Arabidopsis thaliana carrying a site-specific integration of a target gene encoding modified human interferon (dIFN) using endonuclease Cas9. For the targeted insertion, we selected the region of the histone H3.3 gene (HTR5) with a high constitutive level of expression. Our results indicated that Cas9-induced DNA integration occurred with the highest frequency with the construction with donor DNA surrounded by homology arms and Cas9 endonuclease recognition sites. Among the monoclones of the four cell lines with knock-in studied, there is high heterogeneity in the level of expression and accumulation of the target protein. The accumulation of dIFN protein in cell lines with targeted insertions into the target region of the HTR5 gene does not statistically differ from the level of accumulation of dIFN protein in the group of lines with random integration of the transgene. However, one among the monoclonal lines with knock-in has a dIFN accumulation level above 2% of TSP, which is very high.Targeted DNA integration into known locations in the genome has potential advantages over the random insertional events typically achieved using conventional means of genetic modification. We investigated the possibility of obtaining a suspension cell culture of  Escherichia coli, several yeast species, and mammalian cell cultures. More than 50% of all pharmaceutical recombinant proteins are synthesized in mammalian cells [Recombinant proteins are foreign proteins produced in various expression systems. They are mainly used as pharmaceuticals for diagnostics, for the vaccination of humans or animals, and as drugs or monoclonal antibodies . The evean cells ,3. Mammaan cells . Proteinan cells ,5,6. ThePlant cell cultures as a system for the production of recombinant proteins, however, have several drawbacks, the main one of which is associated with a low yield of recombinant protein, which rarely exceeds 100 μg/kg of fresh weight, despite significant efforts to optimize protein expression and stability . In wholThe problems of random integration can be overcome with the use of site-specific endonucleases. Such endonucleases, the most well-known of which is the Cas9 endonuclease, induce double-stranded breaks (DSB) in the target region of the genome, which are repaired by one of the two innate DNA repair systems: the non-homologous end-joining (NHEJ) pathway and the homology-directed repair (HDR) pathway ,12,13. IUsing a donor format can influence the rate of HDR and editing outcomes. Donor templates can be delivered as dsDNA, such as PCR products, linearized or non-linearized plasmids, or ssDNA. The literature provides examples of the use of all types of templates for knock-in; however, ssDNA templates showed their low efficiency in plants, and linear DNA was usually delivered together with circular DNA, so it is difficult to assess the effect of template composition ,19. CircOur study was aimed to test the possibility of integrating target genes into evolutionarily adapted regions of genes, the expression of which provides vital functions of the plant cell (regions of housekeeping genes). It should be emphasized that site-specific insertion of target genes into the genome of a plant cell is a very difficult task . MoreovedIFN gene was chosen as the target recombinant protein. Deltaferon is a recombinant analog of human gamma-interferon, in which Arg-129 is replaced by Gly and Lys-130 is replaced by Ser and 10 C-terminal amino acid residues are deleted. Deltaferon is biologically active and more resistant to proteolysis than natural interferon gamma and has the same phagocytosis-stimulating properties as human IFN-gamma but reduced antiviral activity. According to its biological properties, it can be considered a component of medicines intended for the treatment of severe viral, immune, and oncological diseases [HTR5 (At4g40040). Histone genes are the most important housekeeping genes since they provide the compaction of chromatin necessary for all eukaryotes, take part in the epigenetic regulation of gene expression, and are located in areas of actively transcribed chromatin. In plants, the most well-studied are the genes of histone H3 [A. thaliana contains 15 histone H3 genes, including five H3.1 genes, three H3.3 genes, and five H3.3-like genes. Histone H3.1 genes are expressed only in the S phase of the cell cycle and histone H3.3 genes are constitutively expressed during the entire interphase. Two of the three H3.3 genes HTR5 and HTR8 (At4g40040 and At5g10980) exhibited replication-independent expression in suspension cells [HTR5 gene region looked more convenient for cloning.Deltaferon (dIFN) encoded by the diseases ,23. For stone H3 . The hapon cells ; among tdIFN) to the region of the HTR5 gene, aimed at optimizing the frequency of knock-ins, receiving of transgenic cell suspension lines with the help of biolistic transformation, and a comparative analysis of the accumulation of the target recombinant protein (dIFN) in monoclonal cell lines with knock-in and cell lines with random insertions of the target gene. In addition, we evaluate the transcriptional activity of the HTR5 gene and the adjacent gene encoding the 12 KDa subunit of microsomal signal peptidase in the A. thaliana monoclonal cell lines created. In this work, for the first time, the creation of plant cell suspension culture lines with a site-specific integration of the target gene obtained using CRISPR/Cas9 is described.Here, we describe the creation of three types of genetic constructs for the delivery of the target gene  Heynh  was used as the initial plant material. This cell line deposited in the All-Russian collection of higher plant cell cultures  under No. 85 and designated as NFC-0 [The fast-growing cell line of as NFC-0  was kindas NFC-0  with theA. thaliana U6 promoter was used as an intermediate vector for inserting selected spacer regions into the sgRNA sequence.The plasmids pBlu/gRNA (#59188) for an intermediate cloning step and Cas9 MDC123 (#59184) with the Cas9 endonuclease gene under the control of the 2 × 35S CaMV promoter optimized for expression in Glycine max cells were donated by R. Stupar  from theA. thaliana was used as a template for the amplification of sequences flanking the integration site of the target gene. The signal peptide sequence directing the synthesized protein to the apoplast was amplified on a Daucus carota genomic DNA template. The plasmid pGEX4T-1 was the source of the GST (glutathione S-transferase) gene encoding the tag for affinity purification of the target protein. For the amplification of the nptII gene sequences (neomycin phosphotransferase II) and the CaMV35S promoter of the cauliflower mosaic virus, the pBi121 plasmid was used as a template. The dIFN (deltaferon) gene, a recombinant analog of human interferon gamma for the plant cells expression, was amplified from the pIFN-γ-trp2-Δ plasmid [Nucleotide sequences for creating genetic constructs were obtained by PCR using appropriate oligonucleotides and templates. The genomic DNA of  plasmid .HTR5 was chosen as the region for the integration of the target gene. The CRISPOR software  was used to select the guide RNA for knock-in into the region of the HTR5 gene. The sequence for knock-in was located between the locus of histone H3.3 gene HTR5 (At4g40040) and the adjacent gene encoding the 12 kDa subunit of microsomal signal peptidase (At4g40042), preceding the coding region of the HTR5 gene.The region of the histone H3.3 gene HTR5 gene–1_gRNA_H Up and 1_gRNA_H Lo. The first stage of the construct assembly consisted of hybridization of the selected phosphorylated oligonucleotides and their subsequent integration into the pBlu/gRNA plasmid treated with the BstV2I restriction enzyme . To confirm the insertion of the target sequence, the DNA of the resulting clones was sequenced using a standard T3 primer .1, pIFN (H3.3).2 and pIFN (H3.3).3, were designed. Schemes of their structure are presented in To deliver the desired gene GST gene, primers Up_sig.BglII(62), Lo_dIFN(60), ifn + Up_GST(60), and Lo_GST(GEX) stopA65I(66) were used stopA65I(66) were used to obtain the GST gene sequence. Then primers Up_sig.BglII(62), Lo_GST(GEX) stopA65I(66) and both DNA fragments obtained were used in PCR to obtain the target DNA fragment signal--his6-dIFN-GST.To obtain the target deltaferon gene, with a signal peptide and an attached To obtain the three final constructs, three intermediate plasmids were created. All intermediate plasmids were obtained on the basis of the pUC19 vector.A. thaliana genome flanking the insertion region and a kanamycin resistance gene with a promoter and terminator. To obtain pInt_var.1, three DNA fragments were preliminarily obtained. The first two fragments, LF and RF (flank sequences), were synthesized by two sequential PCRs on a template of A. thaliana genomic DNA with primers: Up_L_HIII and Lo_L_Acc_Sfr (LF) and Up_R_Sfr_Acc and Lo_R_Sal (RF). The third fragment (nptII gene with p35S promoter) was obtained by PCR on the pBi121 plasmid template with primers Lo_A-B_pBI(58) and Up_Xho-pBI(60). Using the LF and RF fragments, as well as the primers Up_Xho-pBI(60), Up_L_HIII, and Lo_R_Sal, an LF-RF DNA fragment containing recognition sites for the Acc65I, Sfr274I restrictases between LF and RF was obtained, which was then inserted into pUC19. A DNA fragment carrying the nptII gene was cloned into pUC-LF-RF at the Sfr274I/Acc65I restriction enzyme recognition sites.The intermediate plasmid pInt_var.1 carries sequences from the A. thaliana HTR5 gene as well as the nptII gene with a promoter and terminator. To obtain the nptII fragment, the primers Up_Sal_gRNA-pBI(60) and Lo_A-B_pBI(58) were used instead of the primers Lo_A-B_pBI(58) and Up_Xho-pBI(60). The resulting DNA fragment was cloned into pUC19 at the SalI and Acc65I restriction enzyme recognition sites. The target signal-his6-dIFN-GST fragment was obtained using the Lo_Acc_gRNA_GST(60) primer instead of Lo_GST(GEX)stopA65I(66).The intermediate plasmid pInt_var.2, instead of the flanking DNA of Arabidopsis, carries the Cas9 endonuclease recognition sites identical to the site of the double-stranded break in front of the A. thaliana HTR5 gene, and the nptII gene with a promoter and terminator. Plasmid pInt_var.3 was obtained similarly to plasmid pInt_var.1, but instead of primers Up_L_HIII and Lo_R_Sal, primers Up_L_HIII_gRNA and