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PurposeEmergence of vancomycin variable enterococci (VVE) poses a challenge to empiric vancomycin therapy. Vancomycin-variable enterococci (VVE) are vanA-positive, yet phenotypically vancomycin-susceptible enterococci that can switch to a vancomycin-resistant phenotype when exposed to vancomycin. The aim of the present study was to determine the prevalence of VVE in India.MethodsIsolates of phenotypically vancomycin susceptible Enterococcus faecium from 20 tertiary care hospitals across India were collected and tested for the presence of vanA, vanR, vanS, vanB and vanC genes by conventional PCR using previously published primers. Isolates positive for vanA gene were considered as VVE.ResultsThe prevalence of VVE was 1.5% (5/340). Only one VVE isolate was positive for vanR and vanS, and all the isolates were negative for vanB and vanC.ConclusionsAlthough the prevalence is low, our finding emphasizes the importance of routinely screening for van genes in enterococci that are phenotypically susceptible. Silenced vanA able to escape detection and revert to resistance during vancomycin therapy represents a new challenge in clinical settings.  相似文献   
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目的 多样的环境因素使得不同产地栽培滇重楼的化学成分也丰富多样,不同居群栽培滇重楼的甾体皂苷类成分具有很大的差异,多源数据融合分析能更全面的表征药材化学信息,建立一个高效而准确的产地鉴别模型,为其资源合理开发利用提供依据。方法 以来自云南和四川的8个产地(保山、楚雄、大理、红河、丽江、成都、文山、玉溪)共366份栽培滇重楼根茎为实验材料,采集其傅里叶变换近红外光谱(FT-NIR)和衰减全反射-傅里叶变换中红外光谱(ATR-FTMIR)数据。采用Kennard-Stone算法将不同产地的样品分为2/3的训练集和1/3的预测集,基于4种特征变量提取方法(CARS、VIP、SPA、SO-Covsel)结合2种数据融合策略(低级数据融合和中级数据融合),建立偏最小二乘产地判别分析模型。根据模型参数交叉验证均方根误差(RMSECV)和预测均方根误差(RMSEP)评估模型的稳定性,模型训练集和预测集准确率(ACC)评估模型分类性能。结果 近红外光谱和中红外光谱均能反应不同产地栽培滇重楼的化学成分差异,在中级数据融合中,基于VIP和SPA提取的特征变量建立的模型正确率均大于94%。相较于中级数据融合,低级数据融合模型得到了最为满意的结果,其预测集分类正确率达到100%。结论 根据近红外和中红外数据建立的低级数据融合PLS-DA模型,能够用于栽培滇重楼的产地鉴别分析。  相似文献   
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PurposeTo investigate the safety and efficacy of an aqueous polyethylene glycol-based liquid embolic agent, Embrace Hydrogel Embolic System (HES), in the treatment of benign and malignant hypervascular tumors.Materials and MethodsA prospective, single-arm, multicenter study included 8 patients, 5 males and 3 females, with a median age of 58.5 years (30–85 years), who underwent embolization in 8 tumors between October 2019 and May 2020. Technical success was defined as successful delivery of HES to the index vessel, with disappearance of >90% of the targeted vascular enhancement or, for portal vein embolization, occlusion of the portal branches to the liver segments for future resection. The volume of HES administered, ease of use (5 point Likert scale), administration time, and adverse events (AEs) were recorded. Evaluation was performed at 7, 30, and 90 days via clinical assessment and blood testing, and follow-up imaging was performed at 30 days.ResultsEight patients were enrolled, and 10 embolizations were performed in 8 lesions. Tumors included hepatocellular carcinoma (n = 4), renal angiomyolipoma (n = 3), and intrahepatic cholangiocarcinoma (n = 1). Technical success was 100%, and the average ease of use was 3.3 ± 1.0 SD. The HES delivery time was 1–28 minutes (median, 16.5 minutes), and the HES volume injected was 0.4–4.0 mL (median, 1.3 mL). All patients reached 30-day follow-up with imaging, and 6 patients reached 90-day follow-up. There were 3 serious AEs in 2 patients that were unrelated to the embolic agent.ConclusionHES resulted in a 100% embolization technical success rate. The product ease of use was acceptable, and no target vessel recanalization was noted on follow-up imaging at 30 days.  相似文献   
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《Asian nursing research.》2021,15(3):157-162
PurposeThis study develops a checklist with guidelines for the methods and important factors to consider in research using structural equation modeling (SEM).MethodThe paper discusses the factors to consider in the process across the three stages of 1) model setting, 2) model evaluation and modification, and 3) interpretation and reporting of SEM-based studies.ResultsThe authors present a checklist for researchers during the stages of model setting, model evaluation and modification, result analysis, and reporting, along with examples of figures and tables with explanations.ConclusionA checklist will help to improve the reporting quality of SEM-based studies.  相似文献   
