KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements.

Tumours use various strategies to evade immune surveillance1,2. Immunotherapies targeting tumour immune evasion such as immune checkpoint blockade have shown considerable efficacy on multiple cancers3,4 but are ineffective for most patients due to primary or acquired resistance5-7. Recent studies showed that some epigenetic regulators suppress anti-tumour immunity2,8-12, suggesting that epigenetic therapies could boost anti-tumour immune responses and overcome resistance to current immunotherapies. Here we show that, in mouse melanoma models, depletion of KDM5B-an H3K4 demethylase that is critical for melanoma maintenance and drug resistance13-15-induces robust adaptive immune responses and enhances responses to immune checkpoint blockade. Mechanistically, KDM5B recruits the H3K9 methyltransferase SETDB1 to repress endogenous retroelements such as MMVL30 in a demethylase-independent manner. Derepression of these retroelements activates cytosolic RNA-sensing and DNA-sensing pathways and the subsequent type-I interferon response, leading to tumour rejection and induction of immune memory. Our results demonstrate that KDM5B suppresses anti-tumour immunity by epigenetic silencing of retroelements. We therefore reveal roles of KDM5B in heterochromatin regulation and immune evasion in melanoma, opening new paths for the development of KDM5B-targeting and SETDB1-targeting therapies to enhance tumour immunogenicity and overcome immunotherapy resistance.

De Novo Elastin Assembly Alleviates Development of Supravalvular Aortic Stenosis-Brief Report.

BACKGROUND: A series of incurable cardiovascular disorders arise due to improper formation of elastin during development. Supravalvular aortic stenosis (SVAS), resulting from a haploinsufficiency of ELN, is caused by improper stress sensing by medial vascular smooth muscle cells, leading to progressive luminal occlusion and heart failure. SVAS remains incurable, as current therapies do not address the root issue of defective elastin. METHODS: We use SVAS here as a model of vascular proliferative disease using both human induced pluripotent stem cell-derived vascular smooth muscle cells and developmental Eln+/- mouse models to establish de novo elastin assembly as a new therapeutic intervention. RESULTS: We demonstrate mitigation of vascular proliferative abnormalities following de novo extracellular elastin assembly through the addition of the polyphenol epigallocatechin gallate to SVAS human induced pluripotent stem cell-derived vascular smooth muscle cells and in utero to Eln+/- mice. CONCLUSIONS: We demonstrate de novo elastin deposition normalizes SVAS human induced pluripotent stem cell-derived vascular smooth muscle cell hyperproliferation and rescues hypertension and aortic mechanics in Eln+/- mice, providing critical preclinical findings for the future application of epigallocatechin gallate treatment in humans.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism.

Predicting RNA solvent accessibility using only primary sequence data can be regarded as sequence-based prediction work. Currently, the established studies for sequence-based RNA solvent accessibility prediction are limited due to the available number of datasets and black box prediction. To improve these issues, we first expanded the available RNA structures and then developed a sequence-based model using modified attention layers with different receptive fields to conform to the stem-loop structure of RNA chains. We measured the improvement with an extended dataset and further explored the model's interpretability by analysing the model structures, attention values and hyperparameters. Finally, we found that the developed model regarded the pieces of a sequence as templates during the training process. This work will be helpful for researchers who would like to build RNA attribute prediction models using deep learning in the future.

Identification of Active Molecules against Thrombocytopenia through Machine Learning.

Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening complications. Discovering new therapeutic agents against thrombocytopenia has proven to be a challenging task using traditional screening approaches. Fortunately, machine learning (ML) techniques offer a rapid avenue for exploring chemical space, thereby increasing the likelihood of uncovering new drug candidates. In this study, we focused on computational modeling for drug-induced megakaryocyte differentiation and platelet production using ML methods, aiming to gain insights into the structural characteristics of hematopoietic activity. We developed 112 different classifiers by combining eight ML algorithms with 14 molecule features. The top-performing model achieved good results on both 5-fold cross-validation (with an accuracy of 81.6% and MCC value of 0.589) and external validation (with an accuracy of 83.1% and MCC value of 0.642). Additionally, by leveraging the Shapley additive explanations method, the best model provided quantitative assessments of molecular properties and structures that significantly contributed to the predictions. Furthermore, we employed an ensemble strategy to integrate predictions from multiple models and performed in silico predictions for new molecules with potential activity against thrombocytopenia, sourced from traditional Chinese medicine and the Drug Repurposing Hub. The findings of this study could offer valuable insights into the structural characteristics and computational prediction of thrombopoiesis inducers.

