ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

Cancer represents a major worldwide disease burden marked by escalating incidence and mortality. While therapeutic advances persist, developing safer and precisely targeted modalities remains imperative. Nanomedicines emerges as a transformative paradigm leveraging distinctive physicochemical properties to achieve tumor-specific drug delivery, controlled release, and tumor microenvironment modulation. By synergizing passive enhanced permeation and retention effect-driven accumulation and active ligand-mediated targeting, nanoplatforms enhance pharmacokinetics, promote tumor microenvironment enrichment, and improve cellular internalization while mitigating systemic toxicity. Despite revolutionizing cancer therapy through enhanced treatment efficacy and reduced adverse effects, translational challenges persist in manufacturing scalability, longterm biosafety, and cost-efficiency. This review systematically analyzes cutting-edge nanoplatforms, including polymeric, lipidic, biomimetic, albumin-based, peptide engineered, DNA origami, and inorganic nanocarriers, while evaluating their strategic advantages and technical limitations across three therapeutic domains: immunotherapy, chemotherapy, and radiotherapy. By assessing structure-function correlations and clinical translation barriers, this work establishes mechanistic and translational references to advance oncological nanomedicine development.
Humans ; Neoplasms/radiotherapy* ; Immunotherapy/methods* ; Nanoparticles/chemistry* ; Animals ; Nanomedicine/methods* ; Drug Delivery Systems/methods* ; Drug Carriers/chemistry* ; Radiotherapy/methods*

This study predicts the quality markers(Q-markers) for the cough-relieving and phlegm-expelling effects of Kening Granules based on pharmacodynamics, plasma drug chemistry, network pharmacology, and pharmacokinetics. Strong ammonia solution spray and phenol red secretion assays were employed to evaluate the cough-relieving and phlegm-expelling effects of Kening Granules. Twentysix absorbed prototype components of Kening Granules were identified by ultra high performance liquid chromatography coupled with QExactive Plus quadrupole/Orbitrap high resolution mass spectrometry(UHPLC-Q-Exactive Plus Orbitrap HRMS). Through network pharmacology, 11 potential active components were screened out for the cough-relieving and phlegm-expelling effects of Kening Granules. The 11 components acted on 40 common targets such as IL6, TLR4, and STAT3, which mainly participated in PI3K/Akt, HIF-1, and EGFR signaling pathways. Pharmacokinetic quantitative analysis was performed for 7 prototype components. Three compounds including azelaic acid, caffeic acid, and vanillin were identified as Q-markers for the cough-relieving and phlegm-expelling effects of Kening Granules based on their effectiveness, transmissibility, and measurability. The results of this study are of great significance for clarifying the pharmacological substance basis, optimizing the quality standards, and promoting the clinical application of Kening Granules.
Drugs, Chinese Herbal/administration & dosage* ; Network Pharmacology ; Cough/blood* ; Male ; Humans ; Animals ; Rats ; Rats, Sprague-Dawley ; Biomarkers/blood* ; Quality Control ; Chromatography, High Pressure Liquid ; Antitussive Agents/chemistry*

To investigate the impact and underlying mechanism of NOP2/Sun RNA methyltransferase 2 (NSUN2) on gastric cancer progression, TCGA database was used and revealed a significant upregulation of NSUN2 expression in gastric cancer tissues. Western blot analysis revealed that NSUN2 was upregulated in gastric cancer cells compared with gastric mucosal epithelial cells. Colony formation assays demonstrated an enhanced colony-forming capacity in NSUN2-overexpressing cells. Furthermore, Transwell assays showed a marked increase in cell migration and invasion upon high NSUN2 expression. Moreover, TCGA database analysis suggested ARMC9 as a potential downstream target of NSUN2. Subsequently, MeRIP-qPCR analysis revealed that NSUN2 overexpression could increase m5C modification of ARMC9 mRNA, and reduce its degradation rate, thus enhancing protein expression. Additionally, ARMC9 overexpression augmented cellular colony formation and migratory and invasive capabilities. These findings indicate that NSUN2 promotes gastric cancer progression by elevating m5C modification of ARMC9 mRNA, increasing its stability and enhancing its expression, therefore, NSUN2 and ARMC9 may serve as potential therapeutic targets for gastric cancer.

