The CyberKnife, an acronym for the stereotactic radiosurgery platform, represents an image-guided stereotactic radiotherapy technique. This technology precisely delivers ionizing radiation to tissues, effectively damaging tumor cells, and is suitable for radiotherapy of both intracranial and extracranial tumors. This article reports the first performance of CyberKnife by radiotherapy at Peking Union Medical College Hospital, including a patient with uncontrolled pituitary adenoma after surgery and radiotherapy, and another patient with vertebral metastasis following targeted therapy for lung adenocarcinoma. The application of CyberKnife technology in radiotherapy has achieved highly accurate dose delivery, enabling targeted irradiation of tumor lesions while minimizing damage to surrounding normal tissues, thereby yielding relatively ideal clinical outcomes.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective:To investigate the expression of kinase-like gene CT10 regulatory fac-tor(CRKL)in intrahepatic cholangiocarcinoma(ICC)and its effect on the proliferation and invasion of intrahepatic cholangiocarcinoma cells.Methods:qRT-PCR detected CRKL transcription in ICC pa-tient tissues and adjacent tissues.Immunohistochemistry assessed CRKL expression in ICC tissues and adjacent tissues,and analyzed its relationship with clinicopathological features.For the inhibition of CRKL expression in QBC939 cells by siRNA technology,the effect of CRKL silencing on AKT and ERK signaling pathway was measured by Western blot.CRKL's influence on QBC939 cell prolifera-tion and invasion was analyzed by MTT,clonogenesis,and Transwell assays.Results:The expres-sion of CRKL in ICC tissues was up-regulated,and there were statistically significant differences in CRKL expression in ICC with different clinicopathological features such as tumor size,lymph node metastasis and TNM stage.Cellular experiments showed a significant decrease in the phosphoryla-tion of AKT and ERK upon inhibition of CRKL,and the proliferation and invasion ability of ICC cells was significantly diminished(P＜0.05).Conclusion:CRKL promotes ICC cell proliferation and invasion through AKT and ERK pathways,offering new molecular targets and directions for targeted therapy.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of gallic acid,protocatechuic acid,neomangiferin,catechin,caffeic acid,mangiferin,isomangiferin,albiflorin,paeoniflorin,vitexin,liquiritin,scutellarin,baicalin,liquiritigenin,timosaponin BⅡ,quercetin,wogonoside,benzoylpaeoniflorin,isoliquiritigenin,honokiol,magnolol,norarecaidine,arecaidine,arecoline,epicatechin,baicalein,glycyrrhizinate and wogonin in Dayuan Drink.METHODS The analysis was performed on a 35℃thermostatic Syncronis C18 column(100 mm×2.1 mm,1.7 μm),with the mobile phase comprising of 0.1%formic acid-acetonitrile flowing at 0.3 mL/min in a gradient elution manner,and electron spray inoization source was adopted in positive and negative ion scanning with select reaction monitoring mode.RESULTS Twenty-eight constituents showed good linear relationships within their own ranges(R2≥0.991 0),whose average recoveries were 95.60%-103.53%with the RSDs of 0.60%-5.45%.CONCLUSION This rapid,simple,selective,accurate and reliable method can be used for the quality control of Dayuan Drink.

Objective:To establish a set of artificial intelligence (AI) verification rules for blood routine analysis.Methods:Blood routine analysis data of 18 474 hospitalized patients from the First Hospital of Jilin University during August 1st to 31st, 2019, were collected as training group for establishment of the AI verification rules,and the corresponding patient age, microscopic examination results, and clinical diagnosis information were collected. 92 laboratory parameters, including blood analysis report parameters, research parameters and alarm information, were used as candidate conditions for AI audit rules; manual verification combining microscopy was considered as standard, marked whether it was passed or blocked. Using decision tree algorithm, AI audit rules are initially established through high-intensity, multi-round and five-fold cross-validation and AI verification rules were optimized by setting important mandatory cases. The performance of AI verification rules was evaluated by comparing the false negative rate, precision rate, recall rate, F1 score, and pass rate with that of the current autoverification rules using Chi-square test. Another cohort of blood routine analysis data of 12 475 hospitalized patients in the First Hospital of Jilin University during November 1sr to 31st, 2023, were collected as validation group for validation of AI verification rules, which underwent simulated verification via the preliminary AI rules, thus performance of AI rules were analyzed by the above indicators. Results:AI verification rules consist of 15 rules and 17 parameters and do distinguish numeric and morphological abnormalities. Compared with auto-verification rules, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score of AI rules in training group were 22.7%, 1.6%, 74.5%, 1.3%, 75.7%, 97.2%, 93.5%, 94.7%, 94.1, respectively.All of them were better than auto-verification rules, and the difference was statistically significant ( P<0.001), and with no important case missed. In validation group, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score were 19.2%, 8.2%, 70.1%, 2.5%, 72.6%, 89.2%, 70.0%, 88.3%, 78.1, respectively, Compared with the auto-verification rules, The false negative rate was lower, the false positive rate and the recall rate were slightly higher, and the difference was statistically significant ( P<0.001). Conclusion:A set of the AI verification rules are established and verified by using decision tree algorithm of machine learning, which can identify, intercept and prompt abnormal results stably, and is moresimple, highly efficient and more accurate in the report of blood analysis test results compared with auto-vefication.

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.