Journal of Neuroscience Methods最新文献

{"title":"Localization of neurons from extracellular footprints","authors":"Mélina Scopin ,&nbsp;Giulia L.B. Spampinato ,&nbsp;Olivier Marre ,&nbsp;Samuel Garcia ,&nbsp;Pierre Yger","doi":"10.1016/j.jneumeth.2024.110297","DOIUrl":"10.1016/j.jneumeth.2024.110297","url":null,"abstract":"<div><h3>Background:</h3><div>High density microelectrode arrays (HD-MEAs) are now widely used for both <em>in-vitro</em> and <em>in-vivo</em> recordings, as they allow spikes from hundreds of neurons to be recorded simultaneously. Since extracellular recordings do not allow visualization of the recorded neurons, algorithms are needed to estimate their physical positions, especially to track their movements when the are drifting away from recording devices.</div></div><div><h3>New Method:</h3><div>The objective of this study was to evaluate the performance of multiple algorithms for neuron localization solely from extracellular traces (MEA recordings), either artificial or obtained from mouse retina. The algorithms compared included center-of-mass, monopolar, and grid-based algorithms. The first method is a barycenter calculation. The second algorithm infers the position of the cell using triangulation with the assumption that the neuron behaves as a monopole. Finally, grid-based methods rely on comparing the recorded spike with a projection of spikes of hypothetical neurons with different positions.</div></div><div><h3>Results:</h3><div>The Grid-Based algorithm yielded the most satisfactory outcomes. The center-of-mass exhibited a minimal computational cost, yet its average localization was suboptimal. Monopolar algorithms gave cell localizations with an average error of less than <span><math><mrow><mn>10</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>, but they had considerable variability and a high computational cost. For the grid-based method, the variability was smaller, with satisfactory performance and low computational cost.</div></div><div><h3>Comparison with Existing Method(s):</h3><div>The accuracy of the different localization methods benchmarked in this article had not been properly tested with ground-truth recordings before.</div></div><div><h3>Conclusion:</h3><div>The objective of this article is to provide guidance to researchers on the selection of optimal methods for localizing neurons based on MEA recordings.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110297"},"PeriodicalIF":2.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optogenetic estimation of synaptic connections in brain slices","authors":"Tetsuhiko Kashima ,&nbsp;Takuya Sasaki ,&nbsp;Yuji Ikegaya","doi":"10.1016/j.jneumeth.2024.110298","DOIUrl":"10.1016/j.jneumeth.2024.110298","url":null,"abstract":"<div><h3>Background</h3><div>Detection of synaptic connections is essential for understanding neural circuits. By using optogenetics, current injection, and glutamate uncaging to activate presynaptic cells and simultaneously recording the subsequent response of postsynaptic cells, the presence of synaptic connections can be confirmed. However, these methods present throughput challenges, such as the need for simultaneous multicellular patch-clamp recording and two-photon microscopy. These challenges lead to a trade-off between sacrificing resolution and experimental throughput.</div></div><div><h3>New method</h3><div>We adopted the laser, typically used for local field ablation, and combined this with post hoc analysis. We successfully approximated the synaptic connection probabilities using only an epi-fluorescence microscope and single-cell recordings.</div></div><div><h3>Results</h3><div>We sequentially stimulated the channelrhodopsin 2-expressing cells surrounding the recorded cell and approximated the synaptic connection probabilities. This probability value was comparable to that obtained from simultaneous multi-cell patch-clamp recordings, which included more than 600 pairs.</div></div><div><h3>Comparison with existing methods</h3><div>Our setup allows us to estimate connection probabilities within 100 s, outperforming existing methods. We successfully estimated synaptic connection probabilities using only the optical path typically used by an epi-fluorescence microscope and single-cell recordings. It may also be suitable for dendritic ablation experiments.</div></div><div><h3>Conclusions</h3><div>The proposed method simplifies the estimation of connection probabilities, which is expected to advance the study of neural circuits in conditions such as autism and schizophrenia where connection probabilities vary. Furthermore, this approach is applicable not only to local circuits but also to long-range connections, thus increasing experimental throughput.