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.

Sleep deprivation (SD) has emerged as a significant modifiable risk factor for Alzheimer’s disease (AD), with mounting evidence demonstrating its multifaceted role in accelerating AD pathogenesis through diverse molecular, cellular, and systemic mechanisms. SD is refined within the broader spectrum of sleep-wake and circadian disruption, emphasizing that both acute total sleep loss and chronic sleep restriction destabilize the homeostatic and circadian processes governing glymphatic clearance of neurotoxic proteins. During normal sleep, concentrations of interstitial Aβ and tau fall as cerebrospinal fluid oscillations flush extracellular waste; SD abolishes this rhythm, causing overnight rises in soluble Aβ and tau species in rodent hippocampus and human CSF. Orexinergic neurons sustain arousal, and become hyperactive under SD, further delaying sleep onset and amplifying Aβ production. At the molecular level, SD disrupts Aβ homeostasis through multiple converging pathways, including enhanced production via beta-site APP cleaving enzyme 1 (BACE1) upregulation, coupled with impaired clearance mechanisms involving the glymphatic system dysfunction and reduced Aβ-degrading enzymes (neprilysin and insulin-degrading enzyme). Cellular and histological analyses revealed that these proteinopathies are significantly exacerbated by SD-induced neuroinflammatory cascades characterized by microglial overactivation, astrocyte reactivity, and sustained elevation of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) through NF‑κB signaling and NLRP3 inflammasome activation, creating a self-perpetuating cycle of neurotoxicity. The synaptic and neuronal consequences of chronic SD are particularly profound and potentially irreversible, featuring reduced expression of critical synaptic markers (PSD95, synaptophysin), impaired long-term potentiation (LTP), dendritic spine loss, and diminished neurotrophic support, especially brain-derived neurotrophic factor (BDNF) depletion, which collectively contribute to progressive cognitive decline and memory deficits. Mechanistic investigations identify three core pathways through which SD exerts its neurodegenerative effects: circadian rhythm disruption via BMAL1 suppression, orexin system hyperactivity leading to sustained wakefulness and metabolic stress, and oxidative stress accumulation through mitochondrial dysfunction and reactive oxygen species overproduction. The review critically evaluates promising therapeutic interventions including pharmacological approaches (melatonin, dual orexin receptor antagonists), metabolic strategies (ketogenic diets, and Mediterranean diets rich in omega-3 fatty acids), lifestyle modifications (targeted exercise regimens, cognitive behavioral therapy for insomnia), and emerging technologies (non-invasive photobiomodulation, transcranial magnetic stimulation). Current research limitations include insufficient understanding of dose-response relationships between SD duration/intensity and AD pathology progression, lack of long-term longitudinal clinical data in genetically vulnerable populations (particularly APOE ε4 carriers and those with familial AD mutations), the absence of standardized SD protocols across experimental models that accurately mimic human chronic sleep restriction patterns, and limited investigation of sex differences in SD-induced AD risk. The accumulated evidence underscores the importance of addressing sleep disturbances as part of multimodal AD prevention strategies and highlights the urgent need for clinical trials evaluating sleep-focused interventions in at-risk populations. The review proposes future directions focused on translating mechanistic insights into precision medicine approaches, emphasizing the need for biomarkers to identify SD-vulnerable individuals, chronotherapeutic strategies aligned with circadian biology, and multi-omics integration across sleep, proteostasis and immune profiles may delineate precision-medicine strategies for at-risk populations. By systematically examining these critical connections, this analysis positions sleep quality optimization as a viable strategy for AD prevention and early intervention while providing a comprehensive roadmap for future mechanistic and interventional research in this rapidly evolving field.

