Cryogel wound dressings based on natural polysaccharides perfectly adhere to irregular wounds for rapid haemostasis and easy disassembly.

Skin injuries can have unexpected surfaces, leading to uneven wound surfaces and inadequate dressing contact with these irregular surfaces. This can decrease the dressing's haemostatic action and increase the healing period. This study recommends the use of sticky and flexible cryogel coverings to promote faster haemostasis and efficiently handle uneven skin wounds. Alginate cryogels have a fast haemostatic effect and shape flexibility due to their macroporous structure. The material demonstrates potent antibacterial characteristics and enhances skin adherence by adding grafted chitosan with gallic acid. In irregular defect wound models, cryogels can cling closely to uneven damage surfaces due to their amorphous nature. Furthermore, their macroporous structure allows for quick haemostasis by quickly absorbing blood and wound exudate. After giving the dressing a thorough rinse, its adhesive strength reduces and it is simple to remove without causing any damage to the wound. Cryogel demonstrated faster haemostasis than gauze in a wound model on a rat tail, indicating that it has considerable potential for use as a wound dressing in the biomedical area.

Intrinsic RNA Targeting Triggers Indiscriminate DNase Activity of CRISPR-Cas12a.

The CRISPR-Cas12a system has emerged as a powerful tool for next-generation nucleic acid-based molecular diagnostics. However, it has long been believed to be effective only on DNA targets. Here, we investigate the intrinsic RNA-enabled trans-cleavage activity of AsCas12a and LbCas12a and discover that they can be directly activated by full-size RNA targets, although LbCas12a exhibits weaker trans-cleavage activity than AsCas12a on both single-stranded DNA and RNA substrates. Remarkably, we find that the RNA-activated Cas12a possesses higher specificity in recognizing mutated target sequences compared to DNA activation. Based on these findings, we develop the "Universal Nuclease for Identification of Virus Empowered by RNA-Sensing" (UNIVERSE) assay for nucleic acid testing. We incorporate a T7 transcription step into this assay, thereby eliminating the requirement for a protospacer adjacent motif (PAM) sequence in the target. Additionally, we successfully detect multiple PAM-less targets in HIV clinical samples that are undetectable by the conventional Cas12a assay based on double-stranded DNA activation, demonstrating unrestricted target selection with the UNIVERSE assay. We further validate the clinical utility of the UNIVERSE assay by testing both HIV RNA and HPV 16 DNA in clinical samples. We envision that the intrinsic RNA targeting capability may bring a paradigm shift in Cas12a-based nucleic acid detection and further enhance the understanding of CRISPR-Cas biochemistry.

Diagnosis of Clinically Significant Portal Hypertension Using CT- and MRI-based Vascular Model.

Background Currently, the hepatic venous pressure gradient (HVPG) remains the reference standard for diagnosis of clinically significant portal hypertension (CSPH) but is limited by its invasiveness and availability. Purpose To investigate a vascular geometric model for noninvasive diagnosis of CSPH (HVPG ≥10 mm Hg) in patients with liver cirrhosis for both contrast-enhanced CT and MRI. Materials and Methods In this retrospective study, consecutive patients with liver cirrhosis who underwent HVPG measurement from August 2016 to April 2019 were included. Patients without hepatic diseases were included and marked as non-CSPH to balance the ratio of CSPH 1:1. A variety of vascular parameters were extracted from the portal vein, hepatic vein, aorta, and inferior vena cava and then entered into a vascular geometric model for identification of CSPH. Diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC). Results The model was developed and tested with retrospective data from 250 patients with liver cirrhosis and 273 patients without clinical evidence of hepatic disease at contrast-enhanced CT examination, including 213 patients with CSPH (mean age, 49 years ± 12 [SD]; 138 women) and 310 patients without CSPH (mean age, 50 years ± 9; 177 women). For external validation, an MRI data set with 224 patients with cirrhosis (mean age, 49 years ± 10; 158 women) and a CT data set with 106 patients with cirrhosis (mean age, 53 years ± 12; 71 women) were analyzed. Significant reductions in mean whole-vessel volumes were observed in the portal vein (ranging from 36.9 cm3 ± 16.0 to 29.6 cm3 ± 11.1; P < .05) and hepatic vein (ranging from 35.3 cm3 ± 21.5 to 22.4 cm3 ± 15.7; P < .05) when CSPH occurred. Similarly, the mean whole-vessel lengths were shorter in patients with CSPH (portal vein: 1.7 m ± 1.2 vs 3.0 m ± 2.4, P < .05; hepatic vein: 0.9 m ± 1.5 vs 1.8 m ± 1.5, P < .05) than in those without CSPH. The proposed vascular model performed well in the internal test set (mean AUC, 0.90 ± 0.02) and external test sets (mean AUCs, 0.84 ± 0.12 and 0.87 ± 0.11). Conclusion A contrast-enhanced CT- and MRI-based vascular model was proposed with good diagnostic consistency for hepatic venous pressure gradient measurement. ClinicalTrials.gov registration nos. NCT03138915 and NCT03766880 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roldán-Alzate and Reeder in this issue.

