthermore, the π-π stacking interactions within biphenyl structures resulted in a significant enhancement of mechanical strength by as more as 37.7%, and also played a positive role in achieving a lower dielectric constant. Significantly, the copolymer exhibited outstanding closed-loop chemical recyclability, allowing for facile depolymerization into bisphenol monomers and the repolymerized copolymer retains its high heat and fire resistance. This work provides a novel insight in the design of high-performance and closed-loop chemical recyclable polymeric materials.

The impact of breastfeeding on the preterm infant's microbiome and metabolome: a pilot study.

BACKGROUND: Human milk is unquestionably beneficial for preterm infants. We investigated how the transition from tube to oral/breastfeeding impacts the preterm infants' oral and gut microbiome and metabolome. METHODS: We analyzed stool, saliva, and milk samples collected from a cohort of preterm infants enrolled in the MAP Study, a prospective observational trial. The microbiome and metabolome of the samples were analyzed from 4 longitudinal sample time points, 2 during tube feeds only and 2 after the initiation of oral/breastfeeding. RESULTS: We enrolled 11 mother-infant dyads (gestational age = 27.9 (23.4-32.2)) and analyzed a total of 39 stool, 44 saliva, and 43 milk samples over 4 timepoints. In saliva samples, there was a shift towards increased Streptococcus and decreased Staphylococcus after oral feeding/breastfeeding initiation (p < 0.05). Milk sample metabolites were strongly influenced by the route of feeding and milk type (p < 0.05) and represented the pathways of Vitamin E metabolism, Vitamin B12 metabolism, and Tryptophan metabolism. CONCLUSION: Our analysis demonstrated that the milk and preterm infant's saliva microbiome and metabolome changed over the course of the first four to 5 months of life, coinciding with the initiation of oral/breastfeeds. IMPACT: The microbiome and metabolome is altered in the infant's saliva but not their stool, and in mother's milk when feeds are transitioned from tube to oral/breastfeeding. We assessed the relationship between the gut and oral microbiome/metabolome with the milk microbiome/metabolome over a longitudinal period of time in preterm babies. Metabolites that changed in the infants saliva after the initiation of oral feeds have the potential to be used as biomarkers for disease risk.

The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

Vehicle-Type Recognition Method for Images Based on Improved Faster R-CNN Model.

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model.

High-Entropy and Component Stoichiometry Tuning Strategies Boost the Sodium-Ion Storage Performance of Cobalt-Free Prussian Blue Analogues Cathode Materials.

Prussian blue analogs (PBAs) are appealing cathode materials for sodium-ion batteries because of their low material cost, facile synthesis methods, rigid open framework, and high theoretical capacity. However, the poor electrical conductivity, unavoidable presence of [Fe(CN)6] vacancies and crystalline water within the framework, and phase transition during charge-discharge result in inferior electrochemical performance, particularly in terms of rate capability and cycling stability. Here, cobalt-free PBAs are synthesized using a facile and economic co-precipitation method at room temperature, and their sodium-ion storage performance is boosted due to the reduced crystalline water content and improved electrical conductivity via the high-entropy and component stoichiometry tuning strategies, leading to enhanced initial Coulombic efficiency (ICE), specific capacity, cycling stability, and rate capability. The optimized HE-HCF of Fe0.60Mn0.10-hexacyanoferrate (referred to as Fe0.60Mn0.10-HCF), with the chemical formula Na1.156Fe0.599Mn0.095Ni0.092Cu0.109Zn0.105 [Fe(CN)6]0.724·3.11H2O, displays the most appealing electrochemical performance of an ICE of 100%, a specific capacity of around 115 and 90 mAh·g-1 at 0.1 and 1.0 A·g-1, with 66.7% capacity retention observed after 1000 cycles and around 61.4% capacity retention with a 40-fold increase in specific current. We expect that our findings could provide reference strategies for the design of SIB cathode materials with superior electrochemical performance.

Investigating the Electrochemical Performance of MnFe2O4@xC Nanocomposites as Anode Materials for Sodium-Ion Batteries.

