Multiple Arms Research Articles

Pexidartinib and standard neoadjuvant therapy in the adaptively randomized I-SPY2 trial for early breast cancer.

We investigated the small-molecule receptor tyrosine kinase-inhibitor of colony-stimulating factor-1 receptor pexidartinib in the stage II/III breast cancer in the I-SPY2 platform trial. I-SPY2 is an adaptive platform trial that features multiple arms of experimental agents administered on a background of standard neoadjuvant therapy with paclitaxel and adriamycin/cyclophosphamide, followed by definitive surgery. The adaptive randomization engine preferentially assigns patients based upon cumulative performance of each agent in a given breast cancer subtype based on hormone receptor and HER2 receptor status. The study endpoint is pathologic complete response. A total of 9 participants were randomized to receive pexidartinib with neoadjuvant paclitaxel before enrollment was halted due to a serious adverse event of vanishing bile duct syndrome. No participants received a full course of the study drug. Although there remains interest in agents targeting CSF-1, hepatic toxicity appears to be a limiting factor for their use in early breast cancer. NCT01042379 ( www. gov/ct2/show/NCT01042379 ).

Machine Learning-based Dispatching for a Wet Clean Station in Semiconductor Manufacturing

The concept of cyber manufacturing has become a critical element in semiconductor fabrication environments, where automation and systemization are integral, for addressing the growing complexity of processes and facilitating predictive capabilities through data integration. This study deals with the dispatching problem to minimize makespan at a wet clean station in semiconductor fabrication using artificial intelligence-enabled manufacturing control techniques. The wet clean station is comprised of sequential chemical and rinsing baths for cleaning wafer lots and multiple robot arms for lot handling. In the station, wafer lots are sequentially immersed in several baths for cleaning to eliminate residual contaminants and stains that cause defects on wafer surfaces. The station can process various types of products, and the specific order of immersion differs depending on the product type. Unlike typical dispatching problems, the information required for dispatching, such as processing times and sequences inside the station, is not available. The only available data are historical logs that record when each lot enters and leaves the station. However, even when cleaning the same product type, the duration that lots spend in the station may vary based on the combination of product types being cleaned simultaneously and the settings of the station. Thus, using the time records, this study proposes a dispatching method based on machine learning models (multiple linear regression, deep neural network, and convolutional neural network). The proposed algorithms were evaluated and verified by comparing them with CPLEX solving a mixed integer programming and dispatching methods used in a semiconductor fab in Korea. Through this experiment, we observed that the proposed models can provide dispatching solutions that are practical and effective in a rapidly changing production setting. These models have the potential to enhance the capacity of a wet clean station and will contribute to artificial intelligence-based manufacturing system control.

Optimizing Neoadjuvant Treatment Response Prediction for Triple-Negative Breast Cancer Using Clinical Trial Data and Deep Auxiliary Learning.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options and poor prognosis. Developing predictive models for TNBC treatment responses is crucial but challenging due to data scarcity and the reliance on cell line data, which limits clinical translational value. Leveraging omics data from clinical trials, particularly through auxiliary learning, offers a potential solution to enhance predictive accuracy and reduce data requirements. In this study, we propose a new approach utilizing deep auxiliary task reweighting learning methods to automatically reweight auxiliary tasks, thereby optimizing the performance of the primary task of predicting TNBC treatment responses. We benchmark various auxiliary learning methods, including ARML, AdaLoss, GradNorm, and OL AUX, against traditional supervised machine learning algorithms and single-task learning baselines. Our results characterize the performance of auxiliary learning across various contexts, including utilizing parallel treatment arms within a multi-arm clinical trial, leveraging treatment arms from different clinical trials, and integrating multiple arms with the same treatment regimens across separate clinical trials. The last scenario also provides an opportunity for validating prediction models on an independent dataset, demonstrating the superior performance of the auxiliary learning models in predicting pathological complete response (pCR) in TNBC patients treated with standardized combinational chemotherapy with Taxane, Anthracycline, and Cyclophosphamide (TAC). Source code and additional resources can be accessed at https://github.com/moonchangin/DeepAux TxPred TNBC.

