Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle.

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer-Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.

Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy.

Purpose: To develop a multivariable normal tissue complication probability (NTCP) model to predict moderate to severe late rectal bleeding following intensity-modulated radiation therapy (IMRT). Methods and materials: Sixty-eight patients with localized prostate cancer treated by IMRT from 2008 to 2011 were enrolled. The median follow-up time was 56 months. According to the criteria of D'Amico risk classifications, there were 9, 20 and 39 patients in low, intermediate and high-risk groups, respectively. Forty-two patients were combined with androgen deprivation therapy. Fifteen patients had suffered from grade 2 or more (grade 2+) late rectal bleeding. The numbers of predictors for a multivariable logistic regression NTCP model were determined by the least absolute shrinkage and selection operator (LASSO). Results: The most important predictors for late rectal bleeding ranked by LASSO were platelet count, risk group and the relative volume of rectum receiving at least 65 Gy (V65). The NTCP model of grade 2+ rectal bleeding was as follows: S = -17.49 + Platelets (1000/µL) * (-0.025) + Risk group * Corresponding coefficient (low-risk group = 0; intermediate-risk group = 19.07; high-risk group = 20.41) + V65 * 0.045. Conclusions: A LASSO-based multivariable NTCP model comprising three important predictors (platelet count, risk group and V65) was established to predict the incidence of grade 2+ late rectal bleeding after IMRT.

Propensity-score-matched evaluation of the incidence of radiation pneumonitis and secondary cancer risk for breast cancer patients treated with IMRT/VMAT.

Propensity score matching evaluates the treatment incidence of radiation-induced pneumonitis (RP) and secondary cancer risk (SCR) after intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) for breast cancer patients. Of 32 patients treated with IMRT and 58 who received VMAT were propensity matched in a 1:1 ratio. RP and SCR were evaluated as the endpoints of acute and chronic toxicity, respectively. Self-fitted normal tissue complication probability (NTCP) parameter values were used to analyze the risk of RP. SCRs were evaluated using the preferred Schneider's parameterization risk models. The dosimetric parameter of the ipsilateral lung volume receiving 40 Gy (IV40) was selected as the dominant risk factor for the RP NTCP model. The results showed that the risks of RP and NTCP, as well as that of SCR of the ipsilateral lung, were slightly lower than the values in patients treated with VMAT versus IMRT (p ≤ 0.01). However, the organ equivalent dose and excess absolute risk values in the contralateral lung and breast were slightly higher with VMAT than with IMRT (p ≤ 0.05). When compared to IMRT, VMAT is a rational radiotherapy option for breast cancer patients, based on its reduced potential for inducing secondary malignancies and RP complications.

Tennis Elbow Diagnosis Using Equivalent Uniform Voltage to Fit the Logistic and the Probit Diseased Probability Models.

To develop the logistic and the probit models to analyse electromyographic (EMG) equivalent uniform voltage- (EUV-) response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG) signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS) 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP) models were established for the VAS score and EMG absolute voltage-time histograms (AVTH). TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27%) developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3-169.7 mV), γ 50 = 0.84 (CI: 0.78-0.90) and TV50 = 155.6 mV (CI: 138.9-172.4 mV), m = 0.54 (CI: 0.49-0.59) for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow.