Application of percutaneous transluminal sharp recanalization in transjugular intrahepatic portosystemic shunt for patients with chronic portal vein occlusion

PURPOSEWe aimed to evaluate the feasibility and safety of a modified technique for portal vein recanalization, percutaneous transluminal sharp recanalization (PTSR), when performing transjugular intrahepatic portosystemic shunt (TIPS) for the treatment of chronic portal vein occlusion (CPVO) and portal hypertension.METHODSNine consecutive patients with CPVO and portal hypertension had undergone TIPS and PTSR procedure after failing in conventional percutaneous catheterization from March 2017 to July 2019. Technical success rates, effectiveness, and complications were evaluated. Follow-up of patients’ clinical outcomes and shunt patency were performed periodically. Primary and secondary shunt patency were analyzed by Kaplan-Meier method.RESULTSThe occluded portal veins were successfully recanalized after failing in conventional percutaneous catheterization, and TIPS procedures were completed in all 9 patients. Two patients suffered from procedure-related complications. A portosystemic pressure gradient <12 mmHg, or a percent reduction of 25% to 50% of baseline, was achieved in all 9 patients after TIPS. During the median follow-up period of 28 months (range, 9–36 months), 1 patient experienced recurrent ascites and the other 8 patients remained asymptomatic. The cumulative rates of primary and secondary shunt patency were 66.67% and 100%, respectively, at 2 years.CONCLUSIONAs a supplementary method, PTSR is a feasible and safe method for portal vein recanalization when performing TIPS for patients with CPVO and portal hypertension

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Molecular mechanism of activation of human musk receptors OR5AN1 and OR1A1 by (R)-muscone and diverse other musk-smelling compounds

We acknowledge support from NSF (CHE-1265679) and NIH (5R01DC014423 subaward) (EB), NIH (5R01 DC014423) (HM), the European Reasearch Council (ERC) and the Engineering Science Research Council (EPSRC) (DO'H), FAPESP and CNPq (RAC), the Chinese Scholarship Council (CSC) for studentship support (MY), National Science Foundation (31070972) (HZ), Science and Technology Commission of Shanghai Municipality (16ZR1418300) (HZ), the Shanghai Eastern Scholar Program (J50201) (HZ). VSB thanks NIH grant 1R01GM106121-01A1 and computational time from NERSC.Understanding olfaction at the molecular level is challenging due to the lack of crystallographic models of odorant receptors (ORs). To better understand the molecular mechanism of OR activation, we focused on chiral (R)-muscone and other musk smelling odorants due to their great importance and widespread use in perfumery and traditional medicine, as well as environmental concerns associated with bioaccumulation of musks with estrogenic/antiestrogenic properties.  We experimentally and computationally examined the activation of human receptors OR5AN1 and OR1A1, recently identified as specifically responding to musk compounds.  OR5AN1 responds at nanomolar concentrations to musk ketone and robustly to macrocyclic sulfoxides and fluorine-substituted macrocyclic ketones; OR1A1 responds only to nitromusks. Structural models of OR5AN1 and OR1A1 based on quantum mechanics/molecular mechanics (QM/MM) hybrid methods were validated through direct comparisons with activation profiles from site-directed mutagenesis experiments and analysis of binding energies for 35 musk-related odorants.  The experimentally found chiral selectivity of OR5AN1 to (R)- over (S)-muscone was also computationally confirmed for muscone and fluorinated (R)-muscone analogs.  Structural models show that OR5AN1, highly responsive to nitromusks over macrocyclic musks, stabilizes odorants by hydrogen bonding to Tyr260 of transmembrane a-helix 6 and hydrophobic interactions with surrounding aromatic residues Phe105, Phe194 and, Phe207.  The binding of OR1A1 to nitromusks is stabilized by hydrogen bonding to Tyr258 along with hydrophobic interactions with surrounding aromatic residues Tyr251 and Phe206.  Hydrophobic/nonpolar and hydrogen bonding interactions contribute, respectively, 77% and 13% to the odorant binding affinities, as shown by an atom-based quantitative structure-activity relationship model.PostprintPeer reviewe

