Biology

An immense scientific effort has been made worldwide due to Covid-19s pandemic magnitude. It has made possible to identify almost 300,000 SARS-CoV-2 different genetic variants, connecting them with clinical and epidemiological findings. Among this immense data collection, that constitutes the biggest evolutionary experiment in history, is buried the answer to what will happen in the future. Will new strains, more contagious than the current ones or resistant to the vaccines, arise by mutation? Although theoretic population genetics is, by far, the most powerful tool we have to do an accurate prediction, it has been barely used for the study of SARS-CoV-2 due to its conceptual difficulty. Having in mind that the size of the SARS-CoV-2 population is astronomical we can apply a discrete treatment, based on the branching process method, Fokker-Plank equations and Kolmogoroffs forward equations, to calculate the survival likelihood through time, to elucidate the likelihood to become dominant genotypes and how long will this take, for new SARS-CoV-2 mutants depending on their selective advantage. Results show that most of the new mutants that will arise in the SARS-CoV-2 meta-population will stay at very low frequencies. However, some few new mutants, significantly more infectious than current ones, will still emerge and become dominant in the population favoured by a great selective advantage. Far from showing a "mutational meltdown", SARS-CoV-2 meta-population will increase its fitness becoming more infective. There is a probability, small but finite, that new mutants arise resistant to some vaccines. High infected numbers and slow vaccination programs will significantly increase this likelihood.

What is the nature of the ageing process? What is the spore survival, that one would expect upon analysing a self-cross, in a wild fission yeast strain? Could this two research questions be, somehow, related? In this manuscript, I am describing some interesting observations obtained while studying fission yeast spore survival values upon genetic crosses. Early findings brought my attention into mainly studying self-crosses (intra-strain crosses in which any cell can be involved in by mating with a sibling cell). This study, yield some interesting findings. As a summary: 1) most fission yeast self-crosses do show low spore survival values; 2) clonally related strains show a high phenotypic variability in self-cross spore survival values; 3) differences in self-cross spore survival values can be detected when comparing zygotic and azygotic matings; 4) self-cross spore survival values are highly affected by environmental factors, mainly producing a reduction in the spore survival values; 5) self-cross spore survival values are "recovered" when cells are subjected to several rounds of meiotic divisions; 6) signs of correlation between spore survival and vegetative cell survival (prior to the entry into meiosis) have been observed in this study. All those observations, among others, are discussed as part of an epigenetic variability that exist in fission yeast populations. A cyclical behaviour, of this epigenetic variability it is proposed, defining an underlying ratchet-like epigenetic mechanisms acting in all cells. In this manuscript, I propose that this mechanism, is, indeed, the main cause of the ageing process.

ObjectiveTo study kinetics and associations between inflammation related proteins in circulation after pediatric allogenic hematopoietic stem cell transplantation (HSCT) to reveal proteomic signatures or individual soluble proteins associated with specific complications post HSCT. MethodsWe used a proteomics method called Proximity Extension Assay to repeatedly measure 180 different proteins together with clinical variables, cellular immune reconstitution, and blood viral copy numbers in 27 children aged 1-18 years during a two-year follow up after allogenic HSCT. Protein profile analysis was done using unsupervised hierarchical clustering and a regression-based method, while Bonferroni-corrected Mann-Whitney U test was used for time point specific comparison of individual proteins against outcome. ResultsAt 6 months after allogenic HSCT, we could identify a protein profile pattern associated with occurrence of the complications chronic graft-versus-host disease, viral infections, relapse, and death. When protein markers were analyzed separately, the plasma concentration of the inhibitory and cytotoxic T cell surface protein FCRL6 (Fc receptor-like 6) was higher in patients with CMV viremia (log2-fold change 1.5 (p0.00099), 2.5 (p=0.00035) and 2.2 (p=0.045) at time points 6, 12 and 24 months). Flow cytometry confirmed that FCRL6 expression was higher in innate-like {gamma}{delta} T cells, indicating that these cells have a role in controlling CMV reactivation in HSCT recipients. ConclusionsThe potentially druggable FCRL6 receptor on cytotoxic T cells appears to have a role in controlling CMV viremia post-HSCT. Our results suggest that system level analysis is a useful addition to the studying of single biomarkers in allogeneic HSCT.

