Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder.

Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.

Improving diagnostic efficacy of primary prostate cancer with combined 99mTc-PSMA SPECT/CT and multiparametric-MRI and quantitative parameters.

Purpose: This prospective study aimed to evaluate the difference between 99mTc-PSMA single-photon emission computed tomography (SPECT)/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of primary prostate cancer (PCa). Materials and methods: Fifty-six men with suspected PCa between October 2019 and November 2022 were prospectively enrolled in this study. The median age of the patients was 70 years (range, 29-87 years). Patients were divided into high-(Gleason score>7, n=31), medium- (Gleason score=7, n=6) and low-risk groups (Gleason score < 7, n=6). All patients underwent 99mTc-PSMA SPECT/CT and mpMRI at an average interval of 3 days (range, 1-7 days). The maximum standardized uptake value (SUVmax), the minimum apparent diffusion coefficient (ADCmin), and their ratio (SUVmax/ADCmin) were used as imaging parameters to distinguish benign from malignant prostatic lesions. Results: Of the 56 patients, 12 were pathologically diagnosed with a benign disease, and 44 were diagnosed with PCa. 99mTc-PSMA SPECT/CT and mpMRI showed no significant difference in the detection of primary PCa (kappa =0.401, P=0.002), with sensitivities of 97.7% (43/44) and 90.9% (40/44), specificities of 75.0% (9/12) and 75.0% (9/12), and AUC of 97.4% and 95.1%, respectively. The AUC of SUVmax/ADCmin was better than those of SUVmax or ADCmin alone. When SUVmax/ADCmin in the prostatic lesion was >7.0×103, the lesion was more likely to be malignant. When SUVmax/ADCmin in the prostatic lesion is >27.0×103, the PCa patient may have lymph node and bone metastases. SUVmax was positively correlated with the Gleason score (r=0.61, P=0.008), whereas ADCmin was negatively correlated with the Gleason score (r=-0.35, P=0.023). SUVmax/ADCmin was positively correlated with the Gleason score (r=0.59, P=0.023). SUVmax/ADCmin was the main predictor of the high-risk group, with an optimal cut-off value of 15.0×103. Conclusions: The combination of 99mTc-PSMA SPECT/CT and mpMRI can improve the diagnostic efficacy for PCa compared with either modality alone; SUVmax/ADCmin is a valuable differential diagnostic imaging parameter.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk.

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

Highly sensitive detection of thyroglobulin based on sandwich-type electrochemical immunoassay.

As a dimeric protein, thyroglobulin (Tg) is an important biomarker for different thyroid cancer (DTC), so designing effective method to detect Tg is of great significance. In this work, by preparing ß-cyclodextrin (CD) functionalized carbon nanotubes (CNTs) nanohybrid (CD-CNTs) as carrier to immobilize primary antibody (Ab1) of Tg, assembling sulfydryl ferrocene (Fc) and secondary antibody (Ab2) on the surface of nanogold (Au) as signaling amplifier (Ab2-Au-Fc), a simple and sensitive sandwich-type electrochemical immunoassay (STEM) of Tg was designed herein for the first time. In brief, CNTs show large surface area and conductivity, while CD offers superior host-guest recognition capability that can bound with Ab1; meanwhile, Fc probe can offer stable electrochemical signal that is proportionable to the concentration of Tg. Under the optimum conditions, the proposed STEM platform shows excellent sensing results for Tg detection: a considerable low analytical detection (0.5 ng mL-1) and wide linearity (2 to 200 ng mL-1), suggesting the designed STEM platform offers potential real applications for detect Tg.

99mTc-rituximab tracer injection for guiding internal mammary sentinel lymph nodes biopsy in primary breast cancer: A prospective observational study.