A New Statistical Analysis of the Morphology of Spiral Galaxies

Morphology is the starting point for understanding galaxies. Elmegreen et al. classified spiral galaxies into flocculent, multiple-arm, and grand-design galaxies based on the regularity of their spiral arm structure. With the release of a vast number of clear spiral galaxy images from the Sloan Digital Sky Survey, we conducted a morphological classification of 5093 blue spiral galaxies. A statistical analysis of this sample shows that the fractions of flocculent, multiple-arm, and grand-design galaxies are 38% ± 1%, 59% ± 1%, and 3% ± 1%, respectively. Redshift has no obvious influence on this classification. However, as the bulge size becomes larger, the fraction of multiple-arm galaxies increases, while that of flocculent galaxies decreases. In addition, we performed a statistical analysis of 3958 galaxies with a clear spiral arm structure, finding that 82% of these galaxies have two arms in their inner regions. We also found that the majority (74%) of the barred spiral galaxies exhibit the characteristics of two inner spiral arms and multiple outer spiral arms, and there is no barred spiral galaxy in this work with four continuous spiral arms from the inner to the outer regions. These results highlight that the spiral arm structure of the Milky Way, according to the current mainstream view of a four-arm galaxy with continuous arms extending from the inner to outer regions, is quite unique. However, our findings align with the spiral morphology of the Milky Way proposed by Xu et al., in which case our Galaxy can be considered typical.

Reverse Selection Designs for Accommodating Multiple Control Arms.

Evaluating a novel treatment in a randomized controlled trial requires comparison against existing therapies. If several existing therapies of similar benefit exist, the identification of a single control regimen may be difficult. For this situation, we propose a reverse selection design which, in its simplest form, includes a single experimental treatment arm and two control arms. Rather than carrying both control arms through the entire trial, the control arms are compared at an early interim analysis, ideally while accrual is ongoing. At this time, the worst-performing control arm is dropped and randomization continues to the remaining arms. At the end of the study, we compare the treatment to the remaining control arm. When no head-to-head comparison of the extant therapies is available or feasible, this design requires a smaller sample size than a traditional three-arm design or two sequential trials in which the extant therapies are compared and the better treatment is used in a subsequent trial as the control arm. This is because the final comparison is only between two arms and because the early interim analysis occurs prior to the end of accrual - yet with enough information such that any substantially better control arm will be selected. We evaluate the operating characteristics of a reverse selection design via simulation and show that it reduces the required sample size needed to compare the treatment against the best control, controls type I error, and likely selects the right control arm to use in the final analysis.

An automated platform trial framework for A/B testing

This paper proposes a platform trial for conducting A/B tests with multiple arms and interim monitoring to investigate the impact of several factors on the expected sample size and probability of early stopping. We examined the performance of three stopping boundaries: O’Brien Fleming (OBF) stopping for either futility or difference (both), Pocock stopping for futility only, and fixed sample size design. We simulated twelve scenarios of different orders of arms based on various effect sizes, as well as considered 1 or 3 interim looks. The overall findings are summarizing in a flowchart to provide intuitive guidance for the design of the platform based on the simulation. We found that it is better to use OBF stopping for both if there is any effective variant and the trial is sufficiently powered to detect the expected effect size. If the study is underpowered to detect a difference, we recommend fixed sample size design to gather as much information as possible, however if the expected sample size is important to minimize, we recommend using Pocock boundaries with futility monitoring. Our results aimed at helping high-tech companies conduct their own studies without requiring extensive knowledge of clinical trial design and statistical methodology.

Qini Curves for Multi-Armed Treatment Rules

Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.

