Cumulin, an Oocyte-secreted Heterodimer of the Transforming Growth Factor-β Family, Is a Potent Activator of Granulosa Cells and Improves Oocyte Quality

Growth differentiation factor 9 (GDF9) and bone morphogenetic protein 15 (BMP15) are oocyte-specific growth factors with central roles in mammalian reproduction, regulating species-specific fecundity, ovarian follicular somatic cell differentiation, and oocyte quality. In the human, GDF9 is produced in a latent form, the mechanism of activation being an open question. Here, we produced a range of recombinant GDF9 and BMP15 variants, examined their in silico and physical interactions and their effects on ovarian granulosa cells (GC) and oocytes. We found that the potent synergistic actions of GDF9 and BMP15 on GC can be attributed to the formation of a heterodimer, which we have termed cumulin. Structural modeling of cumulin revealed a dimerization interface identical to homodimeric GDF9 and BMP15, indicating likely formation of a stable complex. This was confirmed by generation of recombinant heterodimeric complexes of pro/mature domains (pro-cumulin) and covalent mature domains (cumulin). Both pro-cumulin and cumulin exhibited highly potent bioactivity on GC, activating both SMAD2/3 and SMAD1/5/8 signaling pathways and promoting proliferation and expression of a set of genes associated with oocyte-regulated GC differentiation. Cumulin was more potent than pro-cumulin, pro-GDF9, pro-BMP15, or the two combined on GC. However, on cumulus-oocyte complexes, pro-cumulin was more effective than all other growth factors at notably improving oocyte quality as assessed by subsequent day 7 embryo development. Our results support a model of activation for human GDF9 dependent on cumulin formation through heterodimerization with BMP15. Oocyte-secreted cumulin is likely to be a central regulator of fertility in mono-ovular mammals

Geographical Assessment of the Natural Environment at Al-Huwaizah Marsh, Eastern Of Misan Governorate (Iraq)

In this article, Al-Huwaizah Marsh, one of the biggest wetlands in southern Iraq, has been studied. The spatial analysis approach was used to study the spatial relationships between the elements of the natural environment on the one hand, and their relationship to the distribution of organisms in Al-Huwaizah Marsh on the other hand. A geographic information system (GIS) was established and fed with the data contained in the topographical, geological, pedological, and hydrological maps issued by the various Iraqi institutions. The study hypothesis indicated that the abiotic elements of the ecosystem (location, surface, geological structure, climate, and soil) played a direct role in the geographical distribution of biotic elements (animals and plants) of Al-Huwaizah Marsh. Al-Huwaizah Marsh is a natural depression that collects water during floods and is distinguished by providing an ideal environment for different species of birds, fish, mammals, and plants to live. The four most significant waterbodies of Al-Huwaizah Marsh are Al-Edhaim, Umm Al-Na'aj, Al-Sanaf, and AlJakka

Dimensional and hierarchical models of depression using the Beck Depression Inventory-II in an Arab college student sample

Abstract Background An understanding of depressive symptomatology from the perspective of confirmatory factor analysis (CFA) could facilitate valid and interpretable comparisons across cultures. The objectives of the study were: (i) using the responses of a sample of Arab college students to the Beck Depression Inventory (BDI-II) in CFA, to compare the "goodness of fit" indices of the original dimensional three-and two-factor first-order models, and their modifications, with the corresponding hierarchical models (i.e., higher - order and bifactor models); (ii) to assess the psychometric characteristics of the BDI-II, including convergent/discriminant validity with the Hopkins Symptom Checklist (HSCL-25). Method Participants (N = 624) were Kuwaiti national college students, who completed the questionnaires in class. CFA was done by AMOS, version 16. Eleven models were compared using eight "fit" indices. Results In CFA, all the models met most "fit" criteria. While the higher-order model did not provide improved fit over the dimensional first - order factor models, the bifactor model (BFM) had the best fit indices (CMNI/DF = 1.73; GFI = 0.96; RMSEA = 0.034). All regression weights of the dimensional models were significantly different from zero (P Conclusion The broadly adequate fit of the various models indicates that they have some merit and implies that the relationship between the domains of depression probably contains hierarchical and dimensional elements. The bifactor model is emerging as the best way to account for the clinical heterogeneity of depression. The psychometric characteristics of the BDI-II lend support to our CFA results.</p

Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial.

BACKGROUND: Opioid-related adverse events are a serious problem in hospitalized patients. Little is known about patients who are likely to experience opioid-induced respiratory depression events on the general care floor and may benefit from improved monitoring and early intervention. The trial objective was to derive and validate a risk prediction tool for respiratory depression in patients receiving opioids, as detected by continuous pulse oximetry and capnography monitoring. METHODS: PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) was a prospective, observational trial of blinded continuous capnography and oximetry conducted at 16 sites in the United States, Europe, and Asia. Vital signs were intermittently monitored per standard of care. A total of 1335 patients receiving parenteral opioids and continuously monitored on the general care floor were included in the analysis. A respiratory depression episode was defined as respiratory rate ≤5 breaths/min (bpm), oxygen saturation ≤85%, or end-tidal carbon dioxide ≤15 or ≥60 mm Hg for ≥3 minutes; apnea episode lasting \u3e30 seconds; or any respiratory opioid-related adverse event. A risk prediction tool was derived using a multivariable logistic regression model of 46 a priori defined risk factors with stepwise selection and was internally validated by bootstrapping. RESULTS: One or more respiratory depression episodes were detected in 614 (46%) of 1335 general care floor patients (43% male; mean age, 58 ± 14 years) continuously monitored for a median of 24 hours (interquartile range [IQR], 17-26). A multivariable respiratory depression prediction model with area under the curve of 0.740 was developed using 5 independent variables: age ≥60 (in decades), sex, opioid naivety, sleep disorders, and chronic heart failure. The PRODIGY risk prediction tool showed significant separation between patients with and without respiratory depression (P \u3c .001) and an odds ratio of 6.07 (95% confidence interval [CI], 4.44-8.30; P \u3c .001) between the high- and low-risk groups. Compared to patients without respiratory depression episodes, mean hospital length of stay was 3 days longer in patients with ≥1 respiratory depression episode (10.5 ± 10.8 vs 7.7 ± 7.8 days; P \u3c .0001) identified using continuous oximetry and capnography monitoring. CONCLUSIONS: A PRODIGY risk prediction model, derived from continuous oximetry and capnography, accurately predicts respiratory depression episodes in patients receiving opioids on the general care floor. Implementation of the PRODIGY score to determine the need for continuous monitoring may be a first step to reduce the incidence and consequences of respiratory compromise in patients receiving opioids on the general care floor

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified