The Structured Clinical Interview for DSM (SCID) is considered the gold standard assessment for accurate, reliable psychiatric diagnoses; however, because of its length, complexity, and training required, the SCID is rarely used outside of research.This paper aims to describe the development and initial validation of a Web-based, self-report screening instrument  based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the SCID-5-Clinician Version (CV) intended to make accurate, broad-based behavioral health diagnostic screening more accessible within clinical care.First, study staff drafted approximately 1200 self-report items representing individual granular symptoms in the diagnostic criteria for the 8 primary SCID-CV modules. An expert panel iteratively reviewed, critiqued, and revised items. The resulting items were iteratively administered and revised through 3 rounds of cognitive interviewing with community mental health center participants. In the first 2 rounds, the SCID was also administered to participants to directly compare their Likert self-report and SCID responses. A second expert panel evaluated the final pool of items from cognitive interviewing and criteria in the DSM-5 to construct the SAGE-SR, a computerized adaptive instrument that uses branching logic from a screener section to administer appropriate follow-up questions to refine the differential diagnoses. The SAGE-SR was administered to healthy controls and outpatient mental health clinic clients to assess test duration and test-retest reliability. Cutoff scores for screening into follow-up diagnostic sections and criteria for inclusion of diagnoses in the differential diagnosis were evaluated.The expert panel reduced the initial 1200 test items to 664 items that panel members agreed collectively represented the SCID items from the 8 targeted modules and DSM criteria for the covered diagnoses. These 664 items were iteratively submitted to 3 rounds of cognitive interviewing with 50 community mental health center participants; the expert panel reviewed session summaries and agreed on a final set of 661 clear and concise self-report items representing the desired criteria in the DSM-5. The SAGE-SR constructed from this item pool took an average of 14 min to complete in a nonclinical sample versus 24 min in a clinical sample. Responses to individual items can be combined to generate DSM criteria endorsements and differential diagnoses, as well as provide indices of individual symptom severity. Preliminary measures of test-retest reliability in a small, nonclinical sample were promising, with good to excellent reliability for screener items in 11 of 13 diagnostic screening modules (intraclass correlation coefficient [ICC] or kappa coefficients ranging from .60 to .90), with mania achieving fair test-retest reliability (ICC=.50) and other substance use endorsed too infrequently for analysis.The SAGE-SR is a computerized adaptive self-report instrument designed to provide rigorous differential diagnostic information to clinicians. The Structured Clinical Interview for DSM-5 (SCID-5) is currently accepted as the gold standard in psychiatric diagnosis and is regularly used in research settings where the accurate diagnosis of primary and comorbid disorders is required for the appropriate determination of study eligibility and assignment to a research condition -3. The STo streamline use of the SCID in research and to make it more accessible for use in clinical settings, clinician-administered, Web-based versions of the SCID instruments were developed including the NetSCID-5-Clinician Version (NetSCID-5-CV), which covers the same disorders as the SCID-5-CV paper version; the NetSCID-5-Research Version (NetSCID-5-RV), which covers the same diagnostic modules of the paper version of the SCID-5-RV; and the NetSCID-5-Personality Disorder (PD) Version, which covers the 10 DSM-5 PDs across Clusters A, B, and C, as well as other specified PD . In a vaPerhaps primarily because of the need for time-efficient diagnostic practices, routine clinical practice continues to rely predominantly on unstructured clinical interviews , despiteGiven the tension between the need for accurate diagnosis and the limited resource of clinician time in routine clinical practice, especially in primary care, one proposed solution is for patients to take a self-administered diagnostic screening questionnaire before their intake interview with a clinician. Results of this self-report screening measure could focus the clinician’s diagnostic expertise on a differential diagnosis during the face-to-face client interview ,12,21, aThe goal of this study was to develop a computerized adaptive self-report assessment based on the SCID and DSM-5 criteria  completeAs a first step, we authored a set of approximately 1200 unique self-report items that mirrored the questions in the SCID for DSM-IV and corresponded with criteria outlined in the DSM-IV-TR. In anticipation of the release of DSM-5, we also developed items intended to represent the few anticipated changes to diagnostic criteria occurring between DSM-IV and DSM-5 (prospective changes were made available online before the DSM-5’s publication date). TeleSage staff developed these items using a rigorous methodology first developed and successfully implemented in our previous instrument development work . Self-reDuring the item development process, staff members strove to keep items very simple by developing items that omitted lead phrases; omitted contingencies; included only a single concept; omitted idiomatic language; adhered to a 5-point Likert scale  wherever possible; used simple English language words; simple syntax, so as not to exceed a fifth grade reading level; and a consistent timeframe, where applicable, depending on the DSM-specified timeframe. These strategies were aimed at producing items that were easy to read, easy to understand, could be understood by non-native English speakers, and were amenable to direct translation into other languages. For SCID questions that were not straightforward , several simple self-report items were created. For example, to represent depression criteria 1A “In the last month, has there been a time when you were feeling hopeless, depressed, or down most of the day nearly every day,” 5 items were drafted: (1) I felt sad; (2) I felt depressed; (3) I felt irritable; (4) I felt hopeless; and a fifth item relating to “most of the day nearly every day.” We did not use the term “down” as it is idiomatic. We did create an item for hopelessness as it is in the DSM-5, although it is absent in DSM-IV-TR. We also created an item for irritability as it is a criterion for youth.Given that we intended to develop readily understandable, clear expressions of clinical symptoms in simple language, we acknowledge that the content of some of our items may overlap with other existing measures. Indeed, 3 of the 5 items just referenced to represent DSM-5 depression criteria 1A are also present in the Patient-Reported Outcomes Measurement Information System (PROMIS)  item banThe completed item pool was iteratively reviewed by a panel of 7 experts, including Michael First, MD, the primary author of the SCID, 2 other psychiatrists, and 4 psychologists with combined expertise in community mental health, SCID items and administration, and mental health item development. Items were presented in tables populated with the original wording of the DSM criteria, the corresponding SCID item wording, and the proposed self-report items. The expert panel rated the clarity  and correspondence with DSM-defined criteria  of each self-report item. Panel members also identified any missing concepts, offered suggestions for item rewrites, and discussed revised items by email and phone until consensus was reached on a final pool of items.The self-report item pool was divided into 2, with 6 to 7 diagnostic categories (approximately 4 SCID modules) in each half. After engaging in an institutional review board (IRB)-approved informed consent process, participants were given the half of the item pool that corresponded with their individual chart diagnosis. Both halves were then tested and revised over 3 rounds of cognitive interviewing (CI). After each round of CI, session summaries were analyzed by TeleSage staff. All items that posed difficulty for 20% or more of the participants were either omitted or rewritten for the next round of CI.verbal probes and verbal think alouds to determine the perceived meaning of survey questions [CI is a scientific technique that uses uestions . For thiirritable means to you?”) or ask, for example, behaviors . This process continued until the interviewer probed all items. Interviews were recorded on a digital recorder, and the cognitive interviewer took objective, not interpretive, notes during the session pertaining to the participant’s responses as well. After the interview, the cognitive interviewer listened to the audio file as needed and converted the notes from the session into a summary indicating items that were particularly difficult for the participant to answer or caused confusion, and items for which the participant’s interpretation did not reflect the item’s intent. By having participants describe all their thoughts out loud as they work their way through questions, it is possible to identify many of the potential problems that could affect a patient’s response in unintended ways. Using CI to hone questions should improve the likelihood that individual items will ultimately have good psychometric characteristics during quantitative validation.After reading an item aloud, participants marked their responses to the items. In addition, participants were instructed to circle any item they perceived as unclear or confusing as they completed the self-report assessment. Participants were also encouraged to think aloud while they answered each item. After participants completed the self-report items, the cognitive interviewer asked follow-up questions to further assess the reason the participant found each circled item unclear or confusing, while also confirming that the participant understood the meaning and intent of items that were not circled. For example, the cognitive interviewer would point out specific words in the question and ask for the meaning of that word  that the participants completed in the self-report item pool and included the participant’s specific chart diagnosis. To account for any learning effect, participants were randomized so that half of the participants took the SCID first and half completed the self-report items and CI first.An expert panel was convened for this next stage to convert the self-report item pool into the computerized adaptive Screening Assessment for Guiding Evaluation-Self-Report (SAGE-SR). The panel included 2 psychiatrists, 2 clinical psychologists, 1 physician, TeleSage staff members with backgrounds in psychology as well as expertise in mental health item development and SCID administration, and TeleSage staff computer programmers with expertise in computer-adaptive instrument development. To construct an easily understood instrument that could be administered in a time-efficient manner, the SAGE-SR was constructed to have an initial 65-question screener which covered the same 13 diagnostic categories for which items were drafted in stage I. Respondents would need to endorse screener items at a sufficient threshold (set by the expert panel) within each diagnosis to “screen in” and branch to the remaining self-report items necessary to determine if respondents meet criteria for that diagnosis to be included in the final differential diagnosis. Possible diagnoses that could be returned in this differential diagnosis are presented in The expert panel examined the newly released DSM-5 criteria for each of the diagnoses covered by the self-report items to determine the most appropriate items for inclusion on the screener using clinical judgment for best fit and criteria that were “essential” or central to each diagnosis. For example, to meet DSM-5 criteria for major depressive disorder, 5 or more of a series of 9 symptoms must