Alloys ; toxicity ; Cobalt ; toxicity ; Lung ; Lung Diseases ; diagnosis ; therapy ; Tungsten ; toxicity

Air Pollutants ; Dust ; analysis ; Humans ; Lung ; Metals ; adverse effects ; Pneumoconiosis ; Research

Objective To observe the effect of nursing intervention on quality of sleep and life of perimenopausal women with insomnia.Methods A total of 78 perimenopausal women with insomnia were enrolled and divided into intervention group and control group.The control group was treated with conventional medication,while the intervention group received nursing interven-tion additionally.The Pittsburgh Sleep Quality Index(PSQI)was used to assessed the quality of sleep and the WHO Quality of Life-BREF (WHOQOL-BREF)was used to assessed the quality of life after 12 weeks of treatment.Results After 12 weeks treatment,the score of two groups significantly better in all domains of PSQI and WHOQOL-BREF than before the treatment(P <0.05).There were significant difference in all domains score of PSQI and WHOQOL-BREF of the two groups after 12 weeks treatment.Conclusions The nursing intervention can obviously improve the quality of sleep and life of perimenopausal women with insomnia.

Objective To observe the effect of nursing intervention on quality of sleep and life of perimenopausal women with insomnia.Methods A total of 78 perimenopausal women with insomnia were enrolled and divided into intervention group and control group.The control group was treated with conventional medication,while the intervention group received nursing interven-tion additionally.The Pittsburgh Sleep Quality Index(PSQI)was used to assessed the quality of sleep and the WHO Quality of Life-BREF (WHOQOL-BREF)was used to assessed the quality of life after 12 weeks of treatment.Results After 12 weeks treatment,the score of two groups significantly better in all domains of PSQI and WHOQOL-BREF than before the treatment(P <0.05).There were significant difference in all domains score of PSQI and WHOQOL-BREF of the two groups after 12 weeks treatment.Conclusions The nursing intervention can obviously improve the quality of sleep and life of perimenopausal women with insomnia.

OBJECTIVE: To investigate the effect of administration of rosiglitazone, an artificial ligand of PPARγ, on the expression and secretion of apolipoprotein (apoM) in fat-fed, streptozotocin-treated rats, an animal model for type 2-like diabetes. METHODS: Healthy male SD rats were divided into 4 groups: a control group (n=7), a high-fat chow group (HF group, n=8), a diabetes mellitus group (DM group, n=7), and a diabetes mellitus group with rosiglitazone intervention group (RSG group, n=7). Fasting blood glucose (FBG), fasting insulin (FINS), triglyceride (TG) and total cholesterol (TC) were measured at the beginning of the study. The diabetic rats model was established by feeding high fat chow and intraperitoneal injection of streprozotocin. Then the randomly selected treatment group was given rosiglitazone by daily gavage for 8 weeks. All the rats were killed at the fifteenth week, at which time blood and tissues (liver, kidney, adipose) were collected and prepared. The levels of FBG, FINS, TG and TC were assayed. The level of apoM in serum was measured by enzyme-linked immunosorbent assay (ELISA). Reverse transcription polymerase chain reaction (RT-PCR) was used to determine apoM mRNA expression in liver, kidney, and adipose tissues. RESULTS: Compared with either control group or HF group, serum apoM concentration in the DM group was reduced significantly (P<0.05); compared with the DM group, however, serum apoM concentrations in RSG group were increased (P<0.05). The expression of apoM mRNA in liver was highest, in kidney medium, and in adipose tissue extremely low (P<0.05). ApoM mRNA expression in liver and kidney was decreased in both DM and HF groups compared to control group (P<0.05). But, as with serum apoM concentration, apoM mRNA in the liver, kidney and adipose tissues of the RSG group were all increased markedly (P<0.05). The level of serum apoM in SD rats correlated negatively with TG (r=-0.466, P=0.011), TC (r=-0.568, P= 0.001), FBS (r =-0.371, P<0.001), and FINS(r=-0.768, P= 0.048 ). CONCLUSION These results suggest that apoM may participate in the glucose and lipid metabolism by the regulation of PPARγ.
Animals ; Apolipoproteins ; blood ; genetics ; metabolism ; Apolipoproteins M ; Diabetes Mellitus, Experimental ; drug therapy ; metabolism ; Dietary Fats ; administration & dosage ; Lipocalins ; blood ; genetics ; metabolism ; Male ; PPAR gamma ; agonists ; RNA, Messenger ; genetics ; metabolism ; Rats ; Rats, Sprague-Dawley ; Rosiglitazone ; Thiazolidinediones ; therapeutic use

Objective To investigate the morbidity, diagnostic profile and perinatal outcome of pregestational diabetes mellitus (PGDM) in 15 hospitals in Guangdong province. Methods A total of 41338 women delivered in the 15 hospitals during the 6 months,195 women with PGDM(PGDM group) and 195 women with normal glucose test result(control group)were recruited from these tertiary hospitals in Guangdong province from January 2016 to June 2016. The morbidity and diagnostic profile of PGDM were analyzed. The complications during pregnancy and perinatal outcomes were compared between the two groups. In the PGDM group, pregnancy outcomes were analyzed in women who used insulin treatment (n=91) and women who did not (n=104). Results (1)The incidence of PGDM was 0.472%(195/41338). Diabetes mellitus were diagnosed in 59 women (30.3%, 59/195) before pregnancy, and 136 women (69.7%,136/195) were diagnosed as PGDM after conceptions. Forty-six women (33.8%) were diagnosed by fasting glucose and glycohemoglobin (HbA1c) screening. (2) The maternal age, pre-pregnancy body mass index (BMI), prenatal BMI, percentage of family history of diabetes, incidence of macrosomia, concentration of low density lipoprotein were significantly higher in PGDM group than those in control group (all P<0.05). Women in PGDM group had significantly higher HbA1c concentration((6.3±1.3)% vs (5.2±0.4)%), fasting glucose [(6.3±2.3) vs (4.8±1.1) mmol/L], oral glucose tolerance test(OGTT)-1 h glucose((12.6±2.9) vs (7.1± 1.3) mmol/L)and OGTT-2 h glucose [(12.0±3.0) vs (6.4±1.0) mmol/L] than those in control group (P<0.01). (3)The morbidity of preterm births was significantly higher (11.3% vs 1.0%, P<0.01), and the gestational age at delivery in PGDM group was significantly smaller [(37.6±2.3) vs (39.2±1.2) weeks, P<0.01]. Cesarean delivery rate in the PGDM group (70.8% vs 29.7%) was significantly higher than the control group (P<0.01). There was significantly difference between PGDM group and control in the neonatal male/female ratio (98/97 vs 111/84, P=0.033). The neonatal birth weight in PGDM group was significantly higher((3159±700) vs (3451±423) g, P<0.01). And the incidence of neonatal hypoglycemia in the PGDM group was higher than the control group (7.7% vs 2.6%, P=0.036).(4)In the PGDM group, women who were treated with insulin had a smaller gestational age at delivery [(36.9±2.9) vs (37.9±2.5) weeks, P<0.01], and the neonates had a higher neonatal ICU(NICU)admission rate (24.2% vs 9.6% , P<0.01). Conclusions The morbidity of PGDM in the 15 hospitals in Guangdong province is 0.472%. The majority of PGDM was diagnosed during pregnancy; HbA1c and fasting glucose are reliable parameters for PGDM screening. Women with PGDM have obvious family history of diabetes and repeated pregnancy may accelerate the process of diabetes mellitus. Women with PGDM have higher risk for preterm delivery and neonatal hypoglycemia. Unsatisfied glucose control followed by insulin treatment may increase the need for NICU admission.