Aim To analyze the anti-A549 and HI299 lung ade-nocarcinoma activities via using examples of baicalin, astragalo-side, hesperidin and cisplatin based on real time cellular analysis (RTCA) technology, and to build a new strategy for EC50 e-valuation reflecting the time-dimensional characteristic. Methods Using RTCA Software Pro for data analysis and GraphPad Prism and Origin Pro plotting, the in vitro anti-A549 and H1299 lung adenocarcinoma activities of baicalin, astragaloside, hesperidin, and cisplatin were characterized using the endpoint method and time dimension, respectively. Results (X) There were significant differences in EC50 values of A549 and H1299 cells at 24 h and 48 h endpoint methods. (2) The correlation coefficient of the curve fitted with the four-parameter equation was > 0. 9, and the dynamic change of EC50 remained relatively stable (the linear fitting of EC50 at adjacent 4 points I slope 1^1) used to calculate the EC50 value within this time dimension. The EC50 of baicalin, astragaloside, hesperidin and cisplatin on A549 cells was 52. 97 ±1.75 плпо! • L~1(16~48 h) , 62.88 ± 2.91 ijunol • L"1 (32.25 -48 h) , 78.84 ±0.33 плпо1 • L"1 (21.5 -29.75 h), 13.57 ±1.54 плпо1 • L_1(27.5 -48 h), respectively; the EC50 of baicalin, astragaloside, hesperidin and cisplatin on H1299 cells was 43. 71 ± 1. 26 |лто1 • L_1 ( 19. 5 -48 h), 47.23 ±1. 19 |лто1 • L_1(14 -48 h) , 39.45 ±0.24 плпо1 • L"1 (12.75 -46.25 h), 25.97 ±4.76 плпо1 • L"1 (10. 25 -48 h) , respectively. The results showed that the time window for the anti-tumor effect of the test solution/drug was different. Conclusions Based on RTCA technology, it is more accurate and reasonable to select EC50 data that exhibit better fitting, stable changes, and time-dimensional characteristics for the evaluation of anti-tumor activity. In addition, this method of distinguishing different effective time of antitumor drugs can provide a reference for the timing of clinical combination drugs, and this approach will also provide a reference for further related studies.

Objective To study the activity of ten kinds of antipyretic-antidotal traditional Chinese medicine(TCM),including radix tinosporae.herb of blin conyza and turmeric,against extensively drug-resistant Acineto-bacter baumannii(XDR-AB)infection,screen out the extracts of antipyretic-antidotal TCM which have in vivo anti-infection activity,provide a research basis for the discovery of novel antimicrobials against XD-RAB infection.Methods Ten antipyretic-antidotal TCM were extracted with water,50％ethanol and 95％ethanol respectively,and TCM extracts with different concentrations were prepared,which were co-incubated with the model of XDR-AB-infected Caenorhabditis elegans previously optimized by the research group.The in vivo activity of antipyretic-antidotal TCM against XDR-AB infection was judged through the survival rate of Caenorhabditis elegans.Results With the increase of concentration of turmeric and cortex pseudolaricis extracts,the survival rate of XDR-AB-infec-ted nematodes continued to improve.The water extract,50％ethanol extract,and 95％ethanol extract of turmeric at a concentration of 1 000 μg/mL could increase the survival rates of XDR-AB-infected Caenorhabditis elegans to 54.2％(compared to the negative control group,P＜0.001),18.8％,and 13.3％,respectively.The water ex-tract,50％ethanol extract,and 95％ethanol extract of cortex pseudolaricis at a concentration of 1 000 μg/mL could increase the survival rates of XDR-AB-infected Caenorhabditis elegans to 47.4％(compared to the negative control group,P＜0.001),23.8％,and 15.8％,respectively.Conclusion The water extracts of turmeric and cortex pseudolaricis have good activity against XDR-AB infection,and their main chemical components can be tested for in vitro antimicrobial efficacy to discover novel antimicrobial agents against XDR-AB infection.

Objective To explore the predictive role of the triglyceride-glucose(TyG)index in patients with acute ischemic stroke(AIS)treated with alteplase thrombolysis and create a comprehensive predictive model integrating multiple factors for assessing patient out-comes.Methods The clinical data of 302 patients with AIS undergoing alteplase intravenous thrombolysis at the Neurology Department of Fushun Central Hospital from January 2019 to October 2022 were retrospectively analyzed.The patients were categorized into a good prognosis group(n= 193)and a poor prognosis group(n= 109)based on their mRS scores at 90 days post-thrombolysis.Univariate and multivariate logistic regression analyses were employed to identify risk factors influencing adverse outcomes and to establish a predictive model.The predictive performance of the model was assessed using receiver operating characteristic(ROC)curve analysis.Results The results of the multivariate logistic regression analysis revealed that pre-thrombolysis high NIHSS score and TyG index≥9.37 were inde-pendent risk factors for unfavorable prognosis in AIS patients.A predictive model for AIS patient prognosis was successfully established:Logit(Y)=-17.167 + 1.681×TyG index+0.147×pre-thrombolysis NIHSS score.The optimal cutoff value for the TyG index was 9.37.The ROC areas under the curve for predicting unfavorable prognosis in AIS patients at 90 days post-thrombolysis were 0.713 for TyG index,0.705 for pre-thrombolysis NIHSS score,and 0.787 for the combined variable(Y),with the combined variable(Y)exhibiting the largest ROC curve area.Conclusion TyG index≥9.37 and pre-thrombolysis high NIHSS score are independent risk factors for poor prognosis.The combined variable the combined variable(Y)has higher predictive efficiency than the separate variables.