Geo-Sensing-Based Analysis of Urban Heat Island in the Metropolitan Area of Merida, Mexico.

Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation (r> 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies.

Quantity and quality of muscle in patients recently diagnosed with colorectal cancer: a comparison with cancer-free controls.

This cross-sectional study compares the muscle mass, core strength and physical fragility of patients recently diagnosed with colorectal cancer (pRD-CRC) with those of healthy subjects and identifies variables to be considered when designing pre-treatment physical interventions for such patients. Body composition, anthropometric variables, the muscle architecture of the lumbopelvic region, physical fitness and frailty were assessed in 32 pRD-CRC and 29 healthy control subjects. The patients showed a reduction in muscle mass (F = 10.059; P = 0.003), in the width of the lumbar multifidus (F = 21.869; P < 0.001), in the transverse abdominal muscle (U = 323.00; P = 0.042) and in the abdominal strength resistance (F = 12.264; P = 0.001). They were also frailer (P = 0.002) than the controls. These results suggest that pRD-CRC are affected by reduced strength and myopenia, leading to frailty. The early incorporation of these patients into strength-enhancing programs may be advisable.

A Blended Learning System to Improve Motivation, Mood State, and Satisfaction in Undergraduate Students: Randomized Controlled Trial.

BACKGROUND: Smartphone-based learning, or mobile learning (m-learning), has become a popular learning-and-teaching strategy in educational environments. Blended learning combines strategies such as m-learning with conventional learning to offer continuous training, anytime and anywhere, via innovative learning activities. OBJECTIVE: The main aim of this work was to examine the short-term (ie, 2-week) effects of a blended learning method using traditional materials plus a mobile app-the iPOT mobile learning app-on knowledge, motivation, mood state, and satisfaction among undergraduate students enrolled in a health science first-degree program. METHODS: The study was designed as a two-armed, prospective, single-blind, randomized controlled trial. Subjects who met the inclusion criteria were randomly assigned to either the intervention group (ie, blended learning involving traditional lectures plus m-learning via the use of the iPOT app) or the control group (ie, traditional on-site learning). For both groups, the educational program involved 13 lessons on basic health science. The iPOT app is a hybrid, multiplatform (ie, iOS and Android) smartphone app with an interactive teacher-student interface. Outcomes were measured via multiple-choice questions (ie, knowledge), the Instructional Materials Motivation Survey (ie, motivation), the Profile of Mood States scale (ie, mood state), and Likert-type questionnaires (ie, satisfaction and linguistic competence). RESULTS: A total of 99 students were enrolled, with 49 (49%) in the intervention group and 50 (51%) in the control group. No difference was seen between the two groups in terms of theoretical knowledge gain (P=.92). However, the intervention group subjects returned significantly higher scores than the control group subjects for all postintervention assessed items via the motivation questionnaire (all P<.001). Analysis of covariance (ANCOVA) revealed a significant difference in the confusion and bewilderment component in favor of the intervention group (P=.01), but only a trend toward significance in anger and hostility as well as total score. The intervention group subjects were more satisfied than the members of the control group with respect to five out of the six items evaluated: general satisfaction (P<.001), clarity of the instructions (P<.01), clarity with the use of the learning method (P<.001), enough time to complete the proposed exercises (P<.01), and improvement in the capacity to learn content (P<.001). Finally, the intervention group subjects who were frequent users of the app showed stronger motivation, as well as increased perception of greater gains in their English-language competence, than did infrequent users. CONCLUSIONS: The blended learning method led to significant improvements in motivation, mood state, and satisfaction compared to traditional teaching, and elicited statements of subjective improvement in terms of competence in English. TRIAL REGISTRATION: ClinicalTrials.gov NCT03335397; https://clinicaltrials.gov/ct2/show/NCT03335397.

Health status among long-term breast cancer survivors suffering from higher levels of fatigue: a cross-sectional study.

PURPOSE: The aims of this study were to evaluate the health status of long-term breast cancer survivors (LTBCS) suffering from higher levels of fatigue, to highlight their needs, and to establish the key points of intervention support programs. METHODS: A cross-sectional observational study was conducted at the Sport and Health Joint University Institute (iMUDS) between September 2016 and July 2017 with 80 LTBCS that were classified into non-fatigued (≤ 3.9) or fatigued (≥ 4) according to the Piper Fatigue Scale (PFS) total score. The instruments used were the European Organization for Research and Treatment of Cancer Core 30 and its breast cancer (BC) module, the Visual Analog Scale (VAS), the Brief Pain Inventory (BPI), the Scale for Mood Assessment (EVEA), the International Fitness Scale (IFIS), and the Charlson Comorbidity Index. RESULTS: The analysis revealed that 41.2% of LTBCS were considered moderately fatigued and showed significantly higher levels for the categories of "nausea and vomiting" (P = .005), "pain," "dyspnea" and "insomnia" (P < .001), "appetite loss" (P = .002), "financial difficulties" (P = .010), "systemic therapy side effects" (P < .001), "breast symptoms" and "arm symptoms" (P = .002), and "upset by hair loss" (P = .016). In addition, LTBCS presented significantly higher levels of pain in the affected and non-affected arm, "sadness-depression." "anxiety," "anger/hostility" (All: P < .001), and lower general physical fitness (P < .001). The rest of the variables did not show significant differences. CONCLUSION: LTBCS suffering from higher levels of fatigue had lower QoL, higher level of pain, worse mood state, and lower physical fitness.

Modeling the Electrical Activity of the Heart via Transfer Functions and Genetic Algorithms.

Although healthcare and medical technology have advanced significantly over the past few decades, heart disease continues to be a major cause of mortality globally. Electrocardiography (ECG) is one of the most widely used tools for the detection of heart diseases. This study presents a mathematical model based on transfer functions that allows for the exploration and optimization of heart dynamics in Laplace space using a genetic algorithm (GA). The transfer function parameters were fine-tuned using the GA, with clinical ECG records serving as reference signals. The proposed model, which is based on polynomials and delays, approximates a real ECG with a root-mean-square error of 4.7% and an R2 value of 0.72. The model achieves the periodic nature of an ECG signal by using a single periodic impulse input. Its simplicity makes it possible to adjust waveform parameters with a predetermined understanding of their effects, which can be used to generate both arrhythmic patterns and healthy signals. This is a notable advantage over other models that are burdened by a large number of differential equations and many parameters.