be present during the same 2-week period and represent a change from previous functioning ; howeverOnce the initial instrument was constructed and programmed for Web-based administration , TeleSage staff members piloted and tested the Web-based administration of the SAGE-SR to identify any programming glitches. Following this process, healthy participants were recruited to take the SAGE-SR for the purpose of measuring administration time, assessing the appropriateness of the thresholds for screening and differential diagnosis set by the expert panel, identifying any remaining areas of confusion regarding item administration, and for preliminary quantitative validation. A subset of these participants returned for a second session within 1 week for the purpose of assessing test-retest reliability and how consistently participants screened into follow-up sections and received diagnoses for differential diagnostic consideration. All participants underwent a full informed consent process before engaging in any study procedures; all study and consent procedures were IRB-approved before the commencement of participant enrollment. After completing the SAGE-SR, participants were asked to provide feedback on whether they found any items confusing or unclear, whether they found items repetitive, whether the instructions were clear, what they thought about the length of the assessment, how well the progress bar and radio buttons on the device functioned, as well as any other comments they wanted to offer on what worked and what did not.In addition, an initial sample of respondents was recruited from an outpatient public sector mental health clinic based in Tennessee; initial results from this population were used primarily to examine administration time in a clinical population. Further recruitment at this site is ongoing and will be used for future quantitative validation in a clinical population.The expert panel iteratively rated, discussed, and rewrote items until each item scored on average 2.5 or higher for clarity and correspondence with its respective DSM symptom. A consensus was reached on a final pool of 664 items that expert panel members agreed were clear, easy to understand, and collectively represented the items from 8 SCID-IV-CV modules, thus covering the DSM-IV-TR criteria for each of 13 diagnostic categories  represented all 13 diagnostic categories in the self-report items (or 8 SCID-5-CV modules); participants ranged in age from 18 to 68 years (mean 39.9) and were 60% female (30/50), 86% white (43/50), 12% African American (6/50), and 2% Native American (1/50).For the first round of CI, a total of 18 participants responded to approximately half of the final item pool of 664 items. Thus, each self-report item was tested in 9 cognitive interviews in the first round. After each interview, a staff member reviewed the recording of the interview and the cognitive interviewer’s notes from the session singling out the following: (1) items that were understood by everyone and (2) items that were difficult for some participants to answer or which were not interpreted as expected. Overall, by the end of the first round of testing, of the original 664 items, 157 items tested very well, 2 items were omitted, 1 item was split into 2 items, and small modifications were made to many additional items to increase clarity. Sample revised items are presented in For the second round of CI, the 157 items that were understood very clearly were set aside, and 22 participants responded to approximately half of the remaining 506 unique items. Thus, each self-report item in the second round was tested in 11 more cognitive interviews. At the end of round 2, one more item was removed, and minor wording changes were made to several other items.In the third round of CI, the 157 items that worked well in the first round were added back to the item pool to reassess the entire item pool. In addition, 10 CI sessions were conducted, each on half of the modules as before, so that each item received an additional 5 cognitive interviews. There were virtually no misunderstandings in this third round; less than 1% of items were described as confusing by any participant, and there was only 1 instance in which 2 people misunderstood the same item (this item had a content duplicate and was omitted).On conclusion of all 3 rounds of cognitive interviews, the expert panel reviewed the session summaries and agreed on a final set of 661 items that they judged to be clear, concise, and that covered all 13 diagnostic categories. In general, the expert panel erred in keeping items that did well in CI, even if this made for some redundancy as expert panel members knew that the quantitative analysis would enable identification of the most predictive items and allow for future reduction of the item pool.In the first and second rounds of CI, all 40 participants were also given a clinician-administered SCID. This SCID contained the same modules (and diagnostic categories) that the participants completed in the self-report item pool that included their specific chart diagnosis. The responses to all self-report items were compared with the same participant’s responses to the corresponding SCID item(s) to see whether the self-report items would predict the SCID response for the same item or symptom in a real-life application. In all the cases tested, we found that we could identify 1 or more self-report items that predicted each SCID item endorsement. More specifically, where participants selected 4 “often” or 5 “always” on the SAGE-SR , the clinician independently endorsed the associated SCID item on the clinician-administered SCID.Eighty-four participants who denied having sought treatment or received medication for a mental illness in the past two years were recruited in Chapel Hill, NC. To recruit participants, study staff passed out flyers describing the study near the campus of a large university and made calls to campus service organizations to describe the study; some participants were recruited directly by study staff through these efforts and others called in to schedule appointments when they learned about the study secondhand as a result of these recruitment strategies.The resulting sample ranged in age from 18 to 34 (mean 20.2) years, was 74% female (62/84), 5% African American (4/84), 14% Asian (12/84), 7% Hispanic (6/84), and 68% white (57/84). An additional 5% of participants reported being of more than one race (4/84), and 1 participant declined to provide race information (1% or 1/84). All participants were asked to take the SAGE-SR using a tablet or laptop. A total of 42 participants returned within 7 days (mean 5.24 days) to take the SAGE-SR a second time. The 65-item screener covering 13 domains took an average of 7.3 min to administer to this nonclinical sample, with a standard deviation of 2.4 min. When the follow-up items were taken into consideration, the participants took an average 14 min to take the full SAGE-SR, with a standard deviation of 6.8 min. The Tennessee-based clinical sample was recruited via flyers posted in the clinic waiting room. This sample was comprised of 44 participants who ranged in age from 23 to 76 (mean 47.7) years and were 68% female (30/44). Race data was only available for 66% of this sample (29/44); of those that provided race information, the sample was 69% African American (20/29), 3% Asian (1/29), 14% Hispanic (4/29), 10% white (3/29), and 3% other (1/29). As expected, the screener took participants from the clinical sample longer to complete . The full SAGE-SR took on average 24 min to administer in the public sector clinical sample, with a standard deviation of 12.6 min. In contrast, in research populations, the full NetSCID-CV takes 56 min to administer with a standard deviation of 34 min.Feedback from the nonclinical sample indicated that participants found the SAGE-SR easy to navigate and complete and found nearly all items clear; one exception was the reference to “unwanted thoughts” in the section on obsessive-compulsive disorder, which participants indicated was too vague and confusing. To increase clarity, a definition was added to the display screen for this item: “Unwanted thoughts are thoughts that kept coming back to you even when you didn't want them to.” The only other feedback regarding clarity was regarding some lead prompts that were intended to prime participants to think of the particular period when they were experiencing the specific symptoms they endorsed during the screener to assess concurrence of the follow-up symptoms with the screener symptoms. For example, the lead prompt for the follow-up questions intended to explore generalized anxiety disorder initially read, “Because of my anxiety or worry,” but participants responded that reverse-scored questions did not work with this phrase; subsequently, the lead prompt phrase was changed to “During the time(s) when I felt anxious…” After this change, the related concurrency items were well understood.The expert panel convened to review the results from the healthy sample to verify the appropriateness of the screening and diagnostic cutoff criteria. Relatively, few of the nonclinical participants were expected to screen in to take the follow-up questions, and fewer still were expected to meet criteria for inclusion of a diagnosis within the differential. Any items that were endorsed above threshold more than 15% of the time were reviewed by the expert panel. Thresholds for follow-up item administration were intended to be more sensitive, whereas thresholds for diagnosis were intended to be more specific. Minor threshold modifications were made after this review. For example, as mentioned earlier, 3 self-report items represented depressed mood on the screener ; initially, the threshold for screening in to the follow-up depression items was endorsing any of these 3 items as happening at least “sometimes” in the last 30 days. This threshold worked well for the “I felt depressed” and “I felt hopeless” items but was overinclusive for the “I felt sad” item (too many participants screened in), so the screening threshold for that item was changed to at least “often.” In addition, when looking at the consistency with which participants screened in to receive depression follow-up questions, participants who only screened in at 1 time point did so by answering the “I felt sad” screener question at the “sometimes” threshold at that time point; therefore, increasing the threshold for this item also increased the consistency of the screening algorithm.As part of our preliminary look at quantitative validation, test-retest reliability estimates were calculated for the screening items that were always administered in each of the 13 diagnostic categories covered by the SAGE-SR in the nonclinical sample . For the 8 screening modules where the initial screener items included only Likert scale items, we first calculated summary scores within each module and then calculated the intraclass correlation coefficient (ICC) for these summary scores. The screening module for psychotic disorders includes Likert scale items that could indicate hallucinations as well as delusions, so summary scores and ICCs were also calculated for these subcategories of the psychotic disorders screening module. The ICC model used for these analyses was a 2-way mixed model of absolute agreement because the rater was the same at test and retest (self-report). This ICC model was also used to calculate test-retest reliability for the alcohol use disorders screening item, which was a continuous measure of the number of days the participant drank alcohol in the last 30 days. For the remaining 4 modules, categorical items (answered either yes or no) were used for screening purposes  ever experienced serious trauma, (2) witnessed serious trauma, (3) had a close friend or relative who was traumatized, or (4) whether they were repeatedly exposed to trauma through their work). For each of these 7 items, we calculated kappa coefficients as a measure of test-retest reliability; however, it was not possible to calculate a kappa coefficient for the diagnostic screening