Browsing by Subject "Classification"

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  • Bjorck, M.; Kirkpatrick, A. W.; Cheatham, M.; Kaplan, M.; Leppäniemi, Ari; De Waele, J. J. (2016)
    Background: In 2009, a classification system for the open abdomen was introduced. The aim of such a classification is to aid the (1) description of the patient's clinical course; (2) standardization of clinical guidelines for guiding open abdomen management; and (3) facilitation of comparisons between studies and heterogeneous patient populations, thus serving as an aid in clinical research. Methods: As part of the revision of the definitions and clinical guidelines performed by the World Society of the Abdominal Compartment Syndrome, this 2009 classification system was amended following a review of experiences in teaching and research and published as part of updated consensus statements and clinical practice guidelines in 2013. Among 29 articles citing the 2009 classification system, nine were cohort studies. They were reviewed as part of the classification revision process. A total of 542 patients (mean: 60, range: 9-160) had been classified. Two problems with the previous classification system were identified: the definition of enteroatmospheric fistulae, and that an enteroatmospheric fistula was graded less severe than a frozen abdomen. Results: The following amended classification was proposed: Grade 1, without adherence between bowel and abdominal wall or fixity of the abdominal wall (lateralization), subdivided as follows: 1A, clean; 1B, contaminated; and 1C, with enteric leak. An enteric leak controlled by closure, exteriorization into a stoma, or a permanent enterocutaneous fistula is considered clean. Grade 2, developing fixation, subdivided as follows: 2A, clean; 2B, contaminated; and 2C, with enteric leak. Grade 3, frozen abdomen, subdivided as follows: 3A clean and 3B contaminated. Grade 4, an established enteroatmospheric fistula, is defined as a permanent enteric leak into the open abdomen, associated with granulation tissue. Conclusions: The authors believe that, with these changes, the requirements on a functional and dynamic classification system, useful in both research and training, will be fulfilled. We encourage future investigators to apply the system and report on its merits and constraints.
  • Simsek, Burak (Helsingin yliopisto, 2020)
    In this study, a classification scheme is implemented to obtain high resolution snow cover information from Sentinel-2 data using a very simple Bayesian Network (Naive-Bayes) that is trained with ground snow measurement data. Performance comparison of using Bayesian/non-Bayesian Naive-Bayes, different feature sets and different discretization methods is conducted. Results show that Bayesian NB performs the best with up to 0.88 classification accuracy for snow/no-snow classification. Use of most relevant spectral bands rather than all available bands provided improvement in some cases but also performed slighty worse in some, hence not giving a clear answer. However, effect of discretization method was clear, chimerge performed better than equal width binning but it was much slower to a point that it was not practical to discretisize a full Sentinel-2 image’s pixels.
  • Pacheco, Jose Fernando; Silveira, Luis Fabio; Aleixo, Alexandre; Agne, Carlos Eduardo; Bencke, Glayson A.; Bravo, Gustavo A.; Brito, Guilherme R. R.; Cohn-Haft, Mario; Mauricio, Giovanni Nachtigall; Naka, Luciano N.; Olmos, Fabio; Posso, Sergio R.; Lees, Alexander C.; Figueiredo, Luiz Fernando A.; Carrano, Eduardo; Guedes, Reinaldo C.; Cesari, Evaldo; Franz, Ismael; Schunck, Fabio; de Q. Piacentini, Vitor (2021)
    An updated version of the checklist of birds of Brazil is presented, along with a summary of the changes approved by the Brazilian Ornithological Records Committee's Taxonomy Subcommittee since the first edition, published in 2015. In total, 1971 bird species occurring in Brazil are supported by documentary evidence and are admitted to the Primary List, 4.3% more than in the previous edition. Eleven additional species are known only from undocumented records (Secondary List). For each species on the Primary List, status of occurrence in the country is provided and, in the case of polytypic species, the respective subspecies present in Brazilian territory are listed. Explanatory notes cover taxonomic changes, nomenclatural corrections, new occurrences, and other changes implemented since the last edition. Ninety species are added to the Primary List as a result of species descriptions, new occurrences, taxonomic splits, and transfers from the Secondary List due to the availability of documentation. In contrast, eight species are synonymized or assigned subspecific status and thus removed from the Primary List. In all, 293 species are endemic to Brazil, ranked third among the countries with the highest rate of bird endemism. The Brazilian avifauna currently consists of 1742 residents or breeding migrants, 126 seasonal non-breeding visitors, and 103 vagrants. The category of vagrants showed the greatest increase (56%) compared to the previous list, mainly due to new occurrences documented in recent years by citizen scientists. The list updates the diversity, systematics, taxonomy, scientific and vernacular nomenclature, and occurrence status of birds in Brazil.
