Breast cancer (BC) is the most common malignancy and the second leading cause of cancer death among women in the United States. The consumption of natural dietary components such as broccoli sprouts (BSp) and green tea polyphenols (GTPs) has demonstrated exciting potential in reducing the risk of BC through the regulation of epigenetic mechanisms. However, little is known about their impacts on reversing epigenomic aberrations that are centrally involved in the initiation and progression of BC. Previously, we have determined the efficacy of combined BSp and GTPs treatment on the inhibition of the growth of a mammary tumor in a transgenic Her2/neu mouse model. We sought to extend our previous study to identify universal biomarkers that represent common mechanistic changes among different mouse models in response to this dietary regime by including a new transgenic mouse model, C3(1)-SV40 TAg (SV40). As a result, we identified novel target genes that were differentially expressed and methylated in response to dietary botanicals when administered singly (BSp and GTPs) and in combination (BSp + GTPs) in both mouse models. We discovered more differentially expressed and methylated genes in the combination treatment group compared to the singly administered groups. Subsequently, several biological pathways related to epigenetic regulations were identified in response to the combination treatment. Furthermore, when compared to the BSp and GTPs treatment alone, the combinatorial treatment showed a more significant impact on the regulation of the epigenetic modifier activities involved in DNA methylation and histone modifications. Our study provides key insights about the impact of the combined administration of BSp and GTPs on BC using a multi-omics analysis, suggesting a combinatorial approach is more efficacious in preventing and inhibiting BC by impacting key tumor-related genes at transcriptomic and methylomic levels. Our findings could be further extrapolated as a comprehensive source for understanding the epigenetic modifications that are associated with the effects of these dietary botanicals on BC prevention.  Breast cancer (BC) is the most common malignancy and the leading cause of death among women worldwide . The BC Cancer progression is closely related to aberrations of genetic modifications and epigenetic alterations. Histone modifications and DNA methylation are the most important epigenetic mechanisms . DNA metSeveral mechanisms have been proposed to explain the chemopreventive properties of SFN in BSp, including the induction of apoptosis, cell cycle arrest and the activation of phase I Cytochrome P450s (CYP) enzymes and phase 2 detoxifying enzymes ,13,14. SCbl and Zfp800) and several differentially methylated genes that showed overlapped expression or methylation change patterns across both mouse models. Overall, our findings are important by uncovering novel biomarkers in response to this combinatorial dietary regimen that can potentially be used for future breast cancer prevention or therapy.Our results showed that the combined administration of BSp and GTPs showed more prominent effects on BC inhibition by modulating epigenetic profiles. Herein, we performed a correlated analysis between the DNA methylome by reduced representation bisulfite sequencing (RRBS) and RNA transcriptome by RNA sequencing in an SV40 mouse model with the single and combination treatment of BSp and GTPs. Our results showed the cumulative impacts of BSp and GTPs on the transcriptomic and methylomic levels. In comparison with the data from our previous studies in a Her2/neu model, we identified two differentially expressed genes -SV40 TAg (FVB-Tg(C3-1-TAg)cJeg/JegJ), was purchased from Jackson Lab . These mice are known to develop spontaneous ER(−) mammary tumors due to overexpressed SV40 transgene with a median tumor latency of ~15 wks . The femControl = 9) was administered control AIN-93G diet. In the BSp group (NBSp = 7), mice were administered a modified 26% (w/w) BSp diet  as used previously [GTPs = 7) were orally administered with 0.5% GTPs Sunphenon 90D  in drinking water, which contains > 90% polyphenols. Lastly, in the combination group, mice (NCombination = 7) were fed with a combination BSp and GTPs diet as described above. The BSp and GTPs concentrations incorporated in this study were pharmacologically achievable and also possessed translational potential [Female C3(1)-SV40 TAg (SV40) mice were assigned to four groups and administered with the dietary botanicals from prepubescence (3 wks) until termination 20 wks) of the experiment. The control group  and total RNA were extracted from the same frozen mammary tumor that was used for library construction. The gDNA was extracted using a DNAeasy kit  as per the manufacturer’s protocol. DNA concentration and quality were measured using a NanoDrop spectrophotometer  and Qubit dsDNA High sensitivity kit. Subsequently, total RNA was extracted using TRIzol reagent  based on the manufacturer’s protocol and RNA concentration was determined using NanoDrop spectrophotometer. RNA integrity numbers (RIN) were evaluated using an RNA Nano bioanalyzer chip, and only the samples with RIN > 7 were retained for sequencing. After the isolation, RNA and DNA were frozen and kept at −80 ℃ until further use. Control = 6, NBSp = 7, NGTPs = 3 and NCombination = 3), approximately 45 million sequencing reads were generated per sample, which were considered for library construction. In RRBS libraries, the bisulfite-converted libraries yielded an average of 75 million high-quality 75 bp pair-end reads, indicating that the bisulfite conversion efficiency was greater than 98% overall.Both RNA-seq libraries pair-end reads (75 bp × 2) and RRBS libraries single-end reads were sequenced on Illumina NextSeq 500 at the Heflin Center of Genomic Sciences . For each mRNA preparation . Briefly, the RNA-seq raw fastq files were trimmed and further aligned using a pseudo-aligner Salmon  to mousewas ≥1.5 . For the RRBS data, we utilized the pipeline that integrated the assessment of the read qualities , followed by the trimming process , alignment using Bismark (v0.16.34)  and diffSignificant genes at the transcriptomic and methylomic levels were used to analyze gene networks, canonical and functional pathways. The identified differentially expressed genes (DEGs) that were used as an input into the STRING database  to unravp ≤ 0.05 were considered as a threshold to select DEGs across the treatment groups . A methylation change ≥ 10% (FDR ≤ 0.05) was considered a threshold for the methylome analysis to identify DMGs. For other statistical computations and analysis, data were presented as mean ± SD, which were further analyzed using a two-tailed Student’s t-test or one-way ANOVA along with Tukey’s post hoc test.Principal component analysis (PCA) components were calculated from normalized gene expression data across different treatment groups using the prcomp function in R package stats (v3.6.3). Finally, the contributions of each PCA component were extracted using the “get_eigenvalue” function. For transcriptome analysis, fold change (FC) ≥ 1.5 and Gene expression for specific genes of interest was examined by real-time PCR with SYBR GreenER qPCR Supermix . For PCR arrangement, we used 2 µL of cDNA, 4 µL of iTaq SYBR green from Bio-Rad, 2 µL of nuclease-free water, 1 µL of forward and reverse primers for specific genes of interest with a total volume of 10 µL. Upon the preparation of the samples, the gene expression was assessed in triplicates by PCR using the CFX Connect Real-Time System . Specific gene primers for DE across SV40 and Her2/neu mouse models were obtained from Integrated DNA Technologies  . DNA extraction from mammary tumors of control, BSp, GTPs and combination treatment groups was described before. The global DNA methylation status was explicitly indicated by the levels of 5-methylcytosine (5-mC) in total DNA and was determined by the MethylFlash Methylated DNA 5-mC Quantification Kit from EpiGentek. In addition, the MethylFlash Hydroxymethylated DNA 5-hmC Quantification Kit  was employed to quantify global hydroxymethylation status in total DNA samples. The nuclear protein was extracted to determine overall DNMT and HDAC enzymatic activities using the EpiQuik DNMT Activity/Inhibition Assay Ultra Kit (EpiGentek) and the EpiQuik HDAC Activity/Inhibition Assay Kit (EpiGentek), respectively. Moreover, histone acetylation activities were evaluated using EpiQuik Acetyl Histone3-Lysine9 and Histone3-Lysine27 (H3K9 and H3K27) Assay Kits as per the manufacturer’s guidelines.w/w) and 0.5% GTPs in the drinking water, respectively. The concentrations of these diets are physiologically available and demonstrate a practical consumption level by consuming ~2 cups BSp/day or drinking 1–2 cups of green tea for an adult human [We used an SV40 mouse model in this study that can spontaneously develop mammary tumors early in life due to the overexpression of the SV40 oncogene. The dietary concentration for the BSp and GTPs used in this study was formulated as 26% BSp chow diet  treatments on the tumor development in the SV40 transgenic mice. Our results showed that both the single and combinatorial treatment of GTPs and BSp led to the suppression of tumor growth . HoweverCtrl = 6, NBSp = 7, NGTPs = 3 and NCombination = 3) libraries for the RNA-seq and RRBS analysis, respectively. To study the combinatorial effects of BSp and GTPs on the epigenomic and transcriptomic changes in comparison to the BSp and GTPs treatment