Transition metal oxides (TMOs) are important anode materials in sodium-ion batteries (SIBs) due to their high theoretical capacities, abundant resources, and cost-effectiveness. However, issues such as the low conductivity and large volume variation of TMO bulk materials during the cycling process result in poor electrochemical performance. Nanosizing and compositing with carbon materials are two effective strategies to overcome these issues. In this study, spherical MnFe2O4@xC nanocomposites composed of MnFe2O4 inner cores and tunable carbon shell thicknesses were successfully prepared and utilized as anode materials for SIBs. It was found that the property of the carbon shell plays a crucial role in tuning the electrochemical performance of MnFe2O4@xC nanocomposites and an appropriate carbon shell thickness (content) leads to the optimal battery performance. Thus, compared to MnFe2O4@1C and MnFe2O4@8C, MnFe2O4@4C nanocomposite exhibits optimal electrochemical performance by releasing a reversible specific capacity of around 308 mAh·g-1 at 0.1 A·g-1 with 93% capacity retention after 100 cycles, 250 mAh·g-1 at 1.0 A g-1 with 73% capacity retention after 300 cycles in a half cell, and around 111 mAh·g-1 at 1.0 C when coupled with a Na3V2(PO4)3 (NVP) cathode in a full SIB cell.

Cascaded terahertz hollow-core Bragg waveguide: numerical design and experimental demonstration.

A THz hollow-core Bragg waveguide with discontinuous support bridges in both radial and axial directions is proposed. The influence of the support bridges on the transmission loss of the waveguide is demonstrated numerically. The proposed waveguide shows confinement loss two orders of magnitude lower than that of the Bragg waveguide with conventional support bridges. A waveguide sample is fabricated by 3D printing technology, and the experimental results show that the transmission loss is in agreement with that of the simulation results. It is also demonstrated that the transmission loss of the fabricated waveguide is mainly determined by the large absorption loss of the waveguide material used in the experiment.

An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules.

Background US-based diagnosis of thyroid nodules is subjective and influenced by radiologists' experience levels. Purpose To develop an artificial intelligence model based on American College of Radiology Thyroid Imaging Reporting and Data System characteristics for diagnosing thyroid nodules and identifying nodule characteristics (hereafter, MTI-RADS) and to compare the performance of MTI-RADS, radiologists, and a model trained on benign and malignant status based on surgical histopathologic analysis (hereafter, MDiag). Materials and Methods In this retrospective study, 1588 surgically proven nodules from 636 consecutive patients (mean age, 49 years ± 14 [SD]; 485 women) were included. MTI-RADS and MDiag were trained on US images of 1345 nodules (January 2018 to December 2019). The performance of MTI-RADS was compared with that of MDiag and radiologists with different experience levels on the test data set (243 nodules, January 2019 to December 2019) with the DeLong method and McNemar test. Results The area under the receiver operating characteristic curve (AUC) and sensitivity of MTI-RADS were 0.91 and 83% (55 of 66 nodules), respectively, which were not significantly different from those of experienced radiologists (0.93 [P = .45] and 92% [61 of 66 nodules; P = .07]) and exceeded those of junior radiologists (0.78 [P < .001] and 70% [46 of 66 nodules; P = .04]). The specificity of MTI-RADS (87% [154 of 177 nodules]) was higher than that of both experienced and junior radiologists (80% [141 of 177 nodules; P = .02] and 75% [133 of 177 nodules; P = .001], respectively). The AUC of MTI-RADS was higher than that of MDiag (0.91 vs 0.84, respectively; P = .001). In the test set of 243 nodules, the consistency rates between MTI-RADS and the experienced group were higher than those between MTI-RADS and the junior group for composition (79% [n = 193] vs 73% [n = 178], respectively; P = .02), echogenicity (75% [n = 183] vs 68% [n = 166]; P = .04), shape (93% [n = 227] vs 88% [n = 215]; P = .04), and smooth or ill-defined margin (72% [n = 174] vs 63% [n = 152]; P = .002). Conclusion The area under the receiver operating characteristic curve (AUC) of an artificial intelligence model based on the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) was higher than that of a model trained on benign and malignant status based on surgical histopathologic analysis. The AUC and sensitivity of the model based on TI-RADS exceeded those of junior radiologists; the specificity of the model was higher than that of both experienced and junior radiologists. © RSNA, 2022.

Disrupted intrinsic functional brain topology in patients with major depressive disorder.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Impaired robust interhemispheric function integration of depressive brain from REST-meta-MDD database in China.

BACKGROUND: Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations. METHODS: In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns. RESULTS: As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q < 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs. CONCLUSIONS: We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Beam complexity and monitor unit efficiency comparison in two different volumetric modulated arc therapy delivery systems using automated planning.

