Adulteration detection in plant-based medicinal powders is necessary to provide high quality products due to the economic and health importance of them. According to advantages of imaging technology as non-destructive tool with low cost and time, the present research aims to evaluate the ability of the visible imaging combined with machine learning for distinguish original products and the adulterated samples with different levels of chickpea flour. The original products were black pepper, red pepper, and cinnamon, the adulterant was chick pea, and the adulteration levels were 0, 5, 15, 30, and 50 %. The results showed that the accuracies of the classifier based on the artificial neural networks method for classification of black pepper, red pepper, and cinnamon were 97.8, 98.9, and 95.6 %, respectively. The results for support vector machine with one-to-one strategy were 93.33, 97.78 and 92.22 %, respectively. Visible imaging combined with machine learning are reliable technologies to detect adulteration in plant-based medicinal powders so that can be applied to develop industrial systems and improving performance and reducing operation costs.

In swimming, the posture and technique of athletes are crucial for improving performance. However, traditional swimming coaches often struggle to capture and analyze athletes\' movements in real-time, which limits the effectiveness of coaching. Therefore, this paper proposes RL-CWtrans Net: a robot vision-driven multimodal swimming training system that provides precise and real-time guidance and feedback to swimmers. The system utilizes the Swin-Transformer as a computer vision model to effectively extract the motion and posture features of swimmers. Additionally, with the help of the CLIP model, the system can understand natural language instructions and descriptions related to swimming. By integrating visual and textual features, the system achieves a more comprehensive and accurate information representation. Finally, by employing reinforcement learning to train an intelligent agent, the system can provide personalized guidance and feedback based on multimodal inputs. Experimental results demonstrate significant advancements in accuracy and practicality for this multimodal robot swimming coaching system. The system is capable of capturing real-time movements and providing immediate feedback, thereby enhancing the effectiveness of swimming instruction. This technology holds promise.

The dried ripe fruit of Terminalia chebula Retz. is a common Chinese materia medica, and ellagic acid (EA), isolated from the plant, is an important bioactive component for medicinal purposes. This study aimed to delineate the optimal extraction parameters for extracting the EA content from Chebulae Fructus (CF), focusing on the variables of ethanol concentration, extraction temperature, liquid-solid ratio, and extraction time. Utilizing a combination of the response surface methodology (RSM) and an artificial neural network (ANN), we systematically investigated these parameters to maximize the EA extraction efficiency. The extraction yields for EA obtained under the predicted optimal conditions validated the efficacy of both the RSM and ANN models. Analysis using the ANN-predicted data showed a higher coefficient of determination (R2) value of 0.9970 and a relative error of 0.79, compared to the RSM\'s 2.85. The optimal conditions using the ANN are an ethanol concentration of 61.00%, an extraction temperature of 77 °C, a liquid-solid ratio of 26 mL g-1 and an extraction time of 103 min. These findings significantly enhance our understanding of the industrial-scale optimization process for EA extraction from CF.

UNASSIGNED: Osteoporotic Vertebral Compression Fracture (OVCF) substantially reduces a person\'s health-related quality of life. Computer Tomography (CT) scan is currently the standard for diagnosis of OVCF. The aim of this paper was to evaluate the OVCF detection potential of artificial neural networks (ANN).
UNASSIGNED: Models of artificial intelligence based on deep learning hold promise for quickly and automatically identifying and visualizing OVCF. This study investigated the detection, classification, and grading of OVCF using deep artificial neural networks (ANN). Techniques: Annotation techniques were used to segregate the sagittal images of 1,050 OVCF CT pictures with symptomatic low back pain into 934 CT images for a training dataset (89%) and 116 CT images for a test dataset (11%). A radiologist tagged, cleaned, and annotated the training dataset. Disc deterioration was assessed in all lumbar discs using the AO Spine-DGOU Osteoporotic Fracture Classification System. The detection and grading of OVCF were trained using the deep learning ANN model. By putting an automatic model to the test for dataset grading, the outcomes of the ANN model training were confirmed.
UNASSIGNED: The sagittal lumbar CT training dataset included 5,010 OVCF from OF1, 1942 from OF2, 522 from OF3, 336 from OF4, and none from OF5. With overall 96.04% accuracy, the deep ANN model was able to identify and categorize lumbar OVCF.
UNASSIGNED: The ANN model offers a rapid and effective way to classify lumbar OVCF by automatically and consistently evaluating routine CT scans using AO Spine-DGOU osteoporotic fracture classification system.

In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilities and support physicians in diagnosing and treating patients. This paper focuses on modelling glaucoma evolution because it requires early diagnosis, individualized treatment, and lifelong monitoring. Glaucoma is a chronic, progressive, irreversible, multifactorial optic neuropathy that primarily affects elderly individuals. It is important to emphasize that the processed data are taken from medical records, unlike other studies in the literature that rely on image acquisition and processing. Although more challenging to handle, this approach has the advantage of including a wide range of parameters in large numbers, which can highlight their potential influence. Artificial neural networks are used to study glaucoma progression, designed through successive trials for near-optimal configurations using the NeuroSolutions and PyTorch frameworks. Furthermore, different problems are formulated to demonstrate the influence of various structural and functional parameters on the study of glaucoma progression. Optimal neural networks were obtained using a program written in Python using the PyTorch deep learning framework. For various tasks, very small errors in training and validation, under 5%, were obtained. It has been demonstrated that very good results can be achieved, making them credible and useful for medical practice.