Objective: To explore the use of 99mTc-rituximab tracer injection for internal mammary sentinel lymph node (IM-SLN) detection in patients with primary breast cancer. Methods: This prospective observational study enrolled female patients with primary breast cancer between September 2017 and June 2022 at Fujian Provincial Hospital. The participants were divided into the peritumoral group (two subcutaneous injection points on the surface of the tumor), two-site group (injections into the glands at 6 and 12 o'clock around the areola area), and four-site group (injections into the gland at 3, 6, 9, and 12 o'clock around the areola area). The outcomes were the detection rates of the IM-SLNs and axillary sentinel lymph nodes (A-SLNs). Results: Finally, 133 patients were enrolled, including 53 in the peritumoral group, 60 in the two-site group, and 20 in the four-site group. The detection rate of the IM-SLNs in the peritumoral group (9.4% [5/53]) was significantly lower than in the two-site (61.7% [37/60], P<0.001) and four-site (50.0% [10/20], P<0.001) groups. The detection rates of A-SLNs among the three groups were comparable (P=0.436). Conclusion: The two-site or four-site intra-gland injection of 99mTc-rituximab tracer might achieve a higher detection rate of IM-SLNs and a comparable detection rate of A-SLNs compared with the peritumoral method. The location of the primary focus has no impact on the detection rate of the IM-SLNs.

Can 99 Tc m -3PRGD 2 (α ν ß 3 ) and 18 F-FDG dual-tracer molecular imaging change the therapeutic strategy for progressive refractory differentiated thyroid cancer: Case report.

RATIONALE: The management of radioiodine refractory differentiated thyroid cancer (RAIR-DTC) represents a major challenge in thyroid cancer. The American Thyroid Association guidelines recommend the use of tyrosine kinase inhibitors (TKIs) for RAIR-DTC that does not respond to conventional treatment. Currently, imaging modalities that predict the response to TKI treatment based on morphological and functional features are lacking. we report a case of a patient with progressive RAIR lung metastases who underwent 2-deoxy-2-[ 18 F]fluoro-D-glucose and 99technetiumm-three polyethylene glycol spacers-arginine-glycine-aspartic acid ( 99 Tc m -3PRGD 2 ) dual-tracer imaging and investigate the value of this imaging strategy for determining subsequent therapeutic schedules. PATIENT CONCERNS: A 52-year-old man with advanced RAIR-DTC and progressive lung metastasis. After TKI treatment [sorafenib] lost its clinical benefits, the patient's therapeutic response was evaluated as progressive disease. 2-deoxy-2-[ 18 F]fluoro-D-glucose PET/CT and 99 Tc m -3PRGD 2 SPECT/CT were performed. There were multiple FDG-positive lesions in the lung. However, 99 Tc m -3PRGD 2 SPECT/CT showed only 1 lesion in the right middle pulmonary lobe with arginine-glycine-aspartic positivity. DIAGNOSIS: RAIR-DTC. INTERVENTIONS: Radiofrequency ablation was performed for only the lesion with RDG and FDG positivity. OUTCOMES: The patient quickly achieved partial response. LESSONS: This case indicates that for progressive RAIR metastases, patients can benefit more from prioritizing treatment for lesions that are both arginine-glycine-aspartic and FDG positive.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Predicting miRNA-Disease Associations via Node-Level Attention Graph Auto-Encoder.

Previous studies have confirmed microRNA (miRNA), small single-stranded non-coding RNA, participates in various biological processes and plays vital roles in many complex human diseases. Therefore, developing an efficient method to infer potential miRNA disease associations could greatly help understand operational mechanisms for diseases at the molecular level. However, during these early stages for miRNA disease prediction, traditional biological experiments are laborious and expensive. Therefore, this study proposes a novel method called AGAEMD (node-level Attention Graph Auto-Encoder to predict potential MiRNA Disease associations). We first create a heterogeneous matrix incorporating miRNA similarity, disease similarity, and known miRNA-disease associations. Then these matrixes are input into a node-level attention encoder-decoder network which utilizes low dimensional dense embeddings to represent nodes and calculate association scores. To verify the effectiveness of the proposed method, we conduct a series of experiments on two benchmark datasets (the Human MicroRNA Disease Database v2.0 and v3.2) and report the averages over 10 runs in comparison with several state-of-the-art methods. Experimental results have demonstrated the excellent performance of AGAEMD in comparison with other methods. Three important diseases (Colon Neoplasms, Lung Neoplasms, Lupus Vulgaris) were applied in case studies. The results comfirm the reliable predictive performance of AGAEMD.

Head-to-head comparison of 99mTc-PSMA and 99mTc-MDP SPECT/CT in diagnosing prostate cancer bone metastasis: a prospective, comparative imaging trial.

