scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.

The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.

Impact of Using Pre- and Post-Bronchodilator Spirometry Reference Values in a Chinese Population.

RATIONALE: Spirometry reference equations that are derived from a large, nationally representative, general population are warranted in China and the impact of using pre- and post-BD spirometry reference values has yet to be assessed in Chinese populations. OBJECTIVES: To present both the pre-BD and post-BD spirometry reference values for Chinese adults using the China Pulmonary Health (CPH) study. METHODS: A reference population of 17969 healthy, non-smoking participants in the CPH study was used to calculate the pre- and post-BD reference values for the forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC. Both pre- and post-BD reference values were applied to the entire CPH population (50991 individuals) to illustrate the divergence between the use of references in determining the disease prevalence and severity grading. MEASUREMENTS AND MAIN RESULTS: The prevalence of airflow limitation was 5.36% using pre-BD reference and 8.02% using the post-BD reference. Individuals who had post-BD FEV1/FVC below post-BD but higher than pre-BD reference values were found to have significantly higher rates of self-reported respiratory symptoms, and significantly lower values in spirometry indicators than those above post-BD reference values. An additional 3.51% of participants were identified as grade II-IV COPD using the post-BD FEV1 predicted values. CONCLUSION: This study generated and applied pre- and post-bronchodilator spirometry reference values in a nationally representative Chinese adult population. Post-BD reference values may serve as an additional criterion in identifying individuals at risk for obstructive pulmonary diseases, its diagnostic and prognostic values should be further investigated.

Targeting MMP9 in CTNNB1 mutant hepatocellular carcinoma restores CD8+ T cell-mediated antitumour immunity and improves anti-PD-1 efficacy.

OBJECTIVE: The gain of function (GOF) CTNNB1 mutations (CTNNB1 GOF ) in hepatocellular carcinoma (HCC) cause significant immune escape and resistance to anti-PD-1. Here, we aimed to investigate the mechanism of CTNNB1 GOF HCC-mediated immune escape and raise a new therapeutic strategy to enhance anti-PD-1 efficacy in HCC. DESIGN: RNA sequencing was performed to identify the key downstream genes of CTNNB1 GOF associated with immune escape. An in vitro coculture system, murine subcutaneous or orthotopic models, spontaneously tumourigenic models in conditional gene-knock-out mice and flow cytometry were used to explore the biological function of matrix metallopeptidase 9 (MMP9) in tumour progression and immune escape. Single-cell RNA sequencing and proteomics were used to gain insight into the underlying mechanisms of MMP9. RESULTS: MMP9 was significantly upregulated in CTNNB1 GOF HCC. MMP9 suppressed infiltration and cytotoxicity of CD8+ T cells, which was critical for CTNNB1 GOF to drive the suppressive tumour immune microenvironment (TIME) and anti-PD-1 resistance. Mechanistically, CTNNB1 GOF downregulated sirtuin 2 (SIRT2), resulting in promotion of ß-catenin/lysine demethylase 4D (KDM4D) complex formation that fostered the transcriptional activation of MMP9. The secretion of MMP9 from HCC mediated slingshot protein phosphatase 1 (SSH1) shedding from CD8+ T cells, leading to the inhibition of C-X-C motif chemokine receptor 3 (CXCR3)-mediated intracellular of G protein-coupled receptors signalling. Additionally, MMP9 blockade remodelled the TIME and potentiated the sensitivity of anti-PD-1 therapy in HCC. CONCLUSIONS: CTNNB1 GOF induces a suppressive TIME by activating secretion of MMP9. Targeting MMP9 reshapes TIME and potentiates anti-PD-1 efficacy in CTNNB1 GOF HCC.

Salicylic acid inhibits rice endocytic protein trafficking mediated by OsPIN3t and clathrin to affect root growth.

