ABCA4- and RDH12-associated diseases benefit from known mechanisms whereby gene malfunctioning leads to elevated bisretinoid levels in RPE cells. On the other hand, peripherin2/RDS-associated disease (PRPH2/RDS), retinitis pigmentosa (RP), central serous chorioretinopathy (CSC), acute zonal occult outer retinopathy (AZOOR), and ceramide kinase like (CERKL)-associated retinal degeneration all express abnormally high fundus autofluorescence levels without a demonstrated pathophysiological pathway for bisretinoid elevation. We suggest that, while a known link from gene mutation to increased production of bisretinoids (as in ABCA4- and RDH12-associated diseases) causes primary elevation in fundus autofluorescence, a secondary autofluorescence elevation also exists, where an impairment and degeneration of photoreceptor cells by various causes leads to an increase in bisretinoid levels in RPE cells.The method of quantitative fundus autofluorescence (qAF) can be used to assess the levels of bisretinoids in retinal pigment epithelium (RPE) cells so as to aid the interpretation and management of a variety of retinal conditions. In this review, we focused on seven retinal diseases to highlight the possible pathways to increased fundus autofluorescence.  Autofluorescence (AF) is the innate ability of fluorophores to emit light after having absorbed photons of suitable wavelength. The retina exhibits an autofluorescence ,2,3, thePathological increases in bisretinoid formation are a primary feature of diseases associated with deficiencies in ABCA4 and RDH12. Under the latter conditions, these by-products of the visual cycle are understood to form as a direct result of the dysfunction conferred by the disease-causing gene variants. Here, we propose that elevated bisretinoid can also be a secondary feature of some retinal diseases when accelerated bisretinoid formation occurs downstream of photoreceptor cell impairment and degeneration.To compare SW-AF images obtained from different subjects at specific retinal locations, or from the same subject longitudinally, a standardized method is applied. For quantitative fundus autofluorescence (qAF), a scanning laser ophthalmoscope (cSLO) is employed with an excitation light of 488 nm, and emitted light is captured within a range of 500 to 680 nm . ProtocoABCA4-associated disease. Indeed, elevated lipofuscin is considered to be a key feature and early component of the ABCA4-disease process . . complemAcute zonal occult outer retinopathy (AZOOR) is a rare condition of non-genetic origin that affects predominantly young women and is characterized by acute onset, photopsias, and subjective visual field losses ,138. Basceramide kinase-like (CERKL) gene have been reported to cause autosomal recessive RP and cone-rod dystrophy [CERKL gene indicates it has protective functions for photoreceptor cells against oxidative stress through several pathways [Mutations in the ystrophy ,148. Cliystrophy . Currentpathways ,152,153.pathways . So far,pathways . Indeed,pathways ,155. TheFollowing capture of a photon of light, the 11-cis-retinaldehye chromophore of visual pigment isomerizes to all-trans-retinaldehyde, thereby initiating a series of conformational rearrangements leading to the phototransduction cascade. To sustain vision, 11-cis-retinaldehye also has to be re-formed via the visual cycle . BecauseSome of the retinaldehyde released into the photoreceptor disk membrane is immediately accessible to the RDH enzymes; alternatively, all-trans-retinal and excessive 11-cis-retinal can undergo condensation reactions with the primary amine of phosphatidylethanolamine (PE) in the outer segment disc membrane thereby forming the adduct N-retinylidene-PE (NRPE) via a Schiff base linkage (C=C-N) . NRPE is


To date, thousands of highly abundant and conserved single-stranded RNA molecules shaped into ring structures (circRNAs) have been identified. CircRNAs are multifunctional molecules that have been shown to regulate gene expression transcriptionally and post-transcriptionally and exhibit distinct tissue- and development-specific expression patterns associated with a variety of normal and disease conditions, including cancer pathogenesis. Over the past years, due to their intrinsic stability and resistance to ribonucleases, particular attention has been drawn to their use as reliable diagnostic and prognostic biomarkers in cancer diagnosis, treatment, and prevention. However, there are some critical caveats to their utility in the clinic. Their circular shape limits their annotation and a complete functional elucidation is lacking. This makes their detection and biomedical application still challenging. Herein, we review the current knowledge of circRNA biogenesis and function, and of their involvement in tumorigenesis and potential utility in cancer-targeted therapy. Sendai virus) [DCC gene and end-joined in a scrambled order compared to the canonical linear sequence [Sry gene, encoding a crucial molecule responsible for sex determination during embryogenesis, was found to be expressed in adult mice testes exclusively as a 1.23-kb circular RNA [Despite their recent fame, the discovery of circRNAs dates back over 40 years. CircRNAs were first discovered in the murine respirovirus (i virus)  and in pi virus) . The inii virus)  and latei virus) –7. In 19sequence . In paraular RNA –18.in-silico approach identified ~2000 human, 700 nematode and 1900 mouse circRNAs that were more stable than the associated linear mRNAs in vivo [Nevertheless, the lack of evidence of their translation into polypeptides left researchers skeptical about the functional significance of such RNAs, and for several decades circRNAs were commonly disregarded as mis-splicing artifacts or by-products of pre-mRNA processing . With th in vivo .Since their discovery, thousands of circRNAs have been found to be present in multiple organisms and their expression been associated with developmental stages, physiological conditions, and diseases including cancer. This has opened up a new field of study aimed at elucidating their biogenesis and function as an essential part of gene expression programs across eukaryotes Fig. .Fig. 1ThPre-mRNA splicing, facilitated by the spliceosome, is a mechanism that is tightly coupled with transcription . During Although circRNAs are preferentially back-spliced from hundreds of human genes , includiThe maturation of circRNAs is a tightly regulated process , involviA variety of circRNAs can be generated from a single protein-coding or non-coding genomic locus  Table 11 and canIn exon skipping a pre-mRNA is spliced into two RNA molecules consisting of an mRNA from which at least one exon is missed, and a lariat containing the skipped exons making circularization possible. The predominant fraction of circRNAs (over 80%) are known to be exon-derived circRNAs (ecircRNAs) , 20, 21;via back-splicing is also mediated by heterogeneous nuclear ribonucleoprotein L (HNRNPL) which is required for prostate cancer growth in vitro and is aberrantly expressed in human prostate tumors [ARHGAP35 generally associated with poor survival in cancer patients [cis- and trans-acting factors are likely to provide a more complex set of processes that affect circRNA biogenesis [Several RBPs, including quaking (QKI) , musclebe tumors . In thispatients . Furtherpatients , preventpatients , 74. By patients , can stapatients . Overallogenesis .cis [There is added complexity in the formation of circRNAs. When introns are not excised properly and are retained in the newly generated circRNAs, so-called exon–intron circRNAs (EIciRNAs) are generated . Converscis , 68 as tcis , 78, 79.cis . Other tcis , very liMLL/AF9 fusion gene (f-circM9) in leukemia causes pro-proliferative and pro-oncogenic effects [EML4-ALK fusion variant 3b generates a tumor-promoting circRNA named f-circEA [Distinct classes of circRNA can also be generated in pathological contexts including cancer Fig. . For ins effects . Similarf-circEA  and an af-circEA .Some circRNAs may also contain modified nucleotides, such as N6-methyl-adenosine patterns (m6A), that can further diversify their biogenesis –88, as wAlternative explanations of back-splicing should also be contemplated. A proportion of discovered circRNAs might not be functional per se but might offer through their back-splicing a break between transcription of the main gene and translation, allowing post-transcriptional regulatory processes to take place. Offering a more radical perspective, recent studies have also proposed that circRNAs form through pre-mRNA splicing errors and are not able to confer any specific benefit .The number of unique circRNAs produced in human cells  , 92 largFL1 (friend leukemia virus integration 1) (onco) gene and favors breast cancer cell metastasis [Beyond their regulatory effects on alternative pre-mRNA splicing , circRNAtastasis . An intetastasis , 68. Cirtastasis .Fig. 4BiInitial examples of functional circRNAs were shown to work as efficient microRNA sponges, post-transcriptionally regulating the activity of their downstream target genes , as wellCKAP5 mRNA expressing a protein that regulates microtubule function and sustains cell-cycle progression in cancer cells [CTNNB1 mRNA [HMGA2 mRNA by enhancing its interaction with IGF2BP2 to promote malignant progression [In addition, circRNAs can influence the stability of other RNA molecules, including both lncRNAs and mRNAs . An examer cells . LikewisNB1 mRNA . A similgression .PABPN1 by preventing HuR binding to the cognate mRNA [c-MYC ensures the retention in the nucleus of c-Myc protein and consequently promotes tumorigenesis [As well as being required for their back-splicing and gene transcription regulation, circRNAs are also able to engage with RBPs to direct their cellular localization . Given tate mRNA . In thisate mRNA . In NSCLate mRNA . Conversigenesis .via a cap-independent mechanism. As a result, shortened versions of canonical proteins, acting as modulators of dominant negative protein variants, decoys, or alternative protein complexes are generated [SHPRH gene (circSHPRH) which encodes the novel identified protein termed SHPRH-146aa [via the Hippo-YAP pathway [CDH1 gene (C-E-Cad- 254aa), involved in the maintenance of the cancer stemness in glioblastoma by interacting with EGFR and activating downstream STAT3 signaling [Although the majority of circRNAs are expected to be non-coding, both in vivo and in vitro experiments have demonstrated their association with ribosomes and translation into proteins , 121. Bienerated . Howeverenerated , suggestenerated –125. Besenerated , cancer-RH-146aa . TogetheRH-146aa . InhibitRH-146aa . By cont pathway . A similignaling . In hepaignaling  that intignaling . Oncogenignaling . Experimignaling .Clinical applications of circRNAs rely on accurate RNA profiling Box ; this inBox 1 Chapter: Step-by-step methods for investigating circRNAs: from discovery to functionRNAse R treatment: An exoribonuclease that specifically degrades the bulk of linear transcripts and enriches circRNAs in a K+ containing buffer [g buffer , 137.A-tailing + RNAse R treatment: Purified RNA samples are treated with poly(A) polymerase followed by RNase R treatment in a Li+ containing buffer, to remove linear RNAs more efficiently [iciently .Microarrays: Reverse transcribed RNAs (cDNAs) hybridized with probes immobilized on to microarray chips that target circRNA back-splice junctions [unctions .Northern Blotting: Separation of linear and circular RNA molecules based on electrophoretic mobility, immobilized on a nitrocellulose membrane and targeted by complementary radiolabeled or non-radiolabeled oligonucleotide probes [e probes .RNA-seq libraries and deep sequencing: Transcriptome-wide profiling of circRNAs.Initial identification and enrichmentRT-qPCR: cDNAs are amplified and quantified with “divergently oriented” primers designed across the junction sequences.Sanger sequencing: Conventional sequencing to confirm the circular nature of an RNA molecule once amplified.RT-RCA: Reverse transcription by rolling cycle amplification using primers binding to the circRNA junction site [ion site .RNA-FISH: Fixed cells or tissues are hybridized with short fluorescently labeled DNA probes targeting back-splice junctions of a circRNA [ circRNA , 146.LAMP: Double exponential amplification induced by employing stem-loop primers (SLPs) that recognize the junction sequence of circRNAs and form a double stem-loop DNA structure [tructure .Nanostring technology (nanoString nCounter): Capture and reporter color-coded probes that jointly recognize the back-splice junction of a circRNA [ circRNA .Validation of sequence, presence, and relative abundanceSilencing systems: Targeting the junction region of the circRNAs , CRISPR–Cas13 [etc).PR–Cas13 , 157, etOverexpressing systems: To increase circRNA expression .Modulating circRNA levelsRIP: Antibody pulldown of a circRNA directly bound to a specific protein of interest.Biotinylated AONs: Oligonucleotides designed against the circRNA junction to pull down the circRNA.Identification of partner molecules (RBP or miRNAs)RT-qPCR: Using a spike external RNA at a known concentration followed by RT-qPCR to generate a standard curve correlating Ct values with copy number.Droplet digital PCR (ddPCR): Droplets with single cDNA molecules are amplified individually allowing absolute quantification of circRNA copies per input RNA [nput RNA .Assessing circRNA stoichiometry and absolute quantificationRescue experiments: Comparing the biological effects observed from mutated or truncated circRNAs with those of endogenous circRNAs and overexpressed circRNAs.Validating the functional consequences of circRNA interactionsIt’s worth noting that different methods have their own advantages and limitations. Combining multiple approaches can enhance the reliability and accuracy of circRNA detection and validation.RNA-based high-throughput sequencing technologies , have allowed genome-wide annotation and quantification  of a numTraditional methods, such as RT-qPCR using sets of “divergently oriented” primers designed to cover the circRNA back-spliced junction, and Northern blotting, which can separate RNA molecules of different size and abundance based on their speed in electrophoresis , 30, 34,The use of microarrays with probes spanning back-splice junctions has also been employed as a suitable screening tool; however, this approach can sometimes lead to the erroneous detection of linear species and can generate data that is not easily consistent between studies . NonetheIntracellular visualization, localization, and quantification of RNA molecules are critical for studying their biology and function. Several studies have employed RNA fluorescence in situ hybridization (RNA-FISH)  for the Advances in the field of circRNA research have paved the way for a number of novel assays with increased sensitivity and specificity that appear to detect more accurately low abundance circRNAs and hold great promise for their efficient annotation in the future . Among sFAM120A) by competitively binding to IGF2BP2 (a translation inhibitor) [Beyond efficient detection, studies aimed at assessing circRNA function require particular attention. Albeit at different levels, circRNAs and linear RNAs transcribed at the same genomic locus are normally co-expressed. This makes gain- and loss-of-function studies particularly challenging since they are based on targeting the original loci and are therefore likely to affect the cognate linear RNAs too. In terms of selective degradation of circRNAs, of particular note is the newly developed RNA-guided, RNA-targeting Cas13 system (CRISPR–Cas13) , 156, whhibitor) . Strateghibitor)  by emplohibitor) . A succehibitor) .Cutting-edge methodologies, such as the new Oxford Nanopore technology, are particularly suitable for long-read sequencing and may further help in providing information about the entire sequence of circRNAs . FurtherCircRNAs have been linked to a variety of physiological conditions and cell biology features including stemness and pluripotency , 167–172Growing evidence suggests that circRNAs can be used as potential biomarkers for early-cancer detection, clinical diagnosis, prognosis and even used in monitoring response to therapy –177 Fig. Their uSome circRNAs have shown a strong prognostic potential including CiRS-7/CDR1as which has been found highly abundant in intratumoral stromal cells, extensively used as prognostic factor in carcinomas of colon , lung 1 and breaCircRNA panels and/or signatures appear to have a more robust prognostic value than single circRNAs. RNA-seq analysis of frozen tissues collected from post-operation has allowed to profile differential circRNA expression between patients with and without recurrence in four circRNA-based signatures (named circScores), thereby grouping colon cancer patients based on high- or low-risk of recurrence . AccordiCircRNA tissue and cell-type specificity allow better correlation with specific pathologies, including the ability to distinguish between cancer subtypes , 189–192Exosome-derived circRNAs are also useful diagnostic biomarkers depending on relative expression, stability, and exosome coupled targeted delivery pathways . For insSome circRNAs could offer a double utility as both prognostic and diagnostic biomarkers. For example, circ0001785 not only shows higher diagnostic efficiency for breast cancer detection than the two most widely investigated biomarkers serum carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3) , but alsFOXQ1, which ultimately leads to metastasis in nasopharyngeal carcinoma, EMT and docetaxel chemoresistance [ZEB2, a master regulator of EMT [The efficacy of cancer therapy is often limited by intrinsic and acquired resistance. CircRNA expression has been proven to vary in response of tumor cells to chemotherapy, radiotherapy and immunotherapy through several mechanisms, thereby serving as a valuable indicator for clinicians to modify cancer patient treatment. For instance, circCRIM1 competitively binds to miR-422a and prevents the inhibitory effects of miR-422a on its target gene sistance . In endor of EMT . Interesr of EMT .via miR-515-5p [Given their circulating nature, exosomes can also transmit drug resistance between heterogeneous populations of tumor cells. CircRNAs with altered expression in drug-resistant cells can be transferred to drug-sensitive ones. For example, the exosomal circZNF91 functions as an exosomal cargo mediating the signal transmission between hypoxic and normoxic tumor cells in pancreatic cancer and promotes chemoresistance . In a siR-515-5p .via exosomes by HCC cells into the surrounding microenvironment and by sequestering and preventing miR-449c-5p binding to the checkpoint target gene “T cell immunoglobulin and mucin domain 3” (TIM-3), triggers natural killer (NK) cell dysfunction, immune evasion and resistance to anti-PD-1 immunotherapy [Binding of the cell receptor PD-1 to its programmed death-ligand 1 (PD-L1) on tumor cells, activates downstream signaling pathways and inhibits T cell activation. Novel antibody inhibitors, such as anti-PD-1 and anti-PD-L1, are designed to restore the host antitumor immune response effectively, with an overall manageable toxicity compared to chemotherapy and radiotherapy. However, the development of resistance to immune checkpoint inhibitors (ICIs) is one of the major limitations in using novel anti-PD-1 and anti-PD-L1 therapies for treating cancer. CircRNAs play an important role in acquiring resistance features to these therapies by modulating the expression of key cancer pathways and associated immune populations from the tumor microenvironment. For example, in NSCLC circFGFR1 induces resistance to PD-1 antibodies by interacting with miR-381-3p and upregulating its target CXCR4, responsible for NSCLC progression and resistance . In distotherapy .The dysregulation of circRNAs can drive tumorigenesis and metastasis in various cancers, making them promising targets for cancer treatment Fig. . Severalvia sponging miR-16-5p, has been shown to reduce tumor growth in mice efficiently [RNA interference (RNAi) accomplished by cytoplasmic delivery of small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs)  has beeniciently . Repressiciently , a circRiciently .Single-stranded DNA antisense oligonucleotides (AONs) have also been employed to inhibit selectively or degrade oncogenic circRNAs. For example, AONs targeting circLONP2, which enhances metastasis and invasiveness of colorectal cancer cells by favoring the maturation and the exosomal dissemination of miR-7, dramatically reduce the extent of metastasis to foreign organs in vivo .The delivery of RNAi molecules can be better accomplished with the use of nanoparticles. Gold nanoparticles (AuNPs), commonly associated with a linker such as PEG or polyethylenimine, can be conjugated with siRNAs, shRNAs or AONs and ensure a more efficient drug delivery in animal studies thanks to their high stability, and easily modifiable surface. Delivery of AuNPs with siRNAs targeting circDNMT1 has been shown to suppress breast tumor growth successfully as well as enhancing the survival in mice . A similTranslated circRNAs with important oncogenic roles in cancer can also be targeted by specific drug inhibitors. This is the case of HER2-103aa which plays a role in tumorigenesis and shares sequence similarity with HER2 CR1 domain (the domain that is targeted by pertuzumab). Treatment with pertuzumab has proven to reduce significantly the tumorigenicity of both circHER2 and its encoded 103 amino acids polypeptide (HER2-103aa) expressing cells in vivo .CircRNA expression can be increased by direct delivery into cells. This approach is particularly effective in antagonizing oncogenic miRNAs by exploiting the property of circRNAs to act as ceRNAs/miRNA sponges. To obviate possible degradation effects, more efficient systems have adopted cassettes cloned inside lentivirus or adeno-associated virus vectors . Howevervia sponging of one of its 3’ UTR targets, miR-181c-5p [Gain- or loss-of-function-based therapeutic strategies targeting crucial circRNAs associated with cancer therapy resistance have also been investigated to overcome treatment failure. CircRNAs, such as CircRNA-SORE, have been shown to interfere with the activity of tyrosine kinase inhibitors, such as sorafenib. Upregulation of circRNA-SORE in sorafenib-resistant HCC cells ensures the cytoplasmic retention of the oncogenic protein YBX1 and prevents its ubiquitination and degradation by PRP19 in the nucleus and thereby favors resistance to sorafenib. Moreover, circRNA-SORE is loaded into exosomes that are trafficked from sorafenib-resistant cancer cells to sensitive cancer cells, and this helps to spread the resistance phenotype. By treating mice bearing subcutaneous sorafenib-resistant patient-derived xenografts with siRNAs targeting