ObjectiveTo explore the possible mechanisms of Wenyang Huazhuo Formula （温阳化浊方， WHF） in improving reproductive aging from the perspective of the ovarian mechanical microenvironment. MethodsThe experiment included five groups， 3-month group （20 female mice at 3 months of age）， 6-month group （20 female mice at 6 months of age）， 6-month + WHF group （20 female mice at 5 months of age treated with WHF）， 9-month group （20 female mice at 9 months of age）， and 9-month + WHF group （20 female mice at 8 months of age treated with WHF）. The 6-month + WHF group and 9-month + WHF group were orally administered WHF 41.2 g/（kg·d） once daily for 4 consecutive weeks. The other three groups received no intervention. Reproductive hormone levels were measured by ELISA. HE staining was used to count the numbers of various stages of follicles. Ovarian hyaluronic acid （HA） content and collagen fiber content were measured to evaluate the ovarian mechanical microenvironment. Superovulation was performed to observe the number of eggs obtained， as well as the number of offspring and birth weight to assess fertility. The in vitro fertilization and blastocyst culture of oocytes from female offspring in each group were observed to evaluate the effect of WHF on offspring reproductive potential. ResultsCompared with the 3-month group， the 6-month group and 9-month group showed significantly decreased serum levels of gonadotropin-releasing hormone （GnRH）， follicle-stimulating hormone （FSH）， and luteinizing hormone （LH）， decreased ovarian collagen content， and reduced numbers of primordial and secondary follicles. In contrast， the numbers of primary follicles， antral follicles， and atretic follicles increased. The levels of anti-Müllerian hormone （AMH）， ovarian HA content， and the fertilization rate， cleavage rate， and blastocyst formation rate of oocytes from offspring were significantly lower （P＜0.05）. Compared with the 6-month group， the 6-month + WHF group showed significantly reduced serum levels of GnRH， FSH， and LH， with a significant decrease in primary follicles， antral follicles， and atretic follicles as well as increase of AMH levels， ovarian HA content， number of primordial and secondary follicle， egg count， and offspring birth weight （P＜0.05）. Compared with the 9-month group， the 9-month + WHF group exhibited reduced GnRH， FSH， and collagen fiber content， as well as reduced number of primary follicles， antral follicles， and atretic follicles. However， AMH levels， ovarian HA content， number of primordial and secondary follicle， egg count， offspring numbers， birth weight， fertilization rate， cleavage rate， and blastocyst formation rate of oocytes from offspring all significantly increased （P＜0.05）. ConclusionWHF can significantly improve the ovarian reserve， fertility， and reproductive potential in offspring during reproductive mid-life and late-life stages. Its effect may be related to the remodeling of the mechanical microenvironment of aging ovaries. Moreover， the effect on the mechanical microenvironment remodeling of late-stage ovaries and the improvement of the offspring reproductive potential is more significant.

Data analysis models may assist the transmission of traditional Chinese medicine （TCM） experience and clinical diagnosis and treatment， and the possibility of constructing a “data-knowledge” dual-drive model was explored by taking gastric precancerous state as an example. Data-driven is to make clinical decisions around data analysis， and its syndrome-differentiation decision-making research relies on hidden structural models and partially observable Markov decision-making processes to identify the etiology of diseases， syndrome elements， evolution of pathogenesis， and syndrome differentiation protocols； knowledge-driven is to make use of data and information to promote decision-making and action processes， and its syndrome-differentiation decision-making research relies on convolutional neural networks to improve the accuracy of local disease identification and syndrome differentiation. The “data-knowledge” dual-driven model can make up for the shortcomings of single-drive numerical simulation accuracy， and achieve a balance between local disease identification and macroscopic syndrome differentiation. On the basis of previous research， we explored the construction method of diagnostic assisted decision-making platform for gastric precancerous state， and believed that the diagnostic and decision-making ability of doctors can be extended through the assistance of machines and algorithms. Meanwhile， the related research methods were integrated and the core features of gastric precancerous state based on TCM syndrome differentiation and endoscopic pathology diagnosis and prediction were obtained， and the elements of endoscopic pathology recognition based on TCM syndrome differentiation were explored， so as to provide ideas for the in-depth research and innovative application of cutting-edge data analysis technology in the field of intelligent TCM syndrome differentiation.

