Tmem160 contributes to the establishment of discrete nerve injury-induced pain behaviors in male mice

Chronic pain is a prevalent medical problem, and its molecular basis remains poorly understood. Here, we demonstrate the significance of the transmembrane protein (Tmem) 160 for nerve injury-induced neuropathic pain. An extensive behavioral assessment suggests a pain modality- and entity-specific phenotype in male Tmem160 global knockout (KO) mice: delayed establishment of tactile hypersensitivity and alterations in self-grooming after nerve injury. In contrast, Tmem160 seems to be dispensable for other nerve injury-induced pain modalities, such as non-evoked and movement-evoked pain, and for other pain entities. Mechanistically, we show that global KO males exhibit dampened neuroimmune signaling and diminished TRPA1-mediated activity in cultured dorsal root ganglia. Neither these changes nor altered pain-related behaviors are observed in global KO female and male peripheral sensory neuron-specific KO mice. Our findings reveal Tmem160 as a sexually dimorphic factor contributing to the establishment, but not maintenance, of discrete nerve injury-induced pain behaviors in male mice

Machine learning and artificial intelligence in neuroscience: A primer for researchers

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: No data was used for the research described in the article.Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML

Enhancing the trustworthiness of pain research: A call to action.

\ua9 2024 The AuthorsThe personal, social and economic burden of chronic pain is enormous. Tremendous research efforts are being directed toward understanding, preventing, and managing chronic pain. Yet patients with chronic pain, clinicians and the public are sometimes poorly served by an evidence architecture that contains multiple structural weaknesses. These include incomplete research governance, a lack of diversity and inclusivity, inadequate stakeholder engagement, poor methodological rigour and incomplete reporting, a lack of data accessibility and transparency, and a failure to communicate findings with appropriate balance. These issues span pre-clinical research, clinical trials and systematic reviews and impact the development of clinical guidance and practice. Research misconduct and inauthentic data present a further critical risk. Combined, they increase uncertainty in this highly challenging area of study and practice, drive the provision of low value care, increase costs and impede the discovery of more effective solutions. In this focus article, we explore how we can increase trust in pain science, by examining critical challenges using contemporary examples, and describe a novel integrated conceptual framework for enhancing the trustworthiness of pain science. We end with a call for collective action to address this critical issue. Perspective: Multiple challenges can adversely impact the trustworthiness of pain research and health research more broadly. We present ENTRUST-PE, a novel, integrated framework for more trustworthy pain research with recommendations for all stakeholders in the research ecosystem, and make a call to action to the pain research community

Enhancing the trustworthiness of pain research: A Call to Action.

Perspective: Multiple challenges can adversely impact the trustworthiness of pain research and health research more broadly. We present ENTRUST-PE, a novel, integrated framework for more trustworthy pain research with recommendations for all stakeholders in the research ecosystem, and make a call to action to the pain research community.Acknowledgement: As a summary of the key issues discussed and the recommendations of the ENTRUST-PE project some passages of text are included from the full white paper of the project [69]. N.E. O’Connell, J. Belton, G. Crombez, et al. ENTRUST-PE: An Integrated Framework for Trustworthy Pain Evidence OSF Preprints (2024), 10.31219/osf.io/e39ys .The personal, social and economic burden of chronic pain is enormous. Tremendous research efforts are being directed toward understanding, preventing, and managing chronic pain. Yet patients with chronic pain, clinicians and the public are sometimes poorly served by an evidence architecture that contains multiple structural weaknesses. These include incomplete research governance, a lack of diversity and inclusivity, inadequate stakeholder engagement, poor methodological rigour and incomplete reporting, a lack of data accessibility and transparency, and a failure to communicate findings with appropriate balance. These issues span pre-clinical research, clinical trials and systematic reviews and impact the development of clinical guidance and practice. Research misconduct and inauthentic data present a further critical risk. Combined, they increase uncertainty in this highly challenging area of study and practice, drive the provision of low value care, increase costs and impede the discovery of more effective solutions. In this focus article, we explore how we can increase trust in pain science, by examining critical challenges using contemporary examples, and describe a novel integrated conceptual framework for enhancing the trustworthiness of pain science. We end with a call for collective action to address this critical issue.The ENTRUST-PE project (www.entrust-pe.org), on which this article is based, was funded by the Federal Ministry of Education and Research, Germany under the ERA-NET Neuron Co-Fund Scheme (Proposal ID NEURON_NW-016)

Machine learning and artificial intelligence in neuroscience: A primer for researchers

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML