JMIRx Med

PubMed-indexed overlay journal for preprints with post-review manuscript marketplace (What is JMIRx?).

Editor-in-Chief:

Edward Meinert, MA (Oxon), MSc, MBA, MPA, MPH, PhD, CEng, FBCS, EUR ING, Professor of Digital Health and Clinical Artificial Intelligence, Newcastle University, United Kingdom

JMIRx Med (ISSN 2563-6316), which has been accepted for indexing in PubMed and PubMed Central, is an innovative overlay journal to MedRxiv and JMIR Preprints (other preprint servers are invited to join). JMIRx peer-reviews preprints and publishes their revised "version of record" with peer-review reports across a broad range of medical, clinical and related health sciences. Unlike the majority of JMIR journals, papers published in this journal do not require a digital health focus - in fact, most papers we published in the first months of the journal were related to COVID19, but we publish all research that qualifies for preprinting on MedRxiv.

Conceived to address the urgent need to make highly relevant scientific information available as early as possible without losing the quality of the peer-reviewed process, this innovative new journal is the first in a new series of “superjournals”. Superjournals (a type of "overlay" journal) sit on top of preprint servers (JMIRx-Med serves MedRxiv and JMIR Preprints), offering peer-review and everything else a traditional scholarly journal does. Our goal is to rapidly peer review and publish a paper. All JMIRx Med papers must have originated as a preprint.

All JMIRx Med papers are rigorously peer-reviewed, copyedited and XML-tagged. Accepted papers are published along with the related Peer Review Reports and Author Responses to Peer Review Reports, providing an additional layer of transparency to the scholarly publishing process.

There is no Article Processing Fee directly paid by authors for this journal. JMIRx Med is envisioned as a diamond open access and Plan-P compliant journal, which enables Plan P member universities/institutions and funders to subsidize peer review of preprints and publishing in JMIRx Med. Individual PI-led labs, departments and universities can become institutional members, guaranteeing unlimited peer-review of preprints.

If you are not affiliated with a Plan P member organization, we encourage you to provide Plan P membership details to your administrator or sign up for a Principal Investigator (PI) level membership. Further details provided here.

For a limited time only, authors who opt-in during submission to receive PREreview or PeerRef community peer review for their preprint or refer us to their department head/librarian/funder contact will receive a membership-waiver and may publish the preprint in JMIRx Med at no cost to the author. Referral form provided here.

To submit a preprint to JMIRx, authors can self-nominate their existing preprints for publication (which is the equivalent to a traditional journal submission), using the minimalistic JMIRx-Med submission form that essentially only points to the preprint (the preprint needs to be unpublished and should not be under consideration by a journal).

Preprints that have already been peer-reviewed by third-party Plan P accredited peer-review services such as PREreview and PeerRef do not require further peer-review (at the editors' discretion). In the submission process, you can nominate your preprint for a PREreview journal club, which can be used in lieu of traditional peer-review.

For more details on other submission pathways (including for papers not in MedRxiv) and peer-review options see How to submit to a JMIRx journal.

For more information on JMIRx please also see our Knowledge Base article "What is JMIRx?".

Recent Articles

Peer Reviews

Peer Review of “Monte Carlo Dose Estimation of Absorbed Dose to the Hematopoietic Stem Cell Layer of the Bone Marrow Assuming Nonuniform Distribution Around the Vascular Endothelium of the Bone Marrow: Simulation and Analysis Study”

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Peer Reviews

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Authors' Responses to Peer-Reviews

Authors’ Response to Peer Reviews of “Monte Carlo Dose Estimation of Absorbed Dose to the Hematopoietic Stem Cell Layer of the Bone Marrow Assuming Nonuniform Distribution Around the Vascular Endothelium of the Bone Marrow: Simulation and Analysis Study”

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#xRadiologyandImaging

Monte Carlo Dose Estimation of Absorbed Dose to the Hematopoietic Stem Cell Layer of the Bone Marrow Assuming Nonuniform Distribution Around the Vascular Endothelium of the Bone Marrow: Simulation and Analysis Study

Recent studies have shown that hematopoietic stem cells (HSCs) are concentrated around the endothelium of the sinusoidal capillaries. However, the current International Commission on Radiological Protection (ICRP) dosimetry model does not take into account the heterogeneity of the bone marrow tissue and stem cell distribution. If the location of the hematopoietic stem cell layer differs from previous assumptions, it is necessary to re-evaluate the dose. It is especially important for short-range alpha particles, the energy deposited in the target HSC layer can vary greatly depending on the distance from the source region.

Authors' Responses to Peer-Reviews

Authors’ Response to Peer Reviews of “Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models”

This is the author(s)’ responses to peer review reports related to MS ID#65417

Peer Reviews

Peer Review of “Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models”

This is a peer review report related to MS ID#65417

Peer Reviews

Peer Review of “Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models”

This is a peer review report related to MS ID#65417

#xPsychiatryandClinicalPsychology

Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models

Major Depressive Disorder (MDD) is a highly prevalent mental health condition with significant public health implications. Early detection is crucial for timely intervention, but current diagnostic methods often rely on subjective clinical assessments, leading to delayed or inaccurate diagnoses. Advances in neuroimaging and machine learning offer potential for objective and accurate early detection.

Peer Reviews

Peer Review of “Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models”

This is a peer review report related to MS ID#65417

#Live Reviewed Article Collection

Using Electrooculography and Electrodermal Activity During a Cold Pressor Test to Identify Physiological Biomarkers of State Anxiety: Feature-Based Algorithm Development and Validation Study

Anxiety has become a significant health concern affecting mental and physical well-being, with state anxiety—a transient emotional response—linked to adverse cardiovascular and long-term health outcomes. Traditional physiological monitoring lacks the contextual sensitivity needed to assess anxiety in real time. Electrooculography (EOG) and electrodermal activity (EDA), two bio signals measurable by wearables, offer promising avenues for identifying biomarkers of state anxiety (s-anxiety) in naturalistic environments.