Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis.
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ABSTRACT:
SUBMITTER: Gilam G
PROVIDER: S-EPMC8442889 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveWith the increasing societal awareness of the prevalence and impact of acute pain, there is a need to develop an acute pain classification system that both reflects contemporary mechanistic insights and helps guide future research and treatment. Existing classifications of acute pain conditions are limiting, with a predominant focus on the sensory experience (e.g., pain intensity) and pharmacologic consumption. Consequently, there is a need to more broadly characterize and classify the multidimensional experience of acute pain.SettingConsensus report following expert panel involving the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION), American Pain Society (APS), and American Academy of Pain Medicine (AAPM).MethodsAs a complement to a taxonomy recently developed for chronic pain, the ACTTION public-private partnership with the US Food and Drug Administration, the APS, and the AAPM convened a consensus meeting of experts to develop an acute pain taxonomy using prevailing evidence. Key issues pertaining to the distinct nature of acute pain are presented followed by the agreed-upon taxonomy. The ACTTION-APS-AAPM Acute Pain Taxonomy will include the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Future efforts will consist of working groups utilizing this taxonomy to develop diagnostic criteria for a comprehensive set of acute pain conditions.PerspectiveThe ACTTION-APS-AAPM Acute Pain Taxonomy (AAAPT) is a multidimensional acute pain classification system designed to classify acute pain along the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms.ConclusionsSignificant numbers of patients still suffer from significant acute pain, despite the advent of modern multimodal analgesic strategies. Mismanaged acute pain has a broad societal impact as significant numbers of patients may progress to suffer from chronic pain. An acute pain taxonomy provides a much-needed standardization of clinical diagnostic criteria, which benefits clinical care, research, education, and public policy. For the purposes of the present taxonomy, acute pain is considered to last up to seven days, with prolongation to 30 days being common. The current understanding of acute pain mechanisms poorly differentiates between acute and chronic pain and is often insufficient to distinguish among many types of acute pain conditions. Given the usefulness of the AAPT multidimensional framework, the AAAPT undertook a similar approach to organizing various acute pain conditions.
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Project description:Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.
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Project description:Cancer is a complex disease with a high rate of mortality. The characteristics of tumor masses are very heterogeneous; thus, the appropriate classification of tumors is a critical point in the effective treatment. A high level of heterogeneity has also been observed in breast cancer. Therefore, detecting the molecular subtypes of this disease is an essential issue for medicine that could be facilitated using bioinformatics. This study aims to discover the molecular subtypes of breast cancer using somatic mutation profiles of tumors. Nonetheless, the somatic mutation profiles are very sparse. Therefore, a network propagation method is used in the gene interaction network to make the mutation profiles dense. Afterward, the deep embedded clustering (DEC) method is used to classify the breast tumors into four subtypes. In the next step, gene signature of each subtype is obtained using Fisher's exact test. Besides the enrichment of gene signatures in numerous biological databases, clinical and molecular analyses verify that the proposed method using mutation profiles can efficiently detect the molecular subtypes of breast cancer. Finally, a supervised classifier is trained based on the discovered subtypes to predict the molecular subtype of a new patient. The code and material of the method are available at: https://github.com/nrohani/MolecularSubtypes.
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Project description:The study pursued dual goals: To advance mRNA-seq bioinformatics towards unbiased transcriptome capture and to demonstrate its potential for discovery in neuroscience by applying the approach to an in vivo model of neurological disease. We found that 12.4% of known genes were induced and 7% were suppressed in the dysfunctional (but anatomically intact) L4 dorsal root ganglion (DRG) 2 weeks after L5 spinal Nerve Ligation (SNL). A new algorithm for agnostic mapping of pre-mRNA splice junctions (SJ) achieved a precision of 97%.
2010-05-02 | GSE20895 | GEO
Project description:Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.
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Project description:A modern biomedical research project can easily contain hundreds of analysis steps and lack of reproducibility of the analyses has been recognized as a severe issue. While thorough documentation enables reproducibility, the number of analysis programs used can be so large that in reality reproducibility cannot be easily achieved. Literate programming is an approach to present computer programs to human readers. The code is rearranged to follow the logic of the program, and to explain that logic in a natural language. The code executed by the computer is extracted from the literate source code. As such, literate programming is an ideal formalism for systematizing analysis steps in biomedical research. We have developed the reproducible computing tool Lir (literate, reproducible computing) that allows a tool-agnostic approach to biomedical data analysis. We demonstrate the utility of Lir by applying it to a case study. Our aim was to investigate the role of endosomal trafficking regulators to the progression of breast cancer. In this analysis, a variety of tools were combined to interpret the available data: a relational database, standard command-line tools, and a statistical computing environment. The analysis revealed that the lipid transport related genes LAPTM4B and NDRG1 are coamplified in breast cancer patients, and identified genes potentially cooperating with LAPTM4B in breast cancer progression. Our case study demonstrates that with Lir, an array of tools can be combined in the same data analysis to improve efficiency, reproducibility, and ease of understanding. Lir is an open-source software available at github.com/borisvassilev/lir.
