血液系统恶性肿瘤患者化疗后合并中性粒细胞减少性肠炎的诊治研究

目的分析成人血液系统恶性肿瘤患者接受强烈化疗后中性粒细胞减少性肠炎（NE）的发生率、危险因素及预后情况。方法收集2004至2013年接受化疗的1804例血液系统恶性肿瘤患者，记录患者血常规、凝血检测和血液生化检测结果，并记录患者年龄、性别、原发病、既往化疗次数、既往化疗方案中是否使用阿糖胞苷、临床症状、肠壁厚度、中性粒细胞最低计数、中性粒细胞缺乏持续时间、NE的治疗方法和预后等，探讨NE起病诱因、临床特征、腹部B超特点、症状的预后意义及化疗药物对发病的影响等。结果1804例患者中226例（12.5%）化疗后合并NE，化疗后10~19d起病，中位起病时间为化疗后第14天。发生NE后26例患者死亡，病死率11.5%。化疗药物包括阿糖胞苷、临床症状≥4项、中性粒细胞缺乏持续超过7d以及B超下肠壁厚度≥10mm的患者病死率相对较高。结论NE是接受强烈化疗的血液系统肿瘤患者的严重的并发症，发生NE后患者病死率较高。     

凉血通瘀方干预高血压大鼠急性脑出血模型对脑组织中差异miRNA表达的影响＊

目的 研究凉血通瘀方对高血压大鼠急性脑出血模型脑组织miRNA表达的影响，对差异表达的miRNA靶基因进行分析，探索凉血通瘀方可能的药效机制。方法 将自发性高血压大鼠随机分成对照组（B）和实验组（C）。适应性饲养一周后，C组灌胃凉血通瘀方，B组灌胃等体积生理盐水，连续5天，每天1次。构建脑出血模型后收集脑组织，借助全转录组测序技术获得miRNA表达量，与miRBase数据库比对获取已知miRNA，使用miRDeep2预测新miRNA。差异分析软件为DESeq2，筛选阈值为|log₂FC| ≥1 并且P <0.05。对显著差异表达的miRNA进行靶基因预测，对靶基因进行GO功能、KEGG通路富集和PPI网络分析。结果 实验组和对照组对比，共发现21个显著差异表达的miRNA，上调有9个，下调有12个，共预测得到1243个有统计学意义的靶基因。GO富集分析发现，生物过程中突触囊泡分泌的调节、神经递质分泌的调节和神经递质运输的调节占前三位，神经元投射终点、全膜、质膜区域和细胞投射则是主要的细胞成分。分子功能分别为小GTPase绑定、底物特异性跨膜转运蛋白活性和离子跨膜转运体活性。通路分析结果显示，靶基因在癌证通路、pI3K-Akt信号通路、人类乳头瘤病毒感染、神经活性配体-受体相互作用和MAPK通路等分布广泛。采用STRING网站和Cytoscape软件，根据MCC算法筛选出ADRA2C、CASR、CCL28、CCR1、DRD2、GNAT3、GRM2、DYNC1LI1、GABBR1、GNAI1等核心靶基因。结论 凉血通瘀方对脑出血急性期鼠脑组织内miRNA的表达有重要影响；显著差异表达miRNAs可能通过靶向核心基因调控凉血通瘀方干预急性脑出血的病理过程及预后。     

Improving Prenatal Diagnosis of Coarctation of the Aorta

Background

The purpose of the study was to evaluate the association between fetal echocardiographic measurements and the need for intervention (primary coarctation repair, staged coarctation repair, or catheter intervention) in prenatally diagnosed coarctation of the aorta.