Single-tube one-step gel-based RT-RPA/PCR for highly sensitive molecular detection of HIV.

We developed a single-tube one-step gel-based reverse transcription-recombinase polymerase amplification (RT-RPA)/polymerase chain reaction (PCR) (termed "SOG RT-RPA/PCR") to detect the human immunodeficiency virus (HIV). To improve the assay sensitivity, the RNA template is pre-amplified by RT-RPA prior to PCR. To simplify the detection process and shorten the assay time, we embedded PCR reagents into agarose gel, constructing it to physically separate the reagents from the RT-RPA reaction solution in a single tube. Due to the thermodynamic properties of agarose, the RT-RPA reaction first occurs independently on top of the PCR gel at a low temperature (e.g., 39 °C) during the SOG RT-RPA/PCR assay. Then, the RPA amplicons directly serve as the template for the second PCR amplification reaction, which begins when the PCR agarose dissolves due to the elevated reaction temperature, eliminating the need for multiple manual operations and amplicon transfer. With our SOG RT-RPA/PCR assay, we could detect 6.3 copies of HIV RNA per test, which is a 10-fold higher sensitivity than that of standalone real-time RT-PCR and RT-RPA. In addition, due to the high amplification efficiency of RPA, the SOG RT-RPA/PCR assay shows stronger fluorescence detection signals and a shorter detection time compared to the standalone real-time RT-PCR assay. Furthermore, we detected HIV viral RNA in clinical plasma samples and validated the superior performance of our assay. Thus, the SOG RT-RPA/PCR assay offers a powerful method for simple, rapid, and highly sensitive nucleic acid-based molecular detection of infectious diseases.

CRISPR-powered Biosensing Platform for Quantitative Detection of Alpha-fetoprotein by a Personal Glucose Meter.

Alpha-fetoprotein (AFP) is an important protein biomarker of liver cancer, as its serum levels are highly correlated with the progression of disease. Conventional immunoassays for AFP detection rely on enzyme-linked immunosorbent assay analyses with expensive and bulky equipment. Here, we developed a simple, affordable, and portable CRISPR-powered personal glucose meter biosensing platform for quantitative detection of the AFP biomarker in serum samples. The biosensor takes advantage of the excellent affinity of aptamer to AFP and the collateral cleavage activity of CRISPR-Cas12a, enabling sensitive and specific CRISPR-powered protein biomarker detection. To enable point-of-care testing, we coupled invertase-catalyzed glucose production with the glucose biosensing technology to quantify AFP. Using the developed biosensing platform, we quantitatively detected AFP biomarker in spiked human serum samples with a detection sensitivity of down to 10 ng/mL. Further, we successfully applied the biosensor to detect AFP in clinical serum samples from patients with liver cancer, achieving comparable performance to the conventional assay. Therefore, this novel CRISPR-powered personal glucose meter biosensor provides a simple yet powerful alternative for detecting AFP and potentially other tumor biomarkers at the point of care.

Comparison of single-pore non-liposuction near-infrared laparoscopy with conventional open surgery for axillary sentinel lymph node biopsy in patients with early breast cancer: a single-center, small-sample retrospective study.

