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.

The chemical components of Carthami Flos were investigated by using macroporous resin, silica gel column chromatography, reversed-phase octadecylsilane(ODS) column chromatography, Sephadex LH-20, and semi-preparative high-performance liquid chromatography(HPLC). The planar structures of the compounds were established based on their physicochemical properties and ultraviolet-visible(UV-Vis), infrared(IR), high-resolution electrospray ionization mass spectrometry(HR-ESI-MS), and nuclear magnetic resonance(NMR) spectroscopic technology. The absolute configurations were determined by comparing the calculated and experimental electronic circular dichroism(ECD). Six flavonoid C-glycosides were isolated from the 30% ethanol elution fraction of macroporous resin obtained from the 95% ethanol extract of Carthami Flos, and identified as saffloquinoside F(1), 5-hydroxysaffloneoside(2), iso-5-hydroxysaffloneoside(3), isosafflomin C(4), safflomin C(5), and vicenin 2(6). Among these, the compounds 1 to 3 were new chalcone C-glycosides. The compounds 1, 2, 4, and 5 could significantly increase the viability of H9c2 cardiomyocytes damaged by oxygen-glucose deprivation/reoxygenation(OGD/R) at a concentration of 50 μmol·L~(-1), showing their good cardioprotective activity.
Glycosides/pharmacology* ; Flowers/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Carthamus tinctorius/chemistry* ; Chalcones/pharmacology* ; Animals

Objective To explore the relationship between bedtime procrastination and fear of missing out and the intervention effect of aerobic exercise on bedtime procrastination,so as to provide a theoretical basis and practical reference for remedying bedtime procrastination.Methods Totally 988 students were selected through random sampling and then surveyed with the bedtime procrastination scale and the fear of missing out scale.Correlation and regression analyses were performed to explore the relationship between bedtime procrastination and fear of missing out.A total of 36 students were recruited from the 988 students to participate in the exercise intervention and they were assigned into an exercise group and a control group by the random number table method,with 18 students in each group.The exercise group performed aerobic exercise for 12 weeks,while the control group maintained daily activities.The participants' scores on the bedtime procrastination scale and the fear of missing out scale were recorded before and after the intervention and compared.Results The fear of missing out was positively correlated with bedtime procrastination among college students(r=0.214,P<0.001),and it was an important predictive factor for bedtime procrastination(β=0.241,P<0.001).After the intervention,the scores of bedtime procrastination scale decreased in the exercise group(t=2.277,P=0.036),while there was no significant difference in the scores of the control group before and after intervention(t=-0.787,P=0.442).Conclusions A high level of fear of missing out indicates severe bedtime procrastination.And 12-week exercise intervention could remedy bedtime procrastination.
Humans ; Fear ; Exercise ; Male ; Female ; Procrastination ; Young Adult ; Cross-Sectional Studies ; Students/psychology* ; Adult ; Surveys and Questionnaires ; Adolescent

In recent years,significant breakthroughs have been achieved in the immunotherapy for Alzheimer's disease.In line with global advancements,two anti-amyloid-β monoclonal antibodies have been approved and successfully launched in China for clinical use.Lecanemab and Donanemab were officially used in June 2024 and April 2025 in China,respectively.In order to standardize the rational and safe application of anti-amyloid-β monoclonal antibodies for Alzheimer's disease in China,this article integrates recom-mendations from the clinical trials and real-world experience from the author's team and domestic peers to further update the recom-mendations for the clinical use of anti-amyloid-β monoclonal antibody based on the 2024 version.It includes indications for therapy,pre-treatment evaluation and preparation,administration protocols and safety measures during treatment,and post-treatment monitor-ing strategies.

Osteoarthritis (OA) causes chronic pain that significantly impairs quality of life, with current treatments often proving insufficient and accompanied by adverse effects. Recent research has identified the dorsal root ganglion (DRG) and its resident macrophages as crucial mediators of chronic OA pain through neuroinflammation driven by macrophage polarization. We present a novel injectable thermo-sensitive hydrogel system, KAF@PLEL, designed to deliver an anti-inflammatory peptide (KAF) specifically to the DRG. This biodegradable hydrogel enables sustained KAF release, promoting the reprogramming of DRG macrophages from pro-inflammatory to anti-inflammatory phenotypes. Through comprehensive in vitro and in vivo studies, we evaluated the hydrogel's biocompatibility, effects on macrophage polarization, and therapeutic efficacy in chronic OA pain management. The system demonstrated significant capabilities in preserving macrophage mitochondrial function, suppressing neuroinflammation, alleviating chronic OA pain, reducing cartilage degradation, and improving motor function in OA rat models. The sustained-release properties of KAF@PLEL enabled prolonged therapeutic effects while minimizing systemic exposure and side effects. These findings suggest that KAF@PLEL represents a promising therapeutic approach for improving outcomes in OA patients through targeted, sustained treatment.

