On-demand drug delivery systems using nanofibers have attracted significant attention owing to their controllable properties for drug release through external stimuli. Near-infrared (NIR)-responsive nanofibers provide a platform where the drug release profile can be achieved by the on-demand supply of drugs at a desired dose for cancer therapy. Nanomaterials such as gold nanorods (GNRs) exhibit absorbance in the NIR range, and in response to NIR irradiation, they generate heat as a result of a plasmon resonance effect. In this study, we designed poly (N-isopropylacrylamide) (PNIPAM) composite nanofibers containing GNRs. PNIPAM is a heat-reactive polymer that provides a swelling and deswelling property to the nanofibers. Electrospun nanofibers have a large surface-area-to-volume ratio, which is used to effectively deliver large quantities of drugs. In this platform, both hydrophilic and hydrophobic drugs can be introduced and manipulated. On-demand drug delivery systems were obtained through stimuli-responsive nanofibers containing GNRs and PNIPAM. Upon NIR irradiation, the heat generated by the GNRs ensures shrinking of the nanofibers owing to the thermal response of PNIPAM, thereby resulting in a controlled drug release. The versatility of the light-responsive nanofibers as a drug delivery platform was confirmed in cell studies, indicating the advantages of the swelling and deswelling property of the nanofibers and on–off drug release behavior with good biocompatibility. In addition, the system has potential for the combination of chemotherapy with multiple drugs to enhance the effectiveness of complex cancer treatments. On-demand drug delivery systems (DDSs), which are programmable in a patient-friendly manner, can spatially and temporally control drug delivery at a particular site and the rate of drug release over a specific period of time ,2,3. RecAs a result, implantable drug delivery has emerged as an adequate platform for local drug delivery in cancer by aiming at the tumor site and effectively removing the damage by migrating from the bloodstream to the target tissue . Hence, Nanofibers can play an essential role in wound dressing and can be a good drug carrier in skin cancer treatment with promising properties such as curing, controlling the chemical environment around the wound area that can maintain proper moisture, and protecting the wound from further damage . In this3O4, Si, and graphene oxide introduced inside or on the surface of the nanofibers have been prepared, which play important roles in DDSs [Several nanofibers with inorganic nanoparticles such as Au, Ag, Fe in DDSs ,41,42,43 in DDSs ,45. GNRs in DDSs ,47,48. I in DDSs . PNIPAM  in DDSs . Nanofib in DDSs ,52,53. T in DDSs . This me in DDSs . The pho in DDSs .PNIPAM nanofibers containing GNRs with fast thermal/optical response, high heating rate, and high structural stability were prepared through the electrospinning method . Electro4·3H2O), cetyltrimethylammonium bromide (CTAB), sodium borohydride (NaBH4), silver nitrate (AgNO3), ascorbic acid, 11-bromo-1-undecanol (98%), triphenylmethanethiol (97%), methanesulfonyl chloride , trifluoroacetic acid , triisopropylsilane , and triethylamine  were purchased from Sigma-Aldrich  and used as received. Other organic solvents required for ligand synthesis were purchased from Sigma-Aldrich . Fluorescein was purchased from JUNSEI . CPT was purchased from TCI . The 1H-NMR graph was measured with a CDCl3 solvent using a JEOL ECX-400 400 MHz  spectrometer. Cells were obtained from the Korean Cell Line Bank . All cell reagents for in vitro studies such as phosphate-buffered saline solution (PBS), Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), penicillin–streptomycin, and trypsin-ethylenediaminetetraacetic acid (trypsin-EDTA) were all purchased from Sigma-Aldrich . The cell viability was quantified using 3--2,5-diphenyltetrazolium bromide (MTT), which was purchased from Sigma-Aldrich . Fluorescence spectra were collected using a Neosys-2000 UV-Vis spectrophotometer  and a QM-400 spectrophotometer . A diode laser system with a wavelength of 808 nm and continuous-wave operation mode was used as photo-stimulation, and temperature traces were recorded using a Ti95 infrared camera . The cell viability was measured using a microplate reader .Poly(N-isopropylacrylamide) , octaglycidyl polyhedral oligomeric silsesquioxane (OpePOSS) , 2-ethyl-4-methylimidazole (EMI), gold(III) chloride trihydrate (HAuCl11-trimethyl ammonium (TMA), as reported by Jeong et al. [The GNRs used in this study were prepared according to the well-known seed-mediated growth method using CTAB, and the stability of GNRs in organic solvents was ensured through surface modification of the GNRs with HS-Cg et al. .The morphology of GNRs was investigated by transmission electron microscopy (TEM) . GNRs were dispersed in ionized water (IW). A single drop of GNR solution was applied on a carbon-coated copper grid (200 mesh) and allowed to dry at ambient temperature before imaging with a 50 nm scale bar. The SPR spectra of GNRs were examined using UV–Vis spectroscopy  in the wavelength range of 400–1100 nm by adding 2 mL of GNRs solution to a 4 mL quartz cuvette. Zeta potential analysis was determined by adding a maximum of 400 μL of the three different samples to a zeta potential cuvette with a positive and a negative electrode. The equilibration time for each was set at 120 s, with a total of 50 runs. Three separate tests were carried out under these conditions.To make 5 mL of polymeric solution for electrospinning, 0.5 g (10%) of PNIPAM, 0.15 g (3%) of OpePOSS, and 0.01 g (0.2%) of EMI were dissolved in 4 mL of DMF:THF 1:1. The mixture was stirred for 4 h at room temperature. Finally, 1 mL of TMA-GNRs DMF:THF 1:1 solution (200 nM) was added to the obtained solution, and the mixture was stirred for an additional hour to form a uniform solution. A certain polymer concentration that produces the nanofibers of the same diameter and suitable uniformity was chosen. If the concentration is low, the diameter of the resulting fibers becomes nonuniform and some bonds or even the fibers may not be formed. Conversely, at higher concentration, the diameter of the fibers is large; hence, the rate of penetration of water is slow, which affects the response speed of the composite film. The resulting homogeneous polymer solution was injected into a 10 mL plastic syringe. During electrospinning, a direct-current voltage of 13.5 kV was applied to the needle, and the polymer solution was supplied at a flow rate of 0.05 mL/min at room temperature. Furthermore, the distance between the needle and the collection plate was 12 cm. The electrospinning process was performed at 26.4 °C and 45–50% relative humidity (RH) measured by a thermo hygrometer. The prepared nanofibers were placed in a vacuum oven at 160 °C for 4 h to crosslink the PNIPAM nanofibers.The morphologies of nanofiber scaffolds were analyzed using a scanning electron microscope (SEM)  and confocal laser scanning microscopy (CLSM) . The nanofibers containing GNRs and fluorescein were prepared and cut into circles of 1 cm diameter using a biopsy punch. The nanofiber samples were coated with 250 Å of gold via a Denton Desk V Sputter Coater. The SEM images were obtained at an accelerated voltage of 20 kV and 20 μm scale bar. Fiber diameter distribution histograms were quantified using the SEM micrographs. For each sample, 10 random field images were taken, and 10 fibers were measured in each image. The samples from the same nanofibers with a diameter of 1 cm were taken for CLSM. In this analysis, two samples were prepared that were the original dry nanofiber and water-treated nanofiber. The CLSM images were obtained under 63× magnification. All these characterizations were observed at room temperature.2 up to 60 min at 10 min intervals. The drug release was confirmed by fluorescence spectroscopy after every 10 min of the same sample tube. The pulsatile drug release was performed in a cyclic on–off manner by 10 min of no NIR light irradiation and 2 min of NIR light irradiation at a laser power of 1.6 W/cm2 up to 60 min, and the solution was quantified via a QM-400 spectrophotometer after 10 min of no irradiation and 2 min of irradiation. Furthermore, the cumulative drug release with 0.6 W/cm2, 1.1 W/cm2, and 1.6 W/cm2 laser powers was performed. In this experiment, four different samples were used, and the laser power irradiated up to 60 min at 5 min intervals. For all these drug release experiments, all the samples containing the same amount of drug (2 μg) were used and the experiment was performed at room temperature.To study the behavior of NIR-responsive drug release at different irradiation times, nanofibers containing GNRs and fluorescein (model drug) were prepared. The nanofibers were cut into circles of 1 cm diameter using a biopsy punch and added in 1.5 mL of IW. To check the fluorescence intensity of drug release, the sample tube, which was placed 10 cm from the center of a laser probe, was irradiated directly on the nanofiber sample by a diode laser (808 nm) at a laser power of 1.6 W/cm2 incubator. Thereafter, the media were refreshed and the cells with a density of 3 × 103 cells/well were cultured on the