Objective:To analyze patch test results in patients with rosacea, investigate the prevalence of contact sensitization, and identify common allergens, to provide relevant evidence for the diagnosis, treatment, and preventive interventions for rosacea.Methods:A cross-sectional retrospective survey was conducted to analyze the positive contact allergy rate and allergen distribution in 186 rosacea patients who underwent the 60-item China Standard Series Patch Test (CB-1000) at the Department of Dermatology, Peking University First Hospital, from January 2023 to September 2024.Results:Among the 186 patients, 159(85.5%) showed positive reactions in patch tests. The top three allergens with the highest positivity rates were: metal salts, preservatives and fragrances. The top 10 individual allergens with the highest positivity rates were: nickelsulfate hexahydrate 46.8%(87/186), cobaltchloride hexahydrate 29.0%(54/186), hydroperoxides of linalool 15.1%(28/186), carba mix 14.0%(26/186), propolis 9.7%(18/186), fragrance mix Ⅰ 8.6%(16/186), methyl dibromo glutaronitrile 8.1%(15/186), Peru balsam 7.5%(14/186), gold sodium thiosulfate dihydrate 7.5%(14/186), and hydroperoxides of limonene 7.5%(14/186). The overall positivity rate for cosmetics-related allergens was 56.5% (105/186), with the top five allergens being: hydroperoxide of linalool 15.1%(28/186), propolis 9.7%(18/186), fragrance mix Ⅰ 8.6%(16/186), methyl dibromo glutaronitrile 8.1%(15/186), and Peru balsam 7.5%(14/186).Conclusion:The overall positivity rate for patch test allergens in rosacea patients is high. The most common allergen types are metal salts, preservatives, and fragrance agents. Cosmetics-related allergens also show a high positivity rate. Rosacea patients should avoid exposure to these allergens.

Objective:To analyze the interference of an exogenous glucose test on urinary creatinine-related renal injury biomarkers in patients with chronic kidney disease (CKD).Methods:This cross-sectional study enrolled CKD patients who visited Peking University First Hospital between October 2023 and March 2024. The patients (age: 50±18 years) included 90 males and 70 females. Fresh morning urine samples were collected, totaling 160 samples. Each urine sample was divided into 5 aliquots,each containing 225 μl. One aliquot received 75 μl of deionized water as the control. The other aliquots received 75 μl of glucose solutions at concentrations of 120, 480, 960, and 1200 mmol/L, resulting in final glucose concentrations of 30, 120, 240, and 300 mmol/L in the urine samples, respectively. Urinary creatinine in each sample was measured using both the enzymatic method and the picric acid (Jaffe) method. The following ratios were calculated: urinary albumin-to-creatinine ratio (uACR), urinary protein-to-creatinine ratio (uPCR), urinary transferrin-to-creatinine ratio (uTRF/uCr), urinary α1-microglobulin-to-creatinine ratio (uA1M/uCr), urinary immunoglobulin G-to-creatinine ratio (uIgG/uCr), and urinary N-acetyl-β-D-glucosaminidase-to-creatinine ratio (uNAG/uCr).Results:Under high glucose concentrations, significant differences ( P<0.05) were observed between the enzymatic method and the picric acid method in measuring urinary creatinine-related renal injury biomarkers. At glucose concentrations of 30, 120, 240, and 300 mmol/L, the mean percentage biases for creatinine measured by the enzymatic method were -0.19%, -0.27%, -0.20%, and -0.21%, respectively. The mean percentage biases for creatinine measured by the picric acid method were 0.78%, 1.26%, 1.35%, and 1.38%, respectively, showing an increasing deviation between the results before and after glucose addition as the glucose concentration rose. For uACR measurement, the mean absolute biases using the enzymatic method were -0.01, 1.27, 0.95, and 1.10 mg/g at the respective glucose concentrations. Using the picric acid method, the mean absolute biases for uACR were -11.69, -14.98, -16.91, and-18.51 mg/g. The biases of the picric acid method were significantly higher than the those of the enzymatic method, and the absolute value of the mean biases increased with rising glucose concentration. For uPCR, uTRF/uCr, uA1M/uCr, uNAG/uCr, and uIgG/uCr, the deviations measured by the enzymatic method were consistently smaller than those measured by the picric acid method. Conclusions:The measurement of creatinine and related renal injury biomarkers by the enzymatic method is less affected by glucose concentration. In contrast, the measurement results obtained using the picric acid method are significantly affected by glucose concentration.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

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 establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. Methods Five AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analyses were performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene set enrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. Results A total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, and regulation of interleukin 17. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. Conclusion The identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.

Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.

Objective:To evaluate the effectiveness of anti-G straining maneuver (AGSM) in pilots by surface electromyography (sEMG), and to explore the relationships between characteristics of sEMG and anti-G endurance in pilots.Methods:Thirty-eight male high-performance fighter pilots who completed the human centrifuge test at the Air Force Medical Center were selected. Among them, 25 completed the 8.0 G for 10 s anti-G endurance test and 13 completed the 9.0 G for 10 s anti-G endurance test. The sEMG tester was used to keep track of the changes of sEMG in rectus abdominis, rectus femoris, anterior tibial and gastrocnemius muscles while pilots were engaged in AGSM. The anti-G endurance was evaluated according to the changes of visual fields and consciousness. The pilots were divided into 3 groups: the good vision and consciousness group, peripheral visual field narrowing group and endurance endpoint group. The differences in the integral electromyogram (iEMG), mean power frequency (MPF) and muscle input rates between the 3 groups were investigated.Results:A total of 25 pilots completed the 8.0 G for 10 s anti-G endurance test. Among them, 8 (32.0%) were in the good vision and consciousness group, 13 (52.0%) in the peripheral visual field narrowing group and 4 (16.0%) reached the endurance endpoint. Among the 13 pilots who completed the 9.0 G for 10 s anti-G endurance test, 3 (23.1%) were in the good vision and consciousness group, 6 (46.1%) in the peripheral visual field narrowing group, and 4 (30.8%) in the endurance endpoint group. The results of sEMG showed that the iEMG values of the anterior tibialis muscle in pilots under the 9.0 G for 10 s load were significantly different across endurance groups ( H=7.54, P=0.023), and that the iEMG values of the tibialis anterior muscle in the good vision and consciousness group were higher than those in the endurance endpoint group ( P=0.036). The negative slopes of MPF for the rectus abdominis, rectus femoris, anterior tibialis, and gastrocnemius muscles were higher in the good vision and consciousness group than in the other 2 groups, but the differences were not statistically significant ( P>0.05). During the 8.0 G for 10 s anti-G endurance test, there were significant differences in lower limb muscle contribution rates between the 3 groups ( F=4.19, P=0.029). The endurance endpoint group exhibited a lower contribution rate than the good vision and consciousness group ( P=0.025). During the 9.0 G for 10 s anti-G endurance test, there were significant differences in tibialis anterior muscle contribution rates between the 3 groups ( F=4.16, P=0.049). The endurance endpoint group demonstrated a lower contribution rate than the good vision and consciousness group ( P=0.049). Conclusions:The full and balanced activation of abdominal muscles and lower limb muscles, especially the effective mobilization of calf muscles, plays a pivotal role in improving pilots′ AGSM efficiency in high G environments.

