Search

Predicting the onset time of adverse drug events can substantially lessen the negative impact on the prognosis of cancer patients who are often subject of aggressive and highly toxic treatment regimens. However, the laboratory verification of each patient case to study the mechanics of adverse drug events requires costly, time-intensive research. Thus, to alleviate the efforts required to tackle this problem, using computational models is highly desirable. To provide a suite of such applicable models, we used openly available adverse drug event data resources called FAERS and explored various machine learning paradigms to assess their performance in predicting adverse effect onset days (since the beginning of the treatment). Among various machine learning approaches, we observed that the graph-based embedding model, particularly ComplEx, performed better than other, more traditional machine learning approaches. The embedding learned from the ComplEX trained with k-NN regression for the downstream predictive task obtained the lowest root mean square error, which we consider very promising for further research.

The evaluation of strike impact is important for optimal training, conditioning and tactical use. Therefore, the aim of this study was to evaluate ground and pound strikes, in terms of net force variability, across genders and performance levels. Eighty-one participants, professional men (n = 8, 37 ± 6 years, 195 ± 7 cm, 113 ± 27 kg), advanced men (n = 47, 26 ± 8 years, 180 ± 7 cm, 76 ± 11 kg), and advanced women (n = 26, 21 ± 1 years, 167 ± 6 cm, 61 ± 7 kg) performed three strikes from a kneeling position into a force plate on the ground. The elbow strike resulted in the highest impulse and the palm strike in the highest peak force for all three categories. These results support the recommendation that has previously been made to teach the palm strike to beginners and advanced tactical and combat athletes. The direct punch and elbow strike net force were characterized by a double peak curve, where the first peak variability explained 70.2–84% of the net force. The second peak was pronounced in professional men during elbow strikes, which explained 16% of net force variability. The strike type determines the impact net force and its characteristics, where palm strike is typical by highest peak impact tolerance and elbow strike by double force peak with high net force impulse.

Performance in strike combat sports is mostly evaluated through the values of the net force, acceleration, or speed to improve efficient training procedures and/or to assess the injury. There are limited data on the upper limb striking area, which can be a useful variable for contact pressure assessment. Therefore, the aim of this study was to determine the contact area of the upper limb in three different strike technique positions. A total of 38 men and 38 women (n = 76, 27.3 ± 8.5 years of age, 73.9 ± 13.8 kg of body weight, 173.3 ± 8.4 cm of body height) performed a static simulation of punch with a fist, palm strike, and elbow strike, where three segments of the right upper limb were scanned. The analysis of 684 images showed a correlation (r = 0.634) between weight and punch technique position in men and significant differences in elbow strike (p < 0.001) and palm strike (p < 0.0001) between women and men. In both groups, the palm demonstrated the largest area and the elbow the smallest one. These data may be used to evaluate strike contact pressure in future studies in forensic biomechanics and assessment of injury in combat sports and self-defense.