Elimination of Intragrain Defect to Enhance the Performance of FAPbI3 Perovskite Solar Cells by Ionic Liquid.

Ionic liquids have been widely used to improve the efficiency and stability of perovskite solar cells (PSCs), and are generally believed to passivate defects on the grain boundaries of perovskites. However, few studies have focused on the relevant effects of ionic liquids on intragrain defects in perovskites which have been shown to be critical for the performance of PSCs. In this work, the effect of ionic liquid 1-hexyl-3-methylimidazolium iodide (HMII) on intragrain defects of formamidinium lead iodide (FAPbI3) perovskite is investigated. Abundant {111}c intragrain planar defects in pure FAPbI3 grains are found to be significantly reduced by the addition of the ionic liquid HMII, shown by using ultra-low-dose selected area electron diffraction. As a result, longer charge carrier lifetimes, higher photoluminescence quantum yield, better charge carrier transport properties, lower Urbach energy, and current-voltage hysteresis are achieved, and the champion power conversion efficiency of 24.09% is demonstrated. These observations suggest that ionic liquids significantly improve device performance resulting from the elimination of {111}c intragrain planar defects.

Astrocyte-specific inhibition of sigma-1 receptor leads to depressive-like behaviors in mice via activation of NF-κB-induced neuroinflammation.

Major depressive disorder (MDD) is a global health burden characterized by persistent low mood, deprivation of pleasure, recurrent thoughts of death, and physical and cognitive deficits. The current understanding of the pathophysiology of MDD is lacking, resulting in few rapid and effective antidepressant therapies. Recent studies have pointed to the sigma-1 (σ-1) receptor as a potential rapid antidepressant target; σ-1 agonists have shown promise in a variety of preclinical depression models. Hypidone hydrochloride (YL-0919), an independently developed antidepressant by our institute with faster onset of action and low rate of side effects, has recently emerged as a highly selective σ-1 receptor agonist; however, its underlying astrocyte-specific mechanism is unknown. In this study, we investigated the effect of YL-0919 treatment on gene expression in the prefrontal cortex of depressive-like mice by single-cell RNA sequencing. Furthermore, we knocked down σ-1 receptors on astrocytes in the medial prefrontal cortex of mice to explore the effects of YL-0919 on depressive-like behavior and neuroinflammation in mice. Our results demonstrated that astrocyte-specific knockdown of σ-1 receptor resulted in depressive-like behavior in mice, which was reversed by YL-0919 administration. In addition, astrocytic σ-1 receptor deficiency led to activation of the NF-κB inflammatory pathway, and crosstalk between reactive astrocytes and activated microglia amplified neuroinflammation, exacerbating stress-induced neuronal apoptosis. Furthermore, the depressive-like behavior induced by astrocyte-specific knockdown of the σ-1 receptor was improved by a selective NF-κB inhibitor, JSH-23, in mice. Our study not only reaffirms the σ-1 receptor as a key target of the faster antidepressant effect of YL-0919, but also contributes to the development of astrocytic σ-1 receptor-based novel drugs.

Assessing inter-intraspecific variability of leaf vulnerability to embolism for 10 alpine Rhododendron species growing in Southwestern China.

Alpine Rhododendron species are prominent constituents and renowned ornamental plants in alpine ecosystems. Consequently, evaluating the genetic variation in embolism resistance within the genus Rhododendron and predicting their adaptability to future climate change is important. Nevertheless, the assessment of embolism resistance in Rhododendron species remains limited. This investigation aimed to examine leaf vulnerability to embolism across ten alpine Rhododendron species, which are frequently employed as ornamental species in Rhododendron forests in Southwest China. The study analyzed the correlation between embolism resistance and various morphological traits, while also conducting water control experiments to evaluate the relationship between embolism resistance and drought resistance. The outcomes indicated pronounced variations in leaf vulnerability to embolism among species, as reflected by the water potential at 50% of embolized pixels (P50 ). Furthermore, the leaf P50 exhibited a significant positive correlation with vessel diameter (D) (R2 = 0.44, P = 0.03) and vessel wall span (b) (R2 = 0.64, P = 0.005), while displaying a significant negative correlation with vessel reinforcement ((t/b)2 ) (R2 = 0.67, P = 0.004). These findings underscore the reliability of selecting species based on embolism vulnerability to preserve the diversity of alpine ecosystems and foster resilience to climate change.

