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.

ObjectiveThe proteome of biological evidence contains rich genetic information, namely single amino acid polymorphisms (SAPs) in protein sequences. However, due to the lack of efficient and convenient analysis tools, the application of SAP in public security still faces many challenges. This paper aims to meet the application requirements of SAP analysis for forensic biological evidence’s proteome data. MethodsThe software is divided into three modules. First, based on a built-in database of common non-synonymous single nucleotide polymorphisms (nsSNPs) and SAPs in East Asian populations, the software integrates and annotates newly identified exonic nsSNPs as SAPs, thereby constructing a customized SAP protein sequence database. It then utilizes a pre-installed search engine—either pFind or MaxQuant—to perform analysis and output SAP typing results, identifying both reference and variant types, along with their corresponding imputed nsSNPs. Finally, SAPTyper compares the proteome-based typing results with the individual’s exome-derived nsSNP profile and outputs the comparison report. ResultsSAPTyper accepts proteomic DDA mass spectrometry raw data (DDA acquisition mode) and exome sequencing results of nsSNPs as input and outputs the report of SAPs result. The pFind and Maxquant search engines were used to test the proteome data of 2 hair shafts of2 individuals, and both obtained SAP results. It was found that the results of the Maxquant search engine were slightly less than those of pFind. This result shows that SAPTyper can achieve SAP fingding function. Moreover, the pFind search engine was used to test the proteome data of 3 hair shafts from 1 European person and 1 African person in the literature. Among the sites fully matched by the literature method, sites detected by SAPTyper are also included; for semi-matching sites, that is, nsSNPs are heterozygous, both literature method and SAPTyper method had the risk of missing detection for one type of the allele. Comparing the analysis results of SAPTyper with the SAP test results reported in the literature, it was found that some imputed nsSNP sites identified by the literature method but not detected by SAPTyper had a MAF of less than 0.1% in East Asian populations, and therefore they were not included in the common nsSNP database of East Asian populations constructed by this software. Since the database construction of this software is based on the genetic variation information of East Asian populations, it is currently unable to effectively identify representative unique common variation sites in European or African populations, but it can still identify SAP sites shared by these populations and East Asian populations. ConclusionAn automated SAP analysis algorithm was developed for East Asian populations, and the software named SAPTyper was developed. This software provides a convenient and efficient analysis tool for the research and application of forensic proteomic SAP and has important application prospects in individual identification and phenotypic inference based on SAP.

OBJECTIVE To provide a reference for optimizing and promoting the quality standards of Shechuan naolitong granules. METHODS Fifteen batches of Shechuan naolitong granules were used as samples to establish HPLC fingerprints using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）. Similarity evaluation and common peak identification were performed， and orthogonal partial least squares discriminant analysis （OPLS-DA） was used to assess quality differences among different batches and to screen quality differential components. Using salvianolic acid B（SAB） as the internal reference， quantitative analysis of multi-components by single-marker （QAMS） was developed to simultaneously determine geniposidic acid （GA）， chlorogenic acid （CA）， vaccarin （VA）， ferulic acid （FA） and senkyunolide I （SI）. The results were compared with those obtained by the external standard method. RESULTS A total of 13 common peaks were identified in the HPLC fingerprints of 15 batches of samples， and the similarities of the spectra were all above 0.96. Seven chromatographic peaks were identified as GA （peak 3）， CA （peak 6）， VA （peak 8）， FA （peak 9）， SI （peak 11）， SAB（peak 12） and TA（peak 13）. OPLS-DA indicated that the differential quality markers among 15 batches were peaks 5， 11 （SI）， and 12 （SAB）.Using SAB as the internal reference， the relative correction factors for GA， CA， VA， FA and SI were calculated as 1.058 4， 0.594 3， 0.643 3， 0.342 7 and 0.262 8， respectively. The mean content of GA， CA， VA， FA， SI and SAB across the 15 batches of samples were 0.155 0， 0.085 4， 0.140 3， 0.071 8， 0.072 7， 1.276 3 mg/g， respectively， showing no significant difference compared with the ESM （P＞0.05）. CONCLUSIONS The established HPLC fingerprint and QAMS are simple， efficient and economical， providing a reference for the quality control and further development of Shechuan naolitong granules.

