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.

Schistosomiasis is prevalent in 78 countries and territories worldwide, while the eastern and western parts of sub-Sahara Africa bear the highest disease burden due to schistosomiasis. Recently, climate change, international trade and travel, urbanization and war have increased the risk of cross-boundary importation and transmission of schistosomiasis, and schistosomiasis has increasingly become a public health concern in non-endemic countries and territories. Biomphalaria straminea, the intermediate host of Schistosoma mansoni, has colonized in southern China and its habitats continue to move northward. In addition, cross-boundary imported cases of schistosomiasis have been reported occasionally in China. However, the real number of cases may be underestimated greatly due to insufficient diagnostic capacity and weak awareness of case reporting for overseas imported schistosomiasis in healthcare facilities. It is necessary to establish a multi-party collaborative mechanism, improve corresponding systems and technical specifications, reinforce surveillance and early warning, and border management, enhance technical reserves and capability building, and improve the awareness of schistosomiasis prevention and healthcare-seeking among entry-exit personnel, in order to effectively address the threat of cross-boundary imported schistosomiasis.

Objective To analyze the structural and phylogenetic characteristics of the mitochondrial genome from Bulinus globosus, so as to provide a theoretical basis for classification and identification of species within the Bulinus genus, and to provide insights into understanding of Bulinus-schistosomes interactions and the mechanisms of parasite transmission. Methods B. globosus samples were collected from the Ruya River basin in Zimbabwe. Mitochondrial DNA was extracted from B. globosus samples and the corresponding libraries were constructed for high-throughput sequencing on the Illumina NovaSeq 6000 platform. After raw sequencing data were subjected to quality control using the fastp software, genome assembly was performed using the A5-miseq and SPAdes tools, and genome annotation was conducted using the MITOS online server. Circular maps and sequence plots of the mitochondrial genome were generated using the CGView and OGDRAW software, and the protein conservation motifs and structures were analyzed using the TBtools software. Base composition and codon usage bias were analyzed and visualized using the software MEGA X and the ggplot2 package in the R software. In addition, a phylogenetic tree was created in the software MEGA X after sequence alignment with the software MAFFT 7, and visualized using the software iTOL. Results The mitochondrial genome of B. globosus was a 13 730 bp double-stranded circular molecule, containing 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, and 13 protein-coding genes, with a marked AT preference. The mitochondrial genome composition of B. globosus was similar to that of other species within the Bulinus genus. Phylogenetic analysis revealed that the complete mitochondrial genome sequence of B. globosus was clustered with B. truncatus, B. nasutus, and B. ugandae into the same evolutionary clade, and gene superfamily analysis showed that the metabolism-related proteins of B. globosus were highly conserved, notably the cytochrome c oxidase family, which showed a significant consistency. Conclusions This is the first whole mitochondrial genome sequencing to decode the compositional features of the mitochondrial genome of B. globosus from Zimbabwe and its evolutionary relationship within the Bulinus genus, which provides important insights for further understanding of the phylogeny and mitochondrial genome characteristics of the Bulinus genus.

Objective: Adolescent depression is a highly prevalent and disabling mental disorder with unclear pathophysiology and unfavorable treatment outcomes. Recent efforts have been focusing on searching for biomarkers as specific indicators of adolescent depression. We performed a systematic literature review and meta-analysis, specifically including studies with healthy control groups as an inclusion criterion. This approach helps to avoid confounding factors and provides more accurate results regarding the inflammatory and immune biomarkers associated with adolescent depression. Methods: Three electronic databases were searched for studies comparing the means and changes in the biomarkers between depressed adolescent patients and healthy controls published in English until February 2024. Two authors independently performed the screening, quality assessment, and data extraction of the studies. A meta-analysis was conducted on outcomes reported by two or more studies using a random-effects model and presented Forrest plots and test statistics (I2) for heterogeneity analysis. Results: Nine studies were included in the review, including seven case-control studies and two cross-sectional studies. These studies included 24 target biomarkers, 13 of which were quantified in 2 or more studies. Compared to the healthy controls, the depressed adolescents had significantly higher values in ten indicators. Additionally, the depressed adolescents had lower procalcitonin levels than the healthy controls. The two groups showed no significant differences in the remaining 13 biomarkers. Conclusion Our findings offer fresh insights into the pathophysiology of inflammatory and immune aspects of adolescent depression and provide helpful guidance in developing targeted and effective intervention and prevention strategies to address adolescent depression.

The relationship between gut microbiota and depressive disorder has become a research focus in recent years. Within the microbiota-gut-brain axis, the gut microbiota influences the onset and progression of depressive disorder primarily through the tryptophan metabolic pathway. Tryptophan, an essential amino acid in humans, is subject to dual regulation by intestinal microorganisms, which modulate its metabolic balance via inflammatory stimulation and microbial metabolite production. In depression, excessive activation of the kynurenine branch of tryptophan metabolism leads to the accumulation of proinflammatory and neurotoxic metabolites, thereby exacerbating neuroinflammation in the brain. Intervention studies indicate that the antidepressant-like effects of probiotics and traditional Chinese medicine are associated with remodeling of the gut microbiota, restoration of tryptophan metabolic balance, and alleviation of neuroinflammation. Furthermore, targeted inhibition of kynurenine 3-monooxygenase can mitigate neuroinflammation by regulating microglial activity, thus improving depressive-like behaviors. In summary, the metabolite-inflammation axis represents a central node in the interaction regulation between tryptophan metabolism and the microbiota-gut-brain axis. This provides a theoretical foundation for developing novel therapeutic strategies targeting depression through modulation of gut microbiota-mediated tryptophan metabolism.
Tryptophan/metabolism* ; Gastrointestinal Microbiome/physiology* ; Humans ; Depressive Disorder/microbiology* ; Probiotics/therapeutic use* ; Brain/metabolism* ; Kynurenine/metabolism* ; Metabolic Networks and Pathways ; Animals ; Medicine, Chinese Traditional