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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Morphology</journal-id><journal-title-group><journal-title xml:lang="en">Morphology</journal-title><trans-title-group xml:lang="ru"><trans-title>Морфология</trans-title></trans-title-group></journal-title-group><issn publication-format="print">1026-3543</issn><issn publication-format="electronic">2949-2556</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">685115</article-id><article-id pub-id-type="doi">10.17816/morph.685115</article-id><article-id pub-id-type="edn">AYUDGN</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Original Study Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Оригинальные исследования</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Peripheral blood lymphocyte subpopulations as a marker of severe mechanical soft tissue injury in rats: an immunocytochemical study and ROC analysis</article-title><trans-title-group xml:lang="ru"><trans-title>Субпопуляции лимфоцитов периферической крови как маркер тяжёлой механической травмы мягких тканей бедра у крыс: иммуноцитохимическое исследование и ROC-анализ</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>外周血淋巴细胞亚群作为大鼠大腿软组织严重机械损伤的标志物：免疫细胞化学研究和 ROC 分析</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0004-0550-2760</contrib-id><contrib-id contrib-id-type="spin">9390-2689</contrib-id><name-alternatives><name xml:lang="en"><surname>Lanicheva</surname><given-names>Albina Kh.</given-names></name><name xml:lang="ru"><surname>Ланичева</surname><given-names>Альбина Хамитовна</given-names></name><name xml:lang="zh"><surname>Lanicheva</surname><given-names>Albina Kh.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand. Sci. (Medicine), Assistant Professor</p></bio><bio xml:lang="ru"><p>канд. мед. наук, доцент</p></bio><bio xml:lang="zh"><p>MD, Cand. Sci. (Medicine), Assistant Professor</p></bio><email>lanichevaa@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0006-8933-9618</contrib-id><contrib-id contrib-id-type="spin">8269-3077</contrib-id><name-alternatives><name xml:lang="en"><surname>Semchenko</surname><given-names>Valery V.</given-names></name><name xml:lang="ru"><surname>Семченко</surname><given-names>Валерий Васильевич</given-names></name><name xml:lang="zh"><surname>Semchenko</surname><given-names>Valery V.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><bio xml:lang="ru"><p>д-р мед. наук, профессор</p></bio><bio xml:lang="zh"><p>MD, Dr. Sci. (Medicine), Professor</p></bio><email>ivm_omgau_gistology@mail.ru</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Bashkir State Medical University</institution></aff><aff><institution xml:lang="ru">Башкирский государственный медицинский университет</institution></aff><aff><institution xml:lang="zh">Bashkir State Medical University</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Omsk State Agrarian University</institution></aff><aff><institution xml:lang="ru">Омский государственный аграрный университет им. П.А. Столыпина</institution></aff><aff><institution xml:lang="zh">Omsk State Agrarian University</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2026-02-16" publication-format="electronic"><day>16</day><month>02</month><year>2026</year></pub-date><pub-date date-type="pub" iso-8601-date="2026-08-07" publication-format="electronic"><day>07</day><month>08</month><year>2026</year></pub-date><volume>164</volume><issue>3</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><issue-title xml:lang="zh"/><fpage>326</fpage><lpage>337</lpage><history><date date-type="received" iso-8601-date="2025-06-19"><day>19</day><month>06</month><year>2025</year></date><date date-type="accepted" iso-8601-date="2025-09-22"><day>22</day><month>09</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2026, Eco-Vector</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2026, Эко-Вектор</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2026,</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="en">Eco-Vector</copyright-holder><copyright-holder