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1,334 results for “Hematology”
Figure 1 in Blood cells and some hematological parameters of red drum (Linnaeus, 1766) in Vietnam
Figure 1. Erythrocytes of red drum. a - location L1, b - location L2, c - location L3. The red arrow indicates the deformed erythrocyte nuclear/uneven nuclear-matter distribution. The blue arrow indicates the slightly alkaline erythrocytes.
A Phase 1/2 Study of CPI-0610 With and Without Ruxolitinib in Patients With Hematologic and Myeloproliferative Malignancies
ClinicalTrials.gov study NCT02158858. IPD Sharing: YES. Countries: 9. Publications: 3.
Basket Study to Assess Efficacy, Safety and PK of Iptacopan (LNP023) in Autoimmune Benign Hematological Disorders
ClinicalTrials.gov study NCT05086744. IPD Sharing: YES. Countries: 6. Publications: 1.
Phase 1/2 Dose Escalation and Efficacy Study of Anti-CD38 Monoclonal Antibody in Patients With Selected CD38+ Hematological Malignancies
ClinicalTrials.gov study NCT01084252. IPD Sharing: YES. Countries: 17. Publications: 2.
Data from: Hematological parameters vary with life history stage in the pale-breasted thrush (Turdus leucomelas)
<p>The avian life cycle is composed by a progressive sequence of life history stages (LHS). Changes in energy expenditure and exposure to stressors at different LHS require corresponding changes in behavior, physiology, and morphology. Variation in hematological parameters, such hematocrit (Hct), hemoglobin (Hb), and heterophil to lymphocyte ratio (H/L ratio), can have permissive, stimulatory, and preparative actions to help maintain homeostasis through different LHS. Few studies have examined differences in these parameters among different LHS in free-living birds, with most of them restricted to temperate zones. We collected blood samples and measured hematological parameters every week for over a year from a population of a common resident bird species in southeastern Brazil, the pale–breasted thrush (Turdus leucomelas). Hematocrit and hemoglobin concentration were highest during the onset of the reproduction and lowest during molt. Furthermore, H/L ratios were higher at the end of the reproduction, indicating that the breeding season could be the most stressful period of the year for this population of thrushes. There was no difference between sexes for any hematological parameter at any LHS. These results show that there is a permissive physiological effect for Hct and Hb to facilitate LHS transitions and that reproduction could be the most stressful event for this species. Lastly, these results mirror those from temperate species despite distinct environmental differences between these regions.</p>
Basophils as a predictive hematological biomarker of canine visceral leishmaniasis in the treatment with miltefosine
<p><strong>BACKGROUND:</strong> Basophils are initiators of the Th2 response related to the progression of canine visceral leishmaniasis (CanL). Miltefosine has been presented as an alternative in treatment due to its leishmanicide and immunomodulatory effects. Many blood cells have been used as predictive biomarkers, but none of them focused on the immunomodulatory action of miltefosine in the different stages of the disease.</p> <p><strong>OBJECTIVE:</strong> Identifying the predictive hematological biomarkers in dogs with visceral leishmaniasis treated with miltefosine.</p> <p><strong>METHODS:</strong> The animals were divided into three groups: sick dogs; infected dogs, dogs exposed. The animals were submitted to differential blood cell count and CanL diagnosis, through DPP, ELISA and qPCR. The groups were monitored from the time zero (T0) before treatment, and the times twenty (T20) and thirty (T30) days after the beginning of treatment using miltefosine at a dose of 1mL/10kg per day for 28 days.