BACKGROUND: This study aimed to compare the effects of single-pore non-liposuction near-infrared (NIR) endoscopic surgery and traditional open surgery for axillary sentinel lymph node biopsy (SLNB) in patients with early breast cancer (EBC). METHODS: The clinical pathological data of 61 patients with EBC who underwent axillary SLNB using indocyanine green (ICG) combined with carbon nanoparticle suspension (CNS) were retrospectively collected. Thirty patients received SLNB through single-pore non-liposuction NIR endoscopic surgery (endoscopic group), and the remaining 31 received SLNB through open-incision surgery (open group). The success rate, operation time, volume of intraoperative bleeding, postoperative axillary drainage, axillary extubation time, and the occurrence of postoperative complications were compared between the groups along with the total number of sentinel lymph nodes (SLNs), luminous SLNs, stained SLNs, and the pathological positivity rate of the SLNs. RESULTS: All patients underwent SLNB with a 100% success rate. SLNB operation times of the endoscopic group were longer than those of the open group (t = 3.963, P = 0.000), and the volume of axillary drainage was inferior (t = 3.035, P = 0.004). However, there were no differences in the intraoperative bleeding volumes, axillary extubation times, and postoperative complications (P > 0.05). In the Open group, the mean number of SLNs was 5.12 ± 2.16, and the pathological positivity rate was 13.53%; in the Endoscopic group, these numbers were 4.89 ± 1.73 and 12.39%. The mean number of SLNs detected (t = 0.458, P = 0.649) and the pathological positivity rates (χ2 = 0.058, P = 0.810) did not differ between the two groups. All 61 patients were followed for a median of 14.6 months. There were no local recurrences or distant metastases. CONCLUSIONS: Our single-center results reveal that single-hole non-liposuction NIR endoscopic axillary SLNB is not inferior to open SLNB and may be an appropriate option for patients with early breast cancer who desire breast preservation with fewer incisions. TRIAL REGISTRATION: This retrospective study was "retrospectively registered" at the Sixth Affiliated Hospital of South China University of Technology (no. 2020105) and in National Medical Research Registration and Archival Information System ( https://www.medicalresearch.org.cn , number: MR-44-21-004727).

Testing facility location and dynamic capacity planning for pandemics with demand uncertainty.

The outbreak of coronavirus disease 2019 (COVID-19) has seriously affected the whole world, and epidemic research has attracted increasing amounts of scholarly attention. Critical facilities such as warehouses to store emergency supplies and testing or vaccination sites could help to control the spread of COVID-19. This paper focuses on how to locate the testing facilities to satisfy the varying demand, i.e., test kits, caused by pandemics. We propose a two-phase optimization framework to locate facilities and adjust capacity during large-scale emergencies. During the first phase, the initial prepositioning strategies are determined to meet predetermined fill-rate requirements using the sample average approximation formulation. We develop an online convex optimization-based Lagrangian relaxation approach to solve the problem. Specifically, to overcome the difficulty that all scenarios should be addressed simultaneously in each iteration, we adopt an online gradient descent algorithm, in which a near-optimal approximation for a given Lagrangian dual multiplier is constructed. During the second phase, the capacity to deal with varying demand is adjusted dynamically. To overcome the inaccuracy of long-term prediction, we design a dynamic allocation policy and adaptive dynamic allocation policy to adjust the policy to meet the varying demand with only one day's prediction. A comprehensive case study with the threat of COVID-19 is conducted. Numerical results have verified that the proposed two-phase framework is effective in meeting the varying demand caused by pandemics. Specifically, our adaptive policy can achieve a solution with only a 3.3% gap from the optimal solution with perfect information.

[Detection method of early heart valve diseases based on heart sound features].

Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.

Monolithic, 3D-printed lab-on-disc platform for multiplexed molecular detection of SARS-CoV-2.

Multiplexed detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rather than detection targeting a single gene is crucial to ensure more accurate coronavirus disease 2019 (COVID-19) diagnostics. Here, we develop a monolithic, 3D-printed, lab-on-disc platform for multiplexed molecular detection of SARS-CoV-2. The centrifugal lab-on-disc is fabricated in one step using simple 3D printing technology, circumventing the need for aligning and binding multiple layers. By combining isothermal amplification technology, this lab-on-disc platform is capable of simultaneously detecting the nucleoprotein and envelope genes of SARS-CoV-2 as well as an internal control of the human POP7 gene. Within a 50-minute incubation period, 100 copies SARS-CoV-2 RNA can be detected through visual observation according to color and fluorescence changes in the disc. Further, we clinically validated the lab-on-disc platform by testing 20 nasopharyngeal swab samples and demonstrated a sensitivity of 100% and an accuracy of 95%. Therefore, the monolithic, 3D-printed, lab-on-disc platform provides simple, rapid, disposable, sensitive, reliable, and multiplexed molecular detection of SARS-CoV-2, holding promise for COVID-19 diagnostics at the point of care.

