ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

ObjectiveTo investigate the effects of Maxing Kugan decoction （MKD） on inflammatory response and apoptosis in rats with oleic acid （OA）-induced acute lung injury （ALI） and explore its mechanism of action. MethodsSixty Sprague-Dawley （SD） rats were randomly assigned into six groups： a control group， a model group， a dexamethasone-treated group （2 mg·kg^-1）， and three MKD-treated groups at low， medium， and high doses （3.1， 6.2，12.4 g·kg^-1）. Each group was administered either an equivalent volume of normal saline or the corresponding concentration of MKD by gavage for seven consecutive days. The model group and each administration group were used to establish the ALI model by tail vein injection of OA （0.2 mL·kg^-1）. Twelve hours after modeling， blood gas analyses were conducted， and the wet-to-dry （W/D） weight ratio of lung tissue was measured for each group. Additionally， enzyme-linked immunosorbent assay （ELISA） was employed to quantify the levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the bronchoalveolar lavage fluid （BALF） of the rats. Cell damage and apoptosis in lung tissue were examined via hematoxylin-eosin （HE） staining and TdT-mediated dUTP-biotin nick end labeling （TUNEL） assays， and the results were subsequently scored. The expression levels of the p38 mitogen-activated protein kinase （p38 MAPK）/nuclear factor kappa-B （NF-κB） signaling pathway and apoptosis-related proteins and mRNAs were assessed using Western blot and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the control group， the model group exhibited a significant decrease in partial pressure of oxygen （PaO₂）， blood oxygen saturation （SaO₂）， and oxygenation index （PaO₂/FiO₂）， along with a marked increase in partial pressure of carbon dioxide （PaCO₂） and lung W/D ratio （P<0.01）. Additionally， levels of TNF-α， IL-6， and IL-1β in BALF were significantly elevated （P<0.01）. Histopathological analysis of lung tissue showed significant inflammatory infiltration， tissue edema， alveolar septal thickening， and apoptosis of lung tissue. Pronounced increases were observed in the mRNA expression levels of p38 MAPK， NF-κB p65， inhibitor of NF-κB （IκBα）， B-cell lymphoma-2 associated x protein （Bax）， and Caspases-3， as well as the protein expression levels of p-p38 MAPK， p-NF-κB p65， p-IκBα， Bax， Caspases-3， and cleaved Caspases-3， while the mRNA and protein expression of Bcl-2 was downregulated （P<0.01）. Compared with the model group， MKD significantly elevated PaO₂， SaO₂， and PaO₂/FiO₂ while reducing PaCO₂ and W/D ratio in rats （P<0.01）. It also greatly reduced TNF-α， IL-6， and IL-1β levels in BALF （P<0.01） and alleviated inflammatory infiltration， tissue edema， alveolar septal thickening， and apoptosis of lung tissue. Additionally， it downregulated the mRNA expression of p38 MAPK， NF-κB p65， IκBα， Bax， Caspases-3， as well as protein expression of p-p38 MAPK， p-NF-κB p65， p-IκBα， Bax， Caspases-3， and cleaved Caspases-3 in lung tissue （P<0.05， P<0.01）， while significantly upregulating mRNA and protein expression of Bcl-2 （P<0.01）. ConclusionMKD exerts a protective effect on OA-induced ALI rats， potentially through the regulation of the p38 MAPK/NF-κB signaling pathway to inhibit inflammation and apoptosis.

ObjectiveTo investigate the effects of Maxing Kugan decoction （MKD） on inflammatory response and apoptosis in rats with oleic acid （OA）-induced acute lung injury （ALI） and explore its mechanism of action. MethodsSixty Sprague-Dawley （SD） rats were randomly assigned into six groups： a control group， a model group， a dexamethasone-treated group （2 mg·kg^-1）， and three MKD-treated groups at low， medium， and high doses （3.1， 6.2，12.4 g·kg^-1）. Each group was administered either an equivalent volume of normal saline or the corresponding concentration of MKD by gavage for seven consecutive days. The model group and each administration group were used to establish the ALI model by tail vein injection of OA （0.2 mL·kg^-1）. Twelve hours after modeling， blood gas analyses were conducted， and the wet-to-dry （W/D） weight ratio of lung tissue was measured for each group. Additionally， enzyme-linked immunosorbent assay （ELISA） was employed to quantify the levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the bronchoalveolar lavage fluid （BALF） of the rats. Cell damage and apoptosis in lung tissue were examined via hematoxylin-eosin （HE） staining and TdT-mediated dUTP-biotin nick end labeling （TUNEL） assays， and the results were subsequently scored. The expression levels of the p38 mitogen-activated protein kinase （p38 MAPK）/nuclear factor kappa-B （NF-κB） signaling pathway and apoptosis-related proteins and mRNAs were assessed using Western blot and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the control group， the model group exhibited a significant decrease in partial pressure of oxygen （PaO₂）， blood oxygen saturation （SaO₂）， and oxygenation index （PaO₂/FiO₂）， along with a marked increase in partial pressure of carbon dioxide （PaCO₂） and lung W/D ratio （P<0.01）. Additionally， levels of TNF-α， IL-6， and IL-1β in BALF were significantly elevated （P<0.01）. Histopathological analysis of lung tissue showed significant inflammatory infiltration， tissue edema， alveolar septal thickening， and apoptosis of lung tissue. Pronounced increases were observed in the mRNA expression levels of p38 MAPK， NF-κB p65， inhibitor of NF-κB （IκBα）， B-cell lymphoma-2 associated x protein （Bax）， and Caspases-3， as well as the protein expression levels of p-p38 MAPK， p-NF-κB p65， p-IκBα， Bax， Caspases-3， and cleaved Caspases-3， while the mRNA and protein expression of Bcl-2 was downregulated （P<0.01）. Compared with the model group， MKD significantly elevated PaO₂， SaO₂， and PaO₂/FiO₂ while reducing PaCO₂ and W/D ratio in rats （P<0.01）. It also greatly reduced TNF-α， IL-6， and IL-1β levels in BALF （P<0.01） and alleviated inflammatory infiltration， tissue edema， alveolar septal thickening， and apoptosis of lung tissue. Additionally， it downregulated the mRNA expression of p38 MAPK， NF-κB p65， IκBα， Bax， Caspases-3， as well as protein expression of p-p38 MAPK， p-NF-κB p65， p-IκBα， Bax， Caspases-3， and cleaved Caspases-3 in lung tissue （P<0.05， P<0.01）， while significantly upregulating mRNA and protein expression of Bcl-2 （P<0.01）. ConclusionMKD exerts a protective effect on OA-induced ALI rats， potentially through the regulation of the p38 MAPK/NF-κB signaling pathway to inhibit inflammation and apoptosis.

Platelet transfusion refractoriness (PTR) is a common issue among patients with hematological diseases and tumors. This article reviews the diagnostic criteria, influencing factors, and recent prevention and management strategies for immune PTR. The diagnostic criteria typically involve post-transfusion platelet increment (PI), platelet recovery rate (PPR), and corrected count increment (CCI). Both immune and non-immune factors can lead to PTR, with immune factors mainly including HLA and HPA antibodies. Prevention and management strategies include the use of leukocyte-reduced platelets, HLA and HPA antigen-matched platelets, intravenous immunoglobulin therapy, and immunosuppressive strategies. Although various strategies have been proposed and applied in clinical practice, the prevention and management of immune PTR remain challenging. Future research needs to explore more effective individualized treatment strategies, while also considering the potential application of emerging technologies such as nanotechnology in the field of transfusion.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

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.

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.

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.