Regulation of cagA-Helicobacter on gastric PIM2 expression in gastric cancer.

BACKGROUND: The association between infection with cagA-positive H. pylori and an elevated susceptibility to gastric cancer has been firmly established. PIM2 is known to be overexpressed in various types of cancers; however, the specific mechanism by which cagA influences the regulation of PIM2 expression in gastric cancer remains unidentified at present. MATERIALS AND METHODS: A mutant NCTC11637ΔcagA strain of H. pylori and the eukaryotic expression vector pcDNA-cagA were constructed for evaluating PIM2 expression levels in gastric cancer cells (HGC27, SGC7901, and AG) co-cultured with the NCTC11637 and NCTC11637ΔcagA strain, as well as pcDNA-cagA and the empty vector pcDNA3.1 (+). RESULTS: Co-culturing gastric cancer cells with NCTC11637 significantly increased PIM2 expression levels (P< 0.001) compared to the negative control group. Additionally, the expression of PIM2 in cells co-cultured with NCTC11637 was higher than that co-cultured with NCTC11637ΔcagA (P< 0.001). Furthermore, successful construction of the eukaryotic expression vector pcDNA-cagA resulted in a significant increase in PIM2 mRNA expression levels after its transfection into gastric cancer cells compared to the control group after 48 hours. CONCLUSIONS: The findings indicate that H. pylori/cagA A could be one of the key factors in regulating PIM2 expression levels, potentially influencing the progression of H. pylori-related Gastric Cancer.

MSFN: a multi-omics stacked fusion network for breast cancer survival prediction.

Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge. Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multi-omics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction. Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.

The auxin-responsive CsSPL9-CsGH3.4 module finely regulates auxin levels to suppress the development of adventitious roots in tea (Camellia sinensis).

The cutting technique is extensively used in tea breeding, with key emphasis on promoting the growth of adventitious roots (ARs). Despite its importance in tea cultivation, the mechanisms underlying AR development in tea remain unclear. In this study, we demonstrated the essential role of auxins in the initiation and progression of AR and established that the application of exogenous 1-naphthaleneacetic acid-enhanced AR formation in tissue-cultured seedlings and cuttings. Then, we found that the auxin-responsive transcription factor CsSPL9 acted as a negative regulator of AR development by reducing the levels of free indole-3-acetic acid (IAA) in tea plants. Furthermore, we identified CsGH3.4 as a downstream target of CsSPL9, which was activated by direct binding to its promoter. CsGH3.4 also inhibited AR development and maintained low levels of free IAA. Thus, these results revealed the inhibitory effect of the auxin-responsive CsSPL9-CsGH3.4 module on AR development by reducing free IAA levels in tea. These findings have significant theoretical and practical value for enhancing tea breeding practices.

The characteristic VOCs of different parts of Artocarpus heterophyllus fruit based on HS-SPME-GC-MS and PTR-TOF-MS.

Insight investigation on both edible pulps and inedible parts involving inflorescence axis and shreds of Artocarpus heterophyllus Lam were carried out, a total of 98 VOCs and 201 masses were identified by the combination of HS-SPME-GC-MS and PTR-TOF-MS. Among them, according to the consistency of OAV and results of VIP > 1, p < 0.05, compounds methyl isovalerate (A2), 3-methylbutyl acetate (A5) and octanoic acid, ethyl ester (A21) were recognized as aroma markers to distinguish the pulps, shreds and inflorescence axis. Meanwhile, the inflorescence axis (IC50: 1.82 mg/mL) and shreds (IC50: 16.74 mg/mL) exhibited more excellent antioxidant potency than pulps (IC50: 17.43 mg/mL) in vitro. These findings validated the feasibility of coupling HS-SPME-GC-MS and PTR-TOF-MS for rapid detection of characteristic VOCs of this plant, and offered new prospect of fragrance utilization and waste management of the edible and inedible parts of A. heterophyllus fruit.

DP-SSLoRA: A privacy-preserving medical classification model combining differential privacy with self-supervised low-rank adaptation.

BACKGROUND AND OBJECTIVE: Concerns about patient privacy issues have limited the application of medical deep learning models in certain real-world scenarios. Differential privacy (DP) can alleviate this problem by injecting random noise into the model. However, naively applying DP to medical models will not achieve a satisfactory balance between privacy and utility due to the high dimensionality of medical models and the limited labeled samples. METHODS: This work proposed the DP-SSLoRA model, a privacy-preserving classification model for medical images combining differential privacy with self-supervised low-rank adaptation. In this work, a self-supervised pre-training method is used to obtain enhanced representations from unlabeled publicly available medical data. Then, a low-rank decomposition method is employed to mitigate the impact of differentially private noise and combined with pre-trained features to conduct the classification task on private datasets. RESULTS: In the classification experiments using three real chest-X ray datasets, DP-SSLoRA achieves good performance with strong privacy guarantees. Under the premise of ɛ=2, with the AUC of 0.942 in RSNA, the AUC of 0.9658 in Covid-QU-mini, and the AUC of 0.9886 in Chest X-ray 15k. CONCLUSION: Extensive experiments on real chest X-ray datasets show that DP-SSLoRA can achieve satisfactory performance with stronger privacy guarantees. This study provides guidance for studying privacy-preserving in the medical field. Source code is publicly available online. https://github.com/oneheartforone/DP-SSLoRA.

