Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

BACKGROUND:Osteoporotic fracture is the most serious complication of osteoporosis.Previous studies have demonstrated that gut microbiota has a regulatory effect on skeletal tissue and that gut microbiota has an important relationship with osteoporotic fracture,but the causal relationship between the two is unclear. OBJECTIVE:To explore the causal relationship between gut microbiota and osteoporotic fractures using Mendelian randomization method. METHODS:The genome-wide association study(GWAS)datasets of gut microbiota and osteoporotic fracture were obtained from the IEU Open GWAS database and the Finnish database R9,respectively.Using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,Mendelian randomization analyses with random-effects inverse variance weighted,MR-Egger regression,weighted median,simple model,and weighted model methods were performed to assess whether there is a causal relationship between gut microbiota and osteoporotic fracture.Sensitivity analyses were performed to test the reliability and robustness of the results.Reverse Mendelian randomization analyses were performed to further validate the causal relationship identified in the forward Mendelian randomization analyses. RESULTS AND CONCLUSION:The results of this Mendelian randomization analysis indicated a causal relationship between gut microbiota and osteoporotic fracture.Elevated abundance of Actinomycetales[odds ratio(OR)=1.562,95%confidence interval(CI):1.027-2.375,P=0.037),Actinomycetaceae(OR=1.561,95%CI:1.027-2.374,P=0.037),Actinomyces(OR=1.544,95%CI:1.130-2.110,P=0.006),Butyricicoccus(OR=1.781,95%CI:1.194-2.657,P=0.005),Coprococcus 2(OR=1.550,95%CI:1.068-2.251,P=0.021),Family ⅩⅢ UCG-001(OR=1.473,95%CI:1.001-2.168,P=0.049),Methanobrevibacter(OR=1.274,95%CI:1.001-1.621,P=0.049),and Roseburia(OR=1.429,95%CI:1.015-2.013,P=0.041)would increase the risk of osteoporotic fractures in patients.Elevated abundance of Bacteroidia(OR=0.660,95%CI:0.455-0.959,P=0.029),Bacteroidales(OR=0.660,95%CI:0.455-0.959,P=0.029),Christensenellacea(OR=0.725,95%CI:0.529-0.995,P=0.047),Ruminococcaceae(OR=0.643,95%CI:0.443-0.933,P=0.020),Enterorhabdus(OR=0.558,95%CI:0.395-0.788,P=0.001),Eubacterium rectale group(OR=0.631,95%CI:0.435-0.916,P=0.016),Lachnospiraceae UCG008(OR=0.738,95%CI:0.546-0.998,P=0.048),and Ruminiclostridium 9(OR=0.492,95%CI:0.324-0.746,P=0.001)would reduce the risk of osteoporotic fractures in patients.We identified 16 gut microbiota associated with osteoporotic fracture by the Mendelian randomization method.That is,using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,eight gut microbiota showed positive causal associations with osteoporotic fracture and another eight gut microbiota showed negative causal associations with osteoporotic fracture.The results of this study not only identify new biomarkers for the early prediction of osteoporotic fracture and potential therapeutic targets in clinical practice,but also provide an experimental basis and theoretical basis for the study of improving the occurrence and prognosis of osteoporotic fracture through gut microbiota in bone tissue engineering.

