Plant-derived exosome-like nanovesicles (PELNs), characterized by a natural lipid bilayer membrane, have rapidly emerged as a prominent research frontier in medicine owing to their unique biological properties and robust therapeutic potential. This review comprehensively examines the biological profiles, mechanistic functions, and recent engineering advancements of PELNs. In terms of composition, PELNs are uniquely enriched in plant-specific glycolipids, phosphatidylserine, secondary metabolites, and highly stable 2'-O-methylated miRNAs. This distinct molecular makeup endows them with exceptional biocompatibility, negligible immunogenicity, and the capacity for cross-species molecular communication. Mechanistically, PELNs demonstrate profound anti-inflammatory efficacy by suppressing the NF-κB and NLRP3 inflammasome pathways. They also serve as potent immune modulators, driving macrophage M1/M2 polarization and regulating T cell activity. Additionally, PELNs exhibit promising antitumor capabilities, targeting malignancies via reactive oxygen species (ROS) induction, TRAIL pathway activation, and tumor microenvironment remodeling. Crucially, the plant miRNAs encapsulated within PELNs remain highly stable in the gastrointestinal tract, allowing them to selectively alter gene expression in specific gut microbiota communities. This interaction deeply influences host immunity and metabolism, highlighting the vital role in cross-species regulation. Advancements in bioengineering have further expanded the clinical utility of PELNs. Targeted delivery efficiency can be significantly amplified via surface functionalization (e.g., folate and RGD sequences) and state-of-the-art drug loading technologies such as sonication and electroporation. Consequently, engineered PELNs surpass traditional synthetic nanocarriers in penetrating natural physiological barriers, particularly for oral and transdermal drug administration. Despite these advantages, clinical translation is currently hindered by the lack of standardized isolation protocols, challenges in scalable manufacturing, and the need for robust quality control frameworks. Looking forward, the integration of multi-omics approaches and AI-driven “molecular fingerprinting”—coupled with the design of synthetic biomimetic vesicles—will be instrumental in overcoming these bottlenecks, ultimately establishing PELNs as a next-generation platform for precision medicine and targeted nanotherapeutic delivery.

Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

Objective This study conducted molecular biological identification of the viruses carried by Culex pipiens pallens specimens collected in Shunyi District, Beijing in 2023, and observed the changes in the infection rate of the viruses carried by Cx. pipiens pallens at different collection times. Methods Cx. pipiens pallens were collected using carbon dioxide mosquito traps. The mosquito samples were ground in batches and analyzed by molecular biology technologies. The virus infection rate at different collection times was analyzed statistically. Results 17 strains of Culex pipiens pallens densovirus(CppDNV)were identified from Cx. pipiens pallens samples collected in Shunyi District, Beijing, in 2023. The nucleotide sequence analysis of the virus genome coding region showed that CppDNV was a single-stranded DNA virus with a total length of 3 335 nt, encoding 2 non-structural proteins(NS1, NS2)and 1 capsid protein(VP). The nucleotide(amino acid)sequence lengths of the three proteins were 2 376 nt(791 aa),1 092 nt(363 aa)and 1 071 nt(356 aa), respectively. Phylogenetic analysis showed that CppDNV was located in genus Brevihamaparvovirus. Statistical analysis showed no significant difference in infection rates across collection times(χ2=4.429, P=0.194). Conclusions CppDNV was identified in Cx. pipiens pallens in Beijing, and it was stably maintained in this natural population.

