Left Ventricular Wall Stress and Incident Heart Failure in Elderly Community-Dwelling Individuals.

Background: Greater left ventricular (LV) wall stress is associated with adverse outcomes among patients with prevalent heart failure (HF). Less is known about the association between LV wall stress and incident HF. Objectives: The purpose of the study was to identify clinical factors associated with wall stress and test the association between wall stress and incident HF. Methods: We studied 4,601 ARIC (Atherosclerosis Risk In Communities) study participants without prevalent HF who underwent echocardiography between 2011 and 2013. LV end systolic and diastolic wall stress (LVESWS, LVEDWS) were calculated from chamber and wall thickness, systemic blood pressure, and transmitral Doppler E/e' as a surrogate for LV end diastolic pressure. Incident HF was ascertained by International Classification of Diseases (ICD)-9/10 claims for hospitalized HF through December 31, 2016. We used Cox regression to test the association between wall stress and incident HF, adjusted for demographics, traditional cardiovascular risk factors, prevalent coronary artery disease and atrial fibrillation, creatinine, N-terminal pro-B-type natriuretic peptide, troponin, triglycerides, C-reactive protein, LV ejection fraction, and LV mass. Results: The cohort had a median age of 75 years and 58% women, with 18% identifying as Black. Median LVESWS and LVEDWS were 48.8 (25th-75th percentile: 39.3-60.1) and 18.9 (25th-75th percentile: 15.8-22.5) kdynes/cm2, respectively. LVESWS and LVEDWS were modestly related (rho = 0.30, P < 0.001). Over 4.6 years of median follow-up (156 HF events), each 1 kdyne/cm2 greater LVEDWS was significantly associated with higher risk of incident HF (HR: 1.03; 95% CI: 1.01-1.06), while LVESWS was not (HR: 1.00; 95% CI: 0.99-1.01). Conclusions: Among community-dwelling elderly individuals, greater LVEDWS is associated with a higher risk for incident HF.

Candidaemia and Central Line-Associated Candidaemia in a Network of Indian ICUs: Impact of COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Candidaemia is a potentially life-threatening emergency in the intensive care units (ICUs). Surveillance using common protocols in a large network of hospitals would give meaningful estimates of the burden of candidaemia and central line associated candidaemia in low resource settings. We undertook this study to understand the burden and epidemiology of candidaemia in multiple ICUs of India, leveraging the previously established healthcare-associated infections (HAI) surveillance network. Our aim was also to assess the impact that the pandemic of COVID-19 had on the rates and associated mortality of candidaemia. METHODS: This study included adult patients from 67 Indian ICUs in the AIIMS-HAI surveillance network that conducted BSI surveillance in COVID-19 and non-COVID-19 ICUs during and before the COVID-19 pandemic periods. Hospitals identified healthcare-associated candidaemia and central line associated candidaemia and reported clinical and microbiological data to the network as per established and previously published protocols. RESULTS: A total of 401,601 patient days and 126,051 central line days were reported during the study period. A total of 377 events of candidaemia were recorded. The overall rate of candidaemia in our network was 0.93/1000 patient days. The rate of candidaemia in COVID-19 ICUs (2.52/1000 patient days) was significantly higher than in non-COVID-19 ICUs (1.05/patient days) during the pandemic period. The rate of central line associated candidaemia in COVID-19 ICUs (4.53/1000 central line days) was also significantly higher than in non-COVID-19 ICUs (1.73/1000 central line days) during the pandemic period. Mortality in COVID-19 ICUs associated with candidaemia (61%) was higher than that in non-COVID-19 ICUs (41%). A total of 435 Candida spp. were isolated. C. tropicalis (26.7%) was the most common species. C. auris accounted for 17.5% of all isolates and had a high mortality. CONCLUSION: Patients in ICUs with COVID-19 infections have a much higher risk of candidaemia, CLAC and its associated mortality. Network level data helps in understanding the true burden of candidaemia and will help in framing infection control policies for the country.

Simultaneous estimation of paclitaxel and bortezomib via LC-MS/MS: pharmaceutical and pharmacokinetic applications.

