The homeodomain regulates stable DNA binding of prostate cancer target ONECUT2.

The CUT and homeodomain are ubiquitous DNA binding elements often tandemly arranged in multiple transcription factor families. However, how the CUT and homeodomain work concertedly to bind DNA remains unknown. Using ONECUT2, a driver and therapeutic target of advanced prostate cancer, we show that while the CUT initiates DNA binding, the homeodomain thermodynamically stabilizes the ONECUT2-DNA complex through allosteric modulation of CUT. We identify an arginine pair in the ONECUT family homeodomain that can adapt to DNA sequence variations. Base interactions by this ONECUT family-specific arginine pair as well as the evolutionarily conserved residues are critical for optimal DNA binding and ONECUT2 transcriptional activity in a prostate cancer model. The evolutionarily conserved base interactions additionally determine the ONECUT2-DNA binding energetics. These findings provide insights into the cooperative DNA binding by CUT-homeodomain proteins.

Estimating Transition Intensity Rate on Interval-censored Data Using Semi-parametric with EM Algorithm Approach.

Phase IV clinical trials are designed to monitor long-term side effects of medical treatment. For instance, childhood cancer survivors treated with chest radiation and/or anthracycline are often at risk of developing cardiotoxicity during their adulthood. Often the primary focus of a study could be on estimating the cumulative incidence of a particular outcome of interest such as cardiotoxicity. However, it is challenging to evaluate patients continuously and usually, this information is collected through cross-sectional surveys by following patients longitudinally. This leads to interval-censored data since the exact time of the onset of the toxicity is unknown. Rai et al. computed the transition intensity rate using a parametric model and estimated parameters using maximum likelihood approach in an illness-death model. However, such approach may not be suitable if the underlying parametric assumptions do not hold. This manuscript proposes a semi-parametric model, with a logit relationship for the treatment intensities in two groups, to estimate the transition intensity rates within the context of an illness-death model. The estimation of the parameters is done using an EM algorithm with profile likelihood. Results from the simulation studies suggest that the proposed approach is easy to implement and yields comparable results to the parametric model.

A comprehensive meta-analysis of tissue resident memory T cells and their roles in shaping immune microenvironment and patient prognosis in non-small cell lung cancer.

Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint and stimulatory genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification.

ONECUT2 acts as a lineage plasticity driver in adenocarcinoma as well as neuroendocrine variants of prostate cancer.

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 gene targets include the glucocorticoid receptor (GR; NR3C1) and the NE splicing factor SRRM4, which are key drivers of lineage plasticity. Thus, OC2, despite its previously described NEPC driver function, can indirectly activate a portion of the AR cistrome through epigenetic activation of GR. Mechanisms by which OC2 regulates gene expression include promoter binding, enhancement of genome-wide chromatin accessibility, and super-enhancer reprogramming. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC and support enhanced efforts to therapeutically target OC2 as a means of suppressing treatment-resistant disease.

Profiling phagosome proteins identifies PD-L1 as a fungal-binding receptor.

Phagocytosis is the process by which myeloid phagocytes bind to and internalize potentially dangerous microorganisms1. During phagocytosis, innate immune receptors and associated signalling proteins are localized to the maturing phagosome compartment, forming an immune information processing hub brimming with microorganism-sensing features2-8. Here we developed proximity labelling of phagosomal contents (PhagoPL) to identify proteins localizing to phagosomes containing model yeast and bacteria. By comparing the protein composition of phagosomes containing evolutionarily and biochemically distinct microorganisms, we unexpectedly identified programmed death-ligand 1 (PD-L1) as a protein that specifically enriches in phagosomes containing yeast. We found that PD-L1 directly binds to yeast upon processing in phagosomes. By surface display library screening, we identified the ribosomal protein Rpl20b as a fungal protein ligand for PD-L1. Using an auxin-inducible depletion system, we found that detection of Rpl20b by macrophages cross-regulates production of distinct cytokines including interleukin-10 (IL-10) induced by the activation of other innate immune receptors. Thus, this study establishes PhagoPL as a useful approach to quantifying the collection of proteins enriched in phagosomes during host-microorganism interactions, exemplified by identifying PD-L1 as a receptor that binds to fungi.

Implementing a Barcode-Based Whole-Life Cycle Management Process to Improve Robotic Instrument Tracking.

