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.

Animal experiments are widely used in biomedical research for safety assessment, toxicological analysis, efficacy evaluation, and mechanism exploration. In recent years, the ethical review system has become more stringent, and awareness of animal welfare has continuously increased. To promote more efficient and cost-effective drug research and development, the United States passed the Food and Drug Administration (FDA) Modernization Act 2.0 in September 2022, which removed the federal mandate requiring animal testing in preclinical drug research. In April 2025, the FDA further proposed to adopt a series of "new alternative methods" in the research and development of drugs such as monoclonal antibodies, which included artificial intelligence computing models, organoid toxicity tests, and 3D micro-physiological systems, thereby gradually phasing out traditional animal experiment models. Among these cutting-edge technologies, 3D bioprinting models are a significant alternative and complement to animal models, owing to their high biomimetic properties, reproducibility, and scalability. This review provides a comprehensive overview of advancements and applications of 3D bioprinting technology in the fields of biomedical and pharmaceutical research. It starts by detailing the essential elements of 3D bioprinting, including the selection and functional design of biomaterials, along with an explanation of the principles and characteristics of various printing strategies, highlighting the advantages in constructing complex multicellular spatial structures, regulating microenvironments, and guiding cell fate. It then discusses the typical applications of 3D bioprinting in drug research and development,including high-throughput screening of drug efficacy by constructing disease models such as tumors, infectious diseases, and rare diseases, as well as conducting drug toxicology research by building organ-specific models such as those of liver and heart. Additionally,the review examines the role of 3D bioprinting in tissue engineering, discussing its contributions to the construction of functional tissues such as bone, cartilage, skin, and blood vessels, as well as the latest progress in regeneration and replacement. Furthermore, this review analyzes the complementary advantages of 3D bioprinting models and animal models in the research of disease progression, drug mechanisms, precision medicine, drug development, and tissue regeneration, and discusses the potential and challenges of their integration in improving model accuracy and physiological relevance. In conclusion, as a cutting-edge in vitro modeling and manufacturing technology, 3D bioprinting is gradually establishing a comprehensive application system covering disease modeling, drug screening, toxicity prediction, and tissue regeneration.

