Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread.

The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty in a model's predictions. By including the most recent available data in a model, the quality of its predictions can be improved and uncertainties reduced. This paper adapts an existing, large-scale, individual-based COVID-19 model to explore the benefits of updating the model in pseudo-real time. We use Approximate Bayesian Computation (ABC) to dynamically recalibrate the model's parameter values as new data emerge. ABC offers advantages over alternative calibration methods by providing information about the uncertainty associated with particular parameter values and the resulting COVID-19 predictions through posterior distributions. Analysing such distributions is crucial in fully understanding a model and its outputs. We find that forecasts of future disease infection rates are improved substantially by incorporating up-to-date observations and that the uncertainty in forecasts drops considerably in later simulation windows (as the model is provided with additional data). This is an important outcome because the uncertainty in model predictions is often overlooked when models are used in policy.

CD4+ T cell calibration of antigen-presenting cells optimizes antiviral CD8+ T cell immunity.

Antiviral CD8+ T cell immunity depends on the integration of various contextual cues, but how antigen-presenting cells (APCs) consolidate these signals for decoding by T cells remains unclear. Here, we describe gradual interferon-α/interferon-ß (IFNα/ß)-induced transcriptional adaptations that endow APCs with the capacity to rapidly activate the transcriptional regulators p65, IRF1 and FOS after CD4+ T cell-mediated CD40 stimulation. While these responses operate through broadly used signaling components, they induce a unique set of co-stimulatory molecules and soluble mediators that cannot be elicited by IFNα/ß or CD40 alone. These responses are critical for the acquisition of antiviral CD8+ T cell effector function, and their activity in APCs from individuals infected with severe acute respiratory syndrome coronavirus 2 correlates with milder disease. These observations uncover a sequential integration process whereby APCs rely on CD4+ T cells to select the innate circuits that guide antiviral CD8+ T cell responses.

External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults: national validation cohort study in Scotland.

BACKGROUND: The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. METHODS: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. RESULTS: Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. CONCLUSIONS: Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively.

Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model.

INTRODUCTION: Reasons for drug shortages are multi-factorial, and patients are greatly injured. So we needed to reduce the frequency and risk of drug shortages in hospitals. At present, the risk of drug shortages in medical institutions rarely used prediction models. To this end, we attempted to proactively predict the risk of drug shortages in hospital drug procurement to make further decisions or implement interventions. OBJECTIVES: The aim of this study is to establish a nomogram to show the risk of drug shortages. METHODS: We collated data obtained using the centralized procurement platform of Hebei Province and defined independent and dependent variables to be included in the model. The data were divided into a training set and a validation set according to 7:3. Univariate and multivariate logistic regression were used to determine independent risk factors, and discrimination (using the receiver operating characteristic curve), calibration (Hosmer-Lemeshow test), and decision curve analysis were validated. RESULTS: As a result, volume-based procurement, therapeutic class, dosage form, distribution firm, take orders, order date, and unit price were regarded as independent risk factors for drug shortages. In the training (AUC = 0.707) and validation (AUC = 0.688) sets, the nomogram exhibited a sufficient level of discrimination. CONCLUSIONS: The model can predict the risk of drug shortages in the hospital drug purchase process. The application of this model will help optimize the management of drug shortages in hospitals.

Establishment of national standard for anti-SARS-Cov-2 neutralizing antibody in China: The first National Standard calibration traceability to the WHO International Standard.

Neutralizing antibody (NtAb) levels are key indicators in the development and evaluation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccines. Establishing a unified and reliable WHO International Standard (IS) for NtAb is crucial for the calibration and harmonization of NtAb detection assays. National and other WHO secondary standards are key links in the transfer of IS to working standards but are often overlooked. The Chinese National Standard (NS) and WHO IS were developed by China and WHO in September and December 2020, respectively, the application of which prompted and coordinated sero-detection of vaccine and therapy globally. Currently, a second-generation Chinese NS is urgently required owing to the depletion of stocks and need for calibration to the WHO IS. The Chinese National Institutes for Food and Drug Control (NIFDC) developed two candidate NSs (samples 33 and 66-99) traced to the IS according to the WHO manual for the establishment of national secondary standards through a collaborative study of nine experienced labs. Either NS candidate can reduce the systematic error among different laboratories and the difference between the live virus neutralization (Neut) and pseudovirus neutralization (PsN) methods, ensuring the accuracy and comparability of NtAb test results among multiple labs and methods, especially for samples 66-99. At present, samples 66-99 have been approved as the second-generation NS, which is the first NS calibrated tracing to the IS with 580 (460-740) International Units (IU)/mL and 580 (520-640) IU/mL by Neut and PsN, respectively. The use of standards improves the reliability and comparability of NtAb detection, ensuring the continuity of the use of the IS unitage, which effectively promotes the development and application of SARS-CoV-2 vaccines in China.

