A super-massive Neptune-sized planet.

Neptune-sized planets exhibit a wide range of compositions and densities, depending on factors related to their formation and evolution history, such as the distance from their host stars and atmospheric escape processes. They can vary from relatively low-density planets with thick hydrogen-helium atmospheres1,2 to higher-density planets with a substantial amount of water or a rocky interior with a thinner atmosphere, such as HD 95338 b (ref. 3), TOI-849 b (ref. 4) and TOI-2196 b (ref. 5). The discovery of exoplanets in the hot-Neptune desert6, a region close to the host stars with a deficit of Neptune-sized planets, provides insights into the formation and evolution of planetary systems, including the existence of this region itself. Here we show observations of the transiting planet TOI-1853 b, which has a radius of 3.46 ± 0.08 Earth radii and orbits a dwarf star every 1.24 days. This planet has a mass of 73.2 ± 2.7 Earth masses, almost twice that of any other Neptune-sized planet known so far, and a density of 9.7 ± 0.8 grams per cubic centimetre. These values place TOI-1853 b in the middle of the Neptunian desert and imply that heavy elements dominate its mass. The properties of TOI-1853 b present a puzzle for conventional theories of planetary formation and evolution, and could be the result of several proto-planet collisions or the final state of an initially high-eccentricity planet that migrated closer to its parent star.

Attribute-guided prototype network for few-shot molecular property prediction.

The molecular property prediction (MPP) plays a crucial role in the drug discovery process, providing valuable insights for molecule evaluation and screening. Although deep learning has achieved numerous advances in this area, its success often depends on the availability of substantial labeled data. The few-shot MPP is a more challenging scenario, which aims to identify unseen property with only few available molecules. In this paper, we propose an attribute-guided prototype network (APN) to address the challenge. APN first introduces an molecular attribute extractor, which can not only extract three different types of fingerprint attributes (single fingerprint attributes, dual fingerprint attributes, triplet fingerprint attributes) by considering seven circular-based, five path-based, and two substructure-based fingerprints, but also automatically extract deep attributes from self-supervised learning methods. Furthermore, APN designs the Attribute-Guided Dual-channel Attention module to learn the relationship between the molecular graphs and attributes and refine the local and global representation of the molecules. Compared with existing works, APN leverages high-level human-defined attributes and helps the model to explicitly generalize knowledge in molecular graphs. Experiments on benchmark datasets show that APN can achieve state-of-the-art performance in most cases and demonstrate that the attributes are effective for improving few-shot MPP performance. In addition, the strong generalization ability of APN is verified by conducting experiments on data from different domains.

Chemical structure-aware molecular image representation learning.

Current methods of molecular image-based drug discovery face two major challenges: (1) work effectively in absence of labels, and (2) capture chemical structure from implicitly encoded images. Given that chemical structures are explicitly encoded by molecular graphs (such as nitrogen, benzene rings and double bonds), we leverage self-supervised contrastive learning to transfer chemical knowledge from graphs to images. Specifically, we propose a novel Contrastive Graph-Image Pre-training (CGIP) framework for molecular representation learning, which learns explicit information in graphs and implicit information in images from large-scale unlabeled molecules via carefully designed intra- and inter-modal contrastive learning. We evaluate the performance of CGIP on multiple experimental settings (molecular property prediction, cross-modal retrieval and distribution similarity), and the results show that CGIP can achieve state-of-the-art performance on all 12 benchmark datasets and demonstrate that CGIP transfers chemical knowledge in graphs to molecular images, enabling image encoder to perceive chemical structures in images. We hope this simple and effective framework will inspire people to think about the value of image for molecular representation learning.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

Novel mutation leading to splice donor loss in a conserved site of DMD gene causes Duchenne muscular dystrophy with cryptorchidism.