alone, we constructed 19 (NDE genes= 250), amongst which 225 (90%) genes were upregulated and 25 (10%) were down-regulated using a 5% false discovery rate (FDR) and a fold-change (log2 FC) cutoff greater than 2  in response to the combination treatment in both the SV40 and Her2/neu mouse models (Cbl is highly expressed in BC and significantly inhibits the transforming growth factor-β (TGF-β) tumor-suppressive activity [Furthermore, we compared the RNA-seq results in the SV40 model with our previous study in the Her2/neu model . Interese models A. These e models B. Additie models C. Howeveactivity . Our rest-test p-value ≤ 0.05) were color coded with red (upregulated genes) and green (down-regulated genes) as shown in The combination treatment-induced DEGs in the SV40 mice were uploaded into the STRING (v11)  databaseCtrl = 6, NBSp = 7, NGTPs = 3 and NCombination = 3) were designed. Each of these libraries produced a minimum of seven Gb clean reads, which were sequenced and aligned to the reads of the mouse reference NCBI GRCm39 genome using Bismark [To further elucidate the global DNA methylation changes across the different dietary treatment groups, we applied RRBS analyses in the mammary tumor samples of the SV40 mice. A total of nineteen single-end libraries  in the BSp and GTPs treatment group, respectively. Out of 162 identified DMGs in the BSp treatment group, 77 DMGs were hypomethylated and 85 were hypermethylated. Of 636 identified DMGs in the GTPs group, 503 DMGs were hypomethylated and 133 were hypermethylated. These DMGs distributed amongst the various genomic regions across each treatment group are provided in We applied downstream analyses to identify the CpGs methylation levels across the BSp, GTPs and combination treatment groups. A total of 162 and 636 differentially methylated genes (DMGs) were identified (DEGs+DMGs =4 and GTPsDEGs+DMGs =2)   . The comd Zap70) C. To betd Zap70) D. Out ofd Zap70) D followen = 266) is much higher than the overlapped DEGs (n = 2) in the combination group across the two different transgenic mouse models that showed similar responses to the combination treatment in inhibiting BC, suggesting consistent epigenetic landmark changes may respond to this dietary treatment regardless of the genotypic difference. In summary, the combination treatment group showed a more significant impact on both the transcriptomic and methylomic levels in two different mouse models, indicating that epigenetic mechanism-induced gene expression changes may play a role in the regulation of the preventive effects of the combination treatment of BSp and GTPs in inhibiting BC. Because our previous study on the Her2/neu mouse model revealed that combinatorial treatment had a more significant impact on methylation changes than BSp and GTPs treatment alone, we therefore compared the methylomic profile in an SV40 mouse to that in a Her2/neu mouse model . As a reTo better understand the biological functions of DNA methylation changes by combination treatment, we analyzed the functional gene associations in significantly altered methylomic profiles using DAVID. Our results indicated that DMGs-involved multiple cellular pathways were regulated by the combination dietary treatment of BSp and GTPs, such as DNA repair, oxidative phosphorylation, DNA methylation, histone acetylation, covalent chromatin modifications, apoptosis, the cell cycle and many others . These pWe further evaluated the epigenetic-driven mechanisms due to the administration of different dietary groups. Clinical trials have demonstrated that cruciferous vegetable BSp and green tea as well as their phytochemical extracts, including SFN and GTPs, are highly effective therapeutic and chemopreventive agents against various cancer types ,14,16,18In this study, we applied a genome-wide analysis to the multi-omics data and explored the underlying mechanisms by BSp and GTPs treatment  in the BC transgenic SV40 mouse model. This multi-omics approach integrates transcriptomic and methylomic data that facilitate understanding how epigenetic mechanisms can influence gene transcriptional profiling in a genome-wide perspective in response to BSp and/or GTPs dietary administration. We categorized the global DNA methylation and gene expression patterns in mouse mammary tumors from different treatment groups. Our results demonstrated that exposure to BSp, GTPs and the combination treatment (BSp + GTPs) led to changes in the transcriptome and genome-wide DNA methylation profiles across different genes. We identified that the combination treatment exhibited a greater efficacy in inhibiting tumor growth than the single treatment groups. Consistently with the phenotypic trend across different dietary groups, our results also showed that the combination treatment exhibited a more significant impact in modulating the transcriptomic and methylomic profiles than that of any single treatment. This could potentially serve as a critical contributor toward combinatorial approach-induced preventive effects on BC.Cbl and Zfp800) in both mouse models, indicating universal impacts on these genes by combinatorial treatment across the different mouse models. In contrast to the combination treatment group, BSp or GTPs individually did not have overlapping DEGs at transcriptomic levels between the two mouse models. Cbl is a proto-oncogene with E3 ubiquitin-protein ligase activity that is primarily responsible for signal transduction in response to various kinds of stimuli [Cbl is to ubiquitously activate RTKs, thereby suppressing the RTK signaling toward lysosomal degradation [Cbl can also act as a tumor suppressor gene involved in the pathogenesis of different cancer types. For instance, a study demonstrated the primary function of Cbl in restricting tumor cell proliferation and invasion [Cbl gene led to malignant behaviors by directly targeting microRNA (miRNA) miR-124-3p functions [Zfp800 gene expression can inhibit tumor cell proliferation in pancreatic cancer [Zfp800 with various biological pathways, such as cell proliferation, cell growth, etc. Thus, the identification of these two genes that were significantly differentially expressed in both mouse models may provide mechanistic insights into how the combination treatment of BSp and GTPs exhibited the most prominent chemopreventive effects on breast cancer than any single treatment. Cbl and Zfp800 and their related pathways could be target responders to this novel combination treatment and may contribute to its preventive effects against BC. These effects may be universal and independent of the tumor genotype. It is possible that each preclinical model might influence different genes to target the same signaling or metabolic pathways leading to similar outcomes. For example, both models regulate important biological pathways such as DNA repair, the cell cycle, protein transportation and histone acetylation as well as others. The regulation of these important signaling pathways may contribute to the preventive effects of the combination strategy against BC.Our transcriptomic analysis in the SV40 transgenic mouse model identified 250 DEGs in the combination group compared to the BSp and GTPs treatment group with fewer DEGs. Our previous study on the Her2/neu transgenic mouse model reported 895 DEGs in the combination treatment group with even lesser DEGs in the BSp or GTPS treatment groups [ stimuli ,39. The invasion ,42. Anotunctions . Similarc cancer . This stAmpd1, Capn3, Itgp1bp2, Prima1, Tmem233 and Symd1) followed a positive relationship between the DNA methylation and gene transcription regulation . As these gene changes may result in alterations of multiple key cellular pathways, we therefore presume that the combinatorial treatment of BSp and GTPs may reverse aberrant epigenetic landmarks leading to altered key gene expression profiles. Although only 13 genes are identified that may be regulated by DNA methylation, this suggests that other mechanisms may participate in combination treatment-induced gene expression changes beyond epigenetic regulation, and an increased sample size in future studies will help identify more target genes modulated through epigenetic mechanisms. In concordance with our above findings, our study also revealed the combination treatment had a more significant impact on several essential epigenetic modulators, such as the DNMTs, HDAC and acetyl H3-K9 enzymatic activities, than any single treatment of BSp or GTPs in the SV40 model. For example, we found the combinatorial treatment reduced the global DNMT enzymatic activity. This might explain that the majority of the identified DMGs in the combination group were hypomethylated. However, the global cytosine methylation (5-mC) levels remain constant. This discrepancy between DNMT and 5-mC has previously been shown in human prostate cancer, which is associated with the tumor stage and differentiation that can be used as a biomarker for prostate cancer [Aligning with our previous study , our mete cancer .Overall, our studies indicate that the dietary administration of combined BSp and GTPs can induce more significant impacts on BC suppression than any of these nutrients administered alone, which may be due to a possible additive or synergistic impact of this novel dietary regimen on transcriptomic and methylomic profiling across different transgenic BC mouse models. As a result, our research could lead to a novel dietary approach in the prevention of BC and also help the identification of biomarkers in response to this combinatorial intervention.


