In order to evaluate how well existing techniques for transferring NIR calibrations perform for solid pharmaceutical formulations, a study on four assays of active ingredients was undertaken. The study included two configurations of dispersive NIR instruments and one Fourier transform (FT) instrument. Three methods for calibration transfer: slope/bias correction, local centring and piecewise direct standardisation (PDS), were tested and evaluated. Our conclusions are that the calibration transfer methods tested can perform equally well. It was shown that it is possible to transfer calibrations between instruments of different configurations or even of different types, without loosing the prediction ability of the calibration. To achieve a good calibration transfer, a larger variation in the content of the active ingredient in the samples and more samples are needed for the slope and bias correction method compared to the local centring method. For PDS to be a successful calibration transfer method, an optimisation of the number of transfer samples and how they are selected together with various factors specific for this method is needed. Local centring is the preferred transfer method as its performance is excellent yet it is simple to perform, no optimisation is needed, only a few transfer samples are required and the transfer samples do not have to vary in their content of the active ingredient.
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http://dx.doi.org/10.1016/j.jpba.2005.10.042 | DOI Listing |
Talanta
January 2025
Department of Chemical and Biomolecular Engineering, University of Connecticut, CT, 06269, United States. Electronic address:
This study applies a periodic table-guided approach to select and investigate hafnium oxide (HfO), in conjunction with reduced graphene oxide (rGO), for the electrochemical determination of methyl parathion (MP), an organophosphate insecticide. MP poses significant ecological and health risks due to its high toxicity, and despite bans, illegal use has been reported, especially in the global south. To address these challenges, an electrode modified with a nanocomposite of rGO/HfO was first constructed for MP detection.
View Article and Find Full Text PDFJ Neural Eng
January 2025
School of Informatics, The University of Edinburgh, 10 Chricton Street, Edinburgh, EH8 9LE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Objective: Electromyographic (EMG) signals show large variabilities over time due to factors such as electrode shifting, user behaviour variations, etc., substantially degrading the performance of myoelectric control models in long-term use. Previously one-time model calibration was usually required each time before usage.
View Article and Find Full Text PDFReprod Biol Endocrinol
January 2025
Department of Reproductive Medicine Center, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, The People's Republic of China.
Objective: This study aimed to develop a predictive model for the risk of no usable blastocyst formation in patients with normal ovarian reserve undergoing IVF.
Methods: The model was derived from 7,901 patients who underwent their first oocyte retrieval and subsequent blastocyst culture, of which 446 cases have no usable blastocysts formed. Univariate regression analyses, least absolute shrinkage and selection operator regression analysis were used to identify the association of patient and cycle characteristics with the presence of no available blastocyst and to create a nomogram.
Sci Rep
January 2025
Thin Films and Nanoscience Laboratory, Department of Physics, Tripura University, Suryamaninagar, 799022, Tripura, India.
Layer-by-Layer (LbL) technique is the simplest and inexpensive method for preparartion of nano-dimensional thin films for tailoring material behavior having wide range of applications including sensors. Here, spectroscopic behavior of two laser dyes Acriflavine (Acf) and Rhodamine B (RhB) assembled onto LbL films have been investigated. It has been observed that both Acf and RhB form stable LbL films.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Background: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and -nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.
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