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Abstract Cas12 and Cas13 are extensively utilized in molecular diagnostics for their trans-cleavage activities, yet their activation characteristics remain partially understood. Here, we conduct an in-depth investigation of Cas12a, Cas12f1, and Cas13a, uncovering the characteristics of their trans-DNase and trans-RNase activities with noncanonical activators. Our findings reveal that DNA can serve as a direct target for CRISPR-Cas13a, markedly increasing the detection sensitivity for single-base mismatches. Moreover, the trans-cleavage activities of Cas12a and Cas13a can be activated by diverse RNA:DNA and RNA:RNA duplexes, respectively, indicating that the presence of stem–loop structures in crRNAs is not essential for their activation. Notably, Cas12f1, unlike Cas12a, exhibits intrinsic RNase activity independently of activation. Leveraging these insights, we have improved the accuracy of a dual-gene target detection approach that employs the CRISPR-Cas12f1 and Cas13a systems. Our research advances the understanding of the noncanonical activation characteristics of Cas12 and Cas13a, contributing to the field of CRISPR-based diagnostics.

Abstract Background Perioperative hypotension is frequently observed following the initiation of general anesthesia administration, often associated with adverse outcomes. This study assessed the effect of subclavian vein (SCV) diameter combined with perioperative fluid therapy on preventing post-induction hypotension (PIH) in patients with lower ASA status. Methods This two-part study included patients aged 18 to 65 years, classified as ASA physical status I or II, and scheduled for elective surgery. The first part (Part I) included 146 adult patients, where maximum SCV diameter (dSCVmax), minimum SCV diameter (dSCVmin), SCV collapsibility index (SCVCI) and SCV variability (SCVvariability) assessed using ultrasound. PIH was determined by reduction in mean arterial pressure (MAP) exceeding 30% from baseline measurement or any instance of MAP

Abstract Purpose Acute postoperative hypertension (APH) is a common complication during the anesthesia recovery period that can lead to adverse outcomes, including cardiovascular and cerebrovascular accidents. Identification of risk factors for APH will allow for preoperative optimization and appropriate perioperative management. This study aimed to identify risk factors for APH. Patients and methods In this retrospective single-center study, 1,178 cases were included. Data was entered by two investigators, and consistency analysis was performed by another. Patients were divided into APH and non-APH groups. A predictive model was built by multivariate stepwise logistic regression. The predictive ability of the logistic regression model was tested by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Hosmer and Lemeshow goodness-of-fit (GOF) test was performed to reflect the goodness of fit of the model. Calibration curve was created to represent the relationship between predicted risk and observed frequency. Sensitivity analysis was performed to evaluate the robustness of the results. Results Multivariate logistic regression analysis showed that age over 65 years (OR = 3.07, 95% CI: 2.14 ~ 4.42, P

Cervical cancer is a major health concern worldwide, highlighting the urgent need for better early detection methods to improve outcomes for patients. In this study, we present a novel digital pathology classification approach that combines Low-Rank Adaptation (LoRA) with the Vision Transformer (ViT) model. This method is aimed at making cervix type classification more efficient through a deep learning classifier that does not require as much data. The key innovation is the use of LoRA, which allows for the effective training of the model with smaller datasets, making the most of the ability of ViT to represent visual information. This approach performs better than traditional Convolutional Neural Network (CNN) models, including Residual Networks (ResNets), especially when it comes to performance and the ability to generalize in situations where data are limited. Through thorough experiments and analysis on various dataset sizes, we found that our more streamlined classifier is highly accurate in spotting various cervical anomalies across several cases. This work advances the development of sophisticated computer-aided diagnostic systems, facilitating more rapid and accurate detection of cervical cancer, thereby significantly enhancing patient care outcomes.

Objective To test agreement and interchangeability between distal (dRA) and forearm radial arterial (RA) pressures (AP) during general anesthesia (GA) for prone spinal surgery. Methods This prospective observational study involved 40 patients scheduled for GA spinal surgery. The right dRA and left forearm RA were cannulated in all patients to continuously measure invasive blood pressures (IBP). We compared the agreement and trending ability of systolic AP (SAP), diastolic AP (DAP), and mean AP (MAP) at each site 15 minutes after tracheal intubation, start of surgery, 30 and 60 minutes after the start of surgery, and after skin suturing. Results Paired BP values (n = 184) (37 cases) were analyzed. The bias (standard deviation), limits of agreement, and percentage error were: SAP: 0.19 (3.03), −5.75 to 6.12, and 5.04%; DAP: −0.06 (1.75), −3.50 to 3.38, and 5.10%; and MAP: 0.08 (1.52), −2.90 to 3.05, and 3.54%, respectively. The linear regression coefficients of determination were 0.981, 0.982, and 0.988 for SAPs, DAPs, and MAPs, respectively; four-quadrant plot concordance rates were 95.11%, 92.03%, and 92.66%, respectively. Conclusion All arterial BPs showed good agreement and trending capabilities for both the dRA and RA. The dRA may be substituted for the RA in IBP monitoring.