BACKGROUND: To investigate the beam complexity and monitor unit (MU) efficiency issues for two different volumetric modulated arc therapy (VMAT) delivery technologies for patients with left-sided breast cancer (BC) and nasopharyngeal carcinoma (NPC). METHODS: Twelve left-sided BC and seven NPC cases were enrolled in this study. Each delivered treatment plan was optimized in the Pinnacle3 treatment planning system with the Auto-Planning module for the Trilogy and Synergy systems. Similar planning dose objectives and beam configurations were used for each site in the two different delivery systems to produce clinically acceptable plans. The beam complexity was evaluated in terms of the segment area (SA), segment width (SW), leaf sequence variability (LSV), aperture area variability (AAV), and modulation complexity score (MCS) based on the multileaf collimator sequence and MU. Plan delivery and a gamma evaluation were performed using a helical diode array. RESULTS: With similar plan quality, the average SAs for the Trilogy plans were smaller than those for the Synergy plans: 55.5 ± 21.3 cm2 vs. 66.3 ± 17.9 cm2 (p < 0.05) for the NPC cases and 100.7 ± 49.2 cm2 vs. 108.5 ± 42.7 cm2 (p < 0.05) for the BC cases, respectively. The SW was statistically significant for the two delivery systems (NPC: 6.87 ± 1.95 cm vs. 6.72 ± 2.71 cm, p < 0.05; BC: 8.84 ± 2.56 cm vs. 8.09 ± 2.63 cm, p < 0.05). The LSV was significantly smaller for Trilogy (NPC: 0.84 ± 0.033 vs. 0.86 ± 0.033, p < 0.05; BC: 0.89 ± 0.026 vs. 0.90 ± 0.26, p < 0.05). The mean AAV was significantly larger for Trilogy than for Synergy (NPC: 0.18 ± 0.064 vs. 0.14 ± 0.037, p < 0.05; BC: 0.46 ± 0.15 vs. 0.33 ± 0.13, p < 0.05). The MCS values for Trilogy were higher than those for Synergy: 0.14 ± 0.016 vs. 0.12 ± 0.017 (p < 0.05) for the NPC cases and 0.42 ± 0.106 vs. 0.30 ± 0.087 (p < 0.05) for the BC cases. Compared with the Synergy plans, the average MUs for the Trilogy plans were larger: 828.6 ± 74.1 MU and 782.9 ± 85.2 MU (p > 0.05) for the NPC cases and 444.8 ± 61.3 MU and 393.8 ± 75.3 MU (p > 0.05) for the BC cases. The gamma index agreement scores were never below 91% using 3 mm/3% (global) distance to agreement and dose difference criteria and a 10% lower dose exclusion threshold. CONCLUSIONS: The Pinnacle3 Auto-Planning system can optimize BC and NPC plans to achieve the same plan quality using both the Trilogy and Synergy systems. We found that these two systems resulted in different SAs, SWs, LSVs, AAVs and MCSs. As a result, we suggested that the beam complexity should be considered in the development of further methodologies while optimizing VMAT autoplanning.

Construction of Function-Oriented Core-Shell Nanostructures in Hydrogen-Bonded Organic Frameworks for Near-Infrared-Responsive Bacterial Inhibition.

Exploration of effective ways to integrate various functional species into hydrogen-bonded organic frameworks (HOFs) is critically important for their applications but highly challenging. In this study, according to the "bottle-around-ship" strategy, core-shell heterostructure of upconversion nanoparticles (UCNPs) and HOFs was fabricated for the first time via a ligand-grafting stepwise method. The UCNPs "core" can effectively upconvert near-infrared (NIR) irradiation (980 nm) into visible light (540 nm and 653 nm), which further excites the perylenediimide-based HOF "shell" through resonance energy transfer. In this way, the nanocomposite inherits the high porosity, excellent photothermal and photodynamic efficiency, NIR photoresponse from two parent materials, achieving intriguing NIR-responsive bacterial inhibition toward Escherichia coli. This study may shed light on the design of functional HOF-based composite materials, not only enriching the HOF library but also broadening the horizon of their potential applications.

An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors.

Text recognition in natural scene images has always been a hot topic in the field of document-image related visual sensors. The previous literature mostly solved the problem of horizontal text recognition, but the text in the natural scene is usually inclined and irregular, and there are many unsolved problems. For this reason, we propose a scene text recognition algorithm based on a text position correction (TPC) module and an encoder-decoder network (EDN) module. Firstly, the slanted text is modified into horizontal text through the TPC module, and then the content of horizontal text is accurately identified through the EDN module. Experiments on the standard data set show that the algorithm can recognize many kinds of irregular text and get better results. Ablation studies show that the proposed two network modules can enhance the accuracy of irregular scene text recognition.

Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics.

OBJECTIVE. The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules. MATERIALS AND METHODS. A total of 103 patients (training cohort-to-validation cohort ratio, ≈ 3:1) with suspected thyroid nodules who had undergone thyroidectomy and immunohistochemical analysis were enrolled. The immunohistochemical markers were cytokeratin 19, galectin 3, thyroperoxidase, and high-molecular-weight cytokeratin. All patients underwent CT before surgery, and a 3D slicer was used to analyze images of the surgical specimen. Test-retest and Spearman correlation coefficient (ρ) were used to select reproducible and nonredundant features. The Kruskal-Wallis test (p < 0.05) was used for feature selection, and a feature-based model was built by support vector machine methods. The performance of the radiomic models was assessed with respect to accuracy, sensitivity, specificity, corresponding AUC, and independent validation. RESULTS. Eighty-six reproducible and nonredundant features selected from the 828 features were used to build the model. The best performance of the cytokeratin 19 model yielded accuracy of 84.4% in the training cohort and 80.0% in the validation cohort. The thyroperoxidase and galectin 3 predictive models yielded accuracies of 81.4% and 82.5% in the training cohort and 84.2% and 85.0% in the validation cohort. The performance of the high-molecular-weight cytokeratin predictive model was not good (accuracy, 65.7%) and could not be validated. CONCLUSION. A radiomics model with excellent performance was developed for individualized noninvasive prediction of the presence of cytokeratin 19, galectin 3, and thyroperoxidase based on CT images. This model may be used to identify benign and malignant thyroid nodules.

Rapid elimination of scattering in three-dimensional fluorescence spectra via deep learning.

Three-dimensional fluorescence spectra are often affected by scattering effects, traditional scattering elimination methods rely excessively on parameter settings and cannot automatically eliminate scattering in batches, thereby limiting the application of fluorescence spectroscopy technology in rapid online monitoring and analysis of samples. In this study, we have developed a model based on a deep learning CycleGAN to rapidly eliminate scattering from three-dimensional fluorescence spectra. The proposed model efficiently eliminates scattering by simply inputting single or batches of contaminated fluorescent spectra. By training the CycleGAN using a large dataset of simulated three-dimensional fluorescence spectra and employing data augmentation, to the model can transform fluorescence spectra with scattering into ones without scattering. To validate the effectiveness of the proposed methed, we confirmed its generalization and reliability by eliminating scattering from two sets of previously unseen real experimental three-dimensional fluorescence spectra. We evaluated the effectiveness of scattering elimination across various noise levels and scattering widths, using metrics such as the mean absolute error, peak signal-to-noise ratio, structural similarity and cosine similarity. Furthermore, we conducted a component analysis using PARAFAC on the spectra post-scattering elimination, yielding correlation coefficients of >0.97 when compared to that in case of actual components. Finally, we compared the proposed model with traditional mathematical methods, such as blank subtraction and Delaunay triangulation. Results showed that the proposed model can automatically and efficiently eliminate scattering from fluorescence spectra in batches, substantially improving the efficiency of scattering elimination.

Changes in PI3K/AKT and NRF2/HO-1 signaling expression and intestinal microbiota in bleomycin-induced pulmonary fibrosis.

Pulmonary fibrosis is the outcome of the prolonged interstitial pneumonia, characterized by excessive accumulation of fibroblasts and collagen deposition, leading to its development. This study aimed to study the changes in PI3K/AKT and NRF2/HO-1 signaling expression and intestinal microbiota in a rat model of a novel bleomycin-induced pulmonary fibrosis. The findings of our study showed the model was successfully established. The results showed that the alveolar septum in the model was significantly widened and infiltrated by severe inflammatory cells. Alveolar atrophy occurred due to the formation of multiple inflammatory foci. During this period, fibrous tissue was distributed in strips and patches, primarily around the pulmonary interstitium and bronchus. Moreover, lung damage and fibrosis progressively worsened over time. The mRNA expression of HO-1 and NRF2 in the model decreased while the mRNA expression of HIF-1α, VEGF, PI3K and AKT increased. Furthermore, it was observed to decrease the protein expression of E-cad, HO-1 and NRF2, and increase the protein expression of α-SMA and p-AKT. Additionally, this model leaded to an imbalance in the intestinal microbiota. This study demonstrate that the novel pulmonary fibrosis model activates the NRF2/HO-1 pathway and the PI3K/AKT pathway in rat lung tissues, and leading to intestinal barrier disorder.

