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The use of luminescence in biological systems allows one to diagnose diseases and understand cellular processes. Molecular systems, particularly lanthanide(III) complexes, have emerged as an attractive system for application in cellular luminescence imaging due to their long emission lifetimes, high brightness, possibility of controlling the spectroscopic properties at the molecular level, and tailoring of the ligand structure that adds sensing and therapeutic capabilities. This review aims to provide a background in luminescence imaging and lanthanide spectroscopy and discuss selected examples from the recent literature on lanthanide(III) luminescent complexes in cellular luminescence imaging, published in the period 2016–2020. Finally, the challenges and future directions that are pointing for the development of compounds that are capable of executing multiple functions and the use of light in regions where tissues and cells have low absorption will be discussed.

In ecosystems, spatial structure plays a fundamental role in shaping the observed dynamics. In particular, the availability and distribution of unoccupied sites—potential habitats—can strongly affect species persistence. However, mathematical models of ecosystems based on ordinary differential equations (ODEs) often neglect the explicit representation of these unoccupied sites. Here, probabilistic cellular automata (PCA) are used to reproduce two basic ecological scenarios: competition between two species and a predator–prey relationship. In these PCA-based models, unoccupied sites are taken into account. Subsequently, a mean field approximation of the PCA behavior is formulated in terms of ODEs. The variables of these ODEs are the numbers of individuals of both species and the number of empty cells in the PCA lattice. Including the empty cells in the ODEs leads to a modified version of the Lotka–Volterra system. The long-term behavior of the solutions of the ODE-based models is examined analytically. In addition, numerical simulations are carried out to compare the time evolutions generated by these two modeling approaches. The impact of explicitly considering unoccupied sites is discussed from a modeling perspective.

In the context of the ongoing war in the Gaza Strip, the Gaza Health Ministry (GHM) has reported 45 936 fatalities and more than 10 000 individuals missing or under the rubble for the period Oct 7, 2023, to Jan 8, 2025. The scope of this death count is difficult to fully interpret because it does not account for the size and age distribution of the Gaza Strip population. Moreover, the quality of this death count has been questioned. In this study, we evaluated the quality of the GHM death count by comparing GHM data against register data, and we estimated life expectancy losses in the Gaza Strip for the period October, 2023, to September, 2024, ie, the first 12 months of the war. We matched individuals included in the GHM nominative list of killed individuals for the period Oct 7, 2023, to Aug 30, 2024, with individuals included in the refugee register maintained by the UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), which covers about 66% of the Gaza Strip population. We compared proportions of matched fatalities with proportions of registered refugees in the 2017 census. We then used census data, vital registration data, and GHM fatality information since Oct 7, 2023, to produce estimates of life expectancy losses in the Gaza Strip for the first 12 months of the war. We used three scenarios for these life expectancy estimates, based on the different types of counts provided by GHM. These scenarios did not account for the indirect effect of the war. 21 953 (63·9%) of 34 344 individuals in the GHM list of killed individuals (and 19 744 [64·4%] of 30 673 excluding those who were not yet born at the time of the 2017 census) were matched with individuals included in the UNRWA refugee register. This proportion is similar to the proportion of registered refugees in the 2017 census (65·7%), providing additional evidence regarding the reliability of the GHM data. In the central variant, life expectancy in the Gaza Strip decreased by 34·9 years during the first 12 months of the war, about half (–46·3%) the prewar level of 75·5 years. Life expectancy losses were larger for males (–38·0 years [–51·6%]) than for females, but nonetheless, females also suffered large losses (–29·9 years [–38·6%]). Losses between the low and high variants ranged between –31·1 years (–41·1%) and –39·4 years (–52·2%) for both sexes combined. Our approach to estimating life expectancy losses in this study is conservative as it ignores the indirect effect of the war on mortality. Even ignoring this indirect effect, results show that the ongoing war in the Gaza Strip generated a life expectancy loss of more than 30 years during the first 12 months of the war, nearly halving prewar levels. Actual losses are likely to be higher. None.