Abstract Background Individuals affected by autonomic dysfunction are at a higher risk of developing hypotension following anesthesia induction. Dynamic pupillometry has previously been employed as a means of assessing autonomic function. This prospective observational study was developed to determine whether pupillary light reflex (PLR) parameters can reliably predict post-induction hypotension (PIH). Methods This study enrolled patients with lower ASA status (I-II) undergoing elective surgery. PLR recordings for these patients prior to anesthesia induction were made with an infrared pupil camcorder, with a computer being used to assess Average Constriction Velocity (ACV), Maximum Constriction Velocity (MCV), and Constriction Ratio (CR). PIH was defined by a > 30% reduction in mean arterial pressure (MAP) or any MAP recording

Abstract Background Salivary glands produce saliva that play essential roles in digestion and oral health. Derivation of salivary gland organoids from pluripotent stem cells (PSCs) provides a powerful platform to model the organogenesis processes during development. A few studies attempted to differentiate PSCs into salivary gland organoids. However, none of them could recapitulate the morphogenesis of the embryonic salivary glands, and most of the protocols involved complicated manufacturing processes. Methods To generate PSC-derived salivary gland placodes, the mouse embryonic stem cells were first differentiated into oral ectoderm by treatment with BMP4 on day 3. Retinoic acid and bFGF were then applied to the cultures from day 4 to day 6, followed by a 4-day treatment of FGF10. The PSC-derived salivary gland placodes on day 10 were transplanted to kidney capsules to determine the regenerative potential. Quantitative reverse transcriptase–polymerase chain reaction, immunofluorescence, and RNA-sequencing were performed to identify the PSC-derived SG placodes. Results We showed that step-wise treatment of retinoic acid and FGF10 promoted the differentiation of PSCs into salivary gland placodes, which can recapitulate the early morphogenetic events of their fetal counterparts, including the thickening, invagination, and then formed initial buds. The PSC-derived salivary gland placodes also differentiated into developing duct structures and could develop to striated and excretory ducts when transplanted in vivo. Conclusions The present study provided an easy and safe method to generate salivary gland placodes from PSCs, which offered possibilities for studying salivary gland development in vitro and developing new cell therapies.

Abstract Background The novel distal radial artery (dRA) approach is a popular arterial access route for interventional cardiology and neurointerventions. We explored the dRA as an alternative site to the classic forearm radial artery (RA) for perioperative blood pressure monitoring. We hypothesized that dRA catheterization is noninferior to RA for the first attempt success rate. Methods This was a single-center, prospective, randomized controlled, noninferiority study. Adult patients who underwent elective surgery at the Jinling Hospital from May 2021 to August 2021 were enrolled. The primary endpoint was to test the noninferiority of the first attempt success rate between the groups. Secondary endpoints included anatomical characteristics, catheterization time, arterial posterior wall puncture rate, postoperative compression time, dampened arterial pressure waveforms, and complications. Results Totally, 161 patients who received either dRA (n = 81) or RA (n = 80) catheterization were analyzed. The first attempt success rates were 87.7 and 91.3% in the dRA and RA groups, respectively, with a mean difference of − 3.6% (95% CI, − 13.1 to 5.9%). The dRA diameter and cross-sectional area were significantly smaller than those of the RA (P

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most laboratories manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID (Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine. The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that could replace manual quantification for mouse scratching behavior in different itch models and for drug screening.

Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

Reconnaissance unmanned aerial vehicles are specifically designed to estimate parameters and process intercepted signals for the purpose of identifying and locating radars. However, distinguishing quasi-simultaneous arrival signals (QSAS) has become increasingly challenging in complex electromagnetic environments. In order to address the problem, a framework for self-supervised deep representation learning is proposed. The framework consists of two phases: (1) pre-train an autoencoder. For learning the unlabeled QSAS representation, the ConvNeXt V2 is trained to extract features from masked time–frequency images and reconstruct the corresponding signal in both time and frequency domains; (2) transfer the learned knowledge. For downstream tasks, encoder layers are frozen, the linear layer is fine-tuned to classify QSAS under few-shot conditions. Experimental results demonstrate that the proposed algorithm can achieve an average recognition accuracy of over 81% with the signal-to-noise ratio in the range of −16∼16 dB. Compared to existing CNN-based and Transformer-based neural networks, the proposed algorithm shortens the time of testing by about 11× and improves accuracy by up to 21.95%.

