Spontaneous coronary artery dissection: an unpredictable event.

Spontaneous coronary artery dissection (SCAD) is an under-recognized cause of acute coronary syndrome that predominantly affects women in adulthood and is the leading cause of acute myocardial infarction in pregnancy. The most common clinical presentation is ST-segment elevation myocardial infarction (STEMI) or non-STEMI, followed by cardiogenic shock (∼2%), sudden cardiac death (0.8% in autopsy series), cardiac arrest, ventricular arrhythmias (∼5%), and Takotsubo syndrome. The prevalence of SCAD in the general population is largely uncertain due to underdiagnosis. Oral contraceptives, post-menopausal therapy, and infertility treatments are recognized associated factors. The pathological substrates (fibromuscular dysplasia) and triggers (especially emotional stress) are commonly present in affected women. The few cases with a precise genetic aetiology occur in the context of syndromic and non-syndromic connective tissue diseases. The only true certainty in SCAD is the overwhelming prevalence in women. The first event as well as the recurrence (up to 30%, which varies depending on the definition) is largely unpredictable. The treatment strategy is highly individualized and requires extensive additional study in order to optimize outcomes and prevent major adverse cardiovascular events in affected individuals. We have known about SCAD for nearly a century, but we still do not know how best to prevent, diagnose, and treat it, making SCAD a highly important and unmet clinical need.

Genetics and clinics: together to diagnose cardiomyopathies.

The diagnostic paths of hereditary cardiomyopathies (CMPs) include both clinical and molecular genetics. The first step is the clinical diagnosis that guides the decisions about treatments, monitoring, prognostic stratification, and prevention of major events. The type of CMP [hypertrophic cardiomyopathy, dilated cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy (ARVC)] is defined by the phenotype, and the genetic testing may identify the precise cause. Furthermore, genetic testing provides a pre-clinical diagnosis in unaffected family members and the basis for prenatal diagnosis. It can contribute to risk stratification (e.g. LMNA) and can be a major diagnostic criterion (e.g. ARVC). The test can be limited to a single gene when the pre-test diagnostic hypothesis is based on proven clinical evidence (e.g. GLA for Fabry disease). Alternatively, it can be expanded from a multigene panel to a whole exome or whole genome sequencing when the pre-test hypothesis is a genetically heterogeneous disease. In the last decade, the study of larger genomic targets led to the identification of numerous gene variants not only pathogenic (clinically actionable) but also of uncertain clinical significance (not actionable). For the latter, the pillar of the genetic diagnosis is the correct interpretation of the pathogenicity of genetic variants, which is evaluated using both bioinformatics and clinical-genetic criteria about the patient and family. In this context, cardiologists play a central role in the interpretation of genetic tests, performing the deep-phenotyping of variant carriers and establishing the co-segregation of the genotype with the phenotype in families.

Myths to debunk: the non-compacted myocardium.

Left ventricular non-compaction (LVNC) is defined by the triad: prominent trabecular anatomy, thin compacted layer, and deep inter-trabecular recesses. No person, sick or healthy, demonstrates identical anatomy of the trabeculae; their configuration represents a sort of individual dynamic 'cardiac fingerprinting'. LVNC can be observed in healthy subjects with normal left ventricular (LV) size and function, in athletes, in pregnant women, as well as in patients with haematological disorders, neuromuscular diseases, and chronic renal failure; it can be acquired and potentially reversible. When LVNC is observed in patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, restrictive cardiomyopathy, or arrhythmogenic cardiomyopathy, the risk exists of misnaming the cardiomyopathy as 'LVNC cardiomyopathy' rather than properly describe, i.e. a 'DCM associated with LVNC'. In rare infantile CMPs (the paradigm is tafazzinopathy or Barth syndrome), the non-compaction (NC) is intrinsically part of the cardiac phenotype. The LVNC is also common in congenital heart disease (CHD) as well as in chromosomal disorders with systemic manifestations. The high prevalence of LVNC in healthy athletes, its possible reversibility or regression, and the increasing detection in healthy subjects suggest a cautious use of the term 'LVNC cardiomyopathy', which describes the morphology, but not the functional profile of the cardiac disease. Genetic testing, when positive, usually reflects the genetic causes of an underlying cardiomyopathy rather than that of the NC, which often does not segregate with CMP phenotype in families. Therefore, when associated with LV dilation and dysfunction, hypertrophy, or CHD, the leading diagnosis is cardiomyopathy or CHD followed by the descriptor LVNC.

