ABSTRACT
Background At present, the practice of pulmonary rehabilitation for pneumoconiosis in China is in a primary stage. The basis for formulating an individualized comprehensive pulmonary rehabilitation plan is still insufficient, which is one of the factors limiting the development of community-level rehabilitation work. Objective To formulate an exercise prescription based on maximum heart rate measured by cardiopulmonary exercise test (CPET), conduct an individualized comprehensive pulmonary rehabilitation program with the exercise prescription for patients with stable pneumoconiosis, and evaluate its role in improving exercise endurance and quality of life, thus provide a basis for the application and promotion of pulmonary rehabilitation. Methods A total of 68 patients were recruited from the Occupational Disease Prevention Hospital of Jinneng Holding Coal Industry Group Co., Ltd. from April to August 2022 , and were divided into an intervention group and a control group by random number table method, with 34 cases in each group. All the pneumoconiosis patients participated in a baseline test. The control group was given routine drug treatment, while the intervention group received multidisciplinary comprehensive pulmonary rehabilitation treatment on the basis of routine drug treatment, including health education, breathing training, exercise training, nutrition guidance, psychological intervention, and sleep management, whose exercise intensity was determined according to the maximum heart rate provided by CPET. The rehabilitation training lasted for 24 weeks. Patients were evaluated at registration and the end of study respectively. CPET was used to measure peak oxygen uptake per kilogram (pVO2/kg), anaerobic threshold (AT), carbon dioxide equivalent of ventilation (EqCO2), maximum metabolic equivalent (METs), and maximum work (Wmax). The modified British Medical Research Council Dyspnea Questionnaire (mMRC), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), Pittsburgh Sleep Quality Index (PSQI), Chronic Obstructive Pulmonary Disease Assessment Test (CAT), and Short Form of Health Survey (SF-36) were used to evaluate the potential effect of the comprehensive pulmonary rehabilitation program. Results Among the included 68 patients, 63 patients were having complete data, then 31 cases were assigned in the control group and 32 cases in the interventional group. Before the intervention, there was no significant difference in pVO2/kg, AT, EqCO2, METs, or Wmax between the two groups (P>0.05). At the end of the trail, the indicators like pVO2/kg [(19.81±2.38) mL·(min·kg)−1], AT [(14.48±2.33) mL·(min·kg)−1], METs (5.64±0.69), and Wmax [(85.25±14) W] of patients in the intervention group were all higher than those [(13.90±2.37) mL·(min·kg)−1, (11.70±1.94) mL·(min kg)−1, (3.97±0.70), and (61.77±14.72) W, respectively] in the control group (P<0.001); there was no significant difference in EqCO2 between the two groups (P=0.083). Before the trial, there was no significant difference in mMRC, SAS, SDS, PSQI, or CAT scores between the two groups (P>0.05). At the end of the trail, the mMRC score (1.16±0.57), SAS score (27.93±2.12), SDS score (26.48±1.44), PSQI score (1.08±0.88), and CAT score (4.34±3.28) of patients in the intervention group were lower than those [(2.03±0.83), (35.87±6.91), (34.23±6.65), (5.37±3.03), and (13.87±7.53), respectively] in the control group (P<0.001). The SF-36 scores of bodily pain (94.13±10.72), general health (87.50±5.68), vitality (95.31±5.53), mental health (99.88±0.71), and health changes (74.22±4.42) in the intervention group were higher than those [(71.87±32.72), (65.81±15.55), (74.52±16.45), (86.97±16.56), and (29.84±13.50), respectively] in the control group (P<0.001), and no significant difference was found in social functioning and role emotional scores (P>0.05). Conclusion Comprehensive pulmonary rehabilitation can increase the oxygen intake and exercise endurance of pneumoconiosis patients, ameliorate dyspnea symptoms, elevate psychological state and sleep quality, and improve the quality of life.
ABSTRACT
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.
ABSTRACT
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.