Nuclear Configuration, Spindle Morphology and Cytoskeletal Organization of In Vivo Maturing Horse Oocytes

Horse oocytes (n = 37) were recovered in vivo from pre-ovulatory follicles 30 h after an ovulation-inducing hCG injection and were examined by fluorescent staining and confocal microscopy. Percentages of metaphase-I (MI), metaphase-II (MII) and atypical oocytes were 11%, 78% and 11% respectively. Microtubules were concentrated in the meiotic spindle in both MI and MII oocytes. Chromosomes in the metaphase plate were anchored at the equatorial region of the spindle. Spindle orientation was perpendicular to the oolema in all MI oocytes, whereas in MII oocytes, 66% were parallel and 34% were perpendicular. In MII oocytes, the nuclear material in the polar body had no specific organization and was intertwined with microtubules. Discrete foci of microfilaments at the sub-cortical region of the ooplasm formed an F-actin band, as seen in the inner confocal sections. The percentage area of oocyte image with discrete foci and/or the thickness of F-actin band was used to indicate microfilament content. Microfilament content was greater (p < 0.006) in MII oocytes than in MI oocytes and greater (p < 0.03) in MII oocytes with a perpendicular spindle than with a parallel spindle. The perpendicular spindle orientation in MII oocytes may have represented a later stage of maturation. Atypical oocytes were based on microtubules that were detached from the kinetochores and spread in the ooplasm or by microtubules that accumulated as an amorphous mass near the condensed chromatin. This is the first description of the nuclear configuration, spindle morphology and cytoskeletal organization of in vivo maturing horse oocytes

The ability of two different Vibrio spp. bacteriophages to infect Vibrio harveyi, Vibrio cholerae and Vibrio mimicus

Aims: To determine the host range of the Vibrio harveyi myovirus-like bacteriophage (VHML) and the cholera toxin conversion bacteriophage (CTX Φ) within a range of Vibrio cholerae and V. mimicus and V. harveyi, V. cholerae and V. mimicus isolates respectively.\ud \ud Methods and Results: Three V. harveyi, eight V. cholerae and five V. mimicus isolates were incubated with VHML and CTX Φ. Polymerase chain reaction (PCR) was used to determine the presence of VHML and CTX Φ in infected isolates. We demonstrated that it was possible to infect one isolate of V. cholerae (isolate ACM #2773/ATCC #14035) with VHML. This isolate successfully incorporated VHML into its genome as evident by positive PCR amplification of the sequence coding part of the tail sheath of VHML. Attempts to infect all other V. cholerae and V. mimicus isolates with VHML were unsuccessful. Attempts to infect V. cholerae non-01, V. harveyi andV. mimicus isolates with CTX Φ were unsuccessful.\ud \ud Conclusions: Bacteriophage infection is limited by bacteriophage-exclusion systems operating within bacterial strains and these systems appear to be highly selective. One system may allow the co-existence of one bacteriophage while excluding another. VHML appears to have a narrow host range which may be related to a common receptor protein in such strains. The lack of the vibrio pathogenicity island bacteriophage (VPI Φ) in the isolates used in this study may explain why infections with CTX Φ were unsuccessful.\ud \ud Significance and Impact of the Study: The current study has demonstrated that Vibrio spp. bacteriophages may infect other Vibrio spp.\u

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024