微创治疗方法在输尿管上段结石中应用进展

输尿管上段结石为临床中常见泌尿系结石类型之一,如不能及时诊治,可引起重度积水、泌尿系感染,甚至脓毒血症,对患者肾功能、健康造成严重影响。随着微创治疗技术在泌尿系结石中应用,微创治疗方法能降低对患者造成治疗性创伤,降低相关并发症发生率,促进患者康复,了解临床中微创治疗输尿管上段结石方法,对临床中合理治疗输尿管上段结石有重要价值。     

Magnetic resonance elastography with guided pressure waves

Magnetic resonance elastography aims to non-invasively and remotely characterize the mechanical properties of living tissues. To quantitatively and regionally map the shear viscoelastic moduli in vivo, the technique must achieve proper mechanical excitation throughout the targeted tissues. Although it is straightforward, ante manibus, in close organs such as the liver or the breast, which practitioners clinically palpate already, it is somewhat fortunately highly challenging to trick the natural protective barriers of remote organs such as the brain. So far, mechanical waves have been induced in the latter by shaking the surrounding cranial bones. Here, the skull was circumvented by guiding pressure waves inside the subject's buccal cavity so mechanical waves could propagate from within through the brainstem up to the brain. Repeatable, reproducible and robust displacement fields were recorded in phantoms and in vivo by magnetic resonance elastography with guided pressure waves such that quantitative mechanical outcomes were extracted in the human brain.     

补中益气汤对于肝再生线粒体能量代谢的研究概况＊

肝再生的机制非常复杂，线粒体功能障碍所引起的能量供给不足是影响因素之一，但其机理亟待研究。严重肝损害时肝细胞ATP供应减少、线粒体能量代谢异常，导致肝再生受到抑制。补中益气汤为李东垣所创，其具补中益气、升阳举陷之功，有实验证实补中益气汤具有保护线粒体功能、增加线粒体能量代谢的作用，从而促进肝再生。本文综述补中益气汤总方与其中各类中药对线粒体能量代谢的保护作用，从而为促进肝再生提供新的治疗手段并对改善病人预后有重要意义。     

双源CT多定量参数对胃癌的诊断价值及与HER2的相关性

目的:探讨门脉期双源CT多个定量参数与胃腺癌病理分化程度及HER2的相关性。方法: 回顾性分析2018年7月至2019年4月间于陕西省人民医院行双源CT双能量扫描的48例经胃镜活检(21例)或手术病理证实（27例）的胃腺癌及30例正常胃的影像学资料，其中27例HER2指标明确，通过西门子第二代双源CT扫描获得静脉期双能量图像，利用syngo.via软件获得曲线斜率、门脉期碘浓度、标准化碘浓度；将患者分为胃腺癌与正常胃壁组，高、中、低分化胃腺癌组，HER2阳性组（+，++，+++）与HER2阴性组（-）。统计学方法采用Kappa一致性检验、ROC曲线法、两独立样本t检验及方差分析。结果:活检与术后病理结果具有较强的一致性（Kappa系数为0.701），两者无明显差异；胃腺癌与正常胃壁两组间能谱曲线斜率（1.35±0.24、2.19±0.71）及标准化碘浓度（0.31±0.079、0.54±0.157）均具有统计学意义（P<0.05），曲线下面积分别为0.992、0.919；低分化、中分化及高分化胃腺癌能谱曲线斜率值（3.07±0.67，2.63±0.57，2.01±0.39）组间及组内差异均具有统计学意义（P<0.05），低分化、中分化及高分化胃腺癌门脉期标准化碘浓度(0.60±0.167，0.52±0.089，0.36±0.039)组间差异具有统计学意义（P<0.05），中分化组与低分化组差异无统计学意义（P>0.05），高分化组与中、低分化组均具有统计学差异（P<0.05）。HER2阳性组与阴性组的能谱曲线斜率及标准化碘浓度值无统计学差异（P>0.05）。结论:能谱曲线斜率及门脉期标准化碘浓度值有助于对胃腺癌进行诊断并推测病理分化程度；双源CT定量参数与免疫组化指标HER2无相关性。     

北京地区3859名体检人群骨密度调查及骨量异常患病率分析

目的调查北京地区健康体检人群骨密度的情况以及骨量减少和骨质疏松的患病率,为骨质疏松症的防治提供参考。方法选择2017年1月至2018年12月在中日友好医院健康体检中心进行健康体检的人群,排除继发性骨质疏松症及其他影响骨代谢的因素,共3859名。其中男性2067名,女性1792名。年龄20~83岁,平均年龄(51.29±11.18)岁,按性别及年龄每10年一组。采用美国GE公司的LUNAR Prodigy双能X线骨密度仪测量受试者腰椎1~4正位及股骨颈和全髋的骨密度。分析各组不同部位骨密度情况及骨量异常(包括骨量减少和骨质疏松)的患病率。采用SPSS 22.0统计软件进行分析,以P<0.05为差异有统计学意义。结果①男性腰椎1~4骨密度峰值在20~29岁,股骨颈和全髋骨密度峰值在30~39岁。女性各部位骨密度峰值均在30~39岁。②随年龄增长,男性和女性骨量异常患病率均呈上升趋势,50岁以上女性骨量异常患病率显著上升,明显高于同年龄组男性。③30~59岁男性和女性腰椎骨量异常患病率均明显高于髋部;70岁以上男性和60岁以上女性髋部骨量异常患病率明显高于腰椎。结论中老年人群尤其是绝经后女性是骨质疏松症的高危人群;老年人群的骨质疏松筛查可以考虑选择髋部骨密度为主。     

From the Cover: Communication consumes 35 times more energy than computation in the human cortex,but both costs are needed to predict synapse number

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843–1864 (1960)] and [J. Sawada, D. S. Modha, “Synapse: Scalable energy-efficient neurosynaptic computing” in Application of Concurrency to System Design (ACSD) (2013), pp. 14–15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a 108-fold discrepancy between biological and lowest possible values of a neuron’s computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

The purpose of the brain is to process information, but that leaves us with the problem of finding appropriate definitions of information processing. We assume that given enough time and given a sufficiently stable environment (e.g., the common internals of the mammalian brain), then Nature’s constructions approach an optimum. The problem is to find which function or combined set of functions is optimal when incorporating empirical values into these function(s). The initial example in neuroscience is ref. 1, which shows that information capacity is far from optimized, especially in comparison to the optimal information per joule which is in much closer agreement with empirical values. Whenever we find such an agreement between theory and experiment, we conclude that this optimization, or near optimization, is Nature’s perspective. Using this strategy, we and others seek quantified relationships with particular forms of information processing and require that these relationships are approximately optimal (1–7). At the level of a single neuron, a recent theoretical development identifies a potentially optimal computation (8). To apply this conjecture requires understanding certain neuronal energy expenditures. Here the focus is on the energy budget of the human cerebral cortex and its primary neurons. The energy audit here differs from the premier earlier work (9) in two ways: The brain considered here is human not rodent, and the audit here uses a partitioning motivated by the information-efficiency calculations rather than the classical partitions of cell biology and neuroscience (9). Importantly, our audit reveals greater energy use by communication than by computation. This observation in turn generates additional insights into the optimal synapse number. Specifically, the bits per joule optimized computation must provide sufficient bits per second to the axon and presynaptic mechanism to justify the great expense of timely communication. Simply put from the optimization perspective, we assume evolution would not build a costly communication system and then not supply it with appropriate bits per second to justify its costs. The bits per joule are optimized with respect to N, the number of synaptic activations per interpulse interval (IPI) for one neuron, where N happens to equal the number of synapses per neuron times the success rate of synaptic transmission (below).To measure computation, and to partition out its cost, requires a suitable definition at the single-neuron level. Rather than the generic definition “any signal transformation” (3) or the neural-like “converting a multivariate signal to a scalar signal,” we conjecture a more detailed definition (8). To move toward this definition, note two important brain functions: estimating what is present in the sensed world and predicting what will be present, including what will occur as the brain commands manipulations. Then, assume that such macroscopic inferences arise by combining single-neuron inferences. That is, conjecture a neuron performing microscopic estimation or prediction. Instead of sensing the world, a neuron’s sensing is merely its capacitive charging due to recently active synapses. Using this sampling of total accumulated charge over a particular elapsed time, a neuron implicitly estimates the value of its local latent variable, a variable defined by evolution and developmental construction (8). Applying an optimization perspective, which includes implicit Bayesian inference, a sufficient statistic, and maximum-likelihood unbiasedness, as well as energy costs (8), produces a quantified theory of single-neuron computation. This theory implies the optimal IPI probability distribution. Motivating IPI coding is this fact: The use of constant amplitude signaling, e.g., action potentials, implies that all information can only be in IPIs. Therefore, no code can outperform an IPI code, and it can equal an IPI code in bit rate only if it is one to one with an IPI code. In neuroscience, an equivalent to IPI codes is the instantaneous rate code where each message is IPI−1. In communication theory, a discrete form of IPI coding is called differential pulse position modulation (10); ref. 11 explicitly introduced a continuous form of this coding as a neuron communication hypothesis, and it receives further development in ref. 12.Results recall and further develop earlier work concerning a certain optimization that defines IPI probabilities (8). An energy audit is required to use these developments. Combining the theory with the audit leads to two outcomes: 1) The optimizing N serves as a consistency check on the audit and 2) future energy audits for individual cell types will predict N for that cell type, a test of the theory. Specialized approximations here that are not present in earlier work (9) include the assumptions that 1) all neurons of cortex are pyramidal neurons, 2) pyramidal neurons are the inputs to pyramidal neurons, 3) a neuron is under constant synaptic bombardment, and 4) a neuron’s capacitance must be charged 16 mV from reset potential to threshold to fire.Following the audit, the reader is given a perspective that may be obvious to some, but it is rarely discussed and seemingly contradicts the engineering literature (but see ref. 6). In particular, a neuron is an incredibly inefficient computational device in comparison to an idealized physical analog. It is not just a few bits per joule away from optimal predicted by the Landauer limit, but off by a huge amount, a factor of 108. The theory here resolves the efficiency issue using a modified optimization perspective. Activity-dependent communication and synaptic modification costs force upward optimal computational costs. In turn, the bit value of the computational energy expenditure is constrained to a central limit like the result: Every doubling of N can produce no more than 0.5 bits. In addition to 1) explaining the 108 excessive energy use, other results here include 2) identifying the largest “noise” source limiting computation, which is the signal itself, and 3) partitioning the relevant costs, which may help engineers redirect focus toward computation and communication costs rather than the 20-W total brain consumption as their design goal.     

Impact of Food Intake on Liver Stiffness Determined by 2-D Shear Wave Elastography: Prospective Interventional Study in 100 Healthy Patients

The aim was to evaluate the influence of food intake on liver stiffness measurement (LSM), performed with 2-D shear wave elastography (Logiq E9, GE Medical Systems, Wauwatosa, WI, USA). One hundred healthy volunteers were prospectively enrolled. Mean age was 25.8 (19–55) y, and mean body mass index was 22.43 (17.3–30.8) kg/m². Patients fasted for at least 3 h and subsequently ingested a liquid meal of 800 kcal. Liver stiffness and portal vein velocity were measured before and after food intake. Food intake resulted in significantly higher LSM values compared with baseline LSM (5.74 ± 0.94 kPa vs. 4.80 ± 0.94 kPa, p < 0.001). On multiple linear regression analysis, body mass index was significantly positively correlated with the LSM increase after food intake (p?=?0.01). No correlation between the increase in LSM and the increase in post-prandial portal vein velocity was observed (r?=?0.09). In summary, food intake has a significant influence on LSM. There is an 11% risk of misclassifying non-fasting, healthy patients as having significant fibrosis.