OBJECTIVE To analyze the differences in chemical components of Gardenia jasminoides before and after processing with ginger， and to evaluate the quality differences among different producing areas. METHODS Ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry was used to analyze the compositional differences of G. jasminoides before and after processing with ginger. The water content， total ash， and ethanol-soluble extract content of ginger- processed G. jasminoides were determined according to the 2020 edition of Chinese Pharmacopoeia. High performance liquid chromatography was adopted to determine the contents of genipin gentiobioside， geniposide， crocin Ⅰ and crocin Ⅱ in ginger- processed G. jasminoides. RESULTS A total of 49 chemical components were identified from raw G. jasminoides and ginger- processed G. jasminoides， including 14 flavonoids， 15 iridoids， 10 organic acids， 2 alkaloids and 8 other compounds. Among them， 42 components were detected in raw G. jasminoides， 28 in ginger-processed G. jasminoides， and 21 components were common to both. After processing with ginger， raw G. jasminoides lost 21 components （including iridoids， flavonoids， alkaloids， and others）， while 7 chemical components were added （including coumarins， organic acids， organic acid esters， and flavonoids）. For the 15 batches of ginger-processed G. jasminoides， the water content ranged from 5.64% to 7.11%， total ash from 2.92% to 4.87%， and ethanol-soluble extract from 40.61% to 58.02%. The average contents of genipin gentiobioside， geniposide， crocin Ⅰ and crocin Ⅱ were 0.108 7， 0.542 2， 0.565 0， and 0.012 5 mg/g， respectively. CONCLUSIONS After processing with ginger， G. jasminoides loses 21 components， while 7 new components are added. Differences are observed in the water content， total ash， ethanol-soluble extract， and the contents of genipin gentiobioside， geniposide， crocin Ⅰ， and crocin Ⅱ of ginger-processed G. jasminoides from different producing areas. Notably， samples from Fujian exhibit high contents of genipin gentiobioside and ethanol-soluble extract， while samples from Jiangxi have a high content of crocin Ⅰ.

ObjectiveThis paper aims to systematically reveal the effect difference and mechanisms of Zishenwan against chronic prostatitis （CP） before and after salt-processing of Anemarrhenae rhizoma and Phellodendri chinensis cortex based on an integrated strategy of ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry （UPLC-Q-Orbitrap-MS/MS）， network pharmacology， and serum metabolomics. MethodsZishenwan samples before and after salt-processing of Anemarrhenae rhizoma and Phellodendri chinensis cortex were extracted by alcohol-water dual extraction. The chemical components of each sample were detected by UPLC-Q-Orbitrap-MS/MS， and differential components were screened by multivariate statistical analysis. Network pharmacology analysis was performed based on the identified chemical components of Zishenwan to construct a protein-protein interaction （PPI） network of "component， target， and pathway"， and the core components， targets， and pathways of Zishenwan against CP were screened. Forty-two male Sprague-Dawley （SD） rats were randomly divided into a blank group， a model group， a Qianliekang group （1.54 g·kg^-1）， low- and high-dose raw Zishenwan groups （1.8， 5.4 g·kg^-1）， and low- and high-dose salt-processed Zishenwan groups （1.8， 5.4 g·kg^-1）. The CP rat model was established by intraprostatic injection of carrageenan. After one week of recovery， the rats were administered the corresponding drugs for 21 days， while those in the blank group and model group received the same volume of normal saline. After the experiment， serum and tissue samples were collected to evaluate pharmacodynamic indicators including organ indices， histopathology， and inflammatory factors in serum. Subsequently， untargeted serum metabolomics technology was used to analyze metabolite changes and perform pathway enrichment analysis. The network pharmacology was used to construct a network of "differential metabolite， reaction， enzyme， and gene". ResultsA total of 76 components were identified in raw and salt-processed Zishenwan， and 34 differential components were screened by multivariate statistical analysis. Among them， the contents of 14 components， including berberine， berberrubine， and phellodendrine， increased after salt-processing， while the contents of 20 components， such as neomangiferin， decreased. The 28 active components and 185 potential targets were screened out by network pharmacology. The core components included berberine， phellodendrine， magnoflorine， and jatrorrhizine， and the core targets included signal transducer and activator of transcription 3 （STAT3）， protein kinase B1 （Akt1）， and transcription factor AP-1 （JUN）. These targets were significantly enriched in pro-inflammatory signaling pathways such as phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） and mitogen-activated protein kinase （MAPK）. Compared with the model group， all Zishenwan administration groups showed decreased prostate index， reduced levels of interleukin （IL）-1β， IL-18， and B-cell lymphoma-2 （Bcl-2） in serum （P<0.05， P<0.01）， as well as varying degrees of alleviation in histopathological damage. At the same dose， compared with the raw Zishenwan groups， the salt-processed Zishenwan groups showed lower prostate index， pathological scores， and IL-1β， IL-18， and Bcl-2 levels in serum， but the differences were not statistically significant. Metabolomics reveals that 38 differential metabolites were reversed after salt-processed Zishenwan administration. Both raw and salt-processed Zishenwan regulated pathways such as β-alanine metabolism and tryptophan metabolism. In addition to the common regulated pathways， the salt-processed group specifically regulated pantothenate and coenzyme A biosynthesis， pyrimidine metabolism， and arginine and proline metabolism. The intersecting pathways between network pharmacology and metabolomics were tryptophan metabolism and arginine and proline metabolism， with overlapping targets including monoamine oxidase A （MAOA） and arginase 1 （ARG1）. ConclusionThe increased contents of components such as berberine and phellodendrine in salt-processed Zishenwan may enhance its therapeutic effect on CP by inhibiting the PI3K/Akt and MAPK signaling pathways， along with multi-target regulation of tryptophan， arginine， and pantothenate metabolism pathways to comprehensively regulate inflammatory and immune responses.

