Objective To explore the therapeutic effect of LuoFuShan Rheumatism Plaster(LFS)on neuropathic pain(NP)and its molecular mechanism.Methods Mouse models of sciatic nerve chronic constriction injury(CCI)were treated with low,medium,and high doses(2.2,4.4,and 8.8 cm2,respectively)of LFS by topical application for 14 consecutive days.The therapeutic effects were assessed by evaluating the mechanical withdrawal threshold(MWT),paw withdrawal latency(PWL),plasma IL-6 and TNF-α levels,and histopathology of the sciatic nerve.Network pharmacology and molecular docking were used to identify the key targets and signaling pathways.The key targets were verified by RT-qPCR and immunohistochemistry.The biosafety of LFS was evaluated by measuring the organ indices and damage indicators of the heart,liver,and kidneys.Results Compared with the CCI group,LFS dose-dependently increased MWT and PWL,reduced plasma IL-6 and TNF-α levels,and alleviated sciatic nerve inflammation in the mouse models.Network pharmacology identified 378 bioactive compounds targeting 279 NP-associated genes enriched in TLR and TNF signaling.Molecular docking showed that quercetin and ursolic acid in LFS could stably bind to TLR4 and TNF-α.In the mouse models of sciatic nerve CCI,LFS significantly downregulated the mRNA expression levels of Tlr4 and Tnf-α in the spinal cord in a dose-dependent manner and lowered the protein expressions of TLR4 and TNF-α in the sciatic nerve.LFS treatment did not cause significant changes in the organ indices or damage indicators of the heart,liver and kidneys as compared with those in the CCI model group and sham-operated group.Conclusion LFS alleviates NP in mice by suppression of TLR4/TNF-α-mediated neuroinflammation with a good safety profile.

Objective The purpose of writing the"expert consensus on humanistic care for patients in hospice care"(hereinafter referred to as the"consensus")aims to standardize the practice of humanistic care in the field of hospice care,ensuring that humanistic care is integrated throughout the entire service process for hospice care patients and their families.Methods A systematic search was conducted in domestic and foreign databases for literature related to hospice care and humanistic care,including guidelines,expert consensuses,systematic reviews or Meta-analyses,and evidence summaries.High-quality evidence was evaluated,extracted,and summarized to form the initial draft of the"consensus".From June to October 2024,20 experts from the fields of hospice care,nursing humanities,and evidence-based nursing were invited to participate in 1 round of expert consultation.Among them,13 experts were selected for 2 rounds of expert demonstration meetings.After collating and analyzing the experts' opinions,the initial draft was revised and refined,ultimately resulting in the final version of the"consensus".Results The effective response rate of the consultation questionnaire was 100％,with expert authority coefficient of 0.880,judgment coefficient of 0.935,and familiarity level of 0.825.The Kendall harmony coefficient of the expert consultation was 0.134(P＜0.05).The"consensus"consisted of 13 aspects,including the targets and objectives,principles,institutional guarantees,environmental requirements,etc.Conclusion This"consensus"possesses strong scientific rigor and practicality,which can provide guidance and references for the practice of humanistic care in the field of hospice care,promoting the standardization and humanization of hospice care services.

Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

Objective The purpose of writing the"expert consensus on humanistic care for patients in hospice care"(hereinafter referred to as the"consensus")aims to standardize the practice of humanistic care in the field of hospice care,ensuring that humanistic care is integrated throughout the entire service process for hospice care patients and their families.Methods A systematic search was conducted in domestic and foreign databases for literature related to hospice care and humanistic care,including guidelines,expert consensuses,systematic reviews or Meta-analyses,and evidence summaries.High-quality evidence was evaluated,extracted,and summarized to form the initial draft of the"consensus".From June to October 2024,20 experts from the fields of hospice care,nursing humanities,and evidence-based nursing were invited to participate in 1 round of expert consultation.Among them,13 experts were selected for 2 rounds of expert demonstration meetings.After collating and analyzing the experts' opinions,the initial draft was revised and refined,ultimately resulting in the final version of the"consensus".Results The effective response rate of the consultation questionnaire was 100％,with expert authority coefficient of 0.880,judgment coefficient of 0.935,and familiarity level of 0.825.The Kendall harmony coefficient of the expert consultation was 0.134(P＜0.05).The"consensus"consisted of 13 aspects,including the targets and objectives,principles,institutional guarantees,environmental requirements,etc.Conclusion This"consensus"possesses strong scientific rigor and practicality,which can provide guidance and references for the practice of humanistic care in the field of hospice care,promoting the standardization and humanization of hospice care services.