Objective To investigate the impact of continuous nebulization therapy after lung transplantation on pulmonary function and related complications in lung transplant recipients. Methods A retrospective analysis was conducted on the general data of 71 recipients who underwent allogeneic lung transplantation at the Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, from June 2013 to December 2024. Recipients were divided into observation group (those who continued nebulization therapy for more than 3 months after discharge) and control group (those who discontinued nebulization therapy on their own). The main observation indicators were pulmonary function indicators at 6 months after surgery, including forced expiratory volume in the first second as a percentage of predicted value (FEV₁% pred), forced vital capacity as a percentage of predicted value (FVC% pred), ratio of forced expiratory volume in the first second to forced vital capacity as a percentage of predicted value (FEV₁/FVC% pred), forced expiratory flow at 25%, 50% and 75% of forced vital capacity as a percentage of predicted value, and the percentage of predicted value of corrected carbon monoxide diffusion capacity measured by single-breath method, as well as the ratio of corrected carbon monoxide diffusion capacity to alveolar volume as a percentage of predicted value. Additionally, the annual incidence of postoperative pulmonary infections, survival rate and the rate of no severe airway complications were analyzed. Results At 6 months after lung transplantation, the FEV₁% pred and FVC% pred of the observation group were better than those of the control group [FEV₁% pred was 76% (60%, 91%) vs. 67% (62%, 78%), FVC% pred was (75 ± 13)% vs. (69 ± 11)%, both P<0.05]. The observation group had a lower annual incidence of pulmonary infections compared to the control group (P = 0.023), with a risk of 0.485 times that of the control group. There were no statistically significant differences between the two groups in median survival time and the rate of no severe airway complications (both P>0.05). Conclusions Continuous nebulization therapy after lung transplantation may effectively improve pulmonary function, reduce the annual incidence of pulmonary infections, and play a positive role in the long-term maintenance of pulmonary function.

Objective:To investigate the influence of Kruppel-like factor 16 (KLF16) on the proliferation and migration of pancreatic cancer cells.Methods:Immunohistochemical images of KLF16 were collected from 171 pancreatic cancer tissues and their matched paracarcinoma normal pancreas tissues and 8 pancreatic cancer tissues only in GEPIA database. The expression of KLF16 protein was detected by immunohistochemical imaging software. The protein and mRNA expressions of pancreatic cancer cell lines AsPC-1 and MIA PaCa-2 KLF16 were detected by Western blot and quantitative fluorescence PCR. By knockdown or exogenous overexpression of KLF16, the two cells were divided into blank control group (NC group), negative control group (siRNA-NC group), downexpression KLF16 group (siKLF16 group), overexpression control group (OE-NC group) and ovexpression KLF16-OE group (KLF16-OE group). CCK-8 assay, colony formation assay and transwell chamber were used to detect cell proliferation and migration.Results:The KLF16 protein expression level (4.02±1.26 vs 1.73±1.07) and positive expression rate (91.6% vs 13.5%) in pancreatic cancer tissues were significantly higher than those in paracancer normal pancreas tissues, with statistical significance ( P<0.05). After downregulating KLF16 expression and culturing for 24, 48, 72, and 96 hours, the A450 values of both AsPC-1 (0.19±0.02 vs 0.23±0.03, 0.24±0.06 vs 0.36±0.06, 0.45±0.09 vs 0.78±0.10, 0.69±0.04 vs 0.88±0.07) and MIA PaCa-2 cells (0.20±0.03 vs 0.22±0.02, 0.29±0.05 vs 0.31±0.04, 0.47±0.06 vs 0.78±0.10, 0.71±0.02 vs 0.90±0.07) and colony counts [(36±4.32) per well vs (118.51±10.01) per well, (13.6±2.62) per well vs (83.1±9.11) per well], and the number of migrated cells [(16.67±2.05) vs (46.67±5.91), (19.67±1.69) vs (55±4.89)] all decreased significantly. However, after up-regulating the expression of KLF16 and culturing for 24, 48, 72 and 96 h, the A450 value of both AsPC-1 (0.21±0.05 vs 0.20±0.04, 0.48±0.03 vs 0.31±0.04, 0.91±0.09 vs 0.72±0.03, 1.28±0.10 vs 1.05±0.02) and MIA PaCa-2 cells (0.20±0.01 vs 0.19±0.05, 0.44±0.03 vs 0.30±0.04, 0.89±0.06 vs 0.72±0.03, 1.19±0.05 vs 1.01±0.10), and the number of cell colonies [(189±6.37)/per hole vs (108±9.62)/ per hole, (141±12.56)/ per hole vs (80.69±10.32)/ per hole]], migration cell numbers [(79±4.89) per hole vs (50.33±4.11) per hole, (79.66±3.85) per hole vs (51±4.08) per hole] all increased significantly. Conclusions:KLF16 is highly expressed in pancreatic cancer. The up-regulated expression of KLF16 in pancreatic cancer cell lines can promote the proliferation and migration of pancreatic cancer cells.