Objective:To evaluate the applicability of a new anti-G capability assessment trainer (AG-CAT) in high-performance (HP) anti-G maneuver training and positive pressure breathing for high-G (PHP) training for pilots.Methods:A total of 142 fighter pilots who were subjected to anti-G maneuver training at Dujiangyan Special Crew Sanatorium of PLA Air Force between January and November 2023 were enrolled. According to the Guidelines for Aviation Physiological Training, 123 pilots underwent both HP anti-G maneuver training and PHP positive pressure breathing training, 15 received only HP training, and 4 received only PHP training. Based on the training devices used, these pilots were divided into AG-CAT group and an anti-G and anti-hypoxia capability detection instrument (GHyCDI) group. The 2 groups were compared regarding the pedal force of lower limbs, blood pressure, and improvement of +G z tolerance during training. Results:Of the 138 pilots undergoing HP training, 88 used AG-CAT and 50 used GHyCDI. One hundred and twenty-seven pilots participated in PHP training, with 73 in the AG-CAT group and 54 in the GHyCDI group. During HP training, the pedal force of left lower limbs in the AG-CAT group was greater than that of the right limbs and of the GHyCDI group ( t=4.38, 2.64, P<0.001, =0.009). In PHP training, the AG-CAT group exhibited greater pedal force in left limbs than in right ones, while the GHyCDI group showed an opposite trend ( t=2.25, 3.37, P=0.029, 0.002). Systolic and diastolic blood pressures during HP training (with or without anti-G suits) were higher in the AG-CAT group than in the GHyCDI group ( t=3.50, 3.72, 2.55, 4.21, P=0.001,<0.001,=0.012,<0.001). Similarly, during PHP training, both systolic and diastolic pressures were higher in the AG-CAT group ( t=2.03, 3.81, P=0.045,<0.001). The AG-CAT group demonstrated superior improvements in +G z tolerance during HP training (without/with anti-G suits: Z=2.14, 3.21, P=0.049, 0.001) and PHP training ( Z=2.56, P=0.010) compared with the GHyCDI group. Conclusions:AG-CAT shows excellent applicability in aviation physiological training of pilots. Its ergonomic design, practical functionalities and enhanced compatibility with personnel protective equipment can better meet training requirements compared to conventional devices.

Objective: o develop an onset risk prediction nomogram for patients with homocysteine-type (H-type) hypertension (HTH) based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH. Methods: Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai Hospital of Traditional Chinese Medicine, and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine from July 6th 2020 to June 16th 2021, and from August 11th 2023 to January 22nd 2024, were enrolled in this retrospective research. The baselines and clinical biochemical indicators of patients were collected. The SMART-I TCM pulse instrument was applied to gather pulse diagram parameters. Multivariate logistic regression was adopted to analyze the risk factors for HTH. RStudio was employed to construct the nomogram model, receiver operating characteristic (ROC) curve, and calibration curve (bootstrap self-sampling 200 times), and clinical decision curve were drawn to evaluate the model’s discrimination and clinical effectiveness. Results: A total of 168 hospitalized patients with essential hypertension were selected and divided into non-HTH group (n = 29) and HTH group (n = 139). Compared with non-HTH group, HTH group had a lower body mass index (BMI), and higher proportions of male patients and drinkers (P < 0.05). The ventricular wall thickening (VWT) could not be determined. The proportions of left common carotid intima-media wall thickness (LCCIMWT) and serum creatinine (SCR) were higher in HTH group (P < 0.05). The pulse diagram parameter As was significantly higher, and H4/H1 and T1/T were lower in HTH group (P < 0.05). Gender, alcohol consumption, serum creatinine, and the pulse diagram parameter H4/H1 were identified as independent risk factors for HTH (P < 0.05). The nomogram’s area under the ROC curve (AUC) was 0.795 [95% confidence interval (CI): (0.706 6, 0.882 8)], with a specificity of 0.724 and sensitivity of 0.799. After 200 times repeated bootstrap self-samplings, the calibration curve showed that the simulated curve fits well with the actual curve (x2 = 9.5002, P = 0.301 9). The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00. Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.

OBJECTIVE To analyze the differential chemical composition of Olibanum of different origins and to provide some references for the identification of Olibanum origin and the quality standards.METHODS The chemical compositions of Olibanum were analyzed by UPLC-Q/TOF-MS.Multivariate statistical methods were used to analyze Olibanum and processed products to screen the differential compounds in Olibanum of different origins.RESULTS Using positive and negative ion scanning modes,a total of 108 compounds were identified in Olibanum.The compounds were mainly terpenes,among which triterpenes were the highest.And through multivariate statistical analysis,it was found that there were significant differences in the chemical compositions of Olibanum of different regions,with a total of 34 differential compounds;furthermore,specific compounds unique to Olibanum from other sources were identified.CONCLUSION Different regions of Olibanum could be distinguished based on their differential chemical composi-tions.This study could serve as a basis for identifying different origins of Olibanum,and also provide experimental data for subsequent research on the pharmacological substance basis of Olibanum.