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141.
Naim Alkhouri Michael Berk Lisa Yerian Rocio Lopez Yoon-Mi Chung Renliang Zhang Thomas M. McIntyre Ariel E. Feldstein Stanley L. Hazen 《Digestive diseases and sciences》2014,59(7):1617-1624
Background and Aim
Oxidative stress is a core abnormality responsible for disease progression in nonalcoholic fatty liver disease (NAFLD). By employing a highly sensitive liquid chromatography–tandem mass spectrometry (LC/MS/MS) approach we recently were able to define the circulating profile of bioactive lipid peroxidation products characteristic of patients with nonalcoholic steatohepatitis (NASH) and developed the OxNASH score for NASH diagnosis. The aims of this study were to assess the utility of OxNASH as a predictor of NASH and study the association between OxNASH and specific histologic features of NAFLD.Methods
Our cohort consisted of 122 patients undergoing liver biopsy for clinical suspicion of NAFLD. The NAFLD activity score (NAS) was calculated for each patient. Levels of fatty acid oxidation products were quantified using stable isotope dilution LC/MS/MS, and OxNASH was calculated.Results
The mean age of our patients was 49.3 (±11.6) years, and the mean body mass index was 31.5 (±4.8) kg/m2. The majority of patients were Caucasian (82 %) and 48 % were female. OxNASH correlated with NAS and with the individual histologic features of NAFLD, namely, steatosis, inflammation, and ballooning (P < 0.05), with the strongest association being with inflammation [rho (ρ) 0.40, 95 % confidence interval 0.23, 0.57, P < 0.001]. There was also a correlation between the stage of fibrosis and OxNASH (P = 0.001). These associations remained statistically significant after adjustment for multiple confounders.Conclusions
Based on our results, in adult patients with NAFLD, OxNASH correlates with histologic features of NASH and appears to be a promising noninvasive marker. 相似文献142.
Hyperglycemia in Patients Referred for Cardiac Catheterization Is Associated With Preexisting Diabetes Rather Than a Stress‐Related Phenomenon: A Prospective Cross‐Sectional Study 下载免费PDF全文
143.
144.
Hao Helen Hou Aniruddha Rajesh Keyur M. Pandya Joseph W. Lubach Ariel Muliadi Edward Yost Wei Jia Karthik Nagapudi 《Journal of pharmaceutical sciences》2019,108(2):870-879
Usage of the amorphous phase of compounds has become the method of choice to overcome oral bioavailability problems related to poor solubility. Due to the unstable nature of glasses, it is clear that the method of preparation of the amorphous glass will have an impact on physical/chemical stability and in turn in vivo performance. The method of preparation can also have a profound impact on the mechanical properties of the amorphous phase. We have explored the impact of preparation method on the mechanical properties of an amorphous solid dispersion using a development compound, GDC-0810. Three methods were used to generate amorphous solid dispersions (ASDs) of 50% GDC-0810 with hydroxypropyl methylcellulose acetate succinate: (1) spray drying, (2) coprecipitation using overhead mixing, and (3) coprecipitation using resonant acoustic mixing. All 3 methods were found to generate ASDs with good phase mixing and similar glass transition temperatures. Coprecipitated ASD powders (overhead mixing and resonant acoustic mixing) demonstrated superior tabletability and flow properties when compared to the spray drying powder. Careful choice of manufacturing process can be used to tune material properties of ASDs to make them more amenable for downstream operations like tableting. Acoustic mixing has been demonstrated as a scalable new method to make ASDs through coprecipitation. 相似文献
145.
