dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients with neuropathic pain are heterogeneous in etiology, pathophysiology, and clinical appearance. They exhibit a variety of painrelated sensory symptoms and signs (sensory profile). Different sensory profiles might indicate different classes of neurobiological mechanisms, and hence subgroups with different sensory profiles might respond differently to treatment. The aim of the investigation was to identify subgroups in a large sample of patients with neuropathic pain using hypothesis-free statistical methods on the database of 3 large multinational research networks (German Research Network on Neuropathic Pain (DFNS), IMI-Europain, and Neuropain). Standardized quantitative sensory testing was used in 902 (test cohort) and 233 (validation cohort) patients with peripheral neuropathic pain of different etiologies.
Consortia Three large multinational consortia collected phenotypic data of patients with peripheral neuropathic pain (test cohort): the German Research Network on Neuropathic Pain (DFNS), the EUROPAIN, and the NEUROPAIN collaboration. The gathered data comprised demographic, psychometric, and clinical data as well as results of a standardized quantitative sensory assessment that were captured in one joined central database of the DFNS. 40 ach study center used a computer-assisted program for data entry locally in each center (Neuroquast, Statconsult, Magdeburg, Germany). For data export into the central database, a special data export file was created, encrypted, and sent to the central database through e-mail. All centers and investigators underwent a strict quality assessment and certification process to allow future pooling of data across sites and countries.
39, 63 A confirmatory analysis of heterogeneity between the participating centers in healthy subjects and patients painful neuropathies showed a high degree of homogeneity between the different centers, making it possible to analyze the database as a homogenous group. 62 he DFNS (http://www. neuropathischer-schmerz. de) was established to investigate mechanisms and treatments of neuropathic pain and consists of 10 German centers. The study protocol was approved by the ethics committee of the University Hospital Kiel, Germany, and subsequently by the ethics committees of all participating centers. The EUROPAIN consortium (http://www. imieuropain. org) consists of academic study groups working on pain research from Germany, Denmark, and the United Kingdom, a Spanish SME and Europe's most active pharmaceutical companies working in the pain field.
By the latter criterion, we intended to assure that pain was the leading diagnosis and not depression. Because patient selection was done by each individual center, we do not know how many patients were excluded for this reason. Data sets were excluded in case of incomplete records (eg, no precise diagnosis documented, more than one QST variable missing in the affected area, no information about age, sex, or other demographic data) (Fig. 1 ). All subjects signed written informed consent according to the Declaration of Helsinki for participation in the respective study and for transfer of the study records into the central database. The ethics committee of each center approved the study protocol individually. The study is reported according to the STROBE statement. Several centers contributed to more than one consortium, which contributed to uniform clinical standards across consortia.
Validation data set For external validation, patients with PNP, PNI, and PHN who were collected either within the DFNS after the database closure in 2010 (n 5 143) or within the Europain consortia for treatment studies with oxcarbazepine and lidocaine (n 5 90)  13, 14  (not included in the flowchart, Fig. 1 ). Inclusion and exclusion criteria for the patients collected within the DFNS were identical to the criteria for the test data set. Inclusion and exclusion criteria for the patients collected within Europain were identical except that patients did not fill out questionnaires on pain qualities, depression, and pain course over the last 4 weeks. Test and validation data sets were equal in age, sex, pain duration, and current pain intensity. After transforming the individual QST values into z scores, a separate cluster analysis was performed within this data set.
Patients In total, 1848 data sets were included into the combined DFNS/ Europain/Neuropain database. After applying the inclusion/ exclusion criteria, we could assess 902 patients with peripheral neuropathic pain of different etiologies in the test cohort (Fig. 1 ). The validation cohort consisted of 233 patients. Demographic data of the entire patient cohort are shown in Table  1. Most of the patients had long-lasting chronic pain between 1 and 5 years. Pain intensity generally was moderate to severe with average current pain ratings close to 6 on a 0-to-10 Likert scale without relevant differences between the cohorts. Distributions of etiologies differed between the 2 cohorts because of the absence of patients with RAD in the validation cohort. Questionnaires were available from 724 of the 902 patients in the test cohort, but not from the validation cohort.
4. 14 ). Importantly, no specific drug was present in more than half of the patients in any group that shows that the sensory patterns do not result from drug effects. Furthermore, when cluster analyses were applied in the 2 largest groups of medication (tricyclic antidepressants, anticonvulsants), 3 clusters with similar pattern emerge (data not shown). According to the published DFNS reference data, each QST parameter in each patient can be individually rated as within or outside the 95% CI of variability in healthy age-and sex-matched subjects. This analysis is presented in Figure  4. Of patients in cluster 1 ("sensory loss"), more than 50% had significant nonnociceptive sensory loss on an individual basis. Paradoxical   25 ) 20 (  22 ) 20 (25)   Validation data set* 124 (  53 ) 77 (  33 ) 32 (14)   Importantly, no specific drug was present in more than half of the patients in any cluster, which shows that the sensory patterns do not result from drug effects.
Research grant to  Imperial College from Astellas  as part of a  European Commission  and  European Federation of Pharmaceutical Industries and Associations (EFPIA) ,  Innovative Medicines Initiative  Grant (EUROPAIN). M. Segerdahl is an employee of Lundbeck A/S. J. Serra  is an employee of  Neuroscience Technologies S. L. , Barcelona, Spain. S. Sindrup  has received financial support for investigatorinitiated study from  Pfizer. C. Sommer  has received consultation fees from  Air Liquide ,  Astellas ,  Baxalta ,  CSL Behring , and  Genzyme. She received fees for educational talks for  Baxalta, CSL Behring ,  Genzyme ,  Novartis , and  Pfizer.
Conflict of interest statement The NEUROPAIN project is an investigator-initiated European multicenter study with R. Baron as the principle investigator and 10 coinvestigator sites, supported by an independent investigator-initiated research grant from Pfizer Ltd. The funding source had no role in study design, data collection and analysis, or writing of the manuscript. DFNS steering committee: R. Baron, C. Maier, T. T ölle, R. -D. Treede. The EUROPAIN consortium (http://www. imieuropain. org/consortium): R. Baron.
This effect decreases the calculated F V / F M value and allows the detection of such herbicides  (Bolhar-Nordenkampf et al. 1989). Glyphosate application leads to a depletion of free phosphate, leading to an inhibition of ATP synthesis, and isoxaflutole inhibits PQ synthesis. Søbye et al. (2011)  showed that F J and the slopes at the J and I steps can be used to titrate the effects of the herbicides glyphosate and isoxaflutole as well as mixtures of these two compounds. However, looking at the OJIP transients shown in that paper, these parameters seem to reflect mainly the destruction of either the photosynthetic system or alternatively PSII, since the main effect was observed on the F V amplitude. The authors note that the fluorescence data obtained 48 h after the treatment gave the same information as the biomass measurements carried out 3 weeks after the treatment.
Although NK and T cell numbers were temporarily decreased during administration of IL-21, the cells had higher expression of CXCR3, hyaluronanmediated motility receptor (HMMR), IFN-γ, perforin, and granzyme. In addition, NK cells from patients displayed an enhanced cytotoxicity capacity  (151). These results were confirmed in a phase II trial for metastatic melanoma  (155). In the absence of clinical approval, IL-15 and IL-21 are also used ex vivo to expand and activate NK cells for further infusion in patients. NK cells stimulated ex vivo by the leukemic cell line K562 expressing membrane-bound IL-15 or IL-21 showed a strong proliferation and cytolytic activity with a higher proliferation rate and an increase in telomere length for IL-21-activated NK cells  (156). NK cells expanded ex vivo by membrane-bound IL-15 are currently infused into MDS or AML patients (phase I clinical trial #NCT02123836).
The anti-CD16 part of the BiKEs bypasses the disadvantages of classical mAbs  (157). For instance, AFM13, a BiKE targeting CD19 and CD16, has been recently tested in phase I and II trials (#NCT01221571 and #NCT02321592, respectively) in non-Hodgkin lymphoma. In vitro studies demonstrated that CD33 × CD16 BiKEs trigger NK cell activation against AML cell lines and primary targets through CD16 signaling, leading to cytokine and chemokine production  (158). As a consequence, significant increases in NK cell cytolytic activity led to induction of target cell apoptosis at high and low target to effector ratios. In a study based on NK cell isolation from patients with MDS, authors showed that CD33 × CD16 BiKE potently activates blood and marrow MDS-NK cells at all disease stages to lyse CD33 + MDS and CD33 + myeloid-derived suppressor cells (MDSCs) targets  (159).