module for other substance use disorders, given that only 1 individual endorsed use in the past 30 days and did so at both time points, leaving empty cells and constants in the 2-way tables. The remaining test-retest reliability results are presented with 2-tailed 95% CIs (using bootstrap methods for the kappa coefficients) in In determining how to interpret these measures of reliability, we used 2 relevant resources: (1) the presented rationale for interpreting the reliability coefficients used by the researchers conducting the DSM-5 field trials ,33 and (The SAGE-SR was developed as a self-report alternative to the SCID and NetSCID-CV. The development process included the use of an expert panel to draft and iteratively review items as well as review the results of CI regarding item clarity to ensure that the criteria for 13 diagnostic categories commonly seen in clinical practice were well represented in a final pool of 661 well-understood self-report items. Using this item pool, we constructed the SAGE-SR as a 2-part computerized adaptive assessment with an initial 65-item screening instrument from which respondents who meet screening thresholds branch to follow-up questions to determine which diagnoses are returned for a clinician to consider for differential diagnosis.Initial validation efforts with a nonclinical sample yielded promising results; qualitative feedback from participants indicated items and instructions were well understood, whereas the tablet- or laptop-based administration was simple to complete and reasonable in length. Preliminary quantitative validation efforts suggest good consistency in screening algorithms across 2 administration times as well as good to excellent test-retest reliability across all but 1 diagnostic category for the screening items in our small nonclinical sample. The one domain for which test-retest reliability was weakest was mania or hypomania, which has also proven problematic for other researchers attempting to create self-report diagnostic screening assessments ,8,33. ThWe believe that the item development and qualitative validation procedures described above were very comprehensive, but although the initial quantitative feedback indicates that the SAGE-SR has great promise, the quantitative results are preliminary and based on a small nonclinical sample. Clearly, the results of this initial validation study will need further replication in a larger clinical sample. Data collection in clinical samples is ongoing, and more extensive quantitative validation will be presented once that work is complete. In addition, as noted earlier, the SCID is typically the gold standard against which the accuracy of most diagnostic assessments is measured. A cross-validation of the SAGE-SR’s differential diagnosis against the NetSCID-5-CV’s diagnostic algorithms is also currently underway.The SAGE-SR has an initial diagnostic screener that branches to groups of follow-up items to efficiently produce a differential diagnosis. Because the assessment is self-report, it should be possible to use the SAGE-SR in routine clinical care both in specialty behavioral health and in primary care settings. The SAGE-SR offers the promise of providing a rigorous differential diagnosis based on the SCID-5-CV and DSM-5 to a clinician before their meeting with the client so that their face-to-face time can be focused on clarifying that diagnosis in a manner that builds the rapport so inherent in the success of a therapeutic relationship. Indeed, an additional critique offered against the use of either the SCID-5 or other structured clinical interviews in clinical settings is that, despite the diagnostic rigor they provide, it is difficult to build rapport while adhering to a strict and standardized administration protocol .The SAGE-SR helps address the concerns in the field regarding the need for greater diagnostic rigor as well as assessment of possible comorbidities that might be missed in unstructured clinical interviews while doing so in a cost-effective and clinician time-effective manner. The SAGE-SR also fits into the health care movement exemplified by the personal health record in which patients are empowered to provide information to their clinicians and to participate more actively in determining what treatment is most appropriate for them. The SAGE-SR could help primary care practices satisfy the Affordable Care Act’s mandate for screening for depression and alcohol use, while doing so as part of a more comprehensive screen for common behavioral health issues.In addition to its utility for use in routine clinical care in primary care and specialty behavioral health settings, the SAGE-SR offers rigorous coverage of disorders and utility to clinical researchers as well as for epidemiological studies evaluating large number of participants where clinician-based interviewing is not feasible or is prohibitively expensive. The SAGE-SR covers the same diagnostic categories as the SCID-5-CV and all clinical diagnoses in these categories except for psychiatric diagnoses due to another medical condition and substance-induced diagnoses see . Thus, tGiven the move toward measurement-based care -37, the More information about the SAGE-SR is available on the Web  as is a


Microglia are the innate sentinels of the central nervous system (CNS) and are responsible for the homeostasis and immune defense of the CNS. Under the influence of the local environment and cell-cell interaction, microglia exhibit a multidimensional and context-dependent phenotypes that can be cytotoxic and neuroprotective. Recent studies suggest that microglia express multitudinous types of lectins, including galectins, Siglecs, mannose-binding lectins (MBLs) and other glycan binding proteins. Because most studies that examine lectins focus on the peripheral system, the functions of lectins have not been critically investigated in the CNS. In addition, the types of brain cells that contribute to the altered levels of lectins present in diseases are often unclear. In this review, we will discuss how galectins, Siglecs, selectins and MBLs contribute to the dynamic functions of microglia. The interacting ligands of these lectins are complex glycoconjugates, which consist of glycoproteins and glycolipids that are expressed on microglia or surrounding cells. The current understanding of the heterogeneity and functions of glycans in the brain is limited. Galectins are a group of pleotropic proteins that recognize both β-galactoside-containing glycans and non- β-galactoside-containing proteins. The function and regulation of galectins have been implicated in immunomodulation, neuroinflammation, apoptosis, phagocytosis and oxidative bursts. Most Siglecs are expressed at a low level on the plasma membrane and bind to sialic acid residues for immunosurveillance and cell-cell communication. Siglecs are classified based on their inhibitory and activatory downstream signaling properties. Inhibitory Siglecs negatively regulate microglia activation upon recognizing the intact sialic acid patterns and vice versa. MBLs are expressed upon infection in cytoplasm and can be secreted in order to recognize molecules containing terminal mannose as an innate immune defense machinery. Most importantly, multiple studies have reported dysregulation of lectins in neurological disorders. Here, we reviewed recent studies on microglial lectins and their functions in CNS health and disease, and suggest that these lectin families are novel, potent therapeutic targets for neurological diseases. The central nervous system (CNS) consists of neurons and glial cells such as microglia, astrocytes and oligodendrocytes. Microglia are the CNS counterpart of macrophages and differ from other types of glial cells in terms of their origin, morphology and functions  and other glycan binding proteins. Some of these lectins are expressed constitutively, while others are expressed upon stimulation. For instance, microglia generally do not express galectin-3, but its expression can be up-regulated by inflammatory mediators  and bind to β-galactosides with different specificities and affinities  model  of MS patients. Genetic removal of galectin-8 hastens the onset of the disease and worsens the progression. Daily treatment with galectin-8 significantly delays the disease progression of EAE. In particular, galectin-8 inhibits the activity of Th17-positive cells to induce inflammation by influencing cells toward apoptosis  is clinically categorized into three groups, namely, mild, moderate and severe. Its effects can be further divided into primary and secondary injuries. Primary injuries (such as direct damage to the brain parenchyma that may shear axons) are usually irreversible. On the other hand, secondary injuries are the detrimental effects resulting from the primary injury  with a significant increase in microglial activation, along with higher inflammatory response and oxidative damage  aggregates. Accumulated evidence suggest that neurons can release α-Syn to neighboring astrocytes and microglia  are located in the plasma membrane outer leaflet and the luminal side of the intracellular vesicles. Cytosolic galectins  therefore have no access to β-galactosides. In a neuronal model (SY5Y cells), treatments with α-Syn trigger the formation of galectin-3 puncta because the endocytosed α-Syn cannot be effectively degraded and triggers cellular stress , leading to vesicle rupture  represent the best-characterized subgroup of I-type lectins, which contain the immunoglobulin domain that recognizes structural diversity of carbohydrates , a disease that is characterized by lysosomal storage dysfunction, neurodegeneration and early death, the expression of Siglec-1 is significantly up-regulated. These Siglec-1-expressing microglia serve as antigen-presenting cells (APCs) to interact with CD8+ T cells and promote the pathology of the disease. In Siglec-1-deficient CLN mice, microglia express significantly lower inflammatory cytokines  and higher level of anti-inflammatory cytokines . In addition, Siglec-1-deficient CLN mice exhibit reduced axonal degeneration and longer lifespan, supporting a harmful role of Siglec-1  are markedly reduced. Similarly, primary microglia derived from CD33 KO mice showed greater uptake of Aβ than the control cells, while the rates of Aβ degradation are unaffected. Moreover, the KO of CD33 in the APP/PS1 mouse model significantly reduces Aβ plaques in the cortex and hippocampus , while they are absent in human monocytes  that can distinguish microglia from monocytes and macrophages. The level of Siglec-H is also up-regulated in the spinal cord of an ALS mouse model , which is also present in many proteins that do not recognize or bind to any glycan. C-type lectins and protein with CTLD are found in all organism and can be categorized to at least 17 groups based on their domain architecture, such as selectins, collectins, etc. The classification of C-type lectins and their roles in the peripheral immunity have been reviewed extensively elsewhere . Although this development is encouraging, the ability of TD139 to cross the BBB is poor and therefore limits its application to neurological diseases with abnormal up-regulation of Gal3. Further modification of TD139 to increase its permeability across the BBB may pave the way for the development of therapeutic intervention for brain diseases.The concept of lectin-based therapeutics deploys the specific targets of lectins or the delivery of drugs that interacts with lectins to the target site. For example, Galectin-3 has been implicated in numerous neurological disorders and diseases in the peripheral nervous system. Pharmacological suppression of galectin-3 appears to be a promising approach. Specifically, TD139 that binds to galectin-3 and inhibit its functions have passed the clinical phase Ib/IIa trials for treating idiopathic pulmonary fibrosis  N-acetylneuraminic acid effectively target Siglec-7 and Siglec-9 to exert anti-inflammatory effects of macrophages in a human Collectively, emerging studies have shown that modulating the lectins activity via direct interference in the