Lo_R_Sal_gRNA were used for cloning.The intermediate plasmid pInt_var.3 carries sequences from the Arabidopsis genome flanking the insertion region, recognition sites for the Cas9 endonuclease that are identical to the double-strand break site upstream of the The resulting target DNA fragment signal-his6-dIFN-GST was used for cloning within the intermediate plasmids pInt_var.1, pInt_var.2, and pInt_var.3 at the recognition sites of the BglII/Acc65I restriction enzymes to form three target plasmids, pIFN (H3.3).1, pIFN (H3.3).2 and pIFN (H3.3).3.dIFN gene to the region of the HTR5 gene of A. thaliana was carried out using biolistic transformation with the immobilization of plasmids pIFN (H3.3).1, pIFN (H3.3).2 or pIFN (H3.3).3 together with the plasmid Cas9H33 on the gold particles. In total, 10 biolistic transformations were performed for each construct.Delivery of the target For the transformation by the biolistic method, 1 mL of cell suspension was applied to the surface of the SH medium  with theg. Furthermore, monoclones were obtained from these cell cultures. For this, on the 4th day of cultivation, cell suspensions diluted with the culture medium were seeded on Petri dishes with SH medium supplied with the antibiotic kanamycin (100 mg/L), and microcalluses were grown from individual cells/cell aggregates.Three days after the biolistic transformations, the calluses were transferred to a selective SH medium of the same composition supplied with kanamycin at a concentration of 100 mg/L and cultured in a light with an intensity of 20,000 lux at a photoperiod of 18/6 h (day/night). Transfer of the calluses to fresh media of the same composition were carried out weekly. The resulting antibiotic resistant calluses were used to obtain cell suspensions. The suspensions were cultured in the dark, using an orbital shaker at 160× nptII gene, for the target dIFN gene, and for the event of target insertion—when one primer is located in the target region of plant DNA, and the other is located in the transgenic construct.The detection of knock-ins was carried out using PCR and sequencing DNA of kanamycin-resistant calluses. Genomic DNA was isolated from the calluses using CTAB buffer according to the Allen protocol . PCR ananptII gene and the target dIFN gene was confirmed by PCR analysis, but insertion into the specified region of the genome was not confirmed. The detection of insertions of target genes was carried out using PCR and DNA sequencing of calluses resistant to selective agents. Genomic DNA was isolated using CTAB buffer according to the Allen protocol [nptII gene, for the target dIFN gene, and for the event of target insertion—when one primer is located in the target region of plant DNA, and the other is located in the transgenic construct.The control group with random insertion of transgenes included cell lines identified by resistance to the antibiotic kanamycin, in which the presence of the marker protocol . PCR ana4, pH 9.0, 30 mM KCl, 10 mM ammonium sulfate, 0.01% Tween 20, 3 mM MgCl2, 0.2 mM of each deoxyribonucleoside triphosphate, 0.4 μM of each primer, 0.4 μM of each probe, and 0.1 u.a./μL Hot Start DNA polymerase . To create the probes, we used pairs of fluorophore-quencher: FAM-BHQ1 and Cy5-BHQ2. Real-time PCR was carried out in the multiplex version. The level of target gene expression was investigated relative to the HTR5 gene , the gene encoding the 12 kDa subunit of microsomal signal peptidase (primers and probe VAP27-1_f VAP27 -1_r VAP27-1_p), as well as A. thaliana’s own PARP2 gene . The structures of primers and probes are shown in Total RNA from the cell biomass of each monoclonal line was isolated using the ExtractRNA reagent , RNA was treated with DNase I , and 4 µg RNA was used to obtain cDNA . Expression analysis was performed using real-time PCR on a CFX96 amplifier . The reaction was carried out in 20 μL of a reaction mixture of the following composition: 15 ng of the studied cDNA, 50 mM Tris-SOE is the PCR efficiency and Ct the threshold fluorescence cycle in the test and control samples, respectively.Data processing was carried out using Bio-Rad CFX Manager 3.1 software. Normalized expression was calculated using the formula:A. thaliana monoclonal cell lines with target insertions of the dIFN gene in the region of the HTR5 gene and 7 cell lines with insertions of the studied gene into the random regions of the genome. Protein extracts of A. thaliana non-transgenic cell culture served as a negative control. Protein extract of a non-transgenic cell culture of A. thaliana supplied with 1 μg/μL of dIFN, which we had previously synthesized in E. coli, was used as a positive control. dIFN in E. coli cells was synthesized on the same matrix that was used in genetic constructs for the production of knock-ins in plant cells.For ELISA, we used protein extracts obtained from a series of g . The cell precipitate was triturated in liquid nitrogen and a 0.7 g sample was transferred to a 2 mL tube with the addition of 1400 μL of PBS buffer . Then, the extracts were homogenized on Vibra-Cell™ Ultrasonic Liquid Processors VCX130 (USA) according to the program: 5 cycles, amplitude 45%, 10 s on, 15 s off. The extracts were centrifuged for 10 min at 14,000× g  and the supernatant was collected in a separate tube.To extract proteins, 40 mL of a 7-day cell suspension culture was centrifuged for 10 min at 12,000× ® plate . The sorption on the plate was carried out overnight at a temperature of 10 °C. After sorption, the wells were washed with 250 μL of PBST (PBS buffer with the addition of 0.1% Tween 20) and then 250 μL of 0.5% fat-free dry milk in PBS was added to each well and incubated for 30 min at room temperature with shaking, after which the wells were rinsed again with 250 μL PBST. The remaining liquid was removed with a water-jet pump. In total, 100 μL of primary antibodies (anti-interferon gamma (human) antibodies IMS01-145-319, Agrisera, Sweden) diluted in PBS buffer (1:5000) was added to each well and the plates were incubated with shaking for 1 h at room temperature. After washing three times with 300 μL PBST, 100 μL of secondary antibodies (Goat Anti-Chicken IgY (H&L), HRP conjugated, AS09 603, Agrisera, Sweden) diluted in PBS (1:10000) was added to each well and incubated with shaking for 1 h. After washing the wells three times with 300 μL PBST, 100 μL TMB  was added to each well and incubated with shaking for 30 min in the dark at room temperature. The reaction was stopped by adding 100 μL of 0.1 M HCl. Within 15 min after stopping the reaction, the optical density was measured on a Victor X3 2030 device  at a wavelength of 450 nm. All measurements were carried out in three biological replicates. The optical density (OD) value of the negative control was subtracted from the OD of all experimental values and the positive control. The accumulation of the target dIFN protein in the analyzed cell lines was estimated as a percentage of the TSP. The concentration of TSP was determined by the Bradford method [The protein extract (100 μL) in four replicates was introduced into the wells of a 96-well MICROLONdIFN and nptII gene delivery to the target region of the HTR5 gene using three types of genetic constructs are presented in The results of the efficiency of As can be seen in After biolistic transformation, calli cultivated on a culture medium with kanamycin represent a heterogeneous mass of cells, including both cells with knock-in events in the target region and cells with the integration of target genes into random regions of the genome. To obtain monoclonal cell lines, we used one part of the kanamycin-resistant callus for analysis for the presence of knock-ins, while the second part was transferred to a fresh medium. It is quite obvious that such a procedure for selecting cells with knock-ins may be associated with their loss. As can be seen from the data in HTR5 gene, PCR was used with primers, one of which was located inside the transferred construct and the other on the plant DNA region outside the flanking sequence present in the construct  when sequenced from one side and identity with vector DNA  when read from the other side. During the alignment, slight rearrangements were noted at the junction of the plant and vector DNA, especially in line 1 obtained with the construct pIFN(H3.3).2, which is integrated into the genome by the mechanism of NHEJ. The results of sequencing a specific PCR fragment confirmed the targeted insertion of the genetic construct into the target region of the histone gene H3.3. As a result of the biolistic transformation of two types of genetic constructs, 4 cell lines with target gene knock-ins in the target region of the HTR5 gene were obtained, which served as progenitors for the creation of monoclonal cell lines.Alignment  of the nHTR5 gene region obtained using the pIFN(H3.3).2 construct: four for line 1 (1.1–1.4) and two for line 6 (6.2 and 6.3). Nine monoclones were selected from two lines with knock-in obtained using the pIFN(H3.3).3 construct: four for line 38  and five for line 4 .Six monoclones were selected from two lines with the integration of the target gene into the For a comparative analysis of the expression of the target gene delivered to the target region and assessment of the level of accumulation of the target protein in the monoclonal cell lines, seven monoclonal cell lines with insertions of the target gene into random regions of the genome were selected. Monoclones 41 and 51 were selected from cell lines obtained using the pIFN(H3.3).3 construct. Five monoclones were selected from cell lines obtained with a different construct pIFN(H3.3).2 .HTR5 gene, a consistently high level of accumulation of the dIFN protein was not observed. As can be seen in the figure, all four lines differed in the level of accumulation of the target protein. Moreover, in each line, the dIFN content varied between individual monoclones. The highest level of accumulation of the dIFN protein was found in monoclones of line 1; in the case of monoclone 1.4, it reached more than 2% of the TSP. Statistical analysis of the data obtained by the Kruskal–Wallis test revealed significant differences between the monoclones H = 9802, p = 0.0203. Analysis of the data obtained using Dunn’s test, taking into account multiple comparisons, revealed significant differences only between lines 4 and 1 .The