Background:  Blockade of tumour necrosis factor (anti-TNF) is effective in patients with Crohn’s Disease but has been associated with infection risk and neurological complications such as demyelination. Niemann-Pick disease Type C1 (NPC1) is a lysosomal storage disorder presenting in childhood with neurological deterioration, liver damage and respiratory infections. Some NPC1 patients develop severe Crohn’s disease. Our objective was to investigate the safety and effectiveness of anti-TNF in NPC1 patients with Crohn’s disease.Methods: Retrospective data on phenotype and therapy response were collected in 2019-2020 for the time period 2014 to 2020 from patients in the UK, France, Germany and Canada with genetically confirmed NPC1 defects and intestinal inflammation. We investigated TNF secretion in peripheral blood mononuclear cells treated with NPC1 inhibitor in response to bacterial stimuli.Results: NPC1 inhibitor treatedperipheral blood mononuclear cells (PBMCs) show significantly increased TNF production after lipopolysaccharide or bacterial challenge providing a rationale for anti-TNF therapy. We identified 4 NPC1 patients with Crohn’s disease (CD)-like intestinal inflammation treated using anti-TNF therapy . Anti-TNF therapy was associated with reduced gastrointestinal symptoms with no apparent adverse neurological events. Therapy improved intestinal inflammation in 4 patients.Conclusions: Anti-TNF therapy appears safe in patients with NPC1 and is an effective treatment strategy for the management of intestinal inflammation in these patients. Variants in theNPC1 gene account for 95% of the genetic defects in patients with NPC. Patients experience progressive neurological impairment with reduced muscle tone, seizures, speech impairment and early onset dementia as well as hepatosplenomegaly, interstitial lung disease with recurrent pneumonia. Niemann-Pick disease Type C (NPC) is an autosomal recessive lysosomal storage disorder presenting most typically throughout childhood or adolescence. The NPC1 defect disrupts auto-phagosome maturation which impairs antibacterial responses towardsSalmonella typhimurium andAdherent-invasive E coli (AIEC). Mice with NPC1 deficiency can develop mild intestinal inflammation.Around 7% of patients with NPC1 develop inflammatory bowel disease (IBD), in particular Crohn’s disease with weight loss, diarrhoea, perianal fissures and fistula formation. However, anti-TNF treatment can cause neurological side effects such as demyelination and non-demyelinating central nervous system (CNS) inflammation including vasculitis. Since NPC1 patients could be particularly vulnerable to adverse neurological events due to their underlying progressive neurological disease we investigated if anti-TNF is a safe and effective treatment strategy for intestinal inflammation in NPC patients.In paediatric and adult patients with Crohn’s disease anti-TNF agents  are standard of care to induce and maintain remissionIn this retrospective cohort study, we identified patients with NPC1 and intestinal inflammation by contacting specialist centres involved in their care. We recruited patients with NPC1 and intestinal inflammation who had been on anti-TNF agents as part of their management. Data collection took place in 2019 –2020 with data collection from 2014 until 2020. Selection bias was reduced by the use of pre-determined inclusion criteria, and as a consequence, patients with incomplete datasets were excluded. Data was collected from historical medical records to calculate wPCDAI scores to reduce memory bias.Patients with genetically confirmed NPC1 and confirmed intestinal inflammation  treated with anti-TNF agents were identified by contacting specialist facilities responsible for their care in Europe and Canada. We identified 5 patients and retrospective data was collected. Data was obtained from local hospital notes by the patients’ lead clinician. Out of five patients, one was excluded due to incomplete data being available. De-identified patient information was provided by the clinicians responsible for their care. was calculated at baseline, 4 weeks, 10 weeks, 6 months and a year after starting anti-TNF agents. A change in wPCDAI of >17.5 was used to indicate a response to treatment with a wPCDAI <12.5 suggesting remission, wPCDAI > 40 suggesting moderate disease and wPCDAI > 57.5 suggesting severe disease. Potential side effects including unexpected changes in neurological function and infectious complications (e.g. increased frequency of respiratory infections) since starting anti-TNF therapy were recorded.Information on previous treatment and responses  were obtained in addition to response to anti-TNF therapy. The mathematically weighted paediatric Crohn’s disease activity index (wPCDAI)Peripheral blood mononuclear cells (PBMCs) from healthy volunteers supplied from the NHS blood bank as leukocyte cones were obtained via the Oxford GI biobank.Salmonella typhimurium (Multiplicity of infection (MOI): 10) or Adherent invasic E. Coli (AIEC) (MOI: 10). Supernatant was collected and frozen at 0, 2, 4 and 6 hours after stimulation. TNF quantification was performed using an eBioscience ELISA kit  as per manufacturer’s protocol.PBMCs were separated from whole blood by using a Ficoll gradient. PBMCs  were either treated with 2ug/ml U18666A (Sigma Aldrich) drug for 24 hours or were left untreated. After U18666A treatment, PBMCs  were either stimulated with lipopolysaccharide (LPS) (200 ng/ml) orinfected withGraphPad Prism 9 software .R is an open access alternative. Statistical significance was calculated using a Mann-Whitney U test or ANOVA for multiple test comparison. P < 0.05 were considered significant .Data analysis was performed usingWritten informed consent was obtained locally by the patients’ lead clinician from the patients or their guardians for deidentified data to be used for research purposes. Patient data were collected as part of the Oxford IBD cohort study and a sub-project to investigate rare diseases . Anonymous patient data were analyzed. Healthy donor samples were obtained via Oxford GI illnesses biobank. caused a TNF associated inflammatory cytokine response. Healthy volunteer PBMCs were incubated with U18666A before being stimulated with LPS,AIEC or Salmonella. At baseline (0 hour) we did not observe any significant differences in TNF levels between U18666A treated and control cells   . This su. The mean age of Crohn’s disease onset was 8.1 years with a mean treatment period of 27.75 months. Therapy used for control of intestinal inflammation was exclusive enteral nutrition (EEN) (n=1), local steroids (n=1), systemic steroids (n=4), 5-ASA (n=2), adalimumab (n=2), infliximab (n=4), methotrexate (n=1), anti-IL12/23p40 targeting ustekinumab (n=1) and the integrin antagonist vedolizumab (n=1). Two patients received both infliximab and adalimumab (patients 1 and 4) and patient 3 stopped infliximab and was switched to ustekinumab due to loss of effect before restarting both agents on family request. Complete remission was temporarily achieved in one patient (patient 1) using infliximab induction therapy. All other patients achieved a partial response (patients 2–4)  . Two patts 2–4) . In all 4 patients who received anti-TNF therapy, over the 1 year of follow up, we did not observe any adverse neurological events or worsening of neurological function suggesting anti-TNF therapy does not accelerate neurological decline in this patient group. Furthermore, there was no increase in respiratory complications or other serious infection in patients reported in this cohort at 1-year of follow up. Neurological decline was seen in some patients after the first year follow up period however, clinicians felt this was consistent with NPC1 progression. Patient 2 died whilst on anti-TNF therapy due to neurological and respiratory complications including inability to clear secretions. The clinicians felt this was due to NPC progression rather than anti-TNF treatment. However, this patient experienced an initial honeymoon period of improving motor skills, better respiratory function, nutrition, pain control  during the first year of treatment with a significant improvement in quality of life. Patient 1 had an improvement in both a large perianal wound pocket and a vaginal fistula and became able to swallow small amounts again. Unfortunately, the patient did not have an endoscopy prior to starting treatment so it is unclear if this was due to improvement in neurological function or upper gastrointestinal manifestations of Crohn’s disease.Four patients had a partial response to infliximab therapy. One patient had poor disease control on infliximab. Despite this, infliximab appeared to achieve better reported disease control on clinician global assessment than prior immunosuppression, steroids and exclusive enteral nutrition.We assessed patient response to treatment using the wPCDAI scoring system to indicate severity of intestinal symptoms at baseline, 4 weeks, 10 weeks, 6 months and a year. Patient 2 initially had severe Crohn’s disease with a wPCDAI of 82.5 and showed a significant improvement in score to 17.5 over the first 90 days of treatment with anti-TNF therapy . This imin-vitro cell model increased levels of TNF upon LPS and bacterial stimulation suggesting that TNF is a differentially expressed cytokine in NPC1 and providing a molecular rationale of the treatment.NPC1 patients can present with severe Crohn’s-like disease in particular debilitating perianal disease. Anti-TNF therapy in NPC1-IBD patients results in symptom improvement without neurological deterioration at 1 year of follow up. We show in an.Patients with NPC1 suffer from a severe life-limiting disease, the potential risks of TNF therapy (demyelination and increased infection susceptibility) must be balanced against the improvement in quality of life due to effective IBD therapy and symptom control. Our data has shown improved management of Crohn’s-like disease in patients with NPC following anti-TNF therapy without worsening neurological function or infectious complications. Our data suggest that anti-TNF is safe to use in NPC from a neurological perspective. The improvement in IBD activity in NPC1 patients  was asso). Elevated levels of TNF within demyelinating plaques has been associated with the development of multiple sclerosis (MS). Polymorphism in both TNFR1 and TNFR2 have been linked to an increased risk of MS. Similarly, anti-TNF therapies have been linked to CNS demyelination and increased disease burden in patients with multiple sclerosis with a dose-dependent increase in relapse rate found in a trial of the TNF antagonist lenecept. Despite this, anti-TNF associated demyelination occurs independently to the classical risk factors for MS. Cases of anti-TNF associated demyelination have been described in patients with Crohn’s disease, psoriasis, rheumatoid arthritis and ankylosing spondylitis potentially hinting to a rare but relevant safety signal. For example, in 75 patients starting anti-TNF therapy for rheumatoid arthritis or spondyloarthropathies, three patients who developed neurological symptoms demonstrated new cortical lesions consistent with demyelination on MRI. Another case series of four patients with neurological symptoms on anti-TNF therapy demonstrated MRI changes and CSF oligoclonal bands with three patients meeting the diagnostic criteria for multiple sclerosis. However, pathophysiological mechanisms behind these effects are not completely understood. Several mechanisms have been proposed, including poor cerebral penetration of anti-TNF agents due to large molecular size which could prevent immunosuppressive actions in the brain. Another theory is based on the multiple forms and receptors for TNF. Soluble TNF signals through TNFR1 which is a ubiquitous receptor whose signalling can result in both pro and anti-apoptotic effects but more commonly results in inflammation and increased apoptosis through caspase 8 activation. In contrast, signalling from transmembrane TNF is largely through TNFR2. TNFR2 is found on immune cells, endothelial cells and some cells of the CNS and is thought to be neuroprotective and stimulate cell survival through phosphatidylinositol 3-kinase (PI3K) activity. These opposing responses combined with variable receptor expression results in complex consequences of TNF manipulation and could explain why TNF may have both beneficial and deleterious role in multiple sclerosis.TNF can promote neuro-inflammation and neuronal damage as well as having a protective role in some diseases .Data are available under the terms of the et al. aim to investigate the safety and effectiveness of anti-TNF therapy in NPC patients with Crohn's disease symptoms. To do so, they evaluate retrospective data from four patients with confirmed NPC1 mutations and intestinal inflammation and provide in vitro evidence suggesting that TNF expression may be inherently higher in NPC.In this report, Williams This is a well-written article, and I agree with the authors' conclusion that anti-TNF therapy appears to be safe in patients with NPC1 mutations and may be an effective strategy for the management of intestinal inflammation in these patients. I have a couple of suggestions:in vitro model of NPC, does not increase TNF-alpha by itself . They then quantify changes in TNF-a expression in response to bacterial exposure , and show that the response of NPC-like cells is stronger than that of control cells. The authors suggest that this increase is consistent with an increased TNF production in response to LPS and gut bacteria contributing to mucosal inflammation in NPC patients. This, of course, would provide a mechanism for the reported improvement in quality of life in response to anti-TNF in the study patients. While I agree with the authors' overall interpretation of their data, I would suggest expanding the discussion to include the article by Iftakhar-E-Khudaet al. who reported that another U18666A model does display TNF-a increase in the absence of bacterial challenges, albeit within a different time frame. How do those findings fit into the authors' conclusions? The authors show that the treatment of PBMCs with U18666A, which is a widely used approach to generate a chemically-inducedet al., Vincent et al.), especially in view of the link between liver dysfunction and Crohn's disease. The Discussion incorporates published work on the use of anti-TNFa therapies in other diseases. I think it would also be appropriate to include citations reporting the effects of anti-TNFa therapy on NPC liver disease .in vitro experiments on PBMCs from healthy donors, including small molecule inhibition of NPC1 and infection models.The case series is backed up byResults are described clearly.Tables and figures are helpful to the reader and complementary to the information provided in the text.The discussion is well articulated and leads to a relevant conclusive message.It would be relevant and interesting to examine infliximab level and anti-infliximab antibody level in the four patients described. Therapeutic drug monitoring would add further insight into how patients with NPC1 respond to infliximab. Three out of the four patients described had a partial response to IFX, and it’d be relevant to know whether loss of response was due to the development of anti-IFX antibody as opposed to low IFX trough levels. Is this data available and can it be analysed? If not, could the authors discuss whether anti-TNF therapeutic drug monitoring could/should be evaluated in future studies of patients with NPC1 and IBD?Minor commentsMethods, Page 4 – Statistical analysis: “R is an open access alternative” – Can the authors clarify whether R was utilised or not for data analysis in this specific work?[5] Ruemmele FM, Veres G, Kohlo KL, et al. Consensus guidelines of ECCO/ESPGHAN on the medical management of pediatric Crohn’s disease. J Crohns Colitis. 2014; 8(10): 1179-207. These guidelines were updated in 2020 and you may want to add the following updated version:.[5] van Rheenen PF, Aloi M, Assa A, et al. The Medical Management of Paediatric Crohn's Disease: an ECCO-ESPGHAN Guideline Update. Journal of Crohns and Colitis 2020: 1-24References: Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Paediatric gastroenterology with sub-specialist interest in paediatric IBDI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Non-Small Cell Lung Cancer (NSCLC) accounts for approximately 80% to 85% of all lung cancer cases and is the leading cause of cancer death worldwide , 2. BeyoOver the last two decades, F18-fluorodeoxyglucose PET/CT has been widely used for NSCLC, which has the unique advantage of metabolic evaluations with semi-quantitative parameters compared to the traditional images. Although there has been significant progress in FDG PET/CT applications and performance, further advancements can optimize imaging interventions as well as establish emerging approaches, such as radiomics, to improve the management of lung cancer. 8 of 17 published articles in our Research Topic are about the value of radiomics features in NSCLC.Radiomics has been considered an “imaging biomarker”, and it is a quantitative approach with advanced mathematical analysis to medical imaging, which extracts many features from medical images using data-characterization algorithms. By providing 3-D characterization of the tumor, radiomics features based on CT, MRI and/or PET describe intra-tumor heterogeneity and have the potential to uncover disease characteristics. To date, radiomics has been most extensively studied in NSCLC compared to other tumors . PublishZhang et al. reported that with correction of FDG PET/CT radiomics features to EGFR mutation analyses of tissue samples, FDG PET/CT radiomics models could help in discriminating EGFR positive from negative mutations in 173 preoperative patients with NSCLC, which could guide target therapy in patients with EGFR mutations. In another larger multicenter study including 728 patients with lung adenocarcinoma, Zhang et al. reported a two-user friendly nomogram by calculating radiomics score to predict the EGFR mutation status. Wu et al. also demonstrated that pre-treatment CT based radiomics features could reliably predict EGFR mutation in 67 NSCLC patients, and the addition of clinical models further improved radiomics performance.Adequate identification of genotype and gene mutation is the basis for target therapy. Recent research showed correlation between the radiomics features and the epithelial growth factor receptor (EGFR) –7. In thTang et al. analyzed FDG PET/MRI radiomics in 61 NSCLC patients and found that FDG PET/MRI radiomics features revealed different degrees of correlation with different tumors and could predict the preoperative histological classifications of the lesions: adenocarcinoma or squamous cell cancer.Beyond invasive approach to histopathologic diagnosis by surgery or biopsy, radiomics may help in the prediction of histology and stage of the tumors. nd lung primary is of incremental significance of treatment and prognosis in oncologic patients. Zhong et al. evaluated the value of CT radiomics in discriminating the second lung primary from lung metastasis in 252 oncologic patients with suspected lung lesions. 16 radiomics features and 4 clinical-radiographic features were selected to build the final model, which showed good discriminative capacity for the 2nd lung primary and lung metastasis.Differentiation between lung metastasis and the 2Zhou et al. assessed the predictive role of FDG PET/CT based radiomics for tumor microenvironment immune types (TMIT) in 103 treatment-naïve NSCLC patients, including the expressions of programmed death ligand (PD-L1), programmed death 1 (PD-1) and CDF8+ tumor infiltrating lymphocytes. The results suggested that FDG PET/CT radiomics features had good performance in predicting the TMIT.Currently immunotherapy by immune checkpoint blockade is standard care in advanced NSCLC. Radiomics has been used in assessment of PD-L1 expression in NSCLC . Zhou etZha et al. developed and validated a nomogram model based on CT radiomics features for preoperative prediction of visceral pleural invasion in 659 patients with lung adenocarcinoma. The results showed that the nomogram combining clinical and radiomics features had markedly improved accuracy, specificity, positive predictive value, and AUC for predicting visceral pleural invasion.Xu et al.). Li et al. reported a radiomics model based on nodule type stratification for preoperative prediction of micropapillary pattern in lung adenocarcinoma less than 2 cm. They found that ground glass opacity nodule type affected performance of the prediction.The micropapillary pattern is a marker of poor prognosis in NSCLC . AI and deep learning models will facilitate faster clinical translation and implementation of radiomics in NSCLC. However, the significant variability in radiomics features used in different studies, in addition to the lack of reproducibility, suggests that the current data are still preliminary and more comprehensive studies are needed for validation of radiomics applications.The author confirms being the sole contributor of this work and has approved it for publication.