The most common site of metastasis of prostate cancer (PCa) is bone. Skeletal-related events can increase the risk of death in patients with PCa by 28%. Due to the low detection rate of lesions in patients with low prostate-specific antigen (PSA) levels, the value of 99mTc methylene diphosphonate (99mTc-MDP) bone scintigraphy is limited. Prostate-specific membrane antigen (PSMA) is a small molecular probe that can efficiently and specifically detect PCa lesions. This prospective study aimed to evaluate the difference between 99mTc-PSMA single-photon emission computed tomography (SPECT)/CT and 99mTc-MDP SPECT/CT in the detection of bone metastasis in PCa. A total of 74 men with pathologically confirmed PCa from October 2019 to November 2021 were prospectively enrolled in this study. The median age was 70 (range, 55-87) years. All patients underwent both 99mTc-PSMA SPECT/CT and 99mTc-MDP SPECT/CT at an average interval of 12.1 (range, 1-14) days. The detected imaging-positive bone lesions were scored as "typical metastasis" or "equivocal metastasis" by a standard reporting schema. Subsequent therapy modality details were observed through follow-up. Twenty-five of the 74 patients were diagnosed with bone metastases. 99mTc-PSMA SPECT/CT and 99mTc-MDP SPECT/CT detected 20 and 18 bone metastases, with sensitivities of 80.0% (20/25) and 72.0% (18/25), specificities of 100.0% (49/49) and 81.3% (40/49), and AUCs of 88.0% and 84.9%, respectively. There was a significant difference in the AUC between the two imaging methods (P < 0.001). In an analysis of the number of bone metastasis lesions, the proportion of "typical metastasis" versus "equivocal metastasis" detected by the two imaging methods was 26.3:1 (PSMA) and 2.9:1 (MDP), and the difference was statistically significant (P = 0.005). There was a significant difference in the detection of bone metastatic lesions by 99mTc-PSMA and 99mTc-MDP when the maximum diameter of the lesions was ≤ 0.6 cm (P < 0.05). The optimal cut-off value for PSA was 2.635 ng/mL (PSMA) and 15.275 ng/mL (MDP). 99mTc-PSMA SPECT/CT led to a change in management to a more individualized therapy modality for 11 of 74 men (14.9%). 99mTc-PSMA SPECT/CT was superior to 99mTc-MDP SPECT/CT in the detection of bone metastases in PCa, especially for small lesions and in patients with low PSA levels, and demonstrated an additional benefit of providing information on extraskeletal metastases. With regard to therapy, 99mTc-PSMA scans might have utility in improving the subsequent therapy modality.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart.

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.

[Correlation Study on Expression of PD-1 and PD-L1 in Non-small Cell Lung Cancer and Epidermal Growth Factor Receptor Mutations].

BACKGROUND: The treatment mode of lung cancer is epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) as a first-line treatment for patients with EGFR mutant in non-small cell lung cancer (NSCLC). At the same time programmed death receptor 1 (PD-1) and its programmed death receptor ligand 1 (PD-L1) inhibitors therapy as the representative immune checkpoint inhibitors (ICIs) has a significant effect in the treatment of lung cancer. The aim of this study was to investigate the correlation between the expression of PD-1 and PD-L1 in NSCLC and clinicopathologic feature, EGFR gene mutation. METHODS: The protein expression of PD-1 and PD-L1 was detected by immunohistochemistry from 127 patients with NSCLC and EGFR gene mutation was detected by quantitative polymerase chain reaction (qPCR) to analyze its relation with clinicopathologic feature. Also, the correlation between protein expression of PD-1 and PD-L1 and EGFR mutation. RESULTS: The PD-1 positive expression in NSCLC tumor cells and tumor infiltrating immune cells is 53.5% (68/127), PD-L1 is 57.5% (73/127). The PD-1 and PD-L1 expression significantly higher in well-differentiated and moderately-differentiated carcinoma than poorly differentiated carcinoma, I+II than III+IV in clinical staging (P<0.05). The EGFR mutation rate was 46.5% (59/127), correlate with female, without smoking history, adenocarcinoma and well-differentiated and moderately-differentiated patients respectively higher than male, smoking history, squamous carcinoma and poorly differentiated patients (P<0.05). The protein expression of PD-L1 and PD-1 had the consistency in NSCLC patients (kappa=0.107,5, P=0.487). There was a negative correlation between the EGFR mutation and PD-1 and PD-L1 expression (Φ=-0.209, Φ=-0.221, P<0.05). Follow-up of NSCLC patients, the median total survival in under the age of 65, adenocarcinoma, well-differentiated and moderately-differentiated, with PD-L1 expression patients respectively higher than over the age of 65, squamous carcinoma, poorly differentiated, without PD-L1 expression patients (P<0.05). The median survival of hypo expression patients of PD-L1 significantly higher than hyper expression patient (P=0.04). CONCLUSIONS: According to the Chinese Expert Consensus on Standards of PD-L1 immunohistochemistry testing for NSCLC, we tested the PD-L1 expression in NSCLC and then the dominant population of anti-PD-1/PD-L1 treatment was screened out. Patients with EGFR mutation were also detected and EGFR mutation was negatively correlated with the expression of PD-1 and PD-L1 as well. On the basis of PD-L1 expression and EGFR mutation status, it may benefit NSCLC patients from individualized treatment. Meanwhile, patients who were under the age of 65, adenocarcinoma, well-differentiated and moderately-differentiated, hypo expression of PD-L1 have a relatively good prognosis, to provide reference for the prognosis evaluation of NSCLC.

HOPMCLDA: predicting lncRNA-disease associations based on high-order proximity and matrix completion.

In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) have important roles in the biological processes of complex diseases. However, experiments to determine the associations between diseases and lncRNAs are time consuming and costly. Therefore, there is a need to develop effective computational methods for exploring potential lncRNA-disease associations. In this study, we present a computational prediction method based on high-order proximity and matrix completion to predict lncRNA-disease associations (HOPMCLDA). HOPMCLDA integrates explicit similarity and high-order proximity information on lncRNAs and diseases and constructs a heterogeneous disease-lncRNA network to utilize similarity information. Finally, nuclear norm regularization is carried out on the heterogeneous network for the recovery of a lncRNA-disease association matrix. By implementing leave-one-out cross validation (LOOCV) and five-fold cross validation (5-fold CV), we compare HOPMCLDA with five other methods. HOPMCLDA outperforms the other methods, with area under the receiver operating characteristic curve values of 0.8755 and 0.8353 ± 0.0045 using LOOCV and 5-fold CV, respectively. Furthermore, case studies of three human diseases (gastric cancer, osteosarcoma, and hepatocellular carcinoma) confirm the reliable predictive performance of HOPMCLDA.

Conductive Polymer (Graphene/PPy)-BiPO4 Composite Applications in Humidity Sensors.

In this particular experiment, a chain of conductive polymer graphene/polypyrrole (Gr/PPy) and BiPO4-or (Gr/PPy)-BiPO4-materials were prepared and used as moisture-sensitive materials. The structure and morphology of the conductive polymer (Gr/PPy)-BiPO4 materials were analyzed using an X-ray diffractometer, scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. Moreover, properties such as hysteresis loop, impedance, sensing response, and response and recovery time were calculated and evaluated using an inductance-capacitance-resistance analyzer. The data expressed that PPy/BiPO4, as prepared in this study, exhibited excellent sensing properties, with impedance changing by only a few orders of range. Furthermore, the response time and time of recovery were 340 s and 60 s, respectively, and negligible humidity hysteresis occurred at different relative humidities. Therefore, conductive PPy/BiPO4, as prepared in the present study, is an excellent candidate for application in humidity sensors.

Predicting circRNA-Disease Associations Based on Deep Matrix Factorization with Multi-source Fusion.

Recently, circRNAs with covalently closed loops have been discovered to play important parts in the progression of diseases. Nevertheless, the study of circRNA-disease associations is highly dependent on biological experiments, which are time-consuming and expensive. Hence, a computational approach to predict circRNA-disease associations is urgently needed. In this paper, we presented an approach that is based on deep matrix factorization with multi-source fusion (DMFMSF). In DMFMSF, several useful circRNA and disease similarities were selected and then combined by similarity kernel fusion. Then, linear and non-linear characteristics were mined using singular value decomposition (SVD) and deep matrix factorization to infer potential circRNA-disease associations. Performance of the proposed DMFMSF on two benchmark datasets are rigorously validated by leave-one-out cross-validation(LOOCV) and fivefold cross-validation (5-fold CV). The experimental results showed that DMFMSF is superior over several existing computational approaches. In addition, five important diseases, hepatocellular carcinoma, breast cancer, acute myeloid leukemia, colorectal cancer, and coronary artery disease were applied in case studies. The results suggest that DMFMSF can be used as an accurate and efficient computational tool for predicting circRNA-disease associations.

