Light-induced electronic polarization in antiferromagnetic Cr2O3.

In a solid, the electronic subsystem can exhibit incipient order with lower point group symmetry than the crystal lattice. Ultrafast external fields that couple exclusively to electronic order parameters have rarely been investigated, however, despite their potential importance in inducing exotic effects. Here we show that when inversion symmetry is broken by the antiferromagnetic order in Cr2O3, transmitting a linearly polarized light pulse through the crystal gives rise to an in-plane rotational symmetry-breaking (from C3 to C1) via optical rectification. Using interferometric time-resolved second harmonic generation, we show that the ultrafast timescale of the symmetry reduction is indicative of a purely electronic response; the underlying spin and crystal structures remain unaffected. The symmetry-broken state exhibits a dipole moment, and its polar axis can be controlled with the incident light. Our results establish a coherent nonlinear optical protocol by which to break electronic symmetries and produce unconventional electronic effects in solids.

A magnetic continuum in the cobalt-based honeycomb magnet BaCo2(AsO4)2.

Quantum spin liquids (QSLs) are topologically ordered states of matter that host fractionalized excitations. A particular route towards a QSL is via strongly bond-dependent interactions on the hexagonal lattice. A number of Ru- and Ir-based candidate Kitaev QSL materials have been pursued, but all have appreciable non-Kitaev interactions. Using time-domain terahertz spectroscopy, we observed a broad magnetic continuum over a wide range of temperatures and fields in the honeycomb cobalt-based magnet BaCo2(AsO4)2, which has been proposed to be a more ideal version of a Kitaev QSL. Applying an in-plane magnetic field of ~0.5 T suppresses the magnetic order, and at higher fields, applying the field gives rise to a spin-polarized state. Under a 4 T magnetic field that was oriented principally out of plane, a broad magnetic continuum was observed that may be consistent with a field-induced QSL. Our results indicate BaCo2(AsO4)2 is a promising QSL candidate.

Evidence for Bootstrap Percolation Dynamics in a Photoinduced Phase Transition.

Upon intense femtosecond photoexcitation, a many-body system can undergo a phase transition through a nonequilibrium route, but understanding these pathways remains an outstanding challenge. Here, we use time-resolved second harmonic generation to investigate a photoinduced phase transition in Ca_{3}Ru_{2}O_{7} and show that mesoscale inhomogeneity profoundly influences the transition dynamics. We observe a marked slowing down of the characteristic time τ that quantifies the transition between two structures. τ evolves nonmonotonically as a function of photoexcitation fluence, rising from below 200 fs to ∼1.4 ps, then falling again to below 200 fs. To account for the observed behavior, we perform a bootstrap percolation simulation that demonstrates how local structural interactions govern the transition kinetics. Our work highlights the importance of percolating mesoscale inhomogeneity in the dynamics of photoinduced phase transitions and provides a model that may be useful for understanding such transitions more broadly.

Evaluation of the Stiffness of Tissues Surrounding Thyroid Nodules with Shear Wave Elastography.

OBJECTIVES: This study evaluated the stiffness of tissue surrounding thyroid nodules using shear wave elastography (SWE). METHODS: A total of 128 thyroid nodules in 108 patients were examined with conventional ultrasound imaging and SWE. The maximum Young modulus value was measured to evaluate the stiffness of the thyroid nodules (E) and the 2-mm perinodular region (Eshell ). The number of thyroid fibrocytes was evaluated by Masson trichrome staining and image-processing software. The arrangement of the fibrous structure was also classified. RESULTS: The mean age ± SD of the 108 patients was 33.12 ± 13.34 years (range, 18-80 years). Thirty-nine thyroid nodules were classified as benign and 89 as malignant. Eshell was significantly higher for malignant nodules (95.0 ± 21.9 kPa) compared with benign nodules (48.1 ± 17.0 kPa; P < .001). Eshell yielded an area under the receiver operator characteristic curve value of 0.951, which was used for the diagnosis of nodules. There was a high positive correlation between E and Eshell in the malignant group (R = 0.722; P < .001) and a moderate positive correlation in the benign group (R = 0.601; P < .001). Percentages of fibrocytes correlated highly with Eshell in all samples as well as in malignant samples (R = 0.867 and R = 0.729, respectively; P < .001). The degree of disorder of the tissue surrounding thyroid nodules was positively correlated with Eshell (R = 0.833). CONCLUSIONS: Perinodular stiffness has potential to improve diagnosis of thyroid nodules.

