SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition.

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes.

Background: Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving. Objectives: The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients. Methods: We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study. Results: The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01). Conclusion: The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.

Antibiofilm Mechanisms of the Helical G3 Peptide against Staphylococcus epidermidis.

Antibacterial peptides (ABPs) have been recognized as promising alternatives to conventional antibiotics due to their broad antibacterial spectrum, high antibacterial activity, and low possibility of inducing bacterial resistance. However, their antibiofilm mechanisms have not yet reached a consensus. In this study, we investigated the antibiofilm activity of a short helical peptide G3 against Staphylococcus epidermidis, one of the most important strains of medical device contamination. Studies show that G3 inhibits S. epidermidis biofilm formation in a variety of ways. In the initial adhesion stage, G3 changes the properties of bacterial surfaces, such as charges, hydrophobicity, and permeability, by rapidly binding to them, thus interfering with their initial adhesion. In the mature stage, G3 prefers to target extracellular polysaccharides, leading to the death of outside bacteria and the disruption of the three-dimensional (3D) architecture of the bacterial biofilm. Such efficient antibiofilm activity of G3 endows it with great potential in the treatment of infections induced by the S. epidermidis biofilm.

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

Deficiency in SLC25A15, a hypoxia-responsive gene, promotes hepatocellular carcinoma by reprogramming glutamine metabolism.

BACKGROUND & AIMS: The role of solute carrier family 25 member 15 (SLC25A15), a critical component of the urea cycle, in hepatocellular carcinoma (HCC) progression remains poorly understood. This study investigated the impact of SLC25A15 on HCC progression and its mechanisms. METHODS: We systematically investigated the function of SLC25A15 in HCC progression using large-scale data mining and cell, animal, and organoid models. Furthermore, we analyzed its involvement in reprogramming glutamine metabolism. RESULTS: SLC25A15 expression was significantly decreased in HCC tissues, and patients with low SLC25A15 levels had a poorer prognosis. Hypoxia-exposed HCC cells or tissues had lower SLC25A15 expression. A positive correlation between HNF4A, a transcription factor suppressed by hypoxia, and SLC25A15 was observed in both HCC tissues and cells. Modulating HNF4A levels altered SLC25A15 mRNA levels. SLC25A15 upregulated SLC1A5, increasing glutamine uptake. The reactive metabolic pathway of glutamine was increased in SLC25A15-deficient HCC cells, providing energy for HCC progression through additional lipid synthesis. Ammonia accumulation due to low SLC25A15 levels suppressed the expression of OGDHL (oxoglutarate dehydrogenase L), a switch gene that mediates SLC25A15 deficiency-induced reprogramming of glutamine metabolism. SLC25A15-deficient HCC cells were more susceptible to glutamine deprivation and glutaminase inhibitors. Intervening in glutamine metabolism increased SLC25A15-deficient HCC cells' response to anti-PD-L1 treatment. CONCLUSION: SLC25A15 is hypoxia-responsive in HCC, and low SLC25A15 levels result in glutamine reprogramming through SLC1A5 and OGDHL regulation, promoting HCC progression and regulating cell sensitivity to anti-PD-L1. Interrupting the glutamine-derived energy supply is a potential therapeutic strategy for treating SLC25A15-deficient HCC. IMPACT AND IMPLICATIONS: We first demonstrated the tumor suppressor role of solute carrier family 25 member 15 (SLC25A15) in hepatocellular carcinoma (HCC) and showed that its deficiency leads to reprogramming of glutamine metabolism to promote HCC development. SLC25A15 can serve as a potential biomarker to guide the development of precision therapeutic strategies aimed at targeting glutamine deprivation. Furthermore, we highlight that the use of an inhibitor of glutamine utilization can enhance the sensitivity of low SLC25A15 HCC to anti-PD-L1 therapy.

Mesenchymal stem cells derived from CHIR99021 and TGF­ß induction remained on the colicomentum and improved cardiac function of a rat model of acute myocardium infarction.