An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing.

With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.

An integrated self-powered 3D printed sample concentrator for highly sensitive molecular detection of HIV in whole blood at the point of care.

Rapid and efficient biological sample preparation and pretreatment are crucial for highly sensitive, reliable and reproducible molecular detection of infectious diseases. Herein, we report a self-powered, integrated sample concentrator (SPISC) for rapid plasma separation, pathogen lysis, nucleic acid trapping and enrichment at the point of care. The proposed sample concentrator uses a combination of gravitational sedimentation of blood cells and capillary force for rapid, self-powered plasma separation. The pathogens (e.g., HIV virus) in separated plasma were directly lysed and pathogen nucleic acid was enriched by an integrated, flow-through FTA® membrane in the concentrator, enabling highly efficient nucleic acid preparation. The FTA® membrane of the SPISC is easy to store and transport at room temperature without need for uninterrupted cold chain, which is crucial for point of care sampling in resource-limited settings. The platform has been successfully applied to detect HIV virus in blood samples. Our experiments show that the sample concentrator can achieve a plasma separation efficiency as high as 95% and a detection sensitivity as low as 10 copies per 200 µL blood (∼100 copies per mL plasma) with variability less than 7%. The sample concentrator described is fully compatible with downstream nucleic acid detection and has great potential for early diagnostics, monitoring and management of infectious diseases at the point of care.

Multiplexed colorimetric detection of SARS-CoV-2 and other pathogens in wastewater on a 3D printed integrated microfluidic chip.

Severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic has become a global public health emergency. The detection of SARS-CoV-2 and human enteric pathogens in wastewater can provide an early warning of disease outbreak. Herein, a sensitive, multiplexed, colorimetric detection (termed "SMCD") method was established for pathogen detection in wastewater samples. The SMCD method integrated on-chip nucleic acid extraction, two-stage isothermal amplification, and colorimetric detection on a 3D printed microfluidic chip. The colorimetric signal during nucleic acid amplification was recorded in real-time and analyzed by a programmed smartphone without the need for complicated equipment. By combining two-stage isothermal amplification assay into the integrated microfluidic platform, we detected SARS-CoV-2 and human enteric pathogens with sensitivities of 100 genome equivalent (GE)/mL and 500 colony-forming units (CFU)/mL, respectively, in wastewater within one hour. Additionally, we realized smart, connected, on-site detection with a reporting framework embedded in a portable detection platform, which exhibited potential for rapid spatiotemporal epidemiologic data collection regarding the environmental dynamics, transmission, and persistence of infectious diseases.

Circular RNA expression profile in transgenic diabetic mouse kidneys.

BACKGROUND: Diabetic nephropathy is one of the most important complications in patients with diabetes. The etiology and pathogenesis of diabetic nephropathy remain unclear. Several studies have indicated that circular RNAs (circRNAs) play crucial regulatory roles in numerous human diseases and normal physiology; however, to date, no study has focused on the comprehensive expression profile of circRNAs in the kidneys of diabetic mice. Therefore, we aimed to identify differentially expressed circRNAs in diabetic mouse kidneys to explore the possible roles of dysregulated circRNAs in diabetic nephropathy development. RESULTS: Diabetic BKS-Leprem2Cd479/Nju (BKS-DB/Nju) mice and their nondiabetic wild-type littermates of C57BL/KsJ wild-type (WT) mice were used as experimental animals. Among all circRNAs identified by high-throughput RNA sequencing, four circRNAs were upregulated and ten were downregulated in diabetic mouse kidneys compared to those in nondiabetic mouse kidneys. After verification using quantitative reverse transcriptase polymerase chain reaction assays, we found that circR_1084, circR_182, circR_4, circR_50, circR_596, circR_897, and circR_203 were downregulated, whereas circR_627, circR_628, circR_735, and circR_801 were upregulated in the renal tissues of 8- and 16-week-old BKS-DB/Nju mice compared to those in WT mice. CONCLUSION: We studied the circRNA expression profile in the kidneys of diabetic mice. Differentially expressed circRNAs may be useful as candidate biomarkers for diabetic nephropathy. Collectively, our results provide a novel theoretical basis for further investigation of the regulatory roles of circRNA in the etiology and pathogenesis of diabetic nephropathy.

