Experimental Generation of Spin-Photon Entanglement in Silicon Carbide.

A solid-state approach for quantum networks is advantageous, as it allows the integration of nanophotonics to enhance the photon emission and the utilization of weakly coupled nuclear spins for long-lived storage. Silicon carbide, specifically point defects within it, shows great promise in this regard due to the easy of availability and well-established nanofabrication techniques. Despite of remarkable progresses made, achieving spin-photon entanglement remains a crucial aspect to be realized. In this Letter, we experimentally generate entanglement between a silicon vacancy defect in silicon carbide and a scattered single photon in the zero-phonon line. The spin state is measured by detecting photons scattered in the phonon sideband. The photonic qubit is encoded in the time-bin degree of freedom and measured using an unbalanced Mach-Zehnder interferometer. Photonic correlations not only reveal the quality of the entanglement but also verify the deterministic nature of the entanglement creation process. By harnessing two pairs of such spin-photon entanglement, it becomes straightforward to entangle remote quantum nodes at long distance.

Motif-Aware miRNA-Disease Association Prediction Via Hierarchical Attention Network.

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives. The data and codes are available at https://github.com/stevejobws/MotifMDA.

Thermally Evaporated Metal Halide Perovskites and Their Analogues: Film Fabrication, Applications and Beyond.

Metal halide perovskites emerge as promising semiconductors for optoelectronic devices due to ease of fabrication, attractive photophysical properties, their low cost, highly tunable material properties, and high performance. High-quality thin films of metal halide perovskites are the basis of most of these applications including solar cells, light-emitting diodes, photodetectors, and electronic memristors. A typical fabrication method for perovskite thin films is the solution method, which has several limitations in device reproducibility, adverse environmental impact, and utilization of raw materials. Thermal evaporation holds great promise in addressing these bottlenecks in fabricating high-quality halide perovskite thin films. It also has high compatibility with mass-production platforms that are well-established in industries. This review first introduces the basics of the thermal evaporation method with a particular focus on the critical parameters influencing the thin film deposition. The research progress of the fabrication of metal halide perovskite thin films is further summarized by different thermal evaporation approaches and their applications in solar cells and other optoelectronic devices. Finally, research challenges and future opportunities for both fundamental research and commercialization are discussed.

Four-year variation in pathogen distribution and antimicrobial susceptibility of urosepsis: a single-center retrospective analysis.

Background: Urosepsis is a common disease in urology, which is characterized by high treatment costs and high mortality. In the treatment of sepsis, anti-infection therapy is the most important means. However, the effect of empirical anti-infection therapy is often not ideal. Therefore, it is necessary to continuously monitor the prevalence of bacterial isolates in the blood culture of patients with urinary sepsis and their sensitivity to antibacterial drugs. This is of great significance to improve the efficacy of empirical antibiotic therapy for urosepsis. Objective: To elucidate the landscape of prevailing bacterial profiles and their antimicrobial susceptibilities in urosepsis cases, and to furnish robust clinical evidence to underpin the timely initiation of empirical antibiotic treatment. Methods: Collect the basic information and blood culture results of patients with urosepsis hospitalized from 2017 to 2020. Retrospective analysis of bacterial species and antimicrobial susceptibility in urosepsis and changes over 4 years. Results: Gram-negative bacteria (178 isolates, 75.11%) constituted the main pathogens causing urosepsis, followed by Gram-positive bacteria (46 isolates, 19.41%) and fungus (13 isolates, 5.48%). The sensitivity of ertapenem, meropenem, amikacin, and imipenem to Gram-negative bacteria all exceeded 85%. The sensitivity rates of levofloxacin, gentamicin, and ciprofloxacin are decreasing every year (p < 0.05). Tigecycline, vancomycin, and linezolid exhibited excellent sensitivity against Gram-positive bacteria. Among fungi, fluconazole demonstrated universal sensitivity, while itraconazole-resistant isolates have been found, and amphotericin B is still effective. Conclusion: Analysis of blood culture results of patients more accurately reflected the etiology of urosepsis, mainly Escherichia coli, Enterococcus, and Klebsiella pneumoniae. If there are no definitive blood culture results, empiric treatment of urosepsis should not include fluoroquinolone antibiotics. Cefepime, cefoxitin, and ceftazidime are the most sensitive antibiotics to Gram-negative bacteria besides carbapenem antibiotics. In addition, the current situation regarding extended-spectrum ß-lactamase-producing bacteria and carbapenem-resistant Enterobacteriaceae bacteria resistance is extremely concerning with limited therapeutic options available. Strengthening antibiotic management practices and exploring novel antibacterial agents can help mitigate this issue.

Combined Use of Helicobacter pylori Genotyping and CDX2 Expression as a Predictor of Malignant Potential in Gastric Intestinal Metaplasia.