Metal-support spin orders: Crucial effect on electrocatalytic oxygen reduction.

Magnetic property (e.g. spin order) of support is of great importance in the rational design of heterogeneous catalysts. Herein, we have taken the Ni-supported ferromagnetic (FM) CrBr3 support (Nix/CrBr3) to thoroughly investigate the effect of spin-order on electrocatalytic oxygen reduction reaction (ORR) via spin-polarized density functional theory calculations. Specifically, Ni loading induces anti-FM coupling in Ni-Cr, leading to a transition from FM-to-ferrimagnetic (FIM) properties, while Ni-Ni metallic bonds create a robust FM direct exchange, benefiting the improvement of the phase transition temperature. Interestingly, with the increase in Ni loading, the easy magnetic axis changes from out-of-plane (2D-Heisenberg) to in-plane (2D-XY). The adsorption properties of Nix/CrBr3, involving O2 adsorption energy and configuration, are not governed by the d-band center but strongly correlate with magnetic anisotropy. It is noteworthy that the applied potential and electrolyte acidity triggers spin-order transition phenomena during the ORR and induces the catalytic pathway change from 4e- ORR to 2e- ORR with the excellent onset potential of 0.93 V/reversible hydrogen electrode, comparable to the existing most excellent noble-metal catalysts. Generally, these findings offer new avenues to understand and design heterogeneous catalysts with magnetic support.

An inflammation-associated ferroptosis signature optimizes the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma.

Inflammation and ferroptosis crosstalk complexly with immune microenvironment of hepatocellular carcinoma (HCC), thus affecting the efficacy of immunotherapy. Herein, our aim was to identify the inflammation-associated ferroptosis (IAF) biomarkers for contributing HCC. A total of 224 intersecting DEGs identified from different inflammation- and ferroptosis-subtypes were set as IAF genes. Seven of them including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 were used for construction of a risk model which classified HCC patients into two groups (high and low risk). HCC patients in the high-risk group exhibited shorter survival rate and higher immune score, and were predicted to have higher respond rate in immune checkpoint inhibition (ICI) therapy. Levels of the seven genes were significantly changed in HCC tissues in comparison to adjacent tissues. After inserting the gene expression into the risk model, we found that the risk model exhibited the higher diagnostic value for distinguish HCC tissues compared each single gene. Furthermore, HCC tissues from our research group with high-risk score exhibited more cases of microsatellite instability (MSI), heavier tumour mutational burden (TMB), higher expression level of PDL1 and cells with CD8. Knockdown of APOA5 reduced HCC cell proliferation combining with elevating inflammation and ferroptosis levels. In conclusion, we considered APOA5 maybe a novel target for suppressing HCC via simultaneously elevating inflammation and ferroptosis levels, and signature constructed by seven IAF genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 can act as a biomarker for optimising the diagnosis, prognosis evaluation and immunotherapy options in HCC patients.

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

BACKGROUND: To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). METHODS: This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. RESULTS: PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. CONCLUSIONS: PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.

BACKGROUND: To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy. METHODS: Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction. RESULTS: RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score. CONCLUSIONS: AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study.

BACKGROUND: The high level of expertise required for accurate interpretation of prostate MRI. PURPOSE: To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. STUDY TYPE: Retrospective. SUBJECTS: One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test). FIELD STRENGTH/SEQUENCE: 3.0T/scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and apparent diffusion coefficient map. ASSESSMENT: Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology. STATISTICAL TESTS: Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I2 analysis. RESULTS: In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I2  = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05). DATA CONCLUSION: Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Biosensor-based high-throughput screening enabled efficient adipic acid production.

