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Contrast Media & Molecular Imaging is an exciting journal in the area of contrast agents and molecular imaging, covering all areas of imaging technologies with a special emphasis on MRI and PET.
Chief Editor, Professor Zimmer, focuses on the development and use of PET radiotracers for new applications of PET/MRI imaging in neuroscience and pharmacology.
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Prediction of Loss of Muscle Mass in Sarcopenia Using Ultrasonic Diaphragm Excursion
Background. The diagnosis of sarcopenia is based on the mass and function of appendicular skeletal muscle. It is not clear whether diaphragm excursion is related to muscle mass loss. We try to fill the gap by measuring ultrasonic diaphragm excursion during quiet breathing (Dq) and forced deep breathing (Df) and test whether they could predict the muscle mass loss in sarcopenia. Methods. The subjects are recruited from the elderly patients diagnosed with pulmonary nodules in community physical examination. According to the definition, the subjects were divided into group A (who did not meet the diagnostic criteria for muscle mass loss in sarcopenia) and group B (who met the criteria). Participants were assessed for ultrasonic diaphragm excursion, pulmonary function, and cardiopulmonary exercise testing. Logistic regression was used to assess the correlation between right diaphragm excursion and skeletal muscle mass, and receiver-operating characteristic curve (ROC) was applied to determine the best threshold. Results. We recruited 64 elderly participants: 52 in group A (39 males) and 12 in group B (8 males). The Df in group A were higher than in group B (6.02 (5.44–6.60) vs. 4.31 (3.53–5.09) cm, ). The difference also exists in FVC, FEV1.0, PEF, Pimax, WRmax, and VO2max, but neither in Dq. Logical regression showed that Df was negatively related to muscle mass (B = −0.525, OR = 0.591 (0.378–0.926), ), even after adjusted age. Based on ROC, a cutoff value of 5.27 cm (AUC = 0.7783, ) was selected, and Df ≤ 5.27 cm indicates the increase in odds of existing muscle mass loss. Conclusion. Ultrasonic diaphragm excursion in forced deep breath is helpful for predicting muscle mass loss in sarcopenia. The trial is registered with ChiCTR1800019742.
Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning
Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.
Effect of Combining Early Chemotherapy with Zhipu Liujunzi Decoction under the Concept of Strengthening and Consolidating Body Resistance for Gastric Cancer Patients and Nursing Strategy
Objective. To explore the clinical efficacy of combining early chemotherapy with Zhipu Liujunzi decoction under the concept of strengthening and consolidating body resistance for gastric cancer patients and nursing strategy. Methods. The clinical data of 100 patients undergoing radical gastrectomy in our hospital from July 2019 to July 2020 were selected for the retrospective analysis, and the patients were divided into the control group and experimental group according to different treatment methods, with 50 cases in each group. Early chemotherapy after surgery was given to patients in the control group, and on the basis of the aforesaid treatment and under the concept of strengthening and consolidating body resistance, patients in the experimental group took Zhipu Liujunzi decoction and received the nursing strategy, so as to compare their effective rate, adverse reaction rate (ARR), immune function indicators, KPS scores, and nursing satisfaction scores. Results. After treatment, the experimental group obtained significantly higher objective remission rate (ORR) and disease control rate (DCR) (), lower carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels (), higher immune parameters levels (), higher KPS scores and lower TCM symptom scores (), lower PSQI scores, SAS scores, and SDS scores () and higher nursing satisfaction scores (), and lower total accidence rate of toxic side effects () than the control group. Conclusion. Under the concept of strengthening and consolidating body resistance, combining early chemotherapy with Zhipu Liujunzi decoction is a reliable method for improving the immune function and quality of life for gastric cancer patients with higher safety. Such a strategy greatly reduces the tumor marker levels in patients. Further research will be conducive to establishing a better solution for gastric cancer patients.
Nursing Intervention Countermeasures of Robot-Assisted Laparoscopic Urological Surgery Complications
The objective is to explore the application effect of comprehensive nursing intervention in prevention of lower extremity deep vein thrombosis and pulmonary embolism in urological patients undergoing laparoscopic and robot-assisted laparoscopic surgery. From April 2019 to April 2020, 200 patients who received urological laparoscopic surgery and robot-assisted laparoscopic surgery were selected. According to the random number table method, they were divided into control group and observation group, 100 cases in control group and 100 cases in observation group. Patients in control group received routine nursing, while patients in observation group received comprehensive nursing intervention. The skin condition, swelling, pain, and occurrence of deep venous thrombosis and pulmonary embolism of lower extremities in 2 groups were observed. The experimental results showed that the lower limb swelling, lower limb pain, and lower limb deep vein thrombosis in the control group were significantly higher than those in the observation group, but all patients were cured and discharged after taking effective symptomatic treatment and nursing measures in time. In the control group, pulmonary embolism occurred in 3 patients, all of whom died. There was no significant difference in skin changes of lower limbs (), and there were significant differences in other skin changes (). It proved that comprehensive nursing intervention can effectively prevent the formation of lower extremity deep vein thrombosis and pulmonary embolism in urological patients undergoing laparoscopic and robot-assisted laparoscopic surgery with high-risk factors.
Fast Independent Component Analysis Algorithm-Based Functional Magnetic Resonance Imaging in the Diagnosis of Changes in Brain Functional Areas of Cerebral Infarction
The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (). The maximum Dice coefficient of FAST ICA algorithm was 0.967, and FAST ICA algorithm was better obviously than the traditional ICA algorithm (). The cerebral blood flow of the healthy superior frontal gyrus (SFG) and healthy superior marginal gyrus (SMG) of the observation group with good motor function recovery were 1.02 ± 0.22 and 1.53 ± 0.61, respectively; both indicators showed an increasing trend, and those in the experimental group were much higher in contrast to the control group, showing statistically obvious differences (). Besides, the detection results of cerebral blood flow (CBF) in the healthy SFG and healthy SMG were negatively correlated with the results of connection test B. In summary, the fMRI based on the Fast ICA algorithm showed a good diagnostic effect in the changes of brain functional areas in patients with cerebral infarction. The experimental results showed that the cerebral blood flow in the brain area was related to motor or cognitive function. The results of this study provided a reliable reference for the examination and diagnosis of brain functional areas in patients with cerebral infarction.
Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies
Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.