lectin itself or the receptor-ligand interaction in CNS is a promising area for further exploration. Currently, treatment of neurological disorders using a lectin-based approach has not been envisaged, likely owing to an incomplete understanding of the roles of lectins in the CNS and the limited ability of drugs to cross the BBB. A greater understanding of microglial lectins may facilitate the development of novel therapeutic interventions for patients with neurological disorders.JJS designed and wrote the manuscript. YC designed and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lumbar spinal canal stenosis (LSCS) is one of the most common spinal disorders in elderly people, with the number of LSCS patients increasing due to the aging of the population. The ligamentum flavum (LF) is a spinal ligament located in the interior of the vertebral canal, and hypertrophy of the LF, which causes the direct compression of the nerve roots and/or cauda equine, is a major cause of LSCS. Although there have been previous studies on LF hypertrophy, its pathomechanism remains unclear. The purpose of this study is to establish a relevant mouse model of LF hypertrophy and to examine disease-related factors. First, we focused on mechanical stress and developed a loading device for applying consecutive mechanical flexion-extension stress to the mouse LF. After 12 weeks of mechanical stress loading, we found that the LF thickness in the stress group was significantly increased in comparison to the control group. In addition, there were significant increases in the area of collagen fibers, the number of LF cells, and the gene expression of several fibrosis-related factors. However, in this mecnanical stress model, there was no macrophage infiltration, angiogenesis, or increase in the expression of transforming growth factor-β1 (TGF-β1), which are characteristic features of LF hypertrophy in LSCS patients. We therefore examined the influence of infiltrating macrophages on LF hypertrophy. After inducing macrophage infiltration by micro-injury to the mouse LF, we found excessive collagen synthesis in the injured site with the increased TGF-β1 expression at 2 weeks after injury, and further confirmed LF hypertrophy at 6 weeks after injury. Our findings demonstrate that mechanical stress is a causative factor for LF hypertrophy and strongly suggest the importance of macrophage infiltration in the progression of LF hypertrophy via the stimulation of collagen production. Lumbar spinal canal stenosis (LSCS) is a common spinal disorder in elder people. There are approximately 250,000 to 500,000 LSCS patients in the United States, with the number increasing due to the aging of the population . LSCS caIn previous studies, only human samples have been used due to the lack of a relevant animal model. A limitation of these studies was the difficulty in observing the gradual process of LF hypertrophy because the hypertrophied LF obtained from LSCS patients already showed advanced histological changes; the loss of elastic fibers and an excessive accumulation of collagen fibers ,5. Molecin vivo studies directly demonstrating that mechanical stress induces LF hypertrophy.Various possible factors for LF hypertrophy have been considered, such as age, activity level, genetic components, and mechanical stress –14. AmonIn this study, we established a LF hypertrophy mouse model using a novel loading device and examined the influence of consecutive mechanical stress on LF hypertrophy. In addition, we induced macrophage infiltration into the mouse LF by applying micro-injury and examined the pathological role of macrophages in LF hypertrophy.Eight-week-old female C57BL/6 wild-type mice and CAG-EGFP transgenic mice were used in this study . All mice were housed in a temperature- and humidity-controlled environment with a 12 h light–dark cycle. In all animal experiments, the mice were anesthetized intraperitoneally with an anesthetic mixture  every hour in consideration of the anesthetic duration . The aniAfter the mice were transcardially fixed with 4% paraformaldehyde, the lumbar spine was removed and immersed in the same fixative. The spine was decalcified in ethylenediaminetetraacetic acid solution and dehydrated in sucrose solution. The sample was then embedded into OCT compound, frozen in liquid nitrogen, and cut into 10-μm sections on a cryostat. The sections were subjected to hematoxylin-eosin (HE) and Elastica-van Gieson (EVG) staining. For immunostaining, the sections were stained with primary antibodies against Iba1 , laminin , collagen type 1 alpha 1 , and 5-bromo-20-deoxyuridine . Then, the sections were incubated with Alexa Fluor-conjugated secondary antibodies . Nuclear counterstaining was performed using Hoechst 33342 . For BrdU detection, the sections were pre-treated with 2N-HCl for 30 min at 37°C.7 bone-marrow cells prepared from CAG-EGFP mice by flushing the femurs and tibias were transplanted into irradiated wild-type recipient mice as previously described [To examine bone-marrow-derived cell (BMDC) infiltration into the LF under consecutive mechanical stress, 1 × 10escribed . After cTo quantify the number of proliferating cells by consecutive mechanical stress, BrdU (100 μg/g body weight) was intraperitoneally administered daily during the loading period in the two groups.To induce macrophage infiltration, after peeling the lumbar paraspinal muscles and exposing the mouse LF, we applied micro-injury to the LF at the dorsal side with a sharp 30-gauge needle tip. At 1, 2, and 6 weeks after micro-injury, histological and gene expression analyses were performed. Sham surgery was performed on the controls.Human LF was obtained at surgery from 20 LSCS patients  and 10 lumbar disc herniation (LDH) patients . Hypertrophied LF from LSCS patients was then divided into two groups: mild (<4 mm) and severe (>4 mm) hypertrophy (n = 10 per group).Human LF sections were subjected to HE and EVG staining. All procedures were approved by the Kyushu University Institutional Review Board (25–126) and the analyses of the sample were performed after obtaining written informed consent from each patient.All radiographs of the mouse spine were scanned with micro-computed tomography (CT) . For histological and immunohistochemical analyses, images were obtained using a BZ-9000 digital microscope . The thickness of human LF was measured at the facet joint level on axial T1-weighed magnetic resonance imaging (MRI) as previously described .The thicTotal RNA was isolated from LF cells using the RNeasy Mini Kit  and cDNA was synthesized from the total RNA using a PrimeScript reverse transcriptase  according to the manufacturer’s instructions. Quantitative RT-PCR was performed using 20 μl reaction mixture with primers specific to the genes of interest  and SYBRp < 0.05. The data were presented as the mean ± SEM. All statistical analyses were carried out using the JMP software program .Wilcoxon’s rank sum test was used to compare the medians of the data between two groups for the area, width, and thickness of the LF, the area of the collagen and elastic fibers, the qRT-PCR results, and the cell count in the mouse LF. To analyze the differences among three groups in the ratio of elastic fibers to collagen fibers and the cell count in human LF, ANOVA with the Tukey-Kramer post hoc test was performed. Statistical significance was set at First, to evaluate whether the mouse was a feasible experimental animal model for studying LF hypertrophy, we performed histological analyses of the mice lumbar spine. In the axial sections of the spine, HE staining showed that the mouse LF was located between the dural tube and facet joints . In the To establish a LF hypertrophy mouse model, we initially developed a loading device by which the mouse LF was subjected to consecutive mechanical stress for the present experiment . The devTo examine whether mechanical stress indeed induced LF hypertrophy, we compared the axial cross-sectional area of the mouse LF with/without mechanical stress loading . We found the sectional area in the stress group to be about 1.5-fold that in the control group on EVG staining after 12-week mechanical stress . AlthougWe then investigated the effect of mechanical stress on the extracellular matrix (ECM). Previous observations in human samples demonstrated that the major ECM component was elastic fibers, while the minor component was collagen fibers in non-hypertrophied LF, whereas the ratio of collagen fibers was markedly increased in hypertrophied LF ,5. SimilWe next evaluated the severity of LF hypertrophy in the mouse model in comparison to human samples. Non-hypertrophied LF of human showed a dense and regular bundle of elastic fibers, whereas severely hypertrophied LF showed thin, irregular, and fragmented elastic fibers in EVG staining . AdditioIn addition to these ECM component changes, we examined the cellular distribution changes using human and our mouse samples. There were few cells in human non-hypertrophied LF, whereas a significantly increased number of cells was observed in human hypertrophied LF . Also inTo examine the influence of mechanical stress on the activation of fibrosis-related factors in LF cells, we evaluated the gene expression of inflammatory cytokines, growth factors, and angiogenesis-related factors in the mouse model. Quantitative RT-PCR demonstrated that the gene expression of collagens, tumor necrosis factor-α, interleukin-1β, and interleukin-6 were significantly higher in the stress group than in the control group . A signiIn contrast to severely hypertrophied LF of LSCS patients, our mouse model by mechanical stress demonstrated no infiltrating macrophages, increase in TGF-β1 expression, or angiogenesis Figs . We therIn this study, by applying consecutive mechanical bending stress to the mouse LF, we demonstrated that mechanical stress was one of the direct causes of LF hypertrophy. In the mouse hypertrophied LF, increased collagen fibers, proliferating cells, and the gene expression of several fibrosis-related factors were found. In addition, macrophage infiltration with angiogenesis was induced by applying micro-injury to the mouse LF. In this macrophage infiltration model, LF hypertrophy was observed along with the excessive expression of collagen and the increased expression of TGF-β1. These findings suggest that long-term mechanical stress and macrophage infiltration significantly influence the progression of LF hypertrophy.To date, previous studies have reported the common histological characteristics of human hypertrophied LF as follows: collagen deposition, elastic fiber fragmentation, and calcification ,23; inflIn fibrotic diseases of several organs, TGF-β1 has been reported to be an important disease-related factor for collagen production ,27. The Another characteristic of human hypertrophied LF is angiogenesis. While the normal LF is non-vascularized, marked angiogenesis was observed in the area of collagen accumulation in the severely hypertrophied LF . SeveralThere are several limitations associated with the present study. We did not establish a LF hypertrophy mouse model showing advanced histological changes only by mechanical stress. Although we tried to investigate the influence of the combination of mechanical stress and micro-injury on LF hypertrophy, the combination unexpectedly caused an LF tear and the sample could not be analyzed. In addition to this limitation, our mechanical stress mouse model cannot be used to develop an LSCS model because the ratio of the LF to the dural tube was significantly smaller in mice than in humans. Nevertheless, we believe that each model we established in this study showed the pathological characteristics of human hypertrophied LF, at least in part, and is thus useful for a better understanding its pathogenesis.In conclusion, we demonstrated for the first time that consecutive mechanical stress directly brought about LF hypertrophy in a mouse model. In addition, macrophage infiltration following micro-injury was found to be associated with severe LF hypertrophy by stimulating angiogenesis, collagen synthesis, and increased TGF-β1 production.


