Valproic acid counteracts polycyclic aromatic hydrocarbons (PAHs)-induced tumorigenic effects by regulating the polarization of macrophages

Polycyclic aromatic hydrocarbons (PAHs) are common persistent organic pollutants that are carcinogenic, teratogenic and mutagenic, causing a variety of harm to human health. In this study, we investigated the mechanism of how valproic acid (VPA) interferes with the carcinogenesis of PAHs protect normal tissues via the regulation of macrophages’ function. Using the established model of transformed malignant breast cancer by 7,12-dimethylbenz[a]anthracene (DMBA), a representative PAH carcinogen, we discovered VPA induces the polarization of macrophages toward the M1 phenotype in the tumor tissues, facilitates the expression of pro inflammatory cytokines such as IFN-γ, IL-12 and TNF-α, activates CD8+ T cells to secret Granzyme B thus to promote the apoptosis of tumor cells and suppresses the viability of vascular endothelial cells in tissue stroma of tumor. Surprisingly, VPA selectively induces macrophages to polarize towards the M2 phenotype in normal tissues and promotes the expression of anti-inflammatory cytokines such as IL-10 to enhance cell proliferation. Additionally, at the cellular level, VPA can directly regulate the polarization of macrophages to affect the growth of vascular endothelial cells by simulating the living conditions of tumor and normal cells. Collectively, VPA exerts an interventional effect on tumor growth and a protective effect on normal tissues by regulation of selective macrophages’ polarization in their microenvironment

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference time. Sparsity is a natural approach to reduce this cost, but existing methods either require costly retraining, have to forgo LLM's in-context learning ability, or do not yield wall-clock time speedup on modern hardware. We hypothesize that contextual sparsity, which are small, input-dependent sets of attention heads and MLP parameters that yield approximately the same output as the dense model for a given input, can address these issues. We show that contextual sparsity exists, that it can be accurately predicted, and that we can exploit it to speed up LLM inference in wall-clock time without compromising LLM's quality or in-context learning ability. Based on these insights, we propose DejaVu, a system that uses a low-cost algorithm to predict contextual sparsity on the fly given inputs to each layer, along with an asynchronous and hardware-aware implementation that speeds up LLM inference. We validate that DejaVu can reduce the inference latency of OPT-175B by over 2X compared to the state-of-the-art FasterTransformer, and over 6X compared to the widely used Hugging Face implementation, without compromising model quality. The code is available at https://github.com/FMInference/DejaVu

Acupuncture for insomnia symptoms in hypertensive patients: a systematic review and meta-analysis

PurposeIn the realm of pain management, traditional Chinese medicine, specifically acupuncture, has garnered increasing attention. This meta-analysis pioneers the evaluation of acupuncture’s effectiveness in treating insomnia among hypertensive patients.MethodsWe conducted a comprehensive search across several databases—PubMed, Web of Science, Cochrane Library, WANFANG, China National Knowledge Infrastructure (CNKI), Sinomed, and the Chinese Journal of Science and Technology (VIP). Additionally, forward and backward articles of studies published from the inception of these databases until 10 September 2023, were reviewed. This systematic review and meta-analysis included all randomized controlled trials (RCTs) focusing on acupuncture for insomnia in hypertensive patients, without imposing language or date restrictions. We rigorously assessed all outcome measures reported in these trials. The evidence was synthesized by calculating the difference between mean differences (MD) in symptom change. The quality of the evidence was determined using the Cochrane Risk of Bias tool. This study is registered with PROSPERO under number CRD42023461760.ResultsOur analysis included 16 RCTs, comprising 1,309 patients. The findings revealed that acupuncture was significantly more effective than the control group in reducing insomnia symptoms, as indicated by a greater decrease in the PSQI score (MD = −3.1, 95% CI [−3.77 to −2.62], p < 0.00001). Additionally, improvements in both systolic and diastolic blood pressure were more pronounced in the acupuncture group compared to the control group (SBP: MD = −10.31, 95% CI [−16.98 to −3.64], p = 0.002; DBP: MD = −5.71, 95% CI [−8.19 to −3.23], p < 0.00001). These results suggest that acupuncture not only improves sleep quality but also lowers blood pressure in patients suffering from hypertension and insomnia. Further research is warranted to elucidate optimal acupuncture points and the duration of treatment for maximized therapeutic effect.Systematic review registration:https://www.crd.york.ac.uk/prospero, CRD42023461760

