Atherosclerosis of the internal carotid artery (ICA) is an important cause of ischemic stroke. Artery-to-artery embolism is the major stroke mechanism in patients with atherosclerotic carotid disease. This study hypothesized that the atherosclerotic ICA geometry and plaque location would be associated with lesion pattern in patients with acute ischemic stroke.Ischemic stroke patients with symptomatic proximal ICA disease (> 50% diameter stenosis) were enrolled. The carotid plaque location was divided into high-apical and low-body types. The geometric parameters of the ICA (angles between arteries) were measured, and ischemic lesion patterns were classified according to the number, location, and size of the lesions. Factors associated with plaque location and lesion pattern, dichotomized by size, were investigated.p = 0.019) in patients with low-body than high-apical plaques. Low-body plaques were more frequently associated with small scattered or cortical lesions , whereas high-apical plaques were more frequently associated with large lesions having additional lesions . The presence of low-body plaques  was independently associated with the small lesion-only pattern.Of the 93 acute ischemic stroke patients enrolled, 31 had high-apical and 62 had low-body plaques. Hyperlipidemia was more prevalent and the common carotid artery (CCA)-ICA angle was wider (167.7 ± 10.4° vs 162.3 ± 9.8°, Low-body plaques are more frequently associated with small scattered lesions, whereas high-apical plaques are more frequently associated with large lesions having additional lesions. A wide CCA-ICA angle is associated with low-body plaque of the carotid artery. Atherosclerosis of the carotid artery is one of the major causes of ischemic stroke, with artery-to-artery embolism being the major stroke mechanism in patients with atherosclerosis of the carotid artery . ThromboAlthough atherosclerotic plaque of the carotid artery is associated with old age, male sex, and hyperlipidemia , these rThis study was a retrospective analysis of a prospectively collected database of acute ischemic stroke patients (within 7 days from stroke onset) between January 2013 and December 2017. Patients classified by the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) as having large artery atherosclerosis with symptomatic proximal internal carotid artery (pICA) disease were enrolled , 12.Patients were excluded if they had (1) embolic sources from the heart  or the aorta, (2) other uncommon etiologies such as dissection or moyamoya disease, (3) tandem stenotic lesions at the intracranial ICA and MCA or ACA, or (4) results on contrast enhanced magnetic resonance angiography (MRA) that did not allow assessment of geometric properties . The study protocol was approved by the institutional review board of Kyung Hee University Hospital, which waived the need for informed consent because of its retrospective nature.Demographic characteristics, vascular risk factors, concurrent medications, and baseline laboratory results were obtained from the prospectively acquired stroke database. Hyperlipidemia was defined by patient having past history of hyperlipidemia, using a lipid-lowering agent including statins or patient diagnosed hyperlipidemia at admission for LDL-cholesterol levels .On the day of admission, all patients underwent magnetic resonance imaging and MRA in the following sequence: DWI, fluid attenuated inversion recovery imaging, gradient echo imaging, T1- and T2-weighted imaging, and intracranial and extracranial contrast enhanced MRA.The patterns of infarction on DWI were classified according to the number, location, and maximum diameter of lesions , 15. LesThe presence and location of plaques and the degree of stenosis in the pICA were assessed on contrast enhanced MRA. Atherosclerotic plaques of the carotid artery were classified as being high-apical or low-body plaques. High-apical plaques were defined as plaques in the transitional zone of the bulb and the proximal cervical ICA segment, with or without the involvement of the body segment. Low-body plaques were defined as those in the transitional zone of the common carotid artery (CCA) and the bulb, and are located mainly in the lower body segment, with or without the involvement of the apical segment  the degree of stenosis measured by North American Symptomatic Carotid Endarterectomy Trial (NASCET) criteria; 2) the presence of ulcer, defined as a niche in the plaque surface > 2 mm in depth on contrast enhanced extracranial MRA ; 3) carot-tests, and Mann-Whitney U-tests were used as appropriate. In addition, clinical and imaging characteristics were compared according to different lesion patterns and location on DWI. Multivariable binary logistic regression analysis was performed to investigate the independent association between lesion patterns  and the type of plaque and the geometric properties of the carotid bifurcation. Odds ratios (ORs) were calculated, along with 95% confidence intervals (95% CIs). A p value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS 22.0 for Windows .Demographic characteristics, vascular risk factors, concurrent medications, laboratory findings, geometrical factors, and ischemic lesion patterns were compared in patients grouped by plaque location. Pearson chi-square tests, independent Of the 3061 patients with acute ischemic stroke between January 2013 and December 2017, 657 (21.5%) were classified as having large artery atherosclerosis according to the TOAST classification. Among the latter, 117 (17.8%) patients had symptomatic pICA disease. After excluding patients with complete occlusion of the carotid artery, those with no DWI or MRA data, and those with poor MRA quality, the final study population included 93 patients.The 93 patients included 75 (80.6%) men and 18 (19.4%) women of mean age 72.0 ± 8.3 years. Considering the location of the plaque, 43 (46.2%) patients showed atherosclerosis extending from the proximal to the distal area, but in most of the cases atherosclerosis was dominant in one area. Only 8 patients showed discrepancy between the two researchers. Finally, after a consensus meeting, 31 (33.3%) had high-apical and 62 (66.7%) had low-body plaque.p = 0.022). The CCA-ICA angle was significantly wider in patients with low-body than high-apical plaques  and narrower ICA-ECA angle  were independently associated with low-body plaques (Supplementary Table STable p = 0.040), whereas large lesions accompanied by additional lesions were more frequently associated with high-apical plaques (38.7% vs. 11.3%, p = 0.002; Table p = 0.032; Table Small scattered lesions were more frequently associated with low-body plaques (54.8% vs. 32.3%, The present study found that the CCA-ICA angle was wider in patients with low-body than high-apical plaques. These low-body plaques showed a greater association with small scattered lesions and were independently associated with having only small lesions of diameter < 15 mm. Patients with high-apical plaques had a narrower CCA-ICA angle and were more likely to have large ischemic lesions.In agreement with previous findings , the preOur results also showed that the lesion patterns on DWI in acute ischemic stroke patients with symptomatic pICA disease were associated with plaque location, but not with the geometry of the ICA or the presence of ulcer. Small scattered lesions were associated with low-body plaques of the ICA, whereas the presence of a large lesion with additional lesions was associated with high-apical plaques. Dichotomization of lesion patterns according to the presence or absence of a large lesion resulted in low-body plaques being independently associated with the small lesion-only pattern. In high-apical plaques, the bulb is proximal to the stenotic portion with a back flow causing fluid stagnation and thrombus formation, such as in the appendage of the left atrium , 24. ThiThe present study had several limitations. First, its retrospective nature may have caused a selection bias. Second, the number of patients was small, under-powering some of our results, especially the lesion patterns. Third, geometry was analyzed based on two-dimensional anteroposterior images of three-dimensionally reconstructed MRA images. Though three-dimensional analysis of the vascular geometry might strengthen our results, two-dimensional analysis is more practical. Fourth, the present study did not analyze the state of intracerebral hemodynamics, the activation of the collateral circulation, or the presence of microembolic signals. Finally, this study did not pathologically assess the composition and vulnerability of the plaques or the size and composition of the embolus resulting from each stroke mechanism. Despite these limitations, our results suggest the importance of vascular geometry in the development of acute stroke in patients with symptomatic pICA disease.The CCA-ICA angle may be a factor determining the location of atherosclerotic plaques of the carotid artery, probably altering hemodynamics. Differences in the locations of carotid plaques may explain, at least in part, inter-individual differences in the location of lesions in acute ischemic stroke in patients with symptomatic pICA disease.Additional file 1.Additional file 2: Table S1. Multivariable analysis of factors associated with low-body plaques. Table S2. Association independent factors with lesion pattern on diffusion-weighted imaging. Table S3. Association independent factors with lesion location including cortical, subcortical, and cortico-subcortical lesions.


