87 research outputs found

    Evaluation of Cinnamomum osmophloeum

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    Cinnamomum osmophloeum Kanehira belongs to the Lauraceae family of Taiwan’s endemic plants. In this study, C. osmophloeum Kanehira extract has shown inhibition of tyrosinase activity on B16-F10 cellular system first. Whether extracts inhibited mushroom tyrosinase activity was tested, and a considerable inhibition of mushroom tyrosinase activity by in vitro assays was presented. Animal experiments of C. osmophloeum Kanehira were carried out by observing animal wound repair, and the extracts had greater wound healing power than the vehicle control group (petroleum jelly with 8% DMSO, w/v). In addition, the antioxidant capacity of C. osmophloeum Kanehira extracts in vitro was evaluated. We measured C. osmophloeum Kanehira extract’s free radical scavenging capability, metal chelating, and reduction power, such as biochemical activity analysis. The results showed that a high concentration of C. osmophloeum Kanehira extract had a significant scavenging capability of free radical, a minor effect of chelating ability, and moderate reducing power. Further exploration of the possible physiological mechanisms and the ingredient components of skincare product for skin-whitening, wound repair, or antioxidative agents are to be done

    Milk Consumption Across Life Periods in Relation to Lower Risk of Nasopharyngeal Carcinoma: A Multicentre Case-Control Study

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    Background: The much higher incidence of nasopharyngeal carcinoma (NPC) in men suggests sex hormones as a risk factor, and dairy products contain measurable amounts of steroid hormones. Milk consumption has greatly increased in endemic regions of NPC. We investigated the association between NPC and milk consumption across life periods in Hong Kong.Methods: A multicentre case-control study included 815 histologically confirmed NPC incident cases and 1,502 controls who were frequency-matched on age and sex at five major hospitals in Hong Kong in 2014–2017. Odds ratios (ORs) of NPC (cases vs. controls) for milk consumption at different life periods were estimated by unconditional logistic regression, adjusting for sex, age, socioeconomic status score, smoking and alcohol drinking status, exposure to occupational hazards, family history of cancer, IgA against Epstein-Barr virus viral capsid antigen, and total energy intake.Results: Compared with abstainers, lower risks of NPC were consistently observed in regular users (consuming ≥5 glasses of milk [fresh and powdered combined] per month) across four life periods of age 6–12 (adjusted OR 0.74, 95% CI 0.54–0.86), 13–18 (0.68, 0.55–0.84), 19–30 (0.68, 0.55–0.84), and 10 years before recruitment (0.72, 0.59–0.87). Long-term average milk consumption of ≤2.5, >2.5, and ≤12.5, >12.5 glasses per month yielded adjusted OR (95% CI) of 1.00 (0.80–1.26), 0.98 (0.81–1.18), 0.95 (0.76–1.18), and 0.55 (0.43–0.70), respectively (all P-values for trend < 0.05).Conclusion: Consumption of milk across life periods was associated with lower risks of NPC. If confirmed to be causal, this has important implications for dairy product consumption and prevention of NPC

    Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

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    Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Inverse Design of Ligands Using A Deep Generative Model Semi-supervised by A Data-driven Ligand Field Strength Metric

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    Transition metal (TM) complexes exhibit diverse structural and electronic properties. The properties of a TM complex can be tuned through modulating the ligand field strength (LFS) inflicted by its ligands. Current quantification of the LFS of a ligand is mainly derived from experimental measurements on a subset of highly symmetrical TM complexes and is limited in ligand scope. Herein, we report a data-driven method to quantify the LFS of ligands assigned from experimental crystal structures of TM complexes. We first show that the experimental metal-ligand bond lengths of over 4000 mononuclear Fe, Co, and Mn complexes form bimodal distributions. Using gaussian fits on the bimodal distributions, each TM complex is assigned with a spin state label. These spin state labels can then be used to calculate the LFS of the ligands of the complexes. Using the obtained data-driven LFS metric, we establish that a semi-supervised deep generative model, junction tree variational autoencoder (JTVAE), can be employed to predict LFS values. Our model exhibits a mean absolute error (MAE) of 0.047 and root mean squared error of 0.072 on the training set. The model also allows the generation of novel ligands with desirable LFS values

    A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal Complexes

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    Deep generative models (DGMs) have shown great promise in the generation of organic molecules and inorganic materials with chemical sensible structures and optimized properties. However, there is a lack of their applications in transition metal (TM) complexes due to their flexible coordination environment, multiple accessible oxidation and spin states, despite the importance of these complexes in fine chemical synthesis, commodity production, and optical applications. Herein, we propose a joint semi-supervised junction-tree variational autoencoder (SSVAE) and artificial neural network (ANN) classifier model, coined as LiveTransForM (Ligand variational auto-encoder and Transfer learning For transition Metal complexes), for the design of octahedral TM complexes. LiveTransForM allows the design of ligands that build up TM complexes and the prediction of the spin states of the assembled complexes. We show that the accuracy of the classifier is improved when the latent variables from the SSVAE are used as input for the ANN model compared to those from the unsupervised VAE. Input augmentation using the three molecular axes also improves the accuracy of the classifier. 58 complexes with predicted spin states are then generated by LiveTransForM and the accuracy of their spin state labels are validated by density functional theory methods. Two design strategies, single mutation and seeded generation, are also introduced to allow the directed evolution of a parent complex towards a desirable spin state and local modification of seed complexes with similar spin states, respectively

    10:30 am- 12:00 pm, Room: HOH-1 Electricity Consumption and Asset Prices

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    Purdue University, and University of Notre Dame) for helpful comments. We thank Manisha Goswami, Stev

    Global network modulation during thalamic stimulation for Tourette syndrome

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    Background and objectives: Deep brain stimulation (DBS) of the thalamus is a promising therapeutic alternative for treating medically refractory Tourette syndrome (TS). However, few human studies have examined its mechanism of action. Therefore, the networks that mediate the therapeutic effects of thalamic DBS remain poorly understood.Methods: Five participants diagnosed with severe medically refractory TS underwent bilateral thalamic DBS stereotactic surgery. Intraoperative fMRI characterized the blood oxygen level-dependent (BOLD) response evoked by thalamic DBS and determined whether the therapeutic effectiveness of thalamic DBS, as assessed using the Modified Rush Video Rating Scale test, would correlate with evoked BOLD responses in motor and limbic cortical and subcortical regions.Results: Our results reveal that thalamic stimulation in TS participants has wide-ranging effects that impact the frontostriatal, limbic, and motor networks. Thalamic stimulation induced suppression of motor and insula networks correlated with motor tic reduction, while suppression of frontal and parietal networks correlated with vocal tic reduction. These regions mapped closely to major regions of interest (ROI) identified in a nonhuman primate model of TS.Conclusions: Overall, these findings suggest that a critical factor in TS treatment should involve modulation of both frontostriatal and motor networks, rather than be treated as a focal disorder of the brain. Using the novel combination of DBS-evoked tic reduction and fMRI in human subjects, we provide new insights into the basal ganglia-cerebellar-thalamo-cortical network-level mechanisms that influence the effects of thalamic DBS. Future translational research should identify whether these network changes are cause or effect of TS symptoms
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