Severity predictors for multisystemic inflammatory syndrome in children after SARS-CoV-2 infection in Vietnam.

Multisystemic inflammatory syndrome in children (MIS-C) might manifest in a broad spectrum of clinical scenarios, ranging from mild features to multi-organ dysfunction and mortality. However, this novel entity has a heterogenicity of data regarding prognostic factors associated with severe outcomes. The present study aimed to identify independent predictors for severity by using multivariate regression models. A total of 391 patients (255 boys and 136 girls) were admitted to Vietnam National Children's Hospital from January 2022 to June 2023. The median age was 85 (range: 2-188) months, and only 12 (3.1%) patients had comorbidities. 161 (41.2%) patients required PICU admission, and the median PICU LOS was 4 (2-7) days. We observed independent factors related to PICU admission, including CRP ≥ 50 (mg/L) (OR 2.52, 95% CI 1.39-4.56, p = 0.002), albumin ≤ 30 (g/L) (OR 3.18, 95% CI 1.63-6.02, p = 0.001), absolute lymphocyte count ≤ 2 (× 109/L) (OR 2.18, 95% CI 1.29-3.71, p = 0.004), ferritin ≥ 300 (ng/mL) (OR 2.35, 95% CI 1.38-4.01), p = 0.002), and LVEF < 60 (%) (OR 2.48, 95% CI 1.28-4.78, p = 0.007). Shock developed in 140 (35.8%) patients, especially for those decreased absolute lymphocyte ≤ 2 (× 109/L) (OR 2.48, 95% CI 1.10-5.61, p = 0.029), albumin ≤ 30 (g/L) (OR 2.53, 95% CI 1.22-5.24, p = 0.013), or LVEF < 60 (%) (OR 2.24, 95% CI 1.12-4.51, p = 0.022). In conclusion, our study emphasized that absolute lymphocyte count, serum albumin, CRP, and LVEF were independent predictors for MIS-C severity. Further well-designed investigations are required to validate their efficacy in predicting MIS-C severe cases, especially compared to other parameters. As MIS-C is a new entity and severe courses may progress aggressively, identifying high-risk patients optimizes clinicians' follow-up and management to improve disease outcomes.

Evaluation of screening algorithms to detect rectal colonization with carbapenemase-producing Enterobacterales in a resource-limited setting.

Objectives: To improve and rationalize the detection of carbapenemase-producing Enterobacterales (CPE) in rectal swabs in a high-prevalence and resource-constrained setting, addressing surveillance challenges typically encountered in laboratories with limited resources. Methods: A point prevalence survey (PPS) was conducted on 15 August 2022, in a provincial children's hospital in northern Vietnam. Rectal swab samples of all admitted children were collected and plated on a selective medium for carbapenem-resistant Enterobacterales (CRE). Species identification and antimicrobial susceptibility testing (AST) were performed by MALDI-TOF, and VITEK2 XL and interpreted according to CLSI breakpoints (2022). Carbapenemases were detected by the carbapenem inactivation method (CIM) and quantitative real-time PCR (qRT-PCR). Results: Rectal swab samples were obtained from 376 patients. Of 178 isolates growing on the CRE screening agar, 140 isolates were confirmed as Enterobacterales of which 118 (84.3%) isolates were resistant to meropenem and/or ertapenem. CIM and PCR showed that 90/118 (76.3%) were carbapenemase producers. Overall, 83/367 (22.6%) were colonized by CPE. Klebsiella pneumoniae, Escherichia coli and Enterobacter cloacae complex were the most common CPE detected, with NDM as the predominant carbapenemase (78/90; 86.7%). Phenotypic resistance to meropenem was the best predictor of CPE production (sensitivity 85.6%, specificity 100%) compared with ertapenem resistance (95.6% sensitivity, 36% specificity). CIM was 100% concordant with PCR in detecting carbapenemases. Conclusions: These findings underscore the effectiveness of meropenem resistance as a robust indicator of the production of carbapenemases and the reliability of the CIM method to detect such carbapenemases in resource-limited settings where the performance of molecular methods is not possible.

Awareness of having hypertension, diabetes and dyslipidaemia among US adults: The 2011-2018 NHANES data.

AIM: This study aimed to investigate awareness of having hypertension, diabetes and dyslipidaemia and their associated factors among US adults. METHODS: Data from the National Health and Nutrition Examination Survey, including 21,399 adults aged ⩾20 years (pregnant women excluded) collected between 2011 and 2018, were used. Blood pressure was measured using a Baumanometer calibrated mercury true gravity wall model sphygmomanometer. Serum total cholesterol levels were measured using enzymatic assays. The percentage of haemoglobin A1C (HbA1c), which reflects long-term blood glucose levels, was measured and used to identify diabetes. Participants self-reported whether they were told by a doctor that they have hypertension, dyslipidaemia and diabetes. Awareness was defined as alignment between objective and self-reported measures for having the conditions. Sampling weights and the Taylor series linearisation variance estimation method were used in the analyses. RESULTS: The findings showed that 64.06% of people with hypertension, 54.71% of those with dyslipidaemia and 78.40% of those with diabetes were aware of having the respective condition. Age, sex and health insurance were associated with awareness of having all three conditions, but marital status was not associated with any outcome. Weight status was associated with awareness of having hypertension and dyslipidaemia, whereas ethnicity was associated with awareness of having hypertension and diabetes. Relative family income was only associated with awareness of having hypertension. CONCLUSIONS: Large proportions of US adults with hypertension, dyslipidaemia and diabetes are not aware of having the conditions. Interventions targeting groups at higher risk of being unaware of these conditions are needed.

GENA: A knowledge graph for nutrition and mental health.

While a large number of knowledge graphs have previously been developed by automatically extracting and structuring knowledge from literature, there is currently no such knowledge graph that encodes relationships between food, biochemicals and mental illnesses, even though a large amount of knowledge about these relationships is available in the form of unstructured text in biomedical literature articles. To address this limitation, this article describes the development of GENA - (Graph of mEntal-health and Nutrition Association), a knowledge graph that represents relations between nutrition and mental health, extracted from biomedical abstracts. GENA is constructed from PubMed abstracts that contain keywords relating to chemicals, food, and health. A hybrid named entity recognition (NER) model is firstly applied to these abstracts to identify various entities of interest. Subsequently, a deep syntax-based relation extraction model is used to detect binary relations between the identified entities. Finally, the resulting relations are used to populate the GENA knowledge graph, whose relationships can be accessed in an intuitive and interpretable manner using the Neo4J Database Management System. To evaluate the reliability of GENA, two annotators manually assessed a subset of the extracted relations. The evaluation results show that our methods obtain high precision for the NER task and acceptable precision and relative recall for the relation extraction task. GENA consists of 43,367 relationships that encode information about nutrition and health, of which 94.04% are new relations that are not present in existing ontologies of food and diseases. GENA is constructed based on scientific principles, and has the potential to be used within further applications to contribute towards scientific research within the domain. It is a pioneering knowledge graph in nutrition and mental health, containing a diverse range of relationship types. All of our source code and results are publicly available at https://github.com/ddlinh/gena-db.

Contextualized medication event extraction with levitated markers.

Automatic extraction of patient medication histories from free-text clinical notes can increase the amount of relevant information to clinicians for developing treatment plans. In addition to detecting medication events, clinical text mining systems must also be able to predict event context, such as negation, uncertainty, and time of occurrence, in order to construct accurate patient timelines. Towards this goal, we introduce Levitated Context Markers (LCMs), a novel transformer-based model for contextualized event extraction. LCMs are an adaptation of levitated markers -originally developed for relation extraction- that allow pretrained transformer models to utilize global input representations while also focusing on event-related subspans using a sparse attention mechanism. In addition to outperforming a strong baseline model on the Contextualized Medication Event Dataset, we show that LCMs' sparse attention can provide interpretable predictions by detecting relevant context cues in an unsupervised manner.

