Treatment-induced symptoms, depression and age as predictors of sexual problems in premenopausal women with early breast cancer receiving adjuvant endocrine therapy.

PURPOSE: Sexual dysfunction is an important concern of premenopausal women with early breast cancer. We investigated predictors of sexual problems in two randomized controlled trials. METHODS: A subset of patients enrolled in TEXT and SOFT completed global and symptom-specific quality-of-life indicators, CES-Depression and MOS-Sexual Problems measures at baseline, six, 12 and 24 months. Mixed models tested the association of changes in treatment-induced symptoms (baseline to 6 months), depression at 6 months, and age at randomization with changes in sexual problems over 2 years. RESULTS: Sexual problems increased by 6 months and persisted at this level. Overall, patients with more severe worsening of vaginal dryness, sleep disturbances and bone or joint pain at 6 months reported a greater increase in sexual problems at all time-points. Depression scores were significantly associated with sexual problems in the short-term. All other symptoms had a smaller impact on sexual problems. Age was not associated with sexual problems at any time-point. CONCLUSION: Among several key symptoms, vaginal dryness, sleep disturbance, and bone and joint pain significantly predicted sexual problems during the first 2 years. Early identification of these symptoms may contribute to timely and tailored interventions.

ASCD: Automatic sensing and control device for crop irrigation scheduling.

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.