OBJECTIVES: To explore the therapeutic effect of LuoFuShan Rheumatism Plaster (LFS) on neuropathic pain (NP) and its molecular mechanism. METHODS: Mouse models of sciatic nerve chronic constriction injury (CCI) were treated with low, medium, and high doses (2.2, 4.4, and 8.8 cm², respectively) of LFS by topical application for 14 consecutive days. The therapeutic effects were assessed by evaluating the mechanical withdrawal threshold (MWT), paw withdrawal latency (PWL), plasma IL-6 and TNF-α levels, and histopathology of the sciatic nerve. Network pharmacology and molecular docking were used to identify the key targets and signaling pathways. The key targets were verified by RT-qPCR and immunohistochemistry. The biosafety of LFS was evaluated by measuring the organ indices and damage indicators of the heart, liver, and kidneys. RESULTS: Compared with the CCI group, LFS dose-dependently increased MWT and PWL, reduced plasma IL-6 and TNF-α levels, and alleviated sciatic nerve inflammation in the mouse models. Network pharmacology identified 378 bioactive compounds targeting 279 NP-associated genes enriched in TLR and TNF signaling. Molecular docking showed that quercetin and ursolic acid in LFS could stably bind to TLR4 and TNF‑α. In the mouse models of sciatic nerve CCI, LFS significantly downregulated the mRNA expression levels of Tlr4 and Tnf-α in the spinal cord in a dose-dependent manner and lowered the protein expressions of TLR4 and TNF-α in the sciatic nerve. LFS treatment did not cause significant changes in the organ indices or damage indicators of the heart, liver and kidneys as compared with those in the CCI model group and sham-operated group. CONCLUSIONS LFS alleviates NP in mice by suppression of TLR4/TNF-α-mediated neuroinflammation with a good safety profile.
Animals ; Toll-Like Receptor 4/metabolism* ; Neuralgia/metabolism* ; Mice ; Signal Transduction/drug effects* ; Tumor Necrosis Factor-alpha/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Sciatic Nerve/injuries* ; Male ; Molecular Docking Simulation ; Disease Models, Animal ; Interleukin-6

Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

The application of Raman spectroscopy in the field of laboratory medicine is making continuous progress and development. The biosensor platform based on Raman spectroscopy provides a new means for accurate molecular diagnosis of diseases. In particular, as a fast and non-destructive detection method, surface-enhanced Raman scattering has the advantages of simple sample preparation, little interference from water and real-time detection, and shows great application potential in the field of medical examination. At the same time, with the integration of SERS and other technologies, including electrochemistry, new nano-materials, microfluidic, biochip, DNA nano-machine, artificial intelligence and machine learning, it will play a more and more important role in the field of medical laboratory. With the deepening of SERS research and the cross-integration between multiple disciplines, it will be widely used in biomedical detection and is expected to become an important technology platform for the next generation of precision diagnosis.

Objective:To analyze the difference between Z score and previous criteria in the diagnosis characteristics of coronary artery aneurysm (CAA) in Kawasaki disease, and to investigate the clinical distribution of Kawasaki disease CAA in the Z score group.Methods:This study retrospectively analyzed the clinical and echocardiographic data of 2 419 children with Kawasaki disease in Shenzhen Children′s Hospital from January 2009 to December 2019. The traditional criteria and Z score criteria were used to diagnose CAA, and the differences of diagnostic efficiency between the 2 diagnostic methods were analyzed. The clinical distribution characteristics of CAA in children with Kawasaki disease were analyzed by grouping their sex, clinical classification (complete Kawasaki disease, incomplete Kawasaki disease) the sensitivity to intravenous immunoglobulin (IVIG) (IVIG-sensitive Kawasaki disease,IVIG-unresponsive Kawasaki disease). And the course of the disease (≤6 weeks, >6-8 weeks, >8 weeks to 6 months) etc. The χ2 test or Kruskal-Wallis test was used for comparison between the groups, and the Kappa test was used for consistency evaluation.Results:Among the 2 419 children with Kawasaki disease, 1 558 were males and 861 were females. The age of onset was 1.8 (1.0, 3.2) years. The rate of CAA by Z score criteria was higher than that by traditional method (21.9% (529/2 419) vs. 13.9% (336/2 419), χ 2=1 074.94, P<0.001). Compared to the traditional method, the Z score criteria found higher rate of CAA in male patients, patients with incomplete Kawasaki disease, and IVIG-unresponsive patients (25.2% (392/1 558) vs. 16.0% (249/1 558), (32.7% (166/507) vs. 19.5% (99/507), 30.5% (95/312) vs. 24.0% (75/312), χ 2=694.05, 216.19, 184.37, all P<0.001). The Z score criteria was consistent with the traditional method in diagnosing CAA (κ=0.642, P<0.001). Moreover, in the Z score criteria, the rate of CAA in males (25.2%, 392/1 558) was higher than that in females (15.9%, 137/861), higher in incomplete Kawasaki cases (32.7%, 166/507) than that in complete Kawasaki case (19.0%, 363/1 912), and higher in IVIG-unresponsive cases (30.4%, 95/312) than that in IVIG-sensitive cases (20.6%, 434/2 107), with statistically significant differences (χ 2=27.76, 44.38, 15.43, all P<0.001). Coronary Z score of course ≤ 6 weeks was greater than that of course between>6-8 weeks and >8 weeks to 6 months (1.3 (0.7, 2.3) vs. 0.7 (0.3, 1.4), 0.7 (0.3, 1.3), Z=20.65, 13.70, both P<0.001). Conclusions:The rate of CAA in Kawasaki disease by Z score criteria is higher than that by traditional method. In the Z score group, most CAA occur within 6 weeks of the course of the disease, and the rate of CAA in male, incomplete Kawasaki disease, and IVIG-unresponsive is higher.

