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Introduction: Any alteration in nutritional status can compromise the progression of breast cancer, as well as the tolerance and efficacy of chemotherapy. The aims of our study were to assess the nutritional status of breast cancer patients treated with chemotherapy and to identify nutritional factors that may exacerbate chemo-induced digestive toxicity. Methods: This descriptive cross-sectional study was conducted within the medical oncology department of the National Institute of Salah Azaiez. A Mediterranean Diet Score (MDS) was used to evaluate the level of adherence to the Mediterranean Diet. Nutritional status was assessed using 2 methods; body composition evaluation utilising the TANITA BC 601 impedance metre and High authority of health (HAS) criteria of malnutrition. Digestive effects were assessed with World Health Organization (WHO) grades ranging from 0 to 4. High grades 3 and 4 signified toxic grades. Results: We enrolled 107 adult women diagnosed with breast cancer undergoing the same chemotherapy protocol. The participants had an average age of 52.56 ± 9.75 years and an average BMI of 29.85 ± 5.72 kg/m2 with 78.5% classified as overweight. Malnutrition was observed in 41% of patients, while sarcopenia was present in 12%. Chemotherapy-related adverse effects were predominantly constipation (80%), diarrhea (73%), nausea (70%) and vomiting (63.4%) with one-third experiencing high-grade toxicity. Strong adherence to Mediterranean Diet was observed in 48.6% of cases and higher MDS scores were inversely correlated with the severity of nausea (P =.01) and constipation (P =.02). Malnutrition was significantly associated with all digestive toxicities except with constipation. Sarcopenia was associated only with vomiting (P =.005). Conclusion: There is a relationship between nutritional status and digestive toxicity, justifying early nutritional treatment to improve the tolerance and efficacy of chemotherapy. [ABSTRACT FROM AUTHOR]

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η , achieving an R 2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R 2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring. [ABSTRACT FROM AUTHOR]

Little research has been carried out in terms of modeling and control of analgesia. However, emerging new technology and recent prototypes paved the way for several ideas on pain modeling for control. Recently, such an idea has been proposed for measuring the Depth of Analgesia (DoA). In this paper, that solution is further exploited towards obtaining a novel fractional-order model and dedicated controller for DoA. First, clinical data from patients undergoing general anesthesia are used to determine a commensurate fractional-order model of the skin impedance at each sampling period. Second, we provide a proof of concept indicating that fractional order changes due to variations in the infused opioid drug (Remifentanil). Third, a fractional-order model for DoA is developed correlating the changes in the pain index (as the output signal) and the Remifentanil infusion rate (as the input signal). Standard optimization routines are used to estimate the parameters. A database of 19 real patients is used. Lastly, a preliminary fractional-order controller is designed and tested in simulation for the 19 patients. The closed-loop simulation results correspond to the expected clinical outcomes. [ABSTRACT FROM AUTHOR]

Traditionally, amber (Succinite) has been used to alleviate all types of pain, skin allergies, and headaches. However, no studies have been conducted on its antidiabetic and antioxidant effects. In this study, differentiated skeletal muscle C2C12 cells were used to demonstrate the protective effects of amber (AMB) against H2O2-induced cell death. In addition, the effects of AMB on glucose uptake and ATP production were investigated. Our results showed that AMB at 10, 25, and 50 μg/mL suppressed the elevation of ROS production induced by H2O2 in a dose-dependent manner. Moreover, AMB enhanced glucose utilization in C2C12 cells through the improvement of ATP production and an increase in PGC-1α gene expression resulting in an amelioration of mitochondrial activity. On the other hand, AMB significantly increased the gene expression of glucose transporters GLUT4 and GLUT1. Our finding suggests that AMB can be used as a natural supplement for diabetes treatment and for the promotion of skeletal muscle function. [ABSTRACT FROM AUTHOR]

Cross-education has been studied extensively with adults, examining the training effects on contralateral homologous muscles. There is less information on the cross-education effects on contralateral heterologous muscles and scant information comparing these responses between adults and youth. The objective was to compare cross-education training effects in male youth and adults to contralateral homologous and heterologous muscles. Forty-two male children (10-13-years) and 42 adults (18-21-years) were tested before and following an 8-week unilateral, dominant or nondominant arm, chest press (CP) training program or control group (14 subjects each). Unilateral testing assessed dominant and nondominant limb strength with leg press and CP 1 repetition maximum (1RM), knee extensors, elbow extensors (EE), elbow flexors, and handgrip maximum voluntary isometric contraction (MVIC) strength and shot put distance and countermovement jump height. Upper-body tests demonstrated large magnitude increases, with children overall exceeding adults (p = 0.05- p < 0.0001, η² : 0.51, 10.4 ± 11.1%). The dominant trained limb showed significantly higher training adaptations than the nondominant limb for the adults with CP 1RM ( p = 0.03, η² : 0.26, 6.7 ± 11.5%) and EE ( p = 0.008, η² : 0.27, 8.8 ± 10.3%) MVIC force. Unilateral CP training induced significantly greater training adaptations with the ipsilateral vs. contralateral limb ( p = 0.008, η² : 0.93, 27.8 ± 12.7%). In conclusion, children demonstrated greater training adaptations than adults, upper-body strength increased with no significant lower-body improvements, and ipsilateral training effects were greater than contralateral training in adults. [ABSTRACT FROM AUTHOR]

Aims: Monitoring drug safety in real‐world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post‐marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID‐19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co‐clustering method to help the monitoring of drug safety. Methods: A dynamic latent block model (dLBM), based on a time‐dependent co‐clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR). Results: Our model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID‐19 vaccines. The dLBM also highlighted some specific drug–ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding. Conclusions: Co‐clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, 'unsupervisedly', the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports. [ABSTRACT FROM AUTHOR]

Since March 2022, the centralized cytotoxic preparation unit at the Lille University Hospital (Lille, France) is equipped with augmented reality eyewear for preparation and quality control. The technology enables a user-friendly guided step by step preparation process. It also assists the user by identifying vials through data matrix scan and recording photos at different stages of preparation in order to replace the in-process double visual inspection which will now be carried out a posteriori during the release control. In this paper, we evaluate user feedback and model the learning curve for this new tool. The team's feedback was evaluated using the System Usability Scale (SUS) and Short User Experience Questionnaire (SUEQ). Both questionnaires showed very good acceptance of the tool by our teams, with scores of 79.7 for the SUS and 2.014 for the UEQ. Finally, a learning curve was drawn up according to Wright, showing a learning curve of 91%. This study shows that the tool has been very well integrated into our preparation unit. [ABSTRACT FROM AUTHOR]