Forecasting Epidemics Through Nonparametric Estimation of Time-Dependent Transmission Rates Using the SEIR Model.

Deterministic and stochastic methods relying on early case incidence data for forecasting epidemic outbreaks have received increasing attention during the last few years. In mathematical terms, epidemic forecasting is an ill-posed problem due to instability of parameter identification and limited available data. While previous studies have largely estimated the time-dependent transmission rate by assuming specific functional forms (e.g., exponential decay) that depend on a few parameters, here we introduce a novel approach for the reconstruction of nonparametric time-dependent transmission rates by projecting onto a finite subspace spanned by Legendre polynomials. This approach enables us to effectively forecast future incidence cases, the clear advantage over recovering the transmission rate at finitely many grid points within the interval where the data are currently available. In our approach, we compare three regularization algorithms: variational (Tikhonov's) regularization, truncated singular value decomposition (TSVD), and modified TSVD in order to determine the stabilizing strategy that is most effective in terms of reliability of forecasting from limited data. We illustrate our methodology using simulated data as well as case incidence data for various epidemics including the 1918 influenza pandemic in San Francisco and the 2014-2015 Ebola epidemic in West Africa.

On Stable Parameter Estimation and Forecasting in Epidemiology by the Levenberg-Marquardt Algorithm with Broyden's Rank-one Updates for the Jacobian Operator.

Rigorously calibrating dynamic models with time-series data can pose roadblocks. Oftentimes, the problem is ill-posed and one has to rely on appropriate regularization techniques to ensure stable parameter estimation from which forward projections with quantified uncertainty could be generated. If the inversion procedure is cast as nonlinear least squares constrained by a system of nonlinear differential equations, then the system has to be solved numerically at every step of the iterative process and the corresponding parameter-to-data map cannot be used to evaluate the Fréchet derivative analytically. To address challenges related to both instability and Jacobian approximation, we propose a novel regularized Levenberg-Marquardt algorithm with iterative rank-one updates for computation of the derivative operator. In order to test the efficiency of this scheme, we conduct numerical experiments using a mathematical model of infectious disease transmission and real incidence data of historic measles outbreaks in the UK.

Reconstruction of incidence reporting rate for SARS-CoV-2 Delta variant of COVID-19 pandemic in the US.

In recent years, advanced regularization techniques have emerged as a powerful tool aimed at stable estimation of infectious disease parameters that are crucial for future projections, prevention, and control. Unlike other system parameters, i.e., incubation and recovery rates, the case reporting rate, Ψ, and the time-dependent effective reproduction number, Re(t), are directly influenced by a large number of factors making it impossible to pre-estimate these parameters in any meaningful way. In this study, we propose a novel iteratively-regularized trust-region optimization algorithm, combined with SuSvIuIvRD compartmental model, for stable reconstruction of Ψ and Re(t) from reported epidemic data on vaccination percentages, incidence cases, and daily deaths. The innovative regularization procedure exploits (and takes full advantage of) a unique structure of the Jacobian and Hessian approximation for the nonlinear observation operator. The proposed inversion method is thoroughly tested with synthetic and real SARS-CoV-2 Delta variant data for different regions in the United States of America from July 9, 2021, to November 25, 2021. Our study shows that case reporting rate during the Delta wave of COVID-19 pandemic in the US is between 12% and 37%, with most states being in the range from 15% to 25%. This confirms earlier accounts on considerable under-reporting of COVID-19 cases due to the impact of "silent spreaders" and the limitations of testing.

On optimal control at the onset of a new viral outbreak.

We propose a versatile model with a flexible choice of control for an early-pandemic outbreak prevention when vaccine/drug is not yet available. At that stage, control is often limited to non-medical interventions like social distancing and other behavioral changes. For the SIR optimal control problem, we show that the running cost of control satisfying mild, practically justified conditions generates an optimal strategy, u(t), t ∈ [0, T], that is sustainable up until some moment τ ∈ [0, T). However, for any t ∈ [τ, T], the function u(t) will decline as t approaches T, which may cause the number of newly infected people to increase. So, the window from 0 to τ is the time for public health officials to prepare alternative mitigation measures, such as vaccines, testing, antiviral medications, and others. In addition to theoretical study, we develop a fast and stable computational method for solving the proposed optimal control problem. The efficiency of the new method is illustrated with numerical examples of optimal control trajectories for various cost functions and weights. Simulation results provide a comprehensive demonstration of the effects of control on the epidemic spread and mitigation expenses, which can serve as invaluable references for public health officials.

