OBJECTIVE To provide reference for medical institutions to establish the record management mode and review rules of off-label use of 5-aminolevulinic acid （ALA） in photodynamic therapy based on the level of evidence. METHODS All ALA-containing outpatient prescriptions in the rational drug use system in our hospital from January 1， 2024 to December 31， 2025 were retrospectively collected. Based on the drug instructions， the current status of off-label use of ALA in photodynamic therapy was identified . The relevant studies in Micromedex， PubMed， CNKI， Wanfang Data and other databases were systematically searched as the relevant evidence-based evidence of ALA off-label use. According to the Off-label Drug Use Filing Standard of the hospital，the evidence-based evaluation method was used to evaluate the evidence-based evidence of ALA off-label use and carry out hierarchical management. RESULTS A total of 1 803 effective prescriptions were included， of which 676 （37.49%） were off-label use， distributed in the dermatology department （564 prescriptions，83.43%） and the plastic surgery department （112 prescriptions，16.57%）. All 676 prescriptions were off-indications medication， involving ten types of skin diseases， primarily including moderate to severe acne （39.94%）， skin warts （25.44%）， Bowen’s disease （11.98%）， and others. According to evidence-based evidence，off-label uses such as moderate to severe acne， actinic keratosis， and Bowen’s disease were managed according to the evidence categoryⅠ orⅡ.The uses of extramammary Paget’s disease and rosacea were managed according to the evidence category Ⅲ.The uses of lichen sclerosus and keloids were managed according to the evidence category Ⅳ.The results of evidence-based evaluation showed that 92.01% of off-label use in our hospital had high-level evidence-based support （ evidence category was gradeⅠ-Ⅱ）. CONCLUSIONS Off-label uses supported by high-level evidence， such as moderate to severe acne， skin warts， and Bowen’s disease， can be managed under filing category Ⅰ or Ⅱ. For the use of lichen sclerosus and keloids， evidence-based evidence is insufficient and should be strictly restricted.The vast majority of ALA off-label use in our hospital has sufficient evidence-based basis.

Objective:To analyze the characteristics of toxic substances determination standards in workplaces in China, in order to provide basis for further development of national occupational health standards.Methods:In November 2023, Used descriptive analysis methods, conduct a characteristic analysis of the toxic substance determination standards for workplaces in China issued between 2004 and 2024, including the first drafting situation, standard formulation/revision situation, standard determination methods types, and method performance indicators.Results:The The first drafting unit in the GBZ/T 160 and GBZ/T 300 standards that have been issued is mainly composed of disease prevention and control centers (154, accounting for 60.6%) and the hospital of Occupational disease prevention and control (154, accounting for 60.6%). The regional distribution is North China (65 items, accounting for 25.6%), East China (60 items, accounting for 23.7%), and South China (51 items, accounting for 20.2%). 93.1% of the standards were published between 2004 and 2017; Covering 671 measurement methods.The method types are gas chromatography (367, 54.7%), spectrophotometry (98, 14.6%), inductively coupled plasma mass spectrometry (54, 8.0%), etc. The missing items of standard performance indexes mainly focus on penetration capacity, recovery rate and detection limit. The missing items of penetration capacity and recovery rate are mainly distributed in gas chromatography methods; The missing detection limits and minimum detection concentrations are mainly distributed in spectrophotometry and gas chromatography method.Conclusion:Suggest accelerating the update speed of toxic substance determination standards in the workplace, supplementing the supporting determination methods in GBZ 2.1 standard, focusing on solving common problems in existing standards, gradually improving the standards for missing performance indicators, and gradually carrying out national standard transformation in a hierarchical and phased manner for local or group standards with good practical applications.

The incidence of thyroid cancer has been increasing,and early diagnosis and treatment are crucial for improving patient prognosis.With the advancement of artificial intelligence(AI)technology,significant progress has been made in its application in the diagnosis and treatment of thyroid cancer.AI technology has notably enhanced the diagnostic accuracy of thyroid cancer.By optimizing imaging examinations such as ultrasound and CT scans,it can more precisely identify malignant features of thyroid nodules.In fine-needle aspiration biopsy,the integration of AI with genetic testing technologies has improved both the accuracy and efficiency of diagnosis.In terms of treatment,AI assists in intraoperative functional preservation,reducing the risk of surgical trauma.For instance,it can accurately identify the locations of the recurrent laryngeal nerve and parathyroid glands.Additionally,AI is capable of predicting the efficacy of 131I treatment and the risk of complications,thereby guiding postoperative follow-up and management.The core strength of AI technology lies in its powerful data processing and analytical capabilities,enabling it to uncover latent patterns within data and provide a scientific basis for treatment decision-making.Looking ahead,with continuous technological advancements,AI is expected to propel the diagnosis and treatment of thyroid cancer towards greater intelligence and precision.However,challenges such as data privacy and algorithm transparency need to be addressed.This article provides a review of the research progress of AI technology in the fields of diagnosis,treatment,and prognosis prediction of thyroid cancer,explores the current strengths and weaknesses of AI technology,and looks forward to its future development directions while acknowledging challenges like data privacy and algorithm transparency.

Objective To apply the image recognition method based on distance graph convolutional network to the image processing of malaria-infected erythrocytes for realizing the multi-stage recognition of malaria and improving the diagnostic efficiency of malaria.Methods A multi-stage malaria recognition model based on distance graph convolutional network was proposed.A radial basis function was firstly added in KNN graph construction algorithm to construct adjacency matrix and assign weights to the nearest-neighbor nodes according to the similarity between nodes,so as to weaken the effects of the distant nearest-neighbor nodes on the central node.Then,attention mechanism was introduced to update adjacency matrix dynamically in the graph convolutional network for making the model pay attention to near-neighbor nodes with higher similarity,and finally the multi-stage image recognition of malaria-infected erythrocytes was completed.Results Validated on the Malaria-MIT dataset,the experimental results show that compared with original model,the proposed method improved accuracy,precision,recall rate and F1-score to 96.18%,96.23%,96.18%and 96.18%,respectively.Conclusion The proposed approach can effectively accomplish the task of multi-stage image recognition of malaria-infected erythrocytes.

Objective To investigate the effect and potential mechanism of rat mesenchymal stem cells(MSC)on D-galactose-induced brain-tissue aging.Methods A rat brain-aging model was established by injecting D-galactose,and rats in the treatment group received MSC injections via the tail vein.Superoxide dismutase(SOD)activity and malondialdehyde(MDA)levels were assessed in rat brain tissue at the end of the experiment,and pathological changes in brain tissue were observed by hematoxylin-eosin(HE)staining.Expression levels of the inflammatory factors interleukin(IL)-1 and IL-6,the pathway proteins brain-derived neurotrophic factor(BDNF)-tropomyosin receptor kinase B(TrkB),the negative growth regulators p53 and p16,as well as vascular endothelial growth factor(VEGF)and basic fibroblast growth factor(bFGF)were observed by polymerase chain reaction(PCR)and Western Blot.Results Brain levels of SOD activity were significantly increased and MDA levels were significantly decreased in rats in the modle group compared with the treatment group(P＜0.05).The pathological state of the cerebral cortex and hippocampus were improved and the number of neurons and nucleus pulposus ratio in the brain were increased in the treatment group,as shown by HE staining.Expression levels of IL-1,IL-6,p53,and p16 were significantly decreased,while BDNF,TrkB,VEGF,and bFGF were significantly increased in the treatment group compared with the model group,as shown by PCR and Western Blot(P＜0.05).Conclusions These result suggest that MSCs potentially mitigate D-galactose-induced cerebral senescence by concurrently modulating the BDNF-TrkB axis to attenuate oxidative/inflammatory damage,while enhancing the secretion of vasculotrophic(VEGF)and neurotrophic(bFGF)factors for neuronal maintenance.

Objective To investigate the common molecular features of immunothrombosis, thus enhancing the comprehension of thrombosis triggered by immune and inflammatory responses and offering crucial insights for identifying potential diagnostic and therapeutic targets. Methods Differential gene expression analysis and functional enrichment analysis were conducted on datasets of systemic lupus erythematosus (SLE) and venous thromboembolism (VTE). The intersection of differentially expressed genes in SLE and VTE with those of neutrophil extracellular traps (NET) yielded cross-talk genes (CG) for SLE-NET and VTE-NET interaction. Further analysis included functional enrichment and protein-protein interaction (PPI) network assessments of these CG to identify hub genes. Venn diagrams and receiver operating characteristic (ROC) curve analysis were employed to pinpoint the most effective shared diagnostic CG, which were validated using a graft-versus-host disease (GVHD) dataset. Results Differential expression genes in SLE and VTE were associated with distinct biological processes, whereas SLE-NET-CG and VTE-NET-CG were implicated in pathways related to leukocyte migration, inflammatory response, and immune response. Through PPI network analysis, several hub genes were identified, with matrix metalloproteinase 9 (MMP9) and S100 calcium-binding protein A12 (S100A12) emerging as the best shared diagnostic CG for SLE (AUC: 0.936 and 0.832) and VTE (AUC: 0.719 and 0.759). Notably, MMP9 exhibited good diagnostic performance in the GVHD dataset (AUC: 0.696). Conclusion This study unveils the common molecular features of SLE, VTE, and NET, emphasizing MMP9 and S100A12 as the optimal shared diagnostic CG, thus providing valuable evidence for the diagnosis and therapeutic strategies related to immunothrombosis. Additionally, the expression of MMP9 in GVHD highlights its critical role in the risk of VTE associated with immune system disorders.
Humans ; Computational Biology/methods* ; Lupus Erythematosus, Systemic/immunology* ; Protein Interaction Maps/genetics* ; Venous Thromboembolism/therapy* ; Matrix Metalloproteinase 9/genetics* ; Extracellular Traps/metabolism* ; Gene Regulatory Networks ; Thrombosis/immunology* ; Graft vs Host Disease/genetics* ; Gene Expression Profiling

Objective: To construct a deep learning model based on convolutional neural network(CNN)-long short term memory network(LSTM)-Attention to explore the correlation between meteorological and clinical factors and the incidence of ischemic stroke. Methods: A fusion model CNN-LSTM-Attention based on CNN, LSTM, and Attention was constructed by incorporating clinical data and meteorological data of ischemic stroke inpatients. The predictive performance of the model was evaluated by maximum prediction error and root mean square error(RMSE). The impact of different lag days on prediction performance was investigated by selecting lag periods ranging from 1 to 7 days. Results: In both short-term and long-term predictions, the CNN-LSTM-Attention fusion model(short-term: 1.5 and 0.6; long-term: 8.3 and 2.5) showed superior maximum prediction bias and RMSE compared to the LSTM model(short-term: 2.8 and 1.2; long-term: 19.5 and 5.5) and the CNN-LSTM model(short-term: 2.0 and 0.8; long-term: 11.2 and 3.3). After incorporating lag days, the maximum prediction deviation and RMSE for lags of 3 days(short-term: 0.7 and 0.4; long-term: 5.5 and 1.9) and 5 days(short-term: 0.8 and 0.3; long-term: 6.5 and 2.0) in both short-term and long-term forecasts were smaller than lags of 0 days(short-term: 1.5 and 0.6; long-term: 8.3 and 2.5). The maximum prediction deviation and RMSE in the short-term forecast were greater than lag 0 days for both lag 1 days(1.5 and 0.8) and lag 7 days(1.9 and 0.9). In the long-term forecast, the two indicators for lag 1 days(6.8 and 2.4) were lower than those for lag 0 days but higher than those for lag 3 days and 5 days. The maximum prediction deviation for lag 7 days(7.5) was lower than that for lag 0 days, but the RMSE(2.7) is higher than that for lag 0 days. Conclusion The established CNN-LSTM-Attention model demonstrates significant predictive value for the onset of ischemic stroke and can provide reference for the rational allocation of medical resources.