accumulation of the target dIFN protein in the analyzed cell lines, estimated as a percentage of the total soluble protein (TSP), was investigated by ELISA. The results of ELISA are shown in HTR5 gene in A. thaliana cell cultures, this region was chosen as the target region for the delivery of the dIFN gene. In this regard, it was of interest to determine the amount of mRNA of the target gene placed in the region with a high transcriptional activity using the real-time PCR method. dIFN gene expression normalized to the expression of A. thaliana’s own PARP2 gene. It was found that the mRNA of the dIFN gene was not detected in all lines. The highest expression level was observed in monoclones of line 1, as well as in lines with random insertion. In monoclones of lines 38 and 4, expression of the dIFN gene was not detected, which correlated with the low level of protein in these lines.Based on the data of T. Okada et al. , who revt = 1.0345, df = 7 tst (df = 7) = 2.365.For a comparative analysis of the level of accumulation of the target protein integrated into the transcriptionally active region, and the protein delivered to random regions of the genome, the mean values of protein accumulation in monoclones of one cell line were compared. The mean content of dIFN protein in the group of monoclones of line 4 was 0.085%, line 38–0.310%, line 1–0.990% and line 6–0.459%. The mean dIFN content for lines with random insertion was 0.160% (line 44), 0.454% (line 51), 0.282% (line 2), 0.291% (line 8), and 0.194% (line 10). As a result of comparing the mean protein content in the lines with knock-in and random insertion, no significant differences were found by Student’s test, A. thaliana genes the HTR5 gene and the gene encoding the 12 kDa subunit of microsomal signal peptidase, it was of interest to check whether the insertion of the target genes affected their expression. HTR5 gene and the gene encoding the 12 kDa subunit of microsomal signal peptidase in A. thaliana monoclonal cell lines with a knock-in in the region of HTR5 . A cell line (n.t.) obtained from the non-transgenic callus of A. thaliana (control 1) and monoclonal lines with random insertion  were used as controls for comparison. For lines 1, 6, 38, and 4, the data on the expression of the studied genes were pooled, since no significant differences were found between the monoclones for each of these lines. HTR5 genes and 12 kDa subunit of the microsomal signal peptidase gene obtained for all monoclones in each line.Since the site of insertion of the target construct is located in the promoter region of two HTR5gene, the microsomal signal peptidase gene was much weaker expressed in all the analyzed cell lines. Comparative analysis using the nonparametric Kruskal–Wallis test did not reveal significant differences in the expression level of the HTR5 gene and the microsomal signal peptidase gene between the group of lines with site-specific insertion and control groups (lines with random insertion and a non-transgenic line). Thus, it becomes obvious that the level of expression of A. thaliana’s own genes little, if not, differs in lines with knock-in and with random insertion of the construct, which suggests that the insertion of the target construct into the studied target region of the HTR5 gene did not affect the expression of the studied plant genes.As we can see in the figure presented, both the studied genes significantly differed from each other in terms of expression level. Compared to the Despite the attractiveness of using higher plant cell cultures for the commercial production of biopharmaceuticals, the yield of recombinant protein in this expression system is still low compared to animal cell cultures. Typically, the yield of the recombinant protein is about 1% of the TSP ,33. In wThe observed variability in the ability to biosynthesize and accumulate recombinant proteins in plant cells is largely determined by the organization of the transgene insertion region. The optimally high level of target gene expression, planned by the researchers during the design of the genetic construct, may not be realized when the transgene enters the transcriptionally inactive regions of the genome. Thus, the identification of transcriptionally active regions of the genome and directed integration of target genes into these regions opens up new prospects for researchers to increase the synthetic capabilities of plant cells for the production of recombinant proteins.dIFN gene into the region of one of the housekeeping genes—the histone H3.3 gene HTR5.The rapid development of genomic editing methods using CRISPR/Cas9 opens up the opportunity for researchers to deliver target genes to constitutively transcribed regions of the genome and to obtain highly productive lines producing recombinant proteins. Plant cell housekeeping genes that are actively expressed during the entire interphase of the cell cycle can be potential target sites for the integration of target genes. We tried to implement this approach in this study and evaluate the possibilities of increasing the accumulation of recombinant protein in the case of integration of the target Despite a significant increase in the number of works on editing the plant cell genome, the number of experimental works devoted to gene insertion using the knock-in method is still small ,16. The A. thaliana plants [Zea mays [A. thaliana and 4.7% for Zea mays [dIFN gene delivery to the region of the HTR5 gene was detected. At the same time, the inclusion of Cas9 nuclease recognition sites in the flanking regions in the pIFN(H3.3).3 construct made it possible to identify six events of transgene delivery to the target region with the same number of biolistic transformations performed. Although in the case of using the pIFN(H3.3)2 construct, only three desired events were identified, this approach can also be successfully used in the work. The absence of flanking DNA simplifies the process of creating a genetic construct associated with the addition of homology regions to the construct or, on the contrary, their replacement when the target site is changed. Thus, as the experiments have shown, the introduction of Cas9 recognition sites into the genetic construct, intended for its excision from the circular plasmid in the cell, seems to be a very effective approach for integrating transgenes into target regions of plant cell cultures. It seems more promising to us to use a template without flanking homology arms and carrying only the Cas9 recognition sites, since such a design, based on insertion by the NHEJ mechanism, excludes additional stages of construction and, with a fairly high frequency, can lead to the successful production of lines with knock-in.As a rule, most of the genetic constructs intended for the delivery of transgenes to the target region using the CRISPR/Cas9 genomic editing technology include flanking DNA regions homologous to the insertion site ,39,40,41a plants  and Zea Zea mays ,41, a teZea mays . The useZea mays ,41. The Zea mays . Adding HTR5 gene, formed the basis for the creation of monoclonal cell lines with random insertions.It is worth emphasizing that an important problem that remains is the low efficiency of the selection of cells carrying knock-in since plant cells in suspension culture are prone to the formation of cell aggregates. Separating knock-in cells from cells in which the same construct is integrated into a random region of the genome is a laborious task. It is desirable to monitor the presence of knock-in regularly even among cells that have successfully passed selection and carry a confirmed knock-in, since it is known that cells that produce foreign proteins at a high level tend to be displaced from a heterogeneous culture ,42. To rdIFN gene integrated into the region of the HTR5 gene, characterized by high transcriptional activity, and assessing the possibility of increasing the accumulation of the recombinant protein. Contrary to expectations, a high level of accumulation of the dIFN protein was found only among monoclones of line 1, which reached more than 2% of the TSP in the case of monoclone 1.4. In general, the accumulation of the target protein varied both between cell lines and between individual monoclones derived from this line. The region of the HTR5 gene was used as a target for the delivery of the dIFN gene due to its high transcriptional activity in the cells of the A. thaliana suspension culture [The created series of monoclonal cell lines served as a convenient model for analyzing the expression features of the target  culture ; howeverUbi7 gene using TALEN. However, in this work, the target marker genes were inserted into the genome in such a way that they are placed under the control of the native promoter (the Ubi7 gene promoter), and it is possible that the absence of expression variability is associated with this fact. The absence of expression variability within a single insertion site was demonstrated in a study on site-specific gene insertion into the tobacco genome using the Cre/Lox system [The observed high variability in the level of accumulation of the dIFN protein in lines with knock-in can be possibly explained by the fact that during insertion, mutations occur in the regulatory region of the target genes that affect their expression. The contribution of somaclonal variability in the diversity between cell lines in target gene expression and protein accumulation is also not excluded. The variability in the expression of several genes integrated into the rice genome using recombinase is shown in the work . Forsythx system . Howeverx system . The absx system . FurtherHTR5 and 12 kDa subunit of microsomal peptidase genes, a change in the expression of which could reduce the viability of cells, as well as their ability to reproduce.It should be emphasized that the integration of foreign genes in some cases is accompanied by a change in the expression of own plant genes located in the insertion region. It was found that insertions of transgenes can cause both large-scale chromosomal rearrangements and point mutations, as well as affect the epigenetic landscape of the surrounding chromatin . Our anaHTR5 gene does not statistically differ from the level of accumulation of dIFN protein in the group of lines with random integration of the transgene, it is among the knock-ins that lines 1–4 were identified, a monoclone of line 1, the accumulation of protein in which is more than 2% of the TSP. This cell line is of interest as a bioproducer for the accumulation of recombinant interferon gamma and can be used to reveal the potential for increasing the biosynthesis of recombinant protein by optimizing the conditions for cultivating plant cells and optimizing the composition of nutrient media.Even though the accumulation of dIFN protein in cell lines with targeted insertions into the target region of the