BACKGROUND: As one of the most common congenital abnormalities in male births, cryptorchidism has been found to have a polygenic aetiology according to previous studies of common variants. However, little is known about genetic predisposition of rare variants for cryptorchidism, since rare variants have larger effective size on diseases than common variants. METHODS: In this study, a cohort of 115 Chinese probands with cryptorchidism was analysed using whole-genome sequencing, alongside 19 parental controls and 2136 unaffected men. Additionally, CRISPR-Cas9 editing of a conserved variant was performed in a mouse model, with MRI screening used to observe the phenotype. RESULTS: In 30 of 115 patients (26.1%), we identified four novel genes (ARSH, DMD, MAGEA4 and SHROOM2) affecting at least five unrelated patients and four known genes (USP9Y, UBA1, BCORL1 and KDM6A) with the candidate rare pathogenic variants affecting at least two cases. Burden tests of rare variants revealed the genome-wide significances for newly identified genes (p<2.5×10-6) under the Bonferroni correction. Surprisingly, novel and known genes were mainly found on X chromosome (seven on X and one on Y) and all rare X-chromosomal segregating variants exhibited a maternal inheritance rather than de novo origin. CRISPR-Cas9 mouse modelling of a splice donor loss variant in DMD (NC_000023.11:g.32454661C>G), which resides in a conserved site across vertebrates, replicated bilateral cryptorchidism phenotypes, confirmed by MRI at 4 and 10 weeks. The movement tests further revealed symptoms of Duchenne muscular dystrophy (DMD) in transgenic mice. CONCLUSION: Our results revealed the role of the DMD gene mutation in causing cryptorchidism. The results also suggest that maternal-X inheritance of pathogenic defects could have a predominant role in the development of cryptorchidism.

Sharp turns and gyrotaxis modulate surface accumulation of microorganisms.

The accumulation of swimming microorganisms at surfaces is an essential feature of various physical, chemical, and biological processes in confined spaces. To date, this accumulation is mainly assumed to depend on the change of swimming speed and angular velocity caused by cell-wall contact and hydrodynamic interaction. Here, we measured the swimming trajectories of Heterosigma akashiwo (a biflagellate marine alga) near vertical and horizontal rigid boundaries. We observed that the probability of sharp turns is greatly increased near a vertical wall, resulting in significant changes in the distributions of average swimming speed, angular velocity, and rotational diffusivity near the wall (a quantity that has not previously been investigated) as functions of both distance from the wall and swimming orientation. These cannot be satisfactorily explained by standard hydrodynamic models. Detailed examination of an individual cell trajectory shows that wall contact by the leading flagellum triggers complex changes in the behavior of both flagella that cannot be incorporated in a mechanistic model. Our individual-based model for predicting cell concentration using the measured distributions of swimming speed, angular velocity, and rotational diffusivity agrees well with the experiment. The experiments and model are repeated for a cell suspension in a vertical plane, bounded above by a horizontal wall. The cell accumulation beneath the wall, expected from gyrotaxis, is considerably amplified by cell-wall interaction. These findings may shed light on the prediction and control of cell distribution mediated by gyrotaxis and cell-wall contact.

NIGT1.4 maintains primary root elongation in response to salt stress through induction of ERF1 in Arabidopsis.

Plants employ various molecular mechanisms to maintain primary root elongation upon salt stress. Identification of key functional genes, therein, is important for improving crop salt tolerance. Through analyzing natural variation of the primary root length of Arabidopsis natural population under salt stress, we identified NIGT1.4, encoding an MYB transcription factor, as a novel contributor to maintained root growth under salt stress. Using both T-DNA knockout and functional complementation, NIGT1.4 was confirmed to have a role in promoting primary root growth in response to salt stress. The expression of NIGT1.4 in the root was shown induced by NaCl treatments in an ABA-dependent manner. SnRK2.2 and 2.3 were shown to interact with and phosphorylate NIGT1.4 individually. The growth of the primary root of snrk2.2/2.3/2.6 triple mutant was shown sensitive to salt stress, which was similar to nigt1.4 plants. Using DNA affinity purification sequencing, ERF1, a known positive regulator for primary root elongation and salt tolerance, was identified as a target gene for NIGT1.4. The transcriptional induction of ERF1 by salt stress was shown absent in nigt1.4 background. NIGT1.4 was also confirmed to bind to the promoter region of ERF1 by yeast one-hybrid experiment and to induce the expression of ERF1 by dual-luciferase analysis. All data support the notion that salt- and ABA-elicited NIGT1.4 induces the expression of ERF1 to regulate downstream functional genes that contribute to maintained primary root elongation. NIGT1.4-ERF1, therefore, acts as a signaling node linking regulators for stress resilience and root growth, providing new insights for breeding salt-tolerant crops.

