[Rules of prescriptions for heart failure of dilated cardiomyopathy based on latent structure model and association rules].

The latent structure model and association rules analysis were employed to explore the compatibility rules of prescriptions for heart failure of dilated cardiomyopathy, with a view to providing theoretical support for the clinical treatment of this disease based on syndrome differentiation and the formulation of guidelines. The articles about the treatment of heart failure of dilated cardiomyopathy were retrieved from CNKI, Wanfang, VIP, and SinoMed. The database was established in Microsoft Excel 2019. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of Chinese medicine with the frequency greater than 4.00%. Furthermore, the frequency structure model was used to mine the rules of prescriptions for heart failure of dilated cardiomyopathy. The study included 175 traditional Chinese medicine(TCM) prescriptions, involving 128 Chinese medicines, with the cumulative frequency of 1 847. High-frequency medicines included Astragali Radix, Salviae Miltiorrhizae Radix et Rhizoma, Poria, Cinnamomi Ramulus, Glycyrrhizae Radix et Rhizoma, with the main effects of tonifying, activating blood, resolving stasis, and releasing exterior. A total of 17 hidden variables, 34 hidden categories, and 6 comprehensive cluster models, along with 15 core prescriptions, were obtained. According to the prescriptions, the patients mainly had the syndromes of heart-Yang and Qi deficiency, Qi deficiency and blood stasis, heart-kidney Yang deficiency or Qi-Yin deficiency. Fifty-four strong association rules were obtained through association rule analysis. The highest degree of support was observed for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Astragali Radix, while the highest degree of confidence was found for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Cinnamomi Ramulus-Ophiopogonis Radix-Astragali Radix. The heart failure of dilated cardiomyopathy, characterized by internal deficiency and excess manifestations, is attributed to deficiency, stasis, and water. These factors are closely associated with the heart, lung, and spleen. The treatment should follow the principle of invigorating Qi and warming Yang, and meanwhile the method of activating blood and resolve stasis or moving Qi and promoting urination can be adopted according to the specific syndrome of patients.

An in-depth evaluation of federated learning on biomedical natural language processing for information extraction.

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting.

Discrimination and screening of volatile metabolites in atractylodis rhizoma from different varieties using headspace solid-phase microextraction-gas chromatography-mass spectrometry and headspace gas chromatography-ion mobility spectrometry, and ultra-fast gas chromatography electronic nose.

Atractylodis rhizoma is a common bulk medicinal material with multiple species. Although different varieties of atractylodis rhizoma exhibit variations in their chemical constituents and pharmacological activities, they have not been adequately distinguished due to their similar morphological features. Hence, the purpose of this research is to analyze and characterize the volatile organic compounds (VOCs) in samples of atractylodis rhizoma using multiple techniques and to identify the key differential VOCs among different varieties of atractylodis rhizoma for effective discrimination. The identification of VOCs was carried out using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), resulting in the identification of 60 and 53 VOCs, respectively. The orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to screen potential biomarkers and based on the variable importance in projection (VIP ≥ 1.2), 24 VOCs were identified as critical differential compounds. Random forest (RF), K-nearest neighbor (KNN) and back propagation neural network based on genetic algorithm (GA-BPNN) models based on potential volatile markers realized the greater than 90 % discriminant accuracies, which indicates that the obtained key differential VOCs are reliable. At the same time, the aroma characteristics of atractylodis rhizoma were also analyzed by ultra-fast gas chromatography electronic nose (Ultra-fast GC E-nose). This study indicated that the integration of HS-SPME-GC-MS, HS-GC-IMS and ultra-fast GC E-nose with chemometrics can comprehensively reflect the differences of VOCs in atractylodis rhizoma samples from different varieties, which will be a prospective tool for variety discrimination of atractylodis rhizoma.

Effects of thumb-tack needling combined with sporting on older adult patients with knee osteoarthritis: A randomized active-control clinical trial.

