The continuous emergence of SARS-CoV-2 variants as well as other potential future coronavirus has challenged the effectiveness of current COVID-19 vaccines. Therefore, there remains a need for alternative antivirals that target processes less susceptible to mutations, such as the formation of six-helix bundle (6-HB) during the viral fusion step of host cell entry. In this study, a novel high-throughput screening (HTS) assay employing a yeast-two-hybrid (Y2H) system was established to identify inhibitors of HR1/HR2 interaction. The compound IMB-9C, which achieved single-digit micromolar inhibition of SARS-CoV-2 and its Omicron variants with low cytotoxicity, was selected. IMB-9C effectively blocks the HR1/HR2 interaction in vitro and inhibits SARS-CoV-2-S-mediated cell-cell fusion. It binds to both HR1 and HR2 through non-covalent interaction and influences the secondary structure of HR1/HR2 complex. In addition, virtual docking and site-mutagenesis results suggest that amino acid residues A930, I931, K933, T941, and L945 are critical for IMB-9C binding to HR1. Collectively, in this study, we have developed a novel screening method for HR1/HR2 interaction inhibitors and identified IMB-9C as a potential antiviral small molecule against COVID-19 and its variants.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

BACKGROUND:Aberrant expression of some transcription factors involved in the Heghog signaling pathway occurs in patients with polydactyly,and these transcription factors regulate the expression of massive target genes,thereby affecting cellular function.OBJECTIVE:To analyze the transcriptome characterization of patients with polydactyly by single-cell transcriptome analysis.METHODS:The single-cell transcriptome data of mesenchymal and epithelial cells of four patients with polydactyly were downloaded from the GEO public database.Fibroblasts and keratinocytes were categorized into cell subsets,and the transcription factors within each subset were examined.A regulatory network of these transcription factors and their target genes was developed,and the functions of these regulatory factors were analyzed.RESULTS AND CONCLUSION:The transcriptional profiling data of individual cells indicated that the regulatory factors strongly linked to cell functionality in polydactyly include the transcription factors HOXD13,MSX2,LHX2,EMX2,LEF1,CREB3L2,and LHX2 of the HOX family and GLI2 transcription factors.In fibroblasts,HOXD13,MSX2,and LHX2 are involved in polydactyly,whereas HES2 and GLIS1 are crucial for the formation and development of keratinocytes.To conclude,the elevated expression of transcription factors including HOXD13,MSX2,and LHX2 is potentially closely associated with the development of polydactyly.Potential therapeutic strategies that may be offered to correct or prevent polydactyly by targeting specific transcription factors or modulating their activity.

Objective:To construct a quality analysis model by combining the strengths-weaknesses-opportunities-threats(SWOT)analysis method and analytic hierarchy process(AHP),and to explore its application value in the quality management for operation of first aid equipment of hospital.Methods:The advantages,disadvantages,opportunities and threats of management for equipment were analyzed according to SWOT model,and the operation quality of equipment was positioning analyzed by AHP,and comprehensive management countermeasures were formulated from the aspects of personnel,system,preventive maintenance,information management and cooperation between institutes and enterprises.A total of 215 first aid equipment in clinical use in Qilu Hospital of Shandong University from January 2021 to December 2024 were selected.In these equipment,171 equipment from January 2021 to December 2022 were managed by using the conventional management mode,and 195 equipment(including 151 using equipment under the conventional management mode and 44 newly added equipment)from January 2023 to December 2024 were managed by using the strategic management mode with quality analysis model.The operation quality and management level of the two management modes for the equipment were compared.A self-made survey questionnaire was used to investigate the satisfaction scores of nurses of operating equipment,doctors of clinical department,medical engineers and administrators for the service effects of equipment under the two management modes,who use and manage the first aid equipment in hospital.Results:The average failure rate,risk rate,alarm rate and unqualified rate of first aid equipment in hospitals,which adopted the strategic management mode,were respectively(0.72±0.42)%,(1.08±0.70)%,(1.18±0.48)%and(1.33±0.63)%,all of which were lower than those of the conventional management mode,and the differences were statistically significant(t=3.629,2.792,2.179,2.396,P<0.05).The management level scores of monitors,ventilators,defibrillators,high-frequency electrosurgical knives and other first aid equipment,which adopted the strategic management mode,were respectively(94.69±1.68),(95.84±2.52),(95.38±2.85),(95.69±2.02)and(94.17±4.89)points,all of which were higher than those of the conventional management mode,and the differences were statistically significant(t=4.797,2.403,2.390,3.825,2.196,P<0.05).The satisfaction scores of nurses of operating equipment,doctors of clinical department,medical engineers and administrators for first aid equipment of adopting strategic management mode were all higher than those of adopting conventional management mode,and the difference were statistically significant(t=3.666,3.336,4.275,4.292,P<0.05).Conclusion:The quality analysis model of first aid equipment of hospital can improve the operation quality and management level of first aid equipment,and reduce the equipment's failure rate and the unqualified rate of quality inspection,and improve the service effect of first aid equipment.