The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.

BACKGROUND: The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. METHODS: Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. RESULTS: A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. CONCLUSIONS: PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Associations of inflammatory and metabolic biomarkers with incident erosive hand osteoarthritis in the osteoarthritis initiative cohort.

OBJECTIVE: Erosive hand osteoarthritis (eHOA) is a subtype of hand osteoarthritis (OA) that develops in finger joints with pre-existing OA and is differentiated by clinical characteristics (hand pain/disability, inflammation, and erosions) that suggest inflammatory or metabolic processes. METHOD: This was a longitudinal nested case-cohort design among Osteoarthritis Initiative participants who had hand radiographs at baseline and 48-months, and biospecimens collected at baseline. We classified incident radiographic eHOA in individuals with ≥1 joint with Kellgren-Lawrence ≥2 and a central erosion present at 48-months but not at baseline. We used a random representative sample (n = 1282) for comparison. We measured serum biomarkers of inflammation, insulin resistance and dysglycemia, and adipokines using immunoassays and enzymatic colorimetric procedures, blinded to case status. RESULTS: Eighty-six participants developed incident radiographic eHOA. In the multivariate analyses adjusted for age, gender, race, smoking, and body mass index, and after adjustment for multiple analyses, incident radiographic eHOA was associated with elevated levels of interleukin-7 (risk ratio (RR) per SD = 1.30 [95% confidence interval (CI) 1.09, 1.55] p trend 0.01). CONCLUSION: This exploratory study suggests an association of elevated interleukin-7, an inflammatory cytokine, with incident eHOA, while other cytokines or biomarkers of metabolic inflammation were not associated. Interleukin-7 may mediate inflammation and tissue damage in susceptible osteoarthritic finger joints and participate in erosive progression.

Electrophotocatalysis Versus Indirect Electrolysis: Electrochemical Selenocyclization of 3-Aza-1,5-dienes Facilitated by Energy Transfer, Direct Photolysis or N-Hydroxyphthalimide.

Three hybrid electrochemical protocols, which involve the energy transfer, direct photolysis and N-hydroxyphthalimide catalyst, respectively, are presented for the selenylation/cyclization of the fragile substrates of 3-aza-1,5-dienes with diorganyl diselenides to afford 3-selenomethyl-4-pyrrolin-2-ones. The two electrophotocatalytic reactions and the indirect electrolysis one are both regioselective and external-oxidant- and transition-metal-free, and are associated with a broad substrate scope and high Se-economy, and all three methods are amenable to gram-scale syntheses, late-stage functionalizations, sunlight-induced experiments and all-solar-driven syntheses.

Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma, a leading cause of cancer-related mortality, demands precise prognostic indicators for effective management. The presence of spread through air space (STAS) indicates adverse tumor behavior. However, comparative differences between 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography(PET)/computed tomography(CT) and CT in predicting STAS in lung adenocarcinoma remain inadequately explored. This retrospective study analyzes preoperative CT and 18F-FDG PET/CT features to predict STAS, aiming to identify key predictive factors and enhance clinical decision-making. METHODS: Between February 2022 and April 2023, 100 patients (108 lesions) who underwent surgery for clinical lung adenocarcinoma were enrolled. All these patients underwent 18F-FDG PET/CT, thin-section chest CT scan, and pathological biopsy. Univariate and multivariate logistic regression was used to analyze CT and 18F-FDG PET/CT image characteristics. Receiver operating characteristic curve analysis was performed to identify a cut-off value. RESULTS: Sixty lesions were positive for STAS, and 48 lesions were negative for STAS. The STAS-positive was frequently observed in acinar predominant. However, STAS-negative was frequently observed in minimally invasive adenocarcinoma. Univariable analysis results revealed that CT features (including nodule type, maximum tumor diameter, maximum solid component diameter, consolidation tumor ratio, pleural indentation, lobulation, spiculation) and all 18F-FDG PET/CT characteristics were statistically significant difference in STAS-positive and STAS-negative lesions. And multivariate logistic regression results showed that the maximum tumor diameter and SUVmax were the independent influencing factors of CT and 18F-FDG PET/CT in STAS, respectively. The area under the curve of maximum tumor diameter and SUVmax was 0.68 vs. 0.82. The cut-off value for maximum tumor diameter and SUVmax was 2.35 vs. 5.05 with a sensitivity of 50.0% vs. 68.3% and specificity of 81.2% vs. 87.5%, which showed that SUVmax was superior to the maximum tumor diameter. CONCLUSION: The radiological features of SUVmax is the best model for predicting STAS in lung adenocarcinoma. These radiological features could predict STAS with excellent specificity but inferior sensitivity.