Yaron Arbel Amir Sternfeld Adiel Barak Zvia Burgansky-Eliash Amir Halkin Shlomo Berliner Itzhak Herz Gad Keren Ardon Rubinstein Shmuel Banai Ariel Finkelstein 《Atherosclerosis》2014
Background
The “Slow Coronary Flow” (SCF) phenomenon in the presence of angiographically normal coronaries is attributed to microvascular and endothelial dysfunction. The microcirculation can be non-invasively assessed by measuring retinal blood flow velocity.The aim of the present study was to evaluate the efficacy of the “Retinal Functional Imager” (RFI) device as a noninvasive method of diagnosing patients with slow coronary flow.Methods
Coronary blood flow velocity assessed by corrected TIMI Frame Count and retinal arterioles blood flow assessed by RFI were measured in 28 consecutive patients with normal coronary arteries. The patients were divided into 2 groups: a slow coronary flow (SCF) and a normal coronary flow (NCF) groups.Results
Inverse correlation was found between retinal and coronary blood flows so that higher retinal arterial flow velocity was observed in the SCF group (3.8 ± 1.1 mm/s vs. 2.9 ± 0.61 mm/s, respectively, p = 0.022). RFI provided 73% sensitivity and 77% specificity for diagnosing SCF using ROC analysis. Additionally, patients with SCF had higher values of serum LDL cholesterol (104.7 ± 18.93 mg/dl vs. 81.55 ± 14.62 mg/dl in NCF, p = 0.005), Glucose (96.9 ± 23.0 mg/dl vs. 83.55 ± 9.7 mg/dl in NCF, p = 0.024), and lower percentage of statin consumption (40.0% vs. 76.9% in NCF, p = 0.049).Conclusions
Slow coronary blood flow can be non-invasively diagnosed with Retinal Functional Imager. Patients with normal coronary arteries and slow coronary blood flow have high retinal arteriolar blood flow. Early non-invasive diagnosis of SCF might help detect individuals who are at higher risk to develop coronary atherosclerosis, and to provide them with early preventive measures. 相似文献146.
Alejandra C. Ventura Alan Bush Gustavo Vasen Matías A. Goldín Brianne Burkinshaw Nirveek Bhattacharjee Albert Folch Roger Brent Ariel Chernomoretz Alejandro Colman-Lerner 《Proceedings of the National Academy of Sciences of the United States of America》2014,111(37):E3860-E3869
Cell signaling systems sense and respond to ligands that bind cell surface receptors. These systems often respond to changes in the concentration of extracellular ligand more rapidly than the ligand equilibrates with its receptor. We demonstrate, by modeling and experiment, a general “systems level” mechanism cells use to take advantage of the information present in the early signal, before receptor binding reaches a new steady state. This mechanism, pre-equilibrium sensing and signaling (PRESS), operates in signaling systems in which the kinetics of ligand-receptor binding are slower than the downstream signaling steps, and it typically involves transient activation of a downstream step. In the systems where it operates, PRESS expands and shifts the input dynamic range, allowing cells to make different responses to ligand concentrations so high as to be otherwise indistinguishable. Specifically, we show that PRESS applies to the yeast directional polarization in response to pheromone gradients. Consideration of preexisting kinetic data for ligand-receptor interactions suggests that PRESS operates in many cell signaling systems throughout biology. The same mechanism may also operate at other levels in signaling systems in which a slow activation step couples to a faster downstream step.Detecting and responding to a chemical gradient is a central feature of a multitude of biological processes (1). For this behavior, organisms use signaling systems that sense information about the extracellular world, transmit this information into the cell, and orchestrate a response. Measurements of the direction and proximity of the extracellular stimuli usually rely on the binding of diffusing chemical particles (ligands) to specific cell surface receptors. Different organisms have evolved different strategies to make use of this information. Small motile organisms, including certain bacteria, use a temporal sensing strategy, measuring and comparing concentration signals over time along their swimming tracks (2). In contrast, some eukaryotic cells, including Saccharomyces cerevisiae, are sufficiently large to implement a spatial sensing mechanism, measuring concentration differences across their cell bodies (3).The observation that some eukaryotes that use spatial sensing exhibit remarkable precision in response to shallow gradients (1–2% differences in ligand concentration between front and rear) (4, 5) has led to several proposed models in which large amplification is achieved by positive feedback loops in the signaling pathways triggered by