Is there a stronger willingness to pay for photovoltaic power generation with high education in China?

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China.

Disturbance of mitochondrial dynamics in myocardium of broilers with pulmonary hypertension syndrome

1. The following study investigated the relationship between pulmonary hypertension syndrome (PHS) and mitochondrial dynamics in broiler cardiomyocytes.2. An animal model for PHS was established by injecting broiler chickens with CM-32 cellulose particles. Broiler myocardial cells were cultured under hypoxic conditions to establish an in vitro model. The ascites heart index, histomorphology, mitochondrial ultrastructure, and mitochondrial dynamic-related gene and protein expression were evaluated.3. The myocardial fibres from PHS broilers had wider spaces and were wavy and twisted and the number of mitochondria increased. Compared with the control group, the gene and protein expression levels were decreased for Opa1, Mfn1, and Mfn2 in the myocardium of PHS broilers. The gene and protein expression was significantly increased for Drp1 and Mff.4. This study showed that PHS in broilers may cause myocardial mitochondrial dysfunction, specifically by diminishing mitochondrial fusion and enhancing fission, causing disturbances in the mitochondrial dynamics of the heart.

Motif-Aware miRNA-Disease Association Prediction Via Hierarchical Attention Network

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives. The data and codes are available at https://github.com/stevejobws/MotifMDA.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

Modeling the impact of high thermal conductivity paper on the performance and life of power transformers

Degradation of insulation paper is a key contributor to the failure of power transformers. Insulation degradation accelerates at elevated temperatures, which highlights the potential for better thermal management to prolong life. While several studies have analyzed the benefits of high thermal conductivity oil for reducing temperatures inside a transformer, this study is an initial assessment of the benefits of high thermal conductivity paper on transformer life. Blending particulates with cellulosic fibers offers a pathway for high thermal conductivity paper (with good dielectric properties), which can reduce internal temperatures. Presently, life extensions that can be achieved by the use of such thermally conducting papers were estimated, with the thermal conductivity of the paper being the key parameter under study. The analytical-numerical thermal model used in this study was validated against experimental measurements in a distribution transformer, adding confidence to the utility of the model. This model was then used to provide estimates of hot-spot temperature reduction resulting from the use of papers with higher thermal conductivity than baseline. Transformer life was predicted conventionally by tracking the degree of polymerization of paper over time, based on an Arrhenius model. Results indicate that increasing the thermal conductivity of paper from 0.2 W/mK (baseline) to 1 W/mK reduces the hot spot temperature by 10 °C. While degradation significantly depends on the moisture and oxygen content, the model shows that such a temperature reduction can increase life for all conditions, by as much as a factor of three.

The influence of respiratory movement on preoperative CT-guided localization of lung nodules

AIM

To evaluate the motion amplitude of lung nodules in different locations during preoperative computed tomography (CT)-guided localization, and the influence of respiratory movement on CT-guided percutaneous lung puncture.

MATERIALS AND METHODS

A consecutive cohort of 398 patients (123 men and 275 women with a mean age of 53.9 ± 10.7 years) who underwent preoperative CT-guided lung nodule localization from May 2021 to Apr 2022 were included in this retrospective study. The respiratory movement-related nodule amplitude in the cranial-caudal direction during the CT scan, characteristics of patients, lesions, and procedures were statistically analyzed. Univariate and multivariate logistic regression analyses were used to evaluate the influence of these factors on CT-guided localization.

RESULTS

The nodule motion distribution showed a statistically significant correlation within the upper/middle (lingular) and lower lobes (p<0.001). Motion amplitude was an independent risk factor for CT scan times (p=0.011) and procedure duration (p=0.016), but not for the technical failure rates or the incidence of complications. Puncture depth was an independent risk factor for the CT scan times, procedure duration, technical failure rates, and complications (p<0.01). Female, prone, and supine (as opposed to lateral) positions were significant protective factors for pneumothorax, while the supine position was an independent risk factor for parenchymal hemorrhage (p=0.025).

CONCLUSION

Respiratory-induced motion amplitude of nodules was greater in the lower lobes, resulting in more CT scan times/radiation dose and longer localization duration, but showed no statistically significant influence on the technical success rates or the incidence of complications during preoperative CT-guided localization.

Dual-channel hypergraph convolutional network for predicting herb-disease associations

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

[Activation of HIF-1α/ACLY signaling axis promotes progression of clear cell renal cell carcinoma with VHL inactivation mutation]

Objective: To explore the potential pathogenesis of clear cell renal cell carcinoma (ccRCC) based on the HIF-1α/ACLY signaling pathway, as well as to provide new ideas for the treatment of ccRCC. Methods: Seventy-eight ccRCC cases diagnosed at the First Affiliated Hospital of Soochow University, Suzhou, China were collected. The VHL mutation was examined using exon sequencing. The expression of HIF-1α/ACLY in VHL-mutated ccRCC was evaluated using immunohistochemical staining and further validated in VHL-mutated ccRCC cell lines (786-O, A498, UM-RC-2, SNU-333, and Caki-2) using Western blot. The mRNA and protein levels of ACLY were detected using real-time quantitative PCR and Western blot after overexpression or interference with HIF-1α in ccRCC cell lines. HeLa cells were treated with CoCl2 and hypoxia (1%O2) to activate HIF-1α and then subject to the detection of the ACLY mRNA and protein levels. The potential molecular mechanism of HIF-1α-induced ACLY activation was explored through JASPAR database combined with chromatin immunoprecipitation assay (ChIP) and luciferase reporter gene assay. The effect of HIF-1α/ACLY regulation axis on lipid accumulation was detected using BODIPY staining and other cell biological techniques. The expression of ACLY was compared between patients with ccRCC and those with benign lesions, and the feasibility of ACLY as a prognostic indicator for ccRCC was explored through survival analysis. Results: Exon sequencing revealed that 55 (70.5%) of the 78 ccRCC patients harbored a VHL inactivation mutation, and HIF-1α expression was associated with ACLY protein levels. The protein levels of ACLY and HIF-1α in ccRCC cell lines carrying VHL mutation were also correlated to various degrees. Overexpression of HIF-1α in A498 cells increased the mRNA and protein levels of ACLY, and knockdown of HIF-1α in Caki-2 cells inhibited the mRNA and protein levels of ACLY (P<0.001 for all). CoCl2 and hypoxia treatment significantly increased the mRNA and protein levels of ACLY by activating HIF-1α (P<0.001 for all). The quantification of transcriptional activity of luciferase reporter gene and ChIP-qPCR results suggested that HIF-1α could directly bind to ACLY promoter region to transcriptionally activate ACLY expression and increase ACLY protein level (P<0.001 for all). The results of BODIPY staining suggested that the content of free fatty acids in cell lines was associated with the levels of HIF-1α and ACLY. The depletion of HIF-1α could effectively reduce the accumulation of lipid in cells, while the overexpression of ACLY could reverse this process. At the same time, cell function experiments showed that the proliferation rate of ccRCC cells with HIF-1α knockdown was significantly decreased, and overexpression of ACLY could restore proliferation of these tumor cells (P<0.001). Survival analysis further showed that compared with the ccRCC patients with low ACLY expression, the ccRCC patients with high ACLY expression had a poorer prognosis and a shorter median survival (P<0.001). Conclusions: VHL mutation-mediated HIF-1α overexpression in ccRCC promotes lipid synthesis and tumor progression by activating ACLY. Targeting the HIF-1α/ACLY signaling axis may provide a theoretical basis for the clinical diagnosis and treatment of ccRCC.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks

BACKGROUND

As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters.

