ATG16L1, IRGM, NOD2 and LRRK2), crosstalk of multiple phenotypes with autophagy  and autophagy-associated signaling pathways. Moreover, we briefly discuss the role of autophagy in colorectal cancer and current status of autophagy-based drug research for IBD. It should be emphasized that autophagy has cell-specific and environment-specific effects on the gut. One of the problems of IBD research is to understand how autophagy plays a role in intestinal tract under specific environmental factors. A better understanding of the mechanism of autophagy in the occurrence and progression of IBD will provide references for the development of therapeutic drugs and disease management for IBD in the future.Inflammatory bowel disease (IBD) is a group of disorders that cause chronic inflammation in the intestines, with the primary types including ulcerative colitis and Crohn’s disease. The link between autophagy, a catabolic mechanism in which cells clear protein aggregates and damaged organelles, and intestinal health has been widely studied. Experimental animal studies and human clinical studies have revealed that autophagy is pivotal for intestinal homeostasis maintenance, gut ecology regulation and other aspects. However, few articles have summarized and discussed the pathways by which autophagy improves or exacerbates IBD. Here, we review how autophagy alleviates IBD through the specific genes (e.g.,  The common clinical symptoms of IBD include severe diarrhea, abdominal pain, and weight loss, among others. If left untreated, the condition can lead to serious complications or the development of colorectal cancer (CRC). The pathogenesis of inflammatory bowel diseases (IBD) is multifactorial and complex, including an unfavorable environment, susceptibility gene variants, abnormal intestinal microbiota, and a mucosal immune and inflammatory response genetically susceptible to the host microbiota , 2, but Autophagy is a conserved intracellular degradation pathway that helps maintain intracellular homeostasis during stress or malnutrition. Autophagy is prevalent across cells and interacts with other essential cellular homeostatic processes. However, in the intestinal mucosa, cell-type-specific differences in autophagic function may exist . A growivia various pathways, including the genes, cell signaling pathways, ERS, and microorganisms involved. Meanwhile, a brief summary of the current research status of IBD therapeutic agents targeting autophagy is presented. These will further explain the pathogenesis of IBD and provide novel ideas for the development and use of drugs for IBD treatment in the future.Based on the important impact of autophagy on the gut, in this review, we will discuss how autophagy affects intestinal cells and gut function. We will also discuss how autophagy plays a role in the development of IBD 2In eukaryotic cells, autophagy is an important protein degradation system and a tightly regulated catabolic and organelle renewal process . In thisMacroautophagy is a process in which the endoplasmic reticulum, Golgi apparatus, or cytoplasmic membrane surround the material to be degraded into bimodal binding vesicles called autophagosomes. The autophagosome then fuse with lysosomes and degrade the contents. Microphagy implies that the lysosomal membrane directly wraps the long-lived proteins, among other components, and degrades them within the lysosome. CMA is a process of selective protein degradation through a lysosome-dependent mechanism. In this process, intracytoplasmic proteins bind to molecular chaperones and are translocated into the lysosomal compartment and subsequently digested by lysosomal enzymes .via ATG7 and ATG3, leading to the maturation of autophagosomes  and immunity-related GTP-ase family M gene (IRGM) as risk factors for CD brought autophagy into focus in IBD (LRRK2), and nucleotide-binding and oligomerisation domain 2 gene (NOD2) are strongly associated with the development of IBD  are a group of proteases present in the cytoplasm that are closely related to cell growth, differentiation and especially apoptosis. Thr 300-to-Ala (T300A) polymorphism in tability . In a prtability . ATG16L1man IECs . Interleman IECs , 44 and man IECs . Howeverman IECs . A recent manner . ATG16L1intained .3.1.2IRGM is thought to be a shared susceptibility gene for both CD and UC  are considered to increase the susceptibility to CD and UC  and ULK1 but dependent on the activation of BECN1-containing class III PI3 kinases  is the response that activates signaling pathways such as the unfolded protein response (UPR), endoplasmic reticulum overload response, and the caspase-12-mediated apoptotic pathway. These responses can respond to misfolded and unfolded protein aggregation in the lumen of the endoplasmic reticulum and disturbed calcium ion homeostasis. UPR has been found to be initiated by three ER transmembrane sensors, including inositol-requiring enzyme 1 (IRE1), protein kinase R-like ER kinase (PERK) and activation of transcription factor 6 (ATF6). Some of the mediators released by ERS can directly induce autophagic vesicle formation and initiate autophagy . FindingAtg16L1 knockout mice exhibit more severe colonic inflammation owing to deficient autophagy, resulting in the insufficient degradation of ER to nucleus signaling 1, allowing its excessive accumulation and activation -activated protein kinase (AMPK), a key molecule in the regulation of biological energy metabolism, is an AMP-dependent protein kinase. Sodium hydrosulfide (NaHS) activates hepatic autophagy through the AMPK/mTOR pathway and improves non-alcoholic fatty liver disease . In colons of UC . Xiong alleagues  found thes , a component of inflammatory vesicles, is responsible for the activation of caspase-1 and subsequent pro-IL-1β and pro-IL-18 maturation. It has long been shown that single-nucleotide polymorphisms in the NLRP3 gene are associated with susceptibility to CD . There iJesus  production and inhibiting NLRP3 inflammatory vesicles through mitochondrial autophagy  is a key transcription factor in the cellular antioxidant response. The pathway involving heme oxygenase-1 (HO-1) is a key signaling pathway that regulates endogenous oxidative stress. Reportedly, p62 SQSTM1 is a target gene of Nrf2 , and theArab  showed t3.3.4Zhang  deletion, reduced UVRAG levels were observed to correlate with impaired autophagy induction  were knocked out, cell death and ROS production were enhanced by the oxaliplatin treatment of Caco-2 cells (In addition to its direct anti-tumor effects, autophagy inhibits CRC by weakening the inflammatory response in the tumor microenvironment and increase the processing and presentation of tumor-associated antigens. This improves anti-tumor immunity. Some chemotherapeutic agents with immunogenic properties have been shown to exhibit immunogenic anti-tumor properties by inducing autophagic cell death . Bacilluenomenon . In contenomenon . In addi-2 cells , 169. Th5Wang and his collaborators (Gao and his colleagues (In terms of drug research, the therapeutic targets of IBD are primarily elements related to the maintenance of homeostasis in the intestine, and autophagy has emerged as a new potential therapeutic option for it. borators  then assborators . In inteborators . Anakinrborators . Sirolimborators . Meanwhiborators . Howeverborators  that theborators –177. Gaolleagues  confirmeChu and his colleagues olecules and peptides to precisely modulate autophagy in pathological processes. For example, P140 is a peptide that selectively targets the autophagic process, particularly CMA. P140 effectively affects the processing of endogenous antigens as well as downstream deleterious pro-inflammatory events by interfering with the over-activated autophagic process (However, few of the current abundant of activators or inhibitors have strictly selected one autophagic pathway and one target. Therefore, some of the research has turned towards identifying small m  process . Importa process  and P140 process , 181. Pr process . Spermid process . Liu’s s process .Similarly, some herbal extracts or active ingredients have been shown to ameliorate intestinal inflammation by inducing autophagy; e.g., andrographolide inhibits NLRP3 inflammatory vesicles by enhancing autophagy , and treAutophagy, owing to its fundamental role in maintaining homeostasis in the intestine, can be an attractive therapeutic target. Although some progress has been made in the study of drugs that induce autophagy through different mechanisms, the treatment of IBD by autophagy modulation is still in its preliminary stages. It is challenging because of the low pharmacological specificity of its targets, the lack of specificity for specific cell classes, and the autophagy-independent effects. Therefore, extensive experimental studies and large-scale clinical trials are necessary to explore and confirm the results.6via multi-pathway and cell-specific and environment-specific mechanisms. The microbial-autophagy-IBD interaction has been described previously, but the diet-autophagy-IBD interaction is poorly understood. Currently, the effects of vitamin D in diet-autophagy-IBD interaction have been studied. The deficiency of this vitamin has been recognized as an important pathological basis for IBD (Haihua and his colleagues further demonstrated that proanthocyanidins may alleviate DSS-induced UC by inducing autophagy (Autophagy affects the intestine  for IBD . The vit for IBD , reduces for IBD , and pro for IBD . In addi for IBD  and can  for IBD . Haihua utophagy . TherefoTo date, the number of studies on microautophagy and CMA is insufficient in IBD, and it is yet to be confirmed whether autophagy governs the age and gender in IBD onset. In genetics, the development and application of genetic testing will be particularly important in predicting the occurrence, type, and prognosis of IBD as well as the guidance and utility of drugs, especially for patients with refractory IBD. However, these aspects are still in the preliminary stage of research.Overall, the current findings indicate that the absence of autophagy favors the exacerbation rather than the induction of intestinal inflammation. However, the inhibition of autophagy can ameliorate intestinal inflammation under certain conditions, such as after appendectomy . Therefo7Great strides have been made in our understanding of autophagy within intestinal cells both in a healthy and diseased context. A growing number of autophagic genes and proteins have been shown to be closely associated with intestinal inflammatory processes. Autophagy genes or proteins reduce susceptibility to intestinal injury and maintain intestinal homeostasis by providing endoplasmic reticulum stress protection, phagocytosing pathogen invasion, reducing pathogen attachment, and regulating apoptosis Figure 2Autophagy is highly cell-specific and environment-specific. Therefore, strict selection of autophagic pathways or targets without affecting other biological processes is the key to autophagy-based therapeutic strategies for IBD. In the future, additional efforts are needed to validate the suitability of autophagy as a therapeutic target for IBD and to determine whether it is effective and feasible. These efforts should perhaps be devoted to the identification of small molecules or peptides for the precise modulation of the autophagic process and to gradually transfer experimental findings to clinical studies.S-LC, C-ML, WL, Q-SL, D-SH and SY-H retrieved and analyzed concerned literatures. S-LC and Q-SL wrote the manuscript. S-LC, C-ML, WL, M-YZ, J-HZ, S-YH, Y-WH, and YZ revised the manuscript.All authors contributed to the article and approved the submitted version.


Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts.We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations.The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data .The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies.The online version contains supplementary material available at 10.1186/s12911-023-02181-9. Text phenotyping is the task of extracting diseases or traits of patients from clinical notes, which can benefit a wide range of tasks like cohort selection, epidemiological research, and decision making for better clinical care. A particular set of human phenotypes are rare diseases: a rare disease is very uncommon, affecting 5 or fewer people in 10,000, but there are between 6,000 and 8,000 rare diseases and they collectively affect approximately 3.5-5.9% of the population (or 263-446 million persons) globally  anTable In this study, we proposed an ontology-driven and weakly supervised approach for rare disease phenotyping from clinical notes. Unlike the use of ontologies, weak supervision has not been well established in the clinical NLP domain. Our proposed weak supervised deep learning approach requires no human annotation and extends the paradigm from  on weak Traditional clinical NLP relies heavily on strong supervision with manually labelled data. However, with recent data-demanding methods like deep learning, it is time to consider to automatically create labelled data to train models, with the support of rules and resources like ontologies and NER+L tools. Our work on rare diseases provides empirical evidence for the task by applying a weakly supervised NLP pipeline on three clinical note datasets (one for discharge summaries and two for radiology reports) in two institutions in the US and the UK. The improvements on the precision were highly significant (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Our study also demonstrates that NLP can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes see Fig. .While our rule-based weak supervision does not require annotated data, it can bring bias or noise as no simple rule can perfectly predict the labels for a complex task. This bias, although not affecting most predictions for the testing data, was manifested in the slight drop of recall in Text-to-UMLS linking Table . This loAs suggested in our results and other studies , 46, theThere are still, however, some false positive mentions detected by the proposed NLP pipeline, as shown in our analyses of the prediction errors and the identified cohorts in Figs. -4. DisamAlso, we note that our work is highly dependent on existing ontologies and their available matchings to each other. We leveraged and validated the matching among ORDO, UMLS, ICD-10, and ICD-9. The current matchings are generally correct, but not perfect (e.g. 88.4% accuracy of matching between UMLS and ORDO). A more accurate matching among ontologies, potentially corrected with machine learning , will imWhile we only enhanced SemEHR with the weakly supervised phenotype confirmation model, the approach can be adapted to improve other NER+L tools and models to support more accurate rare disease cohort selection and coding. Recently, more packages and environments (e.g. Snorkel , skweak Our work mainly focused on identifying rare disease concepts in the clinical notes, while other physical, behavioural, and physiological characteristics need to be identified so as to establish a clinical diagnosis of a rare disease. We also mainly focused on rare diseases as a whole and the approach can be applied to identify specific rare diseases. Future work needs to extract a wider set of information to enhance rare disease phenotyping, and to facilitate the development of risk prediction tools for rare diseases to support decision making during the COVID-19 pandemic and beyond , 54.Additional file 1.Additional file 2.


Bladder cancer (BC) is a frequently diagnosed cancer with high mortality. Male patients have a higher risk of developing BC than female patients. As a type of caspase-independent cell death, necroptosis plays a significant role in the occurrence and progression of BC. The aberrant function of long non-coding RNAs (lncRNAs) plays an indispensable role in GI. However, the relationship between lncRNA and necroptosis in male patients with BC remains unclear. The clinical information and RNA-sequencing profiles of all BC patients were retrieved from The Cancer Genome Atlas Program. A total of 300 male participants were selected for the study. We conducted to identify the necroptosis-related lncRNAs (NRLs) by Pearson correlation analysis. Subsequently, least absolute shrinkage and selection operator Cox regression were conducted to establish a risk signature with overall survival-related NRLs in the training set and to validate it in the testing set. Finally, we verified the effectiveness of the 15-NRLs signature in prognostic prediction and therapy via survival analysis, receiver operating characteristic curve analysis, and Cox regression. Furthermore, we analyzed the correlation between the signature risk score and pathway enrichment analysis, immune cell infiltration, anticancer drug sensitivity, and somatic gene mutations. We developed 15-NRLs  signature, and divided patients into a high-risk group and low-risk group through the median risk score. Kaplan–Meier and receiver operating characteristic curves showed that the prognosis prediction had satisfactory accuracy. Cox regression analysis indicated that the 15-NRLs signature was a risk factor independent of various clinical parameters. Additionally, immune cell infiltration, half-maximal inhibitory concentration, and somatic gene mutations differed significantly among different risk subsets, implying that the signature could assess the clinical efficacy of chemotherapy and immunotherapy. This 15-NRLs risk signature may be helpful in assessing the prognosis and molecular features of male patients with BC and improve treatment modalities, thus can be further applied clinically. The exact effect of necroptosis on tumors is complex and controversial.,9 Indeed, several studies suggest its involvement as a cancer suppressor that participates in cancer inhibition by activating markers involved in regulated necrosis pathways, while others as cancer promoters by inhibition of tumor immunity and activation of inflammation.,11Necroptosis is the first form of programmed necrosis described with a prominent role in multiple physiological and pathological conditions. Accumulating evidence suggests that lncRNA can act as an oncogene or tumor suppressor to participate in the occurrence and development of cancers.,14 Researchers have clarified that lncRNA can participate in the occurrence and development of cancer through necroptosis.–17Long non-coding RNA (lncRNA) is a kind of cellular transcript that is longer than 200 nt in length and does not code for proteins, which mainly functions in transcription regulation, nuclear domain organization, and protein or RNA molecules regulation.In this study, we obtained lncRNA data and clinical information related to BC from The Cancer Genome Atlas (TCGA) database and constructed a necroptosis-related lncRNAs (NRLs) based risk signature to explore its role in predicting the prognosis of BC. We assume that this risk signature has the ability to predict the prognosis of male patients with BC. The content of our study is to evaluate and verify the prognostic ability and independent prognostic value of risk signature.https://portal.gdc.cancer.gov/) on September 13, 2022. For subsequent analysis, transcriptome profiles were processed by log2. A total of 300 males with BC were included in the current study. The exclusion criteria were a negative histological diagnosis of BC, the presence of a malignancy other than BC, lack of complete clinical data, and female patients. In each sample, the data of the transcriptome profile contains the expression level of detected lncRNAs in each sample. From the clinical information, we selected age, gender, follow-up time, survival status, smoking status, pathological stage, and TNM stage for analyses. Necroptosis-related genes (NRGs) were acquired by searching the Kyoto Encyclopedia of Genes and Genomes  and the Human Gene Database . In addition, the mutation data of BC samples were downloaded in MAF format from the TCGA database. No approval from the ethics committee was needed because all the information was acquired from the TCGA database.The clinical information and RNA-sequencing (RNA-seq) profile associated with all BC patients were retrieved from TCGA  to visualize the lncRNA-mRNA network. In addition, the NRGs regulated by candidate lncRNAs were uploaded to the string  website to construct the PPI network.In order to prove the mutually regulated connection between the NRLs and the corresponding target mRNAs, we use Cytoscape  in R  to screen NRLs related to OS. The cutoff criterion for statistically significant correlations with OS was decided as P < .05.In all NRLs, OS-related lncRNAs play a key role in the onset, progression, and prognosis of tumors. We performed time-dependent univariate Cox regression with the ezcox package . We performed a chi-square test on the clinical data from the 2 sets to ensure that the training set and testing set were unbiased. Finally, each BC patient’s survival risk score (RS) was calculated based on the standardized expression levels of NRLs and the corresponding regression coefficients derived from the least absolute shrinkage and selection operator (LASSO) regression analysis. The calculation is as follows:where RS is an indicator to measure the prognosis of BC patients, and β is the regression coefficient of each variable. The median RS of the training set was utilized as the demarcation point to categorize all included BC samples into the low-risk or high-risk subsets.After risk signatures have been developed, their reliability and robustness in estimating prognostic outcomes must be