  • IMAGEN Consortium; Chavanne, Alice V.; Paillère Martinot, Marie Laure; Penttilä, Jani; Frouin, Vincent (2023)
    Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18–23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with the caudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4–8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction in adolescents.
  • Kinnula, Ville (Helsingin yliopisto, 2021)
    In inductive inference phenomena from the past are modeled in order to make predictions of the future. The mathematical concept of exchangeability for random sequences provides a mathematical justification for the assumption that observations are independently and identically distributed given some underlying parameters estimable from the empirical distribution of the observations. The theory of exchangeability contains basic elements for inductive inference, such as the de Finetti representation theorem for the probability of a general exchangeable sequence, prior probability distributions for the parameters in the representation theorem, as well as the predictive probabilities, or rule of succession, for new observations from the random sequence under consideration. However, entirely unanticipated observations pose a problem for inductive inference. How can one assign a probability for an event that has never been seen before? This is called the sampling of species problem. Under exchangeability, the number of possible different events t has to be known before-hand to be able to assign an equal prior probability 1/t for each event. In the sampling of species problem an assumption of infinite possible events has to be made, leading to the prior probability 1/∞ for each event, which is impossible. Exchangeability is thus inadequate to handle probability distributions for infinite possible events. It turns out that a solution to the sampling of species problem arises from partition exchangeability. Exchangeable random sequences have the same probability of occurring, if the observations in the sequence have identical frequencies. Under partition exchangeability, the sequences have the same probability of occurring when they share identical frequencies of frequencies. In this thesis, partition exchangeability is introduced as a framework of inductive inference by juxtaposing it with the more familiar type of exchangeability for random sequences. Partition exchangeability has parallel elements to exchangeability, in the Kingman representation theorem, the Poisson-Dirichlet distribution for the prior probability distribution, and a corresponding rule of succession. The rules of succession are required in the problem of supervised classification to provide product predictive probabilities to be maximized by assigning the test data into pre-defined classes based on training data. A Bayesian construction of supervised classification is discussed in this thesis. In theory, the best classification performance is gained when assigning the class labels to the test data simultaneously, but because of computational complexity, an assumption is often made where the test data points are i.i.d. with regards to each other. In the case of a known set of possible events these simultaneous and marginal classifiers converge in their test data predictive probabilities as the amount of training data tends to infinity, justifying the use of the simpler marginal classifier with enough training data. These two classifiers are implemented in this thesis under partition exchangeability, and it is shown in theory and in practice with a simulation study that the same asymptotic convergence between the simultaneous and marginal classifiers applies with partition exchangeable data as well. Finally, a small application in single cell RNA expression is explored.
  • Koolen, Ninah; Oberdorfer, Lisa; Rona, Zsofia; Giordano, Vito; Werther, Tobias; Klebermass-Schrehof, Katrin; Stevenson, Nathan; Vanhatalo, Sampsa (2017)
    Objective: To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age. Methods: We collected 231 EEG recordings from 67 infants between 24 and 45 weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography (N = 323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier. Results: Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations. Conclusions: A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages. Significance: This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
  • Garnier Artinano, Tomas; Andalibi, Vafa; Atula, Iiris; Maestri, Matteo; Vanni, Simo (2023)
    IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. MethodsThe system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. ResultsBiophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates. DiscussionOur findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells.
  • Kukkola, Johanna (Helsingin yliopisto, 2022)
    Can a day be classified to the correct season on the basis of its hourly weather observations using a neural network model, and how accurately can this be done? This is the question this thesis aims to answer. The weather observation data was retrieved from Finnish Meteorological Institute’s website, and it includes the hourly weather observations from Kumpula observation station from years 2010-2020. The weather observations used for the classification were cloud amount, air pressure, precipitation amount, relative humidity, snow depth, air temperature, dew-point temperature, horizontal visibility, wind direction, gust speed and wind speed. There are four distinct seasons that can be experienced in Finland. In this thesis the seasons were defined as three-month periods, with winter consisting of December, January and February, spring consisting of March, April and May, summer consisting of June, July and August, and autumn consisting of September, October and November. The days in the weather data were classified into these seasons with a convolutional neural network model. The model included a convolutional layer followed by a fully connected layer, with the width of both layers being 16 nodes. The accuracy of the classification with this model was 0.80. The model performed better than a multinomial logistic regression model, which had accuracy of 0.75. It can be concluded that the classification task was satisfactorily successful. An interesting finding was that neither models ever confused summer and winter with each other.