Ferrous iron-induced increases in capitate glandular trichome density and upregulation of CbHO-1 contributes to increases in blinin content in Conyza blinii.

MAIN CONCLUSION: Ferrous iron can promote the development of glandular trichomes and increase the content of blinin, which depends on CbHO-1 expression. Conyza blinii (C. blinii) is a unique Chinese herbal medicine that grows in Sichuan Province, China. Because the habitat of C. blinii is an iron ore mining area with abundant iron content, this species can be used as one of the best materials to study the mechanism of plant tolerance to iron. In this study, C. blinii was treated with ferrous-EDTA solutions at different concentrations, and it was found that the tolerance value of C. blinii to iron was 200 µM. Under this concentration, the plant height, root length, biomass, and iron content of C. blinii increased to the maximum values, and the effect was dependent on the upregulated expression of CbHO-1. At the same time, under ferrous iron, the photosynthetic capacity and capitate glandular trichome density of C. blinii also significantly increased, providing precursors and sites for the synthesis of blinin, thus significantly increasing the content of blinin. These processes were also dependent on the high expression of CbHO-1. Correlation analysis showed that there were strong positive correlations between iron content, capitate glandular trichome density, CbHO-1 gene expression, and blinin content. This study explored the effects of ferrous iron on the physiology and biochemistry of C. blinii, greatly improving our understanding of the mechanism of iron tolerance in C. blinii.

Synthesis and Characterization of Long-Acting Darunavir Prodrugs.

Antiretroviral therapy (ART) has improved the quality of life in patients infected with HIV-1. However, complete viral suppression within anatomical compartments remains unattainable. This is complicated by adverse side effects and poor adherence to lifelong therapy leading to the emergence of viral drug resistance. Thus, there is an immediate need for cellular and tissue-targeted long-acting (LA) ART formulations. Herein, we describe two LA prodrug formulations of darunavir (DRV), a potent antiretroviral protease inhibitor. Two classes of DRV prodrugs, M1DRV and M2DRV, were synthesized as lipophilic and hydrophobic prodrugs and stabilized into aqueous suspensions designated NM1DRV and NM2DRV. The formulations demonstrated enhanced intracellular prodrug levels with sustained drug retention and antiretroviral activities for 15 and 30 days compared to native DRV formulation in human monocyte-derived macrophages. Pharmacokinetics tests of NM1DRV and NM2DRV administered to mice demonstrated sustained drug levels in blood and tissues for 30 days. These data, taken together, support the idea that LA DRV with sustained antiretroviral responses through prodrug nanoformulations is achievable.

NCPHLDA: a novel method for human lncRNA-disease association prediction based on network consistency projection.

In recent years, an increasing number of biological experiments and clinical reports have shown that lncRNA is closely related to the development of various complex human diseases. Therefore, studying the relationship between lncRNA and disease is necessary. Doing so not only helps to understand the disease mechanism, but also facilitates the diagnosis, treatment, and prognosis of disease. However, understanding the relationship between lncRNA and disease through biological experiments and clinical studies requires considerable time and money. Over the years, many researchers have developed computational methods to predict potential lncRNA-disease associations. In this study, on the basis of the assumption that functionally similar lncRNAs tend to associate with phenotypically similar diseases, and vice versa, we propose a novel computational method called network consistency projection for human lncRNA-disease associations (NCPHLDA) to predict potential lncRNA-disease associations. This method integrates a lncRNA cosine similarity network, a disease cosine similarity network, and the known lncRNA-disease association network. NCPHLDA is not only a parameterless method but also does not require a negative sample. More importantly, NCPHLDA can predict lncRNA without any known associated diseases. AUC values of 0.9273 and 0.9179 ± 0.0043 are obtained by implementing leave-one-out cross-validation and 5-fold cross-validation for NCPHLDA, respectively. Case studies of three diseases (breast cancer, cervical cancer, and hepatocellular carcinoma) indicate that NCPHLDA has reliable predictive performance. The source code of NCPHLDA is freely available at https://github.com/bryanze/NCPHLDA.