Energetic preference and topological constraint effects on the formation of DNA twisted toroidal bundles.

DNA toroids are compact torus-shaped bundles formed by one or multiple DNA molecules being condensed from the solution due to various condensing agents. It has been shown that the DNA toroidal bundles are twisted. However, the global conformations of DNA inside these bundles are still not well understood. In this study, we investigate this issue by solving different models for the toroidal bundles and performing replica-exchange molecular dynamics (REMD) simulations for self-attractive stiff polymers of various chain lengths. We find that a moderate degree of twisting is energetically favorable for toroidal bundles, yielding optimal configurations of lower energies than for other bundles corresponding to spool-like and constant radius of curvature arrangements. The REMD simulations show that the ground states of the stiff polymers are twisted toroidal bundles with the average twist degrees close to those predicted by the theoretical model. Constant-temperature simulations show that twisted toroidal bundles can be formed through successive processes of nucleation, growth, quick tightening, and slow tightening of the toroid, with the two last processes facilitating the polymer threading through the toroid's hole. A relatively long chain of 512 beads has an increased dynamical difficulty to access the twisted bundle states due to the polymer's topological constraint. Interestingly, we also observed significantly twisted toroidal bundles with a sharp U-shaped region in the polymer conformation. It is suggested that this U-shaped region makes the formation of twisted bundles easier by effectively reducing the polymer length. This effect can be equivalent to having multiple chains in the toroid.

A metric learning-based method for biomedical entity linking.

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here.

A term-based and citation network-based search system for COVID-19.

The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their visualization, accelerating identification of relevant documents. It offers a multi-view interactive search and navigation interface, bringing together unsupervised approaches of term extraction and citation analysis. We conducted a user evaluation with domain experts, including epidemiologists, biochemists, medicinal chemists, and medicine students. In general, most users were satisfied with the relevance and speed of the search results. More interestingly, participants mostly agreed on the capacity of the system to enable exploration and discovery of the search space using the graph visualization and filters. The system is updated on a weekly basis and it is publicly available at http://www.nactem.ac.uk/cord/.

Optogenetic Control of Non-Apoptotic Cell Death.

Herein, a set of optogenetic tools (designated LiPOP) that enable photoswitchable necroptosis and pyroptosis in live cells with varying kinetics, is introduced. The LiPOP tools allow reconstruction of the key molecular steps involved in these two non-apoptotic cell death pathways by harnessing the power of light. Further, the use of LiPOPs coupled with upconversion nanoparticles or bioluminescence is demonstrated to achieve wireless optogenetic or chemo-optogenetic killing of cancer cells in multiple mouse tumor models. LiPOPs can trigger necroptotic and pyroptotic cell death in cultured prokaryotic or eukaryotic cells and in living animals, and set the stage for studying the role of non-apoptotic cell death pathways during microbial infection and anti-tumor immunity.

Circularly permuted LOV2 as a modular photoswitch for optogenetic engineering.

Plant-based photosensors, such as the light-oxygen-voltage sensing domain 2 (LOV2) from oat phototropin 1, can be modularly wired into cell signaling networks to remotely control protein activity and physiological processes. However, the applicability of LOV2 is hampered by the limited choice of available caging surfaces and its preference to accommodate the effector domains downstream of the C-terminal Jα helix. Here, we engineered a set of LOV2 circular permutants (cpLOV2) with additional caging capabilities, thereby expanding the repertoire of genetically encoded photoswitches to accelerate the design of optogenetic devices. We demonstrate the use of cpLOV2-based optogenetic tools to reversibly gate ion channels, antagonize CRISPR-Cas9-mediated genome engineering, control protein subcellular localization, reprogram transcriptional outputs, elicit cell suicide and generate photoactivatable chimeric antigen receptor T cells for inducible tumor cell killing. Our approach is widely applicable for engineering other photoreceptors to meet the growing need of optogenetic tools tailored for biomedical and biotechnological applications.