ObjectiveTo investigate the protective effect of Rehmanniae Radix iridoid glycosides （RIG） on the kidney tissue of streptozotocin （STZ）-induced diabetic mice and explore the underlying mechanism. MethodsTwelve of 72 male C57BL/6J mice were randomly selected as the normal group， and the remaining 60 mice were fed with a high-fat diet for six weeks combined with injection of 60 mg·kg^-1 STZ for 4 days to model type 2 diabetes mellitus. The successfully modeled mice were randomized into model， metformin （250 mg·kg^-1）， catalpol （100 mg·kg^-1）， low-dose RIG （RIG-L， 200 mg·kg^-1） and high-dose RIG （RIG-H， 400 mg·kg^-1） groups （n=11）. Mice in each group were administrated with corresponding drugs， while those in the normal group and model group were administrated with the same dose of distilled water by gavage once a day. After 8 weeks of intervention， an oral glucose tolerance test （OGTT） was performed， and the area under the curve （AUC） was calculated. After mice were sacrificed， both kidneys were collected. The body weight， kidney weight， and fasting blood glucose （FBG） were measured. Biochemical assays were performed to measure the serum levels of triglycerides （TG）， total cholesterol （TC）， serum creatinine （SCr）， and blood urea nitrogen （BUN）. Enzyme-linked immunosorbent assay （ELISA） was employed to determine the serum level of fasting insulin （FINS）， and the insulin sensitivity index （ISI） and homeostatic model assessment for insulin resistance （HOMA-IR） were calculated. The pathological changes in kidneys of mice were observed by hematoxylin-eosin staining and Masson staining. The immunohistochemical method （IHC） was employed to assess the expression of interleukin-1 （IL-1）， interleukin-6 （IL-6）， tumor necrosis factor-α（TNF-α）， transforming growth factor-β₁ （TGF-β₁）， and collagen-3 （ColⅢ） in the kidney tissue. The protein levels of TGF-β₁， cell signal transduction molecule 3 （Smad3）， matrix metalloproteinase-9 （MMP-9）， and ColⅢ in kidneys of mice were determined by Western blot. ResultsCompared with the normal group， the model group showcased decreased body weight and ISI （P<0.01）， increased kidney weight， FBG， AUC， FINS， HOMA-IR， TC， TG， SCr， and BUN （P<0.01）， glomerular hypertrophy， capsular space narrowing， and collagen deposition in the kidney， up-regulated protein levels of IL-1， IL-6， TNF-α， TGF-β₁， ColⅢ， and Smad3 （P<0.01）， and down-regulated protein level of MMP-9 （P<0.01） in the kidney tissue. Compared with the model group， the treatment groups had no significant difference in the body weight and decreased kidney weight （P<0.05， P<0.01）. The FBG level declined in the RIG-H group after treatment for 4-8 weeks and in the metformin， catalpol， and RIG-L groups after treatment for 6-8 weeks （P<0.01）. The AUC in the RIG-L， RIG-H， and metformin groups decreased （P<0.05， P<0.01）. The levels of TC， SCr， and BUN in the serum of mice in each treatment group became lowered （P<0.05， P<0.01）. The level of TG declined in the RIG-L， RIG-H， and metformin groups （P<0.05， P<0.01）. The serum level of FINS declined in the catalpol， RIG-L， and metformin groups （P<0.01）. Compared with the model group， the treatment groups showed decreased HOMA-IR （P<0.01）， increased ISI （P<0.01）， alleviated pathological changes in the kidney tissue， and down-regulated expression of IL-1 and TGF-β₁. In addition， the protein levels of IL-6， TNF-α， and ColⅢ in the RIG-H and metformin groups and IL-6 and TNF-α in the RIG-L group were down-regulated （P<0.05， P<0.01）， and the protein levels of IL-6， TNF-α， and ColⅢ in the catalpol group and ColⅢ in the RIG-L group showed a decreasing trend without statistical difference. The protein levels of TGF-β₁， Smad3， and ColⅢ in the RIG-H and metformin groups were down-regulated （P<0.01）. Compared with that in the model group， the protein level of MMP-9 was up-regulated in each treatment group （P<0.01）. ConclusionRIG can improve the renal structure and function of diabetic mice by regulating the TGF-β₁/Smads signaling pathway.

Objective:Based on value orientation,it aimed to scientifically define the concept and connotation of accessibility to novel anticancer drugs,in order to deeply understand the nature and current status of the accessibility issues of novel anticancer drugs,and to provide a reference for the formulation and optimization of policies related to novel anticancer drugs.Methods:Data was collected through literature review and expert interviews,and the concept of drug accessibility was defined using the atomic diagram method.Results:The core images include"affordability","availability","high quality"and"patients".The concept of accessibility to novel anticancer drugs is defined as"the process of ensuring the sustainable supply,equitable access,affordability,and rational use of high-quality anticancer drugs to safeguard the realization of patient benefit goals."The connotation of the value orientation in policies on the accessibility of novel anticancer drugs is profoundly reflected in the multi-dimensional value-driven approach to ensure the ultimate benefit of patients,which includes quality,sustainability,equity,affordability,and rational use.Conclusion:The proposal of the concept and connotation of accessibility provides a theoretical basis for a deep understanding of the accessibility of novel anticancer drugs and offers valuable references for subsequent policy-making and practical operations.