Discontinuation of tyrosine kinase inhibitor (TKI) treatment is emerging as the main therapy goal for Chronic Myeloid Leukemia (CML) patients. The DESTINY trial showed that TKI dose reduction prior to cessation can lead to an increased number of patients achieving sustained treatment free remission (TFR). However, there has been no systematic investigation to evaluate how dose reduction regimens can further improve the success of TKI stop trials.Here, we apply an established mathematical model of CML therapy to investigate different TKI dose reduction schemes prior to therapy cessation and evaluate them with respect to the total amount of drug used and the expected TFR success.Our systematic analysis confirms clinical findings that the overall time of TKI treatment is a major determinant of TFR success, while highlighting that lower dose TKI treatment for the same duration is equally sufficient for many patients. Our results further suggest that a stepwise dose reduction prior to TKI cessation can increase the success rate of TFR, while substantially reducing the amount of administered TKI.Our findings illustrate the potential of dose reduction schemes prior to treatment cessation and suggest corresponding and clinically testable strategies that are applicable to many CML patients. BCR-ABL1 mRNA  after a median of 18 months and a 4-log reduction (MR4) after a median of 45 months (Chronic myeloid leukemia (CML) is a malignancy of the hematopoietic stem cell. The introduction of tyrosine kinase inhibitors (TKI) revolutionized CML treatment as they target the causative BCR-ABL1 oncoprotein ).Our mathematical modelling approach is based on patient data from the DESTINY trial (NCT 01804985), whose primary results have been previously reported . The triclinical reference data set). Initial statistical assessment revealed no or minor differences between the original patient cohort and the reference data set with respect to initial LRATIO, treatment duration, follow-up duration, recurrence times and type of TKI  in terms of a mathematical model which comprises interactions between immune effector cells (Z), quiescent (X) and proliferating (Y) leukemic stem cells (d declines from d=1 (full dose) to d=0.5 or 0.25 (reduced dose) to d=0 (therapy cessation). Details of the model are provided in We describe individual disease dynamics of CML patients  and its logarithm asFor comparison of the model simulation results with clinical time courses of BCR-ABL1 and ABL1 transcripts in leukemic cells. Herein the carrying capacity YK is set to 106. Within the model, we define molecular recurrence as the first time point at which RATIO increases above 0.1% and remains there for at least one month.thereby accounting for the presence of both We obtain patient-specific optimal parameter choices for the model parameters (termed λ) by applying an optimization routine that minimizes the distance between each clinical time course and the corresponding simulation . While we cannot be sure whether a unique global optimum is identifiable, we observed that the measurement uncertainties allowed for many reasonable fits. For these reasons we took a complementary approach in which we explicitly consider the intrinsic heterogeneity of the model solutions. Technically, we used 64 independent optimization runs (i ϵ{1…64}) per patient j, and obtained 64 parameter sets λij that reflect the patient-specific variability within the parameter space (ij. Within the figure there are broader regions for some parameter choices (like the immune parameters Zp and ZK), although the residual error remains in the same order of magnitude. This confirms the visual impression from BCR-ABL1/ABL1 measurements which exceed 0.1% (MR3). In the model, we mirror this as sustained levels of leukemic cells above 0.1% for at least one month. Applying this definition to the simulations for each of the 64 parameter sets λij for all 67 patients, we observed 32 patients with no indication of recurrence, while 35 have several simulations predicting recurrence (including 11 with mixed outcomes and 24 with recurrence predictions only). Viewing this question as a binary classification problem, we applied a receiver operating characteristic . Class B refers to scenarios with a strong immune system in which the steady state for leukemic dominance  is accompanied by a second steady state describing immune control of a sufficiently few leukemic cells . Class C presents with a similar immunological landscape as class B while the size of the remission steady state (ELow) is smaller and more difficult to achieve.We have previously shown that the mathematical model implies different ‘immunological landscapes’ characterized by the existence or absence of typical steady states that can be obtained after therapy stop , 29. Thej, but use a spectrum of 64 optimization runs i, optimal parameter sets λij of the same patient may be classified to different immunological classes A, B or C. Practically, each of the 64 optimal parameter configurations λij for a particular patient j, obtained from fitting the complete data set  remission level (MR4+24M+12M50% scenario) for all 67 patients instead of using the observed time points from the ij belong to class A, B or C. Applying the same sampling approach as above (Varying the full dose TKI treatment time between 12 and 36 months past reaching MR4 . To furtTo address the effect of TKI dose on TFR success we considered a situation where simulated patients receive 36 months full dose treatment past reaching MR4 (MR4 + 36M) and compared it to the reduction scheme discussed above, in which a 24 month full dose treatment after reaching MR4 precedes a 12 month reduction to either 50% of the original dose (MR4 + 24M + 12M50%) or to 25% of the original dose (MR4 + 24M + 12M25%). While some patients in the latter scenarios present with disease recurrence earlier than they would do with the full dose treatment, those patients are likely to fail anyway. To systematically analyze this, we simulated further reduction schemes, all extending over 36 months past reaching MR4. Earlier initiation of dose reduction at 12 months past reaching MR4 leads to a similar fraction of recurrences compared to the above scenarios  hints towards a lower recurrence rate . Our mosee also , 33 as wdim natural killer cells , NCT 01804985) was conducted in accordance with the Declaration of Helsinki and applicable regulatory requirements. The protocol was approved by the North West - Liverpool East Committee of the UK National Research Ethics Service. The patients/participants provided their written informed consent to participate in this study.Conception and design: EK, CB, IR, AF, IG. Development of methodology: EK, CB, TZ, AF, IG. Acquisition of data : RC. Analysis and interpretation of data : EK, CB, AF, IG. Writing, review, and/or revision of the manuscript: EK, CB, TZ, RC, IR, AF, IG. Study supervision: IR, AF, IG. All authors contributed to the article and approved the submitted version.


Anxiety and depressive symptoms are associated with fear of falling and fear of falling-related activity restrictions. However, it remains unknown whether anxiety or depressive symptoms alone could predict fear of falling and activity restrictions in older adults. We sought to determine if anxiety and depressive symptoms alone could be an independent predictor of fear of falling and activity restrictions in community-dwelling older adults.N = 6376) from the National Health and Aging Trends Study. The Generalized Anxiety Disorder Scale 2 and Patient Health Questionnaire 2 were used to assess anxiety and depressive symptoms, respectively. Interview questions included demographics, health-related data, and fall worry levels . Using multinomial logistic regression models, we examined whether anxiety and depressive symptoms (T1) predicted fear of falling and activity restrictions (T2).This longitudinal analysis used waves 5  and 6  data , 10 and 13% of participants reported anxiety and depressive symptoms. About 19% of participants experienced fear of falling but not activity restrictions, and 10% of participants developed activity restrictions in wave 6 (T2), respectively. Participants with anxiety symptoms at T1 had a 1.33 times higher risk of fear of falling (95% CI = 1.02–1.72) and 1.41 times higher risk of activity restrictions (95% CI = 1.04–1.90) at T2. However, having depressive symptoms did not show any significance after adjusting for anxiety symptoms.Anxiety symptoms seemed to be an independent risk factor for future fear of falling and activity restrictions, while depressive symptoms were not. To prevent future fear of falling and activity restrictions, we should pay special attention to older individuals with anxiety symptoms.The online version contains supplementary material available at 10.1186/s12877-022-03406-8. Fear of falling has become a major public health issue among older adults over the past decades, with 21 to 85% having experienced fear of falling with or without falls . AlthougAnxiety and depressive symptoms are the most common psychological symptoms associated with fear of falling and activity restrictions –11, and From the clinical point of view, anxiety and depressive symptoms were two different concepts. According to the Chinese Classification and Diagnostic Criteria of Mental Disorders, 3rd Edition (CCMD-3), anxiety symptoms were upwardly hyperactive and characterized mainly by nervousness without a clear object or specific content. Depressive symptoms were inhibited downward and dominated by a depressed state of mind that could range from grumpy or grief . A prospN = 25) examined the association between anxiety symptoms and fear of falling controlling for depressive symptoms [Several previous studies have indicated that anxiety symptoms are a significant predicting factor for fear of falling and activity restrictions without controlling for depressive symptoms , 24–26. symptoms . An extesymptoms , 25, 28,symptoms , 30. ButTherefore, the purpose of this longitudinal study was to examine the independent effects of anxiety and depressive symptoms on fear of falling and activity restrictions among community-dwelling older adults in the United States (U.S.). We hypothesized that both anxiety and depressive symptoms can be independent predictors of fear of falling and activity restrictions in older adults.We used data from the National Health and Aging Trends Study (NHATS) wave 5 (2015) and wave 6 (2016). Since 2011, the NHATS has collected health information from a nationally representative sample of Medicare beneficiaries aged 65 and older in the U.S. , and theAnxiety symptoms were measured by the 2-item Generalized Anxiety Disorder Scale (GAD-2) . It screDepressive symptoms were measured using the 2-item Patient Health Questionnaire (PHQ-2) . PHQ-2 mFear of falling and activity restrictions were assessed by the two questions, “In the last month, did you worry about falling down?” If the answer was yes, then participants were asked, “In the last month, did this worry ever limit your activities?” Based on the participants’ responses, a three-category variable was created to indicate the fall worry levels: No fear of falling (coded 0), had fear of falling but not activity restrictions (coded 1) and had fear of falling-related activity restrictions (coded 2).We included covariates hypothesized to be associated with our outcomes of interest. These included demographic variables such as age, gender ; race/ethnicity ; education level ; and living arrangement .Health related covariables included (a) the number of chronic illnesses ; (b) whether the participants had dementia (yes/no); (c) the number of activities of daily living (ADL) impairment, ranging from 0 to 4 ; (d) the number of instrumental activities of daily living (IADL) impairment, ranging from 0 to 7 ; (e) whether the participants were bothered by pain in the last month (yes/no); (f) body mass index (BMI) of the participants ; (g) whether the participants were hospitalized over the past year (yes/no); and (h) whether the participants had problems with balance or coordination in the last month (yes/no).Continuous variables were presented as mean ± standard deviations and categorical variables as frequencies and percentages. We used two-sample t-tests to estimate the distribution of age, ADLs, and IADLs across the fall worry levels. Chi-square tests were used to test the differences among groups of fall worry levels for categorical variables.To determine whether anxiety and/or depressive symptoms at T1 could predict the fall worry levels at T2 independently, we performed three sets of multinominal logistic regression models in three steps. First, we modeled the effects of anxiety symptoms at T1 on fall worry levels at T2, in which fall worry levels at wave 6 were treated as the outcome while fall worry levels at T1 were controlled. Next, we conducted similar models replacing anxiety symptoms with depressive symptoms as the main predictor to examine the effects of depressive symptoms on fall worry levels. Finally, we examined the independent effects of anxiety and depressive symptoms on fall worry levels by including them as main predictors simultaneously in the models. In each model set, we first estimated the crude effects (Model 1), followed by the effects adjusted for demographic variables (Model 2), and finally the effects adjusted for demographic and health-related covariates (Model 3).n = 439). Due to the small proportion of missing data and the large sample size, we did not use any techniques to handle the missing data. For all models, relative risk ratios (RRR) and 95% confidence intervals were reported. P values less than 0.05 indicated statistical significance. All analyses were conducted using Stata/SE 15.0 .To improve the robustness of the results, we performed a sensitivity analysis excluding samples who were interviewed by proxy (N = 6376) was on average 78 ± 7.73 years old. The majority were non-Hispanic White (69.1%) and female (58.1%) with more than half of them (54%) having college and above education. About 10 and 14% of participants reported anxiety and depressive symptoms at T1 respectively. Three hundred one participants experienced both anxiety and depressive sympotms. About 21% (n = 1353) of participants had fear of falling and 11% (n = 698) had activity restrictions at T2 , but results became insignificant in the fully adjusted model . The RRR of “Had activity restrictions” was 1.46 times greater among participants with anxiety symptoms compared to those without anxiety symptoms (95% CI = 1.10–1.94). For participants with depressive symptoms, the results of “Had fear of falling but not activity restrictions” showed insignificant in the crude model (Model 1). However, the RRR of “Had activity restrictions” became 1.33 times greater compared to those without depressive symptoms in the fully adjusted model .Figure The results of sensitivity analyses limiting the participants to non-proxy participants only showed similar results to our earlier analyses, supporting that anxiety symptoms were an independent factor of future fear of falling and activity restrictions.  