This study evaluated the effectiveness of an individualized home-based exercise program that included specific turning exercises in improving turning performance in adults identified as having unsteadiness during turning.
Objective: Study aimed to identify the prevalence of falls and associated contributory factors among older Malaysians. Methods: A cross sectional study among community dwelling older adults aged 50 years and above. Self-administered questionnaires on history of falls in the previous 12 months, physical assessment and computerized and clinical measures of balance were assessed on a single occasion. Results: Forty nine (31.0%) participants fell, with 4.4% reported having multiple falls within the previous 12 months. Slips were the most prevalent cause of falls, accounting for 49% of falls. More than half (54.5%) of falls occurred in the afternoon while participants walked inside the home (32.7%), outside home (30.6%), and 36.7% were in community areas. More than half of respondents were identified as having turning instability. Step Test, turn sway, depression, physical activity level and edge contrast sensitivity were significantly worse for fallers (p < 0.05). Multiple logistic regression analysis showed that turning performance, visual acuity and back pain were significantly associated with falls risk, accounting for 72% of the variance of risk factors for falls among studied population. Conclusion: Falls are common among community dwelling older Malaysians. The findings provide information of falls and falls risk factors among community dwelling older adults in Malaysia. Future intervention studies should target locally identified falls risk factors. This study has highlighted the importance of instability during turning as an important fall risk factor.
Many receiver-based Preamble Sampling Medium Access Control (PS-MAC) protocols have been proposed to provide better performance for variable traffic in a wireless sensor network (WSN). However, most of these protocols cannot prevent the occurrence of incorrect traffic convergence that causes the receiver node to wake-up more frequently than the transmitter node. In this research, a new protocol is proposed to prevent the problem mentioned above. The proposed mechanism has four components, and they are Initial control frame message, traffic estimation function, control frame message, and adaptive function. The initial control frame message is used to initiate the message transmission by the receiver node. The traffic estimation function is proposed to reduce the wake-up frequency of the receiver node by using the proposed traffic status register (TSR), idle listening times (ILTn, ILTk), and "number of wake-up without receiving beacon message" (NWwbm). The control frame message aims to supply the essential information to the receiver node to get the next wake-up-interval (WUI) time for the transmitter node using the proposed adaptive function. The proposed adaptive function is used by the receiver node to calculate the next WUI time of each of the transmitter nodes. Several simulations are conducted based on the benchmark protocols. The outcome of the simulation indicates that the proposed mechanism can prevent the incorrect traffic convergence problem that causes frequent wake-up of the receiver node compared to the transmitter node. Moreover, the simulation results also indicate that the proposed mechanism could reduce energy consumption, produce minor latency, improve the throughput, and produce higher packet delivery ratio compared to other related works.
GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.