Objective:To analyze the comprehensive efficiency of resource allocation in urban public hospitals in 21cities of Guangdong Province from 2019 to 2023,aiming at providing empirical evidence for future medical policy makers and hospital managers in resource allocation and management.Methods:Using Data Envelopment Analysis and Malmquist index to evaluate the efficiency of health resource allocation in public hospitals in Guangdong.Results:From 2019 to 2023,the comprehensive efficiency of urban public hospitals in Guangdong Province was mildly ineffective,only 7 cities showing relative effectiveness,and there were significant regional differences in efficiency.Malmquist index analysis showed that the total factor productivity of health resource allocation in urban public hospitals in the province decreased from 2019 to 2020 and kept increasing from 2020 to 2023,mainly due to the increasing of the technological progress index and the scale efficiency index.Horizontally,the total factor productivity of 9 cities in the province has increased,while the total factor productivity of 12 cities has decreased,with significant regional differences.The input-output redundancy analysis shows that only 6 cities have no input-output redundancy.Conclusion:The overall efficiency of resource allocation in urban public hospitals in Guangdong Province from 2019 to 2023 is relatively low,with significant regional differences.In the future,it is necessary to coordinate and plan resource allocation,focus on refined management,strengthen talent training and technological improvement,improve the operational efficiency of existing resources,and promote high-quality development of hospitals.

Objective:To explore the value of ultrasound elastography (USE) in evaluating the stability and prognosis of carotid plaque (CP) in patients with cerebral infarction(CI).Methods:A total of 94 patients with CI admitted to Dongying District People′s Hospital of Dongying City from January 2022 to December 2023 were retrospectively selected as the study objects, all of whom were treated with carotid artery dissection. The presence of CP was confirmed by pathological examination. The head and neck of patients were examined by conventional ultrasound and USE before surgery, respectively, and the diagnostic value of the two methods on CP stability was compared. The difference of strain value and strain rate among different plaque types was compared, and the correlation between different plaque types and the score of ESSEN Stroke Risk Scale (ESRS) was analyzed.Results:The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of conventional ultrasonic detection of CP were 75.00%, 71.43%, 74.00%, 87.10% and 52.63%; and of USE were 90.28%, 89.29%, 90.00%, 95.59%, 78.13%. The strain values and strain rates of stable plaques were lower than those of vulnerable plaques : (0.73 ± 0.11) × 10 3 vs. (2.42 ± 0.57) × 10 3, (6.57 ± 0.84) s -1 vs. (9.36 ± 2.55) s -1, there were statistical differences ( P<0.01). The ESRS scores of patients with vulnerable plaques were mostly ≥3 scores, and those with stable plaques were mostly<3 scores, there was statistical differences ( P<0.01). The scores of National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) 3 months after the onset of stable plaque were lower than those of vulnerable plaque patients: (26.80 ± 8.47) scores vs. (34.67 ± 8.98) scores, (3.97 ± 0.84) scores vs. (4.55 ± 1.61) scores, there were statistical differences ( P<0.01 or <0.05). Conclusions:USE has significant advantages in detecting CP stability in CI patients, which can efficiently and accurately assess the risk of plaque and provide important information for clinical use.