Associations of cytosine deaminase gene polymorphisms with effectiveness of gemcitabine/cisplatin chemotherapy in patients of Xinjiang Uyghur and Han nationality with non-small cell lung cancer.

BACKGROUND: Cytidine deaminase (CDA) polymorphisms may affect the response to gemcitabine/cisplatin chemotherapy in patients with non-small cell lung cancer (NSCLC). This study is designed to investigate the associations of CDA-79A>C and 208G>A polymorphisms and gemcitabine/cisplatin chemotherapy effectiveness in Xinjiang Uyghur and Han patients. METHODS: This prospective cohort study enrolled consecutive patients with stage IIIb/IV NSCLC administered gemcitabine/cisplatin chemotherapy at the First Affiliated Hospital, Medical College of Shihezi University and the First People's Hospital, Kashgar Region. CDA-A79C and CDA-G208A polymorphisms were detected by direct sequencing. Progression-free survival was analyzed by the Kaplan-Meier method. Associations of A79C and G208A polymorphisms with treatment effectiveness and progression-free survival were analyzed using logistic regression and multivariate Cox regression analyses. Subgroup analyses based on ethnicity were performed. RESULTS: The study enrolled 120 patients. A79C and G208A polymorphisms followed the Hardy-Weinberg equilibrium. The frequencies of the AA, AC, and CC genotypes and the A and C alleles of A79C were 52.2%, 29.9%, 17.9%, 67.2%, and 32.8%, respectively, in Han patients and 75.4%, 18.9%, 5.7%, 84.9%, and 5.1%, respectively, in Uyghur patients. Uyghur patients had lower frequencies of A79C-AC/CC genotypes, A79C-C allele, G208A-GA genotype, and G208A-A allele (P<0.05). Compared with A79C-AA, the odds of ineffective chemotherapy were increased for A79C-AC (odds ratio [OR] 2.818; 95% confidence interval [95% CI] 1.031, 7.705; P=0.043) and A79C-CC (OR 9.864; 95% CI 1.232, 78.966; P=0.031). G208A polymorphisms did not influence chemotherapy effectiveness. Chemotherapy was more effective in Han patients than in Uyghur patients for A79C-AC and G208A-GG. Progression-free survival was longer for A79C-AA versus A79C-AC/CC (10 vs. 7 months, P=0.004) and G208A-GA/AA vs. G208A-AA (12 vs. 8 months, P=0.010). Polymorphisms of A79C (hazard ratio [HR] 1.617; 95% CI 1.009, 2.592; P=0.046) and G208A (HR 2.193; 95% CI 1.055, 4.557; P=0.035) were associated with progression-free survival. CONCLUSION: For Uyghur and Han ethnic groups, A79C and G208A polymorphisms can be used as a promising biomarker for the chemotherapy efficacy and prognosis of NSCLC.

Synthesis and characterization of a long-acting emtricitabine prodrug nanoformulation.

PURPOSE: A palmitoylated prodrug of emtricitabine (FTC) was synthesized to extend the drug's half-life, antiretroviral activities and biodistribution. METHODS: A modified FTC prodrug (MFTC) was synthesized by palmitoyl chloride esterification. MFTC's chemical structure was evaluated by nuclear magnetic resonance. The created hydrophobic prodrug nanocrystals were encased into a poloxamer surfactant and the pharmacokinetics (PK), biodistribution and antiretroviral activities of the nanoformulation (NMFTC) were assessed. The conversion of MFTC to FTC triphosphates was evaluated. RESULTS: MFTC coated with poloxamer formed stable nanocrystals (NMFTC). NMFTC demonstrated an average particle size, polydispersity index and zeta potential of 350 nm, 0.24 and -20 mV, respectively. Drug encapsulation efficiency was 90%. NMFTC was readily taken up by human monocyte-derived macrophages yielding readily detected intracellular FTC triphosphates and an extended PK profile. CONCLUSION: NMFTC shows improved antiretroviral activities over native FTC. This is coordinate with its extended apparent half-life. The work represents an incremental advance in the development of a long-acting FTC formulation.