assessed. The validity of the signature is verified from the following 3 aspects: Kaplan–Meier curves were adopted to contrast the OS of the high-risk and low-risk subsets in the training and testing sets using the survival package. The time-dependent receiver operating characteristic (ROC) curves were used to evaluate survival prediction, and areas under the ROC curve were calculated to assess the predictive accuracy and specificity of the NRLs signature. Univariate and multivariate Cox regression analyses on the whole dataset were performed to assess whether the risk signature was an independent prognostic predictor of survival in BC patients.http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html) in R was utilized to carry out the Gene Set Enrichment Analysis.In order to clarify the differences in enriched pathways between the low-risk and high-risk subsets, the clusterProfiler package (http://www.bioconductor.org/packages/release/bioc/html/ESTIMATE.html). The relative proportions of 22 types of human immune cells were extracted by the CIBERSORT package (http://www.bioconductor.org/packages/release/bioc/html/CIBERSORT.html), and the correlation between the risk signature and immune cell characteristics was revealed.The components of immune cells and stromal cells in the tumor microenvironment (TME) of each BC sample were calculated to verify the differences in microenvironment features between low-risk or high-risk subsets using the ESTIMATE package  was used to assess the half-maximal inhibitory concentration (IC50) of ordinary chemotherapy drugs. Wilcoxon signed-rank test was used to analyze the difference in IC50 values between the high-risk subset and low-risk subset.In order to predict the response of BC patients to chemotherapeutic drugs in 2 different risk subgroups, the “oncoPredict” package (http://www.bioconductor.org/packages/release/bioc/html/maftools.html) in R.Information on somatic gene mutations of BC patients was employed to investigate the correlations between tumor mutation burden (TMB) and RS. This hypothesis was confirmed by comparing the mutation spectrum of the 2 low-risk and high-risk subsets with maftools package  using the keyword “necroptosis”; and 636 NRGs were collected from the Human Gene Database  with the keyword “necroptosis gene.”Figure A total of 6 NRGs were differentially expressed between BC tissues and normal bladder tissues, with differentially downregulated Fig. .We first identified 16,876 lncRNAs from the TCGA-BC RNA-seq data, and 1125 lncRNAs were ultimately selected as NRLs using Pearson correlation analysis. An mRNA-lncRNA co-expression network was established to determine the potential effects of NRLs Fig. A. A PPI P < .05, Fig. http://links.lww.com/MD/I903, which illustrates the information of patients in the 2 sets and results of chi-square test.). LASSO Cox regression analysis was conducted using the training set to acquire the lncRNAs with the highest prognostic value via the “glmnet” package (http://www.bioconductor.org/packages/release/bioc/html/glmnet.html)  Fig. B and C. Firstly, the median RS of the training set was utilized as the cutoff value to assort both the training and testing sets into 2 subsets: low-risk and high-risk. Heatmaps were drawn to show the expression profiles of 15-NRLs in low-risk and high-risk subsets (Fig. In order to clarify the differences in enrichment pathway between the low-risk subset and high-risk subset, enrichment analysis was carried out by Gene Set Enrichment Analysis. The results revealed that the significantly enriched in the high-risk subset were negative regulation of proteolysis, negative regulation of hydrolase activity, coated vesicle, negative regulation of catalytic activity, endocytosis, and presynapse Fig. A. EpitheTME was generally composed of tumor cells, stromal cells, and immune cells. TME scores were used to measure the differences in matrix and immune cell permeability between the low-risk subset and high-risk subset. The samples in the high-risk subset had higher immune, interstitial, and evaluation scores and lower levels of tumor purity Fig. A. As shoIn the high-risk subset, BTLA, BTNL3, CD209, CD226, CD27, CD28, CD40LG, CD70, CD86, CTLA-4, KIR2DL1, LGALS9, PDCD1, SIRPA, TIGIT, TNFRSF14, TNFRSF4, TNFRSF9, TNFSF14, and TNFSF4CD276 were highly expressed (Fig. The 2 risk subsets had a statistically significant difference in their responses to the 15 anticancer drugs. Moreover, patients in the low-risk subset were particularly sensitive to PF-4708671 and GSK269962A, which may be applied to BC patients with a lower RS (Fig. Given the important influence of TMB affecting the clinical responses of immune checkpoint inhibitors, we employed the information on somatic gene mutations of BC patients to investigate the correlations between TMB and our risk signature. According to different classified categories, a missense mutation is the most frequent type of mutation in BC Fig. A. The amConsiderable literatures focus on the effect of necroptosis on BC, but there is a lack of research on the effect of NRLs. Therefore, studying the prognostic value and molecular characteristics of NRLs in BC is of great significance for the diagnosis and treatment of BC patients. In this study, NRLs were obtained from TCGA-BC transcriptional data by Pearson correlation analysis. Then univariate analysis and LASSO regression analysis were used to determine the most valuable NRLs for the prognosis of BC. Finally, 15 lncRNAs were selected to create a 4-lncRNAs risk signature associated with necroptosis. AC009974.1, AC140118.2, LINC00323, LINC02872, PCAT19, AC017104.1, AC134312.5, AC147067.2, AL139351.1, AL355922.1, LINC00844, AC069503.1, AP003721.1, DUBR, and LINC02863 form our risk signatures. Additionally, DUBR can suppress migration and invasion of lung adenocarcinoma by inhibiting cell oxidative phosphorylation. LINC00844 overexpression significantly repressed the proliferation, migration, and invasion of hepatocellular carcinoma cells by inactivating the mitogen-activated protein kinase signaling pathway.,21 LINC00844 was a novel coregulator of the androgen receptor that plays a central role in preventing prostate cancer cell migration and invasion. Furthermore, LINC00844 significantly reduced proliferation and elevated apoptosis of prostate cancer through upregulating GSTP1, which played vital role in cell-cycle regulation. PCAT19 can facilitate MELK-driven glioma tumorigenesis through sponging miR-142-5P. Upregulation of PCAT19 reversed the effects of Honokiol and anisomycin on promoting cell proliferation and inhibiting lung cancer cell apoptosis. Moreover, some studies showed that PCAT19 is implicated in PCa cell growth and tumor progression.–28DUBR suppressed the malignant progression of ovarian cancer by downregulating miR-107 to induce SMAC expression and involvement in the XIAP/ caspase 3 signaling pathway.,30 A wide variety of immune and non-immune cells within the TME infrastructure, together with the factors that they secrete, cooperate to create a chronic inflammatory, immunosuppressive, and protumoral environment. Moreover, TME can not only interact with tumor cells, make tumor cells proliferate, and prevent tumor cell apoptosis and metastasis, but also play an important role in the treatment of tumors. In this study, samples in the high-risk subset had higher immune, interstitial, and evaluation scores and lower levels of tumor purity. Further analysis revealed that the expression of B cells naive in the high-risk subset was significantly upregulated, indicating that these abnormal infiltrating immune cells may be correlated with BC initiation and development. In particular, B cells naive was positively correlated with RS, suggesting that the unfavorable prognosis of patients in the high-risk subset may be partly due to the infiltrated degree of B cells. In addition, the safety and efficacy of PD-1 immune checkpoint inhibitors and CTLA-4 inhibitors have been reported to have been evaluated in several tumor treatments with promising results.–35The nature of TME in the evolution of cancer has changed our understanding of cancer development. In theory, the use of chemotherapy in an adjuvant setting may be optimal after surgery.,38 Meanwhile, immunotherapy has demonstrated remarkable success in BC at various stages of the disease. Higher tumor load before surgery or radiotherapy results in a more effective T-cell response. Patients in the low-risk subset may be sensitive to drugs such as Doramapimod and Ribociclib. Some drugs like Gefitinib, Erlotinib, and Carmustinemay be more applicable for patients with a higher RS.Chemotherapy and immunotherapy accompanied by surgery have become the primary approaches for BC.This study had a few limitations. First, relatively few datasets were collected. More independent datasets are thus needed for further validation. Second, more biological experiments are needed to validate and explore the mechanism of action of necroptosis. We are currently conducting clinical trials, but this is a very time-consuming process. We will clinically verify our conclusions in follow-up studies.In short, this study successfully created a 15-NRLs signature to predict the survival of BC patients. In addition, the study of the molecular features based on 15-NRLs signature was essential to expand new strategies and ideas for the treatment of male patients with BC.This 15-NRLs risk signature may be helpful to assess the prognosis and molecular features of male patients with BC and improve treatment modalities, thus can be further applied clinically.Conceptualization: Yuzhou Jin, Gang Deng.Data curation: Yuzhou Jin, Jiacheng Li.Formal analysis: Yuzhou Jin, Gang Deng.Investigation: Yuzhou Jin.Methodology: Yuzhou Jin, Chenhao Tang.Software: Yuzhou Jin, Shenze Yan.Validation: Yuzhou Jin, Kangwei He.Visualization: Yuzhou Jin, Donggang Shan.Writing – original draft: Yuzhou Jin, Gang Deng.Writing – review & editing: Yuzhou Jin, Gang Deng.