  • Palojoki, Sari; Saranto, Kaija; Reponen, Elina; Skants, Noora; Vakkuri, Anne; Vuokko, Riikka (2021)
    Background: It is assumed that the implementation of health information technology introduces new vulnerabilities within a complex sociotechnical health care system, but no international consensus exists on a standardized format for enhancing the collection, analysis, and interpretation of technology-induced errors. Objective: This study aims to develop a classification for patient safety incident reporting associated with the use of mature electronic health records (EHRs). It also aims to validate the classification by using a data set of incidents during a 6-month period immediately after the implementation of a new EHR system. Methods: The starting point of the classification development was the Finnish Technology-Induced Error Risk Assessment Scale tool, based on research on commonly recognized error types. A multiprofessional research team used iterative tests on consensus building to develop a classification system. The final classification, with preliminary descriptions of classes, was validated by applying it to analyze EHR-related error incidents (n=428) during the implementation phase of a new EHR system and also to evaluate this classification’s characteristics and applicability for reporting incidents. Interrater agreement was applied. Results: The number of EHR-related patient safety incidents during the implementation period (n=501) was five-fold when compared with the preimplementation period (n=82). The literature identified new error types that were added to the emerging classification. Error types were adapted iteratively after several test rounds to develop a classification for reporting patient safety incidents in the clinical use of a high-maturity EHR system. Of the 427 classified patient safety incidents, interface problems accounted for 96 (22.5%) incident reports, usability problems for 73 (17.1%), documentation problems for 60 (14.1%), and clinical workflow problems for 33 (7.7%). Altogether, 20.8% (89/427) of reports were related to medication section problems, and downtime problems were rare (n=8). During the classification work, 14.8% (74/501) of reports of the original sample were rejected because of insufficient information, even though the reports were deemed to be related to EHRs. The interrater agreement during the blinded review was 97.7%. Conclusions: This study presents a new classification for EHR-related patient safety incidents applicable to mature EHRs. The number of EHR-related patient safety incidents during the implementation period may reflect patient safety challenges during the implementation of a new type of high-maturity EHR system. The results indicate that the types of errors previously identified in the literature change with the EHR development cycle.
  • Lindfors, Lydia; Sioris, Patrik; Anttalainen, Anna; Korelin, Katja; Kontunen, Anton; Karjalainen, Markus; Naakka, Erika; Salo, Tuula; Vehkaoja, Antti; Oksala, Niku; Hytönen, Vesa; Roine, Antti; Lepomäki, Maiju (2022)
    The primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as “the automated tissue laser analysis system” (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL−1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.
  • Lammers, Gert Jan; Bassetti, Claudio L.A.; Dolenc-Groselj, Leja; Jennum, Poul J.; Kallweit, Ulf; Khatami, Ramin; Lecendreux, Michel; Manconi, Mauro; Mayer, Geert; Partinen, Markku; Plazzi, Giuseppe; Reading, Paul J.; Santamaria, Joan; Sonka, Karel; Dauvilliers, Yves (2020)
    Summary The aim of this European initiative is to facilitate a structured discussion to improve the next edition of the International Classification of Sleep Disorders (ICSD), particularly the chapter on central disorders of hypersomnolence. The ultimate goal for a sleep disorders classification is to be based on the underlying neurobiological causes of the disorders with clear implication for treatment or, ideally, prevention and or healing. The current ICSD classification, published in 2014, inevitably has important shortcomings, largely reflecting the lack of knowledge about the precise neurobiological mechanisms underlying the majority of sleep disorders we currently delineate. Despite a clear rationale for the present structure, there remain important limitations that make it difficult to apply in routine clinical practice. Moreover, there are indications that the current structure may even prevent us from gaining relevant new knowledge to better understand certain sleep disorders and their neurobiological causes. We suggest the creation of a new consistent, complaint driven, hierarchical classification for central disorders of hypersomnolence; containing levels of certainty, and giving diagnostic tests, particularly the MSLT, a weighting based on its specificity and sensitivity in the diagnostic context. We propose and define three diagnostic categories (with levels of certainty): 1/“Narcolepsy” 2/“Idiopathic hypersomnia”, 3/“Idiopathic excessive sleepiness” (with subtypes)
  • Tislevoll, Benedicte Sjo; Hellesoy, Monica; Fagerholt, Oda Helen Eck; Gullaksen, Stein-Erik; Srivastava, Aashish; Birkeland, Even; Kleftogiannis, Dimitrios; Ayuda-Duran, Pilar; Piechaczyk, Laure; Tadele, Dagim Shiferaw; Skavland, Jorn; Panagiotis, Baliakas; Hovland, Randi; Andresen, Vibeke; Seternes, Ole Morten; Tvedt, Tor Henrik Anderson; Aghaeepour, Nima; Gavasso, Sonia; Porkka, Kimmo; Jonassen, Inge; Floisand, Yngvar; Enserink, Jorrit; Blaser, Nello; Gjertsen, Bjorn Tore (2023)
    Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24 h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phosphoERK1/2 24 h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics.
