Optimal Quality Research Articles

Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiography.

The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.

Comparative analysis of the Macroinvertebrate fauna of ecologically stable and traditional garden ponds

Freshwater habitats are considered to be highly vulnerable globally. For some species, mainly invertebrates, artificial ponds created in urban gardens give suitable habitat or function as stepping stones. However, traditional garden ponds use expensive pieces of equipment with high energy demand and might release harmful chemicals into their environment. In contrast, the ecologically stable garden ponds, which are distinguished from the traditional garden ponds by their design and the complex ecosystem that maintains them, offer a solution for the problems of eutrophication and succession and maintain optimal water quality in an environmentally and cost-effective way. This study aimed to compare the aquatic macroinvertebrate fauna diversity between traditional garden ponds and ecologically stable garden ponds designed and constructed by the first author. Five ponds of both types were examined, by collecting biological specimens from 90 sampling points. Altogether 42 species were identified, and one taxon at the genus level. Our statistical analysis indicates that the ecologically stable ponds host a much richer macroinvertebrate fauna in terms of species abundance compared to traditional garden ponds.

High-Quality Evaluation for Invisible Watermarking Based on Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD)

. In this research, we propose an innovative approach that integrates Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) to enhance the quality and security of digital images. The purpose of this technique is to embed imperceptible watermarks into images, preserving their integrity and authenticity. The integration of DCT allows for an efficient transformation of image data into frequency components, forming the basis for embedding watermarks that are nearly invisible to the human eye. In this context, SVD offers an advantage by separating singular values and corresponding vectors, facilitating a more sophisticated watermarking process. The quality evaluation using metrics such as MSE, PSNR, UQI, and MSSIM demonstrates the effectiveness of this approach. Low average MSE values, ranging from 0.0058 to 0.0064, indicate minimal distortion in the watermarked images. Additionally, high PSNR values, ranging from 67.20 dB to 67.22 dB, affirm the high image quality achieved after watermarking. These results validate that the integration of DCT and SVD provides a high level of security while maintaining optimal visual quality in digital images. This approach is highly relevant and effective in addressing the challenges of image protection in this digital era.

Analysis of Service Quality JKN Mobile Application Toward Participants Satisfaction of the Health Social Security Implementing Agency (BPJS) Tegal Regency

Background: JKN Mobile is an innovation by BPJS Kesehatan to provide service access easier for their participants. JKN Mobile application can be accessed everytime and everywhere while continuing to provide optimal service quality for user satisfaction.Objective: This study aims to analyze the correlation between service quality (tangile, reliability, responsiveness, assurance, and empathy) with satisfaction of JKN Mobile’s users at Tegal Regency.Methods: Type of quantitative research using cross sectional research design. Sampling method using accidental sampling technique. Total sample 100 respondents using Lameshow sample size formula. The research instrument used questionnaire.Results: Based on the fisher exact statistical test on the service quality with satisfaction the result is tangible (p=0,001), reliability (p=0,000), responsiveness (p=0,000), assurance (p=0,000), dan empathy (p=0,000). Conclusion: There was a significant relationship between service quality with satisfaction.

Effect of Cysteine with Essential Oils on Quality Attributes and Functional Properties of 'Blanca de Tudela' Fresh-Cut Artichoke.

The commercialisation of fresh-cut artichokes with optimal quality and appearance and a maximum shelf-life is a great challenge for the artichoke market. The use of different anti-browning agents has been previously studied; however, their effect is still limited. Therefore, the objective of this study is the evaluation of the effect of L-cysteine and, in combination with a mixture of essential oils components (eugenol, thymol and carvacrol) on browning, quality and bioactive compounds of fresh-cut artichokes stored for 9 days at 2 °C. Four different treatments were applied to 'Blanca de Tudela' fresh-cut artichokes: cysteine and cysteine with 75, 150 and 300 µL of the essential oils components (EOs) mixture. After 2, 4 and 9 days of storage, physicochemical parameters (weight loss, colour, respiration rate) and functional (total phenolic content, antioxidant activity) were studied. A descriptive sensorial analysis was also carried out to evaluate sensory attributes. Results showed that the application of cysteine and 150 µL of EOs displayed the lowest browning and highest antioxidant properties, as well as the best quality and sensory parameters. The use of this post-harvest treatment on fresh-cut artichokes would result in a natural and eco-friendly solution to improve artichoke quality and shelf-life.

