The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

AIM To study the neoflavonoids from Dalbergia cochinchinensis Pierre ex Laness and their anti-hypoxia/reoxygenation injury activities on H9c2 myocardial cells.METHODS The 70%ethanol extract from D.cochinchinensis was isolated and purified by silica gel,Sephadex LH-20 and reverse-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The CCK-8 method was used to detect their activities on H9c2 cells and protective effects on hypoxia-reoxygenation injury of H9c2 cells,and their structure-activity relationship was analyzed.RESULTS Twelve compounds were isolated and identified as latifolin(1),5-O-methyllatifolin(2),mimosifoliol(3),5-O-methydalbergiphenol(4),dalbergiphenol(5),cearoin(6),2,4-dihydroxy-5-methoxy-benzophenone(7),2-hydroxy-4,5-dimethoxybenzophenone(8),melannoin(9),2,2′,5-trihydroxy-4-methoxybenzophenone(10),dalbergin(11),4-methoxydalbergione(12).The dalbergiphenols and dalbergins had little toxicity to H9c2 cells,and dalbergiphenols had strong activity against hypoxia-reoxygenation injury of H9c2 cells.CONCLUSION Compound 8 is a new natural product.Compounds 4,9 are isolated from this plant for the first time.Dalbergiphenols may be the main neoflavonoids against hypoxia-reoxygenation injury of H9c2 cells.

Objective:To explore the clinical and electrophysiological characteristics of peripheral neuropathy in prediabetic patients.Methods:Subjects aged 20-65 years with high-risk factors of impaired glycemia enrolled in Beijing Tiantan Hospital, Capital Medical University from 2019 to 2022 were recruited to conduct oral glucose tolerance test, after excluding other causes of neuropathy or radiculopathy. Patients with impaired fasting glucose or impaired glucose tolerance were defined by American Diabetes Association criteria. These patients were divided into clinical polyneuropathy (PN) and clinical non-PN groups, according to the 2010 Toronto consensus criteria and the presence of PN symptoms and signs or not. Nerve conduction studies (NCS), F wave, sympathetic skin response (SSR), R-R interval variation (RRIV) and current perception thresholds (CPT) were performed and the abnormal rate was compared between different electrodiagnostic methods and between clinical subgroups.Results:Among the 73 prediabetic patients ultimately enrolled, only 20 (27.4%) can be diagnosed as clinical PN according to the Toronto consensus criteria. The abnormal rate of CPT (68.5%, 50/73) was significantly higher than those of F wave (2.7%, 2/73), lower limb NCS (0, 0/73), upper limb NCS changes of carpal tunnel syndrome (26.0%, 19/73), SSR (6.8%, 5/73) and RRIV (5.5%, 4/73; McNemar test, all P<0.001). With sinusoid-waveform current stimuli at frequencies of 2 000 Hz, 250 Hz and 5 Hz, the CPT device was used to measure cutaneous sensory thresholds of large myelinated, small myelinated and small unmyelinated sensory fibers respectively. CPT revealed a 21.9% (16/73) abnormal rate of unmyelinated C fiber in the hands of prediabetic patients, significantly higher than that of large myelinated Aβ fibers [8.2% (6/73), χ2=5.352, P=0.021]. Both abnormal rates of small myelinated Aδ [42.5% (31/73)] and unmyelinated C fibers [39.7% (29/73)] in the feet of prediabetic patients were significantly higher than that of large myelinated Aβ fibers [11.0% (8/73), χ2=18.508, 15.965, both P<0.001]. Compared with the clinical non-PN group, the abnormal rates of CPT [90.0% (18/20) vs 60.4% (32/53), χ2=5.904, P=0.015] and SSR [20.0% (4/20) vs 1.9% (1/53), P=0.016) were significantly higher in the clinical PN group. Conclusions:Peripheral neuropathies in prediabetic patients are usually asymptomatic or subclinical, and predispose to affect unmyelinated and small myelinated sensory fibers. Selective electrodiagnostic measurements of small fibers help to detect prediabetic neuropathies in the earliest stages of the disease.

Tuberous sclerosis complex(TSC)is a rare genetic disease that can lead to benign dysplasia in multiple organs such as the skin, brain, eyes, oral cavity, heart, lungs, kidneys, liver, and bones. Its main symptoms include epilepsy, intellectual disabilities, skin depigmentation, and facial angiofibromas, whilst incidence is approximately 1 in 10 000 to 1 in 6000 newborns. This case presents a middle-aged woman who initially manifested with epilepsy and nodular depigmentation. Later, she developed a lower abdominal mass, elevated creatinine, and severe anemia. Based on clinical features and whole exome sequencing, the primary diagnosis was confirmed as TSC. Laboratory and imaging examinations revealed that the lower abdominal mass originated from the uterus. CT-guided biopsy pathology and surgical pathology suggested a combination of leiomyoma and abscess. With the involvement of multiple organs and various complications beyond the main diagnosis, the diagnostic and therapeutic process for this patient highlights the importance of rigorous clinical thinking and multidisciplinary collaboration in the diagnosis and treatment of rare and challenging diseases.

Objective: To investigate the factors affecting depression and anxiety symptoms among the elderly, so as to provide the basis for promoting mental health among the elderly. Methods: The elderly aged 60 years and above in Ningbo City, Zhejiang Province were recruited using the multistage stratified random sampling method from June to August 2022, and demographic information, lifestyle and self-rated health status were collected by questionnaires. The symptoms of depression and anxiety were assessed by the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7), respectively. The presence of depressive and anxiety symptoms was determined when both the PHQ-9 score and the GAD-7 score were 10 points and higher. Factors affecting depressive and anxiety symptoms were identified using a multivariable logistic regression model. Results: A total of 7 771 individuals were surveyed, including 3 490 males (44.91%) and 4 281 females (55.09%), and had a mean age of (72.11±6.79) years. The prevalence of depression and anxiety symptoms was 2.05%. Multivariable logistic regression analysis identified residence (urban area, OR=0.316, 95%CI: 0.201-0.498), sedentary duration (<3 h/d, OR=0.349, 95%CI: 0.232-0.525; 3-5 h/d, OR=0.458, 95%CI: 0.313-0.671), physical activity (≥3 times/week, OR=0.551, 95%CI: 0.373-0.815), sleep quality (poor, OR=2.491, 95%CI: 1.738-3.571), social isolation (OR=1.688, 95%CI: 1.148-2.481) and self-rated health (poor, OR=5.857, 95%CI: 3.547-9.671; normal, OR=1.903, 95%CI: 1.234-2.937) as the influencing factors for depression and anxiety symptoms among the elderly. Conclusion The prevalence of depression and anxiety symptoms among the elderly is associated with residence, sedentary duration, sleep quality, physical activity, social interactions and self-rated health status.

Based on the genomic information of Emericella sp. 1454, in conjunction with literature analysis of its secondary metabolite emestrin, this study identified the biosynthetic precursors of emestrin and enhanced its production by supplementing the culture medium with these precursors. In this study, it was found for the first time that the addition of biosynthetic precursor, reduced glutathione, to the culture medium significantly increased emestrin yield. By incorporating 1.5 g of reduced glutathione into 50 g of rice culture medium and fermenting for 15 days, a yield of 30.82 mg of emestrin was obtained, which marked an 11.71-fold increase compared to the original fermentation approach. The method is both simple and cost-effective, establishing a solid foundation for the efficient synthesis of emestrin and similar compounds. Additionally, it serves as an important reference for enhancing the production of other epipolythiodioxopiperazine compounds.