Breast cancer is an important public health problem worldwide, with important disparities in incidence, mortality, and survival rates between developed and developing countries due to inequalities regarding access to measures for the prevention and treatment of the disease. In Brazil, there are higher rates of incidence and a downward trend in mortality in regions of greater socioeconomic development.To evaluate the effect of age, period, and birth cohort on breast cancer mortality in women aged 20 years and older in the states of the Northeast Region of Brazil, an area of high socioeconomic vulnerability, from 1980 to 2019.The death records were extracted from the DATASUS Mortality Information System website  from the Ministry of Health of Brazil. Estimable functions were used to estimate the age-period and cohort models (APC) using the Epi library from the R statistical software version 6.4.1.The average breast cancer mortality rate for the period was 20.45 deaths per 100,000 women. The highest coefficients per 100,000 women were observed in the states of Pernambuco (21.09 deaths) and Ceará (20.85 deaths), and the lowest in Maranhão (13.58 deaths) and Piauí (15.43 deaths). In all of the locations, there was a progressive increase in mortality rates in individuals over 40 years of age, with higher rates in the last five-year period (2015–2019). There was an increase in the risk of death for the five-year period of the 2000s in relation to the reference period (1995–1999) in the Northeast region and in the states of Alagoas, Bahia, Maranhão, Paraíba, and Piauí. In addition, there was an increased risk of death for women born after the 1950s in all locations.The highest mortality rates in all five-year periods analyzed were observed in states with greater socioeconomic development, with an increase in mortality rates in the 2000s, and a higher risk of death in the younger cohorts. Breast cancer is the cancer type with the highest incidence and mortality among women, representing 11.6% of new cancer cases and 6.6% of cancer deaths worldwide in 2018 . Among tThere are disparities in the temporal evolution of the incidence and mortality of this disease between different regions of the world. This is characterized by a higher incidence in countries with high socioeconomic development, and the highest mortality rates in developing countries. In 2018, the incidence in developed countries was 1.73 times higher than that observed in developing countries (54.4 new cases vs 31.3 new cases per 100 thousand women). The opposite was found in relation to mortality (14.9 deaths vs 11.6 deaths per 100 thousand women)  represents the expected mortality rate at age i and period j; θij, number of deaths at age i and period j; Nij denotes the population at risk of death at age i and period j; μ represents the average rate; α1 corresponds to the effect of age group i; βj, the effect of period j; and γk, the effect of cohort k. The cases, yij, are specifed as the y-variate, Poisson errors with log link and ln(Nij) as an offset and then ot the factors age, period and cohort [The effects of Age, Period and Cohort (APC) on breast cancer mortality were estimated for each of the nine states in the Northeast region, considering the Poisson distribution of the number of deaths. The natural logarithm of the expected rate value is a linear function of age, period and cohort effects –18.ln(Ed cohort .L+βL) constitutes the longitudinal trend of age, where αL and βL are linear trend of age and period respectively, whereas the linear trend of the age-specific rates logarithm represents that the drift term is equal to the sum of the period and cohort slopes (βL + γL), where βL and γL are the linear trend of period and cohort, respectively [In the present study age, period and cohort (APC) effect parameters were estimated using the approach proposed by Holford, this method limits the effect analysis to its linear combinations and curvatures. The curvatures represent estimable functions of the parameters and make them constant, despite the parameterization used –18. In aectively , 17.In the present study, the period from 1995 to 1999 was the reference period, the reference cohort was that of 1945–1949, because the central cohorts tend to be more stable and complete than the first and last cohorts , 17.The quality of fit was assessed using deviance statistics, defined as two times the logarithm of the estimated likelihood function of the model. The contribution of the effects was assessed by comparing the deviance of the model with the specifc effect compared to the complete model (age-period-cohort). Results with p ≤ 0.05 were considered significant. Formal testing of the effects done through a sequence of relevant sub-models conveniently arranged, allowing to compare models between adjacent lines .http://www.r-project.org) of the R program (version 4.1) [The risk of death was estimated by relative risk (RR) estimates and 95% confidence intervals according to period and cohort effects. Estimates for the Age Period and Cohort models were made using the Epi library 1.1.18  .In the period of 1980 to 2019 in the Northeast of Brazil, 65,531 deaths due to breast cancer were recorded in women aged 20 years and over . After the correction process, there was an increase of 54.15% in the death records, representing a mortality rate of 20.45 deaths per 100,000 women. After this correction of the death records, there was an increase of about 22.0% in mortality rates in all Northeast states, varying from 22.3% in Pernambuco to 87.18% in Maranhão. The states that showed the greatest increases in the average mortality rates after the correction process of the death records were Maranhão (87.18%), Paraíba (41.64%), and Piauí (39.97%). In the other locations, the percentage increase was around 36%, with the exception of Sergipe (28.19%).In all of the locations under study, a significant percentage increase in breast cancer mortality rates was observed when comparing the first five-year period (1980–1984) to the last five-year period (2015–2019). This increase was over 60%, varying from 62.0% in Alagoas to 222.00% in Maranhão .The states with the highest mortality rates were Pernambuco (21.09 deaths) and Ceará (20.85 deaths), and the lowest mortality rates were Maranhão (13.58 deaths) and Piauí (15.43 deaths) within the stipulated periods, with the highest rates being presented in the five-year period of the 2000s . In all There was an increase in breast cancer mortality rates between the five-year periods from 1980 to 2019, with higher rates presented in the five-year period of the 2000s Figs  and 4.An upward temporal trend was observed in the cohorts of 1900 to 1904 (80 years and older) to 1965 to 1969 (45 to 49 years), with a slight reduction from the 1970 cohort (35 to 44 years). Thus, women residing in the Northeast region, as well as in each of its states, presented higher mortality rates among women from older generations,  as the best fit model for the data , with the exception of Pernambuco, Rio Grande do Norte, and Sergipe, in which the models of best fit were age-cohort and age-drift (sum of the period and cohort slopes (βL + γL), respectively, where βL and γL represented the linear trends for the period and cohort) .With regard to the temporal effect of age after adjusting the APC models, there was a progressive increase in average mortality rates with increasing age, confirming the findings of the exploratory analysis. However, the highest percentage increases between age groups were seen in ages up to 44 years, being over 100%, and the most advanced age groups presented the lowest percentages. The age groups between 55–59 years and 60–64 years stood out, presenting values equal to 8% and 10%, respectively, in relation to their immediate previous ranges .Regarding the effect of the period, there was heterogeneity between the locations under study. In the Northeast region, there was an increased risk of death (RR> 1) from breast cancer in relation to the reference period (1995–1999) in the 1980s (1980–1984 to 1985–1989) and in the five-year periods of 2005–2009, 2010–2014 and 2015–2019. There was a reduction of the risk of death in the following five-year periods: 1990–1994 and 2000–2004. Similar results were observed in the states of Alagoas, Bahia, Maranhão, Paraíba, and Piauí . In CearWomen living in the Northeast region who were born until the cohort from 1940 to 1944 had a lower risk of death from breast cancer (RR <1) when compared to the cohort from 1945 to 1949. Conversely, women born in the 1950s presented a progressive increase in the risk of death from this neoplasm (RR> 1) .The temporal evolution of mortality from diseases and health problems is influenced by access to health services, diagnostic innovations, and improvements in the quality and coverage of death records. In Brazil, after the implementation of the Unified Health System (SUS), there was an increase in access to health services, especially in the 2000s, contributing to the improvement in the quality and coverage of death records –18.The improvement in the quality and coverage of death records did not occur uniformly in all geographic regions. The states of the Northeast region still have a high proportion of records classified as ill-defined cause, incomplete cancer diagnosis, and low death coverage . After tThe states of the Northeast region with the best socioeconomic and health indicators  showed higher breast cancer mortality rates as compared to the states with the greatest socioeconomic vulnerability (Maranhão and Piauí), the opposite was verified in the coefficients of mortality from cervical cancer . A similThe breast cancer mortality rate in the Northeast region in the 2000s is similar to the average mortality rates observed in Brazil in the period of 1980 to 2009 , however, it was lower than the rate presented