A Multi-Stage Progressive Network with Feature Transmission and Fusion for Marine Snow Removal

Improving underwater image quality is crucial for marine detection applications. However, in the marine environment, captured images are often affected by various degradation factors due to the complexity of underwater conditions. In addition to common color distortions, marine snow noise in underwater images is also a significant issue. The backscatter of artificial light on marine snow generates specks in images, thereby affecting image quality, scene perception, and subsequently impacting downstream tasks such as target detection and segmentation. Addressing the issues caused by marine snow noise, we have designed a new network structure. In this work, a novel skip-connection structure called a dual channel multi-scale feature transmitter (DCMFT) is implemented to reduce information loss during downsampling in the feature encoding and decoding section. Additionally, in the feature transfer process for each stage, iterative attentional feature fusion (iAFF) modules are inserted to fully utilize marine snow features extracted at different stages. Finally, to further optimize the network's performance, we incorporate the multi-scale structural similarity index (MS-SSIM) into the loss function to ensure more effective convergence during training. Through experiments conducted on the Marine Snow Removal Benchmark (MSRB) dataset with an augmented sample size, our method has achieved significant results. The experimental results demonstrate that our approach excels in removing marine snow noise, with a peak signal-to-noise ratio reaching 38.9251 dB, significantly outperforming existing methods.

Towards a multi-arm multi-stage platform trial of disease modifying approaches in Parkinson's disease

An increase in the efficiency of clinical trial conduct has been successfully demonstrated in the oncology field, by the use of multi-arm, multi-stage trials allowing the evaluation of multiple therapeutic candidates simultaneously, and seamless recruitment to phase 3 for those candidates passing an interim signal of efficacy. Replicating this complex innovative trial design in diseases such as Parkinson's disease is appealing, but in addition to the challenges associated with any trial assessing a single potentially disease modifying intervention in Parkinson's disease, a multi-arm platform trial must also specifically consider the heterogeneous nature of the disease, alongside the desire to potentially test multiple treatments with different mechanisms of action. In a multi-arm trial, there is a need to appropriately stratify treatment arms to ensure each are comparable with a shared placebo/standard of care arm; however, in Parkinson's disease there may be a preference to enrich an arm with a subgroup of patients that may be most likely to respond to a specific treatment approach. The solution to this conundrum lies in having clearly defined criteria for inclusion in each treatment arm as well as an analysis plan that takes account of predefined subgroups of interest, alongside evaluating the impact of each treatment on the broader population of Parkinson's disease patients. Beyond this, there must be robust processes of treatment selection, and consensus derived measures to confirm target engagement and interim assessments of efficacy, as well as consideration of the infrastructure needed to support recruitment, and the long-term funding and sustainability of the platform. This has to incorporate the diverse priorities of clinicians, triallists, regulatory authorities and above all the views of people with Parkinson's disease.

MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image

This work presents a novel transformer-based method for hand pose estimation-DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propose a novel multi-stage approach from full-scene depth image-MuTr. MuTr removes the necessity of having two different models in the hand pose estimation pipeline-one for hand localization and one for pose estimation-while maintaining promising results. To the best of our knowledge, this is the first successful attempt to use the same model architecture in standard and simultaneously in full-scene image setup while achieving competitive results in both of them. On the NYU dataset, DePOTR and MuTr reach precision equal to 7.85 mm and 8.71 mm, respectively.

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

Analyzing the Effect of Using Axial Impellers in Large-Scale Bioreactors

In high-performance industrial fermentation processes, stirring and aeration may account for significant production costs. Compared to the widely applied Rushton impellers, axial-pumping impellers are known to yield a lower power draw and at the same time improve mixing. However, their lower gas dispersion capability requires stronger agitation, compromising these benefits. Diverse advanced impeller forms have been developed to cope with this challenge. We apply alternating radial and axial impellers and demonstrate strong gas dispersion and energy-efficient mixing for the first time in a large-scale (160 m³ ) bioreactor, based on experimental and CFD simulation data. For equal operating conditions (stirrer speed, aeration rate), this setup yielded similar gas hold-ups and better mixing times (35 %) compared to a classical Rushton-only configuration. Hence, applying a radial impeller on an upper level for improving gas dispersion maintains the benefits of axial impellers in terms of reducing energy demand (up to 50 %). We conclude that this effect is significant only at large-scale, when bubbles substantially expand due to the release of the hydrostatic pressure and have time to coalesce. The work thus extends current knowledge on mixing and aeration of large-scale reactors using classical impeller types. This article is protected by copyright. All rights reserved.

