Adolescent mental health is shaped by both family environments and physical well-being. However, the role of physical sub-health, a state of functional imbalance and fatigue prior to diagnosable illness, remains poorly understood in this process. This study examined whether physical sub-health serves both as a psychosomatic pathway and as a vulnerability factor linking family functioning to adolescent mental health. A cross-sectional survey was conducted among 647 primary and secondary school students across China. Validated scales were used to assess family functioning, physical sub-health (operationalized based on constitution characteristics), and mental health symptoms. Structural equation modeling (SEM) was employed to examine the mediating role of physical sub-health. Furthermore, latent moderated structural equation modeling (LMS) was applied to test its moderating effect. Physical sub-health significantly mediated the association between family functioning and mental health, with the largest indirect effects observed for qi stagnation (β = -0.27, 95

BACKGROUND:The C2-7 Cobb angle is an important parameter in evaluating cervical sagittal alignment, which is widely used for preoperative planning, identifying surgical indications, and postoperative assessment. However, this angle becomes unmeasurable in 28% to 49% of clinical radiographs because of poor visualization of the C7 inferior endplate, limiting treatment planning and radiographic follow-up in cervical alignment assessment. The C2-6 Cobb angle has been proposed as a substitute in previous research, but these studies were limited by small symptomatic cohorts from a single center and lacked both subgroup-specific and external validation. Furthermore, there is currently a lack of reference standards for the clinical use of the C2-6 Cobb angle, and no established machine-learning models are available to accurately predict the C2-7 Cobb angle. QUESTIONS/PURPOSES:(1) Can the C2-6 Cobb angle serve as a reliable substitute for the C2-7 angle? (2) Can machine-learning models accurately predict the C2-7 Cobb angle? METHODS:We conducted a retrospective, multicountry imaging study from January 2020 to January 2025, utilizing standing lateral cervical spine radiographs from a large hospital data set in China and public data sets from Vietnam and India. In China, 11,800 radiographs were initially screened. The inclusion criterion was cervical radiographs of sufficient clarity. The exclusion criterion was cervical radiographs with incomplete visualization of anatomic structures. Following these exclusions, 10,571 radiographs from China were included, comprising 10,000 standard standing lateral radiographs plus 284 implant and 287 flexion-extension radiographs. From the public data sets, 470 radiographs from Vietnam and 62 from India were reviewed, with no radiographs excluded. A total of 11,103 radiographs were available for final analysis. Key variables included demographics (age, sex), symptomatic status, implant status, and radiographic sagittal parameters derived from standing lateral views. Four orthopaedic specialists labeled keypoints on the original radiographs, including the corner points of C2 to C7 and the centroid of C2. An algorithm was employed for precise measurement of the C2-6 and C2-7 Cobb angles. The Pearson correlation coefficient was calculated to assess the strength of the correlation between the C2-6 and C2-7 Cobb angles, and a linear regression analysis was applied to derive a predictive equation for the C2-7 Cobb angle based on the C2-6 Cobb angle. Subsequently, the 10,000 standard Chinese standing lateral radiographs were randomly assigned to the training set (80%) and the testing set (20%). An independent validation set (n = 1103) was established to assess robustness, comprising 284 implant radiographs and 287 flexion-extension radiographs from China, together with 470 from Vietnam and 62 from India. RESULTS:Correlation analysis demonstrated a strong positive correlation between the C2-6 and C2-7 Cobb angles in the overall population (r = 0.92; p < 0.001). Machine-learning models incorporating the C2-6 Cobb angle and other sagittal parameters achieved high predictive accuracy for estimating the C2-7 Cobb angle, with Lasso regression performing best (R 2 = 0.93, mean absolute error [MAE] = 2.57). Additionally, strong performance was observed in the validation set (R 2 = 0.95, MAE = 3.21). In the subgroup analysis for the extension in males group, the linear model achieved the best validation results, with R 2 = 0.94 and MAE = 2.52. CONCLUSION:A strong correlation and high interpretable linear regression results between the C2-6 and C2-7 Cobb angles were observed across different countries, body positions, and implants, suggesting that the C2-6 Cobb angle can serve as a reliable substitute for the C2-7 Cobb angle in radiographic imaging. Further analysis revealed that the C2-6 Cobb angle is approximately 6° smaller than the C2-7 Cobb angle at the population level, which may serve as an important reference for standardized interpretation in clinical evaluation. Machine-learning models achieved high predictive accuracy for estimating the C2-7 Cobb angle, with the best performing model (Lasso regression) achieving an MAE of 2.57, offering an alternative clinical application option. To facilitate clinical use, we provide a freely available online tool ( http://c2-7cobbanglepredictionsystem.online ) that will be maintained for at least 15 years. LEVEL OF EVIDENCE:Level III, diagnostic study.