Human induced pluripotent stem cells (hiPSCs) have been regarded as a potential stem cell source for cell therapy. However, the production of cells with mesenchymal potential from hiPSCs through spontaneous differentiation is time consuming and laborious. In the present study, the combined use of the GSK-3 inhibitor CHIR99021 and TGF-ß was used to obtain mesenchymal stem cell (MSC)-like cells from hiPSCs. During the induction process, the transcription of epithelial-mesenchymal transition (EMT)-related genes N-cadherin and Vimentin in the transformed cells was upregulated, whereas the transcription of E-cadherin and pluripotency-related transcription factors SOX2, OCT4 and NANOG did not change significantly. This indicated that whilst cells were pluripotent, EMT was initiated by the upregulation of transcription of EMT promoting genes. Both SMAD-dependent and independent signalling pathways were significantly activated by the combined induction treatment compared with the single factor induction. The hiPSC-derived MSC-like cells (hiPSC-MSCs) expressed MSC-related markers and acquired osteogenic, chondrogenic and adipogenic differentiation potentials. After being injected into the peritoneal cavity of rats, the hiPSC-MSCs secreted angiogenic and immune-regulatory factors and remained on the colicomentum for 3 weeks. Within an 11-week period, four intraperitoneal hiPSC-MSC injections (1x107 cells/injection) into acute myocardial infarction (AMI) model rats significantly increased the left ventricular ejection fraction, left ventricular fractional shortening and angiogenesis and significantly reduced scar size and the extent of apoptosis in the infarcted area compared with that of the control PBS injection. Symptoms of hiPSC-MSC-induced immune reaction or tumour formation were not observed over the course of the experiment in the hiSPC-MSC treated rats. In conclusion, the CHIR99021 and TGF-ß combined induction was a rapid and effective method to obtain MSC-like cells from hiPSCs and multiple high dose intraperitoneal injections of hiPSC-derived MSCs were safe and effective at restoring cardiac function in an AMI rat model.

Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation.

BACKGROUND: From 2016 to 2021, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations. OBJECTIVE: In order to expedite evidence synthesis and research gap discoveries, this pilot study has the following three aims: (1) to compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) to develop natural language processing (NLP) algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) to compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations. METHODS: We built a tobacco research domain-specific semantic database using data from 2993 paper abstracts from 4 leading tobacco-specific journals, with enrichment from other publicly available sources. We then trained an NLP model to extract named entities after learning patterns and relationships between words and their context in text, which further enriched the semantic database. Using this iterative process, we extracted and assessed studies relevant to LGBTQ+ tobacco control issues, further comparing our findings with an ongoing systematic review that also focuses on evidence synthesis for this demographic group. RESULTS: In total, 33 studies were identified as relevant to sexual and gender diverse individuals' nicotine and tobacco product use. Consistent with the ongoing systematic review, the NLP results showed that there is a scarcity of studies assessing policy impact on this demographic using causal inference methods. In addition, the literature is dominated by US data. We found that the product drawing the most attention in the body of existing research is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults, consistent with the research needs identified by the US health agencies. CONCLUSIONS: Our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. While future research is needed to statistically test the NLP tool's performance, there is potential for NLP tools to fundamentally transform the approach to evidence synthesis.

Few-shot image generation with reverse contrastive learning.

Generative models, such as Generative Adversarial Networks (GANs), have recently shown remarkable capabilities in various generation tasks. However, the success of these models heavily depends on the availability of a large-scale training dataset. When the size of the training dataset is limited, the quality and diversity of the generated results suffer from severe degradation. In this paper, we propose a novel approach, Reverse Contrastive Learning (RCL), to address the problem of high-quality and diverse image generation under few-shot settings. The success of RCL benefits from a two-sided, powerful regularization. Our proposed regularization is designed based on the correlation between generated samples, which can effectively utilize the latent feature information between different levels of samples. It does not require any auxiliary information or augmentation techniques. A series of qualitative and quantitative results show that our proposed method is superior to the existing State-Of-The-Art (SOTA) methods under the few-shot setting and is still competitive under the low-shot setting, showcasing the effectiveness of RCL. Code will be released upon acceptance at https://github.com/gouayao/RCL.

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules.

MOTIVATION: Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. RESULTS: Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein-protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. AVAILABILITY AND IMPLEMENTATION: The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.

Benefit of Using Both Ultrasound Imaging and Clinical Information for Predicting Malignant Soft Tissue Tumors.

OBJECTIVE: Ultrasonography (US) is the primary imaging method for soft tissue tumors (STTs), the diagnostic performance of which still requires improvement. To achieve an accurate evaluation of STTs, we built the diagnostic nomogram for STTs using the clinical and US features of patients with STTs. METHODS: A total of 613 patients with 195 malignant and 418 benign STTs were retrospectively recruited. We used a blend of clinical and ultrasonic features, as well as exclusively US features, to develop two distinct diagnostic models for STTs: the clinical-US model and the US-only model, respectively. The two models were evaluated and compared by measuring their areas under the receiver operating characteristic curve (AUC), calibration, integrated discrimination improvement (IDI) and decision curve analysis. The performance of the clinical-US model was also compared with that of two radiologists. RESULTS: The clinical-US model had better diagnostic performance than the model based on US imaging features alone (AUCs of the clinical-US and US-only models: 0.95 [0.93-0.97] vs. 0.89 [0.87-0.92], p < 0.001; IDI of the two models: 0.15 ± 0.03, p < 0.001). The clinical-US model was also superior to the two radiologists in diagnosing STTs (AUCs of clinical-US model and two radiologists: 0.95 [0.93-0.97] vs. 0.79 [0.75-0.82] and 0.83 [0.80-0.85], p < 0.001). CONCLUSION: The diagnostic model based on clinical and US imaging features had high diagnostic performance in STTs, which could help identify malignant STTs for radiologists.