in the South region with the highest HDI in Brazil  , 28. In The reality that can correlate with the transition from cancer is that there is a relationship between the human development index (HDI) and cancer. Locations with a higher HDI have higher rates of cancer incidence associated with the westernization of habits and lifestyles, while locations with a lower HDI have higher coefficients of neoplasms associated with infection . AnotherIn this sense, the present study showed a progressive increase in the estimated mortality rates with advancing age in all states, with greater magnitude from 65 years of age onwards. Similar results were observed in Mexico, Spain, Taiwan, Germany, and Brazil , 31–34. However, it is essential to highlight that, from the youngest ages up to 44 years, the percentage increases in mortality rates were higher than 100%, while in the more advanced age groups, the magnitude of this growth was less intense, highlighting the age groups between 55–59 and 60–64 years in which reduced percentages and less than 20% were observed. This phenomenon has been verified in other studies and is known as the “Clemmensen hook”, both in incidence and in mortality rates , 36–38. The temporal evolution of breast cancer mortality rates in the Northeast region in all its states showed a significant increase, especially in the five-year period of the 2000s. The increase in breast cancer mortality rates over the last 40 years (1980–2019) in the Northeast states is similar to that observed in developed countries in the period of 1990 to 2013, in which a higher proportional increase was observed in developing countries (46%) in relation to developed countries (8%), although mortality rates were higher in the latter . SimilarRegarding the period effect in the Northeast of Brazil, there was an increased risk of death from breast cancer in the states of Alagoas, Bahia, Maranhão, Paraíba, and Piauí in the quinquennia of the 2000s (2000 to 2019), and in Ceará (2015–2019) in relation to the reference period (1995–1999).It is known that there are differences in the factors correlated to the period effect on the temporal evolution of breast cancer incidence and mortality. Changes in reproductive behavior , the westernization of habits and lifestyles, and access to mammographic examination have been related to the increased incidence of breast cancer in developed and developing countries , 44, 45.The increased risk of death in the most recent periods observed in most states in the Northeast is similar to those observed in Mexico, Russia, Ukraine, and Brazil and Brazilian´s geographic regions , 31, 48.It is expected that, at the beginning of the screening programs, there will be an increase in mortality, since many women who were not previously exposed to secondary prevention (mammography) will be diagnosed in advanced stages of the disease, which reduces their survival and increases the quality of death records. With the consolidation of the screening program, a reduction in mortality is observed due to the increase in the proportion of women diagnosed in the early stages of the disease and the reduction in the proportion of diagnoses in advanced stages , 57.In countries where there has been a reduction in the risk of death from breast cancer since the 1990s, population-based screening programs with coverage above 70% are observed, associated with timely treatment and access to therapeutic innovations in diagnosis, drug treatment  and radiotherapy , 44, 45.The globalization process has generated social, cultural, and economic impacts that have promoted health risks at different intensities between regions of the world , 59. LowRegions with greater socioeconomic vulnerability are unable to provide their population with the available therapeutic resources, contributing to the maintenance of an epidemiological profile in which neglected tropical diseases are associated with an increased prevalence of non-communicable chronic diseases. Disparities are observed between countries in the world and within each country, especially in locations that are excluded from the central circuits of the global economy –61. A reIn Brazil, since the beginning of the 2000s, the Ministry of Health’s prevention and control program has recommended an annual clinical breast examination for women over 40 years old and biannual screening by means of mammography for women aged 50 to 69 years. These guidelines were revised in 2015 , thus maIt is noteworthy that the Northeast region presented a relationship between mammography devices and women well below that recommended by the WHO  . Thus, wAccess to screening and timely treatment with therapeutic innovations can influence the temporal evolution of breast cancer mortality in successive cohorts. In developed countries, an increase in incidence was observed, especially in younger cohorts, due to changes in the reproductive behavior of women, with a high proportion of pregnancies after the age of 30, the option of not being pregnant, a high prevalence of the use of oral contraceptives, and hormonal replacement therapies –68. ThesIn Brazil, with the exception of the Southeast region and the city of São Paulo, an increased risk of death was observed for the younger cohorts , 73, corThe results obtained for the cohorts are corroborating other studies that showed an increased risk of death in younger cohorts in developing countries , 74, 75.In the United Kingdom, the reduction in fertility rates was correlated with the increased risk of cancers related to reproductive factors  . The incThe total fertility rate in Brazil ranged from 7.1 children per woman in the first decade of the 20th century to 1.9 children in 2010 . NullipaA study on the temporal evolution of total fertility rates estimated for 17 cohorts of Brazilian women born between 1890 and 1975 showed a reduction of 6.2 children among women born in 1890–1895 to 2.5 children in the 1970–1975 cohort. Fertility showed a generalized tendency of reduction in all regions, mainly for women born between 1940–1945, but with a different rhythm between geographic regions. In the Northeast, this trend was more accelerated from the cohort born between 1950–1955 , and theIn the Northeast region, the process of declining the total fertility rate started in the early 1970s, dropping from 7.53 children per woman to 3.12 children in 1996 . BetweenPesquisa Nacional de Saúde—PNS) carried out in 2013 showed the high prevalence of consumption of unhealthy foods in the Brazilian population. Although the North and Northeast regions have the lowest consumption percentages in comparison to the other regions, higher consumption of soft drinks, sweets and alcoholic beverages was observed in Pernambuco and Ceará, similar to the states in more developed regions [In addition to the risk factors related to changes in reproductive behaviors, it is important to highlight the factors related to lifestyle—physical inactivity, alcohol consumption, and high-calorie foods—observed in the states of this Brazilian region , 83–85.  regions .The reduction in the risk of breast cancer as result of adopting habits considered healthy has been confirmed in several meta-analyses and systematic reviews –88. In tIf there are no major changes in the PNAO, the risk of death is expected to increase during the coming periods in the younger cohorts from the states in the Northeast region. It is noteworthy that the PNAO was instituted with the objective of contemplating the actions of promotion, prevention, early detection, adequate treatment, and palliative care of cancer, in addition to guaranteeing comprehensive care to the population affected by the disease , 64. HowIn addition, in 2020 and 2021 Brazil has high incidence and mortality rates due to covid-19, which impacted the treatment and control of other morbidities, including breast cancer. Women in fear of the SARS-COV-2 infection have postponed medical appointments and tests for the early detection of breast cancer. In addition, the Oncology Care Network has postponed many surgeries, with a view to reserving beds for the treatment of covid-19. We believe that this situation may have a period effect, increasing the incidence of women diagnosed with an advanced stage of the disease and thereby increasing the risk and death from breast cancer in the next five-year period (2020–2024).Inequalities in the quality of information and coverage of death records between states in the Northeast region represent a limitation of the present study; however, corrections were made, improving the quality of the estimates that were generated. Another limitation is the problem of identifying the effects of the APC models, which are already widespread among researchers and with widely evaluated resolutions. Due to the linearity between the effects of age, period, and cohort, the adjustment allows for obtaining of infinite solutions for the models of maximum likelihood, with different estimates for its parameters, providing the same forecast for any combination of the effects, but making it impossible to estimate the complete model –18. HoweThe main limitation of ecological studies is the impossibility of evaluating the association between the outcome and risk factors, as they present aggregated data on the outcome and exposure, and thus, it is not possible to extrapolate their findings to the individual level . However, it allows us to raise hypotheses about possible contextual factors that may be correlated with the outcome, and these hypotheses can be tested in observational studies with individual data about the outcome and associated factors –17.The highest rates of mortality from breast cancer in all the five-year periods studied were observed in the locations that had the best socioeconomic conditions and access to health services, with a progressive increase in the coefficients of mortality and risk of death in the five-year period of the 2000s, in women born after the 1950 cohort. Such findings may be correlated with the increased prevalence at the population level of risk factors for breast cancer, which was not accompanied by public health measures to promote early detection and timely treatment of the disease, contributing to the increase in mortality rates by this neoplasm over time, especially in women from younger cohorts.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.


