Towards universal health coverage: achievements and challenges of 10 years of healthcare reform in China.

Universal health coverage (UHC) has been identified as a priority for the global health agenda. In 2009, the Chinese government launched a new round of healthcare reform towards UHC, aiming to provide universal coverage of basic healthcare by the end of 2020. We conducted a secondary data analysis and combined it with a literature review, analysing the overview of UHC in China with regard to financial protection, coverage of health services and the reported coverage of the WHO and the World Bank UHC indicators. The results include the following: out-of-pocket expenditures as a percentage of current health expenditures in China have dropped dramatically from 60.13% in 2000 to 35.91% in 2016; the health insurance coverage of the total population jumped from 22.1% in 2003 to 95.1% in 2013; the average life expectancy increased from 72.0 to 76.4, maternal mortality dropped from 59 to 29 per 100 000 live births, the under-5 mortality rate dropped from 36.8 to 9.3 per 1000 live births, and neonatal mortality dropped from 21.4 to 4.7 per 1000 live births between 2000 and 2017; and so on. Our findings show that while China appears to be well on the path to UHC, there are identifiable gaps in service quality and a requirement for ongoing strengthening of financial protections. Some of the key challenges remain to be faced, such as the fragmented and inequitable health delivery system, and the increasing demand for high-quality and value-based service delivery. Given that China has committed to achieving UHC and 'Healthy China 2030', the evidence from this study can be suggestive of furthering on in the UHC journey and taking the policy steps necessary to secure change

Folding-Free ZNE: A Comprehensive Quantum Zero-Noise Extrapolation Approach for Mitigating Depolarizing and Decoherence Noise

Quantum computers in the NISQ era are prone to noise. A range of quantum error mitigation techniques has been proposed to address this issue. Zero-noise extrapolation (ZNE) stands out as a promising one. ZNE involves increasing the noise levels in a circuit and then using extrapolation to infer the zero noise case from the noisy results obtained. This paper presents a novel ZNE approach that does not require circuit folding or noise scaling to mitigate depolarizing and/or decoherence noise. To mitigate depolarizing noise, we propose leveraging the extreme/infinite noisy case, which allows us to avoid circuit folding. Specifically, the circuit output with extreme noise becomes the maximally mixed state. We show that using circuit-reliability metrics, simple linear extrapolation can effectively mitigate depolarizing noise. With decoherence noise, different states decay into the all-zero state at a rate that depends on the number of excited states and time. Therefore, we propose a state- and latency-aware exponential extrapolation that does not involve folding or scaling. When dealing with a quantum system affected by both decoherence and depolarizing noise, we propose to use our two mitigation techniques in sequence: first applying decoherence error mitigation, followed by depolarizing error mitigation. A common limitation of ZNE schemes is that if the circuit of interest suffers from high noise, scaling-up noise levels could not provide useful data for extrapolation. We propose using circuit-cut techniques to break a large quantum circuit into smaller sub-circuits to overcome this limitation. This way, the noise levels of the sub-circuits are lower than the original circuit, and ZNE can become more effective in mitigating their noises

Rationale-Enhanced Language Models are Better Continual Relation Learners

Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by large language models (LLM), into CRE task. Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.Comment: Accepted at EMNLP 202

Natural products targeting autophagy and apoptosis in NSCLC: a novel therapeutic strategy

Lung cancer is the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) being the predominant type. The roles of autophagy and apoptosis in NSCLC present a dual and intricate nature. Additionally, autophagy and apoptosis interconnect through diverse crosstalk molecules. Owing to their multitargeting nature, safety, and efficacy, natural products have emerged as principal sources for NSCLC therapeutic candidates. This review begins with an exploration of the mechanisms of autophagy and apoptosis, proceeds to examine the crosstalk molecules between these processes, and outlines their implications and interactions in NSCLC. Finally, the paper reviews natural products that have been intensively studied against NSCLC targeting autophagy and apoptosis, and summarizes in detail the four most retrieved representative drugs. This paper clarifies good therapeutic effects of natural products in NSCLC by targeting autophagy and apoptosis and aims to promote greater consideration by researchers of natural products as candidates for anti-NSCLC drug discovery

Mutation of a TADR protein leads to rhodopsin and Gq-dependent retinal degeneration in Drosophila

The Drosophila photoreceptor is a model system for genetic study of retinal degeneration. Many gene mutations cause fly photoreceptor degeneration, either because of excessive stimulation of the visual transduction (phototransduction) cascade, or through apoptotic pathways that in many cases involve a visual arrestin Arr2. Here we report a gene named tadr (for torn and diminished rhabdomeres), which, when mutated, leads to photoreceptor degeneration through a different mechanism. Degeneration in the tadr mutant is characterized by shrunk and disrupted rhabdomeres, the light sensory organelles of photoreceptor. The TADR protein interacted in vitro with the major light receptor Rh1 rhodopsin, and genetic reduction of the Rh1 level suppressed the tadr mutation-caused degeneration, suggesting the degeneration is Rh1-dependent. Nonetheless, removal of phospholipase C (PLC), a key enzyme in phototransduction, and that of Arr2 failed to inhibit rhabdomeral degeneration in the tadr mutant background. Biochemical analyses revealed that, in the tadr mutant, the G(q) protein of Rh1 is defective in dissociation from the membrane during light stimulation. Importantly, reduction of G(q) level by introducing a hypomorphic allele of G(alphaq) gene greatly inhibited the tadr degeneration phenotype. These results may suggest that loss of a potential TADR-Rh1 interaction leads to an abnormality in the G(q) signaling, which in turn triggers rhabdomeral degeneration independent of the PLC phototransduction cascade. We propose that TADR-like proteins may also protect photoreceptors from degeneration in mammals including humans

