Processing for deodorization is widely used in the production of animal-derived Chinese medicinal materials. In this study, Heracles Neo ultra-fast gas-phase electronic nose combined with chemometrics was employed to analyze the overall odor difference of Cervi Cornu Pantotrichum(focusing on that derived from Cervus nippon Temminck in this study) before and after processing. The results showed that the electronic nose effectively distinguished between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. HS-GC-MS was used to identify and quantify the volatile components in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum, and 35 and 37 volatile components were detected in the medicinal materials and decoction pieces, respectively. The medicinal materials and decoction pieces contained 28 common volatile components contributing to the odor of Cervi Cornu Pantotrichum. The odor activity value(OAV) of each volatile component was calculated based on the olfactory threshold and relative content. The results showed that there were 17 key odor substances such as isovaleraldehyde, 2-methylbutanal, isobutyraldehyde, hexanal, and methanethiol in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. All of them had bad odor and were the main source of the odor of Cervi Cornu Pantotrichum. The results of principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) showed that there were significant differences in volatile components between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. Based on the thresholds of P<0.05 and Variable Importance in Projection(VIP)>1, 21 differential volatile odor components were screened out. Among them, isopentanol, isovaleraldehyde, 2-methylbutanal, n-nonanal, and dimethylamine were the key differential odor compounds between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. The odor compounds and their relative content reduced, and some flavor substances such as esters were produced after processing with wine, which was the main reason for the reduction of the odor after processing of Cervi Cornu Pantotrichum.
Odorants/analysis* ; Electronic Nose ; Gas Chromatography-Mass Spectrometry/methods* ; Animals ; Volatile Organic Compounds/analysis* ; Deer ; Drugs, Chinese Herbal/chemistry*

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

Objective:To explore the effects of the core symptoms of attention deficit hyperactivity disorder (ADHD) and behavior problems on the sleep of children with ADHD and their correlation.Methods:In this cross-sectional study, a total of 289 children aged 6-12 years with ADHD who visited the Outpatient Department of the Center for Cognition and Sleep at the People′s Hospital of Guangxi Zhuang Autonomous Region from June 2021 to December 2022 were selected.The Swanson, Nolan and Pelham Rating Scale Ⅳ was used to assess ADHD core symptoms (attention deficit and hyperactivity-impulsivity), the Conners Parent Symptom Questionnaire was used to assess behavior problems (conduct, learning, psychosomatic symptoms, and anxiety), and the Sleep Disturbance Scale for Children (SDSC) was used to assess sleep.Multivariate Logistic regression, multivariate linear regression, and path analysis were performed for statistical analysis of the data.Results:Multivariate Logistic regression analysis showed that after controlling age, gender, verbal intelligence quotient and performance intelligence quotient, attention deficit ( P=0.008, OR=2.60, 95% CI: 1.28-5.28), psychosomatic symptoms ( P=0.027, OR=6.77, 95% CI: 1.24-36.93) and anxiety ( P=0.013, OR=3.96, 95% CI: 1.43-11.67) increased the risk of sleep disturbances in children with ADHD (SDSC score >39 points).Path analysis showed that attention deficit ( β=0.244, P=0.001), psychosomatic symptoms ( β=0.114, P=0.046) and anxiety ( β=0.216, P<0.001) directly affected the SDSC score.Hyperactivity-impulsivity indirectly affected the SDSC score through psychosomatic symptoms ( β=0.142, P=0.039), and attention deficit indirectly affected the SDSC score through anxiety ( β=0.321, P<0.001). Conclusions:The severer the core symptoms of ADHD and behavior problems are, the higher the degree of sleep disturbances in children with ADHD is.Psychosomatic symptoms and anxiety are directly associated with the sleep disturbances of ADHD children.Attention deficit has direct or anxiety-mediated indirect impact on the sleep of ADHD children.Hyperactivity-impulsivity indirectly affects the sleep of children with ADHD through psychosomatic symptoms.

