Most studies associated diabetes mellitus (DM) with risk of cancer have focused on the Caucasian population and only a few types of cancer. Therefore, a large and comprehensive nationwide retrospective cohort study involving an Asian population was conducted to evaluate the risk of several major types of cancer among Type 2 DM patients. The study analyzed the nationwide population-based database from 1996 to 2009 released by the National Health Research Institute in Taiwan. Incidence and hazard ratios (HRs) were calculated for specific types of cancer. The overall risk of cancers was significantly greater in the DM cohort [N 5 895,434; HR 5 1.19, 95% confidence interval (CI) 5 1.17-1.20], compared with non-DM controls (N 5 895,434). Several organs in the digestive and urogenital systems showed increased risk of cancer. The three highest HRs were obtained from cancers of the liver (HR 5 1.78, 95% CI 5 1.73-1.84), pancreatic (HR 5 1.52, 95% CI 5 1.40-1.65), and uterus and corpus (HR 5 1.38, 95% CI 5 1.22-1.55). The risk increased with age, and men with DM aged 75 years exhibited the highest risk (HR 5 7.76, 95% CI 5 7.39-8.15). Subjects with DM in this population have a modest increased risk of cancer, similar to the Caucasian population for several specific types of cancer. Old men with DM have the highest risk of cancer. Careful screening for cancer in DM patients is important for early diagnosis and effective treatment.

Background. Knee osteoarthritis (OA) presented with knee pain and limitation of mobility is common, and it may become a chronic problem resulting in major loss of function, with related impaired activity of daily living. Current traditional therapy for knee OA includes pharmacological treatment and physiotherapy, but the efficacies are limited. An alternative noninvasive treatment low-level laser therapy (LLLT) applied to acupoints is still contradictory and the efficacy needs to be assessed. Methods and Materials. We conduct the randomized double-blind control study to investigate the efficacy of a dual-frequency LLLT (combines red light (780 nm) and near-infrared light (830 nm)) in patients suffering knee OA. Participates were randomly assigned into active laser therapy (ALT) and placebo laser therapy (PLT) groups. Subjects in the ALT group were separately treated by laser apparatus at the three acupoints (SP9, SP10, and EX-LE2) on their knee joints under continuous radiation for 15 min at the maximum intensity, three times per week for four weeks. The PLT group used laser apparatus of the same model according to similar procedures without laser light emission. Outcome Measurements including visual analog scale (VAS), pain pressure threshold (PPT), and Lequesne index were used. Results. A total of 30 subjects with two-sided knee OA in both groups completed the experiment. Statistically significant decreases were observed in the Lequesne index (5.27 ± 3.26 vs. 10.83 ± 3.83), conscious VAS 4 weeks after treatment (moving: 2.87 ± 1.13 vs. 5.67 ± 1.72; resting: 0.33 ± 0.62 vs. 2.67 ± 1.29), and the increase was noted in PPT (21.23 ± 1.82 kg vs. 13.02 ± 1.46 kg) in the ALT group compared with the PLT group. Conclusion. It appears that the knee OA pain and disability can be decreased after a dual-frequency LLLT applied to acupoints (SP9, SP10, and EX-LE2). The clinical efficacy of LLLT is highly related to the therapeutic settings of the laser apparatus; hence, more clinical trials with diffident parameter settings are needed to be further clarified.

Falls put older adults at great risk and are related to the body’s sense of balance. This study investigated how to detect the possibility of high fall risk subjects among older adults. The original signal is based on center of pressure (COP) measured using a force plate. The falling group includes 29 subjects who had a history of falls in the year preceding this study or had received high scores on the Short Falls Efficacy Scale (FES). The nonfalling group includes 47 enrollees with no history of falls and who had received low scores on the Short FES. The COP in both the anterior–posterior and mediolateral direction were calculated and analyzed through empirical mode decomposition (EMD) up to six levels. The following five features were extracted and imported to a decision tree algorithm: root-mean-square deviation, median frequency, total frequency power, approximate entropy, and sample entropy. The results showed that there were a larger number of statistically different feature parameters, and a higher classification of accuracy was obtained. With the aid of empirical mode decomposition, the average classification accuracy increased 10% and achieved a level of 99.74% in the training group and 96.77% in the testing group, respectively.