</p> <p><strong>FINDINGS:</strong> Basophils showed an exponential increase in the infected and sick group with an increase of IgG, suggesting a Th2 response. In the exposed group there was reduction of basophils with increase in monocytes and IgG reduction, suggesting a Th1 response.</p> <p><strong>CONCLUSIONS:</strong> We suggest basophils as a predictive biomarker of immunomodulation in the treatment of CanL with miltefosine.</p> <p><strong>BACKGROUND:</strong> Basophils are initiators of the Th2 response related to the progression of canine visceral leishmaniasis (CanL). Miltefosine has been presented as an alternative in treatment due to its leishmanicide and immunomodulatory effects. Many blood cells have been used as predictive biomarkers, but none of them focused on the immunomodulatory action of miltefosine in the different stages of the disease.</p> <p><strong>OBJECTIVE:</strong> Identifying the predictive hematological biomarkers in dogs with visceral leishmaniasis treated with miltefosine.</p> <p><strong>METHODS:</strong> The animals were divided into three groups: sick dogs; infected dogs, dogs exposed. The animals were submitted to differential blood cell count and CanL diagnosis, through DPP, ELISA and qPCR. The groups were monitored from the time zero (T0) before treatment, and the times twenty (T20) and thirty (T30) days after the beginning of treatment using miltefosine at a dose of 1mL/10kg per day for 28 days.</p> <p><strong>FINDINGS:</strong> Basophils showed an exponential increase in the infected and sick group with an increase of IgG, suggesting a Th2 response. In the exposed group there was reduction of basophils with increase in monocytes and IgG reduction, suggesting a Th1 response.</p> <p><strong>CONCLUSIONS:</strong> We suggest basophils as a predictive biomarker of immunomodulation in the treatment of CanL with miltefosine.</p>
Lung microbiome in children with hematological malignancies and lower respiratory tract infections
<p><strong>Background</strong>: Respiratory infectious complications remain a major cause of morbidity and mortality in children with hematological malignancies. Knowledge regarding the lung microbiome in the aforementioned children is limited.</p> <p><strong>Methods</strong>: A prospective cohort was conducted, enrolling 16 children with hematological malignancies complicated with moderate to severe lower respiratory tract infections(LRTIs), versus 21 LRTIs children with age, gender, weight, infection severity matched, with no underlying malignancies, to evaluate the lung microbiome from bronchoalveolar lavage fluid samples in different groups.</p> <p><strong>Results</strong>: Lung microbiome from children with hematological malignancies and LRTIs showed obviously decreased α and β diversity, increased microbial function in infectious disease:bacteria/parasite, drug resistance:antimicrobial and human pathogenesis than the control group, and significantly reduced proportion of <em>Firmicutes</em>, <em>Bacteroidota</em>, <em>Actinobacteriota</em> , increased <em>Proteobacteria</em> at the phylum level, distinctly elevated <em>Parabacteroides</em>, <em>Klebsiella</em>, <em>Grimontia</em>, <em>Escherichia_Shigella</em>, <em>unclassified_Enterobacteriaceae</em> at the genus level than the control group. Besides, it was revealed that α diversity (Shannon), β diversity (Bray Curtis dissimilarity), Proteobacteria at the phylum level, and <em>unclassified_Enterobacteriaceae</em> and <em>Escherichia_Shigella</em> at the genus level were significantly negatively associated with hospitalization course, whereas <em>Firmicutes</em> at the phylum level was established positively correlated with hospitalization course.</p> <p><strong>Conclusions</strong>: Children with hematological malignancies and LRTIs showed obviously decreased α and β diversity, significantly increased function in infectious disease pathogenesis, antimicrobial drug resistance, and unfavorable environment tolerance. Besides, α diversity (Shannon), β diversity (Bray Curtis dissimilarity), <em>Proteobacteria</em> may be used as negatively correlated predictors for hospitalization course in these children whereas <em>Firmicutes</em> is a positively correlated predictor. </p>