RESULTS

Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment.

CONCLUSIONS

BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

[Clinical observation on the efficacy and safety of dupilumab in the treatment of moderate to severe atopic dermatitis]

To investigate the clinical efficacy and safety of dupilumab in the treatment of moderate to severe atopic dermatitis (AD) in China. A small sample self-controlled study before and after treatment was conducted to retrospective analysis patients with moderate to severe AD treated with dupilumab in the department of dermatology of the First Affiliated Hospital of Chongqing Medical University from July 2020 to March 2022. Dupilumab 600 mg was injected subcutaneously at week 0, and then 300 mg was injected subcutaneously every 2 weeks. The condition was evaluated by SCORAD(severity scoring of atopic dermatitis), NRS(numerical rating scale), DLQI(dermatology life quality index) and POEM(patient-oriented eczema measure). The improvement of SCORAD, NRS, DLQI and POEM was analyzed by paired t test and non-parametric paired Wilcoxon. The results showed that a total of 67 patients with moderate to severe AD received dupilumab treatment, of which 41 patients (the course of treatment was more than 6 weeks) had reduced the severity of skin lesions, improved quality of life and reduced pruritus. A total of 23 patients completed 16 weeks of treatment. At 4, 8, 12 and 16 weeks, SCORAD, NRS, DLQI and POEM decreased compared with the baseline, and the differences were statistically significant. SCORAD (50.13±15.19) at baseline, SCORAD (36.08±11.96)(t=6.049,P<0.001) at week 4,SCORAD (28.04±11.10)(t=10.471,P<0.001) at week 8, SCORAD (22.93±9.72)(t=12.428,P<0.001) at week 12, SCORAD (16.84±7.82)(t=14.609,P<0.001) at week 16, NRS 7(6,8) at baseline, NRS 4(3,5)(Z=-3.861,P<0.001) at week 4, NRS 2(1,4)(Z=-4.088,P<0.001) at week 8, NRS 1(0,2)(Z=-4.206,P<0.001) at week 12, NRS 2(0,2)(Z=-4.222,P<0.001) at week 16, DLQI (13.83±5.71) at baseline, DLQI (8.00±4.02)(t=6.325,P<0.001) at week 4, DLQI (5.61±3.50)(t=8.060,P<0.001) at week 8, DLQI (3.96±1.99)(t=8.717,P<0.001) at week 12, DLQI (2.70±1.89)(t=10.355,P<0.001) at week 16, POEM (18.04±6.41) at baseline, POEM (9.70±4.70)(t=7.031,P<0.001) at week 4, POEM (7.74±3.48)(t=8.806,P<0.001) at week 8, POEM (6.35±3.33)(t=10.474,P<0.001) at week 12, POEM (4.26±2.51)(t=11.996,P<0.001) at week 16. In the 16th week, 100%(23 patients), 91.3%(21 patients), 34.8%(8 patients) and 8.7%(2 patients) of 23 patients reached SCORAD30, SCORAD50, SCORAD70, and SCORAD90 statuses, respectively. There were 82.6%(19 patients), 95.7%(22 patients) and 95.7%(22 patients) of 23 patients with NRS, DLQI and POEM improved by≥4 points compared with baseline. Twelve patients with AD who continued to receive dupilumab after 16 weeks showed further improvement in skin lesions. The adverse events were conjunctivitis and injection site reaction. In conclusion, dupilumab is an effective and safe treatment for moderate and severe AD. However, the longer-term efficacy and safety require further studies involving larger sample sizes and a longer follow-up time.

Efficacy and Safety of Local Radiotherapy Combined with Chemotherapy ± Bevacizumab in the Treatment of Patients with Advanced and Recurrent Metastatic Cervical Cancer

PURPOSE/OBJECTIVE(S)

To observe the efficacy and safety of local radiotherapy combined with chemotherapy ± bevacizumab in the treatment of patients with advanced or recurrent metastatic cervical cancer.

MATERIALS/METHODS

A total of 53 patients with advanced and recurrent metastatic cervical cancer who had received local radiotherapy combined with chemotherapy ± bevacizumab in Affiliated Cancer Hospital of Guizhou Medical University from July 2018 to October 2021 were collected. The recurrence types included 21 patients of pelvic recurrence, 7 patients of distant metastasis, 14 patients of complex pelvic recurrence and distant metastasis, and 11 patients of advanced stage (initial diagnosis stage IVB). The primary endpoints were objective response rate (ORR) and disease control rate (DCR). The secondary endpoints were progression-free survival (PFS), overall survival (OS) and incidence of adverse reactions.

RESULTS

(1) Complete response (CR) was achieved in 4 patients (7.5%), partial response (PR) in 34 patients (64.2%), stable disease (SD) in 12 patients (22.6%), and disease progression (PD) in 3 patients (5.7%), ORR was 71.7%, DCR was 94.3%. (2) The follow-up time was 5.3 to 45.7 months, the median OS was 29.3 months, the median PFS was 15.7 months, the one-year and two-year OS were 83.0% and 59.2%, and the 1-year and two-year PFS were 62.2% and 34.4%. (3) Recurrence type, tumor size at the time of recurrence, and efficacy after radiotherapy were significant factors for PFS and OS rates in multivariate analysis. (4) The main adverse reactions were myelosuppression, gastrointestinal reaction and urinary reaction. Grade IV leukopenia occurred at 13.2%, grade IV neutropenia at 11.3%, grade IV thrombocytopenia at 15.1%, and grade IV anemia at 5.7%, all of which were tolerable. The gastrointestinal and urinary reactions were mainly grade I-II, and the incidence of vesical or rectovaginal fistulas was about 7.5% (2 patients had rectovaginal fistulas and 2 patients had vesto-vaginal fistulas).

CONCLUSION

Local radiotherapy combined with chemotherapy ± bevacizumab can improve the efficacy and survival of patients with advanced and recurrent metastatic cervical cancer. Adverse reactions are tolerable and may provide survival benefits in these patients.

Fibulin-1 Regulates Initiation of Successional Dental Lamina

In humans, teeth are replaced only once, and the successional dental lamina (SDL) of the permanent tooth is maintained in a quiescent state until adolescence. Recently, we showed that biomechanical stress generated by the rapid growth of the deciduous tooth inhibits SDL development via integrin β1-RUNX2 signaling at embryonic day 60 (E60) in miniature pigs. However, the mechanism by which RUNX2 regulates SDL initiation within the SDL stem cell niche remains unclear. In the current study, we transcriptionally profiled single cells from SDL and surrounding mesenchyme at E60 and identified the landscape of cellular heterogeneity. We then identified a specific fibroblast subtype in the dental follicle mesenchyme between the deciduous tooth and the SDL of the permanent tooth (DFDP), which constitutes the inner part of the niche (deciduous tooth side). Compared with traditional dental follicle cells, the specific expression profile of DFDP was identified and found to be related to biomechanical stress. Subsequently, we found that RUNX2 could bind to the enhancer regions of Fbln1 (gene of fibulin-1), one of the marker genes for DFDP. Through gain- and loss-of-function experiments, we proved that the biomechanical stress-mediated RUNX2-fibulin-1 axis inhibits the initiation of SDL by maintaining SDL niche homeostasis.

Introducing an Experimental Approach to Predict Spot Scanning Time Parameters for a Superconducting Cyclotron Proton Therapy Machine

PURPOSE/OBJECTIVE(S)

Proton pencil beam scanning (PBS) delivery sequence varies a lot among institutions due to the differences in vendors, machine types, and beamline configurations, which impacts PBS interplay effects and treatment delivery time estimation. This study aims to develop an independent experimental approach to predict the spot scanning time parameters for a clinical superconducting cyclotron proton therapy machine.

MATERIALS/METHODS

This independent experimental approach employed an open-air parallel-plate detector with a temporal resolution of 0.05ms. A series of spot, energy, and dose rate patterns were designed and delivered, including (1) Spot switching time (SSWT) under different spot spacing for IEC-X, IEC-Y directions and diagonal direction (traveling in both X and Y direction) for three energy layers (110, 170 and 230 MeV); The Wilcoxon test is used to validate the prediction of SSWT along the diagonal direction. (2) Energy layer switching time (ELST) with different descending energy gaps for a fixed initial energy and different initial energies for a fixed descending energy gap. (3) Dose rate (MU/min) are measured for different minimum-MU-per-energy-layer (MMPEL), which are compared with the previous publication.

RESULTS

A SSWT jump at 10mm (can be customized) spot spacing is observed because of triggering the machine's "raster mode" threshold. Discontinuous two variable piecewise linear functions were used to fit the SSWT in X/Y for spot spacing and energy. SSWT in X/Y is increasing as spot spacing and energy increase. SSWT in the diagonal direction is determined by the time either in the x-direction or y-direction, whichever takes longer (see Table 1 for one example of validations). ELST is linear depending on descending energy gap. The dose rate dependence on MMPEL is confirmed with previous publications of a similar type of machine.

CONCLUSION

The study provided the first independent quantitative experimental modeling of the beam delivery time parameters without any information from vendors. Such machine-specific delivery sequence models could pave the foundation of precise interplay effect evaluation for clinical decision-making.

Effect of Concurrent Chemoradiotherapy on Regulatory T Cells,CD8/Treg Ratio,PD1 and CTLA-4 in Patients with Cervical Cancer

PURPOSE/OBJECTIVE(S)

To investigate the significance of chemoradiotherapy on regulatory T (Treg) cells, CD8 / Treg ratio, squamous cell carcinoma antigen (SCC), PD1, and CTLA-4 in the peripheral blood of cervical cancer (CC)patients.

MATERIALS/METHODS

A retrospective study was performed 56 cervical cancer patients treated with concurrent chemoradiotherapy from September 06, 2019 to April 19, 2021 were selected, in patients who underwent surgery. Flow cytometry was used to determine the levels of regulatory T cells, CD8 / Treg ratio, squamous cell carcinoma antigen, PD1, and CTLA-4 in the peripheral blood of patients before and after concurrent therapy, Differences in relative level values before and after treatment were calculated using statistical protocols such as the paired samples t-test.

RESULTS

The proportion of CD4+CD25+CD127low Treg in CD4+T cells was (15.96±4.29) % in cervical cancer patients and (9.76±4.21) % in healthy controls, and the difference between the two groups was statistically significant (P < 0.05). In different age groups, Treg, CD8 levels, CD4/CD8 ratio and CD8/Treg ratio before and after radiotherapy and chemotherapy had no significant relationship with age and pathological types (P > 0.05), but CD8/Treg ratio was higher in patients with adenocarcinoma than in patients with squamous cell carcinoma after radiotherapy and chemotherapy, and the difference was statistically significant (Z = -2.076 P = 0.038). For postoperative patients, CD8 levels were lower before and after chemoradiotherapy than after chemoradiotherapy (T = -2.320 P = 0.020). In terms of PD1, regardless of age, pathological type, the level of PD1 after radiotherapy and chemotherapy was higher than that before chemotherapy, and the difference was statistically significant. The level of adenocarcinoma (53.50±10.16) % was significantly higher than that of squamous carcinoma (43.72±11.89) % (T = -2.609 P = 0.011). The PD1 level of patients with cervical cancer radical resection (41.64±13.29) % was lower than that of patients without cervical cancer radical resection (46.84±10.61) %, the difference was statistically significant (T = 2.187 P = 0.031). The PD1 level of patients without pelvic lymph node metastasis (48.84±10.04) was significantly higher than that of patients with pelvic lymph node metastasis (42.96±10.85), and the difference was statistically significant (T = -2.019 P = 0.049), There were no significant differences in vascular positivity, invasion depth, stump positivity, pelvic lymph node positivity and Treg level, CD8 level, CTLA4 level, SCC, CD4/CD8 ratio, CD8/Treg ratio (ALL P > 0.05).

CONCLUSION

The level of Treg cells in patients with cervical cancer is significantly higher than that in healthy people, and it does not decrease immediately after radiotherapy and chemotherapy. Peripheral blood Treg, PD1, CD8 and CD8/Treg can reflect the immune function of the body, which may provide a certain reference for immunotherapy.