STUDY DESIGN:A cross-sectional analysis of 10,000 cervical spine X-rays. OBJECTIVE:This study investigates the variations in C6S and C7S across demographic factors (gender, age, cervical curvature, and symptoms) and explores their correlation. In addition, machine learning models are applied to improve the accuracy of C7S prediction. SUMMARY OF BACKGROUND DATA:The C7S is crucial for assessing cervical balance but is often limited by visibility issues. This study uses a large sample to validate the feasibility of the C6S as a substitute for C7S across diverse populations with varying ages, genders, symptoms, and cervical curvatures. MATERIALS AND METHODS:A retrospective study was conducted on 10,000 subjects who underwent cervical sagittal X-ray imaging. Four orthopedic specialists labeled key points, which were cross-validated, and an algorithm was then used to measure C6S and C7S. Pearson correlation coefficients were calculated to assess the relationship between C6S and C7S, and linear regression derived a predictive equation for C7S. Various machine learning models were compared with improve C7S prediction accuracy. RESULTS:The average angles for C6S and C7S were 15.4° (16.8° in males, 14.7° in females) and 19.1° (21.1° in males, 18.2° in females), respectively, with C7S generally larger than C6S, except in Sigmoid 1 curvature. Males exhibited higher values for both C6S and C7S, and both slopes increased after age 20. Both angles increased significantly with age from 20 to 90 years. A strong positive correlation was found between C6S and C7S ( r >0.75, P <0.001), confirmed by linear regression ( R2 ​​​​​​=0.688). Among the machine learning models, both Ridge regression and linear regression performed better than the others, with R2 =0.855 in predicting C7S. CONCLUSION:The strong correlation between C6S and C7S suggests that C6S can substitute for C7S when visibility is limited. Machine learning models further enhance prediction accuracy, demonstrating promising clinical potential.

Automated Optical Inspection (AOI) technology is crucial for industrial defect detection but struggles with shadows and surface reflectivity, resulting in false positives and missed detections, especially on non-planar parts. To address these issues, a novel defect detection technique based on deep learning and photometric stereo vision was proposed, along with the creation of the Metal Surface Defect Dataset (MSDD). The proposed Stroboscopic Illuminant Image Acquisition (SIIA) method uses a specially arranged illuminant setup and a Taylor Series Channel Mixer (TSCM) to blend multi-angle illumination images into pseudo-color images. This approach enables end-to-end defect detection using universal object detectors. The method involves mapping color space transformations to spatial domain transformations and utilizing hue randomization for data augmentation. Four object detection methods (FCOS, YOLOv5, YOLOv8, and RT-DETR) were validated on the MSDD, achieving an mAP of 86.1%, surpassing traditional methods. The MSDD includes 138,585 single-channel images and 9,239 mixed images, covering eight defect types. This dataset is essential for automated visual inspection of metal surfaces and is freely accessible for research purposes.

患者,男,59岁,2022年9月14日无明显诱因出现腰痛伴左大腿外侧及小腿外侧麻木、疼痛,左足背麻木.症状逐渐加重,2022年9月21日出现右大腿外侧及小腿外侧麻木、疼痛,双下肢疼痛剧烈,难以入睡,行走约20m后双下肢麻木、疼痛加重,休息后未见明显缓解,疼痛视觉模拟评分为9分.于2022年9月28日入院.查体:腰椎生理曲度可,活动度严重受限.腰骶部压痛明显,未引出放射痛.双下肢皮肤感觉正常,双下肢肌力V级,肌张力正常.

