Diabetes, hyperlipidemia, and hyperuricemia have emerged as global chronic metabolic disorders posing significant public health challenges. Edible medicinal plants demonstrate multitarget regulatory effects and favorable safety profiles in the management of chronic metabolic diseases, owing to their bioactive compounds and nutritional synergy. Radix Astragali (RA), a traditional Chinese medicine with medicine–food homology, has a long history of application in China. Based on a review of relevant literature, this article discusses the historical background, modern research, therapeutic functions, and mechanisms of RA in the treatment of chronic metabolic diseases. Modern studies have shown that RA contains a variety of bioactive components and demonstrates efficacy in managing diabetes, hyperlipidemia, and hyperuricemia. Its mechanisms of action involve modulation of the insulin signaling pathway, regulation of lipid metabolism-related enzyme activity, promotion of uric acid excretion, and other processes. Notably, RA exhibits certain common therapeutic effects across hyperglycemia, hyperlipidemia, and hyperuricemia. Through modulation of the gut microbiota and inhibition of inflammatory pathways, RA exerts synergistic, multitarget effects that improve metabolic disorders associated with chronic metabolic diseases.

In recent years, with the rapid development of artificial intelligence technology, the application of deep learning in the field of pathology has been continuously expanding. Particularly, the rise of multimodal data fusion methods has opened up new technical paths for the precise diagnosis, prognosis assessment, and individualized treatment of tumors. By integrating multi-level and multi-source data such as clinical information, pathological omics, molecular omics, and imaging omics, deep learning models can identify potential associated features and key biological mechanisms that are difficult to reveal by a single modality, thereby significantly improving the accuracy of disease classification and the scientific nature of risk stratification. This article systematically reviews the research progress of multimodal data fusion methods based on deep learning in the field of pathology in recent years, focuses on sorting out different types of fusion strategies, evaluates their advantages and challenges in practical clinical applications, and looks forward to future development trends.

Histological staining is the basis of pathological analysis,but the traditional staining method relies on chemical reagents,which not only consumes a lot of resources,but also causes harm to the environment and human health.In recent years,with the rapid development of deep learning technology,virtual staining technology,as a new method,is expected to effectively replace and supplement the traditional histological staining methods.It uses neural networks to analyze unstained tissue images,generate digital images that are highly similar to chemical staining effects,and even realize the mutual conversion between different staining modes,reducing the laboratory's dependence on chemical reagents and providing sustainable research programs.In this paper,the basic principles of virtual staining and its potential applications in histopathology are introduced in detail,and the current challenges and future research directions are discussed.

In recent years, with the rapid development of artificial intelligence technology, the application of deep learning in the field of pathology has been continuously expanding. Particularly, the rise of multimodal data fusion methods has opened up new technical paths for the precise diagnosis, prognosis assessment, and individualized treatment of tumors. By integrating multi-level and multi-source data such as clinical information, pathological omics, molecular omics, and imaging omics, deep learning models can identify potential associated features and key biological mechanisms that are difficult to reveal by a single modality, thereby significantly improving the accuracy of disease classification and the scientific nature of risk stratification. This article systematically reviews the research progress of multimodal data fusion methods based on deep learning in the field of pathology in recent years, focuses on sorting out different types of fusion strategies, evaluates their advantages and challenges in practical clinical applications, and looks forward to future development trends.

Histological staining is the basis of pathological analysis,but the traditional staining method relies on chemical reagents,which not only consumes a lot of resources,but also causes harm to the environment and human health.In recent years,with the rapid development of deep learning technology,virtual staining technology,as a new method,is expected to effectively replace and supplement the traditional histological staining methods.It uses neural networks to analyze unstained tissue images,generate digital images that are highly similar to chemical staining effects,and even realize the mutual conversion between different staining modes,reducing the laboratory's dependence on chemical reagents and providing sustainable research programs.In this paper,the basic principles of virtual staining and its potential applications in histopathology are introduced in detail,and the current challenges and future research directions are discussed.