电针复合TCI靶控输注在单肺通气食管癌开胸手术麻醉中的应用与安全性＊

目的 探讨电针复合靶控输注（Target Controlled Infusion， TCI）在单肺通气食管癌开胸手术麻醉中的应用价值。方法 选取于我院拟行单肺通气食管癌开胸切除手术患者60例作为研究对象。随机将其分为试验组与对照组，对照组30例采取气管插管全麻及TCI靶控输注维持麻醉深度，试验组30例则在对照组方案基础上辅助电针麻醉，记录两组患者手术麻醉时间、药物用量、苏醒时间、并发症；以及术前（T_Ⅰ）、插管前即刻（T_Ⅱ）、插管后1 min（T_Ⅲ）、切皮即刻（T_Ⅳ）、去骨时（T_Ⅴ）、拔管即刻（T_Ⅵ）时平均动脉压（MAP）、平均心率（HR）、BIS值；术前、术后1天、术后3天简易智能精神状态检查量表（MMSE）；并于麻醉诱导前（T₀）、手术开始2 h（T₁）、术后1天（T₂）、术后3天（T₃）时抽取患者外周静脉血检测IL-1β、IL-6、IL-10、TNF-α浓度。结果 试验组手术用时、麻醉时间略低于对照组，但差异无统计学意义（P>0.05），试验组异丙酚、舒芬太尼用量以及苏醒时间均明显低于对照组（P<0.05）；T_Ⅱ时两组平均动脉压（Mean Arterial Pressure， MAP）、心率（Heartrate， HR）较术前明显降低，且试验组MAP明显低于对照组（P<0.05），但两组T_Ⅱ时HR比较无显著差异（P>0.05）；T_Ⅲ、T_Ⅵ时对照组MAP、HR明显高于T_Ⅰ时，而试验组MAP、HR与T_Ⅰ比较无显著差异（P>0.05）。术后1天、3天试验组简易智能精神状态检查量表（Mini-Mental State Examination， MMSE）评分低于对照组，有显著性差异（P<0.05）。T₁、T₂、T₃时试验组白介素-1β（IL-1β）、白介素-6（IL-6）、肿瘤坏死因子（Tumor Necrosis Factor-α， TNF-α）水平明显低于对照组（P<0.05），白介素-10（IL-10）水平明显高于对照组（P<0.05）。结论 采用电针复合TCI靶控输注麻醉方案可有效提升单肺通气食管癌开胸手术麻醉效果，能够降低患者术后认知功能障碍发生风险。     

Low potential for evolutionary rescue from climate change in a tropical fish

Climate change is increasing global temperatures and intensifying the frequency and severity of extreme heat waves. How organisms will cope with these changes depends on their inherent thermal tolerance, acclimation capacity, and ability for evolutionary adaptation. Yet, the potential for adaptation of upper thermal tolerance in vertebrates is largely unknown. We artificially selected offspring from wild-caught zebrafish (Danio rerio) to increase (Up-selected) or decrease (Down-selected) upper thermal tolerance over six generations. Selection to increase upper thermal tolerance was also performed on warm-acclimated fish to test whether plasticity in the form of inducible warm tolerance also evolved. Upper thermal tolerance responded to selection in the predicted directions. However, compared to the control lines, the response was stronger in the Down-selected than in the Up-selected lines in which evolution toward higher upper thermal tolerance was slow (0.04 ± 0.008 °C per generation). Furthermore, the scope for plasticity resulting from warm acclimation decreased in the Up-selected lines. These results suggest the existence of a hard limit in upper thermal tolerance. Considering the rate at which global temperatures are increasing, the observed rates of adaptation and the possible hard limit in upper thermal tolerance suggest a low potential for evolutionary rescue in tropical fish living at the edge of their thermal limits.

