ObjectiveTo investigate whether small epithelial cells (SEC) exist in human hepa- tocellularcarcinoma (HCC) and if so, whether they exhibit immunolabelling for both albumin and cytokeratin 7 (CK7). MethodsThirty cases of human HCC from operative specimens were investi- gated by immunohistochemistry with antibody against albumin, a marker of hepatocyte differentiation and CK7, a marker of biliary differentiation. Ten cases were investigated by electron microscopy and by immuno- electron microscopy. ResultsThe SEC were found in 20 of 30 cases that located around the edges of the tumors and appeared as proliferative small biliary ductules. Under electron microscopy they were of small size, contained sparse cytoplasm, few free ribosomes, intracellular tonofilarnents, and intercellular junctions. Immunoelectron microscopically the SEC exhibited labelling for both albumin and CK7 in 5 out of 10 cases. ConclusionSEC in human HCC are found that represent the same mor- phology like those seen in hepatoblastoma and biliary atresia, co- expressed markers for hepatocytic and biliary differentiation.

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*

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.
X-Rays ; Algorithms ; Diagnosis, Computer-Assisted ; Thorax/diagnostic imaging* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted

<p>OBJECTIVETo study the frequency of rare blood group Lu(a-b-) phenotype in a population from Shanghai region, and to explore the molecular basis of Lu(a-b-) by detecting the Lu and Lu relative mediator gene EKLF/KLF1.p><p>METHODSDonors from Shanghai region were screened for Lutheran blood group by monoclonal anti-Lub using serological methods. Individuals with Lu(b-) were determined Lua, P1 and i antigens. Fifteen exons of the LU gene and 3 exons of the EKLF/KLF1 gene for the identified Lu(a-b-) samples were amplified and sequenced.p><p>RESULTSTen Lu(a-b-) donors were obtained from 44 331 donors from Shanghai region. No homozygous or heterozygous mutations were found in the LU gene, whilst 7 mutations in EKLF/KLF1 gene were identified in the 10 samples.p><p>CONCLUSIONThe frequency of rare Lu(a-b-) blood group in Shanghai was approximately 0.02%, and all the individuals had an In(Lu) phenotype. The molecular basis of such samples may be related to mutations in the EKLF/KLF1 gene.p>
China ; ethnology ; Humans ; Kruppel-Like Transcription Factors ; genetics ; Lutheran Blood-Group System ; genetics ; Mutation ; Phenotype

OBJECTIVE: To create an instrument to determine the mental prisoners' competency to serve a sentence, which is according with the Chinese legal system. METHODS: Integrating the Chinese criminal jurisprudence and the authors' forensic psychiatric experience, the research team created an instrument which called Competency to serve a sentence Rating scale firstly, then used the instrument retrospectively, in the end the validity and reliability of the instrument were inspected and, through an diagnostic test, the feasibility of the instrument was evaluated. RESULTS: Homogeneity reliability of the instrument is 0.8779, the correspondence of the conclusion between the instrument and the expertise is 0.909, except the positive likelihood ratio is 0.0683, the other diagnostic index are better. CONCLUSION The Competency to serve a sentence Rating Scale is feasible.
Adult ; Expert Testimony ; Female ; Forensic Psychiatry ; Humans ; Male ; Mental Competency ; Mental Disorders/psychology* ; Mentally Ill Persons/psychology* ; Prisoners/psychology* ; Psychiatric Status Rating Scales ; Retrospective Studies ; Surveys and Questionnaires ; Young Adult

OBJECTIVE: To observe ultrastructure changes of electrical injury in rats. METHODS: An experimental model of rats suffered from the low voltage were designed. Ultrastructure changes of electrical injured tissues were observed under transmission electron microscope. RESULTS: (1) Plasma of epithelium was concreted in the affected areas and inner membrane system was broken. (2) Hypercontraction bands were observed in skeleton muscles. (3) There was dissolved necrosis and hypercontraction bands in the myocardium. (4) Vacuoles were found in plasma of endothelium of blood vessels on electrical current path, and myelin sheath of nerve fiber were loosed. CONCLUSION The above mentioned ultrastructure changes could be used as assistant diagnostic index of electrocution. The mechanism of the changes were discussed.
Animals ; Electric Injuries/pathology* ; Muscle, Skeletal/ultrastructure* ; Nerve Fibers/ultrastructure* ; Rats ; Rats, Sprague-Dawley

To explore the effects of plasma jet (PJ) and plasma activated water (PAW) on the sterilization of Streptococcus mutans ( S. mutans) and compare the advantages and disadvantages of the two methods, so as to provide a basis for plasma treatment of dental caries and to enrich the treatment means of dental caries, an atmospheric pressure plasma excitation system was built, and the effects of PJ and PAW on the sterilization rate of S. mutans and the changes of temperature and pH during treatment were studied under different excitation voltage ( U _e ) and different excitation time ( t _e ). The results showed that in the PJ treatment, the difference in the survival rate of S. mutans between the treatment group and the control group was statistically significant ( P = 0.007, d=2.66) when U _e = 7 kV and t _e = 60 s, and complete sterilization was achieved at U _e = 8 kV and t _e = 120 s in the PJ treatment. In contrast, in the PAW treatment, the difference in the survival rate of S. mutans between the treatment group and the control group was statistically significant ( P = 0.029, d = 1.71) when U _e = 7 kV and t _e = 30 s, and complete sterilization was achieved with PAW treatment when U _e = 9 kV and t _e = 60 s. Results of the monitoring of temperature and pH showed that the maximum temperature rise during PJ and PAW treatment did not exceed 4.3 °C, while the pH value after PAW treatment would drop to a minimum of 3.02. In summary, the optimal sterilization parameters for PJ were U _e =8 kV and 90 s < t _e ≤ 120 s, while the optimal sterilization parameters for PAW were U _e = 9 kV and 30 s< t _e ≤ 60 s. Both treatment methods achieved non-thermal sterilization of S. mutans, where PJ required only a smaller U _e to achieve complete sterilization, while at pH < 4.7, PAW only required a shorter t _e to achieve complete sterilization, but its acidic environment could cause some chemical damage to the teeth. This study can provide some reference value for plasma treatment of dental caries.
Humans ; Streptococcus mutans ; Dental Caries/therapy* ; Sterilization ; Temperature ; Water

Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100-300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm's significant potential for aiding in the diagnosis of congenital heart disease.
Humans ; Heart Sounds ; Neural Networks, Computer ; Algorithms ; Heart Defects, Congenital

Prostate cancer is the most common malignant tumor in male urinary system, and the morbidity and mortality rate are increasing year by year. Traditional imaging examinations have some limitations in the diagnosis of prostate cancer, and the advent of molecular imaging probes and imaging technology have provided new ideas for the integration of diagnosis and treatment of prostate cancer. In recent years, prostate-specific membrane antigen (PSMA) has attracted much attention as a target for imaging and treatment of prostate cancer. PSMA ligand positron emission tomography (PET) has important reference value in the diagnosis, initial staging, detection of biochemical recurrence and metastasis, clinical decision-making guidance and efficacy evaluation of prostate cancer. This article briefly reviews the clinical research and application progress on PSMA ligand PET imaging in prostate cancer in recent years, so as to raise the efficiency of clinical applications.
Male ; Humans ; Prostate/pathology* ; Ligands ; Positron Emission Tomography Computed Tomography/methods* ; Prostatic Neoplasms/diagnostic imaging* ; Positron-Emission Tomography

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*