The effects of fluid resuscitation according to PiCCO on the early stage of severe acute pancreatitis.

OBJECTIVES: To evaluate the therapeutic effect of early fluid resuscitation under the guidance of Pulse indicator Continuous Cardiac Output (PiCCO) on patients with severe acute pancreatitis (SAP). METHODS: Clinical data of 18 SAP patients (the study group), who had undergone fluid resuscitation under the guidance of PiCCO from October 2011 to October 2013, were analyzed prospectively. Clinical data of 25 cases (control group) who had undergone fluid resuscitation without the guidance of PiCCO from January 2009 to September 2011 were collected. Then, retrospective and prospective case-control study was carried out. RESULTS: During the first 6 h, 0-24 h, 24-48 h, and 0-72 h of admission, the study group received more volume of fluid than the control group. There were significantly faster decline of APACHE II score and the value of blood lactate in study group, as well as the length of ICU stay and the proportion of renal failure at 72 h of admission. According to the 2012 Atlanta classification, six cases in study group turned into moderate SAP (33.30%), significantly higher than the control group (8.00%) (p = 0.0049). The volume of fluid infusion and clinical parameters were linearly relative. CONCLUSIONS: The PiCCO device may be a useful adjunct for fluid resuscitation monitoring in patients with SAP in the early stage. Early fluid resuscitation under the guidance of PiCCO can improve tissue perfusion, reduce the SIRS persistence time and the length of ICU stay. This program did not increase the risk of respiratory failure and influence the mortality.

Multi-omics analysis of human tendon adhesion reveals that ACKR1-regulated macrophage migration is involved in regeneration.

Tendon adhesion is a common complication after tendon injury with the development of accumulated fibrotic tissues without effective anti-fibrotic therapies, resulting in severe disability. Macrophages are widely recognized as a fibrotic trigger during peritendinous adhesion formation. However, different clusters of macrophages have various functions and receive multiple regulation, which are both still unknown. In our current study, multi-omics analysis including single-cell RNA sequencing and proteomics was performed on both human and mouse tendon adhesion tissue at different stages after tendon injury. The transcriptomes of over 74 000 human single cells were profiled. As results, we found that SPP1+ macrophages, RGCC+ endothelial cells, ACKR1+ endothelial cells and ADAM12+ fibroblasts participated in tendon adhesion formation. Interestingly, despite specific fibrotic clusters in tendon adhesion, FOLR2+ macrophages were identified as an antifibrotic cluster by in vitro experiments using human cells. Furthermore, ACKR1 was verified to regulate FOLR2+ macrophages migration at the injured peritendinous site by transplantation of bone marrow from Lysm-Cre;R26RtdTomato mice to lethally irradiated Ackr1-/- mice (Ackr1-/- chimeras; deficient in ACKR1) and control mice (WT chimeras). Compared with WT chimeras, the decline of FOLR2+ macrophages was also observed, indicating that ACKR1 was specifically involved in FOLR2+ macrophages migration. Taken together, our study not only characterized the fibrosis microenvironment landscape of tendon adhesion by multi-omics analysis, but also uncovered a novel antifibrotic cluster of macrophages and their origin. These results provide potential therapeutic targets against human tendon adhesion.

Diamond-Like Carbon Depositing on the Surface of Polylactide Membrane for Prevention of Adhesion Formation During Tendon Repair.