AIM: The aim of this clinical study was to explore the effects of thumb-tack needling combined with sporting (TTNS) therapy on the improvement of pain and joint function in older adult patients with knee osteoarthritis (KOA). METHODS: A total of 120 older adult patients with KOA were randomly assigned to receive TTNS therapy or medicine treatment (Med group) only for 1 month. The patients were followed up for 3 months and clinical efficacies were evaluated using a visual analog scale to assess pain, the Lequesne scoring system to assess motor function, and the Western Ontario and McMaster University Osteoarthritis Index to assess KOA severity. Blood was collected to measure the levels of interleukin-6 and tumor necrotic factor-alpha using enzyme-linked immunosorbent assay. RESULTS: The data suggested that TTNS therapy resulted in a significantly higher clinical efficacy (P = 0.012). Visual analog scale score, Lequesne index, and Western Ontario and McMaster University Osteoarthritis Index of the TTNS group at the time of post-treatment (1 month) and post-follow-up (3 months) were also lower compared with the Med group. Patients in the TTNS group also showed lower levels of serum tumor necrotic factor-alpha and interleukin-6. CONCLUSIONS: TTNS therapy is more efficacious than pharmacological treatment in improving the clinical outcomes of patients with KOA, which suggests its clinical utility in the management of KOA. Geriatr Gerontol Int 2024; 24: 415-420.

Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools.

In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and ß-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and ß-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and ß-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.

[Regularity of prescriptions for sick sinus syndrome based on latent structure combined with association rules].

This study aims to mine the regularity of traditional Chinese medicine(TCM) prescriptions for sick sinus syndrome(SSS) and provide a reference for clinical syndrome differentiation and treatment. The relevant papers were retrieved from CNKI, Wanfang, VIP, and SinoMed with the time interval from inception to January 31, 2023. The relevant information from qualified papers was extracted to establish a library. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of TCMs with the frequency ≥3%, which combined with frequency descriptions, were used to explore the rules of TCM prescriptions for SSS. A total of 192 TCM prescriptions were included, involving 115 TCMs with the cumulative frequency of 1 816. High-frequency TCMs include Aconiti Lateralis Radix Praeparata, Ginseng Radix et Rhizoma, Glycyrrhizae Radix et Rhizoma, Astragali Radix, and Salviae Miltiorrhizae Radix et Rhizoma. The high-frequency medicines mainly had the effects of tonifying, releasing exterior with pungent-warm, and activating blood and resolving stasis. The analysis of the latent structure model yielded 13 hidden variables, 26 hidden classes, 8 comprehensive cluster models, and 21 core prescriptions. Accordingly, the common syndromes of SSS were inferred as heart-Yang Qi deficiency, heart-spleen Yang deficiency, heart-kidney Yang deficiency, Yang deficiency and blood stasis, both Qi and Yin deficiency and blood stasis, and Yin and Yang deficiency. The analysis of association rules predicted 30 strong association rules, among which Ginseng Radix et Rhizoma-Aconiti Lateralis Radix Praeparata had the highest support. SSS is a syndrome with Yang deficiency and Qi deficiency as the root causes and cold, phlegm, and stasis as the manifestations. The clinical treatment of SSS should focus on warming Yang and replenishing Qi, which should be supplemented with the therapies of activating blood and resolving stasis, warming interior and dissipating cold, or regulating Qi movement for resolving phlegm according to the patients' syndromes.

Modulation of oxidized low-density lipoprotein-affected macrophage efferocytosis by mitochondrial calcium uniporter in a murine model.