Objective:To construct a quality analysis model by combining the strengths-weaknesses-opportunities-threats(SWOT)analysis method and analytic hierarchy process(AHP),and to explore its application value in the quality management for operation of first aid equipment of hospital.Methods:The advantages,disadvantages,opportunities and threats of management for equipment were analyzed according to SWOT model,and the operation quality of equipment was positioning analyzed by AHP,and comprehensive management countermeasures were formulated from the aspects of personnel,system,preventive maintenance,information management and cooperation between institutes and enterprises.A total of 215 first aid equipment in clinical use in Qilu Hospital of Shandong University from January 2021 to December 2024 were selected.In these equipment,171 equipment from January 2021 to December 2022 were managed by using the conventional management mode,and 195 equipment(including 151 using equipment under the conventional management mode and 44 newly added equipment)from January 2023 to December 2024 were managed by using the strategic management mode with quality analysis model.The operation quality and management level of the two management modes for the equipment were compared.A self-made survey questionnaire was used to investigate the satisfaction scores of nurses of operating equipment,doctors of clinical department,medical engineers and administrators for the service effects of equipment under the two management modes,who use and manage the first aid equipment in hospital.Results:The average failure rate,risk rate,alarm rate and unqualified rate of first aid equipment in hospitals,which adopted the strategic management mode,were respectively(0.72±0.42)%,(1.08±0.70)%,(1.18±0.48)%and(1.33±0.63)%,all of which were lower than those of the conventional management mode,and the differences were statistically significant(t=3.629,2.792,2.179,2.396,P<0.05).The management level scores of monitors,ventilators,defibrillators,high-frequency electrosurgical knives and other first aid equipment,which adopted the strategic management mode,were respectively(94.69±1.68),(95.84±2.52),(95.38±2.85),(95.69±2.02)and(94.17±4.89)points,all of which were higher than those of the conventional management mode,and the differences were statistically significant(t=4.797,2.403,2.390,3.825,2.196,P<0.05).The satisfaction scores of nurses of operating equipment,doctors of clinical department,medical engineers and administrators for first aid equipment of adopting strategic management mode were all higher than those of adopting conventional management mode,and the difference were statistically significant(t=3.666,3.336,4.275,4.292,P<0.05).Conclusion:The quality analysis model of first aid equipment of hospital can improve the operation quality and management level of first aid equipment,and reduce the equipment's failure rate and the unqualified rate of quality inspection,and improve the service effect of first aid equipment.

