High-Risk Hepatocellular Carcinoma: Hepatic Arterial Infusion Chemotherapy versus Transarterial Chemoembolization.

Objective: To compare the efficacy and safety of hepatic arterial infusion chemotherapy (HAIC) with transarterial chemoembolization (TACE) for the treatment of high-risk hepatocellular carcinoma (hHCC) patients. Methods: Between January 2014 and August 2022, a total of 1765 consecutive patients with hHCC who underwent initial intra-arterial therapies were reviewed and divided into a TACE group (n, 507) and a HAIC group (n, 426). The study used propensity score matching (PSM) to reduce selectivity bias. Overall survival (OS) and progression-free survival (PFS) were compared using Kaplan‒Meier curves with the Log rank test. The objective response rate (ORR), conversion surgery rate (CSR) adverse event (AE) comparison and subgroup analysis were performed between the two groups. Results: After PSM 1:1, 444 patients were divided into two groups. The patients with hHCC who received HAIC had higher median PFS (6.1 vs 3.3 months, P < 0.001) and OS (10.3 vs 8.2 months, P=0.303) than TACE. Higher ORR (24.8% vs 11.7%) and CSR (15.5% vs 8.9%) were found in the HAIC group than in the TACE group (both P < 0.05). The incidence of grade 3/4 AE was 23.9% and 8.1% in the TACE and HAIC groups, respectively. The subgroup analysis suggest that HAIC appeared to particularly benefit patients with tumor diameter of more than 10 centimeters (hazard ratio [HR], 0.6; 95% CI, 0.47-0.77; p, 0.00) and PVTT Vp4 (HR, 0.56; 95% CI, 0.39-0.8; P, 0.01) for PFS outperforming TACE. Conclusion: HAIC can provide better disease control for hHCC than cTACE, with a comparable long-term OS and safety.

Recent five-year progress in the impact of gut microbiota on vaccination and possible mechanisms.

Vaccine is the most effective way to prevent the spread of communicable diseases, but the immune response induced by it varies greatly between individuals and populations in different regions of the world. Current studies have identified the composition and function of the gut microbiota as key factors in modulating the immune response to vaccination. This article mainly reviews the differences in gut microbiota among different groups of vaccinated people and animals, explores the possible mechanism of vaccine immunity affected by gut microbiota, and reviews the strategies for targeting gut microbiota to improve vaccine efficacy.

Clinical Evidence for Association of Acupuncture with Improved Major Depressive Disorder: A Systematic Review and Meta-Analysis of Randomized Control Trials.

INTRODUCTION: Depression is the most significant contributor to non-fatal health reductions worldwide. Acupuncture is the most commonly used complementary alternative therapy to relieve major depressive disorder (MDD) effectively. Nevertheless, the effects of acupuncture for MDD are uncertain. This review aimed to determine the efficacy and safety of acupuncture for MDD. METHODS: Meta-analysis was performed for randomized controlled trials of acupuncture for MDD data from eight databases searched from inception until February 10, 2022. All RCTs with adult participants undergoing acupuncture treatment for MDD were included. The primary outcome measure was the Hamilton rating scale for depression (HAMD). We used random-effects meta-analysis to synthesize the results with a mean difference or odds ratio. Furthermore, the potential heterogeneity was tested through meta-regression/subgroup analyses/sensitive analysis. The quality of evidence for each outcome was assessed by the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: Forty-three studies were included: 9 acupuncture versus sham acupuncture (n = 920), 26 acupuncture versus antidepressants (n = 2169), and 9 acupuncture plus antidepressants versus antidepressants (n = 667). Of the 43 high-quality articles, 24 and 8 were determined to have a low and moderate risk of bias, respectively. The pooled results for HAMD and SDS revealed the clinical benefits of acupuncture or acupuncture plus antidepressants compared to sham acupuncture or antidepressants, with high-quality evidence. Furthermore, high-quality evidence showed that acupuncture led to fewer adverse effects than antidepressants. CONCLUSIONS: Acupuncture or acupuncture plus antidepressants were significantly associated with reduced HAMD scores, with high-quality evidence. Also, more rigorous trials are needed to identify the optimal frequency of acupuncture for MDD and integrate such evidence into clinical care to reduce antidepressant use.

The dose-effect association between acupuncture sessions and its effects on major depressive disorder: A meta-regression of randomized controlled trials.

BACKGROUND: The benefits of acupuncture on Major depressive disorder (MDD) have been well established in previous studies. However, uncertainty exists regarding the dose-effect relationship between acupuncture and MDD. This study aims to explore the association between acupuncture and its effects on MDD based on previously published data. METHODS: Nine databases were searched from inception until 10th September 2021. Randomized controlled trials that compared acupuncture with sham acupuncture, or anti-depressants, were included. The data extraction, and assessing the data quality and risk of bias completed by two researcher, respectively. A non-linear meta-regression approach with restricted cubic spline was used to investigate the dose-effect relationship between acupuncture sessions and their effects on the Hamilton rating scale for depression (HAMD) score. RESULTS: Of the 20,835 citations screened, 62 studies (2269 patients of MDD) were included. The dose-effect meta-analysis suggested that acupuncture session was associated with a decline in HAMD scores. Overall, an increase in the number of acupuncture sessions received was associated with symptom improvement in MDD patients. After 8 acupuncture sessions, the HAMD score decreased from 17.68 (95% CI: -11.81, -4.80) to 8.30 (95% CI: 14.23-21.13). After 24 acupuncture sessions, a decrease in HAMD scores was observed in 51% of cases (95% CI: 48% to 54%). After 36 acupuncture sessions, the effect of improvement in HAMD scores peaked at 66% of cases (95% CI: 59% to 72%). CONCLUSIONS: A dose-effect relationship was found between the number of acupuncture sessions and HAMD scores. 36 acupuncture sessions were associated with optimal clinical response. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021290143.

Balanced Convolutional Neural Networks for Pneumoconiosis Detection.

Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience of radiologists, which affects rapid detection on large populations. Recent research focuses on computer-aided detection based on machine learning. These have achieved high accuracy, among which artificial neural network (ANN) shows excellent performance. However, due to imbalanced samples and lack of interpretability, wide utilization in clinical practice meets difficulty. To address these problems, we first establish a pneumoconiosis radiograph dataset, including both positive and negative samples. Second, deep convolutional diagnosis approaches are compared in pneumoconiosis detection, and a balanced training is adopted to promote recall. Comprehensive experiments conducted on this dataset demonstrate high accuracy (88.6%). Third, we explain diagnosis results by visualizing suspected opacities on pneumoconiosis radiographs, which could provide solid diagnostic reference for surgeons.

Long-term prediction for temporal propagation of seasonal influenza using Transformer-based model.

Influenza is one of the most common infectious diseases worldwide, which causes a considerable economic burden on hospitals and other healthcare costs. Predicting new and urgent trends in epidemiological data is an effective way to prevent influenza outbreaks and protect public health. Traditional autoregressive(AR) methods and new deep learning models like Recurrent Neural Network(RNN) have been actively studied to solve the problem. Most existing studies focus on the short-term prediction of influenza. Recently, Transformer models show superior performance in capturing long-range dependency than RNN models. In this paper, we develop a Transformer-based model, which utilizes the potential of the Transformer to increase the prediction capacity. To fuse information from data of different sources and capture the spatial dependency, we design a sources selection module based on measuring curve similarity. Our model is compared with the widely used AR models and RNN-based models on USA and Japan datasets. Results show that our approach provides approximate performance in short-term forecasting and better performance in long-term forecasting.

Propagation source identification of infectious diseases with graph convolutional networks.

Source identification in networks has drawn considerable interest to understand and control the infectious disease propagation processes. It is usually difficult to achieve both high accuracy and short error distance when we try to solve the problem. This study attempts to introduce the graph convolutional network for the problem of source identification in a given network with different infection rates. First, we put forward a label propagation framework, which can locate the infection source based on both infected and uninfected nodes. Then, a novel Source Identification Graph Convolutional Network (SIGN) framework is proposed inspired by label propagation. Third, we modify the classical cross-entropy loss function and presented neighborhood loss to optimize the average error distance. Finally, extensive experiments are performed on eight datasets with different topologies and varying infection sizes to demonstrate the effectiveness of the proposed algorithms. We compare the proposed method with four mainstream approaches, and our method shows strong performances especially under the large infection size.

Source identification of infectious diseases in networks via label ranking.

BACKGROUND: Outbreaks of infectious diseases would cause great losses to the human society. Source identification in networks has drawn considerable interest in order to understand and control the infectious disease propagation processes. Unsatisfactory accuracy and high time complexity are major obstacles to practical applications under various real-world situations for existing source identification algorithms. METHODS: This study attempts to measure the possibility for nodes to become the infection source through label ranking. A unified Label Ranking framework for source identification with complete observation and snapshot is proposed. Firstly, a basic label ranking algorithm with complete observation of the network considering both infected and uninfected nodes is designed. Our inferred infection source node with the highest label ranking tends to have more infected nodes surrounding it, which makes it likely to be in the center of infection subgraph and far from the uninfected frontier. A two-stage algorithm for source identification via semi-supervised learning and label ranking is further proposed to address the source identification issue with snapshot. RESULTS: Extensive experiments are conducted on both synthetic and real-world network datasets. It turns out that the proposed label ranking algorithms are capable of identifying the propagation source under different situations fairly accurately with acceptable computational complexity without knowing the underlying model of infection propagation. CONCLUSIONS: The effectiveness and efficiency of the label ranking algorithms proposed in this study make them be of practical value for infection source identification.