OS09.7.A GBM AGILE PLATFORM TRIAL FOR NEWLY DIAGNOSED AND RECURRENT GBM: RESULTS OF FIRST EXPERIMENTAL ARM, REGORAFENIB

Abstract BACKGROUND GBM AGILE (NCT03970447;https://www.gcaresearch.org/research/gbm-agile) is a phase 3 Bayesian adaptive platform trial that efficiently tests multiple arms against common control, with 6 arms included to date. Primary endpoint is overall survival (OS). Stage 1 experimental arms are adaptively randomized against other arms. Demonstrated efficacy in stage 1 leads to fixed randomization stage 2. Stages 1 and 2 are combined for registration. Control randomization is fixed. Regorafenib, a multikinase-inhibitor, entered into GBM AGILE as the first arm and therefore was equally randomized against control. Regorafenib showed OS benefit in recurrent disease (RD) in randomized phase 2 REGOMA trial. MATERIAL AND METHODS Patient subtypes in GBM AGILE are newly diagnosed unmethylated (NDU), RD, and—not considered for regorafenib—ND methylated (NDM). Arm indications (signatures) are combinations of subtypes. Control is temozolomide (ND) and lomustine (RD). Efficacy is assessed by OS hazard ratio(HR), arm/control. Efficacy is demonstrated when Bayesian probability of benefit (HR< 1.00) ≥ 98% (roughly analogous P-value: 0.02). Futility occurs at any monthly analysis when Bayesian predictive power (PP) is < 25% for all signatures. Follow-up continues for 12 months after arm’s accrual stops. RESULTS When PP for all 3 pre-defined signatures was < 25%, regorafenib’s accrual was stopped for futility. Regorafenib/control sample sizes were 49/51, 127/128, 176/179 for signatures NDU, RD, and both. Respective PPs: 0.138, 0.030, 0.025—none close to 0.25. Respective mean HRs: 1.26, 1.25, 1.23. Probabilities of benefit (HR< 1.00): 0.35, 0.18, 0.17. At final analysis, mean HRs were 1.07, 1.08, 1.08 with final probabilities of benefit (HR< 1.00) equal to 0.421, 0.312, 0.296—none close to 0.98. CONCLUSION GBM AGILE efficiently and compellingly addressed regorafenib’s role in GBM, in RD and NDU. These findings are germane as they fail to confirm the REGOMA results in RD. GBM AGILE continues to efficiently assess other therapies, including utilizing concurrent and previously accrued controls. This abstract was accepted and previously presented at the 2023 SNO Annual Meeting and published in Neuro-Oncology, Volume 25, Issue Supplement_5, November 2023, Pages v97-v98.

A Complex Meta-Regression Model to Identify Effective Features of Interventions From Multi-Arm, Multi-Follow-Up Trials.

Network meta-analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of research, interventions are often complex, made up of multiple components or features. This makes it difficult to define a common set of interventions on which to perform the analysis. One approach to this problem is component network meta-analysis (CNMA) which uses a meta-regression framework to define each intervention as a subset of components whose individual effects combine additively. In this article, we are motivated by a systematic review of complex interventions to prevent obesity in children. Due to considerable heterogeneity across the trials, these interventions cannot be expressed as a subset of components but instead are coded against a framework of characteristic features. To analyse these data, we develop a bespoke CNMA-inspired model that allows us to identify the most important features of interventions. We define a meta-regression model with covariates on three levels: intervention, study, and follow-up time, as well as flexible interaction terms. By specifying different regression structures for trials with and without a control arm, we relax the assumption from previous CNMA models that a control arm is the absence of intervention components. Furthermore, we derive a correlation structure that accounts for trials with multiple intervention arms and multiple follow-up times. Although, our model was developed for the specifics of the obesity data set, it has wider applicability to any set of complex interventions that can be coded according to a set of shared features.

Automatic tiling method for mosaic ceramic art images based on subpixel edge fitting localization and collaborative operation of multiple manipulators

Mosaic ceramics are not limited to use solely as building materials, they also possess artistic value. Artists can create images by arranging and combining mosaic ceramics, resulting in a perfect fusion of large-scale public art for external walls and ceramic materials. However, the current approach for artists to create mosaic ceramic exterior wall art images involves manual laying and assembling of individual mosaic ceramics. This manual process suffers from issues such as low efficiency, eye fatigue, selection errors, and the risk of high-altitude operations. These challenges significantly impact the quality and efficiency of creating mosaic ceramic exterior wall art image images. To address these problems, this paper proposes an automatic mosaic ceramic art image stitching method based on subpixel edge fitting positioning and collaborative operation of multiple robotic arms. Additionally, a new U + I type conveying method is designed for efficient and space-saving transportation of mosaic ceramics. Experimental results demonstrate a high success rate of recognition, absorption, and placement of multi-color mosaic ceramics using this method reaching 95.45%, with a positioning error within 0.5 mm. The method can also adapt to varying levels of light intensity or noise interference. This approach effectively enhances the quality and efficiency of creating mosaic ceramic exterior wall art images and promotes the development of mosaic ceramic exterior wall public art creation.