Epitranscriptomic subtyping, visualization, and denoising by global motif visualization.

Advances in sequencing technologies have empowered epitranscriptomic profiling at the single-base resolution. Putative RNA modification sites identified from a single high-throughput experiment may contain one type of modification deposited by different writers or different types of modifications, along with false positive results because of the challenge of distinguishing signals from noise. However, current tools are insufficient for subtyping, visualization, and denoising these signals. Here, we present iMVP, which is an interactive framework for epitranscriptomic analysis with a nonlinear dimension reduction technique and density-based partition. As exemplified by the analysis of mRNA m5C and ModTect variant data, we show that iMVP allows the identification of previously unknown RNA modification motifs and writers and the discovery of false positives that are undetectable by traditional methods. Using putative m6A/m6Am sites called from 8 profiling approaches, we illustrate that iMVP enables comprehensive comparison of different approaches and advances our understanding of the difference and pattern of true positives and artifacts in these methods. Finally, we demonstrate the ability of iMVP to analyze an extremely large human A-to-I editing dataset that was previously unmanageable. Our work provides a general framework for the visualization and interpretation of epitranscriptomic data.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma.

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

Ultrasound features and differential diagnosis for superficial nodular fasciitis.

BACKGROUND: Nodular fasciitis (NF) has nonspecific clinical manifestations and is often misdiagnosed as sarcoma. The investigations of imaging methods for NF were limited. OBJECTIVE: To analyze the ultrasound (US) features of NF, and to evaluate the diagnostic value of US for NF. MATERIALS AND METHODS: A total of 61 NF patients were recruited retrospectively, and 551 lesions in the subcutaneous fat layer were included for comparison. We evaluated the ultrasound features of the patients and divided the NF cases into three types. Chi-square test or Fisher exact test were conducted to detect the potential difference in the distributions of three types in the two groups. RESULTS: Among the 61 NF cases, 65.6% were in the upper extremities (n = 40). The proportion of type 1, 2, and 3 were 57.4%, 24.6%, and 18.0%, respectively. NF were significantly more likely locating in the upper extremities than the other soft tissue tumors (p < 0.001). Type 1 and type 2 of sonographic features were significantly more commonly observed in NF than other soft tissue tumors among the three types (p < 0.001). CONCLUSION: The type 1 and type 2 of US features can help to distinguish NF from other lesions. US has great potential to improve the diagnostic accuracy and reduce the unnecessary surgery.

Comparison of computed tomography and magnetic resonance imaging findings and histopathological features of macrotrabecular-massive hepatocellular carcinoma.

Background: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is a novel subtype of HCC, one of eight distinct subtypes, that accounts for 5% of all cases of HCC and is associated with a worse prognosis. Preoperative diagnosis of MTM-HCCs using imaging findings can facilitate patient treatment decision-making. The purpose of this study was to describe computed tomography (CT) and magnetic resonance imaging (MRI) findings of MTM-HCCs and compare these findings with histopathological features. Methods: This retrospective case-control study was performed at Shenzhen People's Hospital. The cohort included 17 patients with surgically confirmed MTM-HCCs and 232 patients with surgically confirmed non-MTM-HCCs who were enrolled by searching the pathological database from January 2018 to June 2022. CT and MRI findings were retrospectively analyzed and compared with pathological features. Student's t-test or Mann-Whitney U test for continuous variables and χ2 test or Fisher's exact test for categorical variables were implemented to compare imaging manifestations between MTM-HCCs and non-MTM-HCCs, as appropriate. Results: Seventeen tumors with a mean diameter of 8.58±2.83 cm were identified in the 17 patients. In addition to the typical findings of hepatocellular carcinomas (HCCs), such as arterial phase hyperenhancement (APHE), wash out, restricted diffusion, capsule and non-uptake at the hepatobiliary phase (HBP), the most common findings in MTM-HCCs were necrosis in 11 patients (64.7%, 11/17), followed by intratumoral arteries in 6 patients (35.3%, 6/17), peritumoral arterial transitive enhancement in 3 patients (17.6%, 3/17) and peritumoral hypointensive areas at the HBP in 3 of 8 patients (37.5%, 3/8) who received gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) enhancement. The tumor size of non-MTM-HCCs was 5.26±1.94 cm, which was smaller than the 8.58±2.83 cm of MTM-HCCs (P<0.001). The frequency of necrosis and intratumoral arteries was significantly higher in MTM-HCCs than in non-MTM-HCCs (necrosis: 64.7% vs. 34.6%, P=0.012; intratumoral arteries: 47.1% vs. 19.7%, P=0.008). Conclusions: MTM-HCCs tend to be large in size with intratumoral artery and intratumoral necrosis, which are characteristics that may distinguish them from non-MTM-HCCs.

