Bing‐Neel syndrome (BNS) remains a rare complication of Waldenstrom Macroglobulinemia. Given the paucity of this disease, treatment guidelines are based on small clinical trials with limited participants. Here, we present a case of primary CNS diffuse large B‐cell lymphoma masqueraded as BNS that developed while on ibrutinib therapy. Even when disease appears stable while on systemic therapy for Waldenstrom Macroglobulinemia, CNS complications may arise, and early recognition can lead to improved quality of life and progression‐free survival during these rare instances. Magnetic resonance (MRI) imaging of the brain demonstrated an enhancing, hypercellular mass centered in the left thalamus with additional foci of signal abnormality and enhancement in the cortex of the left frontal lobe and subcortical white matter Figure . These fRegarding her oncological history, she was initially diagnosed with WM at the age of 67 after workup for complaints of chronic fatigue revealed elevated IgM levels (3370 mg/dl) as well as serum hyperviscosity. Bone marrow biopsy showed a low‐grade B‐cell lymphoma with plasmacytic differentiation and 60%–70% bone marrow involvement. Neoplastic cells were found to be lambda restricted and negative for CD5, CD10, and CD23 by flow cytometry. An increased number of lambda predominant cells were confirmed by flow cytometry and CD138 immunostaining. The patient was started on first‐line therapy with the Bruton tyrosine kinase inhibitor ibrutinib; however, due to worsening adverse effects after 6 months of therapy she transitioned to rituximab, an anti‐CD20 monoclonal antibody. Unfortunately, the patient was found to have worsening IgM levels and serum viscosity while on rituximab monotherapy over the next 6 months. Thus, she was restarted on ibrutinib while continuing rituximab every 3 months and had significant improvement on this combination of therapy.She completed two years of maintenance rituximab and reduced‐dose ibrutinib (140 mg) at time of presentation with the most recent IgM levels of 299 mg/dl prior to the onset of her previously mentioned neurological symptoms. Given her MRI findings, computed tomography (CT) imaging of the head, chest, abdomen, and pelvis was completed, which revealed multiple intracranial lesions but no evidence of lymphadenopathy or neoplastic process elsewhere. She further underwent lumbar puncture for cerebral spinal fluid (CSF) analysis with flow cytometry showing mostly T cells without evidence of B‐cell non‐Hodgkin lymphoma. As there remained high suspicion for central nervous system (CNS) lymphoma, the patient ultimately had a left parietal stereotactic brain biopsy with pathology findings of diffuse aggressive B‐cell non‐Hodgkin lymphoma Figure . ImmunohThe patient was placed on oral dexamethasone 4 mg four times daily with noticeable improvement in her speech and mobility. Due to the patient's poor renal function, she was not a candidate for induction therapy with methotrexate. Thus, she began treatment with whole brain radiation therapy (WBRT) to 30.6 Gy while continuing systemic treatment with ibrutinib. A repeat MRI of the brain two months later demonstrated near resolution of the patient's lymphoma with findings of only a few small foci of nonspecific enhancement adjacent to the biopsy cavity within the left thalamus  is defined by the World Health Organization (WHO) as an indolent lymphoplasmacytic lymphoma (LPL) belonging to the category of non‐Hodgkin B‐cell lymphomas (NHL).In approximately 1% of WM cases, patients develop Bing‐Neel syndrome (BNS) in which there is infiltration of the CNS by lymphoplasmacytic cells. The absolute incidence of BNS is unknown, but in a retrospective cohort study of 1523 WM patients, only 13 patients (0.8%) were diagnosed with BNS, suggesting a very low prevalence.The evaluation for BNS begins with diagnostic testing including gadolinium‐enhanced magnetic resonance imaging (MRI) of the brain and whole spine as well as cerebrospinal fluid (CSF) sampling for cytology, flow cytometry, and mutational analyses.Despite these sensitive imaging techniques, MRI cannot distinguish between other forms of CNS lymphoma thus necessitating further testing with both CSF analysis and tissue biopsy. CSF should be collected and sent for cytology, flow cytometry, in addition to electrophoresis and immunofixation in order to determine whether M‐protein and specific immunoglobulin elevations are present within the CNS.Given the paucity of BNS cases, there remains a lack of universal guidelines pertaining to the management of this disease process. Although there is no standardized approach for treatment, therapy should begin with identifying individuals who are symptomatic from their disease and requires antineoplastic agents that have the ability to penetrate the blood‐brain barrier or agents available for intrathecal administration.Additional potentially targetable mutations have been identified throughout the past decade and thus offer further therapeutic alternatives for the treatment of WM and BNS. WHIM  syndrome caused by heterozygous mutations within the CXCR4 gene is observed in the pediatric population and characterized by chronic noncyclic neutropenia.Our case illustrates the struggle clinician's face when attempting to differentiate between primary CNS lymphoma versus progression to BNS in patients with previously established WM. In our patient's situation, switching from ibrutinib to rituximab had short‐lived efficacy and soon led to worsening serum viscosity and IgM gammopathy. It is possible that she developed CNS signs and symptoms at the time of disease progression from an inadequate response to rituximab monotherapy because of its poor blood‐brain barrier permeability. It is unclear whether or not the patient's other medical conditions including stage III chronic kidney disease and previously treated transitional cell ureteral cancer conferred an increased risk for CNS disease. Resuming therapy with ibrutinib resulted in significant improvements of her lab values and symptoms. However, despite continued treatment and relatively stable disease the patient developed neurological complications and was found to have CNS lymphoma. Diagnostic studies revealed diffuse large B‐cell lymphoma raising suspicion for either the development of primary CNS lymphoma versus progression of WM to BNS. Because large cell transformation in WM is a rare occurrence, it is likely that this case represents primary CNS lymphoma masquerading as BNS. The patient's DLBCL demonstrated a MYD88L265P mutation that is found in both the lymphoplasmacytic cells of WM as well as primary CNS lymphomas. Therefore, clonality studies between bone marrow biopsy and brain tissue are required in such cases in order to confirm a diagnosis of BNS. Unfortunately, bone marrow biopsy was not performed when our patient presented with neurological complications, thus the percentage of lymphoplasmacytic cell infiltration was also unknown at the time. In hindsight, ibrutinib resistance demonstrated through a C481S BTK mutation from the patient's brain biopsy would have further aided in the evaluation for BNS in the event that the patient's WM became resistant to treatment. Once systemic therapy was combined with WBRT there was noted improvement in both her neurological symptoms as well as extent of disease evident on repeat imaging. We believe this case outlines the importance of recognizing early signs and symptoms of CNS involvement within those who have a history of WM, regardless of their current and past treatment regimens. Diagnostic evaluation should be thorough and include biopsy of both intracranial lesions as well as the bone marrow for clonality study comparison when suspicion remains high for BNS in order for there to be clear differentiation between progressions of WM versus development of a primary CNS lymphoma. Early recognition of these disease processes can ultimately lead to more prompt treatment and rapid improvement of symptom burden, extended progression free survival, and overall improved quality of life in this patient population.The authors of this manuscript declare no conflict of interest.Lukas Delasos: contributed to the preparation and presentation of the published work through creation of the initial draft, review of literature, and development of the finalized manuscript. Deep Phachu and Nishka Shetty: contributed to critical review, commentary, and revision of the manuscript. Melissa Sepulveda‐Ramos: contributed to the pathological images and analysis. James Vredenburgh: contributed to critical review and final revisions to the manuscript.This manuscript was completed in accordance with the ethical standards of the institutional research committee.The authors have confirmed during submission that patient consent has been signed and collected in accordance with the journal's patient consent policy.


Dear Editor,Xiao et al evaluated the in‐hospital outcomes of transcatheter aortic valve implantation (TAVI) in patients with and without chronic obstructive pulmonary disease (COPD) by analyzing the National Inpatient Sample (NIS) database from 2011 through 2014.First, given the administrative nature of the NIS database, it lacks details regarding the severity of COPD quantified by the definitive criteria, and the co‐existence of pulmonary hypertension (PH), both of which are important factors for pre‐procedural risk stratification and planning. Patients with mild COPD constitute a markedly different group than oxygen‐dependent COPD. In particular, patients with very low forced expiratory volume in 1‐second (<30% predicted) are at an increased risk of poor outcomes following TAVI.Nonetheless, we would like to commend the authors for conducting this excellent study which supports the effectiveness of TAVI among patients with concomitant COPD despite the increased risk of respiratory complications.


EMBO Molecular Medicine by Kaur et al (2021) reveals that treatment of Wnt‐addicted cancer cells with inhibitors of Wnt signaling induces a state of BRCAness leading to hypersensitivity to PARP inhibitors. This is a new example of induced synthetic lethality that could pave the way for new indications for PARP inhibitors or may contribute to the long‐awaited clinical introduction of therapeutic agents targeting the Wnt pathway.Despite considerable efforts, therapeutic strategies targeting the Wnt pathway are still not clinically available. The pervasive role of Wnt‐βcatenin signaling for the control of stem cells during normal tissue homeostasis makes the on‐target toxicity of available therapeutic grade molecules an important limitation preventing their clinical introduction. The article in this issue of  et al (this issue of EMBO Mol Med) that shows that treatment of Wnt‐addicted cancer cells with inhibitors of Wnt signaling induces a state of BRCAness leading to hypersensitivity to PARP inhibitors.S. Angers discusses a new example of induced‐synthetic lethality reported by Kaur  This state of impaired HRR activity, which predicts the efficacy of PARP inhibitors, is commonly referred to as “BRCAness” as it phenocopies to some extent the germ line inactivation of the BRCA1/2 genes  induces a vulnerable state in tumor cells that can be exploited by a second treatment (in this case a PARP inhibitor) Fig . While PRNF43 mutations but that are resistant to Wnt pathway inhibition, possibly the result of additional mutations evading a requirement for βcatenin signaling for the proliferation of these cells. This result suggests that harnessing this induced synthetic lethal interaction could be effective in the context of tumors resistant to Wnt pathway inhibitors or alternatively slow down the emergence of therapeutic resistance.Finally, the functional interaction between Wnt‐βcatenin signaling and the DNA damage pathways is distinct from the well‐established role of the Wnt pathway in progenitor cell self‐renewal. Consistent with this, synergy between Porcupine and PARP inhibitors was also observed in pancreatic cancer cell lines that harbor


P = .005). This relationship remained significant in the estrogen receptor (ER)‐negative subgroup (P = .047). The univariable odds ratio (OR) of a stroma‐low tumor on pathological complete response (pCR) was 2.46 , which attenuated to 1.90  after adjustment for relevant prognostic factors. Subgroup analyses revealed an OR of 5.91 in univariable analyses for ER‐negativity  and 1.48 for ER‐positivity . In conclusion, a low amount of stroma on pre‐treatment biopsies is associated with a higher MP score and pCR rate. Therefore, the TSR is a promising biomarker in predicting neoadjuvant treatment outcome. Incorporating this parameter in routine pathological diagnostics could be worthwhile to prevent overtreatment and undertreatment.The tumor‐stroma ratio (TSR) has proven to be a strong prognostic factor in breast cancer, demonstrating better survival for patients with stroma‐low tumors. Since the role of the TSR as a predictive marker for neoadjuvant chemotherapy outcome is yet unknown, this association was evaluated for HER2‐negative breast cancer in the prospective DIRECT and NEOZOTAC trials. The TSR was assessed on 375 hematoxylin and eosin‐stained sections of pre‐treatment biopsies. Associations between the TSR and chemotherapy response according to the Miller‐Payne (MP) grading system, and between the TSR and pathological response were examined using Pearson's chi‐square, Cochran‐Armitage test for trend and regression analyses. A stroma‐low tumor prior to neoadjuvant chemotherapy was significantly associated with a higher MP score ( AC‐Tdoxorubicin/cyclophosphamide followed by docetaxelCIconfidence intervalERestrogen receptorFEC‐Tfluorouracil/epirubicin/cyclophosphamide followed by docetaxelH&Ehematoxylin and eosinHER2human epidermal growth factor receptor 2KkappaMPMiller‐PayneORodds ratiopCRpathological complete responseSDstandard deviationTACdocetaxel/doxorubicin/cyclophosphamideTNBCtriple negative breast cancerTSRtumor‐stroma ratioWhat's new?Neoadjuvant chemotherapy is increasingly used in the treatment of early‐stage, operable breast cancer. However, response rates vary widely among patients, calling for predictive biomarkers. Here, the authors show that a low amount of stroma in pre‐treatment biopsies is significantly associated with a higher chemotherapy response, as measured with the Miller‐Payne grading system and a higher complete pathological response rate in HER2‐negative breast cancer patients. The results highlight the tumor‐stroma ratio, which can easily be incorporated into routine pathological diagnostics, as a promising predictive biomarker for neoadjuvant treatment outcome in breast cancer patients.1Breast cancer is the leading type of cancer among women in Europe.The tumor‐microenvironment holds opportunities in the search for a marker to predict treatment efficacy.In order to further elucidate the predictive value of this parameter, in this study, the TSR was evaluated on pre‐treatment biopsies of the tumor, together with both the Miller‐Payne (MP) grading system and the pathological complete response (pCR) rate after neoadjuvant chemotherapy on surgical resection material. Hereby, we aimed to predict response to treatment in HER2‐negative, early‐stage breast cancer patients treated in the Dutch Breast Cancer Research Group (BOOG) phase III, randomized, multicenter DIRECT22.1This study analyzed patients who were included in two prospective, randomized, multicenter trials that were coordinated by the Dutch Breast Cancer Research Group (BOOG). Both databases comprised of clinical data of women with a histologically confirmed diagnosis of HER2‐negative, stage II/III early breast cancer who received neoadjuvant chemotherapy. All therapies consisted of antracycline and taxane containing chemotherapy agents. In the DIRECT trial, patients received either doxorubicin/cyclophosphamide followed by docetaxel (AC‐T) or fluorouracil/epirubicin/cyclophosphamide followed by docetaxel (FEC‐T), according to hospital guidelines. Moreover, the patients were randomly allocated to either a fasting mimicking diet 3 days before and on the day of chemotherapy or to a regular diet. In the NEOZOTAC trial, patients were randomly assigned to receive either docetaxel/doxorubicin/cyclophosphamide (TAC) or TAC combined with zoledronic acid.Detailed study design and inclusion and exclusion criteria of both studies have previously been reported.Both studies were conducted in accordance with the Declaration of Helsinki and were approved by the Ethical Committee of the Leiden University Medical Center in agreement with the Dutch law for medical research involving human subjects.2.2The TSR was visually determined on routine histological hematoxylin and eosin (H&E)‐stained slides of pre‐treatment biopsies, as described previously.2.3Primary endpoints were response to neoadjuvant chemotherapy according to the MP grading system2.4P‐value of less than .05 was considered statistically significant. All data was analyzed using IBM SPSS Statistics (version 25.0 for Windows).Analyses are reported for the total group of patients and stratified by estrogen receptor (ER) status. Continuous, normally distributed variables are shown as mean (SD) and categorical variables are presented as absolute numbers (percentage). Pearson's chi‐square was used to evaluate possible associations between TSR and pCR rate and between TSR and tumor grade, and the Cochran‐Armitage test for trend for TSR and MP response. Logistic regression analysis was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between TSR and pCR rate, adjusting for variables which have been reported to be associated with pCR . The amount of missing values was low and these were, therefore, not imputed. Subgroup analyses of the study groups are provided in the Appendix 33.1The total study population comprised of 375 patients with HER2‐negative, stage II/III breast cancer, amenable to neoadjuvant chemotherapy . The ORs and 95% CIs for the multivariable analyses of the pCR rate are shown in Table P = .119). In the ER‐negative subgroup . This was not the case in the ER‐positive subgroup . Multivariable analysis could not be performed in the ER‐negative subgroup, due to the undersized patient group. However, in the ER‐positive subgroup, there was a 1.34 odds of pCR if patients had a low stromal content . Subgroup analyses of the DIRECT and NEOZOTAC trials revealed statistically significant associations between TSR and pCR rate, similar to the total patient group , with a higher number of grade 3 tumors in the stroma‐low group (73.5%) than in the stroma‐high group (26.5%). This result was also seen in the trial subgroups .The association between the TSR and tumor grade was evaluated for the total group of patients and the subgroups Figure . Only a 4The aim of this study was to evaluate the predictive value of the TSR on the histopathological response to neoadjuvant anthracycline and taxane‐based chemotherapy in patients with stage II/III HER2‐negative breast cancer. TSR, determined on pre‐treatment biopsies, was equally distributed between the two study groups (DIRECT and NEOZOTAC) and it was predictive for MP response and pCR rate in the total trial population. Low stromal content was significantly associated with a higher MP score and a higher pCR rate in univariable analysis. Similarly, subgroup analysis of ER‐negative tumors revealed a significantly higher MP score and pCR rate for stroma‐low tumors. These outcomes suggest a better response to neoadjuvant treatment for patients with stroma‐low tumors, especially for patients with ER‐negative tumors, compared to patients with stroma‐high tumors.This study is a planned subanalysis of the two prospective DIRECT and NEOZOTAC trials. In these studies, the effect of dietary interventions on therapeutic efficacy and the addition of zoledronic acid to the neoadjuvant treatment regimen were assessed, respectively. However, both intervention arms did not significantly affect the pCR rate. Therefore, we expect no influence of the interventions on the main objective of the current study.Importantly, this is the first prospective study investigating the potential of the TSR as a predictor for neoadjuvant chemotherapy outcome in patients with HER2‐negative tumors. A recently published retrospective study analyzing breast cancer biopsies concluded that patients with stroma‐low tumors had a greater chance of good response to neoadjuvant therapy,This study does have some limitations. First of all, the TSR was not assessed in lymph nodes in this study, since sentinel lymph node biopsies are currently not standard of care. It was shown that there can be a difference in the stroma status between the primary tumor and the associated lymph nodes and that combining the TSR of the lymph nodes with the TSR of the primary tumor was very valuable in the prognostication of breast tumors.K) of 0.506,K‐value of TSR scores of breast cancer resection material is higher, ranging from 0.68 to 0.85.Secondly, in this study, the assessment of the TSR was performed on pre‐treatment biopsies, instead of on resection material, while the latter is most commonly used in studies. Although the method of scoring the TSR has proven to be easy and reproducible,At present, our hypothesis is that the favorable response to neoadjuvant chemotherapy in the stroma‐low group of our study can be explained as a consequence of tumor biology. It is suggested that stroma provides a favorable, protective environment for the tumor, thereby leading to more aggressive disease, resistance to chemotherapy and worse outcomes in case of stroma‐high tumors.In combination with tumor grade and ER status, implementation of the TSR into routine pathological diagnostics