Humans ; Thyroid Neoplasms

The neurochemical features of the nitrergic and peptidergic innervation of the porcine esophagus were investigated by means of immunohistochemical methods combined with vagotomy. Neuronal cell bodies in both the submucosal and the myenteric plexus (MP) were detected immunoreactivities for nNOS, VIP, GAL, NPY, PACAP, L-ENK, SP, 5-HT and CB, while CGRP- and SOM-immunoreactive (ir) somata were not encountered. In addition, nNOS- and CB-ir myenteric neurons constituted the separate enteric subpopulations.Double immunostainings with a general neuronal marker (PGP9.5 ) and the specific markers, such as nNOS, VIP and SP revealed (1)nNOS-ir myenteric neurons in the porcine esophagus accounted for a higher percentage (63 % ) of all esophageal intrinsic PGP9.5-ir neurons in comparison of VIP-ir (36%) and SP-ir populations (28%); (2) An increasing rostrocaudal gradient in the number of myenteric neurons per ganglion as well as a significantly higher number of enteric ganglia within both plexuses in the abdominal segment; ( 3 ) The densest nerve fibers within the esophageal musculature were VIP-/GAL-/NPY-ir, some of which also co-expressed nNOS and/or PACAP immunoreactivity. The number of L-ENK- and/or SP-ir fibers was significantly higher in lamina muscularis mucosae ( LMM ) than in tunica muscularis externa (TME). In contrast to reports in other species, CGRP-ir fibers within the porcine esophagus constituted a very limited population and were extrinsic; (4) Vagotomy experiments revealed an obvious decrease of PACAP-and 5-HT-ir nerve fibers within the MP,suggesting that these fibers originate from the vagal nerve, while these nNOS- and/or VIP-/GAL-/NPY-ir fibers innervating both the TME and the LMM did not appear to be significantly affected by the vagotomy procedure, possibly being the intrinsic origin.

The setting and adjustment of ventilator parameters need to rely on a large amount of clinical data and rich experience. This paper explored the problem of difficult decision-making of ventilator parameters due to the time-varying and sudden changes of clinical patient's state, and proposed an expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment based on fuzzy control rule and neural network. Based on the method and the real-time physiological state of clinical patients, we generated a mechanical ventilation decision-making solution set with continuity and smoothness, and automatically provided explicit parameter adjustment suggestions to medical personnel. This method can solve the problems of low control precision and poor dynamic quality of the ventilator's stepwise adjustment, handle multi-input control decision problems more rationally, and improve ventilation comfort for patients.
Humans ; Ventilators, Mechanical ; Respiration, Artificial ; Neural Networks, Computer

OBJECTIVE: To investigate the feasibility of MRI three-dimensional (3D) reconstruction model in quantifying glenoid bone defect by comparing with CT 3D reconstruction model measurement. METHODS: Forty patients with shoulder anterior dislocation who met the selection criteria between December 2021 and December 2022 were admitted as study participants. There were 34 males and 6 females with an average age of 24.8 years (range, 19-32 years). The injury caused by sports injury in 29 cases and collision injury in 6 cases, and 5 cases had no obvious inducement. The time from injury to admission ranged from 4 to 72 months (mean, 28.5 months). CT and MRI were performed on the patients' shoulder joints, and a semi-automatic segmentation of the images was done with 3D slicer software to construct a glenoid model. The length of the glenoid bone defect was measured on the models by 2 physicians. The intra-group correlation coefficient ( ICC) was used to evaluate the consistency between the 2 physicians, and Bland-Altman plots were constructed to evaluate the consistency between the 2 methods. RESULTS: The length of the glenoid bone defects measured on MRI 3D reconstruction model was (3.83±1.36) mm/4.00 (0.58, 6.13) mm for physician 1 and (3.91±1.20) mm/3.86 (1.39, 5.96) mm for physician 2. The length of the glenoid bone defects measured on CT 3D reconstruction model was (3.81±1.38) mm/3.80 (0.60, 6.02) mm for physician 1 and (3.99±1.19) mm/4.00 (1.68, 6.38) mm for physician 2. ICC and Bland-Altman plot analysis showed good consistency. The ICC between the 2 physicians based on MRI and CT 3D reconstruction model measurements were 0.73 [95% CI (0.54, 0.85)] and 0.80 [95% CI (0.65, 0.89)], respectively. The 95% CI of the difference between the two measurements of physicians 1 and 2 were (-0.46, 0.49) and (-0.68, 0.53), respectively. CONCLUSION The measurement of glenoid bone defect based on MRI 3D reconstruction model is consistent with that based on CT 3D reconstruction model. MRI can be used instead of CT to measure glenoid bone defects in clinic, and the soft tissue of shoulder joint can be observed comprehensively while reducing radiation.
Male ; Female ; Humans ; Young Adult ; Adult ; Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Joint Instability ; Shoulder Joint/diagnostic imaging* ; Shoulder Dislocation ; Magnetic Resonance Imaging/methods*