BACKGROUND:Aberrant expression of some transcription factors involved in the Heghog signaling pathway occurs in patients with polydactyly,and these transcription factors regulate the expression of massive target genes,thereby affecting cellular function.OBJECTIVE:To analyze the transcriptome characterization of patients with polydactyly by single-cell transcriptome analysis.METHODS:The single-cell transcriptome data of mesenchymal and epithelial cells of four patients with polydactyly were downloaded from the GEO public database.Fibroblasts and keratinocytes were categorized into cell subsets,and the transcription factors within each subset were examined.A regulatory network of these transcription factors and their target genes was developed,and the functions of these regulatory factors were analyzed.RESULTS AND CONCLUSION:The transcriptional profiling data of individual cells indicated that the regulatory factors strongly linked to cell functionality in polydactyly include the transcription factors HOXD13,MSX2,LHX2,EMX2,LEF1,CREB3L2,and LHX2 of the HOX family and GLI2 transcription factors.In fibroblasts,HOXD13,MSX2,and LHX2 are involved in polydactyly,whereas HES2 and GLIS1 are crucial for the formation and development of keratinocytes.To conclude,the elevated expression of transcription factors including HOXD13,MSX2,and LHX2 is potentially closely associated with the development of polydactyly.Potential therapeutic strategies that may be offered to correct or prevent polydactyly by targeting specific transcription factors or modulating their activity.

Objective:To study the application of 3D printing technology in multi-center fenestrated/branched endovascular repair (F/B-EVAR) for endovascular repair of abdominal aortic diseases.Methods:From Feb 2018 to Mar 2023, The clinical and followup data of 316 cases of abdominal aortic lesions undergoing repair with F/B-EVAR at 69 medical centers nationwide using 3D printing technology to guide physician-modified stent graft were retrospectively analyzed.Results:The mean follow-up time of the patients was 23 months (2-60 months), and 24 cases were lost to follow up, the follow-up rate was 92.4% (292/316), the mean postoperative hospitalization time was (8.2±4.9) days. A total of 944 main abdominal branch arteries were reconstructed. Intraoperative reconstruction of 11 branches failed, with a success rate of 98.8% (933/944). Within 30 days after surgery, 8 patients died (2.5%), and 6 patients died during follow-up, a total of 14 patients died (4.4%). There were 11 cases (3.5%) of spinal cord ischemia and no patient suffered from permanent paraplegia. There were 19 patients (6.0%) with postoperative renal function injury. Internal leakage was found in 26 patients, and the rate of internal leakage was 8.2%.Conclusion:3D printing technology can accurately locate the location of branch arteries, simplifing the surgical process, shortening the learning curve , and improving clinical efficacy.

Endovascular Aneurysm Repair (EVAR) has become an important treatment method for abdominal aortic aneurysms due to its advantages of shorter operative time,faster postoperative recovery,and lower early postoperative mortality. However,the incidence of complications and the postoperative reintervention rates are higher than those of open surgery. The main complications after EVAR include access vessel injury,post-implantation syndrome,stent migration,endoleaks,visceral branch artery occlusion,lower limb ischemia,and stent infection,which are also the primary causes of reintervention. In recent years,the causes and associated risk factors of various postoperative complications of EVAR have attracted widespread attention and discussion,which are of great significance for improving surgical techniques,enhancing postoperative monitoring,and improving patient outcomes. This paper provides a review of the current complications,associated risk factors,and management strategies after EVAR.

Endovascular Aneurysm Repair (EVAR) has become an important treatment method for abdominal aortic aneurysms due to its advantages of shorter operative time,faster postoperative recovery,and lower early postoperative mortality. However,the incidence of complications and the postoperative reintervention rates are higher than those of open surgery. The main complications after EVAR include access vessel injury,post-implantation syndrome,stent migration,endoleaks,visceral branch artery occlusion,lower limb ischemia,and stent infection,which are also the primary causes of reintervention. In recent years,the causes and associated risk factors of various postoperative complications of EVAR have attracted widespread attention and discussion,which are of great significance for improving surgical techniques,enhancing postoperative monitoring,and improving patient outcomes. This paper provides a review of the current complications,associated risk factors,and management strategies after EVAR.