BackgroundSleep disorder in the first trimester is a fairly common health problem， and previous studies have mainly reflected the overall sleep quality through scale assessments， which may not accurately capture the differences among various subtypes. ObjectiveTo explore the latent profiles of sleep quality in first-trimester pregnant women and identify the physiological， psychological and social factors， in order to provide practical references for the development of personalized interventions for sleep disorders in first-trimester pregnant women. MethodsA total of 1 066 first-trimester pregnant women who visited the obstetric outpatient clinic of a tertiary A hospital in Shenzhen from October 2021 to October 2022 were investigated using the general information questionnaire， Pittsburgh Sleep Quality Index （PSQI）， Edinburgh Postnatal Depression Scale （EPDS）， Chinese version of short International Physical Activity Questionnaire （IPAQ-S-C） and Social Capital Assessment Tool in Pregnancy for Maternal Health （SCAT-MH）. Then the sleep profiles were identified through latent profile analysis， and the robust mixture regression model was employed to determine the influencing factors of sleep profiles. ResultsA 3-profile solution showed the best fit： 732 cases （68.67%） of good sleep quality group， 87 cases （8.16%） of low sleep efficiency group， and 247 cases （23.17%） of daytime dysfunction group. In comparison with subjects in good sleep quality group， first-trimester pregnant women in low sleep efficiency group were at a younger age （OR=0.951， 95% CI： 0.922~0.980）， held a Bachelor's degree or above （OR=1.869， 95% CI： 1.260~2.773） and exhibited lower levels of social capital （OR=0.962， 95% CI： 0.951~0.973）， while those in daytime dysfunction group were at an older age （OR=1.072， 95% CI： 1.027~1.120） and had higher levels of depression （OR=1.166， 95% CI： 1.115~1.218）. Pregnant women who were workers （OR=0.507， 95% CI： 0.293~0.876） were less likely to report daytime dysfunction. ConclusionThree latent profiles with significant heterogeneity are derived from the sleep quality of first-trimester pregnant women， and levels of depression and social capital are the main influencing factors of sleep quality. ［Funded by Industry-University-Research Innovation Fund for Chinese Universities （number， 2023HT018）］

Discussing the cognition, attitude and influencing factors of the family members of organ donors towards advance care planning (ACP) to provide a basis for the further promotion of ACP in the field of organ donation. Using qualitative research methods, 8 family members of organ donors were selected purposefully for semi-structured interviews, and the 7-step of Colaizzi was used to analyze and summarize the themes of the interview data. The results showed that the cognition status of the family members of organ donors of ACP could be summarized into 3 themes: problems faced by ACP implementation, positive recognition of the implementation of ACP, factors affecting the promotion of ACP. Organ donors’ family members are unfamiliar with the concept of ACP and have unclear cognition, but think that the implementation of ACP is of positive significance. It is suggested to further strengthen the publicity of ACP, medical autonomy, death education, so as to promote the development of ACP and improve the willing to donate organ.

Tendon-bone healing is a complex biological process. Multiple signaling pathways are involved in tendon-bone healing， including transforming growth factor-β signaling pathway， bone morphogenetic protein signaling pathway， Wnt signaling pathway， fibroblast growth factor signaling pathway and nuclear transcription factor-κB signaling pathway. This paper summarizes the research status of traditional Chinese medicine regulating related signaling pathways to promote tendon-bone healing. It is found that a variety of traditional Chinese medicine monomers or herbal extracts （such as baicalein， icariin， total flavonoids of Drynaria fortunei， parthenolide， total saponins of Panax notoginseng， etc.） and traditional Chinese medicine compounds （such as Taohong siwu decoction， Liuwei dihuang pill， Xujin jiegu liquid， etc.） can promote bone formation， anti-inflammatory， anti-oxidation， by regulating the above signaling pathways， thereby effectively promoting tendon-bone healing.

Biosensor analysis technology is a kind of technology with high specificity that can convert biological reactions into optical and electrical signals. In the development of drugs for Alzheimer's disease (AD), according to different disease hypotheses and targets, this technology plays an important role in confirming targets and screening active compounds. This paper briefly describes the pathogenesis of AD and the current situation of therapeutic drugs, introduces three biosensor analysis techniques commonly used in the discovery of AD drugs, such as surface plasmon resonance (SPR), biolayer interferometry (BLI) and fluorescence analysis technology, explains its basic principle and application progress, and summarizes their advantages and limitations respectively.

Based on the strategy of metabolomics combined with bioinformatics, this study analyzed the potential allergens and mechanism of pseudo-allergic reactions (PARs) induced by the combined use of Reduning injection and penicillin G injection. All animal experiments and welfare are in accordance with the requirements of the First Affiliated Experimental Animal Ethics and Animal Welfare Committee of Henan University of Chinese Medicine (approval number: YFYDW2020002). Based on UPLC-Q-TOF/MS technology combined with UNIFI software, a total of 21 compounds were identified in Reduning and penicillin G mixed injection. Based on molecular docking technology, 10 potential allergens with strong binding activity to MrgprX2 agonist sites were further screened. Metabolomics analysis using UPLC-Q-TOF/MS technology revealed that 34 differential metabolites such as arachidonic acid, phosphatidylcholine, phosphatidylserine, prostaglandins, and leukotrienes were endogenous differential metabolites of PARs caused by combined use of Reduning injection and penicillin G injection. Through the analysis of the "potential allergen-target-endogenous differential metabolite" interaction network, the chlorogenic acids (such as chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, and isochlorogenic acid A) and β-lactam allergens in the combination of the two may be mainly regulated by PLD1, PLA2G12A and CYP1A1. The three upstream signal target proteins mainly activate the arachidonic acid metabolic pathway, promote the degranulation of mast cells, release downstream endogenous inflammatory mediators, and induce PARs.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