circRNA-SORE, researchers have demonstrated that the responsiveness to sorafenib treatment can be restored . In a si-181c-5p . CircRNA-181c-5p .via shRNAs modulates the response to anti-PD-1 treatment and improves overall survival. This may prove an effective method of reversing resistance for ICIs by acting both on tumor cells and on associated dysfunctional immune microenvironment [via the miR-30-5p/snail/DPP-4 axis. Treatment with sitagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor generally used to treat type 2 diabetes, seems to obviate the resistant effects induced by the circMET. A combination of both sitagliptin and anti-PD-1 molecules has been shown to improve antitumor immunity in immunocompetent mice and is likely to be more effective in treating patients with HCC [As discussed earlier, circUHRF1 plays a crucial role in sustaining anti-PD-1 resistance in HCC . Using aironment . Immuno-with HCC .Further research is needed before specific circRNAs can be effectively exploited as clinically useful biomarkers or as therapeutic targets for cancer treatment. On-going studies are currently assessing the potential therapeutic benefits for cancer patients by bringing circRNA biology into clinical practice. Table As sequencing approaches improve in terms of depth, accuracy, and read length , the annMany open questions regarding their biogenesis and biological functions, remain unanswered. It remains unclear whether or not they are exclusively co- or post-transcriptionally generated, how they are ultimately degraded, to what extent their structures might confer functional differences compared to their linear RNA counterparts and what proportion are translated into proteins. Moreover, many functional aspects of circRNAs might have not been uncovered yet due to the limitations of current approaches.From a clinical perspective, the advent of circRNA research has opened up new exciting possibilities in cancer research, for better diagnosis and novel therapies. CircRNAs have been associated with a variety of physiological conditions and cell biology features including stemness and pluripotency. It has also been demonstrated that they have potential utility as valid biomarkers for early-cancer detection, diagnosis, prognosis and even prediction of response to therapies. However, how they impact cancer diagnosis and prevention remains debatable and sometimes even contradictory. CircRNAs often participate in more than one molecular mechanism in many tissues and diseases; targeting circRNAs therapeutically might lead to off-target effects in non-cancerous cells and tissue, making their clinical translation particularly challenging. Moreover, the discovery of multiple miRNAs sponged by the same circRNAs in different cancer types suggests that their contribution to a specific cancer phenotype is likely to be context-dependent. Additional related controversies surround the stoichiometry circRNA–miRNA–mRNA and the role of circRNAs as competitive endogenous RNAs , 223. ThThe integration of artificial intelligence (AI) and circRNA transcriptome analysis holds great promise for advancing our understanding of this complex biological system, undoubtedly facilitating diagnostics and therapeutics in various pathological contexts including cancer. New approaches based on AI might help to identify shared and unique characteristics in features of circRNAs, such as their expression levels, alternative splicing patterns, co-expression networks, or specific structural motifs, all in different conditions. However, the success of using circRNA transcriptome data combined with AI for building classifiers will rely on high-quality data generation, careful experimental design, and rigorous computational analysis. The interpretability of AI models in this context remains challenging, as circRNA properties and functions are still not fully elucidated.In summary, there is undoubtedly tremendous potential for circRNAs to have a significant impact on cancer diagnosis and treatment. The initial studies of circRNAs have necessarily focused on their discovery and characterization; what needs to follow in the near future is more detailed biological validation with patient samples in the context of clinical research. These experiments will need to be further corroborated with detailed functional studies in suitable model organisms. Once a more complete understanding of their functional biology is obtained, it will be possible to exploit the full potential of circRNAs in the clinic.


Lipid rafts are dynamic assemblies of glycosphingolipids, sphingomyelin, cholesterol, and specific proteins which are stabilized into platforms involved in the regulation of vital cellular processes. Cerebellar lipid rafts are cell surface ganglioside microdomains for the attachment of GPI-anchored neural adhesion molecules and downstream signaling molecules such as Src-family kinases and heterotrimeric G proteins. In this review, we summarize our recent findings on signaling in ganglioside GD3 rafts of cerebellar granule cells and several findings by other groups on the roles of lipid rafts in the cerebellum. TAG-1, of the contactin group of immunoglobulin superfamily cell adhesion molecules, is a phosphacan receptor. Phosphacan regulates the radial migration signaling of cerebellar granule cells, via Src-family kinase Lyn, by binding to TAG-1 on ganglioside GD3 rafts. Chemokine SDF-1α, which induces the tangential migration of cerebellar granule cells, causes heterotrimeric G protein Goα translocation to GD3 rafts. Furthermore, the functional roles of cerebellar raft-binding proteins including cell adhesion molecule L1, heterotrimeric G protein Gsα, and L-type voltage-dependent calcium channels are discussed. Glycosphingolipid, one of the glycoconjugates, exists in clusters and forms microdomains containing cholesterol in the cell membrane. This review provides several examples of glycosphingolipid microdomain functions and their molecular mechanism in healthy states and diseases of the cerebellum.Glycosphingolipids are found in the outer leaflet of the plasma membrane of all vertebrate cells and are thought to play functional roles in the regulation of cellular proliferation and differentiation . GanglioThe basic forces driving the formation of lipid rafts are thought to be lipid interactions. Glycosphingolipids are relatively rich in saturated fatty acyl chains, which allows tight packing and confers the characteristic of a high melting temperature. On the other hand, phospholipids are relatively rich in cis-unsaturated fatty acyl chains (kinked structure), which prevents tight packing and confers the characteristic of a low melting temperature. Lipid rafts are thought to exist as phase-separated domains of glycosphingolipids and cholesterol from phospholipids. Src family kinases and the α-subunit of the heterotrimeric G proteins anchor onto the inner leaflet via N-terminal saturated fatty acyl chains lipid modification, palmitoylation, and myristoylation . The natA variety of cell-surface proteins are anchored in the membrane by GPI. GPI-anchored proteins have various functions, such as enzymes, receptors, and adhesion molecules. GPI-anchored proteins have been implicated in transmembrane signaling, nevertheless, they lack intracellular domains. GPI-anchored proteins are mainly associated with lipid rafts. It is thought that association with Src-family kinases in lipid rafts is important for GPI-anchored proteins in signal transduction.Phosphatidyl lnositol Glycan (PIG) and Post GPI Attachment to Proteins (PGAP) from Chinese hamster ovary cell mutants . . 42]. Contactin-2/TAG-1 is expressed by granule cells extending bipolar processes in the inner EGL, and is required for the initial navigation of parallel fibers. Axonin-1, a homolog of chick contactin-2, is also expressed in post-mitotic granule cells as they extend their processes, the parallel fibers. In the absence of axonin-1, these processes still extend, but no longer in a parallel manner, to the pial surface of the chicken cerebellum . FurtherContactin-1/F3 is expressed by older granule cells in the ML, and required for parallel fiber fasciculation. Contactin-1 mutant mice display a severe ataxic phenotype consistent with defects in the cerebellum . AnalysiContactin-6/NB-3 is expressed in granule cells during synaptogenesis with Purkinje cells. Contactin-6/NB-3 KO mice show impairment in motor coordination and synapse formation between parallel fibers and Purkinje cells . IdentifLipid rafts are spatially and compositionally heterogeneous in the cell membrane. For example, DRM rafts are separated into two distinct subfractions in dorsal root ganglionic neurons. The GPI-anchored prion protein, located primarily in the cell body, was relatively soluble in detergent. Thy-1, abundantly expressed in neurites, was highly resistant to detergent solubilization . In primCerebellar rafts function as dynamic membrane microdomains for the attachment of various proteins such as adhesion molecules, receptors, signaling molecules, adaptor proteins, and effector proteins .L1 is a member of the immunoglobulin superfamily implicated in a variety of processes, including neurite elongation, axon fasciculation, and migration of neuronal precursors. Localization of L1 to lipid rafts is developmentally regulated. In mouse cerebella, L1 is detected in the DRM raft fraction only between postnatal days 3–28, corresponding to the period when granule cells migrate and neurites elongate in vivo. Growth cone migration mediated by L1 is inhibited after DRM disruption by micro-scale chromophore-assisted laser inactivation of GM1 gangliosides . c-Src iThe heterotrimeric G protein, Gs α-subunit (Gsα) is enriched predominantly in lipid rafts in subjects with a major depressive disorder . Gsα is 2+ influx through L-type voltage-dependent calcium channels (L-VDCC), which promotes the activity-dependent survival of cerebellar granule cells. Primary cerebellar granule cells can be maintained in a medium containing depolarizing levels of KCl (25 mM), a condition that mimics neuronal activity. Cerebellar granule cells undergo apoptosis when cells are transferred from a medium with 25 mM KCl to a 5 mM KCl medium. The activation of L-VDCC prevents cerebellar granule cells from entering low-K+-induced apoptosis. L-VDCC, protein kinase A (PKA), and calmodulin-dependent protein kinase II (CaMK-II) are associated with lipid rafts of cerebellar granule cells [2 subunit and the steady-state calcium concentration in neuronal somas ([Ca2+]i) to values close to those measured in 5 mM KCl proapoptotic conditions. These observations suggest that lipid rafts play a major role in the control of the PKA- and CaMK-II-induced phosphorylation level of the L-VDCC β2 subunit, thus preventing CGNs from entering apoptosis.Membrane depolarization causes Cale cells . Lipid rA number of studies have examined the role of lipid rafts in physical properties and signaling. In contrast, the role of gangliosides in lipid rafts remained obscure. Recent studies by gene targeting of ganglioside synthases revealed ganglioside-specific phenotypes. Lipid rafts are compositionally and functionally heterogenous in the neuronal membrane. We demonstrated that ganglioside GD3 rafts are involved in the migration of granule cells during the early stage of cerebellar development. Ganglioside species-specific raft functions in cerebellar health and diseases remain to be explored.


Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss’ Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications. Computed tomography (CT) remains the imaging modality of choice for assessment of patients with moderate to severe features of COVID-19, for confirmed cases with worsening respiratory status, and for patients with functional impairment or hypoxia after recovery from COVID-196.Since the COVID-19 pandemic, significant attention has been directed towards developing medical imaging-based Artificial Intelligence (AI) models for rapid diagnosis, risk-stratification and prediction of complications for this disease7 or deep learning (DL) methods, may facilitate the development of advanced imaging analysis tools for diagnosis and prognostication in COVID-19 research8. Through the extraction of quantitative features from standard-of-care medical imaging, they offer non-invasive biomarkers that can guide clinical decision-making11. Such algorithms are reliant on large and high-quality annotated datasets13. However, heterogeneity is inherent in medical imaging datasets, for example due to variation in scanning protocols, contrast enhancement and acquisition parameters including slice thickness and reconstruction kernels between centres15. This represents a common challenge which can introduce confounding factors that may impair a model’s ability to learn effectively from the data and perform reliably22. Imaging biomarker standardization efforts are underway to mitigate against this25. Contrast-enhanced CT imaging is a key example. Its use is clinically dictated and correlates with other variables which are likely to influence model performance26. Furthermore, patient related factors including physiology can significantly impact upon the resultant images between patients injected with the same contrast dose under the same imaging protocol26. Simply excluding images based on contrast status introduces bias, however datasets with a combination of contrast and non-contrast CT images can lead to signal intensity distribution shifts in training data which may impair performance of CT imaging-based predictive models27. This can influence the development of clinically relevant computational algorithms.Radiomics models, either using hand-crafted features6. Direct post-contrast arterial-phase CT is recommended in the context of suspected pulmonary embolism or superimposed bacterial pneumonia, which are the primary differential diagnoses and common complications of severe COVID-19 cases28. Patients with COVID-19 who meet indications for CT imaging are likely to be breathless, coughing or clinically unstable, placing importance on shorter scanning protocols6. If comparable quality non-contrast images could be synthesized from contrast-enhanced CT images, this could enable standardization of contrast and non-contrast imaging for radiomics and DL algorithm training and validation.Imaging guidance for COVID-19 advises single-phase CT with no contrast enhancement or post-contrast series, as radiological features of COVID-19 tend to be confined to the lungs, without involvement of the pleura or mediastinum29. GANs have gained popularity in data synthesis due to their ability to learn global context from large training examples31. In medical imaging, they facilitate domain adaptation for AI-based networks32. Choi et al. developed a three-dimensional (3D) conditional GAN (cGAN or pix2pix) to generate synthetic non-contrast chest CTs, demonstrating higher lesion conspicuity for expert reviewers in mediastinal lymph node assessments when synthetic images were used alongside acquired contrast CTs33. However, whilst their method produced predictions with higher perceptual quality than previous non-adversarial convolutional neural network (CNN)-based networks34, it still relies on paired input images which may not be feasible for real-world applications. The cycle-consistent GAN (cycle-GAN)35 has emerged as a revolutionary training strategy for generating photo-realistic and high-resolution synthetic images using unpaired data38. Chandrashekar et al. deployed a two-dimensional (2D) cycle-GAN to synthesize contrast CT angiograms (CTAs) to negate contrast CTA acquisition39. Xie et al. developed a 3D residual cycle-GAN to predict contrast-enhanced CTs for anatomy localization as well as organ-at-risk (OAR) delineations for radiotherapy treatment planning, without the need to perform contrast CT imaging40. Conversely, Sandfort et al. developed a cycle-GAN model as a data augmentation tool and demonstrated that the addition of synthetic non-contrast CT images in multi-organ segmentation training improved performance for out-of-distribution patient scans41. This is especially pertinent when considering the challenges around using COVID-19 imaging data to develop accurate and generalizable AI models. Therefore, synthesizing non-contrast CT images from contrast enhanced CTs has both clinical and research applications, which merit further development in DL technology. Non-contrast CT images can also be generated using non-AI techniques, such as acquiring the images using dual-energy CT42. The paired images acquired using different energies can be used to generate a virtual subtraction non-contrast image42. However, access to dual energy or multiband CT scanners and radiologist expertise is costly and not widely available, and therefore the development of AI techniques is an important solution.Generative adversarial networks (GANs) present a potential solution31.Whilst previous research demonstrates a promising outlook for the integration of image synthesis in clinical decision-making, in-depth investigation of the performance of these algorithms on large multi-centre and contrast heterogenous COVID-19 datasets remains unexplored43 DL classification task to assess network generalizability as a data homogenization tool. We believe that our study on a large scale, multi-centre COVID-19 dataset provides a valuable evaluation of cycle-GAN generated synthetic CT data in the context of wider AI applications.In this study, we sought to close this research gap with the following contributions. Firstly, we developed a 3D patch-based cycle-GAN to synthesize non-contrast images from contrast CTs. We used 2,078 scans from 1650 patients with COVID-19 pneumonia, and qualitatively evaluated our results with a modified Turing-test for blind classification of synthetic and acquired CT images. We subsequently performed both a handcrafted radiomics and VGG-Net44 (Research Ethics Council (REC) reference number: 20/LO/0688). Patient consent was not required to use this deidentified data as per the respective UK Health Research Authority and UK Research Ethics Council approvals. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from the human experts involved in the study. NCCID is a centralized database containing medical images of hospital patients from over 25 centres across the UK. Images were filtered to include only CT images that included the entire thorax. For example, CT coronary angiograms were excluded. 2,078 CT scans were downloaded together with available metadata and manually labelled as either contrast-enhanced or non-contrast by a clinician  (reference number: 20/HRA/3051), ClinicalTrials.gov identifier: NCT04721444. CT images used for this study were obtained from the National COVID-19 Chest Imaging Database (NCCID)This section details how the 2078 labelled NCCID CT images were pre-processed and describes the development of the cycle-GAN framework including architecture, training, and inference.45. Intensity values outside the range of −1,000 to 1,000 Hounsfield units (HU) were truncated, and the remaining values were scaled to  following division by 1,000 to shorten the dynamic range for cycle-GAN training.From the 2,078 downloaded CT scans, low-dose CT scans, which constituted less than 3% of the dataset were excluded. The remaining 2,019 CT images (from 1650 patients) were resampled to 35, images from domain A are synthesized to the domain B distribution and then reconstructed back to domain A to maintain spatial consistency. This process is simultaneously conducted for domain B synthesis and reconstruction, requiring two generators and two discriminators. Concurrent training of the generator and discriminator generates sets of trained weights that yield realistic synthetic images, which are indistinguishable from real images by the discriminator. Thereby, cycle-GAN promises to be an ideal strategy for style transfer from contrast to non-contrast CT, (and vice versa), using unpaired training data. This is particularly appealing for large multi-centre datasets that constitute large heterogeneity in CT images.Conventionally, GANs consists of a generator and a discriminator, whereby the generator predicts synthetic images from random noise for the discriminator to classify as synthetic or real, during training. In a cycle-GANAB and GBA) and discriminators (DA and DB) were performed using 3D layers , two down-convolution blocks (StridedConv3D-InstanceNorm-Relu), nine residual units, two up-convolutional blocks (StridedTransposedConv3D-InstanceNorm-Relu), and a final Tanh activation layer. The patch-GAN discriminator included three down-convolutional blocks (StridedConv3D-InstanceNorm-LeakyRelu), one 3D convolutional layer and a final sigmoid activation layer. The computational graph for the models and the training code were implemented using TensorFlow 2.4.1 and Keras libraries.We developed a 3D patch-based cycle-GAN where the convolutional operations in the generators  and the structural similarity index (SSIM) loss46 between the input and reconstructed patches  for classifying real and synthetic patches.AB and GBA are the generators, and DA and DB denote the discriminators.The adversarial loss is an essential component of the cycle-GAN loss function that uses the minimax loss between real and synthesized images in domains A and B29 Eqs. –3. To maains Eq. . The ove–4). Network training parameters were informed by our previous work38.From the 2,019 CT images  in the NCCID dataset, 1,171 contrast CTs (929 patients) and 607 non-contrast CTs (522 patients) were used for training, and 119 contrast CTs (100 patients) and 126 non-contrast CTs (100 patients) for testing. During cycle-GAN training, random patches of size During the evaluation phase, synthetic CT volumes were generated using a sliding window algorithm with a stride of 16 voxels Fig. . Intensi47 were calculated to determine classification accuracy, sensitivity, specificity, and interrater agreement reliability respectively  accreditation were asked to review and classify 200 non-contrast single-slice thoracic CT images. 100 images were acquired CT slices and 100 were synthetic non-contrast CT slices generated from our cycle-GAN model, windowed to −1200 and 200 HU. The individual scores were recorded, and percentages and Fleiss’ Kappaely Eqs. .7\docume1 weight/bias regularization applied on all nodes), and a final sigmoid layer for classification. As input to the classifier, we extracted features from each image slice using the pre-trained VGG16 model48; after removing the original classification layer of the VGG16 network and max-pooling across each channel in the preceding layer this resulted in 512 extracted features. To ensure that the features represented regions within the body only, an automatic body contour algorithm was applied prior to feature extraction, consisting of image thresholding (HU > −500), followed by morphological opening , a binary cross entropy loss function, batch size of 28, and validation data size of 48 patients (split evenly between both classes). To determine the distribution of training/validation curves, each of the models were trained in this way 30 times, in each instance randomly sampling 48 different validation patients. The mean and standard error in the mean of the training/validation curves at each epoch were recorded.Using a single slice from each of 200 test patients held back from the NCCID dataset in cycle-GAN training (100 contrast and 100 non-contrast), we trained three classifiers: (i) acquired contrast versus acquired non-contrast, (ii) synthetic non-contrast versus acquired non-contrast, and (iii) acquired contrast versus acquired non-contrast where labels were shuffled to derive performance statistics for a random choice model (i.e. to generate a Null hypothesis). These 200 patients were split into training/test datasets using an 80:20 ratio by stratified sampling. Using the 160 patient training data, the classifier was trained for 2,500 epochs, using Adam optimization (learning rate 1 × 10A “contrast-heterogenous” dataset—this combined CT images from 100 COVID-19 patients held back from the NCCID dataset in cycle-GAN training (50 contrast and 50 non-contrast) and 100 patients with non-COVID-19 pneumonia (30 contrast and 70 non-contrast) .A “contrast-homogenous” dataset—the contrast-enhanced images were replaced by the GAN-generated synthetic non-contrast equivalents (50 COVID-19 and 30 non-COVID-19 patients). For consistency and to exclude potential artifacts from the generators, acquired CTs were replaced by their corresponding predictions from the contrast to non-contrast model. Note that the identity term in the cycle-GAN loss function for network training ensured that the non-contrast images remained unaffected.We undertook an additional assessment by comparing AUC values of machine learning classifiers trained using handcrafted radiomic features to predict COVID-19 vs non-COVID-19 pneumonia from single-slice CT images. Twin datasets were produced for this experiment:45 and single slice lung regions of interest (ROIs) encompassing diseased parenchyma were manually segmented by a clinician using the ITKSnap desktop software49. The