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.

In order to explore the possible role and molecular mechanism of the combined action of leech and bear bile in liver and gallbladder diseases, this study first used network pharmacology methods to screen the components and targets of leech and bear bile, as well as the related target genes of liver and gallbladder diseases. The selected key genes were subjected to interaction network and GO/KEGG enrichment analysis. Then, using sodium oleate induced HepG2 cell lipid deposition model and DL-ethionine induced mouse fatty liver model, the activity of leech and bear bile alone and in combination in reducing liver fat was evaluated in vitro and in vivo, and the expression of key pathway related proteins suggested by network pharmacology was detected by Western blot. The results of network pharmacology analysis showed that the active ingredients of leech and bear bile have 295 intersecting targets with liver and gallbladder related diseases, involving more than 200 signaling pathways, including the PI3K/Akt signaling pathway and phospholipase D signaling pathway closely related to glucose and lipid metabolism. The results of in vitro validation experiments showed that both leech and bear bile, alone and in combination, can significantly inhibit the lipid deposition induced by sodium oleate in human liver cells, reduce the triacylglycerol level in cell culture supernatant, and inhibit the lipid content in liver cells. The observation results of Nile red staining confocal microscopy showed that the combination of leech and bear bile had better activity in reducing lipid deposition in liver cells compared to using them alone. In a mouse fatty liver model, the combination of leech and bear bile can better reduce elevated organ indices, blood lipids, and liver lipid levels, as well as lower the levels of serum liver injury biomarkers. The animals used in this experiment and related disposal meet the requirements of animal welfare. Before the experiment, it was reviewed and approved by IACUC, Institute of Materia Medica, Chinese Academy of Medical Sciences. The Western blot experiment results showed that the combination of leech and bear bile can significantly upregulate the expression levels of p-PI3K and p-Akt proteins, and increase the p-PI3K/PI3K and p-Akt/Akt ratios, which is consistent with the predicted results of network pharmacology. The combination of leech and bear bile has great potential for treating fatty liver disease, and activating the PI3K/Akt pathway may be one of the important mechanisms for reducing lipid deposition in liver cells.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

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 explore the association between bedroom light at night (LAN) exposure and body mass index (BMI) in children at 1 year follow up, so as to provide new strategies for obesity prevention. Methods: From December 2021 to May 2022, cluster random sampling was conducted, involving 648 children from two primary schools in Tianchang, Chuzhou City, Anhui Province, China, to assess bedroom LAN exposure of children during sleep. A questionnaire survey and physical examination were carried out in May 2022. Multivariate linear regression was performed to analyze the correlation between bedroom LAN exposure and BMI variable quantity at 1 year follow up (May, 2023). Results: The median intensity of bedroom LAN exposure during the sleep episode was [1.11(0.35,3.24)lx] in children. The proportion of the sample exposed to an average light intensity of ≥3 lx was 27.5%, while 19.0% was exposed to a LAN intensity of ≥5 lx during the sleep episode. In the multivariable linear regression, after adjusting for covariates, including sex, baseline age, sleep duration, family monthly income, and maternal education level, exposure to a 1 h-average post bedtime LAN intensity of ≥3 lx ( β=0.25, 95%CI =0.05-0.44) and LAN≥5 lx ( β=0.34, 95% CI = 0.12-0.55) was associated with a gain of 0.25 and 0.34 kg/m 2, respectively, in the children s BMI at the 1 year follow up ( P < 0.05). Conclusions A positive correlation was found between bedroom LAN exposure and BMI variable quantity at 1 year follow up in children. Thus, reduced bedroom LAN exposure might be useful for interventions aimed at obesity prevention.