Long in vivo circulating nanomicelles formed by sharp-contrast Janus star polymers derived from β-cyclodextrin grafted with lipids and polyzwitterions

Amphiphilic block polymers have the ability to self-assemble and form nanomicelles, which have been extensively studied as nanocarriers for improving the bioavailability and biodistribution of chemotherapeutic drugs while reducing systemic toxicity. However, polymer micelles often face issues with poor stability in the bloodstream, leading to a short circulation time and leakage of the payload. Here, this work reports a rational design of a sharp-contrast Janus star polymer (SJSP) consisting of multiple arms of superhydrophobic lipid moieties and superhydrophilic polyzwitterion chains attached to a β-cyclodextrin core. The SJSP polymer forms nanomicelles possessing a stable core and a controllable and dense stealth shell that effectively protects them in the bloodstream, preventing payload leakage and blood protein adsorption. It is demonstrated that the hydrophobic/hydrophilic balance can be optimized to achieve strong micellar assembly by adjusting the number of lipid and polyzwitterion arms. The SJSP micelle system shows significantly longer blood circulation time in vivo compared to linear counterparts and other available amphiphilic block copolymer micelle systems. Therefore, the SJSP micelle design offers a promising strategy for developing nanocarriers with potential for translational applications in vivo.

Anatomy of the Star Formation in a Tidally Disturbed Disk Galaxy: NGC 3718

We present a UV, optical, and near-IR study of the star-forming complexes in the nearby peculiar galaxy NGC 3718, using Ultraviolet Imaging telescope, Galaxy Evolution Explorer, Spitzer, and DECam Legacy Survey imaging data. The galaxy has a disturbed optical morphology owing to the multiple tidal arms, the warped disk, and the prominent curved dust lanes, but in the near-IR it appears to be a bulge-dominated galaxy. Its disturbed morphology makes it an excellent case to study star formation in a tidally disturbed galaxy that may have undergone a recent minor merger. To study the distribution and properties of the star-forming clumps (SFCs), we divided the galaxy within the R 25 (B band) radius into three parts—the upper, central, and lower regions. Using the UV band images, we investigated the warped star-forming disk, the extended tidal arms, and the distribution and sizes of the 182 SFCs. Their distribution is 49, 60, and 73 in the galaxy’s upper, central, and lower regions, respectively. We determined the UV color, star formation rates (SFRs), star formation density (ΣSFR), and ages of the SFCs. The central disk of the galaxy has a larger mean ΣSFR that is ∼3.3 and ∼1.6 times higher than the upper and lower regions, respectively. We also find that the SFCs in the central disk are older than those in the tidal arms. Our study thus shows that minor mergers can trigger the inside-out growth of galaxy disks, where the younger SFCs are in outer tidal arms and not in the inner disk.

Most Common Symptomatic Adverse Reactions of Cancer Treatments From US Drug Labels (2015-2021) to Inform Selection of Patient-Reported Outcomes

ObjectivesIncorporating patient-reported outcomes (PROs) to assess symptomatic adverse events (AEs) in cancer clinical trials (CTs) is important to characterize treatment tolerability. Cancer therapies approved over the past decade have expanded the types of expected toxicities. To inform future symptomatic AE PRO item selection, we identified the most common symptomatic adverse reactions from recently approved products. MethodsWe reviewed approvals from 2015 to 2021 for lung, breast, and hematologic cancer indications. Using United States Prescribing Information safety data, we recorded symptomatic adverse reactions reported in ≥20% of patients in the experimental arm of CTs supporting approvals. We calculated the proportion of arms reporting each symptomatic adverse reaction. ResultsIn total, 130 experimental arms were included (lung = 30, breast = 10, hematologic = 90). For all cancer types, fatigue and diarrhea were reported in >50% of the arms. Nausea was reported in ≥50% of the arms for all except lung. Vomiting, decreased appetite, and alopecia, were reported in ≥50% of breast cancer arms. Rash, musculoskeletal pain, and cough were reported in >50% of leukemia/lymphoma arms. Cough was common (50%) in multiple myeloma arms. ConclusionsHeterogeneity in symptomatic adverse reactions across CTs supports the use of item libraries when building a PRO strategy to assess tolerability. Fatigue, diarrhea, and nausea were the most frequent symptomatic adverse reactions reported in contemporary cancer CTs and could provide a starting point when selecting PRO symptomatic AE items. Additional symptomatic AE PRO items should be selected based on the mechanism of action, early clinical data, published literature, and patient and clinician input.