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110298"},"PeriodicalIF":2.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of neuronal differentiation in human adipose-derived stromal cells: morphological, molecular, and ultrastructural insights","authors":"Xiaodong Yuan ,&nbsp;Wen Li ,&nbsp;Yi Yuan ,&nbsp;Xuhong Zhu ,&nbsp;Yan Meng ,&nbsp;Qi Wu ,&nbsp;Qi Yan ,&nbsp;Pingshu Zhang","doi":"10.1016/j.jneumeth.2024.110296","DOIUrl":"10.1016/j.jneumeth.2024.110296","url":null,"abstract":"<div><h3>Objective</h3><div>Adipose-derived stromal cells (ADSCs) have shown promise as a potential source of neural differentiation. In this study, we investigated the morphological, molecular and ultrastructural features of ADSCs during neuronal differentiation.</div></div><div><h3>Methods</h3><div>ADSCs were induced in vitro and their differentiation was examined at different time points. Immunocytochemical staining was performed to detect the expression of neuron-specific markers NSE and MAP-2. Immunofluorescence double labeling and Western blot detected the co-expression of presynaptic markers (CaMKII, SynCAM1, SYN) and postsynaptic markers (PSD-95, Synapsin I). Scanning electron microscopy (SEM) was performed to detect the synaptic structural features of differentiated neurons.</div></div><div><h3>Results</h3><div>ADSCs showed diverse morphological features during differentiation, gradually acquiring a neuron-like spindle shape and organized arrangement. The expression of neuron-specific markers and synaptic markers peaked at 5 h of induction. Scanning electron microscopy showed polygonal protrusions of ADSC-derived neurons, and transmission electron microscopy showed characteristic ultrastructures such as nidus, synaptic vesicle-like structures, and tight junctions.</div></div><div><h3>Conclusion</h3><div>Our findings suggest that ADSCs differentiated for 5 h have neuronal features, including morphological, molecular, and ultrastructural resemblance to neurons, as well as the formation of synaptic structures. These insights contribute to a better understanding of ADSC-based neuronal differentiation and pave the way for future applications in regenerative medicine and neurodegenerative diseases.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110296"},"PeriodicalIF":2.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for quantitative spatial analysis of immunolabeled fibers at regenerative electrode interfaces","authors":"Michael Rosario ,&nbsp;Jingyuan Zhang ,&nbsp;Muhammad Irfan Kaleem ,&nbsp;Nikhil Chandra ,&nbsp;Ying Yan ,&nbsp;Daniel Moran ,&nbsp;Matthew Wood ,&nbsp;Wilson Z. Ray ,&nbsp;Matthew MacEwan","doi":"10.1016/j.jneumeth.2024.110295","DOIUrl":"10.1016/j.jneumeth.2024.110295","url":null,"abstract":"<div><h3>Background</h3><div>Regenerative electrodes are being explored as robust peripheral nerve interfaces for neuro-prosthetic control and sensory feedback. Current designs differ in electrode number, spatial arrangement, and porosity which impacts the regeneration, activation, and spatial distribution of fibers at the device interface. Knowledge of sensory and motor fiber distributions are important in optimizing selective fiber activation and recording.</div></div><div><h3>New Method</h3><div>We use confocal microscopy and immunofluorescence methods to conduct spatial analysis of immunolabeled fibers across whole nerve cross sections.</div></div><div><h3>Results</h3><div>This protocol was implemented to characterize motor fiber distribution within 3 macro-sieve electrode regenerated (MSE), 3 silicone-conduit regenerated, and 3 unmanipulated control rodent sciatic nerves. Total motor fiber counts were 1485 [SD: +/- 50.11], 1899 [SD: +/- 359], and 5732 [SD: +/- 1410] for control, MSE, and conduit nerves respectively. MSE motor fiber distributions exhibited evidence of deviation from complete spatial randomness and evidence of dispersion and clustering tendencies at varying scales. Notably, MSE motor fibers exhibited clustering within the central portion of the cross section, whereas conduit regenerated motor fibers exhibited clustering along the periphery.</div></div><div><h3>Comparison with Existing Methods</h3><div>Prior exploration of fiber distributions at regenerative interfaces was limited to either quadrant-based density analysis of randomly sampled subregions or qualitative description. This method extends existing sample preparation and microscopy techniques to quantitatively assess immunolabeled fiber distributions within whole nerve cross-sections.</div></div><div><h3>Conclusions</h3><div>This approach is an effective way to examine the spatial organization of fiber subsets at regenerative electrode interfaces, enabling robust assessment of fiber distributions relative to electrode arrangement.