Aim & Objective: This study evaluates the potential of combining paclitaxel (PTX) and bortezomib (BTZ) for breast cancer therapy. Materials & Methods: The nanoformulation was optimized via Box-Behnken Design (BBD), with method validation adhering to US-FDA guidelines. Results: Multiple reaction monitoring transitions for PTX, BTZ and internal standard were m/z 855.80→286.60, 366.80→226.00 and 179.80→110.00, respectively. Elution done on C18 Luna column with 0.1% FA in MeOH:10 mM ammonium acetate. The size of nanoformulation was 133.9 ± 1.97 nm, PDI 0.19 ± 0.01 and zeta potential -19.20 ± 1.36 mV. Pharmacokinetics showed higher Cmax for PTX-BTZ-NE (313.75 ± 10.71 ng/ml PTX, 11.92 ± 0.53 ng/ml BTZ) versus free PTX-BTZ (104 ± 13.06 ng/ml PTX, 1.9 ± 0.08 ng/ml BTZ). Conclusion: Future findings will contribute to the treatment of breast cancer using PTX and BTZ.

[Box: see text].

The clinical relevance of the new criteria for cirrhotic cardiomyopathy and future directions.

Cardiac dysfunction in patients with liver disease has been recognized since the 1950s. Initially attributed to shared risk factors, it is now evident that cardiac dysfunction in patients with cirrhosis can occur in the absence of known cardiac, i.e., coronary artery and valvular heart disease, and across all etiologies for cirrhosis. In 1996 this myocardial dysfunction was termed cirrhotic cardiomyopathy (CCM). The pathophysiologic mechanisms underlying CCM include impaired beta-adrenergic membrane function and circulating proinflammatory and cardiotoxic substances. In 2005, the first diagnostic criteria for CCM were introduced enabling greater sensitivity and accuracy of diagnosis. Since 2005, advancements in echocardiographic methods and better understanding of the pathophysiology of cardiac dysfunction in patients with cirrhosis necessitated revision of CCM criteria. Changes in CCM criteria included removal of blunted contractile or heart rate response on stress testing and addition of global longitudinal systolic strain. Refinement of criteria for diastolic dysfunction were also incorporated into the new diagnostic approach. Since 2020, the prevalence of the disorder and clinical considerations for pre-transplant, peritransplant, and post-transplant patients with cirrhosis have been further evaluated and CCM was found to adversely impact clinical outcomes during all three phases of care. Future research considerations should address the timing of universal echocardiographic screening for patients with cirrhosis, utility of biomarkers in aiding CCM diagnosis, impact of CCM on right heart function, and role of anti-remodeling agents post-liver transplant (post-LT).

Restoring initial steps by intermittent theta burst stimulation in complete spinal cord injury patient: a case report.

INTRODUCTION: Spinal cord injury (SCI) causes damage to neurons and results in motor and sensory dysfunction. Intermittent theta burst stimulation (iTBS) has been used to induce neuronal and synaptic plasticity by applying a magnetic field in the brain. The plasticity induced in the cortex has an imperative role in the recovery of motor and sensory functioning. However, the effect of iTBS in complete SCI patients is still elusive. CASE PRESENTATION: We report here the case of a 27-year-old female who sustained an L1 complete spinal cord injury (SCI) with an ASIA score of A. The patient lost all the sensory and motor functions below the level of injury. Intermittent theta burst stimulation (iTBS) was administered at 80% of the resting motor threshold over the M1 motor cortex, along with intensive rehabilitation training to promote sensorimotor function. DISCUSSION: There was a partial recovery in functional, electrophysiological, and neurological parameters. The case report also demonstrates the safety and efficacy of iTBS in complete SCI patients. No adverse event has been observed in the patient during intervention sessions.

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.

OBJECTIVE: Traditional knowledge-based and machine learning diagnostic decision support systems have benefited from integrating the medical domain knowledge encoded in the Unified Medical Language System (UMLS). The emergence of Large Language Models (LLMs) to supplant traditional systems poses questions of the quality and extent of the medical knowledge in the models' internal knowledge representations and the need for external knowledge sources. The objective of this study is three-fold: to probe the diagnosis-related medical knowledge of popular LLMs, to examine the benefit of providing the UMLS knowledge to LLMs (grounding the diagnosis predictions), and to evaluate the correlations between human judgments and the UMLS-based metrics for generations by LLMs. METHODS: We evaluated diagnoses generated by LLMs from consumer health questions and daily care notes in the electronic health records using the ConsumerQA and Problem Summarization datasets. Probing LLMs for the UMLS knowledge was performed by prompting the LLM to complete the diagnosis-related UMLS knowledge paths. Grounding the predictions was examined in an approach that integrated the UMLS graph paths and clinical notes in prompting the LLMs. The results were compared to prompting without the UMLS paths. The final experiments examined the alignment of different evaluation metrics, UMLS-based and non-UMLS, with human expert evaluation. RESULTS: In probing the UMLS knowledge, GPT-3.5 significantly outperformed Llama2 and a simple baseline yielding an F1 score of 10.9% in completing one-hop UMLS paths for a given concept. Grounding diagnosis predictions with the UMLS paths improved the results for both models on both tasks, with the highest improvement (4%) in SapBERT score. There was a weak correlation between the widely used evaluation metrics (ROUGE and SapBERT) and human judgments. CONCLUSION: We found that while popular LLMs contain some medical knowledge in their internal representations, augmentation with the UMLS knowledge provides performance gains around diagnosis generation. The UMLS needs to be tailored for the task to improve the LLMs predictions. Finding evaluation metrics that are aligned with human judgments better than the traditional ROUGE and BERT-based scores remains an open research question.

Dissecting the Interplay Mechanism among Process Parameters toward the Biofabrication of High-Quality Shapes in Embedded Bioprinting.

Embedded bioprinting overcomes the barriers associated with the conventional extrusion-based bioprinting process as it enables the direct deposition of bioinks in 3D inside a support bath by providing in situ self-support for deposited bioinks during bioprinting to prevent their collapse and deformation. Embedded bioprinting improves the shape quality of bioprinted constructs made up of soft materials and low-viscosity bioinks, leading to a promising strategy for better anatomical mimicry of tissues or organs. Herein, the interplay mechanism among the printing process parameters toward improved shape quality is critically reviewed. The impact of material properties of the support bath and bioink, printing conditions, cross-linking mechanisms, and post-printing treatment methods, on the printing fidelity, stability, and resolution of the structures is meticulously dissected and thoroughly discussed. Further, the potential scope and applications of this technology in the fields of bioprinting and regenerative medicine are presented. Finally, outstanding challenges and opportunities of embedded bioprinting as well as its promise for fabricating functional solid organs in the future are discussed.

Ensuring safety and efficacy in combination products: regulatory challenges and best practices.

Combination products, amalgamating drugs, biologics, and medical devices, have revolutionized the healthcare landscape with their potential for innovative therapies. However, the intersection of diverse components within these products presents a complex regulatory environment, demanding rigorous attention to safety and efficacy. This article delves into the intricate landscape of regulatory considerations, safety, and efficacy assessments pertaining to combination products-a category at the intersection of drugs, devices, and biologics. The regulatory framework, primarily governed by the U.S. Food and Drug Administration (FDA), necessitates a nuanced classification determining the regulatory pathway. Collaboration between diverse regulatory centers, such as the Center for Drug Evaluation and Research (CDER) and the Center for Devices and Radiological Health (CDRH), underscores the integrated approach required for these innovative healthcare solutions. Safety considerations unravel the potential risks and adverse events associated with combining diverse components, emphasizing the need for robust risk assessment and mitigation strategies. The evaluation of efficacy involves sophisticated methodologies, clinical trials, and post-market surveillance, with recent advancements incorporating digital technologies. This comprehensive exploration aims to contribute to the evolving understanding and best practices in the regulatory and scientific realms, fostering collaboration and innovation in the development and assessment of combination products.

Adaptive Neural Message Passing for Inductive Learning on Hypergraphs.

Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations. This drawback is mitigated by hypergraphs, in which an edge can connect an arbitrary number of nodes. Most hypergraph learning approaches convert the hypergraph structure to that of a graph and then deploy existing geometric deep learning methods. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperMSG, a novel hypergraph learning framework that uses a modular two-level neural message passing strategy to accurately and efficiently propagate information within each hyperedge and across the hyperedges. HyperMSG adapts to the data and task by learning an attention weight associated with each node's degree centrality. Such a mechanism quantifies both local and global importance of a node, capturing the structural properties of a hypergraph. HyperMSG is inductive, allowing inference on previously unseen nodes. Further, it is robust and outperforms state-of-the-art hypergraph learning methods on a wide range of tasks and datasets. Finally, we demonstrate the effectiveness of HyperMSG in learning multimodal relations through detailed experimentation on a challenging multimedia dataset.

An UltraMNIST classification benchmark to train CNNs for very large images.