could attribute to the prevention of overtreatment of women with breast cancer in the neoadjuvant setting, as well as to the selection of patients that are ideal candidates for such therapies. Results of this study show that patients with stroma‐high, HER2‐negative tumors have a lower rate of pCR after neoadjuvant treatment and have a lower MP response on average, thus less decrease in the amount of tumor cells. Especially for this patient group, considering alternative or additional treatment might be worthwhile. Next to that, the online prediction tool PREDICT can be used in the clinical decision making of early, invasive breast cancer.nab‐paclitaxel are investigated as possible neoadjuvant therapeutic agents in TNBC, did not show similar positive results with respect to pCR rate, the other trials, in which durvalumab (GeParNuevo) and pembrolizumab  were administered as immunotherapeutic agents in combination with chemotherapy, were very promising and led to an increased pCR rate. These outcomes could possibly be very valuable for the hormone receptor‐negative, stroma‐high patients, who do not respond well to treatment solely consisting of neoadjuvant chemotherapy. Moreover, the TSR could play an additional role in assessing the outcomes in these studies.Especially within the triple‐negative breast cancer (TNBC) population of this study, patients with a high amount of stroma showed statistically significantly worse outcomes to neoadjuvant treatment. Therefore, personalized therapy would be of great additional value for this group of women, particularly because of the overall worse outcomes that are associated with TNBC.In conclusion, within this population of HER2‐negative breast cancer patients who were treated with neoadjuvant chemotherapy, a low amount of stroma in pre‐treatment biopsies of the tumor was associated with a higher MP score and pCR rate. Further research including the stroma status of lymph nodes may enhance the predictive effect of the TSR for treatment outcome to optimize patient selection for neoadjuvant therapy.The authors declare no potential conflicts of interest.Both the DIRECT (NCT02126449) and NEOZOTAC (NCT01099436) studies were conducted in accordance with the Declaration of Helsinki and were approved by the Ethical Committee of the Leiden University Medical Center in agreement with the Dutch law for medical research involving human subjects. Informed consent was obtained from all patients. Trial registration: ClinicalTrials.gov NCT01099436 (NEOZOTAC), registered April 7, 2010 and ClinicalTrials.gov NCT02126449 (DIRECT), registered April 30, 2014.Figure S1 Association between the primary endpoint MP response and the stroma status stratified by study cohortFigure S2 Association between the primary endpoint pCR and the stroma status stratified by study cohortFigure S3 Association between the tumor grade and the stroma status for the total study cohort and stratified by study cohortClick here for additional data file.


CRTC3 gene was associated with clinically relevant poorer CRC‐specific survival in both the discovery  and validation analysis . In addition, the GG genotype of rs10161980 (intronic variant of AL139383.1 lncRNA) was associated with worse overall survival in the discovery cohort  and CRC‐specific survival in the validation cohort . Our findings show that common genetic risk factors can also influence CRC survival outcome.Previous studies using additive genetic models failed to identify robust evidence of associations between colorectal cancer (CRC) risk variants and survival outcomes. However, additive models can be prone to false negative detection if the underlying inheritance mode is recessive. Here, we tested all currently known CRC‐risk variants (n = 129) in a discovery analysis of 5675 patients from a Scottish cohort. Significant associations were then validated in 2474 CRC cases from UK Biobank. We found that the TT genotype of the intron variant rs7495132 in the  AJCCAmerican Joint Committee on CancerCIconfidence intervalCRCcolorectal cancerGWASgenome‐wide association studyHRhazard ratioSOCCSStudy of Colorectal Cancer in ScotlandWhat's new?To date, there is a dearth of well‐powered studies examining the effect of common germline variants associated with colorectal cancer risk on subsequent survival outcomes. While studies using additive genetic models have failed to reveal robust associations between colorectal cancer risk variants and survival outcomes, additive models can be prone to false‐negative detection if the underlying inheritance mode is recessive. Here, the authors identified two genetic variants with significant recessive effects on survival outcomes in colorectal cancer. Altogether, the findings provide novel insight into the genetic architecture influencing post‐diagnosis survival in colorectal cancer patients.1prediction of cancer outcomes. Genetic variants involved in CRC pathogenesis may also influence subsequent survival outcomes in affected carriers. However, previous studies of low penetrance common genetic variance found inconsistent results using additive genetic models. Hence, no robust associations have been established between any common germline variation and CRC survival outcome.risk, in order to determine whether such variants also had recessive effects on survival outcomes.Previous evidence have shown a familial contribution to survival outcomes of colorectal cancer (CRC), indicating that heritable germline genetic components can contribute to survival outcome.2r2 < 0.2) associated with CRC risk reported by two recent GWAS analysesWe tested associations between survival outcomes of CRC patients and a total of 129 known genetic variants (linkage disequilibrium P < .05 was the significance threshold after correction). We checked the proportional hazards assumption of the Cox model for each identified variant by testing the association between the Schoenfeld residuals and follow‐up time using the SOCCS cohort.P < .05), which indicated potential violation of the proportional hazards assumption, we fitted a parametric Weibull model to estimate the genetic effects on survival outcomes.We studied 5675 CRC patients with GWAS data from the Study of Colorectal Cancer in Scotland (SOCCS) (discovery set). Age at diagnosis, sex and tumour stage were collected as covariates. Statistical power of discovery analysis was estimated for variants with varied genotype frequencies and effect sizes using the method proposed by FreedmanSignificant associations identified from the SOCCS were then validated in an independent population‐based prospective cohort composed of 2474 incident CRC cases from the UK Biobank. Only age at diagnosis and sex were adjusted in the model due to CRC stage being unavailable in the UK Biobank. Given different covariates used, meta‐analysis combining the two cohorts was not performed. Ethics approval of the study cohorts is presented elsewhere.3Patient characteristics for the study cohorts are presented in Table P < .05; Table P = 3.4 × 10−4, FDR corrected P = .022). However, this effect was not significant on CRC‐specific survival after FDR correction . In addition to rs10161980, the TT genotype of the variant rs7495132 was significantly associated with worse CRC‐specific survival , whereas the association with overall survival did not survive the FDR correction . No significant deviation from the proportional hazards assumption was observed for either variants (P > .05). We then validated the two observed signals using the UK Biobank cohort (N = 2474). The GG genotype of rs10161980 was significantly associated with worse CRC‐specific survival , but not significantly associated with overall survival . As for the other variant rs7495132, we replicated the observed association between the TT genotype and CRC‐specific survival in the UK Biobank , although no significant effect was detected of this variant on overall survival . Kaplan‐Meier survival estimates of these two variants in both discovery and validation analysis are plotted in Figure In the discovery analysis, we found 12 variants associated with overall survival and 9 variants associated with CRC‐specific survival at nominal significance , whereas rs10161980 was observed to be associated with overall survival of female patients from the SOCCS cohort . In addition, we identified significant association between the TT genotype of the variant rs174537 and worse overall survival among female patients from the SOCCS. However, we failed to validate any of these associations by conducting sex‐stratified analysis using the UK Biobank cohort  with significant effects on survival outcomes of CRC. Notably, both variants, although not reaching statistical significance, showed the same direction but smaller magnitude of effects in our previous analysis using additive model,CRTC3 gene which encodes the protein CREB regulated transcription co‐activator 3 (CRTC3) and is associated with human T‐cell leukaemia virus infection. According to the results of Genotype‐Tissue Expression project , the T allele of rs7495132 is associated with lower expression of the CRTC3 gene in multiple human tissues such as musculoskeletal tissue and subcutaneous adipose tissue, although no significant association is present in colonic tissue. Previous evidence showed that the CRTC3 protein can regulate energy balance and is associated with weight gain in a mouse model.P = .595). We also added BMI as a covariate in the survival model, and observed that the BMI level was not associated with survival outcomes , whereas the genetic association between rs7495132 and survival outcomes remained significant in the multivariable model. These results could not rule out adiposity as a potential mediator in the observed genetic association. Future research is needed to further reveal the possible biological function of rs7495132 in the adiposity‐mediated pathway involved in CRC progression.The T allele of rs7495132 was identified as a CRC‐risk increasing allele in the original GWAS meta‐analysis.AL139383.1 LncRNA located in chromosome 13. Currently, little is known for potential biological implications of this gene, and there has been a paucity of evidence that rs10161980 can affect expression of any genes. Notably, the G allele of rs10161980 was identified as the CRC‐risk decreasing allele in the original GWAS.With respect to the other variant rs10161980, it is an intron variant of the Given the limited sample size of the study cohorts and the number of variants tested, our study was underpowered to detect small recessive genetic effects (HR < 1.20), especially for variants with low homozygous genotype frequencies (<0.30). Another limitation is that American Joint Committee on Cancer (AJCC) stage was not adjusted in replication analysis due to unavailable data. We re‐estimate the effects of these two variants in SOCCS adjusted for only age and sex, and the results remained statistically significant (Supplementary Table Our study identifies two CRC‐risk variants (rs10161980 and rs7495132) that have recessive genetic effects on survival outcomes of CRC. Currently AJCC stage is used as a blunt indicator of the need for adjuvant chemotherapy. The genetic associations discovered in our study provide prospects for future investigations to incorporate rs10161980 and rs7495132 genotypes into individual prediction models for survival outcomes. In addition, our findings improve the understanding of the genetic architecture for prognosis of CRC patients. Future research is warranted to illuminate biological mechanisms through which these two variants influence CRC progression. Our findings also suggest that recessive genetic effects should be considered in future genetic association studies on survival of CRC.All authors declared no potential conflicts of interest.Appendix S1: Supplementary InformationClick here for additional data file.