The anatomy of the superficial temporal artery and its branches exhibit individual differences, and traditional anatomical study method cannot objectively, truly, or accurately reflect the characteristics of individual anatomical structures. Based on three-dimensional(3D) reconstruction technology, the microstructure and blood supply range of individual superficial temporal arteries can be accurately explored. This article reviewed the anatomy of superficial temporal artery based on 3D reconstruction for clinical application by extensively reviewing the relevant literature on the research and application of 3D reconstruction of superficial temporal artery by CT angiography and magnetic resonance imaging in recent years, and provided precise anatomical reference for the 3D reconstruction technique of superficial temporal artery and related surgery.

BACKGROUND:Currently,different methods of model establishment have been derived from different injury modes of spinal cord injury.Traditional physical injury modeling methods have their own advantages and disadvantages,and there is a lack of more effective and stable animal models of spinal cord injury. OBJECTIVE:To establish a reproducible,controllable,trauma-free,low-mortality,more stable,widely applicable,and short-term postoperative care rat model of spinal cord injury. METHODS:Forty Sprague-Dawley rats with similar body mass and ages were randomly divided into a control group and an improved group,with 20 rats in each group.Animal models of spinal cord injury in the control group were constructed using a clip model method,while the improved group used a modified ligation method based on the compression method to make the spinal cord injury models using suture ligation based on fenestration.Postoperative comparisons were made between the two groups,assessing urination behavior,hematuria,pyuria(infection rate),mortality,scoliosis rate and Basso-Beattie-Bresnahan locomotor rating scale scores at 1,3,5,and 7 days after modeling. RESULTS AND CONCLUSION:Compared with the conventional modeling method,the modified ligation method based on the compression method resulted in faster recovery of urination behavior,lower hematuria rate,lower infection rate,lower mortality rate,lower scoliosis rate,and more concentrated and stable Basso-Beattie-Bresnahan scores(all below 2 points within 1 week).This proves that the modified ligation method based on compression is more suitable for the establishment of spinal cord injury models in rats.

The incidence of thyroid cancer has been increasing,and early diagnosis and treatment are crucial for improving patient prognosis.With the advancement of artificial intelligence(AI)technology,significant progress has been made in its application in the diagnosis and treatment of thyroid cancer.AI technology has notably enhanced the diagnostic accuracy of thyroid cancer.By optimizing imaging examinations such as ultrasound and CT scans,it can more precisely identify malignant features of thyroid nodules.In fine-needle aspiration biopsy,the integration of AI with genetic testing technologies has improved both the accuracy and efficiency of diagnosis.In terms of treatment,AI assists in intraoperative functional preservation,reducing the risk of surgical trauma.For instance,it can accurately identify the locations of the recurrent laryngeal nerve and parathyroid glands.Additionally,AI is capable of predicting the efficacy of 131I treatment and the risk of complications,thereby guiding postoperative follow-up and management.The core strength of AI technology lies in its powerful data processing and analytical capabilities,enabling it to uncover latent patterns within data and provide a scientific basis for treatment decision-making.Looking ahead,with continuous technological advancements,AI is expected to propel the diagnosis and treatment of thyroid cancer towards greater intelligence and precision.However,challenges such as data privacy and algorithm transparency need to be addressed.This article provides a review of the research progress of AI technology in the fields of diagnosis,treatment,and prognosis prediction of thyroid cancer,explores the current strengths and weaknesses of AI technology,and looks forward to its future development directions while acknowledging challenges like data privacy and algorithm transparency.