Background Diagnosis of pneumoconiosis by radiologist reading chest X-ray images is affected by many factors and is prone to misdiagnosis/missed diagnosis. With the rapid development of artificial intelligence in the field of medical imaging, whether artificial intelligence can be used to read images of pneumoconiosis deserves consideration. Objective Three deep learning models for identifying presence of pneumoconiosis were constructed based on deep convolutional neural network. An optimal model was selected by comparing diagnostic efficiency of the three models. Methods Digital radiography (DR) chest images were collected between June 2017 and December 2020 from 7 hospitals and standard radiograph quality control protocol was also followed. The DR chest images with positive results were classified into the positive group, while those without pneumoconiosis were classified into the negative group. The collected chest radiographs were labeled by experts who had passed the assessment of reading radiographs,and the experts were constantly assessed for consistency in the labeling process based on an expectation-maximization algorithm. The labeled data were cleaned, archived, and preprocessed, and then were grouped into a training set and a verification set. Three deep convolutional neural network models TMNet, ResNet-50, and ResNeXt-50 were constructed and trained by ten-fold cross-validation method to obtain an optimal model. Five hundred cases of DR chest radiographs that were not included in the training set and the validation set were collected, and identified by five senior experts as the gold standard, named the test set. The accuracy rate, sensitivity, specificity, area under curve (AUC), and other indexes of the three models were derived after testing, and the efficiency of the three models was evaluated and compared. Results A total of 24867 DR chest radiographs of the training set and the validation set were collected in this study, including 6978 images in the positive group and 17889 images in the negative group. There were 312 cases of pulmonary abnormalities such as pneumothorax and pulmonary tuberculosis. A total of nine experts labeled the chest radiographs, the labeling consistency rate of pneumoconiosis (non-staging) was above 88%, and the labeling consistency rate of pneumoconiosis staging ranged from 84.68% to 93.66%. The diagnostic accuracy, sensitivity, specificity, and AUC of TMNet were 95.20%, 99.66%, 88.61%, and 0.987, respectively. The indicators of ResNeXt were 87.00%, 89.93%, 82.67%, and 0.911, respectively. Those of ResNet were 84.00%, 85.91%, 81.19%, and 0.912, respectively. All these indexes of TMNet were higher than those of ResNeXt-50 and ResNet-50 models. The AUC differences between TMNet and the other two models were both statistically significant (P<0.001). Conclusion All the three convolutional neural network models can effectively diagnose the presence of pneumoconiosis, among which TMNet provides the best efficiency.

Background Multi-slice spiral computerized tomography (MSCT) can be used as an auxiliary diagnosis of chest radiography in diagnosis of pneumoconiosis, but there are few studies on the correlations between interstitial images and stage classification of coal workers' pneumoconiosis in the existing literature. Objective To present MSCT imaging manifestations and distribution characteristics of coal workers' pneumoconiosis and complications, evaluate correlations between coal workers' pneumoconiosis stages and pulmonary interstitial lesions, and provide a reliable imaging diagnosis basis for pneumoconiosis interstitial lesions. Methods From June 2022 to June 2023, a total of 1002 patients with coal workers' pneumoconiosis confirmed by the pneumoconiosis diagnostic and identification group in the Department of Occupational Diseases of the Emergency General Hospital were enrolled. MSCT was used to observe the abnormal imaging manifestations of the lungs of coal workers' pneumoconiosis patients and the diseases of pulmonary fibrosis related to their own diseases (thickening of the interlobular septum, bronchial perivascular interstitial mass thickening, parenchymal banding, subpleural line, intralobular interstitial thickening, honeycomb, and subpleural interstitial thickening), the occurrence of coal workers' pneumoconiosis and complications (old tuberculosis, active tuberculosis, pneumonia, atelectasis, lung cancer, bronchiectasis), and the density, size, and location of pneumoconiosis nodules. Imaging data were analyzed and statistically processed. Results All 1002 patients were male, with an average age of (60.71±6.87) years and an average dust exposure time of (23.01±7.80) years. Among them, there were 470 patients with stage I, 422 patients with stage II, and 110 patients with stage III. There were significant differences in the distribution of thickening of the interlobular septum, bronchial perivascular interstitial mass thickening, parenchymal banding, intralobular interstitial thickening, subpleural interstitial thickening, and honeycomb across different stages (P<0.05). Statistically significant differences in p, q, and r subsets of round nodules were found in patients with pneumoconiosis at different stages (P<0.05). Observed nodule types included solid nodules, pure ground-glass shadow nodules, and partial solid nodules. There were statistically significant differences in pulmonary tuberculosis and bronchiectasis among different stages of coal workers' pneumoconiosis (P<0.05). There were statistically significant differences in interstitial shadows and patches combined with interstitial shadows among different stages of pneumoconiosis complicated with pneumonia (P<0.05). Conclusion MSCT provides images of the progression of coal workers' pneumoconiosis and have a certain relationship with the stages of coal workers' pneumoconiosis, which is conducive to the formulation of reasonable treatment plans in the early clinical stage. Therefore, in the diagnosis and treatment of pneumoconiosis, a great attention should be paid to the imaging technology of chest computerized tomography, especially the use of MSCT examination.

