TRPC3 suppression ameliorates synaptic dysfunctions and memory deficits in Alzheimer's disease.

Transient receptor potential canonical (TRPC) channels are widely expressed in the brain; however, their precise roles in neurodegeneration, such as Alzheimer's disease (AD) remain elusive. Bioinformatic analysis of the published single-cell RNA-seq data collected from AD patient cohorts indicates that the Trpc3 gene is uniquely upregulated in excitatory neurons. TRPC3 expression is also upregulated in post-mortem AD brains, and in both acute and chronic mouse models of AD. Functional screening of TRPC3 antagonists resulted in a lead inhibitor JW-65, which completely rescued Aß-induced neurotoxicity, impaired synaptic plasticity (e.g., LTP), and learning memory in acute and chronic experimental AD models. In cultured rat hippocampal neurons, we found that treatment with soluble ß-amyloid oligomers (AßOs) induces rapid and sustained upregulation of the TRPC3 expression selectively in excitatory neurons. This aberrantly upregulated TRPC3 contributes to AßOs-induced Ca 2+ overload through the calcium entry and store-release mechanisms. The neuroprotective action of JW-65 is primarily mediated via restoring AßOs-impaired Ca 2+ /calmodulin-mediated signaling pathways, including calmodulin kinases CaMKII/IV and calcineurin (CaN). The synaptic protective mechanism via TRPC3 inhibition was further supported by hippocampal RNA-seq data from the symptomatic 5xFAD mice after chronic treatment with JW-65. Overall, these findings not only validate TRPC3 as a novel therapeutic target for treating synaptic dysfunction of AD but most importantly, disclose a distinct role of upregulated TRPC3 in AD pathogenesis in mediating Ca 2+ dyshomeostasis.

Chlorquinaldol Alleviates Lung Fibrosis in Mice by Inhibiting Fibroblast Activation through Targeting Methionine Synthase Reductase.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited treatment options. Thus, it is essential to investigate potential druggable targets to improve IPF treatment outcomes. By screening a curated library of 201 small molecules, we have identified chlorquinaldol, a known antimicrobial drug, as a potential antifibrotic agent. Functional analyses have demonstrated that chlorquinaldol effectively inhibits the transition of fibroblasts to myofibroblasts in vitro and mitigates bleomycin-induced pulmonary fibrosis in mice. Using a mass spectrometry-based drug affinity responsive target stability strategy, we revealed that chlorquinaldol inhibited fibroblast activation by directly targeting methionine synthase reductase (MTRR). Decreased MTRR expression was associated with IPF patients, and its reduced expression in vitro promoted extracellular matrix deposition. Mechanistically, chlorquinaldol bound to the valine residue (Val-467) in MTRR, activating the MTRR-mediated methionine cycle. This led to increased production of methionine and s-adenosylmethionine, counteracting the fibrotic effect. In conclusion, our findings suggest that chlorquinaldol may serve as a novel antifibrotic medication, with MTRR-mediated methionine metabolism playing a critical role in IPF development. Therefore, targeting MTRR holds promise as a therapeutic strategy for pulmonary fibrosis.

Clinical characteristics and outcomes of patients with candidemia during the COVID-19 pandemic: Insights from experience in the Omicron era.

BACKGROUND: In Taiwan, COVID-19 outbreaks caused by the Omicron variant occurred in 2022. We investigated the incidence of candidemia during COVID-19 pandemic and the mortality of candidemia patients with COVID-19 in Taiwan. METHODS: The incidence of candidemia and fluconazole susceptibility of Candida species before (2015-2019) and during COVID-19 pandemic (2020-2023) at Kaohsiung Chang Gung Memorial Hospital were investigated. The associated factors with mortality in candidemia patients during COVID-19 pandemic were analyzed. Candidemia patients who had COVID-19 within the prior 90 days (case group, n = 34) were propensity-score matched for age, ICU admission, and abdominal surgery in a 1:4 ratio with candidemia patients without COVID-19 (control group, n = 136). RESULTS: Age (adjusted odds ratio [AOR] = 1.02, 95% CI: 1.01-1.03), ICU stay (AOR = 1.84, 95% CI: 1.29-2.62), higher Charlson comorbidity index (AOR = 1.08, 95% CI: 1.03-1.13), corticosteroid use (AOR = 1.50, 95% CI: 1.04-2.17) were associated with increased risk of mortality; abdominal surgery (AOR = 0.47, 95% CI: 0.29-0.74) and infected by Candida parapsilosis (AOR = 0.61, 95% CI: 0.38-0.98) were associated with decreased risk of mortality. After matching, there was no significant difference in mortality rates between the case and control groups. The incidence of candidemia increased from 196 to 278 patients/100,000 admissions during COVID-19 pandemic, while the causative species of candidemia and fluconazole susceptibility rates were similar. CONCLUSION: While the incidence of candidemia increased during COVID-19 pandemic, there was no significant difference in mortality between candidemia patients with and without COVID-19 in the Omicron era.