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

Covalent conjugation of extracellular vesicles with peptides and nanobodies for targeted therapeutic delivery.

Natural extracellular vesicles (EVs) are ideal drug carriers due to their remarkable biocompatibility. Their delivery specificity can be achieved by the conjugation of targeting ligands. However, existing methods to engineer target-specific EVs are tedious or inefficient, having to compromise between harsh chemical treatments and transient interactions. Here, we describe a novel method for the covalent conjugation of EVs with high copy numbers of targeting moieties using protein ligases. Conjugation of EVs with either an epidermal growth factor receptor (EGFR)-targeting peptide or anti-EGFR nanobody facilitates their accumulation in EGFR-positive cancer cells, both in vitro and in vivo. Systemic delivery of paclitaxel by EGFR-targeting EVs at a low dose significantly increases drug efficacy in a xenografted mouse model of EGFR-positive lung cancer. The method is also applicable to the conjugation of EVs with peptides and nanobodies targeting other receptors, such as HER2 and SIRP alpha, and the conjugated EVs can deliver RNA in addition to small molecules, supporting the versatile application of EVs in cancer therapies. This simple, yet efficient and versatile method for the stable surface modification of EVs bypasses the need for genetic and chemical modifications, thus facilitating safe and specific delivery of therapeutic payloads to target cells.

Optogenetic approaches to control Ca2+-modulated physiological processes.

As a versatile intracellular second messenger, calcium ion (Ca2+) regulates a plethora of physiological processes. To achieve precise control over Ca2+ signals in living cells and organisms, a set of optogenetic tools have recently been crafted by engineering photosensitive domains into intracellular signaling proteins, G-protein coupled receptors (GPCRs), receptor tyrosine kinases (RTKs), and Ca2+ channels. We highlight herein the optogenetic engineering strategies, kinetic properties, advantages and limitations of these genetically-encoded Ca2+ channel actuators (GECAs) and modulators. In parallel, we present exemplary applications in both excitable and non-excitable cells and tissues. Furthermore, we briefly discuss potential solutions for wireless optogenetics to accelerate the in vivo applications of GECAs under physiological conditions, with an emphasis on integrating near-infrared (NIR) light-excitable upconversion nanoparticles (UCNPs) and bioluminescence with optogenetics.

Diffusion Magnetic Resonance Imaging Phenotypes Predict Overall Survival Benefit From Bevacizumab or Surgery in Recurrent Glioblastoma With Large Tumor Burden.

BACKGROUND: Diffusion magnetic resonance (MR) characteristics are a predictive imaging biomarker for survival benefit in recurrent glioblastoma treated with anti-vascular endothelial growth factor (VEGF) therapy; however, its use in large volume recurrence has not been evaluated. OBJECTIVE: To determine if diffusion MR characteristics can predict survival outcomes in patients with large volume recurrent glioblastoma treated with bevacizumab or repeat resection. METHODS: A total of 32 patients with large volume (>20 cc or > 3.4 cm diameter) recurrent glioblastoma treated with bevacizumab and 35 patients treated with repeat surgery were included. Pretreatment tumor volume and apparent diffusion coefficient (ADC) histogram analysis were used to phenotype patients as having high (>1.24 µm2/ms) or low (<1.24 µm2/ms) ADCL, the mean value of the lower peak in a double Gaussian model of the ADC histogram within the contrast enhancing tumor. RESULTS: In bevacizumab and surgical cohorts, volume was correlated with overall survival (Bevacizumab: P = .009, HR = 1.02; Surgical: P = .006, HR = 0.96). ADCL was an independent predictor of survival in the bevacizumab cohort (P = .049, HR = 0.44), but not the surgical cohort (P = .273, HR = 0.67). There was a survival advantage of surgery over bevacizumab in patients with low ADCL (P = .036, HR = 0.43) but not in patients with high ADCL (P = .284, HR = 0.69). CONCLUSION: Pretreatment diffusion MR imaging is an independent predictive biomarker for overall survival in recurrent glioblastoma with a large tumor burden. Large tumors with low ADCL have a survival benefit when treated with surgical resection, whereas large tumors with high ADCL may be best managed with bevacizumab.

