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.

Humans ; Adolescent ; Acne Vulgaris/therapy* ; Skin Care ; Surveys and Questionnaires ; Cognition ; China ; Skin

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.

BACKGROUND: The application of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) antibodies has greatly improved the clinical outcomes of lung cancer patients. Here, we retrospectively analyzed the efficacy of PD-1 antibody therapy in locally advanced non-surgical or metastatic lung cancer patients, and preliminarily explored the correlation between peripheral blood biomarkers and clinical responses. METHODS: We conducted a single center study that included 61 IIIA-IV lung cancer patients who received PD-1 antibody treatment from March 2020 to December 2021, and collected the medical record data on PD-1 antibody first-line or second-line treatment. The levels of multiple Th1 and Th2 cytokines in the patient's peripheral blood serum, as well as the phenotype of peripheral blood T cells, were detected and analyzed. RESULTS: All the patients completed at least 2 cycles of PD-1 monoclonal antibody treatment. Among them, 42 patients (68.9%) achieved partial response (PR); 7 patients (11.5%) had stable disease (SD); and 12 patients (19.7%) had progressive disease (PD). The levels of peripheral blood interferon gamma (IFN-γ) (P=0.023), tumor necrosis factor α (TNF-α) (P=0.007) and interleukin 5 (IL-5) (P=0.002) before treatment were higher in patients of the disease control rate (DCR) (PR+SD) group than in the PD group. In addition, the decrease in absolute peripheral blood lymphocyte count after PD-1 antibody treatment was associated with disease progression (P=0.023). Moreover, the levels of IL-5 (P=0.0027) and IL-10 (P=0.0208) in the blood serum after immunotherapy were significantly increased compared to baseline. CONCLUSIONS Peripheral blood serum IFN-γ, TNF-α and IL-5 in lung cancer patients have certain roles in predicting the clinical efficacy of anti-PD-1 therapy. The decrease in absolute peripheral blood lymphocyte count in lung cancer patients is related to disease progression, but large-scale prospective studies are needed to further elucidate the value of these biomarkers.
Humans ; Lung Neoplasms/metabolism* ; Interleukin-5/therapeutic use* ; Tumor Necrosis Factor-alpha/therapeutic use* ; Retrospective Studies ; Programmed Cell Death 1 Receptor ; Biomarkers ; Immunotherapy ; Disease Progression ; B7-H1 Antigen

OBJECTIVE: To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images. METHODS: Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD. RESULTS: A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set. CONCLUSIONS The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.
Humans ; Non-alcoholic Fatty Liver Disease/diagnostic imaging* ; Ultrasonography ; Anthropometry ; Algorithms ; China