Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide. Despite therapeutic advancements over recent decades, the prognosis for patients with metastatic CRC (mCRC) remains poor. Approximately 2%-4% of mCRC cases exhibit human epidermal growth factor receptor 2 (HER2) amplification or overexpression, defining a distinct molecular subtype. This HER2-positive status is strongly associated with primary resistance to anti-epidermal growth factor receptor (EGFR) therapies, which are the standard of care for patients with RAS wild-type tumors. Beyond its well-established role in breast and gastric cancers, HER2 has emerged as a pivotal biomarker and actionable therapeutic target in mCRC. However, selecting appropriate treatment strategies remains challenging due to patient heterogeneity and diverse molecular subtypes. This review systematically summarizes the molecular biology, diagnostic strategies, and advances in targeted therapies for HER2-positive mCRC. On the diagnostic front, we discuss the applications of immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), next-generation sequencing (NGS), and circulating tumor DNA (ctDNA) detection technologies. We highlight discrepancies in diagnostic criteria across key clinical trials—such as HERACLES, DESTINY, and MOUNTAINEER—underscoring the urgent need for standardized, CRC-specific definitions to ensure consistent patient selection and comparability of efficacy data across studies. Although NGS enables comprehensive genomic profiling, its cost-effectiveness relative to traditional methods must be carefully considered. Therapeutically, we summarize clinical trial data for HER2-directed agents, including tyrosine kinase inhibitors (TKIs) such as tucatinib and lapatinib, monoclonal antibodies like trastuzumab, bispecific antibodies, and antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan. We review dual-targeting strategies and note recent FDA approvals that represent significant milestones in second-line treatment. Additionally, we explore the potential of combining immune checkpoint inhibitors with HER2-targeted therapies to enhance antitumor immunity through mechanisms including antibody-dependent cellular cytotoxicity (ADCC) and modulation of the tumor microenvironment. ADCs enable precise delivery of cytotoxic payloads, reducing off-target toxicity while effectively inhibiting oncogenic pathways. A substantial portion of this review is dedicated to dissecting the molecular mechanisms underlying primary and acquired resistance to HER2-targeted therapies—persistent challenges that limit clinical benefit. These mechanisms include reactivation of downstream signaling pathways such as PI3K/AKT/mTOR and MAPK, concurrent mutations in genes like KRAS or BRAF, and alterations in HER2 expression that compromise treatment efficacy. For instance, specific HER2 mutations (e.g., L755S) can reduce drug binding affinity, while ctDNA monitoring facilitates early detection of emerging resistance clones during disease progression, thereby enabling timely therapeutic adjustments. Tumor heterogeneity and dynamic interactions with the microenvironment further complicate resistance patterns observed in clinical practice. HER2-targeted therapy represents a new frontier in precision oncology for mCRC, offering renewed hope for improving patient outcomes. Realizing this potential will require continued optimization of diagnostic algorithms and treatment workflows. Future efforts must focus on overcoming resistance, validating liquid biopsy approaches for dynamic monitoring, and establishing unified clinical guidelines. HER2 has become an essential biomarker for stratifying mCRC patients beyond traditional RAS and BRAF status, underscoring the shift from empiric treatment to biomarker-driven precision medicine. International, multidisciplinary collaboration will be critical to validate emerging biomarkers and refine treatment algorithms globally.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Oral solid dosage forms require processes such as disintegration and dissolution to release the drug before it can be absorbed and utilized by the body. In this manuscript, imaging technology was used to continuously visualize and characterize the in vitro static drug release process of gliclazide modified release tablets from 15 manufacturers, combined with the traditional method of in vitro dissolution testing, to determine the release profile of gliclazide modified release tablets, to evaluate the similarity of the release profiles by using the similarity factor (f₂) method and based on the analysis of the release profiles fitted with a variety of mathematical models. The results indicate that the gliclazide modified release tablets produced by 14 companies are hydrophilic gel matrix tablets. Compared to the reference listed drug, the release profiles of formulations from 11 companies show high similarity (f₂ > 50) to the reference. Among these, formulations with visual characteristics similar to the reference exhibit similar release curves. This study provides an alternative method for the in vitro consistency evaluation of gliclazide modified release tablets, aiming to assess the in vitro release behaviour of generic formulations more accurately and comprehensively.