Salicylic acid (SA) plays important roles in different aspects of plant development, including root growth, where auxin is also a major player by means of its asymmetric distribution. However, the mechanism underlying the effect of SA on the development of rice roots remains poorly understood. Here, we show that SA inhibits rice root growth by interfering with auxin transport associated with the OsPIN3t- and clathrin-mediated gene regulatory network (GRN). SA inhibits root growth as well as Brefeldin A-sensitive trafficking through a non-canonical SA signaling mechanism. Transcriptome analysis of rice seedlings treated with SA revealed that the OsPIN3t auxin transporter is at the center of a GRN involving the coat protein clathrin. The root growth and endocytic trafficking in both the pin3t and clathrin heavy chain mutants were SA insensitivity. SA inhibitory effect on the endocytosis of OsPIN3t was dependent on clathrin; however, the root growth and endocytic trafficking mediated by tyrphostin A23 (TyrA23) were independent of the pin3t mutant under SA treatment. These data reveal that SA affects rice root growth through the convergence of transcriptional and non-SA signaling mechanisms involving OsPIN3t-mediated auxin transport and clathrin-mediated trafficking as key components.

Defining Global Benchmarks for Laparoscopic Right Posterior Sectionectomy/H67: An International Multicenter Study.

OBJECTIVE: We aimed to establish global benchmark outcomes indicators for L-RPS/H67. BACKGROUND: Minimally invasive liver resections has seen an increase in uptake in recent years. Over time, challenging procedures as laparoscopic right posterior sectionectomies (L-RPS)/H67 are also increasingly adopted. METHODS: This is a post hoc analysis of a multicenter database of 854 patients undergoing minimally invasive RPS (MI-RPS) in 57 international centers in 4 continents between 2015 and 2021. There were 651 pure L-RPS and 160 robotic RPS (R-RPS). Sixteen outcome indicators of low-risk L-RPS cases were selected to establish benchmark cutoffs. The 75th percentile of individual center medians for a given outcome indicator was set as the benchmark cutoff. RESULTS: There were 573 L-RPS/H67 performed in 43 expert centers, of which 254 L-RPS/H67 (44.3%) cases qualified as low risk benchmark cases. The benchmark outcomes established for operation time, open conversion rate, blood loss ≥500 mL, blood transfusion rate, postoperative morbidity, major morbidity, 90-day mortality and textbook outcome after L-RPS were 350.8 minutes, 12.5%, 53.8%, 22.9%, 23.8%, 2.8%, 0% and 4% respectively. CONCLUSIONS: The present study established the first global benchmark values for L-RPS/H6/7. The benchmark provided an up-to-date reference of best achievable outcomes for surgical auditing and benchmarking.

NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides.

Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases. With the development of molecular biology techniques, some data-driven tools have emerged to predict NPs. However, it is necessary to improve the predictive performance of these tools for NPs. In this study, we developed a deep learning model (NeuroPred-CLQ) based on the temporal convolutional network (TCN) and multi-head attention mechanism to identify NPs effectively and translate the internal relationships of peptide sequences into numerical features by the Word2vec algorithm. The experimental results show that NeuroPred-CLQ learns data information effectively, achieving 93.6% accuracy and 98.8% AUC on the independent test set. The model has better performance in identifying NPs than the state-of-the-art predictors. Visualization of features using t-distribution random neighbor embedding shows that the NeuroPred-CLQ can clearly distinguish the positive NPs from the negative ones. We believe the NeuroPred-CLQ can facilitate drug development and clinical trial studies to treat neurological disorders.

Assessment of Global and Regional Lung Compliance in Pulmonary Fibrosis With Hyperpolarized Gas MRI.

BACKGROUND: Lung compliance, a biomarker of pulmonary fibrosis, is generally measured globally. Hyperpolarized 129Xe gas MRI offers the potential to evaluate lung compliance regionally, allowing for visualization of changes in lung compliance associated with fibrosis. PURPOSE: To assess global and regional lung compliance in a rat model of pulmonary fibrosis using hyperpolarized 129Xe gas MRI. STUDY TYPE: Prospective. ANIMAL MODEL: Twenty Sprague-Dawley male rats with bleomycin-induced fibrosis model (N = 10) and saline-treated controls (N = 10). FIELD STRENGTH/SEQUENCE: 7-T, fast low-angle shot (FLASH) sequence. ASSESSMENT: Lung compliance was determined by fitting lung volumes derived from segmented 129Xe MRI with an iterative selection method, to corresponding airway pressures. Similarly, lung compliance was obtained with computed tomography for cross-validation. Direction-dependencies of lung compliance were characterized by regional lung compliance ratios (R) in different directions. Pulmonary function tests (PFTs) and histological analysis were used to validate the pulmonary fibrosis model and assess its correlation with 129Xe lung compliance. STATISTICAL TESTS: Shapiro-Wilk tests, unpaired and paired t-tests, Mann-Whitney U and Wilcoxon signed-rank tests, and Pearson correlation coefficients. P < 0.05 was considered statistically significant. RESULTS: For the entire lung, the global and regional lung compliance measured with 129Xe gas MRI showed significant differences between the groups, and correlated with the global lung compliance measured using PFTs (global: r = 0.891; regional: r = 0.873). Additionally, for the control group, significant difference was found in mean regional compliance between areas, eg, 0.37 (0.32, 0.39) × 10-4 mL/cm H2O and 0.47 (0.41, 0.56) × 10-4 mL/cm H2O for apical and basal lung, respectively. The apical-basal direction R was 1.12 ± 0.09 and 1.35 ± 0.13 for fibrosis and control groups, respectively, indicating a significant difference. DATA CONCLUSION: Our findings demonstrate the feasibility of using hyperpolarized gas MRI to assess regional lung compliance. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Contemporary Issues in Urothelial Carcinoma of Upper Urinary Tract.

Upper urinary tract urothelial carcinoma (UTUC) is an uncommon malignancy involving the renal pelvis and ureter. Careful pathologic analysis plays a critical role in the diagnosis and clinical management of UTUC. In combination with clinical and radiologic evaluation, pathologic features can be used to stratify patients into low-risk and high-risk groups. This risk stratification can help clinicians select the optimal treatment for patients with UTUC, such as kidney-sparing (conservative) treatment, radical nephroureterectomy or ureterectomy, and perioperative systemic therapy. However, due to the technical difficulty of obtaining sufficient tissue from the upper urinary tract, it is often challenging for pathologists to accurately grade the tumor and assess tumor invasion in small biopsy specimens. Although the majority of UTUCs are pure urothelial carcinoma, a considerable subset of UTUCs show histologic subtypes or divergent differentiation. Recent studies have identified genetically distinct molecular subtypes of UTUC by examining DNA, RNA, and protein expression profiles. The prognosis of pT3 UTUC, particularly renal pelvic UC, remains controversial, and several studies have proposed subclassification of pT3 UTUC. Lynch syndrome is a significant risk factor for UTUC, and screening tests may be considered in young patients and those with familial histories of the disease. Despite significant progress in recent years, several issues remain to be addressed in the pathologic diagnosis, molecular classification, and treatment of UTUC.

Modeling SHANK3-associated autism spectrum disorder in Beagle dogs via CRISPR/Cas9 gene editing.

Despite intensive studies in modeling neuropsychiatric disorders especially autism spectrum disorder (ASD) in animals, many challenges remain. Genetic mutant mice have contributed substantially to the current understanding of the molecular and neural circuit mechanisms underlying ASD. However, the translational value of ASD mouse models in preclinical studies is limited to certain aspects of the disease due to the apparent differences in brain and behavior between rodents and humans. Non-human primates have been used to model ASD in recent years. However, a low reproduction rate due to a long reproductive cycle and a single birth per pregnancy, and an extremely high cost prohibit a wide use of them in preclinical studies. Canine model is an appealing alternative because of its complex and effective dog-human social interactions. In contrast to non-human primates, dog has comparable drug metabolism as humans and a high reproduction rate. In this study, we aimed to model ASD in experimental dogs by manipulating the Shank3 gene as SHANK3 mutations are one of most replicated genetic defects identified from ASD patients. Using CRISPR/Cas9 gene editing, we successfully generated and characterized multiple lines of Beagle Shank3 (bShank3) mutants that have been propagated for a few generations. We developed and validated a battery of behavioral assays that can be used in controlled experimental setting for mutant dogs. bShank3 mutants exhibited distinct and robust social behavior deficits including social withdrawal and reduced social interactions with humans, and heightened anxiety in different experimental settings (n = 27 for wild-type controls and n = 44 for mutants). We demonstrate the feasibility of producing a large number of mutant animals in a reasonable time frame. The robust and unique behavioral findings support the validity and value of a canine model to investigate the pathophysiology and develop treatments for ASD and potentially other psychiatric disorders.