A calculation model for predicting the spread of the COVID-19 epidemic under quarantine conditions is proposed. The obtained simple analytical ratios allow estimating the factors determining the intensity of the infection spread, including changing requirements for quarantine severity. The presented method of forecasting allows to calculate both the total number of infected persons and the number of active infections. Comparison of the results of calculations according to the proposed model with the statistics for a number of cities shows their satisfactory qualitative and quantitative compliance. The proposed simple model can be useful in preliminary assessment of possible consequences of changing quarantine conditions.

Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemics inflection point and final size. Highlights ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact. These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources. The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May. Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

Chronic stress affects over 300 million individuals worldwide, contributing to a rising incidence of diseases associated with the peripheral immune response triggered by this condition, including depression, inflammatory bowel disease, metabolic syndrome, and coronary heart disease. To establish a structured understanding of these associations, an ontological approach based on Formal Concept Analysis, a mathematical framework for order relations is employed to construct a conceptual hierarchy linking chronic stress to these diseases. Within this framework, the objects represent the set of stress-induced diseases, while the attributes correspond to specific combinations of chemokines and cytokines clinically associated with each condition. The findings of the ontological analysis suggest that stress-related diseases follow a staged progression: an initial induction phase, common to all diseases, characterized by the presence of chemokines and cytokines that induce a state of chronic inflammation (inflammaging); a subsequent progression phase, marked by immune response effector molecules that may be shared across different diseases; and a final consolidation phase, in which specific chemo- and cytokines distinctive to each disease are expressed.

The success of artificial intelligence and machine learning is an incentive to develop new algorithms to increase the rapidity and reliability of medical diagnosis. Here we compared different strategies aimed at processing microscope images used to detect anti-neutrophil cytoplasmic antibodies, an important vasculitis marker: (i) basic classifier methods (logistic regression, k-nearest neighbors and decision tree) were used to process custom-made indices derived from immunofluorescence images yielded by 137 sera. (ii) These methods were combined with dimensional reduction to analyze 1733 individual cell images. iii) More complex models based on neural networks were used to analyze the same dataset. The efficiency of discriminating between positive and negative samples and different fluorescence patterns was quantified with Rand-type accuracy index, kappa index and ROC curve. It is concluded that basic models trained on a limited dataset allowed positive/negative discrimination with an efficiency comparable to that obtained by conventional analysis performed by humans (0.84 kappa score). More extensive datasets may be required for efficient discrimination between different fluorescence patterns generated by different auto-antibody species.

A central issue in immunology is the immunological response against non-self. The prerequisite for a specific immunological response is the exposure to the immune system of a non-self-antigen. Mathematical equations are presented, that define the fraction of all outcomes with a non-self-allele in biallelic systems at the population level in pregnancy and transfusion/transplantation medicine. When designing assays, the mathematical descriptions can be used for estimating the number of genetic markers necessary to obtain a predetermined probability level in detecting non-self-alleles of a given frequency. For instance, the equations can be helpful in the design of assays, where the non-self-allele can be detected by analysis of cfDNA in plasma from pregnant women, to estimate fetal fraction or to monitor changes in cfDNA in plasma of transplantation patients. The equations give exact, quantitative descriptions of all non-self-situations in pregnancy and transfusion/transplantation.

