Tissue engineering a fetal membrane

The aim of this study was to construct an artificial fetal membrane (FM) by combination of human amniotic epithelial stem cells (hAESCs) and a mechanically enhanced collagen scaffold containing encapsulated human amniotic stromal fibroblasts (hASFs). Such a tissue-engineered FM may have the potential to plug structural defects in the amniotic sac after antenatal interventions, or to prevent preterm premature rupture of the FM. The hAESCs and hASFs were isolated from human fetal amniotic membrane (AM). Magnetic cell sorting was used to enrich the hAESCs by positive ATP-binding cassette G2 selection. We investigated the use of a laminin/fibronectin (1:1)-coated compressed collagen gel as a novel scaffold to support the growth of hAESCs. A type I collagen gel was dehydrated to form a material mimicking the mechanical properties and ultra-structure of human AM. hAESCs successfully adhered to and formed a monolayer upon the biomimetic collagen scaffold. The resulting artificial membrane shared a high degree of similarity in cell morphology, protein expression profiles, and structure to normal fetal AM. This study provides the first line of evidence that a compacted collagen gel containing hASFs could adequately support hAESCs adhesion and differentiation to a degree that is comparable to the normal human fetal AM in terms of structure and maintenance of cell phenotype

Emerging Applications of III‐Nitride Nanocrystals

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154944/1/pssa201900885_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154944/2/pssa201900885.pd

Nutritional status of adolescent girls in a selected secondary school of north-eastern part of Nigeria

Background: Adolescence is the most critical period of human life when transitioning occurs from childhood to adulthood. Malnutrition is one of the major global health problems faced by many developing countries across the globe. Objectives: This study aimed to investigate the nutritional status of adolescent girls in a selected secondary school in Nigeria. Methods: A cross-sectional study was conducted on 250 adolescent girls who were available during the study period. A nutrition expert, together with an epidemiologist, gathered anthropometric information and measured the height, weight, and body mass index (BMI) of the participants. The BMI was calculated, and the dietary habits of the participants were reported based on their usual food habits. It is part of our limitations and has been addressed under the limitations. Data were collected using a self-administered and semi-structured questionnaire. Results: The mean age of the adolescent girls was 15.9 ± 0.9 years, and more than half (53.2%) were students of senior secondary class 1 (SS-1). More than half (54.4%) of the adolescent girls had average body weight, 36.0% were underweight, and only 9.6% were overweight. The following socio-demographic factors were significantly associated with the BMI of adolescent girls: Age, class of the students, monthly family income, mothers’ educational status, and area of residence. Close to four-fifths (78.4%) of the participants consumed rice more than twice in a week; more than eight-tenths (88.8%) of the participants consumed milk/milk products at least ≤ 2 in a week. About 84.4% of the participants took red meat at least ≤ 2 in a week; more than half (55.2%) of the participants consumed vegetables and fruits more than twice a week. Most (84.8%) of the participants took lunch regularly, and 91.6% of the adolescent girls took breakfast regularly. Conclusions: The study revealed that nearly one-third of the adolescent girls were underweight, indicating a severe public health concern. Early nutritional screening and interventions are recommended to improve the nutritional status of the adolescent girl school in Nigeria

The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh

Bangladesh is one of the world’s most vulnerable countries for climate change. This observational study examined the association of temperature, humidity and rainfall with six common climate-sensitive infectious diseases in adults (malaria, diarrheal disease, enteric fever, encephalitis, pneumonia and bacterial meningitis) in northeastern Bangladesh. Subjects admitted to the adult medicine ward of a tertiary referral hospital in Sylhet, Bangladesh from 2008 to 2012 with a diagnosis of one of the six chosen climate-sensitive infectious diseases were enrolled in the study. Climate-related data were collected from the Bangladesh Meteorological Institute. Disease incidence was then analyzed against mean temperature, humidity and average rainfall for the Sylhet region. Statistical significance was determined using Mann-Whitney test, Chi-square test and ANOVA testing. 5033 patients were enrolled (58% male, 42% female, ratio 1.3:1). All six diseases showed highly significant (p = 0.01) rises in incidence between the study years 2008 (540 cases) and 2012 (1330 cases), compared with no significant rise in overall all-cause hospital admissions in the same period (p = 0.19). The highest number of malaria (135), diarrhea (266) and pneumonia (371) cases occurred during the rainy season. On the other hand, the maximum number of enteric fever (408), encephalitis (183) and meningitis (151) cases occurred during autumn, which follows the rainy season. A positive (P = 0.01) correlation was observed between increased temperature and the incidence of malaria, enteric fever and diarrhea, and a negative correlation with encephalitis, meningitis and pneumonia. Higher humidity correlated (P = 0.01) with a higher number of cases of malaria and diarrhea, but inversely correlated with meningitis and encephalitis. Higher incidences of encephalitis and meningitis occurred while there was low rainfall. Incidences of diarrhea, malaria and enteric fever, increased with rainfall, and then gradually decreased. The findings support a relationship between weather patterns and disease incidence, and provide essential baseline data for future large prospective studies

The spatial distribution of leprosy in four villages in Bangladesh: An observational study

BACKGROUND: There is a higher case-detection rate for leprosy among spatially proximate contacts such as household members and neighbors. Spatial information regarding the clustering of leprosy can be used to improve intervention strategies. Identifying high-risk areas within villages around known cases can be helpful in finding new cases. METHODS: Using geographic information systems, we created digital maps of four villages in a highly endemic area in northwest Bangladesh. The villages were surveyed three times over four years. The spatial pattern of the compounds--a small group of houses--was analyzed, and we looked for spatial clusters of leprosy cases. RESULTS: The four villages had a total population of 4,123. There were 14 previously treated patients and we identified 19 new leprosy patients during the observation period. However, we found no spatial clusters with a probability significantly different from the null hypothesis of random occurrence. CONCLUSION: Spatial analysis at the microlevel of villages in highly endemic areas does not appear to be useful for identifying clusters of patients. The search for clustering should be extended to a higher aggregation level, such as the subdistrict or regional level. Additionally, in highly endemic areas, it appears to be more effective to target complete villages for contact tracing, rather than narrowly defined contact groups such as households

Efficient Large Language Models: A Survey

Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding and language generation, and thus have the potential to make a substantial impact on our society. Such capabilities, however, come with the considerable resources they demand, highlighting the strong need to develop effective techniques for addressing their efficiency challenges. In this survey, we provide a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from model-centric, data-centric, and framework-centric perspective, respectively. We have also created a GitHub repository where we organize the papers featured in this survey at https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey. We will actively maintain the repository and incorporate new research as it emerges. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of efficient LLMs research and inspire them to contribute to this important and exciting field.Comment: Camera ready version of Transactions on Machine Learning Research (TMLR