Computational approaches for detecting disease-associated alternative splicing events.

Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

CellBRF: a feature selection method for single-cell clustering using cell balance and random forest.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. RESULTS: We develop CellBRF, a feature selection method that considers genes' relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. AVAILABILITY AND IMPLEMENTATION: All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

The effect of multimodal care based on Peplau's interpersonal relationship theory on postoperative recovery in lung cancer surgery: a retrospective analysis.

BACKGROUND: Lung cancer remains a major global health concern due to its high incidence and mortality rates. With advancements in medical treatments, an increasing number of early-stage lung cancer cases are being detected, making surgical treatment the primary option for such cases. However, this presents challenges to the physical and mental recovery of patients. Peplau known as the "mother of psychiatric associations" has formulated a theory of interpersonal relationships in nursing. Through effective communication between nurses and patients over four periods, she has established a good therapeutic nurse-patient relationship. Therefore, this study aimed to explore the effect of perioperative multimodal nursing based on Peplau's interpersonal relationship theory on the rehabilitation of patients with surgical lung cancer. METHODS: We retrospectively analyzed 106 patients with non-small cell lung cancer who underwent thoracoscopic lobectomy at our department between June 2021 and April 2022. Patients were categorized into two groups according to the different nursing intervention techniques. The Peplau's group comprised 53 patients who received targeted nursing interventions, and the control group comprised 53 patients who received conventional nursing care. We observed the patients' illness uncertainty, quality of life, and clinical symptoms in both groups. RESULTS: Patients in the Peplau's group had significantly lower illness uncertainty scores and a significantly higher quality of recovery than those in the control group. However, there were no significant differences in length of post-anesthesia care unit stay, complication rates, and visual analog scores between both groups. CONCLUSION: The multimodal perioperative nursing based on Peplau's interpersonal relationship theory not only reduces the illness uncertainty of patients with lung cancer surgery and improves their QoR but also expands the application of this theory in clinical practice, guiding perioperative nursing of patients with lung cancer. IMPLICATIONS: These findings provide practical information for standardized care in a hectic anesthetic care setting. IMPACT: The assessed anesthesia nursing model helps reduce uncertainty and promote early recovery in patients with cancer at various stages of their disease, which expands the scope of therapeutic practice and existing theories. It also serves as a guide for care in the anesthesia recovery room. REPORTING METHOD: We adhered to the relevant Equator guidelines and the checklist of items in the case-control study report. PATIENT OR PUBLIC CONTRIBUTION: Patients cooperated with medical staff to complete relevant scales.

A newly proposed heatstroke-induced coagulopathy score in patients with heat illness: A multicenter retrospective study in China.

PURPOSE: In patients with heatstroke, disseminated intravascular coagulation (DIC) is associated with greater risk of in-hospital mortality. However, time-consuming assays or a complex diagnostic system may delay immediate treatment. Therefore, the present study proposes a new heatstroke-induced coagulopathy (HIC) score in patients with heat illness as an early warning indicator for DIC. METHODS: This retrospective study enrolled patients with heat illness in 24 Chinese hospitals from March 2021 to May 2022. Patients under 18 years old, with a congenital clotting disorder or liver disease, or using anticoagulants were excluded. Data were collected on demographic characteristics, routine blood tests, conventional coagulation assays and biochemical indexes. The risk factors related to coagulation function in heatstroke were identified by regression analysis, and used to construct a scoring system for HIC. The data of patients who met the diagnostic criteria for HIC and International Society on Thrombosis and Haemostasis defined-DIC were analyzed. All statistical analyses were performed using SPSS 26.0. RESULTS: The final analysis included 302 patients with heat illness, of whom 131 (43.4%) suffered from heatstroke, including 7 death (5.3%). Core temperature (OR = 1.681, 95% CI 1.291 - 2.189, p < 0.001), prothrombin time (OR = 1.427, 95% CI 1.175 - 1.733, p < 0.001) and D-dimer (OR = 1.242, 95% CI 1.049 - 1.471, p = 0.012) were independent risk factors for heatstroke, and therefore used to construct an HIC scoring system because of their close relation with abnormal coagulation. A total score ≥ 3 indicated HIC, and HIC scores correlated with the score for International Society of Thrombosis and Hemostasis -DIC (r = 0.8848, p < 0.001). The incidence of HIC (27.5%) was higher than that of DIC (11.2%) in all of 131 heatstroke patients. Meanwhile, the mortality rate of HIC (19.4%) was lower than that of DIC (46.7%). When HIC developed into DIC, parameters of coagulation dysfunction changed significantly: platelet count decreased, D-dimer level rose, and prothrombin time and activated partial thromboplastin time prolonged (p < 0.05). CONCLUSIONS: The newly proposed HIC score may provide a valuable tool for early detection of HIC and prompt initiation of treatment.