The catalytic and the RNA subunits of human telomerase are required to immortalize equid primary fibroblasts.

Many human primary somatic cells can be immortalized by inducing telomerase activity through the exogenous expression of the human telomerase catalytic subunit (hTERT). This approach has been extended to the immortalization of cell lines from several mammals. Here, we show that hTERT expression is not sufficient to immortalize primary fibroblasts from three equid species, namely donkey, Burchelli's zebra and Grevy's zebra. In vitro analysis of a reconstituted telomerase composed by hTERT and an equid RNA component of telomerase (TERC) revealed a low activity of this enzyme compared to human telomerase, suggesting a low compatibility of equid and human telomerase subunits. This conclusion was also strengthened by comparison of human and equid TERC sequences, which revealed nucleotide differences in key regions for TERC and TERT interaction. We then succeeded in immortalizing equid fibroblasts by expressing hTERT and hTERC concomitantly. Expression of both human telomerase subunits led to telomerase activity and telomere elongation, indicating that human telomerase is compatible with the other equid telomerase subunits and proteins involved in telomere metabolism. The immortalization procedure described herein could be extended to primary cells from other mammals. The availability of immortal cells from endangered species could be particularly useful for obtaining new information on the organization and function of their genomes, which is relevant for their preservation.

The doubling time analysis for modified infectious disease Richards model with applications to COVID-19 pandemic.

In the absence of reliable information about transmission mechanisms for emerging infectious diseases, simple phenomenological models could provide a starting point to assess the potential outcomes of unfolding public health emergencies, particularly when the epidemiological characteristics of the disease are poorly understood or subject to substantial uncertainty. In this study, we employ the modified Richards model to analyze the growth of an epidemic in terms of 1) the number of times cumulative cases double until the epidemic peaks and 2) the rate at which the intervals between consecutive doubling times increase during the early ascending stage of the outbreak. Our theoretical analysis of doubling times is combined with rigorous numerical simulations and uncertainty quantification using synthetic and real data for COVID-19 pandemic. The doubling-time approach allows to employ early epidemic data to differentiate between the most dangerous threats, which double in size many times over the intervals that are nearly invariant, and the least transmissible diseases, which double in size only a few times with doubling periods rapidly growing.

Macrophages and Monocytes: "Trojan Horses" in COVID-19.

We aimed to explore whether variants of SARS-CoV-2 (Chinese-derived strain (D614, lineage A), Italian strain PV10734 (D614G, lineage B.1.1) and Alpha strain (lineage B.1.1.7)) were able to infect monocytes (MN) and monocyte-derived macrophages (MDM) and whether these infected cells may, in turn, be vectors of infection. For this purpose, we designed an in vitro study following the evolution of MN and MDM infection at different time points in order to confirm whether these cells were permissive for SARS-CoV-2 replication. Finally, we investigated whether, regardless of viral replication, the persistent virus can be transferred to non-infected cells permissive for viral replication. Thus, we co-cultured the infected MN/MDM with permissive VERO E6 cells verifying the viral transmission. This is a further in vitro demonstration of the important role of MN and MDM in the dissemination of SARS-CoV-2 and evolution of the COVID-19 disease.

Enhanced gene amplification in human cells knocked down for DNA-PKcs.

Gene amplification, a key mechanism for oncogene activation and drug resistance in tumour cells, involves the generation and joining of DNA double-strand breaks. Amplified DNA can be carried either on intra-chromosomal arrays or on extra-chromosomal elements (double minutes). We previously showed that, in rodent cells deficient in DNA-PKcs, intra-chromosomal amplification is significantly enhanced. In the present work, we studied gene amplification in human HeLa cell lines in which the expression of the DNA-PKcs gene was constitutively inhibited by shRNAs. These cell lines showed an increased sensitivity to ionizing radiations, an enhanced frequency of chromosomal aberrations and an increased rate of occurrence of methotrexate resistant colonies compared to the control cell lines (6-18 times). The main mechanism of resistance to methotrexate was extra-chromosomal amplification of the dihydrofolate reductase gene. These results indicate that, in human cells, inhibition of DNA-PKcs gene expression favours gene amplification occurring via the production of double minutes. In addition, they show that cell lines constitutively expressing shRNAs are good model systems to study the role of specific functions in gene amplification.