Characterization of exosome-mediated propagation of systemic inflammatory responses into the Central Nervous System.

The mechanisms through which systemic inflammation exerts its effect on the CNS are still not completely understood. Exosomes are small (30 to 100 nanometers) membrane-bound extracellular vesicles released by most of the mammalian cells. Exosomes play a vital role in cell-to-cell communication. This includes regulation of inflammatory responses by shuttling mRNAs, miRNAs, and cytokines both locally and systemically to the neighboring as well as distant cells to further modulate their transcriptional and/or translational states and affect the functional phenotype of those cells that have taken up these exosomes. The role of circulating blood exosomes leading to neuroinflammation during systemic inflammatory conditions was further characterized. Serum-derived exosomes from LPS-challenged mice (SDEL) were freshly isolated from the sera of the mice that were earlier treated with LPS and used to study SDEL effects on neuroinflammation. Exosomes isolated from the sera of the mice injected with saline were used as a control. In-vitro studies showed that the SDEL upregulate pro-inflammatory cytokine gene expression in the murine cell lines of microglia (BV-2), astrocytes (C8-D1A), and cerebral microvascular endothelial cells (bEnd.3). To further study their effects in-vivo, SDEL were intravenously injected into normal adult mice. Elevated mRNA expression of pro-inflammatory cytokines was observed in the brains of SDEL recipient mice. Proteomic analysis of the SDEL confirmed the increased expression of inflammatory cytokines in them. Together, these results further demonstrate and strengthen the novel role of peripheral circulating exosomes in causing neuroinflammation during systemic inflammatory conditions.

Enhancing the Cooling Efficiency of Aluminum-Filled Epoxy Resin Rapid Tool by Changing Inner Surface Roughness of Cooling Channels.

In low-pressure wax injection molding, cooling time refers to the period during which the molten plastic inside the mold solidifies and cools down to a temperature where it can be safely ejected without deformation. However, cooling efficiency for the mass production of injection-molded wax patterns is crucial. This work aims to investigate the impact of varying surface roughness on the inner walls of the cooling channel on the cooling efficiency of an aluminum-filled epoxy resin rapid tool. It was found that the cooling time for the injection-molded products can be determined by the surface roughness according to the proposed prediction equation. Employing fiber laser processing on high-speed steel rods allows for the creation of microstructures with different surface roughness levels. Results demonstrate a clear link between the surface roughness of cooling channel walls and cooling time for molded wax patterns. Employing an aluminum-filled epoxy resin rapid tool with a surface roughness of 4.9 µm for low-pressure wax injection molding can save time, with a cooling efficiency improvement of approximately 34%. Utilizing an aluminum-filled epoxy resin rapid tool with a surface roughness of 4.9 µm on the inner walls of the cooling channel can save the cooling time by up to approximately 60%. These findings underscore the significant role of cooling channel surface roughness in optimizing injection molding processes for enhanced efficiency.

Antagonizing interleukin-5 receptor ameliorates dextran sulfate sodium-induced experimental colitis in mice through reducing NLRP3 inflammasome activation.