OBJECTIVE: Efferocytosis dysfunction contributes to the progression and rupture of atherosclerotic plaques. Efferocytosis is crucially modulated by intracytoplasmic Ca2+, and mitochondrial calcium uniporter (MCU) complex proteins serve as key channels for regulating Ca2+ concentration. Therefore, it was speculated that MCU may affect the development of atherosclerosis (AS) by regulating efferocytosis. In the present study, we aimed to investigate whether MCU could affect foam cell formation by regulating efferocytosis. METHODS: We stimulated primary macrophages (Møs) using oxidized low-density lipoprotein (ox-LDL) to mimic the atherosclerotic microenvironment and treated them with Ru360, an MCU-specific inhibitor, and UNC1062, an inhibitor of efferocytosis. Additionally, we conducted double staining to determine the Mø efferocytosis rate. We measured the expression of MCU complexes and efferocytosis-associated proteins using western blotting (WB) and real-time quantitative polymerase chain reaction (RT-qPCR), respectively. In addition, we separately detected the Ca2+ level in the cytoplasm and mitochondria (MT) using Fluo-4 AM and Rhod-2 methods. We separately determined the reactive oxygen species (ROS) level in cytoplasm and MT using dichlorodihydrofluorescein diacetate (DCFH-DA) fluorescent probing method and Mito-SOXTM superoxide indicator staining. Additionally, we conducted the enzyme-linked immunosorbent assay (ELISA) to detect the production of interleukin-6 (IL-6), interleukin-18 (IL-18), interleukin-1ß (IL-1ß), and tumor necrosis factor-alpha (TNF-α). Oil Red O staining was performed to measure cytoplasmic lipid levels. RESULTS: Ru360 attenuated ox-LDL-induced efferocytosis dysfunction, and attenuated the upregulation of MCU and MCUR1 induced by ox-LDL, and meanwhile attenuated the downregulation of MCUb induced by ox-LDL. Ru360 attenuated the decrease of intracytoplasmic Ca2+ concentration induced by ox- LDL, Ru360 also attenuated the ROS production induced by ox- LDL, attenuated the release of IL-6, IL-18, IL-1ß, and TNF-α induced by ox- LDL, and attenuated the increase of intracytoplasmic lipid content induced by ox-LDL. UNC1062 attenuated the effects of Ru360 in reducing inflammatory cytokines and intracytoplasmic lipid content. CONCLUSIONS: In this study, we found that MCU inhibition modulated intracytoplasmic Ca2+ concentration, improved impaired Mø efferocytosis, and reduced ROS generation. Macrophage efferocytosis removed apoptotic cells and prevented the release of inflammatory factor and foam cell formation, and this can be a potential new therapeutic target for alleviating atherosclerosis.

Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts.

Purpose: Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities. Approach: Retrospective full year 2021, 298,827 AP/PA chest x-ray images from 3 academic health centers across the United States and MIMIC-CXR, labeled by self-reported race, were used in this study. The image structure is removed by summing the number of each grayscale value and scaling to percent per image (PPI). The resulting data are tested using multivariate analysis of variance (MANOVA) with Bonferroni multiple-comparison adjustment and class-balanced MANOVA. Machine learning (ML) feed-forward networks (FFN) and decision trees were built to predict race (binary Black or White and binary Black or other) using only grayscale value counts. Stratified analysis by body mass index, age, sex, gender, patient type, make/model of scanner, exposure, and kilovoltage peak setting was run to study the impact of these factors on race prediction following the same methodology. Results: MANOVA rejects the null hypothesis that classes are the same with 95% confidence (F 7.38, P<0.0001) and balanced MANOVA (F 2.02, P<0.0001). The best FFN performance is limited [area under the receiver operating characteristic (AUROC) of 69.18%]. Gradient boosted trees predict self-reported race using grayscale PPI (AUROC 77.24%). Conclusions: Within chest x-rays, pixel intensity value counts alone are statistically significant indicators and enough for ML classification tasks of patient self-reported race.

The Src1-PGC1α-AP1 complex-dependent secretion of substance P induces inflammation and apoptosis in encephalomyocarditis virus-infected mice.