Animal experiments are widely used in biomedical research for safety assessment, toxicological analysis, efficacy evaluation, and mechanism exploration. In recent years, the ethical review system has become more stringent, and awareness of animal welfare has continuously increased. To promote more efficient and cost-effective drug research and development, the United States passed the Food and Drug Administration (FDA) Modernization Act 2.0 in September 2022, which removed the federal mandate requiring animal testing in preclinical drug research. In April 2025, the FDA further proposed to adopt a series of "new alternative methods" in the research and development of drugs such as monoclonal antibodies, which included artificial intelligence computing models, organoid toxicity tests, and 3D micro-physiological systems, thereby gradually phasing out traditional animal experiment models. Among these cutting-edge technologies, 3D bioprinting models are a significant alternative and complement to animal models, owing to their high biomimetic properties, reproducibility, and scalability. This review provides a comprehensive overview of advancements and applications of 3D bioprinting technology in the fields of biomedical and pharmaceutical research. It starts by detailing the essential elements of 3D bioprinting, including the selection and functional design of biomaterials, along with an explanation of the principles and characteristics of various printing strategies, highlighting the advantages in constructing complex multicellular spatial structures, regulating microenvironments, and guiding cell fate. It then discusses the typical applications of 3D bioprinting in drug research and development,including high-throughput screening of drug efficacy by constructing disease models such as tumors, infectious diseases, and rare diseases, as well as conducting drug toxicology research by building organ-specific models such as those of liver and heart. Additionally,the review examines the role of 3D bioprinting in tissue engineering, discussing its contributions to the construction of functional tissues such as bone, cartilage, skin, and blood vessels, as well as the latest progress in regeneration and replacement. Furthermore, this review analyzes the complementary advantages of 3D bioprinting models and animal models in the research of disease progression, drug mechanisms, precision medicine, drug development, and tissue regeneration, and discusses the potential and challenges of their integration in improving model accuracy and physiological relevance. In conclusion, as a cutting-edge in vitro modeling and manufacturing technology, 3D bioprinting is gradually establishing a comprehensive application system covering disease modeling, drug screening, toxicity prediction, and tissue regeneration.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, functional impairment, and neuropsychiatric symptoms. It represents the most prevalent form of dementia among the elderly population. Accumulating evidence indicates that oxidative stress plays a pivotal role in the pathogenesis of AD. Notably, elevated levels of oxidative stress have been observed in the brains of AD patients, where excessive reactive oxygen species (ROS) can cause extensive damage to lipids, proteins, and DNA, ultimately compromising neuronal structure and function. Amyloid β‑protein (Aβ) has been shown to induce mitochondrial dysfunction and calcium overload, thereby promoting the generation of ROS. This, in turn, exacerbates Aβ aggregation and enhances tau phosphorylation, leading to the formation of two pathological features of AD: extracellular Aβ plaque deposition and intracellular neurofibrillary tangles (NFTs). These events ultimately culminate in neuronal death, forming a vicious cycle. The interplay between oxidative stress and these pathological processes constitutes a core link in the pathogenesis of AD. The signaling pathways mediating oxidative stress in AD include Nrf2, RCAN1, PP2A, CREB, Notch1, NF‑κB, ApoE, and ferroptosis. Nrf2 signaling pathway serves as a key regulator of cellular redox homeostasis, exerts important antioxidant capacity and protective effects in AD. RCAN1 signaling pathway, as a calcineurin inhibitor, and modulates AD progression through multiple mechanisms. PP2A signaling pathway is involved in regulating tau phosphorylation and neuroinflammation processes. CREB signaling pathway contributes to neuroplasticity and memory formation; activation of CREB improves cognitive function and reduce oxidative stress. Notch1 signaling pathway regulates neuronal development and memory, participates in modulation of Aβ production, and interacts with Nrf2 toco-regulate antioxidant activity. NF‑κB signaling pathway governs immune and inflammatory responses; sustained activation of this pathway forms “inflammatory memory”, thereby exacerbating AD pathology. ApoE signaling pathway is associated with lipid metabolism; among its isoforms, ApoE-ε4 significantly increases the risk of AD, leading to elevated oxidative stress, abnormal lipid metabolism, and neuroinflammation. The ferroptosis signaling pathway is driven by iron-dependent lipid peroxidation, and the subsequent release of lipid peroxidation products and ROS exacerbate oxidative stress and neuronal damage. These interconnected pathways form a complex regulatory network that regulates the progression of AD through oxidative stress and related pathological cascades. In terms of therapeutic strategies targeting oxidative stress, among the drugs currently used in clinical practice for AD treatment, memantine and donepezil demonstrate significant therapeutic efficacy and can improve the level of oxidative stress in AD patients. Some compounds with antioxidant effects (such asα-lipoic acid and melatonin) have shown certain potential in AD treatment research and can be used as dietary supplements to ameliorate AD symptoms. In addition, non-drug interventions such as calorie restriction and exercise have been proven to exerted neuroprotective effects and have a positive effect on the treatment of AD. By comprehensively utilizing the therapeutic characteristics of different signaling pathways, it is expected that more comprehensive multi-target combination therapy regimens and combined nanomolecular delivery systems will be developed in the future to bypass the blood-brain barrier, providing more effective therapeutic strategies for AD.

Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannan-binding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP. Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays. Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05). Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.

Objective To explore characteristics of clinical parameters and cytokines in patients with drug-induced liver injury(DILI)caused by different drugs and their correlation with clinical indicators. Method The study was conducted on patients who were up to Review of Uncertainties in Confidence Assessment for Medical Tests(RUCAM)scoring criteria and clinically diagnosed with DILI.Based on Chinese herbal medicine,cardiovascular drugs,non-steroidal anti-inflammatory drugs(NSAIDs),anti-infective drugs,and other drugs,patients were divided into five groups.Cytokines were measured by Luminex technology.Baseline characteristics of clinical biochemical indicators and cytokines in DILI patients and their correlation were analyzed. Results 73 patients were enrolled.Age among five groups was statistically different(P=0.032).Alanine aminotransferase(ALT)(P=0.033)and aspartate aminotransferase(AST)(P=0.007)in NSAIDs group were higher than those in chinese herbal medicine group.Interleukin-6(IL-6)and tumor necrosis factor alpha(TNF-α)in patients with Chinese herbal medicine(IL-6:P<0.001;TNF-α:P<0.001)and cardiovascular medicine(IL-6:P=0.020;TNF-α:P=0.001)were lower than those in NSAIDs group.There was a positive correlation between ALT(r=0.697,P=0.025),AST(r=0.721,P=0.019),and IL-6 in NSAIDs group. Conclusion Older age may be more prone to DILI.Patients with NSAIDs have more severe liver damage in early stages of DILI,TNF-α and IL-6 may partake the inflammatory process of DILI.