  • Basso, Alessandra (2021)
    The article advances a new way of thinking about classifications in general and the classification of mental disorders in particular. By applying insights from measurement practice to the context of classification, I defend a notion of epistemic accuracy that allows one to evaluate and improve classifications by comparing different classifying methods to each other. Progress in classification arises from the mutual development of classification systems and classifying methods. Based on this notion of accuracy, the article illustrates with an example how psychiatric classifications can be improved via circumscribed comparisons of different perspectives on mental disorders, without relying on complete models of their complex aetiology. When applying this strategy, the traditional opposition between symptom-based and causal approaches is of little consequence for making progress in the epistemic accuracy of psychiatric classification.
  • Hyder, Rasha; Mads, Jensen; Højlund, Andreas; Kimppa, Lilli; Bailey, Christopher J.; Schaldemose, Jeppe L.; Kinnerup, Martin B.; Østergaard, Karen; Shtyrov, Yury (2021)
    Parkinson's disease (PD) is a neurodegenerative disorder, well-known for its motor symptoms; however, it also adversely affects cognitive functions, including language, a highly important human ability. PD pathology is associated, even in the early stage of the disease, with alterations in the functional connectivity within corticosubcortical circuitry of the basal ganglia as well as within cortical networks. Here, we investigated functional cortical connectivity related to spoken language processing in early-stage PD patients. We employed a patientfriendly passive attention-free paradigm to probe neurophysiological correlates of language processing in PD patients without confounds related to active attention and overt motor responses. MEG data were recorded from a group of newly diagnosed PD patients and age-matched healthy controls who were passively presented with spoken word stimuli (action and abstract verbs, as well as grammatically correct and incorrect inflectional forms) while focussing on watching a silent movie. For each of the examined linguistic aspects, a logistic regression classifier was used to classify participants as either PD patients or healthy controls based on functional connectivity within the temporo-fronto-parietal cortical language networks. Classification was successful for action verbs (accuracy = 0.781, p-value = 0.003) and, with lower accuracy, for abstract verbs (accuracy = 0.688, pvalue = 0.041) and incorrectly inflected forms (accuracy = 0.648, p-value = 0.021), but not for correctly inflected forms (accuracy = 0.523, p-value = 0.384). Our findings point to quantifiable differences in functional connectivity within the cortical systems underpinning language processing in newly diagnosed PD patients compared to healthy controls, which arise early, in the absence of clinical evidence of deficits in cognitive or general language functions. The techniques presented here may aid future work on establishing neurolinguistic markers to objectively and noninvasively identify functional changes in the brain's language networks even before clinical symptoms emerge.
  • Longato, Enrico; Acciaroli, Giada; Facchinetti, Andrea; Hakaste, Liisa; Tuomi, Tiinamaija; Maran, Alberto; Sparacino, Giovanni (2018)
    Many glycaemic variability (GV) indices extracted from continuous glucose monitoring systems data have been proposed for the characterisation of various aspects of glucose concentration profile dynamics in both healthy and non-healthy individuals. However, the inter-index correlations have made it difficult to reach a consensus regarding the best applications or a subset of indices for clinical scenarios, such as distinguishing subjects according to diabetes progression stage. Recently, a logistic regression-based method was used to address the basic problem of differentiating between healthy subjects and those affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D) in a pool of 25 GV-based indices. Whereas healthy subjects were classified accurately, the distinction between patients with IGT and T2D remained critical. In the present work, by using a dataset of CGM time-series collected in 62 subjects, we developed a polynomial-kernel support vector machine-based approach and demonstrated the ability to distinguish between subjects affected by IGT and T2D based on a pool of 37 GV indices complemented by four basic parameters—age, sex, BMI, and waist circumference—with an accuracy of 87.1%.
