Model based control of continuous bioprocesse

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Development of Hydroxyapatite-Titania Composite for Implant Application

Hydroxyapatite is one of the defined bioactive materials. It is a potential candidate ceramic for bone and dental implants since it owns a superior biocompatibility. Due to its brittleness and poor mechanical strength, application of HAp is limited at load bearing site. Various routes have been investigated for improving the mechanical properties of hydroxyapatite. Recent studies show various HAp composites were fabricated to improve mechanical strength. Titanium dioxide has very good biocompatibility. Titanium dioxide also promotes cell growth and proliferation. Titanium dioxide is added in different weight percentages i.e. 5%, 10%, 15% in to HAp. Samples were sintered for different time at 800oC. Developed composite were further characterized for morphology, phase, functional group analysis and compressive strength. It was found that HAp- Titania composite containing 10% Titania has better compressive strength 59.8 MPa with 20-40% porosity than any other developed composite of selected compositio

Deep neural network for prediction and control of permeability decline in single pass tangential flow ultrafiltration in continuous processing of monoclonal antibodies

Single-pass tangential flow filtration (SPTFF) is a crucial technology enabling the continuous manufacturing of monoclonal antibodies (mAbs). By significantly increasing the membrane area utilized in the process, SPTFF allows the mAb process stream to be concentrated up to the desired final target in a single pass across the membrane surface without the need for recirculation. However, a key challenge in SPTFF is compensating for flux decline across the membrane due to concentration polarization and surface fouling phenomena. In continuous downstream processing, flux decline immediately impacts the continuous process flowrates. It reduces the concentration factor achievable in a single pass, thereby reducing the final concentration attained at the outlet of the SPTFF module. In this work, we develop a deep neural network model to predict the NWP in real-time without the need to conduct actual NWP measurements. The developed model incorporates process parameters such as pressure and feed concentrations through inline sensors and a spectroscopy-coupled data model (NIR-PLS model). The model determines the optimal timing for membrane cleaning steps when the normalized water permeability (NWP) falls below 60%. Using SCADA and PLC, a distributed control system was developed to integrate the monitoring sensors and control elements, such as the NIRS sensor for concentration monitoring, the DNN model for NWP prediction, weighing balances, pressure sensors, pumps, and valves. The model was tested in real-time, and the NWP was predicted within <5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm

Streaming Video Analytics On The Edge With Asynchronous Cloud Support

Emerging Internet of Things (IoT) and mobile computing applications are expected to support latency-sensitive deep neural network (DNN) workloads. To realize this vision, the Internet is evolving towards an edge-computing architecture, where computing infrastructure is located closer to the end device to help achieve low latency. However, edge computing may have limited resources compared to cloud environments and thus, cannot run large DNN models that often have high accuracy. In this work, we develop REACT, a framework that leverages cloud resources to execute large DNN models with higher accuracy to improve the accuracy of models running on edge devices. To do so, we propose a novel edge-cloud fusion algorithm that fuses edge and cloud predictions, achieving low latency and high accuracy. We extensively evaluate our approach and show that our approach can significantly improve the accuracy compared to baseline approaches. We focus specifically on object detection in videos (applicable in many video analytics scenarios) and show that the fused edge-cloud predictions can outperform the accuracy of edge-only and cloud-only scenarios by as much as 50%. We also show that REACT can achieve good performance across tradeoff points by choosing a wide range of system parameters to satisfy use-case specific constraints, such as limited network bandwidth or GPU cycles.Comment: 12 page

Treatment of stable and unstable intertrochanteric fractures using proximal femoral nail and their functional assessment using modified Harris hip score

Background: Intertrochanteric fracture is common injury and is found in both elderly and the young. Proximal femoral nail (PFN) has been in widespread use for the same in the recent past. There are advocates both for and against this implant. The aim was to study the results of PFN in the treatment of intertrochanteric fractures and their functional evaluation using the modified Harris hip score.Methods: A prospective study done on 104 patients operated for intertrochanteric fractures with PFN. Patients were followed up at 6 weekly intervals and evaluated at each visit using the modified Harris hip score (HHS).Results: The study consisted of 104 patients aged from 36 to 96 years with average age of 67.8 years. Most patients belonged to 60-80 years category. Malunion was observed in three patients (2.8%) and non union was not seen in any patient. Excellent to good results were seen in 73% patients 18% patients had a fair outcome, 7.7% had poor and 2% patients had very poor outcome.Conclusions: PFN is a good implant for intertrochanteric fracture treatment that demands a sound technique and a good reduction prior to insertion. The assessment of results using HHS has been too similar to those obtained using Kyle’s criteria in the previous studies, proving its role in the same.

Real-time model-based control of single pass tangential flow filtration for production of monoclonal antibodies

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Residual on column host cell protein analysis during lifetime studies of protein A chromatography

Capacity reduction in protein A affinity chromatography with extended cycling during therapeutic antibody manufacture is well documented. Identification of which residual proteins remain from previous cycles during the lifetime of these adsorbent materials is required to understand their role in this ageing process, but represents a significant metrological challenge. Scanning electron microscopy (SEM) and liquid chromatography mass spectrometry (LC–MS/MS) are combined to detect and map this phenomenon of protein carry-over. We show that there is a morphological change at the surface of the agarose resin, revealing deposits on the polymer fibres increasing with cycle number. The amount of residual host cell proteins (HCPs) by LC–MS/MS present on the resin is shown to increase 10-fold between 50 and 100 cycles. During this same period the functional class of the predominant HCPs associated with the resin increased in diversity, with number of proteins identified increasing 5-fold. This ageing is observed in the context of the product quality of the eluate HCP and protein A leachate concentration remaining constant with cycle number

Process validation in manufacturing of biopharmaceuticals / editors, Anurag Rathore, Gail Sofer.

Includes bibliographical references and index.Book fair 2013.p.