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The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicles from the shoot in the image. Eventually, with these obtained i-traits, the yield could be well predicted, and the R2 was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.

Ventricular arrhythmogenesis is a key cause of sudden cardiac death following myocardial infarction (MI). Accumulating data show that ischemia, sympathetic activation, and inflammation contribute to arrhythmogenesis. However, the role and mechanisms of abnormal mechanical stress in ventricular arrhythmia following MI remain undefined. We aimed to examine the impact of increased mechanical stress and identify the role of the key sensor Piezo1 in ventricular arrhythmogenesis in MI. Concomitant with increased ventricular pressure, Piezo1, as a newly recognized mechano-sensitive cation channel, was the most up-regulated mechanosensor in the myocardium of patients with advanced heart failure. Piezo1 was mainly located at the intercalated discs and T-tubules of cardiomyocytes, which are responsible for intracellular calcium homeostasis and intercellular communication. Cardiomyocyte-conditional Piezo1 knockout mice (Piezo1Cko) exhibited preserved cardiac function after MI. Piezo1Cko mice also displayed a dramatically decreased mortality in response to the programmed electrical stimulation after MI with a markedly reduced incidence of ventricular tachycardia. In contrast, activation of Piezo1 in mouse myocardium increased the electrical instability as indicated by prolonged QT interval and sagging ST segment. Mechanistically, Piezo1 impaired intracellular calcium cycling dynamics by mediating the intracellular Ca2+ overload and increasing the activation of Ca2+-modulated signaling, CaMKII, and calpain, which led to the enhancement of phosphorylation of RyR2 and further increment of Ca2+ leaking, finally provoking cardiac arrhythmias. Furthermore, in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), Piezo1 activation remarkably triggered cellular arrhythmogenic remodeling by significantly shortening the duration of the action potential, inducing early afterdepolarization, and enhancing triggered activity.This study uncovered a proarrhythmic role of Piezo1 during cardiac remodeling, which is achieved by regulating Ca2+ handling, implying a promising therapeutic target in sudden cardiac death and heart failure.

Current Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: (1) a bar plot to show the predicted probabilities for each CPT code, (2) an interpretation plot showing how each word in the report combines to form the overall prediction, and (3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of re-assigned codes through facilitating rapid review of false-positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue.

Perivascular adipose-derived stem cells (PV-ADSCs) could differentiate into smooth muscle cells (SMCs), participating in vascular remodeling. However, its underlying mechanism is not well explored. Our previous single-cell RNA-sequencing dataset identified a unique expression of matrix Gla protein (MGP) in PV-ADSCs compared with subcutaneous ADSCs. MGP involves in regulating SMC behaviors in vascular calcification and atherosclerosis. In this study, we investigated MGP’s role in PV-ADSCs differentiation toward SMCs in vitro and in vascular remodeling in vivo . PV-ADSCs were isolated from perivascular regions of mouse aortas. Quantitative reverse transcription–polymerase chain reaction (qRT-PCR), Western blot, and immunofluorescence confirmed higher MGP expression in PV-ADSCs. The MGP secretion increased along PV-ADSCs differentiation toward SMCs in response to transforming growth factor–beta 1 (TGF-β1). Lentivirus knockdown of MGP markedly promoted the bone morphogenetic protein 2 (BMP2) expression and phosphorylation of SMAD1/5/8 in PV-ADSCs, subsequently inhibiting its differentiation toward SMCs. Such inhibition could be partially reversed by further application of BMP2 inhibitors. On the contrary, exogenous MGP inhibited BMP2 expression and SMAD1/5/8 phosphorylation in PV-ADSCs, thereby promoting its differentiation toward SMCs. Transplantation of cultured PV-ADSCs, which was pretreated by MGP knockdown, in mouse femoral artery guide-wire injury model significantly alleviated neointimal hyperplasia. In conclusion, MGP promoted the differentiation of PV-ADSCs toward SMCs through BMP2/SMAD-mediated signaling pathway. This study offers a supplement to the society of perivascular tissues and PV-ADSCs.

BackgroundPerivascular adipose-derived stem cells (PVASCs) can contribute to vascular remodeling, which are also capable of differentiating into multiple cell lineages. The present study aims to investigate the mechanism of PVASC differentiation toward smooth muscle cells (SMCs) and endothelial cells (ECs) as well as its function in neointimal hyperplasia.MethodsSingle-cell sequencing and bulk mRNA sequencing were applied for searching key genes in PVASC regarding its role in vascular remodeling. PVASCs were induced to differentiate toward SMCs and ECs in vitro, which was quantitatively evaluated using immunofluorescence, quantitative real-time PCR (QPCR), and Western blot. Lentivirus transfections were performed in PVASCs to knock down or overexpress TBX20. In vivo, PVASCs transfected with lentivirus were transplanted around the guidewire injured femoral artery. Hematoxylin–eosin (H&E) staining was performed to examine their effects on neointimal hyperplasia.ResultsBulk mRNA sequencing and single-cell sequencing revealed a unique expression of TBX20 in PVASCs. TBX20 expression markedly decreased during smooth muscle differentiation while it increased during endothelial differentiation of PVASCs. TBX20 knockdown resulted in the upregulation of SMC-specific marker expression and activated Smad2/3 signaling, while inhibiting endothelial differentiation. In contrast, TBX20 overexpression repressed the differentiation of PVASCs toward smooth muscle cells but promoted endothelial differentiation in vitro. Transplantation of PVASCs transfected with TBX20 overexpression lentivirus inhibited neointimal hyperplasia in a murine femoral artery guidewire injury model. On the contrary, neointimal hyperplasia significantly increased in the TBX20 knockdown group.ConclusionA subpopulation of PVASCs uniquely expressed TBX20. TBX20 could regulate SMC and EC differentiation of PVASCs in vitro. Transplantation of PVASCs after vascular injury suggested that PVASCs participated in neointimal hyperplasia via TBX20.

Current Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: (1) a bar plot to show the predicted probabilities for each CPT code, (2) an interpretation plot showing how each word in the report combines to form the overall prediction, and (3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of re-assigned codes through facilitating rapid review of false-positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue. [ABSTRACT FROM AUTHOR]