Efficacy of Nimotuzumab plus Concurrent Chemo-Radiotherapy for Unresectable Esophageal Cancer: A Real-World Study

PURPOSE/OBJECTIVE(S)

The esophageal cancer ranked 7th in the morbidity of malignant cancer and the 6th contributed to carcinoma deaths. Most patients are diagnosed of advanced stage at first visiting. The 5-year survival rate of unresectable esophageal cancer is about 20% after the standard treatment of concurrent chemo-radiotherapy. Nimotuzumab, a humanized anti-EGFR antibody, has shown good efficacy and low toxicity in epithelial tumors. This two-center, real-world study evaluated the efficacy and safety of nimotuzumab combined with concurrent chemoradiotherapy in unresectable esophageal squamous cell carcinoma (ESCC).

MATERIALS/METHODS

Totally 503 eligible unresectable ESCC patients from Jan 2014 to Dec 2020 were included. 1:2 nearest neighbor propensity score matching (PSM) was performed to match the Nimo group (nimotuzumab plus concurrent chemo-radiotherapy) and CRT group (concurrent chemo-radiotherapy), and the covariates included age, gender, tumor location, lesion length, TNM stage, clinical stage, and radiotherapy dose. The primary endpoint was overall survival (OS). The secondary endpoints were progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR).

RESULTS

A total of 61 patients were in Nimo group which received nimotuzumab (200 mg/w, 4-6 weeks) combined with concurrent chemo-radiotherapy (chemotherapy: S-1/FP/TP/DP for 2-4 cycles; radiotherapy: 2DRT,3D-CRT or IMRT, 50-70 Gy in 25-35 fractions) and 107 patients in CRT group only received concurrent chemo-radiotherapy. The baseline characteristics were well balanced between the two groups. The efficacy of Nimo group was better than that of CRT group. The ORR was 85.2% vs. 71.0%, (P=0.037), the DCR was 98.4% vs. 91.6%, (P>0.05). The median PFS was 28.07 months vs. 19.54 months, and the 1-, 3- and 5-year PFS rates were 78.2% vs. 72.9%, 37.5% vs. 28.3%, and 29.1% vs. 21.3%, respectively (HR: 0.6860, 95% CI: 0.4902-0.9600, P=0.034). The median OS was 34.93 months vs. 24.30 months and the 1-, 3- and 5-year OS rates were 88.5% vs. 81.3%, 46.8% vs. 35.2% and 37.4% vs. 28.0%, respectively (HR: 0.6701, 95% CI: 0.4792-0.9372, P=0.024). The adverse events including radiation esophagitis, radiation pneumonitis, bone marrow suppression, nausea, vomiting, and rash were no significantly different between the two groups (P>0.05).

CONCLUSION

Nimotuzumab combined with concurrent chemo-radiotherapy improved the ORR, and prolonged PFS and OS in unresectable ESCC patients with a good tolerance.

Silva Classification System for HPV-Related EAC of Stage I ∼ IIIc1p Cervical Adenocarcinoma and Its Effect on Prognosis and Survival

PURPOSE/OBJECTIVE(S)

The proportion of adenocarcinoma in cervical cancer gradually increased and presented a younger trend. The previous pathological classification of cervical adenocarcinoma is difficult to provide reference for clinical treatment. In recent years, Silva classification, a new pathologic system for cervical adenocarcinoma, has been confirmed to be suitable for HPV-associated adenocarcinoma (HPVA), and has shown certain clinical application value in subsequent studies. Therefore, this study will retrospectively analyze the distribution of Silva typing system in patients with HPVA under standard treatment mode and its relationship with prognosis and survival.

MATERIALS/METHODS

From January 2010 to September 2021, 124 cervical adenocarcinoma patients with HPVA were retrospectively included, who underwent radical resection of cervical cancer. The HE staining sections of the patients were divided into SilvaA, SilvaB, and SilvaC types according to the Silva typing system. Kaplan-Meier calculation was used for single-factor analysis, and COX stepwise regression model was used for multi-factor analysis.

RESULTS

Of the 124 patients with HPVA who could be graded according to the Silva system, 16 (12.9%, 16/124) were SilvaA, 27 (21.7%, 27/124) SilvaB, and 81 (65.4%, 81/124) SilvaC. In Silva classification, FIGO staging of Silva A and B was stage I. And FIGO staging of Silva C was more significantly later than the staging of Silva A and B. All lymph node metastases and paruterine infiltrates were found only in Silva C. In addition, the patients with Silva C large mass accounted for a higher proportion (41.7%). SilvaA type cervical adenocarcinoma patients were in a survival state by the end of follow-up. Among Silva B, 3 patients died due to tumor, and the 5-year OS rate were 91.3%. Among SilvaC, 15 patients died due to tumor, and the 5-year OS rate were 76.5%. FIGO stage and lymph node invasion were the influencing factors for survival and prognosis of Silva classification (P <0.05). FIGO stage, tumor size, lymph node invasion, and paralegal invasion were the influencing factors for survival and prognosis of SilvaC patients (P <0.05).

CONCLUSION

Silva model classification system combined with clinicopathological features has certain clinical value for the prognostic guidance of HPVA patients. Among Silva classification, SilvaC had the worst prognosis. Late FIGO stage, lymph node metastasis, and paralegal infiltration are the influencing factors for survival and prognosis of SilvaC type.

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network

MOTIVATION

The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction.

RESULTS

In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets.

AVAILABILITY AND IMPLEMENTATION

Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

[Effects of cadmium chloride on testicular autophagy and blood-testis barrier integrity in prepubertal male rats]

Objective: To study the effects of cadmium chloride (CdCl(2)) exposure on testicular autophagy levels and blood-testis barrier integrity in prepubertal male SD rats and testicular sertoli (TM4) cells. Methods: In July 2021, 9 4-week-old male SD rats were randomly divided into 3 groups: control group (normal saline), low dose group (1 mg/kg·bw CdCl(2)) and high dose group (2 mg/kg·bw CdCl(2)), and were exposed with CdCl(2) by intrabitoneal injection. 24 h later, HE staining was used to observe the morphological changes of testis of rats, biological tracer was used to observe the integrity of blood-testis barrier, and the expression levels of microtubule-associated protein light chain 3 (LC3) -Ⅰ and LC3-Ⅱ in testicular tissue were detected. TM4 cells were treated with 0, 2.5, 5.0 and 10.0 μmol/L CdCl(2) for 24 h to detect the toxic effect of cadmium. The cells were divided into blank group (no exposure), exposure group (10.0 μmol/L CdCl(2)), experimental group[10.0 μmol/L CdCl(2)+60.0 μmol/L 3-methyladenine (3-MA) ] and inhibitor group (60.0 μmol/L 3-MA). After 24 h of treatment, Western blot analysis was used to detect the expression levels of LC3-Ⅱ, ubiquitin binding protein p62, tight junction protein ZO-1 and adhesion junction protein N-cadherin. Results: The morphology and structure of testicular tissue in the high dose group were obvious changed, including uneven distribution of seminiferous tubules, irregular shape, thinning of seminiferous epithelium, loose structure, disordered arrangement of cells, abnormal deep staining of nuclei and vacuoles of Sertoli cells. The results of biological tracer method showed that the integrity of blood-testis barrier was damaged in the low and high dose group. Western blot results showed that compared with control group, the expression levels of LC3-Ⅱ in testicular tissue of rats in low and high dose groups were increased, the differences were statistically significant (P<0.05). Compared with the 0 μmol/L, after exposure to 5.0, 10.0 μmol/L CdCl(2), the expression levels of ZO-1 and N-cadherin in TM4 cells were significantly decreased, and the expression level of p62 and LC3-Ⅱ/LC3-Ⅰ were significantly increased, the differences were statistically significant (P<0.05). Compared with the exposure group, the relative expression level of p62 and LC3-Ⅱ/LC3-Ⅰ in TM4 cells of the experimental group were significantly decreased, while the relative expression levels of ZO-1 and N-cadherin were significantly increased, the differences were statistically significant (P<0.05) . Conclusion: The mechanism of the toxic effect of cadmium on the reproductive system of male SD rats may be related to the effect of the autophagy level of testicular tissue and the destruction of the blood-testis barrier integrity.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Apical Papilla Regulates Dental Follicle Fate via the OGN-Hh Pathway