AIM: To evaluate the repeatability and accuracy of iCare IC100 tonometer in measuring intraocular pressure(IOP)by comparing the correlation and difference with Goldmann applanation tonometry(GAT)and non-contact tonometer(NCT), and to compare the correlation of the three types of IOP measurement with the central corneal thickness(CCT).METHODS: Prospective study. A total of 90 outpatients(90 eyes)in Liaoning Aier Eye Hospital from March 2019 to May 2019 were randomly selected as study subjects. All patients were measured IOP using iCare IC100, NCT, and GAT. The interclass correlation coefficient(ICC)was used to evaluate the repeatability of IOP measured 3 times consecutively using an intraocular tonometer. The correlation and consistency of iCare IC100, GAT and NCT were compared by one-way ANOVA, Pearson linear correlation analysis and Bland-Altman analysis. The linear regression analysis was used to analyze the correlation of the three tonometers with CCT.RESULTS: The mean IOP measured with iCare IC100, GAT and NCT was 19.74±6.90, 19.88±7.07 and 18.47±6.31 mmHg, respectively(F=1.180, P=0.309). The measurements of iCare IC100 with GAT, iCare IC100 with NCT and GAT with NCT were all positively correlated(r=0.930, 0.946, 0.918, all P<0.05), the Bland-Altman analysis showed that the mean differences between iCare IC100 and GAT, iCare IC100 and NCT, GAT and NCT were -0.142±2.61, 1.27±2.24, and 1.41±2.81 mmHg, respectively, with 97%(87/90), 96%(86/90), and 97%(87/90)IOP differences distributed within their 95% confidence intervals. The IOP measured with iCare IC100 and CCT, GAT and CCT and NCT and CCT were all positively correlated(r=0.426, 0.353, 0.451, all P<0.01). The linear regression equations between iCare IC100, GAT and NCT measurement and CCT were iCare IC100 IOP=-19.62+0.074×CCT; GAT IOP=-13.54+0.063×CCT; NCT IOP=-19.65+0.072×CCT; that is, for every 10 μm increase in CCT, iCare IC100 measurement increased by 0.74 mmHg, GAT measurement increased by 0.63 mmHg, and NCT measurement increased by 0.72 mmHg.CONCLUSION: The iCare IC100 tonometer has good repeatability and accuracy in measuring IOP, and the CCT has a greater impact on the measurement of iCare IC100 than the GAT and NCT.

AIM: To evaluate the repeatability and accuracy of iCare IC100 tonometer in measuring intraocular pressure(IOP)by comparing the correlation and difference with Goldmann applanation tonometry(GAT)and non-contact tonometer(NCT), and to compare the correlation of the three types of IOP measurement with the central corneal thickness(CCT).METHODS: Prospective study. A total of 90 outpatients(90 eyes)in Liaoning Aier Eye Hospital from March 2019 to May 2019 were randomly selected as study subjects. All patients were measured IOP using iCare IC100, NCT, and GAT. The interclass correlation coefficient(ICC)was used to evaluate the repeatability of IOP measured 3 times consecutively using an intraocular tonometer. The correlation and consistency of iCare IC100, GAT and NCT were compared by one-way ANOVA, Pearson linear correlation analysis and Bland-Altman analysis. The linear regression analysis was used to analyze the correlation of the three tonometers with CCT.RESULTS: The mean IOP measured with iCare IC100, GAT and NCT was 19.74±6.90, 19.88±7.07 and 18.47±6.31 mmHg, respectively(F=1.180, P=0.309). The measurements of iCare IC100 with GAT, iCare IC100 with NCT and GAT with NCT were all positively correlated(r=0.930, 0.946, 0.918, all P<0.05), the Bland-Altman analysis showed that the mean differences between iCare IC100 and GAT, iCare IC100 and NCT, GAT and NCT were -0.142±2.61, 1.27±2.24, and 1.41±2.81 mmHg, respectively, with 97%(87/90), 96%(86/90), and 97%(87/90)IOP differences distributed within their 95% confidence intervals. The IOP measured with iCare IC100 and CCT, GAT and CCT and NCT and CCT were all positively correlated(r=0.426, 0.353, 0.451, all P<0.01). The linear regression equations between iCare IC100, GAT and NCT measurement and CCT were iCare IC100 IOP=-19.62+0.074×CCT; GAT IOP=-13.54+0.063×CCT; NCT IOP=-19.65+0.072×CCT; that is, for every 10 μm increase in CCT, iCare IC100 measurement increased by 0.74 mmHg, GAT measurement increased by 0.63 mmHg, and NCT measurement increased by 0.72 mmHg.CONCLUSION: The iCare IC100 tonometer has good repeatability and accuracy in measuring IOP, and the CCT has a greater impact on the measurement of iCare IC100 than the GAT and NCT.

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]

Lung cancer exhibits the highest incidence and mortality rates among cancers globally, with a five-year overall survival rate alarmingly below 20%. Targeting autophagy, though a controversial therapeutic strategy, is extensively employed in clinical practice. Current research is actively pursuing various therapeutic strategies using small molecules to exploit the dual function of autophagy. Nevertheless, the pivotal question of enhancing or inhibiting autophagy in cancer therapy merits further attention. This review aims to provide a comprehensive overview of the mechanisms of autophagy in lung cancer. It also explores recent advances in targeting cytotoxic autophagy and inhibiting protective autophagy with small molecules to induce cell death in lung cancer cells. Notably, most autophagy-targeting drugs, primarily natural small molecules, have demonstrated that activating cytotoxic autophagy effectively induces cell death in lung cancer, as opposed to inhibiting protective autophagy. These insights contribute to identifying druggable targets and drug candidates for potential autophagy-related lung cancer therapies, offering promising approaches to combat this disease.