radiomic features were standardized and extracted using TexLAB 2.050. This was achieved using 25 HU intensity bins and for features broadly related to volume, intensity, heterogeneity and wavelet transformations, as previously described50. The subjects were divided into training and validation sets with a 4:1 ratio .The images were resampled to a 51 in R. Hyper-parameters of the final selected models are listed in the Supplementary Material.Starting with the heterogenous dataset, highly correlated features were removed (threshold: 0.9) leaving 122 features, and then feature selection was performed using Kendall’s rank (threshold: 0.2) identifying 24 features . Seven machine learning classifiers (logistic regression (LR), linear-support vector machine (SVM), random forest (RF), partial least squares (PLS), ridge, least absolute shrinkage and selection operator (LASSO) and elastic-net regression) were trained using these features. A receiver operating characteristic curve (ROC) analysis was conducted to evaluate the performance of the model and the area under the ROC curve (AUC) was recorded for validation images. Hyper-parameter optimization was performed via grid-search with 20 repeats of ten-fold cross-validation using the caret package52, and p-values were recorded.The same features selected from the heterogenous dataset were then selected from the homogenous dataset and validation set AUC of the classifiers recorded. AUC values between the heterogenous and homogenous datasets were compared using bootstrap with the pROC package in RFor completeness, this experiment was repeated in reverse, first selecting features from the homogenous dataset, and then comparing performance of the classifiers with these same features selected from the heterogenous dataset . The schematic of our proposed framework is shown in Fig. Patient consent was not required for this study. Please see the section on Datasets & Ethical Approval for further information.The examples of the inferred synthetic non-contrast and synthetic contrast CT images predicted from synthetic non-contrast CT are shown in Fig. 53.The results from our human reader assessment revealed that the experts achieved a mean accuracy of 58.7%, with individual classification accuracies of 62%, 13% and 23% for synthetic CT images  are detailed in Table We further explored the impact of the cycle-GAN on the raw handcrafted radiomic feature values. Figure 56. Whilst non-contrast CT is less commonly used in these settings, it does provide utility in differential diagnosis, for example in depicting hemorrhage or calcification and by serving as reference images to evaluate the degree of enhancement on contrast-enhanced CT56. Contrast-enhanced CT is also widely used in planning radiotherapy to better delineate target volumes and OARs. However, it has been suggested that this may lead to dosimetric errors because of overestimations in tissue electron density56. It would therefore be advantageous to automatically synthesize non-contrast images from contrast CT scans, avoiding the need for additional acquisition of non-contrast CTs. Such approaches would be advantageous to reduce demands on resource-constrained health systems.In response to the global need for improved detection and diagnosis of COVID-19 pneumonia, research attention has been directed towards developing generalizable and efficient AI algorithms that can be applied to large and multi-centre datasets. In this study, we developed a 3D patch-based cycle-GAN to synthesize non-contrast images from contrast-enhanced CTs, using a multi-centre dataset containing 2078 CT scans from 1,650 patients with COVID-19. We hypothesized that this approach could provide contrast-homogenized datasets which may improve performance of future imaging-based AI models. Whilst our investigation primarily focuses on evaluating the efficacy of this technique in the context of COVID-19, its clinical utility may be even wider-reaching, for example finding applications in cancer care. Contrast-enhanced CT plays a key role in lesion detection, characterization, and staging, as well as for radiotherapy treatment planning, guidance, and response assessment41. However, few previous studies have conducted an in-depth analysis of this technique in the context of handcrafted radiomics or qualitative reader assessments. Earlier studies have reported the use of CNNs34, cycle-GAN35 and RadiomicGAN57 for CT standardization, however these techniques rely on paired input images for training which limits their applicability in most real-world scenarios. Selim et al. proposed a framework inspired by cycle-GAN that successfully homogenized CT scans from different vendors using their 2D CT harmonization model (CVH-CT)58. Although they validated their results based on radiomic features, clinical evaluation of their results was not explored.Cycle-GAN is a promising technique in medical imaging research because of its ability to generate photorealistic images from unpaired dataIn this study, we employed VGG16-based DL and handcrafted radiomics classifiers, along with human reader assessments, to evaluate the technical and clinical aspects of GAN-generated images using 3D patch-based training. Our human reader assessment, which involved two radiologists and one clinical oncologist, revealed that the experts failed to correctly identify the synthetic CT images in 67% of cases . Figure We employed a VGG16 to assess the effectiveness of a DL model in distinguishing between acquired and synthetic non-contrast CT images. Even though there was a decrease in classification accuracy when using homogenized datasets, the classifier was still able to differentiate between the two groups better than chance, supporting the notion that there exist nuanced features within the synthetic non-contrast scans that can influence the VGG16 classifier's performance. Whilst there was a slight increase in validation binary cross entropy loss, suggesting some overfitting, the validation curves plateaued for AUC, precision, and recall, indicating that the training was stable.59. In our experiment using a handcrafted radiomics classifier, we hypothesized that replacement of contrast images with GAN-generated synthetic non-contrast images would improve performance of a radiomic classifier trained to distinguish COVID-19 vs non-COVID-19 pneumonia. We were unable to reject the null hypothesis in this experiment, likely due to the GAN radiomic features being sensitive to subtleties within the synthetic images that were not appreciable by eye, further validating our VGG16 classification experiment. Our findings suggest that the use of synthetically homogenized datasets may impair the discriminatory ability of classifiers based on radiomic features. Histograms of absolute percentage difference in the features from non-contrast scans before and after synthesis support that, whilst cycle-GAN produced photorealistic images that were sufficient to pass a modified Turing test, they were not identical at the radiomic feature level.Contrast is known to impact upon the performance of radiomic modelsOverall, our results demonstrate that unsupervised DL algorithms can effectively learn global contexts from large datasets and generate realistic predictions sufficient to pass human reader assessments. However, the synthetic images are distinguishable from acquired images by VGG16-DL and handcrafted radiomics classifiers, likely due to underlying subtleties introduced by the cycle-GAN.60. This was evident in absolute intensity difference maps of some cases that displayed slight variations in texture for synthetic images. Additionally, the heterogeneity in contrast distribution amongst patients may affect the success of cycle-GAN. The NCCID dataset included CT pulmonary angiograms that had marked differences in contrast distribution at the time of acquisition compared to CTs performed for evaluation of lung parenchyma. In these cases, unrealistic non-contrast CT synthesis occurred for some hypo-intense structures. Another consideration is that GANs are notoriously difficult to train and our model may have converged to local minima61. Despite these challenges, our framework was able to generate plausible synthetic contrast chest CTs from test patients.One of the challenges of synthesizing non-contrast images from contrast CT is the difference in image acquisition parameters, which can result in variations in texture and contrast distributionA limitation of this study was the use of small patches for training the cycle-GAN framework. Whilst patch-based methods provide more training examples, they also lead to a reduction in field-of-view (FOV) of training images. We overcame this limitation by using stochastic training and dense inference to generate the final synthetic volumes. However, further research is needed to compare the predictive performance of models with varying input sizes. The use of single-slice ROIs for feature extraction in the radiomics experiment and the variability in contrast distribution due to the timing of contrast injection  were additional limitations of our experiments. Our radiomics analysis was limited by the small dataset, although we were still able to obtain statistically significant results for the SVM classifier. In addition, the non-COVID-19 dataset had an imbalance of contrast and non-contrast images, unlike the balanced dataset of COVID-19 patient scans. However, our AUC values were higher than would be expected if the classifiers were classifying purely based on contrast-status alone.Further work with a larger radiomics dataset and multi-slice volumes is warranted.Though further investigation is required to evaluate the diagnostic abilities of synthetic non-contrast CTs for automated predictions, our experiments provide valuable insight into the use of unsupervised synthesis techniques, such as cycle-GAN, as data augmentation or homogenization strategies. Future studies on unsupervised style-transfer should incorporate additional metrics that guide GANs to not only produce realistic predictions but closely represent the underlying information (e.g. radiomic features) from training images. Furthermore, training DL networks using large and multi-centre data can be influenced by bias, due to factors such as contrast/non-contrast data distribution, image acquisition parameters, or scanner type, which may hinder the cycle-GAN’s performance in effectively learning the optimal mapping between both sets of input images. Utilizing more controlled training datasets or introducing intermediate scan homogenization techniques may further assist the framework learning process, however, caution must be exercised in use of chained-AI algorithms to avoid error propagation and diminishing returns.5. Such models require large datasets, however, may suffer from contrast/scan heterogeneity, adversely affecting model generalizability. We developed and evaluated a cycle-GAN model using a multi-centre dataset of COVID-19 pneumonia for data homogenization. Though synthetic non-contrast images generated by our cycle-GAN model passed human assessment, our findings indicate the presence of subtle features in the synthetic images that are detectable at the radiomic feature level. This implies caution is warranted before GAN-synthesized images are used for data homogenization prior to handcrafted or DL radiomic modeling. Whilst our model shows promise in addressing contrast heterogeneity, it also highlights challenges associated with large-scale datasets and the need for more controlled training datasets or intermediate homogenization steps for improved performance. Overall, our study underscores the need for further research to optimize the use of GANs for medical imaging and improve their clinical utility.Handcrafted and DL CT-based radiomic models are increasingly being used both for COVID-19 and other health conditions in the context of COVID-19 endemicityHandcrafted and deep learning (DL) radiomics are two common imaging-based artificial intelligence (AI) techniques that have been leveraged to develop computed tomography (CT) imaging-based models for COVID-19 and other healthcare research. Such approaches require large datasets for training and analysis, however contrast heterogeneity from real-world medical imaging datasets may impair model performance.In this study, using a multi-centre dataset containing 2078 CT scans from 1,650 patients with COVID-19, we developed a 3D patch-based cycle-GAN to synthesize non-contrast images from contrast-enhanced images to homogenize CT data for the development of future COVID-19 AI models. We initially hypothesized that as COVID-19 reaches endemicity, our framework may offer superior applicability to future CT imaging datasets, compared to those developed in the pre-COVID era.We evaluated the performance of our contrast to non-contrast cycle-GAN to assess network generalizability as a contrast homogenization tool with both a handcrafted radiomic and VGG DL classification task. In a modified Turing-test, human experts identified synthetic vs original images, with an average score of 58.7% (range 52.5–71%) attesting to the photorealism of the synthetic images. Furthermore, Fleiss’ Kappa was 0.06 , demonstrating only slight agreement among human experts. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. Our results suggested that our cycle-GAN model produced synthetic non-contrast images sufficient to pass human assessment, subtle features existed in the synthetic images, potentially introduced by the cycle-GAN or inference strategy which are detectable at the radiomic feature level. This implies caution is warranted before GAN-synthesized images are used for data synthesis prior to radiomic modeling or further clinical studies.Supplementary Information.