nanofibers under the same conditions mentioned above. After 24 h, the supernatant was discarded, and the cells were incubated with trypsin-EDTA for 5 min to detach from the surface of the nanofibers for cell counting.Cell studies were performed to evaluate the biocompatibility and toxicity of the nanofibers containing GNRs and fluorescein using the U-87 MG (brain cancer cells) cell line. The nanofibers were cut into circles of 1 cm diameter using a biopsy punch and attached to the bottom of a 24-well plate. Prior to cell seeding, all wells containing nanofibers were preconditioned overnight in DMEM containing 10% FBS and 1% penicillin/streptomycin at 37 °C in a 5% CO2 incubator before experiments. The samples were irradiated with NIR light at different laser powers for 2, 10, and 20 min to release the drug from the nanofibers to the cells in the 96-well plate. After 2–4 h or incubation, the MTT assay was conducted using a microplate reader at 570 nm absorption. In this analysis, three control groups such as only cells without nanofibers (cells w/o NFs), nanofibers containing GNRs without CPT (NFs+GNRs), and nanofibers containing GNRs with CPT (NFs+GNRs+CPT) were prepared. The test group analysis was performed based on different laser powers. The laser powers used in this experiment were 0.6, 1.1, and 1.6 W/cm2.PNIPAM nanofibers containing GNRs and CPT were prepared. In addition, 1 cm-diameter circles of the nanofiber samples were used, each of which contained 30 μg of payload. Further, 500 cells/well were incubated in a 96-well plate for 24 h in DMEM containing 10% FBS and 1% penicillin/streptomycin at 37 °C in a 5% COp ≤ 0.05, which was considered to be a statistically significant difference. The results of all numerical variables were examined by the statistical mean, standard deviation, and graphical analysis using GraphPad Prism 9.2.0 software . All cell viability tests were performed with three independent samples from each group for all the different assays.All statistical analyses were performed using ANOVA analysis with a test level set at The plasmon-based photothermal effect of GNRs has become an excellent source of controlled drug release with potential applications and outstanding properties in DDSs. As mentioned earlier, GNRs have strong absorption in the NIR wavelength range; hence, in this study, we irradiated GNRs with NIR light to generate heat to shrink the nanofibers, which results in drug release. The GNRs used in this study were prepared using CTAB, which is a toxic surfactant and unstable in organic solvents. Hence, surface modification was performed on the GNRs to ensure good stability in organic solvents. For this purpose, CTAB attached on the surface of the GNRs was exchanged with TMA through the ligand exchange process. The obtained TMA-GNRs exhibited good stability in DMF:THF 1:1, which we used as the electrospinning solution. CTAB-GNRs and TMA–GNRs were characterized through TEM, UV-Vis spectroscopy, and the zeta potential . The TEMIn general, the morphology of nanofibers does not depend only on the electrospinning solution, but also on the parameters of the electrospinning process such as flow rate, high voltage, and distance between the nozzle and the collector. All the parameters used in this method were selected based on the requirements of this study. As shown in 2 was considered as a minimal power. As shown in 2; however, when the nanofibers were exposed to the NIR light, they immediately shrank, and their surface area decreased to 0.3 cm2. Moreover, 2 to 1.6 W/cm2, indicating that the system had a strong photothermal effect owing to the presence of the GNRs. These results also confirmed the presence of GNRs in the matrix of the nanofibers.The crosslinking of PNIPAM and OpePOSS through heat treatment at 160 °C is a promising method for the high and long-time stability of nanofibers in an aqueous solution. Heat-treatment at 160 °C for 0.5, 2 h, 4 h, and 0 h with four different samples confirmed the cross-linking of prepared nanofibers containing GNRs and fluorescein for on-demand drug release in 2 directly irradiated the nanofibers. The drug release was observed every 10 min. The drug release intensity increased after every 10 min upon NIR irradiation. The thermal response to the NIR light by the GNRs was confirmed. When the nanofibers were not irradiated with the NIR light, there was a slight emission of the drug; hence, the slope of the fluorescence graph was low. However, when the nanofibers were irradiated with the NIR light, the drug was released, and the slope of the fluorescence graph increased rapidly. The on–off drug release profile was further confirmed (2). This cyclic process showed that 62.1 ± 1.1% of drug was released until 60 min. In each step, the nanofibers shrunk owing to the increase in temperature upon NIR light irradiation and swelled when we turned off the NIR light. This process ensured the swelling and deswelling property of the nanofibers [2 resulted in drug releases of 31.1 ± 1.4%, 53.7 ± 1.8%, and 90.5 ± 3.5%, respectively, as shown in The characteristics of the controlled drug release, as a photothermal response to the irradiated NIR light, from the nanofibers containing GNRs and fluorescein were confirmed with a fluorescence spectrophotometer . The NIRonfirmed b. The onnofibers . Differe2. As shown in 2, the water temperature increased to the average values of 29.6 ± 0.05, 38.1 ± 0.03, and 47.1 ± 0.03 °C, respectively. These results confirm the capability of hyperthermia therapy for cancer treatment. When the nanofibers were placed in water at room temperature and the NIR laser irradiated the nanofibers, the increase in the water temperature was recorded using an infrared camera  irradiated the nanofibers for 5 min, which provided the fluorescence intensity inside the cells, indicating the release of fluorescein upon NIR light irradiation , nanofibers containing GNRs without CPT (NFs+GNRs), and nanofibers containing GNRs with CPT (NFs+GNRs+CPT). 2 for 2, 10, and 20 min, the cell viabilities were 85.3 ± 6.4%, 76.6 ± 2.7%, and 62.8 ± 4.7%, respectively. Upon the NIR irradiation at 1.1 W/cm2 for 2, 10, and 20 min, the cell viabilities were 82.2 ± 2.03%, 70.9 ± 2.4%, and 53.7 ± 4.1%, respectively. Furthermore, upon the NIR light irradiation at 1.6 W/cm2 for 2, 10, and 20 min, the cell viabilities were 61.8 ± 9.5%, 46.4 ± 14.1%, and 8.5 ± 4.3%, respectively. The highest cell death was achieved upon increasing the irradiation time and laser power. In 2 for 2, 10, and 20 min, 0.6 ± 0.01, 3.2 ± 0.04, and 5.4 ± 0.07 μg of drug were released, respectively. At irradiation with 1.1 W/cm2 of laser power for 2, 10, and 20 min, the released amounts were 1.2 ± 0.4, 9.0 ± 0.2, and 11.6 ± 0.36 μg, respectively. Moreover, upon the NIR light irradiation at 1.6 W/cm2 for 2, 10, and 20 min, the drug release amounts were 3.4 ± 0.06, 14.0 ± 0.2, and 16.9 ± 0.35 μg, respectively. The hyperthermia effect was obtained due to the presence of GNRs in nanofibers. Cell viability decreased as the NIR laser power increased, as seen in 2, that is, as the temperature increased in the range from 41 °C to 45 °C, the cell viability decreased to 88.6 ± 6.2%, 80.7 ± 2.6%, and 72.3 ± 0.05% after 2, 10, and 20 min of NIR light exposure, respectively [2, thereby indicating that the NIR light was less toxic to the cells. Based on these findings, we conclude that our method appears to be promising for on-demand drug release and therapeutic efficacy in cancer treatment.MTT assay was performed to evaluate the therapeutic potential of our platform through cell viability with U87 cells, and CPT was chosen as an anti-cancer drug. Cells were incubated for 24 h at 37 °C in a 5% COectively ,80. The Herein, we developed PNIPAM nanofibers containing GNRs and drugs that can control the drug release through NIR light irradiation. As CTAB–GNRs are stable only in water and are not dispersed in organic solvents, TMA–GNRs were prepared through an exchange reaction with TMA ligands that are well-dispersed in organic solvents. To prevent the PNIPAM nanofibers from dissolving in water below the LCST, stable PNIPAM nanofibers were prepared through a crosslinking reaction with OpePOSS. The PNIPAM nanofibers containing GNRs and drugs obtained through electrospinning have high thermal/optical responsiveness. In this study, the on-demand drug release was achieved through our versatile nanofiber platform. The results showed that the fabricated nanofibers are structurally stable and have a very large surface-area-to-volume ratio for effective delivery of drugs. A strong photothermal effect was observed by introducing the GNRs in the nanofibers. The heat generated by the GNRs upon NIR light irradiation could control the swelling and deswelling property of the nanofibers owing to the thermal sensitivity of PNIPAM, which results in drug release. This optimal method allows both hydrophilic and hydrophobic drugs to be safely introduced into DDSs and control the drug release to treat cancers and other complex diseases. Through cell studies, good biocompatibility of the nanofibers was confirmed. Furthermore, our method may contribute to the application of the sequential release of multiple drugs, which is the scope of our future studies.