Stroke is a global disease that seriously threatens human life. The pathological mechanisms of ischemic stroke include neuroinflammation, oxidative stress, and the destruction of blood vessels at the lesion site. Here, a biocompatible in situ hydrogel platform was designed to target multiple pathogenic mechanisms post-stroke, including anti-inflammation, anti-oxidant, and promotion of angiogenesis. Double-crosslinked responsive multifunctional hydrogels could quickly respond to the pathological microenvironment of the ischemic damage site and mediate the delivery of nitric oxide (NO) and ISO-1 (inhibitor of macrophage migration inhibitory factor, MIF). The hydrogel demonstrated good biocompatibility and could scavenge reactive oxygen species (ROS) and inflammatory cytokines, such as interleukin-6 (IL-6), interleukin-10 (IL-10), and MIF. In a mouse stroke model, hydrogels, when situated within the microenvironment of cerebral infarction characterized by weak acidity and elevated ROS release, would release anti-inflammatory nanoparticles rapidly that exert an anti-inflammatory effect. Concurrently, NO was sustained release to facilitate angiogenesis and provide neuroprotective effects. Neurological function was significantly improved in treated mice as assessed by the modified neurological severity score, rotarod test, and open field test. These findings indicate that the designed hydrogel held promise for sustained delivery of NO and ISO-1 to alleviate cerebral ischemic injury by responding to the brain's pathological microenvironment.

Enamel demineralization, the formation of white spot lesions, is a common issue in clinical orthodontic treatment. The appearance of white spot lesions not only affects the texture and health of dental hard tissues but also impacts the health and aesthetics of teeth after orthodontic treatment. The prevention, diagnosis, and treatment of white spot lesions that occur throughout the orthodontic treatment process involve multiple dental specialties. This expert consensus will focus on providing guiding opinions on the management and prevention of white spot lesions during orthodontic treatment, advocating for proactive prevention, early detection, timely treatment, scientific follow-up, and multidisciplinary management of white spot lesions throughout the orthodontic process, thereby maintaining the dental health of patients during orthodontic treatment.
Humans ; Consensus ; Dental Caries/etiology* ; Dental Enamel/pathology* ; Tooth Demineralization/etiology* ; Tooth Remineralization

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

Objective: To analyze the nucleic acid load of human parvovirus B19 in major commercially available blood products in China, including human albumin, human intravenous immunoglobulin, human rabies immunoglobulin and various coagulation factor products, aiming to provide evidence for improving blood product manufacturing processes and quality control of source plasma. Methods: A total of 98 batches of coagulation factor products were tested for human parvovirus B19 nucleic acid using real-time fluorescent quantitative PCR, including 42 batches of human prothrombin complex, 35 batches of human coagulation factor Ⅷ, and 21 batches of human fibrinogen. Additionally, 6 batches of human albumin, 6 batches of human intravenous immunoglobulin, and 38 batches of human rabies immunoglobulin were tested for human parvovirus B19 nucleic acid. Results: Human parvovirus B19 nucleic acid were undetectable in human albumin, human intravenous immunoglobulin and human rabies immunoglobulin. Among the 98 batches of coagulation factor products tested for human parvovirus B19 nucleic acid, B19 nucleic acid reactivity rate was 69.0% (29/42) for human prothrombin complex batches, but nucleic acid concentration were all significantly lower than 10 IU/mL. The reactivity rate of B19 nucleic acid in 35 batches of human coagulation factor Ⅷ was 48.6% (17/35), with nucleic acid concentration all below 10 IU/mL. The reactivity rate of B19 nucleic acid in 21 batches of human fibrinogen was 61.9% (13/21), with nucleic acid concentration all below 10 IU/mL. Conclusion: No human parvovirus B19 has been detected in human albumin, human intravenous immunoglobulin, or human rabies immunoglobulin. Human parvovirus B19 nucleic acid may exist in commercially available coagulation factor products, highlighting the need for enhanced screening of human parvovirus B19 nucleic acid in these products. It is also recommended that B19 viral nucleic acid testing be conducted on source plasma, particularly for coagulation factor products.