Circular RNA PDK1 targets miR-4731-5p to enhance TNXB expression in ligamentum flavum hypertrophy.

Hypertrophic ligamentum flavum (LF) is a main factor responsible for lumbar spinal stenosis (LSS); however, the exact mechanisms of the pathogenesis of these processes remain unknown. This study aimed to elucidate whether circular RNAs and microRNAs regulate the pathogenesis of LF and LSS, especially focusing on circPDK1 (hsa_circ_0057105), a circRNA targeting pyruvate dehydrogenase kinase 1 and differentially expressed in LF tissues between lumbar disk herniation and LSS patients. The circPDK1/miR-4731 and miR-4731/TNXB (Tenascin XB) interactions were predicted and validated by luciferase reporter assay. Colony formation, wound-healing, and MTT assays were used for estimating cell proliferation and migration. Protein expression levels were evaluated using Western blotting. TNXB expression was verified using immunohistochemistry (IHC). Overexpressing circPDK1 promoted the proliferation, migration, and expression of fibrosis-related protein (alpha smooth muscle actin (α-SMA), lysyl oxidase like 2 (LOXL2), Collagen I, matrix metalloproteinase-2 (MMP-2) and TNXB) in LF whereas miR-4731-5p showed opposite effects. The expression of TNXB was promoted by circPDK1; contrary results were observed with miR-4731-5p. Co-overexpression of miR-4731-5p partially reversed the proliferative and fibrosis-prompting effects of circPDK1 or TNXB. The circPDK1-miR-4731-TNXB pathway may be proposed as a regulatory axis in LF hypertrophy, which might shed light on in-depth research of LSS, as well as providing a novel therapeutic target for LF hypertrophy-induced LSS.

The miR-25802/KLF4/NF-κB signaling axis regulates microglia-mediated neuroinflammation in Alzheimer's disease.

Microglia-mediated neuroinflammation plays a critical role in the occurrence and progression of Alzheimer's disease (AD). In recent years, studies have increasingly explored microRNAs as biomarkers and treatment interventions for AD. This study identified a novel microRNA termed miR-25802 from our high-throughput sequencing dataset of an AD model and explored its role and the underlying mechanism. The results confirmed the miRNA properties of miR-25802 based on bioinformatics and experimental verification. Expression of miR-25802 was increased in the plasma of AD patients and in the hippocampus of APP/PS1 and 5 × FAD mice carrying two and five familial AD gene mutations. Functional studies suggested that overexpression or inhibition of miR-25802 respectively aggravated or ameliorated AD-related pathology, including cognitive disability, Aß deposition, microglial pro-inflammatory phenotype activation, and neuroinflammation, in 5 × FAD mice and homeostatic or LPS/IFN-γ-stimulated EOC20 microglia. Mechanistically, miR-25802 negatively regulates KLF4 by directly binding to KLF4 mRNA, thus stimulating microglia polarization toward the pro-inflammatory M1 phenotype by promoting the NF-κB-mediated inflammatory response. The results also showed that inhibition of miR-25802 increased microglial anti-inflammatory M2 phenotype activity and suppressed NF-κB-mediated inflammatory reactions in the brains of 5 × FAD mice, while overexpression of miR-25802 exacerbated microglial pro-inflammatory M1 activity by enhancing NF-κB pathways. Of note, AD-associated manifestations induced by inhibition or overexpression of miR-25802 via the NF-κB signaling pathway were reversed by KLF4 silencing or upregulation. Collectively, these results provide the first evidence that miR-25802 is a regulator of microglial activity and establish the role of miR-25802/KLF4/NF-κB signaling in microglia-mediated neuroinflammation, suggesting potential therapeutic targets for AD.

Emerging roles of lactate in acute and chronic inflammation.

Traditionally, lactate has been considered a 'waste product' of cellular metabolism. Recent findings have shown that lactate is a substance that plays an indispensable role in various physiological cellular functions and contributes to energy metabolism and signal transduction during immune and inflammatory responses. The discovery of lactylation further revealed the role of lactate in regulating inflammatory processes. In this review, we comprehensively summarize the paradoxical characteristics of lactate metabolism in the inflammatory microenvironment and highlight the pivotal roles of lactate homeostasis, the lactate shuttle, and lactylation ('lactate clock') in acute and chronic inflammatory responses from a molecular perspective. We especially focused on lactate and lactate receptors with either proinflammatory or anti-inflammatory effects on complex molecular biological signalling pathways and investigated the dynamic changes in inflammatory immune cells in the lactate-related inflammatory microenvironment. Moreover, we reviewed progress on the use of lactate as a therapeutic target for regulating the inflammatory response, which may provide a new perspective for treating inflammation-related diseases.

Abnormal temporal variability of thalamo-cortical circuit in patients with moderate-to-severe obstructive sleep apnea.

This study investigated the abnormal dynamic functional connectivity (dFC) variability of the thalamo-cortical circuit in patients with obstructive sleep apnea (OSA) and explored the relationship between these changes and the clinical characteristics of patients with OSA. A total of 91 newly diagnosed patients with moderate-to-severe OSA and 84 education-matched healthy controls (HCs) were included. All participants underwent neuropsychological testing and a functional magnetic resonance imaging scan. We explored the thalamo-cortical dFC changes by dividing the thalamus into 16 subregions and combining them using a sliding-window approach. Correlation analysis assessed the relationship between dFC variability and clinical features, and the support vector machine method was used for classification. The OSA group exhibited increased dFC variability between the thalamic subregions and extensive cortical areas, compared with the HCs group. Decreased dFC variability was observed in some frontal-occipital-temporal cortical regions. These dFC changes positively correlated with daytime sleepiness, disease severity, and cognitive scores. Altered dFC variability contributed to the discrimination between patients with OSA and HCs, with a classification accuracy of 77.8%. Our findings show thalamo-cortical overactivation and disconnection in patients with OSA, disrupting information flow within the brain networks. These results enhance understanding of the temporal variability of thalamo-cortical circuits in patients with OSA.

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.

Recyclable V2O5/g-C3N4 Heterojunction-Catalyzed Visible-Light-Promoted C3-H Trifluoromethylation of Quinoxalin-2-(1H)-ones.

A visible-light-initiated C-H trifluoromethylation of quinoxalin-2(1H)-ones was established using a Z-scheme V2O5/g-C3N4 heterojunction as a recyclable photocatalyst in an inert atmosphere at room temperature under additive-free and mild conditions. A variety of trifluoromethylated quinoxalin-2-(1H)-one derivatives were heterogeneously generated in moderate to high yields, exhibiting good functional group tolerance. Remarkably, the recyclable V2O5/g-C3N4 catalyst could be reused five times with a slight loss of catalytic activity.

Clinical value of prokineticin 2 in the diagnosis of neonatal necrotizing enterocolitis.

