Objective: To compare the consistency between bioelectrical impedance analysis (BIA) and dual energy X ray absorptiometry (DXA) in measuring body composition among Tibetan children and adolescents and to explore the applicability of BIA in plateau region, so as to provide scientific and convenient body composition measurement support among children and adolescents. Methods: From May to June, 2022, a total of 344 Tibetan children and adolescents aged 6-17 years were selected from Golmud Municipal National Middle School and Changjiangyuan Nationality Primary School in Qinghai Province by cluster sampling method, and their fat mass, fat mass percentage and lean mass were measured by DXA and BIA. The consistency and correlation between the two methods were assessed by using the Wilcoxon rank-sum test, Spearman correlation analysis, intraclass correlation coefficient (ICC), and Bland-Altman analysis. Results: DXA measured fat mass and fat mass percentage were significantly higher than those obtained by BIA (6-12 years old: Z =9.91, 11.28; 13-17 years old: Z =9.02, 10.21), while lean mass and lean mass percentage were significantly lower than BIA results (6-12 years old: Z =-11.60, -11.30; 13-17 years old: Z =-10.77, -10.36) (all P < 0.05 ). The two methods showed strong correlations in fat mass and lean mass (all r >0.80, all ICC >0.90), but exhibited poor agreement in fat mass percentage and lean mass percentage (6-12 years old: Lin s CCC =0.64, 0.41; 13-17 years old: Lin s CCC = 0.79 , 0.35). Bland-Altman analysis showed that the difference between the two methods was negatively correlated with the average value in FM%(6-12 years old: r =-0.75, 13-17 years old: r =-0.79, both P <0.01). Conclusion BIA and DXA show high consistency in measuring body fat mass and lean body mass in Tibetan children and adolescents, although some bias is still present in certain individuals.

Antibodies play a critical role in adaptive immune responses and serve as key components in disease diagnosis and treatment. These molecules exhibit dynamic post-translational modifications (PTMs), such as glycosylation and phosphorylation, which regulate their effector functions. To date, nearly all of our knowledge about antibody repertoires has come from B cell receptor (BCR) sequencing (BCR-seq), which facilitates the profiling of clonal composition and the tracing of maturation trajectories within B-cell repertoires. However, circulating antibodies found in bodily fluids—such as serum, saliva, milk, mucosal secretions, and cerebrospinal fluid—exhibit diversities and specificities beyond what BCR-seq alone can predict. Therefore, identifying and quantifying antibody clonotypes at the protein level could enhance diagnosis, prognosis, and treatment strategies in personalized medicine. The critical gap between genotype and phenotype necessitates complementary methodologies that enable the direct characterization of antibody proteins in their native functional states. Mass spectrometry (MS)-based antibody repertoire sequencing (Ab-seq) is currently the only feasible approach for this task and primarily includes database-dependent methods—such as bottom-up, middle-down, and top-down approaches—as well as database-independent de novo sequencing technology. These strategies enable multi-level, high-precision characterization ranging from peptides and domains to intact antibody molecules. Unlike the shotgun strategy commonly used in routine proteomics, obtaining full sequences of all antibodies presents unique challenges. It requires specialized methodological adaptations to address issues related to dynamic range, sequence variation, and sample complexity. This review introduces the technical principles, methodological workflows, and recent applications of various mass spectrometry-based antibody repertoire sequencing (Ab-seq) strategies, with a focus on approaches designed to improve sequence coverage and identification accuracy. These include multi-enzyme digestion, hybrid fragmentation methods, and artificial intelligence-assisted de novo sequencing. By systematically comparing database-dependent techniques—such as bottom-up, middle-down, and top-down approaches—with database-independent de novo sequencing, this review outlines their respective advantages and limitations in terms of sample throughput, sequence coverage, post-translational modification characterization, and data analysis complexity. In addition, this review discusses emerging technological trends, including the integration of ion mobility separation, native mass spectrometry, and artificial intelligence-driven data interpretation, which are expected to enhance the depth and accuracy of antibody characterization. Although current methods continue to face challenges related to sample complexity, dynamic range, and unambiguous sequence variant assignment, we emphasize the importance of integrating BCR-seq and Ab-seq data to construct gene-protein association maps. These maps help validate sequence accuracy and facilitate epitope discovery. This dual-platform strategy helps bridge the gap between genotype and phenotype, thereby enhancing both the resolution and scope of antibody repertoire studies. Such an integrative approach also offers a valuable tool for therapeutic antibody development, structure-function analysis, and precise evaluation of vaccine efficacy.