Porcine Circovirus Type 2 Hijacks Host IPO5 to Sustain the Intracytoplasmic Stability of Its Capsid Protein.

Nuclear entrance and stability of porcine circovirus type 2 (PCV2), the smallest virus in mammals, are crucial for its infection and replication. However, the mechanisms are not fully understood. Here, we found that the PCV2 virion maintains self-stability via the host importin 5 (IPO5) during infection. Coimmunoprecipitation combined with mass spectrometry and glutathione S-transferase pulldown assays showed that the capsid protein (Cap) of PCV2 binds directly to IPO5. Fine identification demonstrated that the N-terminal residue arginine24 of Cap is the most critical to efficient binding to the proline709 residue of IPO5. Detection of replication ability further showed that IPO5 supports PCV2 replication by promoting the nuclear import of incoming PCV2 virions. Knockdown of IPO5 delayed the nuclear transport of incoming PCV2 virions and significantly decreased the intracellular levels of overexpressed PCV2 Cap, which was reversed by treatment with a proteasome inhibitor or by rescuing IPO5 expression. Cycloheximide treatment showed that IPO5 increases the stability of the PCV2 Cap protein. Taken together, our findings demonstrated that during infection, IPO5 facilitates PCV2 replication by directly binding to the nuclear localization signal of Cap to block proteasome degradation. IMPORTANCE Circovirus is the smallest virus to cause immune suppression in pigs. The capsid protein (Cap) is the only viral structural protein that is closely related to viral infection. The nuclear entry and stability of Cap are necessary for PCV2 replication. However, the molecular mechanism maintaining the stability of Cap during nuclear trafficking of PCV2 is unknown. Here, we report that IPO5 aggregates within the nuclear periphery and combines with incoming PCV2 capsids to promote their nuclear entry. Concurrently, IPO5 inhibits the degradation of newly synthesized Cap protein, which facilitates the synthesis of virus proteins and virus replication. These findings highlight a mechanism whereby IPO5 plays a dual role in PCV2 infection, which not only enriches our understanding of the virus replication cycle but also lays the foundation for the subsequent development of antiviral drugs.

REBET: a method to determine the number of cell clusters based on batch effect removal.

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn).

GTFtools: a software package for analyzing various features of gene models.

MOTIVATION: Gene-centric bioinformatics studies frequently involve the calculation or the extraction of various features of genes such as splice sites, promoters, independent introns and untranslated regions (UTRs) through manipulation of gene models. Gene models are often annotated in gene transfer format (GTF) files. The features are essential for subsequent analysis such as intron retention detection, DNA-binding site identification and computing splicing strength of splice sites. Some features such as independent introns and splice sites are not provided in existing resources including the commonly used BioMart database. A package that implements and integrates functions to analyze various features of genes will greatly ease routine analysis for related bioinformatics studies. However, to the best of our knowledge, such a package is not available yet. RESULTS: We introduce GTFtools, a stand-alone command-line software that provides a set of functions to calculate various gene features, including splice sites, independent introns, transcription start sites (TSS)-flanking regions, UTRs, isoform coordination and length, different types of gene lengths, etc. It takes the ENSEMBL or GENCODE GTF files as input and can be applied to both human and non-human gene models like the lab mouse. We compare the utilities of GTFtools with those of two related tools: Bedtools and BioMart. GTFtools is implemented in Python and not dependent on any third-party software, making it very easy to install and use. AVAILABILITY AND IMPLEMENTATION: GTFtools is freely available at www.genemine.org/gtftools.php as well as pyPI and Bioconda.