Targeted next-generation sequencing of 565 neuro-oncology patients at UCLA: A single-institution experience.

BACKGROUND: Targeted next-generation sequencing (NGS) is frequently obtained at the University of California, Los Angeles (UCLA) for clinical characterization of CNS tumors. In this study, we describe the diagnostic reliability of the Foundation Medicine (FM) targeted NGS platform and its ability to explore and identify tumor characteristics of prognostic significance in gliomas. METHODS: Neuro-oncology patients seen at UCLA who have received FM testing between August 2012 and March 2019 were included in this study, and all mutations from FM test reports were recorded. Initial tumor diagnoses and diagnostic markers found via standard clinical methods were obtained from pathology reports. With overall and progression-free survival data, elastic net regularized Cox regression and Cox proportional hazards models were used to determine whether any mutations of unknown significance detected by FM could predict patient outcome in glioblastoma (GBM). RESULTS: Six hundred and three samples tested by FM from 565 distinct patients were identified. Concordance of diagnostic markers was high between standard clinical testing methods and FM. Oligodendroglial markers detected via FM were highly correlated with 1p19q codeletion in IDH mutated gliomas. FM testing of multiple tumor samples from the same patient demonstrated temporal and spatial mutational heterogeneity. Mutations in BCORL1, ERBB4, and PALB2, which are mutations of unknown significance in GBM, were shown to be statistically significant in predicting patient outcome. CONCLUSIONS: In our large cohort, we found that targeted NGS can both reliably and efficiently detect important diagnostic markers in CNS tumors.

An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

OBJECTIVE: This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. MATERIALS AND METHODS: We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those of the neural model. RESULTS: Our method achieved 92.78% lenient micro F1-score, with 95.99% lenient precision, and 89.79% lenient recall, respectively. Experimental results showed that combining the predictions of either multiple models, or of a single model with different settings can improve performance. DISCUSSION: Analysis of the development set showed that our neural models can detect more informative text regions than feature-based conditional random field models. Furthermore, most entity types significantly benefit from subword representation, which also allows us to extract sparse entities, especially nested entities. CONCLUSION: The overall results have demonstrated that the ensemble method can accurately recognize entities, including nested and polysemous entities. Additionally, our method can recognize sparse entities by reconsidering the clinical narratives at a finer-grained subword level, rather than at the word level.

Single-Atom Fluorescence Switch: A General Approach toward Visible-Light-Activated Dyes for Biological Imaging.

Photoactivatable fluorophores afford powerful molecular tools to improve the spatial and temporal resolution of subcellular structures and dynamics. By performing a single sulfur-for-oxygen atom replacement within common fluorophores, we have developed a facile and general strategy to obtain photoactivatable fluorogenic dyes across a broad spectral range. Thiocarbonyl substitution within fluorophores results in significant loss of fluorescence via a photoinduced electron transfer-quenching mechanism as suggested by theoretical calculations. Significantly, upon exposure to air and visible light residing in their absorption regime (365-630 nm), thio-caged fluorophores can be efficiently desulfurized to their oxo derivatives, thus restoring strong emission of the fluorophores. The effective photoactivation makes thio-caged fluorophores promising candidates for super-resolution imaging, which was realized by photoactivated localization microscopy (PALM) with low-power activation light under physiological conditions in the absence of cytotoxic additives (e.g., thiols, oxygen scavengers), a feature superior to traditional PALM probes. The versatility of this thio-caging strategy was further demonstrated by multicolor super-resolution imaging of lipid droplets and proteins of interest.