Walking is a complex cognitive motor task that is commonly completed while performing another task such as talking or making decisions. Gait assessments performed under normal and “dual-task” walking conditions thus provide important insights into health. Such assessments, however, are limited primarily to laboratory-based settings.The objective of our study was to create and test a smartphone-based assessment of normal and dual-task walking for use in nonlaboratory settings.We created an iPhone app that used the phone’s motion sensors to record movements during walking under normal conditions and while performing a serial-subtraction dual task, with the phone placed in the user’s pants pocket. The app provided the user with multimedia instructions before and during the assessment. Acquired data were automatically uploaded to a cloud-based server for offline analyses. A total of 14 healthy adults completed 2 laboratory visits separated by 1 week. On each visit, they used the app to complete three 45-second trials each of normal and dual-task walking. Kinematic data were collected with the app and a gold-standard–instrumented GAITRite mat. Participants also used the app to complete normal and dual-task walking trials within their homes on 3 separate days. Within laboratory-based trials, GAITRite-derived heel strikes and toe-offs of the phone-side leg aligned with smartphone acceleration extrema, following filtering and rotation to the earth coordinate system. We derived stride times—a clinically meaningful metric of locomotor control—from GAITRite and app data, for all strides occurring over the GAITRite mat. We calculated stride times and the dual-task cost to the average stride time  from both measurement devices. We calculated similar metrics from home-based app data. For these trials, periods of potential turning were identified via custom-developed algorithms and omitted from stride-time analyses.P<.001, r2=.98). These correlations were independent of walking condition and pocket tightness. App- and GAITRite-derived stride-time dual-task costs were also highly correlated . The error of app-derived stride times  was unaffected by the magnitude of stride time, walking condition, or pocket tightness. For both normal and dual-task trials, average stride times derived from app walking trials demonstrated excellent test-retest reliability within and between both laboratory and home-based assessments (intraclass correlation coefficient range .82-.94).Across all detected strides in the laboratory, stride times derived from the app and GAITRite mat were highly correlated (The iPhone app we created enabled valid and reliable assessment of stride timing—with the smartphone in the pocket—during both normal and dual-task walking and within both laboratory and nonlaboratory environments. Additional work is warranted to expand the functionality of this tool to older adults and other patient populations. Walking is central to many activities of daily living and is most typically completed while simultaneously performing unrelated cognitive tasks, for example, talking, reading signs, or making decisions. Even in healthy adults, such dual tasking reduces gait speed, prolongs stride time, and increases stride-to-stride movement variability . These pGait assessments are typically completed within clinical or laboratory settings. They are thus inaccessible to those living in remote settings and do not lend themselves well to high-frequency monitoring. Moreover, clinical assessments entail qualitative evaluation, are predisposed to subjective bias, and are often insensitive to subtle gait disturbances -9. LaborSmartphones contain a 3-dimensional accelerometer, a 3-dimensional gyroscope, and a digital compass that are similar in sensitivity to research-grade biomechanical instrumentation. The smartphone, when secured to an individual’s lower back or sternum as they walk, can detect gait events such as heel strikes , as wellIn collaboration with Sage Bionetworks  and supported by the Football Players Health Study at Harvard University, the objective of this study was to create an iPhone-based app enabling the administration of a standardized gait assessment, under both normal and dual-task conditions, within nonlaboratory settings. The app was designed to provide multimedia instructions to the user, acquire data with the phone placed in the user’s pocket, and derive stride times from bouts of walking. We chose stride time because it can be directly derived from gait events , is closely linked to gait speed , and hasThe app was designed to recreate a common laboratory-based dual-task gait assessment, namely, evaluation of walking under normal conditions and again while verbalizing serial subtractions of 3 from a random number between 200 and 999 ,20. The We designed walking assessments to include one 45-second trial of normal walking and one 45-second trial of dual-task walking. Trial start and end cues triggered acquisition of accelerometer, gyroscope, and magnetometer data, which were stored on the phone’s internal storage capacity. Following each assessment, the participant was prompted to answer a multiple-choice question (see following section) presented in text format on the smartphone screen. Kinematic and questionnaire data were then automatically uploaded via Wi–Fi to a remote, cloud-based data server for offline analyses.We recruited men and women aged 18 to 35 years via local advertisement. Exclusion criteria were an inability to walk unassisted; self-report of major disease, such as stroke, Parkinson disease, diabetes mellitus, or cardiovascular disease; history or presence of ulceration, amputation, or other painful symptoms in the lower extremities; drug or alcohol abuse; and hospitalization within the past 6 months. Interested and eligible individuals provided written informed consent as approved by the Hebrew SeniorLife Institutional Review Board .Participants completed 2 laboratory visits separated by 1 week. They completed the same assessments of locomotor control within each visit, during which data were simultaneously collected via the app and a 14-foot instrumented GAITRite mat . Participants additionally used the app to complete walking assessments within their homes on 3 separate days, in between their 2 laboratory visits. No instructions were provided regarding time of day for home assessment completion.Participants completed 2 laboratory visits separated by at least one week. We instructed them to wear comfortable shoes and pants or shorts with front pockets for each visit. The same procedures were completed on each visit to enable testing of between-visit test-retest reliability. Within each visit, participants completed the app walking assessment 3 separate times, such that they completed 3 pairs of normal walking and dual-task walking trials. Trial order was randomized with each pair.Within the laboratory, each walking trial was completed around an oval-shaped, 24-m indoor track. We placed the GAITRite mat along one long side of the track. Each trial began with participants standing just behind the beginning of the mat to ensure that the first footfall of each trial was captured by the mat. Participants used the app instructions to initiate and complete each trial.After all trials, the app prompted participants to use the iPhone touch screen to answer the following multiple-choice question: “How tight is the pocket in which you placed the phone? .” We did this to study the effects of this variable on the ability to collect valid and reliable data over time. The questionnaire was incorporated into the app using the SageBridge online portal (Sage Bionetworks).We asked participants to use the app to complete a walking assessment  at home on 3 separate days in between their laboratory visits. The app provided the same instructions to the participant as during the laboratory visit. Additionally, the app instructed participants to complete the walk in a quiet room or hallway, and to walk continuously throughout the trial, making turns if and when needed. On completion of both trials, participants were prompted to answer the same multiple-choice question regarding pocket tightness as described in the laboratory assessment.The app sampled kinematic data at a frequency of 100 Hz. Raw, 3-dimensional accelerometer and gyroscope time series were each transformed from the device coordinate system to an earth coordinate system using the quaternion rotation matrix. Following this rotation, the z-axis formed a line between the center of the earth and the phone, and was thus approximately vertical to the ground see , part B.We defined stride time as the time elapsed between 2 consecutive heel strikes of the same foot. We calculated it by determining the number of data points between 2 consecutive trough nadirs following relatively high peaks, and then dividing by the sampling frequency of 100 Hz. For all strides that took place on the GAITRite mat, we calculated stride times from both gait mat and app data and used these for analyses.Walking trials completed at home likely included variable amounts of turning. Turning, while itself an important functional measure, alters stride timing . We therThe total angular distance traveled in 1 direction can be calculated by integrating the angular velocity time series between 2 consecutive zero crossings . A 180° turn of the phone’s gyroscope would thus equal 3.14 radians . The AUC related to the 180° turn depicted in P<.05.We performed the following analyses with JMP Pro 13 (SAS Institute) and R version 3.3.1 (R Foundation). We set the significance level for all tests to We examined the validity of app-derived stride times by first assessing their agreement with corresponding stride times derived from the GAITRite mat using a Passing-Bablok orthogonal regression model, an appropriate approach for comparing methods while acknowledging measurement error . Models For each individual stride that occurred on the GAITRite mat, we then calculated the magnitude of error between its stride time as calculated by the app and the GAITRite mat. We produced a Bland-Altman plot of this error to visualize this error as a function of stride time . We used 2-way ANOVA to determine whether the magnitude of error, at the individual stride level, was influenced by task condition or pocket tightness.We examined the test-retest reliability of the app assessment by computing several intraclass correlation coefficients (ICCs). We calculated ICCs separately for normal and dual-task walking trials, for each of the following 4 conditions: (1) across trials within each laboratory assessment, (2) across trials over the 3 home assessments, (3) between the 2 laboratory visits, and (4) between laboratory and home assessments. For conditions 1 and 2, the unit of interest was the average stride time derived from each trial . For conditions 3 and 4, the unit of interest was the average stride time, averaged across all trials of the same condition . We took ICC values greater than .80 to reflect excellent test-retest reliability.P<.05.We used Pearson correlations to examine relationships between average stride times and participant height and body mass. We used 2-way ANOVAs to examine the effects of walking condition , setting , and their interaction on average stride time. Significance level was set to We recruited 14 healthy participants aged 22 to 35 years . All 14 participants completed both laboratory visits and all 3 home assessments. Across all 69 recorded assessments, 10 were completed with self-report of loose-fitting pockets, 24 with tight-fitting pockets, and 35 with pockets of medium tightness. For the 41 assessments completed at home, 11 were conducted in the morning, 13 in the afternoon, and 17 in the evening. Across participants, the average day-to-day variation in the range of timing of the 3 home assessments was 4.0 (SD 4.0) hours. The average number of strides detected in each 45-second home-based trial, after removal of turns, was 21.6 (SD 6.4) (range 13-28 strides).P<.001, r2=.98; For each detected stride across all participants and laboratory trials, stride times derived from the app demonstrated excellent validity as compared with the GAITRite mat. Orthogonal regression analysis revealed that stride times derived from the app were highly correlated with those measured by the GAITRite mat  and was unaffected by either walking conditions  or pocket tightness .The average magnitude of error of individual app-derived stride times, as compared with the corresponding stride time derived from the gait mat, was 16.9 (SD 9.0) ms. Average stride times derived from each app trial—for both normal and dual-task walking—demonstrated excellent test-retest reliability across repeated trials within laboratory assessments, across trials between the 2 laboratory assessments separated by 1 week, and across home assessment days . In geneF1158=4.67, P=.03) when dual tasking  than when walking normally . Testing setting  did not affect average stride times .Average stride times were not significantly correlated with participant height or body mass. Stride times were longer (This study provides a proof-of-concept in healthy adults that a smartphone placed in the front pocket of one’s pants or shorts can provide multimedia instructions to the participant and accurately measure stride times during walking under different experimental conditions. The app can detect major turns and compute average stride times during forward walking with high test-retest reliability within a laboratory or home setting.Body-worn sensors, including those contained within smartphones, can be used to capture the kinematic properties of gait. Previous work has typically secured the smartphone or sensor tightly to the individual’s trunk ,22-24 orWalking in everyday life is frequently conducted while executing cognitive tasks. Serial subtraction is most typically used within laboratory dual-task paradigms because it is easily implemented, disrupts the gait of even healthy adults see , part A,The proposed method of identifying periods of walking that likely included turning, and subsequently removing these periods from the calculation of stride times, led to excellent test-retest reliability of average stride time. It is expected that the future development and application of more sophisticated turn identification algorithms will further improve test-retest reliability of this and other metrics by ensuring that strides included in subsequent analyses were not influenced by turning. Moreover, turning is critical to the navigation of one’s environment, and the kinematic characteristics of turning indeed provide important insight into the integrity of the locomotor control system -39. SuchThis study has provided evidence that stride timing—a clinically meaningful outcome of locomotor control—can be accurately and reliably derived from kinematic data acquired by the smartphone when placed in the pocket when walking. Future work is warranted to establish the capability of this app to derive other clinically meaningful metrics of locomotor control, such as walking speed, swing and stance timing, or stride-time variability ,40,41. F


MicroRNAs (miRNAs) have been reported to contribute to the pathophysiology of multiple sclerosis (MS), an inflammatory disorder of the central nervous system. Here, we propose a new consensus-based strategy to analyse and integrate miRNA and gene expression data in MS as well as other publically available data to gain a deeper understanding of the role of miRNAs in MS and to overcome the challenges posed by studies with limited patient sample sizes. We processed and analysed microarray datasets, and compared the expression of genes and miRNAs in the blood of MS patients and controls. We then used our consensus and integration approach to construct two molecular networks dysregulated in MS: a miRNA- and a gene-based network. We identified 18 differentially expressed (DE) miRNAs and 128 DE genes that may contribute to the regulatory alterations behind MS. The miRNAs were linked to immunological and neurological pathways, and we exposed let-7b-5p and miR-345-5p as promising blood-derived disease biomarkers in MS. The results suggest that DE miRNAs are more informative than DE genes in uncovering pathways potentially involved in MS. Our findings provide novel insights into the regulatory mechanisms and networks underlying MS. Multiple sclerosis (MS) is one of the most common neurological disorders in young adults and the aetiology of this chronic inflammatory disorder of the central nervous system (CNS) still remains largely unknown. Although many advances regarding MS treatments have been made, there is still no cure. MS is characterized by dysregulated immune mechanisms and seems to develop in genetically susceptible subjects as a result of environmental exposuresMature miRNAs are single-stranded endogenous RNAs approximately 22 nucleotides in length that have the ability to posttranscriptionally regulate target messenger RNAs (mRNAs). They bind to the 3′untranslated region of their target mRNAs and translationally repress them or allow for their deadenylation and consequent degradation. It has been shown that the expression of more than 60% of mammalian protein-coding genes is under the control of these small RNAs and that a single miRNA may regulate hundreds of mRNA targetset al. constructed a regulatory meta-network by hierarchically organizing the genomic binding information of 119 transcription-related factors derived from the ENCODE project and merging this information with other information, including miRNA regulationet al. constructed molecular networks from proteomic profiling data derived from MS brain lesions and analysed these networks using four different pathway analysis tools, thereby underlining the relevance of extracellular matrix-mediated focal adhesion and integrin signalling in the development of chronic MS lesionset al. investigated whole-blood gene expression data of MS patients using a variety of computational methods including transcription factor binding motif (TFBM) overrepresentation analysis and functional profiling, and uncovered a network of transcription factors (TFs) that potentially dysregulate several genes in MSet al. created a molecular network based on differentially coexpressed TFs and genes in peripheral blood mononuclear cells (PBMC) of MS patients and performed pathway enrichment analyses to discover regulatory relationships between TFs and target geneset al. assembled an interaction network of IFN-β-responsive miRNAs and genes using several miRNA target databaseset al. generated an interaction network between DE miRNAs and genes in T cells of IFN-β treated MS patients using computationally predicted miRNA targetset al. introduced an approach to construct molecular networks by integrating dysregulated miRNAs in MS, which were uncovered in various studies, and miRNA targets from target gene prediction databasesMuch effort has been devoted to integrating and analysing high-throughput expression and interaction data with the aim of understanding basic principles of human biology and disease. For instance, Gerstein Most of the aforementioned studies were conducted in small patient cohorts without technical replicates and independent validation33In order to obtain a list of miRNAs involved in MS, we preprocessed and analysed four miRNA microarray datasets , Fig. 1.We next determined validated and predicted protein-coding gene targets of the 18 DE miRNAs. Using miRTarBaseWe determined transcription factors (TFs) that regulate the 13 DE miRNAs and/or their targets. Using TransmiRUsing the above information, we assembled the miRNA-based network . Our miRWe next performed a functional analysis on all the nodes of the miRNA-based network using DAVIDWe also categorized the 16 enriched immunology- and neurology-related GO terms into four groups: 1) Innate immune and inflammatory responses; 2) Immune response and immune system development; 3) Immune cells and immune tissue development; and 4) Neuron development and plasticity. miR-125a-5p is present in all subnetworks created from these GO terms, suggesting that this miRNA is crucially implicated in various dysregulated pathways in MS. On the other hand, let-7g-5p, miR-19b-3p, miR-30a-5p, and miR-221-3p are mainly involved in subnetworks created from enriched GO terms corresponding to the second and third category. This indicates that these miRNAs are involved in modulating cells regarding autoimmunity and inflammation in MS as well as in affecting the immune response and immune system development. Additionally, miR-221-3p is associated with all the enriched neurology-related GO terms, providing confidence that this miRNA plays a role in influencing molecular processes relevant to MS. miR-450b-5p is also affiliated to neuron differentiation, neuron development, neuron projection development, and positive regulation of neurogenesis, but not to the regulation of long-term neuronal synaptic plasticity. This finding suggests that miR-450b-5p may be implicated in the early stages of CNS development instead of later stages. In the context of MS, this miRNA may be thus particularly important in the relapsing-remitting phase of the disease.−5) and the interleukin signalling pathway (FDR = 1.1 × 10−3). Taken together, these results strongly support the hypothesis that miRNAs are involved in key dysregulated immunological and neurological pathways in MS.Finally, a pathway enrichment analysis of the miRNA-based network nodes using PANTHERWe next carried out a FBL and feed forward loop (FFL) analysis since it has been shown that miRNAs participating in these types of loops act as regulatory switches giving rise to distinct cellular statesIn order to obtain a list of protein-coding genes relevant in MS, we preprocessed and analysed four microarray datasets . For simSimilar to the steps taken in constructing the miRNA-based network, we uncovered validated and predicted DE gene targets to be included in the gene-based network. For this purpose, we made use of TransmiR and revealed that AKT3 activates both miR-22-3p and miR-22-5p, two miRNAs that are not present in the miRNA-based network. We therefore added these interactions to the gene-based network. Furthermore, we searched DE genes that were also TFs in the HOCOMOCO databases in order to confidently ascertain their putative targets. DDIT3 was the only DE gene present in HOCOMOCO, and we identified 91 predicted targets of DDIT3. We included these 91 interactions in the gene-based network, and therefore identified a total of 93 interactions between DE genes and validated and predicted targets.We uncovered predicted TF-target interactions using the 128 DE genes and the 93 DE gene targets. We thereby determined 315 predicted TF and DE gene interaction pairs, and 233 potential interactions between TFs in HOCOMOCO and DE gene targets. Thirteen of the latter interactions were related to DDIT3, as described in the previous section. Thus, 220 additional interactions could be revealed. Using miR-22-3p and miR-22-5p, we further found 8 predicted TF-miRNA interaction pairs. In total, we gathered 543 interactions for the gene-based network.We next assembled the gene-based network using the 93 interactions between DE genes and their targets, the 315 interactions between TFs and DE genes, and the 228 interactions between TFs and DE gene targets. Therefore, the final gene-based network comprised a total of 636 interactions and 244 nodes . A subseA functional analysis using DAVID with the network nodes revealed six significantly enriched immunology-related terms . This finding suggests that the gene-based network does not reflect specific regulation events related to MS.We did not identify any FBLs, however we discovered the presence of 126 coherent FFLs. This demonstrates that the DE genes may be involved in regulatory loops influencing MS, however the gene-based network is less indicative of MS-associated processes compared to the miRNA-based network. We conclude that more insights regarding dysregulated pathways in MS can be gained by investigating dysregulated miRNAs instead of genes.The complex functions of miRNAs, especially in diseases, are still poorly understood. Due to the limited number of public miRNA microarray expression profiles in MS, it is still unclear, which miRNAs play a pivotal role in this chronic disease. In the present study, we made use of publicly available microarray data and databases with the purpose of identifying blood-derived miRNA and mRNA biomarkers as well as molecular interactions that clarify biochemical mechanisms behind MS. To this end, we created a miRNA- and a gene-based network. Our networks differ from previous studies in the literature in that they are based on a consensus of multiple microarray datasets. Based on our networks, we were able to identify pathways potentially involved in MS and generated a list of blood miRNA biomarkers.Because of the inaccessibility of the nervous system, most MS expression studies involve either post-mortem samples or readily obtainable tissue, in particular blood. In the search for biomarkers, the assumption is that the inflammatory and neurodegenerative processes in the CNS are reflected, at least in part, in peripheral blood cells. Thus, genetic variants altering the expression of MS-relevant miRNAs that are not cell-type specific may lead to changes in multiple cells, including blood cellsThe three microarray platforms associated to the miRNA datasets used in this study  each con+ B cells of systemic lupus erythematosus (SLE) patients+ cells in the blood37in vitro in macrophages and forms a negative FBL with let-7bWe identified two potential MS miRNA biomarkers, let-7b and miR-345, that were significantly upregulated in MS according to all four datasets analysed. miR-345 has also been shown to be overexpressed in CD19In our miRNA-based network, miR-125a, miR-221, miR-300, and miR-450b have 14, 18, 8, and 8 targets, respectively. These four miRNAs regulate more targets than the other nine miRNAs in the network in combination. This may be because the sheer amount of information concerning these miRNAs is greater compared to that of the other miRNAs, or because these miRNAs may have a more important role in MS. We did not observe any overlap between these 48 miRNA targets and the 128 DE genes used to construct the gene-based network. This lack of overlap may reflect the facts that miRNAs regulate their targets posttranscriptionally without necessarily degrading their target mRNAs, and that most genes have multiple regulators, and their expression levels are functions of multiple inputs. Furthermore, despite our careful selection, the microarray experiments that form the basis of our miRNA and gene networks were not all performed on the same cell populations. The up-regulation of a miRNA in, for instance, monocytes may not necessarily be strongly correlated with the expression profiles of its target genes in other PBMCs. Likewise, since the miRNA and gene expression datasets are not paired, but rather, truly independent samples, interindividual differences may potentially mask the largely fine-tuning regulatory effects of miRNAs. The availability of paired miRNA and mRNA expression datasets for large patient cohorts should provide additional insights. Ultimately, validating predicted regulatory mechanisms requires experiments with miRNA mimics/inhibitors.et al. revealed an upregulation of miR-125a in peripheral blood T cells of both IFN-β-treated and untreated MS patients compared to HCsWe provided evidence that certain MS-associated miRNAs are involved in neurological processes and may influence components of the immune system. We exposed that miR-125a, which was increased in expression in MS patients compared to HCs, is associated to 16 enriched immunology- and neurological-related GO terms . RecentlBesides miR-125a, we unveiled three other miRNAs highly connected to immune cells and immune tissue development: let-7g, miR-19b, and miR-30a . In accoOur analysis exposed higher levels of miR-221-3p in in the blood of MS patients compared to HCs and revealed that miR-221 was associated with GO terms concerning immune system development, immune response, neuron development, and neuron plasticity. We also showed that miR-221 was involved in many coherent FFLs. In cell type-specific studies, MS patients were found to exhibit higher levels of this miRNA in regulatory T cellsFinally, another miRNA that potentially participates in neurogenesis and neurodifferentiation is miR-450b. We found it to be downregulated in MS and it was associated with enriched neurology-related GO terms. However it has not yet been connected to neurological disorders. PTPRZ1, a miR-450b target, is expressed in remyeliating oligodendrocytes in MS lesionsCompared to our miRNA-based network, our gene-based network did not shed light on many potential regulatory events behind MS. Despite finding four candidate genes  that were consistently either up- or downregulated in the blood of MS patients in all four datasets, these DE genes were not associated to enriched immunology- or neurology-related terms. Although their gene products have been shown to be involved in neurology575861In conclusion, we presented a consensus-based method to analyse and integrate gene and miRNA expression data as well as other publically available data. Our results revealed that DE miRNAs are more informative than DE genes when uncovering potential molecular pathways involved in MS. We argued that, among others, let-7b-5p and miR-345-5p might be the most promising blood-derived miRNA biomarkers in MS.In this study, we emphasized on identifying candidate miRNA and gene biomarkers that are DE in multiple MS microarray datasets. For this purpose, we developed a consensus and database integration approach to construct a miRNA- and a gene-based disease-associated regulatory network. We downloaded publically available microarray datasets, containing raw or normalized data, together with the corresponding platform specifications from the Gene Expression Omnibus (GEO) database . We exclFor each dataset and each gene and miRNA, we performed t-tests comparing the data of HCs and MS patients. For each miRNA and each gene, we also calculated the fold-change, that is, the ratio of the average expression in MS patients versus the average expression in HCs. A miRNA or gene is upregulated if this ratio is greater than one, downregulated if smaller than one, and unchanged if equal to one. miRNAs that were DE with p-value ≤ 0.05 in at least 3 of the 4 miRNA datasets and that were consistently significantly up- or downregulated in MS were selected for the further functional and interaction analyses. For this study, we used the following two criteria to define consensus: 1) A transcript has to be differentially expressed in three out of the four experiments corresponding to the gene or miRNA datasets in We performed permutation tests in order to demonstrate that our selected DE miRNAs and genes are relevant in MS and that they are not the result of random selection. Firstly, for each miRNA dataset, we randomly selected a number of miRNAs equal to the number of actually DE miRNAs. We then determined the number of miRNAs that were common in at least 3 of the 4 miRNA datasets. We repeated this process 5000 times and, in this way, we calculated a p-value for our actually selected DE miRNAs without taking up- or downregulation into consideration. For a second permutation test, we took into account the direction of DE miRNAs and genes. Similar to the first test, we randomly selected a number of miRNAs corresponding to the number of actually DE miRNAs for each dataset. We then determined the number of miRNAs that were common in at least 3 of the 4 miRNA datasets and consistently expressed either at higher or at lower levels in the MS patient group compared with HCs. We repeated this process 5000 times to calculate a p-value for the actually selected DE miRNAs that were common in at least 3 datasets and always dysregulated in MS in the same direction. We correspondingly carried out these two tests for the selected DE genes to calculate respective p-values.Five databases comprising miRNA targets were used. Of these, miRTarBase (release 3.5) and TarBase (release 6.0) contain validated miRNA targets202223Validated TF-miRNA interactions and their regulation (activation or repression) were exported from the TransmiR database (release 1.2)6526In the miRNA-based network, we included DE miRNAs, their targets, TFs regulating these miRNAs and their targets as well as the type of interaction between these molecules. We assumed that all miRNAs repress their targets, unless otherwise indicated in TransmiR. miRNA target activation is possible but remains a rare eventIn order to assess which functionally related genes  are predominantly represented in either the miRNA- or gene-based network, we performed a functional annotation analysis using DAVID (release 6.7)We created subnetworks based on the enriched GO terms found using DAVID and the nodes in the miRNA-based network. From our complete miRNA-based network, we selected the genes associated to an enriched GO term as well as all their neighbouring nodes in order to associate miRNAs to specific GO terms. We also selected all edges between the genes and their first neighbouring nodes when creating the subnetworks.How to cite this article: Freiesleben, S. et al. Analysis of microRNA and Gene Expression Profiles in Multiple Sclerosis: Integrating Interaction Data to Uncover Regulatory Mechanisms. Sci. Rep. 6, 34512; doi: 10.1038/srep34512 (2016).


Smoking is an important risk factor for mental health-related problems. Numerous studies have supported a bi-directional association between cigarette smoking and depression. Despite the advances in understanding the comorbidity between both problems, the most effective psychological treatment that simultaneously targets smoking and depressive symptomatology remains unclear. The objective of this study is to assess the effectiveness of a cognitive-behavioral intervention for smoking cessation with components of behavioral activation for managing depressed mood.A single blind, three-arm, superiority randomized controlled trial is proposed. Participants will be smokers over 18 years old, who smoke at least 8 cigarettes per day. Participants will be randomized to one of three conditions, using a 2:2:1 allocation ratio: 1) standard cognitive-behavioral smoking cessation treatment; 2) standard cognitive-behavioral smoking cessation treatment plus behavioral activation; or 3) a three-month delayed treatment control group. The primary outcome measures will be biochemically verified point-prevalence abstinence (carbon monoxide in expired air) and significant change from baseline in depressive symptoms to the end of treatment, and at the 3-, 6-, and 12-month follow-up.This study aims to assess the efficacy of a cognitive-behavioral intervention with behavioral activation components for smoking cessation and depressive symptoms, compared to a standard cognitive-behavioral intervention to quit smoking. As the relation between depressive symptoms, even at subclinical levels, and quitting smoking difficulties is well known, we expect that such intervention will allow obtaining higher abstinence rates, lower relapse rates, and mood improvement.ClinicalTrials.gov: NCT02844595. Retrospectively registered 19th July, 2016. The study started in January 2016, and the recruitment is ongoing. Both smoking and depression are leading causes of disability, mortality, and morbidity worldwide –3. The fEvidence suggests that smokers from the general population with depressive symptoms or with a depressive disorder, compared to those without one, present higher levels of cigarette consumption, greater nicotine dependence, are more likely to suffer intense nicotine withdrawal symptoms and to fail when trying to quit smoking, have more difficulties to maintain abstinence, and have a higher risk of smoking relapse over time , 11–13. At the same time, studies conducted both in general and clinical populations have found that smoking cessation is related to mood improvement in people who are able to remain abstinent –17. In aThe U.S. Preventive Services Task Force (USPSTF) found strong evidence that behavioral interventions substantially improve achievement of tobacco cessation . DespiteIn relation to the efficacy of smoking cessation treatments aimed at smokers with depression, Gierisch and colleagues  conducteBehavioral activation (BA) may be considered as a parsimonious treatment option for depression, because it is less complex but just as effective as cognitive-behavioral therapy and anti-depressant medication , 27. MorThe central role of reinforcement in the BA approach makes this intervention a good option to be included in an intervention to quit smoking. Audrain-McGovern and colleagues  found thN = 68] examining whether a behavioral activation treatment for smoking cessation (BATS) could improve abstinence rates and depressive symptoms compared to a standard smoking cessation treatment. They found that the BATS group had greater likelihood of point prevalence abstinence and lower depressive symptoms at the end of the treatment and at 6-month follow-up, compared with the standard smoking cessation treatment group. Also, a recent case series study [In fact, BA integrated in smoking cessation intervention has shown preliminary efficacy for depressive symptoms and improving smoking cessation outcomes among adult smokers with depression. MacPherson and colleagues  carried es study  conducteTaken together, these previous findings suggest a potential benefit of adding BA components to a smoking cessation treatment. Through this intervention approach, exposure to positive reinforcing alternatives to smoking cigarettes would be increased, and the distress of the withdrawal syndrome could be reduced, resulting in a possible improvement of smoking abstinence rates and mood, even in smokers with lower depressive symptoms. Due to the novelty of this smoking cessation intervention, further research is needed with controlled trial designs, larger samples, and longer follow-up periods.The main aims of the present randomized controlled trial are: 1) to assess the efficacy (smoking abstinence rates) of a cognitive-behavioral smoking cessation treatment with elements from behavioral activation for managing depressed mood, compared with a standard smoking cessation treatment, at the end of treatment, and at 3-, 6-, and 12-month follow-ups; and 2) to assess whether the applied cognitive-behavioral smoking cessation treatment plus behavioral activation improves depressed mood, compared with a standard smoking cessation treatment, at the end of treatment and at 3-, 6-, and 12-month follow-ups.A three-arm, single blind superiority randomized controlled design is proposed to assess the effectiveness of a cognitive-behavioral intervention for smoking cessation with components of BA. The overall study design is summarized in Fig. Participants will be recruited through the media, posters in healthcare centers, word of mouth, or they will be referred to the unit by their primary care physician or other specialized services of the healthcare system. The different phases of the study  will be carried out at the Smoking Cessation and Addictive Disorders Unit of the University of Santiago de Compostela, Galicia (Spain). Before participants enroll in the study, informed consent for participation will be obtained.Sample selection will be carried out according to the following inclusion criteria: aged 18 or over; wishing to participate in the treatment program; providing written informed consent; and smoking 8 or more cigarettes per day. Exclusion criteria will be: a diagnosis of severe mental disorder (bipolar disorder and/or psychotic disorder); concurrent substance use disorder ; having participated in the same or similar treatment over the previous year or having received pharmacological smoking cessation treatment  over the previous year; presence of a high life-risk pathology that would require immediate individual intervention ; smoking tobacco products other than cigarettes ; or failing to attend the first treatment session.Two baseline assessment sessions will be carried out in a face-to-face interview. Researchers conducting such assessments will be blind to group allocation, which will occur subsequently. Due to the nature of the study, participants will be aware that they will be assigned to one of the three arms.The randomization will be conducted according to a computer generated allocation sequence (ratio: 2.2.1.): (1) standard cognitive-behavioral smoking cessation treatment; (2) standard cognitive-behavioral smoking cessation treatment plus behavioral activation; or (3) a 3-month delayed treatment control group. After that, both active treatments will be administered in eight weekly 60-min sessions.At the end of treatment, there will be a post-treatment assessment (during session 8) and face-to-face follow-ups at 3, 6, and 12 months  . It is mNicotine Dependence Syndrome Scale (NDSS) , 40. QueNicotine Withdrawal Scale Minnesota (MNWS) . This isStructured Clinical Interview, according to DSM-5 criteria for assessing tobacco use disorder.Screening Questionnaire Major Depressive Episode (MDE) . This isBeck Depression Inventory II (BDI-II) , 44. ThiHamilton Depression Rating Scale (HDRS) , 46. ThiThe Environmental Reward Observation Scale (EROS) , 48. ThiBehavioral Activation for Depression Scale (BADS) , 50. ThiRuminative Response Scale (RRS) , 52. TheUCLA Loneliness Scale (Version 3) , 55. ThiCarbon monoxide in expired air assessment. We will use the Micro + Smokerlyzer  to measure carbon monoxide (CO) in expired air so as to corroborate self-reported abstinence at the end of treatment and at follow-ups, as suggested in previous studies .Sociodemographic characteristics, smoking history, smoking cessation treatment history, depression history, and depression treatment history will be assessed using a face-to-face structured interview. In addition, the following instruments will be used  will conduct the assessment and intervention sessions.Both treatments have been manualized with the aim of training the therapist and improving the intervention implementation. Both of them include a detailed session-by-session protocol and follow-up procedures.All sessions will be video-recorded to supervise the therapist and to assess intervention fidelity. Study supervisors will assess fidelity through the random visualization of treatment sessions in both active arms.Point-prevalence abstinence defined according to Russell Standard (RS) criteria . ParticiSignificant change in depressive symptoms on the Beck Depression Inventory-II (BDI-II) from baseline to the end of treatment, and at 3-, 6-, and 12-month follow-up.Significant change in depressive symptoms on the Hamilton Depression Rating Scale (HDRS) from baseline to the end of treatment, and at 3-, 6-, and 12-month follow-up.Continuous abstinence: in accordance with the Russell Standard , we willReduction of cigarette consumption by 50% or more between baseline and each follow-up will be calculated by the mean number of cigarettes smoked at the end of treatment and in the 7 days prior to the 3-month follow-up. At 6-, and 12-month follow-ups, it will be calculated by the mean number of cigarettes smoked in the past 30 days.Significant change in the Environmental Reward Observation Scale (EROS) scores from baseline to the end of treatment, and at 3-, 6-, and 12-month follow-up.Significant change in the Behavioral Activation for Depression Scale (BADS) scores from baseline to the end of treatment, and at 3-, 6-, and 12-month follow-up.Data will be collected from participants both in paper and electronic format. To make anonymization possible, a unique identity code number for use on trial documents and electronic database will be assigned to each participant. Data collected on paper forms will be kept in a locked filing cabinet. Electronic data will be kept in password-protected computer folders at the Smoking Cessation and Addictive Disorders Unit (University of Santiago de Compostela). Only authorized trial staff will have access to trial documentation.www.clinicaltrials.gov). The participants will be provided with both oral and written information regarding the study prior to obtaining their informed consent.The Bioethics Committee of the University of Santiago de Compostela approved this study, which is registered with the international standard randomized controlled trial number NCT02844595  2010 statements , 59 and To our knowledge, this is the first randomized controlled trial conducted evaluating the efficacy of a cognitive-behavioral smoking cessation intervention (CBSCI) with components of Behavioral Activation (BA). As the relationship between depressive symptoms, even at subclinical levels, and difficulties in abstinence achievement and maintenance is well known, we expect that this intervention will allow obtaining higher abstinence rates, lower relapse rates, and mood improvement after the treatment and at long-term.This study has significant strengths: (1) to date, it will be the largest trial to address the clinical-effectiveness of BA integrated into a cognitive-behavioral smoking cessation treatment; (2) follow-up assessments will be carried out at 3-, 6- and 12-month post-treatment in both active groups (CBSCI vs. CBSCI + BA), which will provide an opportunity to evaluate the long-term treatment effects on abstinence rates and mood; and (3) participants will be included regardless of current depressive symptom level, which will allow us to investigate the influence of subclinical depressive symptomatology in treatment outcomes.We expect to find that an effective, brief, and flexible intervention for depression such as BA could be implemented as a part of a cognitive-behavioral treatment for smoking cessation in order to improve its efficacy. The findings obtained will have significant clinical implications. Because of the relevance of depressive symptoms in the smoking cessation process, this intervention could be a good option to address two problems that have a very high impact on people’s health and quality of life.The study started in January 2016, and the recruitment is ongoing.


We report on the fabrication of desired magneto-photonic devices by a low one-photon absorption (LOPA) direct laser writing (DLW) technique on a photocurable nanocomposite consisting of magnetite ( Nanomaterials with excellent optical, electronic, and magnetic properties are highly desirable for fabrication of functional photonic structures and devices. Various kinds of nanofillers, such as carbon nanostructures , metalliStructures made by magnetic nanocomposites are useful for a wide range of applications including data storage ,10, sensDirect laser writing (DLW) is a well-developed optical lithography technique allowing fabrication of multi-dimensional polymeric nanostructures ,23. In tIn this work, we demonstrated the use of LOPA-based DLW as a simple and robust technique for the fabrication of desired 1D, 2D, and 3D magneto-photonic structures with sub-wavelength size. We first elaborated a photocurable nanocomposite consisting of magnetic nanoparticles (MNPs) and a commercial SU-8 photoresist. The concentration of osensors , actuatoosensors .The photopatternable magneto-polymer nanocomposite consists of The data  and relet method ,33; howeDLW is an important and relevant technique to fabricate 2D and 3D structures and devices with arbitrary shapes at submicrometer resolution. In LOPA-based DLW technique ,34, a grIn the LOPA operation regime, the excitation power and exposure time are important parameters, which directly determine the characterization of fabricated structures. To study the behavior of the photopatternable magnetic nanocomposite, we first investigated the evolution of structure size as a function of laser power and writing speed. Series of submicropillars were fabricated on a film thickness of 2 For most applications of photonic structures, the small size is not only a key parameter but also the minimum distance between two features. We have therefore examined the quality of structures as a function of the structure lattice. Different magneto-photonic structures with a periodicity varying from 2 iodicity ,34. The iodicity . Thanks x- and y-directions is 2 The great advantage of the DLW method is that it allows one to realize any structure on demand. Based on the above investigations of structure size and periodicity, we have demonstrated the use of a LOPA-based DLW technique to pattern arbitrary magnetic structures by using the magneto-polymer nanocomposite. The fabrication parameters were chosen appropriately for each design. Exploiting reversible magnetic field-driven motion of magneto-photonic structures for remote actuation in biomedical applications has recently attracted a growing attention . With th sensors , actuato sensors , magneti sensors .Finally, we expect that it is possible to apply an external magnetic field with tunable magnitude and orientation at the position of the focusing spot during the fabrication process by using a using Helmholtz coils system, in order to realize a magneto-photonic structure with controllable magnetic response of individual feature. Examples of these structures include magnetic vortex, 3D magnetic spiral structures, etc. These magneto-photonic structures will be very interesting for applications in tunable photonic devices or data storage, etc.The copy see .In order to mix Firstly, the glass substrates were pretreated with sonication in acetone within 15 min to eliminate all the surface contamination, soaked in isopropanol to remove exceeding acetone and then rinsed in distilled water. These substrates were finally dried by a Nitrogen gas, followed by 1 min on a hot plate at The DLW experimental setup is illustrated in A regime , the samIn this work, we have successfully demonstrated the fabrication of magneto-photonic sub-microstructures on demand utilizing the LOPA-based DLW technique. By incorporating magnetite nanoparticles into SU-8 polymer matrix with different viscosities, we have found that SU-8 2005 with a viscosity of 45 cSt was the most appropriate photoresist for hosting MNPs. Different concentrations of