Objective To investigate the urinary albumin excretion of the diabetes patients and application value in the monitoring of early impairment in kidney. Methods The random urine samples from diabetes patients and controls within three days were collectod. The changes of urinary albumin excretion within day and between days were analyzed. 24-hour urine albumin was used as a standard to evaluate early kidney damage. The correlations between results of random urine albumin at the different time points and different periods were comparod. The sensitivity and specificity of random urine albumin at the different time points and different periods was evaluated and compared to deduce the best diagnostic porformance of the random urine albumin. Results There are greater variations of the levels of urinary albumin of patients with diabetes and control. After the correction with urine creatinine and urine volume the variations can be reduced (CV:49%±23% and 64%±30%). Urinary albumin excretion rate change rhythmically within the 24 h in healthy and diabetes patients. We found the best correlation between overnight ratio of urinary concentrations of albumin and creatinine (ACR) and 24-hour urinary albumin (R2 = 0.976). It was superior to urina sanguinis (R~2 = 0.900), postprandial urine (R~2 = 0.584) and random urine (R~2 =0.791). When 24 h urinary albumin was taken as the standard, receiver operating characteristic (ROC) curve analysis showed there was significant difference between male and female(male 12.8 μg/mg urine creatinine vs female 27.0 μg/mg urine creatinine),and the the cut-off value of ACR was 27.7μg/mg urine creatinine. When the smallest available negative likelihood ratio (0.011) and the greatest positive likelihood ratio (481.000) were obtained,the concentration of 13.0 μg/mg creatinine and 87.4 μg/mg creatinine were set as the cut-off value of ACR. Conclusions The correction with urinary creatinine can reduce the variation between-days compared with urine volume, but still can not completely eliminate the variability. The ACR of overnight urine has the best correlation with the 24 h urinary albumin and can replace 24 h urinary albumin. Random urine as the most convenient collecting urine samples can also replace 24-hour urinary albumin, but the gender discrepancy need to be considered. When the concentration of 13.0 μg/mg and 87.4 μg/mg was set as a random ACR exclusion value and the confirmative value, it can basically rule out and confirm the existence of microalbuminuria.

Urinary albumin used as an important indicator in diagnosis of renal disease , but there are still many problems in the clinical application .The urine specimen type , threshold value , reference measurement procedures and some more aspect were summarized in this article .

Two non-destructive instrumental methods, infrared spectroscopy (IR) and X-ray diffraction (XRD), were studied for quality evaluation of Lobelia chinensis Lour. (L. chinensis). We obtained the IR spectra and XRD patterns of L. chinensis collected from different sources. The similarity of samples was analyzed by cal-culating the cosine coefficient. The cosine values were in the range of 0.83–0.90, indicating that the main components of L. chinensis samples are similar. Sample L1 and L6 showed a slightly lower similarity than that of L2, L3, L4, L5 detected by the two methods, which revealed that IR and XRD methods exhibited analogous detection ability for quality evaluation of L. chinensis. The two methods could be highly re-commended as simple and rapid detection means for quality evaluation of L. chinensis.

We report a case of a 44-year-old female patient with acute liver failure and hepatic encephalopathy. The patient received artificial liver treatment and underwent allogeneic orthotopic liver transplantation at 14 days after admission. Laboratory examination reported a small number of green cytoplasmic inclusions in neutrophils on the peripheral blood smear at 68 days after admission. The patient eventually died of liver failure at 71 days after admission. Green inclusions are bright green or blue-green inclusions presented in the cytoplasm of neutrophils in Wright-Giemsa stained peripheral blood smears, and is associated with liver failure and high short-term mortality.

Objective: To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. Methods: The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher′s exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models. Results: (1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ²=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ²=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ²=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ²=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively. Conclusions Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.

Objective:To investigate the correlation of hepatocellular carcinoma (HCC) classified by contrast-enhanced ultrasound (CEUS) Liver Imaging Data and Report System (LI-RADS) with differentiation degree and Ki-67 index.Methods:A multicenter, retrospective study was conducted.The clinical and CEUS imaging data of 208 patients with 208 HCC lesions from December 2017 to December 2020 in China CEUS database were included and analyzed. According to the CEUS LI-RADS version 2017 proposed by the American College of Radiology, the HCC was classified. The diagnosis and pathological information of all lesions were confirmed by pathology. The differentiation degree of HCC and the distribution of Ki-67 index in different LI-RADS categories were evaluated, and their correlation was analyzed.Results:The degree of differentiation and Ki-67 index among HCC of different CEUS LI-RADS were statistically significant ( P<0.001, P=0.009). LI-RADS M HCC was more likely to be poorly differentiated and showed a higher Ki-67 index. The category of LI-RADS was positively correlated with the degree of tumor differentiation (tau-b=-0.250, P<0.001) and the Ki-67 index (tau-b=0.178, P=0.002), that is, the higher the category of LI-RADS, the lower differentiation degree and the higher the Ki-67. Conclusions:The CEUS LI-RADS classification of HCC is correlating with the degree of differentiation and Ki-67 index.