PLOS Genetics Editors that results from experiments reported in Fig 5M and 5N were not reproducible in follow-up experiments, raising questions about the validity of the main conclusions.Following publication of this article , the autIn Fig 5M and 5N of this article , the autGiven the results noted above, the authors consider the conclusions, and in particular the results reported in Fig 5M and 5N, invalid. Therefore, the authors are retracting this publication.PLOS Genetics for the inconvenience.The authors apologize to the readers and editors of All authors agreed with the retraction.


The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques . More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image. With advances in computer science and medical imaging, researchers have begun to explore new avenues for making the most of the information buried in medical images. Thus, radiomics emerged as a field of research in its own right and captured the attention of researchers. Radiomics is the extraction of a massive amount of data from conventional medical images, such as standard X-rays, ultrasound, CT scan, MRI, or even PET-scan, in correlation with the diagnosis, the stage of the disease, the therapeutic response, the genomic data, or relatively simple the prognosis . It esseWere we going to use 2D or 3D images?Were we going to use only the tumour pixels, or would we include the peritumoural environment in a bounding box?Indeed, most radiomic studies conducted using conventional learning and published have been conducted with less than 100 patients . We can Initially, on conventional models, we tested the same database to predict recurrence, on 2D versus 3D algorithms, to show the noninferiority or even the superiority of the 2D models. Secondly, we tested the same database to predict recurrence on conventional models using masks from manual tumour segmentation versus bounding box masks to extract radiomic data. And lastly, we built the CNN model based on the results we got from testing conventional machine learning algorithms. In the rest of this report, we will present some generalities on radiomics and machine learning techniques, following the state of the art. We will detail the methodology used to carry out this study. Then, we will present the results obtained and discuss the essential data.This component of the study, which was conducted as a distinct longitudinal research project, intended to elucidate further the relevance of the stable high-frequency characteristics found in the training sample in a different test dataset.The feature extract was the same as the one described previously. We conducted comparisons at two different time points of cognitive stages to see if the levels in stable high-frequency characteristics were altered with cognitive decline. We used survival analyses to see if these features influenced the converting time of individuals.Its informative character. Indeed, radiologists in their daily work rely on this type of footage for most of the interpretationUbiquitous sequence. All MRI protocols included T2 linesIts particular interest in radiomics in rectal cancer which has already been demonstrated by numerous previous studiesThe present study comprises 98 patients, with an average age of 60 years, minimum of 21, maximum of 88 years, and male to female ratio of 2.065. Protocols varied depending on the machines used for image acquisition and among institutions. This variation was taken into account in the data analysis. Among the different MRI sequences available, the T2 sequence was chosen for the examination. This is for various reasons:For reasons of simplicity and computational difficulties, it was decided to ignore the other types of sequences.After manual segmentation, the conventional learning model, based on a 2D radiomic analysis of an image of interest from the MRI baseline Conventional learning model, based on a 2D radiomic analysis of an MRI baseline bounding box Conventional learning model, based on a 3D radiomic analysis of an image of interest from the MRI baseline, after manual segmentation (model 3)After manual segmentation, the conventional learning model, based on a 2D radiomic analysis of a post-MRI image of interest (model 4)Conventional learning model, based on a 2D radiomic analysis of an MRI post bounding box (model 5)After manual segmentation, the conventional learning model, based on a 3D radiomic analysis of a post-MRI image of interest (model 6)Deep learning model, based on a 2D analysis of the two MRI scans (baseline+posttherapy)A DICOM file for T2 (3D) acquisition in the axial plane of the MRI baselineAn NRRD file for 3D segmentation of tumour volume on MRI baselineAn NRRD file for 2D segmentation of the tumour surface on MRI baseline. Segmentation was performed by choosing an image of interest from the acquisition volume image stack, ideally, the one passing through the epicentre of the tumourA DICOM file for T2 (3D) acquisition in the axial plane of the MRI postAn NRRD file for 3D segmentation on the MRI postAn NRRD file for 2D segmentation on the IRM post segmentation was performed by choosing an image of interest from the acquisition volume image stack, ideally, the one passing through the epicentre of the tumour. Reading the DICOM images and performing the segmentation were performed using free software, in everyday use: 3D Slicer, version 4.10.2For each patient, a directory was created to organize the raw data. This directory contained six files:X, Y and Z coordinates were collected on this file. These coordinates will be used for the creation of bounding boxes. The latter is created automatically for all patients on both the MRI baseline and the MRI post. On baseline imaging, the volume or area of interest was defined by any wall thickening or mass syndrome appended to the rectal wall, appearing as an intermediate T2 signal, in diffusion restriction and enhanced after gadolinium injection. On posttreatment imaging, the volume or area of interest was defined by any morphological and signal abnormalities in place of the tumour being treated. The contouring of the lesions was performed by a radiologist manually, image by image for 3D segmentation and manually on a single image for 2D segmentation. Where there was any doubt about the pathological nature of the pixels, they were not taken into account in the segmentation. An Excel file was also created on which were noted the epicentres of the tumours (for the MRI baseline) and the epicentres of posttherapeutic changes (for the MRI post). Thus, after reading all the MRI scans, the Automate the reading of raw imagesAutomate the creation of bounding boxesAutomate the preprocessing of imagesAutomate the extraction of radiomic dataCarry out the selection of informatic dataBuild a random forest model for binary classificationThe output of the model expressed as a binary variable (0 for no recurrence and 1 for recurrence)Train the modelTest the performance of the modelFor each of these models, it has been implemented, the details of which can be found and the objectives of which are as follows:X and Y dimensions and between 2.5 and 4 in the Z dimension. The resampling used a function available on the radiomics library, with as output images of pixels with 1 × 1 mm in the XY plane and 4 mm in z depth. A normalization step used the “normalize” function of radiomics. As a reminder, normalization is a process of changing the intensity dynamics of the pixels so that the samples are comparable. In our case, the dynamics of the intensities of the pixels was fixed on an interval of 0 to 255. In addition to the original image, several filters have been applied to increase the amount of data extracted and make the most of the information in the picture.Due to variation in protocols and the inhomogeneity in pixel size between different patients and different images, The Wavelet filter returns several decompositions The Laplacian of Gaussian filter will be used for contour enhancement, identifying areas of change in intensityThe Square filter squares the intensities of the pixelsThe Square Root takes the square root of the intensities of the pixelsThe Logarithm filter takes the logarithm of the intensities of the pixelsThe Exponential filter, as its name suggests here again, exponentially puts the intensities of the pixelsThe Gradient filter returns the magnitude of the gradientThe LBP2D filter returns a local binary pattern in 2DThe LBP3D filter returns a local binary pattern in 3D using spherical harmonics. The last image returned corresponds to the kurtosis mapA total of 8 filters were applied: Wavelet, LoG (Laplacian of Gaussian), Square, Square Root, Logarithm, Exponential, Gradient, and LBP2D or LBP3D. The data was extracted, in an automated way using the algorithm implemented, from the original image and the images built by applying the eight filters previously mentioned. The extraction process was performed for each model, with over 1000 radiomic data recovered/patient.The whole thing was organized in a data frame on it.The question of selecting data or attributes for classification is a very active line of research in data mining. This selection makes it possible to identify and eliminate the variables that penalize the performance of a complex model insofar as they may be noisy, uninformative, redundant, or not (or not very) reproducible. In addition, the identification of relevant variables considerably facilitates the interpretation and understanding of the radiological aspects of tumours. It also improves the prediction performance of the classification algorithm and overcomes the curse of dimensionality. In our study, the number of variables was much greater than the number of patients or observations (a factor of 10-15), making “selection” necessary. The machine learning literature has described three approaches: the filter, wrapper, and embedded approach. As shown in In this context, we opted for a combinatorial technique, The construction of the predictive model used the “random forest” algorithm (or RF for random forest). It is an algorithm combining many decision trees in a bagging-type approach. According to resampling techniques, bagging or bootstrap is a group of statistical inference methods based on the multiple replications of the studied dataset. Thus, each decision tree receives part of the initial dataset. A decision tree  is a graFor each model trained, we performed an iteration to