Gastric cancer (GC) pathogenesis is complex and heterogeneous, reflecting morphological, molecular and genetic diversity. Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the major histological types. GC may be sporadic or hereditary; sporadic GC is related to environmental and genetic low‐risk factors and hereditary GC is caused by inherited high‐risk mutations, so far identified only for the diffuse histotype. DGC phenotypic heterogeneity challenges the current understanding of molecular mechanisms underlying carcinogenesis. The definition of a DGC‐specific mutational profile remains controversial, possibly reflecting the heterogeneity of DGC‐related histological subtypes [signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS)]. Indeed, DGC and DGC‐related subtypes may present specific mutational profiles underlying the particularly aggressive behaviour and dismal prognosis of DGC vs IGC and PCC‐NOS vs SRCC. In this systematic review, we revised the histological presentations, molecular classifications and approved therapies for gastric cancer, with a focus on DGC. We then analysed results from the most relevant studies, reporting mutational analysis data specifying mutational frequencies, and their relationship with DGC and IGC histological types, and with specific DGC subtypes (SRCC and PCC‐NOS). We aimed at identifying histology‐associated mutational profiles with an emphasis in DGC and its subtypes . We further used these mutational profiles to identify the most commonly affected molecular pathways and biological functions, and explored the clinical trials directed specifically to patients with DGC. This systematic analysis is expected to expose a DGC‐specific molecular profile and shed light into potential targets for therapeutic intervention, which are currently missing. The main diffuse gastric cancer (DGC) histological subtypes are signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS). Clinical trials, targeted therapies and predictive markers of therapy response directed to any of these entities are residual. This systematic review exposes the lack of integrated knowledge in these clinical entities and uses their mutational profiles to rethink therapeutic intervention. DGCdiffuse gastric cancerIGCintestinal gastric cancerPCC‐NOSpoorly cohesive carcinoma not otherwise specifiedSRCCsignet‐ring cell carcinoma1Gastric cancer (GC) is very heterogeneous from the morphological, genetic and molecular standpoints. The molecular heterogeneity is mirrored in the great variability of morphological phenotypes . In this1.1A large number of histopathological classifications have been proposed over time , 6, 7, 8The Laurén classification  distinguThe classification of the World Health Organization (WHO), 5th edition 2019),  rec1.2The DGC subtype in Laurén classification roughly corresponds to the poorly cohesive carcinomas (PCC) in the 2019 WHO classification .DGC/PCC may show great morphological variability, between different tumours from distinct patients, but also within different areas of the same tumour . The classical picture of DGC/PCC displays a tumour composed of poorly cohesive signet‐ring cells (SRCs)  are known since the early 90’s and remain a hallmark of the diffuse histological type [The molecular and genomic analysis of large GC series has identified a large variety of oncogene activating mutations, tumour suppressor gene (TSG)‐inactivating alterations, gene fusions, somatic copy number alterations (sCNAs) and other structural variations, as well as epigenetic and transcriptional changes . Activatades ago , 20, 21 ades ago , 23, 24.ome‐wide , 26, 27.cal type , 33, 34.Genome‐wide strategies allowed to overcome the limitations of single‐gene targeted approaches. Through next‐generation sequencing (NGS) technologies, the knowledge on GC mutational burden and molecular signatures increased which culminated in the development of GC molecular classifications. These classifications based on genomics, transcriptomics and/or epigenomic profiles can be associated with the main GC histotypes Table .The first GC molecular classification was based exclusively on gene expression profiling and directly addressed differences in GC main histological types . The sec1.4Given the complexity and high cost of the experimental approaches needed to stratify GC according to TCGA and ACRG classifications, several studies proposed less costly approaches to reach similar endpoints Fig.  38, 39,, 47, 48.et al. used 14 biomarkers in a GC cohort from the United States and Ahn et al. studied protein or mRNA expression of MLH1, E‐cadherin, p53 and EBV in a South Korean population to derive five GC molecular subgroups and reproducing a combination of TCGA and ACRG classifications [in situ hybridization (ISH) or immunohistochemistry (IHC) were used to study MLH1, p53 and EBV, but excluded the analysis of MSS/EMT (ACRG) or GS (TCGA) [In 2016, Setia ications , 39. In S (TCGA) . In 2018S (TCGA) .FGFR2 and KRAS gene amplification as potential actionable targets in the EMT‐like subgroup [et al. [et al. and Tsai et al. used alternative surrogate biomarkers to define MSS/TP53 subgroups and CIN subtype, based on p21 protein expression and DNA content assessed by DNA flow cytometry, respectively [In parallel, in 2018, ISH and IHC were performed in a cohort mainly composed of IGC to refine previous GC classifications . In 2019subgroup . Wang et [et al.  studied ectively , 48.1.5The research effort that followed the publication of the TCGA and ACRG molecular classifications led not only to the development of cost‐effective strategies to stratify GC patients according to these classifications Fig. , but alsCLDN18‐ARHGAP26 gene fusion, which is enriched in DGC cases [One of the most evident gaps in GC therapy is the lack of targeted therapies for DGC patients, which mainly reside in the TCGA GS group and in the MSS/EMT ACRG group Table . This grGC cases , 50, 51 GC cases , are jusThe remaining GC molecular subgroups are less problematic regarding prognosis and available approved therapies or emerging ones Table . The subDiffuse gastric cancer has long been recognized as an independent GC histopathological entity; however, along the time, DGC‐associated subtypes have been recognized and deeply characterized at the morphological and molecular levels. Despite this recognition, the most two comprehensive GC molecular classifications (TCGA and ACRG) have been blind to these subtypes, and DGC as a whole, mainly cluster in the TCGA GS group and in the MSS/EMT ACRG group. These two molecular subgroups, besides sharing a dominant GC histotype, also share mutations in DGC‐associated genes. Despite the increasing knowledge about GC histopathological and molecular subtypes, the integration of these two aspects and its clinical utility remains poorly explored.In summary, DGC and its subtypes remain a major problem in terms of prognosis, response to conventional chemotherapy and a still arid ground for novel therapeutic interventions.1.6After recognizing a knowledge gap regarding molecular features potentially useful for clinical outcome prediction and therapy design in DGC, our aim was to identify histology‐associated mutational profiles for this GC histotype and its subtypes. We further used these mutational profiles to identify their most affected molecular pathways and biological functions, and explored the clinical trials specifically designed for DGC patients. This systematic analysis is expected to expose a DGC‐specific molecular profile and shed light into potential targets for therapeutic intervention, which are currently missing.2We collected available literature from 1998 to 2020, in order to get an overall view of the morphological and molecular features of sporadic GC and its molecular classifications. Peer‐reviewed published articles were searched with the help of ‘PubMed’ database using the following keywords in different combinations: ‘mutational landscape, mutational signature, gastric cancer, diffuse‐type gastric cancer, poorly cohesive carcinoma and signet‐ring cell carcinoma’. Only peer‐reviewed published articles written in English language, irrespective of their study design and type , were consulted, and in the context of the present study, we selected only articles with relevant data and best fitting our aim.2.1For the recurrent gene analysis, published studies or public cohorts reporting mutational frequencies and their relationship with GC histological types and subtypes of interest  were searched for, selected upon compliance with the following criteria and refined by manual curation:https://www.cbioportal.org/) encompassing ‘gastric/ stomach adenocarcinoma’ cancer NGS data;Studies from CBIOPORTAL  AND (diffuse‐type gastric carcinoma OR diffuse‐type gastric cancer OR gastric adenocarcinoma OR gastric cancer OR poorly cohesive carcinoma) NOT review.(genomic variation) AND (diffuse‐type gastric cancer) NOT review.(target sequencing OR whole‐exome sequencing OR whole‐genome sequencing) AND (diffuse‐type gastric cancer) NOT review.Manual curation was further performed on all publications selected with the above criteria, aiming at finding those presenting histological classification for each tumour, either according to Laurén  for compWhenever gene mutation frequencies per histological type or subtypes were not provided, their calculation was performed based on the formula:According to criteria 1, seven different entries were identified; from these, only three data sets were used due to the availability of Laurén histological classification and specific gene mutation frequencies. According to criteria 2a, there were 162 results from which five publications were selected after manual curation. Criteria 2b retrieved 11 results, from which two had already been found with previous criteria and eight did not comply with our criteria after manual curation; therefore, only one publication was selected. Criteria 2c retrieved 29 results, from which four had already been found with previous criteria and 24 did not comply with our criteria after manual curation. Accordingly, a single new publication was selected based on criteria 2c. Overall, 3 data sets and 7 original articles were selected  terms, biological processes and molecular functions associated with each gene set using Enrichr .2.4www.clinicaltrials.gov , and using as query terms ‘diffuse gastric cancer’, which retrieved 24 trials; ‘signet‐ring cell carcinoma’ with 19 trials; and ‘gastric poorly cohesive carcinoma’ with no results. After identifying only those associated with GC, 12 studies were selected. Only two clinical trials  were specifically directed to DGC patients, and one was directed to patients bearing SRCC (NCT01717924 – ADCI002 Study). Beyond these, and given the promising results recently obtained using anti‐claudin 18.2 antibodies, we manually added the MONO study where almost 50% of patients enrolled had DGC (NCT01197885 – MONO) [Relevant clinical trials directed specifically to DGC patients were selected by searching the website  – MONO) . In totaFor these, a deeper analysis on therapeutic strategy, candidate drugs and molecular targets was performed.2.5There are several limitations in this study, namely the discrepancies between mutational profiles and frequencies among cohorts, which may be due to several factors: the methodological approach applied by different research groups ; the heterogeneity of GC ; and the sample size of the cohorts under analysis . Another issue is the lack of diversity in the geographical origin of the cohorts used in the selected studies for analysis, which reflects data available in the literature. However, the reproducibility of findings across the various molecular classifications (e.g. TCGA and ACRG) indicates that relevant GC‐associated molecular defects likely override population differences. Our analysis was restricted to mutational data, leaving behind transcriptomics and epigenetics, which can provide valuable data. Copy number variation analysis would have been a strength in this study; however, there are very few publications listing CNVs per case or their relative frequency to allow an accurate analysis. In addition, recent data suggested that copy number gains tended to occur more commonly in IGC than in DGC that was the focus of this work . In addi3Strategies for gene mutation detection evolved from single‐gene analysis to the analysis of multigene panels, whole exomes or even whole genomes. This created the opportunity to derive multigene profiles that are valuable to correlate molecular defects with specific histological subtypes.3.1The data compilation performed herein and depicted in Fig. To derive the mutational landscape from DGC, we performed a recurrent gene analysis, as described in the methodology section that provided three mutated gene sets, both for DGC and for IGC: specific mutated genes, transversally mutated genes and enriched mutated genes.3.2CDH1 genetic variants as the hallmark of the DGC histological type [CDH1 is the only gene found to be mutated in all DGC data sets and in none of the IGC data sets (specific DGC‐mutated gene)  and KMT2C (12%) have a previously reported association with DGC; CSMD1 (21%), OBSCN (15%), RYR2 (13%) and TAF1 (12%) have been previously associated with GC but not DGC, and THSD7B (12%) with no previous association with GC.This analysis confirmed cal type , 33, 34,ne) Figs . CDH1 vaThere are other specific DGC‐mutated genes depicted in Fig. 3.3BRCA2 and ERBB2 were specific IGC‐mutated genes , according to four out of six studies.We next focused on the mutational landscape of IGC from six studies, to identify the set of specific IGC‐mutated genes Fig. . No