Current convolutional neural networks (CNNs) are not designed for large scientific images with rich multi-scale features, such as in satellite and microscopy domain. A new phase of development of CNNs especially designed for large images is awaited. However, application-independent high-quality and challenging datasets needed for such development are still missing. We present the 'UltraMNIST dataset' and associated benchmarks for this new research problem of 'training CNNs for large images'. The dataset is simple, representative of wide-ranging challenges in scientific data, and easily customizable for different levels of complexity, smallest and largest features, and sizes of images. Two variants of the problem are discussed: standard version that facilitates the development of novel CNN methods for effective use of the best available GPU resources and the budget-aware version to promote the development of methods that work under constrained GPU memory. Several baselines are presented and the effect of reduced resolution is studied. The presented benchmark dataset and baselines will hopefully trigger the development of new CNN methods for large scientific images.

Cortical intermittent theta burst stimulation on gait pathomechanics and urinary tract dysfunction in incomplete spinal cord injury patients: Protocol for a randomized controlled trial.

Gait impairment and neurogenic bladder are co-existing common findings in incomplete spinal cord injury (iSCI). Repetitive transcranial magnetic stimulation (rTMS), evident to be a promising strategy adjunct to physical rehabilitation to regain normal ambulation in SCI. However, there is a need to evaluate the role of Intermittent theta burst stimulation (iTBS), a type of patterned rTMS in restoring gait and neurogenic bladder in SCI patients. The aim of the present study is to quantify the effect of iTBS on spatiotemporal, kinetic, and kinematic parameters of gait and neurogenic bladder dyssynergia in iSCI. After maturing all exclusion and inclusion criteria, thirty iSCI patients will be randomly divided into three groups: Group-A (sham), Group-B (active rTMS) and Group-C (active iTBS). Each group will receive stimulation adjunct to physical rehabilitation for 2 weeks. All patients will undergo gait analysis, as well assessment of bladder, electrophysiological, neurological, functional, and psychosocial parameters. All parameters will be assessed at baseline and 6th week (1st follow-up). Parameters except urodynamics and gait analysis will also be assessed after the end of the 2 weeks of the intervention (post-intervention) and at 12th week (2nd follow-up). Appropriate statistical analysis will be done using various parametric and non-parametric tests based on results.

Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations.

BACKGROUND: Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations. METHODS: Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings. RESULTS: Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD. CONCLUSIONS: In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate. RESEARCH QUESTION: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation. METHOD: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition. RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers. SIGNIFICANCE: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.

Associations of neighborhood sociodemographic environment with mortality and circulating metabolites among low-income black and white adults living in the southeastern United States.

BACKGROUND: Residing in a disadvantaged neighborhood has been linked to increased mortality. However, the impact of residential segregation and social vulnerability on cause-specific mortality is understudied. Additionally, the circulating metabolic correlates of neighborhood sociodemographic environment remain unexplored. Therefore, we examined multiple neighborhood sociodemographic metrics, i.e., neighborhood deprivation index (NDI), residential segregation index (RSI), and social vulnerability index (SVI), with all-cause and cardiovascular disease (CVD) and cancer-specific mortality and circulating metabolites in the Southern Community Cohort Study (SCCS). METHODS: The SCCS is a prospective cohort of primarily low-income adults aged 40-79, enrolled from the southeastern United States during 2002-2009. This analysis included self-reported Black/African American or non-Hispanic White participants and excluded those who died or were lost to follow-up ≤ 1 year. Untargeted metabolite profiling was performed using baseline plasma samples in a subset of SCCS participants. RESULTS: Among 79,631 participants, 23,356 deaths (7214 from CVD and 5394 from cancer) were documented over a median 15-year follow-up. Higher NDI, RSI, and SVI were associated with increased all-cause, CVD, and cancer mortality, independent of standard clinical and sociodemographic risk factors and consistent between racial groups (standardized HRs among all participants were 1.07 to 1.20 in age/sex/race-adjusted model and 1.04 to 1.08 after comprehensive adjustment; all P < 0.05/3 except for cancer mortality after comprehensive adjustment). The standard risk factors explained < 40% of the variations in NDI/RSI/SVI and mediated < 70% of their associations with mortality. Among 1110 circulating metabolites measured in 1688 participants, 134 and 27 metabolites were associated with NDI and RSI (all FDR < 0.05) and mediated 61.7% and 21.2% of the NDI/RSI-mortality association, respectively. Adding those metabolites to standard risk factors increased the mediation proportion from 38.4 to 87.9% and 25.8 to 42.6% for the NDI/RSI-mortality association, respectively. CONCLUSIONS: Among low-income Black/African American adults and non-Hispanic White adults living in the southeastern United States, a disadvantaged neighborhood sociodemographic environment was associated with increased all-cause and CVD and cancer-specific mortality beyond standard risk factors. Circulating metabolites may unveil biological pathways underlying the health effect of neighborhood sociodemographic environment. More public health efforts should be devoted to reducing neighborhood environment-related health disparities, especially for low-income individuals.

