Heparanase Promotes Engraftment and Prevents Graft versus Host Disease in Stem Cell Transplantation

Heparanase, endoglycosidase that cleaves heparan sulfate side chains of heparan sulfate proteoglycans, plays important roles in cancer metastasis, angiogenesis and inflammation.Applying a mouse model of bone marrow transplantation and transgenic mice over-expressing heparanase, we evaluated the effect of heparanase on the engraftment process and the development of graft-versus-host disease.Analysis of F1 mice undergoing allogeneic bone marrow transplantation from C57BL/6 mice demonstrated a better and faster engraftment in mice receiving cells from donors that were pretreated with heparanase. Moreover, heparanase treated recipient F1 mice showed only a mild appearance of graft-versus-host disease and died 27 days post transplantation while control mice rapidly developed signs of graft-versus-host disease (i.e., weight loss, hair loss, diarrhea) and died after 12 days, indicating a protective effect of heparanase against graft-versus-host disease. Similarly, we applied transgenic mice over-expressing heparanase in most tissues as the recipients of BMT from C57BL/6 mice. Monitoring clinical parameters of graft-versus-host disease, the transgenic mice showed 100% survival on day 40 post transplantation, compared to only 50% survival on day 14, in the control group. In vitro and in vivo studies revealed that heparanase inhibited T cell function and activation through modulation of their cytokine repertoire, indicated by a marked increase in the levels of Interleukin-4, Interleukin-6 and Interleukin-10, and a parallel decrease in Interleukin-12, tumor necrosis factor-alfa and interferon-gamma. Using point mutated inactive enzyme, we found that the shift in cytokine profile was independent of heparanase enzymatic activity.Our results indicate a significant role of heparanase in bone marrow transplantation biology, facilitating engraftment and suppressing graft-versus-host disease, apparently through an effect on T cell activation and cytokine production pattern

Telomere Shortening Sensitizes Cancer Cells to Selected Cytotoxic Agents: In Vitro and In Vivo Studies and Putative Mechanisms

or telomere shortening resulting from inhibition of telomerase activity. In addition, the characteristics and mechanisms of sensitization to cytotoxic drugs caused by telomerase inhibition has not been elucidated in a systematic manner. mouse model. The putative explanation underlying the phenotype induced by telomere shortening may be related to changes in expression of various microRNAs triggered by telomere shortening.To our best knowledge this is the first study characterizing the relative impact of telomerase inhibition and telomere shortening on several aspects of cancer cell phenotype, especially related to sensitivity to cytotoxic drugs and its putative mechanisms. The microRNA changes in cancer cells upon telomere shortening are novel information. These findings may facilitate the development of telomere based approaches in treatment of cancer

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible

Pattern of Relapse and Treatment Response in WNT- Activated Medulloblastoma

Over the past decade, wingless-activated (WNT) medulloblastoma has been identified as a candidate for therapy de-escalation based on excellent survival; however, a paucity of relapses has precluded additional analyses of markers of relapse. To address this gap in knowledge, an international cohort of 93 molecularly confirmed WNT MB was assembled, where 5-year progression-free survival is 0.84 (95%, 0.763-0.925) with 15 relapsed individuals identified. Maintenance chemotherapy is identified as a strong predictor of relapse, with individuals receiving high doses of cyclophosphamide or ifosphamide having only one very late molecularly confirmed relapse (p = 0.032). The anatomical location of recurrence is metastatic in 12 of 15 relapses, with 8 of 12 metastatic relapses in the lateral ventricles. Maintenance chemotherapy, specifically cumulative cyclophosphamide doses, is a significant predictor of relapse across WNT MB. Future efforts to de-escalate therapy need to carefully consider not only the radiation dose but also the chemotherapy regimen and the propensity for metastatic relapses

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Decision making for Pap testing among Pacific Islander women.

This study employed a Multi-Attribute Utility (MAU) model to examine the Pap test decision-making process among Pacific Islanders (PI) residing in Southern California. A total of 585 PI women were recruited through social networks from Samoan and Tongan churches, and Chamorro family clans. A questionnaire assessed Pap test knowledge, beliefs and past behaviour. The three MAU parameters of subjective value, subjective probability and momentary salience were measured for eight anticipated consequences of having a Pap test (e.g., feeling embarrassed, spending money). Logistic regression indicated that women who had a Pap test (Pap women) had higher total MAU utility scores compared to women who had not had a Pap test within the past three years (No Pap women) (adjusted Odds Ratio = 1.10). In particular, Pap women had higher utilities for the positive consequences 'Detecting cervical cancer early, Peace of mind, and Protecting my family', compared to No Pap women. It is concluded that the connection between utility and behaviour offers a promising pathway toward a better understanding of the decision to undergo Pap testing

Synthetic PreImplantation Factor (sPIF) induces posttranslational protein modification and reverses paralysis in EAE mice.

An autoimmune response against myelin protein is considered one of the key pathogenic processes that initiates multiple sclerosis (MS). The currently available MS disease modifying therapies have demonstrated to reduce the frequency of inflammatory attacks. However, they appear limited in preventing disease progression and neurodegeneration. Hence, novel therapeutic approaches targeting both inflammation and neuroregeneration are urgently needed. A new pregnancy derived synthetic peptide, synthetic PreImplantation Factor (sPIF), crosses the blood-brain barrier and prevents neuro-inflammation. We report that sPIF reduces paralysis and de-myelination of the brain in a clinically-relevant experimental autoimmune encephalomyelitis mice model. These effects, at least in part, are due to post-translational modifications, which involve cyclic AMP dependent protein kinase (PKA), calcium-dependent protein kinase (PKC), and immune regulation. In terms of potential MS treatment, sPIF was successfully tested in neurodegenerative animal models of perinatal brain injury and experimental autoimmune encephalitis. Importantly, sPIF received a FDA Fast Track Approval for first in human trial in autommuninty (completed)