【Objective】 To elucidate the possible role of arginine and histidine in the pathogenesis of schizophrenia by exploring the serum levels of semi-essential amino acids (arginine and histidine) in patients with schizophrenia and their correlation with psychiatric symptoms. 【Methods】 We selected 72 inpatients with schizophrenia admitted to The First Affiliated Hospital of Xi’an Jiaotong University from March 2021 to October 2022 and 72 healthy volunteers enrolled in Yanta Community during the same period as the research subjects. Serum arginine and histidine levels were measured in patients with schizophrenia and healthy controls using serum liquid chromatography-mass spectrometry (LC-MS). We used the Positive and Negative Symptom Scale (PANSS) to evaluate the mental symptoms of patients with schizophrenia and analyzed the correlation of serum arginine and histidine levels with disease course, frequency of onset, and PANSS score. 【Results】 The levels of serum arginine (P<0.001) and histidine (P=0.011) in the schizophrenia group were significantly lower than those in the control group. The levels of serum arginine and histidine were significantly negatively correlated with the frequency of onset (rs=-0.410, rs=-0.262), total score of PANSS (rs=-0.298, rs=-0.256), positive factors (rs=-0.299, rs=-0.234) and cognitive impairment factors (rs=-0.251, rs=-0.296). In addition, serum arginine levels had significantly negative correlation with anxiety and depression factors (rs=-0.269, P<0.05). 【Conclusion】 The serum levels of arginine and histidine in patients with schizophrenia are significantly lower than those in healthy individuals, and are negatively correlated with overall mental symptoms, severity of positive symptoms and cognitive impairment. The severity of anxiety and depression symptoms is negatively correlated with arginine levels. The detection of serum arginine and histidine can provide reference for the diagnosis and assessment of the severity of schizophrenia in the future.

Objective To analyze the interactions between different structural types of volatile oil compo-nents(VOCs)and skin lipid molecules,and investigate the mechanism of volatile oil in Chi-nese materia medica(VOCMM)as penetration enhancers. Methods In this study,210 different structural types of VOCs were selected from the VOCMM penetration enhancer database,and the molecular docking experiments were conducted with three main lipid molecules of skin:ceramide 2(CER2),cholesterol(CHL),and free fatty acid(FFA).Each VOC was docked individually with each lipid molecule.Cluster analysis was used to explore the relationship between the binding energy of VOCs and their molecular struc-tures.Nine specific pathogen-free(SPF)Sprague Dawley(SD)rats were randomly divided in-to Control,Nootkatone,and 3-Butylidenephthalide groups for in vitro percutaneous experi-ments,with three rats in each group.The donor pool solutions were 3%gastrodin,3%gas-trodin+3%nootkatone,and 3%gastrodin+3%3-butylidenephthalide,respectively.The pen-etration enhancing effects of VOCs with higher binding energy were evaluated by comparing the 12-hour cumulative percutaneous absorption of gastrodin(Q12,μg/cm2). Results(i)Most of the VOCs were non-hydrogen bonded to the hydrophobic parts of CHL and FFA,and hydrogen bonded to the head group of CER2.Among them,sesquiterpene ox-ides showed the most pronounced binding affinity to CER2.The VOCs with 2-4 rings(in-cluding carbon rings,benzene rings,and heterocycles)demonstrated stronger binding affini-ty for three skin lipid molecules compared with the VOCs without intramolecular rings(P<0.01).(ii)According to the cluster analysis,most of the VOCs that bond well to CER2 had 2-3 intramolecular rings.The non-oxygenated VOCs were bonded to CER2 in a hydrophobic manner.The oxygenated VOCs were mostly bonded to CER2 by hydrogen bonding.(iii)The results of Franz diffusion cell experiment showed that the Q12 of Control group was 260.60±25.09 μg/cm2,and the transdermal absorption of gastrodin was significantly increased in Nootkatone group(Q12=5 503.00±1 080.00 μg/cm2,P<0.01).The transdermal absorption of gastrodin was also increased in 3-Butylidenephthalide group(Q12=495.40±56.98 μg/cm2,P>0.05).(iv)The type of oxygen-containing functional groups in VOCs was also an influencing factor of binding affinity to CER2. Conclusion The interactions between different types of VOCs with different structures in the VOCMM and three skin lipid molecules in the stratum corneum were investigated at the molecular level in this paper.This research provided theoretical guidance and data support for the screening of volatile oil-based penetration enhancers,and a simple and rapid method for studying the penetration-enhancing mechanism of volatile oils.

With the deepening of the reform of the medical and health system and the continuous optimization of the medical order,it is especially important to organize the development of admission and discharge standards and improve the service of preparing patients for discharge.In recent years,the research and application of machine learning technology in the medical field has been intensifying,and it has unique advantages in processing data and risk prediction research.Therefore,this paper reviews the development process,types of machine leaming,the content and effects of its application in patient discharge preparation services,and the current problems,in order to provide references for healthcare professionals to implement the best clinical decisions and further improve the patient discharge preparation service model.

Objective To systematically evaluate the prognostic prediction model for chronic heart failure patients in China, and provide reference for the construction, application, and promotion of related prognostic prediction models. Methods A comprehensive search was conducted on the studies related to prognostic prediction model for Chinese patients with chronic heart failure published in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, VIP, Wanfang, and the China Biological Medicine databases from inception to March 31, 2023. Two researchers strictly followed the inclusion and exclusion criteria to independently screen literature and extract data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models. Results A total of 25 studies were enrolled, including 123 prognostic prediction models for chronic heart failure patients. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.690 to 0.959. Twenty-two studies mostly used random splitting and Bootstrap for internal model validation, with an AUC range of 0.620-0.932. Seven studies conducted external validation of the model, with an AUC range of 0.720-0.874. The overall bias risk of all models was high, and the overall applicability was low. The main predictive factors included in the models were the N-terminal pro-brain natriuretic peptide, age, left ventricular ejection fraction, New York Heart Association heart function grading, and body mass index. Conclusion The quality of modeling methodology for predicting the prognosis of chronic heart failure patients in China is poor, and the predictive performance of different models varies greatly. For developed models, external validation and clinical application research should be vigorously carried out. For model development research, it is necessary to comprehensively consider various predictive factors related to disease prognosis before modeling. During modeling, large sample and prospective studies should be conducted strictly in accordance with the PROBAST standard, and the research results should be comprehensively reported using multivariate prediction model reporting guidelines to develop high-quality predictive models with strong scalability.

Objective To develop deep learning-based auxiliary diagnostic models for diverse pulmonary diffuse cystic diseases,and subsequently evaluate their classification performance to identify the optimal model for clinical diagnosis.Methods A total of 288 patients diagnosed with idiopathic pulmonary fibrosis(IPF),pulmonary lymphangioleiomyomatosis(PLAM),and pulmonary Langerhans cell histiocytosis(PLCH)were prospectively enrolled from the First Affiliated Hospital of Guangzhou Medical University between January 2010 and October 2022,comprising 76 cases of IPF,179 cases of PLAM,and 33 cases of PLCH.A total of 877 CT cases were collected,comprising 232 cases of IPF,557 cases of PLAM,and 88 cases of pulmonary PLCH.Based on the cutoff date of December 31,2019,the CT scans were divided into two datasets:dataset A consisted of 500 CT scans including 185 IPF cases,265 PLAM cases,and 50 PLCH cases;while dataset B comprised 377 CT scans with a distribution of 47 IPFcases,292 PLAMcases,and 38 PLCH cases.The Dataset A was randomly partitioned into training set,validation set,and test set in a ratio of 7∶1∶2.Subsequently,six distinct deep learning neural networks were employed for training after preprocessing and data augmentation.Receiver operating characteristic curves were generated to assess the model performance using metrics such as area under the curve(AUC),accuracy,sensitivity,specificity,and F1 score in order to identify the optimal model.Furthermore,a test set B comprising 30 randomly selected cases from dataset B for each disease type was utilized to evaluate the trained optimal model by employing the same aforementioned metrics.Results In test A,six well-established diagnostic models demonstrated superior classification performance for IPF and LAM,with an AUC greater than 0.9.For LCH,EfficientNet exhibited low classification efficiency with an AUC between 0.6 and 0.7,while Vgg11 showed an AUC between 0.8 and 0.9;the other four models displayed excellent classification efficiency with an AUC greater than 0.9.Except for Inception V3,the remaining five diagnostic models performed poorly in identifying and classifying LCH lesions.Considering multiple indicators,the InceptionV3 model showcased optimal comprehensive performance among the six models,achieving high evaluation parameters such as overall accuracy(94.90%),precision(93.49%),recall(90.84%),and specificity(96.91%).TestB was conducted using the trained InceptionV3 model resulting in an accuracy of 81%,precision of 82%,recall of 81%,and specificity of 90%.Conclusions Six recognition and classification models,developed using deep learning technology in conjunction with pulmonary CT images,demonstrate effective discrimination between LAM,LCH,and IPF.Notably,the model constructed utilizing the InceptionV3 neural network exhibits superior efficiency in accurately recognizing and classifying IPF and LAM.

Objective:To summarize the evidence related to the guidance and management of breastfeeding in pregnant women with gestational diabetes mellitus.Methods:UpToDate, BMJ Best Practice, CINAHL, PubMed, Cochrane Library, Embase, Wanfang database, China National Knowledge Infrastructure, and other databases and professional websites were searched by computer for evidence on breastfeeding guidance and management for pregnant women with gestational diabetes mellitus, including clinical decisions, guidelines, evidence summaries, systematic reviews, expert consensus and randomized controlled trials. The search period was from database establishment to April 1, 2023. Two researchers independently conducted literature quality evaluation, evidence extraction, and integration.Results:A total of 23 articles were included, including 4 clinical decision-making articles, 4 guidelines, 4 evidence summaries, 6 systematic reviews, 1 expert consensus and 4 randomized controlled trials. A total of 21 best pieces of evidence were summarized from six aspects of support and education, knowledge and skills, safety issues, feeding issues, dietary care, and blood glucose monitoring.Conclusions:This study summarizes the best evidence on breastfeeding guidance and management for pregnant women with gestational diabetes and suggests that the medical staff should apply the proof according to the clinical situation and the patient's wish.

Objective:To systematically evaluate and Meta-synthesize qualitative studies on the home management needs of patients with spinal cord injury (SCI) to understand their actual self-management needs and improve the quality of home management for patients with SCI in China.Methods:A comprehensive search was conducted in databases including CNKI, Wanfang, CBM, PubMed, Embase, Web of Science, CINAHL, and Cochrane Library for qualitative research on the home management needs of patients with SCI, with a search timeframe up to November 30, 2023. The methodological quality of the included studies was evaluated using the Joanna Briggs Institute (JBI) Qualitative Assessment and Review Instrument (2016). Results were integrated and analyzed using Meta-synthesize methods.Results:A total of 15 studies were included, from which 58 distinct research findings were extracted. These were categorized into 10 new categories, which were further integrated into four main results: the need for positive emotional support, daily living-related needs, healthcare service needs, and social support needs.Conclusions:Healthcare providers should deeply understand the home management needs of patients with SCI. Utilizing artificial intelligence technology, an integrated support model encompassing hospital, home, and society can be constructed. Establishing a comprehensive home rehabilitation platform for patients with SCI can focus on psychological issues and enhance social support levels, thereby improving patients' quality of life.

Objective:To integrate qualitative research on the real experience of patients with spinal cord injury (SCI) returning to society after discharge, so as to provide a basis for developing transitional care intervention program, and promote patients' reintegration into society.Methods:Qualitative research on the real experience of patients with SCI returning to society after discharge was electronically retrieved on China National Knowledge Infrastructure, WanFang Data, VIP, China Biomedical Literature Service System, Cochrane Library, PubMed, Embase, Web of Science, CINAHL, and so on .The search period was from database establishment to August 30, 2023. The quality evaluation criteria for qualitative research of the JBI Evidence-Based Health Care Center (2016) was used to assess the quality of literature, and Meta-synthesis was used to integrate the results.Results:A total of 16 articles were included, and 51 research results were extracted. Similar research results were summarized and combined to form 12 new categories, which were then synthesized into four integrated results, including experiencing physical and mental discomfort following discharge, facing challenges in reintegrating into society, seeking social support, and adapting to social life through self-adjustment role changes.Conclusions:Patients with SCI have multiple psychological experiences in the process of reintegration into society. Medical and nursing staff should attach importance to their inner needs, help them overcome stress and challenges, provide them with personalized continuous care, and promote their role adaptation and reintegration into society.