the ligand-receptor binding (6, 7). Here, we describe a different mechanism, dependent on ligand-receptor binding dynamics, which improves gradient sensing when the concentration of external ligand is close to saturation. We use the budding yeast S. cerevisiae to study the efficiency of this mechanism.Haploid yeast cells exist in two mating types, MATa and MATα (also referred to as a and α cells). Mating occurs when a and α cells sense each other’s secreted mating pheromones: a-factor and α-factor (αF) (8). The pheromone secreted by the nearby mating partner diffuses, forming a gradient surrounding the sensing cell. Pheromone binds a membrane receptor, Ste2, in MATa yeast (9) that activates a pheromone response system (PRS), which cells use to decide whether to fuse with a mating partner or not. At high enough αF concentrations, cells develop a polarized chemotropic growth toward the pheromone source (4). To do that, the nonmotile yeast determines the direction of the potential mating partner measuring on which side there are more bound pheromone receptors, which are initially distributed homogeneously on the cell surface (10). However, this sensing modality can only work when external pheromone is nonsaturating: If all receptors are bound, cells should not be able to determine the direction of the gradient. Surprisingly, even at high pheromone concentrations, yeast tend to polarize in the correct direction (4, 11). Different amplification mechanisms have been proposed to account for the conversion of small differences in ligand concentration across the yeast cell, as is the case for dense mating mixtures, into chemotropic growth (6).We previously studied induction of reporter gene output by the PRS after step increases in the concentration of αF. We found large cell-to-cell variability, the bulk of which was due to large differences in the ability of individual cells to send signal through the system and in their general capacity to express proteins (12). The level of induced gene expression matches well the equilibrium binding curve of αF to receptor (13, 14), a phenomenon known as dose–response alignment (DoRA), common to many other signaling systems (14). In the PRS, DoRA persists for several hours of stimulation.During these studies, we realized that the binding dynamics of αF to its receptor is remarkably slow: At concentrations near the dissociation constant (Kd), binding takes about 20 min to reach 90% of the equilibrium level (15, 16). This dynamics is slow relative not only to the 90-min cell division cycle but also to the pheromone-dependent activation of the mitogen-activated protein kinase (MAPK) Fus3, which takes 2 to 5 min to reach steady-state levels (14). An unavoidable conclusion is that the machinery downstream of the αF receptor must be using pre-equilibrium binding information for its operation.This observation led us to study the consequences of fast and slow ligand-receptor dynamics on the ability of cells to sense extracellular cues. In biology, the rates of ligand binding and unbinding to membrane receptors span a large range, including many cases with dynamics similar to, or even slower than, that of mating pheromone (e.g., rates for EGF, insulin, glucagon, IFN-α1a, and IL-2 in Receptor Ligand Cell type k− (1/s) Kd (M) τ (at L = Kd), s Ref. Fcε IgE Human basophils 2.50E-05 4.80E-10 20,000.00 (17) Fcγ 2.4G2 monoclonal Fab Mouse macrophage 3.80E-05 7.70E-10 13,157.89 (18) Canabinoid receptor CP55,940 Rat brain 1.32E-04 2.10E-08 3,787.88 (19) IL-2 receptor IL-2 T cells 2.00E-04 7.40E-12 2,500.00 (20) α1-Adrenergic Prazosin BC3H1 3.00E-04 7.50E-11 1,666.67 (21) Glucagon receptor Glucagon Rat hepatocytes 4.30E-04 3.06E-10 1,162.79 (22) Formyl peptide receptor (FPR) fMLP Rat neutrophils 5.50E-04 3.45E-08 909.09 (23) Ste2 (αF receptor) αF S. cerevisiae 1.00E-03 5.50E-09 500.00 (15, 16) IFN Human IFN-α1a A549 1.20E-03 3.30E-10 416.67 (24) Transferrin Transferrin HepG2 1.70E-03 3.30E-08 294.12 (25) EGF receptor EGF Fetal rat lung 2.00E-03 6.70E-10 250.00 (26) TNF TNF A549 2.30E-03 1.50E-10 217.39 (24) Insulin receptor Insulin Rat fat cells 3.30E-03 2.10E-08 151.52 (27) FPR FNLLP Rabbit neutrophils 6.70E-03 2.00E-08 74.63 (28) Total fibronectin receptors Fibronectin Fibroblasts 1.00E-02 8.60E-07 50.00 (29) T-cell receptor Class II MHC-peptide 2B4 T-cells 5.70E-02 6.00E-05 8.77 (30) FPR N-formyl peptides Human neutrophils 1.70E-01 1.20E-07 2.94 (31) cAMP receptor cAMP D. discoideum 1.00E+00 3.30E-09 0.50 (32) IL-5 receptor IL-5 COS 1.47E+00 5.00E-09 0.34 (33) NMDA receptor Glutamate Hippocampal neurons 5.00E+00 1.00E-06 0.10 (34) Adenosine A2A Adenosine HEK 293 (human) 1.75E+01 5.20E-08 0.03 (35) AMPA receptor Glutamate HEK 293 (human) 2.00E+03 5.00E-04 2.50E-04 (36)