A case of lymphoepithelial was reported and the literature were reviewed. Early surgical treatment can help to get the better prognosis. There is currently no effective treatment methods and the disease have no lethal tendency. The early surgical treatment is recommended for its possibility of canceration.
Humans ; Lymphatic Diseases ; pathology ; Male ; Middle Aged ; Salivary Glands ; pathology

Objective To investigate the effects of immune system and immuno-related miRNAs expression in low-dose benzene and its homologue mixed-exposed workers in a short time. Methods A total of 40 workers were recruited from a petrochemical factory ,and their blood samples were collected before and after work to detect the levels of IgA,IgG,IgM,IFN-γ,IL-1β,IL-2,IL-8,TNF-αin serum and miRNA-146a,-155 expression in the peripheral blood mononuclear cell. In the meanwhile ,we measured the individual benzene and its homologue exposure level of recruited workers. Results According to the concentration of benzene ,the subjects were divided into benzene-exposed group(20 workers)and control group(20 workers). And toluene exposure level in benzene-exposed group was significantly higher than that in control group(P<0.05). Significant decrease of IgG was found in benzene-exposed workers after one work shift,compared with control group(P<0.05). Moreover,the interaction between benzene and toluene was significant correlated with the decrease of IL-1β and TNF-α(P < 0.05). Additionally,the interaction between miRNA-155 and miRNA-146a was significant correlated with the decrease of TNF-α(P<0.05). Conclusions:Occupational exposure to low-dose benzene and toluene in a short time could decrease immune function. And there may be an effect of miRNAs in the regulation of cytokine.

Objective To analyze the risk factors of malnutrition in patients with colorectal cancer(CRC)and construct a Nomogram prediction model.Methods A total of 402 hospitalized CRC patients in the Anning First People's Hospital Affiliated to Kunming University of Science and Technology from July 2021 to December 2023 were selected as research objects.The Global Leader-ship Initiative on Malnutrition(GLIM)criteria was used as the diagnostic criteria for malnutrition,and patients were divided into malnutrition group and well-nourished group.A multivariate Logistic regression model was used to analyze the influencing factors of malnutrition in CRC inpatients.A No-mogram prediction model was constructed based on predictive factors,and the discrimination and accuracy of the model were validated by the receiver operating characteristic(ROC)curve and Hosmer-Lemeshow goodness-of-fit test.Finally,the clinical application value of the model was verified by calibration curves and clinical decision curves.Results Among the 402 CRC patients,111 cases had malnu-trition,with a malnutrition rate of 27.61％.There were significant differences in age,tumor stage,long-term bedridden status,the Karnofsky Performance Scale(KPS)score,body mass index(BMI),the Nutritional Risk Screening 2002(NRS-2002),red blood cell(RBC),white blood cell(WBC),hemoglobin(HGB),albumin(ALB),prealbumin(PAB),alanine aminotransferase(ALT),and urea levels between the malnutrition and well-nourished groups(P＜0.05).Multivari-ate Logistic regression analysis showed that age,tumor stage,long-term bedridden status,HGB,KPS score,and PAB were independent risk factors for malnutrition in CRC patients,and the sensi-tivity,specificity and area under the curve(AUC)of the Nomogram prediction model constructed based on these factors were 57.4％,88.0％and 0.821(95％CI,0.773 to 0.870,P＜0.001)re-spectively.Based on internal validation,1 000 samples were drawn by the Bootstrap self-sampling method,with a consistency index of 0.821.The calibration curve and clinical decision curve indica-ted that the Nomogram prediction model had good clinical application value.Conclusion The No-mogram prediction model constructed on 6 factors such as advanced age,TNM classification of stageⅣ,poor KPS score,long-term bedridden status,decreased HGB and decreased PAB has a high predictive value for the risk of malnutrition in CRC patients.

