Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Morinda officinalis is one of the “Four Great Southern Medicines” and is commonly used in clinical practice to treat erectile dysfunction and nocturnal emissions caused by kidney yang deficiency， uterine cold infertility， menstrual disorders， low abdominal pain， rheumatism， and muscle and bone weakness. Based on the principle of “treating raw and cooked differently”， this article summarizes the effects of different processing methods on the chemical constituents and pharmacological effects of M. officinalis . Research has found that processing can affect the content and structure of sugars， iridoids， and anthraquinones in M. officinalis ， thereby regulating its pharmacological effects such as anti-osteoporosis， immune regulation， anti-fatigue， and anti-depression. Among them， salt M. officinalis shows outstanding performance in anti-osteoporosis and enhancing immunity. Although previous studies have preliminarily elucidated the major chemical constituents of M. officinalis and their pharmacological characteristics， the dynamic transformation paths of constituents during the processing remain to be further clarified， especially the es tablishment of a quality marker system associated with the processing technology， to achieve the precision and intelligent development of the processing.

Morinda officinalis is one of the “Four Great Southern Medicines” and is commonly used in clinical practice to treat erectile dysfunction and nocturnal emissions caused by kidney yang deficiency， uterine cold infertility， menstrual disorders， low abdominal pain， rheumatism， and muscle and bone weakness. Based on the principle of “treating raw and cooked differently”， this article summarizes the effects of different processing methods on the chemical constituents and pharmacological effects of M. officinalis . Research has found that processing can affect the content and structure of sugars， iridoids， and anthraquinones in M. officinalis ， thereby regulating its pharmacological effects such as anti-osteoporosis， immune regulation， anti-fatigue， and anti-depression. Among them， salt M. officinalis shows outstanding performance in anti-osteoporosis and enhancing immunity. Although previous studies have preliminarily elucidated the major chemical constituents of M. officinalis and their pharmacological characteristics， the dynamic transformation paths of constituents during the processing remain to be further clarified， especially the es tablishment of a quality marker system associated with the processing technology， to achieve the precision and intelligent development of the processing.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To investigate the expression levels of serum tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein β(14-3-3β)and clara's cell secretory protein 16(CC16)in patients with chronic obstructive pulmonary disease(COPD)complicated by respiratory failure,and their relationship with prognosis.Methods A total of 232 patients with COPD complicated with respiratory failure admitted to Yantian Hospital of Southern University of Science and Technology from April 2020 to October 2023 were enrolled in the COPD complicated with respiratory failure group.According to the severity of the disease,they were divided into mild group(n=67),moderate group(n=73)and severe group(n=92).According to the 28-day prognosis,they were divided into death group(n=73)and survival group(n=159).In addition,80 patients with simple COPD(COPD group)and 80 healthy subjects(control group)were selected at the same time.Enzyme-linked immunosorbent assay(ELISA)was used to detect the expression of serum 14-3-3β and CC16.Multivariate Logistic regression analysis was used to analyze the factors of death in patients with COPD complicated with respiratory failure.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of serum 14-3-3β and CC16 expression on the death of patients with COPD complicated with respiratory failure.Results The expression of serum 14-3-3β in COPD complicated with respiratory failure group was higher than that in COPD group and control group(U=3.894,11.417),the expression of CC16 was lower than that in COPD group and control group(t=5.845,14.306),and the differences were statistically significant(all P<0.05),respectively.The expression of serum 14-3-3β in severe group was higher than that in moderate group and mild group(U=5.179,8.234),the expression of CC16 was lower than that of moderate group and mild group(t=4.090,9.281),and the differences were statistically significant(all P<0.05),respectively.The 28-day mortality rate of 232 COPD patients with respiratory failure was 31.47%(73/232).The expression of serum 14-3-3β in the death group was higher than that in the survival group,and the expression of CC16 was lower than that in the survival group,the differences were statistically significant(U/t=6.790,8.265,all P<0.05).The age of the death group was older than that of the survival group,the degree of airflow limitation and the number of acute exacerbations within 1 year were higher than those of the survival group,and the differences were statistically significant(t/χ2/U=3.895,7.202,3.360,all P<0.05).Age,severe airflow limitation,extremely severe airflow limitation,and the number of acute exacerbations within 1 year,elevated 14-3-3β were independent risk factors for death in patients with COPD complicated with respiratory failure(Wald χ2=3.914～22.668,all P<0.05),and elevated CC16 was an independent protective factor(Wald χ2=23.675,P<0.05).The area under the curve(AUC)of serum 14-3-3β combined and CC16 expression in predicting the death of patients with COPD complicated with respiratory failure which was greater than that of serum 14-3-3β and CC16 expression alone,the differences were statistically significant(Z=3.995,3.813,all P<0.01).Conclusion The increase of serum 14-3-3β expression and the decrease of CC16 expression in patients with COPD complicated by respiratory failure are closely related to the aggravation of the disease and poor prognosis.The combination of serum 14-3-3β and CC16 expression is of high value in predicting the death of patients with COPD complicated with respiratory failure.