Introduction: Magnetic Resonance Imaging (MRI) is essential in diagnosing cervical spondylosis, providing detailed visualization of osseous and soft tissue structures in the cervical spine. However, manual measurements hinder the assessment of cervical spine sagittal balance, leading to time-consuming and error-prone processes. This study presents the Pyramid DBSCAN Simple Linear Iterative Cluster (PDB-SLIC), an automated segmentation algorithm for vertebral bodies in T2-weighted MR images, aiming to streamline sagittal balance assessment for spinal surgeons.Method: PDB-SLIC combines the SLIC superpixel segmentation algorithm with DBSCAN clustering and underwent rigorous testing using an extensive dataset of T2-weighted mid-sagittal MR images from 4,258 patients across ten hospitals in China. The efficacy of PDB-SLIC was compared against other algorithms and networks in terms of superpixel segmentation quality and vertebral body segmentation accuracy. Validation included a comparative analysis of manual and automated measurements of cervical sagittal parameters and scrutiny of PDB-SLIC’s measurement stability across diverse hospital settings and MR scanning machines.Result: PDB-SLIC outperforms other algorithms in vertebral body segmentation quality, with high accuracy, recall, and Jaccard index. Minimal error deviation was observed compared to manual measurements, with correlation coefficients exceeding 95%. PDB-SLIC demonstrated commendable performance in processing cervical spine T2-weighted MR images from various hospital settings, MRI machines, and patient demographics.Discussion: The PDB-SLIC algorithm emerges as an accurate, objective, and efficient tool for evaluating cervical spine sagittal balance, providing valuable assistance to spinal surgeons in preoperative assessment, surgical strategy formulation, and prognostic inference. Additionally, it facilitates comprehensive measurement of sagittal balance parameters across diverse patient cohorts, contributing to the establishment of normative standards for cervical spine MR imaging.

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm’s development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

目的:探讨原发性骨质疏松症(POP)经金天格胶囊治疗后的骨代谢变化.方法:采用随机数字表法将 98例POP患者分为对照组、观察组,各 49 例.对照组给予钙尔奇 D3、骨化三醇,观察组在对照组基础上给予金天格胶囊.两组均持续治疗 6 个月.比较两组治疗 6 个月后的临床疗效,治疗前、治疗 6 个月后的疼痛程度、中医症状积分、骨密度值、骨代谢指标、生活质量评分以及治疗期间的用药安全性.结果:治疗 6 个月后,观察组总有效率为95.92%,高于对照组的 79.59%(P＜0.05).治疗 6 个月后,两组疼痛视觉模拟评分(VAS)、各项中医证候积分、血清碱性磷酸酶(ALP)水平均低于治疗前,且观察组低于对照组,差异有统计学意义(均P＜0.05).治疗 6 个月后,两组腰椎 L2-4、股骨颈的骨密度T值、血钙(Ca)及骨钙素(OC)水平均高于治疗前,且观察组高于对照组,差异有统计学意义(均P＜0.05).治疗 6 个月后,两组简明健康状况量表(SF-36)评分高于治疗前;治疗 3 个月后,观察组高于对照组,差异有统计学意义(均 P＜0.05).两组治疗期间的不良反应总发生率比较,差异无统计学意义(P＞0.05).结论:金天格胶囊治疗POP可缓解临床症状,促进骨密度、骨代谢指标改善,使患者疼痛程度得以缓解,有效提高生活质量,具有较高的临床疗效,且安全性良好.

目的 探究益气化瘀方结合神经妥乐平对脊髓型颈椎病术后患者神经功能恢复、生活质量及机体氧化应激反应的影响.方法 选取2017年6月—2019年12月因脊髓型颈椎病在北京中医药大学东直门医院行颈椎前路人工间盘置换术的患者68例,随机分为对照组和观察组各34例.对照组术后采用神经妥乐平口服治疗,观察组术后采用神经妥乐平和益气化瘀方口服治疗,2组均连续治疗1个月.记录2组患者术前及术后2周、1个月、3个月、6个月、1年的颈椎JOA评分、生活质量NDI评分,于治疗前和治疗1个月后采用酶联免疫吸附法测定患者血清丙二醛(MDA)、总抗氧化能力(T-AOC)、去甲肾上腺素(NE)及皮质醇(Cor)水平.结果 2组患者术后各时间段颈椎JOA评分均明显高于术前(P均<0.05);2组患者术前、术后2周、术后1个月、术后3个月颈椎JOA评分比较差异均无统计学意义(P均>0.05),观察组患者术后3个月、6个月、1年的颈椎JOA评分均明显高于对照组(P均<0.05).2组患者术后各时间段NDI评分均明显低于术前(P均<0.05);观察组患者术后2周、1个月、3个月的NDI评分均明显低于对照组(P均<0.05);术后6个月和术后1年,2组NDI评分比较差异均无统计学意义(P均>0.05).治疗1个月后,观察组血清MDA、NE及Cor水平均明显低于对照组,T-AOC水平明显高于对照组,差异均有统计学意义(P均<0.05).结论 益气化瘀方联合神经妥乐平治疗,在早期可通过消炎止痛、减轻氧化应激反应作用,减轻脊髓型颈椎病术后患者疼痛,保护神经,改善患者生活质量,在长期疗效中能够显著促进患者术后神经功能恢复.