OBJECTIVE To investigate the HRCT and MRI characteristic of external auditory canal cholesteatoma(EACC)in children.METHODS A total of 40 patients(45 lesions)with EACC confirmed by pathology were retrospectively analyzed with HRCT and MRI characteristics and clinical therapeutic value.Imaging findings of 40 patients(45 lesions)with EACC were retrospectively analyzed.RESULTS Soft tissues were found in all the external auditory canal(EAC).Of the forty-five soft tissues,7 manifested as inhomogeneous strip soft tissues and 38 as lesions solid soft tissues;30 located in medial part of the EAC and covered the tympanic membrane,while the other 15 presented as tympanic membrane perforation and involved the tympanic cavity.The MRI of the 3 ears showed high signal on T2/T1 iso-intensity,high signal on DWI,and low signal on ADC.Normal whole bony EAC was observed in 17 cases and enlarged medial EAC in 28 cases.Seven cases only involved in the superior wall,but 38 cases displayed as multiple bone wall involved,of which 6 involved in circumferential walls.Thirty-three cases displayed atactic ear bone margin,11 displayed blunted or disappeared drum shield plate.Destroy of long crus of incus and manubrium mallei occurred in 15 cases,of short crus of incus in 8 cases,of stapes in 2 cases,and mastoiditis in 5 cases.According to the pneumatization degree of mastoid air cell,37 cases were classified into pneumatic type,7 cases into mixed type,and the last one into diploic type.CONCLUSION The children EACC tends to be limited and rarely involved in middle ear and mastoid process.No patient with peri-ear infection was found.Application of HRCT and MRI help accurate location and determination of cholesteatoma.According to the extent of the lesion,selecting the appropriate surgical method is an effective method to remove cholesteatoma,improve hearing and reduce recurrence.

Objective:To analyze the efficacy and safety of nalbuphine for analgesia in patients with non-mechanical ventilation in intensive care unit (ICU).Methods:From December 2018 to August 2021, a multicenter randomized controlled clinical study was conducted to select non-mechanical ventilation patients with analgesic needs admitted to ICU of four hospitals in Henan Province and Guizhou Province. Patients were randomly assigned to nalbuphine group and fentanyl group. The nalbuphine group was given continuous infusion of nalbuphine [0.05~0.20 mg/(kg·h)], and the fentanyl group was given continuous infusion of fentanyl [0.5~2.0 μg/(kg·h)]. The analgesic target was critical-care pain observation tool (CPOT) score<2. The observation time was 48 hours. The primary endpoint was CPOT score, the secondary endpoints were Richmond agitation-sedation score (RASS), ICU length of stay, adverse events, and proportion of mechanical ventilation. The quantitative data of the two groups were compared by t test or Mann-Whitney U test. The enumeration data were compared by chi square test or Fisher exact probability method. The data at different time points between groups were compared by repeated measures analysis of variance. Results:A total of 210 patients were enrolled, including 105 patients in the nalbuphine group and 105 patients in the fentanyl group. There was no significant difference in baseline data between the two groups (all P>0.05). There was no significant difference in CPOT score between nalbuphine group and fentanyl group at each time point after medication ( P>0.05), the CPOT score of both groups at each time point after medication was significantly lower than that before medication, and the analgesic target could be achieved and maintained 2 hours after medication. There was no significant difference in RASS between the two groups at each time point after medication ( P>0.05), which was significantly lower than that before medication, and the target sedative effect was achieved 2 hours after medication. There was no significant difference in ICU length of stay between nalbuphine group and fentanyl group [5.0(4.0,7.5) d vs. 5.0(4.0,8.0) d, P=0.504]. The incidence of delirium, nausea and vomiting, abdominal distension, pruritus, vertigo and other adverse events in the nalbuphine group was lower than that in the fentanyl group (all P<0.05). There was no significant difference in the incidence of other adverse events such as deep sedation, hypotension and bradycardia between the two groups (all P>0.05). The incidence of respiratory depression in nalbuphine group was not significantly different from that in fentanyl group ( P>0.05), but the proportion of mechanical ventilation was significantly lower than that in the fentanyl group [1.9% (2/105) vs. 8.6%(9/105), P=0.030]. Conclusions:Nalbuphine could be used for analgesia in ICU patients with non-mechanical ventilation. The target analgesic effect could be achieved within 2 hours, and it had a certain sedative effect with a low incidence of adverse reactions.