Response adaptive intervention allocation in stepped-wedge cluster randomized trials

BACKGROUND

Stepped-wedge cluster randomized trial (SW-CRT) designs are often used when there is a desire to provide an intervention to all enrolled clusters, because of a belief that it will be effective. However, given there should be equipoise at trial commencement, there has been discussion around whether a pre-trial decision to provide the intervention to all clusters is appropriate. In pharmaceutical drug development, a solution to a similar desire to provide more patients with an effective treatment is to use a response adaptive (RA) design.

METHODS

We introduce a way in which RA design could be incorporated in an SW-CRT, permitting modification of the intervention allocation during the trial. The proposed framework explicitly permits a balance to be sought between power and patient benefit considerations. A simulation study evaluates the methodology.

RESULTS

In one scenario, for one particular RA design, the proportion of cluster-periods spent in the intervention condition was observed to increase from 32.2% to 67.9% as the intervention effect was increased. A cost of this was a 6.2% power drop compared to a design that maximized power by fixing the proportion of time in the intervention condition at 45.0%, regardless of the intervention effect.

CONCLUSIONS

An RA approach may be most applicable to settings for which the intervention has substantial individual or societal benefit considerations, potentially in combination with notable safety concerns. In such a setting, the proposed methodology may routinely provide the desired adaptability of the roll-out speed, with only a small cost to the study's power.

The Concept of "Cancer Stem Cells" in the Context of Classic Carcinogenesis Hypotheses and Experimental Findings

In this Commentary, the operational definition of cancer stem cells or cancer initiating cells includes the ability of certain cells, found in a heterogeneous mixture of cells within a tumor, which are able to sustain growth of that tumor. However, that concept of cancer stem cells does not resolve the age-old controversy of two opposing hypotheses of the origin of the cancer, namely the stem cell hypothesis versus the de-differentiation or re-programming hypothesis. Moreover, this cancer stem concept has to take into account classic experimental observations, techniques, and concepts, such as the multi-stage, multi-mechanism process of carcinogenesis; roles of mutagenic, cytotoxic and epigenetic mechanisms; the important differences between errors of DNA repair and errors of DNA replication in forming mutations; biomarkers of known characteristics of normal adult organ-specific stem cells and of cancer stem cells; and the characteristics of epigenetic mechanisms involved in the carcinogenic process. In addition, vague and misleading terms, such as carcinogens, immortal and normal cells have to be clarified in the context of current scientific facts. The ultimate integration of all of these historic factors to provide a current understanding of the origin and characteristics of a cancer stem cell, which is required for a rational strategy for prevention and therapy for cancer, does not follow a linear path. Lastly, it will be speculated that there exists evidence of two distinct types of cancer stem cells, one that has its origin in an organ-specific adult stem cell that is 'initiated' in the stem cell stage, expressing the Oct4A gene and not expressing any connexin gene or having functional gap junctional intercellular communication (GJIC). The other cancer stem cell is derived from a stem cell that is initiated early after the Oct4A gene is suppressed and the connexin gene is expressed, which starts early differentiation, but it is blocked from terminal differentiation.

In Search of a Unifying Concept in Human Diseases

Throughout the history of biological/medicine sciences, there has been opposing strategies to find solutions to complex human disease problems. Both empirical and deductive approaches have led to major insights and concepts that have led to practical preventive and therapeutic benefits for the human population. The classic definitions of "science" (to know) has been paired with the classic definition of technology (to do). One knew more as the technology developed, and that development was often based on science. In other words, one could do more if science could improve the technology. In turn, this made possible to know more science with improved technology. However, with the development of new technologies of today in biology and medicine, major advances have been made, such as the information from the Human Genome Project, genetic engineering techniques and the use of bioinformatic uses of sophisticated computer analyses. This has led to the renewed idea that Precision Medicine, while raising some serious ethical concerns, also raises the expectation of improved potential of risk predictions for prevention and treatment of various genetically and environmentally influenced human diseases. This new field Artificial Intelligence, as a major handmaiden to Precision Medicine, is significantly altering the fundamental means of biological discovery. However, can today's fundamental premise of "Artificial Intelligence", based on identifying DNA, as the primary nexus of human health and disease, provide the practical solutions to complex human diseases that involve the interaction of those genes with the broad spectrum of "environmental factors"? Will it be "precise" enough to provide practical solutions for prevention and treatments of diseases? In this "Commentary", with the example of human carcinogenesis, it will be challenged that, without the integration of mechanistic and hypothesis-driven approaches with the "unbiased" empirical analyses of large numbers of data, the Artificial Intelligence approach with fall short.

Extending the two-stage single arm phase II clinical trial design to the delayed response scenario

Two-stage single arm designs are widely used in phase II clinical trials with binary endpoints. The trial may be stopped early due to insufficient positive responses in the first stage. There may be some enrolled subjects who have yet to respond by the end of the first stage, and their data are ignored if the first stage results in rejection of the trial. It is possible that the result after the first stage is rejection by a slim margin, while the results of pipeline subjects are quite positive. In this case, combining the data from the two sources may provide a valuable opportunity to rescue a promising treatment that was mistakenly rejected. We propose a novel double-check design to take advantage of the pipeline subjects' data to establish a rescue criterion based on two-stage design. When the rescue criterion is met, the decision to reject the trial at the end of the first stage can be reversed, allowing the trial to continue. A derivation based on a binomial distribution shows that the double-check strategy can strictly preserve the type I error rate. Further examination shows that the strategy can provide a slight increase in overall power and a substantial increase in conditional power when the proportion of positive response at the end of the first stage is at the margin. The extra rescue opportunity's cost is pretty low, only a slight increasing in the expected sample size.

Progressively Inpainting Images Based on a Forked-Then-Fused Decoder Network

Image inpainting aims to fill in corrupted regions with visually realistic and semantically plausible contents. In this paper, we propose a progressive image inpainting method, which is based on a forked-then-fused decoder network. A unit called PC-RN, which is the combination of partial convolution and region normalization, serves as the basic component to construct inpainting network. The PC-RN unit can extract useful features from the valid surroundings and can suppress incompleteness-caused interference at the same time. The forked-then-fused decoder network consists of a local reception branch, a long-range attention branch, and a squeeze-and-excitation-based fusing module. Two multi-scale contextual attention modules are deployed into the long-range attention branch for adaptively borrowing features from distant spatial positions. Progressive inpainting strategy allows the attention modules to use the previously filled region to reduce the risk of allocating wrong attention. We conduct extensive experiments on three benchmark databases: Places2, Paris StreetView, and CelebA. Qualitative and quantitative results show that the proposed inpainting model is superior to state-of-the-art works. Moreover, we perform ablation studies to reveal the functionality of each module for the image inpainting task.

Orientation of Antigen Display on Self-Assembling Protein Nanoparticles Influences Immunogenicity

Self-assembling protein nanoparticles (SAPN) serve as a repetitive antigen delivery platform with high-density epitope display; however, antigen characteristics such as size and epitope presentation can influence the immunogenicity of the assembled particle and are aspects to consider for a rationally designed effective vaccine. Here, we characterize the folding and immunogenicity of heterogeneous antigen display by integrating (a) dual-stage antigen SAPN presenting the P. falciparum (Pf) merozoite surface protein 1 subunit, PfMSP1₁₉, and Pf cell-traversal protein for ookinetes and sporozoites, PfCelTOS, in addition to (b) a homogenous antigen SAPN displaying two copies of PfCelTOS. Mice and rabbits were utilized to evaluate antigen-specific humoral and cellular induction as well as functional antibodies via growth inhibition of the blood-stage parasite. We demonstrate that antigen orientation and folding influence the elicited immune response, and when appropriately designed, SAPN can serve as an adaptable platform for an effective multi-antigen display.

