Dust and the Sick Building Syndrome

Background. In the Danish Town Hall Study" it has previously been shown that the sick building syndrome is a widespread phenomenon. This has given rise to speculations as to whether biologically active components in dust or whether absorbed organic gases and vapours in the indoor climate may be partly responsible for the sick building syndrome. Therefore, we wished to study whether qualitative conditions in dust, of a physical, chemical, and biological nature, are related to the prevalence of symptoms of the sick building syndrome. Material and methods. The study included 12 town halls in the Copenhagen area. A total of 870 persons participated, 584 women and 286 men. All the participants filled out a questionnaire regarding health and working conditions, and dust samples were collected with a special vacuum cleaner in the working environment of the participants, after which the contents of inorganic and organic particles as well as of human source fragments the (hair, nails, skin) and various fibres were determined. Results. There was a significant correlation (P<0.05) between the prevalence of gram-negative bacteria in the indoor climate dust, and general symptoms (fatigue, heavy-headedness, headache, dizziness, concentration problems) (corr.coeff.=0.73), as well as symptoms from the mucous membranes of the upper respiratory tract (corr.coeff.=0.76). Also, there was a significant relation between the prevalence of particles in the dust and the prevalence of symptoms from the mucous membranes (corr.coeff.=0.81). There was a strong and significant correlation between the total amount of volatile organic components (TVOC) and lack of concentration (corr.coeff. =0.85) and feeling heavy-headed (corr.coeff.=0.72). Macromolecular dust (potentially allergenetic material) was significantly correlated to the prevalence of headache and general malaise/dizziness (corr.coeff.=0.66), while the ability of the dust to liberate histamine was significantly related to the prevalence of general malaise, dizziness (corr.coeff.=0.65) and lack of concentration (corr.coeff. =0.58). Further analyses including a number of potential confounders did not change the results of the above analyses. Conclusion. The results of this study support the hypothesis that qualitative properties of dust are important to the sick building syndrome. The study was based on relatively few buildings, but nevertheless strong correlations were found. In perspective, epidemiological intervention studies should be carried out to investigate whether minimizing the amount of both dust and bacteria in the indoor climate affect the prevalence of sick building syndrome.     

Estimating basal tear osmolarity in normal and dry eye subjects

Purpose

Tear osmolarity (tOsm) is used as a measure of severity in dry eye disease (DED) and has been proposed as an index of body hydration. In DED the level of tear hyperosmolarity is compared with that of a control population. It is proposed here that a better index of body hydration and a more valid reference point in DED can be acquired by measuring the tOsm after a period of evaporative suppression.

与甜菜按糖计价相适应的栽培技术研究

甜菜按糖计价要求的主要增产增糖栽培技术为，培育和选用标准型与高糖型新品种，采用翻地２５ｃｍ以上和苗期行间深松２８～３０ｃｍ为土壤耕作制；运用Ｎ１３５Ｐ１２０Ｋ９０的平衡施肥法，提早播期８～１０天，建立了６．７万株／ｈｍ＾２的群体结构，采用农业，化学相结合的防治措施，防治苗期和生育中期病虫害。     

The importance of natural product characterization in studies of their anti-inflammatory activity

The knowledge that natural products provide a rich source for therapeutic discovery has led to the development of many of the world's most commonly used drugs. In view of the growing need for effective anti-inflammatory agents, the potential for natural products to serve as safe and effective therapeutic agents has gained increasing attention. However, polymolecular extracts must be rigorously evaluated and chemically characterized to insure adequate consistency in performance. The research in this field has been plagued by inconsistencies due in part to inadequate chemical characterization and documentation, making comparison of results across studies very difficult. Analytical chemistry and molecular methods now exist to insure sufficient transparency to avoid this limitation. Further, our understanding of the complexity of inflammation has advanced to enable significant insight into the mechanism of action of these natural extracts. Here, we review the inflammatory pathways targeted by many therapeutic agents, discuss the value of natural products as anti-inflammatory agents, review approaches for their biological and chemical evaluation, and highlight challenges to the field. We present two examples highlighting the rigorous use of cell, molecular, and chemical methods for characterization and quality control as templates for future studies of anti-inflammatory activity of natural products.     

