Process Analytical Technology (PAT) for automated, real-time control of continuous manufacturing of mAbs

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Comparison of three clinical and three ultrasonic equations in predicting fetal birth weight

Background: Antenatal assessment of fetal weight is important part in the management decisions during labour, thereby improving perinatal outcome. There are a large number of clinical methods and ultrasonic formulae for predicting fetal birth weight (EBW) with varying degrees of accuracy. This study was an attempt to compare the accuracy of three clinical and three ultrasonic methods in Indian population. The method with highest accuracy can be used in high and low resource setting in a country like ours with diverse resource settings.Methods: This was a prospective non randomized cohort study done on 100 antenatal patients in PGIMER, Dr. RML Hospital; New Delhi from Nov 2011 to Jan 2013 EBW (Expected Birth Weight) was calculated applying the 6 formulae three clinical and three ultrasonic and statistical analysis done after delivery comparing with ABW (Actual Birth Weight).Results: Accuracy in all ABW within 10% of ABW was 94 % with Johnson's method, 92 %with Dares method and 62 % with obstetrical equation. It was 100% with Hadlock 2 equation, 96% with Shepherd's and 86% with Warsoff equation Sensitivity for IUGR i.e. wt <2.5 kg was low in clinical methods, highest was only 46.2% with Johnsons. In ultrasonic methods all the three equations had 100% sensitivity making ultrasound the preferred modality in diagnosing macrosomia.Conclusions: The major finding of this study is that clinical estimation of fetal weight is as accurate as ultrasonographic method of estimation within normal range of birth weight Ultrasonographic methods was statistically more accurate with smaller mean errors and more within 10% of actual birth weight. Johnson formula gave most accuracy in clinical methods Ultrasound should be used to confirm clinical methods if IUGR or Macrosomia is suspected. No single method should be used if EBW is a part of decision but two or more methods should be combined

Assessment of Suspending Properties of Katira Gum: Formulation and Evaluation of Nimesulide Suspension

There are several hydrophilic polymers that have been employed as suspending agents in pharmaceutical suspensions due to their ability to form colloidal gel in aqueous medium. In the present study, katira gum obtained from the bark of Cochlospermum religiosum has been evaluated as suspending agent in nimesulide suspension and compared with acacia gum at concentration of 1-5%. Sedimentation volume, rheology, particle size, degree of flocculation and in-vitro drug release were employed as assessment parameters. The result showed that at all concentrations, katira gum higher suspending capability compared acacia gum. The sedimentation volume was found to increase from 0.36 to 1 (A1-A3) and 0.26 to 0.56 (B1-B3). The viscosity of suspensions (A1 and A2) containing gum katira as suspending agent was found to be 1.35 and 2.4 centipoise and 0.63- 1.05 centipoise (B1- B3). Plots between shear stress and rate of shear were plotted using different concentrations indicates the obedience to newtonian behaviour. Degree of flocculation of gum katira and gum acacia suspension was established to be 1.69 and 1.05 respectively

An Efficient Lightweight Provably Secure Authentication Protocol for Patient Monitoring Using Wireless Medical Sensor Networks

The refurbishing of conventional medical network with the wireless medical sensor network has not only amplified the efficiency of the network but concurrently posed different security threats. Previously, Servati and Safkhani had suggested an Internet of Things (IoT) based authentication scheme for the healthcare environment promulgating a secure protocol in resistance to several attacks. However, the analysis demonstrates that the protocol could not withstand user, server, and gateway node impersonation attacks. Further, the protocol fails to resist offline password guessing, ephemeral secret leakage, and gateway-by-passing attacks. To address the security weaknesses, we furnish a lightweight three-factor authentication framework employing the fuzzy extractor technique to safeguard the user’s biometric information. The Burrows-Abadi-Needham (BAN) logic, Real-or-Random (ROR) model, and Scyther simulation tool have been imposed as formal approaches for establishing the validity of the proposed work. The heuristic analysis stipulates that the proposed work is impenetrable to possible threats and offers several security peculiarities like forward secrecy and three-factor security. A thorough analysis of the preexisting works with the proposed ones corroborates the intensified security and efficiency with the reduced computational, communication, and security overheads

Amino acid selective unlabeling for sequence specific resonance assignments in proteins

Sequence specific resonance assignment constitutes an important step towards high-resolution structure determination of proteins by NMR and is aided by selective identification and assignment of amino acid types. The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments. An alternative approach is the method of amino acid selective `unlabeling' or reverse labeling, which involves selective unlabeling of specific amino acid types against a uniformly C-13/N-15 labeled background. Based on this method, we present a novel approach for sequential assignments in proteins. The method involves a new NMR experiment named, {(CO)-C-12 (i) -N-15 (i+1)}-filtered HSQC, which aids in linking the H-1(N)/N-15 resonances of the selectively unlabeled residue, i, and its C-terminal neighbor, i + 1, in HN-detected double and triple resonance spectra. This leads to the assignment of a tri-peptide segment from the knowledge of the amino acid types of residues: i - 1, i and i + 1, thereby speeding up the sequential assignment process. The method has the advantage of being relatively inexpensive, applicable to H-2 labeled protein and can be coupled with cell-free synthesis and/or automated assignment approaches. A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of N-14 at undesired sites. Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies

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

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Conjugated Quantum Dots Inhibit the Amyloid β (1–42) Fibrillation Process

Nanoparticles have enormous potential in diagnostic and therapeutic studies. We have demonstrated that the amyloid beta mixed with and conjugated to dihydrolipoic acid- (DHLA) capped CdSe/ZnS quantum dots (QDs) of size approximately 2.5 nm can be used to reduce the fibrillation process. Transmission electron microscopy (TEM) and atomic force microscopy (AFM) were used as tools for analysis of fibrillation. There is a significant change in morphology of fibrils when amyloid β (1–42) (Aβ (1–42)) is mixed or conjugated to the QDs. The length and the width of the fibrils vary under modified conditions. Thioflavin T (ThT) fluorescence supports the decrease in fibril formation in presence of DHLA-capped QDs

Rapid Label-free Detection of E. coli using Antimicrobial Peptide Assisted Impedance Spectroscopy

There is an increasing demand for rapid detection of waterborne pathogens to monitor drinking water safety. We demonstrate a compact, label-free sensor array for rapid detection of Escherichia coli (E. coli) in contaminated water samples using antimicrobial peptide assisted impedimetric sensor platform. Interdigitated electrode arrays immobilized with the antimicrobial peptide Colicin V (ColV) were used to screen the affinity towards different bacterial strains by monitoring impedance variations in real-time. This ColV assisted impedance biosensor exhibited high selectivity towards Gram-negative strains particularly towards E. coli strains. This selective detection of E. coli from other strains was observed at 102 cfu mL−1, which is clinically relevant. The sensor can detect E. coli from 102 to 106 cfu mL−1 in water sample at pH 7 to 9. These results show that the antimicrobial peptide ColV assisted impedimetric array is capable of rapid, specific detection of E. coli in contaminated water samples

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 &lt;5% error in three independent test cases, successfully enabling control of the SPTFF step in line with the Quality by Design paradigm