Microgel Encapsulated Nanoparticles for Intra-articular Disulfiram Delivery to Treat Osteoarthritis.

Osteoarthritis (OA) affects numerous patients worldwide, and there are no approved disease-modifying drugs. Repurposing FDA-approved small molecular drugs could be a promising alternative strategy to treat OA. Disulfiram (DSF), a clinically approved drug for treatment of alcoholism, inhibits inflammasome activation and exhibits a protective role in interleukin-1ß-induced cardiac injury. However, its efficacy in treating OA remains to be explored due to its poor water solubility and stability, which limit its use in OA treatment. Here, the anti-inflammatory effect of DSF is evaluated in vitro, and a double-layer encapsulation approach is developed for intra-articular delivery of DSF for OA treatment in vivo. DSF is loaded into poly(lactic-co-glycolic acid)-based nanoparticles and encapsulated in gelatin methacrylate microgels through a microfluidic device. Results show that DSF effectively inhibits the expression of key inflammatory cytokines in OA chondrocytes, and the double-layer encapsulation approach reduces the burst release of DSF and prolongs its retention time in the in vitro study. Sustained release of DSF from microgels mitigates cartilage inflammation and subchondral bone erosion in a monoiodoacetate-induced rat OA model. This work demonstrates the potential of repurposing FDA-approved drugs for OA treatment and provides a promising platform for intra-articular delivery of small molecules for superior therapeutic effect.

Comparative Proteome and Cis-Regulatory Element Analysis Reveals Specific Molecular Pathways Conserved in Dog and Human Brains.

Brain development and function are governed by precisely regulated protein expressions in different regions. To date, multiregional brain proteomes have been systematically analyzed only for adult human and mouse brains. To understand the underpinnings of brain development and function, we generated proteomes from six regions of the postnatal brain at three developmental stages of domestic dogs (Canis familiaris), which are special among animals in terms of their remarkable human-like social cognitive abilities. Quantitative analysis of the spatiotemporal proteomes identified region-enriched synapse types at different developmental stages and differential myelination progression in different brain regions. Through integrative analysis of inter-regional expression patterns of orthologous proteins and genome-wide cis-regulatory element frequencies, we found that proteins related with myelination and hippocampus were highly correlated between dog and human but not between mouse and human, although mouse is phylogenetically closer to human. Moreover, the global expression patterns of neurodegenerative disease and autism spectrum disorder-associated proteins in dog brain more resemble human brain than in mouse brain. The high similarity of myelination and hippocampus-related pathways in dog and human at both proteomic and genetic levels may contribute to their shared social cognitive abilities. The inter-regional expression patterns of disease-associated proteins in the brain of different species provide important information to guide mechanistic and translational study using appropriate animal models.

Porous Mg-Zn-Ca scaffolds for bone repair: a study on microstructure, mechanical properties and in vitro degradation behavior.

Biodegradable porous Mg scaffolds are a promising approach to bone repair. In this work, 3D-spherical porous Mg-1.5Zn-0.2Ca (wt.%) scaffolds were prepared by vacuum infiltration casting technology, and MgF2 and fluorapatite coatings were designed to control the degradation behavior of Mg-based scaffolds. The results showed that the pores in Mg-based scaffolds were composed of the main spherical pores (450-600 µm) and interconnected pores (150-200 µm), and the porosity was up to 74.97%. Mg-based porous scaffolds exhibited sufficient mechanical properties with a compressive yield strength of about 4.04 MPa and elastic modulus of appropriately 0.23 GPa. Besides, both MgF2 coating and fluorapatite coating could effectively improve the corrosion resistance of porous Mg-based scaffolds. In conclusion, this research would provide data support and theoretical guidance for the application of biodegradable porous Mg-based scaffolds in bone tissue engineering.