Gene amplification, radiation sensitivity and DNA double-strand breaks.

DNA double-strand breaks (DSBs) are one of the main types of damage induced by ionizing radiations. Free DNA ends that are not correctly repaired can be engaged in pathways triggering gene amplification. Following gene amplification the copy number of a portion of the genome is increased, leading to an enhanced expression of the genes located in the amplified region. Gene amplification plays an important role in cancer, being one of the mechanisms of oncogene activation; in addition, it can confer resistance to chemotherapeutic agents, through the increase in the copy number of genes coding for drug targets. The presence of gene amplification can have a prognostic and a diagnostic value and can help in orienting therapy in specific tumour types. The amplified DNA is primarily produced through recombination-based pathways and can be located either within chromosomes or on extra-chromosomal acentric elements. Studies on the organization of the amplified DNA in tumour cells and in cultured drug resistant cells have suggested that a single DSB can trigger a cascade of events leading to a large number of copies of a region of the genome. In addition, it has been shown that amplified DNA is unstable, further increasing the long-term effect of the initial event. Gene amplification is a peculiar feature of transformed cells and the ability to amplify is strongly influenced by the cellular genetic background. Genes involved in DNA damage response and in DNA damage repair can play a role in controlling the amplification process, in particular, it has been shown that defects in DSB repair functions can increase the frequency of gene amplification. In this review, we will discuss the biological significance of gene amplification, together with the role of DNA DSBs and DSB repair genes in the generation of amplified DNA.

Spatial dynamics and the basic reproduction number of the 1991-1997 Cholera epidemic in Peru.

After being cholera free for over 100 years, Peru experienced an unprecedented epidemic of Vibrio cholerae O1 that began in 1991 and generated multiple waves of disease over several years. We developed a mechanistic transmission model that accounts for seasonal variation in temperature to estimate spatial variability in the basic reproduction number ([Formula: see text]), the initial concentration of vibrios in the environment, and cholera reporting rates. From 1991-1997, cholera spread following a multi-wave pattern, with weekly incidence concentrated during warm seasons. The epidemic first hit the coastal departments of Peru and subsequently spread through the highlands and jungle regions. The correlation between model predictions and observations was high (range in R2: 58% to 97%). Department-level population size and elevation explained significant variation in spatial-temporal transmission patterns. The overall R0 across departments was estimated at 2.1 (95% CI: 0.8,7.3), high enough for sustained transmission. Geographic-region level [Formula: see text] varied substantially from 2.4 (95% CI: 1.1, 7.3) for the coastal region, 1.9 (0.7, 6.4) for the jungle region, and 1.5 (0.9, 2.2) for the highlands region. At the department level, mean [Formula: see text] ranged from 0.8 to 6.9. Department-level [Formula: see text] were correlated with overall observed attack rates (Spearman ρ = 0.59, P = 0.002), elevation (ρ = -0.4, P = 0.04), and longitude (ρ = -0.6, P = 0.004). We find that both [Formula: see text] and the initial concentration of vibrios were higher in coastal departments than other departments. Reporting rates were low, consistent with a substantial fraction of asymptomatic or mild cases associated with the El Tor cholera biotype. Our results suggest that cholera vibrios, autochthonous to plankton in the natural aquatic environment, may have triggered outbreaks in multiple coastal locations along the Pacific coast of Peru. Our methodology could be useful to investigate multi-wave epidemics of cholera and could be extended to conduct near real-time forecasts and investigate the impact of vaccination strategies.

Pathologic substrate of gastropathy in Anderson-Fabry disease.

In both classic and late-onset AFD, mutations of the GLA gene cause deficient activity of the alpha-galactosidase enzyme resulting in intracellular accumulation of the undigested substrate. Gastrointestinal symptoms (GI) are common but non-specific and imputed to the AFD, irrespective of the demonstration of substrate accumulation in GI cells. We demonstrate substrate accumulation in gastric epithelial, vascular, and nerve cells of patients with classic AFD and, vice versa, absence of accumulation in late-onset AFD and controls.