Post-traumatic peritendinous adhesion presents a significant challenge in clinical medicine. This study proposes the use of diamond-like carbon (DLC) deposited on polylactic acid (PLA) membranes as a biophysical mechanism for anti-adhesion barrier to encase ruptured tendons in tendon-injured rats. The results indicate that PLA/DLC composite membrane exhibits more efficient anti-adhesion effect than PLA membrane, with histological score decreasing from 3.12 ± 0.27 to 2.20 ± 0.22 and anti-adhesion effectiveness increasing from 21.61% to 44.72%. Mechanistically, the abundant C=O bond functional groups on the surface of DLC can reduce reactive oxygen species level effectively; thus, the phosphorylation of NF-κB and M1 polarization of macrophages are inhibited. Consequently, excessive inflammatory response augmented by M1 macrophage-originated cytokines including interleukin-6 (IL-6), interleukin-1ß (IL-1ß), and tumor necrosis factor-α (TNF-α) is largely reduced. For biocompatibility evaluation, PLA/DLC membrane is slowly absorbed within tissue and displays prolonged barrier effects compared to traditional PLA membranes. Further studies show the DLC depositing decelerates the release of degradation product lactic acid and its induction of macrophage M2 polarization by interfering esterase and PLA ester bonds, which further delays the fibrosis process. It was found that the PLA/DLC membrane possess an efficient biophysical mechanism for treatment of peritendinous adhesion.

Interoceptive regulation of skeletal tissue homeostasis and repair.

Recent studies have determined that the nervous system can sense and respond to signals from skeletal tissue, a process known as skeletal interoception, which is crucial for maintaining bone homeostasis. The hypothalamus, located in the central nervous system (CNS), plays a key role in processing interoceptive signals and regulating bone homeostasis through the autonomic nervous system, neuropeptide release, and neuroendocrine mechanisms. These mechanisms control the differentiation of mesenchymal stem cells into osteoblasts (OBs), the activation of osteoclasts (OCs), and the functional activities of bone cells. Sensory nerves extensively innervate skeletal tissues, facilitating the transmission of interoceptive signals to the CNS. This review provides a comprehensive overview of current research on the generation and coordination of skeletal interoceptive signals by the CNS to maintain bone homeostasis and their potential role in pathological conditions. The findings expand our understanding of intersystem communication in bone biology and may have implications for developing novel therapeutic strategies for bone diseases.

Self-Healing Hydrogel Embodied with Macrophage-Regulation and Responsive-Gene-Silencing Properties for Synergistic Prevention of Peritendinous Adhesion.

Antiadhesion barriers such as films and hydrogels used to wrap repaired tendons are important for preventing the formation of adhesion tissue after tendon surgery. However, sliding of the tendon can compress the adjacent hydrogel barrier and cause it to rupture, which may then lead to unexpected inflammation. Here, a self-healing and deformable hyaluronic acid (HA) hydrogel is constructed as a peritendinous antiadhesion barrier. Matrix metalloproteinase-2 (MMP-2)-degradable gelatin-methacryloyl (GelMA) microspheres (MSs) encapsulated with Smad3-siRNA nanoparticles are entrapped within the HA hydrogel to inhibit fibroblast proliferation and prevent peritendinous adhesion. GelMA MSs are responsively degraded by upregulation of MMP-2, achieving on-demand release of siRNA nanoparticles. Silencing effect of Smad3-siRNA nanoparticles is around 75% toward targeted gene. Furthermore, the self-healing hydrogel shows relatively attenuated inflammation compared to non-healing hydrogel. The mean adhesion scores of composite barrier group are 1.67 ± 0.51 and 2.17 ± 0.75 by macroscopic and histological evaluation, respectively. The proposed self-healing hydrogel antiadhesion barrier with MMP-2-responsive drug release behavior is highly effective for decreasing inflammation and inhibiting tendon adhesion. Therefore, this research provides a new strategy for the development of safe and effective antiadhesion barriers.