Minimally invasive surgery can involve the use of robotics to improve patient outcomes. Some robotic systems require special instruments with a designated number of uses. In China, during the reprocessing of the robotic instruments, health care personnel determined that the existing tracking processes were inadequate. They conducted a quality improvement project with the goal of establishing a barcode-based standardized process for tracking robotic instruments. They implemented technology that generated a unique identifier each time a robotic instrument was reprocessed after use. Nurses scanned the identifier when surgeons used the instrument. The findings included the increased accuracy of use documentation and decreases in untraceable sterilization and use records, charging concerns, and average daily and monthly inventory times. An increase in adverse event reports associated with robotic instruments also was noted. The use of barcode technology for robotic instrument tracking continues at the facility and may be expanded for additional specialty instruments.

Impact of COVID-19 Nonpharmaceutical Interventions on Respiratory Syncytial Virus Infections in Hospitalized Children.

BACKGROUND: Nonpharmaceutical interventions (NPIs) targeted at SARS-CoV-2 have remarkably affected the circulation of other respiratory pathogens, including respiratory syncytial virus (RSV). This study aimed to assess the changes in epidemiological and clinical characteristics of RSV infections in hospitalized children before and during the pandemic in Suzhou, China. METHODS: We prospectively enrolled children aged < 18 years who were hospitalized in Soochow University Affiliated Children's Hospital with acute lower respiratory infection (ALRIs) from January 2018 to July 2022. Changes in epidemiological and clinical characteristics of RSV infections were analyzed. RESULTS: Compared with the same period in 2018-2019, the difference in the overall positive rate of RSV was not statistically significant in 2020, while it increased significantly in 2021 (11.8% [662/5621] vs. 20.8% [356/1711], p < 0.001) and 2022 (9.0% [308/3406] vs. 18.9% [129/684], p < 0.001). Specifically, the positive rates declined considerably from October to December 2020 but sharply increased during the summer of 2021. Compared to prepandemic period, RSV infections were more frequently observed in older children during the pandemic. RSV-positive children exhibited milder clinical characteristics during the COVID-19 pandemic, including decreased proportion of patients with hospital stay ≥ 11 days (10.3% vs. 6.7%, p < 0.05), less requirement for oxygen therapy (13.7% vs. 6.9%, p < 0.001), and fewer cases of polypnea (12.2% vs. 9.7%, p < 0.05) and wheeze (50.1% vs. 42.9%, p < 0.001). CONCLUSIONS: The implementation of multilayered NPIs targeted at COVID-19 has affected the activity of RSV. Ongoing monitoring of RSV is warranted as the changing RSV epidemiology can provide valuable insights for future healthcare system planning.

Genome-scale requirements for dynein-based transport revealed by a high-content arrayed CRISPR screen.

The microtubule motor dynein plays a key role in cellular organization. However, little is known about how dynein's biosynthesis, assembly, and functional diversity are orchestrated. To address this issue, we have conducted an arrayed CRISPR loss-of-function screen in human cells using the distribution of dynein-tethered peroxisomes and early endosomes as readouts. From a genome-wide gRNA library, 195 validated hits were recovered and parsed into those impacting multiple dynein cargoes and those whose effects are restricted to a subset of cargoes. Clustering of high-dimensional phenotypic fingerprints revealed co-functional proteins involved in many cellular processes, including several candidate novel regulators of core dynein functions. Further analysis of one of these factors, the RNA-binding protein SUGP1, indicates that it promotes cargo trafficking by sustaining functional expression of the dynein activator LIS1. Our data represent a rich source of new hypotheses for investigating microtubule-based transport, as well as several other aspects of cellular organization captured by our high-content imaging.

ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer.

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that the HOX/CUT transcription factor ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 targets include the glucocorticoid receptor and the NE splicing factor SRRM4, among others. OC2 regulates gene expression by promoter binding, enhancement of chromatin accessibility, and formation of novel super-enhancers. OC2 also activates glucuronidation genes that irreversibly disable androgen, thereby evoking phenotypic heterogeneity indirectly by hormone depletion. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC. Our findings support enhanced efforts to therapeutically target this protein as a means of suppressing treatment-resistant disease.

The homeodomain drives favorable DNA binding energetics of prostate cancer target ONECUT2.

The ONECUT transcription factors feature a CUT and a homeodomain, evolutionarily conserved elements that bind DNA cooperatively, but the process remains mechanistically enigmatic. Using an integrative DNA binding analysis of ONECUT2, a driver of aggressive prostate cancer, we show that the homeodomain energetically stabilizes the ONECUT2-DNA complex through allosteric modulation of CUT. Further, evolutionarily conserved base-interactions in both the CUT and homeodomain are necessary for the favorable thermodynamics. We have identified a novel arginine pair unique to the ONECUT family homeodomain that can adapt to DNA sequence variations. Base interactions in general, including by this arginine pair, are critical for optimal DNA binding and transcription in a prostate cancer model. These findings provide fundamental insights into DNA binding by CUT-homeodomain proteins with potential therapeutic implications. One-Sentence Summary: Base-specific interactions regulate homeodomain-mediated stabilization of DNA binding by the ONECUT2 transcription factor.

Sculponeatin A promotes the ETS1-SYVN1 interaction to induce SLC7A11/xCT-dependent ferroptosis in breast cancer.

BACKGROUND: E26 transformation specificity-1 (ETS1) is a transcription factor that is overexpressed in breast cancer (BC) and promotes tumor progression. Sculponeatin A (stA), a new diterpenoid extracted from Isodon sculponeatus, has no reported antitumor mechanism. PURPOSE: Here, we explored the antitumor activity of stA in BC and further clarified its mechanism. METHODS: Ferroptosis was detected by flow cytometric, glutathione, malondialdehyde, and iron determination assays. The effect of stA on the upstream signaling pathway of ferroptosis was detected by Western blot, gene expression, gene alterations and other approaches. The binding of stA and ETS1 was examined through a microscale thermophoresis assay and a drug affinity responsive target stability assay. An in vivo mouse model experiment was performed to evaluate the therapeutic and potential mechanism of stA. RESULTS: stA exhibits therapeutic potential in BC by inducing SLC7A11/xCT-dependent ferroptosis. stA decreases the expression of ETS1, which is responsible for xCT-dependent ferroptosis in BC. stA inhibits the transcriptional expression of xCT by directly binding to the ETS domain of the ETS1 protein. In addition, stA promotes proteasomal degradation of ETS1 by triggering ubiquitin ligase synoviolin 1 (SYVN1)-mediated ubiquitination. The K318 site of ETS1 mediates ubiquitination of ETS1 by SYVN1. In a mouse model, stA inhibits tumor growth without causing obvious toxicity. CONCLUSION: Taken together, the results confirm that stA promotes the ETS1-SYVN1 interaction to induce ferroptosis in BC mediated by ETS1 degradation. stA is expected to be used in research of candidate drugs for BC and drug design based on ETS1 degradation.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition.

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

Analysis of euploidy rates in preimplantation genetic testing for aneuploidy cycles with progestin-primed versus GnRH agonist/antagonist protocol.

BACKGROUND: Progestins can suppress endogenous luteinising hormone (LH) secretion from the pituitary gland and have shown similar efficacy in terms of collecting competent oocytes and embryos; however, some inconsistencies have been proposed regarding the quality of embryos collected with the use of progestins. This study aimed to evaluate euploidy rates and pregnancy outcomes in preimplantation genetic testing for aneuploidy (PGT-A) cycles using the progestin-primed ovarian stimulation (PPOS) protocol versus the gonadotropin-releasing hormone (GnRH) agonist/antagonist protocol. METHODS: This retrospective cohort study included 608 PGT-A cycles: 146 women in the PPOS group, 160 women in the GnRH agonist group, and 302 women in the GnRH antagonist group. This study was performed at the in vitro fertilisation (IVF) centre of Shanghai First Maternity and Infant Hospital between January 2019 and December 2021. Additionally, 267 corresponding first frozen embryo transfer (FET) cycles were analysed to assess pregnancy outcomes. RESULTS: The euploid blastocyst rate per injected metaphase II(MII) oocytes (14.60% vs. 14.09% vs. 13.94%) was comparable among the three groups (p > 0.05). No significant differences were observed among the three groups regarding pregnancy outcomes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, implantation, miscarriage, ectopic pregnancy, and live birth rates per transfer in the first FET cycles (p > 0.05). CONCLUSIONS: The PPOS protocol had no negative effect on euploid blastocyst formation, and the pregnancy outcomes in FET cycles using the PPOS protocol were similar to those of the GnRH agonist and antagonist protocols. Trial registration This trial was retrospectively registered.

METTL3 is aberrantly expressed in endometriosis and suppresses proliferation, invasion, and migration of endometrial stromal cells.