singAKAP9, DLC1, MACF1, RNF43, NRG1, CTNNA2, GSR and PKHD1 were also identified as specific IGC‐mutated genes. AKAP9 (found in 3/6 studies) was the one with the highest average mutation frequency (15%).Additionally, 3.4TP53, FAT4, ARID1A, SPTA1, PIK3CA, GLI3, LRP2, RIMS2, DCLK1, APC, KRAS, SMAD4, ATM and PTEN , as its mutation frequency was >twofold higher in IGC (17%) as compared to DGC (8%)  and IGC  data sets  has its unique molecular profile.For this, we compared the set of genes mutated in SRCC and in PCC‐NOS, described in two Asian studies and obtained with different technical approaches  Data .3.7CDH1, TP53, ATM and KRAS) were found in two independent data sets per histological subtype  were found enriched in SRCC (27% for both) as compared to PCC‐NOS (10% for both)  were found to be exclusive of SRCC (SRCC‐specific mutated genes)  Fig. . While C3.10RHOA, BRCA2, JAK3, BRCA1 and MSH6), were found exclusively mutated in PCC‐NOS (PCC‐NOS‐specific mutated genes)  Fig. . While RThis mutation frequency‐focused analysis revealed more similarities than differences between SRCC and PCC‐NOS, regarding the set of genes affected in both subtypes. However, some very specific features were also found per subtype, likely supporting their morphological differences.3.11TP53, ATM, APC, ARID1A, SMAD4 and PTEN or oncogenes such as PIK3CA and KRAS   in relation to PCC‐NOS  Fig. . Among tDH1 Fig. . There aIGC Fig. . These dNOS Fig. . Both inNOS Fig. , an assoNOS Fig. .EZH2, SETD2, PDGFRA, BRAF, PTCH1, RPL5, JAK3, VHL, BRCA1, GLI1, MSH6, HDAC9 and ERBB4) in relation to SRCC  compared the use of fluorouracil (5‐FU) combined with cisplatin with the use of the latter plus S‐1 in 361 patients with metastatic DGC (Table The MONO study (NCT01197885), a phase II clinical trial, examined the outcome of zolbetuximab  monotherapy in a series of patients with recurrent or refractory, locally advanced or metastatic and CLDN18.2‐positive gastric, gastro‐oesophageal junction, or oesophageal adenocarcinoma , that is currently recruiting, was designed to address the inherent chemoresistance of SRCC. It evaluates primary surgery vs primary chemotherapy followed by surgery, using epirubicin for topoisomerase II inhibition combined with cisplatin and 5‐FU .The NORDICA phase I study NCT03977220), which is specifically directed to DGC patients, is not yet recruiting and aims at using microtubules stabilization agent (paclitaxel) combined with S‐1  would share a similar mutational profile, and whether DGC arising in the HDGC context would mimic a mutational profile closer to that of SRCC.CDH1 and rarely by CTNNA1 pathogenic or likely pathogenic variants and is dominated by the development of early‐onset DGC, mainly SRCC [The so‐called HDGC syndrome is caused mainly by germline nly SRCC , 67, 68.CDH1 [CTNNA1 [We could only find two studies reporting the somatic mutational landscape of HDGC tumours , 69. The [CTNNA1 .CTNNA1 germline mutated tumour [RHOA, a specific DGC and PCC‐NOS gene , in TCGA GS and ACRG MSS/EMT, mainly encompassing DGC, in comparison with other molecular subgroups.To address this gap in knowledge, and shed light into potential targets for therapeutic intervention, we explored a set of studies reporting on multigene mutational analysis to identify DGC‐associated mutational profiles, molecular pathways and biological functions. We chose this approach, as mutational profiles are rather stable genetic events, easy to detect, interpret and less costly as compared to RNA profiles, methylation patterns or other epigenomic approaches. On the other hand, several tumour‐specific mutations constitute well‐proven, highly sensitive and specific predictive biomarkers of response to selective targeted therapies and are now an indispensable laboratory tool for therapeutic decisions .TP53, ARID1A, SMAD4, PIK3CA, PTEN, KRAS, APC, ATM and SPTA1 are driver and central genes in gastric tumorigenesis, and transversal to the main GC histotypes and DGC subtypes [Our systematic analysis confirms that subtypes .Also, herein we identified specific sets of genes that define the main molecular pathways classically involved in either DGC or IGC. However, tumours emerge and evolve due to a mutational profile that can be shared with other tumour types. We could find that the combination of all recurrently mutated genes either in DGC or in IGC triggered the involvement of similar molecular pathways regardless of being histotype‐specific or common to both histotypes. This may explain why conventional therapies, and even some approved targeted therapies, demonstrate clinical benefit regardless of the GC histological type.CDH1 and RHOA. This result is reflected in the findings from the TCGA GS and ACRG MSS/EMT subgroups, which are dominated by DGC. According to both studies, this subgroup presents the worst prognosis within GC molecular subtypes, resistance to conventional chemotherapy and so far, have no approved targeted therapies [RHOA and less frequent mutations in CDH1 in comparison with early‐onset DGCs [We further showed that the most recurrently mutated and specific genes in DGC, across many studies, were herapies , 37, 49.set DGCs , 71.TTN gene, whose mutations were found specifically in DGC and on average in 40% of the cases. Although not previously related specifically to DGC, TTN was found as one of the top five hub genes of a specific co‐expression module positively correlated with GC pathological tumour and lymph node stages [TTN gene mutations were correlated with poor prognosis and predicted tumour mutation burden and immunotherapy efficacy in GC [FAT3 and KMT2C, herein found mutated on average in 15% and 12% of DGC, respectively, and found previously associated with this cancer histotype [KMT2C loss was shown to promote epithelial‐to‐mesenchymal transition and was associated with poor overall survival.Although none of the ongoing clinical trials directed to DGC patients use these two proteins as targets for therapy Table , it is te stages . In anotcy in GC . Other eistotype , 78, 79.Although our study has not addressed the role of epigenetic changes in DGC, these events are also considered significant carcinogenic factors, and epigenetic regulation has been claimed as a promising molecular target for therapy in GC precision oncology , 80. HerCDH1, TP53, ATM and KRAS mutations as recurrent and common events of both DGC subtypes, again supporting the role of GC driver genes, and RHOA mutations as a specificity of PCC‐NOS [CREBBP and MAP2K4 genes, whose mutations are enriched in SRCC as compared to PCC‐NOS , reporting all mutated genes found for DGC cases (sheet 2), IGC cases (sheet 3) and SRCC/PCC‐NOS (sheet 4).Click here for additional data file.Data S2. GO TERMS, Biological Processes and Molecular Functions retrieved by Enrichr for each genes list: Transversally + Specific DGC/IGC, Specific DGC/IGC genes, Transversally + Enriched + Specific SRCC genes, Specific SRCC genes, Transversally + Enriched + Specific PCC‐NOS genes, and Specific PCC‐NOS genes.Click here for additional data file.Table S1. Sample size and details of sequencing methodologies used in the selected publications for analysis.Click here for additional data file.Table S2. Details of Clinical trial registered at ClinicalTrials.gov directed to gastric cancer and often including DGC patients.Click here for additional data file.


Culture and social context affect the expression and interpretation of symptoms of distress, raising challenges for transcultural psychiatric diagnostics. This increases the risk that mental disorders among migrants and ethnic minorities are undetected, diagnosed late or misdiagnosed. We investigated whether adding a culturally sensitive tool, the DSM-5 core Cultural Formulation Interview (CFI), to routine diagnostic procedures impacts the psychiatric diagnostic process.n = 256) admitted to a psychiatric outpatient clinic were randomized to a control (n = 122) or CFI-enhanced diagnostic procedure (n = 134) group. An intention-to-treat analysis was conducted and the prevalence ratio and corresponding 95% confidence intervals (CI) were calculated across arms for depressive and anxiety disorder diagnoses, multiple diagnoses, and delayed diagnosis.We compared the outcome of a diagnostic procedure that included the CFI with routine diagnostic procedures used at Swedish psychiatric clinics. New patients  of a depressive disorder diagnosis across arms was 1.21 (95% CI = 0.83-1.75), 33.6% of intervention-arm participants vs. 27.9% of controls. The prevalence ratio was higher among patients whose native language was not Swedish . The prevalence ratio of receiving multiple diagnoses was higher for the CFI group among non-native speaking patients, and lower to a statistically significant degree among native Swedish speakers .The results suggest that the implementation of the DSM-5 CFI in routine psychiatric diagnostic practice may facilitate identification of symptoms of certain psychiatric disorders, like depression, among non-native speaking patients in a migration context. The CFI did not result in a reduction of patients with a non-definite diagnosis.ISRCTN51527289, 30/07/2019. The trial was retrospectively registered. Culture and social context affect the expression and interpretation of symptoms of distress –4, potenMigration and refugee status are risk factors for emotional distress and psychiatric disorders –10. RefuTherefore, the increased risk of distress and psychopathology among migrants and refugees, combined with difficulties in communication, require careful psychiatric assessment. Culturally sensitive assessment tools can meet these clinical diagnostic challenges to some extent. A more comprehensive cultural diagnostic evaluation may facilitate understanding of signs of distress and result in greater differentiation among diagnoses and identification of additional comorbid conditions. The DSM-5 Cultural Formulation Interview (CFI)  is a staThis study evaluates whether implementing the core CFI during routine diagnostic procedures leads to changes in the resulting psychiatric diagnoses compared to the usual diagnostic process alone.We hypothesized that:A larger proportion of patients participating in the CFI (“intervention”) would receive an ICD-10 depressive disorder or anxiety disorder diagnosis, compared to patients engaged only in the usual diagnostic routine .A larger proportion of patients in the CFI arm would receive multiple diagnoses after the initial diagnostic procedure, compared to those in UC.A smaller proportion of patients in the CFI arm would still be pending a definite diagnosis  after the initial diagnostic procedure, compared to those in UC.The study was designed as an unblinded, individually randomized, controlled parallel trial. In the “intervention” group, the core CFI was added to the routine diagnostic procedures and compared to the usual care (UC) assessment among patients referred to the Järva psychiatric clinic. The area served by the three outpatient clinics included in the study covers a suburban population in western Stockholm with a high proportion of migrants . The average socioeconomic status of the population is low, with approximately twice the proportions of low-income and unemployed persons as in the Stockholm region overall .ISRCTN51527289, 30/07/2019. The trial was retrospectively registered. The target number of participants was 150 in each arm. The trial ended when this target number had been approached for consent.Patients were given oral and written information, translated into 12 languages, underlining that participation was voluntary and could be withdrawn at any time without negative consequences. Approval was obtained from the Regional Ethical Review Board in Stockholm (2015/243-31/2). Trial registration All patients, 18 years of age or older, who had not been in contact with psychiatric services during the preceding 2 years were potentially eligible to participate in the study. Exclusion criteria were: acute suicide risk; inability to give informed consent (e.g. impaired cognition); or age above 65 ; and emergency presentations (as randomization was not possible in emergency situations). Referred patients who did not attend the clinical assessment visit were excluded. The inclusion or exclusion was made in two steps. First, the referral was reviewed by a clinical team assigned to assess new referrals, and the exclusion criteria applied at this stage. Second, patients could be excluded during the evaluation if they met clinical exclusion criteria at that point. The same clinical team performed the individual randomization to the intervention or UC arms using a lottery system with equal likelihood of assignment. The patients included in the study were asked for informed consent as early as possible during their clinical consultations. This consent was obtained before the patients were informed about their randomization. Of 801 eligible patients, 385 were randomized to the intervention group and 416 to the UC group  oversaw the diagnostic procedures. Intervention group clinicians consisted of 10 psychologists/psychotherapists, 3 psychiatric nurses, 1 counsellor, and 1 mental health auxiliary. UC group clinicians were 10 psychologists/psychotherapists, 4 psychiatric nurses, and 1 occupational therapist. The clinicians were assigned to the study arms by the outpatient clinic managers to maintain a fair balance of professions and clinical experience. In both groups, 85% of the SDPs were performed by a psychologist/psychotherapist. The clinicians performing the SDP in the intervention arm received special training on the CFI, including lectures, role-plays using the CFI, and discussions of clinical cases. In both arms, the