Development and validation of a predictive model for end-stage renal disease in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) affects many populations. This study aims to develop a predictive model and create a nomogram for assessing the risk of end-stage renal disease (ESRD) in patients diagnosed with SLE. Data from electronic health records of SLE patients treated at the Affiliated Hospital of North Sichuan Medical College between 2013 and 2023 were collected. The dataset underwent thorough cleaning and variable assignment procedures. Subsequently, variables were selected using one-way logistic regression and lasso logistic regression methods, followed by multifactorial logistic regression to construct nomograms. The model's performance was assessed using calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Statistical significance was set at P < 0.05. The predictive variables for ESRD development in SLE patients included anti-GP210 antibody presence, urinary occult blood, proteinuria, white blood cell count, complement 4 levels, uric acid, creatinine, total protein, globulin, glomerular filtration rate, pH, specific gravity, very low-density lipoprotein, homocysteine, apolipoprotein B, and absolute counts of cytotoxic T cells. The nomogram exhibited a broad predictive range. The ROC area under the curve (AUC) was 0.886 (0.858-0.913) for the training set and 0.840 (0.783-0.897) for the testing set, indicating good model performance. The model demonstrated both applicability and significant clinical benefits. The developed model presents strong predictive capabilities and considerable clinical utility in estimating the risk of ESRD in patients with SLE.

Muscle LIM Protein Force-Sensing Mediates Sarcomeric Biomechanical Signaling in Human Familial Hypertrophic Cardiomyopathy.

BACKGROUND: Familial hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease and is typically caused by mutations in genes encoding sarcomeric proteins that regulate cardiac contractility. HCM manifestations include left ventricular hypertrophy and heart failure, arrythmias, and sudden cardiac death. How dysregulated sarcomeric force production is sensed and leads to pathological remodeling remains poorly understood in HCM, thereby inhibiting the efficient development of new therapeutics. METHODS: Our discovery was based on insights from a severe phenotype of an individual with HCM and a second genetic alteration in a sarcomeric mechanosensing protein. We derived cardiomyocytes from patient-specific induced pluripotent stem cells and developed robust engineered heart tissues by seeding induced pluripotent stem cell-derived cardiomyocytes into a laser-cut scaffold possessing native cardiac fiber alignment to study human cardiac mechanobiology at both the cellular and tissue levels. Coupled with computational modeling for muscle contraction and rescue of disease phenotype by gene editing and pharmacological interventions, we have identified a new mechanotransduction pathway in HCM, shown to be essential in modulating the phenotypic expression of HCM in 5 families bearing distinct sarcomeric mutations. RESULTS: Enhanced actomyosin crossbridge formation caused by sarcomeric mutations in cardiac myosin heavy chain (MYH7) led to increased force generation, which, when coupled with slower twitch relaxation, destabilized the MLP (muscle LIM protein) stretch-sensing complex at the Z-disc. Subsequent reduction in the sarcomeric muscle LIM protein level caused disinhibition of calcineurin-nuclear factor of activated T-cells signaling, which promoted cardiac hypertrophy. We demonstrate that the common muscle LIM protein-W4R variant is an important modifier, exacerbating the phenotypic expression of HCM, but alone may not be a disease-causing mutation. By mitigating enhanced actomyosin crossbridge formation through either genetic or pharmacological means, we alleviated stress at the Z-disc, preventing the development of hypertrophy associated with sarcomeric mutations. CONCLUSIONS: Our studies have uncovered a novel biomechanical mechanism through which dysregulated sarcomeric force production is sensed and leads to pathological signaling, remodeling, and hypertrophic responses. Together, these establish the foundation for developing innovative mechanism-based treatments for HCM that stabilize the Z-disc MLP-mechanosensory complex.

Inhibition of mitochondrial translocase SLC25A5 and histone deacetylation is an effective combination therapy in neuroblastoma.