Assessing the efficacy of saline flush in frequency-domain optical coherence tomography for intracoronary imaging

BackgroundThe increased amount of contrast media in frequency-domain optical coherence tomography (FD-OCT) imaging during percutaneous coronary intervention (PCI) has raised potential concerns regarding impairment of renal function.ObjectivesThis study aimed to evaluate the effectiveness of heparinized saline flush in FD-OCT-guided PCI and identify clinical factors contributing to optimal image quality.MethodsWe retrospectively collected 100 lesions from 90 consecutive patients, and a total of 200 pullbacks were analyzed for the initial and final evaluation in which saline was used as the flushing medium.ResultsThe study population had a mean age of 73, with 52% having chronic kidney disease (CKD). The median amount of contrast used was 28 ml, and no complications were observed associated with saline flush OCT. Imaging quality was then categorized as excellent, good, or unacceptable. Among the total runs, 87% demonstrated clinically acceptable image quality, with 66.5% classified as excellent images and 20.5% classified as good images. Independent predictors of excellent images included lumen area stenosis ≥ 70% (adjusted odds ratio [OR] 2.37, 95% confidence interval [CI] 1.02–5.47, P = 0.044), and the use of intensive flushing (adjusted OR 2.06, 95% CI 1.11–3.86, P = 0.023) defined as a deep engagement of guiding catheter (GC) or a selective insertion of guide extension catheter (GE). Intensive flushing was performed in 60% of the total pullbacks, and it was particularly effective in improving image quality in the left coronary artery (LCA).ConclusionThe use of saline flush during FD-OCT imaging was safe and feasible, which had a benefit in renal protection with adequate imaging quality.

The associations between whole grain, sugar and nutrients intakes in schoolchildren: a cross-sectional study

BackgroundWhole grains have gained extensive attention for their contribution to optimal diet quality in the child population. However, little is known about the association between whole grain and sugar intakes. This study aimed to determine whole grain intake and its associations with sugar and other nutrients intakes in schoolchildren.MethodsA total of 415 healthy Malaysian schoolchildren aged 9–12 years were recruited in this cross-sectional study, through cluster random sampling. Nutrient and sugar intakes were assessed using 3-day 24-hour diet recalls. Whole grain intake was assessed using a validated whole grain food frequency questionnaire.ResultsIn these 415 children (9.4–12.7 years), a total of 24 of them have been excluded due to over- and under-reported their dietary intake. Ultimate sample size was 391 children. Overall, consumption of whole grain, fiber, calcium and B vitamins were lower than the recommended intake. However, children consumed protein sufficiently. Whole grain intake was a significant predictor of calorie (β = 0.1011; p < 0.001), carbohydrate (β = 0.060; p = 0.002), fat (β = 0.107; p = 0.044), riboflavin (β = 3.537; p = 0.008) and sugar (β = 0.138; p = 0.007) intakes, after controlling for sex, age and ethnicity.ConclusionThe findings provide insight to parents, educators and healthcare professionals in encouraging children to choose whole grain food that is low in sugar and fat. The outcome will also encourage food manufacturing companies to produce healthier whole grain products.

EVALUATION OF METHODS FOR TOTAL RNA EXTRACTION FROM THE ENDOSPERM OF COCOS NUCIFERA VAR. MAKAPUNO IN VIETNAM FOR MOLECULAR ANALYSIS

Sap coconut (Cocos nucifera L. var. makapuno) in Vietnam is a mutant coconut variant; coconut water is in the state of lotus glue, and coconut rice is like cream. For high-quality transcriptome, sequencing, quality, purity and concentration of RNA are the key factors. However, coconut endosperm tissue has higher stiffness and fatness than the leaf tissue, which complicates the extraction process. Moreover, RNA is much more difficult to preserve than DNA. In this study, various RNA extraction methods were examined in Vietnamese waxy coconut endosperm tissue samples. Optimum extraction and preservation of the RNA using the simplest possible chemicals was the objective of this study. The modified CTAB method with LiCl and the TRI reagent method were tested and evaluated. The purity, concentration and quality of RNA after storage were improved. The findings indicated that the TRIsure extraction method with the addition of NaCl and β-mercaptoethanol yielded optimum RNA quality. The RNA concentration was 159 ng/µL, with a purity ratio of 1.94 ± 0.04 for A260/A280 and 1.58 ± 0.02 for A260/230. RNA samples remained stable for up to 3 weeks when stored in absolute ethanol at 8°C–10°C, which significantly reduced their degradation during transportation. This study facilitated the use of simple chemicals for high-quality RNA extraction from coconut endosperm and its preservation for applications in high throughput sequencing.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review.

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Optimization of number of wireless temperature sensors using clustering algorithm for deep learning algorithm-based Kimchi quality prediction

The production and storage of kimchi in a factory require monitoring temperature and humidity to maintain optimal quality. Recently, smart packaging devices such as wireless sensors have been used, but their installation and maintenance are costly and require additional resources. In this study, kimchi is packaged in polyethylene packages and placed in plastic baskets. Thirty baskets are stacked in the middle of the storage room. Wireless sensors placed inside the baskets collect temperature data alongside the kimchi for 60 days. The data is sent to a server through communication unit using 4G network. Two clustering algorithms, Competitive Learning Neural Network (CLNN) and k-means, are used to determine the optimal number and location of sensors. Deep learning algorithms, specifically Long Short-Term Memory (LSTM), predict the acidity and ripeness grade of the kimchi. The study found that the optimal number of temperature sensor groups for CLNN was 6 for acidity and 4 for ripeness grade classification. Increasing the number of sensors improved the kimchi acidity prediction, and the accuracy of ripeness classification increased with the number of sensors. The location of the wireless sensors was determined based on the heights of the stacked plastic baskets using the CLNN + LSTM model. This work presents a process for optimizing sensor placement and evaluating kimchi quality during storage and fermentation using clustering and deep learning techniques to maintain optimal quality.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500m * 500m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

The views of parents of children with cancer and pediatric physical therapists on a network for continuity and optimal quality of care for children with cancer: KinderOncoNet

PurposeChildren with cancer require specific therapeutic guidance. Parents prefer physical therapy close to home, while pediatric physical therapists (PPTs) working in the community may lack specific knowledge. The aim of this study is to determine the needs of parents of children with cancer and PPTs to inform the design and development of a care network, named “KinderOncoNet.”MethodsWe explored the perspectives and needs of parents of children with cancer and PPTs in the community, and we investigated the added value that KinderOncoNet could offer. We used an iterative process; data collection consisted of (1) gathering information from parents of children with cancer and PPTs through a survey and (2) co-creation sessions with stakeholders.ResultsIn total, 98 parents and 177 PPTs participated in the survey. Parents (97%) and PPTs (93%) indicated that the care network would bring added value. All but one parent stressed the importance of a local PPT being aware of both the condition and the side and late effects of oncological treatment. Moreover, 40% of PPTs thought they do not have sufficient knowledge to provide high-quality therapy and that they would embrace opportunities for education. Through the co-creation sessions, a prototype of the care network was conceptualized.ConclusionKinderOncoNet can contribute to the continuity and quality of physiotherapy care for children with cancer during and after the oncological treatment. Such a network would allow for sharing knowledge, developing skills, and improving accessibility and communication in the Netherlands.

