Condition-specific mortality risk can explain differences in COVID-19 case fatality ratios around the globe.

OBJECTIVES: With COVID-19 infections resulting in death according to a hierarchy of risks, with age and pre-existing health conditions enhancing disease severity, the objective of this study is to estimate the condition-specific case fatality ratio (CFR) for different subpopulations in Italy. STUDY DESIGN: The design of the study was to estimate the 'pre-existing comorbidity'-conditional CFR to eventually explain the mortality risk variability reported around in different countries. METHODS: We use the available information on pre-existing health conditions identified for deceased patients 'positive with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)' in Italy. We (i) estimated the total number of deaths for different pre-existing health conditions categories and (ii) calculated a conditional CFR based upon the number of comorbidities before SARS-CoV-2 infection. RESULTS: Our results show a 0.6% conditional CFR for a population with zero pre-existing pathology, increasing to 13.9% for a population diagnosed with one and more pre-existing health conditions. CONCLUSIONS: Condition-specific mortality risks are important to be evaluated during the COVID-19 pandemic, with potential elements to explain the CFR variability around the globe. A careful postmortem examination of deceased cases to differentiate death 'caused by COVID-19' from death 'positive with SARS-CoV-2' is therefore urgently needed and will likely improve our understanding of the COVID-19 mortality risk and virus pathogenicity.

Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand.

Models describing dengue epidemics are parametrized on disease incidence data and therefore high-quality data are essential. For Thailand, two different sources of long-term dengue data are available, the hard copy data from 1980 to 2005, where hospital admission cases were notified, and the electronic files, from 2003 to the present, where clinically classified forms of disease, i.e. dengue fever, dengue haemorrhagic fever, and dengue shock syndrome, are notified using separate files. The official dengue notification data, provided by the Bureau of Epidemiology, Ministry of Public Health in Thailand, were cross-checked with dengue data used in recent publications, where an inexact continuous time-series was observed to be consistently used since 2003, affecting considerably the model dynamics and its correct application. In this paper, numerical analysis and simulation techniques giving insights on predictability are performed to show the effects of model parametrization by using different datasets.

Dynamic noise, chaos and parameter estimation in population biology.

We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models such as multi-strain dynamics to describe the virus-host interaction in dengue fever, even the most recently developed parameter estimation techniques, such as maximum likelihood iterated filtering, reach their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and the deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.

Recurrent and de novo diabetic nephropathy in renal allografts.

BACKGROUND: Histologic findings of diabetic nephropathy (DN) are observed in allografts of patients with pretransplant (PreTx) diabetes mellitus (DM) and in patients who develop DM posttransplant (PostTx). Patients with allograft biopsies (Bx) were retrospectively studied to determine the incidence of recurrent and de novo DN and to ascertain what, if any, risk factors predispose to histologic DN in either patient population. METHODS: From the renal transplant services at four hospitals from 1992 to 2000, the authors identified all patients with PreTxDM and PostTxDM (n=81). Those with renal biopsies performed >/=18 months PostTx were classified according to the presence or absence of histologic DN (Bx-positive, n=23; Bx-negative, n=35). Patients were then subdivided into four categories-recurrent DN (n=16), de novo DN (n=7), no recurrent DN (n=27), and no de novo DN (n=8)-for analyses. RESULTS: Among these 58 patients, 74.1% had PreTx and 25.9% had PostTx diabetes. Of those with histologic DN, 69.6% were recurrent DN and 30.4% were de novo DN, making de novo DN at least as likely to develop as recurrent DN. After the onset of diabetes in the de novo population, the time to development of histologic DN was similar in the recurrent and the de novo patients (6.68+/-3.86 years vs. 5.90+/-3.13 years, P=0.66) and more rapid than previously reported. Apart from a more frequent family history of hypertension in patients with allograft DN compared with those without allograft DN, known risk factors for the development of native DN did not significantly differ among patients in the four cohorts. Proposed risk factors related to transplantation did not correlate with the development of recurrent or de novo DN. CONCLUSION: Among patients with histologic DN, de novo DN occurred at least as frequently as recurrent DN, and the time to onset of histologically apparent DN was more rapid than previously reported. Neither the usual clinical predictors of DN nor clinical variables related to transplantation clearly distinguished the group with DN from the group without it, potentially implicating novel mechanisms in its pathogenesis.

Functional activity of hepatocyte nuclear factor-1 is specifically decreased in amino acid-limited hepatoma cells.

Limitation of cultured rat hepatoma cells for an essential amino acid results in a specific decrease in expression of several genes that are preferentially expressed in the liver, including the serum albumin and transthyretin genes. In the work presented here, we examined whether the coordinate repression of these genes is caused by decreased activity of one or more of the liver-enriched transcription factors, hepatocyte nuclear factor-1 (HNF-1), HNF-3, HNF-4 or C/EBP. To address this question, HepG2 human hepatoma cells were transiently transfected with luciferase reporter constructs containing multiple copies of individual transcription factor binding sites. Limitation for an essential amino acid resulted in specific repression of a construct in which luciferase expression was directed by HNF-1. A single HNF-1 binding site located adjacent to the TATA box plays a major role in transcription directed by the serum albumin promoter in transient transfection assays. Amino acid limitation of cells transfected with an albumin promoter/luciferase reporter construct resulted in specific repression of promoter activity. In addition, bacterial methylation or site-directed mutagenesis of the HNF-1 binding site in the albumin proximal promoter region eliminated the regulation of an albumin promoter-luciferase reporter construct under conditions of amino acid limitation. These results demonstrated that the HNF-1 binding site played a major role in regulation of the albumin promoter by amino acid availability. Deletion analysis of the albumin promoter confirmed regulation through the HNF-1 binding site and also identified a second amino acid regulatory element in the upstream region of the albumin promoter, which has been shown previously to contain a functional binding site for HNF-3. The repression of albumin promoter and HNF-1 reporter constructs in amino acid-limited cells occurred without a change in the DNA binding activity of HNF-1. Moreover, HNF-3 DNA binding activity was also not decreased in amino acid-limited cells. These results suggest that the regulation of transcription by amino acids occurs at the level of transcriptional activation by HNF-1 and HNF-3, rather than by alteration of the DNA binding activity of either factor.

Attractor switching by neural control of chaotic neurodynamics.

Chaotic attractors of discrete-time neural networks include infinitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically, a local control algorithm is derived on the basis of least squares minimization of the future deviations between actual system states and the desired orbit. This delayed control allows a consistent neural implementation, i.e. the same types of neurons are used for chaotic and controlling modules. The control signal is realized with one layer of neurons, allowing selective switching between different stabilized periodic orbits. For chaotic modules with noise, random switching between different periodic orbits is observed.