Exponential-Distance Weights for Reducing Grid-like Artifacts in Patch-Based Medical Image Registration.

Patch-based medical image registration has been well explored in recent decades. However, the patch fusion process can generate grid-like artifacts along the edge of patches for the following two reasons: firstly, in order to ensure the same size of input and output, zero-padding is used, which causes uncertainty in the edges of the output feature map during the feature extraction process; secondly, the sliding window extraction patch with different strides will result in different degrees of grid-like artifacts. In this paper, we propose an exponential-distance-weighted (EDW) method to remove grid-like artifacts. To consider the uncertainty of predictions near patch edges, we used an exponential function to convert the distance from the point in the overlapping regions to the center point of the patch into a weighting coefficient. This gave lower weights to areas near the patch edges, to decrease the uncertainty predictions. Finally, the dense displacement field was obtained by this EDW weighting method. We used the OASIS-3 dataset to evaluate the performance of our method. The experimental results show that the proposed EDW patch fusion method removed grid-like artifacts and improved the dice similarity coefficient superior to those of several state-of-the-art methods. The proposed fusion method can be used together with any patch-based registration model.

Short-Term Effect of Percutaneous Coronary Intervention on Heart Rate Variability in Patients with Coronary Artery Disease.

Myocardial ischemia in patients with coronary artery disease (CAD) leads to imbalanced autonomic control that increases the risk of morbidity and mortality. To systematically examine how autonomic function responds to percutaneous coronary intervention (PCI) treatment, we analyzed data of 27 CAD patients who had admitted for PCI in this pilot study. For each patient, five-minute resting electrocardiogram (ECG) signals were collected before and after the PCI procedure. The time intervals between ECG collection and PCI were both within 24 h. To assess autonomic function, normal sinus RR intervals were extracted and were analyzed quantitatively using traditional linear time- and frequency-domain measures [i.e., standard deviation of the normal-normal intervals (SDNN), the root mean square of successive differences (RMSSD), powers of low frequency (LF) and high frequency (HF) components, LF/HF] and nonlinear entropy measures [i.e., sample entropy (SampEn), distribution entropy (DistEn), and conditional entropy (CE)], as well as graphical metrics derived from Poincaré plot [i.e., Porta's index (PI), Guzik's index (GI), slope index (SI) and area index (AI)]. Results showed that after PCI, AI and PI decreased significantly (p < 0.002 and 0.015, respectively) with effect sizes of 0.88 and 0.70 as measured by Cohen's d static. These changes were independent of sex. The results suggest that graphical AI and PI metrics derived from Poincaré plot of short-term ECG may be potential for sensing the beneficial effect of PCI on cardiovascular autonomic control. Further studies with bigger sample sizes are warranted to verify these observations.

Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals.

Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.

Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy.

Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)-systolic time interval (STI), RRI-diastolic time interval (DTI), HR-corrected QT interval (QTcI)-STI, QTcI-DTI, Tpeak-Tend interval (TpeI)-STI, TpeI-DTI, Tpe/QT interval (Tpe/QTI)-STI, and Tpe/QTI-DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD-mild-to-moderate CHD group, severe CHD-chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD-CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.

Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis.