Survei Penentuan Tingkat Panduan Diagnostik pada CT Scan Kepala dan Abdomen Non-Kontras di Rumah Sakit

Diagnostic reference level (DRL) is still low for monitoring patient doses on hospital CT Scan. The need for radiation monitoring of CT Scan patients is related to radiation protection. CT Scan have a large dose, and as CT Scan develop, it is necessary to monitor the dose of CT Scan, especially those related to DRL. The importance of DRL is related to efforts to optimize radiation protection, which must be monitored regularly so that patients receive the lowest possible dose with optimal image quality. Determination of the local DRL value from quartile 2 (Q2) and national DRL with Quartile 3 (Q3) from the average CTDI vol and total DLP recordings of non-contrast CT Scan of the head and abdomen. From the survey results, local DRL calculations and national DRL contribution or I-DRL are as follows: CT scan of the head based on Q2 and Q3 obtained CTDIvol of 63.45 mGy. Meanwhile, based on Q2, it is 913,68 mGy.cm; in Q3, the DLP is 1096,42 mGy.cm; this value is below the national standard. The CT Scan Abdomen CTDIvol examination based on Q2 obtained 8.08 mGy. and based on Q3 of 10.18 mGy. Both are below the national average of 17 mGy. DLP results based on Q2 got a result of 321.99 mGy.cm, and based on Q3 obtained a result of 419,82 mGy.cm; both values are below the standard. National standards are based on the Head of BAPETEN Decree 1211/K/V/2021. DRL that exceeds the national standard for head CT Scan requires a protocol review to obtain the lowest possible dose, but the image quality remains optimal. It is an effort to optimize radiation protection. Keywords: Diagnostic Reference Level, CT Scan, Head, Abdomen, Non-Contrast

The effect of window design factors on the cooling load in hospitals wards

PurposeThis paper investigates the optimization of window design factors (WDFs) in hospital buildings, particularly in government hospitals within the arid climate of the Qassim region, with the aim of achieving a better cooling load reduction. Continuous monitoring of the hospital ward section is crucial due to patients' needs, requiring optimal indoor air quality and cooling load.Design/methodology/approachThe study identifies the optimal conditions for WDF design to mitigate cooling load, including window-to-wall ratio (WWR), window orientation (WO), room size and U-value (thermal properties), effectively reduce energy consumption in terms of sensible cooling load (MWh/m2) and comply with local codes. Data collection involved a smart weather station, while the Integrated Environmental Solution Virtual Environment (IESVE) software facilitated the simulation process.FindingsKey findings reveal that larger patient rooms were about 40% more energy-efficient than smaller rooms. The northern orientation showed lower energy consumption, and specific WWRs and glazing U-values significantly affected energy loads. In an analysis of U-value changes in energy performance based on the Saudi Building Code (SBC), the presented values did not meet the minimum energy consumption standards. For a valid 40% WWR with a thermal permeability of 2.89, 0.181 MWh/m2 was consumed, while for an invalid 100% WWR with the same permeability but facing the north, 0.156 MWh/m2 was consumed, which is considered an invalid practice. It is vital to follow prescribed standards to ensure energy efficiency and avoid unnecessary costs.Originality/valueFocus lies in emphasizing the significance of adhering to prescribed standards, such as SBC, to guarantee energy efficiency and prevent unwarranted expenses. Additionally, the authors highlight the crucial role of optimizing glazing properties and allocating the WWR appropriately to achieve energy-efficient building design, accounting for diverse orientations and climatic conditions.

Developing inverse motion planning technique for autonomous vehicles using integral nonlinear constraints