Objective To analyze the combined effect of body mass index(BMI)and age with cancer occurrence among a hypertensive population in Minhang District,Shanghai.Methods Participants of this study were 212 394 hypertensive patients without cancer in Minhang District,Shanghai,registered in the electronic health information system from 2007 to 2015.Age and BMI were included as smoothing functions in the generalized additive Cox proportional risk model.The bivariate response model was constructed to visualize results using surface plots and to comprehensively analyze the association of BMI and age with the risk of cancer occurrence.Results A total of 22 141 participants developed cancer by Dec 31,2018.The association between age and the risk of cancer incidence showed an overall linear trend while the association between BMI and the risk of cancer incidence showed an overall"U"shape.BMI at about 26 kg/m2 showed the lowest risk of cancer incidence.The risk of cancer occurrence increased with increasing age in people with different BMIs.The associations between BMI and the risk of cancer incidence were different at different age groups:there was no significant association between BMI and the risk of cancer incidence in the young people(20-44 years).While in the middle-aged and older people aged over 45 years,BMI was associated with the risk of cancer incidence in a"U"shape.The lowest risk of cancer incidence was around the BMI of 26 kg/m2.Conclusion BMI among the population with hypertension should be controlled in a reasonable range,especially in the middle-aged and older population,to prevent cancer occurrence.

AIM To explore the ameliorative effects of Ziyin Mingmu Pills on mouse retinitis pigmentosa(RP)and the possible mechanism.METHODS The RP transgenic mice(rd10)were randomly divided into the model group,the Leding group(0.15 g/kg)and the low and high dose Ziyin Mingmu Pills groups(4.50,9.00 g/kg),in contrast to the C57BL/6 mice of the normal group,with 12 mice in each group.The mice had their retinal pathological changes detected by HE staining;their visual function detected by electroretinogram(ERG);their fundus conditions and retinal thickness detected by optical coherence tomography(OCT);their retinal blood perfusion detected by laser speckle blood flow technique;their mRNA expressions of Shh,Ptc,Smo,Gli1,N-myc and Cyclin mRNA detected by digital PCR;and their protein expressions of Shh,Ptc,Smo,Gli1,N-myc and Cyclin detected by immunofluorescence staining.RESULTS Compared with the normal group,the model group displayed pathological changes in the fundus and retina and decreased amplitudes of ERG a wave and b wave(P＜0.01);decreased retinal thickness(P＜0.01);decreased retinal blood perfusion(P＜0.01);and decreased retinal expressions of Shh,Ptc,Smo,Gli1,N-myc,Cyclin mRNA and protein(P＜0.01).Compared with the model group,the groups intervened with Ziyin Mingmu Pills or Leding shared improved pathological changes in the fundus and retina tissue,and increased retinal thickness(P＜0.01);increased retinal blood flow(P＜0.01);increased amplitudes of ERG a wave and b wave(P＜0.01);and increased retinal Shh,Ptc,Smo,Gli1,N-myc and Cyclin mRNA and protein expressions(P＜0.01).CONCLUSION Ziyin Mingmu Pills can improve the fundus pathological changes and visual function to delay RP in mice because of their efficacy in ameliorating retinal thickness and blood flow possibly by activating Shh signaling pathway.