Immune-mediated hematological disease in dogs is associated with alterations of the fecal microbiota: a pilot study
<div> <h3>Background</h3> <p>The dog is the most popular companion animal and is a valuable large animal model for several human diseases. Canine immune-mediated hematological diseases, including immune-mediated hemolytic anemia (IMHA) and immune thrombocytopenia (ITP), share many features in common with autoimmune hematological diseases of humans. The gut microbiome has been linked to systemic illness, but few studies have evaluated its association with immune-mediated hematological disease. To address this knowledge gap, 16S rRNA gene sequencing was used to profile the fecal microbiota of dogs with spontaneous IMHA and ITP at presentation and following successful treatment. In total, 21 affected and 13 healthy control dogs were included in the study.</p> <h3>Results</h3> <p>IMHA/ITP is associated with remodeling of fecal microbiota, marked by decreased relative abundance of the spirochete <em>Treponema</em> spp., increased relative abundance of the pathobionts <em>Clostridium septicum</em> and <em>Escherichia coli</em>, and increased overall microbial diversity. Logistic regression analysis demonstrated that <em>Treponema</em> spp. were associated with decreased risk of IMHA/ITP (odds ratio [OR] 0.24–0.34), while Ruminococcaceae UCG-009 and Christensenellaceae R-7 group were associated with increased risk of disease (OR =&thinsp;6.84 [95% CI 2–32.74] and 8.36 [95% CI 1.85–71.88] respectively).</p> <h3>Conclusions</h3> <p>This study demonstrates an association of immune-mediated hematological diseases in dogs with fecal dysbiosis, and points to specific bacterial genera as biomarkers of disease. Microbes identified as positive or negative risk factors for IMHA/ITP represent an area for future research as potential targets for new diagnostic assays and/or therapeutic applications.</p> </div>
Hematological and biochemical profiles, infection and habitat quality in an urban rat population
<p>Dataset employed in the analysis of "Hematological and biochemical profiles, infection and habitat quality in an urban rat population". For details in sampling methodology, see article of same name.</p> <p>Composed of three dataset files, with codebooks associated:</p> <ul> <li>animal_data: morphological and parasitological data of captured individuals</li> <li>hematological_data: data on hematological parameters of captured individuals</li> <li>biochemical_data: data on biochemical markers of captured individuals</li> </ul> <p>All individuals are identified by an individual code (rat_ID), and can be cross-linked through the bases.</p> <p>Files are composed of two sheets</p> <p>-data sheet: original data, with regular labels</p> <p>-codebook: comprehensive codebook for the data sheet.</p>
Table 5 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 5.</i> Model selection of apparent survival probability (Phi) for model set 3: morphology and sex.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc ΔQAICc weights likelihood parameters Deviance</td></tr><tr><th><b>{</b><i>Φ</i> <b>(<i>g</i> + length)} 219.90</b></th><td><b>0.000</b></td><td><b>0.191</b></td><td><b>1.000</b></td><td><b>4</b></td><td><b>211.810</b></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + mass)}</th><td>220.44</td><td>0.544</td><td>0.145</td><td>0.762</td><td>4</td><td>212.354</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + sex + length)}</th><td>220.46</td><td>0.567</td><td>0.144</td><td>0.753</td><td>5</td><td>210.334</td></tr><tr><th>{<i>Φ</i> (<i>g</i>)}</th><td>220.57</td><td>0.672</td><td>0.136</td><td>0.715</td><td>3</td><td>214.517</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + sex)}</th><td>220.83</td><td>0.935</td><td>0.120</td><td>0.627</td><td>4</td><td>212.745</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + sex + mass)}</th><td>221.16</td><td>1.267</td><td>0.101</td><td>0.531</td><td>5</td><td>211.034</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + bd)}</th><td>222.23</td><td>2.330</td><td>0.060</td><td>0.312</td><td>4</td><td>214.140</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + cond)}</th><td>222.45</td><td>2.557</td><td>0.053</td><td>0.278</td><td>4</td><td>214.368</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + girth)}</th><td>222.56</td><td>2.662</td><td>0.050</td><td>0.264</td><td>4</td><td>214.472</td></tr></tbody></table><p><i>Note: g</i> = group (SF, TB, and TMMC), bd = blubber depth, cond = the body condition index. <i>c ͡</i> adjustment = 1.15. Highest ranked model highlighted in bold.</p>
Table 3 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 3.</i> Model selection of apparent survival probability (Phi) for model set 1: group and time.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th><b>{<i>Φ</i> (<i>g</i>)}</b></th><td><b>222.45</b></td><td><b>0.000</b></td><td><b>0.538</b></td><td><b>1.000</b></td><td><b>3</b></td><td><b>49.658</b></td></tr><tr><th>{<i>Φ</i> (SF and</th><td>222.95</td><td>0.501</td><td>0.419</td><td>0.779</td><td>2</td><td>52.184</td></tr><tr><th>TMMC v TB)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (SF and</th><td>227.55</td><td>5.102</td><td>0.042</td><td>0.078</td><td>2</td><td>56.786</td></tr><tr><th>TB v TMMC)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (.)}</th><td>233.43</td><td>10.983</td><td>0.002</td><td>0.004</td><td>1</td><td>64.684</td></tr><tr><th>{<i>Φ</i> (<i>t</i>)}</th><td>254.10</td><td>31.655</td><td>0.000</td><td>0.000</td><td>22</td><td>41.095</td></tr><tr><th>{<i>Φ</i> (<i>g × t</i>)}</th><td>320.74</td><td>98.291</td><td>0.000</td><td>0.000</td><td>66</td><td>0.000</td></tr><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{<i>Φ</i> (mass)}</th><td>101.79</td><td>0.000</td><td>0.220</td><td>1.000</td><td>2</td><td>97.704</td></tr><tr><th>{<i>Φ</i> (T4)}</th><td>102.08</td><td>0.287</td><td>0.190</td><td>0.866</td><td>2</td><td>97.991</td></tr><tr><th>{<i>Φ</i> (.)}</th><td>102.42</td><td>0.625</td><td>0.161</td><td>0.732</td><td>1</td><td>100.387</td></tr><tr><th>{<i>Φ</i> (change in</th><td>102.80</td><td>1.013</td><td>0.133</td><td>0.603</td><td>2</td><td>98.717</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (days in rehab)}</th><td>103.47</td><td>1.679</td><td>0.095</td><td>0.432</td><td>2</td><td>99.383</td></tr><tr><th>{<i>Φ</i> (admit date)}</th><td>103.73</td><td>1.942</td><td>0.083</td><td>0.379</td><td>2</td><td>99.646</td></tr><tr><th>{<i>Φ</i> (logOH)}</th><td>104.38</td><td>2.593</td><td>0.060</td><td>0.273</td><td>2</td><td>100.297</td></tr><tr><th>{<i>Φ</i> (mass per day)}</th><td>104.46</td><td>2.666</td><td>0.058</td><td>0.264</td><td>2</td><td>100.370</td></tr></tbody></table><p><i>Note: t</i> = time, <i>g</i> = group (SF = San Francisco, TB = Tomales Bay, and TMMC = The Marine Mammal Center). <i>c ͡</i> adjustment = 1.15. Highest ranked model highlighted in bold.</p><p><i>Note</i>: T4 = total thryroxine, adjustment = 1.15.</p>