Root apical complex, including Hertwig's epithelial root sheath, apical papilla, and dental follicle (DF), is the germinal center of root development, wherein the DF constantly develops into periodontal tissue. However, whether DF development is regulated by the adjacent apical papilla remains largely unknown. In this study, we employed a transwell coculture system and found that stem cells from the apical papilla (SCAPs) inhibit the differentiation and maintain the stemness of dental follicle stem cells (DFSCs). Meanwhile, partial SCAP differentiation markers were upregulated after DFSC coculture. High-throughput RNA sequencing revealed that the Hedgehog (Hh) pathway was significantly downregulated in DFSCs cocultured with SCAPs. Upregulation or downregulation of the Hh pathway can respectively activate or inhibit the multidirectional differentiation of DFSCs. Osteoglycin (OGN) (previously known as mimecan) is highly expressed in the dental papilla, similarly to Hh pathway factors. By secreting OGN, SCAP regulated the stemness and multidirectional differentiation of DFSCs via the OGN-Hh pathway. Finally, Ogn^-/- mice were established using the CRISPR/Cas9 system. We found that the root length growth rate was accelerated during root development from PN0 to PN30 in Ogn^-/- mice. Moreover, the hard tissues (including dentin and cementum) of the root in Ogn^-/- mice were thicker than those in wild-type mice. These phenotypes were likely due to Hh pathway activation and the increased cell proliferation and differentiation in both the apical papilla and DF. The current work elucidates the molecular regulation of early periodontal tissue development, providing a theoretical basis for future research on tooth root biology and periodontal tissue regeneration.

PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel

Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.

Is there a stronger willingness to pay for air quality improvement with high education: new evidence from a survey in China

As a developing country with the largest population and serious environmental pollution in the world, China has made great efforts in air pollution. Air quality improvement depends not only on government administrative regulations but also on public support, especially how much the public is willing to pay for air quality improvement. Higher education will encourage the public to take action to improve air quality. However, the confirmation of the causality relationship between WTP and education has been missing. This study uses the Chinese General Social Survey (CGSS) to find the relationship between the two, and the conclusions are drawn: OLS regression model and instrumental variable both determine the positive influence of education level on air quality improvement WTP, and Heckman model further verifies the robustness of the conclusion. The positive influence of education level is greater in the groups of men, higher income, higher awareness of acid rain, and more air purifiers, and it has a greater impact on married people in rural areas than in urban areas. The function mechanism of education can improve residents' WTP by increasing regional GDP, promoting urbanization level, expanding afforestation areas, decreasing private car ownership and the number of newly registered civil cars, and reducing sulfur dioxide emissions, nitrogen oxides, and smoke (powder) dust. The total social and economic value of air quality improvement in China is 34.572 billion CNY to 672.42 trillion CNY.

Cost-effectiveness analysis of five drugs for treating postmenopausal women in the United States with osteoporosis and a very high fracture risk

PURPOSE

Five strategies were recommended by the American Association of Clinical Endocrinologists/American College of Endocrinology (AACE/ACE) guidelines for the treatment of postmenopausal osteoporosis (PMO) patients with a very high fracture risk. We aimed to assess their cost-effectiveness in the United States (US).

METHODS

A microsimulation Markov model was created to compare the cost-effectiveness of five treatment strategies, including zoledronate, denosumab, abaloparatide, teriparatide, and romosozumab in PMO patients with a recent fracture from the healthcare perspective of the US. The data used in the model were obtained from published studies or online resources. Base-case analysis, one-way deterministic sensitivity analysis (DSA) and probability sensitivity analysis (PSA) were conducted for 65-, 70-, 75-, and 80-year-old patients.

RESULTS

In base case, at 65 years, zoledronate was the cheapest strategy. The incremental cost-effectiveness ratios (ICER, which represent incremental costs per QALY gained) of denosumab, teriparatide, abaloparatide, and romosozumab against zoledronate were $13,020/QALY (quality-adjusted years), $477,331 /QALY, $176,287/QALY, and $98,953/QALY, respectively. Under a willing-to-pay (WTP, which means the highest price a consumer will pay for one unit of a good of service) threshold of $150,000/QALY, denosumab and romosozumab were cost-effective against zoledronate. The PSA results showed that denosumab was the most cost-effective option with WTP thresholds of $50,000/QALY, $100,000/QALY and $150,000/QALY. The results were similar in other age groups. The DSA results indicated that the most common parameters that have important influence on the outcome were drug persistence, incidence of adverse events, the efficacy of drugs on hip fractures and the cost of the drug.

CONCLUSION AND RELEVANCE

Among PMO patients with a very high fracture risk in the US, zoledronate is the cheapest strategy and denosumab is the most cost-effective choice among these five strategies.

Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity

As microRNAs (miRNAs) are involved in many essential biological processes, their abnormal expressions can serve as biomarkers and prognostic indicators to prevent the development of complex diseases, thus providing accurate early detection and prognostic evaluation. Although a number of computational methods have been proposed to predict miRNA-disease associations (MDAs) for further experimental verification, their performance is limited primarily by the inadequacy of exploiting lower order patterns characterizing known MDAs to identify missing ones from MDA networks. Hence, in this work, we present a novel prediction model, namely HiSCMDA, by incorporating higher order network structures for improved performance of MDA prediction. To this end, HiSCMDA first integrates miRNA similarity network, disease similarity network and MDA network to preserve the advantages of all these networks. After that, it identifies overlapping functional modules from the integrated network by predefining several higher order connectivity patterns of interest. Last, a path-based scoring function is designed to infer potential MDAs based on network paths across related functional modules. HiSCMDA yields the best performance across all datasets and evaluation metrics in the cross-validation and independent validation experiments. Furthermore, in the case studies, 49 and 50 out of the top 50 miRNAs, respectively, predicted for colon neoplasms and lung neoplasms have been validated by well-established databases. Experimental results show that rich higher order organizational structures exposed in the MDA network gain new insight into the MDA prediction based on higher order connectivity patterns.