First generation immigrants in many European countries have insufficient mastery of the host language to complete self-report questionnaires. To address this problem, we translated and validated Turkish, Moroccan Arabic and Moroccan Berber versions of the Dutch 48-item Symptom Questionnaire (SQ-48), which is a validated and clinical useful measure of psychopathology.Therefore, this study describes the translation and cross cultural validation of the Turkish, Moroccan Arabic, and Moroccan Berber versions of the 48-item Symptom Questionnaire.Four samples were used: 1) psychiatric outpatients with Turkish or Moroccan background (n=150); 2) non-psychiatric subjects with Turkish or Moroccan background (n=103); 3) native Dutch psychiatric outpatients (n=189); 4) native Dutch non-psychiatric subjects (n=463). Data were analysed by confirmatory factor analysis and receiver operating characteristic curves.The 253 psychiatric non-native patients and controls were on average 38,3 years old , and 61% were women. Internal consistency of SQ-48 subscales across groups was adequate to high, the seven-factor structure of SQ-48 fitted the data adequately in the total sample and in each separate group, and AUC values showed acceptable to excellent discrimination. However, the mean severity scores for all SQ-48 subscales were significantly higher in the immigrant groups than those of the Dutch native group. We found full configural, metric and partial scalar invariance.Psychopathology measured by SQ-48 can largely be interpreted in the same way for persons from different immigrant backgrounds. However, cut-off values for Dutch natives should be ascertained using larger samples as these are likely higher than in Dutch psychiatric patients.None Declared


One of the challenges in clinical translation of cell-replacement therapies is the definition of optimal cell generation and storage/recovery protocols which would permit a rapid preparation of cell-treatment products for patient administration. Besides, the availability of injection devices that are simple to use is critical for potential future dissemination of any spinally targeted cell-replacement therapy into general medical practice. Here, we compared the engraftment properties of established human-induced pluripotent stem cells (hiPSCs)-derived neural precursor cell (NPCs) line once cells were harvested fresh from the cell culture or previously frozen and then grafted into striata or spinal cord of the immunodeficient rat. A newly developed human spinal injection device equipped with a spinal cord pulsation-cancelation magnetic needle was also tested for its safety in an adult immunosuppressed pig. Previously frozen NPCs showed similar post-grafting survival and differentiation profile as was seen for freshly harvested cells. Testing of human injection device showed acceptable safety with no detectable surgical procedure or spinal NPCs injection-related side effects. In preclinical studies, it was demonstrated, for example, that the spinal grafting of human or rat fetal spinal cord-derived NPCs and human ES-derived NPCs leads to amelioration of spasticity and improvement in motor function in rats with spinal ischemic injury46. A similar functional benefit was reported using spinal compression or contusion models of spinal traumatic injury in rodents721. Spinal grafting of human fetal tissue-derived NPCs or glia-restricted precursors has been found to ameliorate neuronal degeneration in rat and mouse models of amyotrophic lateral sclerosis (ALS)23. In general, consistent long-term engraftment and differentiation to all neural cell types  were seen after grafting multilineage NPCs112225.Previous preclinical and clinical studies using fetal tissue-derived or embryonic stem cell-derived neural precursor cells (NPCs) have established the rationale for the use of cell-replacement therapies for the treatment of a variety of spinal neurodegenerative disorders27, has opened new opportunities for effective generation of lineage-committed cell lines2832. By using this approach, extensive data were generated which demonstrate similar properties of iPSCs-derived NPCs  once grafted in vivo in several models of neurodegenerative disorders. Previous studies showed that grafted iPSC-derived NPCs or oligodendrocyte precursor-enriched cells survived and promoted functional recovery after spinal traumatic injury without tumorigenesis in rodents and non-human primates34. Similarly, spinal grafting of iPSC-derived glial-rich NPCs improved survival of ALS SOD1-mice. Grafted cells differentiated preferentially into astrocytes which led to spinal parenchymal upregulation of neurotrophic factors such as VEGF, NT3, GDNF, and increased activity of cell- pro-survival signals such as AKT signaling, which is downstream from the VEGF signal. More recently, it was demonstrated that the iPSC-derived dopaminergic progenitors survived, differentiated toward mature midbrain dopaminergic neurons, and improved neurological deficit after bilateral grafting into putamen in a non-human primate model of Parkinson’s disease.More recently the technology of induced pluripotent stem cells, which permits re-programming of adult somatic cells to the pluripotent stage38, ALS40 or Parkinson’s disease42. In general, so far completed or ongoing clinical trials demonstrate acceptable safety profile. While no desired improvement in ambulatory function was achieved so far in spinal trauma patients, some patients demonstrated one to two levels of neurological improvement as assessed by ISNCSCI motor and sensory scores. These early encouraging data warrant further development and testing of cell-replacement technology in human patients with neurodegenerative disorders.Some of the preclinical studies which employed fetal, ES-derived NPCs, or human iPSCs-NPCs led to the initiation of human clinical trials in patients with spinal traumatic injuryOne of the critical determinants for a successful translation of cell-replacement therapies for the treatment of spinal neurodegenerative disorders (as well as brain-associated pathologies) is (1) the establishment of cell-line production protocols which permit access to a readily available, freeze-thaw transplantable cells that can be used for direct patient grafting without additional sub-culturing, and (2) the availability of spinal cell injection device that is simple to assemble and use by trained spinal neurosurgeons.Accordingly, the goal of the present study was twofold. First, the engraftment properties and safety of established and previously characterized human iPSCs-derived NPCs were tested in the immunodeficient rat. The properties of freshly harvested versus previously frozen/washed NPCs grafted into striata and spinal cord were compared at 2 months post-grafting by immunofluorescence (IF) and differential mRNA sequencing which separate rat-specific versus human-specific mRNA transcripts. Second, a newly developed spinal cell injection device equipped with a spinal pulsation-cancelation magnetic injection needle was tested for acute surgical procedure-related safety in the adult immunosuppressed pig.in vitro and in vivo NPCs grafting experiments. A detailed description of hiPSC-NPCs generation, expansion, and phenotypic/functional characterization is provided in the accompanying paper .The previously established hiPSCs-derived NPC cell line was employed in all The generated NPCs were cultured/expanded in poly-L-ornithine/laminin-coated plastic wells using DMEM/F12-based NPC media containing 0.5% N2 supplement , 1% B27 supplement (Life Technologies), 1% glutamate (Life Technologies), and 1% penicillin-streptomycin (Life Technologies) supplemented with 10 ng/ml recombinant human basic fibroblast growth factor (bFGF)  as a solo mitogen.6 to 6 x 106 cells/pellet), and re-suspended in 1 ml of NPC-proliferating media containing 10% DMSO. DMSO-containing cell suspension was then transferred to 1.5cc cryogenic tubes  and kept in an isopropanol-based freezing container  at -80C overnight. The next day the cryovials were transferred into liquid nitrogen vapor (-150°C) for long-term storage.To freeze proliferating NPCs, cells were dissociated with 0.05% trypsin, pelleted  were washed with DPBS and dissociated with 0.05% Trypsin EDTA (Thermo Fisher Scientific) at 37°C, 5% CO2 incubator for 2–3 min. After adding the Trypsin-inhibitor , cell suspensions were collected in a 15-ml tube and centrifuged for 4 min at 400g. After discarding the supernatant, the pellet was re-suspended-washed in hibernation buffer  and centrifuged again (400g) for 4 min. After washing, the final cell pellet was re-suspended in hibernation media, filtered through 40 μm plastic mesh , and the desired cell density solution was prepared. Second, previously frozen NPCs  were thawed quickly in the water bath (37°C) and transferred to 15cc of hibernation media for wash . The cell pellet was then re-suspended with a hibernation medium and filtered through 40 μm plastic mesh  and targeted cell density solution prepared.Two different NPC preparation protocols were used. Proliferating or previously frozen-washed NPCs, were plated onto glass chamber slides  and induced to terminally differentiate by the withdrawal of bFGF and adding 10 ng/ml BDNF  and 10 ng/ml GDNF  into culture media for 2 weeks. After differentiation, cells were fixed with 4% paraformaldehyde, stained with neuronal and glial markers, and images captured and analyzed with a Fluoview FV1000 confocal microscope . All primary and secondary antibodies are listed in First, the adult athymic rats  were used for intrastriatal and spinal hiPSC-NPC grafting in the rodent component of the in vivo grafting study (n = 7). To graft fresh and previously frozen iPSC-NPCs spinally, the previously described technique was used22. Animals received 10 to 15 spinal NPC injections (0.5 µl each) distributed bilaterally between L2 and L6 spinal segments  using a homemade glass capillary . Each animal received both fresh and frozen NPCs injected either into the left or right side of the spinal cord, respectively. Each animal received an identical number of fresh and frozen NPCs injections. In addition to spinal cord injections, the same animals received a bilateral NPC injection (Fresh or Frozen NPCs) into the striatum. To perform brain injections, anesthetized animals were placed into a stereotaxic apparatus to keep the head in a fixed position. After the scalp was shaved, a sagittal midline skin incision was performed to expose the skull. To permit an intra-parenchymal brain injection a small borrow hole was drilled into the skull