Removal of a partial genomic duplication restores synaptic transmission and behavior in the MyosinVA mutant mouse Flailer.

BACKGROUND: Copy number variations, and particularly duplications of genomic regions, have been strongly associated with various neurodegenerative conditions including autism spectrum disorder (ASD). These genetic variations have been found to have a significant impact on brain development and function, which can lead to the emergence of neurological and behavioral symptoms. Developing strategies to target these genomic duplications has been challenging, as the presence of endogenous copies of the duplicate genes often complicates the editing strategies. RESULTS: Using the ASD and anxiety mouse model Flailer, which contains a partial genomic duplication working as a dominant negative for MyoVa, we demonstrate the use of DN-CRISPRs to remove a 700 bp genomic region in vitro and in vivo. Importantly, DN-CRISPRs have not been used to remove genomic regions using sgRNA with an offset greater than 300 bp. We found that editing the flailer gene in primary cortical neurons reverts synaptic transport and transmission defects. Moreover, long-term depression (LTD), disrupted in Flailer animals, is recovered after gene editing. Delivery of DN-CRISPRs in vivo shows that local delivery to the ventral hippocampus can rescue some of the mutant behaviors, while intracerebroventricular delivery, completely recovers the Flailer animal phenotype associated to anxiety and ASD. CONCLUSIONS: Our results demonstrate the potential of DN-CRISPR to efficiently remove larger genomic duplications, working as a new gene therapy approach for treating neurodegenerative diseases.

Multi-model assessment of potential natural vegetation to support ecological restoration.

Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2-3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.

Thriving arid oasis urban agglomerations: Optimizing ecosystem services pattern under future climate change scenarios using dynamic Bayesian network.

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide.

scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. RESULTS: We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. CONCLUSION: scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE .

CapsNetYY1: identifying YY1-mediated chromatin loops based on a capsule network architecture.

BACKGROUND: Previous studies have identified that chromosome structure plays a very important role in gene control. The transcription factor Yin Yang 1 (YY1), a multifunctional DNA binding protein, could form a dimer to mediate chromatin loops and active enhancer-promoter interactions. The deletion of YY1 or point mutations at the YY1 binding sites significantly inhibit the enhancer-promoter interactions and affect gene expression. To date, only a few computational methods are available for identifying YY1-mediated chromatin loops. RESULTS: We proposed a novel model named CapsNetYY1, which was based on capsule network architecture to identify whether a pair of YY1 motifs can form a chromatin loop. Firstly, we encode the DNA sequence using one-hot encoding method. Secondly, multi-scale convolution layer is used to extract local features of the sequence, and bidirectional gated recurrent unit is used to learn the features across time steps. Finally, capsule networks (convolution capsule layer and digital capsule layer) used to extract higher level features and recognize YY1-mediated chromatin loops. Compared with DeepYY1, the only prediction for YY1-mediated chromatin loops, our model CapsNetYY1 achieved the better performance on the independent datasets (AUC [Formula: see text]). CONCLUSION: The results indicate that CapsNetYY1 is an excellent method for identifying YY1-mediated chromatin loops. We believe that the CapsNetYY1 method will be used for predictive classification of other DNA sequences.

Significance of extraprostatic extension by Grade Groups 1-3 prostatic carcinoma on needle biopsy.

