Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace.

Backgrounds: With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers. Aim: To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward. Methods: Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords. Results: According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement. Conclusion: Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Unveiling the Negative Customer Experience in Diagnostic Centers: A Data Mining Approach.

Introduction: This study aims to identify the negative customer experiences reflected in complaints against diagnostic centers using data mining tools. Methods: Analyzing customer complaints from a consumer complaints website, the Apriori algorithm was employed to uncover frequent patterns and identify key areas of concern. The frequency and distribution of terms used in complaints were also analyzed, and word clouds were generated to visualize the findings. Results: The study revealed that major areas of unfavorable customer experience included delayed test reports, erroneous test results, difficulties scheduling appointments, staff incivility, subpar service, and medical negligence. Discussion: These findings and the proposed model can guide diagnostic centers in incorporating data mining tools for customer experience analysis, enabling managers to proactively address issues and view complaints as opportunities for service improvement rather than legal liabilities.

Analysis of Acupoint Selection Rules for Guasha Treatment of Primary Headaches Based on Data Mining.

Objective: We aimed to understand the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches using data mining technology, providing a reference for the clinical application of Guasha therapy for primary headaches. Methods: Literature related to Guasha therapy for primary headaches in PubMed, Web of Science, Chinese National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Chinese Biomedical Literature Database were searched, up until May 12, 2023. A database of acupoints for Guasha therapy for primary headaches was established in Excel. The frequency of the acupoints used for Guasha in therapy of primary headaches were calculated by SPSS 25.0. The association rules between the acupoints were further described using SPSS Modeler 18.0. Results: A total of 67 papers were included, involving 51 acupoints for Guasha against primary headaches. The most commonly used acupoints were Fengchi, Baihui, Taiyang, Shuaigu, Tianzhu, and Hegu. The common acupoint combinations for Guasha therapy for primary headaches were Fengchi-Taiyang, Fengchi-Baihui, Fengchi-Taiyang-Baihui, Fengchi-Tianzhu-Baihui, and Fengchi-Shuaigu-Taiyang-Baihui. Conclusion: Data mining can effectively analyze the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches, providing a reliable basis for clinical acupoint selection.

Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings.

Wind environment is important in architectural sustainable design, as existing studies show that it can be considerably influenced by building morphologies. This study aimed to develop a data-mining framework to quantitatively evaluate and compare influences on Low-Wind-Velocity Area (LWVA) of common cuboid-form buildings with typical morphological parameters. The data-mining framework was originally developed by integrating multiple computational methods for rapid in-depth iterative analyses, including the generation of building models using parametric modelling, the big data generation based on hybrid model, and the statistical metric analysis method. The hybrid model was created by combining the CFD model and machine learning model. The accuracy and efficiency of the framework were fully demonstrated through the comprehensive validation and analyses of different models. The data of more than fifty thousand building cases with different morphological parameters and relevant wind conditions were generated and analyzed. Influences on LWVA of morphological parameters of cuboid-form building was comprehensively evaluated, including the visualization of multiple parameters, calculation and comparison of several correlation coefficients. It suggested that the reduction of height and width on the windward side would significantly decrease the LWVA and promote the outdoor ventilation. The change of depth would have relatively limited influence on the LWVA. Multivariate regression model-fit and variance analyses were further implemented, and it was found that there was a relatively significant linear correlation between the LWVA and morphological parameters. The equation of multivariate regression model was provided for extra rapid prediction. The study outcome could contribute to efficient evaluation of LWVA and provide useful information for sustainable design.

Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges.

OBJECTIVES: The present study was conducted to evaluate the reproducibility of Lekholm and Zarb classification system (L&Z) for bone quality assessment of edentulous alveolar ridges and to investigate the potential of a data-driven approach for bone quality classification. MATERIALS AND METHODS: Twenty-six expert clinicians were asked to classify 110 CBCT cross-sections according to L&Z classification (T0). The same evaluation was repeated after one month with the images put in a different order (T1). Intra- and inter-examiner agreement analyses were performed using Cohen's kappa coefficient (CK) and Fleiss' kappa coefficient (FK), respectively. Additionally, radiomic features extraction was performed from 3D edentulous ridge blocks derived from the same 110 CBCTs, and unsupervised clustering using 3 different clustering methods was used to identify patterns in the obtained data. RESULTS: Intra-examiner agreement between T0 and T1 was weak (CK 0.515). Inter-examiner agreement at both time points was minimal (FK at T0: 0.273; FK at T1: 0.243). The three different unsupervised clustering methods based on radiomic features aggregated the 110 CBCTs in three groups in the same way. CONCLUSIONS: The results showed low agreement among clinicians when using L&Z classification, indicating that the system may not be as reliable as previously thought. The present study suggests the possible application of a reproducible data-driven approach based on radiomics for the classification of edentulous alveolar ridges, with potential implications for improving clinical outcomes. Further research is needed to determine the clinical significance of these findings and to develop more standardized and accurate methods for assessing bone quality of edentulous alveolar ridges.

Establishment-level safety analytics: a scoping review.

The use of data analytics has seen widespread application in fields such as medicine and supply chain management, but their application in occupational safety has only recently become more common. The purpose of this scoping review was to summarize studies that employed analytics within establishments to reveal insights about work-related injuries or fatalities. Over 300 articles were reviewed to survey the objectives, scope and methods used in this emerging field. We conclude that the promise of analytics for providing actionable insights to address occupational safety concerns is still in its infancy. Our review shows that most articles were focused on method development and validation, including studies that tested novel methods or compared the utility of multiple methods. Many of the studies cited various challenges in overcoming barriers caused by inadequate or inefficient technical infrastructures and unsupportive data cultures that threaten the accuracy and quality of insights revealed by the analytics.

Mining Bodily Cues to Deception.

A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.

Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network.

Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages.

Exploration of the molecular mechanism guiding Xinfeng capsule regulatory mechanism for rheumatoid arthritis inflammation.

OBJECTIVES: Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joint synovium. The traditional Chinese medicine Xinfeng capsule (XFC) has a remarkable alleviating effect on inflammatory symptoms, such as joint pain and swelling, in patients with RA. However, the underlying mechanism of action remains to be elucidated. This study intended to conduct network pharmacology, animal experiments, data mining, and molecular docking to explore the molecular mechanism through which XFC can improve the inflammatory symptoms of RA. METHODS: The Apriori association rules and a random walk model were employed to evaluate the effect of XFC on the clinical inflammatory indexes of RA. The active ingredients and the potential target genes of XFC were obtained from public databases. Based on the search tool for recurring instances of neighboring genes (STRING) database, the Database for Annotation, Visualization and Integrated Discovery (DAVID) database, Cytoscape software, and molecular docking method, the molecular mechanism by which XFC acts on RA was also analyzed. Finally, an adjuvant arthritis rat model was established to verify the effects of XFC on inflammation-related signaling pathways and inflammatory factors. RESULTS: XFC significantly reduced the level of C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and the erythrocyte sedimentation rate (ESR). The docking space structures of the active ingredients in XFC, namely triptolide and quercetin, and the key targets were stable. Inflammation-related biological processes were identified as the key factors involved in the development of RA, and the regulation of the toll-like receptor (TLR) signaling pathway may be the key link for XFC toward improving the inflammatory state of RA. The expression levels of toll-like receptor 4 (TLR4), myeloid differentiation primary response protein MyD88 (MyD88), interleukin-1 receptor-associated kinase 1 (IRAK1), TNF receptor-associated factor 6 (TRAF6), TGF-beta-activated kinase 1 (TAK1), phospho-Inhibitor of NF-κB kinaseß (p-IKKß), phospho-Nuclear factor-k-gene binding (p-NF-κB), and interleukin-1ß (IL-1ß) can all be decreased by XFC. XFC improves joint inflammation symptoms by lowering pro-inflammatory factors tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interferon-γ (INF-γ) levels. CONCLUSIONS: XFC could effectively improve the clinical inflammatory indexes of RA. The active ingredients of XFC improved the inflammatory state of RA by regulating the TLR-signaling pathway.

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

Post-marketing safety concerns with palbociclib: a disproportionality analysis of the FDA adverse event reporting system.

OBJECTIVES: To explore the association between palbociclib and related adverse events (AEs) in the real world through U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: The signal strength of palbociclib-related AEs was done by disproportionality analysis. Clinical priority of palbociclib-related AEs was scored and ranked by assessing five different features. Outcome analysis, time to onset analysis, dose-report /AEs number analysis, and stratification analysis were all performed. RESULTS: There were 61,821 'primary suspected (PS)' reports of palbociclib and 195,616 AEs associated with palbociclib. The four algorithms simultaneously detected 18 positive signals at the SOC level, and 65 positive signals at the PT level. Bone marrow failure, neuropathy, peripheral, pleural effusion, myelosuppression, pulmonary edema, and pulmonary thrombosis were also found to have positive signals. Gender (female vs male, χ2 = 5.287, p = 0.022) and age showed significant differences in serious and non-serious reports. Palbociclib-related AEs had a median onset time of 79 days (interquartile range [IQR] 20-264 days). CONCLUSIONS: The study identified potential Palbociclib-related AEs and offered warnings for special AEs, providing further data for palbociclib safety studies in breast cancer patients. Nonetheless, prospective clinical trials are needed to validate these results and explain their relationship.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques.

