Inhibitors of the renin–angiotensin system: The potential role in the pathogenesis of COVID-19

Coronavirus disease 2019 (COVID-19), which initially began in China, has spread to other countries of Asia, Europe, America, Africa and Oceania, with the number of confirmed cases and suspected cases increasing each day. According to recently published research, it was found that the majority of the severe cases were elderly, and many of them had at least one chronic disease, especially cardiovascular diseases. Angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) are the most widely used drugs for cardiovascular diseases. The clinical effect of ACEIs/ARBs on patients with COVID-19 is still uncertain. This paper describes their potential role in the pathogenesis of COVID-19, which may provide useful in the advice of cardiologists and physicians

MELA: Multilingual Evaluation of Linguistic Acceptability

Recent benchmarks for Large Language Models (LLMs) have mostly focused on application-driven tasks such as complex reasoning and code generation, and this has led to a scarcity in purely linguistic evaluation of LLMs. Against this background, we introduce Multilingual Evaluation of Linguistic Acceptability -- MELA, the first multilingual benchmark on linguistic acceptability with 48K samples covering 10 languages from a diverse set of language families. We establish baselines of commonly used LLMs along with supervised models, and conduct cross-lingual transfer and multi-task learning experiments with XLM-R. In pursuit of multilingual interpretability, we analyze the weights of fine-tuned XLM-R to explore the possibility of identifying transfer difficulty between languages. Our results show that ChatGPT benefits much from in-context examples but still lags behind fine-tuned XLM-R, while the performance of GPT-4 is on par with fine-tuned XLM-R even in zero-shot setting. Cross-lingual and multi-task learning experiments show that unlike semantic tasks, in-language training data is crucial in acceptability judgements. Results in layerwise probing indicate that the upper layers of XLM-R become a task-specific but language-agnostic region for multilingual acceptability judgment. We also introduce the concept of conflicting weight, which could be a potential indicator for the difficulty of cross-lingual transfer between languages. Our data will be available at https://github.com/sjtu-compling/MELA.Comment: Work in progres

Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach

Text recognition methods are gaining rapid development. Some advanced techniques, e.g., powerful modules, language models, and un- and semi-supervised learning schemes, consecutively push the performance on public benchmarks forward. However, the problem of how to better optimize a text recognition model from the perspective of loss functions is largely overlooked. CTC-based methods, widely used in practice due to their good balance between performance and inference speed, still grapple with accuracy degradation. This is because CTC loss emphasizes the optimization of the entire sequence target while neglecting to learn individual characters. We propose a self-distillation scheme for CTC-based model to address this issue. It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models. We refer to the regularized CTC loss as Distillation Connectionist Temporal Classification (DCTC) loss. DCTC loss is module-free, requiring no extra parameters, longer inference lag, or additional training data or phases. Extensive experiments on public benchmarks demonstrate that DCTC can boost text recognition model accuracy by up to 2.6%, without any of these drawbacks.Comment: Ziyin Zhang and Ning Lu are co-first author

Revisiting Acceptability Judgements

In this work, we revisit linguistic acceptability in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language. It is verified by native speakers and is the first acceptability dataset that comes with two sets of labels: a linguist label and a crowd label. Our experiments show that even the largest InstructGPT model performs only at chance level on CoLAC, while ChatGPT's performance (48.30 MCC) is also much below supervised models (59.03 MCC) and human (65.11 MCC). Through cross-lingual transfer experiments and fine-grained linguistic analysis, we provide detailed analysis of the model predictions and demonstrate for the first time that knowledge of linguistic acceptability can be transferred across typologically distinct languages, as well as be traced back to pre-training. Our dataset is publicly available at \url{https://github.com/huhailinguist/CoLAC}

Down-Regulation of MicroRNA-214 Contributed to the Enhanced Mitochondrial Transcription Factor A and Inhibited Proliferation of Colorectal Cancer Cells