xml:lang="ru">Эко-Вектор</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2029-08-07"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://eco-vector.com/for_authors.php#07</ali:license_ref></license></permissions><self-uri xlink:href="https://j-morphology.com/1026-3543/article/view/685115">https://j-morphology.com/1026-3543/article/view/685115</self-uri><abstract xml:lang="en"><p><bold>BACKGROUND: </bold>Injuries and burns are accompanied by changes in lymphocyte subpopulations, reflecting the immune system's response. ROC (receiver operating characteristic) analysis is a classification method that uses ROC curves. An ROC curve is a graph that evaluates the quality of binary classification; it shows the relationship between the proportion of true positives (sensitivity) and the proportion of false positives (1 – specificity). Using an ROC curve, one can identify the main diagnostic criteria for trauma severity and determine the post-traumatic stage based on the total lymphocyte count and the percentage of various subpopulations (CD3+, CD4+, CD8+, CD19+, CD56+). This approach enables prediction of the main pathogenetic mechanisms of wound healing.</p> <p><bold>AIM: </bold>To study, using immunocytochemical methods, the responses of different peripheral blood lymphocyte types (T cells, B cells, and NK cells) to mechanical soft tissue injury of the thigh in rats.</p> <p><bold>METHODS: </bold>The study was performed on outbred male white rats weighing 180–200 g. Using a special device (under ether anesthesia), a mechanical thigh injury comparable in kinetic energy to that caused by a 5.6 mm bullet was induced in the animals. The animals were euthanized by decapitation at 3 and 14 days after injury in the morning; blood was collected from the tail vein beforehand. Quantitative assessment of peripheral blood lymphocyte subpopulations was performed using a panel of monoclonal antibodies; the relative and absolute counts of the following subpopulations were determined: CD3+ (all T lymphocytes); CD4+ (T-helpers); CD8+ (cytotoxic T lymphocytes); CD19+ (B lymphocytes); and CD56+ (NK cells). Statistical analysis (ROC analysis) was performed using STATISTICA v.7.0 (StatSoft Inc., USA).</p> <p><bold>RESULTS: </bold>ROC analysis accurately evaluates the quality of binary classification – the effect of a given factor on the state of the components of the system under study. Here, using ROC analysis, we demonstrated the effect (yes/no) of high-kinetic soft tissue injury of the thigh and the effect of time (3 and 14 days after injury) on the relative counts of individual lymphocyte subpopulations in the peripheral blood of rats.</p> <p><bold>CONCLUSION: </bold>On day 3 post-injury, the relative counts of all lymphocyte subpopulations had changed in response to the injury. By day 14, changes in ROC curves were observed only for B lymphocytes and NK cells. These data indicate that the addition of the time factor primarily affects B lymphocytes and NK cells but does not affect T lymphocytes.</p></abstract><trans-abstract xml:lang="ru"><p><bold>Обоснование. </bold>Травмы и ожоги сопровождаются изменениями субпопуляционного состава лимфоцитов, что отражает системный характер иммунного ответа. ROC-анализ — это анализ классификаций, применяемых с помощью ROC-кривых (от англ. receiver operating characteristic). ROC-кривая представляет собой график, позволяющий оценить качество бинарной классификации; он отображает соотношение между долей объектов от общего количества носителей признака, верно классифицированных как положительные по данному признаку (чувствительностью алгоритма классификации), и долей от общего количества объектов, не несущих признака, но ошибочно классифицированных как положительные. Используя ROC-кривую можно выявить основные диагностические критерии, характеризующие тяжесть травмы, и определить стадии посттравматического периода, основываясь на данных об общем числе лимфоцитов и процентном соотношении различных субпопуляций (CD3<sup>+</sup>, CD4<sup>+</sup>, CD8<sup>+</sup>, CD19<sup>+</sup>, CD56<sup>+</sup>). Такой подход позволяет прогнозировать основные патогенетические механизмы раневого процесса.