Objective To explore the MRI finding of female normal levator ani muscle and the levator ani muscle injuries and to evaluate the correlation between childbirth and levator ani muscle injuries. Methods One hundred asymptomatic nulliparous women(control group)and 200 vaginally primiparous women(study group)were selected as the object of this study.Moreover,the study group was divided into two subgroups:group A(100 cases)with stress incontinence,group B(100 cases)without clinical symptoms.Multiplanar proton density magnetic resonance images were obtained at 0.5 cm intervals from these study individuals.All images were used to analyze the differentiation of MRI features between normal levator ani muscle and levator ani muscle injuries.Results No levator ani injuries were identified in the control group.Fifty-four primiparous women(27%)had visible injuries in their levator ani muscles,42 in group A and 12 in group B.Injuries were identified in the puborectalis muscle in 49 cases and in the iliococeygeus muscle in 5 cases(X~2=41.447,P

To provide the basis for preparation of diagnostic kits and vaccines in Toxoplasma gondii infection, the gene coding for the qualified recombinant p30 protein (SAG1) of this parasite was amplified by PCR, and the amplified gene was cloned into prokaryotic expression vector pET-30a(+) to construct the recombinant plasmid, and then transformed to E.coli DH5α. The positive recombinant plasmid was screened by PCR and double enzymes digestion, and the nucleotide sequence of p30 gene was determined by automated DNA sequencing. Meanwhile, the identified recombinant plasmid was transformed to E.coli BL21(DE3) with the expression of p30 on bacteria induced by IPTG and the expressed protein was identified by SDS-PAGE. The protein obtained was then further purified and refolded, and its biological activity was checked by Western blotting. It was shown that the size of the amplified gene was 750 bp with molecular weight of 30 ku, and this protein could specifically react with monoclonal antibody against p30 protein.

ObjectiveTo study the role and mechanism of resisitin in prophylactic and therapeutic treatments of rosiglitazone,a specific peroxisome proliferator-activated receptor-γ(PPARγ) ligand,in rats with severe acute pancreatitis (SAP) and pancreatitis-associated pulmonary injury.MethodsThe levels of amylase ( AMY ),Resistin,TNF-α,IL-1 β and C reactive protein (CRP) in blood plasma,lung myeloperoxidase ( MPO ) activity,pancreas/body weight ratio and lung wet/dry weight ratio were evaluated.Pancreatic and pulmonary pathology were observed.The expression of resistin in pancreas was detected byimmunohistochemistry.The gene expression of resistin mRNA was investigated by real-time PCR.ResultsBoth prophylactic and therapeutic treatments with rosiglitazone could obviously ameliorate the levels of AMY,resistin,TNF-αt,IL-1β and CRP ( all P ＜ 0.01 ).Compared with the control group,both prophylactic and therapeutic treatment groups were higher( all P ＜ 0.01 ).The prophylactic treatment group was not different from the therapeutic treatment group.Both prophylactic and therapeutic treatments with rosiglitazone could significantly reduce pancreas/body weight ratio,pancreatic pathology,MPO,pulmonary pathology ( all P ＜ 0.01 ).Compared with the SAP group,the expression of resistin mRNA in the prophylactic and therapeutic treatment groups were obviously decreased.ConclusionRosiglitazone could obviously ameliorate pancreatitis and pulmonary injury induced by L-arginine.

This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT).Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%.Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT).Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%.Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future.

This review explores the current applications and future prospects of artificial intelligence (AI) in thoracic imaging, with a particular focus on chest radiography (chest X-ray, CXR) and computed tomography (CT).Current Concepts: Recently developed CXR AI algorithms have improved the efficiency, accuracy, and consistency of radiologists' routine clinical workflows by assisting in the detection of a wide range of thoracic diseases on CXR. These AI systems demonstrate diagnostic performance comparable to that of radiology residents who have limited interpretive experience. Furthermore, generative CXR AI technologies are capable of not only automatically detecting abnormalities such as pulmonary nodules, pneumonia, pneumothorax, and tuberculosis, but also generating radiology reports. These advancements represent a paradigm-shifting innovation that may significantly alter the current landscape of CXR interpretation in thoracic radiology. Although performance varies depending on the specific algorithm and dataset, AI applied to low-dose chest CT has demonstrated diagnostic accuracy ranging from 0.81 to 0.98 for nodule detection and malignancy assessment, with sensitivity ranging from 0.88 to 0.99 and specificity from 0.82 to 0.93. Incorporating AI as a second reader in CT interpretation can reduce reading time by approximately 20%, while also improving sensitivity for pulmonary nodule detection by 5% to 20% and malignant nodule diagnosis by 3% to 15%.Discussion and Conclusion: Both CXR AI and chest CT AI streamline image interpretation by assisting with simple and repetitive tasks. Simultaneously, they provide novel diagnostic insights that are expected to influence and potentially reshape the interpretative patterns of radiologists in the near future.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

No abstract available.
Colon* ; Lymphangioma, Cystic*