Table 7 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 7.</i> Model selection of apparent survival probability (Phi) for model set 5 incorporating top covariates from previous models.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4)}</th><td>248.8571</td><td>0</td><td>0.37137</td><td>1</td><td>4</td><td>240.7709</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 +</th><td>250.1334</td><td>1.2763</td><td>0.19618</td><td>0.5283</td><td>5</td><td>240.0038</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 +</th><td>250.298</td><td>1.4409</td><td>0.18068</td><td>0.4865</td><td>5</td><td>240.1684</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 +</th><td>251.8598</td><td>3.0027</td><td>0.08275</td><td>0.2228</td><td>6</td><td>239.678</td></tr><tr><th>mass + logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + mass)}</th><td>252.2936</td><td>3.4365</td><td>0.06662</td><td>0.1794</td><td>4</td><td>244.2074</td></tr><tr><th>{<i>Φ</i> (<i>g</i>)}</th><td>252.7458</td><td>3.8887</td><td>0.05314</td><td>0.1431</td><td>3</td><td>246.6942</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + mass +</th><td>254.2608</td><td>5.4037</td><td>0.02491</td><td>0.0671</td><td>5</td><td>244.1312</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logOH)}</th><td>254.3069</td><td>5.4498</td><td>0.02434</td><td>0.0655</td><td>4</td><td>246.2207</td></tr></tbody></table><p><i>Note: g</i> = group (SF, TB, and TMMC), T4 = total thyroxine, OH = PCB+DDT+ PBDE+HCH+CHLD, Phi = survival.</p>
Table 4 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 4.</i> Model selection of apparent survival probability (Phi) for model set 2: lipid weight contaminant classes.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th><b>{<i>Φ</i> (<i>g</i>)}</b></th><td><b>220.57</b></td><td><b>0.000</b></td><td><b>0.226</b></td><td><b>1.000</b></td><td><b>3</b></td><td><b>214.517</b></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logPBDE)}</th><td>221.04</td><td>0.473</td><td>0.179</td><td>0.789</td><td>4</td><td>212.955</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logHCH)}</th><td>221.11</td><td>0.544</td><td>0.172</td><td>0.762</td><td>4</td><td>213.026</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logCHLD)}</th><td>221.67</td><td>1.104</td><td>0.130</td><td>0.576</td><td>4</td><td>213.586</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logPCB)}</th><td>222.06</td><td>1.491</td><td>0.107</td><td>0.475</td><td>4</td><td>213.973</td></tr><tr><th>{<i>Φ</i> (<i>g ×</i> logHCH)}</th><td>222.25</td><td>1.683</td><td>0.098</td><td>0.431</td><td>6</td><td>210.070</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logDDT)}</th><td>222.46</td><td>1.894</td><td>0.088</td><td>0.388</td><td>4</td><td>214.376</td></tr></tbody></table><p><i>Note: g</i> = group (SF, TB, and TMMC), PCB = polychlorinated biphenyls, DDT = summed dichlorodiphenyltrichloroethane and its metabolites, PBDE = polybrominated diphenylethers, CHLD = chlordanes, and HCH = hexachlorocyclohexanes. <i>c ͡</i> adjustment = 1.15. Highest ranked model highlighted in bold.</p>
Table 6 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 6.</i> Model selection of apparent survival probability (Phi) for model set 4: blood variables.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th><b>{<i>Φ</i> (<i>g</i> + T4)}</b></th><td><b>217.45</b></td><td><b>0.000</b></td><td><b>0.481</b></td><td><b>1.000</b></td><td><b>4</b></td><td><b>209.366</b></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 + T3)}</th><td>219.49</td><td>2.033</td><td>0.174</td><td>0.362</td><td>5</td><td>209.356</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T3)}</th><td>220.04</td><td>2.587</td><td>0.132</td><td>0.274</td><td>4</td><td>211.953</td></tr><tr><th>{<i>Φ</i> (<i>g</i>)}</th><td>220.57</td><td>3.116</td><td>0.101</td><td>0.211</td><td>3</td><td>214.517</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + wbc)}</th><td>221.49</td><td>4.040</td><td>0.064</td><td>0.133</td><td>4</td><td>213.406</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + IgG)}</th><td>222.04</td><td>4.589</td><td>0.048</td><td>0.101</td><td>4</td><td>213.955</td></tr></tbody></table><p><i>Note: g</i> = group (SF, TB, and TMMC), T4 = total thyroxine, T3 = triiodothyronine, WBC = white blood cell count, IgG = total immunoglobulin. <i>c ͡</i> adjustment = 1.15. Highest ranked model highlighted in bold.</p>