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

Multi-arm multi-stage survival trial design with arm-specific stopping rule

ABSTRACT Traditional two-arm randomized trial designs have played a pivotal role in establishing the efficacy of medical interventions. However, their efficiency is often compromised when confronted with multiple experimental treatments or limited resources. In response to these challenges, the multi-arm multi-stage designs have emerged, enabling the simultaneous evaluation of multiple treatments within a single trial. In such an approach, if an arm meets efficacy success criteria at an interim stage, the whole trial stops and the arm is selected for further study. However when multiple treatment arms are active, stopping the trial at the moment one arm achieves success diminishes the probability of selecting the best arm. To address this issue, we have developed a group sequential multi-arm multi-stage survival trial design with an arm-specific stopping rule. The proposed method controls the familywise type I error in a strong sense and selects the best promising treatment arm with a high probability.

Multiobjective optimization of collaborative robotic task sequence assignment problems under collision-free constraints

This paper proposes a multiobjective optimization approach to address the challenge of collaborative manufacturing with multiple robot arms. Given the necessity for multiple robot arms to work together, the potential for collisions between robotic motions is a significant concern, and the automated task sequence assignment for robots becomes increasingly complex. Previous research has either simplified the collision-free conditions in a limited working area, or employed a master-slave approach to obtain only a local solution. Consequently, we propose a unified global optimization approach for simultaneously addressing various collaborative manufacturing issues, including robotic task sequence assignment (RTSA), multiple inverse kinematics (IK) selection, joint-space collision-free operations and multiple manufacturing objectives. As the optimal collaborative RTSA problem is a combinatorial optimization problem with non-deterministic polynomial-time hard (NP-hard) complexity, this paper presents a hybrid nondominated sorting genetic algorithm III (NSGA-III) method that integrates a Hamming-distance-based method and a greedy strategy within NSGA-III to improve population diversity and solution quality. To validate the efficacy of the proposed approach, simulation experiments were conducted on cooperative manufacturing scenarios, with two objectives: task completion time and task load balancing. The experimental results demonstrate that the proposed approach is effective in obtaining collision-free Pareto solutions. Furthermore, the proposed hybrid NSGA-III method obtains superior solutions compared to the original method in the studied problem, as measured by two performance indices.

Validating process mining in pediatric oncology: Demonstrating protocol adherence and variability in neuroblastoma treatment.

166 Background: Neuroblastoma is the most common extracranial tumor in children. The mortality rate for patients with metastatic disease is over 50%. In the Netherlands, 30 new patients are diagnosed annually. Current clinical treatment guidelines are complex and include multiple treatment arms consisting of chemotherapy, surgery, radiotherapy, and immunotherapy (IT). Although poor patient health and complications are known to cause adaptations to the otherwise standardized treatment regimen, the extent to which these variations occur is unknown. This study aimed to validate the use of process mining, a data-driven technique, to retrospectively examine protocol adherence and variability within current clinical guidelines based upon the GPOH-DCOG NBL2009 neuroblastoma treatment protocol. Methods: Data was extracted from electronic health records, as well as laboratory, pathology, and pharmaceutical databases, to create individual event logs for each patient. Process mining was performed using a fuzzy mining algorithm. The treatment courses for all neuroblastoma patients were mapped. Commonalities and variations were highlighted, and the throughput time for key treatment phases was calculated. Subsequently, conformance with protocol was determined. Results: Analysis of 70 patients treated between 2018-2022 showed 62 distinct treatment processes, revealing significant variations in patient care. Patients with high-risk disease showed the least variation (n=47, 42 variants). Notably, 17 variants were identified among 18 patients who died, indicating a highly personalized approach in the advanced stages of the disease. Subgroup analysis of 17 high-risk patients who received IT consisting of alternating cycles of anti-GD2 antibody and retinoic acid showed 6 variants. 12/17 patients (70.6%) completed the full IT regimen. Other treatment adjustments included shortened anti-GD2 infusions for two patients (5 and 8 days versus the prescribed 10 days, respectively), potentially indicating treatment toxicity. One patient received a 15-day infusion, exceeding the prescribed limit of 11 days. The recorded interval between the administration of anti-GD2 and retinoic acid was a median 5.5 hours (range: 60 seconds – 6.4 days), deviating from the prescribed interval of 24 hours and warranting further investigation. Conclusions: Process mining is a valuable tool for analyzing protocol performance and adherence in neuroblastoma treatment. Despite standardized treatment protocols, significant heterogeneity was observed in the treatment of neuroblastoma patients, especially those with poor prognosis. Throughput time analysis revealed variabilities in the IT regimen that warrant further investigation. This study underscores the potential of data-driven approaches to optimize treatment protocols and support evidence-based practices in clinical oncology.

