Objective To evaluate the safety and efficacy of a self-developed micro-normothermic machine perfusion (NMP) system (micro-perfusion device) for preserving isolated porcine limbs. Methods Five healthy Landrace pigs were selected, and their left and right forelimbs were randomly divided into the NMP group and static cold storage (SCS) group. The NMP group was perfused with the self-developed micro-perfusion device and polymerized hemoglobin perfusate for 32 hours at normothermia, while the SCS group was preserved at 4 ℃. Hemodynamic parameters such as perfusion pressure and flow were monitored. The pH value, partial pressure of oxygen (PO₂), lactic acid (Lac), creatine kinase (CK) and lactate dehydrogenase (LDH) in the perfusate were measured. Hematoxylin-eosin staining was used to assess the muscle tissue structure, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling was employed to evaluate muscle cell apoptosis, and immunohistochemistry staining was applied to detect the expressions of tumor necrosis factor (TNF)-α and interleukin (IL)-6. A mixed-effects model was used to analyze the effects of time and treatment methods on tissue structure, cell apoptosis and inflammatory factors. Results The device could stably maintain a perfusion pressure of (69±15) mmHg and a flow rate of (117±42) mL/min. The pH value and electrolytes of the perfusate were generally stable, with PO₂ maintained at a high level. Lac was maintained at 5.38(3.81, 6.45) mmol/L, while CK and LDH increased over time. After 32 hours of perfusion in the NMP group, both the myocyte spacing and apoptosis rate were better than those in the SCS group. Mixed-effects model analysis showed that there were statistically significant differences in the effects of NMP treatment and SCS treatment on myocyte spacing and apoptosis rate per unit time (both P < 0.05). There were no statistically significant differences in TNF-α and IL-6 between the two groups, and mixed-effects model analysis showed no statistically significant differences in the effects of NMP treatment and SCS treatment on TNF-α and IL-6 per unit time (both P > 0.05). Conclusions The micro-perfusion device used in this study may achieve 32-hour normothermic preservation in a porcine limb amputation model, maintain basic metabolism and ionic homeostasis, reduce muscle structural damage and cell apoptosis without inducing additional inflammatory responses. This technology is expected to significantly extend the time window for replantation of amputated limbs in disaster rescue and long-distance transportation, providing an important technical basis for clinical translation and subsequent replantation research.

Objective To develop and validate a fusion model that integrates a multi-parametric magnetic resonance imaging deep learning algorithm with clinicopathological factors to improve the accuracy of preoperative prediction for lymph node metastasis (LNM) in rectal cancer. Methods A total of 191 rectal cancer patients who received treatment at Zhongshan Hospital (Xiamen), Fudan University from January 2022 to December 2024 (modeling group, n＝141) and at Zhongshan Hospital, Fudan University from January to June 2021 (external validation group, n＝50) were retrospectively included. Magnetic resonance imaging and clinical data of all patients were collected to compare the diagnostic performance of the previously developed algorithm with that of two radiologists. Logistic regression was further employed to identify independent risk factors for LNM, and an integrated model combining ‘‘algorithm + clinicopathological features’’ was constructed. The clinical value of this model was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results In the modeling group, the algorithm’s predictive performance for LNM (AUC＝0.763) was higher than those of two physicians (AUC＝0.675, 0.655). Multivariate analysis results indicated that carcinoembryonic antigen ＞5 μg/L and low differentiation were independent risk factors for LNM (P＜0.01). The fusion model improved predictive performance, with an AUC of 0.872 in the modeling group and 0.830 in the external validation group. DCA analysis showed that the fusion model provided higher net benefits across the clinically common threshold range. Conclusions The fusion model based on multi-parametric magnetic resonance image deep learning algorithm and clinical features shows good predictive performance for LNM of rectal cancer, which is superior to simple algorithms and manual interpretation. It is expected to become an auxiliary tool for preoperative risk stratification and treatment strategy selection of rectal cancer.

