typos

report.tex

@@ -163,8 +163,7 @@
     \end{figure}
 
     In article~\cite{sers}, same as in~\cite{paclitaxel}, authors choosed the approach of collecting their own dataset. Their dataset was based on clinical plasma samples from 60 healthy volunteers which were used as a control group, and 60 nasopharyngeal cancer patients (30 plasma samples from radiotherapy sensitivity patients and 30 plasma samples from radiotherapy resistance patients). All plasma samples were
-    obtained from Fujian Provincial Cancer Hospital. As well as in~\cite{paclitaxel}, authors used unique method called surface enhanced Raman spectroscopy (SERS) to extract molecular profiles of patients plasma. Authors even claim that SERS based on
-    surface plasmon resonance was used for this task for the first time. The SERS spectra were processed by deducting the fluorescence background signal using a fifth-order polynomial fitting method, and then the SERS signals were peak normalized, after which the spectra of the same plasma sample were averaged to represent the final SERS data for that sample.э
+    obtained from Fujian Provincial Cancer Hospital. As well as in~\cite{paclitaxel}, authors used unique method called surface enhanced Raman spectroscopy (SERS) to extract molecular profiles of patients plasma. Authors even claim that SERS based on surface plasmon resonance was used for this task for the first time. The SERS spectra were processed by deducting the fluorescence background signal using a fifth-order polynomial fitting method, and then the SERS signals were peak normalized, after which the spectra of the same plasma sample were averaged to represent the final SERS data for that sample.
 
     Authors of articles~\cite{heterogeneity}, \cite{mitochondria}, \cite{kras} and~\cite{glut} turned to open databases to prepare datasets for their research. Authors of~\cite{heterogeneity} downloaded frozen histopathologic images of 494 ovarian and 70 paracarcinoma tissues with hematoxylin–eosin (HE) staining from TCGA~\cite{tcga}. The corresponding clinical information, genomics, and transcriptomics profiles required for this study were also obtained from this database. Authors of~\cite{mitochondria} also used TCGA. They downloaded  information on 183 esophageal cancer patients (95 squamous cell carcinomas and 88 adenocarcinomas) was obtained, including mRNA expression profiles, clinical features such as survival time and status, age, gender, and pathological stage (T, N, and M). Additionally authors used Gene Expression Omnibus (GEO) database~\cite{geo}. RNA sequencing (RNA-seq) for GSE45670 was downloaded from it.  GSE45670 includes a total of 17 esophageal squamous cell carcinomas (ESCC) that did not respond to preoperative CRT, 11 ESCC that responded to preoperative CRT, and 10 samples from normal esophageal epithelium. The GEO dataset GSE53625 comprises 358 samples, including 179 ESCC tissue samples and an equal number of samples of adjacent normal tissues, along with detailed clinical data for the 179 ESCC patients. The GEO dataset GSE19417 contains data from 76 esophageal adenocarcinoma patients, offering detailed clinical data for 48 of these patients. Authors of~\cite{kras} also took gene expression profile data from GEO database, specifically from accession number GSE137912. Their analysis involved 7612 samples treated with KRAS G12C inhibitors. Among these samples, 4297 were tumor cells that persisted in proliferation, whereas 3315 were tumor cells that had ceased proliferating. Each sample contained the expression of 8687 genes. In~\cite{glut}, authors used datasets from both TCGA and GEO and also from European Genome-Phenome Archive~\cite{ega}. In this study they used