Kirill Veselkov
Assistant Professor AdjunctCards
About
Research
Publications
2026
Publisher Correction: Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning
Hanassab S, Southern J, Olabode A, Laponogov I, Bronstein M, Comninos A, Heinis T, Abbara A, Izzi-Engbeaya C, Veselkov K, Dhillo W. Publisher Correction: Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning. Npj Women's Health 2026, 4: 6. DOI: 10.1038/s44294-025-00122-7.Peer-Reviewed Original Research
2025
Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning
Hanassab S, Southern J, Olabode A, Laponogov I, Bronstein M, Comninos A, Heinis T, Abbara A, Izzi-Engbeaya C, Veselkov K, Dhillo W. Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning. Npj Women's Health 2025, 3: 68. PMID: 41341431, PMCID: PMC12669042, DOI: 10.1038/s44294-025-00117-4.Peer-Reviewed Original ResearchPolycystic ovary syndromePolycystic ovary syndrome treatmentClinically available drugsHormone receptor modulatorsRelevant to polycystic ovary syndromeReceptor modulatorsAnti-inflammatory propertiesAnti-androgenPolycystic ovary syndrome pathophysiologyPharmacological agentsAvailable drugsPrecision nutritionNetwork propagation algorithmPCOS pathophysiologyPolygenic disorderTherapeutic targetCombinatorial prediction of therapeutic perturbations using causally inspired neural networks
Gonzalez G, Lin X, Herath I, Veselkov K, Bronstein M, Zitnik M. Combinatorial prediction of therapeutic perturbations using causally inspired neural networks. Nature Biomedical Engineering 2025, 1-18. PMID: 40925962, DOI: 10.1038/s41551-025-01481-x.Peer-Reviewed Original ResearchDiseased cell statesCombinatorial predictionTherapeutic perturbationsGenetic perturbation datasetsPhenotype-driven approachReverse disease phenotypeLatent representationCombinatorial perturbationsCompetitive performanceNeural networkComputationally intensive approachCell statesDisease phenotypePerturbation datasetsPerturbagensChemical perturbationsPhenotypic signaturesCell linesInverse problemPhenotypeNetworkDrug discoveryFast approachDatasetComputerDevelopment and validation of a diagnostic prediction model for pancreatic ductal adenocarcinoma: VAPOR 1, protocol for a prospective multicentre case–control study
Walsh C, Murray J, Laponogov I, Parker A, Ellis J, Converso V, Austin E, Boshier P, Czajkowski C, Spalding D, Al-Mukhtar A, Frampton A, Roberts K, Pandanaboyana S, Halloran C, Costello E, Kocher H, Mitra V, Hamady Z, Al-Sarireh B, Pathak S, Mitchell W, Siriwardena A, Westlake C, Pereira S, Spiliotis I, Biswas S, Collaborators V, Španěl P, Veselkov K, Sharples L, Hanna G, Arya P, Bhushal S, Behrens A, David D, Griffin J, Hoyles L, Klimowska-Nassar N, Li S, McClymont J, Ni M, Pabari A, Strid J, Adade C, Ahmad R, Caballes M, Emerton J, Ivie S, Jameson E, Joy L, Lin C, Ogunwemimo O, Pai M, Sharma S, Shetty R, Speranzini E, Tarazi M, Thomas-Vedat K, Whitestone L, Hague A, Hawkins D, Roddis M, Steele C, Dodd E, Hammill M, Merali N, Patel B, Wells R, Bates M, Hancox R, McDarby A, Whitehouse A, Brownlee L, Carlisle R, Kuzman M, Obhiozele J, Azebeokha C, Edwards K, Howard L, Rashid Z, Saad A, Adams C, Campbell B, Harris N, Sinclair S, Teixeira A, Ganabady M, Georg P, Kristine K, Knight J, Roy S, Allen M, Ashabi A, Baker M, Chapman C, Hajibandeh S, Hammoda M, Jarvis L, Pittard R, Smith G, Travers J, Flinn W, Gerard A, Kuligowska A, Priestley M, Wangese M, Ambrico C, Taylor M, Jose J, Mohammed T, Cadmore L, Fagan M, Hick R, Whitby L, Acedo P, Hibba S, Hayee T, Roys R, Yang Y, Horne Z, Moran L, Ricketts-Arthur I, Ashraf M, Castillo M, Duarte F, Mariampillai S, Ngumo A, Oakley M, Penn R. Development and validation of a diagnostic prediction model for pancreatic ductal adenocarcinoma: VAPOR 1, protocol for a prospective multicentre case–control study. BMJ Open 2025, 15: e094505. PMID: 40866056, PMCID: PMC12406849, DOI: 10.1136/bmjopen-2024-094505.Peer-Reviewed Original ResearchConceptsPancreatic ductal adenocarcinomaCase-control studySouth East Scotland Research Ethics Committee 02Health and Care Research WalesDuctal adenocarcinomaHealth Research Authority and Health and Care Research WalesClinical prediction modelMulticentre case-control studyProspective multicentre case-control studyPresence of pancreatic ductal adenocarcinomaBreath testPeer-reviewed journalsDetection of pancreatic ductal adenocarcinomaEarly detection of PDACStage 4 diseaseGeneral practitionersEarly detectionTriage of patientsNon-invasive breath testConference presentationsCancer UKAdult participantsDiagnostic prediction modelPatient outcomesLAY SUMMARYSmart CAR-T Nanosymbionts: archetypes and proto-models