Objective:To investigate the expression and diagnostic value of SS18-SSX fusion-specific antibody and SSX C-terminal antibody in synovial sarcoma (SS).Methods:Immunohistochemical (IHC) EnVision method was used to detect the expression of SS18-SSX fusion-specific antibody and SSX C-terminal antibody in 51 genetically confirmed cases of SS and 94 non-SS tumors diagnosed at Nanjing Jinling Hospital from August 2013 to December 2020.Results:IHC staining for SS18-SSX fusion-specific antibody revealed strongly diffuse nuclear staining in 48 of 51 (48/51, 94.1%) SS cases, whereas none of the 94 non-SS tumors showed any staining. IHC staining for SSX C-terminal antibody showed strongly diffuse nuclear staining in all 51 (51/51, 100%) SS cases; six of the 94 (6/94, 6.4%) non-SS tumors showed variable staining, including two cases each of leiomyosarcoma and fibrosarcoma, and one case each of malignant peripheral nerve sheath tumor and embryonal rhabdomyosarcoma. The sensitivity and specificity of SS18-SSX fusion-specific antibody in diagnosing SS were 94.1% and 100% and these of SSX C-terminal antibody were 100% and 93.6%, respectively.Conclusions:SS18-SSX fusion-specific antibody and SSX C-terminal antibody are highly sensitive and specific markers for SS. Immunohistochemistry using these antibodies may replace FISH or molecular genetic testing in most cases.

Objective: To investigate the expression of H3.3 G34W mutant-specific antibody in giant cell tumors of bone (GCTB), and its value in the diagnosis of GCTB. Methods: Immunohistochemical (IHC) EnVision method was used to detect the expression of H3.3 G34W mutant-specific antibody and p63 in 83 GCTBs, 18 aneurysmal bone cysts, 23 chondroblastomas and 28 osteosarcomas diagnosed at Nanjing Jinling Hospital from June 2001 to April 2019. Results: Among the 83 cases of GCTB, 69 cases (69/83, 83.1%) expressed H3.3 G34W. H3.3 G34W expression was found exclusively in the mononuclear cell population with strong and diffuse nuclear staining. H3.3 G34W was expressed in 55 of 57 (96.5%) cases of GCTB in long bones, but only 14 of 26 (53.8%) cases of non-long bone GCTB. All recurrent (9/9)/metastatic GCTB (2/2), post-denosumab GCTB (3/3), primary malignant GCTB (3/3) and secondary malignant GCTB (5/5) also expressed H3.3 G34W. H3.3 G34W was negative in all aneurysmal bone cysts and chondroblastomas. H3.3 G34W was positive in 3 of 28(10.7%) cases of osteosarcomas, and giant cell-rich osteosarcoma(GCRO) was the only histological subtype of osteosarcoma that expressed H3.3 G34W. p63 was expressed in 71.1%(59/83) of GCTB, while the positive rates of p63 in aneurysmal bone cysts,chondroblastomas and osteosarcomas were 3/18, 43.5% (10/23) and 21.4% (6/28) respectively. The sensitivity and specificity of H3.3 G34W mutant-specific antibody in the diagnosis of GCTB were 83.1% and 95.7%. Conclusions H3.3 G34W mutant-specific antibody is a highly sensitive and specific marker for GCTB and helpful for the diagnosis of GCTB and its variants. The limitation of this antibody is that as a mall number of GCTB harbor G34 mutation other than G34W, and thus that cannot be detected. The incidental expression of H3.3 G34W mutant protein in osteosarcoma could be a potential diagnostic dilemma, and the results of H3.3 G34W IHC staining needs careful interpretation.

Objective: To study the clinicopathological features, immunohistochemical phenotype, molecular changes, differential diagnosis and prognosis of eosinophilic solid and cystic renal cell carcinoma (ESC RCC). Methods: A total of 15 cases were selected from 2005 to 2019 at Nanjing Jinling Hospital,Nanjing University School of Medicine for clinicopathological and immunohistochemical analysis, 10 of which were subject to cancer-associated mutation analysis using targeted next-generation sequencing (NGS) panel. A literature review was also performed. Results: The patients′ ages ranged from 15 to 68 years (mean, 33 years). The male-to-female ratio was 1.1∶1.0. During a mean follow-up of 22 months, none of the patients developed tumor recurrence, progression or metastasis. Histologically, the tumors typically demonstrated solid and cystic architectures and the neoplastic cells contained voluminous eosinophilic cytoplasm with prominent granular cytoplasmic stippling. Immunohistochemically, tumor cells in all cases were immunoreactive for CK20. Signal pathway related protein mTOR and S6 were positive in 14/15 and 6/15 cases, respectively. Cathepsin K, Melan A and HMB45 were at least focally positive in 12/15, 6/15 and 2/15 cases, respectively. CK7 and CD10 showed focal immunostain positivity in some cases, while TFE3, TFEB, CA9 and CD117 were negative in all cases. NGS demonstrated TSC1/TSC2 mutations in all tested cases (10/10). Conclusions ESC RCC is a rare tumor that tends to occur in young patients with an indolent behavior. Diagnosis can be established by its distinct clinical and histopathologic findings, immunohistochemical phenotype and molecular genetics. The tumor may be considered as a new subtype of RCC.