CT技术的发展与其在医学上的应用

本文简要介绍了CT技术的原理以及其发展过程,并详细介绍了其在医学上的相关应用,可以给想要掌握CT领域现有技术的人群提供一定参考。     

烟田治蚜与防病（CMV）效果研究再报

经多年多点试验，明确了在烟田进行治蚜仅对降低烟蚜种群数量效果显著，而对防治ＣＭＶ等蚜传口针系病毒病没有效果；分析了银膜驱蚜及麦套阻蚜防病的原因所在。主张在当前既无抗病品种又无较好药剂的情况下，防治ＣＭＶ等蚜传口针系病毒病应以驱（避）前期有翅蚜为主，采取多种措施提高烟株前期抗（耐）病能力为辅的防治策略。     

SW-Net: A novel few-shot learning approach for disease subtype prediction

Few-shot learning is becoming more and more popular in many fields, especially in the computer vision field. This inspires us to introduce few-shot learning to the genomic field, which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions. The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data. Accurate disease sub-type classification allows clinicians to efficiently deliver investigations and interventions in clinical practice. We propose the SW-Net, which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data. Our model is built upon a simple baseline, and we modified it for genomic data. Support-based initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy, and an Entropy regularization term on the query set was appended to reduce over-fitting. Moreover, to address the high dimension and high noise issue, we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples. Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.     

2004年中国省级疾控中心食品检验质控考核结果与分析——酱油和饮料中常见防腐剂和甜味剂含量测定

为了解中国省级疾控中心食品检验能力，食品安全行动计划监测和实验室能力建设工作组于2004年3月-4月采用盲样考核方式，组织中国31个省级疾病预防控制中心对酱油质控样品中常见防腐剂（苯甲酸、山梨酸、对羟基苯甲酸甲酯、对羟基苯甲酸丙酯）和甜味剂（糖精钠、乙酰磺胺酸钾、阿斯巴甜、环己基氨基磺酸钠）和饮料质控样品中8个同样的指标进行了考核检验；对检验结果进行了分析，按照误差（检测值与均值之差的绝对值）在2倍标准差以内为满意结果，在2倍和3倍之间为可疑结果，在3倍之外及未报结果为不满意结果的原则进行评价。29个机构报送了结果，其中17个机构报送了全部8项检验结果。酱油检验8个指标结果均满意的单位占41．9％（13/31），其中苯甲酸、山梨酸、对羟基苯甲酸甲酯、对羟基苯甲酸丙酯、糖精钠、乙酰磺胺酸钾、阿斯巴甜、环己基氨基磺酸钠单个指标满意结果的百分率分别为83．9％、87．1％、64．5％、64．5％、77．4％、61．30k、61．30k、71．0％。饮料检验8个指标结果均满意的占45．2％（14131）。其中苯甲酸、山梨酸、对羟基苯甲酸甲酯、对羟基苯甲酸丙酯、糖精钠、乙酰磺胺酸钾、阿斯巴甜、环己基氨基磺酸钠单个项目满意结果的百分率分别为83．90k、80．6％、54．8％、54．8％、83．9％、71．0％、58．1％、74．2％。检验机构应积极利用实验室间比对平台，及时认真地对待不满意或可疑结果，查找原因，保证数据的准确性。     

Modelling a Fused Deep Network Model for Pneumonia Prediction

Deep Learning (DL) is known for its golden standard computing paradigm in the learning community. However, it turns out to be an extensively utilized computing approach in the ML field. Therefore, attaining superior outcomes over cognitive tasks based on human performance. The primary benefit of DL is its competency in learning massive data. The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully. Specifically, various DL approaches outperform the conventional ML approaches in real-time applications. Indeed, various research works are reviewed to understand the significance of the individual DL models and some computational complexity is observed. This may be due to the broader expertise and knowledge required for handling these models during the prediction process. This research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification purposes. This work incorporates a novel fused Squeeze and Excitation (SE) block with the ResNet model for pneumonia prediction and better accuracy. The expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive. The experimentation is carried out in Keras, and the model’s superiority is compared with various advanced approaches. The proposed model gives 90% prediction accuracy, 93% precision, 90% recall and 89% F1-measure. The proposed model shows a better trade-off compared to other approaches. The evaluation is done with the existing standard ResNet model, GoogleNet+ResNet+DenseNet, and different variants of ResNet models.