A single, systemic dose of methotrexate, a DNA-synthesis (S phase) inhibitor, has been used since 1991 for outpatient treatment of women with stable EP. However, methotrexate has limited clinical and cost effectiveness, restricting its use to 25–30% of these women. There is an unmet need for better medical treatment for EP. Colony stimulating factor-1 (CSF-1) promotes placentation and creates a pro-inflammatory environment that is fundamental for the maintenance of a normal pregnancy. We hypothesised that CSF-1 is also involved in the placentation and maintenance of an EP. Herein, we demonstrate the immunolocalisation of the CSF-1 receptor (CSF-1R) as well as its ligand (CSF-1) in immortalised first trimester trophoblast cells. We show that a specific CSF-1R kinase inhibitor, GW2580, abolishes CSF-1 induced trophoblast cell proliferation and migration and can be cytotoxic. We then demonstrate the expression of CSF-1R and CSF-1 in the cytotrophoblast and syncytiotrophoblast within ectopic implantation sites from women with EP. Our data suggests that CSF-1 is involved in the survival and proliferation of trophoblast cells in EP. This suggests that pharmacological disruption of CSF-1/CSF-1R signaling axis could be the basis of a new therapeutic for EP. EP represent 1–2% of all pregnancies and are a leading cause of maternal morbidity and mortality in the first trimester of pregnancy3. Currently, intramuscular methotrexate (MTX), a chemotherapy drug that targets DNA synthesis of trophoblast cells, is the medical management approach most commonly used to resolve EP4. However, MTX does not immediately resolve the EP (with an average time to resolution 24–28 days), has a high failure rate (25–30%) and is associated with unpleasant side effects5. Moreover, MTX can only resolve selected EP that are small  and that have been diagnosed in women that are haemodynamically stable6. Consequently, laparoscopic surgical management with removal of the FT with the EP is regarded as the gold standard treatment7. However, surgery is not a feasible intervention in low resource settings, has a prolonged recovery time and a risk of surgical complications8. Additionally, it can have implications for fertility, notably if the contralateral tube is damaged10. There is an unmet clinical need for a less toxic, more efficacious and widely accessible EP treatment that leads to more rapid resolution and reduces the requirement for surgery.An ectopic pregnancy (EP) is defined as the implantation of a conceptus outside of the uterus, with > 98% of EPs implanting in the Fallopian tube (FT)11, supports growth and proliferation of extravillous trophoblasts12, and plays important role in the implantation of embryo in the uterus and subsequent placental growth13. This may also be the case in EP as increased macrophage density at the tubal ectopic implantation site has been demonstrated in comparison to the rest of the Fallopian tube14. GW2580 is a CSF-1R antagonist and has recently been used in murine models of epithelial ovarian cancer, Alzheimer’s disease, myocardial infarction and spinal cord injury and has been shown to cause reduced infiltration of macrophages and inhibition of cellular proliferation18. As CSF-1 activity is involved in supporting trophoblast growth we hypothesised that targeting CSF-1 receptor signalling via CSF-1R antagonism could be used as a treatment for EP.Colony stimulating factor-1 (CSF-1) is a glycoprotein which regulates proliferation, differentiation and survival of macrophagesHerein, we demonstrate the expression of CSF-1R as well as CSF-1 in immortalised human first trimester trophoblast cells. We then show that CSF-1R antagonism, using GW2580, can inhibit trophoblast cell proliferation, migration and survival in-vitro. Finally, we confirm the expression of CSF-1R and CSF-1 at human ectopic implantation sites.The immortalised first trimester trophoblast cell line, SW.71, expresses CSF-1 protein  at 18, 24 and 48 h. We found that differential results could be seen at the 18 h time-point only . The placenta relies heavily on CSF-1 signalling25 and is shown to be effective in targeting tumour invasion when used in isolation, and potentiates its efficacy when used in combination with indoximod therapy26. Recently, engineered bi-specific inhibitors have been developed that simultaneously target the unique combination of CSF-1 and integrins27, these dual-specific proteins could bind to and inhibit both CSF-1 and αvβ3 integrin and have shown superior therapeutic potential, as compared to monospecific protein therapeutics. Our recent published work has pointed a role of endometrial receptivity marker and cell adhesion protein, integrin β1 (ITGB1), in the embryo attachment within FT, as a result of past chlamydial infection28. We can speculate that CSF-1 and ITGB1 may function synergistically in promoting embryo implantation and growth within the FT. Taken together, our current and previous studies30 provide a rationale to develop strategies of using a combination therapy approach to treat EP. Since we have demonstrated a role of CSF-1 and ITGB1 and in EP, further investigations are warranted to develop such bi-specific inhibitors that could pave the way to discover more efficient and specific drugs to treat EP. We also believe that it would be of interest to investigate the differences in action of CSF1 on ectopic compared to eutopic pregnancies.GW2580 selectively inhibits the tyrosine kinase domain of CSF-1RIn conclusion, we have demonstrated that CSF-1/CSF-1R system is present at the ectopic embryo implantation site and that CSF-1 promotes trophoblast proliferation, migration and survival in-vitro. We suggest that targeting the CSF-1 system using receptor antagonists may have a novel therapeutic role in the medical management of EP. Targeted drug delivery systems, based on nanoparticles, may be additionally exploited as a new strategy for better therapeutic efficiency, along with reduction of side effects. Further in-vivo investigations are therefore warranted to decipher the role of CSF-1/CSF-1R in development of tubal EP and to determine the efficacy of CSF-1 antagonism for the treatment of EP.All experiments were performed in accordance with relevant guidelines and regulations.2 levels31.Swan.71 (RRID:CVCL_D855) (SW.71), an hTERT immortalised cell line of extravillous trophoblast cells from first trimester placenta, were maintained in Dulbecco’s Modified Eagle Medium  supplemented with 10% fetal bovine serum (FBS), 2mML-glutamine and 50ug/ml gentamycin. The cells were incubated at 37 °C in a humidified atmosphere with 5% COImmunocytochemistry (ICC) was performed to demonstrate that SW.71 cells express the CSF-1 and CSF-1R. SW.71 cells were cultured on positively charged coverslips in 6-well plates until ~ 70% confluent. They were fixed using 4% paraformaldehyde for 10 min followed by methanol on ice for 2 min. Blocking was carried out with BSA and normal goat serum for 1 h. CSF-1 and CSF-1R were immunolocalised using primary antibodies conjugated with Alexa Fluor 488 against CSF-1  and CSF-1R  overnight at 4 °C with blocking peptide as a negative control. The coverslips were stained with VECTASHIELD with DAPI  and visualised with a confocal microscope using Zen 2009 software.32 and CSF-1 ligand  was tested at 50–200 ng/mL33 time periods 24–48 h. Vehicle (DMSO) treated cells were used as the control. For the experiments reported the optimised concentration of CSF-1 was 100 ng/mL over 48 h and the concentration of GW2580 was 20–40 µM.The effect of GW2580 and CSF-1 ligand on proliferation of SW.71 cells, was analysed using CellTiter 96 Aqueous (MTS) kit  as per the manufacturers’ recommended protocol. To optimise concentrations of treatments GW2580  was examined at 5 µM-40 µMViability and cytotoxicity was measured with an ApoTox-Glo triplex assay  as per manufacturer’s protocol.https://www.cse-lab.ethz.ch/software/) in which the boundaries of the scratch are automatically delineated. TScratch software measures the percentage scratch remaining open comparing the two time points.To measure cell migration, a scratch assay was performed. SW.71 cells were seeded at a density of 150,000 per well in a 6-well plate. On the base of the plate, three vertical lines were carved under each well using a scalpel as reference points. Cells were serum starved overnight to synchronise all cell cycles, limiting the influence of proliferation, a scratch was then made horizontally across the confluent cell monolayer using a 200μL pipette tip. Culture media was replaced with increasing concentrations of GW2580, with and without 100 ng/mL CSF-1. The wells containing scratched cells were imaged at the reference points at the start of treatment and then imaged again following an optimised incubation period of 18 h with the treatments. The images were analysed using TScratch (version 1) software (This study was approved by the Scotland A Research Ethics Committee (LREC 04/S1103/20) and written informed consent was taken from all study participants. FT ectopic implantation sites (n = 5) were collected from women aged 18–45 years undergoing laparoscopic salpingectomy for the treatment of tubal EP. Biopsies were transported on ice to the laboratory, fixed in 10% neutral buffered formalin overnight at 4 °C, stored in 70% ethanol, and then wax embedded for further immunohistochemical analysis.34. Briefly, 5 μm sections of ectopic implantation site were deparaffinised in xylene, rehydrated in ethanol and processed for antigen retrieval using citrate buffer in a pressure cooker for 20 min. Sections were quenched for endogeneous peroxidase in 3% H2O2 and non-specific binding sites were blocked using horse serum. Sections were incubated with primary antibodies (1 in 500) against CSF-1  and CSF-1R  overnight at 4 °C. For control sections, isotype control immunogloblins were used at the same concentration. Positive immunostaining was revealed by application of goat anti-mouse peroxidase  secondary antibody followed by, signal amplification using tyramide signal amplification kit (PerkinElmer) and mounted with VECTASHIELD with Dapi (Vector Laboratories). Samples were analysed with a confocal microscope using ZEN 2009 software (https://www.softpedia.com/get/Multimedia/Graphic/Graphic-Viewers/ZEN-2009-Light-Edition.shtml).To determine the expression of CSF-1 and CSF-1R in the trophoblast invading the FT in an EP, immunohistochemistry (IHC) was performed, as previously describedIHC and ICC data was analysed descriptively. Results from in-vitro assays  are presented as the mean ± SEM and are analysed using one-way ANOVA followed by Dunn’s multiple comparisons test. All statistical analysis was performed with the use of GraphPad Prism 6.