Objective To investigate the expression of myelin transcription factor 1-like(MYT1L)during amyotrophic lateral sclerosis(ALS)progression and its association with neuronal degeneration through bioinformatics analysis combined with in vivo and in vitro experiments.Methods Bioinformatics analysis of the GSE106803 dataset from the Gene Expression Omnibus(GEO)database revealed significant down-regulation of MYT1L in spinal cords of ALS transgenic mice carrying the human superoxide dismutase 1 mutant gene(hSOD1G93A)compared to the wild-type(WT)mice.hSOD1G93A transgenic mice and their WT littermates were selected to analyze MYT1L mRNA and protein changes in spinal cord tissues at different disease stages using Real-time PCR and Western blotting.Double immunofluorescent staining was used to determine the distribution and cellular localization of MYT1L in the spinal cord of mice at the middle stage of the disease.An ALS cellular model was established using hSOD1G93A mutant NSC34 cells,with hSOD1WT NSC34 cells as controls.MYT1L expression and distribution were assessed in these cells via Real-time PCR,Western blotting,and immunofluorescent staining.Based on the GSE76220 dataset from the GEO database,differentially expressed genes(DEGs)between MYT1L high-and low-expression groups in lumbar spinal motor neurons of ALS patients were identified,followed by Gene Ontology(GO)functional enrichment analysis.MYT1L overexpression was induced in the ALS cellular model to evaluate alterations in cell viability and neurite outgrowth.Results In the GSE106803 dataset,MYT1L expression was significantly down-regulated in the spinal cord of ALS mice.Animal experiments confirmed progressive reductions in MYT1L mRNA and protein levels in spinal cord tissues of ALS mice during mid-and late-disease stages.Compared to the WT group,MYT1L expression decreased in motor neurons of the lumbar spinal cord gray matter anterior horn in ALS mice,while it increased in astrocytes.In vitro,hSOD1G93Amutant NSC34 cells exhibited significantly reduced MYT1L expression than controls,with MYT1L localized to both the cytoplasm and nucleus.DEGs between MYT1L high-and low-expression groups in lumbar spinal cord motor neurons of ALS patients(GSE76220 dataset)were enriched in synaptic-related functions through GO analysis.Overexpression of MYT1L in hSOD1G93A mutant NSC34 cells enhanced cell viability and promoted neurite outgrowth.Conclusion Aberrantly low expression of MYT1L is closely associated with ALS pathogenesis.Overexpression of MYT1L promotes neurite growth and exerts protective effects on ALS motor neurons,suggesting its therapeutic potential.

Objective To compare the long-term prognosis differences between surgical radical resection and endoscopic resection for early gastric cancer patients based on the SEER database. Methods A total of 1 437 patients with stage Tis to T_1b gastric adenocarcinoma were selected from the SEER database from January 1, 2004 to December 31, 2013. They were divided into a surgery group (n=1 257) and an endoscope group (n=180) according to the treatment method. Kaplan-Meier survival curve and Cox regression model were used to analyze survival outcomes. Results The patients in the surgery group were younger than those in the endoscope group ([67.63±12.97] years old vs [71.29±10.82] years old), with higher rates of T₁ stage (97.45% vs 87.78%) and lymph node metastasis (19.73% vs 5.00%, all P＜0.001). The median follow-up time for all patients was 37 (15, 66) months, and the mortality rate of gastric cancer in the endoscope group was lower than that in the surgery group (23.33% vs 27.13%, P＜0.001). Univariate Cox analysis showed that treatment modality, age, sex, T stage, lymph node metastasis were all associated with early gastric cancer mortality (all P＜0.05), and the risk of death in the endoscope group was 43% of that in the surgery group (HR=0.43, P=0.015). After adjusting for multiple factors, there was no statistically significant difference in mortality risk between the two groups (P=0.067), but after excluding lymph node positive patients, the mortality risk in the endoscope group was 46% of that in the surgery group (HR=0.46, P=0.048). Conclusions For early gastric cancer patients with negative lymph nodes, endoscopic resection may provide better survival benefits than surgical procedures, suggesting that it can be the preferred treatment strategy for patients with low risk of lymph node metastasis.

The ocular surface is composed of a variety of tissues,of which limbal stem/progenitor cells(LSCs)are es-sential for maintaining the steady state of the ocular surface.Once LSCs become diseased,resulting in limbal corneal stem cell deficiency(LSCD),the ocular surface epithelial cells will change their cellular identity through processes such as epi-thelial-mesenchymal transition(EMT),transdifferentiation and dedifferentiation.Cell differentiation is tightly controlled by genetic programming and plays a key role in the transformation of the cellular state.The EMT process allows epithelial cells to exhibit mesenchymal cell characteristics,which may cause subepithelial fibrosis in corneal disease and is regulated by a variety of factors.Transdifferentiation is the transformation of one type of differentiated cell into another.In ocular surface stress,conjunctival epithelium may be transformed into corneal epithelium,and non-ocular surface cells may be trans-formed into corneal epithelium,but this process faces ethical and cellular stability challenges.Dedifferentiation,on the other hand,refers to the restoration of a differentiated cell's ability to divide and differentiate,and ocular surface cells also undergo de-differentiation under certain conditions and may be involved in the repair process of the ocular surface.Inten-sive study of these cellular changes provides new ideas for the treatment of ocular surface diseases,although the exact mechanisms are not yet fully understood.Microenvironmental signaling plays a crucial role in this process,so care must be taken when applying the findings to avoid triggering other pathological changes.

