Objective: To compare the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) model, the Prophet model, and the Bayesian structural time series (BSTS) model in forecasting the incidence of hand, foot, and mouth disease (HFMD) , so as to provide a basis for optimizing the early warning system of this disease. Methods: Weekly incidence data of HFMD in Longgang District, Shenzhen City from 2014 to 2024 were collected. The HFMD incidence data from 2014-2019 and 2023 were used as the training set to construct SARIMA, Prophet, and BSTS models, while the data from 2024 were used as the test set to compare and evaluate the predictive performance of the three models. The technique for order preference by similarity to ideal solution (TOPSIS) method was employed to calculate the C-value. This approach integrates multiple evaluation metrics, such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE), to comprehensively assess model performance. Results: A total of 150 111 cases of HFMD were reported in Longgang District from 2014 to 2024, with an average annual incidence of 400.72/105. The weekly incidence fluctuated between 0 and 63.78/105, exhibiting a bimodal seasonal pattern characterized by a primary peak from May to July and a secondary peak from September to October. In the training set, all three models demonstrated a good fit to the bimodal epidemic trend of HFMD, with the BSTS model achieving the best fit. The BSTS model yielded performance metrics as follows: MAE=0.124, MSE=0.050, RMSE=0.223, SMAPE=0.021, and a C-value of 1.000. In the test set, all three models, including SARIMA, Prophet, and BSTS, performed well for short-term predictions (≤16 weeks), with the Prophet model showing relatively superior predictive performance. However, the prediction accuracy of all models declined as the forecast horizon extended. During the primary peak period (May-July), the Prophet model exhibited better predictive performance, whereas the BSTS model performed relatively better during the secondary peak period (September-October). Conclusions For the short-term forecasting of weekly HFMD incidence, the Prophet model outperformed both the SARIMA and BSTS models. During the primary peak period, the Prophet model demonstrated superior predictive performance, whereas the BSTS model exhibited better accuracy in forecasting the secondary peak period.

Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

Traditional Chinese medicine(TCM) solid waste is characterized by widespread availability, renewability, and substantial production volume. In the context of the "dual carbon" goals, the pyrolysis of TCM solid waste for producing fuel gas for recycling in pharmaceutical production has emerged as a crucial strategy for optimizing the energy structure in the TCM industry and developing renewable energy. This paper comprehensively reviews both internal and external factors that influence the pyrolysis of TCM solid waste. Internal factors encompass moisture content, particle size, ash content, and the morphology of the raw materials, while external factors include pyrolysis conditions, equivalence ratios, types of gasifiers, and gasifying agents. Furthermore, this paper details the challenges associated with the pyrolysis of TCM solid waste, such as the dispersion of feedstocks, the diversity of resources, the complexity of the pyrolysis process, and the variations in gasifier performance. Finally, this paper proposes measures to address these challenges. This paper aims to provide insights into the development of a circular economy for TCM resources and the advancement of low-carbon energy utilization in the TCM industry.
Pyrolysis ; Carbon/chemistry* ; Medicine, Chinese Traditional ; Solid Waste/analysis* ; Drugs, Chinese Herbal/chemistry* ; Gases/chemistry*

The combination of Aidi Injection(ADI) and doxorubicin(DOX) is a common strategy in the treatment of cancer, which can achieve synergistic anti-tumor effects while attenuating the cardiotoxicity caused by DOX. This study aims to investigate the mechanism of ADI in attenuating DOX-induced cardiotoxicity by multi-omics. DOX was used to induce cardiotoxicity in mice, and the cardioprotective effects of ADI were evaluated based on biochemical indicators and pathological changes. Based on the results, transcriptomics, proteomics, and metabolomics were employed to analyze the changes of endogenous substances in different physiological states. Furthermore, data from multiple omics were integrated to screen key regulatory pathways by which ADI attenuated DOX-induced cardiotoxicity, and important target proteins were selected for measurement by ELISA kits and immunohistochemical analysis. The results showed that ADI significantly reduced the levels of cardiac troponin T(cTnT) and N-terminal pro-B-type natriuretic peptide(NT-proBNP) and effectively ameliorated myocardial fibrosis and intracellular vacuolization, indicating that ADI showed therapeutic effect on DOX-induced cardiotoxicity. The transcriptomics analysis screened out a total of 400 differentially expressed genes(DEGs), which were mainly enriched in inflammatory response, oxidative stress, and myocardial fibrosis. After proteomics analysis, 70 differentially expressed proteins were selected, which were mainly enriched in the inflammatory response, cardiac function, and energy metabolism. A total of 51 differentially expressed metabolites were screened by the metabolomics analysis, and they were mainly enriched in multiple signaling pathways, including the inflammatory response, lipid metabolism, and energy metabolism. The integrated data of multiple omics showed that linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism pathways played an important role in DOX-induced cardiotoxicity, and ADI may exert therapeutic effects by modulating these pathways. Target validation experiments suggested that ADI significantly regulated abnormal protein levels of cyclooxygenase-1(COX-1), cyclooxygenase-2(COX-2), prostaglandin H2(PGH2), and prostaglandin D2(PGD2) in the model group. In conclusion, ADI may attenuate DOX-induced cardiotoxicity by regulating linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism, thus alleviating inflammation of the body.
Doxorubicin/toxicity* ; Animals ; Mice ; Cardiotoxicity/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Proteomics ; Metabolomics ; Injections ; Humans ; Multiomics