The mitochondrion is a gatekeeper of apoptotic processes, and mediates drug resistance to several chemotherapy agents used to treat cancer. Neuroblastoma is a common solid cancer in young children with poor clinical outcomes following conventional chemotherapy. We sought druggable mitochondrial protein targets in neuroblastoma cells. Among mitochondria-associated gene targets, we found that high expression of the mitochondrial adenine nucleotide translocase 2 (SLC25A5/ANT2), was a strong predictor of poor neuroblastoma patient prognosis and contributed to a more malignant phenotype in pre-clinical models. Inhibiting this transporter with PENAO reduced cell viability in a panel of neuroblastoma cell lines in a TP53-status-dependant manner. We identified the histone deacetylase inhibitor, suberanilohydroxamic acid (SAHA), as the most effective drug in clinical use against mutant TP53 neuroblastoma cells. SAHA and PENAO synergistically reduced cell viability, and induced apoptosis, in neuroblastoma cells independent of TP53-status. The SAHA and PENAO drug combination significantly delayed tumour progression in pre-clinical neuroblastoma mouse models, suggesting that these clinically advanced inhibitors may be effective in treating the disease.

Targeting a thrombopoietin-independent strategy in the discovery of a novel inducer of megakaryocytopoiesis, DMAG, for the treatment of thrombocytopenia.

Thrombocytopenia is a thrombopoietin (TPO)-related disorder with very limited treatment options, and can be lifethreatening. There are major problems with typical thrombopoietic agents targeting TPO signaling, so it is urgent to discover a novel TPO-independent mechanism involving thrombopoiesis and potential druggable targets. We developed a drug screening model by the multi-grained cascade forest (gcForest) algorithm and found that 3,8-di-O-methylellagic acid 2- O-glucoside (DMAG) (10, 20 and 40 µM) promoted megakaryocyte differentiation in vitro. Subsequent investigations revealed that DMAG (40 mM) activated ERK1/2, HIF-1b and NF-E2. Inhibition of ERK1/2 blocked megakaryocyte differentiation and attenuated the upregulation of HIF-1b and NF-E2 induced by DMAG. Megakaryocyte differentiation induced by DMAG was inhibited via knockdown of NF-E2. In vivo studies showed that DMAG (5 mg/kg) accelerated platelet recovery and megakaryocyte differentiation in mice with thrombocytopenia. The platelet count of the DMAG-treated group recovered to almost 72% and 96% of the count in the control group at day 10 and 14, respectively. The platelet counts in the DMAG-treated group were almost 1.5- and 1.3-fold higher compared with those of the irradiated group at day 10 and 14, respectively. Moreover, DMAG (10, 25 and 50 mM) stimulated thrombopoiesis in zebrafish. DMAG (5 mg/kg) could also increase platelet levels in c-MPL knockout (c-MPL-/-) mice. In summary, we established a drug screening model through gcForest and demonstrated that DMAG promotes megakaryocyte differentiation via the ERK/HIF1/NF-E2 pathway which, importantly, is independent of the classical TPO/c-MPL pathway. The present study may provide new insights into drug discovery for thrombopoiesis and TPO-independent regulation of thrombopoiesis, as well as a promising avenue for thrombocytopenia treatment.

Differentiation syndrome and coagulation disorder - comparison between treatment with oral and intravenous arsenics in pediatric acute promyelocytic leukemia.

Realgar-Indigo naturalis formula (RIF), with A4S4 as a major ingredient, is an oral arsenic used in China to treat pediatric acute promyelocytic leukemia (APL). The efficacy of RIF is similar to that of arsenic trioxide (ATO). However, the effects of these two arsenicals on differentiation syndrome (DS) and coagulation disorders, the two main life-threatening events in children with APL, remain unclear. We retrospectively analyzed 68 consecutive children with APL from South China Children Leukemia Group-APL (SCCLG-APL) study. Patients received all-trans retinoic acid (ATRA) on day 1 of induction therapy. ATO 0.16 mg/kg day or RIF 135 mg/kg·day was administrated on day 5, while mitoxantrone was administered on day 3 (non-high-risk) or days 2-4 (high-risk). The incidences of DS were 3.0% and 5.7% in ATO (n = 33) and RIF (n = 35) arms (p = 0.590), and 10.3% and 0% in patients with and without differentiation-related hyperleukocytosis (p = 0.04), respectively. Moreover, in patients with differentiation-related hyperleukocytosis, the incidence of DS was not significantly different between ATO and RIF arms. The dynamic changes of leukocyte count between arms were not statistically different. However, patients with leukocyte count > 2.61 × 109/L or percentage of promyelocytes in peripheral blood > 26.5% tended to develop hyperleukocytosis. The improvement of coagulation indexes in ATO and RIF arms was similar, with fibrinogen and prothrombin time having the quickest recovery rate. This study showed that the incidence of DS and recovery of coagulopathy are similar when treating pediatric APL with RIF or ATO.

