Objective To explore the effects of CYP2C19, CYP2C9 and CYP3A5 genotypes on the plasma concentration of voriconazole in children. Methods Collected blood samples from 50 hospitalized children with invasive fungal infections who received intravenous voriconazole from January 2020 to December 2020. High performance liquid chromatography was used to detect the blood trough concentration of voriconazole, and the time-of-flight mass spectrometry detection system was used to detect the genotypes of CYP2C19, CYP2C9 and CYP3A5, and the effects of children’s genotyping on the plasma concentration, efficacy and adverse reactions of voriconazole were analyzed. Results The total effective rate of 50 children with IFI was 84% （42/50 cases） after voriconazole treatment. The incidence of adverse reactions was 20% （10/50 cases）. The measured plasma concentration of voriconazole ranged from 0.56~7.62 μg/ml. Combined with the different mutation types of CYP2C19 gene loci, three metabolic activities were produced: fast, medium and slow, and the test results showed that there were 16 cases of fast metabolism, 27 cases of intermediate metabolism and 7 cases of slow metabolism. There was a significant difference in plasma concentrations among the three groups （F=15.359, P<0.001）, and the drug concentrations in the fast metabolic group were significantly lower than those in the intermediate metabolic and slow metabolic groups. The mutations of CYP2C9 and CYP3A5 had no significant effect on the plasma concentrations of the drugs, which were （F=2.213, P=0.086 and F=0.757, P=0.475）. Conclusion Voriconazole had significant efficacy in the treatment of invasive fungal infections in children, and the adverse reactions were mild. CYP2C19 genotype was significantly related to the rate of drug metabolism and was an important factor affecting blood drug concentration, the detection of drug concentration and genotype of voriconazole was helpful to adjust the effective drug dose clinically and would achieve more scientific and individualized treatment.

OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

Objective To examine the impact of extreme temperature and drought stress on the survival of Bulinus globosus, so as to provide the theoretical evidence for the genomic research of Bulinus in absence of reference genes. Methods B. globosus snail samples were collected from Kiwani Shehia in Pemba Island, Zanzibar, Tanzania, and offspring snails were obtained through laboratory breeding and reproduction. A total of 120 10-week-old B. globosus snails from the same generation were selected and randomly assigned into four groups, including the high-temperature drought (HD) group, normal temperature drought (D) group, low-temperature drought (LD) group, and the control (C) group, of 30 snails in each group. Snails in HD, D, and LD groups were placed in beakers containing dry soil at the bottom and subsequently housed in climate chambers at 35, 26 ℃, and 10 ℃, respectively, while snails in Group C were maintained in 500 mL petri dishes containing dechlorinated tap water at 26 ℃. Following 3 days of breeding, living snails in each group were collected, and soft tissues were dissected and isolated. Total RNA was extracted from snail soft tissues for library construction, followed by high-throughput sequencing on the Illumina HiSeq 4000 sequencing system. De novo transcriptome assembly was performed using the Trinity software, and the longest transcripts were selected as unigenes. Gene functional annotations of unigenes were conducted using the Diamond software against Gene Ontology (GO) knowledgebase, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, NCBI non-redundant (NR) protein sequences database, Protein Family (Pfam) database, and UniProtKB/Swiss-Prot (Swiss-Prot) knowledgebase. GO and KEGG enrichment analyses of differentially expressed genes (DEGs) were performed using the topGO and clusterProfiler software, respectively. In addition, four relevant genes were selected for validation using a real-time quantitative PCR (qRT-PCR) assay to verify the reliability of transcriptome sequencing results. Results Following 3 days of breeding, there were 7, 20, 28, and 30 survival B. globosus snails in HD, LD, D, and C groups, with corresponding survival rates of 23.33% (7/30), 66.67% (20/30), 93.33% (28/30), and 100.00% (30/30), respectively (χ² = 52.72, P < 0.001). De novo transcriptome assembly generated 176 942 unigenes, with annotation rates of 0.98%, 13.49%, 26.46%, 12.48%, and 14.39% against GO knowledgebase, KEGG pathway database, NR protein sequences database, Pfam database, and Swiss-Prot knowledgebase, respectively. There were 33 up-regulated and 72 down-regulated genes in Group D, 483 up-regulated and 815 down-regulated genes in Group HD, and 245 up-regulated and 172 down-regulated genes in Group LD relative to in Group C. Following removal of overlapping genes across groups and unmatched genes, 11 candidate genes were identified. GO and KEGG analyses revealed 3 heat shock protein (HSP)-related DEGs in these 11 candidate genes, which were annotated as HSP12.2, HSP70, and HSP20 genes and were all significantly up-regulated in each treatment group. Three immune and nervous system-related DEGs were identified, and were all significantly down-regulated in each treatment group, which were involved in the neural cell adhesion molecule L1-like protein pathway, fibrinogen binding protein pathway, and leukocyte elastase inhibitor-like protein pathway. qRT-PCR assay quantified that the expression trends of four genes related to temperature and drought stress across different treatment groups were highly consistent with transcriptome sequencing data. Conclusion The survival rate of B. globosus significantly reduces under combined stresses of extreme temperature and drought, possibly due to an imbalance in its cellular homeostasis regulatory system.

Based on review of global and Chinese schistosomiasis control progress and the evolution of control strategies, this article focuses on Chinese practical experiences of schistosomiasis control and systematically summarizes five key determinants for schistosomiasis transmission risk, including source of infections, intermediate host snails, high-risk populations, natural environments, and social factors. To address these risks and challenges associated with these determinants, the article reviews the advances in techniques for assessment of schistosomiasis transmission risk and their applications, including conventional risk assessment approaches, mathematical model-based tools for prediction of schistosomiasis transmission risk, and indicator-systembased techniques for assessment of schistosomiasis transmission risk. This review underscores the essential role of interdisciplinary integration and dynamic management in precision schistosomiasis control and recommends the intensification of verification of field adaptation and dynamic updates of indicator systems to promote the widespread application of assessment tools across diverse regions and contexts, so as to provide strategic guidance and methodological support to achieve the target for elimination of schistosomiasis across China in 2030.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Berberine is a kind of isoquinoline alkaloid extracted from the roots and rhizomes of many medicinal plants,such as Coptis chinensis of Ranunculus family,Phellodendron chinensis of rutaceae family,and Berberine Sanacanthus family.In recent years,with the deepening of research,berberine has shown re-markable prevention and treatment effect in a variety of neuro-psychiatric disease models.This paper summarizes the research progress of berberine in neuropsychiatric diseases and provides theoretical support for further clinical prevention and treatment of neuropsychiatric diseases.

Objective:To establish and validate a prediction model based on least absolute shrinkage and selection operator（LASSO）regression and classification and regression tree（CART）algorithm for acute kidney injury（AKI）in PICU.Methods:The prospective derivation cohort consisted of 350 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from September 2020 to January 2021.The external data set consisting of 866 critically ill children admitted to the PICU of Children′s Hospital of Soochow University from February 2021 to February 2022 was employed for the external validation.Clinical data was obtained from the electronic medical record system，including demographic characteristics，laboratory data and the pediatric risk of mortality Ⅲ（PRISM Ⅲ）score.The variables associated with AKI were identified using LASSO regression.Subsequently，a decision tree prediction model was built using the CART algorithm.The predictive value of decision tree prediction model was evaluated using the receiver operating characteristic（ROC）curve，calibration curve，and decision curve analysis.Results:Among the 350 children in the derivation cohort，107（30.6%）developed AKI during the PICU stay；and of 866 children in the external validation cohort，165（19.1%）developed AKI during the PICU stay.The LASSO regression screened 16 candidate variables for further analysis，and the decision tree model ultimately identified 4 variables more closely associated with AKI，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein.The decision tree model exhibited high accuracy with AUC of 0.92，0.88，and 0.86 in the training，internal validation，and external validation cohorts，respectively.The model demonstrated good calibration and clinical applicability based on the calibration curve and decision curve analysis.Conclusion:The decision tree model based on the 4 identified clinical indicators，including fold change in serum creatinine from baseline，urine volume，PRISM Ⅲ，and C-reactive protein，is effective for the early prediction of AKI.

AIM:To investigate the effects and underlying mechanisms of paeoniflorin(PF)on lipopolysac-charide(LPS)-induced intestinal injury in septic mice,using a combination of network pharmacology,molecular docking,and animal experiments.METHODS:Network pharmacology was used to identify key active components and therapeutic targets of Red Peony for treating sepsis.Molecular docking was performed to explore the binding affinity be-tween PF and silent information regulator 1(SIRT1).An LPS-induced mouse model of sepsis with intestinal injury was es-tablished.Samples were collected 24 h after modeling,and hematoxylin-eosin(HE)staining was performed to observe pathological changes in intestinal tissues.Chiu's scoring system was utilized to evaluate the extent of intestinal injury.En-zyme-linked immunosorbent assay(ELISA)was employed to measure levels of inflammatory factors in intestinal tissues,including interleukin-1β(IL-1β)and IL-18,as well as indicators of intestinal permeability such as diamine oxidase(DAO)and intestinal-type fatty acid-binding protein(I-FABP),alongside serum levels of D-lactate and the aerobic gly-colysis product L-lactate.Western blot analysis was performed to assess changes in protein levels of SIRT1,M2-type pyru-vate kinase(PKM2),and NOD-like receptor protein 3(NLRP3)in intestinal tissues.RESULTS:Network pharmacolo-gy suggested that paeoniflorin,an active component of Red Peony,treats sepsis by targeting SIRT1 among other proteins.Molecular docking revealed a strong binding affinity of PF with SIRT1.In vivo experimentation revealed significant patho-logical damage in intestinal tissues in the LPS group compared to the control group as evidenced by HE staining.Chiu's score,along with levels of IL-1β,IL-18,D-lactate,and L-lactate were significantly elevated,while DAO and I-FABP levels were reduced(P＜0.05).SIRT1 expression decreased,while PKM2 and NLRP3 levels increased(P＜0.05).In contrast,the LPS+PF group displayed reduced intestinal histopathological injury,lower Chiu's scores,and decreased levels of IL-1β,IL-18,D-lactate,and L-lactate,along with increased DAO and I-FABP levels(P＜0.05).Notably,SIRT1 protein expression increased while PKM2 and NLRP3 levels decreased(P＜0.05).Furthermore,compared to the LPS+PF group,the LPS+PF+EX527 group exhibited exacerbated intestinal histopathological injury,increased Chiu's scores,as well as elevated levels of IL-1β,IL-18,D-lactate,and L-lactate,alongside reduced DAO and I-FABP levels(P＜0.05),decreased SIRT1 expression,and increased PKM2 and NLRP3 levels(P＜0.05).CONCLUSION:Paeoni-florin effectively alleviates intestinal injury in mice with sepsis,potentially through the upregulation of SIRT1 expression and the inhibition of PKM2-mediated aerobic glycolysis,which subsequently reduces the activation of NLRP3 inflamma-somes,mitigates the release of inflammatory factors,and lessens intestinal inflammation.

AIM To explore the clinical effects of Jinfukang Oral Liquid combined with thymosin α1 on patients with non-small cell lung cancer due to Dual Deficiency of Qi and Yin.METHODS Seventy-five patients were randomly assigned into thymosin α1 group(15 cases)for 4-week administration,Jinfukang Oral Liquid group(30 cases)for 4-week administration,and combination group(30 cases)for 4-week administration.The changes in TCM clinical syndrome effects,immunity indices(CD3+T,Th,CTL,total NK,CD56dim CD16+NK,NKT,Treg,MDSC),lethality/inhibition ratios(CTL/Treg,total NK/Treg,NKT/Treg,CTL/MDSC,total NK/MDSC,NKT/MDSC)and FACT-L scores were detected.RESULTS The Jinfukang Oral Liquid group and combination group demonstrated higher total effective rates than the thymosin α1 group(P＜0.05).After the treatment,the Jinfukang Oral Liquid group and combination group displayed increased NKT(P＜0.05)and decreased MDSC(P＜0.05),which were more obvious than those in the thymosin α1 group(P＜0.05),and higher NKT was observable in the Jinfukang Oral Liquid group(P＜0.05);the Jinfukang Oral Liquid group and combination group displayed increased lethality/inhibition ratios(P＜0.05),among which NKT/Treg,CTL/MDSC,total NK/MDSC,NKT/MDSC were higher than those in the thymosin α1 group(P＜0.05),and higher CTL/MDSC,NKT/MDSC were observable in the Jinfukang Oral Liquid group(P＜0.05);the Jinfukang Oral Liquid group(except for physiological status,society and family status)and combination group(except for society and family status)displayed increased FACT-L scores(P＜0.05).CONCLUSION For the patients with non-small cell lung cancer due to Dual Deficiency of Qi and Yin,Jinfukang Oral Liquid single use or combined with thymosin α1 can enhance peripheral blood immune surveillance,inhibit immune escape,restore the balanced state of tumor immune responses,and improve TCM syndromes and life quality.