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"He, Lu"
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Research on object detection and recognition in remote sensing images based on YOLOv11
This study applies the YOLOv11 model to train and detect ground object targets in high-resolution remote sensing images, aiming to evaluate its potential in enhancing detection accuracy and efficiency. The model was trained on 70,389 samples across 20 target categories. After 496 training epochs, the loss functions (Box_Loss, Cls_Loss, and DFL_Loss) demonstrated rapid convergence, indicating effective optimization in target localization, classification, and detail refinement. The evaluation metrics yielded a precision of 0.8861, a recall of 0.8563, a map
50
of 0.8920, a map
50–95
of 0.8646, and an F1 score of 0.8709, highlighting the model’s high accuracy and robustness in addressing complex detection tasks. Furthermore, 80% of the test samples achieved confidence scores exceeding 85%, confirming the reliability of YOLOv11 in multiclass and multiobject detection scenarios. These findings suggest that YOLOv11 holds significant promise for remote sensing image target detection, demonstrating exceptional detection performance while offering robust technical support for intelligent remote sensing image analysis. Future studies will focus on expanding the dataset, refining the model architecture, and improving its performance in detecting small targets and processing complex scenes, paving the way for its broader applications in environmental protection, urban planning, and multiobject detection.
Journal Article
Aerosolized antibiotics for ventilator-associated pneumonia: a pairwise and Bayesian network meta-analysis
Background
Aerosolized antibiotics have been proposed as a novel and promising treatment option for the treatment of ventilator-associated pneumonia (VAP). However, the optimum aerosolized antibiotics for VAP remain uncertain.
Methods
We included studies from two systematic reviews and searched PubMed, EMBASE, and Cochrane databases for other studies. Eligible studies included randomized controlled trials and observational studies. Extracted data were analyzed by pairwise and network meta-analysis.
Results
Eight observational and eight randomized studies were identified for this analysis. By pairwise meta-analysis using intravenous antibiotics as the reference, patients treated with aerosolized antibiotics were associated with significantly higher rates of clinical recovery (risk ratio (RR) 1.21, 95% confidence interval (CI) 1.09–1.34;
P
= 0.001) and microbiological eradication (RR 1.42, 95% CI 1.22–1.650;
P
< 0.0001). There were no significant differences in the risks of mortality (RR 0.88, 95% CI 0.74–1.04;
P
= 0.127) or nephrotoxicity (RR 1.00, 95% CI 0.72–1.39;
P
= 0.995). Using network meta-analysis, clinical recovery benefits were seen only with aerosolized tobramycin and colistin (especially tobramycin), and microbiological eradication benefits were seen only with colistin. Aerosolized tobramycin was also associated with significantly lower mortality when compared with aerosolized amikacin and colistin and intravenous antibiotics. The assessment of rank probabilities indicated aerosolized tobramycin presented the greatest likelihood of having benefits for clinical recovery and mortality, and aerosolized colistin presented the best benefits for microbiological eradication.
Conclusions
Aerosolized antibiotics appear to be a useful treatment for VAP with respect to clinical recovery and microbiological eradication, and do not increase mortality or nephrotoxicity risks. Our network meta-analysis in patients with VAP suggests that clinical recovery benefits are associated with aerosolized tobramycin and colistin (especially tobramycin), microbiological eradication with aerosolized colistin, and survival with aerosolized tobramycin, mostly based on observational studies. Due to the low levels of evidence, definitive recommendations cannot be made before additional, large randomized studies are carried out.
Journal Article
Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost
Background
Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.
Methods
Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model.
Results
A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800–0.838], 0.797 [95% CI 0.781–0.813] and 0.857 [95% CI 0.839–0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value.
Conclusions
Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.
Journal Article
One-dimensional extended Su–Schrieffer–Heeger models as descendants of a two-dimensional topological model
The topological phase diagrams and finite-size energy spectra of one-dimensional extended Su–Schrieffer–Heeger (SSH) models with long-range hoppings on the trimer lattice are investigated in detail. Due to the long-range hoppings, the band structure of the original SSH model becomes more complicated and new phases with the large Zak phase can emerge. Furthermore, a seeming violation of bulk-edge correspondence occurs in the one-dimensional topological system whose band topology stems from the inversion symmetry. The one-dimensional models are mapped onto a two-dimensional topological model when a parameter of the one-dimensional models is regarded as an additional degree of freedom. As Fourier components of the derived two-dimensional model, phase boudaries and the finite-size spectra of one-dimensional models can be recovered from the model in the higher spatial dimensions. Then the origin of edge modes of one-dimensional models can be understood from two dimensions and we give a reasonable explanation of the violation of bulk-edge correspondence in one spatial dimension. In fact, we may give a general perspective that the topological properties of one-dimensional (lower-dimensional) systems can be found their origin from two-dimensional (higher-dimensional) systems.
Journal Article
Enhancing security in instant messaging systems with a hybrid SM2, SM3, and SM4 encryption framework
With the rapid integration of instant messaging systems (IMS) into critical domains such as finance, public services, and enterprise operations, ensuring the confidentiality, integrity, and availability of communication data has become a pressing concern. Existing IMS security solutions commonly employ traditional public-key cryptography, centralized authentication servers, or single-layer encryption, each of which is susceptible to single-point failures and provides only limited resistance against sophisticated attacks. This study addresses the research gap regarding the complementary advantages of SM2, SM3, and SM4 algorithms, as well as hybrid collaborative security schemes in IMS security. This paper presents a hybrid encryption security framework that combines the SM2, SM3, and SM4 algorithms to address emerging threats in IMS. The proposed framework adopts a decentralized architecture with certificateless authentication and performs all encryption and decryption operations on the client side, eliminating reliance on centralized servers and mitigating single-point failure risks. It further enforces an encrypt-before-store policy to enhance data security at the storage layer. The framework integrates SM2 for key exchange and authentication, SM4 for message encryption, and SM3 for integrity verification, forming a multi-layer defense mechanism capable of countering Man-in-the-Middle (MITM) attacks, credential theft, database intrusions, and other vulnerabilities. Experimental evaluations demonstrate the system’s strong security performance and communication efficiency: SM2 achieves up to 642 times faster key generation and 2.2 times faster decryption compared to RSA-3072; SM3 improves hashing performance by up to 11.5% over SHA-256; and SM4 delivers up to 22% higher encryption efficiency than AES-256 for small data blocks. These results verify the proposed framework’s practicality and performance advantages in lightweight, real-time IMS applications.
Journal Article
Targeting Cancer Stem Cells to Overcome Chemoresistance
Cancers are heterogeneous at the cell level, and the mechanisms leading to cancer heterogeneity could be clonal evolution or cancer stem cells. Cancer stem cells are resistant to most anti-cancer treatments and could be preferential targets to reverse this resistance, either targeting stemness pathways or cancer stem cell surface markers. Gold nanoparticles have emerged as innovative tools, particularly for photo-thermal therapy since they can be excited by laser to induce hyperthermia. Gold nanoparticles can be functionalized with antibodies to specifically target cancer stem cells. Preclinical studies using photo-thermal therapy have demonstrated the feasibility of targeting chemo-resistant cancer cells to reverse clinical chemoresistance. Here, we review the data linking cancer stem cells and chemoresistance and discuss the way to target them to reverse resistance. We particularly focus on the use of functionalized gold nanoparticles in the treatment of chemo-resistant metastatic cancers.
Journal Article
Composite dietary antioxidant index was negatively associated with the prevalence of diabetes independent of cardiovascular diseases
Aim
The association between composite dietary antioxidant index (CDAI) and diabetes remains unknown. Our study was to investigate the association of CDAI with diabetes.
Methods
A total of 11,956 participants were enrolled from the National Health and Nutrition Examination Surveys (NHANES). The CDAI was calculated from the intake of six dietary antioxidants. Multivariable logistic regressions were performed to explore the associations between CDAI and the prevalence of diabetes and glycemic index. Non-linear associations were explored using restricted cubic splines.
Results
In the multivariate logistic regression model, the odds ratio (95% confidence interval) of CDAI associating with obesity was 0.98 (0.97-1.00; p = 0.033). Compared with the lowest quartile, the highest quartile was related to 0.84-fold risk of diabetes (0.71–0.99; p = 0.035). However, CDAI was not independently associated with fasting glucose and hemoglobin A1c.
Conclusion
CDAI was negatively associated with diabetes and the relationship was independent of other traditional risk factors.
Journal Article