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110295"},"PeriodicalIF":2.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142348461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Isolation of ependymal cilia from mouse brain” [J. Neurosci. Method. 409 (2024) 110198]","authors":"Akihiro Mizuno ,&nbsp;Kazuhito Takeuchi ,&nbsp;Yuichi Nagata ,&nbsp;Hideyuki Harada ,&nbsp;Taiki Yamamoto ,&nbsp;Takayuki Ishikawa ,&nbsp;Sachi Maeda ,&nbsp;Fumiharu Ohka ,&nbsp;Hironori Ueno ,&nbsp;Ryuta Saito","doi":"10.1016/j.jneumeth.2024.110293","DOIUrl":"10.1016/j.jneumeth.2024.110293","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110293"},"PeriodicalIF":2.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional MRI study with conductivity signal changes during visual stimulation","authors":"Hyug-Gi Kim ,&nbsp;Youngeun Yoon ,&nbsp;Mun Bae Lee ,&nbsp;Jeongin Jeong ,&nbsp;Jiyoon Lee ,&nbsp;Oh In Kwon ,&nbsp;Geon-Ho Jahng","doi":"10.1016/j.jneumeth.2024.110288","DOIUrl":"10.1016/j.jneumeth.2024.110288","url":null,"abstract":"<div><h3>Background</h3><div>Although blood oxygen level-dependent (BOLD) functional MRI (fMRI) is a standard method, major BOLD signals primarily originate from intravascular sources. Magnetic resonance electrical properties tomography (MREPT)-based fMRI signals may provide additional insights into electrical activity caused by alterations in ion concentrations and mobilities.</div></div><div><h3>Purpose</h3><div>This study aimed to investigate the neuronal response of conductivity during visual stimulation and compare it with BOLD.</div></div><div><h3>Materials and methods</h3><div>A total of 30 young, healthy volunteers participated in two independent experiments using BOLD and MREPT techniques with a visual stimulation paradigm at 3 T MRI. The first set of MREPT fMRI data was obtained using a multi-echo spin-echo (SE) echo planar imaging (EPI) sequence from 14 participants. The second set of MREPT fMRI data was collected from 16 participants using both a single-echo SE-EPI and a single-echo three-dimensional (3D) balanced fast-field-echo (bFFE) sequence. We reconstructed the time-course Larmor frequency conductivity to evaluate hemodynamics.</div></div><div><h3>Results</h3><div>Conductivity values slightly increased during visual stimulation. Activation strengths were consistently stronger with BOLD than with conductivity for both SE-EPI MREPT and bFFE MREPT. Additionally, the activated areas were always larger with BOLD than MREPT. Some participants also exhibited decreased conductivity values during visual stimulations. In Experiment 1, conductivity showed significant differences between the fixation and visual stimulation blocks in the secondary visual cortex (SVC) and cuneus, with conductivity differences of 0.43 % and 0.47 %, respectively. No significant differences in conductivity were found in the cerebrospinal fluid (CSF) areas between the two blocks. In Experiment 2, significant conductivity differences were observed between the two blocks in the SVC, cuneus, and lingual gyrus for SE-EPI MREPT, with differences of 0.90 %, 0.67 %, and 0.24 %, respectively. Again, no significant differences were found in the CSF areas.</div></div><div><h3>Conclusion</h3><div>Conductivity values increased slightly during visual stimulation in the visual cortex areas but were much weaker than BOLD responses. The conductivity change during visual stimulation was less than 1 % compared to the fixation block. No significant differences in conductivity were observed between the primary visual cortex (PVC)-CSF and SVC-CSF during fixation and visual stimulations, suggesting that the observed conductivity changes may not be related to CSF changes in the visual cortex but rather to diffusion changes. Future research should explore the potential of MREPT to detect neuronal electrical activity and hemodynamic changes, with further optimization of the MREPT technique.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110288"},"PeriodicalIF":2.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets","authors":"Wonmi Gu ,&nbsp;Juhui Gim ,&nbsp;Dohyun Lee ,&nbsp;Heejong Eom ,&nbsp;Jae Jun Lee ,&nbsp;Seong Shoon Yoon ,&nbsp;Tae-Young Heo ,&nbsp;Jaesuk Yun","doi":"10.1016/j.jneumeth.2024.110294","DOIUrl":"10.1016/j.jneumeth.2024.110294","url":null,"abstract":"<div><h3>Background</h3><div>The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs principle (replacement, reduction and refinement), and other factors make it challenging to obtain NHPs individuals for research. Consequently, there is a need for methods that can comprehensively analyze small datasets using artificial intelligence (AI).