ObjectiveTo explore the effect and mechanism of Jianpi Xiaoai prescription （JPXA） in ameliorating the 5-fluorouracil （5-FU） resistance of colon cancer. MethodsA HCT116/5-FU resistant cell line was established. Different concentrations （10%， 15%， 20%） of JPXA-containing serum and drug-free serum were used for intervention， and 10% fetal bovine serum （10% FBS）， fibroblast growth factor receptor （FGFR） inhibitor （AZD4547）， and recombinant fibroblast growth factor 2 （FGF2） were set as the control groups. Sensitive HCT116 cells were used in the FGF2 group， while HCT116/5-FU cells were used in other groups. Drug resistance， the level of FGF2 in the cell culture medium， the mRNA level of FGF2 in cells， and the protein levels of FGF2/FGFR and phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） were determined. The drug-resistant cells were transplanted into the axilla of nude mice to establish a tumor model. The modeled mice were allocated into model， JPXA （15 g·kg^-1）， 5-FU （0.02 g·kg^-1）， JPXA+5-FU （15 g·kg^-1+0.02 g·kg^-1）， AZD4547 （0.012 5 g·kg^-1）， and AZD4547+5-FU （0.012 5 g·kg^-1+0.02 g·kg^-1） groups. The tumor growth and the protein levels of FGF/FGFR and PI3K/Akt in each group were observed. ResultsThe survival rate of HCT116/5-FU cells decreased in all the JPXA groups with different concentrations. The cell survival rate was decreased most obviously in the 20% JPXA group. The level of FGF2 in the cell culture medium and the mRNA level of FGF2 in cells of each JXPA group decreased， and the decrease was the most significant in the 20% group （P<0.01）. HCT116/5-FU cells showed up-regulated protein levels of FGF2 and phosphorylated fibroblast growth factor receptor 1 （p-FGFR1）， but down-regulated protein level of FGFR1 （P<0.01）. JPXA down-regulated the expression of FGF2 and p-FGFR1 and up-regulated the expression of FGFR1 （P<0.05）. In addition， JPXA down-regulated the expression levels of phosphorylated protein kinase B （p-Akt） and phosphorylated mammalian target of rapamycin （p-mTOR）， while up-regulating the expression levels of Akt and Bcl-2-asociated death promoter （Bad） （P<0.05）. Animal experiments showed that the JPXA combined with 5-FU significantly inhibited the growth of drug-resistant tumors， reduced the protein levels of FGF2， p-FGFR1， phosphorylated phosphatidylinositol-3-kinase （p-PI3K）， p-Akt， and p-mTOR， and increased the expression of Bad. It indicated that JPXA can inhibit the FGF2/FGFR1 signaling in colon cancer and regulate PI3K/Akt and downstream signaling pathways. ConclusionJPXA can ameliorate the chemotherapy resistance of colon cancer through down-regulating FGF2 expression and inhibiting the activation of the PI3K/Akt signaling pathway.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Hemoglobin (Hb) concentration is a key clinical biomarker for diagnosing and managing anemia, ischemic stroke, perioperative blood loss, and chronic diseases such as renal failure. Traditional venous blood sampling remains the gold standard due to its high accuracy, but its invasive nature limits frequent testing, real time monitoring, and large scale screening. This has driven growing interest in non-invasive Hb detection technologies over the past decade. Among these, optical methods are the most promising because of their safety, potential for continuous monitoring, and compatibility with portable or wearable devices. This paper systematically reviews major advances in optical non invasive Hb detection from the last ten years. We focus on near-infrared spectroscopy branches—photoplethysmography (PPG) and dynamic spectrum (DS)—and also cover color analysis/RGB imaging, Raman spectroscopy, and photoacoustic spectroscopy. For each technology, we explain its detection principles, analyze advantages and limitations, and summarize optimization strategies reported in recent literature. PPG, based on pulsatile blood volume changes, underpins many commercial continuous monitors. However, its accuracy is constrained by motion artifacts, individual physiological variations (e.g., skin tone, tissue thickness), and low AC signal to noise ratio. In contrast, DS—an advanced derivative of PPG—uses a differential principle to extract absorbance changes between systolic and diastolic peaks. This theoretically eliminates interference from static tissues (skin, bone, venous blood) and common mode noise (e.g., ambient light), positioning DS as a more robust framework for high precision Hb quantification. Beyond spectral methods, color analysis/RGB imaging offers a hardware minimalist approach. By analyzing images of vascular rich, thin tissues (e.g., conjunctiva, nail beds, palms), it enables Hb estimation using smartphone cameras. Recent advances have shifted from manual RGB feature