clinical staff had extensive experience working with multi-cultural populations.Two independent groups of clinical staff  and anxiety disorders (ICD-10 F40-F41) identified after the SDP, as they are the most common diagnoses among newly referred patients. Other disorders, such as PTSD, were not analysed because of their small proportion in this sample. As secondary outcomes, we compared the proportions of patients who were still under observation, without an identified diagnosis, at the end of the SDP (delayed diagnosis) and the prevalence of multiple comorbid diagnoses. The effect of the intervention was defined as the prevalence ratio of the above outcome measures between study conditions. Patients whose native language was not Swedish were identified for separate analyses.Data were extracted from electronic health records for visits between August 2015 and May 2017 by the first author (MIW), who was not blinded to participants’ group assignment. Diagnostic data were collected as formal ICD-10 codes documented by the clinicians in the patient’s health record after the mandatory SDP was completed. To control for information bias, diagnostic ICD-10 codes were confirmed by the last author (SB).n = 134) or UC (n = 122) arms. As a measure of association, we calculated the prevalence ratio (PR) and corresponding 95% confidence intervals (CI) of diagnoses of depressive and anxiety disorders, of multiple diagnoses, and of delayed diagnoses at the end of the respective diagnostic procedures. Then, the same analyses were conducted by stratifying patients into two language groups, native Swedish speakers and non-Swedish native speakers, to test whether the CFI might be more useful in clinical situations potentially affected by language difficulties. Some patients were referred to the clinic with a notation indicating the referring clinician’s suspicion of a specific diagnosis. To control for possible preconceptions about primary diagnoses of depression or anxiety disorder from referral information, subgroup analyses were conducted for patients with and without suspicion of diagnosis upon referral, including patients’ native language status. Finally, to ensure equivalent assessment quality between the two arms, separate analyses were conducted for two SDP components (MINI and PHQ-9) and for appointments with a psychiatrist vs. other professionals, using prevalence ratio or mean/median estimates. All analyses were conducted with SAS version 9.4 . Statistical significance was set at p = 0.05 throughout.The primary statistical analysis was conducted as intention-to-treat, including all eligible patients randomized to the intervention  or were native Swedish speakers (53.9%)  and the UC groups (21.9) . The number of multiple diagnoses was higher for the intervention group in the subset of patients who were non-Swedish native speakers, while the reverse was true to a statistically significant degree among native Swedish speakers  . The proportion of patients without a definite diagnosis was higher in the CFI group among non-native Swedish speaking patients  compared to native Swedish speakers  . Assignments of depressive, anxiety, multiple, and delayed diagnoses were similar for this group across the two study arms overall, but the proportion of individuals with a particular diagnostic outcome in the group without a specific diagnostic suspicion at baseline was higher among non-Swedish native speakers in the CFI arm for each diagnostic condition: 36.8% vs. 30.0% (depression), 31.6% vs. 20.0% (anxiety), 36.8% vs. 10.0% (multiple), and 36.8% vs. 20.0% (delayed), respectively.We assessed the prevalence of diagnostic outcomes among participants who were referred without suspicion of a specific diagnosis , of using the MINI, and of PHQ-9 scores. There were no between-group differences in the percentage of appointments with a psychiatrist, the frequency of using the MINI, or in PHQ-9 scores calculated as a median  or a mean  Table .Table 3KIn this pragmatic randomized controlled trial, we compared the usual assessment process with a novel diagnostic procedure that included a culturally sensitive interview protocol, in three psychiatric outpatient clinics serving a multicultural catchment area in Stockholm, Sweden.The CFI had a small effect on diagnostic categorization but varied by diagnostic group. Compared to the usual care (UC) group, patients who were assessed using the CFI were slightly more likely to receive a diagnosis of depressive disorder. This effect was due to the impact of the CFI on the assessment of patients whose native language was not Swedish. The associations were not statistically significant, possibly due to the small sample size. By contrast, no effect from the CFI on the detection of anxiety disorders was observed in the overall sample.Contrary to our hypothesis, we did not find a higher prevalence of multiple comorbid diagnoses when using the CFI in the overall sample. Instead, we found that native language modified the association in the same way as for depressive disorder diagnoses: higher proportion of diagnosed patients in the group whose native language was not Swedish and lower proportion among native Swedish speakers; this finding attained statistical significance in the latter group. Among patients whose native language was not Swedish it appears that the CFI contributed to improved identification of comorbidity. Also contrary to our hypothesis, use of the CFI did not result in a reduction in the proportion of patients without a definite diagnosis at the conclusion of the initial diagnostic procedure. The proportion of patients without a definite diagnosis was higher among non-native Swedish speakers, while there was no association among native Swedish speakers.Our findings suggest that using the CFI may affect the diagnostic categorization in diverse ways among different groups of patients. This correlates with findings from an earlier study in which a culturally sensitive assessment showed diverse effects on diagnostic categorization among different subgroups of patients in a study population with psychotic disorder . The lanThere appears to be a small advantage of the CFI in detecting depression and multiple diagnoses among the non-Swedish speaking patients, indicating that it could be particularly helpful in diagnostic evaluation of symptoms in migrant groups. Previous studies have raised serious questions about the conceptual appropriateness of applying diagnostic systems to migrants whose language of distress does not incorporate conventional psychiatric symptoms commonly found within diagnostic systems , 21. SymThe improved identification of depression and comorbidity in the non-native-speaking group of patients could be due to the culturally sensitive information contributing to a more thorough assessment. Also, the questions might help the patients to describe their situation in greater detail, using their own clinical terms when describing their problems and resulting in a more nuanced clinical characterization. Our higher prevalence of depression diagnoses may represent an undetected overrepresentation of depression disorders at referral among non-native Swedish speakers that was identified in clinical situations where the CFI was used.In a study performed after this RCT was completed, the clinicians in the intervention arm were interviewed about their experience of using the CFI. They said that they approached the patients’ problems in a new manner and that this affected their clinical reasoning and assessment . It is pOur expectation, that integrating the CFI in the ordinary diagnostic process would lead to a more rapid completion of the diagnostic assessment, was not supported; rather, our results point to the opposite. This could be because the answers provided by the CFI required a longer time for analysis, leading to the need for supplementary information. This may have prolonged the assessment process and postponed the diagnostic categorization. This explanation is speculative but deserves to be explored in future studies. According to the SDP procedure in psychiatric care in Stockholm, a diagnostic categorization should be made within 3 clinical appointments, in this study expanded to 4. We do not know what the potential proportions of delayed diagnosis would be with an extended timeframe.Our findings are not explained by different proportions of patients referred with a suspicion of depressive or anxiety disorder, by differences in use of routine diagnostic instruments , or in the proportion of assessments conducted by a psychiatrist. However, it is possible that our findings were affected by the CFI having a greater impact among patients who were referred without the suspicion of a specific diagnosis. The CFI may have contributed information that helped identify symptoms of depression and comorbidity among non-native Swedish-speaking patients in particular. This would reproduce the results of an earlier study in which a culturally sensitive assessment tool helped refine diagnostic assessments and enhanced the identification of comorbidity .Our findings of the CFI impact on diagnostic categorization are small and could be due to chance. Almost none of the associations were statistically significant, revealing the difficulty of attempting to detect small effects with a relatively small sample. Further, clinicians’ extensive experience with multi-cultural populations at our study sites may also have reduced an added advantage of using the CFI.The CFI effects on two of the four diagnostic outcomes were in the direction we hypothesized, based on our understanding of the expected higher impact of CFI in the group whose native language is not Swedish. The CFI was associated with the other two diagnostic outcomes in opposite patterns.Our findings may also be due to the generic effect of adding another diagnostic instrument to the assessment, regardless of the specific cultural focus of the CFI . Since tIn this clinical context, the CFI was never performed by the clinicians making the final diagnosis, but by other members of the clinical team, elevating the importance of team communication and accuracy when collecting information from the health record. Although responses to each CFI question were documented in the health record, it is unclear how the psychiatrists weighed this information when formulating the diagnosis. Differential attention across psychiatrists to the CFI documentation could contribute to the variation observed in the effect of the CFI and may, together with a wealth of experience among the clinicians in multicultural assessments, explain why the CFI did not have a larger effect on diagnostic categorization. Engaging the psychiatrists in how to utilise the CFI documentation and formulate a couple of questions in their own practice about the patient’s understanding of illness, based on the CFI data, might result in an active engagement with the CFI information.This study, to the best of our knowledge, is the first to evaluate the effect of the CFI on psychiatric diagnostic categorization. The study took place in a naturalistic setting, with advantages and disadvantages. One advantage is that the core CFI was evaluated in a real-life context with associated challenges, such as heavy workload for clinicians, high influx of new patients, clinician turnover, and restricted time for communication between professionals. Many patients were excluded at first admission or never approached for consent, which may be a limitation of the study, although the numbers of excluded patients were comparable between groups. The number of patients who did not give consent, or withdrew their consent, was small. Also, an interpreter was used in almost twice as many consultations in the intervention group as in the UC group, although these numbers were small. How this might have affected the results is uncertain.An important limitation was that CFI was not conducted by the psychiatrists in charge of the formal diagnostic categorization, and we lack information on how they weighed the CFI information in their deliberations. This diagnostic process corresponds with clinical praxis in Swedish psychiatric outpatient settings. The division of tasks in clinical teams is common, constituting an area for further development in the implementation of the CFI and a potential limitation of its diagnostic advantage. An additional limitation is that the researcher extracting the diagnostic evaluations from clinical files were not blinded to patients’ study assignment.A strength of the study in terms of internal validity is that the psychiatrists who made the final formal diagnostic categorization did not receive any training in the CFI, preventing inequalities in training from affecting the diagnostic results. However, this may have reduced the effect of the CFI. The clinicians in both groups were experienced in working in a multicultural area, which possibly increased the acceptability of the CFI.The results suggest that the implementation of the DSM-5 CFI in routine psychiatric diagnostic practice may facilitate identification of symptoms of certain psychiatric disorders, like depression, among non-native speaking patients in a migration context. However, the CFI did not result in a reduction of patients with a non-definite diagnosis at initial evaluation. When the CFI is not conducted by the same staff assigning the final diagnosis, the diagnostic advantage of the CFI, when added to the usual procedure, may be limited without further work on how to integrate it in the diagnostician’s practice. Further evaluations of the implementation of the CFI in DSM-5 in psychiatric clinical care and praxis are needed.