Table 1 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 1.</i> Mean and range for covariate values by group (SF = San Francisco, TB = Tomales Bay and TMMC = The Marine Mammal Center). The geometric mean is shown for contaminant concentrations (ng/g lipid weight).</p><table><tbody><tr><th></th><th>SF</th><th>TB</th><th>TMMC</th></tr></tbody><tbody><tr><th>Sample size</th><td>19</td><td>7</td><td>21</td></tr><tr><th>Morphology</th><td></td><td></td><td></td></tr><tr><th>Mass (kg)</th><td>19 (13–27)</td><td>20 (16–25)</td><td>18 (13–25)</td></tr><tr><th>Length (cm)</th><td>85 (72–100)</td><td>88 (83–95)</td><td>86 (79–96)</td></tr><tr><th>Girth (cm)</th><td>72 (61–86)</td><td>74 (65–83)</td><td>69 (58–93)</td></tr><tr><th>Blubber depth</th><td>19 (14–25)</td><td>19 (14–23)</td><td>18 (13–22)</td></tr><tr><th>(mm) Body condition</th><td>1.0 (<i>−</i> 3.0–7.5)</td><td>0.1 (<i>−</i> 1.9–1.9)</td><td><i>−</i> 1.0 (<i>−</i> 5.4–2.8)a</td></tr><tr><th>Sex (male, female)</th><td>9, 10</td><td>5, 2</td><td>9, 12</td></tr><tr><th>Blood IgG (mg/mL) WBC (/μL) T4 (nmol/L)</th><td>24 (20–33) 7.4 (4.3–11.1)b 28 (10–58)</td><td>26 (21–29) 8.3 (4.9–13.6)a, b 46 (18–62)</td><td>29 (26–32)a 10.1 (6.2–15.0)a 19 (7 40)a –</td></tr><tr><th>T3 (nmol/L)</th><td>0.86 (0.53–2.32)</td><td>0.61 (0.39–0.94)</td><td>0.88 (0.52–1.46)</td></tr><tr><th>Contaminants PCB</th><td>9,777a</td><td>1,794</td><td>1,148</td></tr><tr><th>DDT</th><td>(2,594–30,075) 7,179</td><td>(668–10,627) 3,897</td><td>(229–7,528) 1,616a</td></tr><tr><th>PBDE CHLD HCH OH</th><td>(2,738–24,436) 1,053a (360–2,874) 373 (125–883) 27 (12–57) 18,601a</td><td>(992–17,380) 192 (40–1,117) 252 (126–817) 43a (20 70) – 6,302b</td><td>(318–5,885) 154 (61–876) 99a (47–461) 24 (17–42) 3,160</td></tr><tr><th></th><td>(5,836–57,855)</td><td>(1,952–27,880)</td><td>(672–14,599)</td></tr></tbody></table><p><i>Note</i>: IgG = serum immunoglobulin, WBC = total white blood cell count, T4 = serum total thyroxine, T3 = serum total triiodothyronine, PCB = polychlorinated biphenyls, DDT = summed dichlorodiphenyltrichloroethane and its metabolites, PBDE = polybrominated diphenylethers, CHLD = chlordanes, HCH = hexachlorocyclohexanes, and OH = the five contaminant classes summed. Superscript letters represent significant differences among groups.</p>
Table 8 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 8.</i> Weekly survival estimate, standard error, and 95% confidence interval from the top model: {<i>Φ</i> (<i>g</i> + T4)}.</p><table><tbody><tr><th></th><th>Estimate</th><th>SE</th><th>LCL</th><th>UCL</th></tr></tbody><tbody><tr><th>SF</th><td>0.914</td><td>0.021</td><td>0.864</td><td>0.947</td></tr><tr><th>TMMC</th><td>0.886</td><td>0.028</td><td>0.820</td><td>0.930</td></tr><tr><th>TB</th><td>0.972</td><td>0.020</td><td>0.891</td><td>0.993</td></tr></tbody></table>
Table 2 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 2.</i> Results for the significant generalized linear model of summed organohalogen contaminants (OH) controlling for group (SF = San Francisco, TB = Tomales Bay and TMMC = The Marine Mammal Center)</p><table><tbody><tr><th>Dependent</th><th>Model</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>variable</th><td>parameter</td><td>Estimate</td><td>SE</td><td><i>t</i></td><td><i>P</i></td></tr><tr><th>OH</th><td>intercept</td><td>13.853</td><td>1.626</td><td>8.52</td><td><0.005</td></tr><tr><th></th><td>location_TB</td><td><i>−</i> 0.929</td><td>0.319</td><td><i>−</i> 2.91</td><td>0.006</td></tr><tr><th></th><td>location_TMMC</td><td><i>−</i> 1.706</td><td>0.226</td><td><i>−</i> 7.554</td><td><0.005</td></tr><tr><th></th><td>length</td><td><i>−</i> 0.047</td><td>0.019</td><td><i>−</i> 2.486</td><td>0.017</td></tr></tbody></table>
Dietary Phytoimmunostimulant Enhanced Hematological Status and Survival Rate of Common Carp (Cyprinus carpio L.)