Overestimation of Oxygen Saturation Measured by Pulse Oximetry in Hypoxemia. Part 1: Effect of Optical Pathlengths-Ratio Increase.

On average, arterial oxygen saturation measured by pulse oximetry (SpO2) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO2), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO2 on 17 cyanotic newborns were performed by means of a Nellcor pulse oximeter (POx), based on light with two wavelengths in the red and infrared regions (660 and 900 nm), and by means of a novel POx, based on two wavelengths in the infrared region (761 and 820 nm). The SpO2 readings from the two POxs showed higher values than the invasive SaO2 readings, and the disparity increased with decreasing SaO2. SpO2 measured using the two infrared wavelengths showed better correlation with SaO2 than SpO2 measured using the red and infrared wavelengths. After appropriate calibration, the standard deviation of the individual SpO2-SaO2 differences for the two-infrared POx was smaller (3.6%) than that for the red and infrared POx (6.5%, p < 0.05). The overestimation of SpO2 readings in hypoxemia was explained by the increase in hypoxemia of the optical pathlengths-ratio between the two wavelengths. The two-infrared POx can reduce the overestimation of SpO2 measurement in hypoxemia and the consequent risk of occult hypoxemia, owing to its smaller increase in pathlengths-ratio in hypoxemia.

Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infection.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has prevailed globally since November 2021. The extremely high transmissibility and occult manifestations were notable, but the severity and mortality associated with the Omicron variant and subvariants cannot be ignored, especially for immunocompromised populations. However, no prognostic model for specially predicting the severity of the Omicron variant infection is available yet. In this study, we aim to develop and validate a prognostic model based on immune variables to early recognize potentially severe cases of Omicron variant-infected patients. Methods: This was a single-center prognostic study involving patients with SARS-CoV-2 Omicron variant infection. Eligible patients were randomly divided into the training and validation cohorts. Variables were collected immediately after admission. Candidate variables were selected by three variable-selecting methods and were used to construct Cox regression as the prognostic model. Discrimination, calibration, and net benefit of the model were evaluated in both training and validation cohorts. Results: Six hundred eighty-nine of the involved 2,645 patients were eligible, consisting of 630 non-ICU cases and 59 ICU cases. Six predictors were finally selected to establish the prognostic model: age, neutrophils, lymphocytes, procalcitonin, IL-2, and IL-10. For discrimination, concordance indexes in the training and validation cohorts were 0.822 (95% CI: 0.748-0.896) and 0.853 (95% CI: 0.769-0.942). For calibration, predicted probabilities and observed proportions displayed high agreements. In the 21-day decision curve analysis, the threshold probability ranges with positive net benefit were 0~1 and nearly 0~0.75 in the training and validation cohorts, correspondingly. Conclusions: This model had satisfactory high discrimination, calibration, and net benefit. It can be used to early recognize potentially severe cases of Omicron variant-infected patients so that they can be treated timely and rationally to reduce the severity and mortality of Omicron variant infection.

Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning.

The COVID-19 pandemic has provided stiff challenges for planning and resourcing in health services in the UK and worldwide. Epidemiological models can provide simulations of how infectious disease might progress in a population given certain parameters. We adapted an agent-based model of COVID-19 to inform planning and decision-making within a healthcare setting, and created a software framework that automates processes for calibrating the model parameters to health data and allows the model to be run at national population scale on National Health Service (NHS) infrastructure. We developed a method for calibrating the model to three daily data streams (hospital admissions, intensive care occupancy, and deaths), and demonstrate that on cross-validation the model fits acceptably to unseen data streams including official estimates of COVID-19 incidence. Once calibrated, we use the model to simulate future scenarios of the spread of COVID-19 in England and show that the simulations provide useful projections of future COVID-19 clinical demand. These simulations were used to support operational planning in the NHS in England, and we present the example of the use of these simulations in projecting future clinical demand during the rollout of the national COVID-19 vaccination programme. Being able to investigate uncertainty and test sensitivities was particularly important to the operational planning team. This epidemiological model operates within an ecosystem of data technologies, drawing on a range of NHS, government and academic data sources, and provides results to strategists, planners and downstream data systems. We discuss the data resources that enabled this work and the data challenges that were faced.