using a dental drill. The NPCs  were injected into the striatum  using a 34G needle interconnected with a digital microinjector .All animal studies were approved by the University of California, San Diego Institutional Animal care and Use Committee (Protocol No. S01193). After cell grafting rats survived for 2 months (n = 4) or 6 months (n = 3). At sacrifice, animals were terminally anesthetized and transcardially perfusion-fixed with 4% paraformaldehyde  or brain and spinal cord tissue harvested fresh and snap-frozen for mRNA sequencing .Second, adult minipig model was employed for spinal cell grafting (n = 3). Adult female Gottingen-Minnesota minipigs were anesthetized and the L2-L3 spinal segment was exposed after partial dorsal laminectomy of the L2-3 vertebra as previously described. Dura was then cut open. Animals then received a total of 10 injections of Frozen-hiPSC-NSCs . From the day of the cell grafting, animals were continuously immunosuppressed by tacrolimus (0.6 mg/kg/day). After cell grafting animals survived for 2 days..At the end of survival, rats were anesthetized with 2 mg pentobarbital and 0.25 mg phenytoin  and transcardially perfused with 200 ml of heparinized saline followed by 250 ml of 4% paraformaldehyde (PFA) in PBS. Brain and spinal cord sections were then prepared and stained with a combination of human-specific and non-specific antibodies  as previ2.For quantitative and qualitative analysis, brain and spinal cord sections taken from immunodeficient rats at 2 months after hiPSC-NPC grafting were used. In qualitative and quantitative analysis of combined antibodies-stained sections, six sections  taken from each animal with identified grafts were used. Sections were stained with hNUMA antibody in combination with neuronal and glial markers including DCX, NeuN, hGFAP, vimentin, Olig2, and Ki67. The total number of double-stained grafted cells was then counted and expressed as % of the total hNUMA stained cell population. Quantitative analysis of grafted hNUMA+ cells-occupied regions in striatum and spinal cord was performed on minimum of 10 sections/animal  and expressed as hNUMA+ area in mmOn the day of sacrifice, pigs were deeply anesthetized with pentobarbital (100 mg/kg) and transcardially perfused with ice-cold, heparinized  PBS , followed by 4% paraformaldehyde . The spinal cords were then removed and postfixed for 24 h in 4% paraformaldehyde at 4°C. After postfixation, spinal cord tissue was cryoprotected in 10%, 20%, and 30% sucrose with 0.02% sodium azide and 10- to 40-µm-thick sections cut on a cryostat. Staining protocol is identical as described for NPCs-grafted rat.. Reads were aligned with HISAT2 to a combined rat (Rn6) and human (Hg38) genome index. We used -k 20 and—score- min L,0,-0.4 with otherwise default settings. Aligned reads were sorted by read name and then evaluated for species of origin with an in-house Perl script. The vast majority of reads only aligned to a single genome, but for those that aligned to both genomes we used the HISAT2 alignment score to guide species assignment. If a read mapped equally well to both genomes then it was flagged as ambiguous and excluded from downstream analysis. Otherwise, the read was assigned to the species for which it had the best alignment score. Species assigned reads were quantified using Kallisto. Human reads were quantified against the full Gencode annotation (v28) and rat assigned reads were quantified against the Refgene annotation for the Rn6 genome (obtained from the UCSC Genome Browser). Kallisto “quant” was run in single-end mode with average fragment length of 200 and SD of 60. All downstream analysis was performed in R. Human and rat quantified expressions were combined into a single table and treated as a single-species for normalization. After calculating TPM scaled expression, we split the human and rat gene-sample read count matrices apart for further analysis.Total RNA was isolated from flash frozen grafted spinal cord and striatum samples (6 months post-grafting) using the Qiagen RNeasy Plus Mini kit. Approximately 1 to 2 μg of RNA was isolated from each sample, with RIN scores of ~9.0, as determined by TapeStation analysis . mRNA sequencing libraries were prepared using the Illumina Stranded mRNA Library kit, and sequenced at the UC San Diego IGM Genomics Center, using the Illumina NovaSeq 6000 platform. Paired-end 100bp reads were received in FASTQ format and were prepared for downstream analysis by trimming known barcodes and then selecting post-trimmed reads for those with average base quality >15. The assignment of transcripts to species of origin (human or rat) was performed using our previously published methods. To assess the degree of motor dysfunction in cell-grafted pig, previously developed 14-grades scoring system was used. In addition, exacerbated pain response to non-noxious mechanical stimuli such as light skin brush was tested at 48 h after cell grafting (ie at the time point of sacrifice).After cell grafting rats were evaluated periodically  for any signs of motor dysfunction  using BBB scoring systemP < 0.05 and **P < 0.01 were considered to be statistically significant. The GraphPad Prism software  was used for all analyses.All data are reported as the mean ± SEM. An unpaired two-tailed Student’s t-test was used for single comparisons between the grafted Fresh-hiPSC-NPCs and the grafted Frozen-hiPSC-NPCs for all histological examinations. In each case, *First, continuously in vitro cultured-proliferating or previously frozen NPCs were induced to differentiate for 2 weeks in vitro and the differentiation profile compared using quantitative and qualitative IF,  were grafted into striata and spinal cord of the immunodeficient rat. After grafting animals survived for 2 to 6 months and the engraftment properties analyzed by IF and mRNA sequencing of human-specific transcripts . tive IF, . Second,nscripts . Third, nscripts .Using a brightfield microscopy, a similar morphological appearance of continuously cultured NPCs versus freshly plated NPCs from frozen stock (2 days post-plating) was observed prior to the differentiation , B.in vitro induction using differentiation media (see M&M for details). Staining with nestin (proliferating NPCs marker) and MAP2  antibodies showed a high density of nestin/MAP2-stained cells, suggesting an early stage of neuronal differentiation (not shown), while a relatively high density of vimentin+ cells  was seen . The viability (trypan blue extrusion test) ranged between 93% and 94% for freshly harvested NPCs and was between 88% and 97% for previously frozen and washed NPCs . Each animal received three bilateral intrastriatal injections, and 10 lumbar spinal cord injections of both freshly harvested and previously frozen-washed NPCs targeted at ipsi- or contralateral site, respectively. After cell injection, animals survived for 2 months and the presence of grafted cells studied using a combination of human-specific or non-specific neuronal or glial antibodies.We next compared the survival and differentiation of freshly harvested versus previously frozen and washed NPCs after grafting into striata and spinal cord of immunodeficient adult rat. Supplemental Fig. 2A–F).Neurological assessment (BBB score) showed no detectable motor dysfunction for the duration of 2-6 months of survival. Quantitative comparative analysis of hNUMA+ cells in striata and spinal cord , showed similar grafts size in both grafted cell population with no significant difference between freshly harvested or previously frozen-washed NPCs . Intense immunoreactivity was seen for early post-mitotic neuronal marker DCX as well as more mature neuronal marker NeuN  and 4A–D47. An array of known cellular markers was used to profile the human-derived transcripts. Consistent with the immunofluorescent analysis of spinal cord and striatal grafts, we detected the clear presence of human-specific transcripts corresponding to neuronal, astrocyte, and oligodendrocyte lineage markers . An analysis of leukocyte markers (the cells used for reprogramming and generation of iPSCs) demonstrated a low level of expression for CD14 and FUT4 (CD15), with undetectable levels of other markers . Analysis of the reprogramming factors  showed undetectable levels of OCT4, low levels of expression of KLF4 and MYC, and high levels of expression for SOX2. This strong expression of SOX2 is indicative of the persistence of immature neuronal precursors in the grafted tissue .In order to analyze the differentiation profiles of engrafted NPCs 6 months post-transplantation, we dissected tissue samples from the striatum and spinal cord that contained transplanted cells. mRNA sequencing was performed on this chimeric tissue, and reads were assigned to the correct species of origin (rat vs. human) using the bioinformatics protocols we have developed previously markers . We next markers . Multipl markers . AnalysiFirst, a stainless steel self-anchoring spinal platform equipped with bilateral muscle retractors and spinal clamps  is equipped with the “magnetic spring” needle tip. This system permits a free movement of the needle previously placed into spinal parenchyma and which follows the dorso-ventral pulsation of the spinal cord. This spring effect is achieved by creating a magnetic-repelling effect by positioning two ring magnets at the tip of the needle and which are oriented with the identical magnetic N< - > N field toward each-other (thus causing repelling-spring effect), , . and our ongoing spinal trauma trial, clinical-grade human fetal spinal cord-derived NPCs previously stored in liquid nitrogen (LN) were washed and shipped in hibernation buffer at 4°C to the clinical site. After a viability test was performed (the cut-off was 70% viability), the NPCs were used directly for spinal grafting without sub-culturing.A transplantation protocol likely to be used in a clinical setting will employ previously frozen hiPSC-NPCs stored in clinical cell banks. In a recently completed ALS trialin vivo grafting.To test this likely cell-preparation-grafting scenario, the hiPSC-NPCs generated in our current study were stored-frozen in LN for 4 weeks. On the day of grafting, the hiPSC-NPCs were washed 3x in hibernation buffer, viability test performed and cells were kept at 4°C for 2–3 h prior to being used for . After cell injections, animals survived for 2 days while being evaluated neurologically for any signs of motor deficit and allodynia . After 2 days, animals were perfusion-fixed with 4% paraformaldehyde and the presence of grafted cells in spinal cord sections confirmed with