Enhancing the conversational agent with an emotional support system for mental health digital therapeutics

As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines.

[Present situation of myopia among primary and junior high school students in Yinzhou District, Ningbo City, Zhejiang Province]

Objective: To determine the situation of myopia among primary and junior high school students from 2019 to 2021 in Yinzhou District, Ningbo City, Zhejiang Province. Methods: Cross-sectional study. Department of Ophthalmology, The Affiliated People's Hospital of Ningbo University, carried out a cross-sectional study by reviewing the results of five times visual acuity screens among primary and junior high school students from 2019 to 2021 in Yinzhou District, Ningbo City, Zhejiang Province. The myopia rate, High myopia rate and spherical equivalent refraction were calculated according to the uncorrected distance visual acuity and non-cycloplegic subjective refraction. Chi-square test and analysis of variance were used to analysis the difference of myopia among term, sex and eye. Results: The visual acuity screen had been completed five times from 2019 to 2021 in Yinzhou District, with a total of 458 654 people, of which 454 812 people met the inclusion criteria. The myopia rate of each screen is 56.6%(50 443/89 122),52.5%(48 463/92 311),63.7%(57 968/91 002),53.2%(48 351/90 886),64.4%(58 920/91 491). The rate of Myopia increased gradually with promoting to high grade, and it was obviously in low grade,up to 17.6%. Conclusion: The myopia rate of primary and junior high school students was raising volatility from 2019 to 2021 in Yinzhou District, Ningbo City, Zhejiang Province.

RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction

BACKGROUND

Drug repositioning is a very important task that provides critical information for exploring the potential efficacy of drugs. Yet developing computational models that can effectively predict drug-disease associations (DDAs) is still a challenging task. Previous studies suggest that the accuracy of DDA prediction can be improved by integrating different types of biological features. But how to conduct an effective integration remains a challenging problem for accurately discovering new indications for approved drugs.

METHODS

In this paper, we propose a novel meta-path based graph representation learning model, namely RLFDDA, to predict potential DDAs on heterogeneous biological networks. RLFDDA first calculates drug-drug similarities and disease-disease similarities as the intrinsic biological features of drugs and diseases. A heterogeneous network is then constructed by integrating DDAs, disease-protein associations and drug-protein associations. With such a network, RLFDDA adopts a meta-path random walk model to learn the latent representations of drugs and diseases, which are concatenated to construct joint representations of drug-disease associations. As the last step, we employ the random forest classifier to predict potential DDAs with their joint representations.

RESULTS

To demonstrate the effectiveness of RLFDDA, we have conducted a series of experiments on two benchmark datasets by following a ten-fold cross-validation scheme. The results show that RLFDDA yields the best performance in terms of AUC and F1-score when compared with several state-of-the-art DDAs prediction models. We have also conducted a case study on two common diseases, i.e., paclitaxel and lung tumors, and found that 7 out of top-10 diseases and 8 out of top-10 drugs have already been validated for paclitaxel and lung tumors respectively with literature evidence. Hence, the promising performance of RLFDDA may provide a new perspective for novel DDAs discovery over heterogeneous networks.

6-Shogaol protects against isoproterenol-induced cardiac injury in rats through attenutating oxidative stress, inflammation, apoptosis and activating nuclear respiratory factor-2/heme oxygenase-1 signaling pathway

The current study investigated the preventive effect of 6-Shogaol on isoproterenol hydrochloride (ISO)-induced myocardial cardiac injury. 6-Shogaol (50 mg/kg b.w.) was administered for 14 days at pretreatment and ISO-induction (85 mg/kg b.w.) for the last two days (13th and 14th days) by subcutaneous injection. Cardiac markers in serum like creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), cardiac troponins T (cTn T) and I (cTn I) increased in ISO-induced rats. Moreover, lipid peroxidative markers like thiobarbituric acid reactive substances (TBARS) and lipid hydroperoxides (LOOH) were raised, and the activities/level of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and reduced glutathione (GSH) were diminished in ISO-treated heart tissue. In addition, inflammatory and nuclear respiratory factor (Nrf)-2 signalling molecules were upregulated in ISO-induced ischemic rats. 6-Shogaol pretreatment decreased the activities of cardiac and lipid peroxidative markers and enhanced the antioxidant status in ISO-induced cardiac injury rats. Further, 6-Shogaol pretreatment inhibited serum inflammatory markers: tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), nuclear factor-kappaB (NF-κB), Nrf-2 molecule and heme oxygenase (HO)-1 in ISO-induced cardial damage rats. We noticed the effect of 6-Shogaol inhibited pro-apoptotic genes like B-cell lymphoma 2 (Bcl-2)-associated X protein (Bax), Fas, caspase-3, -8, -9, cytochrome C, and inflammatory genes and increased Bcl-2 expression in ISO-treated rats. The cardioprotective activity of 6-Shogaol in rats with ISO-induced myocardial damage may be due to its ability to reduce oxidative stress, inflammation, and apoptosis, perhaps via the Nrf-2/HO-1 signalling pathway.

[Cadmium induces apoptosis of mouse spermatocytes (GC-2 spd) by promoting mitochondrial fission]