acquire the confusion matrix and calculate a precision factor on a test cohort representing 50% of the initial dataset.This was done by comparing the resulting 5 AUC values for each of the six models. Considering the nonnormal distribution and the comparison of different groups of values (>2), a nonparametric test such as Kruskal-Wallis was necessary. The alpha risk threshold for concluding a difference was set at 0.05.To build a powerful and efficient neural network, an extensive database was necessary. To do this, we grouped all the images in a single database, namely, the 98 pictures of the MRI baseline ×2 (raw and bounding box) and the 98 photos of the MRI post ×2 . In total, we had 392 images. Patients were randomized into training and validation cohorts at a ratio of 0.8. The images were resampled to have dimensions of 224 × 224 to avoid changing the basic architecture of the neural network.To take full advantage of the potential of the neural network, we have added a data augmentation factor. Kora's library contained functions capable of handling the initial images and performing some modifications to build images with different information.randomization for each of the data random rotations in an interval of 20°x and y axes over lengths corresponding to 0.2 of the matrix dimensiondisplacements in the last to horizontal flipsThis increase in data called onThe present study comprises a total of 98 patients. With an average age 60 years, minimum 21, maximum 88 years, and male to female ratio 2.065, given the imbalance between the number of patients having recurrence (24/98) and the number of patients without recurrence (64/98), a sampling technique increasing the size of the minority sample was necessary to have the same number of observations per sample. For this, we applied SMOTE (for the acronym synthetic minority oversampling technique).The summary of performance results for each model is shown in 1045 radiomic data were extracted for each patient.After applying the selection algorithm, 28 data items were saved as the radiomic signature.1-Display Script. Results of the radiomic data selection for model 1]On an iteration in the form of a confusion matrixThe result of testing the model's performance on the validation cohort is shown:(ii) On all the cross-validation iterations in the form of ROC curves (receiver operating characteristic) (not given here)Note that on this confusion matrix, the performance was more significant than 0.9Note in particular an area under the average curve estimated at 0.83 ± 0.082-Display Script. Confusion matrix for model 1= = = Confusion Matric = = =[[36 6][0 32]]On this confusion matrix, on the first column, we find the number of patients without recurrence, on the second column the number of patients with recurrence, on the first line the number of patients labelled by the model as being at low risk of recurrence, and on the second line the number of patients labelled by the model as being at high risk of recurrence.1046 radiomic data were extracted for each patient.After applying the selection algorithm, 22 data items were saved as the radiomic signature.Results of radiomic data selection for The result of testing the model's performance on the validation cohort is shown:](ii) On all the iterations of the cross-validation in the form of ROC curves. Note in particular an area under the average curve estimated at 0.80 ± 0.12Note that on this confusion matrix, the performance was greater than 0.65Confusion matrix for = = = Confusion Matric = = =[[27 12][14 21] ]The performance in terms of AUC was compared between the six models using the Kruskal-Wallis test. The result is shown in the screenshot below.The results in fact show that there is no difference between the performances of the 6 models.Script. Performance comparison of the 6 conventional learning models(1)from scipy.stats import kruskal(2)X6 = (3)X5 = (4)X4 = (5)X3 = (6)X2 = (7)X1 = stat, p = kruskal (9)print )(10)# interpret(11)alpha = 0.05(12)if p > alpha:∗print("Same distributions (fail to reject H0)')(13)-----(14)else:∗print("Different distributions (reject H0)')(15)-----Statistics-2.675, p = 0.750Identical distributions (fail to reject H0)The trainable dataset was calculated as 134,268,738.In Performance of Each Model. The results of conventional learning models agree with what has been published previously. Indeed, the six models showed a specific predictive capacity, with p values remaining below the alpha threshold of 0.05, once again underlining the potential of radiomics as a predictive factor, in particular of the risk of recurrence of locally advanced rectal neoplasias and its interest in the selection of high-risk patientsComparison of Model Performance. In our study, the Kruskal-Wallis test highlights two main results:The lack of significant difference between models using 2D data vs. models using 3D dataThe noninferiority of models using the bounding box compared to models using tumour contouringWhat more do we bring to literature? Their study evaluating 2D vs. 3D data used CT images on lung sections –13. HoweConversely, MRI is very efficient for evaluating rectal cancer, with minimal indications in evaluating bronchopulmonary cancer. To our knowledge, no previous study has assessed the performance of 2D vs. 3D texture data on MRI images. The advantage of 2D data is undeniable in terms of calculation time and simplicity of the models. The results of our study, therefore, support the use of 2D data.x, y, and z coordinates of that epicentre which are done automatically. We will add pixels on either side of these coordinates according to a number that we define.The performance of models using the bounding box remains comparable to other conventional learning models. Although not inferior, it was not superior in terms of prediction within sample size limits. This bounding box role has already been evoked by Hosni A and Al. who has suggested the presence of information at the level of the immediate peritumoural environment –22. AlthOur CNN model did not show any predictive potential.Error related to the type of pixel valuesThe architecture of the neural network itselfLearning misses too high or too lowImage preprocessing“A dying ReLU”A neural network that is too deepFinally, the lack of correlation between entry (MRI images) and prognosisIt is claimed that there is no error related to the type of pixel values. These have been verified several times. The values were expressed in float64We do not think the error is related to the architecture of the network. We tried several experiments with the addition and removal of different layersConcerning the learning rate, again, several experiments were tested with values between 0.001 and 0.5. The problem persists with all of the values testedAccording to the state-of-the-art data, the preprocessing of the images has been correctly carried outy = 0.03x when x < 0, with x as input and y as output). This leaky ReLU function was supposed to solve the problem of neuronal death, but the model remains in single-class prediction, even after changing the activation functionsThe concept of dying ReLU refers to the fragility of the ReLU activation function. When a large gradient passes through the ReLU neuron, it may change the weights so that this neuron will not activate during subsequent iterations. The result is that the dead ReLU neuron will always give the same output. To overcome this problem, one tested instead of the ReLU functions the “leaky ReLU” function. According to the following formula, this was supposed to give a slight positive gradient when the input was negative (The network depth does not seem to be a problem since different depths have been tested, from the 11-layer model to the 19-layer modelConventional machine learning models were able to capture the predictive information buried in the MRI images of our database in correlation to the risk of recurrence. Therefore, we reject the hypothesis that the failure of the deep learning model can be justified by the lack of correlation between the data and the prognosisTo understand this result as well as possible, we first checked the outputs of the model. These were all of the same class: either it is 0 or it is 1. Secondly, we sought to understand the reasons behind this neural network giving as outputs a unique style. A small tour of the literature allows us to collect some hypotheses concerning this problem:Therefore, it turns out that convolutional neural networks process information from MRI images entirely differently than conventional learning techniques. Where traditional methods receive texture data as input resulting from a straightforward and relatively easy form of engineering, the texture analysis performed in the dark by CNNs which appears different and challenging to understand. Thus, CNNs are not to date an automatic equivalent to conventional learning techniques, contrary to what was assumed at the start of the study. Although they have the advantage of certain automaticity and simplicity of execution, they deserve their “black box” qualifier.This study evaluated and compared six conventional learning models and one deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recurrence. In conventional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in more of the tumour itself. We built a 16-layer convolutional neural network model in deep learning, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image. Conventional education is highly effective, with each model having radiomic signatures capable of accurately predicting the risk of recurrence. Conversely, deep learning was unable to learn patterns correlated with prognosis. It does not constitute an automatic substitute for more conventional techniques, contrary to what has been suggested. Comparing the performance of traditional learning models with each other highlights two main facts. First, where 3D texture data has the disadvantage of being complex and requiring time and significant computational capacity, 2D texture data has shown equivalent performance with the advantage of simplicity and lower cost in computing skills. Second, at the risk of being time-consuming, the manual segmentation before the extraction of texture data in conventional learning can be replaced by the quasiautomatic creation of bounding boxes, less costly in time and energy, and including a peritumoural environment potentially valuable for the performance of the model.