Inflammatory bowel disease (IBD) is a condition characterized by inflammation in the gastrointestinal tract. Reducing intestinal inflammation is a promising approach for treating IBD. The nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing protein 3 (NLRP3) inflammasome, a critical component of the innate immune system, is implicated in the pathogenesis of IBD. Therefore, inhibiting NLRP3 inflammasome activation is a potential therapeutic strategy for IBD. In this study, we investigated the effects of the interleukin-5 (IL-5) receptor antagonist YM-90709 on dextran sulfate sodium-induced experimental colitis in mice. We found that YM-90709 reduced the expressions of IL-1ß and caspase-1 p20 in the colon and ameliorated colitis. Furthermore, we identified YM-90709 as an effective agent for inhibiting NLRP3 inflammasome activation. Knockdown of IL-5 receptor or using an inhibitor of STAT5, a key transcription factor downstream of the IL-5/IL-5 receptor signal pathway, also reduced NLRP3 inflammasome-dependent IL-1ß release and ASC speck formation. Our study is the first to demonstrate that the NLRP3 inflammasome may be a downstream signal of IL-5/IL-5 receptor and that YM-90709 protects against IBD by inhibiting IL-5 receptor. These findings suggest a new strategy for regulating intestinal inflammation and managing IBD.

MESPEUS: a database of metal coordination groups in proteins.

MESPEUS is a freely accessible database which uses carefully selected metal coordination groups found in metalloprotein structures taken from the Protein Data Bank. The database contains geometrical information of metal sites within proteins, including 40 metal types. In order to completely determine the metal coordination, the symmetry-related units of a given protein structure are taken into account and are generated using the appropriate space group symmetry operations. This permits a more complete description of the metal coordination geometry by including all coordinating atoms. The user-friendly web interface allows users to directly search for a metal site of interest using several useful options, including searching for metal elements, metal-donor distances, coordination number, donor residue group, and structural resolution. These searches can be carried out singly or in combination. The details of a metal site and the metal site(s) in the whole structure can be graphically displayed using the interactive web interface. MESPEUS is automatically updated monthly by synchronizing with the PDB database. An investigation for the Mg-ATP interaction is given to demonstrate how MESPEUS can be used to extract information about metal sites by selecting structure and coordination features. MESPEUS is available at http://mespeus.nchu.edu.tw/.

Learning Generative Models Using Denoising Density Estimators.

Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised machine learning. We introduce a new generative model based on denoising density estimators (DDEs), which are scalar functions parametrized by neural networks, that are efficiently trained to represent kernel density estimators of the data. Leveraging DDEs, our main contribution is a novel technique to obtain generative models by minimizing the Kullback-Leibler (KL)-divergence directly. We prove that our algorithm for obtaining generative models is guaranteed to converge consistently to the correct solution. Our approach does not require specific network architecture as in normalizing flows (NFs), nor use ordinary differential equation (ODE) solvers as in continuous NFs. Experimental results demonstrate substantial improvement in density estimation and competitive performance in generative model training.

A Predictive Model of Severe Cytokine Release Syndrome After Coadministration of CD19- and CD22-Chimeric Antigen Receptor T-Cell Therapy in Children With B-Cell Hematological Malignancies Based on Patient-Reported Outcomes.

BACKGROUND: Chimeric antigen receptor T-cell therapy-related severe cytokine release syndrome (sCRS) has seriously affected the life safety of patients. OBJECTIVE: To explore the influencing factors of sCRS in children with B-cell hematological malignancies and build a risk prediction model. METHODS: The study recruited 115 children with B-cell hematological malignancies who received CD19- and CD22-targeted chimeric antigen receptor T-cell therapy. A nomogram model was established based on symptomatic adverse events and highly accessible clinical variables. The model discrimination was evaluated by the area under the receiver operating characteristic curve. The calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow test. The bootstrap self-sampling method was used to internally validate. RESULTS: Thirty-seven percent of the children experienced sCRS. Indicators included in the nomogram were tumor burden before treatment, thrombocytopenia before pretreatment, and the mean value of generalized muscle weakness and headache scores. The results showed that the area under the receiver operating characteristic curve was 0.841, and the calibration curve showed that the probability of sCRS predicted by the nomogram was in good agreement with the actual probability of sCRS. The Hosmer-Lemeshow test indicated that the model fit the data well (χ2 = 5.759, P = .674). The concordance index (C-index) obtained by internal validation was 0.841 (0.770, 0.912). CONCLUSIONS: The nomogram model constructed has a good degree of discrimination and calibration, which provides a more convenient and visual evaluation tool for identifying the sCRS. IMPLICATIONS FOR PRACTICE: Incorporation of patient-reported outcomes into risk prediction models enables early identification of sCRS.