Objective: To study the underlying mechanism of cadmium-induced apoptosis of mouse spermatocytes (GC-2 spd) . Methods: In March 2021, GC-2 spd cells were exposed to different concentrations of CdCl(2) for 24 hours, namely 5 μmol/L CdCl(2) (low-dose) group and 10 μmol/L CdCl(2) (high-dose) group, and unexposed GC-2 spd cells were used as control group. Mitochondrial morphology was observed in the cells stained with Mito-Track Red CMXRos fluorescent probes by confocal microscopy and the mitochrondrial membrane potential was measured by flow cytometry with JC-1 fluorescent probes. Mitochrondrial proteins, cytosolic proteins and total cellular proteins of GC-2 spd cells were extracted using cell mitochondria isolation kit and RIPA buffer, respectively. The expression of mitochondrial homeostasis regulatory proteins (FIS1 and OPA1), and apoptosis-related proteins (Cytochrome c and cleaved Caspase-3) were examined by Western blot. Results: Compared with the cells in the control group, the relative ratio of JC-1 red/green fluorescence signal in the cells of the low-dose and high-dose CdCl(2) groups decreased significantly (0.740±0.071, 0.570±0.028), with a statistically significant difference (P=0.017, 0.004) ; The morphology of mitochondria changed from long tube to point, and the proportion of cells containing point mitochondria increased significantly (45.1%±3.7% and 25.7%±4.9%), the difference was statistically significant (P=0.005, 0.001) ; The relative expression level of mitochondrial FIS1 in cells of low and high dose CdCl(2) groups was significantly higher (1.271±0.120, 1.693±0.155), the difference was statistically significant (P=0.046, 0.000) ; The relative expression level of OPA1 decreased significantly (0.838±0.050, 0.682±0.040), and the difference was statistically significant (P=0.049, 0.001). Compared with the control group, the relative expression level of cytochrome c protein in the cytoplasm of cells in the low dose group of CdCl(2) was not significantly increased (1.249±0.151), and the difference was not statistically significant (P=0.075). However, the relative expression level in the cytoplasm of cells in the high dose group of CdCl(2) was significantly increased (2.355±0.110), and the difference was statistically significant (P=0.000) ; The relative expression level of Cytochrome c in mitochondria of low and high dose CdCl(2) groups decreased significantly (0.681±0.043, 0.619±0.114), with a statistically significant difference (P=0.004, 0.001) ; Moreover, the level of cleaved Caspase-3 protein in cells gradually increased (5.486±0.544, 11.493±1.739), the difference was statistically significant (P=0.004, 0.000) . Conclusion: Cadmium induced cleaved Caspase-3 mediated apoptosis of GC-2 spd cells via promoting mitochrondrial fission and the release of Cytochrome c from the mitochrondria to the cytosol.

A geometric deep learning framework for drug repositioning over heterogeneous information networks

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach

Background

The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance.

Results

In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.

Circadian nuclear receptor Rev-erbalpha is expressed by platelets and potentiates platelet activation and thrombus formation

Background

Adverse cardiovascular events have day/night patterns with peaks in the morning, potentially related to endogenous circadian clock control of platelet activation. Circadian nuclear receptor Rev-erbα is an essential and negative component of the circadian clock.

Purpose

We aim to investigate the expression profile and biological function of Rev-erbα in platelets.

Methods and results

Here we report the presence and functions of circadian nuclear receptor Rev-erbα in human and mouse platelets. Both human and mouse platelet Rev-erbα showed a circadian rhythm that positively correlated with platelet aggregation. Global Rev-erbα knockout and platelet-specific Rev-erbα knockout mice exhibited defective in hemostasis as assessed by prolonged tail-bleeding times. Rev-erbα deletion also reduced ferric chloride-induced carotid arterial occlusive thrombosis, prevented collagen/epinephrine-induced pulmonary thromboembolism, and protected against microvascular microthrombi obstruction and infarct expansion in an acute myocardial infarction model. In vitro thrombus formation assessed by CD41-labeled platelet fluorescence intensity was significantly reduced in Rev-erbα knockout mouse blood. Platelets from Rev-erbα knockout mice exhibited impaired agonist-induced aggregation responses, integrin αIIbβ3 activation and α-granule release. Consistently, pharmacological inhibition of Rev-erbα by specific antagonists decreased platelet activation markers in both mouse and human platelets. Mechanistically, mass spectrometry and co-immunoprecipitation analyses revealed that Rev-erbα potentiated platelet activation via oligophrenin-1-mediated RhoA/ERM (ezrin/radixin/moesin) pathway.

Conclusion

We provide the first evidence that circadian protein Rev-erbα is functionally expressed in platelets and potentiates platelet activation and thrombus formation. Rev-erbα may serve as a novel therapeutic target for managing thrombosis-based cardiovascular disease.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): This work was supported by grants from the National Science Fund for Distinguished Young Scholars (81625002), the National Natural Science Foundation of China (81930007).

Identifying Protein Complexes From Protein-Protein Interaction Networks Based on Fuzzy Clustering and GO Semantic Information

Protein complexes are of great significance to provide valuable insights into the mechanisms of biological processes of proteins. A variety of computational algorithms have thus been proposed to identify protein complexes in a protein-protein interaction network. However, few of them can perform their tasks by taking into account both network topology and protein attribute information in a unified fuzzy-based clustering framework. Since proteins in the same complex are similar in terms of their attribute information and the consideration of fuzzy clustering can also make it possible for us to identify overlapping complexes, we target to propose such a novel fuzzy-based clustering framework, namely FCAN-PCI, for an improved identification accuracy. To do so, the semantic similarity between the attribute information of proteins is calculated and we then integrate it into a well-established fuzzy clustering model together with the network topology. After that, a momentum method is adopted to accelerate the clustering procedure. FCAN-PCI finally applies a heuristical search strategy to identify overlapping protein complexes. A series of extensive experiments have been conducted to evaluate the performance of FCAN-PCI by comparing it with state-of-the-art identification algorithms and the results demonstrate the promising performance of FCAN-PCI.

[Clinical characteristics of 272 437 patients with different histopathological subtypes of primary esophageal malignant tumors]

Objective: To characterize the histopathological subtypes and their clinicopathological parameters of gender and onset age by common, rare and sparse primary esophageal malignant tumors (PEMT). Methods: A total of 272 437 patients with PEMT were enrolled in this study, and all of the patients were received radical surgery. The clinicopathological information of the patients was obtained from the database established by the State Key Laboratory of Esophageal Cancer Prevention & Treatment from September 1973 to December 2020, which included the clinical treatment, pathological diagnosis and follow-up information of esophagus and gastric cardia cancers. All patients were diagnosed and classified by the criteria of esophageal tumor histopathological diagnosis and classification (2019) of the World Health Organization (WHO). The esophageal tumors, which were not included in the WHO classification, were analyzed separately according to the postoperative pathological diagnosis. The χ² test was performed by the SPSS 25.0 software on count data, and the test standard α=0.05. Results: A total of 32 histopathological types were identified in the enrolled PEMT patients, of which 10 subtypes were not included in the WHO classification. According to the frequency, PEMT were divided into common (esophageal squamous cell carcinoma, ESCC, accounting for 97.1%), rare (esophageal adenocarcinoma, EAC, accounting for 2.3%) and sparse (mainly esophageal small cell carcinoma, malignant melanoma, etc., accounting for 0.6%). All the common, rare, and sparse types occurred predominantly in male patients, and the gender difference of rare type was most significant (EAC, male∶ female, 2.67∶1), followed with common type (ESCC, male∶ female, 1.78∶1) and sparse type (male∶ female, 1.71∶1). The common type (ESCC) mainly occurred in the middle thoracic segment (65.2%), while the rare type (EAC) mainly occurred in the lower thoracic segment (56.8%). Among the sparse type, malignant melanoma and malignant fibrous histiocytoma were both predominantly located in the lower thoracic segment (51.7%, 66.7%), and the others were mainly in the middle thoracic segment. Conclusion: ESCC is the most common type among the 32 histopathological types of PEMT, followed by EAC as the rare type, and esophageal small cell carcinoma and malignant melanoma as the major sparse type, and all of which are mainly occur in male patients. The common type of ESCC mainly occur in the middle thoracic segment, while the rare type of EAC mainly in the lower thoracic segment. The mainly sparse type of malignant melanoma and malignant fibrous histiocytoma predominately occur in the lower thoracic segment, and the remaining sparse types mainly occur in the middle thoracic segment.

[Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy]

OBJECTIVE

To develop a nomogram to predict the long-term survival of patients with esophageal cancer following esophagectomy.

METHODS

We collected the data of 7215 patients with esophageal carcinoma from the Surveillance, Epidemiology, and End Results (SEER) database during the period from 2004 and 2016. Of these patients, 5052 were allocated to the training cohort and the remaining 2163 patients to the internal validation cohort using bootstrap resampling, with another 435 patients treated in the Department of Cardiothoracic Surgery of Jinling Hospital between 2014 and 2016 serving as the external validation cohort.

RESULTS

In the overall cohort, the 1-, 3-, and 5-year cancer-specific mortality rates were 14.6%, 35.7% and 41.6%, respectively. Age (≥80 years vs < 50 years, P < 0.001), gender (male vs female, P < 0.001), tumor site (lower vs middle segment, P=0.013), histology (EAC vs ESCC, P=0.012), tumor grade (poorly vs well differentiated, P < 0.001), TNM stage (Ⅳ vs Ⅰ, P < 0.001), tumor size (> 50 mm vs 0-20 mm, P < 0.001), chemotherapy (yes vs no, P < 0.001), and LNR (> 0.25 vs 0, P < 0.001) were identified as independent risk factors affecting long-term survival of the patients. The nomograms established based on the model for predicting the survival probability of the patients at 1, 3 and 5 years after operation showed a C-index of 0.726 (95% CI: 0.714-0.738) for predicting the overall survival (OS) and of 0.735 (95% CI: 0.727-0.743) for cancer-specific survival (CSS) in the training cohort. In the internal validation cohort, the C-index of the nomograms was 0.752 (95% CI: 0.738-0.76) for OS and 0.804 (95% CI: 0.790-0.817) for CSS, as compared with 0.749 (95% CI: 0.736-0.767) and 0.788 (95%CI: 0.751-0.808), respectively, in the external validation cohort. The nomograms also showed a higher sensitivity than the TNM staging system for predicting long-term prognosis.

CONCLUSION

This prognostic model has a high prediction efficiency and can help to identify the high-risk patients with esophageal carcinoma after surgery and serve as a supplement for the current TNM staging system.

Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction

Background

Protein–protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system.

Methods

In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs.

Results

To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network.

Conclusion

The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.

AB0784 Symptoms compatible with Rome IV functional bowel disorder in patients with ankylosing spondylitis

Background

Clinical manifestations of gut problems except for inflammatory bowel disease (IBD) have not been well-established in patients with ankylosing spondylitis (AS). One study investigated that 30% patients with axial spondyloarthritis (axSpA) had irritable bowel syndrome (IBS) symptoms meeting Rome III criteria.[1]

Objectives

To determine the frequency of symptoms meeting Rome IV functional bowel disorder (FBD) in patients with AS, investigate factors associated with FBD symptoms, and assess whether having FBD symptoms might influence AS disease activity.

Methods

In this cross-sectional study, we consecutively enrolled 153 AS patients without known colonic ulcer and 56 sex- and age-matched controls to evaluate FBD (or its subtypes) symptoms.[2] In AS group, logistic regression models were used to explore whether demographic data, disease activity, level of gut inflammation, drug use, and fibromyalgia [3] were associated with presence of gut symptoms. Finally, potential impacts of gut symptoms on AS disease status were assessed in linear regression models.

Results

Sixty (39.2%) of 153 AS patients had FBD symptoms, which was more prevalent than controls (23.2%). Besides, symptoms compatible with IBS and chronic diarrhea were detected in 18 and 43 AS patients respectively. For AS group, multivariable logistic regression analyses showed that symptoms of FBD, IBS, and chronic diarrhea were negatively associated with using non-steroidal anti-inflammatory drug (NSAID), and positively associated with comorbid fibromyalgia, respectively. In exploration about effects of FBD (or its subtypes) symptoms on AS disease activity by multivariable linear regression analyses, FBD symptoms and chronic diarrhea had positive associations with assessments of AS respectively.

Conclusion

Patients with AS had frequent symptoms compatible with FBD, IBS, and chronic diarrhea, proportions of which were lower in those with NSAID-use. The improvement of FBD symptoms, especially chronic diarrhea, might be conducive to disease status of AS patients.

References

[1]Wallman JK, et al. Ann Rheum Dis. 2020;79:159-61.

[2]Mearin F, et al. Gastroenterology. 2016;18:S0016-5085(16)00222-5.

[3]Wolfe F, et al. J Rheumatol. 2011;38:1113-22.

Figure 1.

Frequencies with symptoms meeting FBD criteria

Table 1.

Univariable and multivariable associations between gut symptoms and assessments of AS

Gut symptomsUnivariableMultivariableβpβpASDAS-CRPaFBD symptoms0.2340.1120.294< 0.001IBS symptoms0.0390.863Chronic diarrhea0.2170.1720.3010.002BASDAIbFBD symptoms0.747< 0.0010.764< 0.001IBS symptoms0.2020.560Chronic diarrhea0.7610.0020.845< 0.001BAS-GcFBD symptoms0.936< 0.0010.979< 0.001IBS symptoms0.0590.889Chronic diarrhea0.9030.0030.9490.001ASAS HIdFBD symptoms1.941< 0.0011.6730.003IBS symptoms2.2630.0081.7690.046Chronic diarrhea1.5000.0151.3430.030BASFIeFBD symptoms0.4330.0490.4280.048IBS symptoms0.2960.376Chronic diarrhea0.4480.0600.4250.069BASMIfFBD symptoms-0.3730.190-0.4930.075IBS symptoms-0.4420.304Chronic diarrhea-0.1790.564　　

Besides gut symptoms, other clinical variables (Block-1) being chosen into hierarchical multivariable models were as follows: aHLA-B27, lnCRP, and lnESR; bHLA-B27 and lnESR; cHLA-B27 and lnCRP; dsex and TNFi; eHLA-B27, lnESR, and TNFi; fage and lnESR. Missing data ranging from 1-7%.

Disclosure of Interests

None declared