The geometric bone model of patients is an important basis for individualized biomechanical modeling and analysis, formulation of surgical planning, design of surgical guide plate, and customization of artificial joint. In this study, a rapid three-dimensional (3D) reconstruction method based on statistical shape model was proposed for femur. Combined with the patient plain X-ray film data, rapid 3D modeling of individualized patient femur geometry was realized. The average error of 3D reconstruction was 1.597-1.842 mm, and the root mean square error was 1.453-2.341 mm. The average errors of femoral head diameter, cervical shaft angle, offset distance and anteversion angle of the reconstructed model were 0.597 mm, 1.163°, 1.389 mm and 1.354°, respectively. Compared with traditional modeling methods, the new method could achieve rapid 3D reconstruction of femur more accurately in a shorter time. This paper provides a new technology for rapid 3D modeling of bone geometry, which is helpful to promote rapid biomechanical analysis for patients, and provides a new idea for the selection of orthopedic implants and the rapid research and development of customized implants.
Humans ; Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Femur/surgery* ; Femur Head ; Lower Extremity

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
Animals ; Support Vector Machine ; Whales ; Eye Movements ; Algorithms

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Imagery, Psychotherapy ; Imagination

Spirometra erinaceieuropaei casein kinase I (SeCKI) was analyzed using bioinformatical methods to predict its structure and function based on the deduced amino acid sequence from full length cDNA sequence of SeCKI gene with online sites and software programs. The longest open reading frame contains 448 amino acids, 50 kDa and theoretical pI of 4.73, with a complete tubulin domain, a SMART tubulin_C domain and a low complexity region. SeCKI has no signal sequence and no transmembrane domain, but is predicted to be located extracellularly. The secondary structure of SeCKI contains 12 α-helixes, 11 β-strands and 22 coils. SeCKI had 19 potential antigenic epitopes and 25 HLA-I restricted epitopes. Based on phylogenetic analysis of SeCKI sequence, S. erinaceieuropaei has the closest evolutionary status with Hymenolepis microstoma. Information from this study could provide important insights into the identification of diagnostic antigens and molecular targets of antisparganum drugs.

The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model-SMILESynergy. First, the drug text data-simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.
Electric Power Supplies ; Neural Networks, Computer ; Antineoplastic Agents/pharmacology*

Liposome is an ideal drug carrier with many advantages such as excellent biocompatibility, non-immunogenicity, and easy functionalization, and has been used for the clinical treatment of many diseases including tumors. For the treatment of tumors, liposome has some passive targeting capability, but the passive targeting effect alone is very limited in improving the drug enrichment in tumor tissues, and active targeting is an effective strategy to improve the drug enrichment. Therefore, active targeting liposome drug-carriers have been extensively studied for decades. In this paper, we review the research progresses on active targeting liposome drug-carriers based on the specific binding of the carriers to the surface of tumor cells, and summarize the opportunities, challenges and future prospects in this field.
Drug Carriers/therapeutic use* ; Drug Delivery Systems ; Humans ; Liposomes/therapeutic use* ; Neoplasms/drug therapy*