Extremely Simple and Rapid HPLC Analysis of Tocilizumab in Human Serum with Selective Precipitation Using Alkylamine.

An assay using HPLC with fluorescence (FL) detection method for monitoring native FL of tocilizumab (TCZ) in human serum combined with extremely simple and rapid pretreatment without any antigen-antibody reaction was developed. Good separation of TCZ was achieved within 13 min on a Presto FF-C18 column (100 × 4.6 mm i.d., 2 µm). Simple pretreatment with acetonitrile containing primary and secondary alkylamines having longer than C3 in the alkyl chain removed immunoglobulin G subclass 1 and TCZ could be recovered selectively. The spiked calibration curve of TCZ in human serum showed good linearity in the range of 40-1000 µg/mL (r > 0.997). The lower limit of quantitation (S/N = 10) of the TCZ was 19.7 µg/mL. The accuracy was within 103.5-114.9%, and the intra- and inter-day precisions as relative standard deviations were less than 5.3 and 7.8% (n = 5), respectively. The recovery of TCZ was 42.2 ± 3.4% (n = 3). The TCZ in pretreated sample was confirmed to be stable for 6 h (>95%) at room temperature and 24 h (>95%) at 4 °C. The proposed method is considered extremely superior to the previous methods in terms of time requirement for analysis. Therefore, the developed method may be more useful than conventional methods in urgent situations, such as confirming therapeutic efficacy of cytokine-release syndrome by 2019 coronavirus disease.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.

BACKGROUND: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. METHODS: We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. RESULTS: Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. CONCLUSION: Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

Evaluation of the discrimination and calibration of predictive scores of mortality in ECMO for patients with COVID-19.

BACKGROUND: The criteria for the selection of COVID-19 patients that could benefit most from ECMO organ support are yet to be defined. In this study, we evaluated the predictive performance of ECMO mortality predictive models in patients with COVID-19. We also performed a cost-benefit analysis depending on the mortality predicted probability. We conducted a retrospective cohort study in COVID-19 patients who received ECMO at two tertiary care hospitals between March 2020 to July 2021. MATERIALS AND METHODS: We evaluated the discrimination (C-statistic), calibration (Cox calibration), and accuracy of the prediction of death due to severe ARDS in V-V ECMO score (PRESERVE), the Respiratory Extracorporeal Membrane Oxygenation Survival Score (RESP) score, and the PREdiction of Survival on ECMO Therapy-Score (PRESET) score. In addition, we compared the RESP score with Plateau pressure instead of Peak pressure. RESULTS: We included a total of 36 patients, 29 (80%) of them male and with a median (IQR) APACHE of 10 (8-15). The PRESET score had the highest discrimination (AUROCs 0.81 [95%CI 0.67-0.94]) and calibration (calibration-in-the-large 0.5 [95%CI -1.4 to 0.3]; calibration slope 2.2 [95%CI 0.7/3.7]). The RESP score with Plateau pressure had higher discrimination than the conventional RESP score. The cost per QALY in the USA, adjusted to life expectancy, was higher than USD 100 000 in patients older than 45 years with a PRESET > 10. CONCLUSION: The PRESET score had the highest predictive performance and could help in the selection of patients that benefit most from this resource-demanding and highly invasive organ support.

A simple in-host model for COVID-19 with treatments: model prediction and calibration.

In this paper, we provide a simple ODEs model with a generic nonlinear incidence rate function and incorporate two treatments, blocking the virus binding and inhibiting the virus replication to investigate the impact of calibration on model predictions for the SARS-CoV-2 infection dynamics. We derive conditions of the infection eradication for the long-term dynamics using the basic reproduction number, and complement the characterization of the dynamics at short-time using the resilience and reactivity of the virus-free equilibrium are considered to inform on the average time of recovery and sensitivity to perturbations in the initial virus free stage. Then, we calibrate the treatment model to clinical datasets for viral load in mild and severe cases and immune cells in severe cases. Based on the analysis, the model calibrated to these different datasets predicts distinct scenarios: eradication with a non reactive virus-free equilibrium, eradication with a reactive virus-free equilibrium, and failure of infection eradication. Moreover, severe cases generate richer dynamics and different outcomes with the same treatment. Calibration to different datasets can lead to diverse model predictions, but combining long- and short-term dynamics indicators allows the categorization of model predictions and determination of infection severity.