BACKGROUND: Necrotizing enterocolitis (NEC) is an inflammatory and necrotizing intestinal emergency that occurs in preterm infants and low birth weight newborns, however no specific serum markers for the diagnosis of NEC has been identified so far. Prokineticin 2 (PK2) is a newly identified immunomodulator in a variety of inflammatory diseases. METHODS: Serum samples were collected from healthy neonatal controls and patients with NEC newly admitted to the Children's Hospital of Chongqing Medical University from January 1, 2021 to January 1, 2022 (before clinical intervention and after surgical treatment). ELISA was used to measure serum PK2 levels, and values, including white blood cell count (WBC), neutrophil-lymphocyte ratio (NLR), procalcitonin (PCT), C-reactive protein (CRP) and platelet (PLT), were analysed; the efficiency of PK2 combined with the above biomarkers in the differential diagnosis of NEC was compared using receiver operating characteristic (ROC) curve analysis. RESULTS: Serum PK2 levels in the NEC group (n = 53) were significantly lower than those in the control group (n = 18), but increased to near-normal levels after the postoperative recovery period. The NLR value of NEC group was higher than that of control group (P < 0.05). There was no significant difference in WBC and PLT count between NEC group and control group (P > 0.05). Serum CRP and PCT levels in NEC group were significantly higher than those in control group (P < 0.001 for CRP and P < 0.05 for PCT, respectively). After surgery, serum CRP, NLR and PCT levels were lower than before surgery, while serum PK2 levels were higher than before surgery (P < 0.05). The areas under the ROC curve (AUC) of PK2, PCT and CRP for the diagnosis of NEC were 0.837, 0.662 and 0.552, respectively. The AUC of PK2 combined with PCT, PK2 combined with CRP, and PK2 combined with PCT and CRP were 0.908, 0.854, and 0.981, respectively. PK2 exhibited the highest diagnostic efficacy for NEC. CONCLUSION: PK2 has higher diagnostic efficacy than PCT and CRP in the diagnosis of NEC, the combination of PK2 and PCT or CRP can significantly improve its diagnostic efficiency, especially when the three are combined at the same time.

Prediction of miRNA-disease associations by neural network-based deep matrix factorization.

MicroRNA(miRNA) is a class of short non-coding RNAs with a length of about 22 nucleotides, which participates in various biological processes of cells. A number of studies have shown that miRNAs are closely related to the occurrence of cancer and various human diseases. Therefore, studying miRNA-disease associations is helpful to understand the pathogenesis of diseases as well as the prevention, diagnosis, treatment and prognosis of diseases. Traditional biological experimental methods for studying miRNA-disease associations have disadvantages such as expensive equipment, time-consuming and labor-intensive. With the rapid development of bioinformatics, more and more researchers are committed to developing effective computational methods to predict miRNA-disease associations in roder to reduce the time and money cost of experiments. In this study, we proposed a neural network-based deep matrix factorization method named NNDMF to predict miRNA-disease associations. To address the problem that traditional matrix factorization methods can only extract linear features, NNDMF used neural network to perform deep matrix factorization to extract nonlinear features, which makes up for the shortcomings of traditional matrix factorization methods. We compared NNDMF with four previous classical prediction models (IMCMDA, GRMDA, SACMDA and ICFMDA) in global LOOCV and local LOOCV, respectively. The AUCs achieved by NNDMF in two cross-validation methods were 0.9340 and 0.8763, respectively. Furthermore, we conducted case studies on three important human diseases (lymphoma, colorectal cancer and lung cancer) to validate the effectiveness of NNDMF. In conclusion, NNDMF could effectively predict the potential miRNA-disease associations.

Research progress on the mechanism of action of Salvia miltiorrhiza and its components in the treatment of endometrial carcinoma.

The incidence and mortality of endometrial carcinoma (EC) are increasing year by year. Although the curative effect of surgery and commonly used drugs is clear, it is accompanied by obvious side effects, and safe and effective means are urgently needed to promote the curative effect and decrease the toxicity of drugs. Traditional Chinese medicine has been passed down for thousands of years in China and has proved to be advantageous in the treatment of various cancers and the auxiliary enhancement and reduction of toxicity. This paper reviewed the role and internal mechanism of Salvia miltiorrhiza in preventing and treating endometrial carcinoma by referring to relevant literature and works, so as to more comprehensively understand and grasp the research status, effective components, curative effect and effective mechanism of S. miltiorrhiza in preventing and treating endometrial carcinoma, and provide ideas and basis for clinical use and basic research.