10 and 13% of participants at T1 reported experiencing symptoms of anxiety or depression, and approximately 20 and 11% reported “Had fear of falling but not activity restrictions” or “Had activity restrictions” at T2, respectively; (2) anxiety symptoms were associated with a higher risk ratio of “Had fear of falling but not activity restrictions” and “Had activity restrictions” 1 year later adjusting for depressive symptoms; and (3) depressive symptoms did not seem to have any association with fear of falling and activity restrictions.N = 25) with a high proportion of women (88%). However, we can say that the results of our study can be better interpreted and generalizable because of the large sample size and a more balanced female proportion (58.1%) [Consistent with previous studies , 34, our (58.1%) .We also found that anxiety symptoms increased the risk of future activity restrictions independently. This result also supported the previous studies , 26. ForOur results showed that depressive symptoms had no significant associations with fear of falling and activity restrictions in the fully adjusted model. This result was a surprise because previous studies showed opposite results . For exaThe different results of the association between depressive symptoms and activity restrictions with or without controlling for anxiety symptoms should be highlighted. Taking anxiety symptoms into account, the association between depressive symptoms and activity restrictions became insignificant, suggesting anxiety symptoms may weaken the effects of depressive symptoms on activity restrictions. Further investigations are needed to clarify the role of anxiety symptoms on the pathway from depressive symptoms to activity restrictions and determine its effect size.Our findings provided some evidence that anxiety symptoms may be able to predict future levels of fear of falling and activity restrictions. This finding has implications for guiding the strategies for preventing fear of falling and activity restrictions among older adults from the context of tertiary prevention. First, the knowledge of the scientific relationship between anxiety symptoms and future levels of fear of falling and activity restrictions should be disseminated especially to older adults and their caregivers. They should be aware that anxiety symptoms affect future fear of falling and activity restrictions levels, and subsequently may lead to more adverse health outcomes such as falls and functional impairment. Second, anxiety symptoms should be screened for early detection and diagnosis. Then we can identify and treat older adults with a high risk of developing fear of falling and activity restrictions better as secondary prevention. The assessment tool used in this study, GAD-2 is readily available and easy to use in the community  and therThe strengths of this study include its longitudinal design and a large nationally representative sample of the study population. However, several study limitations should be noted. Selected variables were all based on retrospective self-reported interviews. Recall bias may influence the accuracy of the data and may have caused a lack of significant associations between depressive symptoms with fear of falling and activity restrictions. The GAD-2 and PHQ-2 were used to measure participants’ anxiety and depressive symptoms respectively. These tools were originally developed for not diagnostic but screening purposes. Therefore, the data may be limited to the state of emotion at the time of the interview.Based on the nationally representative sample of Medicare beneficiaries, we found that anxiety symptoms were an independent risk factor for potential future fear of falling and activity restrictions. Depressive symptoms did not have any associations with fear of falling and activity restrictions. As a future strategy for preventing fear of falling and activity restrictions, special attention should be paid to older adults with anxiety symptoms in the communities.Additional file 1: Supplemental Table S1. Independent effects of anxiety and depressive symptoms at T1 and on fall worry levels at T2 controlling for each other, excluding proxy respondents. .


The contribution of brain regions to visuospatial abilities according to sex differences and gender identity is inconsistently described. One potential explaining factor may be the different tasks employed requiring a variable load of working memory and other cognitive resources. Here we asked to 20 cis and 20 transgender participants to undergo functional Magnetic Resonance Imaging during performance of a judgement line of orientation test that was adapted to explore the basic visuospatial processing while minimizing the working memory load. We show that V1 activation may be viewed as a brain area with enhanced activation in males, regardless of participants’ gender identity. On its turn, gender identity exclusively influences the visuospatial processing of extrastriate visual areas (V5) in women with gender dysphoria. They showed enhanced V5 activation and an increased functional connectivity between V5 and V1. Overall our neuroimaging results suggest that the basic visuospatial abilities are associated with different activations pattern of cortical visual areas depending on the sex assigned at birth and gender identity.The online version contains supplementary material available at 10.1007/s11682-022-00638-5. Over the past 30 years innovative technologies and methods have generated a growing pile of studies exploring gender differences in brain functioning and anatomy , defined as the discrepancy between the experienced gender and the sex assigned at birth (WHO). The clinically significant distress—which may result from this incongruence—is usually defined as Gender Dysphoria (GD). Notably, the idea of the sexual dimorphic brain as the anatomical substrate of psychosexual development has been widely explored and research has focused on the influence and shaping role of genes and gonadal hormones on sexual morphological or funtional differentiation of the brain  and of people with GD (10 transgender women and 10 transgender men) during execution of an automatic version of JLO task that has been introduced with the purpose of minimizing involvement of additional resources as WM Kesler, .The aim of the present study is to investigate i) whether this specific version of the JLO can capture differences in brain activations between cisgender subjects; ii) whether men and women with GD show modified activation patterns.The study was approved by the local ethics committee (2013/0016117) and written informed consent was obtained from each participant in conformity with the Helsinki Declaration and later amendments or comparable ethical standard.Forty subjects, namely 20 cisgender individuals  and 20 trans individuals  of similar age  and education  were examined  by means of a mirror attached to the head coil. The task was an adapted version  equipped with Omega HP gradients with maximum amplitude of 45 mT/m and slew rate of 200 T/m/s for each axis and a 32-phased-array-element head coil. After scout and T2 weighted FLAIR images to check for possible brain abnormalities, 3D T1-weighted imaging and fMRI were obtained in all subjects.T1-weighted MR images were acquired with a sagittal high-resolution 3D sequence .For the fMRI experiment we utilized a T2*-weighted echo-planar imaging (EPI) sequence with parameter setted similarly to previous studies from our group . Canonical preprocessing was applied . We adopted the following filtering steps: temporal high-pass with cutoff at 50 s; spatial smoothing using a 2 mm full width half maximum Gaussian kernel. Co-registration of fMRI images to the individual high-resolution T1-weighted image was performed using a 6-degree of freedom registration. The individual high resolution T1-weighted images were co-registered to the standard space Montreal Neurological Institute 152 (MNI152) brain with an affine transformation (12 degree of freedom) followed by a nonlinear transformation. fMRI images were co-registered to the MNI152 standard space using the transformation previously computed when co-registering the individual high-resolution T1-weighted images to the MNI152 standard space 1 mm T1-weighted template.Volumes acquired with fMRI were analyzed using the FMRIB Software Library  and accounted for in a confound matrix at the subject-level analysis. Each stimulus delta functions sequence was convolved with a double gamma hemodynamic response function, whereas the temporal derivative was included in the model and temporal filtering applied.To explore activity related to the execution of the JLO task, a General Linear Model (GLM) of ORIENTATION vs COLOUR contrast was set at subject-level analysis including a distinct explanatory variable to account for baseline volumes. Each model explanatory variable was convolved with a double gamma hemodynamic response function, whereas temporal derivatives were included, and temporal filtering applied.Because the experimental design involved randomized intervals stimuli, we reduced autocorrelation in the data by applying voxel-wise pre-whitening. To establish between-group differences, we used an unpaired t test with a mixed effects model where age was included as nuisance covariate. All group analyses were performed in the MNI152 standard space. For all statistical analyses the resulting Z (Gaussianized T/F) statistic images were thresholded using clusters determined by Z > 2.3, and a corrected p-value < 0.01. To anatomically map the significant clusters in the resulting Z statistic images probabilistic atlases were used  analysis of the areas exhibiting significantly different BOLD contrast in the GI/GD participants when compared with their respective cisgender groups (TW vs CM and TMvs CW). To this aim we quantified relevant coupling of a cortical ROI to the rest of the brain in a voxelwise analysis using a Task-residual functional connectivity approach  and corrected for multiple comparisons with threshold-free cluster enhancement (TFCE) method  = 1.09, p < 0.01] showed a statistically significant difference of education among groups. Multiple post-hoc pair-wise comparisons corrected with Bonferroni method revealed that CM and CW showed statistically significant higher scores than TM and TW (respectively—p = 0.004 and p < 0.001).Another one-way ANOVA  showed a statistically significant difference of scores (corrected for education) among the four groups of subjects. Multiple post-hoc pair-wise comparisons corrected with Bonferroni method revealed that only CM showed statistically significant higher scores than TW (p = 0.004). See Supplemetary Table For this reason Raven matrices assessement’ score was corrected with education and therefore computed on a percentile basis in 1–5 grades. A one-way ANOVA [F = 5.005, p = 0.60.Behavioural data are summarized in Table Clusters of significantly higher activation when comparing the four groups of subjects are detailed in Table No clusters of significantly decreased activation were found in all comparisons.The FC analysis of the left Visual Cortex V (V5) revealed by TM > CW contrast demonstrated increased connectivity between the same area with bilateral V1 and Superior Parietal Lobule (SPL) – see Table spurious tasks, such as MRT, which require significant involvement of WM  were compared to cis, the primary visual cortex V1 was more activated Table . The finWhen contrasting the transgender groups with the corresponding cisgender groups (of the same sex assigned at birth: TW vs CM and TM vs CW) we observed a statistically significant higher activation of V5 in the TM > CW contrast. Notably, this difference was not observed in the mirror contrast between the groups with male sex assigned at birth (i.e. TW vs CM).Overall, considering the above results concerning the sex assigned at birth and those related to the perceived gender identity, one might hypothesize the existence of a link between the higher V1 activation observed in subjects with male sex assigned at birth (both CM and TW) and the increased activation of V5 in females assigned at birth with perceived male gender identity (TM). In fact, taking into account both the absence of differences in behavioral performance between the groups and the enhanced functional connectivity between V5 and V1 Table  in TM, oper se, but rather in the WM elaboration of information during complex visuospatial processing . Hence, V1 functioning in visuospatial processes might resemble that of other typical sexual dimorphic brain areas . Indeed, a sexual dimorphic visuospatial functioning seems reasonable because V1 is well-known for being an area devoted to the elaboration of the orientation of visual stimuli  represents an epiphenomenon related to the general effort of these individuals to resemble the male brain functioning. This possibility is corroborated by the fact that the SPL was the only brain region, beside V1, that we found to be enhanced in our FC analysis of V5 and this region is well-known also to absolve WM related functions .Supplementary file1 (XLS 22 kb)Below is the link to the electronic supplementary material.