Anser anser) to test the hypothesis that geese originating at five latitudinally separated capture sites in Sweden have different migration strategies. We also assessed individual consistency in movement strategy over consecutive annual cycles. We used the scale‐independent net squared displacement modeling framework to quantify variables of autumn and spring migration for geese from each capture site: distance, timing, and duration. Our results demonstrate a positive correlation between migration distance and latitudinal origin. Geese from the northernmost site on average migrated farther south and about 15 times as far as the short‐moving or resident geese from the two southernmost sites. Movement strategies of individual geese varied considerably both within and among capture sites. Individual consistency in movement strategy from one annual cycle to the consecutive was high in geese from the northern sites moving the farthest, whereas the resident or short‐moving geese from the southernmost sites generally showed lower or no individual consistency. These changes have come about during a time span so short  that it can unlikely be explained by classical Darwinian between‐generation adaptation. Consequently, and given that young geese follow their parents during their first migration, we presume an important role of within‐family, inter‐generation change as a driver behind the large‐scale changed migration habits in Swedish Greylag geese.Knowledge about intraspecific and individual variation in bird migration behavior is important to predict spatiotemporal distribution, patterns of phenology, breeding success, and interactions with the surrounding environment . Such variation is key to adaptive, evolutionary responses, i.e., how individuals respond spatiotemporally to the environment to maximize fitness. In this study we used GPS location data from one to three full annual cycles from 76 Greylag geese ( Anser anser, to test the hypothesis that geese originating at five latitudinally separated capture sites in Sweden have different migration strategies. Our results demonstrate that migration strategies of individual geese varied considerably both within and among capture sites, with increasingly longer migration distances for geese originating at higher latitudes.Knowledge about intraspecific and individual variation in bird migration behavior is key to predict adaptive and evolutionary responses, i.e., how individuals respond spatiotemporally to the environment to maximize fitness. In this study we used GPS location data from one to three full annual cycles from 76 Greylag geese,  Such variation is also key to adaptive responses studied within the evolutionary paradigm; i.e., how individuals respond spatiotemporally to selective regimes in an optimal way to maximize fitness  that force individuals to change distance or timing of migration, it may also change because of evolutionary adaptation to long‐term environmental change . Research accumulated over the last two decades documents significant changes in timing of migration in a wide range of avian species  or Made‐by‐Theo (Theo Gerrits)  were removed completely from the dataset to avoid inter‐individual autocorrelation in movement. Consequently, data from 76 individuals were used in subsequent analyses . In total, 82 individuals from the five capture sites were tracked for at least one annual cycle. We defined the annual cycle as starting on July 1, when all geese were flightless and resident in a restricted area for breeding and molting. We only included individuals with location data for more than 349 days of the annual cycle . Locations for all geese were visually inspected in ArcGIS (vers. 10.7), and in cases where individuals obviously moved together in pairs or flocks, the individuals with the least number of locations (2.32) based on a straight line from the starting location (July 1 each year) to all consecutive daily locations for each individual and annual cycle (July 1 year t to June 30 year t + 1). These calculations were made in the R package adehabitatLT version 1.6  based on the following equation , θa the date of reaching half of the asymptotic distance during autumn migration, θs the date of reaching half of the spring migration distance to the sites of origin , and φa and φs the number of days lapsed to cover a quarter of the distance moved (from 1/2 to 3/4 of the asymptotic migration distance) in autumn and spring , respectively. The equation is divided into two sub‐equations to allow for variation in movements in autumn versus spring   were used as response variable, capture site as a fixed‐effect variable of distance, duration and timing of autumn and spring movement, and goose ID as a random effect on migration distance.To identify differences in migration strategy between geese of different origin, we used the NSD values for the full annual cycles as response variable, and explored the effect of capture site  with the complement of annual cycle  as explanatory variable for distance, duration, and timing of autumn and spring movement .n = 43, Table To test for individual consistency in movement strategy over consecutive annual cycles, we extracted data from individuals that had NSD covering at least two consecutive annual cycles (3NA–1388) and Örebro , and 15 and 4 times as far as those from the southernmost sites Svedala  and Kristianstad  , timing of autumn and spring movement (dates), and duration of autumn and spring movement (days), was explained by the latitudinal origin of geese . Our results demonstrate a positive correlation between migration distance and latitudinal origin of geese. Geese from the northernmost site Hudiksvall  on average migrated 1.4 and 1.2 times as far as those from the more centrally located sites Nyköping  and did not generate any distinct migration patterns . As a result, there were wide confidence intervals around the predicted estimates of timing and duration of both autumn and spring movement, covering the full annual cycle of geese from Svedala  for Hudiksvall, Nyköping, and Örebro, and for migrating individuals from Kristianstad. Geese from Örebro reached half their migration distance by the beginning of October , followed by those from Nyköping a week later , Hudiksvall by the end of October , and the few geese migrating from Kristianstad reaching half their migration distance by the end of November . Spring migration timing corresponded to the approximate order of autumn migration timing, i.e., geese had covered half of their migration distance back to Örebro by the second week of February , followed by geese from Kristianstad  and Nyköping  roughly a week later, and geese from Hudiksvall by early March .The general consistency in order of timing of autumn and spring migration for geese with different latitudinal origin indicates that the duration of the migration period is relatively similar in geese from all capture sites, except for those from Svedala and Kristianstad, which moved only locally. The geese from Hudiksvall, which migrated the farthest and fastest in autumn , were the ones with the longest migration duration  in spring , followed by geese from Nyköping , and Örebro  . Resident or short‐moving geese from the southern sites  generally showed lower or no individual consistency, with only a few individuals showing consistent movements from one annual cycle to the next  from one annual cycle to the next was high in the geese migrating the farthest, i.e., the northernmost and central capture sites  showed distinct migration patterns, whereas only a few individuals from the southern site Kristianstad migrated, albeit short distances, and the geese from Svedala were relatively resident, only displaying local movements. Taken together, these findings corroborate the hypothesis that present‐day Swedish Greylag geese exhibit a leapfrog migration pattern lomonsen  and Bertlomonsen  and refu4.1Among the three northern capture sites, where all geese clearly migrated, the duration of autumn migration was rather similar despite differences in migration distance. The geese that migrated the farthest  thus also migrated the fastest. Yet, they did not start autumn migration earlier, as geese from Örebro and Nyköping had reached a quarter of their migration distance earlier than those from Hudiksvall. This pattern fits with findings from Greylag geese in Norway, where northern breeders migrated later than southern breeders. However, contrary to our results, northern breeders in Norway migrated slower than southern breeders   were resident in a very restricted area, with only short‐distance excursions locally or regionally. The number of Greylag geese wintering in southern Sweden has increased steadily from almost zero to ⁓60,000 during the last 30 years  indicated low or no individual consistency Table . Our sec4.4This study demonstrates a present‐day leapfrog migration pattern in Swedish Greylag geese, and differences in migration strategy at the level of capture site (latitude of breeding or molting area). We argue this pattern is robust because: (a) it conforms to the spatial predictions of the leapfrog hypothesis; (b) at the level of capture site, migration distance increased with latitude; (c) only 2 of 22 geese from the two southern sites reached the winter areas of geese from the three capture sites farther north; and (d) only one bird from the three northern capture sites wintered in the core winter area of geese from the two southern sites.Grus americana) highlights the importance of such variability by showing that older and innovative individuals attempting new strategies can be drivers of rapid change in migration patterns , leading to asymmetrical competition. Under these premises, a chain migration pattern will change into a leapfrog pattern if there is an accentuated increase in breeding suitability toward the north or in wintering suitability toward the south in the flyway , assuming that density‐dependent processes operate ; Data curation (lead); Formal analysis (lead); Visualization (lead); Writing – original draft ; Writing – review & editing . Camilla Olsson: Conceptualization ; Data curation ; Formal analysis (supporting); Writing – review & editing . Johan Elmberg: Conceptualization ; Data curation (supporting); Funding acquisition (lead); Project administration (lead); Writing – original draft ; Writing – review & editing . Nils Bunnefeld: Conceptualization ; Methodology (lead); Writing – review & editing . Niklas Liljebäck: Conceptualization ; Funding acquisition (supporting); Project administration (supporting); Writing – review & editing . Johan Månsson: Conceptualization ; Data curation (supporting); Funding acquisition (lead); Project administration (lead); Writing – review & editing .Supplementary MaterialClick here for additional data file.


JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.Time is a central element of the sexual dimorphic patterns of development, pathology, and aging of the skeleton. Because the transcriptome is a representation of the phenome, we hypothesized that both sex and sex‐specific temporal, transcriptomic differences in bone tissues over an 18‐month period would be informative to the underlying molecular processes that lead to postnatal sexual dimorphism. Regardless of age, sex‐associated changes of the whole bone transcriptomes were primarily associated not only with bone but also vascular and connective tissue ontologies. A pattern‐based approach used to screen the entire Gene Expression Omnibus (GEO) database against those that were sex‐specific in bone identified two coordinately regulated gene sets: one related to high phosphate–induced aortic calcification and one induced by mechanical stimulation in bone. Temporal clustering of the transcriptome identified two skeletal tissue‐associated, sex‐specific patterns of gene expression. One set of genes, associated with skeletal patterning and morphology, showed peak expression earlier in females. The second set of genes, associated with coupled remodeling, had quantitatively higher expression in females and exhibited a broad peak between 3 to 12 months, concurrent with the animals' reproductive period. Results of phenome‐level structural assessments of the tibia and vertebrae, and in vivo and in vitro analysis of cells having osteogenic potential, were consistent with the existence of functionally unique, skeletogenic cell populations that are separately responsible for appositional growth and intramedullary functions. These data suggest that skeletal sexual dimorphism arises through sex‐specific, temporally different processes controlling morphometric growth and later coupled remodeling of the skeleton during the reproductive period of the animal. © 2021 The Authors.  Differences in sex hormones, responsiveness to mechanical loading, and the insulin‐like growth factor axis have each been mechanistically linked to the sexual dimorphism of skeletal tissue development. Although both males and females lose bone mass with aging, bone loss in humans and other mammals has been shown to greatly accelerate in females when systemic levels of estrogen decrease at menopause or after ovariectomy or after genetic deletion of the estrogen receptor. The loss of estrogen increases bone turnover and has molecular effects on the interactions among the three cell types in bone  that regulate bone remodeling. There are also intrinsic sex‐associated differences in postnatal skeletal morphogenesis associated with earlier sexual maturation and earlier cessation in overall skeletal growth in females, which itself leads to a smaller skeletal mass in females compared with males. Finally, emerging data suggest that there are immense sex‐specific differences in the coupled remodeling of the skeleton during the temporal period of female fecundity. Thus, the lower baseline skeletal mass of women, in combination with the increase in bone loss in the menopausal period, puts women at greater risk for osteoporotic fractures as they age.Most mammals exhibit skeletal sexual dimorphism, with males commonly showing larger structural sizes of their skeletal elements and greater overall bone mass compared with females.5, 6Time is a central element to the development, pathology, and aging of the skeleton. Because the transcriptome is a representation of the phenome, we hypothesized that characterization of sex‐specific, temporal transcriptomic differences would be informative to understanding the underlying biological, molecular, and cellular processes that lead to postnatal sexual dimorphism in skeletal development, homeostasis, and aging. A series of bioinformatics approaches were used to analyze the sex‐associated changes of the bone transcriptome in male and female animals over an 18‐month period of postnatal life. We identified genes that showed differential quantitative sex‐specific expression regardless of animal age, as well as those whose temporal patterns of expression over the life span differed between the sexes. We then examined the correspondence between these gene ontologies and sex‐specific bone phenotypes, at both tissue and cellular levels.n = 26 males, n = 36 females), which were obtained from a C57BL/6 colony maintained at Boston University and housed under a 12‐hour light/dark cycle. All analyses on these mice were performed at 3, 6, 9, 12, and 18 months of age. A second set of 3‐month‐old female C57BL/6 mice (N = 6) was obtained from Jackson Laboratory  and was used as an external control for this study. A schematic of the work flow of the experiments is presented in Fig. N = 4) C57BL/6 mice obtained from Jackson Laboratory.All animal experiments were approved by the Institutional Animal Care and Use Committee at Boston University. Micro‐computed tomography (microCT), transcriptomic, histological, and cell culture studies comparing male to female aging were all performed in the same mice . Methods for sample processing, normalization of microarray data, and quality control measures for examining for batch effects and comparing individual samples by principal components are fully described in the Supplementary All microarray laboratory procedures were performed at the Boston University Microarray and Sequencing Resource. Raw CEL files and RMA normalization were as previously described.22 Rawt test  across all time points in a subset of 37 wild‐type male and female animals, using non‐log‐transformed expression data (Supplementary q < 0.25) between sexes was used for initial ontology assessments and is summarized in Supplemental Table  was used to identify data sets within the NCBI Gene Expression Omnibus (GEO) that display coordinate differential gene expression of the sex‐specific expression gene set that was identified in bone. Full details of this approach are found in the Supplementary The effect of sex on gene expression was determined by performing Student's two‐sample al Table 23 was Data were averaged over the four replicates and standardized by the gene‐specific mean and standard deviation at each time point. The analysis was then performed on these data, treating temporal gene‐expression profiles from males and females as distinct objects that were then analyzed jointly in a single EPEM. Although a common approach is to cluster the data for each sex separately and compare the two sets of clusters, using EPEM, different cluster numbers may be obtained for each sex, and cluster labels need to be remapped as labels, which are then assigned independently. Therefore, our approach clusters the data together such that temporal gene expression profiles across sex are treated as distinct objects, allowing us to determine if genes across sexes are partitioned into the same or different clusters. The entire data set generated from this analysis is presented in Supplemental Table Temporal clustering of gene expression was performed using an entropy penalized expectation maximization (EPEM) algorithm with an underlying mixed‐effects model using a third‐order fixed‐effect (FE) polynomial and a second‐order gene‐specific random‐effect (RE) polynomial to allow for gene‐specific heterogeneity within a cluster.24 Dathttp://www.ingenuity.com/) and Metascape (https://metascape.org/gp/index.html#/main/step1) were used for all biological and functional assessments of gene groups identified by the various computational approaches used in this study. Only ontology groupings with modified Fisher exact test p < 0.05 were considered to be significantly overrepresented for these assessments. For the analysis using the t test approach, the entire group of genes in Supplemental Table Ingenuity pathway analysis (IPA) software  that were used for microCT were immediately placed in 4% paraformaldehyde for 1 week at 4°C, after which the bones were stored in phosphate‐buffered saline at 4°C. Samples were scanned at a nominal resolution of 8 μm/voxel and 16 μm/voxel for analysis of trabecular and cortical bone, respectively. Standard trabecular and cortical parameters then were calculated. Full details of the microCT analysis are fully described in the Supplementary At euthanasia, bones (lculated.26 Fuln = 5) of tibias per time point and sex was selected for histological analysis. Bones were processed, blocks were sectioned, and histomorphometric analysis was carried out as previously described. Histomorphometry measurements for osteoclasts were made from 5 slides per mouse from the same bones as used for microCT. Periosteal measurements were made from 8‐week‐old male and female mice (n = 2 per sex). The analysis of periosteal tissues was performed of femurs from 8‐week‐old C57BL/6 mice (n = 2 per sex). Tissues were processed in the same manner for standard histomorphometry. Details of these analyses and immunological analysis for cathepsin K antibody staining are fully described in the Supplementary After completion of the micro‐CT scans, a subset  for analyses.Bone marrow stromal cell populations were prepared from individual mice in the Boston University colony at 3, 6, 9, and 12 months of age. Cells were collected from both femora and the tibia contralateral to those used for the microCT and histological assessments.20 In Using multiple linear regression, the effects of bone site, age, sex, and their interactions were assessed for each of the microCT outcome measures . For assessments of cell culture studies, statistical analyses were performed with Prism 5 , and data are presented as mean ± standard error (SE). Gene expression data from the in vitro experiments were log transformed and then compared using two‐factor ANOVA . The sample sizes for each analysis are indicated in the figure legends.q cut‐off , 353 differentially expressed genes (DE) genes between males and females were identified  score to each sample in each data set, with positive and negative SA scores indicating whether the genes in the signature are coordinately regulated with respect to the other samples in the data set in the same or opposite manner, respectively, as in the original signature. Each data set is then assessed using Fisher's exact test to determine whether it is significantly enriched in samples with strong positive and negative SA scores. For this analysis, we used the DE group having FDR  This data set is significantly enriched in samples with strong positive or negative association with the sex‐specific gene expression signature    and have