Clay-polymer nanocomposites for effective water treatment: opportunities, challenges, and future prospects.

The metal intoxication and its associated adverse effects to humans have led to the research for development of water treatment technologies from pollution hazards. Therefore, development of cheaper water remediation technologies is more urgent than ever. Clays and clay minerals are naturally occurring, inexpensive, non-toxic materials possessing interesting chemical and physical properties. As a result of interesting surface properties, these have been developed as efficient absorbent in water remediation. Recently, clay-polymer nanocomposites have provided a cost-effective technological platform for removing contaminants from water. Covering research advancements from past 25 years, this review highlights the developments in clay-polymer nanocomposites and their advanced technical applications are evaluated with respect to the background and issues in remediation of toxic metals and organic compounds from water. The extensive analysis of literature survey of more than two decades suggests that future work need to highlight on advancement of green and cost-effective technologies. The development of understanding of the interaction and exchange between toxin and clay-polymer composites would provide new assembly methods of nanocomposites with functional molecules or nanomaterials need to be extended to increase the detection and extraction limit to parts per trillion.

A Versatile Photocrosslinkable Silicone Composite for 3D Printing Applications.

Embedded printing has emerged as a valuable tool for fabricating complex structures and microfluidic devices. Currently, an ample of amount of research is going on to develop new materials to advance its capabilities and increase its potential applications. Here, we demonstrate a novel, transparent, printable, photocrosslinkable, and tuneable silicone composite that can be utilized as a support bath or an extrudable ink for embedded printing. Its properties can be tuned to achieve ideal rheological properties, such as optimal self-recovery and yield stress, for use in 3D printing. When used as a support bath, it facilitated the generation microfluidic devices with circular channels of diameter up to 30 µm. To demonstrate its utility, flow focusing microfluidic devices were fabricated for generation of Janus microrods, which can be easily modified for multitude of applications. When used as an extrudable ink, 3D printing of complex-shaped constructs were achieved with integrated electronics, which greatly extends its potential applications towards soft robotics. Further, its biocompatibility was tested with multiple cell types to validate its applicability for tissue engineering. Altogether, this material offers a myriad of potential applications (i.e., soft robotics, microfluidics, bioprinting) by providing a facile approach to develop complicated 3D structures and interconnected channels.

The chronic use of serotonin norepinephrine reuptake inhibitors facilitates dyskinesia priming in early Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) patients are frequently exposed to antidepressant medications (ADMs). Norepinephrine (NE) and serotonin (5HT) systems have a role in levodopa-induced dyskinesias (LID) pathophysiology. METHODS: We performed a longitudinal analysis on the PPMI cohort including drug-naïve PD patients, who are progressively exposed to dopamine replacement therapies (DRTs) to test the effect of ADM exposure on LID development by the 4th year of follow-up. RESULTS: LID prevalence (according to MDS UPDRS score 4.1 ≥ 1) was 16% (42/251); these patients were more likely women (p = 0.01), had higher motor (p < 0.001) and depression scores (p = 0.01) and lower putaminal DAT binding ratio (p = 0.01). LID were associated with the exposure time to L-DOPA (2.2 ± 1.07 vs 2.6 ± 0.9, p = 0.02) and to the exposure to ADMs, in particular to SNRI (4.8% vs 21.4%, p < 0.001). The latter persisted after correcting for significant covariates (e.g., disease duration, cognitive status, motor impairment, depression, dopaminergic denervation). A similar difference in LID prevalence in PD patients exposed vs non-exposed to SNRI was observed on matched data by the real-world TriNetX repository (22% vs 13%, p < 0.001). DISCUSSION: This study supports the presence of an effect of SNRI on LID priming in patients with early PD. Independent prospective cohort studies are warranted to further verify such association.