AlzCode: a platform for multiview analysis of genes related to Alzheimer's disease.

MOTIVATION: Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. RESULTS: We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function and clinical severity) and general data such as protein-protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Atlas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. AVAILABILITY AND IMPLEMENTATION: AlzCode is freely available at: http://www.alzcode.xyz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Results from a systematic programme of evaluating COVID-19 reinfection cases in the early phase of the pandemic, Singapore.

OBJECTIVES: The objectives of this study were to describe the coronavirus disease caused by SARS-CoV-2 (COVID-19) reinfection evaluation algorithm used in the early phase of the pandemic in Singapore and analyze the clinical and laboratory characteristics of the cases evaluated. METHODS: We performed a retrospective case-control analysis including all COVID-19 cases evaluated for possible reinfection under the local COVID-19 reinfection evaluation programme between 1 June 2020-30 June 2021. Whole genome sequencing (WGS) was used as confirmatory testing. We compared all reinfection ("RI") cases against those who were evaluated but eventually assessed not to be reinfection ("non-RI"). RESULTS: There were 74 possible reinfection cases evaluated through the programme, of which 32 were subsequently classified as RI. There was strong statistical evidence that RI cases had a longer interval between 1st and 2nd episode (mean 297 days; 95%-confidence interval (CI) 267-327) compared to non-RI cases (mean 186 days; 95%-CI 144-228). The cycle threshold (Ct) value of initial polymerase chain rection (PCR) at 2nd episode was also found to be significantly lower in RI cases (mean 23; 95%-CI 20-26) compared to non-RI cases (mean 34; 95%-CI 32-36). There was no significant difference in the proportion of individuals who had fever, acute respiratory symptoms or asymptomatic in both groups. Delta and beta variants were most commonly identified from WGS and provide indication of re-infection as these were not 'wild-type' and were not circulating during the time period of the index infection. CONCLUSIONS: Using a combination of serologic, microbiologic and genomic criteria to evaluate possible reinfection cases is useful and can provide a framework for evaluation that may be modified for future similar situations.

Knowledge, perceptions and behaviours of endoscopists towards the use of artificial intelligence-aided colonoscopy.

BACKGROUND: Recent developments in artificial intelligence (AI) systems have enabled advancements in endoscopy. Deep learning systems, using convolutional neural networks, have allowed for real-time AI-aided detection of polyps with higher sensitivity than the average endoscopist. However, not all endoscopists welcome the advent of AI systems. METHODS: We conducted a survey on the knowledge of AI, perceptions of AI in medicine, and behaviours regarding use of AI-aided colonoscopy, in a single centre 2 months after the implementation of Medtronic's GI Genius in colonoscopy. We obtained a response rate of 66.7% (16/24) amongst consultant-grade endoscopists. Fisher's exact test was used to calculate the significance of correlations. RESULTS: Knowledge of AI varied widely amongst endoscopists. Most endoscopists were optimistic about AI's capabilities in performing objective administrative and clinical tasks, but reserved about AI providing personalised, empathetic care. 68.8% (n = 11) of endoscopists agreed or strongly agreed that GI Genius should be used as an adjunct in colonoscopy. In analysing the 31.3% (n = 5) of endoscopists who disagreed or were ambivalent about its use, there was no significant correlation with their knowledge or perceptions of AI, but a significant number did not enjoy using the programme (p-value = 0.0128) and did not think it improved the quality of colonoscopy (p-value = 0.033). CONCLUSIONS: Acceptance of AI-aided colonoscopy systems is more related to the endoscopist's experience with using the programme, rather than general knowledge or perceptions towards AI. Uptake of such systems will rely greatly on how the device is delivered to the end user.

Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore.

BACKGROUND: Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps. METHODS: A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy. RESULTS: A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement. CONCLUSIONS: Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.

One-year review of real-time artificial intelligence (AI)-aided endoscopy performance.

BACKGROUND: Colonoscopies have long been the gold standard for detection of pre-malignant neoplastic lesions of the colon. Our previous study tried real-time artificial intelligence (AI)-aided colonoscopy over a three-month period and found significant improvements in collective and individual endoscopist's adenoma detection rates compared to baseline. As an expansion, this study evaluates the 1-year performance of AI-aided colonoscopy in the same institution. METHODS: A prospective cohort study was conducted in a single institution in Singapore. The AI software used was GI Genius™ Intelligent Endoscopy Module, US-DG-2000309 © 2021 Medtronic. Between July 2021 and June 2022, polypectomy rates in non-AI-aided colonoscopies and AI-aided colonoscopies were calculated and compared. Some of the AI-aided colonoscopies were recorded and video reviewed. A "hit" was defined as a sustained detection of an area by the AI. If a polypectomy was performed for a "hit," its histology was reviewed. Additional calculations for polyp detection rate (PDR), adenoma detection rate (ADR), and adenoma detection per colonoscopy (ADPC) were performed. Cost analysis was performed to determine cost effectiveness of subscription to the AI program. RESULTS: 2433 AI-aided colonoscopies were performed between July 2021 and June 2022 and compared against 1770 non-AI-aided colonoscopies. AI-aided colonoscopies yielded significantly higher rates of polypectomies (33.6%) as compared with non-AI-aided colonoscopies (28.4%) (p < 0.001). Among the AI-aided colonoscopies, 1050 were reviewed and a final 843 were included for additional analysis. The polypectomy to "hit" ratio was 57.4%, PDR = 45.6%, ADR = 32.4%, and ADPC = 2.08. Histological review showed that 25 polyps (3.13%) were sessile-serrated adenomas. Cost analysis found that the increased polypectomy rates in AI-aided colonoscopes led to an increase in revenue, which covered the subscription cost with an excess of USD 20,000. CONCLUSION: AI-aided colonoscopy is a cost effective means of improving colonoscopy quality and may help advance colorectal cancer screening in Singapore.

Divergent evolutionary trajectories of influenza B viruses underlie their contemporaneous epidemic activity.

Influenza B viruses have circulated in humans for over 80 y, causing a significant disease burden. Two antigenically distinct lineages ("B/Victoria/2/87-like" and "B/Yamagata/16/88-like," termed Victoria and Yamagata) emerged in the 1970s and have cocirculated since 2001. Since 2015 both lineages have shown unusually high levels of epidemic activity, the reasons for which are unclear. By analyzing over 12,000 influenza B virus genomes, we describe the processes enabling the long-term success and recent resurgence of epidemics due to influenza B virus. We show that following prolonged diversification, both lineages underwent selective sweeps across the genome and have subsequently taken alternate evolutionary trajectories to exhibit epidemic dominance, with no reassortment between lineages. Hemagglutinin deletion variants emerged concomitantly in multiple Victoria virus clades and persisted through epistatic mutations and interclade reassortment-a phenomenon previously only observed in the 1970s when Victoria and Yamagata lineages emerged. For Yamagata viruses, antigenic drift of neuraminidase was a major driver of epidemic activity, indicating that neuraminidase-based vaccines and cross-reactivity assays should be employed to monitor and develop robust protection against influenza B morbidity and mortality. Overall, we show that long-term diversification and infrequent selective sweeps, coupled with the reemergence of hemagglutinin deletion variants and antigenic drift of neuraminidase, are factors that contributed to successful circulation of diverse influenza B clades. Further divergence of hemagglutinin variants with poor cross-reactivity could potentially lead to circulation of 3 or more distinct influenza B viruses, further complicating influenza vaccine formulation and highlighting the urgent need for universal influenza vaccines.

Correction: Zhang, N.; Lin, C. The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights. Sensors 2023, 23, 1621.

There was an error in the original publication [...].

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights.

Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.

CDER167, a dual inhibitor of URAT1 and GLUT9, is a novel and potent uricosuric candidate for the treatment of hyperuricemia.

