ObjectiveTo identify the pathogen species responsible for the wilt disease of Tetradium ruticarpum in Chongqing， investigate there biological characteristics， and screen effective fungicides， so as to provide a theoretical basis for disease control in production. MethodsThe pathogen was isolated via the tissue culture method. Pathogenicity was verified according to Koch's postulates. The pathogen was identified based on morphological characteristics and multi-gene phylogenetic analysis. The mycelial growth rate method was used for biological characterization of the pathogen and fungicide screening. ResultsThe pathogen colonies were nearly circular with irregular edges， white， short， velvety aerial hyphae， and pale purple undersides. Macroconidia were colorless， sickle-shaped， with 3-5 septa， while microconidia were transparent， elliptical， aseptate or with 1-2 septa. Multi-gene phylogenetic analysis showed that the pathogen clustered in the same clade as Fusarium fujikuroi with 100% support， which， combined with morphological characteristics， identified the pathogen causing wilt of T. ruticarpum in Chongqing as F. fujikuroi. The optimal conditions for the mycelial growth of F. fujikuroi were mung bean agar （MBA） with glucose as the carbon source， beef extract and yeast powder as nitrogen sources， 28 ℃， pH 7.0， and alternating light/dark conditions. The optimal conditions for sporulation were potato dextrose agar （PDA） with glucose as the carbon source， beef extract as the nitrogen source， 28 ℃， pH 7.0， and complete darkness. Among chemical fungicides， phenazine-1-carboxylic acid exhibited the strongest inhibitory effect on F. fujikuroi. Shenqinmycin and tetramycin were the most effective bio-fungicides. ConclusionThis study is the first to report F. fujikuroi as the causal agent of wilt disease in T. rutaecarpa. The chemical fungicide phenazine-1-carboxylic acid and the bio-fungicides shenqinmycin and tetramycin showed strong inhibitory effects against F. fujikuroi.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

BACKGROUND: Growing evidence suggests that long non-coding RNAs (lncRNAs) exert pivotal roles in fostering chemoresistance across diverse tumors. Nevertheless, the precise involvement of lncRNAs in modulating chemoresistance within the context of gallbladder cancer (GBC) remains obscure. This study aimed to uncover how lncRNAs regulate chemoresistance in gallbladder cancer, offering potential targets to overcome drug resistance. METHODS: To elucidate the relationship between gemcitabine sensitivity and small nucleolar RNA host gene 1 ( SNHG1 ) expression, we utilized publicly available GBC databases, GBC tissues from Renji Hospital collected between January 2017 and December 2019, as well as GBC cell lines. The assessment of SNHG1, miR-23b-3p, and phosphatase and tensin homolog (PTEN) expression was performed using in situ  hybridization, quantitative real-time polymerase chain reaction, and western blotting. The cell counting kit-8 (CCK-8) assay was used to quantify the cell viability. Furthermore, a GBC xenograft model was employed to evaluate the impact of SNHG1 on the therapeutic efficacy of gemcitabine. Receiver operating characteristic (ROC) curve analyses were executed to assess the specificity and sensitivity of SNHG1. RESULTS: Our analyses revealed an inverse correlation between the lncRNA SNHG1 and gemcitabine resistance across genomics of drug sensitivity in cancer (GDSC) and Gene Expression Omnibus (GEO) datasets, GBC cell lines, and patients. Gain-of-function investigations underscored that SNHG1 heightened the gemcitabine sensitivity of GBC cells in both in vitro and in vivo  settings. Mechanistic explorations illuminated that SNHG1 could activate PTEN -a commonly suppressed tumor suppressor gene in cancers-thereby curbing the development of gemcitabine resistance in GBC cells. Notably, microRNA (miRNA) target prediction algorithms unveiled the presence of miR-23b-3p binding sites within SNHG1 and the 3'-untranslated region (UTR) of PTEN . Moreover, SNHG1 acted as a sponge for miR-23b-3p, competitively binding to the 3'-UTR of PTEN , thereby amplifying PTEN expression and heightening the susceptibility of GBC cells to gemcitabine. CONCLUSION The SNHG1/miR-23b-3p/PTEN axis emerges as a pivotal regulator of gemcitabine sensitivity in GBC cells, holding potential as a promising therapeutic target for managing GBC patients.
Humans ; Deoxycytidine/pharmacology* ; PTEN Phosphohydrolase/genetics* ; Gemcitabine ; RNA, Long Noncoding/metabolism* ; MicroRNAs/genetics* ; Gallbladder Neoplasms/genetics* ; Cell Line, Tumor ; Animals ; Mice ; Drug Resistance, Neoplasm/genetics* ; Mice, Nude ; Antimetabolites, Antineoplastic ; Gene Expression Regulation, Neoplastic