Quality control is a key link in the modernization process of traditional Chinese medicine(TCM). Studies have shown that the effects of active components in TCM depend on not only their chemical composition but also their suitable physical forms and states. The physical phase structures, such as micelles, vesicles, gels, and nanoparticles, can improve the solubility, delivery efficiency, and targeting precision of active components. These structures significantly enhance the pharmacological activity while reducing the toxicity and side effects, demonstrating functional activity surpassing that of active components and highlighting the key effects of "structures" on "functions" of active components. Taking the physical phase structure as a breakthrough point, this paper outlines the common types of TCM physical phase structures. Furthermore, this paper explores how to realize the quality upgrading of TCM through the precise regulation of physical phase structures based on the current applications and potential of TCM physical phase structures in processing to increase the efficacy and reduce the toxicity, compounding and decocting processes, drug delivery systems, and quality control, aiming to provide novel insights for the future quality control of TCM.
Quality Control ; Drugs, Chinese Herbal/standards* ; Medicine, Chinese Traditional/standards* ; Humans ; Drug Delivery Systems

Interferon regulatory factor 4 (IRF4) is a critical transcription factor that governs the differentiation of cluster of differentiation 4⁺ (CD4⁺) T cells. The pathogenesis and progression of psoriasis are primarily attributed to an immune imbalance stemming from the overproduction of interleukin-17A (IL-17A) by T lymphocytes. However, the role of IRF4 in psoriasis remains unexplored. In this study, we found that IRF4 activity is increased in the cutaneous lesions of patients with psoriasis in response to stimulation by IL-23A and IL-1β. This IRF4 elevation heightens its binding to the E1A binding protein p300 (EP300) promoter, triggering the transcription of downstream retinoic acid receptor-related orphan receptor-γt (RORγt) and increasing the secretion of IL-17A, thereby establishing the IL-1β/IL-23A-IRF4-EP300-RORC-IL-17A inflammatory cascade in psoriasis. The alleviation of imiquimod (IMQ)-induced psoriatic-like symptoms was achieved through the creation of a Irf4 ^-/- gene deletion mouse model and pharmacological inhibition using antisense oligonucleotides targeted for Irf4. This amelioration was accompanied by a decreased number of IL-17A-producing CD4⁺ T cells in the skin. The findings of this study suggest that IRF4 plays a crucial role in the promotion of inflammation and exacerbation of IMQ-induced psoriasiform dermatitis. Consequently, IRF4 targeting could be a promising therapeutic strategy.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

In recent decades, the prevalence of hyperuricemia and gout has increased dramatically due to lifestyle changes. The drugs currently recommended for hyperuricemia are associated with adverse reactions that limit their clinical use. In this study, we report that berberine (BBR) is an effective drug candidate for the treatment of hyperuricemia, with its mechanism potentially involving the modulation of gut microbiota and its metabolite, succinic acid. BBR has demonstrated good therapeutic effects in both acute and chronic animal models of hyperuricemia. In a clinical trial, oral administration of BBR for 6 months reduced blood uric acid levels in 22 participants by modulating the gut microbiota, which led to an increase in the abundance of Bacteroides and a decrease in Clostridium sensu stricto_1. Furthermore, Bacteroides fragilis was transplanted into ICR mice, and the results showed that Bacteroides fragilis exerted a therapeutic effect on uric acid similar to that of BBR. Notably, succinic acid, a metabolite of Bacteroides, significantly reduced uric acid levels. Subsequent cell and animal experiments revealed that the intestinal metabolite, succinic acid, regulated the upstream uric acid synthesis pathway in the liver by inhibiting adenosine monophosphate deaminase 2 (AMPD2), an enzyme responsible for converting adenosine monophosphate (AMP) to inosine monophosphate (IMP). This inhibition resulted in a decrease in IMP levels and an increase in phosphate levels. The reduction in IMP led to a decreased downstream production of hypoxanthine, xanthine, and uric acid. BBR also demonstrated excellent renoprotective effects, improving nephropathy associated with hyperuricemia. In summary, BBR has the potential to be an effective treatment for hyperuricemia through the gut-liver axis.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.