Objective:To evaluate the relevant factors to optimize the learning curve and the impact of the basic skills of endoscopic technology on the learning curve of gasless transaxillary posterior endoscopic thyroidectomy.Methods:A retrospective analysis was performed to evaluate the clinical outcomes of 50 patients who underwent Glandular Ultrasound-Assisted (GUA) thyroid surgery by a surgeon with a background in endoscopic thyroid surgery via the thoracic-areolar approach, and 50 patients operated on by a surgeon without such experience at the Thyroid Surgery Department of Jilin University China-Japan Union Hospital from Apr. to Dec. 2023. The patients were divided into two groups: the Endoscopic Experience Group and the Non-Endoscopic Experience Group. The Cumulative Sum Control Chart (CUSUM) was applied to construct learning curves for both groups, dividing the technical exploration period from the mastery period. The analysis compared the surgical time, postoperative first-day drainage volume, number of central lymph nodes dissected rates, and postoperative complications between the two groups and across the two phases.Results:The analysis of the learning curve revealed that the inflection point of the Endoscopic Experience Group was 15, while of the Non-Endoscopic Experience Group was 18. The learning curve was divided into the technical exploration stage and the proficient mastery stage. The operative time of technical exploration stagde was significantly longer than of proficient mastery stage of both group (183.46±36.13min vs.144.40±26.14min, P<0.001; 186.89±48.91min vs.131.59±22.90min; P<0.001) . The operative time in the proficient mastery stage of the Endoscopic Experience Group was longer than that of the Non-Endoscopic Experience Group (144.40±26.15min vs. 131.59±22.90min, P<0.05) . The postoperative drainage volume in the Endoscopic Experience Group was lower than that in Non-Endoscopic Experience Group in both stages (65.40±32.48mL vs.93.22±30.67mL, 57.40±15.35mL vs.78.50±28.30mL, P<0.05) , and the postoperative drainage volume in the proficient mastery stage of the Non-Endoscopic Experience Group was significantly lower than in the technical exploration stage (93.22±30.67mL vs.78.50±28.30mL, P<0.05) .No significant differences in central lymph node dissection numbers or postoperative complications were observed between the groups at both stages. Conclusions:There is a specific learning curve in the early stage of gasless transaxillary posterior endoscopic thyroidectomy. After crossing the learning curve, the operation time is obviously shortened with the improvement of the operator's surgical technique.Having a basic understanding of endoscopic technology in the early stage can reduce the occurrence of postoperative drainage, but has a minimal impact on the learning curve.

Objective:To evaluate the relevant factors to optimize the learning curve and the impact of the basic skills of endoscopic technology on the learning curve of gasless transaxillary posterior endoscopic thyroidectomy.Methods:A retrospective analysis was performed to evaluate the clinical outcomes of 50 patients who underwent Glandular Ultrasound-Assisted (GUA) thyroid surgery by a surgeon with a background in endoscopic thyroid surgery via the thoracic-areolar approach, and 50 patients operated on by a surgeon without such experience at the Thyroid Surgery Department of Jilin University China-Japan Union Hospital from Apr. to Dec. 2023. The patients were divided into two groups: the Endoscopic Experience Group and the Non-Endoscopic Experience Group. The Cumulative Sum Control Chart (CUSUM) was applied to construct learning curves for both groups, dividing the technical exploration period from the mastery period. The analysis compared the surgical time, postoperative first-day drainage volume, number of central lymph nodes dissected rates, and postoperative complications between the two groups and across the two phases.Results:The analysis of the learning curve revealed that the inflection point of the Endoscopic Experience Group was 15, while of the Non-Endoscopic Experience Group was 18. The learning curve was divided into the technical exploration stage and the proficient mastery stage. The operative time of technical exploration stagde was significantly longer than of proficient mastery stage of both group (183.46±36.13min vs.144.40±26.14min, P<0.001; 186.89±48.91min vs.131.59±22.90min; P<0.001) . The operative time in the proficient mastery stage of the Endoscopic Experience Group was longer than that of the Non-Endoscopic Experience Group (144.40±26.15min vs. 131.59±22.90min, P<0.05) . The postoperative drainage volume in the Endoscopic Experience Group was lower than that in Non-Endoscopic Experience Group in both stages (65.40±32.48mL vs.93.22±30.67mL, 57.40±15.35mL vs.78.50±28.30mL, P<0.05) , and the postoperative drainage volume in the proficient mastery stage of the Non-Endoscopic Experience Group was significantly lower than in the technical exploration stage (93.22±30.67mL vs.78.50±28.30mL, P<0.05) .No significant differences in central lymph node dissection numbers or postoperative complications were observed between the groups at both stages. Conclusions:There is a specific learning curve in the early stage of gasless transaxillary posterior endoscopic thyroidectomy. After crossing the learning curve, the operation time is obviously shortened with the improvement of the operator's surgical technique.Having a basic understanding of endoscopic technology in the early stage can reduce the occurrence of postoperative drainage, but has a minimal impact on the learning curve.

OBJECTIVES: To investigate the underlying causes and clinical manifestations in infants undergoing colonoscopy, and to analyze changes in disease spectrum. METHODS: Clinical data from 180 infants who underwent a total of 184 colonoscopies at the Department of Gastroenterology, Children's Hospital Affiliated to Shandong University from January 2015 to December 2024 were retrospectively analyzed. Patients were grouped by age: ≤6 months (n=41) and >6-12 months (n=139); and by examination period: 2015-2019 (n=83) and 2020-2024 (n=97). Primary causes for performing colonoscopy, final diagnoses, and disease spectrum evolution were assessed. RESULTS: Among 184 colonoscopies, the leading causes prompting examination were hematochezia (37.8%, 68/180), diarrhea (36.7%, 66/180), and co-occurring hematochezia and diarrhea (21.1%, 38/180). Causes for performing colonoscopy differed significantly by age group (P<0.05). Colonic polyps were only detected in the >6-12 months group (P<0.05). Compared to the 2015-2019 group, the 2020-2024 group had fewer food allergy-related gastrointestinal diseases (P<0.05) but more colitis (P<0.05). CONCLUSIONS Colonoscopy is essential for diagnosing infantile digestive disorders, with disease spectra varying by age and time period.
Humans ; Infant ; Colonoscopy ; Male ; Female ; Retrospective Studies ; Infant, Newborn ; Diarrhea/etiology* ; Gastrointestinal Hemorrhage/etiology*