ObjectiveCell adhesion is a critical process that regulates cellular physiological functions. Quantitative characterization of adhesion dynamics is essential for elucidating the intrinsic mechanical mechanisms underlying cellular activities. Although atomic force microscopy-based single-cell force spectroscopy is widely used for single-cell adhesion measurements, it requires complex chemical modifications for preparation of live-cell probes, leading to limitations such as cumbersome operation, low throughput, and potential impacts on cell viability. Fluidic force microscopy, which combines atomic force microscopy with microfluidic probes, is a technique allowing the operation of force-controlled nanopipettes in aqueous environments. By applying negative or positive pressure via a pressure controller, a single living cell can be captured onto or released from the cantilever under physiological conditions. This procedure offers a simple workflow and high assay throughput for single-cell adhesion measurements without the need for chemical functionalization. In this study, fluidic force microscopy-based single-cell force spectroscopy was adopted to achieve long-term quantitative characterization of single-cell adhesion dynamics in a simpler and more efficient manner, comparing the dynamic differences in adhesion establishment between two cell lines with different differentiation levels. MethodsHEK 293T and hTERT RPE-1 cells were non-invasively captured on the cantilever of a fluidic force microscope via its integrated microfluidic system during 40 h of adhesion culture. Cell-substrate detachment assays were performed, and force-distance curves were recorded to extract key mechanical adhesion parameters, including adhesion force, adhesion energy, and maximum detachment distance. These measurements were combined with real-time monitoring of cell spreading area to systematically characterize the dynamic evolution of single-cell adhesion. ResultshTERT RPE-1 cells rapidly entered a stable adhesion phase within 1 h after seeding, with both area-normalized adhesion force and area-normalized adhesion energy reaching peak values. In contrast, HEK 293T cells required 4 h to achieve stable adhesion. Subsequently, the adhesion force, adhesion energy and maximum detachment distance of hTERT RPE-1 and HEK 293T cells stabilized at approximately 240 nN vs. 30 nN, 2.2 pJ vs. 0.12 pJ and 6 μm vs. 4 μm, respectively. hTERT RPE-1 cells reached the peak of area-normalized adhesion parameters earlier than HEK 293T cells, with their peak area-normalized adhesion force and area-normalized adhesion energy being substantially elevated relative to HEK 293T cells. HEK 293T cells presented stronger linear correlations among adhesion energy, maximum detachment distance and adhesion force compared with hTERT RPE-1 cells. For both cell lines, cell spreading area exhibited a weak correlation with adhesion force. Whereas the area-normalized adhesion parameters of HEK 293T cells remained relatively constant throughout the adhesion process, hTERT RPE-1 cells exhibited elevated values in the early phase, followed by a gradual decline. These results indicated distinct dynamic adhesion patterns between the two cell types, with hTERT RPE-1 cells exhibiting stronger adhesion strength and higher adhesion efficiency. ConclusionIn this study, fluidic force microscopy-based single-cell force spectroscopy was successfully applied to perform long-term in situ quantitative measurement of the adhesion dynamics in single adherent cells. The approach revealed divergent adhesion patterns between HEK 293T and hTERT RPE-1 cells, suggesting a close association between cell differentiation and adhesion behaviors. These findings provide quantitative mechanical evidence for further understanding the underlying mechanisms of cell adhesion.