Oncolytic Adenovirus in Cancer Immunotherapy

Tumor-selective replicating "oncolytic" viruses are novel and promising tools for immunotherapy of cancer. However, despite their first success in clinical trials, previous experience suggests that currently used oncolytic virus monotherapies will not be effective enough to achieve complete tumor responses and long-term cure in a broad spectrum of cancers. Nevertheless, there are reasonable arguments that suggest advanced oncolytic viruses will play an essential role as enablers of multi-stage immunotherapies including established systemic immunotherapies. Oncolytic adenoviruses (oAds) display several features to meet this therapeutic need. oAds potently lyse infected tumor cells and induce a strong immunogenic cell death associated with tumor inflammation and induction of antitumor immune responses. Furthermore, established and versatile platforms of oAds exist, which are well suited for the incorporation of heterologous genes to optimally exploit and amplify the immunostimulatory effect of viral oncolysis. A considerable spectrum of functional genes has already been integrated in oAds to optimize particular aspects of immune stimulation including antigen presentation, T cell priming, engagement of additional effector functions, and interference with immunosuppression. These advanced concepts have the potential to play a promising future role as enablers of multi-stage immunotherapies involving adoptive cell transfer and systemic immunotherapies.

Adaptive platform trials using multi-arm, multi-stage protocols: getting fast answers in pandemic settings

Global health pandemics, such as coronavirus disease 2019 (COVID-19), require efficient and well-conducted trials to determine effective interventions, such as treatments and vaccinations. Early work focused on rapid sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), subsequent in-vitro and in-silico work, along with greater understanding of the different clinical phases of the infection, have helped identify a catalogue of potential therapeutic agents requiring assessment. In a pandemic, there is a need to quickly identify efficacious treatments, and reject those that are non-beneficial or even harmful, using randomised clinical trials. Whilst each potential treatment could be investigated across multiple, separate, competing two-arm trials, this is a very inefficient process. Despite the very large numbers of interventional trials for COVID-19, the vast majority have not used efficient trial designs. Well conducted, adaptive platform trials utilising a multi-arm multi-stage (MAMS) approach provide a solution to overcome limitations of traditional designs. The multi-arm element allows multiple different treatments to be investigated simultaneously against a shared, standard-of-care control arm. The multi-stage element uses interim analyses to assess accumulating data from the trial and ensure that only treatments showing promise continue to recruitment during the next stage of the trial. The ability to test many treatments at once and drop insufficiently active interventions significantly speeds up the rate at which answers can be achieved. This article provides an overview of the benefits of MAMS designs and successes of trials, which have used this approach to COVID-19. We also discuss international collaboration between trial teams, including prospective agreement to synthesise trial results, and identify the most effective interventions. We believe that international collaboration will help provide faster answers for patients, clinicians, and health care systems around the world, including for future waves of COVID-19, and enable preparedness for future global health pandemics.

Adaptive platform trials using multi-arm, multi-stage protocols: getting fast answers in pandemic settings