Background::Histological healing is closely associated with improved long-term clinical outcomes and lowered relapses in patients with ulcerative colitis (UC). Here, we developed a novel diagnostic criterion for assessing histological healing in UC patients.Methods::We conducted a retrospective cohort study in UC patients, whose treatment was iteratively optimized to achieve mucosal healing at Shanghai Tenth People’s Hospital of Tongji University from January 2017 to May 2022. We identified an inflammatory cell enumeration index (ICEI) for assessing histological healing based on the proportions of eosinophils, CD177 + neutrophils, and CD40L + T cells in the colonic lamina propria under high power field (HPF), and the outcomes (risks of symptomatic relapses) of achieving histological remission vs. persistent histological inflammation using Kaplan-Meier curves. Intrareader reliability and inter-reader reliability were evaluated by each reader. The relationships to the changes in the Nancy index and the Geboes score were also assessed for responsiveness. The ICEI was further validated in a new cohort of UC patients from other nine university hospitals. Results::We developed an ICEI for clinical diagnosis of histological healing, i.e., Y = 1.701X 1 + 0.758X 2 + 1.347X 3 - 7.745 (X 1, X 2, and X 3 represent the proportions of CD177 + neutrophils, eosinophils, and CD40L + T cells, respectively, in the colonic lamina propria under HPF). The receiver operating characteristics curve (ROC) analysis revealed that Y <-0.391 was the cutoff value for the diagnosis of histological healing and that an area under the curve (AUC) was 0.942 (95% confidence interval [CI]: 0.905-0.979) with a sensitivity of 92.5% and a specificity of 83.6% ( P <0.001). The intraclass correlation coefficient (ICC) for the intrareader reliability was 0.855 (95% CI: 0.781-0.909), and ICEI had good inter-reader reliability of 0.832 (95% CI: 0.748-0.894). During an 18-month follow-up, patients with histological healing had a substantially better outcome compared with those with unachieved histological healing ( P <0.001) using ICEI. During a 12-month follow-up from other nine hospitals, patients with histological healing also had a lower risk of relapse than patients with unachieved histological healing. Conclusions::ICEI can be used to predict histological healing and identify patients with a risk of relapse 12 months and 18 months after clinical therapy. Therefore, ICEI provides a promising, simplified approach to monitor histological healing and to predict the prognosis of UC.Registration::Chinese Clinical Trial Registry, No. ChiCTR2300077792.