We asked if sensation-seeking is linked to premorbid personality characteristics in patients with addictive disorders, or the characteristics follow the sensation-seeking activity. We interpreted the former as a state associated with normal rates of dopamine synthesis, and the latter as a trait of individuals with abnormally high rates of synthesis. We previously determined dopaminergic receptor density in striatum, and we now tested the hypothesis that an elevated dopaminergic condition with increased extracellular dopamine and receptor density follows increased dopamine synthesis capacity in highly sensation-seeking individuals, as measured by positron emission tomography of 18 men with tracer fluorodopa (FDOPA). We detected a site in left caudate nucleus where the volume of distribution of FDOPA-derived metabolites correlated negatively with FDOPA metabolite turnover, consistent with decreased metabolite breakdown in highly sensation-seeking subjects. High rates of sensation-seeking attenuated the dopamine turnover in association with a low rate of dopamine recycling, low dopamine oxidation, and elevated extracellular dopamine and receptors in caudate nucleus. In contrast, low rates of sensation-seeking were associated with rapid dopamine recycling, rapid dopamine oxidation, low extracellular dopamine, and low receptor density. We conclude that the modulation of dopaminergic neurotransmission associated with sensation-seeking is a state of sensation-seeking, rather than a trait of personality following abnormal regulation of dopaminergic neurotransmission.The online version contains supplementary material available at 10.1007/s11682-021-00509-5. Dopaminergic neurotransmission is implicated in reward, motivation, decision making, reinforcement learning, and behavioral addiction, which may compel individuals to engage in risky activities. Sensation seeking is associated with risky behavior, including pathological gambling, alcohol and drug use in adolescence, and predicts development of substance abuse , the rate of loss of DA metabolites from brain tissue (kloss), and the apparent volume of distribution of fluorodopamine (VD), indicative of dopamine storage capacity and turnover.Previous reports from this group showed that DA synthesis capacity is elevated in patients with the trait of psychosis .We did not preselect participants on the basis of score. The ranking is based on the response to questions about individual proclivity to engage in novel or risky activities. At first contact, we accepted only healthy, right-handed candidates who took no medication for central nervous disorders and had no metallic foreign objects. We screened the candidates for signs of psychological or psychiatric symptoms by means of a formal Structured Clinical Interview for the DSM-IV (SCID).Twenty-one men aged 27.6 ± 6.5  passed the first contact and then gave written informed consent to the study as approved by the official Central Danish Regional Science Ethics Committee. All research was performed in accordance with relevant guidelines/regulations. Of these, only 18 men completed the experiments for technical reasons. The mean age of the 18 successful participants was 27.7 ± 6.4 , and the mean of their ZSSS scores was 24.4 ± 3.8  with a median of 25 used to divide the participants into two groups, below and above ZSSS of 25. The ZSSS scores of the two groups averaged 21.6 ± 3.0 (SD) for the low group (n = 9) and 27.3 ± 1.7 (SD) for the high group (n = 9).Subjects fasted overnight before the PET experiment, and a dose of 200 mg (p.o.) Carbidopa (Merck Sharpe and Dohme) was given one hour before the PET recording to minimize the decarboxylation of FDOPA in peripheral tissues , and calculated the blood-brain clearance , the diffusion rate from brain , and their ratio  for both FDOPA and OMFD. This model entails the constraints of a common blood-brain partition ratio for the two large neutral amino acids (FDOPA: − 3). It also assumes that the ratio of the two unidirectional clearances , , and a first-order rate constant, expressing the diffusion from brain of [18F]fluorodopamine together with its acidic metabolites from brain as a single compartment . Of necessity, and by definition, the term kloss is a kinetic simplification based upon the assumption that an equilibrium is obtained between the sum of all [18F]fluorodopamine pools in the vesicles and cytosol of dopamine neurons, and other cells containing DOPA decarboxylase, and the several pools of deaminated metabolites, irrespective of their site of formation ,  of decarboxylated FDOPA metabolites trapped in brain (ml cm− 3). The measure of brain radioactivity includes activity distributed in the plasma volume , as well in the precursor pool  and the effective distribution volume. The composite of the three distribution volumes  constitutes the total tracer distribution  as an index of the steady-state dopamine storage used in the present study. The IOM consists of a set of first-order differential equations that rearrange to a linearized solution, which permits us to calculate parametric maps of VD  from the magnitude of R2 value at the degrees of freedom (n = 15) of this non-linear regression analysis.We first stratified the 18 subjects into two subgroups at the median of the ZSSS scores for preliminary subgroup comparison, and calculated mean VD), divided into the highest and lowest ZSSS value groups, were averaged in common space and subtracted to reveal a difference map of values at t > 3  scores in the present population, are shown in Fig. VD value of ventral striatum between the two subgroups, nor in cerebellum  set of first-order differential equations that rearrange to a linearized solution that yielded parametric maps of VD measures with scores of the ZSSS, we performed regression analysis on the former against the latter for the 18 healthy male volunteers included in the study. We detected voxels (145 mm3) with Student’s t-statistics greater than 2.8 by voxel-wise mapping of linear regression analysis between the VD-parametric maps and the ZSSS scores. To obtain average estimates, we defined a region representing the cluster of t values above the selected threshold (t > 2.8) shown in Fig. VD, K, and kloss were 4.2 ml cm− 3, 0.010 ml cm− 3 min− 1, and 0.0034 min− 1, respectively. The apparent volume of distribution (VD) revealed an inverse relation with the elimination and turnover constant (kloss), in the shape of the hyperbola shown in Fig. R2 = 0.76, P < 0.0001), as expected when the inherent clearance of FDOPA from plasma to brain (K) is unrelated to the sensation-seeking propensity.To test the prediction of a correlation of ls 145 mm with StuA-C.The average individual regional estimates of the turnover rate constants kloss were negatively correlated with the ZSSS scores (P = 0.0033) as shown in Fig. K) were not correlated to the ZSSS scores (P = 0.63) as shown in Fig. VD with the ZSSS scores must be highly positive, as shown in Fig. We determined the linear correlations between the estimates of the remaining parameters of the region of interest and the individual ZSSS scores, as shown in Figs. 4We tested the association between the sensation-seeking propensity assessed by Zuckerman’s sensation-seeking scale, and states of dopamine synthesis and breakdown obtained by kinetic analysis of brain PET images after administration of FDOPA . Alternatively, if no underlying abnormality of dopamine synthesis capacity can be detected, it follows that the sensation-seeking propensity can be regarded a state of mind that underlies the learning of an addictive behavior.VD) was inversely proportional to the dopamine turnover rate as indicated by the rate of loss (kloss), while the estimates of synthesis capacity (K) revealed no correlation with the scores of sensation-seeking propensity. The three parameters identified by the analysis define the kinetics of regional dopamine turnover and storage capacity, of which the DA synthesis capacity is known to be abnormal in patients with neuropsychiatric disorders, including Parkinson’s disease, schizophrenia, and alcoholism  is very small, long dynamic PET sessions may be necessary for the accurate quantitation of the rate of slow continuous loss of metabolites from brain tissue. For this reason, we extended the recording to three hours for these young normal control subjects with different sensation-seeking levels, in order to better differentiate subtle changes in the low magnitude range of kloss estimates. As predicted, the individual kinetic estimates of the group had a very limited range, which replicated the results of earlier studies of FDOPA with the inlet-outlet model (IOM)   activity in association with increased trapping of radiolabeled fluoro dopamine and its metabolites in the dopaminergic terminals of the dorsal striatum of the highly sensation-seeking individuals, linked to increased reuptake of dopamine that allows efficient re-trapping of previously released DA. Due to the structure of the analysis, the three kinetic estimates are interrelated, such that kloss and VD maintain a hyperbolic relationship. The metabolism of intracellular fluorodopamine is mainly subject to degradation by MAO, but the velocity of the entire metabolism, including the breakdown by catechol-O-methyltransferase (COMT) and passive efflux to CSF, is reflected in the magnitude of kloss  imply increased vesicular sequestration of fluorodopamine from the cytosol, associated with increased synaptic release and redistribution to the extracellular space. Thus, reduced values of kloss naturally lead to increased vesicular fluorodopamine trapping, consistent with the results of increased VD in the highly sensation-seeking individuals.The correlation of ZSSS scores with parametric maps of kloss, and the reported turnover constant of MAO (kMAO) − 1. The value of SI for the dorsal striatum cluster is approximately 6,000, in good agreement with the ratio obtained from the values of the vesicular (48 nmol cm− 3) and non-vesicular (0.015 nmol cm− 3) dopamine contents  can be simplified to,Vi is the distribution volume of fluorodopamine in the cytosolic in non-vesicular space. Adding the effective plasma (V0) and the precursor pool (Vf) volumes to both sides yields the expression,Vf does not covary with the ZSSS scores in the present study, because of the unchanged dopamine synthesis capacity. Based on the result of the linear correlation between values of VD and the ZSSS scores, we conclude that sensation-seeking propensity scored as ZSSS in the healthy volunteers is proportional to the product of Vi and SI,α and β are constants. In the relationship, Vi represents the non-vesicular concentration of fluorodopamine, relative to arterial FDOPA. This means that sensation-seeking propensity is inversely associated with dopamine degradation activity, via regulation of the non-vesicular DA concentration.The intracellular dopamine concentration is regulated by a complex mechanism involving DOPA decarboxylase, vesicular transporters (VMAT2), dopamine reuptake sites (DAT), and monoamine oxidase (MAO) that cannot readily be disentangled by current methods. Instead, we introduce a sequestration index (SI), defined as the ratio of vesicular to non-vesicular dopamine contents, indicative of the degree of sequestration, or protection of fluorodopamine from degradation. The magnitude of the SI can be evaluated from the magnitudes of kloss and measures of platelet MAO-B activity, as presented in Fig. MAO modulates human mood, animals’ behavior and vulnerability to psychiatric disorder. MAO-B activity in blood platelets has been used as a surrogate marker of central MAO-B activity , while the FDOPA transfer coefficient or clearance (K), described as a measure of synthesis capacity, remained unchanged in reference to the K vs kloss correlation previously presented for several diseases , which appears to delineate a basic dopaminergic function that is insensitive to the actual state of sensation-seeking  in left dorsal striatum, where dopamine signaling serves to control motivated behavior. The average turnover rate for loss of DA metabolites from brain tissue correlated negatively with the ZSSS scores, whereas the apparent volume of distribution of fluorodopamine correlated positively. We suggest that the elevated DA store of high sensation-seekers reveals a state of mind which is the cause rather than the consequence of a neurobiological trait.(PNG 162 KB)(PNG 106 KB)Below is the link to the electronic supplementary material.


Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware of how vulnerabilities and their exploits are changing gives helpful insights into combating new threats. Applying dynamic topic modeling to a time-stamped cybersecurity document collection shows how the significance and details of concepts found in them are evolving. We correlate two different temporal corpora, one with reports about specific exploits and the other with research-oriented papers on cybersecurity vulnerabilities and threats. We represent the documents, concepts, and dynamic topic modeling data in a semantic knowledge graph to support integration, inference, and discovery. A critical insight into discovering knowledge through topic modeling is seeding the knowledge graph with domain concepts to guide the modeling process. We use Wikipedia concepts to provide a basis for performing concept phrase extraction and show how using those phrases improves the quality of the topic models. Researchers can query the resulting knowledge graph to reveal important relations and trends. This work is novel because it uses topics as a bridge to relate documents across corpora over time. Cybersecurity is a crucial computing area vital to our society due to the rise in cyberattacks and the damage they can do . The risResearchers have used artificial intelligence techniques to extract information from documents such as security bulletins, after-action reports, and descriptions of new software vulnerabilities for many years. Most works in this area have used language understanding technology that extracts references to entities, such as malware instances, software products, IP addresses or process names, and relations between them. Although these data have been helpful for many purposes, they have not addressed temporal aspects of how the cybersecurity landscape has changed over the years. As computer exploits grow in sophistication, understanding past exploits and their evolution could provide insight into new exploits before they occur. A temporal analysis of cybersecurity documents may detect completely novel attacks, such as “poisoning” a machine learning system’s training data, soon after being anticipated or observed.A work by Applying a temporal analysis to cybersecurity language understanding can be done by applying dynamic topic models (DTMs) to documents that span a period of time. We show how to do this using two cyber-related document collections: 17 years of malware reports from Symantec and 20 years of cybersecurity research papers from the arXiv repository. We identify changes in trends over time for each and correlate the two models to show how new malware instances and behavior described in the first collection trigger and influence research changes in the second collection.Domain-specific terminology, meanings, and jargon found in a narrow field like cybersecurity make concept spotting, information extraction, and natural language understanding difficult. The heavy use of acronyms and multi-word phrases with non-compositional semantics exacerbates the problem. To address these issues, we extract common cybersecurity concepts from Wikipedia data and identify phrases and acronyms that refer to them.For the cybersecurity domain, applying and using dynamic topic models is still an unexplored area of research. Using DTMs and performing cross-domain analysis between multiple data sets extracted from cybersecurity collections are also a novel contribution. We put forth this work to show how temporal analysis by means of cross-domain understanding can be applied to cybersecurity and could be used to foster a document-based search tool.• Man in the middle attacks that intercept communication• Brute force attacks that obtain and exfiltrate protected information• Denial of service attacks that flood networks or services to lessen and prevent access• Phishing attacks that use deception to fool people into disclosing sensitive information• Social engineering attacks that manipulate users to obtain access to information• Malware attacks that compromise data or resource integrity, confidentiality, and/or availabilityAs the Internet and its use in everyday tasks has become ubiquitous, so has cybersecurity-related crimes. Cybersecurity attacks can be divided into those involving software, hardware, and networks , and canDynamic topic modeling (DTM)  providesResearch by More recent research by We use dynamic topic models (DTMs) to evolve topics over time in data collection. A key innovation to our method is using Wikipedia concepts to provide domain context for preprocessing the documents. Typically, a bag-of-words approach is used for methods such as topic modeling. However, in domains with technical terminology, we have found that using concepts that include domain-relevant phrases and explicitly looking for those concepts during document preprocessing improves topic modeling results .Automatically extracting domain concepts from a text collection is a challenging problem. An important contribution of this work is our approach to automatic domain concept extraction using Wikipedia concepts. In our previous work , we showFor scientific data and domains with domain-specific jargon, starting with known concepts provides a means for building a suitable set of phrases to use during extraction. As we showed in our previous work and highlight in In our current work, we build on this previous approach by using the Wikipedia concept hierarchy to find domain-specific concepts. We exploit Wikipedia concepts related to cybersecurity as a context model for training the DTM. Since Wikipedia concepts are easily mapped to concepts in DBpedia , WikidatWith this context in place, we preemptively search for mentions of cybersecurity concepts in the text of each document before standard text processing methods are used, such as stop word removal, low-frequency term removal, and lexical-based processing. In addition to the Wikipedia-based concept search, we apply standard text processing to find other words of interest subject to stop word removal, low-frequency removal, and lexical-based processing.We generate three files from the collection: one large vocabulary file across all time slices, a file that defines the words for each document, and a file that indicates to the dynamic topic modelling system how many files exist per time slice. We then use the dynamic topic modelling system to generate the topic model. The whole process is governed by a knowledge graph created and updated as documents in the repository are processed. The knowledge graph is then populated as the dynamic topic models learns latent topics over time, as shown in Initially, the knowledge graph encapsulates the concepts from Wikipedia for the specific domain. As more documents are processed, it includes graphs of the documents in the collection(s) and their properties, including their discovered concepts. We add graphs of the topics and topic probabilities to the knowledge graph during the topic modeling process. For cybersecurity, this is particularly useful when looking for documents with common exploit properties.We captured a total of 3,836 concepts from Wikipedia which were used to establish the context for the topic modeling portion. We started with the concept phrases “cybersecurity,” “computer security,” “cyber security,” and “cyber.” For each phrase, we retrieved Wikipedia pages; then for each page retrieved, the outgoing links were found on that page. We perform a one-level traversal to formulate the concept list. The longer this list of concepts, the longer is the preprocessing time. When we increased the traversal to three levels, processing time doubled. Some example concepts are shown in When we perform the preprocessing step, which is the step that finds the word and word phrases in the text and accumulates their frequencies, we then treat the acronym for a word phrase as if that phrase was found and increase its frequency count. The task of automatically generating possible acronym is difficult because acronyms can be confused with stop words and can also be polysemous. For example, a simple Wikidata query shows that 32 entities have an English name or alias matching “CPA.”We use a general heuristic to extract acronyms from phrases that takes the first letter of each word in the phrase (excluding pronouns) and generates an acronym by concatenating the letters. We captured 245 cybersecurity acronyms based on the cybersecurity Wikipedia concepts. Some example acronyms based on our concepts are shown in “W97M.Coldape macro virus” is also seen as “W97M.Coldape virus” and “W97M.Coldape.” When calculating word frequencies, treating these as the same concept is important for statistical significance. Our topic model ontology, described in more detail in In particular, for the cybersecurity domain, another issue that commonly arises relates to variations of phrases. For example, In this work, we used dynamic topic models to model multiple document collections over time. The output from preprocessing is a set of files that are required by DTM including the vocabulary file representing the vocabulary for the document collection, a file which indicates the number of time slices, and a file that defines the words and frequencies for each document. We then apply our cross-domain topic modeling methodology to find topic pairs that have low divergence scores based on Jensen–Shannon divergence .n most probable terms and generate a new vector of term V that consists of the top terms from V, if the term exists in For each topic in domain 1 described by After we perform the above process for every pair of topics across The smaller the threshold, the fewer the pairs will be used to obtain documents across the two domains. Given a pair of topics below the divergence threshold, documents for each topic are discovered based on a second threshold, which defines how much the document mixture model should pertain to the paired topic. This method can be compared to an unsupervised clustering technique in that documents are grouped together based on two metrics: one measuring how much the topic contributes to the document’s mixture and the other measuring how low the divergence is between any given topic pairs. We use this method to map unknown documents to a known set of documents to understand the unknown document better.The further contribution of this work is the topic model ontology (TMO)  which prBased on our early work , we furtN terms, which map back to keywords and concepts captured when processing each document, as shown in graph fragment in the upper left corner of We also capture relationships between documents and topics while generating the topic model. In our knowledge graph, we use the TMO schema to represent each topic and its top In addition, as we perform cross-domain correlations, we are able to represent topic mappings ontologically, as shown in the graph in the bottom left portion of We evaluated two different data sets over time for this work. The first is a collection of research papers from arXiv categorized by their authors as relevant to Cryptography and Security. Documents in this set tend, on average, to be relatively long. The second is a collection of Symantec Malware reports that are uncategorized. Their documents tend, on average, to be shorter. The number of arXiv papers increases steadily over time, and the number of Symantec Malware reports is strongly