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Soil microorganisms can be used as one of the important indicators of wetland ecosystem restoration. To study the effects of different restoration stages on soil microbial community composition and diversity in Naolihe Wetland, we employed a "time and space parallel" method. Four restoration stages, namely corn field (Corn), short-term restoration wetland (2 years, ST), long-term restoration wetland (8 years, LT) and natural wetland (>25 years, NW), were selected to represent the restoration time and geographical location in Naolihe Nature Wetland. We investigated the composition and diversity of soil microbial communities in different restoration wetland (from corn fields to natural wetlands) by using 16S rRNA and ITS rRNA gene sequencing. We also performed chemical experiments to measure soil enzyme activity and physicochemical properties at each sampling site. The results showed that soil physicochemical properties and enzyme activities significantly differed with the extension of wetland restoration years (p < 0.05). Proteobacteria, Acidobacteria, and Actinobacteria are the most dominant phyla in bacterial. The alpha diversity of soil bacteria was the highest in the corn field (Corn), and ST-LT-NW first decreased and then increased with the extension of wetland restoration years. There are two most dominant phyla (Ascomycota and Mucoromycota) in fungal. However, the alpha diversity of soil fungi was the lowest in the Corn and LT stage, and ST-LT-NW first decreased and then increased with the extension of wetland restoration years. The research findings indicated that the changes in soil physicochemical properties with the extension of wetland restoration years play a significant role in shaping the structure and diversity changes of soil microbial communities. Through the analyses of bacterial and fungal functions using the FUNGuild and FAPROTAX databases, the results showed that the abundance of aerobic bacteria in the soil increased more than that of anaerobic bacteria as the wetland restoration years prolonged, while the abundance of saprotrophic, symbiotic, and pathogenic fungi in the soil significantly decreased with the prolonged wetland restoration years. This study will help us better understand the process of restoration after farmland abandonment, providing valuable reference information for the implementation of a series of wetland ecological restoration projects in the future.

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BACKGROUND: Schwannomas are uncommon tumors originating from Schwann cells, forming the neural sheath. They account for approximately 2%-6% of all mesenchymal tumors and are most commonly identified in peripheral nerve trunks, with rarity in the gastrointestinal tract. Among gastrointestinal locations, the stomach harbors the majority of nerve sheath tumors, while such occurrences in the sigmoid colon are exceptionally infrequent. CASE SUMMARY: This study presented a clinical case involving a 60-year-old female patient who, during colonoscopy, was diagnosed with a submucosal lesion that was later identified as a nerve sheath tumor. The patient underwent surgical resection, and the diagnosis was confirmed through immunohistochemistry. This study highlighted an exceptionally uncommon occurrence of a nerve sheath tumor in the sigmoid colon, which was effectively managed within our department. Additionally, a comprehensive review of relevant studies was conducted. CONCLUSION: The preoperative diagnosis of nerve sheath tumors poses challenges, as the definitive diagnosis still relies on pathology and immunohistochemistry. Although categorized as benign, these tumors have the potential to demonstrate malignant behavior. Consequently, the optimal treatment approach entails the complete surgical excision of the tumor, ensuring the absence of residual lesions at the margins.

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RATIONALE: Sarcomatoid hepatocellular carcinoma (SHC) is an uncommon variant of hepatocellular carcinoma (HCC), characterized by HCC features combined with sarcomatoid histology and manifestations. The simultaneous occurrence of HCC and hepatosarcomatoid carcinoma is infrequent. This report presents a distinctive instance of HCC coexisting with hepatic sarcomatoid carcinoma in a 56-year-old male. The case exhibits an unusual clinical presentation, diagnosis, treatment, and prognosis. Through the presentation of this case, we aspire to contribute novel concepts to shape forthcoming strategies encompassing SHC diagnosis and treatment. PATIENT CONCERNS: The 56-year-old male patient was admitted to the hospital, due to discovering a hepatic mass lasting for over 2 months. DIAGNOSES: Ultimately, combined hepatocellular and SHC diagnosis was conclusively confirmed through histopathological and imaging examinations. INTERVENTION: In this case, our approach encompassed hepatectomy coupled with ultrasound-guided radiofrequency ablation for HCC. Intraoperative ultrasound localization was employed for accurate tumor identification, followed by postoperative hepatic artery embolization to facilitate meticulous tumor resection. OUTCOMES: He underwent hepatic arteriography chemoembolization treatment and is currently stable, experiencing regular chemotherapy follow-up visits. LESSONS: The presence of distinct tumor types concurrently can influence treatment choices and prognosis. Given the intricate nature of this condition, crafting an optimal treatment strategy necessitates the incorporation of variables such as the patient age, tumor characteristics, liver function, and other pertinent factors.

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BACKGROUND: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. OBJECTIVE: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. METHODS: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. RESULTS: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. CONCLUSION: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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