Understanding the cognitive processes that contribute to mental pain in individuals with psychotic disorders is important for refining therapeutic strategies and improving patient outcomes. This study investigated the potential relationship between mental pain, mind wandering, and self-reflection and insight in individuals diagnosed with psychotic disorders. We included individuals diagnosed with a ‘schizophrenia spectrum disorder’ according to DSM-5 criteria. Patients in the study were between 18 and 65 years old, clinically stable, and able to provide informed consent. A total of 34 participants, comprising 25 males and 9 females with an average age of 41.5 years (SD 11.5) were evaluated. The Psychache Scale (PAS), the Mind Wandering Deliberate and Spontaneous Scale (MWDS), and the Self-Reflection and Insight Scale (SRIS) were administered. Statistical analyses involved Spearman’s rho correlations, controlled for potential confounders with partial correlations, and mediation and moderation analyses to understand the indirect effects of MWDS and SRIS on PAS and their potential interplay. Key findings revealed direct correlations between PAS and MWDS and inverse correlations between PAS and SRIS. The mediation effects on the relationship between the predictors and PAS ranged from 9.22% to 49.8%. The largest statistically significant mediation effect was observed with the SRIS-I subscale, suggesting that the self-reflection and insight component may play a role in the impact of mind wandering on mental pain. No evidence was found to suggest that any of the variables could function as relationship moderators for PAS. The results underscore the likely benefits of interventions aimed at reducing mind wandering and enhancing self-reflection in psychotic patients . Further research will be essential to elucidate the underlying mechanisms. Psychiatric disorders are conditions characterized by disturbances in thought, emotion, and behavior that impair functioning and reduce the quality of life . PsychotMental pain, mind wandering and self-reflection are dimensions that have been extensively described in the psychiatric and psychological literature, especially in psychosis ,7,8. MenPsychache may be linked to mind wandering, a common cognitive phenomenon where the focus drifts from the present moment to internal thoughts, consisting of daydreaming, future planning, or recalling memories ; this phAnother dimension that has been extensively described in the psychiatric and psychological literature is self-reflection. This cognitive process, characterized by introspection, involves the deliberate exploration and examination of one’s own thoughts, feelings, and behaviors ,21, reprThe Research Domain Criteria (RDoC) initiative, spearheaded by the National Institute of Mental Health, underscores the importance of dimensional and transdiagnostic approaches in mental health research, and it advocates for the integration of biological research ,27. Our Mental pain in psychotic disorders has been the focus of several studies. It has been recognized as a risk factor for suicide, generalizable between different types of populations ,46,47,48Previous literature has suggested the potential role of mind wandering in the severity of psychiatric disorders , particuPrevious research investigated self-reflection as a predictor of well-being , and sugSelf-reflection might exacerbate mental pain, since an act of introspection could potentially lead to rumination or dwelling on negative thoughts and feelings ,65. SimiPsychotic patients often experience a sense of ontological suffering that is often difficult to address through traditional psychiatric symptom measures, traditional scales, and assessment tools . The valIn individuals diagnosed with psychotic disorders, we hypothesized that unpleasant or negative content experienced in mind wandering may exacerbate mental pain. Additionally, we hypothesized that enhanced self-insight could facilitate adaptive coping strategies, thereby mitigating psychache. The study aims to investigate the interrelationship among mental pain, mind wandering, and self-insight in this population. We expect these factors to be interrelated, acting as either mediators or moderators in their association with mental pain. By focusing on the often-neglected dimensions of individual suffering, we seek to provide actionable data for the refinement of therapeutic interventions and patient well-being.Patients were recruited by convenience sampling in the “Policlinico G. Rodolico” outpatient clinic  from September 2022 to April 2023. Patients visiting the clinic for routine check-ups were approached by attending psychiatrists, who introduced the study and assessed initial eligibility based on predefined inclusion and exclusion criteria. Eligible patients were individuals with a clinical diagnosis of a schizophrenia spectrum disorder according to DSM-5 criteria ; aged beWe employed the Psychache Scale (PAS) of Holden et al. to assess mental pain , which hTo evaluate the incidence and nature of mind wandering, we used the Mind Wandering Deliberate and Spontaneous Scale (MWDS) ,72,73. TThe Self-Reflection and Insight Scale (SRIS) is a psychological measurement tool to assess an individual’s capacity for self-awareness . Good psAfter securing informed consent, the participants were requested to complete paper forms that encompassed the scales. These questionnaires were conducted in a quiet, private environment to reduce potential distractions. During the data-gathering process, a member of the research team was present to ensure the accuracy and completeness of the information being collected and to help participants to understand their assignments.p-value of less than 0.05 was deemed statistically significant for all the above-mentioned analyses. Bonferroni correction was applied to adjust the significance level for further interpreting correlations robustness between constructs under the most conservative assumption of Type I error occurrence, setting a corrected alpha threshold at 0.0033 . Moderation and mediation analyses did not require Bonferroni correction as these tests are theory-driven and evaluate specific, interdependent relationships among variables. Applying Bonferroni correction in such instances would be overly conservative, increasing the risk of Type II errors without appropriately controlling for Type I errors [All data analyses were conducted using Jamovi , an openI errors . In our I errors .The complete sample, with no missing data, was comprised of a total of 34 participants. This included 25 males and 9 females. The mean age was 41.5 (11.5 SD), ranging from 21 to 63. The mean years of illness were 12.2 .Complete characteristics of the total sample are reported in p = 0.022)A moderate direct correlation between PAS-Total and MW-D A moderate direct correlation between PAS-Total and MW-S A moderate direct correlation between PAS-Total and MW-Total A moderate inverse correlation between PAS-Total and SRIS-SR A strong inverse correlation between PAS-Total and SRIS-I A moderate inverse correlation between PAS-Total and SRIS-Total A moderate inverse correlation between MW-S and SRIS-SR A moderate inverse correlation between MW-S and SRIS-I A weak inverse correlation between MW-Total and SRIS-SR A weak inverse correlation between MW-Total and SRIS-I A weak inverse correlation between MW-Total and SRIS-Total , most of our findings concerning PAS-Total correlations retained their statistical significance, except for PAS-Total’s correlation with SRIS-Total and PAS-Total’s correlation with MW-D. However, the relationships between MWDS and SRIS lost their significance, barring the total scale’s inverse correlation, which remained significant.cρ) between variables, accounting for potential confounders.The robustness of the correlation’s strength and significance persisted in the partial correlations and throughout all sensitivity analyses.Some correlations even increased in strength, and all achieved statistical significance, including those correlations that previously did not.Correlational strength changes are represented in Sensitivity analyses revealed that these changes occurred mostly when age was accounted for.However, it is worth noting that the significance of the correlations for SRIS-SR and SRIS-Total was lost after applying the Bonferroni correction . Conversely, SRIS-I correlations maintained their significance after the correction, except for the correlation with MW-Total.The confounders were not inter-correlated, with a few exceptions . As expeMost confounders showed no significant correlation with psychometric measures, with the exceptions being education, which had moderate direct correlations with SRIS scales, and the diagnosis of schizophrenia/schizoaffective disorder, which had a moderate inverse correlation with SIRS-SR .The results indicate that the indirect effects (mediation) of the variables range from 9.22% to 49.8% across different predictors. In the case of MW scales, this range goes from 18% to 49.8%. When SRIS scales are the predictors, the direct effects are overwhelmingly dominant, with percentages ranging from 63.5% to 90.78%. The highest level of mediation is observed when MW-S is the predictor and SRIS-I is the mediator, with nearly half (49.8%) of the effect being indirect. When the predictors were MW-S and MW-Total, SRIS-I’s estimate of the positive indirect effects reached statistical significance; the paths’ betas are shown in None of the relationships between the predictors and moderators are statistically significant.c(ρ = 0.807). This suggests that amplified mind wandering in subjects with psychosis might trigger mental pain. Conversely, it is plausible to hypothesize that those experiencing higher degrees of mental pain engage more in mind wandering as a form of coping, escaping their distress in this way, partly due to the reduced insight capacity accompanying mental pain—as shown by our mediation analysis. It should be considered that the MW-D subscale had less correlation strength with PAS-Total, suggesting that deliberate mind wandering might act as a buffer, reducing the adverse effects of spontaneous mind wandering on mental pain. This could be attributed to its less harmful impact on self-reflection abilities, as shown by our data, potentially due to its link to more creative and productive thought processes [To the best of our knowledge, this study is the first to investigate the connection between the dimensions of mental pain, mind wandering, and self-reflection in patients with a clinical diagnosis of schizophrenia spectrum disorder. Our analysis revealed that MW scales have a moderate direct correlation with PAS-Total, reaching the range of strong and very strong when accounting for confounders in partial correlations, the strongest being with MW-Total rocesses ,89,90,91cρ = −0.735). Our research adds to previous findings [In contrast, SRIS scales displayed an inverse correlation with PAS-Total, ranging from moderate to strong, even when accounting for confounders. Specifically, among the SRIS subscales, SRIS-I had the strongest inverse correlation with PAS  . AdditioThe potential role of self-reflection in mitigating mental pain also deserves exploration. Once we establish a correlation, it is essential to delve into the underlying mechanisms. These could be due to the improved coping skills fostered by self-reflection  or the pWhile self-administered psychometric measures are valid and reliable, they are prone to typical biases and could offer less precision than direct performance measurements, when available . Mind waOur research identified a direct association between mind wandering and mental pain in individuals diagnosed with psychotic conditions. Self-reflection, particularly self-insight, showed an inverse correlation. Mediation analyses revealed that the impact of mind wandering on mental pain may be partly attributed to a deficit in self-insight. This finding suggests that self-insight could be a crucial focus in the cognitive treatment of these conditions. Moderation analyses indicated that the levels of self-insight and mind wandering did not significantly modify each other’s effects on mental pain, implying potential separate interventions for these aspects.


Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model , easy integration of any OBO  ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.https://kghub.org. Addressing scientific challenges such as climate change and treatment of complex or rare diseases requires integration of heterogeneous data from multiple disciplines. These datasets differ in terminology, units, granularity, and perspective, among other factors, and are difficult to combine using traditional relational databases. Knowledge graphs (KGs), which offer a more flexible and powerful way to link together heterogeneous datasets, are increasingly used to integrate data in various domains including biology, ecology, biomedicine, and personalized health , DBpedia [2007 , Harmonizome (https://maayanlab.cloud/Harmonizome/), CROssBAR (https://emmaa.indra.bio/), while BioKDE  and can reveal structural patterns. Including ontology data supports approaches to detect “emergent” and otherwise nonobvious associations between nodes. In the above example, incorporating diseases from the Mondo disease ontology  Foundry . Drug repurposing is a popular use case for graph ML on KGs, as observations about drugs in therapeutic use can be integrated with basic research data from many other sources  that provide more context for ML models. There have been notable successes in predicting new protein targets for existing drugs using graph ML over the past several years . Graph MKG-Hub is a collection of tools and libraries for building and reusing KGs . It inclhttps://biocypher.org/tutorial.html), Biolink Model is used as an ontological backbone for all extended classes that may express more narrowly focused or detailed descriptions of the Biolink class hierarchy. While Biolink maintainers welcome additions and encourage extensions to make their way back into the main model, the extendable LinkML framework helps maintain an active community that is responsive to changing biology and adaptable to many disparate use cases. A more detailed description of how Biolink Model is used in KG-Hub is contained in the supplement , and associations that guide how entities should relate to one another. Biolink represents a wide array of biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype, and establishes mappings to existing biomedical ontologies and reuses existing ontology term definition in its structure (pplement .https://github.com/monarch-initiative/kghub-downloader). KGX (Knowledge Graph Exchange) is a KG serialization standard supported by a Python library and command line utilities that help transform data into Biolink Model compliant graphs. Koza (https://github.com/monarch-initiative/koza) is a declarative transformation framework that uses user-defined templates to both document and define the parsing and transformation of data into KGX format. KG-Hub uses these tools to transform data sources into standalone Biolink Model compliant graphs. KG-Hub orchestrates the transformation using a download.yaml configuration file and a declarative transformation configuration (enabled by Koza), and a Python control script. The kghub-downloader module uses the download.yaml file to document and manage retrieval of the data from a variety of possible sources including a local file, a URL or via queries to an Elasticsearch API (e.g. ChEMBL API). The declarative transformation configuration (as documented in the Koza framework here: https://github.com/monarch-initiative/koza) and accompanying transformation script, process raw input data into KGX TSV format. KGX TSV format is a flattened serialization of the nodes and edges in a graph as tab-separated values. In addition, the declarative nature of the configuration file also serves as documentation and data instance validation for the transform . An example of this process may be found at https://github.com/Knowledge-Graph-Hub/kg-example and in ontology transform in the kg-example example process is a single Python script capable of transforming any OWL ontology from the OBO Foundry). The final step in each graph’s ETL process is to merge the individual transform products into one final graph. This step is handled by the KGX merge function and defined by a configuration file (merge.yaml). Once transformed, each subgraph and the merged graph are available from a centralized open data repository , allowing users to reuse, mix, and match subgraphs, as well as share the final merged graph.KGs are constructed with an extract, transform, and load (ETL) process driven by the KGX and Koza toolkits and the kghub-downloader module  S3 is used to host files on KG-Hub, providing consistent uptime for KG-Hub. The data repository is available at https://github.com/Knowledge-Graph-Hub/kg-cookiecutter). The kg-cookiecutter guides users through a series of prompts to customize their KG project code repository, while relying on KG-Hub to specify best practices, provide basic configuration and setup, and wiring to deploy new KGs consistently. The kg-cookiecutter uses the more generic, cookiecutter framework to make its templates (https://cookiecutter.readthedocs.io/en/stable/). The kg-cookiecutter also provides example download and merge configuration files, as well as an example transformation script that specifies the preferred location for the resulting graph and data. It stores all project materials according to KG-Hub design patterns, ensuring that the directory for each project contains the KG itself and all necessary code, data, and configurations to rebuild the graph. Users are able to customize the rebuild frequency of their KG-Hub graph using a custom Jenkins configuration. Jenkins is a continuous integration framework that helps coordinate and build systems (https://www.jenkins.io/), where systems are broadly defined as containers for code coupled with data.KG-Hub provides a default template to generate modular KGs, the kg-cookiecutter (https://neo4j.com/) during the merge stage of the ETL process, or the KGX TSVs can be loaded directly into Neo4j using the KGX library. These Neo4j databases can then be queried using the Cypher query language (https://www.w3.org/TR/rdf-sparql-query/), a popular language for querying graph data in RDF format. KGX TSV files can also be loaded into a tool like Cytoscape for visualization, querying, and browsing.The availability of biomedical KGs in standard formats supports a variety of downstream use cases. KGs can be loaded into a Neo4j graph database (Below we describe the design patterns (reusable solutions) we developed to address commonly occurring problems in the construction and use of KGs. These design patterns can be repurposed for other KG efforts.https://github.com/Knowledge-Graph-Hub), with reusable, modular software for downloading data, transforming data into subgraphs, and merging subgraphs into KGs (https://github.com/biolink/kgx/blob/master/specification/kgx-format.md#kgx-format-as-tsv), and the merge step combines the subgraphs from each upstream source into a single, merged KGX TSV. The transform step in some KG projects (e.g. KG-IDG) relies on Koza (https://github.com/monarch-initiative/koza), a Python package that facilitates the ingestion of data into KGX TSV format. The merge step uses KGX (https://github.com/biolink/kgx) to perform ID normalization and combine the subgraphs.The KG-Hub framework includes ETL Python code for transforming upstream data into KGs , and both nodes and edges may have one or more properties . A property graph model offers the features needed for a variety of downstream applications, such as storage in Neo4j, while also remaining sufficiently flexible to transform products to other formats (e.g. n-triples or RDF/XML).https://linkml.io), then use the new model in place of Biolink in KG-Hub’s graph assembly pipeline.One of the identifying features of a KG built with KG-Hub is data harmonized according to Biolink Model. Domain knowledge in a KG that conforms to Biolink is represented using associations. An association minimally includes a subject and an object related by a Biolink Model predicate, together comprising its core triple (statement or primary assertion). A key step in KG development is identifying the concepts and relationships in Biolink Model that map to the data source being transformed. KG-Hub provides examples and guidance in selecting Biolink categories for any KG. In addition, KGX automatically assigns Biolink categories to data based on namespaces and mappings to external resources curated in Biolink Model. During data loading, nodes and edges are typed with Biolink categories and association types. With a single model that spans data sources and transformed KGs, KG-Hub facilitates analysis of KG contents in a clearly defined way, and aids interoperability of data between KG projects. For example, a KG of biolink:chemicals, biolink:proteins, and biolink:diseases can be filtered to just interactions between chemicals and proteins while retaining all subcategories. For use cases not covered by Biolink Model, users