Guar gum, a low-cost sustainable biopolymer, for wastewater treatment: A review.

Rapid population growth and the resultant pollution of freshwater resources have created a water stress condition reducing the availability of safe and affordable water. Guar gum, a biocompatible macromolecule obtained from the endosperm of the seeds of Cyamopsis tetragonolobus, is a fascinating raw material for multifunctional adsorbents. This review assembled the work conducted by various researchers over the past few decades and discussed the structure, properties, and different modifications methods employed to develop versatile guar gum-based adsorbent. The paper also summarized the recent progress of guar gum-based nanocomposites for the remediation of multiple hazardous substances such as organic dyes, toxic heavy metal ions, oil-water separation as well as inhibiting the growth of bacterial pathogens. Thus, the important contribution of guar gum composites to safeguard the water quality is highlighted which will overcome the limitations and streamline the future course of innovative research.

Deep learning-based tool affects reproducibility of pes planus radiographic assessment.

Angle measurement methods for measuring pes planus may lose consistency by errors between observers. If the feature points for angle measurement can be provided in advance with the algorithm developed through the deep learning method, it is thought that the error between the observers can be reduced. A total of 300 weightbearing lateral radiographs were used for the development of the deep learning-based algorithm, and a total of 95 radiographs were collected for the clinical validation test set. Meary angle (MA) and calcaneal pitch (CP) were selected as measurement methods and measured twice by three less-experienced physicians with the algorithm-based tool and twice without. The intra- and inter-observer agreements of MA and CP measures were assessed via intra-class correlation coefficient. In addition, verification of the improvement of measurement performance by the algorithm was performed. Interobserver agreements for MA and CP measurements with algorithm were more improved than without algorithm. As for agreement with reference standard, combining the results of all readers, both MA and CP with algorithm were greater than those without algorithm. The deep learning algorithm tool is expected to improve the reproducibility of radiographic measurements for pes planus, especially by improving inter-observer agreement.