Wastewater surveillance of SARS-CoV-2 at intra-city level demonstrated high resolution in tracking COVID-19 and calibration using chemical indicators.

Wastewater-based epidemiology has proven to be a supportive tool to better comprehend the dynamics of the COVID-19 pandemic. As the disease moves into endemic stage, the surveillance at wastewater sub-catchments such as pump station and manholes is providing a novel mechanism to examine the reemergence and to take measures that can prevent the spread. However, there is still a lack of understanding when it comes to wastewater-based epidemiology implementation at the smaller intra-city level for better granularity in data, and dilution effect of rain precipitation at pump stations. For this study, grab samples were collected from six areas of Seattle between March-October 2021. These sampling sites comprised five manholes and one pump station with population ranging from 2580 to 39,502 per manhole/pump station. The wastewater samples were analyzed for SARS-CoV-2 RNA concentrations, and we also obtained the daily COVID-19 cases (from individual clinical testing) for each corresponding sewershed, which ranged from 1 to 12 and the daily incidence varied between 3 and 64 per 100,000 of population. Rain precipitation lowered viral RNA levels and sensitivity of viral detection but wastewater total ammonia (NH4+-N) and phosphate (PO43--P) were shown as potential chemical indicators to calibrate/level out the dilution effect. These chemicals showed the potential in improving the wastewater surveillance capacity of COVID-19.

The contribution of asymptomatic SARS-CoV-2 infections to transmission on the Diamond Princess cruise ship.

A key unknown for SARS-CoV-2 is how asymptomatic infections contribute to transmission. We used a transmission model with asymptomatic and presymptomatic states, calibrated to data on disease onset and test frequency from the Diamond Princess cruise ship outbreak, to quantify the contribution of asymptomatic infections to transmission. The model estimated that 74% (70-78%, 95% posterior interval) of infections proceeded asymptomatically. Despite intense testing, 53% (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. The data did not allow identification of the infectiousness of asymptomatic infections, however low ranges (0-25%) required a net reproduction number for individuals progressing through presymptomatic and symptomatic stages of at least 15. Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. Control measures, and models projecting their potential impact, need to look beyond the symptomatic cases if they are to understand and address ongoing transmission.

Calibration of Methods for SARS-CoV-2 Environmental Surveillance: A Case Study from Northwest Tuscany.

The current pandemic has provided an opportunity to test wastewater-based epidemiology (WBE) as a complementary method to SARS-CoV-2 monitoring in the community. However, WBE infection estimates can be affected by uncertainty factors, such as heterogeneity in analytical procedure, wastewater volume, and population size. In this paper, raw sewage SARS-CoV-2 samples were collected from four wastewater treatment plants (WWTPs) in Tuscany (Northwest Italy) between February and December 2021. During the surveillance period, viral concentration was based on polyethylene glycol (PEG), but its precipitation method was modified from biphasic separation to centrifugation. Therefore, in parallel, the recovery efficiency of each method was evaluated at lab-scale, using two spiking viruses (human coronavirus 229E and mengovirus vMC0). SARS-CoV-2 genome was found in 80 (46.5%) of the 172 examined samples. Lab-scale experiments revealed that PEG precipitation using centrifugation had the best recovery efficiency (up to 30%). Viral SARS-CoV-2 load obtained from sewage data, adjusted by analytical method and normalized by population of each WWTP, showed a good association with the clinical data in the study area. This study highlights that environmental surveillance data need to be carefully analyzed before their use in the WBE, also considering the sensibility of the analytical methods.

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.

Selection, Characterization, Calibration, and Distribution of the U.S. Serology Standard for Anti-SARS-CoV-2 Antibody Detection.

The SARS-CoV-2 pandemic resulted in a demand for highly specific and sensitive serological testing to evaluate seroprevalence and antiviral immune responses to infection and vaccines. Hence, there was an urgent need for a serology standard to harmonize results across different natural history and vaccine studies. The Frederick National Laboratory for Cancer Research (FNLCR) generated a U.S. serology standard for SARS-CoV-2 serology assays and subsequently calibrated it to the WHO international standard (National Institute for Biological Standards and Control [NIBSC] code 20/136) (WHO IS). The development included a collaborative study to evaluate the suitability of the U.S. serology standard as a calibrator for SARS-CoV-2 serology assays. The eight laboratories participating in the study tested a total of 17 assays, which included commercial and in-house-derived binding antibody assays, as well as neutralization assays. Notably, the use of the U.S. serology standard to normalize results led to a reduction in the inter-assay coefficient of variation (CV) for IgM levels (pre-normalization range, 370.6% to 1,026.7%, and post-normalization range, 52.8% to 242.3%) and a reduction in the inter-assay CV for IgG levels (pre-normalization range, 3,416.3% to 6,160.8%, and post-normalization range, 41.6% to 134.6%). The following results were assigned to the U.S. serology standard following calibration against the WHO IS: 246 binding antibody units (BAU)/mL for Spike IgM, 764 BAU/mL for Spike IgG, 1,037 BAU/mL for Nucleocapsid IgM, 681 BAU/mL for Nucleocapsid IgG assays, and 813 neutralizing international units (IU)/mL for neutralization assays. The U.S. serology standard has been made publicly available as a resource to the scientific community around the globe to help harmonize results between laboratories.

A comparison and calibration of integer and fractional-order models of COVID-19 with stratified public response.

The spread of SARS-CoV-2 in the Canadian province of Ontario has resulted in millions of infections and tens of thousands of deaths to date. Correspondingly, the implementation of modeling to inform public health policies has proven to be exceptionally important. In this work, we expand a previous model of the spread of SARS-CoV-2 in Ontario, "Modeling the impact of a public response on the COVID-19 pandemic in Ontario, " to include the discretized, Caputo fractional derivative in the susceptible compartment. We perform identifiability and sensitivity analysis on both the integer-order and fractional-order SEIRD model and contrast the quality of the fits. We note that both methods produce fits of similar qualitative strength, though the inclusion of the fractional derivative operator quantitatively improves the fits by almost 27% corroborating the appropriateness of fractional operators for the purposes of phenomenological disease forecasting. In contrasting the fit procedures, we note potential simplifications for future study. Finally, we use all four models to provide an estimate of the time-dependent basic reproduction number for the spread of SARS-CoV-2 in Ontario between January 2020 and February 2021.

Three-Dimensional Foot Position Estimation Based on Footprint Shadow Image Processing and Deep Learning for Smart Trampoline Fitness System.

In the wake of COVID-19, the digital fitness market combining health equipment and ICT technologies is experiencing unexpected high growth. A smart trampoline fitness system is a new representative home exercise equipment for muscle strengthening and rehabilitation exercises. Recognizing the motions of the user and evaluating user activity is critical for implementing its self-guided exercising system. This study aimed to estimate the three-dimensional positions of the user's foot using deep learning-based image processing algorithms for footprint shadow images acquired from the system. The proposed system comprises a jumping fitness trampoline; an upward-looking camera with a wide-angle and fish-eye lens; and an embedded board to process deep learning algorithms. Compared with our previous approach, which suffered from a geometric calibration process, a camera calibration method for highly distorted images, and algorithmic sensitivity to environmental changes such as illumination conditions, the proposed deep learning algorithm utilizes end-to-end learning without calibration. The network is configured with a modified Fast-RCNN based on ResNet-50, where the region proposal network is modified to process location regression different from box regression. To verify the effectiveness and accuracy of the proposed algorithm, a series of experiments are performed using a prototype system with a robotic manipulator to handle a foot mockup. The three root mean square errors corresponding to X, Y, and Z directions were revealed to be 8.32, 15.14, and 4.05 mm, respectively. Thus, the system can be utilized for motion recognition and performance evaluation of jumping exercises.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture.

BACKGROUND: Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form. OBJECTIVE: In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation. METHOD: For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 µg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS. RESULTS: The models' validation criteria displayed excellent recoveries and lower errors of prediction. CONCLUSIONS: The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results. HIGHLIGHTS: Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.