IF.To perform spinal cell injection in adult pig, a dorsal laminectomy of L3-L4 vertebra was performed and the human injection device mounted over the laminectomy site , C. DuraAll cell-grafted animals showed normal ambulatory motor function with no overt signs of motor weakness, muscle spasticity, or allodynia. Staining of spinal cord sections with hNUMA and nestin antibodies showed the presence of nestin+ grafted NPCs . This wain vivo-grafted NPCs once cells were harvested fresh from proliferating NPCs culture or prepared from previously frozen/washed NPCs stock and grafted into striata and lumbar spinal cord of the immunodeficient rat. By using a novel human spinal cell injection system, we also studied the safety of spinal cell injection procedure in adult pig receiving multiple spinal injections of NPCs. Overall, we demonstrate that independent of the format how the NPCs were prepared before grafting (ie freshly harvested from in vitro proliferating NPCs or previously frozen/washed and grafted NPCs), grafted NPCs show robust engraftment and neuronal and glial differentiation. No detectable tumor formation was seen between 2 and 4 months post grafting. No injection procedure-related side effect  was seen in spinally grafted pigs with NPCs grafts identified in targeted gray matter regions (Laminae III-VII).By using previously established and characterized human iPSCs-derived NPCs , we directly compared the survival, differentiation, and safety/tumorigenicity of in vivo transplantation (ie single-cell suspension or neurospheres) is important as it defines the level of precision how the desired cell dose can be delivered. We have opted for the use of in vitro cell monolayer NPCs culture to expand established human iPSCs-derived NPCs. As described in the accompanying paper , the use of cell culture monolayer system has several specific advantages including (1) high consistency in expanding morphologically defined, homogenous population of NPCs which maintain the expression of neural precursors markers and in vitro differentiation-defined multipotency, (2) ability to effectively prepare a single cell suspension with a minimal degree of cell clumping, and (3) reliable cell freezing and cell recovery after freeze/thaw procedure. As such, the use of this protocol, defined by reliable and predictable cell expansion and post-freezing cell recovery in a single cell suspension format makes this approach a preferable technology for the generation of clinical-grade NPCs.One important requirement in developing a clinical CNS cell-replacement therapy protocol(s) is the ability to consistently deliver a targeted cell dose to the injection site. As such, the format of how the cells are cultured, stored, and prepared for 40.Several previously completed human trials have employed similar cell-derivation and cell-freezing-recovery protocols. In some of these trials, for example, previously frozen fetal tissue-derived NPCs were prepared at a storage facility to a final cell concentration and shipped overnight in hibernation buffer at 4°C to the clinical site. After the viability test of shipped-cell suspension was completed, the cells were directly used for spinal injections49. Comparable long-term survival and differentiation of human NPCs derived from fetal tissue, ES lines, or iPSC line were demonstrated in naive animals50 or in several animal models of neurodegenerative diseases including spinal traumatic injury52, spinal ischemic injury46, stroke5358, brain traumatic injury5961, or ALS62.In our current study, we have demonstrated a comparable grafted-cell survival and differentiation once NPCs were grafted as freshly harvested proliferating NPCs or previously frozen, thawed, and grafted NPCs. Using IF a robust neuronal differentiation was seen with a subpopulation of grafted neurons acquiring inhibitory or excitatory neurotransmitter phenotype at 2–6 months post grafting. The presence of GFAP+ astrocytes and Olig2+oligodendrocytes was also seen in NPCs-grafted striata and spinal cord. Using species-specific mRNA sequencing of human NPCs grafts, we also confirmed the presence of neuronal and glial differentiation-associated transcripts and the lack of endoderm and mesoderm cell derivatives. These data are similar to our previous studies demonstrating the safety and differentiation profile of human ES-derived NPCs or porcine skin fibroblasts-derived iPSC-NPCs grafted spinally as xenografts or allogeneic or syngeneic graftsin vivo grafting studies. Similarly, our data demonstrate that the iPSCs-derived NPCs are equivalent (as defined by post-grafting differentiation characteristics and lack of tumorigenesis) to the NPC lines derived from fetal tissue from ES cells.Jointly, our current study demonstrates that the use of an NPC manual-selection from human iPSCs and NPCs monolayer-expansion protocol is a highly effective approach in generating sufficient quantities of NPCs which can be stored-frozen and used directly in subsequent 64. The XYZ manipulator is used for syringe/needle positioning and parenchymal injection. While effective in delivering cells into specific spinal cord targets these delivery systems lack the ability to synchronize the needle position with spinal cord pulsation . This may lead, in some cases, to spinal cord injury and/or local bleeding at and around the needle injection tract. More recently a free-floating injection needle was developed where the movement of the spinal cord  is effectively eliminated (concerning the position of needle tip). This needle system was successfully used in human ALS and spinal trauma trials40. One of the relative limitations of this system is the need for multiple needle-assembly manipulation steps before each injection can be performed. As such, the system is rather complicated to use and requires a detailed and repetitive training of neurosurgeon(s) to master the injection procedure.Previous large animal and clinical studies have employed several spinal injection devices to deliver cells into spinal parenchyma. Conceptually, two different injections systems were used: (1) a hand-held injection needle directly attached to the syringe, or (2) an injection needle/syringe attached to an external frame with or without XYZ manipulator or in ALS patients receiving lumbar and/or cervical NPCs grafts40. In the pigs injected using our new injection device, no cell-injection procedure-related side effect  was seen if assessed between 24 and 48 h after cell delivery and with grafted cells identified in the targeted gray matter region. Jointly, these data demonstrate that this injection system can effectively and safely be used in future human clinical trials designed to deliver cells into spinal parenchyma (gray or white mater).We have developed a new human spinal injection system that is composed of a self-anchoring spinal platform, a fine XYZ manipulator firmly attached to the spinal platform, and a magnetic spring-effect-inducing injection needle. The use of this needle, which follows the dorso-ventral movement of the spinal cord (ie pulsation) after the needle is placed into the parenchyma (due to spring effect), effectively eliminates a potential needle-induced spinal cord injury caused by the spinal cord pulsation. The second important feature of the injection system is the simplicity of its operation while performing multiple-repetitive injections. After the injection needle is attached to the XYZ manipulator, a one-hand operation is only required to control the motion of the XYZ manipulator in all three axes. As such, repetitive injection can be performed with high safety and simplicity and can easily be completed by any trained neurosurgeon with the expertise in performing spinal laminectomy and durotomy. In our current safety study, using this injection device in adult pigs, we have performed a total of 10 bilateral injections of NPCs. This injection design (ie the number and location of injections delivered) is similar to the one we have recently used in competed Phase I human spinal trauma trial where 6 injections were delivered around the injury epicenterIn vivo grafted previously frozen and washed NPCs show long-term engraftment, neuronal and glial differentiation, and with the lack of tumor formation. The use of a novel human spinal “magnetic” injection needle with a simple operation design was shown to deliver NPCs into adult pig spinal cord with no detectable injection-procedure-related side effect. Accordingly, the use of hiPSCs-NPCs generation protocol and human injection device may represent an additional cell source and clinical cell delivery system to be considered in perspective human spinal cell-replacement clinical trials.In summary, we have demonstrated a successful generation of human iPSCs-derived NPCs cell lines by using an NPC manual-selection protocol and expansion of cells in a monolayer cell culture. Click here for additional data file.Supplemental material, sj-jpg-1-cll-10.1177_09636897221107009 for Expandable Sendai-Virus-Reprogrammed Human iPSC-Neuronal Precursors: In Vivo Post-Grafting Safety Characterization in Rats and Adult Pig by Yoshiomi Kobayashi, Michiko Shigyo, Oleksandr Platoshyn, Silvia Marsala, Tomohisa Kato, Naoki Takamura, Kenji Yoshida, Akiyoshi Kishino, Mariana Bravo-Hernandez, Stefan Juhas, Jana Juhasova, Hana Studenovska, Vladimir Proks, Shawn P. Driscoll, Thomas D. Glenn, Samuel L. Pfaff, Joseph D. Ciacci and Martin Marsala in Cell TransplantationClick here for additional data file.Supplemental material, sj-jpg-2-cll-10.1177_09636897221107009 for Expandable Sendai-Virus-Reprogrammed Human iPSC-Neuronal Precursors: In Vivo Post-Grafting Safety Characterization in Rats and Adult Pig by Yoshiomi Kobayashi, Michiko Shigyo, Oleksandr Platoshyn, Silvia Marsala, Tomohisa Kato, Naoki Takamura, Kenji Yoshida, Akiyoshi Kishino, Mariana Bravo-Hernandez, Stefan Juhas, Jana Juhasova, Hana Studenovska, Vladimir Proks, Shawn P. Driscoll, Thomas D. Glenn, Samuel L. Pfaff, Joseph D. Ciacci and Martin Marsala in Cell TransplantationClick here for additional data file.Supplemental material, sj-jpg-3-cll-10.1177_09636897221107009 for Expandable Sendai-Virus-Reprogrammed Human iPSC-Neuronal Precursors: In Vivo Post-Grafting Safety Characterization in Rats and Adult Pig by Yoshiomi Kobayashi, Michiko Shigyo, Oleksandr Platoshyn, Silvia Marsala, Tomohisa Kato, Naoki Takamura, Kenji Yoshida, Akiyoshi Kishino, Mariana Bravo-Hernandez, Stefan Juhas, Jana Juhasova, Hana Studenovska, Vladimir Proks, Shawn P. Driscoll, Thomas D. Glenn, Samuel L. Pfaff, Joseph D. Ciacci and Martin Marsala in Cell Transplantation