IDH1,2 mutations that are central criteria for glioma diagnosis. The aim of this study was to achieve super-resolution (SR) MRSI using deep learning to image tumor metabolism in patients with mutant IDH glioma.Magnetic resonance spectroscopic imaging (MRSI) can be used in glioma patients to map the metabolic alterations associated with d-2-hydroxyglutarate (2HG). To further enhance structural details of metabolic maps we used prior information from high-resolution anatomical MR imaging. SR MRSI was compared to ground truth by Mann–Whitney U-test of peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM), feature-based similarity index measure (FSIM), and mean opinion score (MOS).We developed a deep learning method based on generative adversarial network (GAN) using Unet as generator network to upsample MRSI by a factor of 4. Neural networks were trained on simulated metabolic images from 75 glioma patients. The performance of deep neuronal networks was evaluated on MRSI data measured in 20 glioma patients and 10 healthy controls at 3T with a whole-brain 3D MRSI protocol optimized for detection of Deep learning SR improved PSNR by 17%, SSIM by 5%, FSIM by 7%, and MOS by 30% compared to conventional interpolation methods. In mutant IDH glioma patients proposed method provided the highest resolution for 2HG maps to clearly delineate tumor margins and tumor heterogeneity.Our results indicate that proposed deep learning methods are effective in enhancing spatial resolution of metabolite maps. Patient results suggest that this may have great clinical potential for image guided precision oncology therapy. Develop a deep learning method for super-resolution MRSI using GAN deep neural networks to upsample metabolic maps by a factor of 4.Combine deep learning and variational methods to further improve the final super-resolution MRSI results.Demonstrate deep learning super-resolution MRSI in mutant IDH glioma patients to obtain the highest resolution of 2HG maps that delineate tumor margins and tumor heterogeneity.IDH1,2 mutations that are central criteria for glioma diagnosis. The spatial and temporal patterns of MRSI metabolic lesions correlate with tumor progression, treatment response, and clinical outcome. Importantly, tumor appearance and dynamics in MRSI are different compared to anatomical MRI. However, because of low metabolite concentrations MRSI is acquired at low resolution , which may result in reduced structural details, with less ability to probe tumor heterogeneity, small and early tumors. To address this challenge, in this work we developed and investigated the performance of deep learning methods for super-resolution MRSI in glioma patients. Such methods can be used for image guided precision oncology therapy.Magnetic resonance spectroscopic imaging (MRSI) can be used in glioma patients to map the metabolic alterations associated with IDH1,2 mutations that are central criteria for glioma diagnosis in the most recent classification 2021 WHO of brain tumors.1 The hallmark metabolic alteration of cancer IDH1,2 mutations is the de novo overproduction of d-2-hydroxyglutarate (2HG)2 as an “oncometabolite” that drives the epigenome3 and microenvironment4 toward tumor progression. Due to its high specificity, 2HG is a valuable imaging biomarker for diagnosing,6 monitoring, assessing tumor burden,7 treatment response,9 and pharmacodynamics of targeted therapy in mutant IDH1 glioma.10Magnetic resonance spectroscopic imaging (MRSI) can be used in glioma patients to map the metabolic alterations associated with 1H-MRSI in the human brain can measure up to 20 metabolites11 that probe-specific molecular mechanisms in neurological diseases, including brain tumors.12 The spatial and temporal patterns of MRSI metabolic lesions correlate with tumor progression, treatment response, and clinical outcome.14 Importantly, tumor appearance and dynamics in MRSI are different compared to anatomical MRI.15 However, because of low metabolite concentrations MRSI is acquired at low resolution  to compensate for lower SNR. In this scenario, structural detail is reduced and small lesions might be missed since boundaries of lesions are blurred. An efficient approach for improving spatial resolution is to upsample MRSI using super-resolution (SR) methods16 that recover fine structural details. Recently, SR MRSI has been demonstrated using patch-based,17 feature nonlocal means (FNLM),18 and deep learning.2019 in healthy volunteers showed upsampled MRSI by deep learning in combination with prior anatomical MRI. Here, inspired by the work of Iqbal et al., we extended the Unet model21 with the framework of the generative adversarial network (GAN)22 in order to build more robustness for larger variability in patient data.In this work, we developed and investigated the performance of deep learning methods for SR MRSI, particularly in glioma patients. The earlier work of Iqbal et al.18 We hypothesize that deep learning may provide better initialization for upsampling SR methods compared to conventional interpolation methods  or more advanced variational methods such as weighted total variation (wTV).In our patient study, we took a 2-step approach to upsample low-resolution (LS) MRSI: (1) using deep learning alone (DLmethod_1), and (2) combining deep learning with prior high-resolution (HR) MRI. As metabolic and anatomical lesions can differ in shape, contrast, and texture, this raises the possibility that spurious spatial features can be introduced from anatomical MRI into MRSI. Our 2-step approach has been motivated by the need to deal with this circumstance. In addition, we studied 2 ways of combining deep learning with prior HR MRI: (1) using a sequential method (DLmethod_2) in which MRSI is first upsampled by deep learning and subsequently reinterpolated by FNLM weights based on prior MRI, and (2) using a simultaneous method (DLmethod_3) in which both initialized SR MRSI and HR MRI data are input together in the deep learning model. For the first method, we leveraged the FNLM, which we showed to be robust in glioma patients with respect of introducing false structural details from MRI into MRSI.A reliable SR MRSI method may increase throughput and feasibility of metabolic imaging in clinical setup. The availability of MR scanners is limited and minimizing the acquisition time is key in providing advanced imaging to more patients and decrease the costs. On the other hand, computing power has advanced greatly, it is more ubiquitous and has lower costs, hence allowing to efficiently shift the burden from image acquisition to image processing. Here, we aim to capitalize on the increased compute performance with the goal to improve the quality and time efficiency for SR MRSI metabolic imaging for neuro-oncology applications in glioma patients.23 To solve this issue, we resorted to simulations of realistic MRSI metabolic maps in order to generate sufficient training datasets similar to our prior work.18 To obtain patient training MRSI datasets we started from anatomical FLAIR and MEMPRAGE images acquired with 1 mm isotropic resolution in 75 glioma patients. FLAIR images were used to segment tumors (TM) by ITK_SNAP.24 The healthy part of brain was segmented by FSL25 on MPRAGE images into white matter (WM), gray matter (GM), and corticospinal fluid (CSF). The tumor and healthy brain segmentations were combined to generate very high-resolution (VHR) MRSI maps according toOne of the obstacles to employ deep neural network (DNN) to upsample metabolic MRSI maps is that there are not sufficient HR training data measured in patients. The need of training data becomes even more demanding when more convolutional layers are used in DNN. Measurement of HR MRSI is prohibitive in patients due to long measurement times and lack of wide availability of pulse sequences that can acquire HR MRSI efficiently.τ is chosen from , and the overall image intensity range is normalized to 1. The VHR MRSI data of size (256 × 256 × 176) were down sampled subsequently in k-space to generate LR MRSI data (46 × 46 × 32). The ground truth HR MRSI data (184 × 184 × 128), corresponding to LR–HR upsampling factor of 4, were generated in 2 ways: (1) by down sampling MRSIVHR in k-space, and (2) by upsampling the LR MRSI with the FNLM SR pipeline that has been demonstrated in 18. The first type of HR MRSI data has sharper structural details, similar to anatomical imaging, and have been used to train the neural networks for the DLmethod_2 and DLmethod_3. The second type of HR MRSI has less sharp anatomical edges, similar to the quality of MRSI acquired with advanced HR protocols,26–29 and have been used to train the neural networks for DLmethod_1. In total each 9600 datasets for the 2 types of ground truth HR were simulated for training and validation. In addition, we employed data augmentation that flips and randomly crops the minibatch data during training process.where ILR, our goal is to find an operator F such that F(ILR) is similar enough to the ground truth metabolite map IHR, where F could be linear or nonlinear. Mathematically, this can be written asGiven a preprocessed LR metabolite map F−1 represents an inverse operator of F and n is the noise term. For the deep learning method, F is usually a nonlinear operator. For the variational model-based reconstruction method, it is usually a linear operator.where ILR, we explore 2 types of neural networks, where the first is the so-called Unet typically used for image segmentation. Considering that Unet performs pixel-wise transformations, it can be reframed for SR tasks as initially recognized by Iqbal et al.19 The dense Unet architecture implemented by Iqbal et al. is able to carry over features from layer to layer and allow feature reuse throughout the network.30 However, the extensive feature reuse could introduce spurious features more easily considering the fact the anatomical images are also given as the input to the neural network, in particular when there are differences between metabolic and anatomical images in patients, such as different texture or extent of lesions. In our implementation instead of introducing densely connected networks in the Unet architecture we employed a similar neural network architecture as Ronneberger et al.,21 where the Unet architecture consists of 2 paths. The contraction path, which aims at extracting significant features from