OBJECTIVE: Gastrointestinal metaplasia (GIM) has a close relationship with gastric cancer (GC), but it is unclear how to judge which GIM could develop into GC. This study aimed to assess the role of CDX2 and its association with Helicobacter pylori (H.pylori) genotypes in GIM. METHODS: CagA and vacA genes were identified via PCR in 466 H. pylori-positive gastric tissues, including gastritis (n=104), GIM diagnosed endoscopically (GIM-1; n=82), gastric cancer (GC; n=173), and paired adjacent GIM tumors resected surgically (GIM-2; n=107). GIM was subclassified per the HID- AB pH2.5-PAS as follows: type I (n=23), type II (n=43), and type III (n=16) in GIM-1; type I (n=8), type II (n=40), and type III (n=59) in GIM-2. CDX2 expression was evaluated immunohistochemically. RESULTS: In GIM-1, the infection rate of vacAm2 (55.8%) and vacAs1m2 (53.5%) was higher in subtype II than in others (P<0.05), while that of vacAm1 (49.2%) and vacAs1m1 (33.9%) was higher in subtype III than in others. The cagA+ rate was higher in subtypes I (75.0%) and III (64.4%) than in subtype II (40.0%; P<0.05) respectively. CDX2 was upregulated in subtype I than in subtypes II and III in GIM-1 and GIM-2. In GIM-2 and GC, CDX2 was downregulated in vacAm1, vacAs1m1, and cagA+ (P<0.05). The predominant genotype was vacAs1m2 in subtype II of GIM-1, CDX2 expression remaining unaltered; however, the predominant genotype was cagA+ vacAs1m1 in subtypes II and III of GIM-2, negatively correlated with CDX2 expression. CONCLUSION: These GIM subtypes (cagA+ vacAs1m1 H. pylori-positive GIM with negative CDX2 expression) resemble GC and should be evaluated similar to cancerous GIM.

Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Propofol improves survival in a murine model of sepsis via inhibiting Rab5a-mediated intracellular trafficking of TLR4.

BACKGROUND: Propofol is a widely used anesthetic and sedative, which has been reported to exert an anti-inflammatory effect. TLR4 plays a critical role in coordinating the immuno-inflammatory response during sepsis. Whether propofol can act as an immunomodulator through regulating TLR4 is still unclear. Given its potential as a sepsis therapy, we investigated the mechanisms underlying the immunomodulatory activity of propofol. METHODS: The effects of propofol on TLR4 and Rab5a (a master regulator involved in intracellular trafficking of immune factors) were investigated in macrophage (from Rab5a-/- and WT mice) following treatment with lipopolysaccharide (LPS) or cecal ligation and puncture (CLP) in vitro and in vivo, and peripheral blood monocyte from sepsis patients and healthy volunteers. RESULTS: We showed that propofol reduced membrane TLR4 expression on macrophages in vitro and in vivo. Rab5a participated in TLR4 intracellular trafficking and both Rab5a expression and the interaction between Rab5a and TLR4 were inhibited by propofol. We also showed Rab5a upregulation in peripheral blood monocytes of septic patients, accompanied by increased TLR4 expression on the cell surface. Propofol downregulated the expression of Rab5a and TLR4 in these cells. CONCLUSIONS: We demonstrated that Rab5a regulates intracellular trafficking of TLR4 and that propofol reduces membrane TLR4 expression on macrophages by targeting Rab5a. Our study not only reveals a novel mechanism for the immunomodulatory effect of propofol but also indicates that Rab5a may be a potential therapeutic target against sepsis.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

A deep-learning-based genomic status estimating framework for homologous recombination deficiency detection from low-pass whole genome sequencing.

Genome-wide sequencing allows for prediction of clinical treatment responses and outcomes by estimating genomic status. Here, we developed Genomic Status scan (GSscan), a long short-term memory (LSTM)-based deep-learning framework, which utilizes low-pass whole genome sequencing (WGS) data to capture genomic instability-related features. In this study, GSscan directly surveys homologous recombination deficiency (HRD) status independent of other existing biomarkers. In breast cancer, GSscan achieved an AUC of 0.980 in simulated low-pass WGS data, and obtained a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 1.3 × 10-4, compared with BRCA-intact samples). In ovarian cancer, GSscan obtained higher HRD risk scores in BRCA-deficient samples in both simulated data and clinical samples (p = 2.3 × 10-5 and p = 0.039, respectively, compared with BRCA-intact samples). Moreover, HRD-positive patients predicted by GSscan showed longer progression-free intervals in TCGA datasets (p = 0.0011) treated with platinum-based adjuvant chemotherapy, outperforming existing low-pass WGS-based methods. Furthermore, GSscan can accurately predict HRD status using only 1 ng of input DNA and a minimum sequencing coverage of 0.02 × , providing a reliable, accessible, and cost-effective approach. In summary, GSscan effectively and accurately detected HRD status, and provide a broadly applicable framework for disease diagnosis and selecting appropriate disease treatment.

