MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Predictors of post-healing recurrence in patients with diabetic foot ulcers: A systematic review and meta-analysis.

BACKGROUND: Diabetic foot ulcer is one of the most prevalent, serious, and costly consequences of diabetes, often associated with peripheral neuropathy and peripheral arterial disease. These ulcers contribute to high disability and mortality rates in patients and pose a major challenge to clinical management. OBJECTIVE: To systematically review the risk prediction models for post-healing recurrence in diabetic foot ulcer (DFU) patients, so as to provide a reference for clinical staff to choose appropriate prediction models. METHODS: The authors searched five databases (Cochrane Library, PubMed, Web of Science, EMBASE, and Chinese Biomedical Database) from their inception to September 23, 2023, for relevant literature. After data extraction, the quality of the literature was evaluated using the Predictive Model Research Bias Risk and Suitability Assessment tool (PROBAST). Meta-analysis was performed using STATA 17.0 software. RESULTS: A total of 9 studies involving 5956 patients were included. The recurrence rate after DFU healing ranged from 6.2 % to 41.4 %. Nine studies established 15 risk prediction models, and the area under the curve (AUC) ranged from 0.660 to 0.940, of which 12 models had an AUC≥0.7, indicating good prediction performance. The combined AUC value of the 9 validation models was 0.83 (95 % confidence interval: 0.79-0.88). Hosmer-Lemeshow test was performed for 10 models, external validation for 5 models, and internal validation for 6 models. Meta-analysis showed that 14 predictors, such as age and living alone, could predict post-healing recurrence in DFU patients (p < 0.05). CONCLUSION: To enhance the quality of these risk prediction models, there is potential for future improvements in terms of follow-up duration, model calibration, and validation processes.

Risk prediction models for intensive care unit-acquired weakness in critically ill patients: A systematic review.

BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICU-AW) is a critical complication that significantly worsens patient prognosis. It is widely thought that risk prediction models can be harnessed to guide preventive interventions. While the number of ICU-AW risk prediction models is increasing, the quality and applicability of these models in clinical practice remain unclear. OBJECTIVE: The objective of this study was to systematically review published studies on risk prediction models for ICU-AW. METHODS: We searched electronic databases (PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang Database) from inception to October 2023 for studies on ICU-AW risk prediction models. Two independent researchers screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies. RESULTS: A total of 2709 articles were identified. After screening, 25 articles were selected, encompassing 25 risk prediction models. The area under the curve for these models ranged from 0.681 to 0.926. Evaluation of bias risk indicated that all included models exhibited a high risk of bias, with three models demonstrating poor applicability. The top five predictors among these models were mechanical ventilation duration, age, Acute Physiology and Chronic Health Evaluation II score, blood lactate levels, and the length of ICU stay. The combined area under the curve of the ten validation models was 0.83 (95% confidence interval: 0.77-0.88), indicating a strong discriminative ability. CONCLUSIONS: Overall, ICU-AW risk prediction models demonstrate promising discriminative ability. However, further optimisation is needed to address limitations, including data source heterogeneity, potential biases in study design, and the need for robust statistical validation. Future efforts should prioritise external validation of existing models or the development of high-quality predictive models with superior performance. REGISTRATION: The protocol for this study is registered with the International Prospective Register of Systematic Reviews (registration number: CRD42023453187).

Comparison of tooth movement and biological response resulting from different force magnitudes combined with osteoperforation in rabbits

Abstract Objective To compare tooth movement rate and histological responses with three different force magnitude designs under osteoperforation in rabbit models. Methodology 48 rabbits were divided into three groups: Group A, Group B, and Group C, with traction force of 50 g, 100 g, 150 g, respectively. Osteoperforation was performed at the mesial of the right mandibular first premolar, the left side was not affected. One mini-screw was inserted into bones between two central incisors. Coil springs were fixed to the first premolars and the mini-screw. Tooth movement distance was calculated, and immunohistochemical staining of PCNA, OCN, VEGF, and TGF-β1 was analyzed. Results The tooth movement distance on the surgical side was larger than the control side in all groups (P<0.01). No significant intergroup difference was observed for the surgical side in tooth movement distance among the three groups (P>0.05). For the control side, tooth movement distance in Group A was significantly smaller than Groups B and C (P<0.001); no significant difference in tooth movement distance between Group B and Group C was observed (P>0.05). On the tension area of the moving premolar, labeling of PCNA, OCN, VEGF and TGF-β1 were confirmed in alveolar bone and periodontal ligament in all groups. PCNA, OCN, VEGF and TGF-β1 on the surgical side was larger than the control side in all groups (P<0.001). Conclusion Osteoperforation could accelerate orthodontic tooth movement rate in rabbits. Fast osteoperforation-assisted tooth movement in rabbits was achieve with light 50 g traction.