Background: and Purpose Accurately predicting long-term functional outcomes of basilar artery occlusion (BAO) remains challenging. We compared the predictive performance of the baseline, 24-hour, and 72-hour National Institutes of Health Stroke Scale (NIHSS) scores for 90-day BAO functional outcomes using the Acute Basilar Artery Occlusion: Endovascular Thrombectomy versus Standard Medical Treatment (ATTENTION) trial data. We identified the optimal assessment time point, determined treatment-specific NIHSS cutoff values, and explored the role of early neurological function in treatment effects. Methods: This retrospective post hoc analysis included 324 patients with acute BAO with baseline NIHSS scores ≥10 and complete NIHSS assessments at each time point. The primary outcome was a favorable 90-day functional outcome (modified Rankin Scale score, 0–3). Receiver operating characteristic curve analysis was used to assess the predictive ability of NIHSS scores. The optimal 72-hour NIHSS predictive cutoff values were determined for the endovascular treatment (EVT) and best medical management (BMM) subgroups. Results: The 72-hour NIHSS score showed the highest predictive accuracy for the primary outcome (area under the receiver operating characteristic curve [AUC]: 0.954), outperforming the 24-hour (AUC: 0.903) and baseline (AUC: 0.688) scores; its optimal predictive cut-off value was ≤11 in the EVT group (sensitivity: 85.6%, specificity: 92.9%, positive predictive value [PPV]: 91.8%, negative predictive value [NPV]: 87.4%) and ≤9 in the BMM group (sensitivity: 84.6%, specificity: 95.1%, PPV: 84.6%, NPV: 95.1%). Conclusions The 72-hour NIHSS score outperformed the baseline and 24-hour scores in predicting 90-day functional outcomes and mediating the effects of EVT. Treatment-specific 72-hour NIHSS cut-off values may guide early risk stratification and prognostic assessments.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

Objective To explore the feasibility and image quality of respiratory-gated total-body ¹⁸F-FDG PET/CT imaging with a 4-minute acquisition in patients with malignant tumors. Methods Sixty-two patients with pathologically confirmed malignant tumors (118 lesions measured) who underwent total-body ¹⁸F-FDG PET/CT examination were consecutively enrolled at the Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, from September 23, 2021 to November 23, 2021, and their clinical and imaging data were retrospectively collected. PET data was acquired over 12 minutes with simultaneous respiratory waveform recorded. The data were reconstructed into four image sets: gated-12 group and gated-4 group (using all counts and the first 4-minute counts, respectively) and ungated-12 group and ungated-6 group (using all counts and the first 6-minute counts, respectively) reconstructed with respiratory gating that retained counts corresponding to the half respiratory window near end-expiration. Image quality assessment and quantitative analysis of lesions were performed and compared among the four image sets, and subgroup analyses were performed according to lesion location, size, and metabolic activity. Results The effective counts of 4 sets of PET images were highest in ungated-12 group, followed by ungated-6 group and gated-12 group, and lowest in gated-4 group (P＜0.001). Regardless of the metabolic level and size of the lesions, compared with the ungated-12 group and the ungated-6 group, the lesions in the gated-12 group showed higher maximum, average, and peak standardized uptake values (SUV_max, SUV_mean, and SUV_peak), tumor liver uptake ratio, tumor blood pool uptake ratio, and smaller metabolic volume (P＜0.05); There was no statistically significant difference in the above indicators between the gate-12 group and the gate-4 group. The background noise of gate-4 group images is slightly higher than that of gate-12 group images (Liver SUV_SD: [0.17＋0.05] vs [0.14＋0.04]); Blood pool SUV_SD: (0.08 ± 0.03) vs (0.06 ± 0.03). There was a statistically significant difference in subjective scores of PET image quality among the four groups (P＜0.001). The subjective scores of image quality in the gated-12 group (4.79 ± 0.41) were not significantly different from those in the non gated-12 group (4.97 ± 0.17) and non gated-6 group (4.79 ± 0.45), but were higher than those in the gated-4 group ([4.17 ± 0.45], P＜0.001)， And the scores have good consistency (Kappa＞0.75). Conclusion In total-body PET/CT imaging for malignant tumors, 4-minute respiratory-gated acquisition can achieve comparable quantitative performance to longer respiratory gating imaging while significantly shortening acquisition time. This approach demonstrates promising potential for clinical application.