Substance P (SP), a neuropeptide consisting of 11 amino acid residues, is involved in the pathogenesis of encephalomyocarditis virus (EMCV)-induced myocarditis by stimulating the production of proinflammatory cytokines. However, the underlying mechanism that regulates SP production is still unknown. In this study, we report the transcriptional regulation of the Tachykinin Precursor 1 (TAC1) gene that encodes SP by a transcriptional complex composed of Steroid Receptor Coactivator 1 (Src1), Peroxisome proliferator-activated receptor-gamma coactivator 1 (PGC1α), and Activator Protein 1 (AP1) transcription factor. Infection of mice with EMCV induced the accumulation of PGC1α and increased TAC1 expression, thereby promoting the secretion of SP, initiating apoptosis, and elevating proinflammatory cytokine levels. In vitro overexpression of the Src1-PGC1α-AP1 members also induced TAC1 expression, increased the SP concentration, initiated apoptosis, and elevated proinflammatory cytokine concentrations. Depletion or inhibition of the Src1-PGC1α-AP1 complex reversed these effects. The administration of gossypol, an Src1 inhibitor, or SR1892, a PGC1α inhibitor, to EMCV-infected mice attenuated myocarditis. Taken together, our results reveal that the upregulation of TAC1 and the secretion of SP in EMCV-induced myocarditis are dependent on the Src1-PGC1α-AP1 complex. Targeting the Src1-PGC1α-AP1 complex may represent a new therapeutic strategy for myocarditis.

Regularity of prescriptions for sick sinus syndrome based on latent structure combined with association rules / 中国中药杂志

This study aims to mine the regularity of traditional Chinese medicine(TCM) prescriptions for sick sinus syndrome(SSS) and provide a reference for clinical syndrome differentiation and treatment. The relevant papers were retrieved from CNKI, Wanfang, VIP, and SinoMed with the time interval from inception to January 31, 2023. The relevant information from qualified papers was extracted to establish a library. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of TCMs with the frequency ≥3%, which combined with frequency descriptions, were used to explore the rules of TCM prescriptions for SSS. A total of 192 TCM prescriptions were included, involving 115 TCMs with the cumulative frequency of 1 816. High-frequency TCMs include Aconiti Lateralis Radix Praeparata, Ginseng Radix et Rhizoma, Glycyrrhizae Radix et Rhizoma, Astragali Radix, and Salviae Miltiorrhizae Radix et Rhizoma. The high-frequency medicines mainly had the effects of tonifying, releasing exterior with pungent-warm, and activating blood and resolving stasis. The analysis of the latent structure model yielded 13 hidden variables, 26 hidden classes, 8 comprehensive cluster models, and 21 core prescriptions. Accordingly, the common syndromes of SSS were inferred as heart-Yang Qi deficiency, heart-spleen Yang deficiency, heart-kidney Yang deficiency, Yang deficiency and blood stasis, both Qi and Yin deficiency and blood stasis, and Yin and Yang deficiency. The analysis of association rules predicted 30 strong association rules, among which Ginseng Radix et Rhizoma-Aconiti Lateralis Radix Praeparata had the highest support. SSS is a syndrome with Yang deficiency and Qi deficiency as the root causes and cold, phlegm, and stasis as the manifestations. The clinical treatment of SSS should focus on warming Yang and replenishing Qi, which should be supplemented with the therapies of activating blood and resolving stasis, warming interior and dissipating cold, or regulating Qi movement for resolving phlegm according to the patients' syndromes.

[Medication rule of traditional Chinese medicine for heart failure after myocardial infarction: based on data mining].