BACKGROUND & AIMS: Noninvasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We investigated the ability of deep convolutional neural network (CNN) analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH. METHODS: We collected liver and spleen images from patients who underwent contrast-enhanced CT or MR analysis within 14 days of transjugular catheterization for hepatic venous pressure gradient measurement. The CT cohort comprised participants with cirrhosis in the CHESS1701 study, performed at 4 university hospitals in China from August 2016 through September 2017. The MR cohort comprised participants with cirrhosis in the CHESS1802 study, performed at 8 university hospitals in China and 1 in Turkey from December 2018 through April 2019. Patients with CSPH were identified as those with a hepatic venous pressure gradient of 10 mm Hg or higher. In total, we analyzed 10,014 liver images and 899 spleen images collected from 679 participants who underwent CT analysis, and 45,554 liver and spleen images from 271 participants who underwent MR analysis. For each cohort, participants were shuffled and then sampled randomly and equiprobably for 6 times into training, validation, and test data sets (ratio, 3:1:1). Therefore, a total of 6 deep CNN models for each cohort were developed for identification of CSPH. RESULTS: The CT-based CNN analysis identified patients with CSPH with an area under the receiver operating characteristic curve (AUC) value of 0.998 in the training set (95% CI, 0.996-1.000), an AUC of 0.912 in the validation set (95% CI, 0.854-0.971), and an AUC of 0.933 (95% CI, 0.883-0.984) in the test data sets. The MR-based CNN analysis identified patients with CSPH with an AUC of 1.000 in the training set (95% CI, 0.999-1.000), an AUC of 0.924 in the validation set (95% CI, 0.833-1.000), and an AUC of 0.940 in the test data set (95% CI, 0.880-0.999). When the model development procedures were repeated 6 times, AUC values for all CNN analyses were 0.888 or greater, with no significant differences between rounds (P > .05). CONCLUSIONS: We developed a deep CNN to analyze CT or MR images of liver and spleen from patients with cirrhosis that identifies patients with CSPH with an AUC value of 0.9. This provides a noninvasive and rapid method for detection of CSPH (ClincialTrials.gov numbers: NCT03138915 and NCT03766880).

Dynamic Aqueous Multiphase Reaction System for One-Pot CRISPR-Cas12a-Based Ultrasensitive and Quantitative Molecular Diagnosis.

Recently, CRISPR-Cas technology has opened a new era of nucleic acid-based molecular diagnostics. However, current CRISPR-Cas-based nucleic acid biosensing has a lack of the quantitative detection ability and typically requires separate manual operations. Herein, we reported a dynamic aqueous multiphase reaction (DAMR) system for simple, sensitive and quantitative one-pot CRISPR-Cas12a based molecular diagnosis by taking advantage of density difference of sucrose concentration. In the DAMR system, recombinase polymerase amplification (RPA) and CRISPR-Cas12a derived fluorescent detection occurred in spatially separated but connected aqueous phases. Our DAMR system was utilized to quantitatively detect human papillomavirus (HPV) 16 and 18 DNAs with sensitivities of 10 and 100 copies within less than 1 h. Multiplex detection of HPV16/18 in clinical human swab samples were successfully achieved in the DAMR system using 3D-printed microfluidic device. Furthermore, we demonstrated that target DNA in real human plasma samples can be directly amplified and detected in the DAMR system without complicated sample pretreatment. As demonstrated, the DAMR system has shown great potential for development of next-generation point-of-care molecular diagnostics.

Annotation of genes involved in high level of dihydromyricetin production in vine tea (Ampelopsis grossedentata) by transcriptome analysis.

BACKGROUND: Leaves of the medicinal plant Ampelopsis grossedentata, which is commonly known as vine tea, are used widely in the traditional Chinese beverage in southwest China. The leaves contain a large amount of dihydromyricetin, a compound with various biological activities. However, the transcript profiles involved in its biosynthetic pathway in this plant are unknown. RESULTS: We conducted a transcriptome analysis of both young and old leaves of the vine tea plant using Illumina sequencing. Of the transcriptome datasets, a total of 52.47 million and 47.25 million clean reads were obtained from young and old leaves, respectively. Among 471,658 transcripts and 177,422 genes generated, 7768 differentially expressed genes were identified in leaves at these two stages of development. The phenylpropanoid biosynthetic pathway of vine tea was investigated according to the transcriptome profiling analysis. Most of the genes encoding phenylpropanoid biosynthesis enzymes were identified and found to be differentially expressed in different tissues and leaf stages of vine tea and also greatly contributed to the biosynthesis of dihydromyricetin in vine tea. CONCLUSIONS: To the best of our knowledge, this is the first formal study to explore the transcriptome of A. grossedentata. The study provides an insight into the expression patterns and differential distribution of genes related to dihydromyricetin biosynthesis in vine tea. The information may pave the way to metabolically engineering plants with higher flavonoid content.