Single Lead ECG-based Ventricular Repolarization Classification for Early Identification of Unexpected Ventricular Fibrillation.

Malignant ventricular arrhythmia (especially ventricular fibrillation (VF)) is the main reason which causes sudden cardiac death (SCD). This paper presents an automatic SCD-patient classifier we developed to identify patients with unexpected VF using 60-minutes continuous single-lead electrocardiograms (ECG) signals before that. Patients are classified as having SCD if the majority of their recorded ventricular repolarization (VR) is recognized as characteristic of unexpected VF. Thus, the classifier's underlying task is to recognize individual VR delineated from single-lead ECG signals as SCD VR, where VR from non-SCD patients are used as controls. With the reported clinical practices of SCD, we extracted five morphological and temporal features (both commonly used and newly developed ones) from ECG signals for VR classification. To evaluate classification performance, we trained and tested k nearest neighbor classifier, a decision tree classifier, and a Naïve Bayes classifier using five-fold cross validation on 36 one-hour ECG signals (18 from patients at risk of SCD and 18 from control people). We compared the performance of these three classifiers, and the patient-classification sensitivity is approximately 98.02-99.51%. Moreover, the k nearest neighbor with a higher accuracy (98.89%) and specificity (98.27%) performed better than the other two. Importantly, the results show obvious superiorities of performance over that in the same duration and of usefulness over several minutes given by related works.Clinical Relevance- This could be integrated into a real-time, long-term out-of-hospital SCD predictor to improve the warning veracity and bring forward the warning time, especially for patients with implantable cardiac defibrillators or pacemakers, etc..

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibrillation (AF) with special emphasis on the combination of deep learning based algorithm and modified patch-based ECG lead. Fifty-five consecutive patients were monitored for AF in around 24 hours by patch-based ECG devices along with a standard 12-lead Holter. Catering to potential positional variability of patch lead, four typical positions on the upper-left chest were proposed. For each patch lead, the performance of automated algorithms with four different convolutional neural networks (CNN) was evaluated for AF detection against blinded annotations of two clinicians. A total of 349,388 10-second segments of AF and 161,084 segments of sinus rhythm were detected successfully. Good agreement between patch-based single-lead and standard 12-lead recordings was obtained at the position MP1 that corresponds to modified lead II, and a promising performance of the automated algorithm with an R-R intervals based CNN model was achieved on this lead in terms of accuracy (93.1%), sensitivity (93.1%), and specificity (93.4%). The present results suggest that the optimized patch-based ECG lead along by deep learning based algorithms may offer the possibility of providing an accurate, easy, and inexpensive clinical tool for mass screening of AF.

Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum.

Atrial Fibrillation (AF) is one of the arrhythmias that is common and serious in clinic. In this study, a novel method of AF classification with a convolutional neural network (CNN) was proposed, and particularly two cardiac rhythm features of R-R intervals and F-wave frequency spectrum were combined into the CNN for a good applicability of mobile application. Over 23 patients' ten-hours of Electrocardiogram (ECG) records were collected from the MIT-BIH database, and each of which was segmented into 10s-data fragments to train the designed CNN and evaluate the performance of the proposed method. Specifically, a total of 83,461 fragments were collected, 49,952 fragments of which are the normal fragments (type-N) and the others are the AF fragments. As results, the obtained average accuracy of the proposed method combining the two proposed features is 97.3%, which is shown a relative higher accuracy comparing with either that of the detection with the feature of R-R intervals (95.7%) or that with the feature of F-wave frequency spectrum (93.9%). Additionally, the sensitivity and the specificity of the present method are both of a high level of 97.4% and 97.2%, respectively. In conclusion, the CNN based approach by combining the R-R interval series and the F-wave frequency spectrum would be effectively to improve the performance of AF detection. Moreover, the proposed classification of AF with 10s-data fragments also could be potentially useful for a wearable real-time monitoring application for a pre-hospital screening of AF.