The study considers issues of elaborating and validating a technique of autonomous vehicle motion planning based on sequential trajectory and speed optimization. This method includes components such as representing sought-for functions by finite elements (FE), vehicle kinematic model, sequential quadratic programming for nonlinear constrained optimization, and Gaussian N-point quadrature integration. The primary novelty consists of using the inverse approach for obtaining vehicle trajectory and speed. The curvature and speed are represented by integrated polynomials to reduce the number of unknowns. For this, piecewise functions with two and three degrees of freedom (DOF) are implemented through FE nodal parameters. The technique ensures higher differentiability compared to the needed in the geometric and kinematic equations. Thus, the generated reference curves are characterized by simple and unambiguous forms. The latter fits best the control accuracy and efficiency during the motion tracking phase. Another advantage is replacing the nodal linear equality constraints with integral nonlinear ones. This ensures the non-violation of boundary limits within each FE and not only in nodes. The optimization technique implies that the spatial and time variables must be found separately and staged. The trajectory search is accomplished in the restricted allowable zone composed by superposing an area inside the external and internal boundaries, based on keeping safe distances, excluding areas for moving obstacles. Thus, this study compares two models that use two and three nodal DOF on optimization quality, stability, and rapidity in real-time applications. The simulation example shows numerous graph results of geometric and kinematic parameters with smoothed curves up to the highest derivatives. Finally, the conclusions are made on the efficiency and quality of prognosis, outlining the similarities and differences between the two applied models.

A systematic techno-enviro-socio-economic design optimization and power quality of hybrid renewable microgrids

A systematic techno-enviro-socio-economic design optimization and power quality of hybrid renewable microgrids

Knowledge-Informed Sparse Learning for Relevant Feature Selection and Optimal Quality Prediction

Industrial data are usually collinear, which can cause pure data-driven sparse learning to deselect physically relevant variables and select collinear surrogates. In this paper, a novel two-step learning approach to retaining knowledge-informed variables (KIV) is proposed to build inferential models. The first step is an improved knowledge-informed Lasso (KILasso) algorithm by removing penalty on the KIVs to produce a series of candidate subsets that guarantee the retention of the KIVs. The candidate subsets are then used to run the KILasso or ridge regression again to select the best sets of variables and estimate the final model. Two new algorithms are proposed and applied to datasets from an industrial boiler process and the Dow Chemical challenge problem. It is demonstrated that some important physically-relevant variables are deselected by pure data-driven sparse methods, but they are retained using the proposed knowledge-informed methods with superior prediction performance.

P196: The effects of individual music therapy on well-being of nursing home residents with dementia: study protocol of a randomized controlled trial

Background:Dementia is often associated with Neuropsychiatric Symptoms (NPS) such as agitation, hallucinations, anxiety, that can cause distress for the resident with dementia in long-term care settings and can impose emotional burden on the environment. NPS are often treated with psychotropic drugs, which, however, frequently cause side effects. Alternatively, non-pharmacological interventions can improve well-being and maintain an optimal quality of life (QoL) of those living with dementia. Music therapy is a non-pharmacological intervention that can reduce NPS and improve well-being of persons with dementia.Objective:The main aim of this study is to assess the effects of individual music therapy on well-being controlled for providing individual attention in nursing home residents with dementia and NPS.Methods:The research is conducted at eight facilities of one nursing home organization in the Netherlands. The participants in the intervention group receive 30 minutes of individual music therapy (MT) in their own room by a music therapist twice a week for 12 weeks. The participants in the control group receive 30 minutes of individual attention in their own room by a volunteer twice a week for 12 weeks. Assessments will be done at baseline, 6 weeks and 12 weeks. An independent observer, blinded for the intervention or control condition, assesses directly observed well-being (primary outcome) and pain before and after the sessions. Nurses assess other secondary outcomes unblinded, i.e., perceived quality of life and NPS assessed with validated scales. The sleepy duration is will be assessed by a wrist device called MotionWatch. Information about psychotropic drug use is derived from electronic medical chart review.Results:We will present baseline data and preliminary results.Discussion:The outcomes refer to both short-term and long-term effects consistent with therapeutic goals of care for a longer term. We hope to overcome limitations of previous study designs such as non- blinded designs and pragmatic designs in which music facilitators that were not only music therapists but occupational therapists and nurses. This study should lead to more focused recommendations for practice and further research into non-pharmacological interventions in dementia.Trial registration:The trial is registered at the International Clinical Trials Registry Platform (ICTRP) search portal in the Netherlands Trial Registration number NL7708, registration date 04-05-2019.