Discovery of a selective NLRP3-targeting compound with therapeutic activity in MSU-induced peritonitis and DSS-induced acute intestinal inflammation.

The aberrant activation of the nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3) inflammasome is known to contribute to the pathogenesis of various human inflammation-related diseases. However, to date, no small-molecule NLRP3 inhibitor has been used in clinical settings. In this study, we have identified SB-222200 as a novel direct NLRP3 inhibitor through the use of drug affinity responsive target stability assay, cellular thermal shift assay, and surface plasmon resonance analysis. SB-222200 effectively inhibits the activation of the NLRP3 inflammasome in macrophages, while having no impact on the activation of NLRC4 or AIM2 inflammasome. Furthermore, SB-222200 directly binds to the NLRP3 protein, inhibiting NLRP3 inflammasome assembly by blocking the NEK7 - NLRP3 interaction and NLRP3 oligomerization. Importantly, treatment with SB-222200 demonstrates alleviation of NLRP3-dependent inflammatory diseases in mouse models, such as monosodium urate crystal-induced peritonitis and dextran sulfate sodium-induced acute intestinal inflammation. Therefore, SB-222200 holds promise as a lead compound for the development of NLRP3 inhibitors to combat NLRP3-driven disease and serves as a versatile tool for pharmacologically investigating NLRP3 biology.

Effect of abnormal values of three temperature indicators on ischemic stroke hospital admissions in Guangzhou, China.

Abnormal temperature has important effects on the occurrence of ischemic stroke (IS). However, relatively less efforts have been taken to systematically unravel the association between various abnormal temperature and IS hospital admission. Focusing on three temperature indicators (i.e., mean temperature, maximum temperature, and minimum temperature), this study attempts to analyse how their abnormal values affect IS hospital admission. The dataset covers the period between September 17, 2012 and August 28, 2018, and includes a total of 1464 cases who were admitted to the hospital for the first onset of IS and lived in the main urban area of Guangzhou. The study adopts the time-stratified case-crossover analysis. Abnormal values of temperature were measured using the 2.5th and 97.5th quantile values of each temperature indicator, with the former refers to a low value whereas the latter a high one. The effects of abnormal temperature on IS hospital admission were assessed through calculating the relative risks induced by the low and high values (the median values of each temperature indicators were taken as the references). The results show that the risk window periods for IS hospital admission associated with the low values of the temperature indicators are the lags of 3-7 days and 18-19 days. The risks of high temperature values on IS admission, however, are insignificant with either one-day lag or cumulative lag. As to different population groups, females show higher risks of IS hospital admission at low temperature values than males; and elderly people, compared with young people, are more vulnerable to low temperature values. To cities with similar climate of Guangzhou, particular attention should be paid to the impact of low temperature values, especially the low value of minimum temperature, on IS admission, and to females and elderly people who are more sensitive to abnormal temperatures.

Towards More Reliable Confidence Estimation.

As a task that aims to assess the trustworthiness of the model's prediction output during deployment, confidence estimation has received much research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important characteristics that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both characteristics in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. Besides, we also incorporate our framework with a modified meta optimization rule, which converges the confidence estimator to flat meta minima. We show the effectiveness of our framework through extensive experiments on various tasks including monocular depth estimation, image classification, and semantic segmentation.

Modification of low temperature-related hospital admissions for cardiovascular diseases by multiple green space indicators at multiple spatial scales: Evidence from Guangzhou, China.