Adipic acid is an industrially important chemical, but the current approach to synthesize it can be of serious pollution to the environment. Rencently, bio-based production of adipic acid has significantly advanced with the development of metabolic engineering and synthetic biology. However, genetic heterogeneity-caused decrease of product titer has largely limited the industrialization of chemicals like adipic acid. Therefore, in the attempt to overcome this challenge, we constitutively expressed the reverse adipate degradation pathway, designed and optimized an adipic acid biosensor, and established a high-throughput screening platform to screen for high-performance strains based on the optimized biosensor. Using this platform, we successfully screened a strain with an adipic acid titer of 188.08 mg·L-1. Coupling the screening platform with fermentation optimization, the titer of adipic acid reached 531.88 mg·L-1 under shake flask fermentation, which achieved an 18.78-fold improvement comparing to the initial strain. Scale-up fermentation in a 5-L fermenter utilizing the screened high-performance strain was eventually conducted, in which the adipic acid titer reached 3.62 g·L-1. Overall, strategies developed in this study proved to be a potentially efficient method in reducing the genetic heterogeneity and was expected to provide guidance in helping to build a more efficient industrial screening process. KEY POINTS: • Developed a fine-tuned adipic acid biosensor. • Established a high-throughput screening platform to screen high-performance strains. • The titer of adipic acid reached 3.62 g·L-1 in a 5-L fermenter.

Can Virtual Reality-Assisted Therapy Offer Additional Benefits to Patients With Vestibular Disorders Compared With Conventional Vestibular Physical Therapy? A Meta-analysis.

OBJECTIVE: To determine whether virtual reality-assisted therapy (VRAT) significantly improves the treatment of peripheral or central vestibular disorders when compared with conventional vestibular physical therapy (CVPT) alone. Indicators of vestibular symptoms are used to determine this. DATA SOURCES: Two reviewers independently searched PubMed, EMBASE, ClinicalTrials.gov, Web of Science, and the Cochrane Collaboration database from January 2010 to January 2022 for studies reporting on VRAT in vestibular disorders. STUDY SELECTION: Randomized controlled trials (RCTs) were included that mainly focused on the following measures: the Dizziness Handicap Inventory (DHI), Simulator Sickness Questionnaire, visual analog scale, and balance measures such as the Activities-specific Balance Confidence Scale (ABC), timed Up and Go test, sensory organization test, and center of pressure. The primary outcome was assessment of symptomatic changes before and after VRAT. DATA EXTRACTION: Two authors independently conducted the literature search and selection. After screening, meta-analysis was performed on the RCTs using RevMan 5.3 software. DATA SYNTHESIS: The results showed that VRAT produced significantly greater improvement than CVPT alone in scores of DHI-Total (standardized mean difference [SMD]: -7.09, 95% confidence interval [CI]: [-12.17, -2.00], P=.006), DHI-Functional (SMD=-3.66, 95% CI: [-6.34, -0.98], P=.007), DHI-Physical (SMD=-3.14, 95% CI: [-5.46, -0.83], P=.008), and DHI-Emotional (SMD=-3.10, 95% CI: [-5.13, -1.08], P=.003). ABC scores did not show improvement (SMD: 0.58, 95% CI: [-3.69, 4.85], P=.79). Subgroup analysis showed that DHI-Total between-group differences were insignificant for central vestibular disorders (SMD=-1.47, 95% CI: [-8.71, -5.78], P=.69), although peripheral disorders showed significant improvements (SMD=-9.58, 95% CI: [-13.92, -5.25], P<.0001). However, the included studies showed high heterogeneity (I2>75%). CONCLUSIONS: VRAT may offer additional benefits for rehabilitation from vestibular diseases, especially peripheral disorders, when compared with CVPT alone. However, because of high heterogeneity and limited data, additional studies with a larger sample size and more sensitive and specific measurements are required to conclusively determine the evidence-based utility of virtual reality.

Claisen Condensation Reaction Mediated Pimelate Biosynthesis via the Reverse Adipate-Degradation Pathway and Its Isoenzymes.

Pimelic acid is an important seven-carbon dicarboxylic acid, which is broadly applied in various fields. The industrial production of pimelic acid is mainly through a chemical method, which is complicated and environmentally unfriendly. Herein, we found that pimelic acid could be biosynthesized by the reverse adipate-degradation pathway (RADP), a typical Claisen condensation reaction that could be applied to the arrangement of C-C bond. In order to strengthen the supply of glutaryl-CoA precursor, PA5530 protein was used to transport glutaric acid. Subsequently, we discovered that the enzymes in the BIOZ pathway are isoenzyme of the RADP pathway enzymes. By combining the isoenzymes of the two pathways, the titer of pimelic acid reached 36.7 mg ⋅ L-1 under the optimal combination, which was increased by 382.9 % compared with the control strain B-3. It was also the highest titer of pimelic acid biosynthesized by Claisen condensation reaction, laying the foundation for the production of pimelic acid and its derivatives.