This study aims to explore the medication rule of traditional Chinese medicine(TCM) for heart failure after myocardial infarction via data mining. To be specific, articles on the treatment of the disease with Chinese medicine were retrieved from CNKI, Wanfang, VIP, and SinoMed and related information was collected. A database was created with Microsoft Excel 2019, and SPSS Clementine 12.0 and IBM SPSS Statistics 23.0 were applied for association rules analysis, cluster analysis, and factor analysis. Finally, a total of 81 TCM prescriptions were screened out, involving 91 medicinals with cumulative use frequency of 740. The main syndromes were Qi deficiency and blood stasis, Yang Qi deficiency and blood stasis together with retained morbid fluid, deficiency of both Qi and Yin and blood stasis. The medicinals with high-frequency were Astragali Radix, Salviae Miltiorrhizae Radix et Rhizoma, Ginseng Radix et Rhizoma, Poria, and Aconiti Lateralis Radix Praeparata. The effects of the medicinals were tonifying deficiency, activating blood and resolving stasis, and promoting urination and draining dampness. The association rules analysis yielded "Astragali Radix-Salviae Miltiorrhizae Radix et Rhizoma" "Astragali Radix-Aconiti Lateralis Radix Praeparata-Salviae Miltiorrhizae Radix et Rhizoma" "Aconiti Lateralis Radix Praeparata-Ginseng Radix et Rhizoma-Salviae Miltiorrhizae Radix et Rhizoma-Astragali Radix" combinations. Cluster analysis yielded 6 basic formulas for heart failure after myocardial infarction. Factor analysis extracted a total of 8 common factors. Heart failure after myocardial infarction is characterized by the syndrome of deficiency in nature and excess in superficiality. The core pathogenesis is "deficiency" "stasis" "retained morbid fluid", particularly "deficiency". This disease is closely related to the heart, lung, and spleen. The basic treatment principle is replenishing Qi and activating blood, and warming Yang, excreting water, and nourishing yin should also be emphasized. The common basic prescriptions, such as Siwu Decoction, Shengmai Powder, Xuefu Zhuyu Decoction, Linggui Zhugan Decoction, and Shenfu Decoction, have been discovered. This study provided data for clinical medication and drug development for heart failure after myocardial infarction.

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.

OBJECTIVE: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations. MATERIALS AND METHODS: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP). RESULTS: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation. CONCLUSION: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.

Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods: A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results: Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion: AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.

Identification and quality control strategy of impurities in Zhengqing Fengtongning injection.

Zhengqing Fengtongning injection is the sterile aqueous solution of Sinomenine Hydrochloride extracted from the root and stem of Sinomenium acutum, and is widely used to treat rheumatoid arthritis. Due to the processes of extraction, separation, purification, preparation and storage, some related impurities might be formed, which may cause side effects on patients. It is important to rapidly separate and identify the related impurities to ensure the safe use of Zhengqing Fengtongning injection. However, there are few literatures about the impurity in Zhengqing Fengtongning injection. In this work, ultra-high performance liquid chromatography- quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was developed to analyze impurities in both Zhengqing Fengtongning injection and its drug substance, with Sinomenine Hydrochloride as its active pharmaceutical ingredient (API). Six impurities of the Zhengqing Fengtongning injection were found. Structures of impurities 1 and 6 were confirmed by NMR and other impurities were identified from the fragmentation pattern of Sinomenine, the similarity of molecular weight and fragment ions in references. Finally, the HPLC analytical technique was developed to achieve the quantification of impurities 1 and 6. In addition, some reasonable suggestions are put forward on the quality control of Zhengqing Fengtongning injection and its drug substance based on the processes and structural characteristics of the related substances. The technical system established in this paper is helpful to strengthen the quality control of Zhengqing Fengtongning injection and improve production, and can also provide references for the production and quality control of similar drugs.

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review.

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.

An integration of UPLC-Q-TOF/MS and chemometrics analysis for the holistic quality evaluation of different geographical Paederia scandens.