Machine Learning Models for Predicting Air Pollution

Abstract: The air quality monitoring system collects data of pollutants from different location to maintain optimum air quality. In the current situation, it is the critical concern,. The introduction of hazardous gases into the atmosphere from industrial sources, vehicle emissions, etc. pollutes the air. Today, the amount of air pollution in large cities has surpassed the governmentset air quality index value and reached dangerous levels. It has a significant effect on a human health. The prediction of air pollution can be done by the Machine Learning (ML) algorithms. Machine Learning (ML) combines statistics and computer science to maximize the prediction power. ML is used in order to calculate the Air Quality Index. Various sensors and an Arduino Uno microcontroller are utilized to collect the dataset. Then by using K- Nearest Neighbor (KNN) algorithm, the air quality is predicted.

Novel Combinatorial Therapeutic Options in NEAT1-Depleted Multiple Myeloma Cells By the Integration of in Vitro and in silico Drug Screening Approaches

Multiple myeloma (MM) is an incurable plasma cells malignancy characterized by high genomic instability and clinical heterogeneity. Transcriptional landscape in MM is characterized by a marked de-regulation of long noncoding RNAs (lncRNAs) that influence disease progression and therapy resistance. We have shown that the lncRNA NEAT1 is overexpressed in MM patients and demonstrated that NEAT1 silencing negatively impact MM cells viability, both in vitro and in vivo suggesting that it may result in higher sensitivity to standard MM treatments. Herein, we adopted in vitro high throughput (HTS) and in silico drug screening approaches to explore the NEAT1 role in chemo-resistance scenario and identify novel putative therapeutic combination strategies. For the HTS approach, a library of 320 selective inhibitors covering 123 targets involved in proliferative and survival pathways, was screened in NEAT1-KD AMO-1 MM cells and their relative scramble condition. Silencing of total NEAT1 was achieved through the gymnotic delivery of LNA-gapmeRs. Synergy score, calculated with Bliss analysis, identified 19 compounds having a synergistic effect, in combination with NEAT1 KD, leading to a decreased MM cells proliferation, compared to single treatments. These inhibitors span different cellular targets, grouped into five categories: cell cycle checkpoint, transcriptional regulators, protein tyrosine kinases, receptors channels, epigenetics regulators. Among the top scoring candidates, Aurora kinase A inhibitors were the most promising compounds able to individually exert a synergistic activity with total NEAT1-KD in vitro. Additionally, transcriptomic analysis was performed by bulk RNA sequencing in NEAT1-KD AMO-1 cell line. Libraries with optimal quality and quantity were run on NextSeq 500. Differential gene expression analysis identified 752 down-regulated genes and 957 up-regulated genes. This signature was used to query the Connectivity Map (CMap) database. Compounds mimicking NEAT1 KD transcriptional signature were selected with the highest connectivity score (&amp;gt; 90). Among the top scoring molecules, Aurora kinase A (AURKA) inhibitors were identified as able to mimic NEAT1 KD transcriptional signature. Both approaches identified AURKA inhibitors as promising compounds able to affect NEAT1 functions. Based on this evidence, we started the validation of two different AURKA inhibitors on a panel of MM cell lines. Cell proliferation was assessed after 96 hours of NEAT1 silencing and 72 hours of drug treatment with Trypan-exclusion method and with live-cell imaging and analysis by taking advantage of Incucyte S3 TM technology. The synergy score was calculated with CompuSyn software. WB was used to assess the drugs on-target activity and qRT-PCR was used to validate the efficiency of NEAT1 KD. These data, suggest that NEAT1 KD and treatment with AURKA inhibitors can have similar biological outcome and can affect similar molecular pathways on MM cells. Overall, our results indicate that NEAT1 silencing combined with Aurora kinase A inhibition could represent a promising therapeutic option for MM patients, supporting the need to further characterize the molecular mechanisms underlying the synergistic effect observed in MM cells.