Retinal ischemia/reperfusion injury (RI) is a common cause of irreversible visual impairment and blindness in elderly and critical unmet medical need. While no effective treatment is available for RI, microglial activation and local immune responses in the retina are thought to play important roles in the pathophysiology of neurodegeneration. While survival and activation of microglia depend critically on colony-stimulating factor receptor (CSF-1R) signaling, it remains unclear if targeting the retinal immune microenvironments by CSF-1RAb after RI is sufficient to rescue vision and present a potentially effective therapy. Here we used rodent models of RI and showed that retinal ischemia induced by acute elevation of intraocular pressure triggered an early activation of microglia and macrophages in the retina within 12 h. This was followed by lymphocyte infiltration and increased production of pro-inflammatory cytokines. Intravitreal injection of CSF-1R neutralizing antibody (CSF-1RAb) after RI significantly blocked microglial activation and the subsequent T cell recruitment. This also led to improved retinal ganglion cell survival and function measured by cell quantification and electroretinogram positive scotopic threshold responses, as well as increased visual acuity and contrast sensitivity as assessed by optomotor reflex-based assays, when compared to the isotype-treated control group. Moreover, the administration of CSF-1RAb efficiently attenuated inflammatory responses and activation of human microglia in culture, suggesting a therapeutic target with human relevance. These results, together with the existing clinical safety profiles, support that CSF-1RAb may present a promising therapeutic avenue for RI, a currently untreatable condition, by targeting microglia and the immune microenvironment in the retina to facilitate neural survival and visual function recovery. Ischemia is a broad term to describe a restriction of blood supply to tissues and a key contributing factor to neural damage in the brain and retina, such as that resulted from stroke, diabetes, diabetic retinopathy, glaucoma, etc. Acute retinal ischemia, including central artery or vein occlusions, is a stroke-equivalent most common cause of irreversible vision loss in elderly . It induEvidence has been shown that retinal ischemia/reperfusion injury (RI) is accompanied by glial activation, neuroinflammation, and induction of a cytokine cascade , 3. Micrvia CSF-1 receptor (CSF-1R) (Colony-stimulating factor-1(CSF-1) is a growth factor essentially involved in the proliferation, differentiation, and survival of microglia (CSF-1R) , 14. Imp(CSF-1R)  and atte(CSF-1R) . Neverth(CSF-1R) –18, and (CSF-1R) , leading(CSF-1R) . In addi(CSF-1R) . Thus, iIn the present study, we proposed to test if targeting CSF-1R after RI suppressed microglial activation, changed the immune microenvironment in the retina. Our data showed that intravitreal injection of CSF-1RAb after RI attenuated retinal ganglion cell degeneration and vision loss through countering microglial activation and retinal immune responses, rather than depleting the microglia. These results establish the translational potential of CSF-1RAb for treating RI.Male Sprague-Dawley rats weighing 200–250 g  and C57BL/6J wild-type mice  were housed under a 12 h light/dark cycle and kept under pathogen-free conditions. All animals were handled in accordance with the National Institute of Health and the Association for Research in Vision and Ophthalmology, and all experimental procedures and the use of animals were approved and monitored by the Animal Care Committee of the Eye and ENT Hospital of Fudan University and Schepens Eye Research Institute of Massachusetts Eye and Ear. Animals were selected randomized for treatment and observed blinded.Retinal ischemia/reperfusion injury (RI) was induced in mouse and rat as previously described –24. BrieTo detect the dynamic changes of retinal microglia, macrophage and lymphocyte infiltration, and T cell responses following acute RI, animals were transcardially perfused with saline. Retinas were dissected and digested in papain for 15 min in 37°C and stopped by the ovomucoid protease inhibitor. After filtration through a 70 µm nylon cell strainer , cell numbers are counted. Cells were then washed in IsoFlow  and reacted with following primary antibodies: APC-conjugated anti-rat CD11b antibody , PE-CY7-conjugated anti-rat CD45 antibody , Pacific Blue-conjugated anti-mouse CD11b antibody , PE-CY7-conjugated anti-mouse CD45 antibody . For Ki67 detection, dissociated retinal cells were permeabilized with Perm/Wash buffer and stained with Alexa Flour 488-conjugated anti-rat Ki67 antibody . To analyze T cells in the superior cervical lymph nodes (LNs) , superiovia Image J by two researchers in a masked fashion.RGC and microglia was immunolabeled in retinal flat-mounts using a standard protocol as previously described . To prepTotal RNA was extracted from the retina and the cells using Quick-RNA Microprep Kit  according to the manufacturer’s protocol. The mRNA was then converted to cDNA using a SuperScript IV First-Strand Synthesis System  according to the manufacturer’s instructions. Quantitative polymerase chain reaction (qPCR) analysis was performed using SYBR Green I dye  and KAPA SYBR FAST kit . All primers were synthesized by Sangon Biotech  and Integrated DNA Technologies (USA). The sequences of all primers are listed in Intravitreal injection was performed as previously described . CSF-1RA2 and 1.7E-4 cd.s/m2 by an average of 40 responses per intensities. Data were processed by the software included in the ERG recorder system . The pSTR was measured from the baseline to the peak of the positive deflection.Positive scotopic threshold response (pSTR) of electroretinogram (ERG) is an established surrogate measure for RGC functions . Animals2; white stripe, 205 cd/m2). As mouse reached the threshold of spatial frequency detectability, tracking reflex responses to the moving gratings was no longer detectable. The highest spatial frequency that reflex response was detected from a mouse was determined to be the final VA of the mouse. The contrast sensitivity (CS) threshold was measured by setting sinusoidal gratings at a fixed spatial frequency. As the contrast reduced, the mouse ceased to track the gratings and the threshold was detected, which was presented as Michelson contrast according to the brightness of the screen (maximum-minimum)/(maximum+minimum). The reciprocal value of the threshold which was determined at five spatial frequencies  was finally calculated as the CS.The setup of the OMR assay was established as previously described . Briefly2 flasks  and incubated at 37°C with 5% CO2. The medium was changed 48 h later to remove cellular debris. After 10–14 days of incubation, culture flasks were placed on a shaker, shaking at a speed of 65 rpm for 5 h in the incubator. After most microglia had detached, the culture media was carefully aspirated, collected, and centrifuged and cells were resuspended in fresh MEM. Microglia were seeded onto 12-well plate and settled for 3–5 days before experiments. The medium was changed three times a week. Microglia were stimulated with lipopolysaccharide  and adenosine triphosphate  to induce inflammatory response. Various doses of CSF-1RAb  were added to the culture medium after LPS and ATP stimulation. Isotype IgG was served as a control. Microglia were harvested 24 h after stimulation for RNA extraction and qPCR analysis.Primary microglia were isolated from C57BL/6J mouse pups aged postnatal day 1–5 after euthanasia, as previously described , 35. TheHuman microglia cell line, HMC3 , was purchased from ATCC and cells were cultured in Eagle’s Minimum Essential Medium  supplemented with 10% FBS . Cells were plated in T75 flasks  and incubated at 37°C with 5% CO2. The medium was changed every 48 h. Cells were then seeded onto 24-well plates and stimulated with LPS and ATP as previously described  was used to analyze the cytokine arrays using filtered supernatant collected from primary mouse microglial cultures at 48 h incubation in the presence or absence of CSF-1RAb LPS and ATP. Assays were carried out according to the manufacturer’s protocol as previously described . The arrP < 0.05 were considered to be statistically significant. Data were expressed as mean ± S.E.M.Statistics were analyzed using GraphPad Prism 6 software . Increasing evidence suggests that retinal ischemia induces systemic immune responses, which can potentially affect cellular responses of the contralateral eye; therefore, different groups of animals, rather than the contralateral eye, were used as controls. Statistical differences among time points and doses were made by One-way ANOVA and Dunnett’s multiple comparisons test. Statistical differences among treatment groups were made by Two-way ANOVA and Tukey’s multiple comparisons test. Probability value of high), from microglia, which express a moderate level of CD45 (CD45mid). In addition, CD11b was used to separate macrophage (CD11bhighCD45high) and microglia (CD11bhighCD45mid) from infiltrated lymphocytes, which are CD11b negative (CD11blowCD45high) (P = 0.047), suggesting an early induction of retinal immune responses. The numbers of microglia and macrophages reached a peak of a 6–8 folds increase over the controls at day 7 after RI. Lymphocyte infiltration (CD11blowCD45high) was detectable beginning at day 2 (P = 0.025) post injury while it also reached a peak by day 7. Thus, 1 