Objective To evaluate the application value of the MR three-dimensional double inversion recovery(3D DIR)sequence in the diagnosis of optic neuritis(ON).Methods A retrospective analysis was conducted on MRI images from 40 patients with sus-pected ON.All patients underwent conventional T2 WI fat suppression(FS)and 3D DIR sequences.The lesion detection rate and diag-nostic accuracy of the intraorbital,intracanalicular,and intracranial segments of the optic nerve were compared between the two sequences,respectively.The receiver operating characteristic(ROC)curves were used to assess the diagnostic performance,and the intraclass correlation coefficient(ICC)were used to analyze interobserver consistency.Imaging assessments were independently performed by two senior and two junior radiologists.Results The sensitivity(SEN),specificity(SPE),and accuracy(ACC)in lesion detection of 3D DIR sequence were significantly better than those of conventional T2 WI FS sequence[odds ratio(OR)221 vs 104,P＜0.001].Notably,3D DIR sequence exhibited superior performance in detecting lesions in the intraorbital segment[area under the curve(AUC)0.915,OR=102]and intracanalicular segment(AUC 0.858,OR=51)compared with conventional T2WI FS sequence.Additionally,3D DIR sequence significantly improved diagnostic consistency among junior radiologists(ICC value increased from 0.469 to 0.655),bring-ing their diagnostic performance closer to that of senior radiologists(AUC improved to 0.883,ACC reached 90.1％).Conclusion The 3D DIR sequence has outstanding diagnostic efficacy in detecting ON lesions,significantly improving ACC and interobserver consistency,thereby offering strong support for the precise diagnosis of ON.

The ocular surface is composed of a variety of tissues,of which limbal stem/progenitor cells(LSCs)are es-sential for maintaining the steady state of the ocular surface.Once LSCs become diseased,resulting in limbal corneal stem cell deficiency(LSCD),the ocular surface epithelial cells will change their cellular identity through processes such as epi-thelial-mesenchymal transition(EMT),transdifferentiation and dedifferentiation.Cell differentiation is tightly controlled by genetic programming and plays a key role in the transformation of the cellular state.The EMT process allows epithelial cells to exhibit mesenchymal cell characteristics,which may cause subepithelial fibrosis in corneal disease and is regulated by a variety of factors.Transdifferentiation is the transformation of one type of differentiated cell into another.In ocular surface stress,conjunctival epithelium may be transformed into corneal epithelium,and non-ocular surface cells may be trans-formed into corneal epithelium,but this process faces ethical and cellular stability challenges.Dedifferentiation,on the other hand,refers to the restoration of a differentiated cell's ability to divide and differentiate,and ocular surface cells also undergo de-differentiation under certain conditions and may be involved in the repair process of the ocular surface.Inten-sive study of these cellular changes provides new ideas for the treatment of ocular surface diseases,although the exact mechanisms are not yet fully understood.Microenvironmental signaling plays a crucial role in this process,so care must be taken when applying the findings to avoid triggering other pathological changes.

Objective To explore the effect of different ways of out-of-plane shield on the tube current and cumulative radiation dose of CT scan.Methods The CT water membrane was scanned using Philips 128 row iCT scanner in different scanning methods:the 1-3 times,without the out-of-plane shield,the total length of localizer was 150 mm,270 mm,and 390 mm,respectively;the fourth time,the out-of-plane shield was used but was not present in the localizer;from the 5th to 7th pass,with out-of-plane shield and develop it in the localizer,with out-of-plane shield development lengths of 60 mm,180 mm,and 300 mm,respectively,and the out-of-plane shield was not within the formal scanning range.The tube current and cumulative radiation dose of various scanning methods were compared and a line graph of the tube current for scanning each layer was figured.Results(1)There was no statistically significant difference in tube current when there was no out-of-plane shield but the length of the localizer was different(P＞0.05).(2)When the pitch was 0.8,1.0,and 1.15 respectively,with out-of-plane shield but no shield object in the localizer,the scanning tube current was lower than that without shield scanning(P＜0.05).(3)When scanning with three different lengths of out-of-plane shield(60 mm,180 mm,300 mm)in the localizer,regardless of the pitch,the tube current was greater than that of unshield scanning(P＜0.05).(4)When there was a shield in the localizer,the closer to the shield,the higher the tube current in the formal scan.(5)The cumulative dose of scans with out-of-plane shield but without the presence of shield in the localizer was smaller than that of unshield scanning.However,with shield and the presence of shield scanning in the localizer,the cumulative radiation dose was greater than that of unshield scanning.Conclusion The presence of out-of-plane shield in the localizer may increase the tube current and radiation dose for formal scanning,and the closer to the shield,the more significant the increase in tube current.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.