Baena J, Victoria J, Toro-Pedroza A, Aragón C, Ortiz-Guzman J, Garcia-Robledo J, Torres D, Rios-Serna L, Albornoz L, Rosales J, Cañas C, Adolfo Cruz-Suarez G, Osorio F, Fleitas T, Laponogov I, Loukanov A, Veselkov K. Smart CAR-T Nanosymbionts: archetypes and proto-models. Frontiers In Immunology 2025, 16: 1635159. PMID: 40873579, PMCID: PMC12379055, DOI: 10.3389/fimmu.2025.1635159.Peer-Reviewed Original ResearchConceptsSolid tumorsT cellsChimeric antigen receptor T cellsPatient's own T cellsPrediction of treatment responseCancer treatmentCAR-T therapyCAR-T productsLipid nanoparticle formulationImmune cell functionPersonalized cancer treatmentCAR-THematologic malignanciesTumor microenvironmentTarget tumorsTreatment responseCAR constructsAutoimmune diseasesPatient stratificationNanoparticle formulationAdvanced therapiesGene deliveryTumorMRNA transfectionCancer cellsNon-invasive breath testing to detect colorectal cancer: protocol for a multicentre, case–control development and validation study (COBRA2 study)
Fadel M, Murray J, Woodfield G, Belluomo I, Laponogov I, Parker A, Converso V, Ellis J, Wheatstone P, Hepburn J, Groves C, Monahan K, Saunders B, Španěl P, Veselkov K, Cross A, Kontovounisios C, Sharples L, Hanna G. Non-invasive breath testing to detect colorectal cancer: protocol for a multicentre, case–control development and validation study (COBRA2 study). BMC Cancer 2025, 25: 1230. PMID: 40731329, PMCID: PMC12309184, DOI: 10.1186/s12885-025-14520-2.Peer-Reviewed Original ResearchConceptsFecal immunochemical testDetection of CRCClinical prediction modelFecal immunochemical test resultsSecondary care referralsTrial registrationThe studyCase-control designCare referralImmunochemical testPlanned colonoscopyValidation studyBowel preparationPresence of CRCRegistrationThe studyOutpatient clinicPopulation of symptomatic patientsBackgroundColorectal cancerColorectal cancerHigh patient acceptanceNon-invasive breath testBreath testCRC groupPatient benefitParticipantsPatient acceptanceThe Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations
Higgins K, Nyssen O, Southern J, Laponogov I, Veselkov D, Gisbert J, Kanonnikoff T, Veselkov K. The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations. Nature Communications 2025, 16: 6472. PMID: 40659612, PMCID: PMC12259899, DOI: 10.1038/s41467-025-61329-5.Peer-Reviewed Original ResearchConceptsNon-bismuth quadruple therapyH. pyloriBismuth-based therapyProspective clinical validationOptimal treatment strategyGastric cancer burdenDeep Q-learningHelicobacter pylori managementAntibiotic allergyQuadruple therapyOptimal therapyPotential of AIConcurrent medicationsHarness artificial intelligencePre-treatment indicatorsClinical decision-makingEuropean RegistryTreatment strategiesPatient characteristicsQ-learningGastric cancerHp-EuRegTreatment selectionTherapyTreatment recommendationsA multi-centre, stratified, open, randomized, comparator-controlled, parallel group phase II trial comparing adjuvant treatment with 177Lu-DOTATATE to standard of care in patients after resection of neuroendocrine liver metastases (NELMAS).
Frilling A, Baum R, Veselkov K, Del Peral E, Martinez M, Lovelle M, Wu J, Park S, Clift A, Eccles A, Hubber J, Wasan H, Modlin I. A multi-centre, stratified, open, randomized, comparator-controlled, parallel group phase II trial comparing adjuvant treatment with 177Lu-DOTATATE to standard of care in patients after resection of neuroendocrine liver metastases (NELMAS). Journal Of Clinical Oncology 2025, 43: tps4225-tps4225. DOI: 10.1200/jco.2025.43.16_suppl.tps4225.Peer-Reviewed Original ResearchGastro-entero-pancreatic neuroendocrine tumorsNeuroendocrine liver metastasesDisease-free survivalStandard of careGastro-entero-pancreaticResection of LMNeuroendocrine tumorsLu-DOTATATEOverall survivalTumor recurrenceLiver resectionAdjuvant treatmentResection of neuroendocrine liver metastasesEarly detection of recurrent diseaseEfficacy of adjuvant therapyPeptide receptor radionuclide therapyDetection of recurrent diseaseDisease-free survival probabilityAssociated with favorable overall survivalStandard of care armAdjuvant treatment conceptsGa-DOTATATE PET/CTLiver-directed therapiesMacroscopic complete resectionProspective open-label
2024
Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes
Rita L, Southern J, Laponogov I, Higgins K, Veselkov K. Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes. Machine Learning And Knowledge Extraction 2024, 6: 2738-2752. DOI: 10.3390/make6040131.Peer-Reviewed Original ResearchEarly Detection of Macular Atrophy Automated Through 2D and 3D Unet Deep Learning
Wei W, Patel R, Laponogov I, Cordeiro M, Veselkov K. Early Detection of Macular Atrophy Automated Through 2D and 3D Unet Deep Learning. Bioengineering 2024, 11: 1191. PMID: 39768009, PMCID: PMC11726850, DOI: 10.3390/bioengineering11121191.Peer-Reviewed Original ResearchAge-related macular degenerationOptical coherence tomographyMacular atrophyEarly detectionVolumetric optical coherence tomographyDice similarity coefficient scoreMacular degenerationCoherence tomographyFollow-upMonitoring PatientsPatientsDetection of MAClinical decisionsAtrophyHuman gradersScoresLesionsTomographyEndpointDegenerationEyes