Parameter inference for stochastic single-cell dynamics from lineage tree data

Background With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. Most of currently available methods treat single-cell trajectories independently, ignoring the mother-daughter relationships and the information provided by the population structure. However, this information is essential if a process of interest happens at cell division, or if it evolves slowly compared to the duration of the cell cycle. Results In this work, we propose a Bayesian framework for parameter inference on single-cell time-lapse data from lineage trees. Our method relies on a combination of Sequential Monte Carlo for approximating the parameter likelihood function and Markov Chain Monte Carlo for parameter exploration. We demonstrate our inference framework on two simple examples in which the lineage tree information is crucial: one in which the cell phenotype can only switch at cell division and another where the cell state fluctuates slowly over timescales that extend well beyond the cell-cycle duration. Conclusion There exist several examples of biological processes, such as stem cell fate decisions or epigenetically controlled phase variation in bacteria, where the cell ancestry is expected to contain important information about the underlying system dynamics. Parameter inference methods that discard this information are expected to perform poorly for such type of processes. Our method provides a simple and computationally efficient way to take into account single-cell lineage tree data for the purpose of parameter inference and serves as a starting point for the development of more sophisticated and powerful approaches in the future

Excess Mortality in a Nephrology Clinic during First Months of Coronavirus Disease-19 Pandemic: A Pragmatic Approach

BACKGROUND: Excess mortality is defined as mortality above what would be expected based on the non-crisis mortality rate in the population of interest. AIM: In this study, we aimed to access weather the coronavirus disease (COVID)-19 pandemic had impact on the in-hospital mortality during the first 6 months of the year and compare it with the data from the previous years. METHODS: A retroprospective study was conducted at the University Clinic of Nephrology Skopje, Republic of Macedonia. In-hospital mortality rates were calculated for the first half of the year (01.01–30.06) from 2015 until 2020, as monthly number of dead patients divided by the number of non-elective hospitalized patents in the same period. The excess mortality rate (p-score) was calculated as ratio or percentage of excess deaths relative to expected average deaths: (Observed mortality rate–expected average death rate)/expected average death rate *100%. RESULTS: The expected (average) overall death mortality rate for the period 2015–2019 was 8.9% and for 2020 was 15.3%. The calculated overall excess mortality in 2020 was 72% (pscore 0.72). CONCLUSION: In this pragmatic study, we have provided clear evidence of high excess mortality at our nephrology clinic during the 1st months of the COVID-19 pandemic. The delayed referral of patients due to the patient and health care system-related factors might partially explain the excess mortality during pandemic crises. Further analysis is needed to estimate unrecognized probable COVID-19 deaths

AVETH Survey on Supervision of Doctoral Students

Good supervision is a key factor for the success of doctoral studies. But there are multiple good ways. Depending on the field of studies, the supervisor ‘style’, and the students’ specific needs, multiple approaches can lead to good results. This variety of supervision contexts makes it difficult to have an overview of the supervision practices, and their actual impact on students’ satisfaction. Thus, in fall 2017, AVETH conducted a survey on the doctoral supervision practices at ETH Zurich, with two objectives: (i) draw a picture of the actual supervision practices at ETH, and (ii) investigate the doctoral students’ satisfaction with respect to their supervision and the impact of specific practices. Based on 1’594 completed survey answers (corresponding to a response rate of ~36%) this report summarizes the findings. It appears that 62% of doctoral students are generally satisfied (Grade 6 and above – See figure below) and 40% are very satisfied (Grade 8 and above) with their supervision. However these numbers vary a lot across departments. Furthermore, there are relations between satisfaction and (i) the number of years of the doctoral thesis, (ii) the opportunities for scientific interactions (both within and outside of the group), and (iii) formal or informal appraisal interviews. Finally, 24% of the survey respondents stated that they experience some kind of ‘abuse of power’ from their supervisor, ranging from lack of scientific freedom to pressure to work long hours or on weekends. This survey offers a factual description of supervision practices at ETH Zurich and raises some alert flags on practices, which should be monitored and/or prevented. Using this new information, AVETH will work together with the ETH School Board to propose a set of ‘supervision guidelines’, which will hopefully contribute to improve everyone’s situation at ETH

AVETH follow-up survey on salary and duties of ETH doctoral students

The present survey was conducted as a follow-up of the AVETH survey in 2014. Based on 1′052 completed answers this report summarizes the current employment situation and the corresponding opinion of doctoral students at ETH Zurich about their salary rate and additional duties

C-reactive protein in patients with normal perfusion and mild to moderate perfusion defects who have undergone myocardial perfusion imaging with 99m-Tc sestamibi gated spect.

High-sensitivity C-reactive protein (CRP) has been extensively used in recent years to assess cardiovascular risk more thoroughly. A significant association between elevated CRP, a prevalence of coronary artery disease (CAD) and adverse cardiac events has been found. Stress myocardial SPECT perfusion imaging (MPI) is an accurate noninvasive technique for detecting CAD. The aim of our study was to find out if there are any differences in the CRP levels between patients with normal myocardial perfusion and mild to moderate perfusion defects, detected with 99m-Tc sestamibi gated SPECT MPI. We prospectively studied 127 patients (79 men, 48 women) suspected of having CAD or with previously confirmed CAD, who were referred for MPI. According to the findings of the stress study, they were divided into two groups: with normal/ near normal myocardial perfusion (n = 85) and with a mild to moderate perfusion defect (n = 42). Levels of CRP in the former group were significantly lower (2.7 mg/L vs. 4.2 mg/L, p = 0.01). There were significantly more men (78.6% vs. 54%, p = 0.000*) and smokers (26% vs. 15%, p = 0.003), also the rates of PCI were significantly higher (36% vs. 15%, p = 0.006) in patients with mild to moderate perfusion defects. The two groups did not differ significantly in age, type of stress, presence of most risk factors for CAD, previous myocardial infarction and CABG. The results of our study have shown that patients with mild to moderate perfusion defects on stress myocardial perfusion SPECT imaging have significantly higher levels of C-reactive protein, compared to those with normal/near normal myocardial perfusion