Multiple linear regression modeling: Prediction of cheese curd dry matter during curd treatment

An in-vitro three-dimensional surgical simulation technique to predict tibial tunnel length in transtibial posterior cruciate ligament reconstruction

BACKGROUND

During the transtibial posterior cruciate ligament (PCL) reconstruction, drilling depth excessively longer than the tibial tunnel length (TTL) is an important reason to cause popliteal neurovascular bundle injury when preparing the tibial tunnel. This study aims to develop an in-vitro three-dimensional surgical simulation technique to determine the TTL in anteromedial (AM) and anterolateral (AL) approaches.

METHODS

A total of 63 knees' 3-dimensional (3D) computed tomography models were included in this study. The SuperImage system was used to reconstruct the 3D knee model and locate the tibial PCL site. The established 3D knee model and the coordinates of the tibial PCL site were imported into Rhinoceros 3D modeling software to simulate AM and AL tibial tunnel approaches with different tibial tunnel angles (TTA). The TTL and the tibial tunnel height (TTH) were measured in this study.

RESULTS

In AM and AL tibial tunnel approaches, the TTL showed a strong correlation with the TTA (for AM: r = 0.758, p < 0.001; for AL: r = 0.727, p < 0.001). The best fit equation to calculate the TTL based on the TTA was Y = 1.04X + 14.96 for males in AM approach, Y = 0.93X + 17.76 for males in AL approach, Y = 0.92X + 14.4 for females in AM approach, and Y = 0.94X + 10.5 for females in AL approach.

CONCLUSION

Marking the TTL on the guide pin or reamer could help to avoid the drill bit over-penetrated into the popliteal space to damage the neurovascular structure.

Engineering artificial casein micelles for future food: Is casein phosphorylation necessary?

Industrial-scale production of recombinant proteins for food products may become economically feasible but correct post-translational modification of proteins by microbial expression systems remains a challenge. For efficient production of hybrid products from bovine casein and recombinant casein, it is therefore of interest to evaluate the necessity of casein post-translational phosphorylation for the preparation of hybrid casein micelles and study their rennet-induced coagulation. Our results show that dephosphorylated casein was hardly incorporated into artificial casein micelles but was capable of stabilising calcium phosphate nanoclusters with an increased size through adsorption on their surface. Thereby, dephosphorylated casein formed larger colloidal particles with a decreased hydration. Furthermore, the presence of increasing amounts of dephosphorylated casein resulted in increasingly poor rennet coagulation behaviour, where dephosphorylated casein disrupted the formation of a coherent gel network by native casein. These results emphasise that post-translational phosphorylation of casein is crucial for their assembly into micelles and thereby for the production of dairy products for which the casein micelle structure is a prerequisite, such as many cheese varieties and yoghurt. Therefore, phosphorylation of future recombinant casein is essential to allow its use in the production of animal-free dairy products.

A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients

Background

Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention.

Methods

This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients’ outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model.

Results

The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively.

Conclusions

Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.

Predictive modeling of the early stages of semi-solid food ripening: Spatio-temporal dynamics in semi-solid casein matrices

A mechanistic, spatio-temporal model to predict early stage semi-solid food ripening, exemplary for semi-solid casein matrices, was created using software based on the finite element method (FEM). The model was refined and validated by experimental data obtained during 8 wk of ripening of a casein matrix that was inoculated by one single central injection of starter culture. The resulting spatio-temporal distributions of lactococci strains, lactose, lactic acid/lactate and pH allowed us to optimize a number of parameters of the predictive model. Using the optimized model, the agreement between simulation and experiment was found to be satisfactory, with the pH matching best. The predictive model unveiled that effective diffusion of substrate and metabolites were crucial for an eventual homogeneous distribution of the measured substances. Hence, while using the optimized parameters from the single injection model, an injection technology for starter culture to inoculate and ferment casein matrices homogeneously was developed by means of solving another optimization problem with respect to injection positions. The casein matrix inoculated by the proposed injection pattern (21 injections, distance = 19 mm) showed sufficient homogeneity (bacterial activity and pH distribution) after the early stages of ripening, demonstrating the potential of application of the injection technology for fermentation of casein-based foods e.g. cheese.

Correlation Between Local Air Temperature and the COVID-19 Pandemic in Hubei, China

Objective: To clarify the correlation between temperature and the COVID-19 pandemic in Hubei.

Methods: We collected daily newly confirmed COVID-19 cases and daily temperature for six cities in Hubei Province, assessed their correlations, and established regression models.

Results: For temperatures ranging from −3.9 to 16.5°C, daily newly confirmed cases were positively correlated with the maximum temperature ~0–4 days prior or the minimum temperature ~11–14 days prior to the diagnosis in almost all selected cities. An increase in the maximum temperature 4 days prior by 1°C was associated with an increase in the daily newly confirmed cases (~129) in Wuhan. The influence of temperature on the daily newly confirmed cases in Wuhan was much more significant than in other cities.

Conclusion: Government departments in areas where temperatures range between −3.9 and 16.5°C and rise gradually must take more active measures to address the COVID-19 pandemic.

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.