may extend the model or even create a new LinkML data model (https://www.jenkins.io/). In addition, a new build is triggered with each update to the ETL software used in the pipeline. The build system provides error messages and guidance when volatile upstream data breaks the ETL code or fails validation.In designing KG-Hub, we kept in mind that biomedical data sources are often volatile and may undergo massive updates at any time. Downloading upstream data is a frequent point of failure in bioinformatics ETL pipelines, due to issues such as changes in URLs and network instability. KG-Hub caches the most recent version of upstream data that was successfully downloaded for each data source to ensure that the ingestion can proceed even when these issues arise. KG-Hub captures data source versions and which version of its own software is used in each build and adds provenance to the data as it moves through the build process. Provenance tracking enables users and systems downstream to trace the transformation of data from its source to the resulting KG. KG-Hub also produces a permanent URL (PURL) for all artifacts in the build ensuring that downstream consumers can reproduce the results of each build even if the upstream sources have changed. Each versioned KG-Hub build, or pipeline run, is scheduled to run on a monthly basis for each KG project, using continuous integration/continuous delivery software (transformed) in the build directory for each project, with PURLs. This design allows projects to easily ingest the transformed subgraph from an upstream data source that was transformed by a different KG-Hub project, eliminating duplication of effort and encouraging alignment of data across KG projects.KG-Hub is designed to allow and encourage reuse of transformed data across different projects. Each KG project produces a subgraph representing the data from each of the upstream sources that it ingests and transforms. These subgraphs are stored separately in a subdirectory  as an interlocutor between OBO ontologies and their graph representation.Ontologies provide a convenient means to incorporate knowledge from domains of interest into KGs in order to contextualize instance-level data. For example, to harmonize knowledge about human diseases and disease phenotypes, one might incorporate the Mondo disease ontology (https://kghub.org/), a browsable interface with graph data and other artifacts (https://kghub.io/), and a GitHub organization for source code (https://github.com/knowledge-Graph-Hub/) are also available.KG-Hub currently includes seven biomedical KG projects that integrate data pertaining to COVID-19 biology .KG-Hub projects typically emit the representation of the graph in several formats, including minimally KGX TSV format, but also RDF/ntriples and Blazegraph journal file formats. The Blazegraph journal file provides the ability to load the data into a Blazegraph instance, as well as to analyze and compare different builds of various KG projects using a tool such as Blazegraph runner (https://github.com/biolink/kgx). KG-Hub projects store graph data in KGX TSV format (https://github.com/biolink/kgx/blob/master/specification/kgx-format.md) and are therefore natively compatible with KGX. In addition to using KGX in the ETL process (in the merge step to combine subgraphs from different upstream sources), it can be used downstream to convert to other data formats, and also to load a Neo4j graph database.KGX is a tool for working with graph data that provides utilities for converting between common graph formats, extracting subgraphs, merging graphs, and loading data into graph databases and a list of all KG-Hub projects that are available through the API:from grape.datasets import   get_available_graphs_from_repositoryget_available_graphs_from_repository(‘kghub’)This API can also retrieve a specific build for a given KG-Hub project:Tight integration with GRAPE allows KG-Hub graphs to be easily downloaded, queried, and analyzed, and also allows procedures such as node embedding and graph neural networks to be applied to these KGs. In addition to reducing the difficulty in finding and accessing KG data, integration of KG-Hub with GRAPE addresses the computational challenges associated with analyzing large KGs, as GRAPE is able to efficiently load and train graph ML models on very large KGs (https://github.com/Knowledge-Graph-Hub/neat-ml) is a Python package that allows graph ML algorithms to be applied to KG-Hub projects in a simple, declarative, YAML-driven way. This YAML file serves to describe the exact task, and after completion serves as provenance and documentation. An example YAML file used in the KG-IDG project is shown in NEAT (All KG-Hub artifacts are stored on cloud storage (AWS S3). During the build process, index.html files are constructed to make these artifacts browsable, and these are made public using a content delivery solution (AWS CloudFront). To run ML tasks, we have integrated with a cloud computing platform, Google Cloud Platform (GCP). Within our continuous integration software configuration (Jenkins), we create new GCP instances to run the ML task, after which ML artifacts are uploaded to our browsable S3 cloud storage An automated scheduler script runs each pipeline  on GCP and returns results to the project directory on KG-Hub.https://github.com/Knowledge-Graph-Hub/kg-cookiecutter). In addition to providing consistent project structure, the template includes example Python code for ETL functions, as well as tests and automation configurations.We have created a template to facilitate new KG projects Provide the KG in additional serialization formats as needed . After merging transformed sources with KGX, the resulting graph may then be examined and analyzed further with GRAPE and NEAT, as described above.As all KG-Hub components are open-source, they may be replicated in other software environments and infrastructure. If an organization would like to set up their own, private KG-Hub, they may do so by setting up each graph project as a private GitHub repository based on the kg-cookiecutter template. They may then set up their own AWS S3 cloud storage as a data repository, or elect to use other cloud storage providers supporting static web page hosting, such as Google Cloud Storage or Microsoft’s Azure Storage. Automated KG updating may be performed through a combination of GitHub Actions and an instance of the Jenkins automation server, as we have done, but may alternatively be accomplished through other continuous integration systems and data orchestration platforms such as Dagster or Apache Airflow.KG-Hub addresses many hurdles involved in assembling, sharing, and using KGs. While KGs provide an elegant method to integrate biological data, work involving KGs remains challenging for a variety of technical and sociological reasons. Analyses of KGs are difficult to reproduce without versioned builds of KGs (as provided by KG-Hub), and interpretation of these analyses are often confounded by lack of provenance and lack of access to upstream data. Integrating data across multiple KGs is challenging due to incompatibilities in format, schema, and data representation. KG-Hub helps to lower these barriers.KG-Hub could be considered the analog of an OBO Foundry for KGs. Much of KG-Hub’s philosophy echoes that of the OBO Foundry . These tutorials provide practice examples of how KG-Hub can be used, especially for downstream graph ML use cases such as node embedding and link prediction using GRAPE, and automated graph ML using NEAT.Demonstrations of relevant use cases are provided in the KG-Hub tutorials (https://github.com/Knowledge-Graph-Hub/knowledge-graph-hub-support).KG-Hub is designed to be straightforward to use and comprehensible to both seasoned KG engineers and beginning researchers. Some understanding of graph theory, ontologies, and the idiosyncrasies of various graph representations (e.g. how relationships between entities are described within triples versus the property graph model that is used in KG-Hub) helps. For ontologies in particular, users must remain aware of the limitations of each set of axioms and terms and how they relate to a chosen domain. If chemicals are to be represented by ChEBI terms, e.g. all newly added chemical nodes should be linked to ChEBI identifiers. If the community identifies needs outside KG-Hub’s current technical capabilities, we will explore further improvements. Our first point of contact for technical requests is the KG-Hub Support code repository on GitHub (Future improvements to KG-Hub will largely depend on community needs and contributions. We will continue to include new KG projects and provide self-contained versions of other graph collections. We foresee creating further valuable assets for graph-based biological and biomedical data analysis. KG-Hub will continue to be an evolving resource for driving reproducible, standardized KG construction and reuse.btad418_Supplementary_DataClick here for additional data file.