the input images, is mainly involved with standard convolutional and max pooling layers. The expanding path works toward identifying important features locally from a finer resolution using transposed convolutions and feature concatenation. Since no dense layer is involved and only convolutional layers are used, our Unet is essentially a fully convolutional network. The input for Unet is an initialized SR IISR by bicubic interpolation. The activation function, Relu, is employed in all layers.To upsample 31 which formulates a GAN by treating the Unet as the generator network. The generative adversarial model typically involves 2 components, the generator network Gθ and the discriminator network Dη, where θ denotes the parameters in generator network and η denotes the parameters in discriminator network. The parameters are comprised by weights (W) and biases (B) in all the layers (L) of the network that are learned during training. Our ultimate goal is to learn a generator function G that is able to reconstruct from the initialized SR image (IISR) an image G(IISR) such that the reconstructed image is as similar as possible to the ground truth metabolite map IHR. However, for GAN the generator network Gθ is trained to generate SR metabolite maps so that the simultaneously trained differential discriminator Dη cannot distinguish the generated maps from the ground truth HR metabolite maps, which can be solved via the following saddle point optimization problemFurthermore, to augment the performance of the Unet we introduced a discriminator network,31. Throughout the generator network, ParametricReLU (PRelu) activation is utilized but no max pooling is involved. In total, there are 8 convolutional layers in the discriminator network, in which 3 × 3 convolutional kernels are used. The number of kernels is increased from 64 to 512 kernels. Image size is reduced by strided convolutions when the number of features is doubled. To obtain a probability for sample classification, 2 dense layers and a final sigmoid activation function are employed following the final obtained feature maps.The architecture of the proposed Unet–GAN model is shown in The cost function for the optimization problem is defined asN training samples, where λ ≥ 0 is the regularization parameter. When only Unet (the generator network) is used, we set λ = 0, that is, only MSE is used in the cost function for computing pixel-wise loss. The cost function is referred to as the perceptual loss function when λ > 0, which contributes significantly to the superior performance by GAN. Both Unet and GAN networks were trained in 2 ways: (1) using as input only the MRSI data (DLmethod_1 and DLmethod_2), and (2) using as input both the MRSI and prior MRI (DLmethod_3). Note that DLmethod_2 is DLmethod_1 combined with FNLM, hence for both methods the training is done similarly. The block diagram for training of all deep learning methods is shown in which consists of 2 components, mean square error (MSE) and adversarial loss over 32 was used with a learning rate of 1e−4 for minimizing the cost function. GAN employed Unet as a pretrained model and was trained with batch size of 16 and the convergence reached after 20k iterations too. The Adam optimizer was used with a learning rate of 1e−5 for first 10k iterations and 1e−6 for last 10k iterations, where the first and second momentum terms were set 0.9 and 0.999, respectively.We trained our networks on a PowerEdge R730 server (Dell) with 24 CPU cores (Intel Xeon E5-2687W v4 3.0 GHz) and 128 GB RAM  running Linux Centos 7.6 using Tensorflow packages in Python 3.6. Unet was trained with batch size of 16 until convergence reached, which took 20k iterations. The Adam optimizerIDH1 glioma patients and 10 healthy control subjects were recruited for this study. All participants gave written informed consent with an IRB approved protocol. IDH1-mutational status in patients was tested by anti-human R132H antibody (DIANOVA).33Twenty mutant k-space encoding. Acquisition, reconstruction and processing were performed as described in 18: (1) metabolic maps from 20 brain metabolites were obtained in all subjects, and (2) 2 metabolic ratios that have high contrast to noise ratio for tumors were calculated in patients: HGG = ([2HG] + [Glutamine])/[Glutamate] and TCN = [total Choline]/[total N-acetyl-aspartate].Whole-brain 3D MR spectroscopic imaging data were acquired at 3T on a Tim Trio scanner  equipped with a 32-channel head coil and a gradient coil capable of 40 mT m maximum amplitude (200 mT m s slew rate), using a pulse sequence optimized for 2HG detection with adiabatic spin echo excitation and weighted 3D stack-of-spiral 34: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), feature similarity index measure (FSIM), and mean opinion score (MOS). For MOS 3 experts were asked to evaluate and compare the SR images relative to ground truth based on 4 criteria scored from 1 (worst) to 5 (best) as following: (1) tumor boundaries, (2) local texture and intensity distribution inside the tumor, (3) healthy brain anatomical landmarks such as ventricles, sulci, gray–white matter border, and (4) healthy brain local texture and intensity distribution. The Mann–Whitney U-test in Matlab (Mathworks) was used to verify for statistically significant differences between the image quality metrics of different methods.Results of the deep learning SR methods were compared to the ground truth using image quality metricsFirst, we investigated the performance of MRSI upsampling using Unet and GAN without prior MRI (DLmethod_1). Results obtained in 3 representative healthy subjects and 3 patients are shown in Upsampling results combining deep learning and prior MRI (DLmethod_2 and DLmethod_3) are shown in Quantitative analysis of the image qualitative metrics is summarized in Applications of all the deep learning methods to in vivo measured data are presented in 35 Alternatively, further improvements of deep learning metabolic maps (DLmethod_1) can be obtained by using FNLM and prior MRI (DLmethod_2). This hybrid approach is less susceptible to introduce spurious features and could be a safer choice in using prior anatomical MRI to increase spatial resolution of MRSI in glioma patients. DLmethod_3 may be more appropriate in healthy subjects and patients with diffuse brain involvement where metabolites and anatomy follow a more similar spatial pattern.Our results indicate that the proposed deep learning methods can effectively enhance the spatial resolution and structural details of metabolite maps. This can be further improved by the aid of HR anatomical MR images. Deep learning methods without prior MRI (DLmethod_1) recover structural information and tissue contrast better than conventional interpolation methods (bicubic or wTV). The performance of DNNs without prior MRI, in particular GAN, is close to combination of conventional methods and FNLM prior MRI. This is particularly useful for patient data where lesions may appear differently in metabolic images versus anatomical images. The input of prior MRI together with MRSI in DNNs (DLmethod_3) provides the highest structural detail in healthy volunteers, however in patients features of the anatomical lesions may be inadvertently fused to the metabolic lesions when the 2 lesions are truly dissimilar at high resolution. In this regard, use of deep learning methods with MRSI data alone may avoid this unwanted effect in patients, while providing important structural improvements. Deep learning methods alone may be useful also in the case there is motion between the MRSI and MRI. The use of MRI to upsample MRSI assumes that the 2 data are well aligned, which may not be true if the subjects have moved between the 2 scans. Due to limited spatial resolution of MRSI it is hard to correct for motion in postprocessing.34 In particular, the human experts found that GAN provided sharper boundaries for brain structure and tumors, while with Unet the boundaries between different structures are slightly smoother.There is a small discrepancy between MOS and other image quality metrics in evaluating Unet and GAN results. Unet tends to have slightly higher PSNR, SSIM, and FSIM, while GAN has slightly higher MOS scores. This is not completely unexpected especially for subtle image differences such as between Unet and GAN. Image quality metrics have limitations, and it is hard to design a comprehensive metric that encompass all the perception details important for the human visual system.37 Objective treatment response to standard chemotherapy8 or targeted mutant IDH inhibitors10 can be assessed comprehensively by imaging tumor metabolism with sufficient structural detail to probe regional changes and tumor volume. Metabolic imaging brings more specificity to mapping of the tumors, which could disambiguate confounding effects of response and progression in anatomical imaging.The results for the MRSI data acquired in glioma patients suggest that the proposed methods have great clinical potential for guiding neurosurgery, radiation, and chemotherapy to deliver precision oncology healthcare. Determining tumor margins is important for the planning of surgery and radiation, in particular for mutant IDH gliomas where maximal resection and radiotherapy benefits patient overall survival.26–29 however their use is limited at the moment by very complex pulse sequences and reconstruction techniques which are available to only a small group of investigators. As these acquisition methods continue to disseminate more data will become available for training and validation. On the other hand, even if advanced sequences become more widely available the acquisition time of HR MRSI will still be long for many routine clinical exams and SR methods as shown here will be beneficial in reducing scan times in patients. Although, we demonstrated SR MRSI in glioma patients, our framework is generally applicable to other neurological or psychiatric diseases where metabolic imaging is important to probe disease mechanisms.Our preliminary results in patients are promising, but further validation and verification of the proposed methodology are necessary. The main limitation in validation is the lack of sufficient MRSI data acquired at high resolution. Recently, very advanced acquisition methods have been developed,vdac071_suppl_Supplementary_MaterialClick here for additional data file.