The accuracy of the solubility parameters obtained by the IGC experiment is verified by molecular dynamics simulation. On the basis of this, the influence of repeated units of polyvinyl alcohol (PVA) on solubility parameters is studied, so as to determine the appropriate chain length of the PVA for simulation verification calculation. The results show that the solubility parameters are not much different when the PVA chain length is 30 and above; the numerical trends of the solubility parameters of PVA88 and PVA99 at room temperature are the same as the results of molecular dynamics simulation; the dispersive surface energy 99 with a higher alcoholysis degree is higher than that of PVA88 with a lower alcoholysis degree. The surface specific adsorption free energy  and molecular dynamics simulation methods have been used to characterize the solubility parameters and surface parameters of polymers, which can provide quantitative reference for the further study of the surface and interface compatibility of polymer components in the future. In this paper, the solubility parameters and surface parameters of two kinds of common alcoholysis, PVA Polyvinyl alcohol (PVA) is a kind of environmental friendly water-soluble polymer material  with gooThe properties of PVA are mainly affected by its average polymerization degree and alcoholysis degree ,13,14,152CTx/PVA foam by the freeze-drying process, and it possesses excellent EM absorption ability. This lightweight absorption-dominated electromagnetic interference (EMI) shielding materials minimize the twice pollution of the reflected electromagnetic (EM) wave and have a broad application prospect. Tatiana et al. [The research on PVA as the matrix of composite materials not only studies the influence of PVA structure ,21 but aa et al.  preparedIn this paper, the solubility parameters and interaction parameters  of two kinds of commonly used PVA with different alcoholysis degrees were studied, and we calculated the data of part of the surface energy to transparentize the physical parameters such as the surface of the PVA. The solubility parameters, thermodynamic parameters, and some surface properties of PVA were calculated indirectly by the inverse gas chromatography (IGC) method and computer simulation. At the same time, the accuracy of solubility parameters obtained by the IGC method is calculated and verified by using the Forcite module of computer simulation software . Materials Studio is a material calculation software that can help researchers construct and analyze structural models of materials and predict the relevance properties of materials.This paper provides a quantitative reference for future research on the relationship between the surface characteristics and structure of PVA composites by using the IGC method and computer simulation to calculate the relevant parameters of PVA with different alcoholysis degrees. It can also help other researchers choose the appropriate type of PVA when studying the PVA composites.The experimental drugs and instruments used in this study are listed in The particles of PVA2488 and PVA2499 were crushed by a pulverizer, and the powder of more than 180 mesh was selected and put into a wide-mouth bottle for standby. The difference of PVA2488 and PVA2499 is that they have different alcoholysis degrees; the alcoholysis degree of PVA2488 is 88, while that of PVA2499 is 99.w/w)). The dry mixture was filled in a stainless-steel column having a length of 1.0 m and an inner diameter of 1/8 inch. The column was connected to an Agilent 6890 N gas chromatogram, and the column was aged for 24 h with N2 as the protection gas at 180 °C column temperature. After the aging, an IGC test could be carried out. A Hamilton syringe was used to inject 0.4 μL of probe solvent by hand, and three parallel injections were performed. The retention time was recorded. The solubility parameters and surface parameters of PVA were calculated according to the IGC theoretical formula. Test temperature: 110 °C, 120 °C, 130 °C, 140 °C, 150 °C.PVA2488 was dissolved in acetone, and then, 6201 pickled red vectors were added to the PVA solution (volume of the discs was 1–1.2 times that of acetone) at a ratio of 1:10 . Finally, the solubility parameters of PVA with different chain lengths were calculated by the Forcite module, so as to determine the appropriate PVA chain length for further molecular dynamics research.100) instead of PVA with an alcoholysis degree of 99 to study. PVA with an alcoholysis degree of 88 (PVA88) was established by changing the occupancy ratio of two kinds of repeat units, and the single-chain models of PVA88 and PVA100 were iterated 5000 times by the steepest descent method and conjugate gradient method respectively to obtain the lowest energy. Then, the single-chain structure was annealed several times by the annealing program in the Forcite module, which was heated from 290 to 590 K at 50 K intervals and cooled back at 20 K intervals until the energy was no longer changed. Through this process, the energy balance of the system was accelerated. Then, the optimized PVA chain was used to establish its amorphous structure through amorphous cells (ACs). In order to avoid the ring-pricking phenomenon of a PVA long chain, the lower initial density of 0.6 g/cm−3 was set, and the smart algorithm was used to optimize its geometry for 10,000 iterations to obtain the equilibrium system. The AC amorphous equilibrium system was simulated by molecular dynamics (MD) [Since PVA with an alcoholysis degree of 99 is close to complete alcoholysis, for the convenience of modeling, we use complete alcoholysis PVA (PVAics (MD) ,31. FirsUsing the IGC method , the pror of the probe solvent and n-pentane tm, m is the mass of the fixed-phase PVA, and T is the column temperature.Among them, J is the non-ideal gas compression factor, F is the carrier velocity, i and Po are the inlet and outlet pressure of the stainless-steel column.PThe retention volumes of probe solvents on PVA2488 and PVA2499 at different temperatures are listed in Comparing the retention volumes of PVA2488 and PVA2499, it can be seen that the retention volumes of PVA2488 and alkane probe solvents are larger than those of PVA2499, but the retention volumes of PVA2499 and non-alkane probe solvents are larger than those of PVA2488, which may be due to the fact that PVA2499 has more hydroxyl groups and weaker interaction with alkane non-polar probe solvents than other solvents.From the lculated ,34. The fficient , which iThe values of The Flory–Huggins interaction parameter  solvent , and theThe solubility parameter  Formula  (8):(8)δThe solubility parameters The calculated results of solubility parameter Since the IGC method needs to vaporize the solvent, the solubility parameter at 298.15 K cannot be measured, so extrapolation is required. By measuring the solubility parameters at 383.15 K, 393.15 K, 403.15 K, 413.15 K, and 423.15 K, using the origin software for linear fitting, the linear relationship between the solubility parameters of PVA2488 and PVA2499 and the temperature was obtained, and the solubility parameter at 298.15 K was obtained.−3)0.5, respectively. PVA2499 has a higher solubility parameter, which is probably due to the fact that PVA2499 has more -OH groups, and compared with -COOCH3 groups, -OH has a greater contribution to the solubility parameter delta, and PVA2499 has more hydrophilic hydroxyl groups, forcing a large number of hydrogen bonds between molecules, which makes the cohesive force of the polymer chain stronger, thus making the solubility parameter of PVA2499 larger.It can be seen from 88 model reasonable, the PVA chain with 50 repeat units is selected for molecular dynamics simulation. The random stereoscopic model of PVA100 with a repeat unit n = 50 and the random stereoscopic model of PVA88 with a repeat unit ratio m:n = 6:44 0.5, respectively. The solubility parameters of PVA88 and PVA100 calculated by molecular dynamics simulation were 22.68 and 24.36 (J/cm−3)0.5, respectively. The solubility parameters of PVA with different alcoholysis degrees were studied by the IGC method and molecular dynamics method. The solubility parameters of PVA2488 and PVA2499 measured by the IGC method were 23.74 and 25.62  of the material can be derived from the temperature–volume diagram obtained by MD. In the following research, we will continue to study the molecular dynamics in depth and explore the simulation calculation of other parameters such as TG by this method.