study the clinicopathological features, immunohistochemical phenotype, molecular changes, differential diagnosis and prognosis of eosinophilic solid and cystic renal cell carcinoma (ESC RCC). Methods A total of 15 cases were selected from 2005 to 2019 at Nanjing Jinling Hospital,Nanjing University School of Medicine for clinicopathological and immunohistochemical analysis, 10 of which were subject to cancer?associated mutation analysis using targeted next?generation sequencing (NGS) panel. A literature review was also performed. Results The patients′ ages ranged from 15 to 68 years (mean, 33 years). The male?to?female ratio was 1.1∶1.0. During a mean follow?up of 22 months, none of the patients developed tumor recurrence, progression or metastasis. Histologically, the tumors typically demonstrated solid and cystic architectures and the neoplastic cells contained voluminous eosinophilic cytoplasm with prominent granular cytoplasmic stippling. Immunohistochemically, tumor cells in all cases were immunoreactive for CK20. Signal pathway related protein Mtor and S6 were positive in 14/15 and 6/15 cases, respectively. Cathepsin K, Melan A and HMB45 were at least focally positive in 12/15, 6/15 and 2/15 cases, respectively. CK7 and CD10 showed focal immunostain positivity in some cases, while TFE3, TFEB, CA9 and CD117 were negative in all cases. NGS demonstrated TSC1/TSC2 mutations in all tested cases (10/10). Conclusions ESC RCC is a rare tumor that tends to occur in young patients with an indolent behavior. Diagnosis can be established by its distinct clinical and histopathologic findings, immunohistochemical phenotype and molecular genetics. The tumor may be considered as a new subtype of RCC.

Objective: To study the molecular features of metanephric adenoma (MA) and discuss their values in differential diagnosis. Methods: BRAF V600E immunohistochemistry (IHC) using the mutation-specific VE1 monoclonal antibody and Sanger sequencing of BRAF mutations were performed on 21 MAs, 16 epithelial-predominant Wilms tumors (e-WT) and 20 the solid variant of papillary renal cell carcinomas (s-PRCC) respectively. p16 protein was detected by IHC also. Fluorescence in situ hybridization (FISH) analyses using centromeric probes for chromosome 7 and 17 were performed on the three renal tumors in parallel. Results: Fourteen (14/21, 66.7%) of 21 MA cases demonstrated diffuse, moderate to strong cytoplasmic BRAF V600E IHC staining and the BRAF V600E protein expression was detected in 2 (2/16) of 16 e-WT cases for the first time, whereas all s-PRCCs were negative (P<0.05). All cases (including 14 MAs and 2 e-WTs) with diffuse, moderate to strong cytoplasmic BRAF V600E IHC staining were confirmed to harbor BRAF V600E missense mutations using Sanger sequencing, and no BRAF mutations were detected in cases with negative BRAF V600E protein expression. One case (1/21, 4.8%) showed trisomy of chromosome 7 alone, and another one (1/21, 4.8%) showed trisomy of chromosome 17 alone in 21 MAs. Two cases (2/16) of 16 e-WTs showed trisomy of chromosome 17 alone. In 20 s-PRCCs, trisomy of chromosomes 7 alone was reported in 2 cases (2/20), trisomy of chromosome 17 alone in 3 cases (3/20) and trisomy of chromosome 7 and 17 in 14 cases (14/20). The total positive rates of trisomy of chromosome 7 and/or 17 in MAs, e-WTs and s-PRCCs were 9.6% (2/21), 2/16 and 95.0% (19/20). p16 protein was positive in 81.0% (17/21) MAs, whereas the positive rates in e-WTs and s-PRCCs were 2/16 and 5.0% (1/20). Conclusions Most MAs harbor BRAF V600E mutations, and MAs lack the gains of chromosome 7 and 17 that are characteristic of papillary renal cell carcinoma. These molecular features can be used to distinguish MA from its mimics. BRAF V600E IHC using the mutation-specific VE1 monoclonal antibody provides an effective method in BRAF V600E mutations detection of renal tumors. p16 is overexpressed in MA, and the finding suggests that the low proliferative rate of the tumor might be attributed to BRAF V600E-induced senescence mediated by p16.