Sound Touch Elastography for Noninvasive Assessment of Liver Stiffness in Patients With Chronic Heart Failure.

Heart failure (HF) can damage various organs, including the liver, a phenomenon known as "cardiohepatic syndrome." The latter is characterized by liver congestion and hepatic artery hypoperfusion, which can lead to liver damage. In this study, we aimed to assess liver damage quantitatively in chronic HF (CHF) with sound touch elastography (STE). A total of 150 subjects were enrolled, including HF with reduced ejection fraction (HFrEF) groups (left ventricular ejection fraction ≤40%, n = 45), HF with mildly reduced ejection fraction (HFmrEF) groups (left ventricular ejection fraction between 41% and 49%, n = 40), and right-sided HF (RHF) groups (n = 25); normal groups (n = 40). Liver stiffness measurement (LSM) was performed in all subjects by STE. The other hepatic parameters were also measured. The LSM was 5.4 ± 1.1 kPa in normal subjects and increased slightly to 5.9 ± 0.7 kPa in patients with HFmrEF. However, the HFrEF and RHF groups had significantly higher LSMs of 8.4 ± 2.0 kPa and 10.3 ± 2.7 kPa, respectively. The LSM of HFrEF was significantly higher than that of HFmrEF, whereas the increase in LSM in patients with RHF was significant relative to HFmrEF and HFrEF. In addition, the other parameters showed abnormal values in only RHF and HFrEF. In conclusion, STE is a useful clinical technique for the noninvasive evaluation of liver stiffness associated with CHF, which could help patients with CHF manage their treatment regimens.

A hydrogel-based ratiometric fluorescent sensor relying on rhodamine B labelled AIE-featured hyperbranched poly(amido amine) for heparin detection.

The fluorescent flexible sensor for point-of-care quantification of clinical anticoagulant drug, Heparin (Hep), is still an urgent need of breakthrough. In this research, a hyperbranched poly(amido amine) (HPA) was decorated with tetraphenylethene (TPE) and Rhodamine B (RhB), constructing a ratiometric fluorescent sensor (TR-HPA) for Hep. When the sensor was exposed to Hep, the TPE units within the probe skeleton would aggregate, resulting in an increasing fluorescent emission at 483 nm. The 580 nm of fluorescence came from RhB enhance, simultaneously, due to the fluorescence resonance energy transfer. As a result, there are two good linear correlation between the fluorescence emission ratio (E483/E580) of TR-HPA and the Hep concentration over a range of 0-1.0 µM, with a low limit of detection of 3.0 nM. Furthermore, we incorporate the TR-HPA probe into a polyvinyl alcohol (PVA) hydrogel matrix to create a flexible fluorescent sensing system platform, denoted as TR-HPA/PVA. This approach offers a straightforward visual detection method by causing a fluorescence color change from pink to blue when trace amounts of Hep are present. The hydrogel-based fluorescent sensor streamlines the detection procedures for Hep in biomedical applications. It shows great potential in rapid and point-of-care human blood clotting condition monitoring, making it suitable for next-generation wearable medical devices.

NIR-II photothermal conversion and imaging based on a cocrystal containing twisted components.

On account of the scarcity of molecules with a satisfactory second near-infrared (NIR-II) response, the design of high-performance organic NIR photothermal materials has been limited. Herein, we investigate a cocrystal incorporating tetrathiafulvalene (TTF) and tetrachloroperylene dianhydride (TCPDA) components. A stable radical was generated through charge transfer from TTF to TCPDA, which exhibits strong and wide-ranging NIR-II absorption. The metal-free TTF-TCPDA cocrystal in this research shows high photothermal conversion capability under 1064 nm laser irradiation and clear photothermal imaging. The remarkable conversion ability-which is a result of twisted components in the cocrystal-has been demonstrated by analyses of single crystal X-ray diffraction, photoluminescence and femtosecond transient absorption spectroscopy as well as theoretical calculations. We have discovered that space charge separation and the ordered lattice in the TTF-TCPDA cocrystal suppress the radiative decay, while simultaneously strong intermolecular charge transfer enhances the non-radiative decay. The twisted TCPDA component induces rapid charge recombination, while the distorted configuration in TTF-TCPDA favors an internal non-radiative pathway. This research has provided a comprehensive understanding of the photothermal conversion mechanism and opened a new way for the design of advanced organic NIR-II photothermal materials.