Endometriosis (EM) is one of the leading gynecological disorders, and associated with excessive functioning of endometrial stromal cells (ESCs). The current study was conducted to determine the expression and role of methyltransferase-like 3 (METTL3) in the proliferation, invasion, and migration of ESCs in EM. The documented expression levels of METTL3, microRNA (miR)-21-5p, and WNT inhibitory factor 1 (WIF1) in eutopic (Eut) and ectopic (Ect) endometrial tissues and ESCs were determined by a combination of real-time quantitative polymerase chain reaction and Western blot assay. After transfection with pcDNA3.1-METTL3, miR-21-5p mimic, and WIF1 small interfering RNA, cell counting kit-8, colony formation, and Transwell assays were performed in the Ect ESCs (Ect-ESCs). Subsequently, the binding of miR-21-5p to METTL3 was analyzed, along with quantification of the N6-methyladenosine (m6A) level, the enrichments of METTL3 and m6A on WIF1, and the mRNA stability of WIF1. In our findings, METTL3 was downregulated in the EM tissues and cells. METTL3 overexpression intrinsically reduced the proliferation, invasion, and migration of Ect-ESCs. miR-21-5p inhibited the METTL3 expression while METTL3 enhanced the mRNA stability and expression of WIF1 via m6A modification. Additionally, a negative correlation of METTL3 was identified with miR-21-5p along with a positive correlation with the WIF1 mRNA in EM tissues. The miR-21-5p overexpression or WIF1 downregulation enhanced the proliferation, invasion, and migration of Ect-ESCs. Collectively, miR-21-5p inhibited the METTL3-mediated m6A modification and mRNA stability of WIF1, thereby facilitating the proliferation, invasion, and migration of Ect-ESCs.

Phytoremediation potential of hybrid Pennisetum in cadmium-contaminated soil and its physiological responses to cadmium.

Cadmium (Cd) contamination in soil is a global problem. Recently, phytoremediation with plants, possessing high biomass and moderate Cd enrichment ability, has received excessive attention as a cost-effective method for Cd remediation from the soil. In this study, the plant growth, physiological responses, Cd concentration, accumulation, and distribution of the C4 grass hybrid Pennisetum (HP) were studied in different levels of Cd-contaminated soil in a pot experiment. Furthermore, a field trial was also conducted to accurately assess its practical phytoremediation potential in natural Cd-contaminated fallow filed. The results showed that HP possessed effective antioxidant enzymes to scavenge ROS and strong physiological coordination in response to Cd stress. The HP had a considerable Cd enrichment ability, and the maximal Cd uptake of 1.08 mg plant-1 was achieved at 60 mg kg-1 Cd in the pot. The maximal concentration of Cd in the aboveground parts and roots of HP were 49.33 mg kg-1 and 103.33 mg kg-1, respectively, when soil Cd was 70 mg kg-1 in the pot. The bioconcentration factor (BCF) of Cd in the aboveground parts was less than 1, while the BCF in the root was greater than 1, and the translocation factor (TF) was less than 0.5 in all Cd treatment groups. A total of 46.89-65.46% absorbed Cd stored in the aboveground parts in the pot. The Cd concentration in roots of HP was significantly higher compared to those in leaves and stems, and all BCFs were greater than 1.5 in a lightly Cd-contaminated field (0.35 mg kg-1). Furthermore, HP had high aboveground dry biomass up to 54.63 t ha-1 and accumulated 16.13 g ha-1 Cd in its aboveground parts in the field, which was accounted for about 91.54% of the total Cd extracted by the plant. The soil Cd concentration was reduced by 60.00% after planting HP. Our results suggest that HP is a potential phytoextractor for Cd in lightly Cd-contaminated soil as well as a phytostabilizer under strong Cd stress in the pot.

Status and Determinants of Symptoms of Anxiety and Depression among Food Delivery Drivers in Shanghai, China.

(1) Background: The psychological status of employees, especially vulnerable populations, has received considerable research attention. However, as a newly emerging and popular occupation in the gig industry, food delivery drivers have received little attention. The majority of these workers are immigrants who are already in a precarious position due to a lack of available jobs, inadequate medical care, poor diets, and communication and acculturation difficulties even before they take these jobs, which involve long working hours and exposure to the elements. (2) Methods: To examine the anxiety and depression symptoms of these workers and possible influencing factors, a cross-sectional study was conducted with a sample of food delivery drivers working for the Meituan Company (one of the largest e-platform companies in China). Anxiety and depression scales were adapted from the GAD-7, and the PHQ-9 was used to assess participants' related symptoms. Differences were compared in terms of sociodemographic, work situation, and lifestyle variables. Binary logistic regressions were conducted to analyze the effects of various factors on the two psychological dimensions. (3) Results: Among the 657 participants, the proportions of participants reporting anxiety and depression symptoms were 46.0% and 18.4%, respectively. Lack of communication with leaders (ORAN = 2.620, 95% CI: 1.528-4.493, p < 0.001; ORDE = 1.928, 95% CI: 1.039-3.577, p = 0.037) and poor sleep quality (ORAN = 2.152, 95% CI: 1.587-2.917, p < 0.001; ORDE = 2.420, 95% CI: 1.672-3.504, p < 0.001) were significant risk factors for both anxiety and depression symptoms. Women (OR = 2.679, 95% CI: 1.621-4.427, p < 0.001), those who climbed ≥31 floors per day (OR = 2.415, 95% CI: 1.189-4.905, p = 0.015), and those with a high frequency of breakfast consumption (OR = 3.821, 95% CI: 1.284-11.369, p = 0.016) were more likely to have anxiety symptoms. Participants who earned less than 5000 RMB (OR = 0.438, 95% CI: 0.204-0.940, p = 0.034), were unwilling to seek medical help (OR = 3.549, 95% CI: 1.846-6.821, p < 0.001), or had a high frequency of smoking (OR = 5.107, 95% CI: 1.187-21.981, p = 0.029) were more likely to be depressive. (4) Conclusion: The existence of communication channels with leaders and good sleep quality are protective factors for anxiety and depression symptoms. Participants who were female, climbed ≥31floors per day, and had a high frequency of eating breakfast were more likely to have anxiety symptoms, while earning less, unwillingness to seek medical help, and a high frequency of smoking were risk factors for depression symptoms.

RIPK3 modulates sarcoma through immune checkpoint HAVCR2.

Sarcomas is a complex group of malignant diseasse with undetermined molecular mechanisms. Receptor interacting serine/threonine kinase 3 (RIPK3) is a necroptosis- and apoptosis-related marker that has been implicated in several immune-associated diseases and aggressive malignant tumours. In the present study, publicly available transcriptome sequencing data were collected from The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET) databases and extensive data mining was performed, focusing on RIPK3 and its potential function in the modulation of gene expression and signaling pathways, immune checkpoints and cell infiltration. Analysis of TCGA and TARGET data revealed 603 up- and 260 downregulated genes in the higher RIPK3 expression group compared with the lower RIPK3 expression groups, with transmembrane channel like 8 and transmembrane protein 97 as the top up- and downregulated genes, respectively. Further pathway analysis revealed that the overexpressed genes were enriched in 'cytokine-cytokine receptor interaction'. Higher RIPK3 was found to be associated with improved survival, the immune checkpoint gene hepatitis A virus cellular receptor 2 (HAVCR2) and an improved response to immune blockade therapy. The potential modulation of HAVCR2 expression by RIPK3 was confirmed by reverse transcription-quantiative PCR in KHOS and 143B human osteosarcoma cell lines. Immune cell infiltration analysis revealed that RIPK3 was positively associated with macrophage and monocyte infiltration, suggesting that RIPK3 executes its function through these immune cells. These findings led to the hypothesis that increased RIPK3 expression may result in improved survival, possibly by regulating the immune checkpoint HAVCR2. In conclusion, the present study comprehensively elucidated the RIPK3 profile with regard to sarcoma survival, transcriptome expression, immune checkpoint therapy and immune cell infiltration. These findings suggest that RIPK3 is potentially a therapeutic target for sarcoma.

A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma.

Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes. Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis. Results: Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high- and low-ECM index groups with significantly different overall survival rates in four datasets. High-ECM index patients exhibited a worse prognosis than low-ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/). Conclusion: In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients.

Current development and prospects of deep learning in spine image analysis: a literature review.

Background and Objective: As the spine is pivotal in the support and protection of human bodies, much attention is given to the understanding of spinal diseases. Quick, accurate, and automatic analysis of a spine image greatly enhances the efficiency with which spine conditions can be diagnosed. Deep learning (DL) is a representative artificial intelligence technology that has made encouraging progress in the last 6 years. However, it is still difficult for clinicians and technicians to fully understand this rapidly evolving field due to the diversity of applications, network structures, and evaluation criteria. This study aimed to provide clinicians and technicians with a comprehensive understanding of the development and prospects of DL spine image analysis by reviewing published literature. Methods: A systematic literature search was conducted in the PubMed and Web of Science databases using the keywords "deep learning" and "spine". Date ranges used to conduct the search were from 1 January, 2015 to 20 March, 2021. A total of 79 English articles were reviewed. Key Content and Findings: The DL technology has been applied extensively to the segmentation, detection, diagnosis, and quantitative evaluation of spine images. It uses static or dynamic image information, as well as local or non-local information. The high accuracy of analysis is comparable to that achieved manually by doctors. However, further exploration is needed in terms of data sharing, functional information, and network interpretability. Conclusions: The DL technique is a powerful method for spine image analysis. We believe that, with the joint efforts of researchers and clinicians, intelligent, interpretable, and reliable DL spine analysis methods will be widely applied in clinical practice in the future.