Identification of the Association Between Toll-Like Receptors and T-Cell Activation in Takayasu's Arteritis.

To explore the relationships between Toll-like receptors (TLRs) and the activation and differentiation of T-cells in Takayasu's arteritis (TAK), using real-time fluorescence quantitative polymerase chain reaction, mRNA abundance of 29 target genes in peripheral blood mononuclear cells (PBMCs) were detected from 27 TAK patients and 10 healthy controls. Compared with the healthy control group, the untreated TAK group and the treated TAK group had an increased mRNA level of TLR2 and TLR4. A sample-to-sample matrix revealed that 80% of healthy controls could be separated from the TAK patients. Correlation analysis showed that the inactive-treated TAK group exhibited a unique pattern of inverse correlations between the TLRs gene clusters (including TLR1/2/4/6/8, BCL6, TIGIT, NR4A1, etc) and the gene cluster associated with T-cell activation and differentiation (including TCR, CD28, T-bet, GATA3, FOXP3, CCL5, etc). The dynamic gene co-expression network indicated the TAK groups had more active communication between TLRs and T-cell activation than healthy controls. BCL6, CCL5, FOXP3, GATA3, CD28, T-bet, TIGIT, IκBα, and NR4A1 were likely to have a close functional relation with TLRs at the inactive stage. The co-expression of TLR4 and TLR6 could serve as a biomarker of disease activity in treated TAK (the area under curve/sensitivity/specificity, 0.919/100%/90.9%). The largest gene co-expression cluster of the inactive-treated TAK group was associated with TLR signaling pathways, while the largest gene co-expression cluster of the active-treated TAK group was associated with the activation and differentiation of T-cells. The miRNA sequencing of the plasma exosomes combining miRDB, DIANA-TarBase, and miRTarBase databases suggested that the miR-548 family miR-584, miR-3613, and miR-335 might play an important role in the cross-talk between TLRs and T-cells at the inactive stage. This study found a novel relation between TLRs and T-cell in the pathogenesis of autoimmune diseases, proposed a new concept of TLR-co-expression signature which might distinguish different disease activity of TAK, and highlighted the miRNA of exosomes in TLR signaling pathway in TAK.

Screening and identification of potential biomarkers and therapeutic targets for systemic sclerosis-associated interstitial lung disease.

Objectives: This study aims to analyze gene expression in lung tissue and lung fibroblasts of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) to identify potential biomarkers and therapeutic targets and to examine its possible role in the pathogenesis of SSc-ILD. Patients and methods: We obtained datasets from Gene Expression Omnibus (GEO) database, and used Robust Rank Aggregation to calculate the co-expressed differentially-expressed-genes (DEGs) in three chips, then analyzed the function, signaling pathways and the protein-protein interaction network of the DEGs. Finally, we verified the DEGs related to SSc-ILD by three databases of Comparative Toxicogenomics Database (CTD), GENE, and DisGeNET, respectively. Results: There were 16 co-expressed DEGs related to SSc-ILD in three GEO series, of which six genes were upregulated, and 10 genes were downregulated. The CTD included 29,936 genes related to SSc, and the GENE and DisGeNET databases had 429 genes related to SSc. Conclusion: The results of gene differential expression analysis suggest that interleukin-6, chemokine ligand 2, intercellular adhesion molecule 1, tumor necrosis factor alpha-induced protein 3, pentraxin 3, and cartilage oligomeric matrix protein may be implicated in the pathogenesis of SSc-ILD and are expected to be potential biomarkers and therapeutic targets for SSc-ILD.

Analysis of COVID-19 Prevention and Control Effects Based on the SEITRD Dynamic Model and Wuhan Epidemic Statistics.

Since December 2019, millions of people worldwide have been diagnosed with COVID-19, which has caused enormous losses. Given that there are currently no effective treatment or prevention drugs, most countries and regions mainly rely on quarantine and travel restrictions to prevent the spread of the epidemic. How to find proper prevention and treatment methods has been a hot topic of discussion. The key to the problem is to understand when these intervention measures are the best strategies for disease control and how they might affect disease dynamics. In this paper, we build a transmission dynamic model in combination with the transmission characteristics of COVID-19. We thoroughly study the dynamical behavior of the model and analyze how to determine the relevant parameters, and how the parameters influence the transmission process. Furthermore, we subsequently compare the impact of different control strategies on the epidemic, the variables include intervention time, control duration, control intensity, and other model parameters. Finally, we can find a better control method by comparing the results under different schemes and choose the proper preventive control strategy according to the actual epidemic stage and control objectives.