This systematic review was conducted with the objective of clarifying the association between malnutrition and the risk of developing kala-azar in humans. The research question was formulated using the PECO acronym: "Is malnutrition a risk factor for the development of kala-azar?" The search was carried out between September and November 2022, with an update in October 2024, in the PubMed, Embase, CAPES Journal Portal databases, and the grey literature. Original studies including individuals exposed to kala-azar with an evaluation of their nutritional status were included. Of the 1,261 records identified, five studies met the inclusion criteria, comprising four cohort studies and one case-control study. Four studies evaluated the association between malnutrition and clinical disease, and one evaluated the association with asymptomatic infection. Only two studies demonstrated a statistically significant association between malnutrition and kala-azar. A meta-analysis was conducted with two studies using R Studio software. The relative risk found was higher for malnourished individuals, although without statistical significance, possibly due to the high heterogeneity among the studies. A higher risk of kala-azar was observed in malnourished children, although without statistical concordance among findings. The methodological quality of the studies was considered low, with a high risk of bias, especially regarding the classification between primary and secondary malnutrition. It is concluded that there is evidence suggesting an association between malnutrition and kala-azar, but it is not possible to affirm this causal relationship with certainty. Further studies with greater methodological rigor, longer follow-up, and larger sample sizes are needed to confirm whether malnutrition modifies the risk of developing kala-azar.

High risk HPV infection is the etiological factor of Cervical Cancer (CC) and other types of cancer of epithelial origin. HPV 16 and 18 infections are associated with 70% of CC worldwide. At the present time, there is a vaccine that prevents this infections. In Mexico, the HPV vaccine was introduced in 2009. Even if the current vaccine is effective, some models indicate a possible scenario of Vaccine-induced Pathogen Strain Replacement (VPSR). In this report, we performed the molecular detection of HPV in a group of HPV-vaccinated Mexican women to explore a putative scenario of VPSR. We used biological samples from women who went for their routine Pap. The study included eighteen women older than 18 years of age and HPV-vaccinated. As the number of cases analyzed is relatively small, we supplemented the study with an agent-based direct computer simulation. The outcome of the numerical experiments and the analyzed cases complement each other and show that in three different scenarios, there is an increase in HPV cases approx 10 years after vaccination of the first cohort of women. The prevalence of non-vaccine HPV types increases when compared to prevalence of vaccine HPV types. This result could be interpreted as the phenomenon of Vaccine-induced Pathogenic Strain Replacement.

Designing prophylactic strategies for newborns requires understanding of the factors that contribute to immunity and resistance to infection. We analyzed 1,892,035 mother-newborn pairs in which both the mother and newborn were observed continuously for at least one year before and after birth. As part of this study, we considered maternal exposures to infections and immune disorders during pregnancy, exposures to anti-infection medications by both mother and newborn, as well as the newborns delivery type and reported complications. According to our analyses, infection rates and immune disorder rates were over-dispersed among newborns. The most consequential factors predicting newborns immune health were preterm birth, with 276.3% and 193.9% risk increases for newborn bacterial infections. Newborn anti-infective prescriptions were associated with considerable increases in risk of diseases affecting immune health, while maternal prescriptions were associated with fewer outcomes and with mixed signs. The Cesarean section mode of delivery, the mothers age, the sex of the newborn, and the mothers exposure to infections all showed significant but smaller effects on the newborns immune health. Female newborn appeared to be better protected against diseases with immune system etiology, except for miscellaneous infections.

Nearly 500 million individuals are affected by diabetes worldwide. This very high prevalence is combined with a North-South gradient and a seasonality of diagnostics which all suggest the role of climate in diabetes etiology. However, only little is known about the impact of climate on diabetes. This article aims to understand the association of climatic variables with type 1 and type 2 diabetes (T1D and T2D) for 72 countries worldwide (1989-2021). T1D is, on average, more prevalent at extreme latitudes whereas T2D prevalence is higher near equator (P < 0,001). Sunshine, temperature, solar irradiance and daylength (photoperiod) are negatively associated with T1D prevalence and positively associated with T2D in simple regression (P < 0,001). Multicollinearity of climatic variables is considered as a challenge, and it is assessed with VIF and optimized with multiple regression. After adjustment, only photoperiod is associated with T1D prevalence (r2=0,45) and sunshine with T2D prevalence (r2=0,48). T1D monthly incidences are approximated with a cosine regression (RR=1,53) which is significantly associated with photoperiod along the year in Europe (P < 0,05). The relation between photoperiod and T1D has never been reported before in an ecological study and a short review is developed in the discussion.