目的:基于网络药理学分析黄芪桂枝五物汤治疗颈椎病的分子生物学机制.方法:通过中药系统药理数据库和分析平台、疾病靶点数据库分别获取黄芪桂枝五物汤的药物成分、成分靶点及颈椎病疾病靶点并获取交集靶点,使用STRING数据库对交集靶点构建蛋白质相互作用关系网络并获取关键靶点,应用Cytoscape软件分别构建化合物靶点网络图及蛋白质互相作用关系(protein-protein interaction,PPI)网络,通过DAVID平台进行基因本体功能(gene ontology,GO)和通路分析.结果:筛选出中药活性成分41个,交集靶点187个,其中PPI网络分析中排名靠前的靶点包括丝氨酸/苏氨酸蛋白激酶(RAC serine/threonine-protein kinase,AKT)1、肿瘤坏死因子(tumor necrosis factor,TNF)、表皮生长因子受体(epidermal growth factor receptor,EGFR)、半胱氨酸蛋白酶(Caspase,CASP)3等;GO及通路富集分析结果,关键蛋白富集在细胞核、细胞质、质膜等位置,参与RNA聚合、DNA复制、药物反应、信号传导等生物过程,发挥蛋白质、酶、转录因子、DNA等生命物质的结合作用,并影响蛋白质异二聚体、转录因子活性.其关键靶点主要通过参与磷脂酰肌醇3-激酶(Phosphatidylinositol-3-kinase,PI3K)/蛋白激酶B(protein kinase B,Akt)信号通路、丝裂原活化蛋白激酶(mitogen-activated protein kinase,MAPK)信号通路、核因子κB(nuclear factor kappa-B,NF-κB)等信号通路对颈椎病进行调控.结论:黄芪桂枝五物汤中槲皮素、β-谷甾醇、山柰酚等化合物发挥核心作用,并参与PI3K-Akt、MAPK、NF-κB等信号通路,发挥治疗颈椎病的作用.

手足综合征(hand-food syndrome,HFS)是一种由化疗药物引起的手足部皮肤不良反应,临床表现为手足皮肤颜色暗沉、水泡、皮肤开裂、脱皮、肿胀等,严重者甚至指甲脱落[1,2].目前HFS发病机制尚不清楚,可能与COX炎症受体、卡培他滨药物累积、酶和转运体代谢有关[3,4].由于发病机制尚未明确,故而没有有效的干预措施.对于发生严重HFS的患者只能予以减药、停药,极大影响了患者治疗进展.中药外用泡洗手足在临床运用中发挥了一定疗效,不仅缓解HFS患者症状,使其治疗过程得以连贯,同时生活质量也得到较大提升[5].

The mechanism of radiotherapy or chemotherapy-induced oral mucositis is not yet clear.And model establishment is needed in further study.In order to summarize the methods of model establishment and make a comparison,literature databases including Web of Science、Pubmed and CNKI were searched for related researches from January 2015 to January 2021. Hamsters, mice, rats,guinea pigs and miniature pigs were chosen to be modeling animals and modeling methods could be classified into: chemotherapy, chemotherapy combined with superficial mucosal irritation, radiotherapy, radiotherapy combined with superficial mucosal irritation and chemoradiotherapy. Advantages and disadvantages had been analyzed in this study to provide reference for following studies.

目的:运用网络药理学方法探讨身痛逐瘀汤治疗腰椎间盘突出症(LDH)的作用机制.方法:利用中药系统药理学数据库与分析平台(TCMSP)、化学专业数据库获取身痛逐瘀汤中各味中药的相关化学成分,于SwissTargetPrediction平台实现活性成分靶点预测;分别于人类基因数据库(GeneCards)、在线人类孟德尔遗传数据库(OMIM)、DisGeNET数据库检索LDH疾病靶点;将中药成分靶点与LDH靶点取交集,即身痛逐瘀汤治疗LDH的潜在作用靶点;应用Cytoscape软件构建身痛逐瘀汤治疗LDH的中药-化学成分-交集靶点网络图;利用String数据库完成蛋白质-蛋白质相互作用分析及关键靶点的筛选,运用Cytoscape构建网络图;通过DAVID平台对关键靶点进行基因本体(GO)富集分析和京都基因和基因组百科全书(KEGG)富集分析,初步揭示其作用机制.以髓核细胞作为实验研究对象,分为单纯压力组与中药组(加入身痛逐瘀汤中药血清干预),置于静水压加载装置,1 MPa加压干预下作用2、4、6 h,应用凋亡试剂盒检测髓核细胞的凋亡情况,通过蛋白质印迹法检测髓核细胞核因子κB p65、基质金属蛋白酶-13(MMP-13)、胱天蛋白酶3蛋白的表达情况.结果:筛选得到身痛逐瘀汤121个活性成分和871个作用靶点,LDH疾病相关靶点438个,身痛逐瘀汤治疗LDH的潜在作用靶点75个,关键作用靶点69个;GO及KEGG富集分析显示关键靶点主要通过参与癌症通路、促分裂原活化的蛋白激酶信号通路、磷脂酰肌醇-3-激酶-蛋白激酶B信号通路、肿瘤坏死因子信号通路、核因子κB信号通路等信号通路对LDH进行调控.身痛逐瘀汤可降低髓核细胞凋亡,降低核因子κB p65、MMP-13、胱天蛋白酶3的蛋白表达水平(P<0.05).结论:身痛逐瘀汤通过多成分、多靶点、多通路治疗LDH,其作用机制可能与核因子κB信号通路的抑制以及细胞外基质的分解代谢相关.

脊髓型颈椎病是临床常见的颈椎退行性病变疾患,其症状较重且常随病情进展而加重,因此需及时手术治疗,目前多采用减压融合术,但术后部分患者仍残留脊髓神经受损症状,西医目前尚无有效方法,而中医近年相关研究取得满意效果,能有效改善患者术后残留症状,并加速其脊髓神经功能康复.本文就脊髓型颈椎病术后脊髓神经功能康复的中医药治疗研究进行综述.

目的 挖掘中医治疗恶性肿瘤放化疗性口腔黏膜炎的用药规律.方法 通过检索中国知网、万方数据库、维普网自建库至2019年7月中医治疗放化疗性口腔黏膜炎的文献,使用Excel、R-studio、SPSS 20.0等工具,对纳入药物进行关联规则分析及因子分析.结果 符合纳入标准的方剂共75首,中药155味,使用频数≥10的药物共11味,以苦寒、甘寒药为主;对这11味中药进行关联规则分析发现提升度较高组合有14对,对去除甘草的频数≥10的10味中药进行关联规则分析,发现提升度均较高;因子分析提取4个公因子.结论 中医治疗放化疗性口腔黏膜炎用药以清热解毒、养阴生津、活血生肌止痛为主.

目的 运用网络药理学的方法探讨青娥丸治疗PMO可能的分子作用机制.方法 运用TCMSP、ETCM、SymMap数据库及文献挖掘获取青娥丸主要活性成分,通过SwissTargetPrediction平台进行成分靶标预测;经GeneCards、OMIM和DisGeNET数据库获取PMO相关靶点,与成分靶点取交集,获得青娥丸治疗PMO的潜在作用靶点;应用Cytoscape软件构建青娥丸治疗PMO的中药-活性成分-交集靶点网络图,使用String数据库及Cytoscape软件对交集靶点进行蛋白质相互作用网络分析,依据节点度值筛选关键靶点;通过DAVID平台对关键靶点进行GO和KEGG富集分析,以探究青娥丸治疗PMO的分子作用机制.结果 筛选出青娥丸活性成分40个,包括飞燕草素、槲皮素、山奈酚等核心成分;青娥丸治疗PMO的潜在作用靶点178个;对交集靶点进行蛋白质互相作用网络分析获取关键靶点68个,包括MAPK1、AKT1、PIK3CA、JAK2等核心靶点;关键靶点基因的GO及KEGG富集分析显示:关键靶标主要在质膜、膜筏、细胞核等位置发挥作用,通过一氧化氮生物合成过程的正调控、信号转导、蛋白质磷酸化等生物过程,发挥激酶活性、与蛋白质结合、与蛋白激酶结合等功能.关键靶点主要通过参与癌症通路、PI3K-Akt信号通路、HIF-1信号通路、以及雌激素、催乳素、甲状腺素类信号通路对PMO进行调控.结论 青娥丸中飞燕草素、槲皮素、山奈酚等核心成分,可能通过参与PI3K-Akt、HIF-1、雌激素、催乳素等信号通路,作用于MAPK1、AKT1、PIK3CA、JAK2等基因靶点,调节骨代谢,治疗PMO.