The effect of acupuncture on emotional disorders in patients with insomnia: A protocol for systemic review and meta-analysis.

BACKGROUND: Insomnia with high incidence is usually accompanied by many other diseases, especially mental disorders with the under-diagnosis and under-treatment. Some studies demonstrated that acupuncture may be effective for emotional disorders accompanied by insomnia. The systematic review protocol is designed to guiding analysis the effectiveness and safety of acupuncture for emotional disorders in patients with insomnia. METHODS: Seven databases, Cochrane central register of controlled trials, Medline, Embase, China National Knowledge Infrastructure, Chinese Biomedical Literature database, VIP database and Wanfang database, will be searched from initial to December 2020. Randomized controlled trials (RCTs) of acupuncture for insomnia with emotional disorders (depression and anxiety) outcomes, which were reported in Chinese or English, will be included. The primary outcome is the change of degree of anxiety and depression. Study selection, data extraction and assessment of the risk of bias will be performed independently by 2 or more reviewers. Available data will be synthesized and statistically analyzed in RevMan V.5.3. The model of fixed effects will be used for the pooled data when the heterogeneity tests show little or no statistical heterogeneity (I2 < 50%). The random-effects model will be taken with heterogeneous data (50% ≤ I2 < 75%). RESULTS: The effect of acupuncture on emotional disorders in patients with insomnia will be assessed on Hamilton anxiety Scale, Hamilton anxiety Scale, Pittsburgh Sleep Quality Index, Insomnia Severity Index, Self-rating Anxiety Scale, Self-rating Depressive Scale and the number of participants secede and the number of patients reported adverse events. CONCLUSION: the emotional disorders interaction with insomnia and the increase of risk on disease evolving and insomnia-related burden, it is so momentous to know that the role of insomnia treatment on comorbidities. We should concern about the management of emotional disorders when treat insomnia, and acupuncture treatment anxiety and depression caused by insomnia may be effective. ETHICS AND DISSEMINATION: Ethics approval is not be needed because the data will not contain individual patient data, and there are no concerns about privacy. The results of this meta-analysis will be disseminated through publication in a peer-reviewed academic journal or relevant conference. INPLASY REGISTRATION NUMBER: INPLASY2020100115.

Thermal Decomposition Mechanism and Kinetics Study of Plastic Waste Chlorinated Polyvinyl Chloride.

Chlorinated polyvinyl chloride (CPVC), as a new type of engineering plastic waste, has been used widely due to its good heat resistance, mechanical properties and corrosion resistance, while it has become an important part of solid waste. The pyrolysis behaviors of CPVC waste were analyzed based on thermogravimetric experiments to explore its reaction mechanism. Compared with polyvinyl chloride (PVC) pyrolysis, CPVC pyrolysis mechanism was divided into two stages and speculated to be dominated by the dehydrochlorination and cyclization/aromatization processes. A common model-free method, Flynn-Wall-Ozawa method, was applied to estimate the activation energy values at different conversion rates. Meanwhile, a typical model-fitting method, Coats-Redfern method, was used to predict the possible reaction model by the comparison of activation energy obtained from model-free method, thereby the first order reaction-order model and fourth order reaction-order model were established corresponding to these two stages. Eventually, based on the initial kinetic parameter values computed by model-free method and reaction model established by model-fitting method, kinetic parameters were optimized by Shuffled Complex Evolution algorithm and further applied to predict the CPVC pyrolysis behaviors during the whole temperature range.

Customer-centered careflow modeling based on guidelines.

In contemporary society, customer-centered health care, which stresses customer participation and long-term tailored care, is inevitably becoming a trend. Compared with the hospital or physician-centered healthcare process, the customer-centered healthcare process requires more knowledge and modeling such a process is extremely complex. Thus, building a care process model for a special customer is cost prohibitive. In addition, during the execution of a care process model, the information system should have flexibility to modify the model so that it adapts to changes in the healthcare process. Therefore, supporting the process in a flexible, cost-effective way is a key challenge for information technology. To meet this challenge, first, we analyze various kinds of knowledge used in process modeling, illustrate their characteristics, and detail their roles and effects in careflow modeling. Secondly, we propose a methodology to manage a lifecycle of the healthcare process modeling, with which models could be built gradually with convenience and efficiency. In this lifecycle, different levels of process models are established based on the kinds of knowledge involved, and the diffusion strategy of these process models is designed. Thirdly, architecture and prototype of the system supporting the process modeling and its lifecycle are given. This careflow system also considers the compatibility of legacy systems and authority problems. Finally, an example is provided to demonstrate implementation of the careflow system.