RtNet: a deep hybrid neural network for the identification of acute rejection and chronic allograft nephropathy after renal transplantation using multiparametric MRI.

BACKGROUND: Reliable diagnosis of the cause of renal allograft dysfunction is of clinical importance. The aim of this study is to develop a hybrid deep-learning approach for determining acute rejection (AR), chronic allograft nephropathy (CAN) and renal function in kidney-allografted patients by multimodality integration. METHODS: Clinical and magnetic resonance imaging (MRI) data of 252 kidney-allografted patients who underwent post-transplantation MRI between December 2014 and November 2019 were retrospectively collected. An end-to-end convolutional neural network, namely RtNet, was designed to discriminate between AR, CAN and stable renal allograft recipient (SR), and secondarily, to predict the impaired renal graft function [estimated glomerular filtration rate (eGFR) ≤50 mL/min/1.73 m2]. Specially, clinical variables and MRI radiomics features were integrated into the RtNet, resulting in a hybrid network (RtNet+). The performance of the conventional radiomics model RtRad, RtNet and RtNet+ was compared to test the effect of multimodality interaction. RESULTS: Out of 252 patients, AR, CAN and SR was diagnosed in 20/252 (7.9%), 92/252 (36.5%) and 140/252 (55.6%) patients, respectively. Of all MRI sequences, T2-weighted imaging and diffusion-weighted imaging with stretched exponential analysis showed better performance than other sequences. On pairwise comparison of resulting prediction models, RtNet+ produced significantly higher macro-area-under-curve (macro-AUC) (0.733 versus 0.745; P = 0.047) than RtNet in discriminating between AR, CAN and SR. RtNet+ performed similarly to the RtNet (macro-AUC, 0.762 versus 0.756; P > 0.05) in discriminating between eGFR ≤50 mL/min/1.73 m2 and >50 mL/min/1.73 m2. With decision curve analysis, adding RtRad and RtNet to clinical variables resulted in more net benefits in diagnostic performance. CONCLUSIONS: Our study revealed that the proposed RtNet+ model owned a stable performance in revealing the cause of renal allograft dysfunction, and thus might offer important references for individualized diagnostics and treatment strategy.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer.

OBJECTIVES: To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer. METHODS: Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration. RESULTS: SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort. CONCLUSION: The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making. KEY POINTS: • Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Prevalence and risk factors for depression and anxiety in adult patients with epilepsy: Caregivers' anxiety and place of residence do mater.

OBJECTIVES: This study was aimed to assess the prevalence of anxiety and depression and the possible contributions of the caregiver's anxiety and depression, disease status, and socio-demographic characteristics to psychopathological comorbidities among adult patients with epilepsy. METHODS: A total of 262 participants (131 adult patient-caregiver pairs) were enrolled in this study. The Hamilton Rating Scale for Depression (HAM-D) and the Hamilton Rating Scale for Anxiety (HAM-A) were applied to evaluate the depression and anxiety status among adult patients with epilepsy and their caregivers, respectively. We collected caregivers' anxiety and depression, patients' sociodemographic characteristic data, and disease status as independent variables using stepwise multiple linear regression analysis that were correlated to the degree of anxiety and depression among these adult patients with epilepsy. RESULTS: Among adult patients with epilepsy, 46 (35.11%) subjects showed anxiety symptoms (HAM-A scores > 6), and 48 (36.64%) had depression symptoms (HAM-D scores > 6). Caregivers' anxiety levels and place of residence were significant independent predictors of both anxiety and depression levels among adult patients with epilepsy. CONCLUSIONS: Adult patients with epilepsy are at a high risk of suffering from anxiety and depression. Caregivers' anxiety and place of residence are definite independent predictors for anxiety and depression severity among adult patients with epilepsy. Therefore, clinicians should be careful in closely monitoring the psychological status of adult patients with epilepsy and their caregivers. Furthermore, the government and medical institutions should increase educational awareness about epilepsy and its cure, especially among adult patients with epilepsy who live in rural areas and consider offering a multidisciplinary management program to improve these patients' psychological status.

Long-term exposure to copper induces mitochondria-mediated apoptosis in mouse hearts.