Mode recognition is a basic task to interpret the behavior of multi-functional radar. The existing methods need to train complex and huge neural networks to improve the recognition ability, and it is difficult to deal with the mismatch between the training set and the test set. In this paper, a learning framework based on residual neural network (ResNet) and support vector machine (SVM) is designed, to solve the problem of mode recognition for non-specific radar, called multi-source joint recognition framework (MSJR). The key idea of the framework is to embed the prior knowledge of radar mode into the machine learning model, and combine the manual intervention and automatic extraction of features. The model can purposefully learn the feature representation of the signal on the working mode, which weakens the impact brought by the mismatch between training and test data. In order to solve the problem of difficult recognition under signal defect conditions, a two-stage cascade training method is designed, to give full play to the data representation ability of ResNet and the high-dimensional feature classification ability of SVM. Experiments show that the average recognition rate of the proposed model, with embedded radar knowledge, is improved by 33.7% compared with the purely data-driven model. Compared with other similar state-of-the-art reported models, such as AlexNet, VGGNet, LeNet, ResNet, and ConvNet, the recognition rate is increased by 12%. Under the condition of 0–35% leaky pulses in the independent test set, MSJR still has a recognition rate of more than 90%, which also proves its effectiveness and robustness in the recognition of unknown signals with similar semantic characteristics.

Abstract Background Organ replacement regenerative therapy based on human adult stem cells may be effective for salivary gland hypofunction. However, the generated tissues are immature because the signaling factors that induce the differentiation of human salivary gland stem cells into salivary glands are unknown. Methods Isolated human submandibular gland stem/progenitor cells (hSMGepiS/PCs) were characterized and three-dimensionally (3D) cultured to generate organoids and further induced by fibroblast growth factor 10 (FGF10) in vitro. The induced spheres alone or in combination with embryonic day 12.5 (E12.5) mouse salivary gland mesenchyme were transplanted into the renal capsules of nude mice to assess their development in vivo. Immunofluorescence, quantitative reverse transcriptase-polymerase chain reaction, calcium release analysis, western blotting, hematoxylin–eosin staining, Alcian blue–periodic acid-Schiff staining, and Masson’s trichrome staining were performed to assess the structure and function of generated tissues in vitro and in vivo. Results The isolated hSMGepiS/PCs could be long-term cultured with a stable genome. The organoids treated with FGF10 [FGF10 (+) group] exhibited higher expression of salivary gland–specific markers; showed spatial arrangement of AQP5+, K19+, and SMA+ cells; and were more sensitive to the stimulation by neurotransmitters than untreated organoids [FGF10 (−) group]. After heterotopic transplantation, the induced cell spheres combined with mouse embryonic salivary gland mesenchyme showed characteristics of mature salivary glands, including a natural morphology and saliva secretion. Conclusion FGF10 promoted the development of the hSMGepiS/PC-derived salivary gland organoids by the expression of differentiation markers, structure formation, and response to neurotransmitters in vitro. Moreover, the hSMGepiS/PCs responded to the niche in mouse embryonic mesenchyme and further differentiated into salivary gland tissues with mature characteristics. Our study provides a foundation for the regenerative therapy of salivary gland diseases.

Copy number changes play an important role in the development of cancer and are commonly associated with changes in gene expression. Persistence curves, such as Betti curves, have been used to detect copy number changes; however, it is known these curves are unstable with respect to small perturbations in the data. We address the stability of lifespan and Betti curves by providing bounds on the distance between persistence curves of Vietoris–Rips filtrations built on data and slightly perturbed data in terms of the bottleneck distance. Next, we perform simulations to compare the predictive ability of Betti curves, lifespan curves (conditionally stable) and stable persistent landscapes to detect copy number aberrations. We use these methods to identify significant chromosome regions associated with the four major molecular subtypes of breast cancer: Luminal A, Luminal B, Basal and HER2 positive. Identified segments are then used as predictor variables to build machine learning models which classify patients as one of the four subtypes. We find that no single persistence curve outperforms the others and instead suggest a complementary approach using a suite of persistence curves. In this study, we identified new cytobands associated with three of the subtypes: 1q21.1-q25.2, 2p23.2-p16.3, 23q26.2-q28 with the Basal subtype, 8p22-p11.1 with Luminal B and 2q12.1-q21.1 and 5p14.3-p12 with Luminal A. These segments are validated by the TCGA BRCA cohort dataset except for those found for Luminal A.