25-Hydroxyvitamin D Levels and the Risk of Dementia and Alzheimer's Disease: A Dose–Response Meta-Analysis

Background and Purpose: Conclusions of previous cohort studies on the relationship between 25-hydroxyvitamin D level and the risk of dementia and Alzheimer's disease were not consistent. Thus, we performed a dose–response meta-analysis to evaluate this relationship by summarizing cohort studies.Methods: Pubmed, Embase, Cochrane, and Web of Science databases were searched for relevant studies. Cohort studies concerning the association between 25-hydroxyvitamin D level and dementia or Alzheimer's disease were included. Results of studies were pooled and the dose–response relationship was determined using a random-effect model.Results: Ten cohort studies, with 28,640 participants were included. A significant inverse relationship was found between 25-hydroxyvitamin D level and the risk of dementia and Alzheimer's disease. In addition, we found a linear dose–response relationship in that a 10 nmol/L increase in 25-hydroxyvitamin D level may lead to a 5% decrease in the risk of dementia (relative risk, 0.95; 95% confidence interval, 0.93–0.98) and 7% in the risk of Alzheimer's disease (relative risk, 0.93; 95% confidence interval, 0.89–0.97).Conclusion: Plasma or serum 25-hydroxyvitamin D concentration was inversely related to the risk of dementia and Alzheimer's disease, consistent with a linear dose–response relationship

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions

Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers. In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads -- naively requiring a full pass (or caching of activations) over the input sequence for each generated token -- similarly to attention-based models. In this paper, we seek to enable $\mathcal O(1)$ compute and memory cost per token in any pre-trained long convolution architecture to reduce memory footprint and increase throughput during generation. Concretely, our methods consist in extracting low-dimensional linear state-space models from each convolution layer, building upon rational interpolation and model-order reduction techniques. We further introduce architectural improvements to convolution-based layers such as Hyena: by weight-tying the filters across channels into heads, we achieve higher pre-training quality and reduce the number of filters to be distilled. The resulting model achieves 10x higher throughput than Transformers and 1.5x higher than Hyena at 1.3B parameters, without any loss in quality after distillation

Impact of Urbanization on PM2.5-Related Health and Economic Loss in China 338 Cities

According to the requirements of the Healthy China Program, reasonable assessment of residents’ health risks and economic loss caused by urban air pollution is of great significance for environmental health policy planning. Based on the data of PM2.5 concentration, population density, and urbanization level of 338 Chinese cities in the year of 2015, the epidemiological relative risk (RR) was adopted to estimate the negative health effects caused by exposure to PM2.5. Meanwhile, the Value of Statistical Life (VSL) and Cost of Illness (COI) methods were used to calculate economic loss. The results show that PM2.5 pollution remains serious in 2015, which brings about many people suffering from all kinds of fearful health problems especially premature death and related diseases. The mortality and morbidity increase dramatically, and the total direct economic loss related to PM2.5 pollution in 2015 was 1.846 trillion yuan, accounting for 2.73% of total annual GDP. In addition, there was a strong correlation between urbanization level and health risks as well as economic loss, which implies that people who live in highly urbanized cities may face more severe health and economic losses. Furthermore, 338 cities were divided into four categories based on urbanization level and economic loss, of which the key areas (type D) were the regions where an increase in monitoring and governance is most needed. In the process of urbanization, policy makers should pay more attention to health costs and regional differentiated management, as well as promote the construction of healthy cities more widely

[In Press] Global research trends in radiotherapy for breast cancer : a systematic bibliometric analysis

