0~3岁婴幼儿家长托育服务需求现状及优化策略——基于Kano模型的分析

托育服务是我国老龄少子化背景下重要的民生工程。为探讨0~3岁婴幼儿家长对托育服务的需求层次结构并对托育服务提出对策建议,本研究基于魅力质量理论与Kano模型,通过访谈法与现有文献资料确定了0~3岁婴幼儿家长对托育服务的6个需求维度与41个需求要素。本研究通过Kano问卷收集0~3岁婴幼儿家长的托育服务需求信息,根据Better-Worse系数与平均满意度指标(ASC)确定了2个基本型需求维度、2个期待型需求维度、1个魅力型需求维度、1个无差异型需求维度以及12个基本型需求要素、8个期待型需求要素、13个魅力型需求要素、8个无差异型需求要素及其重要性排序,并据此对家庭托育服务需求特点开展讨论。基于上述分析,研究提出了以下四条托育服务优化策略:完善机构监督制度,保障托育服务质量;健全师资准入培训,提高托育人员素养;做好个性需求管理,提供多元服务形式;加大政策宣传普及,加快普惠托育建设。

Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection:A retrospective study

BACKGROUND Paradoxically,patients with T4N0M0(stage II,no lymph node metastasis)colon cancer have a worse prognosis than those with T2N1-2M0(stage III).However,no previous report has addressed this issue.AIM To screen prognostic risk factors for T4N0M0 colon cancer and construct a prognostic nomogram model for these patients.METHODS Two hundred patients with T4N0M0 colon cancer were treated at Tianjin Medical University General Hospital between January 2017 and December 2021,of which 112 patients were assigned to the training cohort,and the remaining 88 patients were assigned to the validation cohort.Differences between the training and validation groups were analyzed.The training cohort was subjected to multi-variate analysis to select prognostic risk factors for T4N0M0 colon cancer,followed by the construction of a nomogram model.RESULTS The 3-year overall survival(OS)rates were 86.2%and 74.4%for the training and validation cohorts,respectively.Enterostomy(P=0.000),T stage(P=0.001),right hemicolon(P=0.025),irregular review(P=0.040),and carbohydrate antigen 199(CA199)(P=0.011)were independent risk factors of OS in patients with T4N0M0 colon cancer.A nomogram model with good concordance and accuracy was constructed.CONCLUSION Enterostomy,T stage,right hemicolon,irregular review,and CA199 were independent risk factors for OS in patients with T4N0M0 colon cancer.The nomogram model exhibited good agreement and accuracy.

Cascading multi-segment rupture process of the 2023 Turkish earthquake doublet on a complex fault system revealed by teleseismic P wave back projection method

In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality rate.SAH lesions are generally diffusely distributed,showing a variety of scales with irregular edges.The complex characteristics of lesions make SAH segmentation a challenging task.To cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH segmentation.Specifically,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale features.Second,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder features.Third,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed UDT.Extensive experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

Panoptic UAV:Panoptic Segmentation of UAV Images for Marine Environment Monitoring

UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience,low cost and convenient maintenance.In marine environmental monitoring,the similarity between objects such as oil spill and sea surface,Spartina alterniflora and algae is high,and the effect of the general segmentation algorithm is poor,which brings new challenges to the segmentation of UAV marine images.Panoramic segmentation can do object detection and semantic segmentation at the same time,which can well solve the polymorphism problem of objects in UAV ocean images.Currently,there are few studies on UAV marine image recognition with panoptic segmentation.In addition,there are no publicly available panoptic segmentation datasets for UAV images.In this work,we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV.First,to deal with the large intraclass variability in scale,deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features.Second,due to the complexity and diversity of marine images,boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision.Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset.

Single Tooth Segmentation on Panoramic X-Rays Using End-to-End Deep Neural Networks

In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.

Factors Associated with Acute Respiratory Infections in Children Aged 0 - 5 Years in the Yénawa District of Cotonou (Benin) in 2023

Introduction: Acute respiratory infections remain one of the main causes of mortality in children aged 0 to 5. This work aimed to study the associated factors with the occurrence of acute respiratory infections in children 0 to 5 years old in Yénawa, Cotonou in 2023. Subjects and Method: It was an analytical cross-sectional study of children aged 0 - 5 years and their mothers in Yénawa, selected by four-degree random sampling. The sampling size, calculated using the Schwartz formula, was 126 children and 126 mothers. The dependent variable was the occurrence of acute respiratory infections. The independent variables were classified into four groups: socio-demographic and economic characteristics, behavioral factors, child-related factors, and environmental factors. Data collected by observation and questionnaire survey were analyzed using STATA version 15 software. Associated factors were investigated by bivariate analysis and multiple logistic regression, at the 5% significance level. Results: A total of 126 children aged 0 - 5 years and 126 mothers were surveyed, aged 23.5 (11 - 36) months and 30 (18 - 48) years respectively. The prevalence of acute respiratory infections was 74.60% (CI95% = 66.89 to 82.30). The associated factors were the mother’s age between 18 and 28 (OR = 10.77;CI95% = 1.89 to 61.27;p = 0.007), the use of charcoal/wood for cooking (OR = 7.36;IC = 1.99 to 27.10;p = 0.003)), children's poor personal hygiene (OR = 8.87;IC = 2.92 to 26.97;p 0.001)), and cohabitation with domestic animals (OR = 7.27;IC = 1.67 to 31.71;p = 0.015). Conclusion: Communicating with mothers about the factors identified will help reduce the prevalence of acute respiratory infections in children aged 0 to 5.

Meibomian glands segmentation in infrared images with limited annotation

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble annotations.The viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,respectively.Furthermore,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for training.This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs.It holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

Determining the surface fault-rupture hazard zone for the Pazarcık segment of the East Anatolian fault zone through comprehensive analysis of surface rupture from the February 6,2023,Earthquake(Mw 7.7)

Following surface rupture observations in populated areas affected by the KahramanmaraşEarthquake(Mw 7.7)on February 6th,2023,along the Pazarcık segment of the East Anatolian Fault Zone(EAFZ),this study presents novel insights into physical criteria for delineating surface fault-rupture hazard zones(SRHZs)along ruptured strike-slip faults.To achieve this objective,three trench studies across the surface rupture were conducted on the Pazarcık segment of the EAFZ to collect field data,and earthquake recurrence intervals were interpreted using Bayesian statistics from previously conducted paleoseismological trenchings.The results of the proposed model indicate that the Pazarcık segment produced five significant surface-rupturing earthquakes in the last∼11 kyr:E1:11.13±1.74 kyr,E2:7.62±1.20 kyr,E3:5.34±1.05 kyr,E4:1.82±0.93 kyr,and E5:0.35±0.11 kyr.In addition,the recurrence intervals of destructive earthquakes on the subject in question range from 0.6 kyr to 4.8 kyr.Considering that the last significant earthquake occurred in 1513,the longest time since the most recent surface fault rupturing earthquake on this particular segment was 511 years.These results indicate that,in terms of the theoretical recurrence interval of earthquakes that can create surface ruptures on the Pazarcık segment,the period in which the February 6,2023,earthquake occurred was within the end of the expected return period.As a result,the potential for a devastating earthquake in the near future is not foreseen on the same fault.Finally,the SRHZ proposed for the Pazarcık section of Gölbaşıvillage was calculated as a 61-meter-wide offset on the fault lineament to reduce the negativities that may occur in the ruptured area in the future.It is recommended to take into account this width in the settlement of this area and nearby areas.

Successful emergency surgical intervention in acute non-STsegment elevation myocardial infarction with rupture:A case report

BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.