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays. The proposed method is tested against other pneumothorax segmentation techniques using a dataset of 'chest X-rays' with 'pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

Lactate dehydrogenase to albumin ratio is associated with in-hospital mortality in patients with acute heart failure: Data from the MIMIC-III database.

The effect of the lactate dehydrogenase to albumin ratio (LAR) on the survival of patients with acute heart failure (AHF) is unclear. We aimed to analyze the impact of LAR on survival in patients with AHF. We retrieved eligible patients for our study from the Monitoring in Intensive Care Database III. For each patient in our study, we gathered clinical data and demographic information. We conducted multivariate logistic regression modeling and smooth curve fitting to assess whether the LAR score could be used as an independent indicator for predicting the prognosis of AHF patients. A total of 2,177 patients were extracted from the database. Survivors had an average age of 69.88, whereas nonsurvivors had an average age of 71.95. The survivor group had a mean LAR ratio of 13.44, and the nonsurvivor group had a value of 17.38. LAR and in-hospital mortality had a nearly linear correlation, according to smooth curve fitting (P < 0.001). According to multivariate logistic regression, the LAR may be an independent risk factor in predicting the prognosis of patients with AHF (odd ratio = 1.09; P < 0.001). The LAR ratio is an independent risk factor associated with increased in-hospital mortality rates in patients with AHF.

A real­world pharmacovigilance study of FDA adverse event reporting system events for daratumumab.

BACKGROUND: Daratumumab, a first-in-class humanized IgG1κ monoclonal antibody that targets the CD38 epitope, has been approved for treatment of multiple myeloma by FDA. The current study was to evaluate daratumumab-related adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). RESEARCH DESIGN AND METHODS: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were employed to quantify the signals of daratumumab-associated AEs. RESULTS: Out of 10,378,816 reports collected from the FAERS database, 8727 reports of daratumumab-associated AEs were identified. A total of 183 significant disproportionality preferred terms (PTs) were retained. Unexpected significant AEs such as meningitis aseptic, leukoencephalopathy, tumor lysis syndrome, disseminated intravascular coagulation, hyperviscosity syndrome, sudden hearing loss, ileus and diverticular perforation were also detected. The median onset time of daratumumab-related AEs was 11 days (interquartile range [IQR] 0-76 days), and most of the cases occurred within 30 days. CONCLUSION: Our study found potential new and unexpected AEs signals for daratumumab, suggesting prospective clinical studies are needed to confirm these results and illustrate their relationship.

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime.

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

Early Bird or Night Owl: Insights into Dutch Students' Study Patterns using the Medical Faculty's E-learning Registrations.

Phenomenon: Educational activities for students are typically arranged without consideration of their preferences or peak performance hours. Students might prefer to study at different times based on their chronotype, aiming to optimize their performance. While face-to-face activities during the academic schedule do not offer flexibility and cannot reflect students' natural learning rhythm, asynchronous e-learning facilitates studying at one's preferred time. Given their ubiquitous accessibility, students can use e-learning resources according to their individual needs and preferences. E-learning usage data hence serves as a valuable proxy for certain study behaviors, presenting research opportunities to explore students' study patterns. This retrospective study aims to investigate when and for how long undergraduate students used medical e-learning modules. Approach: We performed a cross-sectional analysis of e-learning usage at one medical faculty in the Netherlands. We used data from 562 undergraduate multimedia e-learning modules for pre-clinical students, covering various medical topics over a span of two academic years (2018/19 and 2019/20). We employed educational data mining approaches to process the data and subsequently identified patterns in access times and durations. Findings: We obtained data from 70,805 e-learning sessions with 116,569 module visits and 1,495,342 page views. On average, students used e-learning for 16.8 min daily and stopped using a module after 10.2 min, but access patterns varied widely. E-learning was used seven days a week with an hourly access pattern during business hours on weekdays. Across all other times, there was a smooth increase or decrease in e-learning usage. During the week, more students started e-learning sessions in the morning (34.5% vs. 19.1%) while fewer students started in the afternoon (42.6% vs. 50.8%) and the evening (19.4% vs. 27.0%). We identified 'early bird' and 'night owl' user groups that show distinct study patterns. Insights: This retrospective educational data mining study reveals new insights into the study patterns of a complete student cohort during and outside lecture hours. These findings underline the value of 24/7 accessible study material. In addition, our findings may serve as a guide for researchers and educationalists seeking to develop more individualized educational programs.