Objective:To evaluate the clinical application of urine sediment score (USS) in early diagnosis, etiological differentiation, staging and prognosis of acute kidney injury (AKI), and to investigate the diagnostic efficacy of independent USS and its combination with blood urea nitrogen(Bun) serum creatinine(sCr) and uric acid(UA) in AKI.Methods:From August 23 to September 28, 2023, 9 020 morning urine samples of hospitalized patients in the First Hospital of Jilin University were detected by Sysmex UF5000.A total of 3 226 ssamples with small and round cell (SRC) > 1/μl and/or CAST>1/μl were screened for microscopic examination, and 404 cases with positive renal tubular epithelial cells and/or cast were enrolled in this study. There were 218 males and 186 females, aged 59.5 (49.0, 71.0) years. The 404 cases were divided into the USS AKI group (345 cases) and the USS non-AKI group (59 cases) according to the USS results based on the microscopic findings. According to Kidney Disease: Improving Global Outcomes (KDIGO) criteria, they were divided into KDIGO criteria AKI group (63 cases) and KDIGO criteria non-AKI group (341 cases), and the AKI group was divided into renal AKI group (33 cases) and non-renal AKI group (30 cases). According to the clinical diagnosis recorded in the medical records, they were divided into clinically diagnosed AKI group (29 cases) and clinically diagnosed non-AKI group (375 cases).The χ 2 test or Fisher exact test was used to compare USS in different AKI causes and stages. Logistic regression was used to calculate the odds ratio of renal AKI and stage 3 AKI. The area under the receiver operating characteristic curve was used to evaluate the sensitivity and specificity of USS, sCr, UA and Bun alone and in combination in the diagnosis of AKI, and the best cut-off value, sensitivity and specificity in the diagnosis of AKI were calculated. P < 0.05 was considered statistically significant. Results:The USS was used to identify the etiology of KDIGO standard AKI group,and there were significant differences in USS between renal AKI group and non-renal AKI group (χ 2=11.070, P<0.001). Compared to USS=1, the odds ratio of renal AKI was 8.125 when USS≥2 (95% CI 2.208—29.901). There was a statistically significant difference in the comparison of USS between groups in each stage of the AKI staging study based on USS (χ 2=15.724, P<0.05). Compared to USS=1, the odds ratio of stage 3 AKI was 9.714 when USS≥2 (95% CI 1.145-82.390). The AUC of independent USS in the diagnosis of AKI was 0.687 (95% CI 0.618-0.757, P<0.001), the specificity was 65.7% and the sensitivity was 61.9%. The AUC of USS combined with Bun, sCr, UA in the diagnosis of AKI was 0.794 (95% CI 0.608-0.980, P<0.05), the specificity was 82.4%, and the sensitivity was 88.9%. Conclusions:There wasan increased likelihood of renal AKI or stage 3 AKI while USS≥2,and whose combination with Bun, sCr and UA will improve the diagnostic efficiency of AKI.

Objective:To explore the relationship between the Prognostic Nutritional Index (PNI) and the HALP score with the prognosis of patients with middle-to-advanced prostate cancer. Methods:A total of 168 patients with middle-to-advanced prostate cancer admitted to three comprehensive tertiary hospitals were observed from 2013 to 2018. Patient medical records were reviewed,and overall survival (OS) and progression-free survival (PFS) were followed up. The receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) and the optimal cut-off value of PNI and HALP scores for predicting patients' prognosis. The Kaplan-Meier method was used to draw survival curves,and the Log-rank test and Cox regression were used to analyze the influencing factors of patient OS and PFS. Results:The follow-up period was 55 (38.5,63) months,with 87 deaths (51.79％) during this period. The median OS and PFS times were 50 months and 45 months,respectively. The 3-year OS and PFS survival rates were 72.02％ and 64.29％,respectively. The areas under the ROC curve for predicting patients' prognosis with PNI and HALP scores were 0.912 and 0.828,respectively,with optimal cut-off values of 46.3 and 31.64 (P<0.05). Multivariate Cox regression showed that PNI<46.3,HALP score<31.64,and age ≥ 60 years were risk factors for OS with HR (95％ CI) of 6.016 (3.273～11.056),2.537 (1.531～4.205),and 1.776 (1.221～2.967),respectively (P<0.05). AJCC stage Ⅳ,PNI<46.3,and HALP<31.64 were risk factors for PFS with HR (95％ CI) of 2.777 (1.381～5.419),5.940 (3.245～10.872),and 2.481 (1.498～4.109),respectively (P<0.05). Conclusion:PNI and HALP scores can predict the prognosis of patients with middle-to-advanced prostate cancer.

Vascular calcification is a highly regulated process of ectopic calcification in cardiovascular system while no effective intervention can be clinically performed up to date.As vascular calcification undergoes a common regulatory mechanism within bone formation,bone morphogenetic protein 7(BMP-7)main-tains contractile phenotype of vascular smooth muscle cells and further inhibits vascular calcification via promoting the process of osteoblast differentiation,reducing ectopic calcification pressure by increasing bone formation and reducing bone resorption.This work systematically reviews the role of BMP-7 in vascular calcifi-cation and the possible mechanism,and their current clinical application as well.The current proceedings may help develope early diagnostic strategy and therapeutic treatment with BMP-7 as a new molecular marker and potential drug target.The expec-tation could achieve early prevention and intervention of vascular calcification and improve poor prognosis on patients.