Copper is a trace element necessary for the normal functioning of organisms, but excessive copper contents may be toxic to the heart. The goal of this study was to investigate the role of excessive copper accumulation in mitochondrial damage and cell apoptosis inhibition. In vivo, the heart copper concentration and cardiac troponin I (c-TnI) and N-terminal forebrain natriuretic peptide (NT-pro-BNP) levels increased in the copper-laden model group compared to those of the control group. Histopathological and ultrastructural observations revealed that the myocardial collagen volume fraction (CVF), perivascular collagen area (PVCA) and cardiomyocyte cross-sectional area (CSA) were markedly elevated in the copper-laden model group compared with the control group. Furthermore, transmission electron microscopy (TEM) showed that the mitochondrial double-layer membrane was incomplete in the copper-laden model groups. Furthermore, cytochrome C (Cyt-C) expression was downregulated in mitochondria but upregulated in the cytoplasm in response to copper accumulation. In addition, Bcl-2 expression decreased, while Bax and cleaved caspase-3 levels increased. These results indicate that copper accumulation in cardiomyocyte mitochondria induces mitochondrial injury, and Cyt-C exposure and induces apoptosis, further resulting in heart damage.

Qualitative and quantitative analysis of the major components in Qinghao Biejia decoction by UPLC-Orbitrap Fusion-MS/MS and UPLC-QQQ-MS/MS and evaluation of their antibacterial activities.

OBJECTIVE: In the present study, the chemical components of Qinghao Biejia decoction (QBD) were qualitatively and quantitatively analyzed using UPLC-Orbitrap Fusion-MS/MS and UPLC-QQQ-MS/MS techniques, followed by identification of each component's origin and evaluation of the antibacterial activity of QBD and its components. METHODS: High-resolution mass spectrometry was used to obtain information on the precise molecular weight, retention time, and fragmentation ion peaks of the compounds used to identify the components of QBD and establish a method for their quantification. In vitro assays including determination of the minimal inhibitory concentration and growth curves were used to assess the antibacterial activity of QBD and its components. RESULTS: A total of 39 components, including fatty acids, phenolic acids, amino acids, flavonoids, coumarins, terpenoids, and alkaloids, were identified by UPLC-Orbitrap Fusion-MS/MS. A high-performance analytical method was also established to quantify 12 components of QBD. The content of mangiferin was relatively high (estimated to be 814 µg/g). The results of the antibacterial assays indicated that mangiferin exhibits antibacterial effects against two strains causing respiratory tract infections. CONCLUSIONS: The present study suggests that mangiferin may serve as a natural compound which shows high antibacterial activity. The results can aid the discovery and analysis of the active antimicrobial components present in QBD and further provide a reference for quality assessment of multi-component herbal prescriptions.

Ag22 Nanoclusters with Thermally Activated Delayed Fluorescence Protected by Ag/Cyanurate/Phosphine Metallamacrocyclic Monolayers through In-Situ Ligand Transesterification.

The composition of protection monolayer exerts great influence on the molecular and electronic structures of atomically precise monolayer protected metal nanoclusters. Four isostructural Ag/cyanurate/phosphine metallamacrocyclic monolayer protected Ag22 nanoclusters are synthesized by kinetically controlled in-situ ligand formation-driven strategy. These eight-electron superatomic silver nanoclusters feature an unprecedented interfacial bonding structure with diverse E-Ag (E=O/N/P/Ag) interactions between the Ag13 core and metallamacrocyclic monolayer, and displays thermally activated delayed fluorescence (TADF), benefiting from their distinct donor-acceptor type electronic structures. This work not only unmasks a new core-shell interface involving cyanurate ligand but also underlines the significance of high-electron-affinity N-heterocyclic ligand in synthesizing TADF metal nanoclusters. This is the first mixed valence Ag0/I nanocluster with TADF characteristic.

Optimized MRI Assessment for Clinically Significant Prostate Cancer: A STARD-Compliant Two-Center Study.