Noteworthy, MGD006, a CD123 × CD3 BiKE is tested in a phase I trial (#NCT02152956), confirming the current explosive attention to BiKEs as potent therapeutic tools for AML and other cancers. In the same way, a CD30 × CD16 bispecific tetravalent chimeric antibody (TandAb) was used in a phase I clinical trial in patients with relapsed or refractory Hodgkin lymphoma showing good tolerance and tumor targeting  (160). However, with respect to AML treatment, the use of BiKEs remains limited due to the heterogeneity of tumor antigen in this disease. Recently, several new reagents were developed in attempt to enhance the targeting of malignant cells. Gleason et al. have generated a trispecific mAb (TriKE) directed against CD19, CD20, and CD16  (159). This TriKE, efficiently engaged NK cells against CD19 + CD20 + leukemic targets, as proven by a strong cytotoxicity and IFN-γ production.
IPH2101 has also been assessed in vitro in combination with lenalidomide and potentiates NK-cell cytotoxicity toward autologous myeloma cells. This combination is currently being tested in a phase I clinical trial in MM patients (#NCT01217203)  (176). The second generation anti-KIR lirilumab was also shown to synergize with Lenalidomide to increase NK cytotoxicity of myeloma patients treated with Daratumumab (anti-CD38)  (177). In vitro and in vivo lirilumab enhances NK activity against CD20 + lymphoma cells  (167). With respect to AML, lirilumab is currently tested in patients in CR for long-term maintenance (#NCT01687387), and for the treatment of patients with refractory/relapsed AML (#NCT02399917). Although safe, this therapeutic mAb did not induce impressive clinical improvement so far.
First of all, cytotoxic effectors expressing KIRs (NK cells, αβ CD8 + , and γδ T cells) use other inhibitory KIRs that are not targeted by lirilumab (NKG2A, KIR3DL, and CD85j/ ILT2). In an autologous setting, it is likely that these cells may still remain tolerant to leukemic cells. In addition, lirilumab also recognizes KIR2DS1 and KIR2DS2; blocking these receptors may, in contrast, unfavor tumor cell clearance  (168). Noteworthy, a newly engineered mAb directed against NKG2A has been generated by Innate Pharma (IPH2201) and is currently tested in clinical trials (phase I/II) in ovarian cancer, squamous cell carcinoma, and refractory CLL (#NCT02459301, #NCT02331875, and #NCT02557516, respectively). It is tempting to speculate on the efficacy of this new reagent in AML treatment, as these cells are expected to express HLA-E.
The PD-1/PD-L1 axis was also involved in the modulation of NK-cell functions against MM  (180). Interestingly, IFN-γ can induce the expression of PD-L1 on AML cells leading to the inhibition of the antileukemic response mediated by T-lymphocytes  (181)  and NK cells  (182). Therefore, the anti-PD-1 mAb Nivolumab is tested in a phase II clinical trial in AML patients in remission with high risk of relapse (#NCT02532231).
AT and ND acknowledge the support by the Association Laurette Fugain (#ALF/No. 2011/01), Institut National du Cancer (grants #R09081HHA and #RPT12008HHA), and Assistance Publique-Hopitaux de Paris translational research grant in Biology 2010 (#RTB10002). ZK was supported by the Association pour la Recherche sur le Cancer (#DOC20100600956). AN was supported by the Qatar Foundation. DO laboratory is supported by the Fondation pour la Recherche Médicale (Equipe FRM DEQ20140329534) and Institut National du Cancer. DO is a senior scholar of the Institut Universitaire de France.
