深部热疗配合输卵管通水术治疗继发性不孕症的疗效研究

目的:探析对继发性不孕症患者行以深部热疗配合输卵管通水术治疗的疗效。方法:于2018年1月~2019年1月择取某院收治的40例继发性不孕症患者,按照数字奇偶法将其分成对照组和研究组,对照组20例患者予以输卵管通水术治疗,研究组20例患者予以输卵管通水术配合深部热疗治疗,对照分析两组临床效果。结果:从临床总有效率上来看,研究组高于对照组(P<0.05);治疗前两组TNF-α、IL-6对比无统计学差异,治疗后研究组TNF-α、IL-6均优于对照组(P<0.05)。结论:对继发性不孕症患者来说,深部热疗配合输卵管通水术治疗的效果显著。     

Electrophysiological differences between upper and lower limb movements in the human subthalamic nucleus

ObjectiveFunctional processes in the brain are segregated in both the spatial and spectral domain. Motivated by findings reported at the cortical level in healthy participants we test the hypothesis in the basal ganglia of Parkinson’s disease patients that lower frequency beta band activity relates to motor circuits associated with the upper limb and higher beta frequencies with lower limb movements.MethodsWe recorded local field potentials (LFPs) from the subthalamic nucleus using segmented “directional” DBS leads, during which patients performed repetitive upper and lower limb movements. Movement-related spectral changes in the beta and gamma frequency-ranges and their spatial distributions were compared between limbs.ResultsWe found that the beta desynchronization during leg movements is characterised by a strikingly greater involvement of higher beta frequencies (24–31 Hz), regardless of whether this was contralateral or ipsilateral to the limb moved. The spatial distribution of limb-specific movement-related changes was evident at higher gamma frequencies.ConclusionLimb processing in the basal ganglia is differentially organised in the spectral and spatial domain and can be captured by directional DBS leads.SignificanceThese findings may help to refine the use of the subthalamic LFPs as a control signal for adaptive DBS and neuroprosthetic devices.     

Structures at Risk From an Intermetatarsal Screw for Lapidus Bunionectomy: A Cadaveric Study

The Lapidus bunionectomy is performed to treat hallux valgus. Recurrence of the deformity remains a concern. A transverse intermetatarsal screw spanning the base of the first metatarsal to the base of the second can increase stability. The neurovascular bundle is located within the proximity of this screw. In this study, we assessed the structures at risks with the use of this technique. In 10 specimens, a guide wire was placed, and a 4.0-mm cannulated screw was inserted. The neurovascular bundle was dissected and inspected for direct trauma to the neurovascular bundle, and the proximity of the screw was measured using a digital caliper. Ten cadaveric specimens were used. The dorsalis pedis artery and deep peroneal nerve were free from injury in 9 of 10 specimens. In those 9 specimens, the neurovascular bundle was located dorsal in relation to the screw. The mean distance of the screw to the neurovascular bundle was 7.1 ± 3.3 mm. The mean distance from the screw to the first tarsometatarsal joint (TMTJ) was 14.7 ± 4.3 mm. The mean distance from the screw as it entered the second metatarsal to the second TMTJ was 18.0 ± 7.2 mm. In 1 specimen, the screw was found to be traversing through the neurovascular bundle. The distance from the screw to the first TMTJ was 15.0 mm. The distance of the screw from where it entered the second metatarsal to the second TMTJ was 24.0 mm. Although the intermetatarsal screw avoided the neurovascular cases in most instances, there is some anatomic risk to the neurovascular bundle. Further study is warranted to evaluate clinical results using the intermetatarsal screw for the modified Lapidus procedure.     

Nationwide Trends in Catheter-Directed Therapy Utilization for the Treatment of Lower Extremity Deep Vein Thrombosis in Medicare Beneficiaries

PurposeTo determine overall and provider specialty trends in the use of catheter-directed therapy for lower extremity deep vein thrombosis (DVT) treatment in the Medicare population.Materials and MethodsUsing data obtained from 2007–2017 Centers for Medicare & Medicaid Services 5% research identifiable files, all claims associated with acute and chronic lower extremity DVT were identified. The annual volume of 2 services—venous percutaneous transluminal thrombectomy (current procedural terminology [CPT] code 37187) and venous infusion for thrombolysis (CPT code 37201 from 2007 to 2012 and CPT code 37212 from 2013 to 2017)—was examined for trends in DVT intervention. Utilization rates based on region and the place of service were calculated. The results were further categorized based on primary operator type (radiology, cardiology, surgery, and other).ResultsThe total number of DVT interventions increased over time, with 4.27 service counts per 100,000 beneficiaries in 2007 increasing to 13.4 by 2017, a growth rate of 12.09%. Radiologists performed the majority of interventions each year, except in 2013, in which they performed 46.6% of interventions, whereas surgeons and cardiologists combined performed the other 53.4%. In 2017, radiologists performed 7.56 services per 100,000 beneficiaries, which was 56.8% of the total count, more than those performed by surgeons, cardiologists, and unspecified providers combined.ConclusionsCatheter-directed therapy is increasingly being used for the treatment of DVT, with its use undergoing a nearly 12-fold increase from 2007 to 2017 in the Medicare population. Radiologists remained the dominant provider of these services throughout the majority of study period, with a relative reduction in market share from 72% in 2007 to 57% in 2017.     

Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PurposeTo show that a deep learning (DL)–based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the “ground truth segmentation” than a conventional thresholding-based model.Materials and MethodsThis post hoc analysis included 36 patients with a diagnosis of hepatocellular carcinoma or other solid liver tumors who underwent conventional transarterial chemoembolization with an intraprocedural cone-beam CT. Semiautomatic segmentation of Lipiodol was obtained. Subsequently, a convolutional U-net model was used to output a binary mask that predicted Lipiodol deposition. A threshold value of signal intensity on cone-beam CT was used to obtain a Lipiodol mask for comparison. The dice similarity coefficient (DSC), mean squared error (MSE), center of mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist-segmented Lipiodol deposits) to obtain accuracy metrics for the 2 masks. These results were used to compare the model versus the threshold technique.ResultsFor all metrics, the U-net outperformed the threshold technique: DSC (0.65 ± 0.17 vs 0.45 ± 0.22, P < .001) and MSE (125.53 ± 107.36 vs 185.98 ± 93.82, P = .005). The difference between the CM predicted and the actual CM was 15.31 mm ± 14.63 versus 31.34 mm ± 30.24 (P < .001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22 ± 0.84 versus 2.58 ± 3.52 (P = .048) for the current model’s prediction and threshold technique, respectively.ConclusionsThis study showed that a DL framework could detect Lipiodol in cone-beam CT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intraprocedurally.     

Efficacy and safety of cold snare polypectomy for sessile serrated polyps ≥ 10 mm: A systematic review and meta-analysis

BackgroundCold snare polypectomy (CSP) is a promising technique for the removal of sessile serrated polyps (SSPs) ≥ 10 mm. However, the efficacy and safety of this technique remain undetermined.AimsWe aimed to comprehensively evaluate the efficacy and safety of CSP for SSPs ≥ 10 mm.MethodsPubMed, EMBASE, Web of Science and Cochrane Library were searched up to January 2021.ResultsA total of 10 studies consisting of 1727 SSPs (range, 10–40 mm) from 1021 patients were included. The overall rates of technical success, adverse events (AEs) and residual SSPs were 100%, 0.7% and 2.9%, respectively. Subgroup analysis showed that the rates of technical success and AEs were comparable between CSP and cold endoscopic mucosal resection (EMR) (99.9% vs. 100% and 1.3% vs. 0.5%, respectively), between the proximal and distal colon (100% vs. 99.9% and 0.3% vs. 0, respectively), and between polyps of 10–19 mm and ≥20 mm (99.8% vs. 100% and 0.9% vs. 0, respectively). However, subgroup analysis showed that the rate of residual SSPs was slightly lower in CSP compared with cold EMR (1.3% vs. 3.9%), as well as in polyps of 10–19 mm compared with those ≥20 mm (3.1% vs. 4.7%).ConclusionCSP was an effective and safe technique for removing SSPs ≥ 10 mm.     

基于交叉对比神经网络的心音分类

目的:通过交叉对比神经网络（CCNN）实现心音信号的自动分类，从而对心血管疾病进行早期诊断。方法:实验基于PhysioNet/Cinc 2016心音数据库。训练集和测试集数据来自互斥的健康受试者/病理患者，并以4:1的比例进行划分，输入CCNN。CCNN利用深度卷积神经网络进行特征提取，结合基于信息的相似度度量理论（IBS），对特征向量间的相似性进行度量并分类。结果:实验结果得出灵敏度为0.834 6，特异性为0.962 3，最终大赛综合得分为0.898 5。结论:CCNN使用交叉对比的输入模式扩充数据量，引入信号间的对比信息，同时在神经网络的训练过程中应用统计学思想，使网络具备良好的泛化性，更加适应医学数据量较少的场景，在心音分类中取得较好的结果。     