BACKGROUND: It is rare for extraprostatic extension (EPE) on biopsy to be seen with Grade Groups (GG) 1-3 (Gleason scores 3 + 3 = 6; 3 + 4 = 7; 4 + 3 = 7) prostatic adenocarcinoma, and there is no data whether this finding should be a contraindication for performing radical prostatectomy (RP). METHODS: Thirty eight cases with GG 1-3 prostatic adenocarcinoma as the highest grade in the case with EPE on biopsy were identified from our consultation files. Highly unfavorable findings at RP were those that if they could have been predicted preoperatively, might have factored into the decision of whether to proceed with surgery. For these purposes, highly unfavorable pathology at RP was defined as either the presence of seminal vesicle invasion or lymph node metastases or GG5 (Gleason score 9-10). RESULTS: Among 37 patients with clinical follow-up data, 18 (49%) received radiation and/or hormonal therapy (RT/HT), 13 patients (35%) either underwent (n = 11) or are planning (n = 2) RP, and 6 patients (16%) received either ablation therapy or active surveillance. Based on the 11 RP pathology reports, 8 were GG2, one GG3 with tertiary pattern 5, and two GG3. Ten cases were reported to have EPE and six cases had positive margins. Only one had highly unfavorable pathology with pT3bN1 disease. The only difference between the RP and the RT/HT groups in their pretreatment parameters was the mean age of the RP patients was 61 compared with 69 for the RT/HT men (p = 0.02); the lack of many cases with highly unfavorable pathology at RP cannot be attributable to a selection bias of men with lower volume cancer on biopsy or lower serum prostate-specific antigen levels choosing RP over RT/HT. CONCLUSIONS: Despite EPE on biopsy, most men do not have highly unfavorable pathology at RP, and this treatment should remain an option in this setting.

scCNC: a method based on capsule network for clustering scRNA-seq data.

MOTIVATION: A large number of studies have shown that clustering is a crucial step in scRNA-seq analysis. Most existing methods are based on unsupervised learning without the prior exploitation of any domain knowledge, which does not utilize available gold-standard labels. When confronted by the high dimensionality and general dropout events of scRNA-seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicate cell type assignment. RESULTS: In this article, we propose a semi-supervised clustering method based on a capsule network named scCNC that integrates domain knowledge into the clustering step. Significantly, we also propose a Semi-supervised Greedy Iterative Training method used to train the whole network. Experiments on some real scRNA-seq datasets show that scCNC can significantly improve clustering performance and facilitate downstream analyses. AVAILABILITY AND IMPLEMENTATION: The source code of scCNC is freely available at https://github.com/WHY-17/scCNC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying Men Who Can Remain on Active Surveillance Despite Biopsy Reclassification to Grade Group 2 Prostate Cancer.

PURPOSE: Men on active surveillance with Grade Group 1 prostate cancer who reclassify to Grade Group 2 on surveillance biopsy often leave active surveillance. We aimed to identify subgroups of men who can safely remain on active surveillance despite preoperative reclassification to Grade Group 2. MATERIALS AND METHODS: We studied 249 active surveillance patients with surveillance biopsies classified as Grade Group 1 or Grade Group 2 who underwent radical prostatectomy. Perineural invasion, cancer volume, linear length and maximum percentage of Gleason pattern 4, and prostate-specific antigen density were evaluated. Radical prostatectomy adverse pathology was defined by any of: pN1; ≥pT3; ≥Grade Group 2 with ≥20% Gleason pattern 4; intraductal carcinoma; large cribriform glands. RESULTS: A multivariable logistic regression model incorporating prostate-specific antigen density and perineural invasion stratified radical prostatectomy adverse pathology risk among Grade Group 1 and Grade Group 2 active surveillance patients. 57% (39/68) of Grade Group 1 men reclassified to Grade Group 2 while on active surveillance had favorable radical prostatectomy pathology. Those without biopsy perineural invasion and with low prostate-specific antigen density were more likely to have favorable radical prostatectomy pathology. CONCLUSIONS: Most Grade Group 1 men who enter active surveillance and subsequently reclassify to Grade Group 2 have favorable findings at radical prostatectomy and can remain on active surveillance. Among patients reclassified to Grade Group 2, those with low prostate-specific antigen density and without perineural invasion had the lowest risk of radical prostatectomy adverse pathology, comparable to (or below) that of Grade Group 1 patients who were not reclassified to Grade Group 2 preoperatively. Prostate-specific antigen density and perineural invasion stratify risk in active surveillance patients reclassified to Grade Group 2 and, if concordant with other clinicopathological and radiographic findings, can enable more patients to remain on active surveillance. Reclassification to Grade Group 2 alone should not disqualify men from remaining on active surveillance.