In particular, the use of infant formula in less economically developed countries is linked to poorer health outcomes because of the prevalence of unsanitary preparation conditions, including lack of clean water and lack of sanitizing equipment.[6] A formula-fed child living in unclean conditions is between 6 and 25 times more likely to die of diarrhea and four times more likely to die of pneumonia than a breastfed child.[7] Rarely, use of powdered infant formula (PIF) has been associated with serious illness, and even death, due to infection with Cronobacter sakazakii and other microorganisms that can be introduced to PIF during its production. Although C. sakazakii can cause illness in all age groups, infants are believed to be at greatest risk of infection. Between 1958 and 2006, there have been several dozen reported cases of C. sakazakii infection worldwide. The WHO believes that such infections are under-reported.[8]
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Use of infant formula has been cited for association with numerous increased health risks. Studies have found infants in developed countries who consume formula are at increased risk for acute otitis media, gastroenteritis, severe lower respiratory tract infections, atopic dermatitis, asthma, obesity,[48] type 1 and 2 diabetes, sudden infant death syndrome (SIDS), eczema and necrotizing enterocolitis when compared to infants who are breastfed.[49][50][51][52] Some studies have found an association between infant formula and lower cognitive development, including iron supplementation in baby formula being linked to lowered I.Q. and other neurodevelopmental delays;[53][54] however other studies have found no correlation.[49] Causation, however, has not been established for negative long-term health effects of infant formula; studies analyzing health outcomes for breastfed vs. formula fed babies are primarily observational in nature and are plagued with confounding factors such as socioeconomic status, education level, and maternal preexisting conditions (such as obesity, which is associated with both low milk production and childhood obesity). When confounding factors are controlled for, differences between long-term health of breastfed and formula fed infants decrease.[55]
Kc, P; Virtanen, M; Pentti, J; Kivimäki, M; Vahtera, J; Stenholm, S; (2020) Does working beyond the statutory retirement age have an impact on health and functional capacity? The Finnish Retirement and Aging cohort study. Occupational and Environmental Medicine 10.1136/oemed-2020-106964. (In press).
Kivimäki, M; Batty, GD; Pentti, J; Shipley, MJ; Sipilä, PN; Nyberg, ST; Suominen, SB; ... Vahtera, J; + view all Kivimäki, M; Batty, GD; Pentti, J; Shipley, MJ; Sipilä, PN; Nyberg, ST; Suominen, SB; Oksanen, T; Stenholm, S; Virtanen, M; Marmot, MG; Singh-Manoux, A; Brunner, EJ; Lindbohm, JV; Ferrie, JE; Vahtera, J; - view fewer (2020) Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. The Lancet Public Health, 5 (3) e140-e149. 10.1016/S2468-2667(19)30248-8.
Ning, K; Gondek, D; Patalay, P; Ploubidis, GB; (2020) The association between early life mental health and alcohol use behaviours in adulthood: A systematic review. PLoS One, 15 (2) , Article e0228667. 10.1371/journal.pone.0228667.
Large-scale volcanism has played a critical role in the long-term habitability of Earth. Contrary to widely held belief, volcanism, rather than impactors, has had the greatest influence on and bears most of the responsibility for large-scale mass extinction events throughout Earth's history. We examine the timing of large igneous provinces (LIPs) throughout Earth's history to estimate the likelihood of nearly simultaneous events that could drive a planet into an extreme moist or runaway greenhouse, leading to the end of volatile cycling and causing the heat death of formerly temperate terrestrial worlds. In one approach, we make a conservative estimate of the rate at which sets of near-simultaneous LIPs (pairs, triplets, and quartets) occur in a random history statistically the same as Earth's. We find that LIPs closer in time than 0.1-1 million yr are likely; significantly, this is less than the time over which terrestrial LIP environmental effects are known to persist. In another approach, we assess the cumulative effects with simulated time series consisting of randomly occurring LIP events with realistic time profiles. Both approaches support the conjecture that environmental impacts of LIPs, while narrowly avoiding grave effects on the climate history of Earth, could have been responsible for the heat death of our sister world Venus.
Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.
Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.
To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and
Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated 2ff7e9595c
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