Objective To investigate the practical effects of pediatric rapid sequence intubation(RSI)nursing coordination training based on the LSPPDM(learn,see,practice,prove,do,maintain)framework in order to provide evidence for optimizing pediatric RSI nursing training programs.Methods Nurses from the intensive care unit(ICU)of Children's Hospital,Fudan University during Feb 2023 and Jan 2024 were divided into the experimental group(n=35)and the control group(n=35)by block randomization.The experimental group received LSPPDM framework-based training,while the control group underwent conventional training with theoretical lectures and procedural demonstrations.Outcomes included training satisfaction,theoretical knowledge and procedural skill assessment scores,team collaboration compliance and RSI procedure time were compared between the two groups.Results The experimental group demonstrated significantly higher training satisfaction(123.80±2.04 vs.117.26±9.82,P<0.05),superior post-training theoretical knowledge and procedural skills(P<0.05),enhanced team collaboration compliance(P<0.05),and shorter RSI completion time(P<0.05)compared with the control group.Conclusion Pediatric RSI nursing coordination training based on the LSPPDM framework can effectively increase training satisfaction,promote theoretical and procedural skills and reduce completion time in nurses.

Objective:To analyze the predictive role of WT1 expression levels pre-and early post-transplantation on relapse and overall survival(OS)in patients with acute myeloid leukemia(AML)undergoing allogeneic hematopoietic stem cell transplantation(allo-HSCT)during their first complete remission(CR1).Methods:A retrospective analysis was conducted on the clinical data of 107 adult AML patients who underwent allo-HSCT during their CR1 at our center between May 2012 and December 2021.The predictive role of bone marrow WT1 expression levels before transplantation and at 3 and 6 months post-transplantation on relapse and OS was explored in combination with relevant clinical factors.Results:The median follow-up time for the 107 patients was 70(range:11-117)months.Among the patients,15 cases died.Kaplan-Meier survial analysis showed that the 3-year overall survival(OS)rate was 85.0％.20 patients experienced relapse,with a median time to relapse of 8(range:0.5-44)months and a l-year cumulative relapse rate of 13.1％.The overall median value of WT1 before transplantation,3 months after transplantation,and 6 months after transplantation was 0.26％(range:0％-23.64％),with an upper quartile value of 0.74％.No statistically significant differences in WT1 expression levels were observed among the pre-transplantation,3-month post-transplantation,and 6-month post-transplantation time points(P=0.227).Univariate analysis showed that patients with WT1 levels＞0.74％at 3 months post-transplantation had a higher 1-year relapse rate(P=0.029)and lower 3-year OS rate(P＜0.001)compared to patients with WT1 levels ≤0.74％.Other significant factors affecting 1-year relapse included stem cell source(P=0.041)and chronic graft-versus-host disease(cGVHD)(P=0.013).For 3-year OS,additional influencing factors were genetic high risk(P=0.048)and stem cell source(P=0.016).Multivariate analysis revealed that WT1 level＞0.74％at 3 months post-transplantation had a trend to affect 1-year relapse rate(HR=3.309,95％CI:0.958-11.431,P=0.058),while the absence of cGVHD was an independent risk factor for 1-year relapse(HR=3.473,95％CI:0.749-16.100,P=0.037).Only WT1 level＞0.74％at 3 months post-transplantation was an independent risk factor for 3-year OS(HR=6.886,95％CI:2.402-19.738,P＜0.001).Conclusion:High WT1 expression level at 3 months post-transplantation in AML patients undergoing allo-HSCT during CR1 affects the 1-year relapse rate and 3-year OS,and is an independent risk factor affecting 3-year OS.These findings suggest that dynamic monitoring of WT1 expression levels has certain value in prognostic assessment of AML patients who received allo-HSCT during CR1.