been specifically associated with regulation of mineral metabolism (Pthr1) and Wnt regulation (Sfrp2). Four of these genes had an overall sex‐specific difference in their expression with FDRq <0.05 and p values <0.00018 and two with FDRq <0.07 and p values <0.0003 , gene expression was profiled in the aortic and thoracic arteries of female DBA/2 mice maintained on a standard or high‐phosphate diet for 14 days.29 Thi28) Fig. A. A sub Nine predicted clusters with unique temporal patterns of gene expression were obtained  in the entire data set were then selected. A total of 705 genes were selected out of the total transcriptome: 269 of these genes (38%) were in common groups compared with 436 (62%) in the discordant groups  and 10,306 genes were in groups on the off‐diagonal . The genes in each group with gene ontologies that both (i) are related to skeletal tissues, skeletal cell types, skeletal development, skeletal morphology, or skeletal pathologies; and (ii) have significant overrepresentation (modified Fisher exact  were discordantly expressed between the sexes. The primary upstream regulator associated with osteoclast differentiation Tnfsf11 (RANKL) was also in the common group, while both Tnf, a major driver of numerous skeletal inflammatory diseases, and Ctnnb1, a primary intracellular signal transducer of Wnt signaling, were in the discordant group , as well as most of the subgroup of genes that had been identified as overlapping between the two experiments showing coordinate regulation of the sex‐specific differentially expressed genes  differentiation and known genes associated with osteoblasts (OB) and osteoclast functions  within intact bones ; however, younger female cells expressed greater levels of the osteocyte marker Dmp1 than male cultures when grown under osteoinductive conditions.Correspondence between the sex‐specific transcriptomic differences and the functional activities of specific skeletal cell populations in bone was next examined. Osteogenesis was examined by assessing the expression of mRNAs representative of osteogenesis and lineage progression . The temporal expression profiles of these mRNAs followed those found for osteoblasts; however, these mRNAs were always expressed at higher levels in females over the entire 18 months , whereas at later times these genes diminished in both sexes but did not show large differences between the sexes  must be systemic because they transcend variant embryonic development and/or embryonic tissue origins that are associated with the axial and appendicular development. Second, females are more metabolically active than males and this high rate of coupled remodeling is associated with the period when they are sexually reproductive. An interesting paradox to these data is the much higher levels of gene expression in the females than in males, even though males accrue an overall higher mass of bone than females. This would suggest that the overall rate of extracellular matrix accumulation is not simply a function of the levels of gene activity but under some forms of posttranscriptional control.Our temporal transcriptomic data showed gene‐level linkage between two sets of gene ontologies that affect bone quality  and/or metabolic skeletal functions  and specific patterns of temporal expression. The group of gene ontologies associated with morphogenesis show elevated expression in females at earlier times than in males. The other group of ontologies associated with mineral metabolism, osteoblasts, and osteoclasts had higher expression in females concurrent with the period of female fecundity. These observations suggest two conclusions about the underlying regulatory mechanisms controlling sexual dimorphism of skeletal tissues. First, while morphometric data have all shown that in human and mice, development and aging of the axial and appendicular skeleton is dimorphic,39 the Thus, even though female mice do not undergo menopause, the higher bone remodeling activity in the females until the end of their period of fecundity modestly compensates for a constant rate of bone loss found in both sexes after reaching skeletal maturity.Our conclusions related to an overall higher rate of coupled remodeling are all strongly supported by the histological, cell culture, and bone tissue analysis. The analysis of specific bone mRNA expression and osteoclast mRNA expression using qRT‐PCR further validated the inferred conclusions made from the transcriptomic analysis. The females also exhibited fewer but thicker trabeculae, indicative of more rounds of coupled remodeling that leads to loss of the smallest trabeculae but thickening of the remaining larger ones.16 Thu In mature animals, these bone compartments also have different functional capacities to contribute to bone repair. A recent study comparing skeletogenic stem cells from the intramedullary space and periosteal surfaces provided further evidence that bone contained separate populations of skeletogenic stem cells with distinct physiologic functions. This study showed that periosteal cell populations were defined by their unilineage potential to form only bone, their plasticity to form cartilage and bone in response to bone trauma, and their inability to support hematopoiesis. In contrast, the intramedullary population was defined by its ability to support hematopoiesis and its inherent osteogenic/chondrogenic potential. Our observed differences in transcriptomes of male and females that define skeletal sexual dimorphism would further suggest that there are developmental differences in skeletogenic stem cell populations that contribute to sexual dimorphism. This conclusion is based both on differences in periosteal thickness and on the higher expression of genes associated with maintenance of stem cell pluripotency in the periosteum of females at earlier time points than males. The finding that the stem cell maintenance gene Nanog is also preferentially expressed in the periosteal skeletal stem cell population and our observation of its earlier peak expression in females compared with males provides further evidence consistent with the conclusion that there is sexual dimorphism in the functionally different osteogenic cell populations. Finally, such findings of sexual dimorphism in the two separate populations are supported by both the unique quantitative and temporal transcriptomic differences that are associated with the earlier cessation of appositional growth in females over males in contrast to quantitatively higher levels of gene expression associated with remodeling found in females over males, which largely reflects intramedullary bone cell activity.Cortical and intramedullary bone compartments arise and develop during embryogenesis and then during postnatal growth by very different mechanisms.40 In The primary strength of this study was the use of multiple approaches to assess the sex‐specific differences in transcriptomic expression of bone tissues. Using a basic statistical approach to determine the sex‐specific expression differences characterizes the most significant sex‐associated differences in gene expression within bone tissues independent of age. The openSESAME program was used to determine if bone tissue–specific group of differentially expressed genes was also expressed in other tissues and under what experimental or biological conditions they were expressed. Finally, using the entropy penalized expectation maximization (EPEM) algorithm modeled the sex‐specific temporal differences in transcriptomic expression of the bone tissues as male and female animals develop and age, thereby identifying sexually dimorphic biological processes that affect the development and aging processes of bone tissue. Other information that was gained from the analysis of the marrow was inclusive of quantifiable changes in inflammatory and immunological expression profiles that are found in both the quantitative and temporal shifts in gene expression for ontologies associated with “inflama‐aging,” which has been put forth as a major driver to systemic aging.This study also had a number of tradeoffs and technical limitations. The use of whole bone organ RNA inclusive of the marrow and periosteum for our transcriptomic analysis does not examine the gene expression in specific cell types. Thus, the specific cell types that express specific genes and the biological processes that are regulated by age and sex in a given cell type can only be inferred. While cortical or reamed trabecular bone cleaned of adherent tissues would have enabled us to examine changes more specifically in osteoblast and osteocyte gene expression, our study would have been limited to these cell types. Therefore, it is our opinion that whole organ assessments of bulk tissue RNA inclusive of the marrow and periosteum, which are informative of osteoclasts and periosteal cell gene expression, have an overall value to understanding age‐ and sex‐related changes within the bone tissue, since these cells are primarily related to remodeling and appositional modeling, growth, and response to loading.43 Otht test of the unlogged expression values without correcting for time to assess the male to female DE. As a response to these technical deficiencies, we have performed a comparative analysis using both moderated and non‐moderated approaches, as well as comparing the t test to ANOVA methods that are summarized in Supplemental A second set of limitations for this study are related to how we performed our array data's statistical analysis. At the time that these studies were carried out in 2011 to 2015, our core facility did not have the pipeline set up to apply the moderated “limma” approach, which is now the standard for correcting for batch effects found in microarray data. We, furthermore, used a basic  On the other hand, there are strain‐specific variations in many of these same skeletal structural features, rates of bone growth, and age‐dependent bone loss with these differences found in both sexes. Other comparative studies between strains show different susceptibility to the development of obesity and diabetes. In this regard, future studies of strain‐specific differences in sexual dimorphic aging and disease pathogenesis will be quite informative to the specific genetic variants that interact with sex. Finally, it should be noted that the biological processes that we have inferred from our gene expression in mice related to sexual dimorphisms in bone, earlier cessation in appositional growth, and higher coupled remodeling during period active sexual reproduction are consistent