Hematuria after nocturnal exercise of a man: A case report.

BACKGROUND: A man experienced multiple episodes of macroscopic hematuria following nocturnal exercise. Urinary stones and tumors were considered the two most likely causes. The patient had two hobbies: Consuming health care products in large quantities and engaging in late-night running. CASE SUMMARY: Health care products contain a large amount of calcium phosphate, and we hypothesize that this could induce the formation of small phosphate stones. After exercise, the urinary system is abraded, resulting in bleeding. The patient was advised to stop using the health care products. Consequently, the aforementioned symptoms disappeared immediately. However, the patient resumed the above two habits one year later; correspondingly, the macroscopic hematuria reappeared. CONCLUSION: This finding further confirmed the above inference and allowed for a new avenue to determine the cause of the patient's hematuria.

Investigation into the association between physical health parameters and tongue image characteristics in college students

Objective: To investigate the correlation between physical health and tongue signs in college students. Methods. Twelve hundred and fourteen college students in the first year of a university in Ningbo were randomly selected as the study subjects, all of whom met the inclusion criteria to test their physical health and perform Chinese medicine tongue diagnosis. All collected data were later imported into an Excel sheet to create a database, and SPSS19.0 statistical software was used for data statistics and analysis. The mean of the measured data was expressed as standard deviation ±, the count data was expressed as frequency, the categorical data was expressed as X2 test, and the analysis was performed using binary logistic regression. Results. (1) Among each constitution type, the agreement rates from highest to lowest were: damp-heat constitution (85.17%), blood stasis constitution (83.33%), calm constitution (79.78%), yin deficiency constitution (75.68%), yang deficiency constitution (74.47%), phlegm-damp constitution (69.70%), qi deficiency constitution (65.00%), qi-yu constitution (0.00%), special endowment physique (0.00%). (2) The two factors of gender and obesity had a small effect on the agreement rate of the results of the tongue image and health status questionnaire in identifying TCM constitution, and the difference was not statistically significant (p>0.05). (3) Two factors, the degree of understanding of the health status questionnaire and the environment in which the tongue images were taken, had a greater influence on the agreement rate between the tongue images and the health status questionnaire in identifying the TCM physique, and the difference was statistically significant (p < 0.05). (4) Logistic regression analysis showed that lung capacity, sitting forward bend, 1000m, standing long jump for boys, 50m and 800m for girls were the influencing factors of normal tongue image (AU)

Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood.

BACKGROUND AND OBJECTIVE: Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood. METHODS: A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded. RESULTS: For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI. CONCLUSION: AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.

Preoperative assessment of complicated appendicitis through stress reaction and clinical manifestations.

This study assessed the severity of the disease through the preoperative clinical manifestations and inflammatory reaction indicators of acute appendicitis, and established a score table to predict complicated appendicitis (CA).The clinical data of 238 patients with acute appendicitis in our hospital were retrospectively analyzed, which included 18 patients with acute simple appendicitis (7.6%), 170 patients with acute purulent appendicitis (72.0%), and 48 patients with acute gangrene and perforation (20.3%). The clinical manifestations and inflammatory reaction indicators were analyzed by univariate logistic regression. Multivariate logistic regression analysis was performed to screen out the independent risk factors of CA. The ß coefficients of independent risk factors entering the multivariate model were assigned by rounding, and the total score was the sum of values of all factors. Finally, verification and analysis were performed for the predictive model, and the operating characteristic curve (ROC) curve was drawn. Then, the area under the curve (AUC) was compared with the THRIVE scale, and the Hosmer-Lemeshow method was used to evaluate whether the model fitted well.The multivariate logistic regression analysis of independent risk factors was performed, and the values were rounded to the variable assignment based on the ß coefficient values. The plotted ROC and AUC was calculated as 0.857 (P < .001). Using the Hosmer-Lemeshow method, the X-value was 12.430, suggesting that the prediction model fitted well.The scoring system can quickly determine whether this is a CA, allowing for an earlier and correct diagnosis and treatment. Furthermore, the scoring system was convenient, economical, and affordable. Moreover, it is easy to popularized and promote.