Calcyclin-binding protein contributes to cholangiocarcinoma progression by inhibiting ubiquitination of MCM2.

Background: Cholangiocarcinoma (CCA) represents the epithelial cell cancer with high aggressiveness whose five-year survival rate is poor with standard treatment. Calcyclin-binding protein (CACYBP) shows aberrant expression within several malignant tumors, but the role of CACYBP in CCA remains unknown. Methods: Immunohistochemical (IHC) analysis was used to identify CACYBP overexpression in clinical samples of CCA patients. Moreover, its correlation with clinical outcome was revealed. Furthermore, CACYBP's effect on CCA cell growth and invasion was investigated in vitro and in vivo using loss-of-function experiments. Results: CACYBP showed up-regulation in CCA, which predicts the dismal prognostic outcome. CACYBP had an important effect on in-vitro and in-vivo cancer cell proliferation and migration. Additionally, knockdown of CACYBP weakened protein stability by promoting ubiquitination of MCM2. Accordingly, MCM2 up-regulation partly reversed CACYBP deficiency's inhibition against cancer cell viability and invasion. Thus, MCM2 might drive CCA development by Wnt/ß-catenin pathway. Conclusions: CACYBP exerted a tumor-promoting role in CCA by suppressing ubiquitination of MCM2 and activating Wnt/ß-catenin pathway, hence revealing that it may be the possible therapeutic target for CCA treatment.

Hypoxia-Responsive lncRNA AC115619 Encodes a Micropeptide That Suppresses m6A Modifications and Hepatocellular Carcinoma Progression.

Long noncoding RNAs (lncRNA) regulate a number of aspects of cancer biology. Recent research has shown that lncRNAs can encode micropeptides that mediate their functions in tumors. Here, we revealed that the liver-specific putative lncRNA, AC115619, is expressed at low levels in hepatocellular carcinoma (HCC) and encodes a micropeptide, designated as AC115619-22aa. AC115619 played a crucial role in the regulation of tumor progression and was a prognostic indicator in HCC. The encoded micropeptide AC115619-22aa inhibited the progression of HCC by binding to WTAP and impeding the assembly of the N6-methyladenosine (m6A) methyltransferase complex, which regulates the expression of tumor-associated genes, such as SOCS2 and ATG14. AC115619 was cotranscribed with the adjacent upstream coding gene APOB, and hypoxia induced transcriptional repression of both APOB and AC115619 by controlling HIF1A/HDAC3 and HNF4A signaling. In animal and patient-derived models, AC115619-22aa reduced global m6A levels and suppressed tumor growth. In conclusion, this study establishes AC115619 and its encoded micropeptide as potential prognostic markers and therapeutic targets for patients with HCC. SIGNIFICANCE: A micropeptide encoded by lncRNA AC115619 impedes formation of the m6A methylation complex to lower m6A levels and reduce the growth of hepatocellular carcinoma.

Ultrasound Assessment of Entheseal Sites and Anterior Chest Wall in Ankylosing Spondylitis: A Cross-Sectional Study.

OBJECTIVES: The study was designed to evaluate entheseal sites and anterior chest wall (ACW) of patients with ankylosing spondylitis (AS) using ultrasound (US) and investigate the correlation between disease activity and US score. METHODS: This prospective cross-sectional study included 104 patients with AS and 50 control subjects. Each patient underwent US scanning of 23 entheses and 11 sites of the ACW. The US features, including hypoechogenicity, thickness, erosion, calcification, bursitis, and Doppler signal, were evaluated. Disease activity was assessed based on C reactive protein (CRP), erythrocyte sedimentation rate (ESR), disease activity score-C reactive protein (ASDAS-CRP), and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). RESULTS: The most commonly involved entheses on US were the Achilles tendon (AT) and quadriceps tendon (QT). The most involved site of ACW was the sternoclavicular joint (SCJ). Compared with the control group, significant differences were observed in the AS group in the rates of US enthesitis and ACW in AT (P = .01), SCJ (P = .00), and costochondral joint (CCJ) (P = .01). Patients with high or very high disease activity had a higher erosion score (P = .02). The erosion score was weakly positively associated with CRP, ESR, BASDAI, ASDAS-CRP, and ASDAS-ESR (correlation coefficient: 0.22-0.45). CONCLUSIONS: The most commonly involved entheseal sites on US were AT and QT, while the site of ACW was SCJ. The US assessment of AS should take the ACW into account. High disease activity might indicate erosion in AS.

