ObjectiveTo investigate the mechanisms by which the combination of berberine （BBR） and baicalin （BAI） ameliorates type 2 diabetes mellitus （T2DM） due to internal accumulation of dampness-heat from the perspectives of gut microbiota and metabolomics. MethodsAntibiotics were used to induce pseudo-sterile mice. Thirty pseudo-sterile mice were randomized into a normal fecal microbiota transplantation group （n=10） and a T2DM （syndrome of internal accumulation of dampness-heat） fecal microbiota transplantation group （n=20）. The mice were then administrated with suspensions of fecal microbiota from healthy volunteers and a patient with T2DM due to internal accumulation of dampness-heat by gavage， respectively. Each mouse received 200 µL suspension every other day for a total of 15 times to reshape the gut microbiota. The T2DM model mice were then assigned into a model group （n=8） and a BBR-BAI group （n=11）. BBR was administrated at a dose of 200 mg·kg^-1， and BAI was administrated in a ratio of BBR-BAI 10∶1 based on preliminary research findings. The administration lasted for 8 consecutive weeks. Fasting blood glucose （FBG）， glycated hemoglobin （HbA1c）， insulin （INS）， triglycerides （TG）， total cholesterol （CHOL）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， and alanine aminotransferase （ALT） levels were measured to evaluate the effects of the BBR-BAI combination on glucose and lipid metabolism and liver function in T2DM mice. Hematoxylin-eosin staining was employed to observe pathological changes in the colon tissue. The expression of claudin-1， zonula occludens-1 （ZO-1）， and occludin in the colon tissue was determined by Western blot. Real-time quantitative polymerase chain reaction（Real-time PCR） was employed to assess the levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the colon tissue. The fecal microbiota composition and differential metabolites were analyzed by 16S rRNA sequencing and ultra-high performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry （UPLC-Q-TOF-MS）， respectively. ResultsThe BBR-BAI combination lowered the FBG， HbA1c， and INS levels （P<0.05， P<0.01） and alleviated insulin resistance （P<0.01） in T2DM mice. Additionally， BBR-BAI elevated the levels of ZO-1， occludin， and claudin-1 （P<0.05， P<0.01） and down-regulated the expression levels of TNF-α， IL-1β， and IL-6 in the colon （P<0.05， P<0.01）. The results of 16S rRNA sequencing showed that BBR-BAI increased the relative abundance of Ligilactobacillus， Phascolarctobacterium， and Akkermansia （P<0.05）， while significantly decreasing the relative abundance of Alistipes， Odoribacter， and Colidextribacter （P<0.05）. UPLC-Q-TOF-MS identified 28 differential metabolites， which were primarily involved in arachidonic acid metabolism and α-linolenic acid metabolism. ConclusionBBR-BAI can ameliorate T2DM due to internal accumulation of dampness-heat by modulating the relative abundance of various bacterial genera in the gut microbiota and the expression of fecal metabolites.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Sepsis is a systemic inflammatory response caused by severe infection or trauma, and is one of the common causes of acute lung injury(ALI) and acute respiratory distress syndrome(ARDS). Sepsis-acute lung injury(SALI) is a critical clinical condition with high morbidity and mortality. Its pathogenesis is complex and not yet fully understood, and there is currently a lack of targeted and effective treatment options. Pyroptosis, a novel form of programmed cell death, plays a key role in the pathological process of SALI by activating inflammasomes and releasing inflammatory factors, making it a potential therapeutic target. In recent years, the role of traditional Chinese medicine(TCM) in regulating signaling pathways related to pyroptosis through multi-components and multi-targets has attracted increasing attention. TCM may intervene in pyroptosis by inhibiting the activation of NLRP3 inflammasomes and regulating the expression of Caspase family proteins, thus alleviating inflammatory damage in lung tissues. This paper systematically reviews the molecular regulatory network of pyroptosis in SALI and explores the potential mechanisms and research progress on TCM intervention in cellular pyroptosis. The aim is to provide new ideas and theoretical support for basic research and clinical treatment strategies of TCM in SALI.
Pyroptosis/drug effects* ; Humans ; Sepsis/genetics* ; Acute Lung Injury/physiopathology* ; Animals ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Inflammasomes/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics*

OBJECTIVE: Left ventricular remodeling induced by myocardial ischemia/reperfusion injury (MI/RI) is a common cardiac dysfunction. Accumulating evidence has demonstrated that autophagy plays a vital role in protecting against ventricular remodeling. This study aims to investigate the performance of Compound Danshen Tablets (CDT) in rescuing ventricular remodeling and whether autophagy as the potential mechanism. METHODS: The left anterior descending arteries of rats were temporarily ligated for 30 min to construct the MI/RI model. Ventricular remodeling was induced by reperfusion for 28 d, during which the MI/RI rats were administered CDT (300 mg/kg and 600 mg/kg), atorvastatin (2 mg/kg), and diltiazem (16 mg/kg). Cardiac function and structure were examined by echocardiography. Immunohistochemistry, Masson's trichrome staining, and hematoxylin-eosin (HE) staining were utilized to assess the fibrosis and histological alterations in the heart tissue. The expression of autophagy-related proteins was detected using Western blotting. RESULTS: CDT attenuated the cardiac dysfunction, structural changes, histopathological changes and fibrosis induced by MI/RI. CDT significantly enhanced the level of Beclin1 and microtubule-associated protein 1 light chain 3 beta (LC3β), and reduced p62 levels in MI/RI rats. Moreover, CDT significantly increased the phosphorylation of adenosine monophosphate-activated protein kinase (AMPK) and inhibited mammalian target of rapamycin (mTOR) phosphorylation. CONCLUSION CDT ameliorated MI/RI-induced ventricular remodeling by activating autophagy and improving autophagic flux via the AMPK/mTOR signaling pathway.

Acute respiratory distress syndrome (ARDS) is a common respiratory emergency, but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures. Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS, but the application of hydrogen has flammable and explosive safety concerns. Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance, thus improving ARDS in patients and animal models. Coral calcium hydrogenation (CCH) is a new solid molecular hydrogen carrier prepared from coral calcium (CC). Whether and how CCH affects acute lung injury in ARDS remains unstudied. In this study, we observed the therapeutic effect of CCH on lipopolysaccharide (LPS) induced acute lung injury in ARDS mice. The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable, demonstrating a significant improvement compared to the untreated ARDS model group. CCH treatment significantly reduced pulmonary hemorrhage and edema, and improved pulmonary function and local microcirculation in ARDS mice. CCH promoted mitochondrial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2 (Trx2), improved lung mitochondrial dysfunction induced by LPS, and reduced oxidative stress damage. The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.