A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models. In the current era characterized by abundant information, individuals are confronted with a deluge of extensive data ,2,3,4. NNumerous approaches have been proposed for implementing an RS. Among them, the Latent Feature Analysis (LFA) model has gained significant popularity in industrial applications due to its efficiency and scalability . When apIn recent times, the rapid advancement of deep learning has led to the widespread adoption of deep neural networks (DNNs) ,23,24 inIt proposes an AutoLFA model that aggregates the merits of both the LFA model and the DNN-based model by a customized self-adaptive weighting strategy;Theoretical analyses and model designs are provided for the proposed AutoLFA model;Extensive experiments on five real recommendation datasets are conducted to evaluate the proposed AutoLFA model. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the related state-of-the-art models.Upon the aforementioned discussions, it becomes apparent that the LFA and DNN-based models have distinct advantages and disadvantages. Consequently, relying solely on either the LFA model or the DNN-based model cannot ensure optimal recommendation performance across diverse real-world application scenarios. To tackle this challenge, this study proposes a novel hybrid recommendation model called AutoLFA, which combines Autoencoder  and LFA 1-norm regularization , and if Ewt(i) is set according to align (10) during training, the following alignment holds:For an AutoLFA model, assuming the Clwhere I is the maximum iteration.I linearly. This leads us to the following proposition.By setting Proposition 1.With where the limit as I approaches infinity, const = 19.45.Remark 1.t(I) | t = 1, 2} + const, with Proposition 1 indicates that Cl(I) is constrained by min{ClRQ 1: Does the proposed AutoLFA model outperform state-of-the-art models in accurately predicting user behavior data?RQ 2: How does the AutoLFA model self-adaptively control the ensemble weights of its base models during the training process to ensure optimal performance?RQ 3: Are the base models of AutoLFA diversified in their ability to represent the same user behavior data matrix, thereby enhancing the performance of AutoLFA?RQ 4: What is the impact of the number of Latent Features and hidden units in the base models on the accuracy of AutoLFA?In this section, we aim to address the following research questions (RQs) through subsequent experiments:Datasets: For our experiments, we utilize five commonly used user-item datasets, as summarized in Evaluation Metrics: The primary objective of representing the user-item matrix is to predict missing ratings accurately. To assess the prediction accuracy of the tested models, we employ two evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE), which are calculated according to [rding to .Baselines: Our proposed MMA model is compared against seven state-of-the-art models: AutoRec , MF, and FML , and NRR, SparseFC, IGMC, and GLocal-K (deep-learning models). A brief description of these competing models is provided in Implementation Details: For all datasets, we set the learning rate to 0.001 for two models. We set the number of hidden units for the Autoencoder to 500 and the number of latent factors for the LFA model 30 to achieve better performance. The final testing results are obtained from the best-performing model, which exhibits the lowest prediction error on the validation set during training. The training process terminates when the preset threshold for training iterations is reached. All experiments are conducted on a GPU server with two 2.4 GHz Xeon Gold 6240 R CPUs, 376.40 GB RAM, and 4 Tesla V100 GPUs.p-values. The Friedman test compares the performance of multiple models across multiple datasets using F-rank values, with lower values indicating higher prediction accuracy. The comparative experiment results are normalized for better interpretation before conducting the Wilcoxon signed-ranks test and the Friedman test. The statistical analysis results of loss/tie/win, the Wilcoxon signed-ranks test, and the Friedman test are presented in the third-to-last, second-to-last, and last rows of AutoLFA achieves the lowest RMSE/MAE in most cases, with only ten cases of loss and one case of a tie in comparison. The total count of loss/tie/win cases is 7/1/62.p-values are below the significance level of 0.1, indicating that AutoLFA outperforms all competitors in terms of prediction accuracy.All AutoLFA obtains the lowest F-rank among all participants, confirming its highest accuracy across all datasets.These observations highlight that AutoLFA achieves the highest prediction accuracy for predicting missing user data compared to other models.LFA-based models generally exhibit higher computational efficiency compared to DNN-based models, as they are trained on observed user behavior data, unlike DNN-based models.Due to their complex data form and architecture, GNN-based models consume significant computational resources and time. From Except for slightly longer time consumption on dataset D4, AutoLFA’s time consumption falls between LFA-based and GNN-based models. It is slightly higher than the original Autoencoder-based model but faster than other DNN-based models in most cases.Based on these observations, we can conclude that the relatively simple structure of the base models in AutoLFA allows for acceptable total time costs after ensembling two base models.To discuss the self-adaptive control of AutoLFA in ensembling different variant models and ensuring its performance, we monitor the variations of ensemble weights between its base models.Monitoring ensemble weight variations: In most cases e.g., a–d, the In some instances, the LFA-based model’s weight may exceed that of the Autoencoder-based model. For example, in In conclusion, based on the experimental results and observations above, we can infer that AutoLFA effectively leverages different types of models. By aggregating these models in the ensemble stage, AutoLFA surpasses other state-of-the-art models in predicting missing ratings with only minor sacrifices in computational resources.µ) and standard deviation (σ). The measurements of the full width at half maximum (FWHM) and the height of the Gaussian curve are also presented. From The distribution of Latent Features in the Autoencoder-based model tends to have more values concentrated at the extremes , as shown in After encoding, the Autoencoder-based model’s Latent Features are more likely to exhibit unusually high values within specific ranges. In contrast, in the LFA-based model, there are no extreme values, as depicted in In some cases, the distribution of Latent Features in the Autoencoder-based model appears to be slightly more uniform compared to the LFA-based model, as illustrated in In order to investigate the diversity of the base models of AutoLFA and their abilities to predict the user behavior data matrix, we visually analyze the encoder output of the Autoencoder-based model, which represents the Latent Features of an Autoencoder model, and the Latent Features of the LFA-based model. The distribution of these Latent Features for the base models across all datasets is depicted in The observed information above indicates that the Autoencoder-based and LFA-based models have distinct representation characteristics, allowing AutoLFA to benefit from their different representation abilities. Consequently, AutoLFA ensures accurate prediction of missing ratings.Increasing the number of Latent Features/hidden units from 2/20 to 20/300 results in a rapid improvement in the accuracy of AutoLFA. During this range, AutoLFA substantially increases accuracy without incurring significant computational costs.Once the number of Latent Features/hidden units reaches 25/400, the rate of accuracy improvement becomes less prominent in We further investigate the impact of the number of Latent Features and hidden units in the base models on AutoLFA. These observations suggest that setting the number of Latent Features/hidden units as 30/500 allows AutoLFA to achieve optimal accuracy in most cases without imposing significant computational resource demands. Although this setting may not yield the highest accuracy in certain cases, it remains relatively close to the optimal value.This paper proposes a novel hybrid recommendation model by combining Autoencoder and LFA models, termed AutoLFA. Its main idea is two-fold: (1) It leverages an Autoencoder and a Latent Feature Analysis (LFA) model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths, and (2) it integrates the Autoencoder and LFA models using a customized self-adaptive weighting strategy. As such, the merits of the LFA and DNN-based models are combined into the AutoLFA model, making it achieve superior recommendation performance under various real-world applications. The experiments investigate four research questions on five real recommendation datasets. The results verify that the proposed AutoLFA outperforms several state-of-the-art models. In the future, we plan to aggregate more variants of LFA-based and deep neural networks (DNNs)-based models to achieve better recommendation performance.


However, further investigation is needed to determine its effect on the development of resistance and adverse effects.(1) Background: Antibiotics are received by a majority of adult intensive care unit (ICU) patients. Guidelines recommend antibiotic de-escalation (ADE) when culture results are available; however, there is less guidance for patients with negative cultures. The purpose of this study was in investigate ADE rates in an ICU population with negative clinical cultures. (2) Methods: This single-center, retrospective, cohort study evaluated ICU patients who received broad-spectrum antibiotics. The definition of de-escalation was antibiotic discontinuation or narrowing of the spectrum within 72 h of initiation. The outcomes evaluated included the rate of antibiotic de-escalation, mortality, rates of antimicrobial escalation, AKI incidence, new hospital acquired infections, and lengths of stay. (3) Results: Of the 173 patients included, 38 (22%) underwent pivotal ADE within 72 h, and 82 (47%) had companion antibiotics de-escalated. Notable differences in patient outcomes included shorter durations of therapy ( Most patients in adult intensive care units (ICUs) receive antibiotics for suspected or confirmed infection. A 2009 study found that ICUs across numerous countries reported that 51% of their patients had confirmed infection, and 71% were receiving antibiotics . A 2019 Staphylococcus aureus (MRSA). In addition to creating a standardized definition, an algorithm was developed to assist with ADE, which separated antibiotics into two categories: pivotal and companion antibiotics. Pivotal antibiotics were defined as the central agent to the regimen, which is typically a beta-lactam antibiotic with Gram-negative coverage. Companion antibiotics could include agents used to broaden the spectrum of coverage of the pivotal antibiotic, such as anti-MRSA agents. For patients with positive cultures, it is suggested to perform ADE within 24 h of definitive culture results, and previous studies have shown that ADE appears to be more common in patients with microbiological documented infections [In 2020, the European Society of Intensive Care Medicine (ESICM) and the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) released a position statement on ADE in critically ill patients . This tafections . HoweverThough ESICM/ESCMID provides some guidance on patients with culture-negative infections, de-escalating antimicrobial therapy in these patients remains a challenge. Clinicians often feel hesitant de-escalating broad-spectrum antibiotics without microbiology to guide them, even with evidence of clinical improvement. As an example, one large study conducted by Desphande and colleagues