DoMYB5 and DobHLH24, Transcription Factors Involved in Regulating Anthocyanin Accumulation in Dendrobium officinale.

As a kind of orchid plant with both medicinal and ornamental value, Dendrobium officinale has garnered increasing research attention in recent years. The MYB and bHLH transcription factors play important roles in the synthesis and accumulation of anthocyanin. However, how MYB and bHLH transcription factors work in the synthesis and accumulation of anthocyanin in D. officinale is still unclear. In this study, we cloned and characterized one MYB and one bHLH transcription factor, namely, D. officinale MYB5 (DoMYB5) and D. officinaleb bHLH24 (DobHLH24), respectively. Their expression levels were positively correlated with the anthocyanin content in the flowers, stems, and leaves of D. officinale varieties with different colors. The transient expression of DoMYB5 and DobHLH24 in D. officinale leaf and their stable expression in tobacco significantly promoted the accumulation of anthocyanin. Both DoMYB5 and DobHLH24 could directly bind to the promoters of D. officinale CHS (DoCHS) and D. officinale DFR (DoDFR) and regulate DoCHS and DoDFR expression. The co-transformation of the two transcription factors significantly enhanced the expression levels of DoCHS and DoDFR. DoMYB5 and DobHLH24 may enhance the regulatory effect by forming heterodimers. Drawing on the results of our experiments, we propose that DobHLH24 may function as a regulatory partner by interacting directly with DoMYB5 to stimulate anthocyanin accumulation in D. officinale.

City-scale distribution of airborne antibiotic resistance genes.

Concerns around urban air quality have been increasing worldwide due to large-scale urbanization. A large volume of work has been focused on the chemical pollutants in the air and their impacts on human health. However, the profile of airborne microbial contaminants, especially antibiotic resistance genes (ARGs), is largely understudied. Here, high-throughput quantitative PCR (HT-qPCR) was employed to explore the temporal and spatial distribution of airborne ARGs from 11 sites with various functional zones and different urbanization levels within Xiamen, China. A total of 104 unique ARGs and 23 mobile genetic elements (MGEs) were detected across all samples. Temporal shift was observed in the distribution of ARG profiles, with significantly higher relative abundance of ARGs detected in summer than that in spring. Temperature is the key predictor of the total relative abundance of ARGs and MGEs in summer, while PM2.5 and PM10 were the two most important factors affecting the abundance in spring. Our findings suggest that urban aerosols accommodate rich and dynamic ARGs and MGEs, and emphasize the role of temperature, air quality and anthropogenic activities in shaping the profile of ARGs.

Emerging photodynamic/sonodynamic therapies for urological cancers: progress and challenges.

Photodynamic therapy (PDT), and sonodynamic therapy (SDT) that developed from PDT, have been studied for decades to treat solid tumors. Compared with other deep tumors, the accessibility of urological tumors (e.g., bladder tumor and prostate tumor) makes them more suitable for PDT/SDT that requires exogenous stimulation. Due to the introduction of nanobiotechnology, emerging photo/sonosensitizers modified with different functional components and improved physicochemical properties have many outstanding advantages in cancer treatment compared with traditional photo/sonosensitizers, such as alleviating hypoxia to improve quantum yield, passive/active tumor targeting to increase drug accumulation, and combination with other therapeutic modalities (e.g., chemotherapy, immunotherapy and targeted therapy) to achieve synergistic therapy. As WST11 (TOOKAD® soluble) is currently clinically approved for the treatment of prostate cancer, emerging photo/sonosensitizers have great potential for clinical translation, which requires multidisciplinary participation and extensive clinical trials. Herein, the latest research advances of newly developed photo/sonosensitizers for the treatment of urological cancers, and the efficacy, as well as potential biological effects, are highlighted. In addition, the clinical status of PDT/SDT for urological cancers is presented, and the optimization of the photo/sonosensitizer development procedure for clinical translation is discussed.