h acute RI induced a prolonged phase of retinal immune responses involving activation of microglia and infiltrations of macrophages and lymphocytes which peaked on day 7 after RI.To study the chronological changes of retinal immune microenvironment induced by RI, we investigated dynamics of immune cell activation and infiltration in the rat retinas. Adult SD rats were subjected to acute elevation of intraocular pressure to 110 mmHg for 1 h . MicroglD45high) , 40 FiFihigh, f1, S, G2, M), except in the resting phase (G0) , and it quickly reached a peak on day 2 but decreased after day 7  . In the + cells were resting microglia displaying ramified cell morphology; whereas, RI induced a large increase in Iba-1+ cells within a day, and many exhibited amoeba-like morphology, larger cell bodies and shorter dendrites (P < 0.001)  and interleukin-1β (IL-1β). To further characterize the phenotypes of microglia and infiltrated macrophages after RI, we quantified the morphological differences of Iba-1 immunolabeled cells, which reacts with both microglia and macrophages. These included dendritic lengths, body area, and number of microglia in retinal flat-mounts. In control retinas, most Iba-1via intravitreal injection, starting at day 1 post RI and injected weekly for a total of 4 weeks (highCD45mid) and macrophages (CD11bhighCD45high) that were peaked on day 7 post RI in mice (+ IFN-γ-producing T helper (Th1) cells, which participate in the propagation of RGC damage (+ T cell infiltration into the retina was peaked on day 14 after RI (P = 0.021)  T cells in the draining lymph nodes of the retina—the superior cervical lymph nodes. In agreement with the above finding, significant increase of Th1 cells was noted in the draining lymph nodes of vehicle control group compared to non-injected control mice, and this was again attenuated in CSF-1RAb-treated mice  and shorter dendrites (P = 0.003) in the retina with RI that was subjected to vehicle treatment (P < 0.001). Remarkably, CSF-1RAb treatment significantly attenuated RI-induced increase of GFAP immunolabeling (P = 0.002), as was confirmed by quantification of immune-intensity in flat-mount retinas  marker Brn3a were performed to allow identification of microglia and macrophages in the ganglion cell layer  in the vehicle-treated retinas (P = 0.012) post RI compared to vehicle-treated group /National Eye Institute (NEI) (Grants EY025913 and EY025259 to DC), Massachusetts Eye and Ear Summit Fund to DC, the Core Grant for Vision Research from NIH/NEI to the Schepens Eye Research Institute (P30EY03790), the Bright Focus Foundation to K-SC, and the Major Program of National Natural Science Foundation of China (Grant 81790641 to XS).DC and K-SC are co-inventors on the patent application for targeting CSF-1/CSF-1R to treat optic neuropathy.The remaining 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.


Amyloid plaques are small (~ 50 μm), highly-dense aggregates of amyloid beta (Aβ) protein in brain tissue, supposed to play a key role in pathogenesis of Alzheimer’s disease (AD). Plaques´ in vivo detection, spatial distribution and quantitative characterization could be an essential marker in diagnostics and evaluation of AD progress. However, current imaging methods in clinics possess substantial limits in sensitivity towards Aβ plaques to play a considerable role in AD screening. Contrast enhanced X-ray micro computed tomography (micro CT) is an emerging highly sensitive imaging technique capable of high resolution visualization of rodent brain. In this study we show the absorption based contrast enhanced X-ray micro CT imaging is viable method for detection and 3D analysis of Aβ plaques in transgenic rodent models of Alzheimer’s disease. Using iodine contrasted brain tissue isolated from the Tg-F344-AD rat model we show the micro CT imaging is capable of precise imaging of Aβ plaques, making possible to further analyze various aspects of their 3D spatial distribution and other properties. The classical “amyloid cascade hypothesis”4 states that the amyloid beta (Aβ) plaques are the causative agent of Alzheimer's pathology. The Aβ plaques are small (~ 50 μm), dense objects composed of clumped fibrils of amyloid β (Aβ), a peptidic product of the amyloid precursor protein. Their formation induces a local inflammation resulting in progressive cellular loss and related cognitive inability. It has been therefore assumed that the reduction of Aβ deposits might help to control the AD course. Numerous approaches have been designed to reduce the Aβ content in the brain tissue5. In order to test the effectiveness of such an AD therapy, there is a substantial need of a non-invasive, precise, and reproducible imaging method to analyze the location and size of the Aβ plaques in the brain.Alzheimer's disease (AD) is the most common cause of dementia worldwide, extensively interfering with personal, social, and health care levels of human society. AD progression is characterized by a gradual deterioration of cognitive abilities, starting with often unnoticed declarative memory impairment, followed by problems with spatial orientation, and ending with an inability to cope with daily life routine. The rare early onset form can affect individuals in their 40 s or 50 s, but the AD risk rapidly increases after the age of 65. While the etiology of AD remains still unclear, there are several hypothesized mechanisms reflecting molecular changes observed in brain tissue of AD patients, mainly the deposits of amyloid beta and hyperphosphorylated tau proteinIn the large majority of studies on mouse and rat models, the amyloid plaques were quantified histologically in sliced and stained brain tissue. While the histology and immunohistochemistry still remain important routines for characterizing the plaque properties, imaging techniques represent a powerful tool for a precise 3D analysis of plaque distribution.mus musculus). The experiments were performed with synchrotron X-ray sources using variations of phase contrast imaging to visualize the Aβ plaques11. Overall, in all published papers on the topic of Aβ plaques detection, the authors were able to identify the Aβ plaques in the mouse brain using phase contrast imaging, some even performing quantitative analyses11. However, imaging at synchrotron facilities is often limited by the narrow field of view resulting in a need of several scans for the imaging of one brain. Also, the access to synchrotron facilities is very limited and burdened by the high price of the CT scans.So far all the attempts to image the amyloid plaques in rodent brain by Computed Tomography (CT) have been performed on mouse brain . The micro CT imaging of rodent brain has been previously used for the visualization of vascular system of mouse brain filled with radio-opaque silicone rubber Microfil40 for the staining of a much larger rat brain. The lab based industrial micro CT device GE Phoenix v|tome|x L 240 was utilized to scan the contrast enhanced brains of a recent rat AD model to determine whether amyloid plaques could be detected by contrast enhanced micro CT imaging. Further, the exploration of the possibilities of Aβ plaque analyses provided by obtained high resolution micro CT data was pursued. This experimental setup promises a faster, more precise and accessible alternative to the synchrotron based micro CT imaging of the Aβ plaque deposits.However, it has not been previously determined whether the absorption based contrast enhanced X-ray micro CT imaging is capable of detecting and quantifying amyloid plaques in the brains of rodents. In this study, we optimized the mouse brain staining protocolWe used brains from three 18 months old female rats . The micro CT scan slices of iodine stained brains from transgenic and wild type samples are shown in the Fig. Beta amyloid is the main component of amyloid plaques. Because of their dense protein content, we expected the applied iodine based contrasting protocol would preferably stain the tissue around the amyloid plaques, resulting in their “negative” highlight in the micro CT data. The golden standard for beta amyloid detection is its immunostaining by binding to a specific antibody tagged by fluorescent probe. Here, we used this approach to validate the identity of lower density loci detected by micro CT in the transgenic samples. To confirm the identity of the suspect plaques, the iodine stained brain samples were washed in ethanol solution after the micro CT scanning, cut into histological sections and finally specifically stained for the amyloid protein. For selected histological slices we identified their respective sections in the micro CT data. The resulting comparison is displayed in Fig. In the industrial micro CT setups that use the cone beam geometry, the dimension of the sample is one of the main factors that determine the resulting voxel size of the obtained dataset. To acquire a 3D distribution of Aβ plaques in the best possible details, we decided to image an isolated part of the brain and to scan it again with a smaller voxel size. We focused on the dorsal hippocampus as this structure is severely impaired by the amyloid accumulation and cellular loss in AD. The dissected dorsal part of the hippocampus is showed in Fig. 2 (IQR 862.3 µm2) from CT data, whereas only 28.9 µm2 (IQR 105.4 µm2) from the histological sections, respectively. The plaque size histograms are depicted in Fig. 2), accounting for its low median values. Besides the invisibility of smallest deposits on the scans, the