with human features of bone sexual dimorphism.A final limitation of our study was the use of a single inbred strain of mouse to carry out this study. On the one hand, the strain we used for this study is the most common one used to generate transgenic variants for most research studies. It should also be noted that comparisons across all of the inbred strains show similar sexual dimorphism in numerous skeletal structural features such as overall cortical cross‐sectional area and cortical thickness and broadly similar differences in trabecular features.44 On These data in general show that the timing of expression of many different biological processes is sexually dimorphic and would affect the different paces of tissue aging and the development of skeletal pathologies in males and in females. More specifically, this report provides evidence that skeletal sexual dimorphism arises through sex‐specific, temporally different processes controlling morphometric growth and coupled remodeling. The data lend credence to the idea that these processes are controlled by functionally distinct skeletogenic populations of cells. Finally, the data presented here illustrate the power of using time as a dynamic factor in transcriptomic analysis to leverage underlying transcriptomic features that are informative of biological processes.All authors state that they have no conflicts of interest.https://publons.com/publon/10.1002/jbm4.10579.The peer review history for this article is available at AppendixS1. Supplementary.Click here for additional data file.Table S1. Summary of the Technical History of the RNA Preparations and Microarray AnalysisSheet one contains the technical history of the preparation of the RNA including the age, sex, and genotype of the mouse, the mouse ID applied to all further specimens and data points from that animal, the DOB of mouse date of RNA isolation, individual who isolated the RNA, RIN value of the sample, and any technical notes related to the sample. Sheet two gives concentration values for each RNA sample. Sheet three contains the cDNA concentrations.Click here for additional data file.Table S2. Sex Differential Summary of the 353 Genes Identified by t Tests of Male Versus Female, Across All Samples Without Correcting for Time Point Non‐Log Transformed Values up to FDR q Threshold at 0.025t test value, and FDRq value are included.Gene entrez ID, gene symbol, gene description Click here for additional data file.Table S3. Summary of Coordinate Rodent Series From the SESAME Analysis (as Described in the Text)Click here for additional data file.Table S4. Complete Data Set for Gene Expression Data Used for EPEM Algorithm Cluster TablesSheet one contains mean values across all time and unlogged values for male and female groups and the unlogged expression ratio between males and females. Sheet two contains the male‐to‐female cluster comparisons aligned to the cross tabulation between clusters. Gene entrez ID, gene symbol, and gene description are included with mean age sex group values. Sheet 2 and sheet 3 contain the concordant and discordant genes groups. Gene entrez ID, gene symbol, mean age and sex group values, mean values across all time and unlogged values for male and females groups, and the unlogged expression ratio between males and females are presented. Sheet 4 contains skeletal‐associated gene ontologies used in the manual annotation of the discordant and concordant gene sets used for secondary ontology assessments.Click here for additional data file.Table S5.A. Ontology Groups Used for Targeted Assessments in Fig. 6 (as Described in the Text)Table S5B. Complete Data Set for Gene Expression Data Used for Selected Cluster Comparisons in Fig. 6A–C)Top headers are for manual curation of the different gene sets in Fig. 6. Gene entrez ID, gene symbol, gene description and cluster comparisons aligned to the cross tabulation between clusters, mean age sex and group values, mean values across all time, unlogged values for male and female groups, and the unlogged expression ratio between males and females are presented.Click here for additional data file.Table S6. MicroCT Data Means by Sex and AgeSheets 1 to 3 are the individual microCT values for tibia trabecular, vertebrae trabecular, and tibia cortical parameter, respectively. Specimen ID, age, sex, bone, and data features are denoted in each sheet.Click here for additional data file.


To assess health care workers' compliance with infection prevention and control measures in different health care sectors in Qatar during COVID‐19 pandemic.Being the first line of defence against COVID‐19 infection, health care workers are particularly at increased risk of getting infected. Compliance with infection prevention and control measures is essential for their safety and the safety of patients.A web‐based national survey was conducted between November 2020 and January 2021 targeting all health care workers in governmental, semi‐governmental and private health care sectors.p < .001). During the pandemic, 49.7% of health care workers were fully compliant with personal protective equipment (PPE) use; 83.1% were fully compliant with hand hygiene. Overall, 44.1% were fully compliant with infection prevention and control measures (PPE and hand hygiene). Nationality, health sector, profession and frequency of interactions with suspected or confirmed COVID‐19 cases were significantly associated with compliance with overall infection prevention and control measures. The most reported barriers were work overload and shortages of PPE and handwashing agents.Of 1,757 health care workers, 49.9% were between 30 and 39 years of age; the majority (47.5%) were nurses. Participants reported a significant increase in the median self‐rated compliance scores during the pandemic compared with before it (Compliance of health care workers with infection prevention and control measures needs further improvement.Frequent quality checks, provision of adequate supplies and behaviour change interventions are recommended strategies for hospital and nursing administrators to improve health care workers' compliance. Health care workers—the first line of defence in the fight against COVID‐19—are particularly at risk of getting infected while taking care of infected patients  through 27 health centres distributed all over the country, and secondary and tertiary care through Hamad Medical Corporation with a number of designated hospitals. The semi‐governmental sector includes six health care facilities. The private sector includes over 40 private hospitals and clinics. We included health care workers in clinical positions , while excluded those in administrative positions.2.2A web‐based self‐administered survey was developed using Microsoft Forms software. Because of the low response rate generally encountered in web‐based surveys and in order to improve the external validity of our study, we invited all eligible health care workers in PHCC , semi‐governmental and private facilities to take the survey. They were contacted via e‐mail with an information letter and a link to the electronic version of the questionnaire. The letter stated the purpose of the study, and that the participation is voluntary. Taking the survey implied informed consent, and participants were free to terminate the survey at any time they desired. The survey was anonymous, and confidentiality of information was assured. Weekly reminders were sent to maximize the response rate.2.3We developed a questionnaire that was adopted from different surveys . And were asked about the frequency of performing hand hygiene  at five moments that are as follows: before touching a patient, before any clean or aseptic procedure is performed, after exposure to body fluid, after touching a patient and after touching patient's surroundings. Health care workers who answered all the questions as ‘always as recommended’ were considered as fully compliant. We also asked the participants to rate their overall perceived compliance with proper use of PPE and hand hygiene before and during the pandemic on a ten‐point scale . Barriers to the appropriate use of infection prevention and control measures as recommended were assessed by asking health care workers to select one or more options from a list of barriers for PPE and hand hygiene separately. They were also able to specify other barriers that were not listed.2.5p values less than .05 were considered significant.Data analysis was performed using IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. Descriptive statistics were presented as frequencies and percentages for categorical variables. Continuous not normally distributed variables were presented as medians and interquartile ranges. Chi‐square test was used to determine the differences between categorical variables. The Wilcoxon signed rank test was used to test the differences in the self‐rated compliance with infection prevention and control measures before and during COVID‐19 pandemic taking into consideration the self‐rated compliance on the ten‐point scale as an ordinal dependent variable. Rank biserial correlation was calculated to measure the effect size for these comparisons . Three multivariable logistic regression models were executed to determine the predictors of full compliance with infection prevention and control measures, one for appropriate use of PPE, one for hand hygiene, and one for overall infection prevention and control measures (both PPE and hand hygiene). The associations between risk factors and outcomes were presented as adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs). Goodness of fit was assessed using Hosmer–Lemeshow test. 2.6This study was performed in line with the principals of Declaration of Helsinki. Approval was obtained from the relevant health institutions under protocol ID PHCC/DCR/2020/07/073.33.1As shown in Table 3.2p values < .001 and large effect sizes (r = .87 and .89), respectively. We assessed compliance with infection prevention and control measures using a checklist adopted from WHO risk assessment tool for health care workers in the context of COVID‐19 , and , with ‐19 WHO, . Accordi3.3p values < .001) compared to the null model. The selection of independent variables to be included in the models was based on clinical and statistical relevance. In the first model (Table p < .001), pharmacists compared to physicians , and health care workers at the private sector compared to those at the governmental sector (PHCC). On the other hand, dentists were more likely to be fully compliant with PPE compared