Hypercalcemia upon denosumab withdrawal in primary hyperparathyroidism: a case report and literature review.

Denosumab discontinuation is associated with a rapid increase in bone resorption and a decrease in bone mineral density. Spontaneous vertebral fractures may occur as a side effect of the rebound of bone resorption. Cases of rebound-linked hypercalcemia have also been described, moderate in women with osteoporosis and breast cancer and severe in children receiving oncological doses of denosumab. We report the case of an adult woman with primary hyperparathyroidism and moderate hypercalcemia, treated with denosumab for osteoporosis, who developed severe hypercalcemia and spontaneous vertebral fractures (SVFs) after denosumab discontinuation. An 86-year-old woman with densitometric osteoporosis was treated for 3 years with 60 mg of subcutaneous denosumab every 6 months. She was known to have primary hyperparathyroidism, with a serum albumin-corrected calcium of 2.82 mmol/l (NV 2.15-2.5) at the end of denosumab effect. Nine months after the last denosumab injection, she was hospitalized due to worsening overall health. Clinical evaluation revealed severe hypercalcemia (calcium 3.35 mmol/l). Very high values of bone turnover markers (BTMs) suggested a rebound effect due to denosumab discontinuation. An X-ray showed multiple new SVFs. After injection of denosumab 60 mg, serum calcium rapidly decreased and BTMs were dramatically reduced. A surgical approach by minimally invasive parathyroidectomy allowed for definite resolution of hyperparathyroidism and hypercalcemia. This case suggests that hypercalcemia can be a side consequence of denosumab discontinuation, which can become severe when other causes of hypercalcemia, such as primary hyperparathyroidism, are present

Mechanistic within-host models of the asexual; Plasmodium falciparum; infection: a review and analytical assessment

BACKGROUND: Malaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host's immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models. METHODS: Mechanistic within-host models of parasite dynamics were identified through a review of published literature. For a subset of these, model code was reproduced and descriptive statistics compared between the models using fitted data. Through simulation and model analysis, key features of the models were compared, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability. RESULTS: The assessed within-host malaria models generally replicate infection dynamics in malaria-naive individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitaemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models. CONCLUSIONS: This study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, it is proposed that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterization and large stochasticity which inaccurately represent unknown disease mechanisms

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. METHODS: A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. RESULTS: We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. CONCLUSIONS: Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions

Phthalates: Potential sources and control measures

The issue of endocrine disruptors is a subject of debate in the agri-food sector and questioning for consumers through the media. Among these compounds, some of the family of phthalates, are used in the composition of some materials used in wineries. This makes it a worrying issue in terms of image and safety. The objective ok this work was to identify the sources of phthalates and the factors favoring their diffusion in wines to propose preventive and curative solutions to the wine makers. The modalities were therefore selected in different French wine regions to represent the diversity of oenological practices. This survey also led to examine more thoroughly the parameters involved in the situations that favored migration of the highest levels. Additional experiments were performed specifically on certain sources or contributing factors. The first identified sources include epoxy resin, fiberglass reinforced polyester as well as PVC materials (pipes …). Favorable factors include alcohol content, temperature, duration and contact area

Comprehensive Evaluation of Rare Pituitary Lesions: A Single Tertiary Care Pituitary Center Experience and Review of the Literature.

The 2017 World Health Organization classification of central nervous system and endocrine tumors have introduced significant changes in the diagnostic criteria for pituitary lesions. The aim of our paper is to describe the epidemiological, clinico-pathological, and radiological features of a single consecutive institutional surgical series of rare pituitary lesions, using these new criteria. Of the 316 endoscopic endonasal trans-sphenoidal approaches performed for pituitary lesions between 2010 and 2018, 15 rare lesions were encountered. These included metastases, pituitary carcinomas, pituicytomas, granular cell tumor, primary pituitary lymphomas, germinoma, mixed gangliocytoma-adenoma, hypophysitis, and pituitary hyperplasia. Their clinical, radiological, and pathological features are herewith presented along with a literature review that enabled us to propose an algorithm to facilitate a diagnosis for rare pituitary lesions

Phthalates: Potential sources and control measures

The issue of endocrine disruptors is a subject of debate in the agri-food sector and questioning for consumers through the media. Among these compounds, some of the family of phthalates, are used in the composition of some materials used in wineries. This makes it a worrying issue in terms of image and safety. The objective ok this work was to identify the sources of phthalates and the factors favoring their diffusion in wines to propose preventive and curative solutions to the wine makers. The modalities were therefore selected in different French wine regions to represent the diversity of oenological practices. This survey also led to examine more thoroughly the parameters involved in the situations that favored migration of the highest levels. Additional experiments were performed specifically on certain sources or contributing factors. The first identified sources include epoxy resin, fiberglass reinforced polyester as well as PVC materials (pipes …). Favorable factors include alcohol content, temperature, duration and contact area