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.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

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.

INTRODUCTION: Lecanemab has shown promise in treating early Alzheimer's disease (AD), but its safety and efficacy in Chinese populations remain unexplored. This study aimed to evaluate the safety and 6-month clinical outcomes of lecanemab in Chinese patients with mild cognitive impairment (MCI) or mild AD. METHODS: In this single-arm, real-world study, participants with MCI due to AD or mild AD received biweekly intravenous lecanemab (10 mg/kg). The study was conducted at Hainan Branch, Ruijin Hospital Shanghai Jiao Tong University School of Medicine. Patient enrollment and baseline assessments commenced in November 2023. Safety assessments included monitoring for amyloid-related imaging abnormalities (ARIA) and other adverse events. Clinical and biomarker changes from baseline to 6 months were evaluated using cognitive scales (mini-mental state examination [MMSE], montreal cognitive assessment [MoCA], clinical dementia rating-sum of boxes [CDR-SB]), plasma biomarker analysis, and advanced neuroimaging. RESULTS: A total of 64 patients were enrolled in this ongoing real-world study. Safety analysis revealed predominantly mild adverse events, with infusion-related reactions (20.3%, 13/64) being the most common. Of these, 69.2% (9/13) occurred during the initial infusion and 84.6% (11/13) did not recur. ARIA-H (microhemorrhages/superficial siderosis) and ARIA-E (edema/effusion) were observed in 9.4% (6/64) and 3.1% (2/64) of participants, respectively, with only two symptomatic cases (one ARIA-E presenting with headache and one ARIA-H with visual disturbances). After 6 months of treatment, cognitive scores remained stable compared to baseline (MMSE: 22.33 ± 5.58 vs . 21.27 ± 4.30, P = 0.733; MoCA: 16.38 ± 6.67 vs . 15.90 ± 4.78, P = 0.785; CDR-SB: 2.30 ± 1.65 vs . 3.16 ± 1.72, P = 0.357), while significantly increasing plasma amyloid-β 42 (Aβ42) (+21.42%) and Aβ40 (+23.53%) levels compared to baseline. CONCLUSIONS: Lecanemab demonstrated a favorable safety profile in Chinese patients with early AD. Cognitive stability and biomarker changes over 6 months suggest potential efficacy, though high dropout rates and absence of a control group warrant cautious interpretation. These findings provide preliminary real-world evidence for lecanemab's use in China, supporting further investigation in larger controlled studies. REGISTRATION ClinicalTrials.gov , NCT07034222.
Humans ; Alzheimer Disease/drug therapy* ; Male ; Female ; Aged ; Middle Aged ; Cognitive Dysfunction/drug therapy* ; Aged, 80 and over ; Amyloid beta-Peptides/metabolism* ; Biomarkers ; East Asian People

To explore the anti-depression effect of Suanzaoren Decoction(SZRD), the regulatory effects on endogenous metabolites in the liver of rats with depression induced by chronic unpredictable mild stress(CUMS) were analyzed by using LC-MS metabolomics. The rats were randomly divided into normal control group, model group, low-dose SZRD group, high-dose SZRD group, and positive drug group. The CUMS depression model was replicated by applying a variety of stimuli, such as fasting and water deprivation, ice water swimming, hot water swimming, day and night reversal, tail clamping, and restraint for rats. Modeling and treatment were conducted for 56 days. The behavioral indexes of rats in each group, including body weight, open field test, sucrose preference test, and tail suspension test, were observed. Plasma samples and liver tissue samples were collected, and the contents of 5-hydroxytryptamine(5-HT), dopamine(DA), and norepinephrine(NE) in plasma were measured using enzyme-linked immunosorbent assay(ELISA). Meanwhile, the regulatory effects of SZRD on the liver metabolic profile of CUMS model rats were analyzed by the LC-MS metabolomics method. The results show that SZRD can significantly improve the depression-like behavior of CUMS model rats and increase the neurotransmitter levels of 5-HT, DA, and NE in plasma. A total of 24 different metabolites in the rats' liver are identified using the LC-MS metabolomics method, and SZRD can reverse 13 of these metabolites. Metabolic pathway analysis indicates that nine metabolic pathways are found to be significantly associated with depression, and in the low-dose SZRD group, four pathways can be regulated, including pentose phosphate pathway, purine metabolism, inositol phosphate metabolism, and sphingolipid metabolism. In the high-dose SZRD group, two metabolic pathways can be regulated, including sphingolipid metabolism and glycerol glycerophospholipid metabolism. Sphingolipid metabolism is a metabolic pathway that can be regulated by SZRD at different doses, so it is speculated that it may be the primary pathway through which SZRD can alleviate metabolic disturbances in the liver of CUMS model rats.
Animals ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Depression/metabolism* ; Male ; Liver/drug effects* ; Rats, Sprague-Dawley ; Antidepressive Agents/administration & dosage* ; Serotonin/blood* ; Humans ; Disease Models, Animal ; Behavior, Animal/drug effects*

This study employed 16S r RNA gene high-throughput sequencing and metabolomics to explore the mechanism of Angelicae Dahuricae Radix polysaccharides(RP) in the treatment of ulcerative colitis(UC). A mouse model of UC was induced with 2. 5% dextran sulfate sodium. The therapeutic effects of RP on UC in mice were evaluated based on changes in body weight, disease activity index( DAI), and colon length, as well as pathological changes. RT-qPCR was performed to assess the m RNA levels of interleukin(IL)-6, IL-1β, tumor necrosis factor(TNF)-α, myeloperoxidase(MPO), mucin 2(Muc2), Occludin, Claudin2, and ZO-1 in the mouse colon tissue. ELISA was employed to measure the expression of IL-1β and TNF-α in the colon tissue. The intestinal permeability of mice was evaluated by the fluorescent dye permeability assay. Immunohistochemistry was employed to detect the expression of Muc2 and occludin in the colon tissue. Changes in gut microbiota and metabolites were analyzed by 16S r RNA sequencing and ultra-high-performance liquid chromatography coupled with quadrupole-orbitrap mass spectrometry( UPLC-Q-Exactive Plus Orbitrap MS), respectively. The results indicated that low-dose RP alleviated general symptoms, reduced colonic inflammation and intestinal permeability, and promoted Muc2 secretion and tight junction protein expression in UC mice. In addition, low-dose RP increased gut microbiota diversity in UC mice and decreased the relative abundance of harmful bacteria such as Ochrobactrum and Streptococcus. Twenty-seven differential metabolites were identified in feces, and low-dose RP restored the levels of disturbed metabolites. Notably, arginine and proline metabolism were the most significantly altered amino acid metabolic pathways following lowdose RP intervention. In conclusion, RP can ameliorate general symptoms, inhibit colonic inflammation, and maintain intestinal mucosal barrier integrity in UC mice by modulating gut microbiota composition and arginine and proline metabolism.
Animals ; Gastrointestinal Microbiome/drug effects* ; Colitis, Ulcerative/genetics* ; Mice ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Polysaccharides/administration & dosage* ; Angelica/chemistry* ; Humans ; Colon/metabolism* ; Disease Models, Animal ; Mucin-2/metabolism* ; Tumor Necrosis Factor-alpha/metabolism*