Global health pandemics, such as coronavirus disease 2019 (COVID-19), require efficient and well-conducted trials to determine effective interventions, such as treatments and vaccinations. Early work focused on rapid sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), subsequent in-vitro and in-silico work, along with greater understanding of the different clinical phases of the infection, have helped identify a catalogue of potential therapeutic agents requiring assessment. In a pandemic, there is a need to quickly identify efficacious treatments, and reject those that are non-beneficial or even harmful, using randomised clinical trials. Whilst each potential treatment could be investigated across multiple, separate, competing two-arm trials, this is a very inefficient process. Despite the very large numbers of interventional trials for COVID-19, the vast majority have not used efficient trial designs. Well conducted, adaptive platform trials utilising a multi-arm multi-stage (MAMS) approach provide a solution to overcome limitations of traditional designs. The multi-arm element allows multiple different treatments to be investigated simultaneously against a shared, standard-of-care control arm. The multi-stage element uses interim analyses to assess accumulating data from the trial and ensure that only treatments showing promise continue to recruitment during the next stage of the trial. The ability to test many treatments at once and drop insufficiently active interventions significantly speeds up the rate at which answers can be achieved. This article provides an overview of the benefits of MAMS designs and successes of trials, which have used this approach to COVID-19. We also discuss international collaboration between trial teams, including prospective agreement to synthesise trial results, and identify the most effective interventions. We believe that international collaboration will help provide faster answers for patients, clinicians, and health care systems around the world, including for each further wave of COVID-19, and enable preparedness for future global health pandemics.

A review of available software for adaptive clinical trial design

BACKGROUND/AIMS

The increasing cost of the drug development process has seen interest in the use of adaptive trial designs grow substantially. Accordingly, much research has been conducted to identify barriers to increasing the use of adaptive designs in practice. Several articles have argued that the availability of user-friendly software will be an important step in making adaptive designs easier to implement. Therefore, we present a review of the current state of software availability for adaptive trial design.

METHODS

We review articles from 31 journals published in 2013-2017 that relate to methodology for adaptive trials to assess how often code and software for implementing novel adaptive designs is made available at the time of publication. We contrast our findings against these journals' policies on code distribution. We also search popular code repositories, such as Comprehensive R Archive Network and GitHub, to identify further existing user-contributed software for adaptive designs. From this, we are able to direct interested parties toward solutions for their problem of interest.

RESULTS

Only 30% of included articles made their code available in some form. In many instances, articles published in journals that had mandatory requirements on code provision still did not make code available. There are several areas in which available software is currently limited or saturated. In particular, many packages are available to address group sequential design, but comparatively little code is present in the public domain to determine biomarker-guided adaptive designs.

CONCLUSIONS

There is much room for improvement in the provision of software alongside adaptive design publications. In addition, while progress has been made, well-established software for various types of trial adaptation remains sparsely available.

A Multi-Stage Plasmodium vivax Malaria Vaccine Candidate Able to Induce Long-Lived Antibody Responses Against Blood Stage Parasites and Robust Transmission-Blocking Activity

Malaria control and interventions including long-lasting insecticide-treated nets, indoor residual spraying, and intermittent preventative treatment in pregnancy have resulted in a significant reduction in the number of Plasmodium falciparum cases. Considerable efforts have been devoted to P. falciparum vaccines development with much less to P. vivax. Transmission-blocking vaccines, which can elicit antibodies targeting Plasmodium antigens expressed during sexual stage development and interrupt transmission, offer an alternative strategy to achieve malaria control. The post-fertilization antigen P25 mediates several functions essential to ookinete survival but is poorly immunogenic in humans. Previous clinical trials targeting this antigen have suggested that conjugation to a carrier protein could improve the immunogenicity of P25. Here we report the production, and characterization of a vaccine candidate composed of a chimeric P. vivax Merozoite Surface Protein 1 (cPvMSP1) genetically fused to P. vivax P25 (Pvs25) designed to enhance CD4+ T cell responses and its assessment in a murine model. We demonstrate that antibodies elicited by immunization with this chimeric protein recognize both the erythrocytic and sexual stages and are able to block the transmission of P. vivax field isolates in direct membrane-feeding assays. These findings provide support for the continued development of multi-stage transmission blocking vaccines targeting the life-cycle stage responsible for clinical disease and the sexual-stage development accountable for disease transmission simultaneously.

KECA Similarity-Based Monitoring and Diagnosis of Faults in Multi-Phase Batch Processes

Multiple phases with phase to phase transitions are important characteristics of many batch processes. The linear characteristics between phases are taken into consideration in the traditional algorithms while nonlinearities are neglected, which can lead to inaccuracy and inefficiency in monitoring. The focus of this paper is nonlinear multi-phase batch processes. A similarity metric is defined based on kernel entropy component analysis (KECA). A KECA similarity-based method is proposed for phase division and fault monitoring. First, nonlinear characteristics can be extracted in feature space via performing KECA on each preprocessed time-slice data matrix. Then phase division is achieved with the similarity variation of the extracted feature information. Then, a series of KECA models and slide-KECA models are established for steady and transitions phases respectively, which can reflect the diversity of transitional characteristics objectively and preferably deal with the stage-transition monitoring problem in multistage batch processes. Next, in order to overcome the problem that the traditional contribution plot cannot be applied to the kernel mapping space, a nonlinear contribution plot diagnosis algorithm is proposed, which is easier, more intuitive and implementable compared with the traditional one. Finally, simulations are performed on penicillin fermentation and industrial application. Specifically, the proposed method detects the abnormal agitation power and the abnormal substrate supply at 47 h and 86 h, respectively. Compared with traditional methods, it has better real-time performance and higher efficiency. Results demonstrate the ability of the proposed method to detect faults accurately and effectively in practice.

Assessing the impact of efficacy stopping rules on the error rates under the multi-arm multi-stage framework

BACKGROUND

The multi-arm multi-stage framework uses intermediate outcomes to assess lack-of-benefit of research arms at interim stages in randomised trials with time-to-event outcomes. However, the design lacks formal methods to evaluate early evidence of overwhelming efficacy on the definitive outcome measure. We explore the operating characteristics of this extension to the multi-arm multi-stage design and how to control the pairwise and familywise type I error rate. Using real examples and the updated nstage program, we demonstrate how such a design can be developed in practice.

METHODS

We used the Dunnett approach for assessing treatment arms when conducting comprehensive simulation studies to evaluate the familywise error rate, with and without interim efficacy looks on the definitive outcome measure, at the same time as the planned lack-of-benefit interim analyses on the intermediate outcome measure. We studied the effect of the timing of interim analyses, allocation ratio, lack-of-benefit boundaries, efficacy rule, number of stages and research arms on the operating characteristics of the design when efficacy stopping boundaries are incorporated. Methods for controlling the familywise error rate with efficacy looks were also addressed.

RESULTS

Incorporating Haybittle-Peto stopping boundaries on the definitive outcome at the interim analyses will not inflate the familywise error rate in a multi-arm design with two stages. However, this rule is conservative; in general, more liberal stopping boundaries can be used with minimal impact on the familywise error rate. Efficacy bounds in trials with three or more stages using an intermediate outcome may inflate the familywise error rate, but we show how to maintain strong control.

CONCLUSION

The multi-arm multi-stage design allows stopping for both lack-of-benefit on the intermediate outcome and efficacy on the definitive outcome at the interim stages. We provide guidelines on how to control the familywise error rate when efficacy boundaries are implemented in practice.

Intensified LOHC-Dehydrogenation Using Multi-Stage Microstructures and Pd-Based Membranes

Liquid organic hydrogen carriers (LOHC) are able to store hydrogen stably and safely in liquid form. The carrier can be loaded or unloaded with hydrogen via catalytic reactions. However, the release reaction brings certain challenges. In addition to an enormous heat requirement, the released hydrogen is contaminated by traces of evaporated LOHC and by-products. Micro process engineering offers a promising approach to meet these challenges. In this paper, a micro-structured multi-stage reactor concept with an intermediate separation of hydrogen is presented for the application of perhydro-dibenzyltoluene dehydrogenation. Each reactor stage consists of a micro-structured radial flow reactor designed for multi-phase flow of LOHC and released hydrogen. The hydrogen is separated from the reactors’ gas phase effluent via PdAg-membranes, which are integrated into a micro-structured environment. Separate experiments were carried out to describe the kinetics of the reaction and the separation ability of the membrane. A model was developed, which was fed with these data to demonstrate the influence of intermediate separation on the efficiency of LOHC dehydrogenation.