comparison of the detailed images in Fig. 2.The next step was designed to assess the sensitivity of amyloid plaques detection using micro CT in contrast to the standard immunohistological staining. After the CT measurement, we sliced the isolated hippocampal tissue sample. Despite the dehydrated and iodine stained tissue turned out to be highly fragile while slicing, we were able to select four well preserved tissue sections and immunostained them for the Aβ presence. Then the corresponding micro CT sections were identified and their match was evaluated Fig. a,b. The  IQR 862. µm2 from IQR 862. µm2 from3) we identified in total 1666 individual plaques. The volume of the smallest individual deposit was 895 µm3, indicating the lower limit for amyloid plaque identification in the present micro CT data. The biggest identified Aβ plaque had a volume of 721,552 µm3, the dataset of measured volumes was characterized by median value 38,423 µm3 and IQR of 57,512 µm3. The distribution of the Aβ plaques in 3D space with their color coded volumes is shown in Fig. The data obtained from the micro CT scan of the dissected hippocampus was chosen for a subsequent 3D analysis. Since the Aβ plaques had similar contrast values as other tissue structures, they could not be detected with global thresholding methods. Hence, a manual segmentation of the plaques was performed. After defining the region of interest, all segmented plaques were counted and measured. In the dissected hippocampus  and the experiments have been performed with synchrotron X-ray sources using variations of phase contrast imaging to visualize the Aβ plaques in a brain. TOMCAT beamline of the Swiss Light Source, Astolfo et al.6 demonstrated that synchrotron-based X-ray phase contrast imaging can be used for the 3D visualization and basic quantification of Aβ plaques in the mouse brain. At the same synchrotron facility the Pinzer et al.7 analyzed the neocortex of mouse brain via differential phase contrast imaging and accompanied their findings with complementary thioflavin S staining of the brain to confirm the presence of the Aβ plaques in brain in CT data. The diffraction enhanced imaging phase contrast X-ray imaging technique was applied by the Connor et al.8 at National Synchrotron Light Source, Brookhaven National Laboratory, USA. In that study, several scans of the one brain were needed in order to obtain a full scan of the brain. A comparison of the affected and wild type mouse brain was shown and the identity of Aβ plaques was confirmed by applying specific immunostaining on the Aβ plaques. The X-ray phase contrast tomography was applied for the purpose of the amyloid plaque imaging specifically to describe the microenvironment of the Aβ plaque at European Synchrotron Radiation Facility, Grenoble, France9. Finally, the research group of Noda-Saita et al.10 at High Energy Accelerator Research Organization, Ibaraki, Japan used phase-contrast X-ray CT imaging to describe the density of Aβ plaques supporting their findings with a complementary immunodetection of Aβ plaques in the mouse brain and with scans of control brains.The detection of amyloid plaques in rodent brain by Computed Tomography (CT) has been attempted so far by using the mouse brain .The immunodetection of amyloid plaques performed on histological slices is the golden standard of identification of plaques in the brain tissue. In this paper, we used this method to validate our findings. In order to precisely correlate the micro CT data with the immunodetection of the plaques, we used the brain which had been previously scanned on micro CT. Then, we performed a classical histological sectioning and carried out the immunodetection of the plaques. The comparison of data from the same brain analyzed by both techniques showed a good agreement between immunodetection and micro CT imaging. Even though the immunodetection is indisputably more sensitive in detection of smaller plaques, it lacks a convenient approach to the 3D analysis across large brain volumes. This comparison thus highlights the advantages of contrast enhanced micro CT imaging. The immunostaining based affirmation of plaque identity detected by micro CT shows that the absorption-based contrast enhanced micro CT imaging is robust in detection of amyloid plaques. While its applicability as described in this report is not suitable for in vivo experiments for the staining protocol toxicity or because of the high irradiation dose, we believe the CT brain imaging approach in the context of Alzheimer’s disease research has a considerable potential for further development.Brains from three 18 months old female rats  obtained from the local breeding colony at Faculty of Medicine in Pilsen, Charles University, were used to collect the data. All protocols followed in this study were approved by the Ethical Committee of the Ministry of Education, Youth and Sports of the Czech Republic  according to the Guide for the Care and Use of Laboratory Animals .At the beginning of the experiments the rats were overdosed with pentobarbital and intracardially perfused with saline, followed by 4% PFA. Then the brains were manually extracted and post-fixed in 4% PFA for another 3 h. Afterwards, the samples were dehydrated in ethanol solutions of different concentrations which increased: 30%, 50%, 70%, 80% and 90%. The process lasted 12 h for each concentration. After the dehydration, samples were submerged in a staining solution consisting of 1% iodine in 90% methanol for 72 h, where the staining solution was refreshed after the first 24 h. Then the samples were washed in 50% ethanol and embedded in 1% agarose gel. In one of the transgenic animals, after the whole brain scan, the dorsal hippocampus was dissected and embedded in 1% agarose gel for another scan.Before scanning, all samples were embedded in 1% agarose gel in 15 ml Falcon tubes in order to prevent movement during the imaging procedure. The micro CT scanning was performed using a laboratory system GE Phoenix v|tome|x L 240 , equipped with a 180 kV/15 W maximum power nanofocus X-ray tube and a high contrast flat panel detector dynamic 41|100 . The measurements were carried out in an air-conditioned cabinet (21 °C). The parameters for each scan are indicated in the Table 41. The segmentation of plaques in the hippocampus sample was carried out using Avizo 9.5 software (Thermo Fisher Scientific)42. In the first step we isolated the hippocampus from the background by creating the corresponding region of interest (ROI). Blood vessels were segmented by global thresholding based on the grey level (vessels appeared as the darkest part since they did not contain any iodine), continuity, and their resulting prolonged and branched 3D shape. The amyoid plaques were segmented manually in Avizo 9.5 software by selecting the plaque areas across the sections under the following criteria: the area of the plaque should be distinguishably darker then the surrounding tissue and its volume should extend across at least 3 following sections. The boundaries of smaller plaques were selected section by section, in bigger plaques (some were spread over more than 30 sections) we interpolated between every other slice while any inconsistencies were additionally manually corrected.All 3D visualizations and measurements were performed in VG Studio MAX 3.4 software (Volume Graphics GmbH)For the analysis of Aβ distribution we generated datasets of plaques coordinates (Matlab R2020a). First, the coordinates of plaques centroids were extracted from the segmented 3D binary data to represent the experimental dataset. Then the random dataset of “plaque’s” centroids was created within the volume of the sample leaving out its vascular system. The resulting coordinates then represented centroids of randomly distributed Aβ plaques with total amount corresponding to the experimental dataset. The analysis of Aβ plaque position in relation to the blood vessels was carried out in VG studio. Both real plaques centroid positions and simulated plaques centroid positions were imported in form of binary image data. The distance of the centroid to the nearest blood vessel was measured via pore analysis module. Subsequently, the intra-plaques distances were analyzed separately in simulated plaque centroids and real plaque centroids. The nearest neighboring centroid of each centroid was found using 3D Euclidean distances analysis in Matlab R2020a. Repeating combinations of the nearest neighbors were excluded from the subsequent analysis.https://www.statskingdom.com/170median_mann_whitney.html).The non-parametric Mann–Whitney test was applied to compare the detected and random datasets . The slices were then deparaffinized and rehydrated. Phosphate buffer saline  was applied for washing. To block the non-specific binding, normal goat serum  was used. The sections were then incubated overnight at 4 °C with the primary antibody  against amyloid beta. Next day, the sections were washed with 1X PBS (0.01 M) thrice  to remove the unbound antibody remnants. Staining was visualized with an Alexa Fluor 647-conjugated goat anti-rabbit antibody (Jackson ImmunoResearch Laboratories) applied in 1:500 dilution at 37 °C for 4 h. All the sections were then counterstained with DAPI to label the nuclei and glass mounted using fluoroshield mounting medium (Merck). The Amyloid beta plaques were visualized using fluorescent microscopy (Olympus), and quantified in open source Fiji image analysis softwareSupplementary InformationSupplementary Video 1.