limited their evaluation of ADE in non-critically ill patients with pneumonia specifically to patients with negative cultures. This study highlighted provider hesitancy upon de-escalation in culture-negative patients, with an overall de-escalation rate of only 13% across 164 hospitals. Despite low de-escalation rates, an improvement in patient outcomes was found in those who successfully underwent de-escalation . IDSA guThis single-center, retrospective, cohort study evaluated patients admitted to the 100-bed ICU tower of an academic medical center from September 2018 until December 2019. Eligible patients were ≥18 years old, received a broad-spectrum antibiotic regimen (defined below) within 24 h of ICU admission, had a total duration of antibiotic therapy of 5 days or greater, and had negative clinical cultures. Patients were excluded if they received antibiotics for peri-operative prophylaxis, esophageal/bowel perforations, facial trauma or penetrating brain injury, febrile neutropenia, or acute exacerbations of cystic fibrosis. If patients received more than one antibiotic course upon the same admission, only the first antibiotic course of ≥5 days was included. This study was approved by the medical center’s Institutional Review Board (protocol number #2020V0252).Clostridiodes difficile infections, ICU all-cause mortality, and all-cause mortality within 30 days of ICU discharge.Patients with negative clinical cultures were separated into two study groups: those that underwent pivotal drug de-escalation or those that did not undergo pivotal drug de-escalation. Once the patients were stratified into their respective study groups, relevant data were collected on the password-protected database, REDCap . The folNegative clinical cultures were defined as those finalizing with no growth or no evidence of a single offending pathogen, including normal flora . These patients with negative clinical cultures were also evaluated for culture appropriateness based on the site of the presumed infection. At a minimum, the patients had to have had the following cultures drawn depending on their site of presumed infection: pneumonia = blood and respiratory; urinary tract infection (UTI) = blood and urine; abdominal infection = blood and wound; skin and soft tissue infection (SSTI) = blood; sepsis of unknown source = blood. Blood and urine cultures were considered appropriate if obtained within 24 h of antibiotic initiation. Respiratory cultures had to be obtained within 48 h of antibiotic initiation. The site of the presumed infection was assessed as defined by The International Sepsis Forum Consensus Conference .Pseudomonas aeruginosa and either an antimicrobial with MRSA coverage for a suspected MRSA infection or double P. aeruginosa coverage. Pivotal antibiotics were the anti-pseudomonal beta-lactams, including piperacillin/tazobactam, cefepime, ceftazidime, and meropenem. The companion antibiotics were the agents added for MRSA coverage  or double P. aeruginosa coverage .For the purposes of this study, a broad-spectrum regimen was one consisting of at least one beta-lactam antimicrobial active against P. aeruginosa [Group 1: amoxicillin, ampicillin, amoxicillin/clavulanate, ampicillin/sulbactam, ceftriaxone, cefpodoxime, cefuroxime, cefdinir, cephalexin;Group 2: piperacillin/tazobactam, cefepime, ceftazidime, aztreonam, levofloxacin, ciprofloxacin;Group 3: meropenem.Patients were considered to have undergone de-escalation if antibiotics were narrowed in spectrum within 72 h of therapy initiation. During the study period, there was no institutional protocol for de-escalation. Adapted from the Duke Antimicrobial Stewardship Outreach Network (DASON) antibiotic rankings, pivotal antibiotics were stratified into different groups based on the spectrum of activity and ability to cover ruginosa . PivotalCompanion antibiotic de-escalation occurred if the antibiotic was discontinued completely. As part of data collection, the incidence of antibiotic escalation was evaluated as well, and was defined as either switching from one pivotal antibiotic to another in a higher group or an addition to antibiotic therapy without de-escalation .t-test or the Mann–Whitney U test, as appropriate. No imputation was made for any missing data. Statistical analysis was performed via SPSS 27  and statistical significance was defined as p < 0.05.Categorical data were assessed using chi-square or Fisher’s exact test, and continuous data were assessed using Student’s p = 0.010). A full list of demographics can be seen in Nine hundred and sixty-one patients were screened for enrollment, yielding 173 patients who could be included. The most common reasons for exclusion were either positive cultures or not receiving at least 5 days of antimicrobial therapy. Of the 173 patients included in this study, 38 patients (22.0%) underwent pivotal antibiotic de-escalated within the first 72 h following antimicrobial initiation. Companion antibiotics were discontinued by 72 h in 82 patients (47.4%), and 81/82 (98%) of the companion antibiotics were anti-MRSA agents. Patients who underwent pivotal antibiotic de-escalation were more likely to no longer require vasopressors (p = 0.016), and positivity for the MRSA PCR was low in both groups. Only 15% of patients had their antibiotics managed by an Infectious Disease team.The most common indication for antibiotics was pneumonia (50.9%) . Piperacp < 0.001), ICU length of stay (p < 0.001), and durations of antibiotic therapy (p = 0.003), and a lower incidence of AKI (p = 0.031). No statistical difference was found for the development of antibiotic resistance. The remaining secondary outcomes can be found in Patients who underwent pivotal ADE had shorter hospital length of stay . The baseline characteristics were balanced between the groups, with most patients presenting with comorbidities, similar APACHE II scores, and community-acquired infections. The rate of companion antibiotic de-escalation was higher (47.4%), potentially due to the availability of rapid diagnostic testing for MRSA. Patients who underwent de-escalation were more likely to have vasopressors discontinued by day 3, which may have played a role in the decision to change antibiotic therapy. In addition, ADE did not appear to be correlated with any negative outcomes. Patients undergoing de-escalation did not have a higher mortality, but had a significantly shorter length of stay and duration of antibiotic therapy.p = 0.095). De-escalation was also found to be associated with reductions in all negative outcomes , except C. difficile infections. Most hospitals included in this study had de-escalation rates lower than ours, despite only including non-critically ill patients, but the outcomes were similar.The low rate of de-escalation found in our study is similar to what has been described in previous literature. One of the largest studies evaluating ADE in critically ill patients was the DetermInants of Antimicrobial use aNd de-escalAtion (DIANA) trial . This muWhile several previous publications show similar de-escalation rates to ours, other smaller studies do exhibit higher rates of de-escalation. Liu and colleagues found that 63% of patients had antibiotics de-escalated at 72 h . The higp < 0.01) [S. aureus and P. aeruginosa in respiratory cultures growing “commensal respiratory flora”, led to a significant increase in ADE [Given the variable and sometimes low rate of ADE in critically ill patients, it is important to identify the predictors of ADE. One important factor in promoting ADE appears to be microbiologic culture results ,16,17. A < 0.01) . Similar < 0.01) . In a ste in ADE . Becausee in ADE ,20. The C. difficile infection in patients that continued their initial regimen, but this was not statistically significant. Another outcome evaluated was the development of resistance because there is evidence suggesting that the use of broad-spectrum antibiotics is associated with this outcome, as described above. [When evaluating the potential negative effects associated with not performing ADE, our study found a statistically significant difference in the development of AKI, with patients undergoing de-escalation demonstrating a decreased rate of AKI. This, in part, could be attributed to the withdrawal of singular nephrotoxic agents or the de-escalation of nephrotoxic combination therapy. For instance, the high rate of piperacillin–tazobactam and vancomycin used in this cohort may contribute to this correlation, given that the combination is associated with AKI . There wd above.  However,d above. . Other sd above. ,23. We fp = 0.03). In contrast, data gathered in our study show that patients who underwent de-escalation had a statistically significant decrease in their total duration of antimicrobial therapy. One reason for this could be explained by more patients having been diagnosed as having sepsis of an unknown source in the non-ADE arm. Without a defined source, it may have been more challenging for providers to perform ADE.One concern associated with de-escalation is that some evidence suggests that de-escalation may be associated with an increased duration of therapy . Leone aFinally, a question remains on the best time to perform ADE in critically ill patients. We evaluated ADE within 72 h after antibiotic initiation, since that is generally when culture results are available. In patients with positive cultures, de-escalation could be considered even sooner. If rapid diagnostics are available, these tests can not only facilitate organism identification, but also the presence of resistance for some tests, such as PCR-based blood culture identification panels. The MRSA PCR is another example of a test that has been implemented at many institutions as a tool for de-escalation. For example, Mergenhagen and colleagues previously found that the negative predictive value (NPV) of MRSA PCR screening used to rule out invasive MRSA infection was 96.5%, describing their utility with de-escalation and empiric avoidance . OutsideP. aeruginosa were assessed to ensure that patients were indicated for broad-spectrum antibiotics empirically. During the study period, antimicrobial stewardship and ICU pharmacist presence increased, which may have affected de-escalation rates in the latter years.This study did have important limitations. This is a small, single-center study, limiting generalizability to other centers. In addition, the retrospective nature of the study introduces potential selection bias. As previously mentioned, patients with earlier improvement (vasopressors discontinued by day 3) were more likely to undergo ADE, which may be why clinical outcomes were similar between the groups. Multiple infection types were included, but pneumonia was the most common; therefore, one should exercise caution when applying the results of this study to less represented disease states. No risk factors for MRSA or ADE of empiric broad-spectrum antimicrobials is recommended by experts, clinical practice guidelines, and professional organizations. However, ADE remains difficult and infrequent in critically ill patients, particularly those without positive clinical cultures. This study adds to the current literature regarding ADE in this population. Our results showed that ADE was rare, but was associated with shorter length of stay and duration of therapy, and lower incidences of AKI, without increased mortality. One area for improvement identified was the obtainment of cultures, and providing education should be prioritized going forward in order to assist with de-escalation. Further study is warranted to corroborate these findings, explore the impact on resistance development, and determine the applicability of these findings to infectious states other than pneumonia. Health systems should continue to advocate for ADE in patients, including those who are critically ill, to prevent adverse drug events and the development of antimicrobial resistance.