diff --git a/report.tex b/report.tex
index 004a9b6..43cb103 100644
--- a/report.tex
+++ b/report.tex
@@ -105,7 +105,7 @@
         \begin{tabular}{lrrl}
             \!\!\!Student, & \hspace{2cm} & & \\
             \!\!\!group 5130201/20102 & \hspace{2cm} & \underline{\hspace{3cm}} &Tishenko А. А. \\\\
-            \!\!\!Supervisor & \hspace{2cm} &  \underline{\hspace{3cm}} &  Motorin D. E. \\\\
+            \!\!\!Supervisor, Ph. D. & \hspace{2cm} &  \underline{\hspace{3cm}} &  Motorin D. E. \\\\
             &&\hspace{4cm}
         \end{tabular}
         \begin{flushright}
@@ -134,9 +134,23 @@
     % \subsection{Первый подраздел}
     % Текст первого подраздела
 
-    % \section*{Заключение}
-    
-    % \addcontentsline{toc}{section}{Заключение}
+    % \section*{Conclusion}
+    % \addcontentsline{toc}{section}{Conclusion}
+    % Conclusion text
 
-    % Текст заключения
+    \newpage
+    % \section*{Literature}
+    % \addcontentsline{toc}{section}{Literature}
+    
+    \vspace{-1.5cm}
+    \begin{thebibliography}{0}
+        \bibitem{paclitaxel}
+        Lu Xin, Wen Xiao, Huanzhi Zhang, Yakun Liu, Xiaoping Li, Pietro Ferraro, Feng Pan, Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning, 2024.
+        \bibitem{heterogeneity}
+        Qiuli Zhu, Hua Dai, Feng Qiu, Weiming Lou, Xin Wang, Libin Deng, Chao Shi, Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer, 2024.
+        \bibitem{mitochondria}
+        Ziyu Liu, Zahra Zeinalzadeh, Tao Huang, Yingying Han, Lushan Peng, Dan Wang, Zongjiang Zhou, DIABATE Ousmane, Junpu Wang, Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer, 2024.
+        \bibitem{sers}
+        Jun Zhang, Youliang Weng, Yi Liu, Nan Wang, Shangyuan Feng, Sufang Qiu, Duo Lin, Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction, 2024.
+    \end{thebibliography}
 \end{document}
\ No newline at end of file