Screening of Effective Insecticides Against

In recent years, with the rapid development of the sericulture industry, the pests of mulberry trees are also increasing. Diaghania pyloalis Walker is one of the main pests of mulberry trees, has strong fecundity and very large food intake, especially in Sichuan and Chongqing areas. In order to find out the insecticides which can effectively control D. pyloalis, the screening test of high-efficiency control efficacy was carried out. In the summer of 2020, the 3rd instar larvae of D. pyloalis were selected as the test objects, and the indoor bioassay of 10 insecticides on D. pyloalis was carried out, and the results of the indoor bioassay were tested by field experiments in the autumn. The results show that 2000 times liquid of Dursban, Spinetoram, Chlorantraniliprole, and 1000 times liquid of Dichlorvos and Phoxim had the best control effect on D. pyloalis. Secondly, 2000 times liquid of Imidacloprid and Chlorfenapyr had the better control effect on D. pyloalis. Finally 2000 times liquid of Pymetrozine and Cyromazine had poor control effect, and 1000 times liquid of Azadirachtin had the worst control effect on D. pyloalis. To control D. pyloalis, 2000 dilution of Dursban and Spinetoram, and 1000 times liquid of Dichlorvos can be used in sericultural production. When using Chlorantraniliprole to control D. pyloalis, the leaves of mulberry should be picked with long interval to avoid affecting the quality of cocoons, and it is not recommended to use Cyromazine, Azadirachtin to control D. pyloalis

Association between Chronic Obstructive Pulmonary Disease and Lung Cancer: A Case-Control Study in Southern Chinese and a Meta-Analysis

<div><h3>Background</h3><p>Lung cancer and chronic obstructive pulmonary disease (COPD) share a common risk factor in cigarette smoking and a large portion of patients with lung cancer suffer from COPD synchronously. We therefore hypothesized that COPD is an independent risk factor for lung cancer. Our aim was to investigate the intrinsic linkage of COPD (or emphysema, chronic bronchitis and asthma) and lung cancer.</p> <h3>Methods</h3><p>The present hospital-based case-control study included 1,069 patients with newly diagnosed lung cancer and 1,132 age frequency matched cancer-free controls. The odds ratios (ORs) for the associations between each previous pulmonary disease and lung cancer were estimated with logistic regression models, adjusting for age, sex, family history of cancer, BMI and pack year smoking. In meta-analysis, the pooled effects of previous pulmonary diseases were analyzed with random effects models; and stratification analyses were conducted on smoking status and ethnicity.</p> <h3>Results</h3><p>In the case-control study, previous COPD was associated with the odds for increased risk of lung cancer (OR = 1.29, 95% confidence interval [CI] = 1.00∼1.68); so were emphysema (OR = 1.55, 95%CI = 1.03∼2.32) and chronic bronchitis (OR = 1.22, 95%CI = 0.99∼1.67); while asthma was associated with odds for decreased risk of lung cancer (OR = 0.29, 95%CI = 0.16∼0.53). These associations were more pronounced in smokers (<em>P</em><.05 for all strata), but not in non-smokers. In meta-analysis, 35 studies (22,010 cases and 44,438 controls) were identified. COPD was significantly associated with the odds for increased risk of lung cancer (pooled OR = 2.76; 95% CI = 1.85–4.11), so were emphysema (OR = 3.02; 95% CI = 2.41–3.79) and chronic bronchitis (OR = 1.88; 95% CI = 1.49–2.36); and these associations were more pronounced in smokers than in non-smokers (<em>P</em><.001 respectively). No significant association was observed for asthma.</p> <h3>Conclusion</h3><p>Previous COPD could increase the risk of lung cancer, especially in smokers.</p> </div

Association between previous pulmonary diseases, tobacco smoking or alcohol exposure and the risk of lung cancer in Southern Chinese.

a<p>adjusted by age, sex, family history of cancer,, BMI and pack year smoking in the multivariate logistic regression analysis.</p

Flowchart of selection of studies for inclusion in the meta-analysis.

<p>Flowchart of selection of studies for inclusion in the meta-analysis.</p

Figure 2

<p><b>A.</b> Pooled effect estimates of the lung cancer risk associated with a previous history of COPD, stratified by smoking status and overall. B. Pooled effect estimates of the lung cancer risk associated with a previous history of emphysema, stratified by smoking status and overall. <b>C.</b> Pooled effect estimates of the lung cancer risk associated with a previous history of chronic bronchitis, stratified by smoking status and overall. <b>D.</b> Pooled effect estimates of the lung cancer risk associated with a previous history of asthma, stratified by smoking status and overall.</p

Associations of lung cancer with previous pulmonary diseases, stratified by smoking status.

a<p>: Data are presented by n (%);</p>b<p>: adjusted by gender, age, family history of cancer, and BMI in the multivariate logistic regression analysis.</p