BACKGROUND: Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke. METHODS: This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year after stroke onset, respectively. We performed Logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes. RESULTS: Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (median time from stroke onset: 43 h, Q1-Q3: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P  <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P  <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P  <0.01). CONCLUSIONS: Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 to 4 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes. REGISTRATION ClinicalTrials.gov , NCT03222024.
Humans ; Male ; Female ; Prospective Studies ; Ischemic Stroke/mortality* ; Aged ; Middle Aged ; Aged, 80 and over ; Stroke ; Brain Ischemia

Heart failure (HF) is a common end-stage clinical manifestation of cardiovascular diseases, imposing substantial health-related burdens worldwide. With its high mortality rates and poor long-term prognosis, there is a pressing need for novel therapies. SIRT1, a nicotinamide adenine dinucleotide (NAD⁺)-dependent deacetylase, has anti-cardiovascular aging properties and other cardioprotective effects, attracting much research attention in recent years. In addition, SIRT1 plays an important role in HF pathophysiology. This review summarized the roles of SIRT1 and its activators in HF, the changes of SIRT1 gene expression in cardiac tissues from animal models and HF patients, and the current status of clinical trials investigating SIRT1 activators as potential therapies for HF. This will provide new ideas for further exploration of pathological mechanisms and the development of clinical prevention strategies for HF.
Heart Failure/metabolism* ; Sirtuin 1/genetics* ; Humans ; Animals

Based on ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology and network pharmacology, this study explored the pharmacodynamic substances and potential mechanisms of Huazhuo Sanjie Chubi Decoction in the treatment of gouty arthritis(GA). UPLC-Q-Exactive Orbitrap-MS technology was used to identify the components in Huazhuo Sanjie Chubi Decoction, and the qualitative analysis of its active ingredients was carried out, with a total of 184 active ingredients identified. A total of 897 active ingredient targets were screened through the PharmMapper database, and 491 GA-related disease targets were obtained from the OMIM, GeneCards, CTD databases. After Venn analysis, 60 intersecting targets were obtained. The component target-GA target network was constructed through the Cytoscape platform, and the STRING database was used to construct a protein-protein interaction network, with 16 core targets screened. The core targets were subjected to Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses, and the component-target-pathway network was constructed. It was found that the main active ingredients of the formula for the treatment of GA were phenols, flavonoids, alkaloids, and terpenoids, and the key targets were SRC, MMP3, MMP9, REN, ALB, IGF1R, PPARG, MAPK1, HPRT1, and CASP1. Through GO analysis, it was found that the treatment of GA mainly involved biological processes such as lipid response, bacterial response, and biostimulus response. KEGG analysis showed that the pathways related to the treatment of GA included lipids and atherosclerosis, neutrophil extracellular traps(NETs), IL-17, and so on. In summary, phenols, flavonoids, alkaloids, and terpenoids may be the core pharmacodynamic substances of Huazhuo Sanjie Chubi Decoction in the treatment of GA, and the pharmacodynamic mechanism may be related to SRC, MMP3, MMP9, and other targets, as well as lipids and atherosclerosis, NETs, IL-17, and other pathways.
Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Arthritis, Gouty/metabolism* ; Chromatography, High Pressure Liquid/methods* ; Humans ; Mass Spectrometry/methods* ; Protein Interaction Maps/drug effects*