Purpose: Breast cancer is the most common malignant tumor in women. Radiotherapy (RT) is an important adjunctive therapy for breast cancer, but the current international research trend of RT in breast cancer treatment and management is unclear. This bibliometric analysis was conducted to investigate the current trends and hot topics in this area. Materials and methods: The Web of Science Core Collection (WoSCC; Clarivate) database was searched, VOSviewer 1.6.18 and CiteSpace 6.1.R2 software were employed for the quantitative and qualitative analysis. Results: 12,268 publications were included in this bibliometric analysis. There was an increasing trend of publications and international collaborations in the topic. The United States and The University of Texas MD Anderson Cancer Center were the most productive countries and institutions, respectively. The analysis of journals showed researches focused on both basic and clinical medicine on breast cancer RT. Park Won published the most papers and Fisher B had the most co-citations. The most co-cited paper was published in the Lancet. Survival, risk, chemotherapy, mastectomy, and surgery were regarded as current research hotspots through the analysis of keywords. Conclusion: Through quantitative and qualitative bibliometric analyses, this study provides insights into the research trends and potential research hotspots on breast cancer RT

Congenital Hepatic Fibrosis in Children and Adults: Clinical Manifestations, Management, and Outcome—Case Series and Literature Review

Background. Congenital hepatic fibrosis is a hereditary fibropolycystic disease caused by ductal plate malformation. It is characterized by portal hypertension, but the manifestations, management, and outcome vary in children and adults. To raise awareness of medical staff, we have comprehensively compared the clinical features of congenital hepatic fibrosis between children and adults. Methods. We retrospectively enrolled all patients diagnosed with congenital hepatic fibrosis at the Huashan Hospital from August 2015 to August 2017 and analyzed their familial, clinical, laboratory, imaging, treatment, and follow-up data in detail. In addition, we reviewed cases with congenital hepatic fibrosis reported in the past 20 years in China and analyzed them according to the patients’ age. Results. A total of eight patients were diagnosed with congenital hepatic fibrosis in the study, including four children and four adults. The onset age of the children, who suffered from severe complications of portal hypertension and needed liver transplantation, ranged from 1 to 15 years old. The disorder developed in adults aged 26 to 60 years old. Three adults complained of recurrent abnormal liver function at the onset of illness, and they mainly received conservative treatments. The literature review included 30 children and 33 adults. In comparison, hepatomegaly was more common in children than in adults (57% vs. 21%, p=0.004). Malformation of kidneys and bile duct abnormalities were common, and multisystem involvement included eyes, other digestive organs, and genital and central nervous systems. Conclusions. Serious complications of portal hypertension developed in children requiring liver transplantation, while adults often had mild-to-moderate liver injuries upon onset. Adults with CHF varied a lot in clinical manifestations. Multiorgan involvement and unusual course are helpful to make a diagnosis. Timely histological assessment by liver biopsy and multidisciplinary cooperation are crucial for definitive diagnosis and early intervention

Fullerene-Based Macro-Heterocycle Prepared through Selective Incorporation of Three N and Two O Atoms into C-60

A 14-membered heterocycle is created on the C-60 cage skeleton through a multistep procedure. Key steps involve repeated PCl5-induced hydroxylamino N-O bond cleavage leading to insertion of nitrogen atoms, and also piperidine-induced peroxo O-O bond cleavage leading to insertion of oxygen atoms. The hetero atoms form one pyrrole, two pyran, and one diazepine rings in conjunction with the C-60 skeleton carbon atoms. The fullerene-based macrocycle showed unique reactivities towards fluoride ion and copper salts

Decentralized Training of Foundation Models in Heterogeneous Environments

Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months. These models are typically trained in specialized clusters featuring fast, homogeneous interconnects and using carefully designed software systems that support both data parallelism and model/pipeline parallelism. Such dedicated clusters can be costly and difficult to obtain. Can we instead leverage the much greater amount of decentralized, heterogeneous, and lower-bandwidth interconnected compute? Previous works examining the heterogeneous, decentralized setting focus on relatively small models that can be trained in a purely data parallel manner. State-of-the-art schemes for model parallel foundation model training, such as Megatron, only consider the homogeneous data center setting. In this paper, we present the first study of training large foundation models with model parallelism in a decentralized regime over a heterogeneous network. Our key technical contribution is a scheduling algorithm that allocates different computational "tasklets" in the training of foundation models to a group of decentralized GPU devices connected by a slow heterogeneous network. We provide a formal cost model and further propose an efficient evolutionary algorithm to find the optimal allocation strategy. We conduct extensive experiments that represent different scenarios for learning over geo-distributed devices simulated using real-world network measurements. In the most extreme case, across 8 different cities spanning 3 continents, our approach is 4.8X faster than prior state-of-the-art training systems (Megatron)