Based on the correlation between Qi and blood in traditional Chinese medicine， the collateral disease theory puts forward that the Qi-collateral go hand in hand with the vessel-collateral of the brain， and to be as close as lips to teeth in structure and function， which is an important basis for the function of brain governing mind. And this theory proposes that deficiency/stagnancy of collateral-Qi， stagnation of collaterals and loss of consciousness are the main pathogenesis of Alzheimer's disease（AD）， which is different from the research strategy of modern medicine focusing on neurons. It is suggested that it is necessary to treat AD from two aspects， including neuronal protection（elimination of pathological products such as β-amyloid and phosphorylated tau protein） and cerebral microvascular protection（protection of cerebral microvascular structure and function， promotion of therapeutic angiogenesis and increase of cerebral blood flow. Tongxinluo capsules is a representative drug for dredging collaterals developed under the guidance of the therapeutic principle of collaterals need circulation， it can protect microvessels and play a neuroprotective role mediated by vascular protection. Clinical studies have confirmed that Tongxinluo capsules can effectively treat AD， vascular dementia and cognitive impairment related diseases， which can provide new ideas and effective treatment ways to prevent and treat AD from neurovascular protection in a comprehensive manner.

BACKGROUND: Currently, the effect of the 2022 nationwide coronavirus disease 2019 (COVID-19) wave on the perioperative prognosis of surgical patients in China is unclear. Thus, we aimed to explore its influence on postoperative morbidity and mortality in surgical patients. METHODS: An ambispective cohort study was conducted at Xijing Hospital, China. We collected 10-day time-series data from December 29 until January 7 for the 2018-2022 period. The primary outcome was major postoperative complications (Clavien-Dindo class III-V). The association between COVID-19 exposure and postoperative prognosis was explored by comparing consecutive 5-year data at the population level and by comparing patients with and without COVID-19 exposure at the patient level. RESULTS: The entire cohort consisted of 3350 patients (age: 48.5 ± 19.2 years), including 1759 females (52.5%). Overall, 961 (28.7%) underwent emergency surgery, and 553 (16.5%) had COVID-19 exposure (from the 2022 cohort). At the population level, major postoperative complications occurred in 5.9% (42/707), 5.7% (53/935), 5.1% (46/901), 9.4% (11/117), and 22.0% (152/690) patients in the 2018-2022 cohorts, respectively. After adjusting for potential confounding factors, the 2022 cohort (80% patients with COVID-19 history) had a significantly higher postoperative major complication risk than did the 2018 cohort (adjusted risk difference [aRD], 14.9% (95% confidence interval [CI], 11.5-18.4%); adjusted odds ratio [aOR], 8.19 (95% CI, 5.24-12.81)). At the patient level, the incidence of major postoperative complications was significantly greater in patients with (24.6%, 136/553) than that in patients without COVID-19 history (6.0% [168/2797]; aRD, 17.8% [95% CI, 13.6-22.1%]; aOR, 7.89 [95% CI, 5.76-10.83]). Secondary outcomes of postoperative pulmonary complications were consistent with primary findings. These findings were verified through sensitivity analyses using time-series data projections and propensity score matching. CONCLUSION: Based on a single-center observation, patients with recent COVID-19 exposure were likely to have a high incidence of major postoperative complications. REGISTRATION NCT05677815 at https://clinicaltrials.gov/ .
Female ; Humans ; Adult ; Middle Aged ; Aged ; Cohort Studies ; COVID-19/complications* ; Pandemics ; Retrospective Studies ; Postoperative Complications/epidemiology*