Testing many treatments within a single protocol over 10 years at MRC Clinical Trials Unit at UCL: Multi-arm, multi-stage platform, umbrella and basket protocols

There is real need to change how we do some of our clinical trials, as currently the testing and development process is too slow, too costly and too failure-prone often we find that a new treatment is no better than the current standard. Much of the focus on the development and testing pathway has been in improving the design of phase I and II trials. In this article, we present examples of new methods for improving the design of phase III trials (and the necessary lead up to them) as they are the most time-consuming and expensive part of the pathway. Key to all these methods is the aim to test many treatments and/or pose many therapeutic questions within one protocol.

Controlling the family-wise error rate in multi-arm, multi-stage trials

BACKGROUND AND AIMS

Multi-arm, multi-stage trials have recently gained attention as a means to improve the efficiency of the clinical trials process. Many designs have been proposed, but few explicitly consider the inherent issue of multiplicity and the associated type I error rate inflation. It is our aim to propose a straightforward design that controls family-wise error rate while still providing improved efficiency.

METHODS

In this article, we provide an analytical method for calculating the family-wise error rate for a multi-arm, multi-stage trial and highlight the potential for considerable error rate inflation in uncontrolled designs. We propose a simple method to control the error rate that also allows for computation of power and expected sample size.

RESULTS

Family-wise error rate can be controlled in a variety of multi-arm, mutli-stage trial designs using our method. Additionally, our design can substantially decrease the expected sample size of a study while maintaining adequate power.

CONCLUSION

Multi-arm, multi-stage designs have the potential to reduce the time and other resources spent on clinical trials. Our relatively simple design allows this to be achieved while weakly controlling family-wise error rate and without sacrificing much power.

A review of statistical designs for improving the efficiency of phase II studies in oncology

Phase II oncology trials are carried out to assess whether an experimental anti-cancer treatment shows sufficient signs of effectiveness to justify being tested in a phase III trial. Traditionally such trials are conducted as single-arm studies using a binary response rate as the primary endpoint. In this article, we review and contrast alternative approaches for such studies. Each approach uses only data that are necessary for the traditional analysis. We consider two broad classes of methods: ones that aim to improve the efficiency using novel design ideas, such as multi-stage and multi-arm multi-stage designs; and ones that aim to improve the analysis, by making better use of the richness of the data that is ignored in the traditional analysis. The former class of methods provides considerable gains in efficiency but also increases the administrative and logistical issues in running the trial. The second class consists of viable alternatives to the standard analysis that come with little additional requirements and provide considerable gains in efficiency.

Cellular association and assembly of a multistage delivery system

The realization that blood-borne delivery systems must overcome a multiplicity of biological barriers has led to the fabrication of a multistage delivery system (MDS) designed to temporally release successive stages of particles or agents to conquer sequential barriers, with the goal of enhancing delivery of therapeutic and diagnostic agents to the target site. In its simplest form, the MDS comprises stage-one porous silicon microparticles that function as carriers of second-stage nanoparticles. Cellular uptake of nontargeted discoidal silicon microparticles by macrophages is confirmed by electron and atomic force microscopy (AFM). Using superparamagnetic iron oxide nanoparticles (SPIONs) as a model of secondary nanoparticles, successful loading of the porous matrix of silicon microparticles is achieved, and retention of the nanoparticles is enhanced by aminosilylation of the loaded microparticles with 3-aminopropyltriethoxysilane. The impact of silane concentration and reaction time on the nature of the silane polymer on porous silicon is investigated by AFM and X-ray photoelectron microscopy. Tissue samples from mice intravenously administered the MDS support co-localization of silicon microparticles and SPIONs across various tissues with enhanced SPION release in spleen, compared to liver and lungs, and enhanced retention of SPIONs following silane capping of the MDS. Phantom models of the SPION-loaded MDS display negative contrast in magnetic resonance images. In addition to forming a cap over the silicon pores, the silane polymer provides free amines for antibody conjugation to the microparticles, with both VEGFR-2- and PECAM-specific antibodies leading to enhanced endothelial association. This study demonstrates the assembly and cellular association of a multiparticle delivery system that is biomolecularly targeted and has potential for applications in biological imaging.