燃煤型氟中毒对男性生殖系统的影响

燃煤型氟中毒是我国特有的一种氟中毒类型,研究发现燃煤型氟中毒对男性生殖系统有一定程度的影响,其中,重度氟中毒地区男性精液的含氟量、精子存活率、精子浓度及不育症的患病率高于轻氟区和非氟中毒病区,血清中卵泡刺激素(FSH)、黄体生成素(LH)也高于轻氟区和非氟中毒区,但是抑制素B、血清睾酮以及雌二醇却有不同程度的降低。提示燃煤型氟中毒也跟其他氟中毒一样,会对男性生殖器官的结构、精子的生成以及生殖内分泌等多方面造成影响,最终导致男性生殖能力的下降。     

自贡市极薄煤层煤矿煤工尘肺X射线影像分析

目的了解极簿煤层煤矿工作场所职业卫生状况并探讨其与煤工尘肺X射线影像特征的关系。方法对15家煤矿井下、地面作业岗位开展职业病危害因素检测,对确诊的672例煤工尘肺病例的X射线胸片进行影像表现的统计分析。结果采煤、掘进、地面卸煤(岩石)岗位游离二氧化硅含量最低5.9%,最高58.4%,平均含量大于10%。极薄煤层煤矿工人工作场所接触的粉尘应为煤矽混合尘,工作场所粉尘浓度最低1.05 mg/m3,最高16.04 mg/m3。所测定的104份煤矿井下、地面工作场所粉尘样品浓度平均值均超过了国家职业卫生标准的限值。672份煤工尘肺X射线胸片表现形态为圆形小阴影的病例占90.18%。以圆形小阴影为主的X线胸片中q型占71.12%,p型25.41%r,型3.47%。Ш期煤工尘肺均表现为大阴影。Ⅰ期煤工尘肺小阴影主要分布于中下肺区的占67.33%;Ⅱ期煤工尘肺小阴影主要分布于上中下肺区的95.80%。Ш期煤工尘肺大阴影主要分布于上肺区和上中肺区。结论自贡市煤矿工人接触的粉尘为煤矽混合尘,煤工尘肺病患者X线影像形态大小以q型为主。     

陕南燃煤污染区环境砷与地方性砷中毒发病关系的调查

目的 查清陕南燃煤污染区的环境砷污染及地方性砷中毒的发病情况和危害程度,确认该区域是否为燃煤型地方性砷中毒病区。方法 对陕南5县18乡(镇)20个调查点的燃煤、室内空气、玉米、辣椒、干菜、饮水、土壤砷进行调查,同时对该区域进行流行病学对比分析,对煤砷与室内空气砷水平及其与患者检出率之间作相关分析。结果 煤砷与室内空气砷呈正相关(r=0.6607,P<0.001);煤砷与患者检出率之间呈正相关(r=0.500 2,P<0.025);煤砷与煤质关系密切,煤质愈好,含砷愈低;调查发现轻、中、重度患者(不含对照点)193人,疑似患者34人,病情以轻、中度患者为主,占检出患者总数的86.01％,并且男性多于女性,中老年人多于和重于年轻人。结论 该调查区域是燃煤型砷污染区,是继贵州省之后新发现的又一个燃煤污染型砷中毒病区。     

Comparison of calcipotriol and coal tar in conjunction with sun exposure in chronic plaque psoriasis: a pilot study

This study was a left-right comparison of the efficacy of 0.005% calcipotriol ointment and 5% coal tar ointment in conjunction with sun exposure in 10 patients with stable plaque psoriasis. After four weeks of therapy, the calcipotriol treated site showed a significantly faster fall in PASI compared to the coal tar treated site. At eight weeks, this difference was not significant with both sides showing comparable improvement in lesions, as shown by PASI values. There were no significant side effects from either therapy. We conclude that both calcipotriol and coal tar ointments have comparable efficacy in treating stable plaque psoriasis when used simultaneously with sun exposure, although the initial response to calcipotriol is faster.     

不同生活燃料与村民呼吸道健康关系的流行病学调查

通过安徽省南方两个自然村中364名村民进行的呼吸道症状、体征、肺功能、免疫指标检查和居室及生活燃料的调查,发现燃料类型与呼吸道疾病率间的差异:燃煤组扁桃体炎(58.3％)、咽炎(55.1％)高于柴草组(43.0％及37.2％);VC％组间无差异,然而燃煤组人群唾液溶菌酶含量偏高,血清IgG含量偏低,反映出不同燃料污染室内空气对人群健康的损害。     

The Triaxial Test of Polypropylene Fiber Reinforced Fly Ash Soil

Recently, soil reinforcement using arranged or randomly distributed fibers has attracted increasing attention in geotechnical engineering. In this study, polypropylene (PP) fibers with three lengths (6, 12, and 24 mm) and three mass percentages (0.5%, 1%, and 1.5%) were used to reinforce a coal fly ash soil (FAS) mixture. Unconsolidated, undrained triaxial tests were carried out in order to study the mechanical properties of the polypropylene fiber-reinforced FAS mixture and evaluate the impact of fiber on the shear strength of the FAS mixture. It is found that the fiber length of 12 mm could significantly improve the shear strength of the polypropylene fiber reinforced FAS mixture, and little effect is shown on the shear strength while using a fiber length of 24 mm. Additional fibers enhance the energy absorption capacity of the FAS specimens and therefore the highest energy absorption capacity occurs when the fiber content is 1% and the fiber length is 12 mm. The peak deviator stress enhances impressively with the addition of polypropylene fiber. The impact of fiber on the peak deviator stress is the largest when fiber content is within 1.0%. The fiber length has little effect on the peak deviator stress when it exceeds 12 mm.     

Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms

Purpose

The purpose of this study is to propose an efficient coal workers' pneumoconiosis (CWP) clinical prediction system and put it into clinical use for clinical diagnosis of pneumoconiosis.

Methods

Patients with CWP and dust-exposed workers who were enrolled from August 2021 to December 2021 were included in this study. Firstly, we chose the embedded method through using three feature selection approaches to perform the prediction analysis. Then, we performed the machine learning algorithms as the model backbone and combined them with three feature selection methods, respectively, to determine the optimal predictive model for CWP.

Results

Through applying three feature selection approaches based on machine learning algorithms, it was found that AaDO₂ and some pulmonary function indicators played an important role in prediction for identifying CWP of early stage. The support vector machine (SVM) algorithm was proved as the optimal machine learning model for predicting CWP, with the ROC curves obtained from three feature selection methods using SVM algorithm whose AUC values of 97.78%, 93.7%, and 95.56%, respectively.

Conclusion

We developed the optimal model (SVM algorithm) through comparisons and analyses among the performances of different models for the prediction of CWP as a clinical application.