放化疗性口腔黏膜炎是放化疗的一种常见的不良反应,其发病机制通过五个阶段模型进行阐述,其中包括一系列生物学信号的传导,尚未完全明确.目前缺乏较为理想的药物治疗方法.中药作为多靶点治疗方法,多个临床研究已证实其治疗效果.目前对于中药治疗口腔黏膜炎的作用机制研究体现在调节炎症因子,抑制炎症反应,包括下调促炎细胞因子、调节抗炎因子的水平;抗氧化应激损伤,包括上调抗氧化剂、降低过氧化产物的水平;促进黏膜修复,促进生长因子的分泌、加速黏膜修复细胞迁移;改善口腔环境,表现为抑制口腔的酸性环境;抑制细菌生长,包括抗革兰阴性菌活性、防止微生物感染;止痛,包括减轻机械性疼痛超敏反应及自发痛等方面.但多数研究仍局限于单一的作用机制,并未从多层面、多靶点进行研究.对日本汉方药Hangeshashinto的研究从抗炎、抗菌、抗氧化、止痛、促进修复多方面进行阐述,较为充分体现了中药的多靶点作用机制.该文对中药的作用机制研究进行阐述,为进一步的研究提供参考.

目的 分析食管癌前病变反流性食管炎(RE)及Barrett食管的舌象转化规律.方法 基于食管癌高发区人群筛查,通过倾向性评分匹配方法以1∶2分别匹配轻度、中重度反流性食管炎及Barrett食管人群,匹配因素包括人群一般特征、生活习惯及相关疾病,采用多因素Logistic回归及倾向性评分匹配方法(PSM)匹配后舌象特征分布规律及食管炎癌转化过程中舌象特征变化规律.结果 经过一般特征、生活习惯、相关疾病PSM匹配,组间各变量间均衡可比(|d| ＞0.25),匹配后组间各变量之间基本均衡,PS进一步校正通过多因素Logistic回归分析,轻度RE与灰黑苔[OR=1.540 (1.120,2.119)]、绛舌[OR=1.529 (1.028,2.275)]、暗红舌[OR=1.291 (1.128,1.479)]、紫舌[OR=1.532 (1.237,1.896)]明显相关;中重度RE与紫舌[OR=1.681 (1.068,2.648)]及灰黑苔[OR=4.719(1.858,11.987)]明显相关;Barrett食管与紫舌[OR =3.447 (1.206,9.853)]相关. 结论 紫舌是RE向Barrett食管转变的危险舌象.

Depression is associated with poorer outcomes in a wide spectrum of surgeries but the specific effects of depression in patients undergoing cervical spine surgery are unknown. This study aimed to evaluate the prevalence and impact of pre-surgical clinical depression on pain and other outcomes after surgery for cervical degenerative disc disease using a national representative database. Data of patients with cervical myelopathy and radiculopathy were extracted from the 2005-2014 US Nationwide Inpatient Sample (NIS) database. Included patients underwent anterior discectomy and fusion (ACDF). Acute or chronic post-surgical pain, postoperative complications, unfavorable discharge, length of stay (LOS) and hospital costs were evaluated. Totally 215,684 patients were included. Pre-surgical depression was found in 29,889 (13.86%) patients, with a prevalence nearly doubled during 2005-2014 in the US. Depression was independently associated with acute or chronic post-surgical pain (aOR: 1.432), unfavorable discharge (aOR: 1.311), prolonged LOS (aOR: 1.152), any complication (aOR: 1.232), respiratory complications/pneumonia (aOR: 1.153), dysphagia (aOR: 1.105), bleeding (aOR: 1.085), infection/sepsis (aOR: 1.529), and higher hospital costs (beta: 1080.640) compared to non-depression. No significant risk of delirium or venous thrombotic events was observed in patients with depression as compared to non-depression. Among patients receiving primary surgery, depression was independently associated with prolonged LOS (aOR: 1.150), any complication (aOR:1.233) and postoperative pain (aOR:1.927). In revision surgery, no significant associations were found for prolonged LOS, any complication or pain. In conclusion, in the US patients undergoing ACDF, pre-surgical clinical depression predicts post-surgical acute or chronic pain, a slightly prolonged LOS and the presence of any complication. Awareness of these associations may help clinicians stratify risk preoperatively and optimize patient care.