FUNDiNG AT and ND acknowledge the support by the Association Laurette Fugain (#ALF/No. 2011/01), Institut National du Cancer (grants #R09081HHA and #RPT12008HHA), and Assistance Publique-Hopitaux de Paris translational research grant in Biology 2010 (#RTB10002). ZK was supported by the Association pour la Recherche sur le Cancer (#DOC20100600956). AN was supported by the Qatar Foundation. DO laboratory is supported by the Fondation pour la Recherche Médicale (Equipe FRM DEQ20140329534) and Institut National du Cancer. DO is a senior scholar of the Institut Universitaire de France.
ACKNOWLEDGMENTS We thank all the authors for their contribution to this research topic, as well as the reviewers who accepted to evaluate the manuscripts published here. LN is part of the  LABEX TULIP  ( ANR-10-LABX-41  and  ANR-11-IDEX-0002-02 ) and is supported by grants from the  Agence Nationale de la Recherche  ( XANTHOMIX ANR-2010-GENM-013-02 ,  XOPAQUE ANR-10-JCJC-1703-01 , and CROpTAL  ANR 14-CE19-0002-01 ). TL is supported by grants from the  DFG  ( LA1338 ) and the  Two Blades Foundation.
There is an early level of recognition of the need to revise the present model of clinical trials. Timely changes to clinical practice have been suggested by the recent National Cancer Institute Precision Medicine Initiatives for the new National Clinical Trials Network,  [31]  but most molecular testing continues to be used only as means to streamline the enrollment in clinical trials. In order to accommodate the n = 1 trial model, early discussions have been initiated about creation of a "cancer knowledge network",  [10]  where information from the numerous case studies of truly individualized cancer treatments could be shared and evaluated. A case in point is the early effort to collect data from patients using targeted therapies in the NCI-Molecular Analysis for Therapy Choice (NCI-MATCH) Trial.
The approach of this consortium has some similarities to the efforts extended by the ECOG-ACRIN www. impactjournals. com/oncotarget Cancer Research Group, NMTRC, SWOG, Alliance for Clinical Trials in Oncology, NRG Oncology Group and the multiple sites participating in the NCI National Clinical Trial Network for establishing the MATCH trial. But it differs, in its use of using bio-marker driven, molecularly-targeted metronomic combination therapy. The consortium(s) stresses the use of a multi-target, multi-modality approach rather than enrollment on single agent trials. The hope is that sufficient amount of data will be accumulated to provide the necessary evidence to inspire other organizations to extend the examination of tumor tissue to include genomic, proteomic and metabolomics examination of the host as well as of the tumor, and promote individualized cancer therapies.
Because only a very small number of patients is going to have overlapping molecular alterations and as such require the same combination of agents, traditional populationbased statistical approaches comparing two disparately treated groups may not be applicable, and novel statistical approaches using predictive models of cancer growth are going to be needed. The data from all individual patients treated by a precision medicine approach will be stored in a single de-identified database to be shared not only with the consortium members but also with other clinicians and researchers interested in using targeted approaches. The additional benefit of sharing information of these N = 1 trials is going to be learning about the changed pharmacokinetics as combinations of different agents are being used.
To implement precision medicine -and incorporate individual differences in genomic make-up and individual biological characteristics into treatment decisions -we will require the development and easy access to largescale genomic, proteomic, biologic and health information databases. While some protein-protein interaction (PPI) networks are already publicly available on the Internet, therapies is dependent on the ability to standardize selection of therapeutic targets and low-dose metronomic backbones. The diagnosis of patient's molecular profile should be based not only on the genomic analysis of the patient's and the patient, but also on detecting the target proteins and their activation in the tissues. In order to incorporate, and consolidate the vast amount of information computer-assisted complex sociotechnical systems need to be employed to provide tumor boards with upto-date information about the best molecular targets.
Finally, to continuously improve the quality of the information provided to tumor boards, AI should be used to inform future decisions. www. impactjournals. com/oncotarget they are, at least at present, mostly complex interaction maps developed by academic biologists over the last 50 years. Of concern is that because they are maintained by academic institutions with varied levels of funding, they may be of varied levels of information integrity, and of different ability to integrate emerging information or to provide for any corrections/additions driven by new information. Due to the clear and potentially immediate impact precision medicine can exert on cancer therapies much of the information in these databases are dedicated to oncology. However, the long term goal should be to generate a broad ranging source of information about diseased and physiologic states that would be useable for general medical purposes.