A Data Mining Study for Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Carpal Tunnel Syndrome.

Background: Carpal tunnel syndrome (CTS) is the most prevalent upper limb compressive neuropathy. A considerable number of clinical trials and meta-analyses have provided evidence supporting the effectiveness of acupuncture in treating CTS. Nevertheless, the ideal choice of acupoints remains ambiguous. Objective: A data mining analysis was conducted with the objective of determining the most effective acupoint combinations and selection for CTS. Methods: A search was conducted across seven Chinese and English electronic bibliographic databases spanning from their inception to March 2023. Selected were clinical trials that evaluated the efficacy of acupuncture therapy for CTS, with or without randomised controlled methods. Data extraction mainly included acupoint prescriptions. Information such as first author, study design and study setting were also extracted. The principal outcomes comprised the clinical manifestations linked to CTS. Statistical descriptions were generated using Excel 2019. The analysis of association rules was conducted using SPSS Modeler 18.0. Using SPSS Statistics 26.0, exploratory factor analysis and cluster analysis were conducted. Results: 142 trials (including 86 RCTs and 56 non RCTs) were identified, and 193 groups of effective prescriptions involving 68 acupoints were extracted. The most frequently used acupoints were Da-ling (PC7), Nei-guan (PC6), He-gu (LI4), Wai-guan (TE5), and Yang-xi (LI5). The most frequently used meridians were the pericardial meridian and the large intestine meridian. The majority of special acupoints used were Five-shu points and Yuan-source points, with acupoints on the upper limbs being the most frequently used. The core acupoint groups were analyzed and 11 groups of association rules, 8 factors, and 5 effective cluster groups were obtained. Conclusion: The evidence-based acupoint selection and combinations of acupuncture therapy for carpal tunnel syndrome were provided by the findings of this study.

Radiomics signature of osteoarthritis: Current status and perspective.

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis. The translational potential of this article: Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way.

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.

Dose-response relationship of levodopa with dyskinesia in Parkinson's disease: A systematic review and meta-analysis.

Despite existing evidence linking dyskinesia to levodopa, the primary treatment for Parkinson's, the dose-response relationship and risk factors remain uncertain. In this study, the risk for dyskinesia in patients with Parkinson's disease receiving levodopa was evaluated via meta-analysis and meta-regression approaches to examine dyskinesia risk factors more reliably and improve treatment strategies and patient care. The PubMed and Embase databases were searched to identify randomized controlled trials comparing levodopa with other anti-Parkinson's drugs published in English before June 31, 2023. The primary outcome was dyskinesia, and a risk of bias assessment was performed. In total, 24 studies met the inclusion criteria; 21 had a low risk of bias, and 3 had a high risk of bias. These studies included 4698 patients with Hoehn and Yahr Grade I-III Parkinson's disease. Our meta-analysis showed that the risk of dyskinesia was higher for levodopa than for other anti-Parkinson's drugs (odds ratio: 2.52 [95% confidence interval: 1.84-3.46]). Dyskinesia was not related to age (slope coefficient: 0.185 [0.095]; P = 0.061), disease duration (slope coefficient: 0.011 [0.018]; P = 0.566), or treatment duration (slope coefficient: 0.008 [0.007]; P = 0.216). The mean levodopa equivalent dose (slope coefficient: 0.004 [0.001]; P = 0.001) in the experimental group and the differences in drug doses between the experimental and control groups were correlated with the risk of dyskinesia. Results of randomized controlled trials supported an association between the levodopa dose and dyskinesia in patients with Parkinson's disease. Compared with levodopa users, users of other anti-Parkinson's drugs had a lower incidence of dyskinesia. Age, disease duration, and treatment duration were not correlated with dyskinesia. These findings suggest that anti-Parkinson's drugs other than levodopa, particularly in cases of early-stage Parkinson's disease, should be considered to reduce the risk of dyskinesia.