</div></div><div><h3>New methods</h3><div>We employed AI to identify cocaine dependence patterns from collected data. First, we collected behavioral data from cocaine SA marmosets (<em>Callithrix jacchus</em>) to develop a dependence prediction model. SHapley Additive exPlanations (SHAP) values were used to demonstrate the importance of various variables. Additionally, we collected positron emission tomographic (PET) images showing dopamine transporter (DAT) binding potential and developed an algorithm for PET image segmentation.</div></div><div><h3>Results</h3><div>The prediction model indicated that the Random Forest (RF) algorithm performed best, with an area under the curve (AUC) of 0.92. The top five variables influencing the model were identified using SHAP values. The PET image segmentation model achieved an accuracy of 0.97, a mean squared error of 0.02, an intersection over union (IoU) of 0.845, and a Dice coefficient of 0.913.</div></div><div><h3>Comparison with existing methods and conclusion</h3><div>Utilizing data from the marmoset SA experiment, we developed an ML-based dependence prediction model and analyzed variable importance rankings using SHAP. AI-based imaging segmentation methods offer a valuable tool for evaluating DAT availability in NHPs following chronic cocaine administration.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110294"},"PeriodicalIF":2.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating and maintaining brain organoids at various levels of complexity","authors":"Floris G. Wouterlood","doi":"10.1016/j.jneumeth.2024.110291","DOIUrl":"10.1016/j.jneumeth.2024.110291","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110291"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel DRL-guided sparse voxel decoding model for reconstructing perceived images from brain activity","authors":"Xu Yin ,&nbsp;Zhengping Wu ,&nbsp;Haixian Wang","doi":"10.1016/j.jneumeth.2024.110292","DOIUrl":"10.1016/j.jneumeth.2024.110292","url":null,"abstract":"<div><h3>Background</h3><p>Due to the sparse encoding character of the human visual cortex and the scarcity of paired training samples for {images, fMRIs}, voxel selection is an effective means of reconstructing perceived images from fMRI. However, the existing data-driven voxel selection methods have not achieved satisfactory results.</p></div><div><h3>New method</h3><p>Here, a novel deep reinforcement learning-guided sparse voxel (DRL-SV) decoding model is proposed to reconstruct perceived images from fMRI. We innovatively describe voxel selection as a Markov decision process (MDP), training agents to select voxels that are highly involved in specific visual encoding.</p></div><div><h3>Results</h3><p>Experimental results on two public datasets verify the effectiveness of the proposed DRL-SV, which can accurately select voxels highly involved in neural encoding, thereby improving the quality of visual image reconstruction.</p></div><div><h3>Comparison with existing methods</h3><p>We qualitatively and quantitatively compared our results with the state-of-the-art (SOTA) methods, getting better reconstruction results. We compared the proposed DRL-SV with traditional data-driven baseline methods, obtaining sparser voxel selection results, but better reconstruction performance.</p></div><div><h3>Conclusions</h3><p>DRL-SV can accurately select voxels involved in visual encoding on few-shot, compared to data-driven voxel selection methods. The proposed decoding model provides a new avenue to improving the image reconstruction quality of the primary visual cortex.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110292"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis","authors":"Yulia Bezsudnova,&nbsp;Andrew J. Quinn,&nbsp;Ole Jensen","doi":"10.1016/j.jneumeth.2024.110279","DOIUrl":"10.1016/j.jneumeth.2024.110279","url":null,"abstract":"<div><h3>Background</h3><div>Multivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography.</div></div><div><h3>New method</h3><div>To optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We determined the least required number of sensors needed for robust MVPA for image categorization experiments.</div></div><div><h3>Results</h3><div>We found that the use of signal space separation (SSS) without a proper regularization significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied.</div></div><div><h3>Comparison with existing methods</h3><div>The power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed.</div></div><div><h3>Conclusions</h3><div>When designing MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters without a proper regularization to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110279"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}