</p> <p><bold>Цель исследования</bold> — с использованием иммуноцитохимических методов изучить реакции различных типов лимфоцитов периферической крови (Т-клеток, В-клеток и NK-клеток) в ответ на механическую травму мягких тканей бедра у крыс.</p> <p><bold>Методы. </bold>Исследование выполнено на белых беспородных самцах крыс массой 180–200 г. С помощью специальной установки у животных (под эфирным наркозом) индуцировали механическое повреждение бедра, соизмеримое по кинетической энергии с повреждением от пули калибра 5,6 мм. Животных выводили из эксперимента через 3 и 14 суток после травмы в утренние часы путём декапитации; предварительно осуществляли забор крови из хвостовой вены. Количественную оценку субпопуляционного состава лимфоцитов периферической крови выполняли с помощью панели моноклональных антител; определяли относительное и абсолютное количество клеток, относящихся к следующим субпопуляциям: CD3<sup>+</sup> — все Т-лимфоциты; CD4<sup>+</sup>, CD8<sup>+</sup> и CD19<sup>+</sup> — В-лимфоциты; CD56<sup>+</sup> — NK-клетки. Статистическая обработка данных (ROC-анализ) проведена с помощью прикладных программ STATISTICA v.7.0 (StatSoft Inc., США).</p> <p><bold>Результаты. </bold>ROC-анализ позволяет точно оценить качество бинарной классификации — влияние какого-либо фактора на состояние компонентов, составляющих систему изучения. В данной работе с помощью ROC-анализа показано влияние (да/нет) фактора наличия высококинетической травмы мягких тканей бедра и фактора времени (3 и 14 суток после повреждения) на относительное содержание отдельных субпопуляций лимфоцитов в периферической крови крыс.</p> <p><bold>Заключение. </bold>Установлено, что на 3-и сутки посттравматического периода под действием повреждения у животных изменилось относительное содержание всех субпопуляций лимфоцитов. Через 14 суток изменения ROC-кривых выявлены только для В-лимфоцитов и NK-клеток. Полученные данные свидетельствуют о том, что присоединение фактора времени влияет в основном на В-лимфоциты и NK-клетки, но не затрагивает Т-лимфоциты.</p></trans-abstract><trans-abstract xml:lang="zh"><p><bold>论证。</bold>创伤和烧伤伴随着淋巴细胞亚群组成的变化，反映了全身免疫反应。ROC 分析是对应用 ROC 曲线进行分类的分析。ROC 曲线（英文为 Receiver Operating Characteristic）是一种图表，可用于评估二元分类的质量；它显示了携带某一特征的对象中被正确分类为该特征阳性的比例（分类算法的灵敏度）与不携带该特征但被错误分类为阳性的对象总数的比例之比。利用 ROC 曲线，可以识别表征损伤严重程度的主要诊断标准，并根据淋巴细胞总数和各种亚群（CD3+、CD4+、CD8+、CD19+、CD56+）的百分比数据确定创伤后阶段的划分。这种方法有助于预测伤口愈合过程的主要发病机制。</p> <p><bold>目的。</bold>本研究的目的是利用免疫细胞化学方法研究大鼠大腿软组织受到机械创伤后，各种类型的外周血淋巴细胞（T 细胞、B 细胞和 NK 细胞）的反应。</p> <p><bold>方法。</bold>本研究采用体重 180-200 克的白色非纯种雄性大鼠。使用特殊装置，在乙醚麻醉下，对大鼠大腿造成机械损伤，其动能与 5.6 毫米子弹造成的损伤相当。分别于伤后 3 天和 14 天早晨，通过断头处死实验动物；此前已采集尾静脉血。使用一组单克隆抗体对外周血淋巴细胞亚群组成进行定量分析；测定以下亚群细胞的相对和绝对数量：CD3+ — — 所有 T 淋巴细胞；CD4+、CD8+ — — T 淋巴细胞亚群；CD19+ — — B 淋巴细胞；CD56+ — — NK 细胞。使用 STATISTICA v.7.0 应用软件（StatSoft 公司，美国）进行统计数据处理（ROC 分析）。</p> <p><bold>结果。</bold>ROC 分析能够精确评估二元分类的质量 — — 即任何因素对研究系统中各组成部分状态的影响。在本研究中，ROC 分析用于展示大腿高动态软组织损伤的存在（是/否）以及时间因素（损伤后 3 天和 14 天）对大鼠外周血中各淋巴细胞亚群相对丰度的影响。</p> <p><bold>结论。</bold>研究发现，在创伤后第 3 天，在损伤的影响下，动物体内所有淋巴细胞亚群的相对含量发生了变化。14 天后，仅在 B 淋巴细胞和 NK 细胞中检测到 ROC 曲线的变化。所得数据表明，时间因素的加入主要影响 B 淋巴细胞和 NK 细胞，但不影响 T 淋巴细胞。</p></trans-abstract><kwd-group xml:lang="en"><kwd>mechanical trauma</kwd><kwd>lymphocyte subpopulations</kwd><kwd>ROC analysis</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>механическая травма</kwd><kwd>субпопуляции лимфоцитов</kwd><kwd>ROC-анализ</kwd></kwd-group><kwd-group xml:lang="zh"><kwd>机械创伤</kwd><kwd>淋巴细胞亚群</kwd><kwd>ROC 分析</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Suhoparova EP, Zinoviev EV, Khrustaleva IE, Kniaazeva ES. 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