Objective Data mining technology was used to mine the medication rules of the prescriptions used in the treatment of pediatric atopic dermatitis by Chinese medical master XUAN Guo-Wei.Methods The medical records of effective cases of pediatric atopic dermatitis treated by Professor XUAN Guo-Wei at outpatient clinic were collected,and then the medical data were statistically analyzed using frequency statistics,association rule analysis and cluster analysis.Results A total of 242 prescriptions were included,involving 101 Chinese medicinals.There were 23 commonly-used herbs,and the 16 high-frequency herbs(frequency＞100 times)were Glycyrrhizae Radix et Rhizoma,Saposhnikoviae Radix,Glehniae Radix,Perillae Folium,Ophiopogonis Radix,Cynanchi Paniculati Radix et Rhizoma,Microctis Folium,Dictamni Cortex,Scrophulariae Radix,Coicis Semen,Cicadae Periostracum,Lilii Bulbus,Rehmanniae Radix,Kochiae Fructus,Sclerotium Poriae Pararadicis,and Euryales Semen.The analysis of the medicinal properties showed that most of the herbs were sweet and cold,and mainly had the meridian tropism of the spleen,stomach and liver meridians.The association rule analysis yielded 24 commonly-used drug combinations and 20 association rules.Cluster analysis yielded 2 core drug combinations.Conclusion For the treatment of pediatric atopic dermatitis,Professor XUAN Guo-Wei focuses on the clearing,supplementing and harmonizing therapies,and the medication principle of"supporting the healthy-qi to eliminate the pathogen,and balancing the yin and yang"is applied throughout the treatment.

Protein as the allergens could lead to allergy. In addition, a widespread class of allergens were known as glycans of N-glycoprotein. N-glycoprotein contained oligosaccharide linked by covalent bonds with protein. Recently,studies implicated that allergy was associated with glycans of heterologous N-glycoprotein found in food, inhalants, insect toxins, etc. The N-glycan structure of N-glycoprotein allergen has exerted an influence on the binding between allergens and IgE, while the recognition and presentation of allergens by antigen-presenting cells (APCs) were also affected. Some researches showed thatN-glycan structure of allergen was remodeled by N-glycosidase, such as cFase I, gpcXylase, as binding of allergen and IgE partly decreased. Thus, allergic problems caused by N-glycoproteins could potentially be solved by modifying or altering the structure ofN-glycoprotein allergens, addressing the root of the issue. Mechanism of N-glycans associated allergy could also be elaborated through glycosylation enzymes, alterations of host glycosylation. This article hopes to provide a separate insight for glycoimmunology perspective, and an alternative strategy for clinical prevention or therapy of allergic diseases.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with dysfunctions related to thinking, learning, and memory of the brain. AD has multiple pathological characteristics with complicated causes, constructing a suitable pathological model is crucial for the research of AD. Microfluidic chip technology integrates multiple functional units on a chip, which can realize microenvironmental control similar to the physiological environment. It is well applied in the construction of pathological model, early diagnosis as well as drug screening of AD. This paper focuses on the construction of AD microfluidic chips model from the perspective of cell type, culture formats and the chips structure as well as the research progress of microfluidic chips in AD application based on the pathological characteristics of AD, which will provide a reference for further elucidation of AD mechanism and drug development.

Ferroptosis is an iron-dependent form of regulated cell death,which is distinct from apoptosis,ne-crosis,and pyroptosis.Recent studies have found that activators of ferroptosis,such as Erastin,can activate autophagy-re-lated proteins,induce the formation of autophagosomes,and ultimately release ferric ions to mediate ferroptosis.This pro-cess,called ferritinophagy,is initiated by the binding of an autophagic cargo receptor protein,nuclear receptor coactivator 4(NCOA4),to iron-laden ferritin.The transfer of NCOA4-ferritin to the lysosome by ferritinophagy results in the proteoly-sis of ferritin,and,in turn,the release of its iron content and lipid-reactive oxygen species(ROS)accumulation.Ferritin-ophagy has been closely associated with central nervous system disorders,circulatory system diseases,and cancer.Fur-thermore,the regulation mechanism of ferritinophagy is also a hot topic in the study of iron-dependent cell death process.With the in-depth study of ferritinophagy,great progress has been made in the study of key components of ferritinophagy as well as its molecular mechanisms and processes.However,a comprehensive summary of the methods for detecting ferritin-ophagy is still unclear.To further deepen the understanding of ferritinophagy and its detection methods,this review focus-es on the concept,characteristics,methods,and precautions during detection of ferritinophagy.This review provided ex-perimental reference for subsequent researchers and promoting the progress of research related to ferritinophagy.