correlated to the frequency of security exploit events. We describe each of these data sets in more detail below.type of the report with the types “trojan,” “worm,” and “virus” as the highest types present across reports. We show the distribution by year in The Symantec Malware reports data set is a set of reports which describe malware incidents and actions to take to combat the malware. They are typically short, with an average of about 9 KB size or approximately 4,500 words. There are a total of 16,167 files with a total size of approximately 149.6 MB. This data set spans 17 years, from 2000 to 2016. These data are unlabeled. However, in each report, there is a text that indicates the The arXiv Cryptography and Security data set is a set of research papers related to cryptography and security. They are typically longer in size, with an average of about 50 KB size or approximately 20,000 words. There are a total of 3,913 files with a total size of approximately 215.5 MB. The documents are categorized as “Cryptography and Security,” but in addition to this base category, many are further tagged by the other categories, with about 94 categories in total. This data set spans 20 years from 1997 to 2016, as shown in To assess the value of our approach, we performed two different experiments. One experiment assesses the improvement in the quality of the topics using concept phrases which are obtained from Wikipedia. A second experiment measures how the dynamic topics can be used in understanding how the cybersecurity domain is changing over time.In this experiment, we tested how the topic model performs given the Wikipedia-provided concepts as the context for the models in comparison with a standard bag-of-words model without a predefined context. We used the arXiv data set, labeled by category. We split the data set into a train and test set (60/40 split). We then built two separate topic models, one which has the concept-provided context and the second which does not use a context but rather a standard bag of words. We performed the similarity portion of the experiment without the temporal component in order to isolate the concept context portion of this work strictly. We evaluate document similarity by grouping documents by the subcategory type.We generated topic models for the arXiv data set with and without the concept context using the training set and then used the test set for evaluation. We measured similarity defined by the average probabilities across documents for each topic. We can observe two things from the heat maps. For certain subcategories of papers, we see stronger similarity between train and test in the concept model, such as the mathematics graph theory train test section in the heat maps. Sometimes, this difference in similarity is subtle but still evident, as in the mathematics number theory train test section in the heat maps. Small differences can indicate significant improvement in topic word probabilities that are correlated to a given set of documents. As we will show in quantum cryptography was a concept defined by our knowledge graph. The word photon is seen in the top ten most probable words, but it is not in the top ten most likely words for the topic without context concepts.When comparing the dynamic topic modelling using 100 topics and the full data sets, the first difference we observed is the vocabulary size. With the concept model, the size of the vocabulary is 13,016, and the vocabulary size for the non-concept model is 11,824. Between the two models, as presented in the first example in intrusion detection is a concept specific to the domain and found in the text. In the topic model without concepts, intrusion detection is never found. The only topic that mentions intrusion and detection contains words that are more generic and less specific to the domain.The second difference is since the model is guided by the domain concepts, words that are harder to interpret because they are more generic move farther toward the tails of the probability distribution. In the second example in Although the approach we describe is meant to improve human topic understanding, we also evaluated how the cybersecurity domain concepts affected classification performance by running a classification experiment using the concept-based method and compared this with a bag-of-words method and an automatic phrase extraction method called Phrasemachine In this experiment, we measured how reliable the model is for understanding specific concept evolution. We build the dynamic topic model for the arXiv data set and the Symantec Malware data set. We compare topic evolution for a range of 20–100 topics using intervals of 20. We also experimented with the variance hyperparameter in the DTM which controls how much time variance is allowed among topics; however, we used a low variance of 0.05.When we build topic models over time, topics evolve over time based on the documents in the collection at that time point. Our observation is as we increased the number of topics, we saw more granularity among topics. Topics represented more narrow mixtures. We also observed concepts that drop off of one topic and fall into another at various points in time. Deciding on the number of topics can be based on a measure called perplexity, which approximately gives a good estimate. Another approach is to observe how the topics change visually as the number of topics increases and choosing the total number of topics based on the requirements of the problem being solved. For cybersecurity, we suggest using a larger number of topics for more granularity. Since discovery is a big part of the cybersecurity process, that is, looking for information based on past exploits, the more granular the model, the more information can be inferred. However, we have found that since concepts can be represented in different topics simultaneously over time due to the co-occurrence of the concept with other words, plotting the concept over time across all topics provides a visual trend of how the concept is changing over time.malware as a use case and observing all topics for a given model, Using the concept malware was detected on February 16, 2006 (According to Wikipedia, the first ever 16, 2006 . The modmalware in the top ten most probable terms for these two trends. The first trend which has a smaller spike starting around 2006 shows association of malware with how to protect computing devices using antivirus software. The second trend which has a more pronounced spike relates to mobile computing and malware. The malware presence in the top ten most probable terms is also observed in the models created with 60 topics and 80 topics.In In the 100-topic model, the third trend observed has a moderate spike around 2008 but then steeply increases from 2011 onward and is shown in malware detection was in 2006, in the 100-topic model, as shown in malware was in the top ten most probable words for a particular topic in 2000. Indeed, a Symantec Malware report did specifically reference malware with a time stamp of 2000. In fact, this report details an exploit titled Infostealer which was found on December 8, 1997.Although the first official When observing the same trend information for the arXiv Cryptography and Security research papers, as shown in malware is found in the top ten probable words by 2007, as shown in the first table in For the arXiv Cryptography and Security research papers, However, since the arXiv data set is a collection of research papers rather than a set of detailed exploit reports, the effects are less pronounced. Also, there tends to be a delay in concepts reflected in research papers due to the time to perform research and write the paper. The Symantec reports, in contrast, are typically focused on a single narrow vulnerability, exploit or attack, and of a more urgent nature.Given this type of analysis, one could use these models to understand how long it takes to build momentum among published work for a particular attack type. We could potentially use this information to identify the time period that might have the most relevant work for a given attack type or to predict future attacks based on currently popular research concepts.Modeling the topic modeling process in support of cross-domain mapping provides the benefit of finding documents that are similar across domains through mapped topics. This could be invaluable for scientific domains and domains such as cybersecurity, where discovering new information relevant to cybersecurity concepts can yield information to help identify potential threats.adware, malware, microsoft, and windows_xp. When we found documents that were correlated and shared concept occurrences, we discovered research papers that may be informative. A research paper published in 2004 entitled “Modelling the costs and benefits of Honeynets” has many concepts in common, including malware, computer_crime, forensic, honeynet_project, keystroke_logging, and network_security. This document could provide insight as to how to thwart a potential attack of this type. Another research paper published in 2016 with the title “Combating Malicious DNS Tunnel” described similar attacks and included the following concepts: command_and_control, denial_of_service, fraud, malware, mcafee, national_institute_of_standards_and_technology, and theft.In the cross-domain case, where we built topic models for both the arXiv Cryptography and Security research papers and the Symantec reports, finding relevant research papers both before an attack and after an attack can help in the information discovery process. For example, a Symantec report dated 2007 included the following concepts: biometric, cryptosystem, information_security, mobile_device, and transport_layer_security. This allowed us to recognize that it was similar to a later document published in 2016 with the title “Attacks on Fitness Trackers Revisited: A Case-Study of Unfit Firmware Security.” That document had many overlapping concepts, such as information_security and transport_layer_security.The discovery of related documents also applies to documents within a single domain. For example, after modeling documents from the arXiv Cryptography and Security research papers, we were able to find a document published in 2012 related to biometrics with the title “Secure voice based authentication for mobile devices: Vaulted Voice Verification.” We modeled this document in terms of known concepts that included Cybersecurity threats are increasing. As systems evolve, new vulnerabilities are discovered because threats are also evolving and developing new attack strategies, leading to the creation of new categories of attacks. Tools to help model and understand such trends in cybersecurity threats and attacks are useful in helping combat them. In this work, we have provided insight into how to use dynamic topic models for cybersecurity documents to understand how the concepts found among documents are changing over time. We demonstrated an approach that uses an ontology of cybersecurity concepts extracted from Wikipedia to find phrases that improve the readability of the topics and provide better human understanding of the topics. We represent the results of the dynamic topic model as a knowledge graph that can also be used for inference and information discovery.Our future work will include automatic concept acquisition using phrase extraction methods. As the knowledge graph is built over time, we will apply a method for automatically identifying new concepts that are representative of the domain. Our preliminary work on this shows that it can improve the models and produce better results. We also plan to build a user interface to support knowledge search and discovery over a learned topic model knowledge graph. This capability will enable domain experts to run searches against the knowledge graph, discover new knowledge, and serve as active components in human-in-the-loop architectures.