Objective:To develop an intelligent vision examination and management system based on mobile terminal, and evaluate its practical application.Methods:A diagnostic test study was adopted.Fifty children and adolescents (100 eyes) aged from 3 to 14 years, with an average age of (8.16±4.58) years, were enrolled in Zhongshan Ophthalmic Center from March to May, 2020.An intelligent vision examination and management system for Android and IOS devices, which applied technologies of real-time range measurement, speech recognition, and gesture recognition to achieve the real-time interaction in the vision examination, was developed using JAVA language and xcode11.The uncorrected visual acuity of subjects was tested with the intelligent vision system and the standard eye chart in hospital.The difference in the number of cooperative eyes between the two methods was analyzed by χ2 test.The LogMAR acuity of the two methods were compared by Wilcoxon rank sum test and the consistency between the two methods was analyzed by Kappa test.The study adhered to the Declaration of Helsinki, and the study protocol was approved by an Ethics Committee of Zhongshan Ophthalmic Center (No.2020KYPJ095). Written informed consent was obtained from guardians of each subject prior to any examination. Results:The intelligent vision examination and management system was successfully developed for Android and IOS devices, which was loaded with precise vision examination at 3 meters, intelligent interaction, result output and automatic filing functionalities.The inspection range of visual acuity was from 0.1 to 1.5.The size and resolution of eye chart met the national standard.There was no significant difference in the number of cooperative eyes during examination between traditional eye chart (99/100) and intelligent vision examination system (98/100) ( χ2=0.338, P=0.561). There was no significant difference in examination results between traditional eye chart 0.3 (0.2, 0.5) LogMAR and intelligent vision examination system 0.3 (0.2, 0.5) LogMAR ( Z=-1.602, P=0.109), and there was a high consistency between the two methods (Kappa coefficient=0.885). Conclusions:The intelligent vision examination and management system based on the mobile terminal is of high accuracy and practicability, which can effectively meet the public demand for at-home vision testing and long-term tracking.The system can be promoted and applied as a new mode for personalized eye health management.

Objective:To compare the diagnostic accuracy of lung ultrasonography and radiography in detection of acute traumatic intrathoracic injuries in children.Methods:A retrospective analysis was performed in 46 cases of children with chest trauma in Shenzhen Children′s Hospital from January 2017 to January 2021. The diagnostic efficiency of lung ultrasound and radiography in children with acute traumatic hemopneumothorax and lung contusion were compared. Computed tomography scan was used as gold standard.Results:The sensitivity and specificity of lung ultrasound were 0.79 and 0.98 for pneumothorax, 0.86 and 0.75 for hemothorax, 0.86 and 0.80 for pulmonary contusion, respectively. Area under the ROC curve of ultrasonography in detection of pneumothorax, hemothorax, and pulmonary contusion was 0.889 (95% CI=0.798-0.979, P<0.001), 0.804 (95% CI=0.707-0.901, P<0.001), and 0.831 (95% CI=0.623-1.000, P=0.013), respectively. Area under the ROC curve of radiography was 0.674 (95% CI=0.544-0.803, P=0.008) for detection of pneumothorax, 0.645 (95% CI=0.517-0.772, P=0.026) for hemothorax, and 0.547 (95% CI=0.289-0.805, P=0.724) for pulmonary contusion. Comparison of area under the ROC curve declared the significant superiority of ultrasonography in detection of pneumothorax, hemothorax and pulmonary contusion (all P<0.05). Conclusions:Lung ultrasound has higher sensitivity and accuracy than chest radiography in the initial evaluation of chest trauma. For children with acute chest trauma, lung ultrasound should be the first choice.