extraction to deep learning models and spectral super resolution that reconstruct hyperspectral data from RGB inputs, significantly improving screening accuracy. Our academic perspective emphasizes critical and integrative analysis. We highlight persistent challenges that hinder clinical translation: profound individual biological variability (skin optics, microvascular architecture), sensitivity to measurement conditions (pressure, ambient light), and a lack of standardized validation protocols and multi center trials. A central thesis is that no single optical method is universally superior; each involves trade offs between accuracy, complexity, cost, and practicality. Looking forward, we posit that the next performance leap will come from multimodal information fusion—combining PPG, electrocardiogram (ECG), bioimpedance, or different optical modalities to compensate for individual differences and environmental noise. AI and deep learning are essential not only for image analysis but also for automated, end to end feature extraction from complex waveforms like PPG sequences. Advancing hardware (tunable lasers, quantum dot LEDs, novel sensor designs) is crucial to improve signal fidelity and portability. Finally, we advocate for clinical scenario specific optimization and rigorous standardized evaluation frameworks to gain regulatory approval (e.g., FDA, NMPA) and achieve widespread clinical acceptance. In conclusion, this review synthesizes a decade of progress. Optical non-invasive Hb detection has evolved from proof of concept studies to emerging products and validated screening tools, but the journey toward reliable, clinic ready quantitative devices continues. The convergence of smarter algorithms, fused sensing modalities, and focused clinical validation offers the most promising path to transform this potential into routine medical practice, ultimately enabling personalized, continuous, and accessible hematological management.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Alzheimer's disease (AD) is a neurodegenerative disease with clinical hallmarks of progressive cognitive impairment. Synergistic effects of the Aβ-Tau cascade reaction are tightly implicated in AD pathology, and microglial NLRP3 inflammasome activation drives neuronal tauopathy. However, the underlying mechanism of how Aβ mediates NLRP3 inflammasome remains unclear. Herein, we determined that oligomeric Aβ (o-Aβ) bound to microglial Kv2.1 and promoted Kv2.1-dependent potassium efflux to activate NLRP3 inflammasome resulting in neuronal tauopathy by using Kv2.1 inhibitor drofenine (Dfe) as a probe. The underlying mechanism has been intensively investigated by assays with Kv2.1 knockdown in vitro (si-Kv2.1) and in vivo (AAV-ePHP-si-Kv2.1). Dfe deprived o-Aβ of its capability to promote microglial NLRP3 inflammasome activation and neuronal Tau hyperphosphorylation by inhibiting the Kv2.1/JNK/NF-κB pathway while improving the cognitive impairment of 5×FAD-AD model mice. Our results have highly addressed that the Kv2.1 channel is required for o-Aβ-driven microglial NLRP3 inflammasome activation and neuronal tauopathy in AD model mice and highlighted that Dfe as a Kv2.1 inhibitor shows potential in the treatment of AD.

The liver regenerative capacity is crucial for patients with end-stage liver disease following partial hepatectomy (PHx). The specific bacteria and mechanisms regulating liver regeneration post-PHx remain unclear. This study demonstrated dynamic changes in the abundance of Parabacteroides distasonis (P. distasonis) post-PHx, correlating with hepatocyte proliferation. Treatment with live P. distasonis significantly promoted hepatocyte proliferation and liver regeneration after PHx. Targeted metabolomics revealed a significant positive correlation between P. distasonis and β-hydroxybutyric acid (BHB), as well as hyodeoxycholic acid and 3-hydroxyphenylacetic acid in the gut after PHx. Notably, treatment with BHB, but not hyodeoxycholic acid or 3-hydroxyphenylacetic acid, significantly promoted hepatocyte proliferation and liver regeneration in mice after PHx. Moreover, STAT3 inhibitor Stattic attenuated the promotive effects of BHB on cell proliferation and liver regeneration both in vitro and in vivo. Mechanistically, P. distasonis upregulated the expression of fatty acid oxidation-related proteins, and increased BHB levels in the liver, and then BHB activated the STAT3 signaling pathway to promote liver regeneration. This study, for the first time, identifies the involvement of P. distasonis and its associated metabolite BHB in promoting liver regeneration after PHx, providing new insights for considering P. distasonis and BHB as potential strategies for promoting hepatic regeneration.