Comparative Study of Acute Coronary Syndrome with Persistent ST-Segment Elevation (STEMI) between Diabetics and Non-Diabetics in Dakar, Senegal

Introduction: This study aimed to compare the frequency of diabetic and non-diabetic patients admitted for ST-elevation myocardial infarction (STEMI), assess their epidemiological, clinical, and paraclinical profiles, and evaluate their therapeutic strategies and outcomes. Methodology: A descriptive, analytical, comparative study with prospective and retrospective data collection was conducted from April 1, 2020, to March 31, 2021. Diabetic and non-diabetic patients with STEMI admitted to a cardiology department were included. STEMI diagnosis was based on clinical and electrocardiographic criteria showing persistent ST-segment elevation in at least two leads. All patients included in the study signed a written, informed consent form. Data analysis was performed using SPSS, with a p-value ≤ 0.05 considered statistically significant. Results: STEMI prevalence was 15.27%, with 37.11% of patients being diabetic and 62.89% non-diabetic. Diabetic patients had a mean age of 59.2 ± 10.9 years, while non-diabetics averaged 58 ± 13.4 years. Diabetics were predominantly female (72.2%), whereas non-diabetics were mainly male (83.6%). Smoking was less frequent among diabetics (25% vs. 47.54%), but hypertension, obesity, and sedentary lifestyle were more common. Diabetics had an average of 3.5 ± 1.1 risk factors compared to 2.6 ± 1.2 in non-diabetics. Admission delay was longer for diabetics (34.8 ± 51.6 hours vs. 23.3 ± 52.3 hours). Chest pain was the main symptom in both groups. Electrocardiograms showed that anterior and inferior infarctions were most frequent. Triple vessel disease and severe complications, such as cardiogenic shock, were more common in diabetics, who also had higher mortality (5.56% vs. 3.28%). Conclusion: Diabetic STEMI patients represent a high-risk group with distinct clinical features, longer admission delays, and a greater accumulation of cardiovascular risk factors, emphasizing the need for targeted interventions.

Clinical Study of Applying Enhanced Recovery after Surgery Concept in Single-Segment Lumbar Spinal Stenosis Surgery

Objective: With the aging population and changes in lifestyle, lumbar spinal stenosis has become a common spinal disorder. Treatment modalities have been advancing, and the application of Enhanced Recovery After Surgery (ERAS) principles provides a new approach to postoperative recovery in patients. This study aims to investigate the clinical application effects of ERAS principles in single-level lumbar spinal stenosis surgery. Methods: This study included 64 patients who underwent lumbar fusion surgery in the Spinal Surgery Department of Baise People’s Hospital from July 2022 to July 2024. These patients were divided into an experimental group (ERAS group, 33 cases) and a control group (conventional group, 31 cases) based on perioperative care, receiving ERAS principles and traditional treatment, respectively. A comparison was made between the two groups in terms of gender, age, BMI, intraoperative blood loss, postoperative length of hospital stay, postoperative complications, hospital costs, VAS scores (preoperative/postoperative day 3), and ODI scores (preoperative/postoperative day 3). Results: There were no significant differences in gender, age, and BMI between the ERAS group and the conventional group (gender: χ2 = 0.5008, P = 0.4792;age: 54.55 ± 8.51 years vs. 57.39 ± 8.16 years, P = 0.0892;BMI: 25.11 ± 2.70 vs. 24.77 ± 2.75, P = 0.3098). However, during surgery, patients in the ERAS group had significantly less blood loss than those in the conventional group (197.58 ± 195.51ml vs. 438.71 ± 349.22 ml, P = 0.0006), and the postoperative length of hospital stay was significantly shorter (7.00 ± 2.24 days vs. 11.55 ± 5.23 days, P = 0.0000). On postoperative day 3, VAS scores were significantly better in the ERAS group compared to the conventional group (3.70 ± 0.88 vs. 4.32 ± 0.87, P = 0.0031), and the ODI scores showed significant improvement as well (46.00 ± 3.04 vs. 48.00 ± 3.39, P = 0.0078). Although there were no significant differences in postoperative complications and hospital costs (complications: 3 cases vs. 0 cases, P = 0.2154;hospital costs: 63524.29 ± 17891.80 RMB vs. 58733.84 ± 13280.82 RMB, P = 0.1154), ERAS demonstrated better postoperative recovery outcomes in single-level lumbar spinal stenosis surgery. Conclusion: The study results support the implementation of ERAS principles in single-level lumbar spinal stenosis surgery to promote rapid recovery, reduce healthcare resource consumption, and improve overall patient satisfaction.

Tenda AX12路由器0-Day栈溢出漏洞挖掘方法

随着5G技术对物联网发展的加速,预计到2025年将会有约250亿台物联网设备连接到人们的生活。其中承担物联网设备网络管理角色的路由器使用量非常大,但是路由器存在众多安全问题,通过路由器设备进行攻击,可以非法获取用户信息。为了维护网络安全,提前发现路由器的漏洞具有重要的研究意义。本文以Tenda AX12路由器为研究对象,从固件入手对其进行0-Day栈溢出漏洞挖掘研究,并提出了基于危险函数追踪的逆向分析漏洞挖掘方法。首先从危险函数中分析函数所在前端的对应位置,将前后端对应;然后对固件中的Web服务进行分析,对其中可能发生栈溢出的httpd二进制代码进行危险函数分析,该方法使用反汇编代码对危险函数的普通形式和展开形式进行定位,并对危险函数进行参数分析和动态检测;接着通过搭建仿真模拟机在模拟机上运行该服务的二进制文件,并在Web前端页面对潜在漏洞位置进行数据包捕捉;最后根据前期分析的危险函数参数情况对包进行改写并发送,以此来触发漏洞,验证漏洞的存在性,同时验证该危险函数是否发生栈溢出。为了更真实地确定漏洞存在,我们又在真实设备上验证漏洞的真实存在性和可利用性。实验结果表明了该漏洞的挖掘检测方法的有效性,我们分别在不同型号的路由器上挖掘到4个0-Day漏洞,并且经过与SaTC工具进行对比实验结果表明该检测方法能够更准确的定位到出现漏洞的函数位置。

2018—2020年福建地区猪瘟病毒E0基因遗传变异分析

为了了解2018—2020年福建地区猪瘟病毒流行及遗传变异情况,收集福建省不同地区临床发病猪的血液和肺脏、脾脏、淋巴结等组织样本1464份,采用RT-nPCR的方法对样品进行CSFV检测,对部分CSFV阳性样品E0基因进行克隆测序。结果显示,CSFV总体阳性率为1.9%(29/1464),并获得13株CSFV E0基因序列。同源性分析发现所得13株CSFV E0基因之间的同源性在82.2%-99.7%,其中11株CSFV E0基因与1.1亚型代表毒株HCLV的核苷酸同源性为99.0%~99.9%,与Shimen株的核苷酸同源性94.1%~95.0%;1株与2.1c型参考毒株HNSD-2012核苷酸同源性为98.2%;1株与2.1d亚型毒株CSFV/JPN/2018核苷酸同源性为99.6%。遗传进化分析发现,10株CSFV与HCLV、C-ZJ-2008、Shimen和Brescia处于同一分支,属于1.1亚型;1株CSFV与HNSD-2012等位于同一分支,属于2.1c亚亚型;1株与CSFV/JPN/2018等位于同一分支,属于2.1d亚亚型。氨基酸序列分析发现,13株毒株与HCLV等代表毒株在重要的Rnase活性位点高度保守,其中2株2.1亚型毒株与HCLV毒株相比,其在第35位非关键氨基酸残基上发生由G→E的突变。结果表明福建地区CSFV流行情况趋向多样化,提示仍需加强对CSFV流行及遗传变异的持续监测,为猪瘟的诊断及有效防控奠定基础。