Objective:To assess the role of Delta-RBC parameters in the automated hematocrit analyzer RET channel for the recognition of cold agglutinins (CA) and to explore the value of RET channel optical method parameters in correcting interference with CA.Methods:This is a retrospective study. The Cas group included 68 samples with Cas interference and the control group included 45 samples without CA interference. All specimens were collected from Zhongshan Hospital Fudan University Outpatient Department from January 2022 to January 2023. Specimens in both the CA and Control group were examined using the CBC+RET channel at room temperature. Recorded and calculated the Impedance method test parameters RBC-I, HGB, HCT-I, MCH-I, MCV-I, MCHC-I and the Optical method test parameters RBC-O, HCT-O, MCH-O, R-MFV, MCHC-O, Delta-RBC. Examined the specimens in the CA group using the CBC channel after prewarmed at 37 ℃ for 2 h, and Impedance method parameters RBC-I 37 ℃ 2 h, HGB 37 ℃ 2 h, HCT-I 37 ℃ 2 h, MCH-I 37 ℃ 2 h, MCV-I 37 ℃ 2 h, MCHC-I 37 ℃ 2 h were recorded. The ROC curves were used to analyze the discrimination efficacy of Delta-RBC in identifying CA interference, and the Bland-Altman method was used to analyze the consistency between the results of the optical method RBC parameters at room temperature and the results of the impedance method after prewarming. The correlation analysis was performed using Pearson and Spearman correlation analysis to analyze the results of the RBC parameters before and after prewarming in the CA group. Result:If Delta-RBC was used as diagnostic indicators for the identification of CA interference, the best cut-off value was 1.065, with AUCs of 0.998. In the CA group, the correlation coefficients between RBC-O, HCT-O, R-MFV, MCH-O, MCHC-O, and RBC-I 37 ℃ 2 h, HCT-I 37 ℃ 2 h, MCV-I 37 ℃ 2 h, MCH-I 37 ℃ 2 h, MCHC-I 37 ℃ 2 h were 0.985, 0.981, 0.729, 0.870, and 0.649, respectively. The percentages within the limits of agreement of the percentage differences between the results of the two methods were 95.6%, 92.6%, 95.6%, 94.1%, and 95.6%, respectively. Conclusions:The RBC parameter Delta-RBC from RET channel optical method can be used as an indicator to effectively assist in the clinical determination of the presence of CA. Reporting results using the optical method RBC parameters of the RET channel can correct the interference of CA without specimen pre-treatment and obtain more correct results of complete blood counts.

Objective:To investigate the clinical characteristics, treatment and prognosis of newly-treated patients with primary central nervous system lymphoma (PCNSL).Methods:Clinical data of 117 newly-treated PCNSL patients who were admitted to the First Hospital of Jilin University, the Fifth Medical Center of Chinese PLA General Hospital, Harbin Medical University Cancer Hospital, and Cancer Hospital of Chinese Academy of Medical Sciences & Peking Union Medical College from August 2009 to February 2018 were retrospectively analyzed. The patients' age, sex, Eastern Cooperative Oncology Group (ECOG) physical status (PS) score, pathological type, involvement of deep brain tissue, number of lesions, cerebrospinal fluid protein concentration, International Extranodal Lymphoma Study Group (IELSG) score, Memorial Sloan Kettering Cancer Center (MSKCC) score, treatment strategy, and response after the first-line therapy were analyzed using univariate and multivariate Cox proportional hazards models to identify the independent influencing factors for progression-free survival (PFS) and overall survival (OS) of PCNSL patients. Kaplan-Meier method was used for survival analysis.Results:In 117 newly-treated PCNSL patients, 59 cases (50.4%) presented with increased intracranial pressure or focal neurological symptoms at diagnosis; there were 65 cases (55.6%) with single lesions and 52 cases (44.4%) with multiple lesions; 1 patient (0.9%) had lymphoma of T-cell origin, and 116 cases (99.1%) had diffuse large B-cell lymphoma (DLBCL). Among 95 evaluable patients, 41 patients (43.2%) achieved complete remission (CR), 20 patients (21.1%) achieved partial remission (PR), 16 patients (16.8%) achieved stable disease (SD), and 18 patients (18.9%) had progressive disease (PD). In 117 patients with median follow-up of 66.0 months (95% CI 57.9-74.1 months), the median PFS and OS were 17.4 months (95% CI 11.5-23.3 months) and 45.6 months (95% CI 20.1-71.1 months), respectively. The 2-, 3- and 5-year PFS rates were 41.2%, 28.6% and 19.3%, and OS rates were 63.7%, 52.4% and 46.3%, respectively. Univariate Cox regression analysis showed that baseline high-risk MSKCC score group was an adverse prognostic factor for PFS ( P = 0.037), and the first-line chemotherapy with ≥4 cycles of high-dose methotrexate (HDMTX), HDMTX in combination with rituximab, ≥4 cycles of rituximab in combination with HDMTX, and achieving CR or ≥PR after the first-line treatment reduced the risk of disease progression and prolonged the PFS time (all P <0.01); age >60 years old, ECOG-PS score of 2-4 points, elevated cerebrospinal fluid protein concentration, high-risk IELSG score, and high-risk MSKCC score were adverse prognostic factors for OS, and ≥4 cycles of HDMTX and achieving CR or ≥PR after the first-line treatment were favorable factors for OS. Multivariate Cox regression analysis verified that rituximab in combination with HDMTX (yes vs. no: HR = 0.349, 95% CI 0.133-0.912, P = 0.032) and achieving ≥PR after the first-line chemotherapy (yes vs. no: HR = 0.028, 95% CI 0.004-0.195, P < 0.001) were independent favorable factors for PFS; age >60 years old (>60 years old vs. ≤60 years old: HR = 10.878, 95% CI 1.807-65.488, P = 0.009) was independent unfavorable factor for OS, while ≥4 cycles of HDMTX treatment (≥4 cycles vs. <4 cycles: HR = 0.225, 95% CI 0.053-0.947, P = 0.042) was independent favorable factor for OS. Conclusions:The older the PCNSL patients at initial treatment, the worse the prognosis. Intensive and continuous treatment for achieving deeper remission may be the key for improving the outcome of PCNSL patients.