In Italy, 311,364 cases and 35,851 deaths of people who tested positive for SARS-CoV-2 were registered as of September 29th, 2020. To avoid the spreading of the virus, mathematical models predicting the course of infections spread1 become the basis to plan stringent countermeasures. We applied a published algorithm to real data up to September 27th, modeling two scenarios where predicted and real data were compared: a conservative scenario with a lockdown still ongoing and a scenario reflecting what actually happened in Italy, where the lockdown has been removed. Results revealed that the number of individuals in life-threatening condition is much lower than predicted, as well as the number of symptomatic individuals. Contrarily, the number of asymptomatic individuals is much higher than predicted. This suggest that human beings are not passive victims, but active fighters able to change the course of the infection creating adaptive strategies against the infections spread.

A calculation model has been proposed to forecast the spread of the [C]OVID-19 epidemic under quarantine conditions. The resulting simple analytical relationships allow for the assessment of factors determining the intensity of the spread of infection, including the changing requirements for quarantine severity. The prediction method presented makes it possible to calculate both the total number of infected persons and the maximum rate of spread of infection. Following the publication of this work in May 2020, in October this year there was a new surge in the virus epidemic, the intensity of which depends on the populations compliance with the rules of hygiene and social distance. Comparison of the results of the model calculations with the statistics for Berlin shows that they are of satisfactory quality. In particular, it shows that with an epidemic growth rate of around 1,000 people/day, unless additional quarantine measures are taken, the total number of infections can be expected to approach 100,000 within approximately six months. It is shown that the intensity of the viruss spread depends on the socio-demographic composition of the population in different districts of Berlin and age structure. The possible impact of behavioural factors dependent on the psychological state of people on the spread of the epidemic, which can be assessed by analysing changes in heart rate, is discussed.

Parthenogenesis, clonal propagation by only female, is a common asexual reproduction model. Without sexual gene recombination, it is hypothesized that the deficiency of genotypic variation and accumulation of deleterious mutations reduce the fitness of parthenogenetic lineages confronted with environmental fluctuations, which is also regarded as evolutionary dead end. There should be specific life-history strategies to explain why parthenogenesis has been existing successfully. We constructed a family pedigree for rotifer spanning six generations, comprising 1200 individuals with identical genetic background in uniform condition, tracing back to the inception of parthenogenesis from single dormant egg. The individual fitness represented by lifespan and fecundity exhibits rich variation, and seems to be determined before birth by maternal stochastic investment among clutches regardless of maternal aging. Alike to "Do not put all your eggs in one basket", this bet-hedging strategy spreads risk of environmental unpredictability. Despite the absence of sexual recombination, the phenotypic fitness failed to achieve fixation and heritability, instead demonstrating transgenerational compensation and trade-offs phenomenon. More siblings mean less children, and vice versa. This can be regarded as intrinsic and innate non-density-dependent self-regulation strategy of population, as limited by the conservation of disposable energy for allocation among offspring. Those strategies are conducive to explain the adaptability of parthenogenesis in evolution.