BACKGROUND: Extreme temperatures have an adverse effect on the occurrence of cardiovascular diseases (CVDs). Previous literatures tend to discuss the modification of CVDs occurrence by green space under high temperature. Relatively less attention is paid to the modification under low temperature. The variation of different attributes and spatial scales of green space in affecting CVDs occurrence are also overlooked. METHODS: This study collected a total of 4364 first-time admission cases due to CVDs in a tertiary hospital in Guangzhou from 2012 to 2018, measured the scale of green space by greening rate (GR) and percentage of landscape (PLAND), the distribution of green space by patch density (PD), mean nearest neighbor distance (ENN_MN) and largest patch index (LPI), and the accessibility of green space by green patch accessibility index (GPAI). Using the time stratified case crossover design method, the modification of low temperature-related CVDs occurrence by the above green space indicators is evaluated in an area with a radius of 100-1000 m which is further divided at an interval of 100 m. RESULTS: We found high GR, high PLAND, high PD, low ENN_MN, high LPI, and low GPAI corresponds to low risk of CVDs occurrence, the optimal modification scale of each green space indicator, which is radius corresponding to the maximum risk difference between high and low indicator subgroups, is around 800 m (GR), 600 m (PLAND and PD), 500 m (GPAI), and 300 m (LPI and ENN_MN), respectively. As the temperature decreases further, the health benefit from low GPAI at the optimal scale is weakened, whereas the benefits from the others are strengthened. CONCLUSIONS: Low temperature related CVDs occurrence risk can be modified by multiple green space indicators, and these modifications have spatial scale effect. Our findings have important theoretical and practical significance for the formulation and implementation of local green space policies.

The impact of sarcopenia on overall survival in patients with pan-RAS wild-type colorectal liver metastasis receiving hepatectomy.

Sarcopenia has been associated with conventional chemotherapy-related toxicity, postoperative complications and poor overall survival in patients with genotype-unselected metastatic colorectal cancer (mCRC). This study aimed to evaluate the prognostic implications of sarcopenia and its change after perioperative cetuximab plus doublet chemotherapy and hepatectomy in patients with RAS wild-type colorectal liver metastasis (CRLM). Patients with CRLM from 2007 to 2018 in Chang Gung Research Database were retrospectively analyzed. Baseline characteristics as well as skeletal muscle index (SMI) at baseline and dynamic changes after interventions were collected. A multivariate Cox proportional hazard model was used to evaluate the effect of each parameter on overall survival (OS), and the Kaplan-Meier method was used to establish survival curves. A two-sided p value < 0.05 was considered statistically significance. Of 214 RAS wild-type mCRC patients who received both cetuximab and doublet chemotherapy, 77 who received upfront or subsequent hepatectomy were included in this study. The median follow-up time was 2.3 years. The rate of sarcopenia was higher in the patients who received neoadjuvant cetuximab-containing regimens than in those who received upfront hepatectomy (95% versus 63%, p = 0.001). Increased SMI after perioperative systemic therapy remained independently associated with better OS in multivariate analysis [hazard ratio (HR) = 0.27/10% increase, p = 0.013). The patients with sarcopenia had a trend of worse OS than those without sarcopenia (median OS: 4.5 versus 3.6 years, log-rank p = 0.282). Improvement in sarcopenia ([SMI after intervention - initial SMI]/initial SMI × 100%) is an important prognostic factor for OS. Future research is warranted to investigate direct interventions for sarcopenia and the impact on OS.

GradMDM: Adversarial Attack on Dynamic Networks.

Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robustness of dynamic neural networks against energy-oriented attacks targeted at reducing their efficiency. Specifically, we attack dynamic models with our novel algorithm GradMDM. GradMDM is a technique that adjusts the direction and the magnitude of the gradients to effectively find a small perturbation for each input, that will activate more computational units of dynamic models during inference. We evaluate GradMDM on multiple datasets and dynamic models, where it outperforms previous energy-oriented attack techniques, significantly increasing computation complexity while reducing the perceptibility of the perturbations https://github.com/lingengfoo/GradMDM.

Dopamine-independent development and maintenance of mouse striatal medium spiny neuron dendritic spines.