It is a growing trend among researchers to make their data publicly available for experimental reproducibility and data reusability. Sharing data with fellow researchers helps in increasing the visibility of the work. On the other hand, there are researchers who are inhibited by the lack of data resources. To overcome this challenge, many repositories and knowledge bases have been established to date to ease data sharing. Further, in the past two decades, there has been an exponential increase in the number of datasets added to these dataset repositories. However, most of these repositories are domain-specific, and none of them can recommend datasets to researchers/users. Naturally, it is challenging for a researcher to keep track of all the relevant repositories for potential use. Thus, a dataset recommender system that recommends datasets to a researcher based on previous publications can enhance their productivity and expedite further research. This work adopts an information retrieval (IR) paradigm for dataset recommendation. We hypothesize that two fundamental differences exist between dataset recommendation and PubMed-style biomedical IR beyond the corpus. First, instead of keywords, the query is the researcher, embodied by his or her publications. Second, to filter the relevant datasets from non-relevant ones, researchers are better represented by a set of interests, as opposed to the entire body of their research. This second approach is implemented using a non-parametric clustering technique. These clusters are used to recommend datasets for each researcher using the cosine similarity between the vector representations of publication clusters and datasets. The maximum normalized discounted cumulative gain at 10 (NDCG@10), precision at 10 (p@10) partial and p@10 strict of 0.89, 0.78 and 0.61, respectively, were obtained using the proposed method after manual evaluation by five researchers. As per the best of our knowledge, this is the first study of its kind on content-based dataset recommendation. We hope that this system will further promote data sharing, offset the researchers’ workload in identifying the right dataset and increase the reusability of biomedical datasets.Database URL: http://genestudy.org/recommends/#/ In the Big Data era, extensive amounts of data have been generated for scientific discoveries. However, storing, accessing, analyzing and sharing a vast amount of data are becoming major bottlenecks for scientific research. Furthermore, making a large number of public scientific data findable, accessible, interoperable and reusable is a challenging task.The research community has devoted substantial effort to enable data sharing. Promoting existing datasets for reuse is a major initiative that gained momentum in the past decade . Many reAn initiative was taken by the developers of DataMed (https://datamed.org) to solve the aforementioned issues for the biomedical community by combining biomedical repositories together and enhancing the query searching based on advanced natural language processing (NLP) techniques , 2. DataGoogle Dataset Search (https://toolbox.google.com/datasetsearch) provides the facility to search datasets on the web, similar to DataMed. While DataMed indexes only biomedical domain data, indexing in Google Dataset Search covers data across several domains. Datasets are created and added to repositories frequently, which makes it difficult for a researcher to know and keep track of all datasets. Further, search engines such as DataMed or Google Dataset Search are helpful when the user knows what type of dataset to search for, but determining the user intent in web searches is a difficult problem due to the sparse data available concerning the searcher . To overRecommendation systems, or recommenders, are an information filtering system that deploys data mining and analytics of users’ behaviors, including preferences and activities, for predictions of users’ interests on information, products or services. Research publications in recommendation systems can be broadly grouped as content-based or collaborative filtering recommendation systems . This arDataset recommendation is a challenging task due to the following reasons. First, while standardized formats for dataset metadata exist , no suchDT) so that the rank score indicates the relatedness of DT to a given source dataset (DS). The rank scores provide information on the likelihood of a DT to contain linking candidates for DS. Reference  from GEO and researcher’s publications  from PubMed using name and curriculum vitae (CV) for developing a dataset recommendation system. A vector space model (VSM) is used to compare publications and datasets. We propose two novel ideas:A method for representing researchers with multiple vectors reflecting each researcher’s diverse interests.A system for recommending datasets to researchers based on their research vectors.For the datasets, we focus on GEO (https://www.ncbi.nlm.nih.gov/geo/). GEO is a public repository for high-throughput microarray and next-generation sequence functional genomics data. It was found that an average of 21 datasets was added daily in the last 6 years (i.e. 2014–19). This gives a glimpse of the increasing number of datasets being made available online, considering that there are many other online data repositories as well. Many of these datasets were collected at significant expense, and most of these datasets were used only once. We believe that reusability of these datasets can be improved by recommending these to appropriate researchers.Efforts on restructuring GEO have been performed by curating available metadata. In reference , the autWe developed this dataset recommendation system for researchers as a part of the dataset reusability platform (GETc Research Platform(http://genestudy.org/)) for GEO developed at the University Texas Health Science Center at Houston. This website recommends datasets to users using their publications.The rest of the article is organized in the following manner. Section The proposed dataset recommendation system requires both dataset metadata and the user profile for which datasets will be recommended. We collected metadata of datasets from the GEO repository, and researcher publications from PubMed using their names and CVs. The data collection methods and summaries of data are discussed next.GEO is one of the most popular public repositories for functional genomics data. As of December 18, 2019, there were 122 222 series of datasets available in GEO. Histograms of datasets submitted to GEO per day and per year as presented in Figure For the present experiment, metadata such as title, summary, submission date and name of dataset creator(s) were collected from GEO and indexed in a database, as shown in Figure A researcher’s academic interest can be extracted from publications, grants, talks, seminars and much more. All this information is typically available in the CV, but it is presented in the form of titles/short texts. Here, short texts imply limited information. Further, lack of standardization in CV formats poses challenges to parse the CVs. In this work, an alternative approach was undertaken, which is outlined next.Title and year of the researcher’s publications were present in the CV. However, we required title, abstract and year of publication for our experiment. A researcher’s list of publications (titles and abstracts) are easier to get from web sources such as Google Scholar, PubMed, Semantic Scholar and others. Unfortunately, the full texts of most scientific articles are not publicly available. Thus, for the present experiment, we used only the title and abstract of publications in identifying the researcher’s areas of research.Given a researcher, we searched the researcher’s name in PubMed using Entrez API (https://www.ncbi.nlm.nih.gov/books/NBK25 501/) and collected all the publications. Multiple researchers with exact same name might exist, thus, querying the name in PubMed might sometime result in publications from other researchers as well. This is a typical challenge of author disambiguation. However, there are a few attempts that have been undertaken to resolve the issue of author disambiguation, and one of them is ORCID (https://orcid.org). A researcher needs to provide ORCID id to access his/her ORCID details. However, to the best of our knowledge, many researchers in the biomedical domain did not have an associated ORCID account. Thus we used a simple method to disambiguate the authors by using their CVs. Initially, the recommendation system prompts a researcher to provide his/her name and a CV (or list of publications). Next, we collected the publications  for a researcher from PubMed by searching his/her name. For removing the publications of other authors with the same name, titles of all collected publications from PubMed were matched against the titles present in the CV. In the case of a match, publications were kept for further processing. An overview of the technique used for the researcher’s publication collection is provided in Figure One of the limitations of the above publication collection method is that the publications could not be collected if they were not listed in PubMed. Further, the datasets used in the present experiments were from the biomedical domain, and the publications not listed in PubMed were less pertinent to biomedical datasets. For example, someone’s biomedical interests (in PubMed) may be more reliable markers for biomedical datasets than a theoretical computer science or statistics paper. Another downside is if the researcher’s CV may not be fully up-to-date.This section describes how the two main objects of interest (datasets and publications of researchers) were embedded in a vector space and then how these vectors were compared in order to make recommendations. First, both datasets and papers were treated as text objects: the text of a dataset includes its title and summary, while the text of a paper includes its title and abstract. Pre-processing was performed on both a researcher’s publications and datasets by removing the low-value stopwords, links, punctuation and junk words. Further, the nltk WordNet lemmatizer (https://www.nltk.org/_modules/nltk/stem/wordnet.html) was used to get the root forms of the words. Next, we describe the methods used for converting datasets and researchers into vectors.VSMs can be built from text in a variety of ways, each of which has its distinct advantages and thus merit experimentation. For the present experiment, we used TF-IDF because it achieved better results for related literature recommendation for datasets in .TF-IDF: For vocabulary W, each unique word w ∈ W is assigned a score proportional to its frequency in the text  and its inverse frequency in the full collection . We tuned parameters such as minimum document frequency (min-df) and maximum