In Brazil, around 80% of snakebites are caused by snakes of the genus Bothrops. A three-dimensional culture model was standardized and used to perform treatments with Bothrops erythromelas venom (BeV) and its antivenom (AV). The MRC-5 and L929 cell lines were cultured at increasing cell densities. Morphometric parameters were evaluated through images obtained from an inverted microscope: solidity, circularity, and Feret diameter. L929 microtissues (MT) showed better morphometric data, and thus they were used for further analysis. MT viability was assessed using the acridine orange and ethidium bromide staining method, which showed viable cells in the MT on days 5, 7, and 10 of cultivation. Histochemical and histological analyses were performed, including hematoxylin/eosin staining, which showed a good structure of the spheroids. Alcian blue staining revealed the presence of acid proteoglycans. Immunohistochemical analysis with ki-67 showed different patterns of cell proliferation. The MT were also subjected to pharmacological tests using the BeV, in the presence or absence of its AV. The results showed that the venom was not cytotoxic, but it caused morphological changes. The MT showed cell detachment, losing their structure. The antivenom was able to partially prevent the venom activities.

Let a population be composed of members of a criminal organization and judges of the judicial system, in which the judges can be co-opted by this organization. In this article, a model written as a set of four nonlinear differential equations is proposed to investigate this population dynamics. The impact of the rate constants related to judges’ co-optation and ex-convicts’ recidivism on the population composition is explicitly examined. This analysis reveals that the proposed model can experience backward and transcritical bifurcations. Also, if all ex-convicts relapse, organized crime cannot be eradicated even in the absence of corrupt judges. The results analytically derived here are illustrated by numerical simulations and discussed from a crime-control perspective.

The Ultimatum Game is a simplistic representation of bargaining processes occurring in social networks. In the standard version of this game, the first player, called the proposer, makes an offer on how to split a certain amount of money. If the second player, called the responder, accepts the offer, the money is divided according to the proposal; if the responder declines the offer, both players receive no money. In this article, an agent-based model is employed to evaluate the performance of five distinct strategies of playing a modified version of this game. A strategy corresponds to instructions on how a player must act as the proposer and as the responder. Here, the strategies are inspired by the following basic emotions: anger, fear, joy, sadness, and surprise. Thus, in the game, each interacting agent is a player endowed with one of these five basic emotions. In the modified version explored in this article, the spatial dimension is taken into account and the survival of the players depends on successful negotiations. Numerical simulations are performed in order to determine which basic emotion dominates the population in terms of prevalence and accumulated money. Information entropy is also computed to assess the time evolution of population diversity and money distribution. From the obtained results, a conjecture on the emergence of the sense of fairness is formulated.

Leprosy, or Hansen’s disease, is a Neglected Tropical Disease (NTD) caused by Mycobacterium leprae that mainly affects the skin and peripheral nerves, causing neuropathy to varying degrees. It can result in physical disabilities and functional loss and is particularly prevalent amongst the most vulnerable populations in tropical and subtropical regions worldwide. The persistent stigma and social exclusion associated with leprosy complicate eradication efforts exacerbate the wider challenges faced by NTDs in sourcing the necessary resources and attention for control and elimination. The introduction of Multidrug Therapy (MDT) significantly lowers the global disease burden. Despite this breakthrough in the treatment of leprosy, over 200,000 new leprosy cases are reported annually across more than 120 countries, emphasizing the need for ongoing detection and management efforts. Artificial Intelligence (AI) has the potential to transform leprosy care by accelerating early detection, improving accurate diagnosis, and enabling predictive modeling to improve the quality for those affected. The potential of AI to provide information to assist healthcare professionals in interventions that reduce the risk of disability, and consequently stigma, particularly in endemic regions, presents a promising path to reducing the incidence of leprosy and improving integration social status of patients. This systematic literature review (SLR) examines the state of the art in research on the use of AI for leprosy care. From an initial 657 works from six scientific databases (ACM Digital Library, IEEE Xplore, PubMed, Scopus, Science Direct and Springer), only 30 relevant works were identified, after analysis of three independent reviewers. We have excluded works due duplication, couldn’t be retrieved and quality assessment. Results show that current research is focused primarily on the identification of symptoms using image based classification using three main techniques, neural networks, convolutional neural networks, and support vector machines; a small number of studies focus on other thematic areas of leprosy care. A comprehensive systematic approach to research on the application of AI to leprosy care can make a meaningful contribution to a leprosy-free world and help deliver on the promise of the Sustainable Development Goals (SDG).