<p>This study determined the effectiveness of mango leaves (<em>Mangifera indica</em>), guava leaves (<em>Psidium guajava</em>), and noni leaves (<em>Morinda citrifolia</em>), in increasing the immune parameters of common carp (<em>Cyprinus carpio </em>L.).<strong> </strong>This research was conducted using a one-factor completely randomized design, consisting of experimental controls (commercial feed without phytoimmunostimulants = T0), and commercial feed which was supplemented with mango leaves (T1), guava leaves (T2), and noni leaves (T3). These were used in 3 replicate experiment in common carp that had been reared in floating net cages in the Koto Panjang PLTA Reservoir. Fish in groups of 100 were fed at 5% of body weight per day for 60 days with feed supplemented with 1.5 g of plant material/100g of diet. The parameters observed were erythrocyte and leukocyte profile and survival rate<strong>. </strong>The results showed that the addition of plant leaves to the feed significantly increased the profile of erythrocytes of common carp (P<0.05) with the best result resulting from supplementation with guava leaves. Here, the hematological profiles were erythrocytes (1.82-1.97 x106 cells/mm<sup>3</sup>), hemoglobin (8.53-9.2 g/dL), hematocrit (35.67-39.67%,) total leukocytes )3.21-3.87 x104 cells/mm<sup>3</sup>), phagocytic index (21.33-27.00%), lymphocytes (83.33-84.33 %), monocytes (8.00-9.33%), neutrophils (7.33-7.67%), blood glucose (1.67-104 mg/dL) and survival rate (95.67%).</p>
Sargassum sp extract improve Hematological profile of Tilapia fish (Oreochromis niloticus)
<p>Strategies to increase body resistance and prevent disease in cultivation include using vaccines, antibiotics, and probiotics. Today, the use of antibiotics with natural ingredients is becoming a trend. One of the natural ingredients that contain high antioxidants and antibiotics is <em>Sargassum</em> sp. This research was conducted from March to May 2022 at the Biotechnology Laboratory, Faculty of Fisheries and Marine, Universitas Riau. This research was conducted in two stages, namely 1) the sensitivity of extracts of <em>Sargassum</em> sp. and 2) the application of <em>Sargassum</em> sp. extract orally in tilapia (<em>O. niloticus</em>). The results showed that the extract of <em>Sargassum</em> sp. was able to inhibit the growth o<em>f Aeromonas hydrophila </em>bacteria with a clear zone of 6.5-15.0 mm, which is classified as resistant. At doses of 2000, 2500, and 3000 ppm, it did not cause death in fish for 96 hours (LD<sub>50</sub>). Hematological parameters can be a sign of the health status of fish. Tilapia given to the addition of <em>Sargassum</em> sp. with different doses gave an effect between treatments, both after 30 days of rearing and post-test against <em>A.hydrophila</em> bacteria (p<0.05). The results showed that the hematology of fish fed with <em>Sargassum</em> sp. extract was in the normal or healthy range. Healthy tilapia had erythrocyte counts ranging from 1.34-2.11 x10<sup>6</sup> cells/mm<sup>3</sup>, hematocrit 26.17-33.19%, hemoglobin 6.26-11.2 g/dL and total leukocytes 1.01-1.50 x10<sup>4</sup> cells/mm<sup>3</sup> and total erythrocytes 5.88-9.13x10<sup>4</sup> cells/ mm<sup>3</sup>. A dose of 3000 ppm provided the highest health improvement against <em>A.hydrophila</em> bacterial infection</p>
Hematology and health status of Pangasianodon hypophthalmus fed with Moringa oleifera enriched pellets and infected with Aeromonas hydrophila
<p><em>Moringa oleifera</em> leaves can be used to improve the health of fish in general. To understand the effectiveness of moringa leaves powder addition in improving the immunity of <em>Pangasianodon hypophthalmus</em> toward <em>Aeromonas hydrophila</em> attack, This study has been conducted from June to September 2022. The fish was fed with moringa powder enriched pellets and then infected with <em>A.hydrophila</em>. There were 4 treatments applied, namely Negative Control (no moringa and no infection), Positive Control (no moringa, infection), T1 (5 g/kg, infection), T2 (10 g/kg, infection), and T3 (15 g/kg, infection). The fingerlings of <em>P.hypopthalmus</em> (3.5±0.5g BW) were reared for 45 days (1 fish/4L water) and fed 3 times/day, 5% of body weight. <em>A.hydrophila</em> was infected through injection (0.1 mL of 108 CFU/mL) on the 31st day. The hematology of the fish was checked on the 31st day and the 14th day after infection (or on the 45th day of the research). Results showed that after being treated with moringa for 30 days, the hematology of fish in all treatments showed almost no difference. However, the Phagocytic Index was slightly higher in the moringa-treated fish, they were around 20.33% (in NC and PC) and 23.33-25.67% (in T1, T2, and T3) respectively. On the 14th day after the infection, the Phagocytic Index increased to 26.67%; 29.00%, and 30.33% in T1, T2, and T3 respectively. There was no Positive Control fish that survive by the end of the experiment, while 88.89 – 97.77% of moringa-treated fish survive, and the infection wound was completely cured. Data obtained indicate the Moringa addition in the fish feed pellets is effective to improve the immunity of <em>P.hypopthalmus</em> against <em>A.hydrophila</em> infection</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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