A POTENTIAL SOLUTION To address the difficulty accessing, curating and interpreting large data, a clinician-relevant computer assisted search of available information of the publicly available databases needs to be created. While more information than ever is available to the clinician, the information is not only overwhelming, it is also dispersed across varied and copious sources, few of which are geared to clinical applications. Automated systems that can trawl, collect and align available relevant information and provide assistive interpretations for clinicians would significantly alleviate this problem. We can begin by accessing available information in publicly available academically or National Institute of Health curated databases and incorporate cancer knowledge networks as they become available.
Such augmented human intelligence can improve the ability of an institutional tumor board to understand and interpret all of the available gamut of molecular information and remaining current on published medical information. The computerized system, containing a variety of artificial intelligence technologies can integrate a wide variety of information and apply an "understanding" of cancer biology in order to guide a tumor board in designing the most effective therapy for each of its unique patients. The system can do so by incorporating and cross-referencing information from multiple modalities, integrating this information in a clinical oncology context, and providing mathematical analysis of molecular pathways relevant to the patient's specific (identified) molecular changes. The information incorporated into this stream can come not only from traditional academically curated databases, but also from medical and popular scientific literature sources, public media as well as health/ fitness tracking databases as recovered through social media.
Those targets are 'assumed' by experts to be necessary for cancer progression based on the fact that some of these candidate genes have been around for decades. They may be considered universal driver genes just by virtue of our familiarity with them and their commonness. While these candidate approaches help alleviate the information glut, they are based on insufficient information given our relative paucity and incomplete knowledge about the role genetic mutations may play in the host, in tumor specific host tissues, and/ or in cancer biology. While BRAF V600E and BRAF V600K mutations are established driver genes for neuroectodermal tumors such as melanoma, the use of BRAF fusions, and non BRAF V600E or non BRAF V600K gene alterations in gliomas will have to be established. [24, 34]  To use and organize the continuously emerging and heterogeneous information being deposited into genomic (The Cancer Genome Atlas, TCGA; Gene Expression Omnibus, GEO; the NCI's Database of Genomic Structural Variation; dbVar etc), proteomic (UniProt, Swiss-Prot end may others), and metabolomics (Kyoto Encyclopedia of Genes and Genomes, KEGG; and other) databases, as well as the concerted effort to identify and catalog genomic vulnerabilities across hundreds of cancer cell lines (Broad Institute's Project Achilles), new computational tools for repeated and potentially automated analysis of large data sets need to be developed.
One previously described novel physical method for prioritization of targets applies a thermodynamic interpretation to gene expression, and then uses a topological filter to identify a set of potential therapeutic targets by their predicted effect on survival. [42, 43]  The method makes use of publically available proteinprotein interaction networks (PPI networks). These PPIs are online repositories of interaction datasets compiled by international teams of academicians and researchers, and comprehensively curated into networks akin to telecommunication or social network maps. The thermodynamic entropy method considers these PPI networks a closed system where all interactions tend to equilibrium, and where entropy is a measure of the PPI network disorder. Because degree entropy of PPI networks for different cancers, correlates with likelihood of survival of patients with this cancer,  [43]  one can calculate the effect of eliminating a specific target (or eliminating multiple targets).
This approach has demonstrated promising results, and points to the benefits arising from incorporating multidisciplinary perspectives to cancer models. Another previously described method performs a pan-cancer analysis of mutated networks. [44]  This unbiased and open-ended analysis had revealed 16 significantly mutated subnetworks that were not previously thought to play significant role in cancer, and demonstrated that rare combinations of mutations, across multiple PPI networks may provide new insights and new opportunities for diagnostics and therapeutics across cancer types. The PPI approach can be used in a number of ways. For instance, one can overlay transcriptional data from a single patient onto a PPI network, or a data set from The Cancer Genome Atlas (TCGA). As an example of the later, TCGA transcription data from a population of patients with glioblastoma multiforme (GBM) was overlaid on the BioGrid PPI network.