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Project description:mRNA-seq is a paradigm-shifting technology because of its superior sensitivity and dynamic range and its potential to capture transcriptomes in an agnostic fashion, i.e., independently of existing genome annotations. Implementation of the agnostic approach, however, has not yet been fully achieved. In particular, agnostic mapping of pre-mRNA splice sites has not been demonstrated. The present study pursued dual goals: (1) to advance mRNA-seq bioinformatics toward unbiased transcriptome capture and (2) to demonstrate its potential for discovery in neuroscience by applying the approach to an in vivo model of neurological disease. We have performed mRNA-seq on the L4 dorsal root ganglion (DRG) of rats with chronic neuropathic pain induced by spinal nerve ligation (SNL) of the neighboring (L5) spinal nerve. We found that 12.4% of known genes were induced and 7% were suppressed in the dysfunctional (but anatomically intact) L4 DRG 2 wk after SNL. These alterations persisted chronically (2 mo). Using a read cluster classifier with strong test characteristics (ROC area 97%), we discovered 10,464 novel exons. A new algorithm for agnostic mapping of pre-mRNA splice junctions (SJs) achieved a precision of 97%. Integration of information from all mRNA-seq read classes including SJs led to genome reannotations specifically relevant for the species used (rat), the anatomical site studied (DRG), and the neurological disease considered (pain); for example, a 64-exon coreceptor for the nociceptive transmitter substance P was identified, and 21.9% of newly discovered exons were shown to be dysregulated. Thus, mRNA-seq with agnostic analysis methods appears to provide a highly productive approach for in vivo transcriptomics in the nervous system.
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Project description:The study pursued dual goals: To advance mRNA-seq bioinformatics towards unbiased transcriptome capture and to demonstrate its potential for discovery in neuroscience by applying the approach to an in vivo model of neurological disease. We found that 12.4% of known genes were induced and 7% were suppressed in the dysfunctional (but anatomically intact) L4 dorsal root ganglion (DRG) 2 weeks after L5 spinal Nerve Ligation (SNL). A new algorithm for agnostic mapping of pre-mRNA splice junctions (SJ) achieved a precision of 97%. mRNA-seq of L4 DRG 2 weeks and 2 months after L5 spinal nerve ligation. CONTROL and SNL were used to identify differential gene expression between chronic pain and standard conditions in Rattus norvegicus. CONTROL and SNL and PILOT were used to perform 'agnostic splice site discovery' in the nervous system transcriptome in Rattus norvegicus
2010-05-01 | E-GEOD-20895 | biostudies-arrayexpress
Project description:In recent years, preclinical pain research has failed to develop genuinely new analgesics for clinical use. This fact is reflected by a high number of patients, limited drug efficacy accompanied by side effects, and a long-term opioid intake. Two main aspects have been addressed, which hinder translation: the use of non-relevant pain models and a mismatch between pain-related outcomes in preclinical and clinical studies. Conversely, disease-specific pain models that mirror more closely the clinical situation and multidimensional behavioral outcome measures that objectively and reproducibly assess relevant pain-related symptoms in a preclinical setting could improve translation. Mechanistically, a matter of debate is the role of Ly6G+ neutrophil granulocytes (NGs) for pain. NGs are essential to eliminate pathogens and promote the wound healing process. For this purpose, there is a need to release various pro- and anti-inflammatory mediators, some of which could ameliorate or enhance pain. However, the contribution of NGs to different pain entities is contradictory for reflex-based tests, and completely unknown in the context of non-evoked pain (NEP) and movement-evoked pain (MEP). First, we combined withdrawal reflex-based assays with novel video-based assessments for NEP- and MEP-related behavior in two mouse pain models. The pain models utilized in this study were incision (INC) and pathogen/adjuvant-induced inflammation (CFA), translating well to postsurgical and inflammatory pain entities. Second, we depleted NGs and applied a set of behavioral assessments to investigate the role of NG migration in different pain modalities. Our comprehensive behavioral approach identified pain-related behaviors in mice that resemble (NEP) or differentiate (MEP) behavioral trajectories in comparison to mechanical and heat hypersensitivity, thereby indicating modality-dependent mechanisms. Further, we show that injury-induced accumulation of NGs minimally affects pain-related behaviors in both pain models. In conclusion, we report a novel assessment to detect NEP in mice after unilateral injuries using a more unbiased approach. Additionally, we are capable of detecting an antalgic gait for both pain entities with unique trajectories. The different trajectories between MEP and other pain modalities suggest that the underlying mechanisms differ. We further conclude that NGs play a subordinate role in pain-related behaviors in incisional and inflammatory pain.
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