Lactate promoted cisplatin resistance in NSCLC by modulating the m6A modification-mediated FOXO3/MAGI1-IT1/miR-664b-3p/IL-6R axis.

BACKGROUND: Cisplatin resistance is one of the major obstacles in non-small cell lung cancer (NSCLC) treatment. Intriguingly, elevated lactate levels were observed in cisplatin-resistant cells, which spurred further investigation into their underlying biological mechanisms. METHODS: Lactate levels were measured by lactate detection kit. Cisplatin-resistance NSCLC cells were established using progressive concentration of cisplatin. Cell viability, proliferation, and apoptosis were detected by CCK-8, EdU, and flow cytometry, respectively. Cell proliferation in vivo was determined by immunohistochemistry of Ki67 and apoptotic cells were calculated by the TUNEL. MeRIP-PCR was used to measure FOXO3 m6A levels. The interactions of genes were analyzed via RIP, ChIP, Dual-luciferase reporter, and RNA pull-down, respectively. RESULTS: Elevated lactate levels were observed in both NSCLC patients and cisplatin-resistance cells. Lactate treatment increased cisplatin-resistance cell viability in vitro and promoted tumor growth in vivo. Mechanistically, lactate downregulated FOXO3 by YTHDF2-mediated m6A modification. FOXO3 transcriptionally reduced MAGI1-IT1 expression. FOXO3 overexpression inhibited the lactate-induced promotion of cisplatin resistance in NSCLC, which were reversed by MAGI1-IT1 overexpression. MAGI1-IT1 and IL6R competitively bound miR-664b-3p. FOXO3 overexpression or MAGI1-IT1 knockdown repressed lactate-mediated cisplatin resistance in vivo. CONCLUSION: Lactate promoted NSCLC cisplatin resistance through regulating FOXO3/MAGI1-IT1/miR-664b-3p/IL6R axis in YTHDF2-mediated m6A modification.

Magnetoresistance properties in nickel-catalyzed, air-stable, uniform, and transfer-free graphene.

A transfer-free graphene with high magnetoresistance (MR) and air stability has been synthesized using nickel-catalyzed atmospheric pressure chemical vapor deposition. The Raman spectrum and Raman mapping reveal the monolayer structure of the transfer-free graphene, which has low defect density, high uniformity, and high coverage (>90%). The temperature-dependent (from 5 to 300 K) current-voltage (I-V) and resistance measurements are performed, showing the semiconductor properties of the transfer-free graphene. Moreover, the MR of the transfer-free graphene has been measured over a wide temperature range (5-300 K) under a magnetic field of 0 to 1 T. As a result of the Lorentz force dominating above 30 K, the transfer-free graphene exhibits positive MR values, reaching ∼8.7% at 300 K under a magnetic field (1 Tesla). On the other hand, MR values are negative below 30 K due to the predominance of the weak localization effect. Furthermore, the temperature-dependent MR values of transfer-free graphene are almost identical with and without a vacuum annealing process, indicating that there are low density of defects and impurities after graphene fabrication processes so as to apply in air-stable sensor applications. This study opens avenues to develop 2D nanomaterial-based sensors for commercial applications in future devices.

Schizophrenia in the genetic era: a review from development history, clinical features and genomic research approaches to insights of susceptibility genes.

Schizophrenia is a devastating neuropsychiatric disorder affecting 1% of the world population and ranks as one of the disorders providing the most severe burden for society. Schizophrenia etiology remains obscure involving multi-risk factors, such as genetic, environmental, nutritional, and developmental factors. Complex interactions of genetic and environmental factors have been implicated in the etiology of schizophrenia. This review provides an overview of the historical origins, pathophysiological mechanisms, diagnosis, clinical symptoms and corresponding treatment of schizophrenia. In addition, as schizophrenia is a polygenic, genetic disorder caused by the combined action of multiple micro-effective genes, we further detail several approaches, such as candidate gene association study (CGAS) and genome-wide association study (GWAS), which are commonly used in schizophrenia genomics studies. A number of GWASs about schizophrenia have been performed with the hope to identify novel, consistent and influential risk genetic factors. Finally, some schizophrenia susceptibility genes have been identified and reported in recent years and their biological functions are also listed. This review may serve as a summary of past research on schizophrenia genomics and susceptibility genes (NRG1, DISC1, RELN, BDNF, MSI2), which may point the way to future schizophrenia genetics research. In addition, depending on the above discovery of susceptibility genes and their exact function, the development and application of antipsychotic drugs will be promoted in the future.