STAT transcription factor is indispensable for oogenesis in silkworm.

Signal Transducer and Activator of Transcription (STAT) proteins represent a critical transcription factor family with multifaceted roles in diverse fundamental eukaryotic processes. In Drosophila, STAT exerts a pivotal regulatory influence on oogenesis, governing the early differentiation of follicular cells and ensuring proper encapsulation of germ-line cells. However, the role of STAT in egg development in silkworms remains unknown. In the present study, using CRISPR/Cas9 technology, we successfully generated a strain of silkworms with targeted deletion of the STAT-L gene, which resulted in significant reproductive abnormalities observed in female moths, including shortened fallopian tubes and reduced egg production. The ovaries dissected from STAT-L knockout silkworms during the pupal stage of silkworm exhibited varying degrees of fusion among egg chambers. Additionally, paraffin sections of prepupal ovaries also revealed evidence of egg chambers fusion. To elucidate the molecular mechanism underlying the role of the STAT-L gene regulation on egg development in silkworm, we performed ovarian transcriptomic analysis following STAT-L knockout. Our findings indicated that STAT-L gene can modulate Notch signaling pathway by down-regulating APH-1 gene expression. These results suggest that STAT-L gene plays a crucial role in normal egg chamber formation in silkworms, potentially through its influence on Notch signaling pathway expression.

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.

Rapid detection of markers in green coffee beans with different primary processing treatments of Coffea arabica L. from Yunnan.

The characteristic flavor of Coffea arabica from Yunnan is largely attributed to the primary processing treatments through affecting the VOCs accumulation. Therefore, a rapid and comprehensive detection technique is needed to accurately recognize VOCs in green coffee beans with different pretreatment methods. Hence, we conducted volatile profiles and identified nine markers of three different primary processed green coffee beans from the major production areas in Yunnan with the combined of HS-SPME-GC-MS and PTR-TOF-MS. The relationships between the chemical composition and the content of VOCs in green coffee beans were elucidated. Among the markers, palmitic acid (F3), linoleic acid (F6), α-ethylidene phenylacetaldehyde (T4), and phytane (T8) contributed to the antioxidant activity of sun-exposed green coffee beans. In conclusion, the analytical technology presented here provided a general tool for an overall and rapid understanding of a detailed volatile profiles of green coffee beans in Yunnan.

Maternal tobacco exposure during pregnancy and atopic dermatitis in offspring: A systematic review and meta-analysis.

The main purpose of this review was to examine the evidence of the relationship between active smoking or passive smoking during pregnancy and atopic dermatitis in offspring. The protocol was written following the PRISMA Checklist and was registered in the PROSPERO database (registration number CRD42022381136). We implemented a comprehensive search in PubMed, Embase and Web of Science databases to identify all potentially related articles from inception through 1 December 2022. We assessed cohort studies and case-control studies using the Newcastle-Ottawa Scale (NOS), and the Joanna Briggs Institute (JBI) critical appraisal tool to assess the quality of cross-sectional studies. Heterogeneity was investigated by using Cochrane Q tests and I2 statistics. In addition, according to the research design, population source and population size, the reasons for the heterogeneity were analysed. A total of 15 observational studies were included in this analysis. Our meta-analysis suggests that atopic dermatitis in offspring is not associated with active smoking during pregnancy (pooled OR, 0.96 [95% CI 0.86-1.07]); however, it is related to passive smoking (OR, 1.52 [95% CI 1.36-1.70]). Passive smoking during pregnancy is associated with an increased risk of eczema development in offspring. More research is needed to explore the risk of active smoking and eczema development in offspring, especially the association between measurements of pregnancy cotinine levels in maternal body fluids and AD in offspring.

Drug-drug interactions prediction based on deep learning and knowledge graph: A review.

Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.

Microbial community, pathogenic bacteria and high-risk anti-biotic resistance genes at two tropical coastal beaches adjacent to villages in Hainan, China.

OBJECTIVE: The aim of the study was to explore the correlation between characteristics of microbial community, pathogenic bacteria and high-risk antibiotic-resistant genes, between coastal beaches and a multi-warm-blooded host, as well as to determine potential species biomarkers for faecal source contamination on tropical coastal beaches in China. MATERIAL AND METHODS: The 'One-Health' approach was used in a microbiological study of beaches and warm-blooded hosts. The microbial.community was analyzed using 16S rRNA gene amplicons and shotgun metagenomics on Illumina NovaSeq. RESULTS: The Chao, Simpson, Shannon, and ACE indices of non-salt beach were greater than those of salt beaches at the genus and OTU levels (P < 0.001). Bacteroidota, Halanaerobiaeota, Cyanobacteria, and Firmicutes were abundant on salt beaches (P<0.01). Human-sourced microorganisms were more abundant on salt beaches, which accounted for 0.57%. Faecalibacterium prausnitzii and Eubacterium hallii were considered as reliable indicators for the contamination of human faeces. High-risk carbapenem-resistant Klebsiella pneumoniae and the genotypes KPC-14 and KPC-24 were observed on salt beaches. Tet(X3)/tet(X4) genes and four types of MCR genes co-occurred on beaches and humans; MCR9.1 accounted for the majority. Tet(X4) found among Cyanobacteria. Although rarely reported at Chinese beaches, pathogens, such as Vibrio vulnificus, Legionella pneumophila, and Helicobacter pylori, were observed. CONCLUSIONS: The low microbial community diversity, however, did not indicate a reduced risk. The transfer of high-risk ARGs to extreme coastal environments should be given sufficient attention.

Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility.

Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies [Formula: see text]-differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.

Effect of domestic COVID-19 vaccine on the plasma D-dimer levels of early pregnant women in China.

Objective: To investigate the effect of COVID-19 vaccination on plasma D-dimer levels in early pregnant women. Methods: A total of 834 early pregnant women(gestational age ≤ 13 weeks), who visited Northwest Women and Children's Hospital between December 2020 and April 2022, were selected. There were 696 women in the healthy group (group A) and 138 in the group with a history of adverse pregnancy and childbirth (group B). The plasma D-dimer levels of all participants were tested, and the COVID-19 vaccine history of all participants was collected using a survey questionnaire. Results: The plasma D-dimer levels did not differ between group A and the group B (p = 0.1327). In the group A, 470 were vaccinated and 226 were unvaccinated. The D-dimer levels of vaccinated individuals were lower than those of unvaccinated individuals (p = 0.0047). In the group B, 84 were vaccinated and 54 were unvaccinated; no difference in D-dimer levels was found between the vaccinated and unvaccinated individuals (p = 0.0542). In the group A, the D-dimer levels of the unvaccinated group were not different from those of women vaccinated with one dose (p = 0.208), but they were higher than those who received two doses (p = 0.019) or three doses (p = 0.003). And, no significant difference in D-dimer levels was found among women who received different vaccine brands and with different vaccination times. Conclusion: This study preliminarily indicates that COVID-19 vaccination does not increase D-dimer levels in early pregnant women.

CXCL10 mediates CD8+ T cells to facilitate vessel normalization and improve the efficacy of cetuximab combined with PD-1 checkpoint inhibitors in colorectal cancer.

The immunotherapy and anti-EGFR targeted treatment occupying a pivotal position in colorectal cancer (CRC), is still limited to a group of patients who display specific molecular alterations and inevitably escape from resistance, further studies are still needed in colorectal cancer. We found that chemokine ligand 10 (CXCL10) expression correlates with intratumoral CD8+ T cell infiltration and reprograms tumor vasculatures in colorectal cancer. CXCL10 overexpression not only suppressed tumor growth but also increased CD8+ T cell infiltration and induced tumor vascular normalization in vivo. Additionally, the growth inhibition and tumor vascular normalization induced by CXCL10 can be reversed by the depletion of CD8+ T cells in vivo. Mechanically, CXCL10 interacts with VCAN to mediate tumor vascular normalization. The VCAN expression correlated inversely with the expression of CXCL10 and the infiltration of CD8+ T cells in CRC. Elevated CXCL10 expression sensitized colorectal cancer cells to cetuximab/anti-PD1 combination therapy compared with cetuximab or anti-PD1 alone. We propose that CXCL10 could be used to increase the anti-EGFR therapy and immunotherapy effect, targeting both tumor vessels and immune cells in colorectal cancer.

KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China.

Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (ß = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (ß = - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (ß = - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (ß = - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.

Correction: Structural diversity, bioactivities, and biosynthesis of natural diterpenoid alkaloids.

Correction for 'Structural diversity, bioactivities, and biosynthesis of natural diterpenoid alkaloids' by Yong Shen et al., Nat. Prod. Rep., 2020, 37, 763-796, https://doi.org/10.1039/D0NP00002G.

