Objective To systematically review international research on health communication, and to provide valuable insights and reference for China's health communication research and practice. Methods This study included 693 articles published from January 2023 to April 2024 in two authoritative academic journals in the field of health communication, ^“Health Communication^” and the ^“Journal of Health Communication^”. A systematic review was conducted on the themes, theoretical foundations, research methods, and populations of international health communication research. Results The findings in this study revealed that international health communication research topics were diverse, with hotspots including social media, health information behavior, health misinformation, stigmatization, trust, and risk perception. The results showed that 34% of the articles were based on theoretical foundations, and 93.3% employed research methods, focusing on adolescents, parents, women, and other key populations. Conclusion Domestic health communication research can expand its perspective from ^“information transmission^” to ^“social interaction^”, innovate theories and methods from ^“single paradigm" to ^“multi-integration^” and shift focus from a ^“mass perspective^” to ^“targeted care^” for the health of all populations. Domestic health communication practice can delve into the localization of social media health communication practices, the comprehensive management of health misinformation, and the critical application of new technologies.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Objective:To construct univariate and multivariate relapse free survival (RFS) prediction models for breast cancer patients with diabetes mellitus type 2 (T2DM) and to compare and select the model with higher predictive performance.Methods:A total of 912 breast cancer patients treated at the First Affiliated Hospital of Dalian Medical University from January 2010 to December 2016 were included, of which 202 patients had T2DM and 710 patients did not. Kaplan-Meier survival curve was drawn based on whether patients had T2DM, and log-rank test was performed based on whether patients had T2DM. All patients were randomly divided into a training set ( n=640) and a validation set ( n=272) at a ratio of 7∶3. Univariate and multivariate Cox proportional risk regression models were used to analyze RFS in breast cancer patients with the survival package. The "rms" package was employed to construct univariate and multivariate RFS prediction models for breast cancer patients with T2DM. Clinical decision curves and calibration curves were used to validate the models. The receiver operator characteristic (ROC) curve was used to compare and analyze the prediction performance of the two models. Results:There were no statistically significant differences between the training set and the validation set patients in terms of age, T2DM, surgical approach, axillary management methods, T stage, N stage, molecular sub-type, estrogen receptor (ER) 1, ER2, progesterone receptor (PR) , ER and PR consistency, Ki67, human epidermal growth factor receptor 2 (HER2) (all P>0.05) . There was a statistically significant difference in histological grade ( χ2=7.59, P=0.022) . Survival analysis showed that the 5-year RFS rate was 83.7% in patients with T2DM and 92.3% in patients without T2DM ( χ2=16.61, P<0.001) . Univariate analysis revealed that age ( HR=1.04, 95% CI: 1.03-1.06, P<0.001) , T2DM ( HR=2.31, 95% CI: 1.49-3.55, P<0.001) , surgical approach ( HR=2.39, 95% CI: 1.20-4.77, P=0.013) , axillary management methods ( HR=2.62, 95% CI: 1.72-3.98, P<0.001) , T stage (T 2: HR=2.13, 95% CI: 1.36-3.31, P<0.001; T 3: HR=6.90, 95% CI: 3.35-14.22, P<0.001) , N stage (N 2: HR=3.87, 95% CI: 2.12-7.07, P<0.001; N 3: HR=8.61, 95% CI: 4.71-15.75, P<0.001) , molecular sub-type (Luminal B: HR=2.74, 95% CI: 1.17-6.36, P=0.019; HER2 +: HR=3.64, 95% CI: 1.38-9.58, P=0.009; TNBC: HR=4.40, 95% CI: 1.71-11.34, P=0.002) , ER1 (>10%: HR=0.57, 95% CI: 0.37-0.90, P=0.016) , ER2 ( HR=0.57, 95% CI: 0.37-0.89, P=0.015) , and PR ( HR=0.56, 95% CI: 0.37-0.86, P=0.008) were all factors influencing RFS in breast cancer patients. Multivariate analysis demonstrated that age ( HR=1.04, 95% CI: 1.02-1.06, P<0.001) , T2DM ( HR=1.82, 95% CI: 1.16-2.85, P=0.009) , T stage (T 2: HR=1.60, 95% CI: 1.01-2.54, P=0.046; T 3: HR=2.64, 95% CI: 1.22-5.72, P=0.014) , N stage (N 2: HR=3.72, 95% CI: 2.01-6.88, P<0.001; N 3: HR=5.34, 95% CI: 2.78-10.25, P<0.001) , and ER1 (>10%: HR=0.63, 95% CI: 0.39-0.99, P=0.046) were independent factors influencing RFS in breast cancer patients. Based on the 10 and 5 variables with P<0.05 in the univariate and multivariate analyses respectively, the nomograms of the univariate and multivariate prediction models were constructed to evaluate the influence of factors such as T2DM on the postoperative RFS of breast cancer patients. Clinical decision curves and calibration curves indicated that both models had high predictive value for RFS in breast cancer patients, and the predictive results were highly consistent with the actual observed results. ROC curve analysis showed that there was no statistically significant difference in the area under the curve (AUC) of the two models for predicting the RFS rates of breast cancer patients in the training set and validation set at 36, 60, and 84 months (all P>0.05) , indicating that the predictive efficacy of the two models was comparable. The multivariate model is more suitable for clinical application because it uses fewer variables. Conclusions:Breast cancer patients with T2DM have poorer prognosis. Age, T2DM, T stage, N stage, and ER1 are independent factors influencing postoperative RFS in breast cancer patients. The multi-factor prediction model of RFS in breast cancer patients based on T2DM is more suitable for clinical application due to its higher predictive efficacy and fewer variables.