Role of Ferroptosis in Bone Homeostasis and Traditional Chinese Medicine Intervention： A Review / 中国实验方剂学杂志

Osteoporosis （OP） is a systemic metabolic bone disease characterized by bone microstructure degeneration and bone mass loss， which has a high prevalence and disability rate. Effective prevention and treatment of OP is a major difficulty in the medical community. The nature of OP is that multiple pathological factors lead to the imbalance of human bone homeostasis maintained by osteoblasts and osteoclasts. Ferroptosis is a non-apoptotic cell death pathway， and its fundamental cause is cell damage caused by iron accumulation and lipid peroxidation. Studies have shown that ferroptosis is involved in and affects the occurrence and development of OP， which leads to OP by mediating the imbalance of bone homeostasis. Ferroptosis is an adjustable form of programmed cell death. The intervention of ferroptosis can regulate the damage degree and death process of osteoblasts and osteoclasts， which is beneficial to maintain bone homeostasis， slow down the development process of OP， improve the clinical symptoms of patients， reduce the risk of disability， and improve their quality of life. However， there are few studies on ferroptosis in OP. Traditional Chinese medicine （TCM） is a medical treasure with unique characteristics and great application value in China. It has been widely used in China and has a long history. It has the multi-target and multi-pathway advantages in the treatment of OP， with high safety， few toxic and side effects， and low treatment cost， and has a significant effect in clinical application. The intervention of TCM in ferroptosis to regulate bone homeostasis may be a new direction for the prevention and treatment of OP in the future. This article summarized the regulatory mechanisms related to ferroptosis， discussed the role of ferroptosis in bone homeostasis， and reviewed the current status and progress of active ingredients in TCM compounds and monomers in the regulation of OP through ferroptosis， so as to provide a theoretical basis for the participation of TCM in the prevention and treatment of OP in the future.

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used. METHODS: In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance. RESULTS: A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900. CONCLUSIONS: Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

Regioselectivity Switch between Enantioselective 1,2- and 1,4-Addition of Allyl Aryl Ketones with 2,3-Dioxopyrrolidines.

A vinylogous addition reaction of allyl aryl ketones with good yields and excellent regioselectivity catalyzed by squaramide catalysts has been developed. A series of chiral tertiary alcohols and bicyclic pyrrolidones could be synthesized in good to excellent yields, enantioselectivities, and diaseteroselectivities. Both experimental results and DFT calculations indicate that 1,2-addition reaction is favorable when the reaction is employed at a lower temperature, while the 1,4-addition/cyclization pathway is favorable when the reaction is employed at a higher temperature. Furthermore, the formation of compound 4 can potentially arise from either the 1,4-addition/cyclization pathway or retro-aldol reaction of compound 3, followed by subsequent 1,4-addition/cyclization.

Mechanism of synergistic remediation of soil phenanthrene contamination in paddy fields by rice-crab coculture and bioaugmentation with Pseudomonas sp.

Polycyclic aromatic hydrocarbons (PAHs) are persistent and harmful pollutants with high priority concern in agricultural fields. This work constructed a rice-crab coculture and bioaugmentation (RCM) system to remediate phenanthrene (a model PAH) contamination in rice fields. The results showed that RCM had a higher remediation performance of phenanthrene in rice paddy compared with rice cultivation alone, microbial addition alone, and crab-rice coculture, reaching a remediation efficiency of 88.92 % in 42 d. The concentration of phenanthrene in the rice plants decreased to 6.58 mg/kg, and its bioconcentration effect was efficiently inhibited in the RCM system. In addition, some low molecular weight organic acids of rice root increased by 12.87 %∼73.87 %, and some amino acids increased by 140 %∼1150 % in RCM. Bioturbation of crabs improves soil aeration structure and microbial migration, and adding Pseudomonas promoted the proliferation of some plant growth-promoting rhizobacteria (PGPRs), which facilitated the degradation of phenanthrene. This coupling rice-crab coculture with bioaugmentation had favorable effects on soil enzyme activity, microbial community structure, and PAH degradation genes in paddy fields, enhancing the removal of and resistance to PAH contamination in paddy fields and providing new strategies for achieving a balance between production and remediation in contaminated paddy fields.

Measurement of the 5S1/2 to 5D5/2 two-photon clock transition frequency of rubidium-85 in high vacuum.

We present a scheme to precisely resolve the unperturbed line shape of an optical rubidium clock transition in a high vacuum, by which we avoided the systematic errors of "collision shift" and "modulation shift." The spectral resolution resolved by this scheme is significantly improved such that we can use "Zeeman broadening" to inspect the stray magnetic field, through which we were able to compensate the magnetic field inside the Rb cells to be below 10-3 Gauss. We thus update the absolute frequency of the clock transition and propose a standard operation procedure (SOP) for the clock self-calibration.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.