Clinical impact of the PAI-1 4G/5G polymorphism in Chinese patients with venous thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) is a life-threatening cardiovascular syndrome that characterized by the imbalance of hemostasis and thrombosis and the formation of thrombi in the blood vessels. The aim of this study was to elucidate the clinical impact of the PAI-1 4G/5G polymorphism in Chinese patients with VTE. METHODS: A total of 169 subjects (89 VTE, 10 hyperbilirubinemia, 10 hyperlipidemia and 60 healthy controls) were recruited at Peking Union Medical College Hospital. The accuracy of the TaqMan-MGB RT-PCR method for detecting F5 G1691A (FVL) and PAI-1 4G/5G polymorphisms was evaluated by using sequencing method as the gold standard. Besides, the association of the PAI-1 4G/5G polymorphism with susceptibility, treatment efficacy and recurrence status of VTE in Chinese population were explored. Eventually, the plasma PAI-1 antigen levels and PAI-1 4G/5G polymorphisms were determined on additional 64 subjects (32 VTE and 32 healthy controls) simultaneously. RESULTS: The TaqMan-MGB RT-PCR method was proven to be highly accurate in determining the FVL and PAI-1 4G/5G polymorphisms without interference from bilirubin and lipids in the samples. No obvious correlation of the PAI-1 4G/5G polymorphism with VTE was observed in our study by using five genetic models (allele, genotype, dominant, recessive and additive). Additionally, we also observed that individuals with the 4G/5G genotype had lower neutrophil counts and neutrophil-to-lymphocyte ratio (NLR) than the 5G/5G genotype. Furthermore, we found that the patients with the 5G/5G genotype were more likely to achieve complete recanalization compared to the 4G/4G genotype. In addition, individuals carrying the 5G/5G genotype were more likely to develop a recurrence-free status as compared to individuals with the 4G/4G or 4G/5G genotypes. PAI-1 antigen levels in the VTE group were significantly higher than those in the HC group. However, there was no significant difference in the antigen levels of PAI-1 among subjects carrying various genotypes in the VTE group or HC group. CONCLUSION: The PAI-1 4G/5G polymorphism has potential value in assessing the prognosis of Chinese patients with VTE. Our study has laid the foundation for the application of PAI-1 4G/5G polymorphism in the personalized management and monitoring of patients with VTE.

Cytokine receptor DOME controls wing disc development in Bombyx mori.

In multicellular organisms, the JAK/STAT signaling pathway is involved in cell proliferation, differentiation, apoptosis, and immune regulation. Through activation of the Stat92E transcription factor, JAK/STAT signaling induced proper wing development in Drosophila. Domeless (DOME) was the first identified invertebrate JAK/STAT receptor. However, the function of DOME in Bombyx mori development remains unclear, especially in wing morphogenesis. In this study, we isolated the cytokine receptor DOME gene in B. mori and evaluated its function in DOME-knockout models. We found that overexpression of DOME at the cellular level upregulated the expression of JAK/STAT pathway-related genes, promoted proliferation, and inhibited apoptosis. The results of the interference with DOME had the opposite effects with those of overexpression at the cellular level. Using CRISPR/Cas9 technology, we constructed a DOME-knockout transgenic silkworm strain (KO-DOME) and found that the wings of the pupa and moth stages were vesicle-shaped and smaller than those of the wild-type silkworm. Some KO-DOME silkworms were unable to extend their wings from the pupal case after eclosion. We detected the expression of cyclin and apoptosis-related genes in the wing disc of the moth stage and found that some cyclin genes, such as CyclinA, CyclinB, and CyclinD, were downregulated, whereas apoptotic genes, such as Caspase1, Caspase3, and Caspase8, were upregulated. We propose that DOME regulates cell proliferation and apoptosis by affecting the JAK/STAT signaling pathway, ultimately influencing the development of wing discs. Our study provides empirical evidence for the biological function of the silkworm DOME gene, which is essential for the normal development of wings.

The Effects of Chinese Herbal Decoction Combined with Recombinant Human Interferon α2b on MRI Imaging, Tumor Markers, and Immune Function in Patients with Renal Cell Carcinoma.

BACKGROUND: To explore the clinical efficacy of traditional Chinese medicine decoction combined with recombinant human interferon α2b in the treatment of renal cell carcinoma (RCC). METHODS: 68 RCC patients were divided into the control group and treatment group (n = 34). The control group was treated with recombinant human interferon α2b, and the treatment group was treated with traditional Chinese medicine decoction on the basis of the control group. The clinical efficacy and life quality were observed. At the same time, the changes of immune function before and after treatment were compared. RESULTS: After one course of treatment, the effective rate and clinical benefit rate of the treatment group were higher than those of the control group. The Karnofsky score in the treatment group was better than that in the control group. In improving the immune function, the treatment group was better than the control group in increasing CD3+ and CD4+ and reducing CD8+. CONCLUSION: Traditional Chinese medicine decoction combined with recombinant human interferon α2b has a good effect on the treatment of RCC. It can not only improve the common clinical symptoms of patients but also improve the quality of life and cellular immune function of patients.