Objective To analyze the risk factors of malnutrition in patients with colorectal cancer(CRC)and construct a Nomogram prediction model.Methods A total of 402 hospitalized CRC patients in the Anning First People's Hospital Affiliated to Kunming University of Science and Technology from July 2021 to December 2023 were selected as research objects.The Global Leader-ship Initiative on Malnutrition(GLIM)criteria was used as the diagnostic criteria for malnutrition,and patients were divided into malnutrition group and well-nourished group.A multivariate Logistic regression model was used to analyze the influencing factors of malnutrition in CRC inpatients.A No-mogram prediction model was constructed based on predictive factors,and the discrimination and accuracy of the model were validated by the receiver operating characteristic(ROC)curve and Hosmer-Lemeshow goodness-of-fit test.Finally,the clinical application value of the model was verified by calibration curves and clinical decision curves.Results Among the 402 CRC patients,111 cases had malnu-trition,with a malnutrition rate of 27.61％.There were significant differences in age,tumor stage,long-term bedridden status,the Karnofsky Performance Scale(KPS)score,body mass index(BMI),the Nutritional Risk Screening 2002(NRS-2002),red blood cell(RBC),white blood cell(WBC),hemoglobin(HGB),albumin(ALB),prealbumin(PAB),alanine aminotransferase(ALT),and urea levels between the malnutrition and well-nourished groups(P＜0.05).Multivari-ate Logistic regression analysis showed that age,tumor stage,long-term bedridden status,HGB,KPS score,and PAB were independent risk factors for malnutrition in CRC patients,and the sensi-tivity,specificity and area under the curve(AUC)of the Nomogram prediction model constructed based on these factors were 57.4％,88.0％and 0.821(95％CI,0.773 to 0.870,P＜0.001)re-spectively.Based on internal validation,1 000 samples were drawn by the Bootstrap self-sampling method,with a consistency index of 0.821.The calibration curve and clinical decision curve indica-ted that the Nomogram prediction model had good clinical application value.Conclusion The No-mogram prediction model constructed on 6 factors such as advanced age,TNM classification of stageⅣ,poor KPS score,long-term bedridden status,decreased HGB and decreased PAB has a high predictive value for the risk of malnutrition in CRC patients.

Objective:To evaluate the diagnostic efficacy and practicality of the Jaundice color card (JCard) as a screening tool for neonatal jaundice.Methods:Following the standards for reporting of diagnostic accuracy studies (STARD) statement, a multicenter prospective study was conducted in 9 hospitals in China from October 2019 to September 2021. A total of 845 newborns who were admitted to the hospital or outpatient department for liver function testing due to their own diseases. The inclusion criteria were a gestational age of ≥35 weeks, a birth weight of ≥2 000 g, and an age of ≤28 days. The neonate′s parents used the JCard to measure jaundice at the neonate′s cheek. Within 2 hours of the JCard measurement, transcutaneous bilirubin (TcB) was measured with a JH20-1B device and total serum bilirubin (TSB) was detected. The Pearson′s correlation analysis, Bland-Altman plots and the receiver operating characteristic (ROC) curve were used for statistic analysis.Results:Out of the 854 newborns, 445 were male and 409 were female; 46 were born at 35-36 weeks of gestational age and 808 were born at ≥37 weeks of gestational age. Additionally, 432 cases were aged 0-3 days, 236 cases were aged 4-7 days, and 186 cases were aged 8-28 days. The TSB level was (227.4±89.6) μmol/L, with a range of 23.7-717.0 μmol/L. The JCard level was (221.4±77.0) μmol/L and the TcB level was (252.5±76.0) μmol/L. Both the JCard and TcB values showed good correlation ( r=0.77 and 0.80, respectively) and agreements (96.0% (820/854) and 95.2% (813/854) of samples fell within the 95% limits of agreement, respectively) with TSB. The JCard value of 12 had a sensitivity of 0.93 and specificity of 0.75 for identifying a TSB ≥205.2?μmol/L, and a sensitivity of 1.00 and specificity of 0.35 for identifying a TSB ≥342.0?μmol/L. The TcB value of 205.2?μmol/L had a sensitivity of 0.97 and specificity of 0.60 for identifying TSB levels of 205.2 μmol/L, and a sensitivity of 1.00 and specificity of 0.26 for identifying TSB levels of 342.0 μmol/L. The areas under the ROC curve (AUC) of JCard for identifying TSB levels of 153.9, 205.2, 256.5, and 342.0 μmol/L were 0.96, 0.92, 0.83, and 0.83, respectively. The AUC of TcB were 0.94, 0.91, 0.86, and 0.87, respectively. There were both no significant differences between the AUC of JCard and TcB in identifying TSB levels of 153.9 and 205.2 μmol/L (both P>0.05). However, the AUC of JCard were both lower than those of TcB in identifying TSB levels of 256.5 and 342.0 μmol/L (both P<0.05). Conclusions:JCard can be used to classify different levels of bilirubin, but its diagnostic efficacy decreases with increasing bilirubin levels. When TSB level are ≤205.2 μmol/L, its diagnostic efficacy is equivalent to that of the JH20-1B. To prevent the misdiagnosis of severe jaundice, it is recommended that parents use a low JCard score, such as 12, to identify severe hyperbilirubinemia (TSB ≥342.0 μmol/L).