Toll-like Receptors and Thrombopoiesis.

Platelets are the second most abundant blood component after red blood cells and can participate in a variety of physiological and pathological functions. Beyond its traditional role in hemostasis and thrombosis, it also plays an indispensable role in inflammatory diseases. However, thrombocytopenia is a common hematologic problem in the clinic, and it presents a proportional relationship with the fatality of many diseases. Therefore, the prevention and treatment of thrombocytopenia is of great importance. The expression of Toll-like receptors (TLRs) is one of the most relevant characteristics of thrombopoiesis and the platelet inflammatory function. We know that the TLR family is found on the surface or inside almost all cells, where they perform many immune functions. Of those, TLR2 and TLR4 are the main stress-inducing members and play an integral role in inflammatory diseases and platelet production and function. Therefore, the aim of this review is to present and discuss the relationship between platelets, inflammation and the TLR family and extend recent research on the influence of the TLR2 and TLR4 pathways and the regulation of platelet production and function. Reviewing the interaction between TLRs and platelets in inflammation may be a research direction or program for the treatment of thrombocytopenia-related and inflammatory-related diseases.

Epigallocatechin gallate facilitates extracellular elastin fiber formation in induced pluripotent stem cell derived vascular smooth muscle cells for tissue engineering.

Tissue engineered vascular grafts possess several advantages over synthetic or autologous grafts, including increased availability and reduced rates of infection and thrombosis. Engineered grafts constructed from human induced pluripotent stem cell derivatives further offer enhanced reproducibility in graft production. One notable obstacle to clinical application of these grafts is the lack of elastin in the vessel wall, which would serve to endow compliance in addition to mechanical strength. This study establishes the ability of the polyphenol compound epigallocatechin gallate, a principal component of green tea, to facilitate the extracellular formation of elastin fibers in vascular smooth muscle cells derived from human induced pluripotent stem cells. Further, this study describes the creation of a doxycycline-inducible elastin expression system to uncouple elastin production from vascular smooth muscle cell proliferative capacity to permit fiber formation in conditions conducive to robust tissue engineering.

Discovery of a novel megakaryopoiesis enhancer, ingenol, promoting thrombopoiesis through PI3K-Akt signaling independent of thrombopoietin.

Thrombocytopenia, a most common complication of radiotherapy and chemotherapy, is an important cause of morbidity and mortality in cancer patients. However, there are still no approved agents for the treatment of radiation- and chemotherapy-induced thrombocytopenia (RIT and CIT, respectively). In this study, a drug screening model for predicting compounds with activity in promoting megakaryocyte (MK) differentiation and platelet production was established based on machine learning (ML), and a natural product ingenol was predicted as a potential active compound. Then, in vitro experiments showed that ingenol significantly promoted MK differentiation in K562 and HEL cells. Furthermore, a RIT mice model and c-MPL knock-out (c-MPL-/-) mice constructed by CRISPR/Cas9 technology were used to assess the therapeutic action of ingenol on thrombocytopenia. The results showed that ingenol accelerated megakaryopoiesis and thrombopoiesis both in RIT mice and c-MPL-/- mice. Next, RNA-sequencing (RNA-seq) was carried out to analyze the gene expression profile induced by ingenol during MK differentiation. Finally, through experimental verifications, we demonstrated that the activation of PI3K/Akt signaling pathway was involved in ingenol-induced MK differentiation. Blocking PI3K/Akt signaling pathway abolished the promotion of ingenol on MK differentiation. Nevertheless, inhibition of TPO/c-MPL signaling pathway could not suppress ingenol-induced MK differentiation. In conclusion, our study builds a drug screening model to discover active compounds against thrombocytopenia, reveals the critical roles of ingenol in promoting MK differentiation and platelet production, and provides a promising avenue for the treatment of RIT.

Macrophage cell membrane-based nanoparticles: a new promising biomimetic platform for targeted delivery and treatment.