Objective:This study aimed to integrate RNA-seq data to identify key genes associated with macrophage-ferroptosis and develop a prognostic model for hepatocellular carcinoma(HCC).Methods:We retrieved a dataset from the GEO database containing transcriptome data and clinical information for 163 samples.Macrophage-related genes were identified using WGCNA and immune infiltration analyses.Ferroptosis-related genes from the FerrDbV2 database were intersected with differentially expressed genes to obtain significant macrophage-ferroptosis-related genes.Hub genes were screened via protein interaction network construction,and key genes were identified using four machine learning algorithms.These genes exhibited significant expression differences between cancer and normal tissues and were closely linked to patient prognosis.ROC curve and KM survival analyses were performed,and expression levels were validated at the transcriptome and proteome levels.Results:Four key genes RRM2,KIF20A,PCK2,and PDK4 were identified and evaluated.RRM2 and KIF20A demonstrated high importance in classification prediction models and reliable performance in ROC and KM analyses(AUC＞0.9,P＜0.05).These genes regulate cancer cell proliferation/survival and macrophage polarization/function,influencing the tumor microenvironment and HCC progression.Conclusion:RRM2 and KIF20A regulate cancer cell proliferation and survival,modulate macrophage polarization and function,and influence the immune response in the tumor microenvironment.They can serve as prognostic biomarkers and potential immunotherapy targets for hepatocellular carcinoma.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.

Aim To investigate the therapeutic effects of a newly developed Tadalafil tablet on pulmonary fi-brosis induced by paraquat(PQ)in rats,as well as its impact on histone acetylation levels in epithelial cells.Methods SD rats were randomly divided into four groups:the control group(control),the model group(PQ),the Tadalafil new tablet treatment group(N-Tad,1 mg·kg-1),and the positive control drug treatment group(Cialis,5 mg·kg-1).The model group and treatment group rats were intraperitoneally injected with PQ(30 mg·kg-1).Two hours after the initial treatment,the rats in the treatment group re-ceived N-Tad or Cialis via gavage,while the control and model groups were administered an equal volume of physiological saline by gavage once daily for 28 days.The weight gain rate and lung tissue index for each group of rats were calculated.Additionally,the effects of N-Tad treatment on lung tissue structural damage and collagen deposition in rats with PQ-in-duced pulmonary fibrosis were observed using HE stai-ning,Masson trichrome staining,and immunohisto-chemical techniques.By employing the Western blot technique,the effects of Tadalafil intervention on the expression of the epithelial marker E-cadherin(E-Cad),the stromal marker fibronectin(Fn),and the histone acetylation marker acetylated histones(Ac-his-tones)in A549 cells were observed.Results Com-pared to the control group,rats with PQ-induced pul-monary fibrosis exhibited a significant decrease in the rate of body weight growth,an increase in lung tissue index(P＜0.05),and a notable increase in the expression and distribution of the fibrosis marker alpha-smooth muscle actin(α-SMA)in lung tissue.The structure of the lung tissue was disrupted,accompanied by the deposition of interstitial collagen fibers.Both N-Tad and Cialis treatments could significantly enhance the rate of weight gain,decrease the lung tissue index,inhibit the expression of α-SMA,and reduce the depo-sition of interstitial collagen in the lung tissue of rats with pulmonary fibrosis.Notably,low-dose N-Tad treatment was comparable to high-dose Cialis treat-ment.At the cellular level,Tadalafil significantly in-hibited the high expression of Fn induced by transfor-ming growth factor beta 1(TGF-β1)in A549 cells.It also upregulated the expression of E-cadherin and sig-nificantly increased the levels of acetylated histones(P＜0.05).Conclusions N-Tad promotes histone acetylation in alveolar epithelial cells,significantly in-hibits epithelial-mesenchymal transition,increases E-cadherin expression,and improves lung tissue structur-al damage and collagen deposition caused by PQ.Ad-ditionally,it offers the advantage of a lower effective dose compared to Cialis,providing a new option for the treatment of pulmonary fibrosis.

Chronic kidney disease(CKD)is a chronic disease characterized by renal structural damage and dysfunction.At present,there is still a lack of effective therapeutic drugs and prevention and treatment methods for CKD in clinical practice.More and more studies have shown that necroptosis,as a new type of programmed cell death,plays a vital role in the onset and progression of CKD.Targeting key molecules in the necroptosis pathway,such as RIPK1,RIPK3 and MLKL,the development of small molecule inhibitors has become an emerging strategy for the treatment of CKD,and has shown significant potential to pro-tect the kidneys and alleviate renal fibrosis in a variety of in vitro and in vivo models.Therefore,this article summarizes the re-search progress of the mechanism of necroptosis in recent years,and focuses on the potential role of necroptosis in the pathogene-sis of CKD and the therapeutic potential of targeting this path-way,providing a new perspective and research direction for the prevention and treatment of CKD in the future.