Urate transporter 1 (URAT1) and glucose transporter 9 (GLUT9) are important targets for the development of uric acid-lowering drugs. We previously showed that the flexible linkers of URAT1 inhibitors could enhance their potency. In this study we designed and synthesized CDER167, a novel RDEA3710 analogue, by introducing a linker (methylene) between the naphthalene and pyridine rings to increase flexibility, and characterized its pharmacological and pharmacokinetics properties in vitro and in vivo. We showed that CDER167 exerted dual-target inhibitory effects on both URAT1 and GLUT9: CDER167 concentration-dependently inhibited the uptake of [14C]-uric acid in URAT1-expressing HEK293 cells with an IC50 value of 2.08 ± 0.31 µM, which was similar to that of RDEA3170 (its IC50 value was 1.47 ± 0.23 µM). Using site-directed mutagenesis, we demonstrated that CDER167 might interact with URAT1 at S35 and F365. In GLUT9-expressing HEK293T cells, CDER167 concentration-dependently inhibited GLUT9 with an IC50 value of 91.55 ± 15.28 µM, whereas RDEA3170 at 100 µM had no effect on GLUT9. In potassium oxonate-induced hyperuricemic mice, oral administration of CDER167 (10 mg·kg-1 · d-1) for 7 days was more effective in lowering uric acid in blood and significantly promoted uric acid excretion in urine as compared with RDEA3170 (20 mg·kg-1 · d-1) administered. The animal experiment proved the safety of CDER167. In addition, CDER167 displayed better bioavailability than RDEA3170, better metabolic stability and no hERG toxicity at 100 µM. These results suggest that CDER167 deserves further investigation as a candidate antihyperuricemic drug targeting URAT1 and GLUT9.

Identification of genes associated with endometrial cell ageing.

Ageing of the uterine endometrium is a critical factor that affects reproductive success, but the mechanisms associated with uterine ageing are unclear. In this study, we conducted a qualitative examination of age-related changes in endometrial tissues and identified candidate genes as markers for uterine ageing. Gene expression patterns were assessed by two RNA-sequencing experiments using uterine tissues from wild type (WT) C57BL/6 mice. Gene expression data obtained by RNA-sequencing were validated by real-time PCR. Genes expressing the pro-inflammatory cytokines Il17rb and chemokines Cxcl12 and Cxcl14 showed differential expression between aged WT mice and a group of mice composed of 5- and 8-week-old WT (young) animals. Protein expression levels of the above-mentioned genes and of IL8, which functions downstream of IL17RB, were analysed by quantitative immunohistochemistry of unaffected human endometrium tissue samples from patients in their 20s and 40s (10 cases each). In the secretory phase samples, 3,3'- diaminobenzidine staining intensities of IL17RB, CXCL12 and CXCL14 for patients in their 40s were significantly higher than that for patients in their 20s, as detected by a Mann-hitney U test. These results suggest that these genes are candidate markers for endometrial ageing and for prediction of age-related infertility, although confirmation of these findings is needed in larger studies involving fertile and infertile women.

Biotin exposure-based immunomagnetic separation coupled with sodium dodecyl sulfate, propidium monoazide, and multiplex real-time PCR for rapid detection of viable Salmonella Typhimurium, Staphylococcus aureus, and Listeria monocytogenes in milk.

In this study, we established a rapid and sensitive method for the detection of viable Salmonella Typhimurium, Staphylococcus aureus, and Listeria monocytogenes in milk using biotin-exposure-based immunomagnetic separation (IMS) combined with sodium dodecyl sulfate (SDS), propidium monoazide (PMA), and multiplex real-time PCR (mRT-PCR). We used IMS to lessen the assay time for isolation of target bacteria. We then optimized the coupling conditions and immunomagnetic capture process. The immunoreaction and incubation times for 5 µg of mAb coupled with 500 µg of streptavidin-functionalized magnetic beads using a streptavidin-biotin system were 90 and 30 min, respectively. Treatment with SDS-PMA before mRT-PCR amplification eliminated false-positive outcomes from dead bacteria and identified viable target bacteria with good sensitivity and specificity. The limit of detection of IMS combined with the SDS-PMA-mRT-PCR assay for the detection of viable Salmonella Typhimurium, Staph. aureus, and L. monocytogenes in spiked milk matrix samples was 10 cfu/mL and remained significant even in the appearance of 106 cfu/mL of nontarget bacteria. The entire detection process was able to identify viable bacteria within 9 h. The combination of biotin-exposure-mediated IMS and SDS-PMA-mRT-PCR has potential value for the rapid and sensitive detection of foodborne pathogens.