n-gram size. For the present study, we kept maximum n-gram size = 2 (i.e. unigrams and bigrams) as including the higher n-gram increases the sparsity as well as computational complexity.We converted each dataset into a vector using TF-IDF. For each dataset, the title and summary were preprocessed and normalized and then converted into a single vector. Finally, each publication vector (or publication cluster vector) is compared with dataset vectors to generate the recommendation score. Different methods for representing a researcher’s papers as vectors are discussed next.vr) in the same vector space using Equation .where It is evident that a researcher will be interested in datasets recommended for his/her current work rather than the work performed a few years back. Thus, we penalized each of the paper vectors from a different year, as stated in Equation :t is the difference between the current year and year of publication. k is the decaying function to decrease the rate proportional to its current value, and for the present study, we kept k=0.05.where .mouse genomics and HIV vaccines may not be interested in mouse vaccines).The baseline method for creating a researcher vector may be helpful for new researchers without many publications, whereas an established researcher may have multiple areas of expertise with multiple papers in each. Also, if the number of papers is imbalanced in multiple areas, then the above baseline method may not work. With a highly imbalanced set of publications this would obviously bias dataset recommendation to the dominant interest. For a more balanced set of interests that are highly dispersed, this mixture would result in the ‘centroid’ of these interests, which could be quite distinct from the individual interests. Both these cases are undesirable. The centroid of a researcher’s interests may not be of much interest to them , and it was observed that the datasets recommended for a researcher were biased toward a single research area with the largest number of publications. For example, Researcher 1 has a dominant number of publications on A critical limitation of the above baseline approach is that researchers can have multiple areas of expertise. We can easily build multiple vectors, each corresponding to a different expertise if we know how to properly group/cluster a researcher’s papers according to expertise or topic. However, parametric methods such as k-means clustering and latent Dirichlet allocation require specifying a priori how many clusters/topics to utilize. Generalizing the number of clusters is not possible due to a varying number of publications of researchers. Instead, our insight is that the more publications a researcher has, the more interests or areas of expertise he/she likely has as well, but this should be modeled as a ‘soft’ constraint rather than a ‘hard’ constraint. We propose to employ the non-parametric Dirichlet Process Mixture Model (DPMM)  to clustDPMM: We employed a Gibbs Sampling-based Dirichlet Process Mixture Modeling for text clustering. DPMM offers the following advantages over its traditional counterparts. First, the number of text clusters need not be specified; second, it is relatively scalable and third, it is robust to outliers . The datasets are related to gene expressions, while the articles collected from PubMed contain a variety of topics related to biomedicine and life sciences which may not be suitable for building a word embedding in the current study (since some of these articles are highly unrelated to the type of information in GEO). The articles before 1998 were removed as the research on micro-array data started during that year  and dataset vector (vd).where The high-level system architecture of the dataset recommendation system is shown in Figure Three dataset recommendation systems are evaluated in this article: a baseline method using the researcher’s vector generation method and two proposed methods using the proposed researcher’s vector generation method.The baseline system uses the researcher’s vector using Equation  of the bThe cluster vectors are generated using the modified Equation . Here, cith cluster), indicating different research fields that a researcher might have, as mentioned in Section This system uses multiple pseudo vectors for multiple clusters of a researcher  of words and MeSH terms in the publications, separately. IACCS was the mean cosine similarity of words or MeSH terms for each pair of papers in a given cluster. Considering a cluster of size Equation :xi and xj are the list of MeSH terms or words of the ith and jth paper, respectively, and where, n clusters . Considering ci and cj are the list of MeSH terms or words of all the papers in the ith and jth clusters, respectively, and where, For the baseline comparison, publication vectors are created using TF-IDF, then K-Means is used to compute the publication clusters. K-Means is a parametric unsupervised clustering. We implemented K-Means with two and five clusters separately for comparison purposes. On the other hand, the tuning parameter proposed for DPMM resulted in a variable number of clusters for different researchers, and these clusters were used for comparison.Being a novel task, no prior ground truth annotations exist for publication-driven dataset recommendation. Thus, we performed a manual evaluation for each developed dataset recommendation system. We asked researchers to rate each retrieved dataset based on their publications or publication clusters. The researchers included in this study have already worked on the datasets from GEO and published papers on these datasets. The rating criterion was how likely they want to work on the retrieved datasets. We asked them to rate using one to three ‘stars’, with three stars being the highest score. Later, normalized discounted cumulative gain (NDCG) at 10 and Precision at 10 (P@10) were calculated to evaluate different systems. The ratings are:1 star [not relevant]: This dataset is not useful at all.2 star [partially relevant]: This dataset is partially relevant to the publication cluster. The researcher has already used this dataset or maybe work on it in the future.3 star [most relevant]: This dataset is most relevant to the publication cluster, and the researcher wants to work on this dataset as soon as possible.The primary evaluation metric used in this work is NDCG, which is a family of ranking measures widely used in IR applications. It has advantages compared to many other measures. First, NDCG allows each retrieved document to have a graded relevance, while most traditional ranking measures only allow binary relevance (i.e. each document is viewed as either relevant or not relevant). This enables the three-point scale to be directly incorporated into the evaluation metric. Second, NDCG involves a discount function over the rank while many other measures uniformly weight all positions. This feature is particularly important for search engines as users care about top-ranked documents much more than others . NDCG israting(i) is the ith dataset rating provided by users. For the present study, we set p = 10 for the simplicity of manual annotation.where The NDCG@10 for the baseline and MIDR systems is calculated using the ratings of only the top ten retrieved datasets. For the MIDR system (separate), there were multiple publication clusters for a single user, and for each publication cluster we recommended datasets separately. NDCG@10 was calculated for each publication cluster using the top ten datasets and later averaged to get a final NDCG@10 for a single researcher. For NDCG@10 calculation, the 1-star, 2-star and 3-star are converted to 0, 1 and 2, respectively. We also calculated P@10  for the baseline and proposed systems. Strict considers only 3-star, while partial considers both 2- and 3-star results. The results presented in this study were evaluated using a total of five researchers (with an average of 32 publications) who already worked on GEO datasets. This is admittedly a small sample size, but is large enough to draw coarse comparisons on this novel task.We compared DPMM clustering with K-Means as mentioned in Section Researcher-specific results of the dataset recommendation system are shown in Table The proposed MIDR system obtained the average NDCG@10, P@10 (P) and P@10 (S) of 0.89, 0.78 and 0.61, respectively. The proposed MIDR (separate) system obtained the average NDGC@10, P@10 (P) and P@10 (S) of 0.62, 0.45 and 0.31, respectively. For calculating NDCG@10 and P@10 in the proposed MIDR (separate) system, individual cluster scores were calculated first, and then divided by the total number of clusters.The proposed MIDR system performed better than the baseline system. The MIDR system recommended a variety of datasets involving multiple clusters/research fields as opposed to the baseline system recommended datasets from a single research field with the maximum number of publications.Performances of the baseline and proposed MIDR (separate) systems could not be directly compared. Evaluation of the MIDR (separate) system was performed over multiple clusters with ten datasets recommended for each cluster. In contrast, evaluation of the baseline system was performed only on 10 datasets, for example, for Researcher 1 in Table HIV keywords and those papers were not published recently. We penalized the papers according to the year of publication for all methods. However, the top datasets contained ‘HIV’ or related keywords for the baseline method. We manually checked the top 100 results and found that those were relevant to HIV. Whereas, the proposed MIDR system clustered the publications into different groups , which resulted in recommendations for different research fields. Therefore, Researcher 1 had the flexibility to choose the datasets after looking at the preferred clusters in the proposed MIDR or MIDR (separate) system.For Researcher 1 in Table Similarly, the results of the MIDR and MIDR (separate) systems could not be directly compared. Evaluation of the MIDR system was performed based on 10 datasets recommended for each researcher, whereas evaluation of MIDR (separate) system was performed based on 10 recommended datasets for each research field (cluster), which could be more than 10 datasets if a researcher had more than one research fields (clusters). Hence, the NDCG@10 and P@10 scores of MIDR (separate) system were less than the MIDR system.For researchers looking to find specific types of datasets, a keyword-based IR system might be more useful. For researcher who generally wanted to find datasets related to their interests, but did not have a particular interest in mind, could benefit from our system. For instance, if a researcher wanted a regular update of datasets relevant to their interest, our method would be better suited. However, this proposed system may not be useful to early-stage