BACKGROUND: There is a requirement for a personalized strategy to make MRI more accessible to men with suspicion of clinically significant prostate cancer (CSPC). PURPOSE: To evaluate an optimized (Op)-MRI compared with biparametric (Bp)-MRI and multiparametric (Mp)-MRI for the diagnosis of CSPC. STUDY TYPE: Two-center, retrospective. SUBJECTS: A total of 346 patients from center 1 and 292 patients from center 2. FIELD STRENGTH/SEQUENCE: 3.0T scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. ASSESSMENT: Four radiologists interpreted the Bp-MRI (T2 WI and DWI) and Mp-MRI (T2 WI, DWI, and DCE) independently according to the Prostate Imaging Reporting and Data System (PI-RADS). For Op-MRI, two radiologists used an adjusted decision rule on Bp-MRI-assessed PI-RADS 3 lesions by determining early enhancement of DCE. Pathologies at biopsy and/or prostatectomy specimens were used as standard references. STATISTICAL TESTS: Performance was assessed using receiver operating characteristic (ROC) curves. Kappa statistics were used to assess interobserver variability. RESULTS: Interreader agreement was excellent for all three MRI assessments (all κ values >0.80). Op-MRI had comparable sensitivity (senior/junior: 90.9% [261/287] / 91.6% [263/287]) and higher specificity (78.1% [274/351] /74.4% [261/351]) compared with Mp-MRI (sensitivity: 92.3% [265/287] / 93.7% [269/287]; specificity: 67.8% [238/351] / 68.1% [239/351]) and Bp-MRI (sensitivity: 91.6% [263/287] / 93.4% [268/287]; specificity: 71.2% [250/351] / 70.1% [246/351]) for the diagnosis of CSPC. Compared to Mp-MRI, Op-MRI spared biopsy in 80.7% (515/638) of DCE scans with similar performance accuracy. Compared to Bp-MRI, Op-MRI downgraded 25.2% (31/123) of lesions at a cost of missing 6.5% (3/46) of malignancies, and upgraded 45.5% (56/123) of lesions with a positive predictive value of 62.5% (35/56) in 123 equivocal findings. DATA CONCLUSION: The Op-MRI, using an adjusted PI-RADS decision rule, did not compromise diagnostic accuracy with sparing biopsy in 80.7% of DCE scans compared to Mp-MRI, and outperformed Bp-MRI by regrading PI-RADS lesions. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.

OBJECTIVES: To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes. METHODS: A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models' performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC). RESULTS: The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49-83.23%) to 97.02% (95% CI, 95.22-98.16%) and 87.94% (95% CI, 85.08-90.31%) to 98.83% (95% CI, 97.60-99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63-95.23%) and 88.21% (95% CI, 85.12-90.73%) for the two test cohorts, respectively. CONCLUSION: Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy. TRIAL REGISTRATION: Clinical trial number: ChiCTR1900027676 KEY POINTS: • Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy. • Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes. • Management of patients becomes more precise based on the DCNN model.

Retinal dehydrogenase 5 (RHD5) attenuates metastasis via regulating HIPPO/YAP signaling pathway in Hepatocellular Carcinoma.

Retinal dehydrogenase 5 (RDH5) is an important enzyme in the visual cycle. Several studies have reported that the RDH family may play crucial roles in tumor prognosis. However, the role of RDH5 in tumor prognosis is still unclear. We examined the mRNA level of RDH5 by using q-PCR in hepatocellular carcinoma (HCC) and adjacent non-cancerous tissues. The proliferation rate of HCC cells was detected by MTS assay, and the invasive ability was examined by transwell and scratch wound assays. The YAP protein localization and expression were visualized by immunofluorescence in two different cell lines. CpG islands in the promoter region were predicted by using the methprimer database. Clinical characteristics of a patient cohort data came from The Cancer Genome Atlas database. RDH5 was significantly downregulated in hepatocellular carcinoma tissues, and low RDH5 expression was associated with metastasis and poor patient prognosis. Functional assays revealed that the RDH5 promoter is methylated in HCC cell lines. Moreover, overexpressing RDH5 can suppress metastasis by reversing the epithelial-mesenchymal transition (EMT) process, and RDH5 also inhibits cell proliferation in HCC cell lines. Furthermore, suppressing RDH5 can activate the Hippo/YAP signaling pathway and promote the nuclear translocation of YAP. Clinical data demonstrated that RDH5 is an independent prognostic factor in HCC. In our study, we provided the first evidence that RDH5 plays a crucial role in suppressing proliferation and metastasis, and the RDH5 promoter is methylated in hepatocellular carcinoma. And as an important regulator, RDH5 can suppress the Hippo/YAP signaling pathway. Taken together, it revealed that RDH5 might be a potential therapeutic target in HCC patients.