Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Stiff person syndrome (SPS) is a rare neuro-immune disorder characterized by exaggerated startle responses and muscle spasms in response to minor stimuli. SPS is often misdiagnosed as panic disorder or agoraphobia. This paper reported a case of a patient with SPS presenting symptoms similar to panic attack. The patient had been misdiagnosed as dystonia, anxiety disorder and panic attack. The patient was finally correctly diagnosed through multiple disciplinary team discussions. This study integrates a comprehensive understanding of symptomatology and patient-reported experiences with targeted neurological evaluations and supplementary diagnostic tools to improve the accuracy of etiological diagnoses. This study aims to enhance the ability of psychiatrists to recognize somatic diseases presenting with psychiatric symptoms and to improve clinical diagnostic and treatment levels.

Objective:To apply artificial intelligence（AI）in classifying ovarian tumors on ultrasound images，and compare the diagnostic results of several sonographers with varying seniority levels.Methods:A total of 645 patients diagnosed with adnexal masses via gynecological ultrasound examination at Qilu Hospital of Shandong University from January 2021 to December 2024 were enrolled. Three deep learning architectures，i.e.，Alexnet，Densenet121，and Resnet50 were developed and used to internally test the classification effectiveness of ovarian tumors，while the optimal model was selected for external testing. Two junior sonographers and two senior sonographers were recruited to independently diagnose ovarian tumors in the external test dataset. Subsequently，the benign and malignant results of the model's predictions were disclosed to each sonographer，and their revised diagnoses on the same external test data in combination with the best AI model were recorded.Results:The optimal model achieved an accuracy of 0.941，sensitivity of 0.936，and specificity of 0.944 on the internal test dataset，and maintained robust performance on the external test dataset with accuracy of 0.891，sensitivity of 0.880，and specificity of 0.907. Compared to junior sonographers，the optimal model demonstrated significantly higher sensitivity in discriminating benign from malignant ovarian tumors（0.880 vs. 0.723，0.602；all P<0.05）. No statistically significant difference was observed in diagnostic accuracy between the optimal model and senior sonographer 1（ P=0.05）. With assistance from the optimal model，junior sonographers achieved significant improvements in both sensitivity and specificity（sensitivity：0.723 vs. 0.843，0.602 vs. 0.819；specificity：0.778 vs. 0.833，0.685 vs. 0.741；all P<0.05）. Conclusions:The optimal model achieves comparable performance to that of senior sonographers in ovarian tumor classification. With model assistance，the diagnostic performance of junior sonographers is significantly improved.

Stiff person syndrome (SPS) is a rare neuro-immune disorder characterized by exaggerated startle responses and muscle spasms in response to minor stimuli. SPS is often misdiagnosed as panic disorder or agoraphobia. This paper reported a case of a patient with SPS presenting symptoms similar to panic attack. The patient had been misdiagnosed as dystonia, anxiety disorder and panic attack. The patient was finally correctly diagnosed through multiple disciplinary team discussions. This study integrates a comprehensive understanding of symptomatology and patient-reported experiences with targeted neurological evaluations and supplementary diagnostic tools to improve the accuracy of etiological diagnoses. This study aims to enhance the ability of psychiatrists to recognize somatic diseases presenting with psychiatric symptoms and to improve clinical diagnostic and treatment levels.

In the context of non-alcoholic fatty liver disease (NAFLD), characterized by dysregulated lipid metabolism in hepatocytes, the quest for safe and effective therapeutics targeting lipid metabolism has gained paramount importance. Sanhuang Xiexin Tang (SXT) and Baihu Tang (BHT) have emerged as prominent candidates for treating metabolic disorders. SXT combined with BHT plus Cangzhu (SBC) has been used clinically for Weihuochisheng obese patients. This retrospective analysis focused on assessing the anti-obesity effects of SBC in Weihuochisheng obese patients. We observed significant reductions in body weight and hepatic lipid content among obese patients following SBC treatment. To gain further insights, we investigated the effects and underlying mechanisms of SBC in HFD-fed mice. The results demonstrated that SBC treatment mitigated body weight gain and hepatic lipid accumulation in HFD-fed mice. Pharmacological network analysis suggested that SBC may affect lipid metabolism, mitochondria, inflammation, and apoptosis-a hypothesis supported by the hepatic transcriptomic analysis in HFD-fed mice treated with SBC. Notably, SBC treatment was associated with enhanced hepatic mitochondrial biogenesis and the inhibition of the c-Jun N-terminal kinase (JNK)/nuclear factor-kappa B (NF-κB) and extracellular signal-regulated kinase (ERK)/NF-κB pathways. In conclusion, SBC treatment alleviates NAFLD in both obese patients and mouse models by improving lipid metabolism, potentially through enhancing mitochondrial biogenesis. These effects, in turn, ameliorate inflammation in hepatocytes.
Humans ; Mice ; Animals ; Non-alcoholic Fatty Liver Disease/metabolism* ; NF-kappa B/metabolism* ; Organelle Biogenesis ; Retrospective Studies ; Mice, Inbred C57BL ; Obesity/metabolism* ; Liver ; Inflammation/metabolism* ; Body Weight ; Lipid Metabolism ; Lipids ; Diet, High-Fat/adverse effects*

Second-generation antipsychotics are widely used in psychiatric disorders such as schizophrenia, however long-term use of second-generation antipsychotics is associated with an increased risk of metabolic syndrome. The etiological mechanisms of metabolic syndrome induced by antipsychotics remain unclear while relationships between intestinal flora and metabolic disorders has attracted increasing attention. Studies have shown that patients with metabolic syndrome induced by antipsychotics have unique characteristics of intestinal flora, and exploratory treatments targeting gut flora lead to preliminary clinical benefits. Intestinal flora may involve in the occurrence and development of metabolic syndrome through regulation of immune, inflammatory, nerve, gene expression and other aspects. In this review, relevant studies investigating the correlations between intestinal flora and metabolic syndrome induced by antipsychotics are reviewed to provide insights for future etiological mechanism research and treatment.

Second-generation antipsychotics are widely used in psychiatric disorders such as schizophrenia, however long-term use of second-generation antipsychotics is associated with an increased risk of metabolic syndrome. The etiological mechanisms of metabolic syndrome induced by antipsychotics remain unclear while relationships between intestinal flora and metabolic disorders has attracted increasing attention. Studies have shown that patients with metabolic syndrome induced by antipsychotics have unique characteristics of intestinal flora, and exploratory treatments targeting gut flora lead to preliminary clinical benefits. Intestinal flora may involve in the occurrence and development of metabolic syndrome through regulation of immune, inflammatory, nerve, gene expression and other aspects. In this review, relevant studies investigating the correlations between intestinal flora and metabolic syndrome induced by antipsychotics are reviewed to provide insights for future etiological mechanism research and treatment.

Objective: To explore the relationship between HCM pathogenic gene mutations and clinical phenotypes by analyzing the prenatal diagnosis and genetic characteristics of a pregnant woman from a family with hypertrophic cardiomyopathy (HCM). Methods: The clinical data of the proband and her family members was collected. The DNA was extracted from the peripheral blood, amniotic fluid cells and cultured amniotic fluid cells of proband. Next generation sequencing (NGS) was utilized for screening pathogenetic loci of the proband. The suspected mutation sequences of HCM pathogenic candidate genes MYH7 and MYBPC3 were directly sequenced after PCR. Pathogenicity prediction of amniotic fluid cells was performed by using genetic data and bioinformatics software, such as Mutation taster, PolyPhen-2 and ANTHEPROT. Results: The sequencing results showed that heterozygous mutations of MYH7 c.1988G＞A (p.Arg663His) and MYBPC3 c.151G＞A (p.Ala51Thr) were found in the proband. The phenotype of her father was normal, and no abnormal mutations were detectable. Her mother also showed normal phenotype but carried MYBPC3 c.151G＞A heterozygous mutation. Only MYH7 c.1988G＞A heterozygous mutation was found in the fetus and no abnormal variation of MYBPC3 was showed. The prediction of mutation effect and analysis of protein structure and function revealed that the two missense mutations could affect the hydrophobicity and antigenicity of the protein. The genetic data demonstrated MYH7 c.1988G＞A was defined as a pathogenic mutation. Conclusion MYH7 c.1988G＞A should be a newly generated pathogenic mutation in the proband, or caused by reproductive chimerism of her parents. MYBPC3 c.151G＞A mutation may promote the occurrence of HCM. Although the fetus only carries MYH7 c.1988G＞A, her phenotype may still display as HCM.

0.05).CONCLUSION:Scheme B has better compliance and is more economical as compared with scheme A.