The COVID-19 pandemic highlighted the importance of civil registration and vital statistics (CRVS) systems for tracking and monitoring mortality outcomes during a public health crisis, especially in low- and middle-income countries. Alternative mortality data sources, such as censuses and surveys, offer an opportunity to assess the impact of health crises on countries with incomplete CRVS systems. Our aim is to show that data on retrospective household deaths collected in household surveys produce informative adult mortality numbers that can be useful in estimating mortality in the context of the COVID-19 pandemic in countries with incomplete CRVS systems, such as Peru. Using data on household deaths in the previous five years from the National Demographic and Family Health Surveys of Peru (ENDES) from 2018, 2019, 2021, and 2022, we estimate the probability of dying between ages 20 and 64 (45q20) and compare the results with estimates from the World Population Prospects (WPP) 2022 revision and with estimates attained using the sibling survival method. We verify that 45q20 estimates from ENDES household death information fall close to those reported by the WPP 2022. However, these estimates have high confidence intervals due to the small sample size. The sibling survival method consistently estimates lower adult mortality probabilities, even in pandemic years. Despite the difference in magnitude between the WPP 2022 estimates and those from household deaths, both provide a picture of an increase in the probability of dying among adults during the pandemic period. This is not reflected in estimates made using the sibling survival method.

The use of luminescence in biological systems allows us to diagnose diseases and understand cellular processes. Persistent luminescent materials have emerged as an attractive system for application in luminescence imaging of biological systems; the afterglow emission grants background-free luminescence imaging, there is no need for continuous excitation to avoid tissue and cell damage due to the continuous light exposure, and they also circumvent the depth penetration issue caused by excitation in the UV-Vis. This review aims to provide a background in luminescence imaging of biological systems, persistent luminescence, and synthetic methods for obtaining persistent luminescent materials, and discuss selected examples of recent literature on the applications of persistent luminescent materials in luminescence imaging of biological systems and photodynamic therapy. Finally, the challenges and future directions, pointing to the development of compounds capable of executing multiple functions and light in regions where tissues and cells have low absorption, will be discussed.

Entomopathogenic nematodes (EPNs) are closely associated with Popillia japonica and potentially used as their biological control agents, although field results proved inconsistent and evoked a continual pursuit of native EPNs more adapted to the environment. Therefore, we surveyed the Azorean Archipelago to isolate new strains of Heterorhabditis bacteriophora and to evaluate their virulence against the model organism Galleria mellonella under laboratory conditions. Six strains were obtained from pasture and coastal environments and both nematode and symbiont bacteria were molecularly identified. The bioassays revealed that Az172, Az186, and Az171 presented high virulence across the determination of a lethal dose (LD50) and short exposure time experiments with a comparable performance to Az29. After 72 hours, these virulent strains presented a mean determination of a lethal dose of 11 infective juveniles cm-2, a lethal time (LT50) of 34 hours, and achieved 40% mortality after an initial exposure time of only 60 minutes. Az170 exhibited an intermediate performance, whereas Az179 and Az180 were classified as low virulent strains. However, both strains presented the highest reproductive potential with means of 1700 infective juveniles/mg of larvae. The bioassays of the native EPNs obtained revealed that these strains hold the potential to be used in biological control initiatives targeting P. japonica because of their high virulence and locally adapted to environmental conditions.