双侧深低温停循环顺行性脑灌注对主动脉弓替换术患者脑损伤的影响

目的 探讨双侧深低温停循环(Deep hypothermia and circulatory arrest,DHCA)+顺行性脑灌注(Anterograde cerebral perfusion,ACP)对主动脉弓替换术患者脑损伤的影响。方法 选择2018年1月-2020年1月本院实施主动脉弓替换术的DebakeyⅠ型主动脉夹层患者96例,根据不同的脑保护技术将患者分为单侧ACP组和双侧ACP组,各48例; 2组均进行DHCA,单侧ACP组经右侧腋动脉进行单侧ACP,双侧ACP组经右侧腋动脉和左颈总动脉进行双侧ACP。结果 全部患者痊愈出院,出院前CT复查显示主动脉弓和升主动脉的人工血管血流畅通,无人工血管扭曲和造影剂渗漏等状况。双侧ACP组术后短暂性脑损伤发生率为2.08%(1/48),明显低于单侧ACP组的16.67%(8/48)(P<0.05)。双侧ACP组术后苏醒时间为(13.18±3.42)h,明显短于单侧ACP组的(16.98±4.18)h(P<0.05)。体外循环开始后2组血清神经元特异性烯醇化酶(Neuron-specific enolase,NSE)、中枢神经特异性蛋白 100-β亚型(Specific protein 100-β,S100-β)水平均逐渐升高,且在体外循环结束时达到峰值,随后开始降低; 脑灌注5min后双侧ACP组血清NSE,S-100β水平明显低于单侧ACP组(P<0.05)。结论 双侧深低温停循环顺行性脑灌注对主动脉弓替换术患者脑损伤的影响程度更小,其机制可能是通过降低血清NSE,S-100β水平,进而有效保护脑组织。     

AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16 h−1Mpc and the HR halo mass function to within 10% down to 1011 M⊙. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

As telescopes and satellites become more powerful, observational data on galaxies, quasars, and the matter in intergalactic space becomes more detailed and covers a greater range of epochs and environments in the Universe. Our cosmological simulations (see, e.g., ref. 1) must also become more detailed and more wide-ranging in order to make predictions and test the effects of different physical processes and different dark-matter candidates. Even with supercomputers, we are forced to decide whether to maximize either resolution or volume, or else compromise on both. These limitations can be overcome through the development of methods that leverage techniques from the artificial intelligence (AI) revolution (see, e.g., ref. 2) and make superresolution (SR) simulations possible. In the present work, we begin to explore this possibility, combining knowledge and existing superscalable codes for petascale-plus cosmological simulations (3) with machine learning (ML) techniques to effectively create representative volumes of the Universe that incorporate information from higher-resolution models of galaxy formation. Our first attempts, presented here, involve simulations with dark matter and gravity only, and extensions to full hydrodynamics will follow. This hybrid approach, which will imply offloading simulations to neural networks (NNs) and other ML algorithms, has the promise to enable the prediction of quasar, supermassive black hole, and galaxy properties in a way that is statistically identical to full hydrodynamic models, but with a significant speed-up.Adding details to images below the resolution scale (SR image enhancement) has become possible with the latest advances in deep learning (DL; ML with NN; ref. 4), including generative adversarial networks (GANs; ref. 5). The technique has applications in many fields, from microscopy to law enforcement (6). It has been used for observational astronomical images by (7), to recover galaxy features from below the resolution scale in degraded Hubble Space Telescope images. Besides SR image enhancement, DL has started to find applications in cosmological simulations. For example, refs. 8 and 9 showed how NNs can predict the nonlinear formation of structures given simple linear theory predictions. NN models have also been trained to predict galaxies (10, 11) and 21-cm emission from neutral hydrogen (12) from simulations that only contain dark matter. GANs have been used in ref. 13 to generate image slices of cosmological models and to generate dark-matter halos from density fields (14). ML techniques other than DL find many applications, too. For example, Kamdar et al. (15) have applied extremely randomized trees to dark-matter simulations to predict hydrodynamic galaxy properties.Generating mocks for future sky surveys requires large volumes and high accuracy, a task that quickly becomes computationally prohibitive. To alleviate the cost, recently, Dai and Seljak (16) developed a Lagrangian-based parametric ML model to predict various hydrodynamical outputs from the dark-matter density field. In other work, Dai et al. (17, 18) sharpened the particle distribution using a potential gradient descent method starting from low-resolution (LR) simulations. Note, however, that these approaches did not aim to enhance the spatial or mass resolution of a simulation.On the DL side, recently, Ramanah et al. (19) explored using the SR technique to map density fields of LR cosmological simulations to that of the high-resolution (HR) ones. While the goal is similar, our work has the following three key differences. First, instead of focusing on the dark-matter density field, we aim to enhance the number of particles and predict their displacements, from which the density fields can be inferred. This approach allows us to preserve the particle nature of the N-body simulations and therefore to interpret the SR outputs as simulations themselves. Second, we test our technique at a higher SR ratio. Compared to ref. 19, which increased the number of Eulerian voxels by 8 times, we increase the number of particles and thus the mass resolution by a factor of 512. Finally, to facilitate future applications of SR on hydrodynamic simulations in representative volumes, we test our method at much smaller scales and in large simulations whose volume is much bigger than that of the training data.     

直肠癌淋巴结转移的深度神经网络评判研究进展

正目前,临床上对于直肠癌常用的影像评估方法有MRI、螺旋CT、PET-CT、直肠腔内超声(ERUS)等。而MRI作为首选检查方式,对肿瘤位置、浸润深度、淋巴结转移、血管侵犯、环周切缘及周围器官侵犯等方面的评估均具有明显优势~([1-2])。通过MRI诊断淋巴结的方法通常是影像科医师逐层浏览每一幅图像,从中识别淋巴结的形状、界限及密度来判断,这种传统方式耗时较长且存在主观偏倚,导致