Globally, both mean and extreme environmental temperatures are increasing due to climate change with mean temperatures predicted to increase by 0.3–4.8 °C by the end of the century (1, 2). Aquatic ectotherms are particularly vulnerable to rising temperatures as their body temperature closely tracks the environmental temperature (3). These organisms can avoid thermal stress by migrating to cooler waters, acclimating, and/or adapting genetically (4–6). For species with a limited dispersal ability (e.g., species from shallow freshwater habitats; ref. 7), acclimation and evolutionary adaptation are the only possible strategies. Furthermore, for ectotherms living at the edge of their upper thermal limits, an increase in extreme temperatures may generate temperature peaks that exceed physiological limits and cause high mortality (5, 8–10). Although this is expected to cause strong selection toward higher upper thermal tolerance, it is largely unknown, particularly within vertebrates, whether and at what rate organisms may adapt by evolving their thermal limits (11–14). These are important issues because constrained or limited evolvability (15) of upper thermal tolerance could lead to population extinctions as climate change increases the severity of heat waves.Ectotherms can also increase their thermal limits through physiological and biochemical adjustments, in a process known as thermal acclimation when they are exposed to elevated temperatures for a period of time (16, 17). Thermal acclimation, sometimes called thermal compensation, is here used interchangeably with the term physiological plasticity as outlined by Seebacher et al. (18). In the wild, individuals may experience days or weeks of warmer temperatures prior to a thermal extreme. Through physiological plasticity, the severity of an ensuing thermal extreme may be reduced, thus increasing the chance for survival (19). Furthermore, in some cases, adaptation can be accelerated by plasticity (20–22). This requires that the physiological mechanisms responsible for acclimation are also (at least partly) involved in the acute response; that is, that there is a positive genetic correlation between physiological plasticity and (acute) upper thermal tolerance. It is therefore crucial to quantify the evolutionary potential of upper thermal tolerance of fish populations threatened by climate change (23, 24) and to understand the link between the evolutionary response of upper thermal tolerance and physiological plasticity.Previously detected evolution of upper thermal tolerance generally points toward a slow process (12, 13, 25–31). However, estimates of the evolutionary potential in upper thermal tolerance mostly come from studies on Drosophila (12, 25, 27, 32), and empirical evidence in aquatic ectotherms and specifically vertebrates is limited. The few studies that have been performed on fish show disparate responses to selection on heat tolerance even within the same species. Baer and Travis (33) detected no response to selection yet Doyle et al. (34) and Klerks et al. (28) detected selection responses with heritabilities of 0.2 in killifish (Heterandria formosa). Despite the typical asymmetry of thermal performance curves (3, 35), studies in vertebrates are limited to unidirectional estimates of evolutionary potential (28, 31, 33) or do not account for the direction of evolution when estimating heritability in upper thermal tolerance from breeding designs (36, 37). Furthermore, while several studies have found that populations with different thermal histories have evolved different levels of heat tolerance (29–31), we still lack a good understanding of how physiological plasticity within a generation, in response to a short heat exposure, interacts with genetic changes during evolution of thermal tolerance.To investigate possible asymmetry in the evolutionary potential of upper thermal tolerance in a vertebrate species, we artificially selected offspring of wild-caught zebrafish (Danio rerio) to increase and decrease upper thermal tolerance for six generations. Furthermore, to disentangle the contribution of acclimation from the genetic response to increase upper thermal tolerance, we selected two lines that were exposed to a period of warm acclimation prior to a thermal challenge. The size (>20,000 phenotyped fish) and duration (six generations) of this study are unique in a vertebrate species for a climate change-relevant selection experiment, and the results provide critical and robust information on how tropical fish may adapt to a changing climate.Being a freshwater and tropical species, zebrafish are likely to be especially vulnerable to climate change (7, 38). In the wild, zebrafish can already be found living only a few degrees below their thermal limits (17, 39) and live in shallow streams and pools (40) that have the potential to rapidly warm during heat waves. Zebrafish therefore represent a species living at the edge of its thermal limit in which rapid adaptation of thermal tolerance would be particularly beneficial for its survival. Wild-caught zebrafish originating from different sites in West Bengal, India (17, 40), were used to maximize the genetic diversity of the parental population. These wild-caught zebrafish (n = 2,265) served as parents of the starting F₀ generation (n = 1,800) on which we selected upper thermal tolerance for six generations. Upper thermal tolerance was measured as the critical thermal maximum (CT_max), a commonly used measure of an organism’s acute upper thermal tolerance (16, 41). CT_max is defined as the temperature at which an individual loses equilibrium (i.e., uncontrolled and disorganized swimming in zebrafish; ref. 42) during thermal ramping. Measuring CT_max is rapid, repeatable, and does not appear to harm zebrafish (42). CT_max is ecologically relevant because it is highly correlated with both tolerance to slow warming (43) and to the upper temperature range boundaries of wild aquatic ectotherms (9).Our selection experiment consisted of four treatment groups (Up-selected, Down-selected, Acclimated Up-selected, and Control) with two replicate lines in each treatment. We established these lines by selecting fish on their CT_max in the F₀ generation with each line consisting of 150 individuals (see Methods for further details of F₀ generation). The offspring of those fish formed the F₁ generation that consisted of 450 offspring in each line. At each generation, the Up, Down, and Control lines were all held at optimal temperature (28 °C) (39), whereas the Acclimated Up-selected lines were acclimated to a supraoptimal temperature (32 °C) for 2 wk prior to selection (17). From the F₁ to F₆ generations, we measured CT_max for all 450 fish in each line and selected the 33% with the highest CT_max in the Up-selected and in the Acclimated Up-selected lines, and the 33% with the lowest CT_max in the Down-selected lines. In the Control lines, 150 fish were randomly selected, measured, and retained. Thus, CT_max was measured on a total of 3,000 fish per generation and 150 individuals remained in each of the eight lines after selection, forming the parents for the next generation. The nonselected lines (Control) represented a control for the Up-selected and Down-selected lines, while the Up-selected lines represented a control for the Acclimated Up-selected lines, because these two treatments solely differed by the acclimation period to which the latter were exposed before selection. Thus, differences in CT_max between Up-selected and Acclimated Up-selected lines represent the contribution of physiological plasticity to upper thermal tolerance. If the difference between these two treatments increases during selection, it would suggest that plasticity increases during adaptation to higher CT_max (i.e., the slope the reaction norm describing the relationship between CT_max and acclimation temperature would become steeper).After six generations of selection, upper thermal tolerance had evolved in both the Up-selected and the Down-selected lines (Fig. 1). In the Up-selected lines, upper thermal tolerance increased by 0.22 ± 0.05 °C ($\bar{x}$ ± 1 SE) compared to the Control lines whereas the Down-selected lines displayed a mean upper thermal tolerance 0.74 ± 0.05 °C lower than the Control (Fig. 1B; estimates for replicated lines combined). The asymmetry in the response to selection was confirmed by the estimated realized heritability, which was more than twice as high in the Down-selected lines (h² = 0.24; 95% CI: 0.19–0.28) than in the Up-selected lines (h² = 0.10; 95% CI: 0.05–0.14; Fig. 2).Open in a separate windowFig. 1.Upper thermal tolerance (CT_max) of wild-caught zebrafish over six episodes of selection. Duplicated lines were selected for increased (Up-selected, orange lines and triangles) and decreased (Down-selected, blue lines and squares) upper thermal tolerance. In addition, we had two Control lines (green dashed lines and diamonds). The Up, Down, and Control lines were all acclimated to a temperature of 28 °C. In addition, two lines were selected for increased upper thermal tolerance after 2 wk of warm acclimation at 32 °C (Acclimated Up-selected, red lines and circles). At each generation, the mean and 95% CIs of each line are shown (n ∼ 450 individuals per line). (A) Absolute upper thermal tolerance values. (B) The response to selection in the Up and Down lines centered on the Control lines (dashed green line). Difference between Up-selected and Acclimated-Up lines are shown in Fig. 3. The rate of adaptation (°C per generation) is reported for each treatment using estimates obtained from linear mixed effects models using the Control-centered response in the Up-selected and Down-selected lines and the absolute response for the Acclimated-Up lines (SE = ±0.01 °C in all lines).Open in a separate windowFig. 2.Realized heritability (h²) of upper thermal tolerance (CT_max) in wild-caught zebrafish. The realized heritability was estimated for each treatment as the slope of the regression of the cumulative response to selection on the cumulative selection differential using mixed effect models passing through the origin with replicate as a random effect. Slopes are presented with their 95% CIs (shaded area) for the Down-selected lines (blue) and Up-selected lines (orange). Data points represent the mean of each replicate line (n ∼ 450) over six generations of selection. Average selection differentials are 0.57 (Down) and 0.39 (Up), respectively, see SI Appendix, Table S1 for more information.At the start of the experiment (F₀), warm acclimation (32 °C) increased thermal tolerance by 1.31 ± 0.05 °C (difference in CT_max between the Up-selected and Acclimated Up-selected lines in Figs. 1A and ​and3),3), which translates to a 0.3 °C change in CT_max per 1 °C of warming. In the last generation, the effect of acclimation had decreased by 25%, with the Acclimated-Up lines having an average CT_max 0.98 ± 0.04 °C higher than the Up lines (Fig. 3). This suggests that, despite a slight increase in CT_max in the Acclimated Up-selected lines during selection, the contribution of plasticity decreased over the course of the experiment.Open in a separate windowFig. 3.Contribution of acclimation to the upper thermal tolerance in the Acclimated-Up selected lines at each generation of selection. The contribution of acclimation was estimated as the difference between the Up and Acclimated-Up selected lines. Points and error bars represent the estimates (±SE) from a linear mixed effects model with CT_max as the response variable; Treatment (factor with two levels: Up and Acclimated Up), Generation (factor with seven levels), and their interaction as the predictor variables; and replicate line as a random factor.During the experiment, the phenotypic variation of CT_max that was left-skewed at F₀ increased in the Down-selected lines and decreased in the Up-selected lines (Fig. 4). At the F₆ generation, phenotypic variance was four times lower in the Up-selected lines (0.09 ± 0.01 and 0.12 ± 0.02 °C²; variance presented for each replicate line separately and SE obtained by nonparametric bootstrapping) than in the Down-selected lines (0.41 ± 0.03 and 0.50 ± 0.04 °C²), which had doubled since the start of the experiment (F₀: 0.20 ± 0.01 °C², see SI Appendix, Fig. S1). In the Acclimated Up-selected lines, the phenotypic variance that was already much lower than the Control at the F₀ also decreased and reached 0.06 ± 0.01 °C² and 0.07 ± 0.01 °C² for the two replicates at the last generation (SI Appendix, Fig. S1).Open in a separate windowFig. 4.Distribution of upper thermal tolerance (CT_max) in selected lines. (A) Distribution for each line at each generation (F₀ to F₆). In the F₀ generation, histograms show the preselection distribution in gray for the nonacclimated fish, in dark green for the Control lines, and in red for the Acclimated-Up fish. In all subsequent generations the Down-selected lines are in blue, the Up-selected lines in yellow, the Control lines in dark green, and Acclimated-up lines in red. All treatments use two shades, one for each replicate line. Dashed lines represent the mean CT_max for each line (n ∼ 450 individuals). (B) Distribution of upper thermal tolerance at the start (F₀, in gray) and the end (F₆, in blue and yellow) of the experiment for the Up-selected and Down-selected lines. The dashed gray line represents the mean of the Up-selected and Down-selected lines in the F₀ generation preselection (n ∼ 900 individuals). Dashed blue and yellow lines represent the mean CT_max for Up and Down-selected lines for the F₆ generation (n ∼ 450 individuals).Together with the asymmetrical response to selection and the lower response of the Acclimated Up-selected lines, these changes in phenotypic variance suggest the existence of a hard-upper limit for thermal tolerance (e.g., major protein denaturation (44), similar to the “concrete ceiling” for physiological responses to warming (14)). Such a hard-upper limit is expected to generate a nonlinear mapping of the genetic and environmental effects on the phenotypic expression of CT_max. This nonlinearity will affect the phenotypic variance of CT_max when mean CT_max approaches its upper limit (SI Appendix, Fig. S2A). For example, with directional selection toward higher CT_max, genetic changes in upper thermal tolerance will translate into progressively smaller phenotypic changes. Similarly, warm acclimation that shifts CT_max upwards will also decrease phenotypic variation in CT_max (see differences in phenotypic variance between control and Acclimated lines at the F₀). This hard ceiling can also explain why an evolutionary increase in CT_max reduces the magnitude of physiological plasticity in CT_max achieved after a period of acclimation (Fig. 3 and see SI Appendix, Fig. S2B). If the sum of the genetic and plastic contributions to CT_max cannot exceed a ceiling value, this should generate a zero-sum gain between the genetic and plastic determinants of thermal tolerance. An increase in the genetic contribution to CT_max via selection should thus decrease the contribution of plasticity. Selection for a higher CT_max should therefore negatively affect the slope of the reaction norm of thermal acclimation because acclimation will increase CT_max more strongly at low than high acclimation temperature (SI Appendix, Fig. S2B).To test this hypothesis, we measured CT_max in all selected lines at the final generation (F₆) after acclimation to 24, 28, and 32 °C. At all three acclimation temperatures, the Acclimated-Up lines did not differ from the Up-selected lines (average difference 0.14 ± 0.08 °C; 0.12 ± 0.09 °C; 0.14 ± 0.09 °C; at 24, 28, and 32 °C respectively; Fig. 5). This suggests that warm acclimation prior to selection did not affect the response to selection. However, considering the within-treatment differences in CT_max between fish acclimated to 28 and 32 °C, we show that the gain in CT_max due to acclimation decreases in both the Up and Acclimated-Up treatments compared to the Control and Down treatments (SI Appendix, Fig. S3). This confirms a loss of thermal plasticity in both Up-selected treatments (Up and Acclimated-Up) at higher acclimation temperatures. Notably, the loss of thermal plasticity is not evident in fish acclimated to 24 and 28 °C, possibly because at these temperatures CT_max remains further away from its hard upper limit.Open in a separate windowFig. 5.Upper thermal tolerance (CT_max) of the selected lines measured at the last generation (F₆) after acclimation at 24, 28, and 32 °C. The response is calculated as the mean difference in upper thermal tolerance (CT_max) relative to the Control lines. Large points and whiskers represent mean ±1 SE for each treatment (n = 120 individuals): Up-selected (orange triangles), Down-selected (blue squares), Acclimated Up-selected (red circles), and Control (green diamonds). Smaller translucent points represent means of each replicate line (n = 60 individuals). See SI Appendix, Fig. S3 for absolute CT_max values and model estimates.Acclimated Up-selected lines are perhaps the most ecologically relevant in our selection experiment. In the wild, natural selection on upper thermal tolerance may not result from increasing mean temperatures but through rapid heating events such as heat waves (45). During heat waves, temperature may rise for days before reaching critical temperatures. This gives individuals the possibility to acclimate and increase their upper thermal tolerance prior to peak temperatures. Our results show that while warm acclimation allowed individuals to increase their upper thermal tolerance, it did not increase the magnitude or the rate of adaptation of upper thermal tolerance.For the past two decades it has been recognized that rapid evolution, at ecological timescales, occurs and may represent an essential mechanism for the persistence of populations in rapidly changing environments (24, 46, 47). Yet, in the absence of an explicit reference, rates of evolution are often difficult to categorize as slow or rapid (48). For traits related to thermal tolerance or thermal performance, this issue is complicated by the fact that the scale on which traits are measured (temperature in °C) cannot meaningfully be transformed to a proportional scale. This prevents us from comparing rates of evolution between traits related to temperature with other traits measured on different scales (49, 50). However, for thermal tolerance, the rate of increase in ambient temperature predicted over the next century represents a particularly meaningful standard against which the rate of evolution observed in our study can be compared.In India and surrounding countries where zebrafish are native, heat waves are predicted to increase in frequency, intensity, and duration, and maximum air temperatures in some regions are predicted to exceed 44 °C in all future climate scenarios (51). Air temperature is a good predictor of water temperature in shallow ponds and streams where wild zebrafish are found (17, 40, 52, 53). Thus, strong directional selection on the thermal limits of zebrafish is very likely to occur in the wild. At first sight, changes in the upper thermal tolerance observed in our study (0.04 °C per generation) as well as the heritability estimates (Down-selected: h² = 0.24, Up-selected: h² = 0.10) similar to those obtained in fruit flies (Drosophila melanogaster) selected for acute upper thermal tolerance (Down-selected: h² = 0.19, Up-selected: h² = 0.12; ref. 12), suggest that zebrafish may just be able to keep pace with climate change and acutely tolerate temperatures of 44 °C predicted by the end of the century. However, several cautions make such an optimistic prediction unlikely.First, such an extrapolation assumes a generation time of 1 y, which is likely for zebrafish but unrealistic for many other fish species. Second, such a rate of evolution is associated with a thermal culling of two-thirds of the population at each generation, a strength of selection that may be impossible to sustain in natural populations exposed to other selection pressures such as predation or harvesting. Third, the heritability and rate of adaptation toward higher upper thermal tolerance observed here may be considered as upper estimates because of the potentially high genetic variance harbored by our parental population where samples from several sites were mixed. While mixing of zebrafish populations often occurs in the wild during monsoon flooding (54, 55), there are likely to be some isolated populations that may have a lower genetic diversity and adaptation potential than our starting population. Finally, and most importantly, the reduced phenotypic variance and decreased acclimation capacity with increasing CT_max observed in our study suggest the existence of a hard-upper limit to thermal tolerance that will lead to an evolutionary plateau similar to those reached in Drosophila selected for increased heat resistance over many generations (12, 56). Overall, the rate of evolution observed in our study is likely higher than what will occur in the wild and, based on this, it seems unlikely that zebrafish, or potentially other tropical fish species, will be able to acutely tolerate temperatures predicted by the end of the century. It is possible that other fish species, especially those living in cooler waters and with wider thermal safety margins, will display higher rates of adaptation than the ones we observed here, and more studies of this kind in a range of species are needed to determine whether slow adaptation of upper thermal tolerance is a general phenomenon.Transgenerational plasticity (e.g., epigenetics) has been suggested to modulate physiological thermal tolerance (57). However, the progressive changes in CT_max observed across generations in our study indicate that these changes were primarily due to genetic changes because effects of transgenerational plasticity are not expected to accumulate across generations. Therefore, the effects of transgenerational plasticity in the adaptation of upper thermal tolerance may be insufficient to mitigate impacts of climate change on zebrafish, yet the potential contribution of transgenerational plasticity is still an open question.By phenotyping more than 20,000 fish over six generations of selection, we show that evolution of upper thermal tolerance is possible in a vertebrate over short evolutionary time. However, the evolutionary potential for increased upper thermal tolerance is low due to the slow rate of adaptation compared to climate warming, as well as the diminishing effect of acclimation as adaptation progresses. Our results thus suggest that fish populations, especially warm water species living close to their thermal limits, may struggle to adapt with the rate at which water temperatures are increasing.     