Synthetic nanoparticles with surface bioconjugation are promising platforms for targeted therapy, but their simple biological functionalization is still a challenging task against the complex intercellular environment. Once synthetic nanoparticles enter the body, they are phagocytosed by immune cells by the immune system. Recently, the cell membrane camouflage strategy has emerged as a novel therapeutic tactic to overcome these issues by utilizing the fundamental properties of natural cells. Macrophage, a type of immune system cells, plays critical roles in various diseases, including cancer, atherosclerosis, rheumatoid arthritis, infection and inflammation, due to the recognition and engulfment function of removing substances and pathogens. Macrophage membranes inherit the surface protein profiles and biointerfacing properties of source cells. Therefore, the macrophage membrane cloaking can protect synthetic nanoparticles from phagocytosis by the immune cells. Meanwhile, the macrophage membrane can make use of the natural correspondence to accurately recognize antigens and target inflamed tissue or tumor sites. In this review, we have summarized the advances in the fabrication, characterization and homing capacity of macrophage membrane cloaking nanoparticles in various diseases, including cancers, immune diseases, cardiovascular diseases, central nervous system diseases, and microbial infections. Although macrophage membrane-camouflaged nanoparticles are currently in the fetal stage of development, there is huge potential and challenge to explore the conversion mode in the clinic.

Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identify TF binding sites and predict their affinities. Recently, deep learning (DL) algorithms have achieved promising results in the prediction of DNA-TF binding, however, various deep learning architectures have not been systematically compared, and the relative merit of each architecture remains unclear. To address this problem, we applied four different deep learning architectures to SELEX-seq and HT-SELEX data, covering three species and 35 families. We evaluated and compared the performance of different deep neural models using 10-fold cross-validation. Our results indicate that the hybrid CNN + DNN model shows the best performances. We expect that our study will be broadly applicable to modeling and predicting TF binding specificity when more high-throughput affinity data are available.

Whole transcriptome sequencing and integrated network analysis elucidates the effects of 3,8-Di-O-methylellagic acid 2-O-glucoside derived from Sanguisorba offcinalis L., a novel differentiation inducer on erythroleukemia cells.

Acute erythroid leukemia (AEL) is a rare and aggressive hematologic malignancy with no specific treatment. Sanguisorba officinalis L. (S. officinalis), a well-known traditional Chinese medicine, possesses potent anticancer activity. However, the active components of S. officinalis against AEL and the associated molecular mechanisms remain unknown. In this study, we predicted the anti-AML effect of S. officinalis based on network pharmacology. Through the identification of active components of S. officinalis, we found that 3,8-Di-O-methylellagic acid 2-O-glucoside (DMAG) not only significantly inhibited the proliferation of erythroleukemic cell line HEL, but also induced their differentiation to megakaryocytes. Furthermore, we demonstrated that DMAG could prolong the survival of AEL mice model. Whole-transcriptome sequencing was performed to elucidate the underlying molecular mechanisms associated with anti-AEL effect of DMAG. The results showed that the total of 68 miRNAs, 595 lncRNAs, 4030 mRNAs and 35 circRNAs were significantly differentially expressed during DMAG induced proliferation inhibition and differentiation of HEL cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the differentially expressed miRNAs, lncRNAs, mRNAs and circRNAs were mainly involved in metabolic, HIF-1, MAPK, Notch pathway and apoptosis. The co-expression networks showed that miR-23a-5p, miR-92a-1-5p, miR-146b and miR-760 regulatory networks were crucial for megakaryocyte differentiation induced by DMAG. In conclusion, our results suggest that DMAG, derived from S. officinalis might be a potent differentiation inducer of AEL cells and provide important information on the underlying mechanisms associated with its anti-AEL activity.

DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence.