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Proteomic genotyping detects single amino acid polymorphisms to infer the genotype of corresponding non-synonymous SNPs. Like any DNA genotype, these inferences can be used to estimate random match probability. Fingermarks are a common source of biological evidence that is sample limited and a highly variable source of identifying DNA. Genetically variant peptides from fingermarks, that contain single amino acid polymorphisms, are an additional source of identifying genetic information. To discover these peptide biomarkers epidermal corneocytes from 9 subjects were isolated, processed, digested with trypsin and applied to mass spectrometry. The resulting proteomic and matching exome datasets were used to discover, characterize and validate 60 genetically variant peptides. An average of 28.8 ± 4.4 genetically variant peptides were detected from each subject resulting in a total of 264 SNP allele inferences with 260 true and 4 false positives, a false discovery rate of 1.5%. Random match probabilities were estimated using the genotype frequencies from the matching major populations in the 1000 Genomes Project. Estimates ranged up to a value of 1 in 1.7 × 108, with a median probability of 1 in 2.4 × 106. Furthermore, the proteomically-inferred genotypes are likely to be compatible with the STR-based random match probability estimates since the closest STR locus was 2.2 Mb from the nearest GVP-inferred SNP. This project represents a novel mode of genetic information that can be obtained from fingermarks and has the potential to complement other methods of human identification including analysis of ridge patterns or touch DNA.  相似文献   
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《Vaccine》2021,39(19):2668-2675
Relapsing malaria caused by Plasmodium vivax is a neglected tropical disease and an important cause of malaria worldwide. Vaccines to prevent clinical disease and mosquito transmission of vivax malaria are needed to overcome the distinct challenges of this important public health problem. In this vaccine immunogenicity study in mice, we examined key variables of responses to a P. vivax Duffy binding protein vaccine, a leading candidate to prevent the disease-causing blood-stages. Significant sex-dependent differences were observed in B cell (CD80+) and T cell (CD8+) central memory subsets, resulting in significant differences in functional immunogenicity and durability of anti-DBP protective efficacy. These significant sex-dependent differences in inbred mice were in the CD73+CD80+ memory B cell, H2KhiCD38hi/lo, and effector memory subsets. This study highlights sex and immune genes as critical variables that can impact host responses to P. vivax antigens and must be taken into consideration when designing clinical vaccine studies.  相似文献   
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《Clinical neurophysiology》2021,132(10):2639-2653
ObjectiveThis study brought together over 60 transcranial magnetic stimulation (TMS) researchers to create the largest known sample of individual participant single and paired-pulse TMS data to date, enabling a more comprehensive evaluation of factors driving response variability.MethodsAuthors of previously published studies were contacted and asked to share deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to variability in response to single and paired-pulse TMS data.Results687 healthy participant’s data were pooled across 35 studies. Target muscle, pulse waveform, neuronavigation use, and TMS machine significantly predicted an individual’s single-pulse TMS amplitude. Baseline motor evoked potential amplitude, motor cortex hemisphere, and motor threshold (MT) significantly predicted short-interval intracortical inhibition response. Baseline motor evoked potential amplitude, test stimulus intensity, interstimulus interval, and MT significantly predicted intracortical facilitation response. Age, hemisphere, and TMS machine significantly predicted MT.ConclusionsThis large-scale analysis has identified a number of factors influencing participants’ responses to single and paired-pulse TMS. We provide specific recommendations to minimise interindividual variability in single and paired-pulse TMS data.SignificanceThis study has used large-scale analyses to give clarity to factors driving variance in TMS data. We hope that this ongoing collaborative approach will increase standardisation of methods and thus the utility of single and paired-pulse TMS.  相似文献   
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