Modeling and motion simulation study of a three-dimensional sewing device

Abstract In response to the sewing demands of steering wheels and their unique shapes, a multi-robot arm cooperative three-dimensional sewing device was designed to achieve efficient and precise sewing operations. Utilizing SolidWorks, a collaborative three-dimensional sewing device involving multiple robotic arms was engineered, and its mechanism model was established in ADAMS software for motion simulation analysis to evaluate the device’s performance and motion characteristics. This three-dimensional sewing device is capable of fulfilling the sewing requirements of special curved surface items such as steering wheels, enhancing production efficiency and saving labor. Taking the constructed physical prototype as the research subject, motion experiments simulating the sewing process were conducted. Combined with the simulation results, curves, and three-dimensional motion effects, the feasibility of the design was successfully verified, providing a foundation for subsequent sewing trajectory planning and motion control of the device.

Preparation and characterization of lignin copolymerized phenolic resins with superior stiffening effect for natural rubber composites

The development of lignin-derived functional additives for rubber composites has gained growing importance in light of biomass valorization. This work aims at preparing phenolic novolak resin with lignin as one building block for reinforcing natural rubber. A one-pot two-step synthetic approach involving phenolation and copolymerization was developed to prepare lignin-phenol-formaldehyde resins (LPF), wherein 30 wt% of phenol was substituted by lignin. With increasing the ratio of formaldehyde/phenol functionality (F/P), resins show increased molecular weight, elevated glass transition temperature and declined curing activity. The resin with F/P of 0.68/1 exhibits a higher stiffening effect compared to the control without lignin. This enhancement is attributed to lignin's ability to form a hyperbranched structure with multiple phenolic arms as implied by GPC results, acting as a crosslinking hub and facilitating the formation of an effective network. Furthermore, we introduced cardanol with a flexible alkene chain to prepare cardanol-lignin-phenol-formaldehyde resins (CLPF). The CLPF15 resin with cardanol/lignin/phenol of 15/30/55 wt% achieves the maximum stiffening effect due to improved compatibility with natural rubber. This study provides insights into the design and optimization of lignin-copolymerized resins for enhancing the mechanical properties of rubber composites.

Evaluation of lavender and rose aromatherapies on the success of inferior alveolar nerve block in symptomatic irreversible pulpitis: A randomized clinical trial

Trial designThis is a prospective, block-randomized, blinded, multiple arm and parallel-group superiority clinical trial. MethodsSeventy-eight patients satisfying the recruitment standards, were randomly allocated into three groups as follows: Group I (n = 26) – Inferior alveolar nerve block (IANB) devoid of aromatherapy (AT); Group II (n = 26) - IANB with lavender AT and Group III - IANB with rose AT (n = 26) with the help of the ultrasonic aroma diffuser (with respective oils)for 20 min/2 h in operatories 1,2 and 3 respectively. For AT, 3–4 drops of lavender and rose-conditioned oils were added from a 100 ml solution containing 100 mg of these medicinal plants. The pre-operative (PRO) and access opening (AO) pain as well as the anxiety of patients were recorded using the Visual Analog Scale (VAS) and Modified Dental Anxiety Scale (MDAS) respectively. Data thus obtained was entered into the Excel sheet and subjected to statistical tests (analysis of variance and paired t-test). The p-value less than 0.05 was considered statistically significant. ResultsGroup I showed non-significant disparity between PRO and AO for both VAS as well as MDAS (p = 0.62, p = 0.71). However, group II (p = 0.04, p = 0.02) and group III (p = 0.03, p = 0.01) revealed significant differences between PO - AO VAS and MDAS. MDAS and VAS intergroup comparison revealed a significant difference among groups I and II (p = 0.03, p = 0.04), and groups I and III (p = 0.02, p = 0.03). However non-significant disparity was observed among groups II and III (p = 0.85, 0.34). Moreover, there was a statistically significant reduction in anxiety levels in females compared to males after rose AT (p = 0.02). Nevertheless, groups I and II did not show any gender predilection for anxiety as well as pain. ConclusionAlleviation of dental anxiety as well as reduction in pain during AO of teeth with SIP can be achieved using Lavender and rose AT. In female patients, rose AT can be preferred over lavender AT.