Objective To analyze the association of serum anti-glutamic acid decarboxylase antibody(GAD-Ab),C-peptide,and urinary albumin-to-creatinine ratio(UACR)with the changes in white matter and cognitive function in elderly patients with end-stage diabetic nephropathy(ESDN).Methods A total of 128 elderly patients with ESDN admitted to Nantong Sixth People's Hospital from November 2023 to November 2024 were enrolled.According to the Fazekas classification,they were assigned to white matter lesion group(grades 1-3)or non-white matter lesion group(grade 0).The levels of GAD-Ab,C-peptide,and UACR were compared between the two groups.The correlations between white matter lesions and the levels of GAD-Ab,C-peptide,and UACR in elderly ESDN patients were investigated.According to the Mini-Mental State Examination(MMSE)score,the patients were assigned to non-cognitive dysfunction group(with an MMSE score of 27-30)or the cognitive dysfunction group(with an MMSE score of<27).The basic data of the two groups were compared.The factors influencing cognitive function in elderly ESDN patients were analyzed,and ROC curve was used to evaluate the correlation between influencing factors and the occurrence of cognitive dysfunction in elderly ESDN patients.Results Among 128 elderly patients with ESDN,81 had white matter lesions,accounting for 63.28%.The levels of GAD-Ab,C-peptide and UACR in the white matter lesion group were all higher than those in the non-white matter lesion group(P<0.05).The Pearson correlation analysis showed that the white matter lesions in ESDN patients were positively correlated with the levels of GAD-Ab,C-peptide and UACR(P<0.05).There were 42 patients with cognitive dysfunction,accounting for 32.81%.There were significant differences in the 24 h urine protein quantitation(Upro),C-peptide,GAD-Ab,UACR,GFR,procalcitonin and hs-CRP levels between the cognitive dysfunction group and the non-cognitive dysfunction group(P<0.05).Binary Logistic regression analysis showed that 24 h Upro(OR=1.006,95%CI:1.003 to 1.008),GAD-Ab(OR=34.923,95%CI:5.779 to 211.058),C-peptide(OR=2.891,95%CI:1.669 to 5.010),UACR(OR=1.066,95%CI:1.032 to 1.102),procalcitonin(OR=1.221,95%CI:1.103 to 1.352),and hs-CRP(OR=1.471,95%CI:1.232 to 1.757)were risk factors for cognitive dysfunction in elderly ESDN patients,while rGFR level(OR=0.967,95%CI:0.950 to 0.984)was a protective factor for cognitive dysfunction in elderly ESDN patients(P<0.05).ROC curve analysis showed that the sensitivities of GAD-Ab,C-peptide,and UACR in evaluating cognitive dysfunction in elderly ESDN patients were 68.30%,73.20%,and 65.90%,respectively.The specificities were 63.20%,64.40%,and 72.40%,respectively.Their combination had a relatively high value in predicting cognitive dysfunction in elderly ESDN patients(AUC=0.892).Conclusion GAD-Ab,C-peptide,and UACR are associated with white matter lesions in elderly ESDN patients,and the combination of the three indexes has a relatively high value in predicting cognitive dysfunction in elderly ESDN patients.

Objective:To analyze mortality risk factors in patients with severe pneumonia and investigate their varying influences across different time periods.Methods:A total of 134 patients with severe pneumonia admitted to the Emergency Department of Xiang’an Hospital, Xiamen University, between June 2019 and February 2020 were enrolled. All patients were treated in the EICU and followed up for four years. Based on outcomes, they were categorized into a death group ( n=77) and a survival group ( n=57). COX regression analysis was employed to identify mortality risk factors at different time points, while logistic regression analysis was used to assess risk factors influencing mortality during hospitalization, ICU stay, 1-month, and 1-year follow-up periods. Results:Mortality rates were 11.9% ( n=16) during ICU admission, 20.8% ( n=28) during hospitalization, 16.4% ( n=22) within 1 month, and 31.3% ( n=42) within 1 year. By the end of the follow-up, 57.4% ( n=77) of patients had died. Ten mortality risk factors were identified, with the number increasing over time. During ICU admission and hospitalization, significant risk factors included total bilirubin levels, APACHE-II score, invasive ventilation, ARDS, and vasopressor use in the ICU. One-month mortality risk additionally involved bacterial infection. One-year mortality risk further incorporated advanced age and chronic heart failure. By the end of follow-up, acute kidney injury (AKI) during ICU admission also emerged as a contributing factor, while higher body weight was identified as a protective factor. Conclusions:The number of mortality risk factors in severe pneumonia patients increases progressively over time. Early-stage factors during hospitalization and ICU admission exert a stronger impact on short-term mortality, whereas bacterial infection, advanced age, and chronic heart failure become increasingly significant in later stages. These findings highlight the dynamic nature of risk factors and underscore the importance of tailored monitoring and intervention strategies at different disease phases.

Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Objective To investigate the effects and potential mechanisms of Marsdeniae Tenacissimae Caulis(Tongguanteng)injection and extract in human osteosarcoma cells proliferation,migration,invasion,and apoptosis.Methods MNNG/HOS,Saos-2 osteosarcoma cells,and normal bone marrow mesenchymal stem cells(BMSC)were cultured in vitro.Cells were incubated with different concentrations of Tongguanteng injection and Tongguanteng extract(40,60,80 mg/mL).Cell proliferation was evaluated by CCK-8 assay and plate colony formation assay.Cell migration and invasion were evaluated by scratch assay and Transwell assay.Cell apoptosis was evaluated by Hoechst33342 staining and Annexin-V/PI double staining assay.Bax,Bcl-2 and Caspase-3 mRNA expression were detected using RT-qPCR.The protein expressions of JAK1,p-JAK1,STAT3,p-STAT3 and MMP9 were detected by Western blot.Results Compared with the control group,both Tongguanteng injection and extract significantly decreased the survival rate of MNNG/HOS and Saos-2 cells,inhibited cell clone formation,migration,and invasion,induced cell apoptosis(P<0.05,P<0.01),promoted Bax mRNA and protein expression,inhibited Bcl-2 mRNA and protein expression,and up-regulated Caspase-3 mRNA and Cleaved Caspase-3 protein expression.Tongguanteng injection could significantly down-regulate the expressions of p-JAK1,p-STAT3 and MMP9 protein expression in Saos-2 cells(P<0.05,P<0.01).Conclusion Both Tongguanteng injection and Tongguanteng extract can significantly inhibit proliferation,migration and invasion of human osteosarcoma MNNG/HOS and Saos-2 cells,and induce apoptosis,with no significant difference in anti-tumor effect.The mechanism may be related to the inhibition of the activation of JAK1/STAT3 signaling pathway.

DNA methylation,a crucial biochemical process within the human body,fundamentally alters gene expression without modifying the DNA sequence,resulting in stable changes.The changes in DNA methylation are closely related to numerous biological processes including cellular proliferation and differentiation,embryonic development,and the occurrence of immune diseases and tumor.Specifically,abnormal DNA methylation plays a crucial role in the formation,progression,and prognosis of chronic myeloid leukemia(CML).Moreover,DNA methylation offers substantial potential for diagnosing and treating CML.Accordingly,understanding the precise mechanism of DNA methylation,particularly abnormal changes in the methylation of specific genes in CML,can potentially promote the development of novel targeted therapeutic strategies.Such strategies could transform into clinical practice,effectively aiding diagnosis and treatment of CML patients.

Objective: QL1604 is a highly selective, humanized monoclonal antibody against programmed death protein 1. We assessed the efficacy and safety of QL1604 plus chemotherapy as first-line treatment in patients with advanced cervical cancer. Methods: This was a multicenter, open-label, single-arm, phase II study. Patients with advanced cervical cancer and not previously treated with systemic chemotherapy were enrolled to receive QL1604 plus paclitaxel and cisplatin/carboplatin on day 1 of each 21-day cycle for up to 6 cycles, followed by QL1604 maintenance treatment. Results: Forty-six patients were enrolled and the median follow-up duration was 16.5 months. An 84.8% of patients had recurrent disease and 13.0% had stage IVB disease. The objective response rate (ORR) per Response Evaluation Criteria in Advanced Solid Tumors (RECIST) v1.1 was 58.7% (27/46). The immune ORR per immune RECIST was 60.9% (28/46).The median duration of response was 9.6 months (95% confidence interval [CI]=5.5–not estimable). The median progression-free survival was 8.1 months (95% CI=5.7–14.0). Fortyfive (97.8%) patients experienced treatment-related adverse events (TRAEs). The most common grade≥3 TRAEs (>30%) were neutrophil count decrease (50.0%), anemia (32.6%), and white blood cell count decrease (30.4%). Conclusion QL1604 plus paclitaxel-cisplatin/carboplatin showed promising antitumor activity and manageable safety profile as first-line treatment in patients with advanced cervical cancer. Programmed cell death protein 1 inhibitor plus chemotherapy may be a potential treatment option for the patient population who have contraindications or can’t tolerate bevacizumab, which needs to be further verified in phase III confirmatory study.