RATIONALE: Due to the special geographical location and climate of China, there are large differences in the chemical composition and content of Paederia scandens (PS) from different origins, which will have a large impact on its efficacy. METHODS: An efficient quality control method for PS was established by combining ultra-high-performance liquid chromatography (UPLC) and electrospray ionization mass spectrometry analyses. First, a UPLC-quadrupole time-of-flight mass spectrometry system was employed to identify the chemical composition of PS from seven different origins. Then, the chemical variation in 73 batches of PS samples was subsequently investigated by quantitation of four marker compounds. RESULTS: A total of 15 common compounds were identified in the samples of PS from seven origins. And four of the marker compounds were chosen based on VIP values to characterize the differences between PS samples of different origins. The linearity ranged between 0.005 and 2.500 mg/mL; the correlation coefficients (r2 ) ranged from 0.999 to 1; the limits of detection ranged from 0.013 to 0.033 µg/mL; and the relative standard deviations for repeatability, precisions, and stabilities were below 0.2%, 1.6%, and 0.6%, respectively. CONCLUSION: The results show that the method can be effective and comprehensive in evaluating the quality of PS from different origins. And this comprehensive strategy proved to be a powerful technique used to differentiate between different geographical herbs.

Bioinformatics analysis and experimental validation of differentially expressed genes in mouse articular chondrocytes treated with IL-1ß using microarray data.

Osteoarthritis (OA) is the most prevalent chronic degenerative disease that affects the health of the elderly. The present study aimed to identify significant genes involved in OA via bioinformatics analysis. A gene expression dataset (GSE104793) was downloaded from the Gene Expression Omnibus. Bioinformatics analysis was then performed in order to identify differentially expressed genes (DEGs) between untreated chondrocytes and chondrocytes cultured with interleukin-1ß (IL-1ß) for 24 h. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using Metascape. A protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes. Gene set enrichment analysis (GSEA) was performed using GSEA software. Furthermore, chondrocytes were extracted and treated with IL-1ß (10 ng/ml) for 24 h, and reverse-transcription quantitative PCR was used to confirm differential expression of hub genes. Patient samples were also collected to verify the bioinformatic analysis results. Based on the cut-off criteria used for determination of the DEGs, a total of 844 DEGs, including 498 upregulated and 346 downregulated DEGs, were identified. The DEGs were mainly enriched in the GO terms and KEGG pathways 'inflammatory response', 'negative regulation of cell proliferation', 'ossification', 'taxis', 'blood vessel morphogenesis', 'extracellular structure organization', 'mitotic cell cycle process' and 'TNF signaling pathway'. The majority of the PCR results, namely the differential expression of kininogen 2, complement C3, cyclin B1, cell division cycle 20, cyclin A2, 1-phosphatidylinositol 4-kinase, BUB1 mitotic checkpoint serine/threonine kinase, kinesin family member 11, cyclin B2 and BUB1 mitotic checkpoint serine/threonine kinase B were consistent with the bioinformatics results. Collectively, the present observations provided a regulation network of IL-1ß-stimulated chondrocytes, which may provide potential targets of OA therapy.

Substance P prevents doxorubicin­induced cardiomyocyte injury by regulating apoptosis and autophagy: In vitro and in vivo evidence.

The function of substance P (SP) in myocardial ischemia is well understood, but its effects on congestive heart failure are unclear. The present study aimed to use in vitro and in vivo approaches to investigate the effects of SP on doxorubicin­induced cardiomyocyte injury. Pathological changes, apoptosis, cardiomyocyte ultrastructure and molecular mechanisms were evaluated in vitro and in vivo. The effects of SP on cell viability of H9c2 myocardial cells were evaluated using the Cell Counting Kit­8 and flow cytometry. B­cell lymphoma 2 (Bcl­2), Bcl­2­associated X protein (Bax), Beclin­1 and microtubule­associated protein 1A/1B­light chain 3 (LC3) were detected by western blotting. Heart failure in rats was established by intraperitoneal injection of doxorubicin. The in vitro data demonstrated that SP at concentrations of 1 µg/ml inhibited doxorubicin­induced apoptosis of H9c2 cells. Administration of doxorubicin reduced Bcl­2, Beclin­1 and LC3 expression levels in H9c2 cells, while having no effect on Bax levels. Administration of SP to these doxorubicin­treated cells did not affect Bcl­2 or Bax expression, but further reduced Beclin­1 while inhibiting the reduction in LC3 expression. In vivo, food intake was significantly increased in rats in the SP group compared with the model group. Cardiomyocytes in the heart­failure group underwent dysfunctional autophagy as ascertained by transmission electron microscopy. Compared with the heart­failure group, these pathological changes, including loss of striations and vacuolation, were inhibited by SP treatment, which promoted Bax expression, reduced Beclin­1 expression and inhibited the reduction in LC3 expression. Taken together, SP reduced cardiomyocyte apoptosis in doxorubicin­induced cardiomyocyte injury, likely by promoting autophagy, which suggested that SP is a potential therapeutic target for doxorubicin­induced heart failure.