Objective Under the background of"breaking the Five-Only"and the evaluation reform of healthcare professionals'titles,it analyzed the willingness of healthcare professionals in public hospitals to disseminate health knowledge and its incentives and constraints.Furthermore,it explored potential pathways to improve the willingness of healthcare professionals to popular science and disseminate health knowledge.Methods From November to Decem-ber 2022,a self-designed online questionnaire was used to survey healthcare professionals in four tertiary hospitals in Shanghai.The questionnaire included basic information of healthcare professionals,analysis of the current situa-tion and demands of healthcare professionals in health knowledge dissemination,willingness of healthcare profes-sionals to disseminate health knowledge and influencing factors.Results A total of 762 healthcare professionals partici-pated in this survey,79.9%(608/762)expressed willingness to promote the dissemination of health knowledge.A multiple-factor logistic regression analysis revealed that intermediate professional title,achieving personal value and so-cial responsibility,increasing patient resources,professional title promotion,science promotion-related awards,being included in performance appraisals,and difficulty in capturing science promotion skills were the influential factors affecting the dissemination of health knowledge among healthcare professionals(P＜0.05).Conclusion The willing-ness of healthcare professionals to disseminate health knowledge was strong,and hospitals should motivate health-care professionals to disseminate health knowledge through building a long-term incentive mechanism,strengthening training in science popularization ability,and improving humanistic qualities.

Objective To predict the potential suitable habitat of Haemaphysalis concinna in Heilongjiang Province under different climatic scenarios. Methods The geographic locations of ticks in Heilongjiang Province from 1980 to 2022 were captured from literature review and field ticks monitoring data from Harbin Center for Disease Control and Prevention in Heilongjiang Province, and the tick distribution sites with spatial correlations were removed using the software ArcGIS 10.2. The environment data under historical climatic scenarios from 1970 to 2000 and the climatic shared socioeconomic pathways (SSP) 126 scenario model from 2021 to 2040 and from 2041 to 2060 were downloaded from the WorldClim website, and the elevation (1 km, 2010), population (1 km grid population dataset of China, 2010) and annual vegetation index (1 km, 2010) data were downloaded from the Resource and Environmental Science and Data Center, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences. The contribution of environmental factors to H. concinna distribution was evaluated and environmental variables were screened using the software MaxEnt 3.4.1 and R package 4.1.0, and the areas of suitable habitats of H. concinna and changes in center of gravity were analyzed using the maximum entropy model in Heilongjiang Province under different climatic scenarios. In addition, the accuracy of the maximum entropy model for prediction of H. concinna distribution was assessed using the area under curve (AUC) of the receiver operating characteristic curve. Results A total of 79 H. concinna distribution sites and 24 environmental variables were collected, and 70 H. concinna distribution sites and 9 environmental factors that contributed to distribution of the potential suitable habitats of H. concinna in Heilongjiang Province were screened. The three most significant contributing factors included precipitation seasonality, annual precipitation, and mean temperature of the driest quarter, with cumulative contributions of 60.7%. The total area of suitable habitats of H. concinna was 29.05 × 10⁴ km² in Heilongjiang Province under historical climatic scenarios, with the center of gravity of suitable habitats located at (47.31° N, 129.16° E), while the total area of suitable habitats of H. concinna reduced by 0.97 × 10⁴ km² in Heilongjiang Province under the climatic SSP126 scenario from 2041 to 2060, with the center of gravity shifting to (47.70° N, 129.28° E). Conclusions The distribution of suitable habitats of H. concinna strongly correlates with temperature and humidity in Heilongjiang Province. The total area of potential suitable habitats of H. concinna may appear a tendency towards a decline with climatic changes in Heilongjiang Province, and high-, medium- and low-suitable habitats may shift.