researchers due to fewer publications. They may take advantage of the available dataset retrieval systems such as DataMed, Omicseq and Google Dataset Search; or the text-based dataset searching that we provided on the website.For some clusters, evaluators rated all recommended datasets as one star. In most of these cases, we observed that the research field of that cluster was out of the scope of GEO. In this case, the NDCG@10 score was close to 1, but the P@10 score was 0. This may be one of the reasons why NDCG@10 scores were much higher compared to P@10 scores.Initially, we had not identified whether the clusters were related to GEO or not. We recommended datasets for these unrelated clusters. For example, Researcher 2 in Table Later, we identified a threshold by averaging the similarity scores of publications and datasets for each cluster, and were able to remove the clusters which were not related to GEO. The threshold was set to 0.05 for the present study, i.e. a cluster was not considered for evaluation or showing recommendation if the average similarity score of the top 10 datasets for that cluster was less than or equals to 0.05. This threshold technique improved the results of proposed systems by 3% for Researcher 2. However, a thorough investigation on threshold involving datasets from different biomedical domains is needed for future work.Further, a dislike button for each cluster may be provided, and users may press the dislike button if that cluster is not related to GEO datasets. Later, this information can be used to build a machine learning-based system to identify and remove such clusters from further processing. This will improve the usefulness and reduce time complexity of the proposed recommendation system..The researchers’ names are searched in PubMed to collect their publications. Many recent conference/journal publications are not updated in PubMed. Further, if the researcher has most of his/her publications that did not belong to the biomedical domain, then there is a low chance of getting those papers in PubMed. This makes the dataset recommendation task harder. Authors might later be able to include a subset of their non-PubMed articles for consideration in dataset recommendation (e.g. bioRxiv preprints), but this work is currently limited to PubMed publications only.We used PubMed name search to find the titles of a researcher’s papers. Finally, the titles were matched with the text in the CV to get publications. If there is any typo in the CV, then that publication would be rejected from being processed in further steps. As we do not fully parse the CV, instead just performing string matching to find publications, there is a high chance of rejecting publications with small typos.The manual evaluation was performed by five researchers only. For each cluster, 10 datasets were recommended, and each researcher has to evaluate an average of 40 datasets. It was a time-consuming task for evaluators to check each of the recommended datasets. For manual evaluation, we required the human judges with expertise on the GEO datasets, which was challenging to find. Further research will entail the scaling of this evaluation process.We developed the GETc research platform that recommends datasets to researchers using the proposed methods. A researcher needs to provide his/her name (as in PubMed) and CV (or list of publications) in the website. After processing his/her publications collected from PubMed, the recommendation system recommends datasets from GEO. Researchers can provide feedback for the datasets recommended by our system based on the evaluation criteria mentioned in Section This work is the first step toward developing a dataset recommendation tool to connect researchers to relevant datasets they may not otherwise be aware of. The maximum NDGC@10, P@10 (P) and P@10 (S) of 0.89, 0.78 and 0.61 were achieved based on the proposed method (MIDR) using five evaluators. This recommendation system will hopefully lead to greater biomedical data reuse and improved scientific productivity. Similar dataset recommendation can be developed for different datasets from both biomedical and other domains.The next goal is to identify the clusters which are not related to datasets and used for recommendations in the present article. These clusters can be removed from further experiments. Later, we plan to implement other embedding methods and test the dataset recommendation system on a vast number of users. A user-specific feedback-based system can be developed to remove datasets from the recommendations. Several additional dataset repositories can be added in the future. Other APIs can also be added to retrieve more complete representation of researcher’s publication history.Availability:  http://genestudy.org/recommends/#/


Corynebacterium striatum and other Corynebacterium species were historically considered contaminants, they are recently being identified as pathogens with increasing frequency. Case Summary. We report the case of a 78-year-old gentleman with a three-year history of knee pain and established diagnosis of osteoarthritis referred for consideration for total knee arthroplasty. He had no knee pain with passive range-of-motion. Plain films demonstrated bony erosions atypical for osteoarthritis. Joint aspiration white blood cell count was 30,548/mm3, but multiple cultures were positive for C. striatum. The infection was successfully treated with open irrigation and debridement, complete synovectomy, and six weeks of intravenous daptomycin.  While C. striatum septic arthritis of a native joint and only the third case of C. striatum septic arthritis of the knee.C. striatum. The infection was successfully treated with open irrigation and debridement, complete synovectomy, and six weeks of intravenous daptomycin. C. striatum. The infection was successfully treated with open irrigation and debridement, complete synovectomy, and six weeks of intravenous daptomycin.  To our knowledge, this is the first report of chronic  Corynebacterium striatum is a catalase-positive, gram-positive bacillus that is among the normal flora of the skin and mucous membranes [C. striatum and other Corynebacterium species were historically considered contaminants, they are recently being identified as pathogens with increasing frequency [C. striatum causing opportunistic infections in hospitalized patients and those with indwelling or implanted devices [C. striatum causing acute septic arthritis in a native joint [C. striatum septic arthritis and osteomyelitis of the knee in a patient referred for total knee arthroplasty.Chronic septic arthritis is a rare condition characterized by insidious onset of joint pain and joint destruction. The most common pathogens associated with chronic septic arthritis are mycobacteria or fungi . Corynebembranes . While Crequency . Specifi devices –6. To dave joint –12. HereThis is the case of a 78-year-old gentleman with a past medical history of osteoarthritis, diabetes mellitus, and prostate cancer who presented to our institution with three years of left knee pain.The history begins in 2013, when the patient began to have progressive left generalized knee pain. Symptoms were exacerbated with activity and relieved with rest. The patient denied fevers as well as history of trauma or surgery to the knee. He had tried activity modification, physical therapy, and hinged knee brace without relief. Two months prior to presentation in our clinic, in June 2016, the patient had an intra-articular steroid injection within the left knee that provided relief for 2 days. He performed bed-to-chair transfers only with the use of a walker at the time of presentation in August 2016. The patient was retired, and the remainder of the family and social history were noncontributory. He was referred for consideration for total knee arthroplasty.The patient's medications included aspirin 81 mg po daily, atorvastatin 40 mg po daily, carvedilol 12.5 mg po daily, furosemide 80 mg po daily, meloxicam 7.5 mg po daily, metformin 500 mg po bid, metolazone 5 mg po daily, mirtazapine 30 mg po daily, extended release oxycodone 10 mg po q12 hours, and phenytoin 125 mg po tid. He had no known drug allergies.On physical examination, this Caucasian gentleman was 5 feet 11 inches tall and weighed 262 pounds. His left knee skin was intact and without erythema. The patient had left knee pain with weight bearing and transferred from his wheelchair to the examination table with an antalgic gait. Left knee medial and lateral joint lines and femoral condyles were tender to palpation. He had no pain with passive range of motion of the left knee, which demonstrated a 20-degree flexion contracture and 100 degrees of flexion. Plain films of the left knee demonstrated joint space narrowing with erosive features on both sides of the joint .3 , erythrocyte sedimentation rate (ESR) was 68 mm/hr (reference range 0-10 mm/hr), and C-reactive protein (CRP) was 63.7 mg/L (reference range 0.0-7.0 mg/L). The patient's history of diabetes mellitus and prostate cancer, together with his elevated inflammatory markers and radiographic findings, suggested a differential diagnosis of septic arthritis versus malignancy. The patient was admitted to the hospital for further workup.Given the atypical appearance of his radiographs, the patient elected to obtain inflammatory markers. Serum white blood cell count was 8,800/mmCorynebacterium striatum and negative for crystals. The higher of the two aspirate white blood cell (WBC) counts was 30,548/mm3 with 96% neutrophils.Computed tomography (CT) scan of his left knee demonstrated bony destruction of both the distal femur and proximal tibia without evidence of malignancy . Two sucC. striatum septic arthritis of the left knee.Our diagnosis was chronic The patient elected to proceed with open left knee irrigation and debridement (I&D), which was performed through a medial parapatellar arthrotomy. Turbid synovial fluid and exuberant synovitis were observed. A total synovectomy was performed. Tibial and femoral articular surfaces were debrided of all necrotic or devitalized tissue and then contoured smoothly. A small drill was used to enter the femoral and tibial canals, and no purulence was encountered. Multiple specimens of soft tissue and bone were sent for culture and pathology. A hemovac drain was placed in the joint, and the wound closed in multiple layers with monofilament absorbable suture.C. striatum and negative for mycobacteria and fungi. Pathology demonstrated acute and chronic inflammation consistent with infection, without evidence of malignancy , that aid the diagnosis of infection when there is ambiguity in clinical presentation. NGS sequences the entire genome of pathogens within a sample. This highly sensitive modality has proven effective in identifying the infecting organism in culture-negative infections . Howeverpathogen .