to physicians , so as health care workers who deal with suspected or confirmed COVID‐19 cases frequently (every shift) compared to those who never deal with such cases . In the second model (Table p = .022), as well as those with nationalities of all origins compared to those with nationalities of Asia‐Pacific origin. On the other hand, allied health professionals were more likely to be compliant with hand hygiene compared to physicians , and health care workers who received previous training on hand hygiene were more than two times more likely to be compliant compared to those who did not . In the third model (Table p < .001). Compared to health care workers in the governmental sector, those working in the semi‐governmental sector were more likely to be fully compliant with infection prevention and control measures , whereas those in the private sector were less likely to be fully compliant . Those dealing frequently (every shift) with suspected or confirmed COVID‐19 cases were about two times more likely to be fully compliant than those who never deal with such cases .Three multivariable logistic regression models were executed to determine the predictors of compliance with infection prevention and control measures. One for compliance with PPE (during both interactions with suspected or confirmed COVID‐19 cases and while performing an AGP), one for compliance with hand hygiene at the five moments, and a third one for compliance with overall infection prevention and control measures (with both PPE and hand hygiene). All models were of good fit and were statistically significant , discomfort caused by PPE such as N95 respirators or face shields 31.3%), and work overload and lack of time (23.9%). For hand hygiene, skin irritation caused by handwashing agents was the most reported barrier (22.7%), followed by work overload and lack of time (19.1%), and shortage of handwashing agents (14.7%). On the other hand, 32.8% and 50.6% of participants reported no barriers at all for PPE use or practicing hand hygiene, respectively , their compliance with overall infection prevention and control measures was found to be moderate (44.1%). The highest compliance rate was found with hand hygiene at the five moments (83.1%), and the lowest with PPE during patient interactions (52.6%). This study shows gaps in infection prevention and control compliance across different health professional groups with higher compliance rates among dentists and lower compliance with pharmacists compared to physicians. Health care sector, nationality and frequency of dealing with suspected or confirmed COVID‐19 cases were found to be predictors of compliance with PPE and with overall infection prevention and control measures. On the other hand, gender, nationality, profession and previous training on hand hygiene were found to be associated with hand hygiene compliance. Several barriers were reported to the proper use of infection prevention and control measures including work overload and shortages of PPE and handwashing agents. Compliance of health care workers with infection prevention and control measures needs to be further improved.5.1Frequent quality checks, continuous monitoring, provision of adequate supplies  and behaviour change interventions are top strategies that can be enforced by policymakers, safety managers of health care institutions, hospital and nursing administrators to improve compliance. Conducting further research that involves direct observation of infection prevention and control related practices is needed.None.This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.This study was performed in line with the principals of Declaration of Helsinki. Approval was obtained from the Institutional Review Board of the Primary Health Care Corporation (PHCC) under protocol ID PHCC/DCR/2020/07/073 to carry out the survey at PHCC level, and an exempt certificate was obtained from The Health Research Governance Department at Ministry of Public Health (MOPH) to carry out the survey at the semi‐governmental and private sectors.


Moreover, we show that increasing electrolyte pH (pH 10 to pH 13) tunes the local field strength, which in turn indirectly enhances the activity of HER by tuning the near‐surface cation concentration. Interestingly, a too high near‐surface cation concentration (at high pH and high cation concentration) leads to a lowering of the HER activity, which we ascribe to a blockage of the surface by near‐surface cations.In this work we study the role of alkali metal cation concentration and electrolyte pH in altering the kinetics of the hydrogen evolution reaction (HER) at gold (Au) electrodes. We show that at moderately alkaline pH (pH 11), increasing the cation concentration significantly enhances the HER activity on Au electrodes (with a reaction order ≈0.5). Based on these results we suggest that cations play a central role in stabilizing the transition state of the rate‐determining Volmer step by favorably interacting with the dissociating water molecule (*H–OH δ−–cat+).The kinetics of the hydrogen evolution reaction (HER) at gold electrodes is affected by the concentration of alkali metal cations and electrolyte pH. At moderately alkaline pH, increasing the cation concentration greatly enhances the HER activity. Cations are proposed to help stabilize the transition state of the rate‐determining Volmer step by a favorable interaction with the dissociating water molecule (*H–OH We stress that Equations (1) and (2) are not supposed to model our data quantitatively; they are only meant to illustrate the various interrelated effects of cations in a simple model.where + cations due to their hydrolysis at more alkaline conditions (pKhydrolysis=14.2) can lower the OH− surface concentration [OH−]s as the bulk electrolyte pH becomes more alkaline, thus countering the increase in the Γcat,s.Alternatively, it can also be argued that the buffering effect of Nacat,s can also be tuned via the electrolyte pH. Essentially, we observe that increasing electrolyte pH leads to a corresponding increase in the near surface cation concentration, at a constant potential on the RHE scale, and reaches saturation at pH 12. These results can be reconciled with the previously reported pH dependence of the interfacial electric field (ΔE=E−Epzc) due to the positive shift in the Epzc with the increasing electrolyte pH (Epzc=Eopzc + 0.059 pHsurface vs. RHE). An important consequence in terms of Equation (1) and (2) is that it may reproduce the pH dependence of HER on the RHE scale as an implicit function of the cation dependence of HER kinetics. Experimental results shown in the previous section confirm this prediction as we observe an increase in the HER activity on Au with the increasing pH , Equation (1) predicts a pH independent Γcat,s and hence a pH independent reaction rate at a fixed potential . This agrees with the expectation that for a rate‐limiting electrochemical water dissociation step an inherent pH dependence should not exist since no protons or hydroxide ions are involved in the reactant side of the rate limiting reaction (H2O + e− + * → H−* + OH−). However, the experimental results on the NHE scale show that the HER kinetics become more sluggish with the increasing pH  for a constant cation concentration (0.1 M) in comparison with the reaction orders obtained in cation concentration at moderately alkaline pH (≈0.5 at pH 11).It should be noted here, that on a pH independent scale (NHE or Ag/AgCl) where the H Figure , suggestδ−–cat+). Moreover, with the help of kinetic measurements and interfacial capacity measurements we elucidate the indirect role of bulk pH in tuning the near‐surface cation concentration and shed light on the convoluted nature of pH effects for HER activity. Essentially, we observe that on the RHE scale increasing pH results in increasing near‐surface cation concentration, thereby improving the HER kinetics. Interestingly, it appears that in addition to these effects, electrolyte pH also affects the HER kinetics directly, by tuning the pre‐exponential factor of the reaction. However, the overall activity trends demonstrate that near‐surface cation concentration is the more important parameter in describing the HER activity in alkaline media on Au electrodes.From the experimental evidence presented in this paper we conclude that there exists an intricate interrelation between the cation and the pH effects on the HER kinetics in the alkaline media. We have shown here that on Au electrodes, the rate for the sluggish Volmer step is increased when the near‐surface cation concentration is increased. Based on these results, we propose that the cations tune the kinetic energy barrier for HER by favorably interacting with the transition state of the rate determining step , even becoming inhibitive above a threshold concentration. This suggests that the optimal concentration of cations for promoting HER varies depending on the degree of stabilization required by the transition state of the reaction indicating that the observed activity trends are strongly dependent on the strength of the metal‐water‐cation interactions. Therefore, in order to optimize the activity of HER on different electrocatalysts it is imperative to study these interfaces individually.To conclude, this work has elucidated the convoluted role of interfacial field strength and the electrolyte cation concentration in tuning the rate of the alkaline Volmer step. Essentially, we consider both the bulk cation concentration and the electrolyte pH affect the local surface concentration of cations, the latter by influencing the local field strength. Interfacial cations alter the kinetic barrier of the water dissociation step by transiently stabilizing the transition state in which the hydroxyl ion splits off from the water molecule. Moreover, the electrolyte pH also affects the nature of the adsorbed hydrogen at the interface, which further indicates a possible pH dependence of the hydrogen bond strength. Our results demonstrate that the different interfacial parameters that are generally proposed to play a key role in the HER kinetics, all play out simultaneously at the gold‐electrolyte interface, thereby making it very difficult to experimentally decouple these effects and identify a single activity descriptor for HER in alkaline media. Instead, different electrode‐electrolyte combinations need to be probed individually in order to assess which parameter is more important for describing the HER activity on a given surface.The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.