  • Vuokko, Riikka; Vakkuri, Anne; Palojoki, Sari (IOS PRESS, 2021)
    Studies in Health Technology and Informatics
    Vaccination information is needed at individual and at population levels, as it is an important part of public health measures. In Finland, a vaccination data structure has been developed for centralized information services that include patient access to information. Harmonization of data with national vaccination registry is ongoing. New requirements for vaccination certificates have emerged because of COVID-19 pandemic. We explore, what is the readiness of Finnish development of vaccination data structures and what can be learned from Finnish harmonization efforts in order to accomplish required level of interoperability.
  • the Academic Research Consortium of Infective Native Aortic Aneurysm (ARC of INAA); Sörelius, Karl; Wyss, Thomas R.; Adam, Donald; Heinola, Ivika; Weiss, Salome (2023)
    Objective: There is no consensus regarding the terminology, definition, classification, diagnostic criteria, and algorithm, or reporting standards for the disease of infective native aortic aneurysm (INAA), previously known as mycotic aneurysm. The aim of this study was to establish this by performing a consensus study. Methods: The Delphi methodology was used. Thirty-seven international experts were invited via mail to participate. Four two week Delphi rounds were performed, using an online questionnaire, initially with 22 statements and nine reporting items. The panellists rated the statements on a five point Likert scale. Comments on statements were analysed, statements revised, and results presented in iterative rounds. Consensus was defined as ≥ 75% of the panel selecting “strongly agree” or “agree” on the Likert scale, and consensus on the final assessment was defined as Cronbach's alpha coefficient > .80. Results: All 38 panellists completed all four rounds, resulting in 100% participation and agreement that this study was necessary, and the term INAA was agreed to be optimal. Three more statements were added based on the results and comments of the panel, resulting in a final 25 statements and nine reporting items. All 25 statements reached an agreement of ≥ 87%, and all nine reporting items reached an agreement of 100%. The Cronbach's alpha increased for each consecutive round (round 1 = .84, round 2 = .87, round 3 = .90, and round 4 = .92). Thus, consensus was reached for all statements and reporting items. Conclusion: This Delphi study established the first consensus document on INAA regarding terminology, definition, classification, diagnostic criteria, and algorithm, as well as reporting standards. The results of this study create essential conditions for scientific research on this disease. The presented consensus will need future amendments in accordance with newly acquired knowledge.
  • Casado-Izquierdo, Pedro; Rio-Machin, Ana; Miettinen, Juho J,; Bewicke-Copley, Findlay; Rouault-Pierre, Kevin; Krizsán, Szilvia; Parsons, Alun; Rajeeve, Vinothini; Miraki-Moud, Farideh; Taussig, David; Bödör, Csaba; Gribben, John; Heckman, Caroline; Fitzgibbon, Jude; Cutillas, Pedro R. (2023)
    Acute myeloid leukaemia (AML) patients harbouring certain chromosome abnormalities have particularly adverse prognosis. For these patients, targeted therapies have not yet made a significant clinical impact. To understand the molecular landscape of poor prognosis AML we profiled 74 patients from two different centres (in UK and Finland) at the proteomic, phosphoproteomic and drug response phenotypic levels. These data were complemented with transcriptomics analysis for 39 cases. Data integration highlighted a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia, which we term MLLGA and MLLGB. MLLGA presented increased DOT1L phosphorylation, HOXA gene expression, CDK1 activity and phosphorylation of proteins involved in RNA metabolism, replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples. MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and inosine-5-monosphosphate dehydrogenase (IMPDH) relative to other cases. Intermediate-risk KMT2A-MLLT3 cases were mainly represented in a third group closer to MLLGA than to MLLGB. The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia, suggesting a role of the nucleolar activity in sensitivity to treatment. In summary, our multilayer molecular profiling of AML with poor prognosis and KMT2A-MLLT3 karyotypes identified a phosphoproteomics signature that defines two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia. These data provide a rationale for the potential development of specific therapies for AML patients characterised by the MLLGA phosphoproteomics signature identified in this study.
  • WSES-AAST Expert Panel; Coccolini, Federico; Moore, Ernest E.; Kluger, Yoram; Leppäniemi, Ari; Catena, Fausto (2019)
    Renal and urogenital injuries occur in approximately 10-20% of abdominal trauma in adults and children. Optimal management should take into consideration the anatomic injury, the hemodynamic status, and the associated injuries. The management of urogenital trauma aims to restore homeostasis and normal physiology especially in pediatric patients where non-operative management is considered the gold standard. As with all traumatic conditions, the management of urogenital trauma should be multidisciplinary including urologists, interventional radiologists, and trauma surgeons, as well as emergency and ICU physicians. The aim of this paper is to present the World Society of Emergency Surgery (WSES) and the American Association for the Surgery of Trauma (AAST) kidney and urogenital trauma management guidelines.