To assess the current status of early surgical site infections following prosthetic implantation in plastic surgery across four provincial-level administrative regions in China, and to explore the effectiveness of standardized training for infection prevention and control.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the completion rate of tumor evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients in the national multicenter real-world database.Methods:The prospective real-world study was conducted. The clinicopathological data of 1 074 patients who underwent surgical treatment for mid and low rectal cancer in 47 national medical institutions, including Peking Union Medical College Hospital et al, from May 12,2023 to May 11,2024 were collected. Observation indicators: (1) clinical characteristics of patients with mid and low rectal cancer; (2) initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer; (3) initial imaging evaluation of patients with mid and low rectal cancer; (4) imaging evaluation after neoadjuvant therapy for patients with mid and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M( Q1, Q3). Count data were described as absoluter numbers and/or percentages. Results:(1) Clinical characteristics of patients with mid and low rectal cancer. Of the 1 074 patients, there were 713 males and 361 females, aged 63(56,70)years. The body mass index of 1 074 patients was 24(21,26)kg/m 2.For American Society of Anesthesiologists classification, there were 147 cases of stage Ⅰ, 641 cases of stage Ⅱ, 157 cases of stage Ⅲ, 2 cases of stage Ⅳ, and there were 127 cases missing data. (2) Initial colonoscopy and pathologic evaluation of tumors in patients with mid and low rectal cancer. Of the 1 074 patients, there were 787 cases (73.28%) undergoing complete colonoscopy, and there were only 197 cases (18.34%) undergoing immunohistochemical evaluation of all four mismatch repair proteins. (3) Initial imaging evaluation of patients with mid and low rectal cancer. Of the 1 074 patients, there were 842(78.40%) patients completing magnetic resonance imaging (MRI) or ultrasound evaluation, and there were 914(85.10%) patients completing chest, abdomen, and pelvis enhanced computed tomography (CT) evaluation. In the 149 patients completing rectal ultrasound evaluation, there were 122 cases (81.88%) comple-ting T staging evaluation, and there were 81 cases (54.36%) completing N staging evaluation. In the 808 patients completing rectal MRI evaluation, there were 708 cases (87.62%) completing T staging evaluation, and there were 590 cases (73.02%) completing N staging evaluation. (4) Imaging evalua-tion after neoadjuvant therapy for patients with mid and low rectal cancer. Of the 388 patients with neoadjuvant therapy, there were 332 patients (85.57%) completing MRI or ultrasound evaluation, and there were 327 patients (84.28%) completing chest, abdomen, and pelvis enhanced CT evalua-tion. In the 70 patients completing rectal ultrasound evaluation, there were 65 cases (92.86%) com-pleting T staging evaluation, and there were 49 cases (70.00%) completing N staging evaluation. In the 327 patients completing rectal MRI evaluation, there were 246 cases (75.23%) completing T staging, and there were 228 cases (69.72%) completing N staging evaluation. Conclusion:The com-pletion rate of tumor imaging evaluation at initial assessment and after neoadjuvant therapy for mid and low rectal cancer patients on a national scale is relatively good.

Objective To explore the impact of suture configuration and fixation type on the biome-chanical strength of rotator cuff repair,using a factorial design study.Methods Sixteen fresh-frozen porcine shoulder samples were randomized into an anchorless double-row suture bridge transosseous su-tures(DS)group,an anchored double-row suture bridge transosseous-equivalent(DE)group,an an-chorless X-BOX construct transosseous sutures(XS)group,and an anchored X-BOX construct transos-seous-equivalent(XE)group,each of four,according to suture configuration(double-row suture bridge,traditional X-BOX construct)and fixation type(suture anchors,transosseous sutures).Then,their fatigue resistance(first-cycle excursion,gap length difference ratio,and the percentage of ex-posed footprints)and the failure strength(the maximum failure load and the re-tear type)were mea-sured using a biomechanical material testing machine.Results Different suture configurations affected failure strength(F=39.559,P<0.001),with the double-row suture bridge groups(693.07±58.35 N,746.76±138.57 N)showing significantly higher failure strength,compared to the traditional X-BOX groups(462.90±18.91 N,421.43±90.76 N).However,the fixation type did not significantly im-pact failure strength(F=1.161,P=0.302).Moreover,the suture configuration influenced the gap differ-ence ratio(F=7.781,P=0.016),but had no significant correlation with other fatigue resistance indica-tors(P>0.05).Meanwhile,failure strength and fatigue resistance were not correlated with fixation type,and the interaction between suture and fixation type(P>0.05).The incidence of failure types for the four suture configurations was as follows:Type I tendon tear:XS>XE>DS=DE;type II tendon tear:DS>XE>XS=DE;fixing material-related failure:DE>DS=XE=XS.Conclusion The failure strength and gap formation ratio in rotator cuff repair under fatigue loading are influenced by suture configuration,whereas no significant association has been observed with respect to fixation method,whether using transosseous sutures or suture anchors.