连续变截面箱梁主墩0号块复杂受力模式与力学仿真分析

连续变截面箱梁是钢箱梁桥中应用较多的型式,具有受力复杂、施工变形控制难的特点.依托雄安新区骨干路网京雄高速高碑店连接线白沟河特大桥项目,结合钢箱梁结构异性结构设计理论,采用线性监测、毫米波雷达监测、埋设应变片应力监测与数值仿真分析相结合的方法,获得连续变截面箱梁主墩0号块各项受力变形数据,得到了连续变截面箱梁主墩0号块复杂受力模式的工作机制.结果表明:1)针对线性监测、内应力监测与毫米波雷达监测,提出了连续变截面箱梁主墩0号块控制阀值范围,内应力变化范围为0.71~12.58 MPa,沉降量0.78~-0.2 mm;2)对比分析线性监测与内应力监测数据,并对其进行拟合回归,构建了主墩0号块变形受力模型;3)通过提取主墩0号块毫米波雷达波谱图,获取其变形异常点数据,分析得出了连续变截面箱梁主墩0号块失稳破坏工作机制.上述研究成果可为连续变截面箱梁主墩0号块设计参数优化、主墩施工质量控制与施工工艺优化提供理论支撑与技术指导.

0~3岁婴幼儿托育教养人员生存状态的地区差异及影响因素分析

关注0~3岁婴幼儿托育教养人员的生存状态有利于其专业成长和队伍稳定。通过对东北、华东、西部三个地区666名托育教养人员生存状态的实证调查,发现托育教养人员生存状态总体水平中等偏上且存在地区差异,政府保障是影响东北地区托育教养人员生存状态的关键因素,机构管理是影响华东地区托育教养人员生存状态的潜在因素,个体主观是影响西部地区托育教养人员生存状态的驱动因素。基于调查结果,提出改善托育教养人员生存状态的建议。

全腹CT评分与Fagotti评分对晚期卵巢癌患者行R0减灭术术前评估的效果比较

目的对比全腹CT评分与Fagotti评分在晚期卵巢癌(OC)患者行R0减灭术术前评估中的效果。方法选取2021年1月至2022年6月新疆维吾尔自治区人民医院收治的60例晚期OC患者作为研究对象,均行全腹CT评分,之后对患者行腹腔镜探查术,术中行Fagotti评分。对比不同Fagotti评分患者的基线资料及全腹CT评分,分析Fagotti评分的影响因素,对比两种评分对晚期OC患者行R0减灭术术前评估效果的一致性。结果60例患者全腹CT评分为0~9分,平均(3.05±1.02)分。≥3分者27例,占45.00%;<3分者占33例,占55.00%。Fagotti评分为0~14分,平均(7.79±2.01)分。≥8分者29例,占48.33%;<8分者占31例,占51.67%。校正国际妇产科联盟(FIGO)分期、肿瘤位置、分化程度、恶性腹水因素后,晚期OC患者全腹CT评分仍与Fagotti评分有关(P<0.05);全腹CT评分与Fagotti评分术前评估晚期OC患者行R0减灭术具有较高的一致性(Kappa指数=0.799,95%CI:0.547~1.052,χ^(2)=35.353,P<0.001)。结论全腹CT评分与Fagotti评分在术前评估晚期OC患者行R0减灭术方面具有较高的一致性,全腹CT评分在一定程度上可取代Fagotti评分对晚期OC患者行R0减灭术的术前评估。

经腹入路与经后腹腔入路腹腔镜肾部分切除术治疗T1aN0M0期肾癌的效果分析

目的对比经腹入路与经后腹腔入路腹腔镜肾部分切除术(LPN)治疗T1aN0M0期肾癌的效果,为临床提供参考。方法回顾性分析2021年1月至2024年1月徐州市肿瘤医院收治的100例进行LPN治疗的T1aN0M0肾癌患者的临床资料,根据手术方法的不同分为对照组(50例,接受经腹入路LPN治疗)和观察组(50例,接受经后腹腔入路LPN治疗)。比较两组患者围术期指标水平、应激指标水平和术后并发症发生情况。结果观察组患者手术时间与术后住院时间均短于对照组(均P<0.05);两组患者手术成功率、术中出血量比较,差异均无统计学意义(均P>0.05)。术后1 d,两组患者丙二醛(MDA)、皮质醇(Cor)水平均高于术前,但观察组均低于对照组(均P<0.05)。两组患者术后并发症总发生率比较,差异无统计学意义(P>0.05)。结论与经腹入路相比,经后腹腔入路LPN治疗T1aN0M0期肾癌在缩短手术时间和术后住院时间及减轻应激反应等方面具有优势,值得临床应用。