Enhancing Virtual Distillation with Circuit Cutting for Quantum Error Mitigation

Virtual distillation is a technique that aims to mitigate errors in noisy quantum computers. It works by preparing multiple copies of a noisy quantum state, bridging them through a circuit, and conducting measurements. As the number of copies increases, this process allows for the estimation of the expectation value with respect to a state that approaches the ideal pure state rapidly. However, virtual distillation faces a challenge in realistic scenarios: preparing multiple copies of a quantum state and bridging them through a circuit in a noisy quantum computer will significantly increase the circuit size and introduce excessive noise, which will degrade the performance of virtual distillation. To overcome this challenge, we propose an error mitigation strategy that uses circuit-cutting technology to cut the entire circuit into fragments. With this approach, the fragments responsible for generating the noisy quantum state can be executed on a noisy quantum device, while the remaining fragments are efficiently simulated on a noiseless classical simulator. By running each fragment circuit separately on quantum and classical devices and recombining their results, we can reduce the noise accumulation and enhance the effectiveness of the virtual distillation technique. Our strategy has good scalability in terms of both runtime and computational resources. We demonstrate our strategy's effectiveness through noisy simulation and experiments on a real quantum device.Comment: 8 pages, 5 figure

Towards Usable Parental Control for Voice Assistants

Voice Personal Assistants (VPA) have become a common household appliance. As one of the leading platforms for VPA technology, Amazon created Alexa and designed Amazon Kids for children to safely enjoy the rich functionalities of VPA and for parents to monitor their kids' activities through the Parent Dashboard. Although this ecosystem is in place, the usage of Parent Dashboard is not yet popularized among parents. In this paper, we conduct a parent survey to find out what they like and dislike about the current parental control features. We find that parents need more visuals about their children's activity, easier access to security features for their children, and a better user interface. Based on the insights from our survey, we present a new design for the Parent Dashboard considering the parents' expectations

The two-way knowledge interaction interface between humans and neural networks

Despite neural networks (NN) have been widely applied in various fields and generally outperforms humans, they still lack interpretability to a certain extent, and humans are unable to intuitively understand the decision logic of NN. This also hinders the knowledge interaction between humans and NN, preventing humans from getting involved to give direct guidance when NN's decisions go wrong. While recent research in explainable AI has achieved interpretability of NN from various perspectives, it has not yet provided effective methods for knowledge exchange between humans and NN. To address this problem, we constructed a two-way interaction interface that uses structured representations of visual concepts and their relationships as the "language" for knowledge exchange between humans and NN. Specifically, NN provide intuitive reasoning explanations to humans based on the class-specific structural concepts graph (C-SCG). On the other hand, humans can modify the biases present in the C-SCG through their prior knowledge and reasoning ability, and thus provide direct knowledge guidance to NN through this interface. Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process. Furthermore, human involvement and prior knowledge can directly and effectively contribute to enhancing the performance of NN

Development of a new stroke scale in an emergency setting

Background: Early identification of stroke is crucial to maximize early management benefits in emergency departments. This study aimed to develop and validate a new stroke recognition instrument for differentiating acute stroke from stroke mimics in an emergency setting. Methods: A prospective observational cohort study among suspected stroke patients presenting to Emergency Department in the Second Affiliated Hospital of Guangzhou Medical University was conducted from May 2012 to March 2013. The symptoms and signs of suspected stroke patients were collected. Logistic regression analysis was used to identify the factors associated with acute stroke. The symptoms and signs closely associated with acute stroke were selected to develop the new stroke scale, Guangzhou Stroke Scale (GZSS). The diagnostic value of GZSS was then compared with ROSIER, FAST and LAPSS. The primary outcome was confirmed stroke by CT within 24 h. Results: Four hundred and sixteen suspected stroke patients (247 ischemia, 107 hemorrhage, 4 transient ischemic attack, 58 non-stroke) were assessed. A new stroke scale, GZSS (total score from −1 to 8.5), was developed and consisted of nine parameters: vertigo (−1), GCS ≤ 8 (+2), facial paralysis (+1), asymmetric arm weakness (+1), asymmetric leg weakness (+1), speech disturbance (+0.5), visual field defect (+1), systolic blood pressure ≥145 mmHg (+1) and diastolic blood pressure ≥95 mmHg (+1). Among the four scales, the discriminatory value (C-statistic) of GZSS was the best (AUC: 0.871 (p < 0.001) when compared to ROSIER (0.772), LAPSS (0.722) and FAST (0.699). At an optimal cut-off score of >1.5 on a scale from −1 to 8.5, the sensitivity and specificity of GZSS were 83.2 and 74.1 %, whilst the sensitivities and specificities of ROSIER were 77.7 and 70.7 %, FAST were 76.0 and 63.8 %, LAPSS were 56.4 and 87.9 %. Conclusion: GZSS had better sensitivity than existing stroke scales in Chinese patients with suspected stroke. Further studies should be conducted to confirm its effectiveness in the initial differentiation of acute stroke from stroke mimics. Keywords: Diagnosis, Stroke, Stroke mimics, ROSIER scale, FAST scale, LAPSS scale, Emergency department, China Abbreviations: AUC, area under the ROC curve; CT, computed tomography; DWI, diffusion weighted imaging; FAST, the face arm speech test; GCS, Glasgow Coma Scale; IQR, inter quartile range; LAPSS, the Los Angeles Prehospital Stroke Screen; MRI, magnetic resonance imaging; NIHSS, National Institute of Health stroke scale; OR, odds ratio; ROC, receiver operating characteristic; ROSIER, the Recognition of Stroke in the Emergency Room scale; TIA, transient ischemic attac