UNASSIGNED: Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future.
UNASSIGNED: The current literature about the latest business and scientific evolutions and trends was reviewed.
UNASSIGNED: In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products\' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient.
UNASSIGNED: Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

Carbon dioxide rebreathing (CO2 rebreathing) significantly influences respiratory drive and the work of breathing during BiPAP ventilation. We analyzed CO2 movement during BiPAP ventilation to find a method of real time detection of CO2 rebreathing without the need of CO2 concentration measurement sampled from the circuit (method expensive and not routinely used). Observational study during routine care in 15 bed university hospital ICU. At 18 patients who required BiPAP ventilation, intubated or during noninvasive ventilation, during weaning period airflow, pressure and CO2 concentration signals were registered on both sides of venting port and 17 respiratory parameters were measured or calculated for each of 4747 respiratory cycles analyzed. Based on CO2 movement (expiration-inspiration sequences) 3 types of cycle were identified, type I and II do not induce rebreathing but type III does. To test differences between the 3 types ANOVA, t-tests, and canonical discriminant analysis (CDA) were used. Then a multilayer perceptron (MLP) network, a type of artificial neural network, using the above parameters (excluding CO2 concentration) was applied to automatically identify the three types of respiratory cycles. Of the 4747 respiratory cycles, 1849 were type I, 1545 type II, and 1353 type III. ANOVA and t-tests showed significant differences between the types of respiratory cycles. CDA confirmed a correct apportionment of 93.9% of the cycles; notably, of 97.9% of type III. MLP automatically classified the respiratory cycles into the three types with 98.8% accuracy. Three types of respiratory cycles could be distinguished based on CO2 movement during BiPAP ventilation. Artificial neural networks can be used to automatically detect respiratory cycle type III, the only inducing CO2 rebreathing.

Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. Materials: a database of 1256 SCI patients admitted for rehabilitation was analyzed. Clinical and demographic data and SCI characteristics were used to predict functional outcomes using both ANN and linear regression models. The former was structured with input, hidden, and output layers, while the linear regression identified significant variables affecting outcomes. Both approaches aimed to evaluate and compare their accuracy for rehabilitation outcomes measured by the Spinal Cord Independence Measure (SCIM) score. Results: Both ANN and linear regression models identified key predictors of functional outcomes, such as age, injury level, and initial SCIM scores (correlation with actual outcome: R = 0.75 and 0.73, respectively). When also alimented with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors, like motor completeness and complications during hospitalization, showing an improvement in its accuracy (R = 0.87). Conclusions: ANN seemed to be not widely superior to classical statistics in general, but, taking into account complex and non-linear relationships among variables, emphasized the impact of complications during the hospitalization on recovery, particularly respiratory issues, deep vein thrombosis, and urological complications. These results suggested that the management of complications is crucial for improving functional recovery in SCI patients.

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

The objectives of this study were to evaluate the energy partition patterns of growing pigs fed diets with different net energy (NE) levels based on machine learning methods, and to develop prediction models for the NE requirement of growing pigs. Twenty-four Duroc × Landrace × Yorkshire crossbred barrows with an initial body weight of 24.90 ± 0.46 kg were randomly assigned to 3 dietary treatments, including the low NE group (2,325 kcal/kg), the medium NE group (2,475 kcal/kg), and the high NE group (2,625 kcal/kg). The total feces and urine produced from each pig during each period were collected, to calculate the NE intake, NE retained as protein (NEp), and NE retained as lipid (NEl). A total of 240 sets of data on the energy partition patterns of each pig were collected, 75% of the data in the dataset was randomly selected as the training dataset, and the remaining 25% was set as the testing dataset. Prediction models for the NE requirement of growing pigs were developed using algorithms including multiple linear regression (MR), artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF), and the prediction performance of these models was compared on the testing dataset. The results showed pigs in the low NE group showed a lower average daily gain, lower average daily feed intake, lower NE intake, but greater feed conversion ratio compared to pigs in the high NE group in most growth stages. In addition, pigs in the 3 treatment groups did not show a significant difference in NEp in all growth stages, while pigs in the medium and high NE groups showed greater NEl compared to pig in the low NE group in growth stages from 25 to 55 kg (P < 0.05). Among the developed prediction models for NE intake, NEp, and NEl, the ANN models demonstrated the most optimal prediction performance with the smallest root mean square error (RMSE) and the largest R2, while the RF models had the worst prediction performance with the largest RMSE and the smallest R2. In conclusion, diets with varied NE concentrations within a certain range did not affect the NEp of growing pigs, and the models developed with the ANN algorithm could accurately achieve the NE requirement prediction in growing pigs.
Net energy (NE) can unify the energy value of the feed with the energy requirements of the pig more accurately and is the optimal system for accurately predicting the growth performance of pigs. The evaluation of the NE partition pattern is difficult and costly, thus, establishing a predicted model is a more efficient way. This study was conducted to evaluate the energy partition patterns of growing pigs fed diets with different NE levels based on machine learning methods. Diets with varied NE concentrations within a certain range did not affect the growth performance and NE requirement for lipid deposition in growing pigs. Among the 4 models developed to predict NE requirements, the artificial neural networks model had the highest accuracy, while the multiple linear regression model had the highest interpretability.