Striatal medium spiny neurons (MSNs) and striatal dopamine (DA) innervation are profoundly important for brain function such as motor control and cognition. A widely accepted theory posits that striatal DA loss causes (or leads to) MSN dendritic atrophy. However, examination of the literature indicates that the data from Parkinson's disease (PD) patients and animal PD models were contradictory among studies and hard to interpret. Here we have re-examined the potential effects of DA activity on MSN morphology or lack thereof. We found that in 15-day, 4- and 12-month old Pitx3 null mutant mice that have severe DA denervation in the dorsal striatum while having substantial residual DA innervation in the ventral striatum, MSN dendrites and spine numbers were similar in dorsal and ventral striatum, and also similar to those in normal mice. In 15-day, 4- and 12-month old tyrosine hydroxylase knockout mice that cannot synthesize L-dopa and thus have no endogenous DA in the entire brain, MSN dendrites and spine numbers were also indistinguishable from age-matched wild-type (WT) mice. Furthermore, in adult WT mice, unilateral 6-OHDA lesion at 12 months of age caused an almost complete striatal DA denervation in the lesioned side, but MSN dendrites and spine numbers were similar in the lesioned and control sides. Taken together, our data indicate that in mice, the development and maintenance of MSN dendrites and spines are DA-independent such that DA depletion does not trigger MSN dendritic atrophy; our data also suggest that the reported MSN dendritic atrophy in PD may be a component of neurodegeneration in PD rather than a consequence of DA denervation.

Identification of thrombotic biomarkers in orthopedic surgery patients by plasma proteomics.

BACKGROUND: Due to the poor specificity of D-dimer, more accurate thrombus biomarkers are clinically needed to improve the diagnostic power of VTE. METHODS: The plasma samples were classified into low-risk group (n = 6) and high-risk group (n = 6) according to the Caprini Thrombosis Risk Assessment Scale score. Data-independent acquisition mass spectrometry (DIA-MS) was performed to identify the proteins in the 12 plasma samples. Bioinformatics analysis including volcano plot, heatmap, KEGG pathways and chord diagram analysis were drawn to analyze the significantly differentially expressed proteins (DEPs) between the two groups. Then, another 26 plasma samples were collected to verify the key proteins as potential biomarkers of VTE in orthopedic surgery patients. RESULTS: A total of 371 proteins were identified by DIA-MS in 12 plasma samples. Volcano plotting showed that there were 30 DEPs. KEGG pathway enrichment analysis revealed that the DEPs were majorly involved in the blood coagulation pathway. The chord diagram analysis demonstrated that proteins SAA1, VWF, FLNA, ACTB, VINC, F13B, F13A and IPSP in the DEPs were significantly related to blood coagulation. VWF and F13B were selected for validation experiments. ELISA test showed that, as compared with those in the low-risk group, the level of VWF in the high-risk sera was significantly increased. CONCLUSIONS: The level of VWF in the high-risk group of thrombosis after orthopedic surgery was significantly higher than that in the low-risk group of preoperative thrombosis, suggesting that VWF may be used as a potential thrombus biomarker in orthopedic surgery patients.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis.

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Comparison of the association between different ozone indicators and daily respiratory hospitalization in Guangzhou, China.

Background: Epidemiological studies have widely proven the impact of ozone (O3) on respiratory mortality, while only a few studies compared the association between different O3 indicators and health. Methods: This study explores the relationship between daily respiratory hospitalization and multiple ozone indicators in Guangzhou, China, from 2014 to 2018. It uses a time-stratified case-crossover design. Sensitivities of different age and gender groups were analyzed for the whole year, the warm and the cold periods. We compared the results from the single-day lag model and the moving average lag model. Results: The results showed that the maximum daily 8 h average ozone concentration (MDA8 O3) had a significant effect on the daily respiratory hospitalization. This effect was stronger than for the maximum daily 1 h average ozone concentration (MDA1 O3). The results further showed that O3 was positively associated with daily respiratory hospitalization in the warm season, while there was a significantly negative association in the cold season. Specifically, in the warm season, O3 has the most significant effect at lag 4 day, with the odds ratio (OR) equal to 1.0096 [95% confidence intervals (CI): 1.0032, 1.0161]. Moreover, at the lag 5 day, the effect of O3 on the 15-60 age group was less than that on people older than 60 years, with the OR value of 1.0135 (95% CI: 1.0041, 1.0231) for the 60+ age group; women were more sensitive than men to O3 exposure, with an OR value equal to 1.0094 (95% CI: 0.9992, 1.0196) for the female group. Conclusion: These results show that different O3 indicators measure different impacts on respiratory hospitalization admission. Their comparative analysis provided a more comprehensive insight into exploring associations between O3 exposure and respiratory health.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.