Biomechanical behavior of brain injury caused by sticks using finite element model and Hybrid-III testing

Objective: To study the biomechanical mechanism of head injuries beaten with sticks, which is common in the battery or assaultive cases. Methods: In this study, the Hybrid-III anthropomorphic test device and finite element model (FEM) of the total human model for safety (THUMS) head were used to determine the biomechanical response of head while being beaten with different sticks. Total eight Hybrid-III tests and four finite element simulations were conducted. The contact force, resultant acceleration of head center of gravity, intracranial pressure and von Mises stress were calculated to determine the different biomechanical behavior of head with beaten by different sticks. Results: In Hybrid-III tests, the stick in each group demonstrated the similar kinematic behavior under the same loading condition. The peak values of the resultant acceleration for thick iron stick group, thin iron stick group, thick wooden stick group and thin wooden stick group were 203.4 g, 221.1 g, 170.5 g and 122.2 g respectively. In finite element simulations, positive intracranial pressure was initially observed in the frontal comparing with negative intracranial pressure in the contra-coup site. Subsequently the intracranial pressure in the coup site was decreasing toward negative value while the contra-coup intracranial pressure increasing toward positive values. Conclusions: The results illustrated that the stiffer and larger the stick was, the higher the von Mises stress, contact force and intracranial pressure were. We believed that the results in the Hybrid-III tests and THUMS head simulations for brain injury beaten with sticks could be reliable and useful for better understanding the injury mechanism.     