Objective:To explore the role of spinal Reelin protein (RELN) in neuropathic pain and its related mechanisms.Methods:A rat model of neuropathic pain induced by chronic constriction injury (CCI) was established using the sciatic nerve ligation method. The mechanical threshold and thermal threshold of the injured side and contralateral side in the sham-operation group and CCI group were compared. Western blot was used to detect the differences in the expressions of spinal RELN, calcium/calmodulin-dependent protein kinase Ⅱ (CAMKⅡ) and phosphorylated extracellular signal-regulated kinase 1/2 (p-ERK1/2) proteins. On the 7th day after CCI modeling, the CCI model rats were further divided into three groups: CCI group (without any treatment), CCI+ RELN overexpression group (intrathecal injection of 15 μl of 5 μg/μl RELN overexpression plasmid, once a day for 2 consecutive days) and CCI+ PBS group (intrathecal injection of PBS). The mechanical threshold and thermal threshold among the three groups were compared, and Western blot was used again to detect the differences in the expressions of RELN, CaMKⅡ and p-ERK1/2 proteins in the three groups.Results:CCI successfully induced neuropathic pain in rats. On the 7th day after CCI, compared with the contralateral hind paw or the injured hind paw in the sham-operation group, the mechanical threshold and thermal threshold of the injured hind paw in the CCI group were significantly lower, with statistically significant differences (all P<0.001). Western blot results showed that compared with the sham-operation group, the protein expression of RELN in the spinal dorsal horn of the injured side in the CCI group was lower ( P=0.031), the protein expression of CAMKⅡ and the level of p-ERK1/2 were higher (all P<0.05), and there was no statistically significant difference in the level of ERK1/2 among the groups ( P>0.05). The thermal threshold and mechanical threshold of the injured side in the CCI+ RELN overexpression group were significantly higher than those in the CCI group and CCI+ PBS group (all P<0.05). Western blot results showed that 24 hours after the transfection of RELN overexpression plasmid, compared with the CCI+ PBS group, the protein expression of RELN in the CCI+ RELN overexpression group was significantly increased, with a statistically significant difference ( P<0.05), indicating that the transfection of RELN overexpression plasmid was successful. Compared with the CCI+ PBS group, the protein expression of CAMKⅡ and the phosphorylation level of ERK2 in the CCI+ RELN overexpression group were lower (all P<0.05), while there was no statistically significant difference in the phosphorylation level of ERK1 ( P>0.05). Conclusions:The overexpression of the RELN gene in the spinal cord weakens the maintenance of neuropathic pain by inhibiting the activation of the CAMKⅡ/ERK2 pathway, which suggests that RELN may become a new target for pain treatment.

Objective:To analyze the clinical characteristics of locally advanced rectal cancer patients with pathological complete response (pCR) after neoadjuvant chemoradiotherapy combined with immunotherapy.Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 46 patients with locally advanced rectal cancer who were admitted to 6 medical centers, including Beijing Friendship Hospital of Capital Medical University et al, from June 2021 to November 2022 were collected. There were 29 males and 17 females, aged (61±4)years. Patients received neoadjuvant chemoradiotherapy combined with immune checkpoint inhibitor therapy, and under-went radical total mesorectal excision during 6-12 weeks after radiotherapy. Observation indicators: (1) comparison of clinical characteristics between pCR and non-pCR patients;(2) postoperative complications and adverse reactions of pCR and non-pCR patients. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Results:(1) Comparison of clinical characteristics between pCR and non-pCR patients. Before neoadjuvant therapy, there were 14 cases aged ≥50 years and 6 cases aged <50 years in pCR patients, versus 25 cases and 1 case in non-pCR patients, showing a significant difference between the two groups ( P<0.05). After neoadjuvant therapy, cases in clinical stage T0, T1, T2, T3, T4 were 11, 1, 5, 3, 0 for pCR patients versus 7, 4, 2, 11, 2 for non-pCR patients, cases of tumor regression grade 1, 2, 3, 4 were 11, 8, 1, 0 for pCR patients versus 7, 14, 4, 1 for non-pCR patients, cases in low-risk, medium-risk, high-risk of neoadjuvant rectal scoring and grading were 20, 0, 0 for pCR patients versus 4, 18, 4 for non-pCR patients, respectively, showing significant differences in above indicators between the two groups ( Z=-2.256, -2.104, -5.458, P<0.05). (2) Postoperative complications and adverse reactions of pCR and non-pCR patients. Postoperative complications occurred in 2 cases of pCR patients and 5 cases of non-pCR patients, postoperative adverse reactions occurred in 11 cases of pCR patients and 10 cases of non-pCR patients, showing no significant difference between the two groups ( P>0.05). Conclusion:Compared with locally advanced rectal cancer patients aged ≥50 years, those aged <50 years have significant benefits from neoadjuvant chemoradiotherapy combined with immunotherapy. Clinical T staging and magnetic resonance imaging-detected tumor regression grade after neoadjuvant therapy have predictive value for patients with pCR .