LAM Genes Contribute to Environmental Stress Tolerance but Sensibilize Yeast Cells to Azoles.

Lam proteins transport sterols between the membranes of different cellular compartments. In Saccharomyces cerevisiae, the LAM gene family consists of three pairs of paralogs. Because the function of paralogous genes can be redundant, the phenotypes of only a small number of LAM gene deletions have been reported; thus, the role of these genes in yeast physiology is still unclear. Here, we surveyed the phenotypes of double and quadruple deletants of paralogous LAM2(YSP2)/LAM4 and LAM1(YSP1)/LAM3(SIP3) genes that encode proteins localized in the junctions of the plasma membrane and endoplasmic reticulum. The quadruple deletant showed increased sterol content and a strong decrease in ethanol, heat shock and high osmolarity resistance. Surprisingly, the quadruple deletant and LAM2/LAM4 double deletion strain showed increased tolerance to the azole antifungals clotrimazole and miconazole. This effect was not associated with an increased rate of ABC-transporter substrate efflux. Possibly, increased sterol pool in the LAM deletion strains postpones the effect of azoles on cell growth. Alternatively, LAM deletions might alleviate the toxic effect of sterols as Lam proteins can transport toxic sterol biosynthesis intermediates into membrane compartments that are sensitive to these compounds. Our findings reveal novel biological roles of LAM genes in stress tolerance and suggest that mutations in these genes may confer upregulation of a mechanism that provides resistance to azole antifungals in pathogenic fungi.

Numerical study of discretization algorithms for stable estimation of disease parameters and epidemic forecasting.

In this paper we investigate how various discretization schemes could be incorporated in regularization algorithms for stable parameter estimation and forecasting in epidemiology. Specifically, we compare parametric and nonparametric discretization tools in terms of their impact on the accuracy of recovered disease parameters as well as their impact on future projections of new incidence cases. Both synthetic and real data for 1918 "Spanish Flu" pandemic in San Francisco are considered. The discrete approximation of a time dependent transmission rate is combined with the Levenberg-Marquardt algorithm used to solve the nonlinear least squares problem aimed at fitting the model to limited incidence data for an unfolding outbreak. Our simulation study highlights the crucial role of a priori information at the early stage of an epidemic in mitigating the lack of stability in over-parameterized models with insu cient data. Fortunately, our results suggest that a balanced combination of problem-oriented regularization techniques is one way in which scientists can still draw useful conclusions about system parameters and in turn generate reliable forecasts that policy makers could use to guide control interventions.

A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm.

Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks. For instance, simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts. In the absence of reliable information about transmission mechanisms of emerging infectious diseases, phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease. In this article, our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014-15 Ebola epidemic in West Africa.

Constitutive expression of the human peroxiredoxin V gene contributes to protection of the genome from oxidative DNA lesions and to suppression of transcription of noncoding DNA.

Peroxiredoxins belong to a family of antioxidant proteins that neutralize reactive oxygen species. One member of this family, peroxiredoxin I (PRDX1), suppresses DNA oxidation. Peroxiredoxin V (PRDX5) has been cloned as a transcriptional corepressor, as a peroxisomal/mitochondrial antioxidant protein, and as an inhibitor of p53-dependent apoptosis. Promoters of mammalian PRDX5 genes contain clusters of antioxidant response elements, which can bind the transcription factor NRF2. However, we found that expression of the human PRDX5 gene in situ was not stimulated by the oxidative agent menadione. Silencing of the NRF2 gene in the absence of oxidative stress by specific siRNA did not decrease PRDX5 protein concentration. We also constructed clones of human lung epithelial cells A549 with siRNA-mediated knockdown of the PRDX5 gene. This led to a significant increase in 8-oxoguanine formation in cell DNA. In the PRDX5 knockdown clone, an increase in transcripts containing sequences of alpha-satellite and satellite III DNAs was also detected, suggesting that this protein may be required for silencing of heterochromatin. Together, these results suggest that constitutively expressed PRDX5 gene plays an important role in protecting the genome against oxidation and may also be involved in the control of transcription of noncoding DNA.