[Determination of five saponins in Xuesaitong Dropping Pills by micellar electrokinetic chromatography and evaluation method of between-batch consistency].

The present study determined five saponins in Xuesaitong Dropping Pills(XDP) by micellar electrokinetic chromatography(MEKC), and evaluated between-batch consistency by MEKC fingerprints and similarity analysis. A background buffer was composed of 20 mmol·L~(-1) sodium tetraborate-20 mmol·L~(-1) boric acid solution(pH 8.5), 55 mmol·L~(-1) sodium dodecyl sulfate(SDS), 23 mmol·L~(-1) ß-cyclodextrin, and 13% isopropyl alcohol. All separations were performed at 25 ℃,20 kV and the detection wavelength was set at 203 nm. The separation channel was a fused silica capillary with a dimension of 75 µm I.D. and a total length of 50.2 cm(effective length of 40.0 cm). The contents of notoginsenoside R_1, and ginsenosides Rg_1, Re, Rb_1, Rd were determined with their quality control ranges set. The fingerprints of XDP were established and the between-batch consistency was evaluated by similarity analysis. The contents of five saponins from the 19 batches of XDP were stable in the fixed ranges. Statistical analysis was carried out on the results of multiple batches of samples, and the specific quality control ranges were recommended as follows: notoginsenoside R_1 21.92-34.16 mg·g~(-1), ginsenosides Rg_1 83.54-131.78 mg·g~(-1), ginsenosides Re 13.58-19.82 mg·g~(-1), ginsenosides Rb_1 89.40-129.90 mg·g~(-1), and ginsenosides Rd 22.34-35.67 mg·g~(-1). Eleven characteristic peaks were identified in the fingerprints. Five peaks, notoginsenoside R_1 and ginsenosides Rg_1, Re, Rb_1, Rd, were identified with reference standards. The similarities of the 19 batches of samples were all above 0.988, indicating good between-batch consistency. This method is green and simple, and can be used for the quantitative determination and quality evaluation of XDP. It can also provide references for the quality control of other Chinese medicinal dripping pills.

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals.

Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation. Objective: Investigate real-time performance of an AI-enabled COVID-19 diagnostic support system across a 12-hospital system. Design: Prospective observational study. Setting: Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International), Indiana University (Indiana, USA), and Emory University (Georgia, USA). Participants: Internal (training, temporal, and real-time validation): 51,592 CXRs; Public: 27,424 CXRs; External (Indiana University): 10,002 CXRs; External (Emory University): 2002 CXRs. Main Outcome and Measure: Model performance assessed via receiver operating characteristic (ROC), Precision-Recall curves, and F1 score. Results: Patients that were COVID-19 positive had significantly higher COVID-19 Diagnostic Scores (median .1 [IQR: 0.0-0.8] vs median 0.0 [IQR: 0.0-0.1], p < 0.001) than patients that were COVID-19 negative. Pre-implementation the AI-model performed well on temporal validation (AUROC 0.8) and external validation (AUROC 0.76 at Indiana U, AUROC 0.72 at Emory U). The model was noted to have unrealistic performance (AUROC > 0.95) using publicly available databases. Real-time model performance was unchanged over 19 weeks of implementation (AUROC 0.70). On subgroup analysis, the model had improved discrimination for patients with "severe" as compared to "mild or moderate" disease, p < 0.001. Model performance was highest in Asians and lowest in whites and similar between males and females. Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.