Since the onset of the global pandemic caused by the emergence and spread of SARS-CoV-2 in early 2020, numerous studies have been conducted worldwide to understand our immune response to the virus. This study investigates the humoral response elicited by vaccination and by SARS-CoV-2 infection in the poorly studied food and retail workers in the Quebec City area. The 1.5-year study period spans from early 2021, when vaccination became available in this region, to mid-2022, following waves of virulence due to the emergence of the first Omicron variants. Cross-correlated with data on workplace protective measures, pre-existing conditions, activities and other potentially relevant factors, this longitudinal study applies recently developed ELISA data transformation to our dataset to obtain normal distribution. This unlocked the possibility to use the ANOVA-Welsh method for statistical analysis to obtain a statistical perspective of the serological response. Our work allows the identification of factors contributing to statistically relevant differences in the humoral response of the cohort and strengthens the utility of the use of decentralized approaches to serological analysis.

According to the World Health Organization (WHO), dengue is the most common acute arthropod-borne viral infection in the world. The spread of dengue and other infectious diseases is closely related to human activity and mobility. In this paper we analyze the effect on the total number of dengue cases within a population after introducing mobility restrictions as a public health policy. To perform the analysis, we use a complex metapopulation in which we implement a compartmental propagation model coupled with the mobility of individuals between the patches. This model is used to investigate the spread of dengue in the municipalities of Caldas (CO). Two scenarios corresponding to different types of mobility restrictions are applied. In the first scenario, the effect of restricting mobility is analyzed in three different ways: a) limiting the access to the endemic node but allowing the movement of its inhabitants, b) restricting the diaspora of the inhabitants of the endemic node but allowing the access of outsiders, and c) a total isolation of the inhabitants of the endemic node. In this scenario, the best simulation results are obtained when endemic nodes are isolated during a dengue outbreak, obtaining a reduction of up to 22.51% of dengue cases. Finally, the second scenario simulates a total isolation of the network, i.e., mobility between nodes is completely limited. We have found that this control measure reduces the number of total dengue cases in the network by up to 42.67%. Author summaryFor the World Health Organization, dengue is a disease of public health concern. In recent years there is an increasing trend in the number of dengue cases despite existing prevention and control campaigns. The mobility of the population is considered an important factor in dengue dispersion. In this paper, we are interested in addressing how restrictions to human mobility might affect the incidence of dengue in a region. Our research is relevant because the model can be adapted to other regions or scales, and the mobility control measures can be taken into account for the development of public health policies in endemic regions.

The SIR (susceptible-infected-removed) model, statistical approach to the parameter identification and the official WHO daily data about the confirmed cumulative number of cases were used to make some estimations for the dynamics of the coronavirus pandemic dynamics in Ukraine, Italy and Austria. The volume of the data sets and the influence of the information about the initial stages of the epidemics were discussed in order to have reliable long-time predictions. The final sizes and durations for the pandemic in these countries are estimated.

The visible and real sizes the COVID-19 epidemic in Ukraine were estimated with the use of the number of laboratory-confirmed cases (accumulated in May and June 2021), the generalized SIR-model and the parameter identification procedure taking into account the difference between registered and real number of cases. The calculated optimal value of the visibility coefficient shows that most Ukrainians have already been infected with the coronavirus, and some more than once, i.e., Ukrainians have probably achieved a natural collective immunity. Nevertheless, a large number of new strains and short-lived antibodies can cause new pandemic waves. In particular, the beginning of such a wave, we probably see in Ukraine in mid-July 2021. The further dynamics of the epidemic and its comparison with the results of mathematical modeling will be able to answer many important questions about the natural immunity and effectiveness of vaccines.

A correlation hypothesis between the level of vaccination and the rate of spread of the new Covid-19 variant is investigated based on the case and vaccination data from European and North American jurisdictions available in the public domain at the time point of past the crest of the Omicron wave in most jurisdictions. Statistical variables describing the rate of the spread based on observed new case statistics defined and discussed. An unexpected moderate positive correlation between the rate of the variant spread measured by two related parameters and vaccination level based on the dataset in the study is reported. While negative correlation was not statistically excluded, the analysis of the data in the study statistically excluded moderate to strong negative correlation. The results of this work, if confirmed by further independent studies can have implications for development of policies aimed at controlling future course of the Covid-19 pandemic.