H型高血压患者舌面诊图像颜色特征参数分析＊

目的 分析H型高血压患者的舌面诊图像颜色参数特征，探讨H型高血压患者的舌诊、面诊变化规律。方法 运用上海中医药大学自行研制的Smart TCM-1型中医舌面一体仪，采集高血压患者舌面诊图像，提取特征参数，分析健康对照组、H型高血压组与非H型高血压组患者舌面颜色参数特征。结果 ①在舌色各项参数中，H型高血压组舌尖部R值、B值、V值均显著小于健康对照组（P < 0.01）；非H型高血压组舌尖部B值显著小于健康对照组（P < 0.01），S值较健康对照组显著增大（P < 0.05）；H型高血压组舌尖部R、V值均明显小于非H型高血压组（P < 0.05）。在舌苔各项参数中，H型高血压组舌中H值、V值均明显小于健康对照组（P < 0.05）；非H型高血压组舌中V值、舌右V值均显著小于健康对照组（P < 0.01）；H型高血压组舌中H值明显小于非H型高血压组（P < 0.05），右侧舌苔S值明显大于非H型高血压组（P < 0.05）。②H型高血压组面色参数鼻G值、下颌G值、口唇R值、口唇V值均明显小于健康对照组（P < 0.05）；非H型高血压组前额H值、目眶H值、脸颊H值、鼻H值、下颌H值、整体H值均明显大于健康对照组（P < 0.05）；H型高血压组前额H值、目眶G值、目眶H值、脸颊H值、鼻G值、鼻H值、下颌R值、下颌G值、下颌H值、下颌V值、口唇R值、口唇G值、口唇V值、整体R值、整体G值、整体H值、整体V值均明显小于非H型高血压组（P < 0.05）。结论 H型高血压患者苔色偏黄，以舌中部为主，且舌右侧黄苔积聚较明显；H型高血压患者面色为黄中带红，口唇、下颌部更为晦暗。H型高血压患者的舌、面诊特征参数的变化，与高血压病阳亢湿盛病机相符。