Background/Aims: Colon cancer, also known as colorectal cancer (CRC), is one of the most common malignant tumors globally. Although significant advances have been made for developing novel therapeutics, the mechanisms of progression of colorectal cancer are still poorly understood. Methods: In this study, we identified down-regulation of microRNA-214 (miR-214) as the contributing factor for CRC. Mitochondrial transcription factor A (TFAM) and miR-214 expression in tumor samples from colorectal cancer patients and cancer cell lines were examined by reverse transcription and real-Time PCR (qPCR) or Western Blotting. Results: Our data demonstrated that miR-214 was significantly down-regulated in the tissue samples from CRC patients as well as CRC derived cell lines. TFAM overexpression was also observed in CRC patients and identified as a target for miR-214. Knockdown of TFAM by miR-214 mimics significantly inhibited the proliferation of CRC cell lines. Also, down-regulation of TFAM inhibited nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) nuclear translocation and the expression of NF-κB depended genes. Conclusion: In conclusion, our data suggested that down-regulation of MiR-214 contributed to the enhanced TFAM expression and decreased proliferation of CRC cells

The effect of peak serum estradiol level during ovarian stimulation on cumulative live birth and obstetric outcomes in freeze-all cycles

ObjectiveTo determine whether the peak serum estradiol (E2) level during ovarian stimulation affects the cumulative live birth rate (CLBR) and obstetric outcomes in freeze-all cycles.MethodsThis retrospective cohort study involved patients who underwent their first cycle of in vitro fertilization followed by a freeze-all strategy and frozen embryo transfer cycles between January 2014 and June 2019 at a tertiary care center. Patients were categorized into four groups according to quartiles of peak serum E2 levels during ovarian stimulation (Q1-Q4). The primary outcome was CLBR. Secondary outcomes included obstetric and neonatal outcomes of singleton and twin pregnancies. Poisson or logistic regression was applied to control for potential confounders for outcome measures, as appropriate. Generalized estimating equations were used to account for multiple cycles from the same patient for the outcome of CLBR.Result(s)A total of 11237 patients were included in the analysis. Cumulatively, live births occurred in 8410 women (74.8%). The live birth rate (LBR) and CLBR improved as quartiles of peak E2 levels increased (49.7%, 52.1%, 54.9%, and 56.4% for LBR; 65.1%, 74.3%, 78.4%, and 81.6% for CLBR, from the lowest to the highest quartile of estradiol levels, respectively, P<0.001). Such association remained significant for CLBR after accounting for potential confounders in multivariable regression models, whereas the relationship between LBR and peak E2 levels did not reach statistical significance. In addition, no significant differences were noticed in adverse obstetric and neonatal outcomes (gestational diabetes mellitus, pregnancy-induced hypertension, preeclampsia, placental disorders, preterm birth, low birthweight, and small for gestational age) amongst E2 quartiles for either singleton or twin live births, both before and after adjustment.ConclusionIn freeze-all cycles, higher peak serum E2 levels during ovarian stimulation were associated with increased CLBR, without increasing the risks of adverse obstetric and neonatal outcomes

The Spatial Association of Gene Expression Evolves from Synchrony to Asynchrony and Stochasticity with Age

For multicellular organisms, different tissues coordinate to integrate physiological functions, although this systematically and gradually declines in the aging process. Therefore, an association exists between tissue coordination and aging, and investigating the evolution of tissue coordination with age is of interest. In the past decade, both common and heterogeneous aging processes among tissues were extensively investigated. The results on spatial association of gene changes that determine lifespan appear complex and paradoxical. To reconcile observed commonality and heterogeneity of gene changes among tissues and to address evolution feature of tissue coordination with age, we introduced a new analytical strategy to systematically analyze genome-wide spatio-temporal gene expression profiles. We first applied the approach to natural aging process in three species (Rat, Mouse and Drosophila) and then to anti-aging process in Mouse. The results demonstrated that temporal gene expression alteration in different tissues experiences a progressive association evolution from spatial synchrony to asynchrony and stochasticity with age. This implies that tissue coordination gradually declines with age. Male mice showed earlier spatial asynchrony in gene expression than females, suggesting that male animals are more prone to aging than females. The confirmed anti-aging interventions (resveratrol and caloric restriction) enhanced tissue coordination, indicating their underlying anti-aging mechanism on multiple tissue levels. Further, functional analysis suggested asynchronous DNA/protein damage accumulation as well as asynchronous repair, modification and degradation of DNA/protein in tissues possibly contributes to asynchronous and stochastic changes of tissue microenvironment. This increased risk for a variety of age-related diseases such as neurodegeneration and cancer that eventually accelerate organismal aging and death. Our study suggests a novel molecular event occurring in aging process of multicellular species that may represent an intrinsic molecular mechanism of aging