Objective: To analyze the nucleic acid load of human parvovirus B19 in major commercially available blood products in China, including human albumin, human intravenous immunoglobulin, human rabies immunoglobulin and various coagulation factor products, aiming to provide evidence for improving blood product manufacturing processes and quality control of source plasma. Methods: A total of 98 batches of coagulation factor products were tested for human parvovirus B19 nucleic acid using real-time fluorescent quantitative PCR, including 42 batches of human prothrombin complex, 35 batches of human coagulation factor Ⅷ, and 21 batches of human fibrinogen. Additionally, 6 batches of human albumin, 6 batches of human intravenous immunoglobulin, and 38 batches of human rabies immunoglobulin were tested for human parvovirus B19 nucleic acid. Results: Human parvovirus B19 nucleic acid were undetectable in human albumin, human intravenous immunoglobulin and human rabies immunoglobulin. Among the 98 batches of coagulation factor products tested for human parvovirus B19 nucleic acid, B19 nucleic acid reactivity rate was 69.0% (29/42) for human prothrombin complex batches, but nucleic acid concentration were all significantly lower than 10 IU/mL. The reactivity rate of B19 nucleic acid in 35 batches of human coagulation factor Ⅷ was 48.6% (17/35), with nucleic acid concentration all below 10 IU/mL. The reactivity rate of B19 nucleic acid in 21 batches of human fibrinogen was 61.9% (13/21), with nucleic acid concentration all below 10 IU/mL. Conclusion: No human parvovirus B19 has been detected in human albumin, human intravenous immunoglobulin, or human rabies immunoglobulin. Human parvovirus B19 nucleic acid may exist in commercially available coagulation factor products, highlighting the need for enhanced screening of human parvovirus B19 nucleic acid in these products. It is also recommended that B19 viral nucleic acid testing be conducted on source plasma, particularly for coagulation factor products.

Soy is a vital source of plant carbohydrates.However,it poses significant allergenic risks,particularly to young children and animals.Among the various proteins in soy,β-conglycinin,which con-stitutes approximately 30％of total soy carbohydrates,is a primary allergen.Undigested β-conglycinin can lead to intestinal damage by inhibiting cell growth,disrupting the cytoskeleton,and inducing apopto-sis.It can also enter the lymphatic and circulatory systems,triggering allergic reactions.Conventional ELISA methods for detecting β-conglycinin rely on polyclonal or monoclonal antibodies,which are limited by their large molecular weight,difficulty in accessing the protein core,and sensitivity to acidic and bas-ic conditions.To address these limitations,this study aimed to develop nanobodies(Nbs)against β-con-glycinin.Nbs,derived from the variable regions of heavy-chain antibodies found in camelids,have a mo-lecular weight approximately one-tenth that of conventional antibodies.They offer advantages such as small size,stable structure,high specificity,and strong affinity.A female alpacas was immunized five times using β-conglycinin,which showed a heavy chain antibody potency of 1∶16 000 by ELISA.Pe-ripheral blood lymphocytes were subsequently isolated and total RNA was extracted.The variable region of the heavy-chain antibody was amplified via PCR,and recombinant plasmids were constructed and transformed into the E.coli competency strain ER2738.The resulting library contained about 3.5×108 CFU/mL,which increased to 1.15×1012 PFU/mL after phage rescue,with a 100％Nbs gene insertion rate,indicating high diversity.Its Nbs phage output was significantly enriched by four rounds of solid-phase elution with an enrichment rate of 155.9.Four rounds of solid-phase panning yielded 35 positive clones,all of which shared the same amino acid sequence upon sequencing.The selected Nb was ex-pressed in a prokaryotic system,and its binding ability to β-conglycinin was confirmed using Western blotting and ELISA.The results demonstrated excellent specificity and affinity.This research lays the groundwork for developing a rapid and efficient detection method for β-conglycinin using Nbs,potentially enhancing food safety and allergen management.