Correlation of commercially available quantitative MGMT (O-6-methylguanine-DNA methyltransferase) promoter methylation scores and GBM patient survival.

BACKGROUND: Between 2011 and 2016, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation testing at University of California Los Angeles (UCLA) was performed through LabCorp, using a threshold of 2 to distinguish MGMT methylated from unmethylated tumors. In this study, we sought to determine whether the magnitude of the methylation score correlated with outcome. METHODS: We identified 165 newly diagnosed glioblastoma (GBM) isocitrate dehydrogenase (IDH) wild-type and temozolomide-treated upfront patients at UCLA and Kaiser Permanente Los Angeles with LabCorp-derived quantitative MGMT scores obtained on pretreatment tissue samples. Using LabCorp's threshold, we found 102 unmethylated and 63 methylated patients. We then further substratified each group based on the magnitude of the score, and performed Kaplan-Meier and Cox regression analyses of overall survival (OS) and progression-free survival (PFS). RESULTS: We validated that the standard LabCorp threshold of 2 could separate our cohort by survival, showing longer OS and PFS for MGMT methylated patients vs unmethylated patients. Cox regression analysis confirmed that MGMT (<1) patients had worse outcome, with OS and PFS hazard ratios of 2.375 (P = .053) and 2.463 (P = .023), respectively, when compared to the MGMT (1-1.99) patients. Contrary to our expectation, when we substratified the ≥2 (methylated) group, we did not find a dose-dependent relationship between the magnitude of MGMT methylation and improved survival. CONCLUSIONS: The MGMT unmethylated group contains a partially methylated group (greater than 1) that shares survival benefits similar to the methylated group. However, we did not demonstrate an association of very high methylation scores with increased survival. These findings will require validation in additional independent clinical data sets.

Patterns of long-term survivorship following bevacizumab treatment for recurrent glioma: a case series.

Aim: Long-term survivors (LTS) after glioma recurrence while on bevacizumab (Bev) therapy are rarely reported in the current literature. The purpose of this case series is to confirm the existence of and describe a large cohort of recurrent glioma LTS treated with Bev (Bev-LTS). Patients & methods: We identified Bev-LTS as patients with post-Bev initiation survival times of ≥3 years among 1397 Bev treated recurrent glioma patients. Results: Among 962 grade-IV, 221 grade III, and 214 grade II Bev-treated glioma patients, we identified 28 (2.9%), 14 (6.3%) and 8 (3.7%) Bev-LTS patients, respectively. 45 Bev-LTS patients recurred on Bev, with 36 of those patients continuing therapy. Conclusion: Our study shows that a small portion of grade-IV, -III, and -II glioma patients can have long-term survival on Bev therapy even after Bev recurrence.

KIN-4/MAST kinase promotes PTEN-mediated longevity of Caenorhabditis elegans via binding through a PDZ domain.

PDZ domain-containing proteins (PDZ proteins) act as scaffolds for protein-protein interactions and are crucial for a variety of signal transduction processes. However, the role of PDZ proteins in organismal lifespan and aging remains poorly understood. Here, we demonstrate that KIN-4, a PDZ domain-containing microtubule-associated serine-threonine (MAST) protein kinase, is a key longevity factor acting through binding PTEN phosphatase in Caenorhabditis elegans. Through a targeted genetic screen for PDZ proteins, we find that kin-4 is required for the long lifespan of daf-2/insulin/IGF-1 receptor mutants. We then show that neurons are crucial tissues for the longevity-promoting role of kin-4. We find that the PDZ domain of KIN-4 binds PTEN, a key factor for the longevity of daf-2 mutants. Moreover, the interaction between KIN-4 and PTEN is essential for the extended lifespan of daf-2 mutants. As many aspects of lifespan regulation in C. elegans are evolutionarily conserved, MAST family kinases may regulate aging and/or age-related diseases in mammals through their interaction with PTEN.