MOTIVATION: Various bacterial pathogens can deliver their secreted substrates also called effectors through Type III secretion systems (T3SSs) into host cells and cause diseases. Since T3SS secreted effectors (T3SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. However, the effectors display high level of sequence diversity, therefore making the identification a difficult process. There is a need to develop a novel and effective method to screen and select putative novel effectors from bacterial genomes that can be validated by a smaller number of key experiments. RESULTS: We develop a deep convolution neural network to directly classify any protein sequence into T3SEs or non-T3SEs, which is useful for both effector prediction and the study of sequence-function relationship. Different from traditional machine learning-based methods, our method automatically extracts T3SE-related features from a protein N-terminal sequence of 100 residues and maps it to the T3SEs space. We train and test our method on the datasets curated from 16 species, yielding an average classification accuracy of 83.7% in the 5-fold cross-validation and an accuracy of 92.6% for the test set. Moreover, when comparing with known state-of-the-art prediction methods, the accuracy of our method is 6.31-20.73% higher than previous methods on a common independent dataset. Besides, we visualize the convolutional kernels and successfully identify the key features of T3SEs, which contain important signal information for secretion. Finally, some effectors reported in the literature are used to further demonstrate the application of DeepT3. AVAILABILITY AND IMPLEMENTATION: DeepT3 is freely available at: https://github.com/lje00006/DeepT3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences.

Deep learning has proven to be a powerful method with applications in various fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can use different deep learning architectures and data sets for rapid modeling. However, the available implementations of neural networks using these toolkits are usually designed for a specific research and are difficult to transfer to other work. Here, we present autoBioSeqpy, a tool that uses deep learning for biological sequence classification. The advantage of this tool is its simplicity. Users only need to prepare the input data set and then use a command line interface. Then, autoBioSeqpy automatically executes a series of customizable steps including text reading, parameter initialization, sequence encoding, model loading, training, and evaluation. In addition, the tool provides various ready-to-apply and adapt model templates to improve the usability of these networks. We introduce the application of autoBioSeqpy on three biological sequence problems: the prediction of type III secreted proteins, protein subcellular localization, and CRISPR/Cas9 sgRNA activity. autoBioSeqpy is freely available with examples at https://github.com/jingry/autoBioSeqpy.

Modeling elastin-associated vasculopathy with patient induced pluripotent stem cells and tissue engineering.

Elastin-associated vasculopathies are life-threatening conditions of blood vessel dysfunction. The extracellular matrix protein elastin endows the recoil and compliance required for physiologic arterial function, while disruption of function can lead to aberrant vascular smooth muscle cell proliferation manifesting through stenosis, aneurysm, or vessel dissection. Although research efforts have been informative, they remain incomplete as no viable therapies exist outside of a heart transplant. Induced pluripotent stem cell technology may be uniquely suited to address current obstacles as these present a replenishable supply of patient-specific material with which to study disease. The following review will cover the cutting edge in vascular smooth muscle cell modeling of elastin-associated vasculopathy, and aid in the development of human disease modeling and drug screening approaches to identify potential treatments. Vascular proliferative disease can affect up to 50% of the population throughout the world, making this a relevant and critical area of research for therapeutic development.

Number of natural teeth, denture use and mortality in Chinese elderly: a population-based prospective cohort study.

BACKGROUND: The associations between the number of natural teeth/denture use and all-cause mortality remain unclear due to lake of investigation for the potential interaction between tooth loss and denture use and for the potential changes in these exposures over time in older adults. We undertake this study to evaluate the associations of the number of natural teeth and/or denture use with mortality in Chinese elderly. METHODS: This is a prospective cohort study of 36,283 older adults (median age: 90). The number of natural teeth and denture use were collected with structured questionnaire. We evaluated hazard ratios (HRs) and confidence intervals (CIs) using a Cox proportional hazards model adjusting for demographic factors, education, income, lifestyle factors, and comorbidities. RESULTS: We documented 25,857 deaths during 145,947 person-years of observation. Compared to those with 20+ teeth, tooth loss was associated with a gradual increase in mortality, with an adjusted HR of 1.14 (95% CI, 1.06 to 1.23) for those with 10-19 teeth, 1.23 (95% CI, 1.15 to 1.31) for those with 1-9 teeth, and 1.35 (95% CI, 1.26 to 1.44) for those without natural teeth. Denture use was associated with lower risk of mortality (adjusted HR 0.81; 95% CI, 0.77 to 0.84). Subgroup analyses indicated that the benefit of denture use was greater in men than in women (P = 0.02) and tended to decrease with age (P < 0.001). The effects of denture use did not differ among various degrees of tooth loss (P = 0.17). CONCLUSIONS: Tooth loss was associated with an increased risk of mortality in older adults. Denture use provided a protective effect against death for all degrees of tooth loss however, this effect appeared to be modified by sex and age.