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Mass spectrometry imaging （MSI） is an emerging molecular imaging technique that combines high spatial resolution and the ability to acquire molecular information. This technology can directly obtain spatial distribution maps of metabolites, proteins, lipids and other molecules in tumor tissues, reveal the abnormal molecular changes during the occurrence and development of liver cancer, and make up for the deficiency of information at the molecular level in traditional imaging （such as CT, MRI）. In liver cancer research, MSI has demonstrated unique advantages and application potential in early diagnosis and screening, precise classification, tumor heterogeneity analysis, and exploration of molecular mechanisms. The principle characteristics of MSI technology and its latest progress in the early diagnosis, classification and molecular mechanism research of liver cancer were reviewed, and the current challenges and future development directions were discussed, which aimed to provide a new perspective and theoretical basis for an in-depth understanding of the pathological mechanism of liver cancer and promoted its precise diagnosis and treatment.

BackgroundIndividuals with chronic multimorbidity exhibit a high prevalence of sleep disturbances， which impair their physical and psychological well-being and impede chronic disease management. Although sleep quality is closely influencing with psychological functioning， empirical evidence regarding the longitudinal trajectories of sleep quality and their influencing factors in this population remains insufficient. ObjectiveTo investigate the longitudinal trajectories of sleep quality and associated factors among patients with chronic multimorbidity from hospitalization through 6 months post-discharge， aiming to facilitate the early identification of high-risk individuals and the development of personalized sleep interventions. MethodsA total of 228 inpatients with chronic multimorbidity， primarily with cardiovascular and metabolic chronic conditions， were enrolled via random sampling at The Third People's Hospital of Fuyang from January 2023 to September 2024. Baseline assessments were conducted during hospitalization using the general socio-demographic questionnaire， the Sleep Dysfunction Rating Scale （SDRS）， the Ruminative Response Scale （RRS）， and the Hospital Anxiety and Depression Scale （HADS）. Sleep quality was further evaluated using the SDRS at 1-， 3-， and 6-month post-discharge. Latent class growth model was employed to delineate trajectories of sleep quality. Multivariate logistic regression analysis was subsequently performed to identify influencing factors of sleep quality. ResultsA total of 201 patients with chronic diseases were included.Based on four predefined assessment time points （baseline， 1 month， 3 months， 6 months post-discharge）， the latent class growth model identified three distinct trajectories of sleep quality： persistent high sleep disturbance （n=72， 35.82%）， moderate sleep disturbance-progression （n=73， 36.32%）， and low sleep disturbance （n=56， 27.86%）. Multivariate logistic regression analysis revealed that， compared with the low sleep disturbance group， multiple factors increased the odds of membership in the persistent high sleep disturbance group： per capita monthly household income of <3 000 yuan （β=13.131， P<0.01） or 3 000–5 000 yuan （β=5.913， P<0.05）， aged 45–65 years （β=9.536， P<0.05）， presence of 3 （β=7.792， P<0.05） or >3 comorbid chronic conditions （β=6.626， P<0.05）， as well as higher RRS （β=0.334， P<0.01） and HADS score （β=1.628， P<0.01）. Additionally， patients aged 45–65 years （β=2.777， P<0.05）， with 3 （β=3.802， P<0.01） or >3 comorbid chronic conditions （β=2.463， P<0.01）， and with higher RRS （β=0.111， P<0.01） and HDAS scores （β=0.350， P<0.05） also demonstrated significantly increased odds of belonging to the moderate sleep disturbance–progression group. ConclusionThere is significant population heterogeneity in sleep quality trajectories among patients with chronic multimorbidity， with age， per capita household monthly income， number of comorbidities， rumination， and anxiety/depression symptoms being identify as the primary influencing factors.