BackgroundMany studies have shown that patients with depressive disorder show impairments in prospective memory， while it is deemed necessary for facilitating their social reintegration， and the current research findings are inconsistent regarding the factors affecting prospective memory of patients with depressive disorder. ObjectiveTo explore the impact of target characteristics （emotional valence and salience） on prospective memory in patients with depressive disorder， so as to provide references for the training and recovery of prospective memory function in these patients. MethodsFrom January to December 2022， 53 patients with depressive disorder were recruited from the outpatient department of Inner Mongolia Autonomous Mental Health Center. Meanwhile， 45 healthy individuals were concurrently recruited from surrounding communities as control group. An experiment with a 2 （participant type： depressive disorder， healthy control） ×2 （target salience： salient， non-salient） ×3 （emotional valence： positive， neutral， negative） factorial design was conducted. The positive/neutral/negative emotional pictures from Chinese Affective Picture System （CAPS） were used for emotional stimulation. A dual-task experimental paradigm was adopted， and the response time and accuracy in prospective memory task and ongoing task were recorded for participants with different target characteristics. Results①In the prospective memory task， the main effect of participant type was statistically significant， with the depressive group showing lower accuracy （F=14.892， P<0.01） and longer response time （F=10.642， P=0.002） compared with control group. ② The main effect of target emotional valence on accuracy （F=7.575， P=0.001） and response time （F=3.196， P=0.044） in the prospective memory task was statistically significant. Simple effect analysis revealed that depressive group yielded a shorter response time and higher accuracy rate under negative conditions compared with positive and neutral conditions （P<0.05 or 0.01）. ③ The main effect of target salience on accuracy （F=6.659， P=0.012） and response time （F=10.106， P=0.002） in the prospective memory task was also statistically significant， with higher accuracy and shorter response time for salient targets compared with non-salient targets. ConclusionPatients with depressive disorder demonstrate preferential attention to and processing of negative stimuli in prospective memory tasks， while increasing target salience may facilitate spontaneous processing of prospective memory task in patients with depressive disorder. ［Funded by Inner Mongolia Health Commission Medical Health Science and Technology Project （number， 202202104）］

Epilepsy is a chronic neurological disorder affecting ~65 million individuals worldwide. Abnormal synaptic plasticity is one of the most important pathological features of this condition. We investigated how ubiquitin-specific peptidase 47 (USP47) influences synaptic plasticity and its link to epilepsy. We found that USP47 enhanced excitatory postsynaptic transmission and increased the density of total dendritic spines and the proportion of mature dendritic spines. Furthermore, USP47 inhibited the degradation of the ubiquitinated α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) subunit glutamate receptor 1 (GluR1), which is associated with synaptic plasticity. In addition, elevated levels of USP47 were found in epileptic mice, and USP47 knockdown reduced the frequency and duration of seizure-like events and alleviated epileptic seizures. To summarize, we present a new mechanism whereby USP47 regulates excitatory postsynaptic plasticity through the inhibition of ubiquitinated GluR1 degradation. Modulating USP47 may offer a potential approach for controlling seizures and modifying disease progression in future therapeutic strategies.
Animals ; Receptors, AMPA/metabolism* ; Neuronal Plasticity/physiology* ; Seizures/physiopathology* ; Disease Models, Animal ; Mice, Inbred C57BL ; Mice ; Ubiquitin Thiolesterase/genetics* ; Male ; Excitatory Postsynaptic Potentials/physiology* ; Ubiquitination ; Dendritic Spines/metabolism* ; Hippocampus/metabolism*

With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

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/.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

OBJECTIVE To investigate and analyze the pharmaceutical care needs of the elderly， thus providing a reference for improving the pharmaceutical care for the elderly. METHODS Based on the Kano model， a questionnaire was designed， and 1 200 community-dwelling elderly in the main urban area of Chongqing were selected as the survey subjects. The study analyzed the attributes and urgency of their pharmaceutical care needs to put forward optimization strategies. RESULTS A total of 1 200 questionnaires were distributed in the study， and 1 062 valid questionnaires were collected， with an effective response rate of 88.50%. The gender distribution of respondents was relatively balanced， with the majority aged between 60 and 69 （43.41%）， and generally possessing a relatively low level of educational attainment. The results showed that medication education and medication consultation were must-be needs； home-based pharmaceutical care was an expected need； drug reorganization， medication monitoring， pharmaceutical science popularization， and pharmaceutical ward round were attractive needs； internet-based pharmaceutical care was indifferent need. The urgent order of demand was medication education ＞ medication consultation ＞ home-based pharmaceutical care ＞ pharmaceutical science popularization ＞ drug reorganization ＞ medication monitoring ＞ pharmaceutical ward round ＞ internet-based pharmaceutical care. CONCLUSIONS The community elderly in Chongqing have high expectations for pharmaceutical care as a whole. Medical institutions should fully guarantee the two essential needs of medication education and medication consultation， and focus on ensuring the expected needs for home-based pharmaceutical care. Efforts should be made to develop the four attractive needs of pharmaceutical science popularization， drug reorganization， medication monitoring， and pharmaceutical ward round， and actively carry out age-friendly adaptations for internet-based pharmaceutical care.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Pre-admission is a critical initiative to optimize medical service processes and alleviate the challenge of "difficult access to healthcare. "However, there is currently a lack of standardized protocols for pre-admission procedures. This study aims to systematically analyze key nodes and risk factors in pre-admission process design and propose optimization strategies, providing a foundation for policy formulation and hospital practices. By constructing a "forward-reverse" dual-process model of pre-admission and identifying risk points based on stakeholder theory (patients, hospitals, healthcare administration, and insurance), the study reveals that while pre-admission can reduce the average length of stay, improve bed turnover rates, and enhance patient satisfaction, it also presents risks such as cross-period financial settlement, challenges in insurance policy adaptability, demands for information system integration, and the need for defining medical safety boundaries. To optimize the pre-admission process and mitigate these risks, this study explores framework improvements in areas including eligibility criteria, mode selection, cost settlement, transition between pre-admission and inpatient status, and cancellation of pre-admission, offering practical guidance for public hospitals. The authors argue that pre-admission requires tripartite collaboration among hospitals, insurers, and healthcare administrations: hospitals should establish top-level design, continuously refine processes, and implement dynamic risk assessment mechanisms; insurance providers should support cross-period settlement policies; and healthcare administrations should issue guiding policies or standardized protocols. Through multi-department coordination and collaborative efforts, the optimization and innovation of pre-admission processes can be advanced, ultimately delivering more efficient and convenient healthcare experiences for patients.