Coronary vessel development is a highly coordinated process during heart formation. Abnormal development and dysfunction of the coronary network are contributory factors in the majority of heart disease. Understanding the molecular mechanisms that regulate coronary vessel formation is crucial for preventing and treating the disease. We report a zebrafish gene-trap vinculin b (vclb) mutant that displays abnormal coronary vessel development among multiple cardiac defects. The mutant shows overproliferation of epicardium-derived cells and disorganization of coronary vessels, and they eventually die off at juvenile stages. Mechanistically, Vclb deficiency results in the release of another cytoskeletal protein, paxillin, from the Vclb complex and the upregulation of ERK and FAK phosphorylation in epicardium and endocardium, causing disorganization of endothelial cells and pericytes during coronary vessel development. By contrast, cardiac muscle development is relatively normal, probably owing to redundancy with Vcla, a vinculin paralog that is expressed in the myocardium but not epicardium. Together, our results reveal a previously unappreciated function of vinculin in epicardium and endocardium and reinforce the notion that well-balanced FAK activity is essential for coronary vessel development. [ABSTRACT FROM AUTHOR]

In the brain, AMPA-type glutamate receptors are major postsynaptic receptors at excitatory synapses that mediate fast neurotransmission and synaptic plasticity. α/β-Hydrolase domain-containing 6 (ABHD6), a monoacylglycerol lipase, was previously found to be a component of AMPA receptor macromolecular complexes, but its physiological significance in the function of AMPA receptors (AMPARs) has remained unclear. The present study shows that overexpression of ABHD6 in neurons drastically reduced excitatory neurotransmission mediated by AMPA but not by NMDA receptors at excitatory synapses. Inactivation of ABHD6 expression in neurons by either CRISPR/Cas9 or shRNA knockdown methods significantly increased excitatory neurotransmission at excitatory synapses. Interestingly, overexpression of ABHD6 reduced glutamate-induced currents and the surface expression of GluA1 in HEK293T cells expressing GluA1 and stargazin, suggesting a direct functional interaction between these two proteins. The C-terminal tail of GluA1 was required for the binding between of ABHD6 and GluA1. Mutagenesis analysis revealed a GFCLIPQ sequence in the GluA1 C terminus that was essential for the inhibitory effect of ABHD6. The hydrolase activity of ABHD6 was not required for the effects of ABHD6 on AMPAR function in either neurons or transfected HEK293T cells. Thus, these findings reveal a novel and unexpected mechanism governing AMPAR trafficking at synapses through ABHD6. [ABSTRACT FROM AUTHOR]

During inflammation, the proper inflammatory infiltration of neutrophils is crucial for the host to fight against infections and remove damaged cells and detrimental substances. IL-1β and NADPH oxidase-mediated reactive oxygen species (ROS) have been implicated to play important roles in this process. However, the cellular and molecular basis underlying the actions of IL-1β and ROS and their relationship during inflammatory response remains undefined. In this study, we use the zebrafish model to investigate these issues. We find that, similar to that of NADPH oxidase-mediated ROS signaling, the Il-1β-Myd88 pathway is required for the recruitment of neutrophils, but not macrophages, to the injury-induced inflammatory site, whereas it is dispensable for bacterial-induced inflammation. Interestingly, the Il-1β-Myd88 pathway is independent of NADPH oxidase-mediated ROS signaling and critical for the directional migration, but not the basal random movement, of neutrophils. In contrast, the NADPH oxidase-mediated ROS signaling is required for both basal random movement and directional migration of neutrophils. We further document that ectopic expression of Il-1β in zebrafish induces an inflammatory disorder, which can be suppressed by anti-inflammatory treatment. Our findings reveal that the Il-1β-Myd88 axis and NADPH oxidase-mediated ROS signaling are two independent pathways that differentially regulate neutrophil migration during sterile inflammation. In addition, Il-1β overexpressing Tg(hsp70:mil-1β_eGFP;lyz:DsRed2)hkz10t;nz50 transgenic zebrafish provides a useful animal model for the study of chronic inflammatory disorder and for anti-inflammatory drug discovery. [ABSTRACT FROM AUTHOR]