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ETHNOPHARMACOLOGICAL RELEVANCE: Leaves of Cecropia pachystachya are described in the folk medicine as possessing antitusive, expectorant, antiasthmatic and hypoglycemic effects. AIM OF THE STUDY: To investigate the hypoglycemic and antioxidant effects of methanolic extract from the leaves of Cecropia pachystachya. The total amount of phenolic and flavonoids compounds was determined and the qualitative composition of the extract was analyzed. MATERIALS AND METHODS: The hypoglycemic effect of the extract was tested in normal, glucose loading and alloxan-induced diabetic rats. The antioxidant activity was assessed by DPPH free radical scavenging and reduction power assays. The total amount of phenolic and flavonoids compounds was determined by Folin-Denis and AlCl(3) reagent method, respectively. The qualitative composition of the extract was analyzed using a HPLC-DAD system. RESULTS: The glucose tolerance test showed that in diabetic rats, the extract caused a significant hypoglycemic effect with a blood glucose reduction of 68% after 12 h. The administration of the extract in alloxan-induced diabetic rats also produced a significant reduction in the blood glucose levels at all points being more pronounced at 90 min (reduction of 60%). After 120 min, no significant difference was observed between the blood levels of the rats treated with the extract and those treated with the standard drugs (metformin and glibenclamide). The extract also presented relevant antioxidant activity with IC(50)=3.1 µg/ml (DPPH assay) and EC(50)=10.8 µg/ml (reduction power). Results were compared with the reference antioxidants quercetin, rutin, and ascorbic acid. The content of flavonoids was 83 mg/g plant and that of phenolics was 326 mg/g plant. Chlorogenic acid and the C-glycosylated flavones, orientin and isoorientin, were identified in the extract. CONCLUSIONS: In conclusion, the findings showed that the folk medicinal plant Cecropia pachystachya possesses hypoglycemic and antioxidant effects which confirmed the traditional use of the plant in the treatment of diabetes. Chlorogenic acid and the C-glycosylated flavonoids may explain these activities. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or experimental testing. However, no ML model is robust or predictive in all real-world scenarios. Therefore, uncertainty quantification (UQ) in ML predictions has gained importance in recent years. Many investigations have focused on developing methodologies that provide accurate uncertainty estimates for ML-based predictions. Unfortunately, there is no UQ strategy that consistently provides robust estimates about model's applicability on new samples. Depending on the dataset, prediction task, and algorithm, accurate uncertainty estimations might be unfeasible to obtain. Moreover, the optimum UQ metric also varies across applications, and previous investigations have shown a lack of consistency across benchmarks. Herein, the UNIQUE (UNcertaInty QUantification bEnchmarking) framework is introduced to facilitate a comparison of UQ strategies in ML-based predictions. This Python library unifies the benchmarking of multiple UQ metrics, including the calculation of nonstandard UQ metrics (combining information from the dataset and model), and provides a comprehensive evaluation. In this framework, UQ metrics are evaluated for different application scenarios, e.g., eliminating the predictions with the lowest confidence or obtaining a reliable uncertainty estimate for an acquisition function. Taken together, this library will help to standardize UQ investigations and evaluate new methodologies.

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 99 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series., (© 2023. The Author(s).)

Machine-learning and deep-learning models have been extensively used in cheminformatics to predict molecular properties, to reduce the need for direct measurements, and to accelerate compound prioritization. However, different setups and frameworks and the large number of molecular representations make it difficult to properly evaluate, reproduce, and compare them. Here we present a new PREdictive modeling FramEwoRk for molecular discovery (PREFER), written in Python (version 3.7.7) and based on AutoSklearn (version 0.14.7), that allows comparison between different molecular representations and common machine-learning models. We provide an overview of the design of our framework and show exemplary use cases and results of several representation-model combinations on diverse data sets, both public and in-house. Finally, we discuss the use of PREFER on small data sets. The code of the framework is freely available on GitHub.

In spite of extensive studies on human walking, less research has been conducted on human walking gait adaptation during interaction with another human. In this paper, we study a particular case of interactive locomotion where two humans carry a rigid object together. Experimental data from two persons walking together, one in front of the other, while carrying a stretcher-like object is presented, and the adaptation of their walking gaits and coordination of the foot-fall patterns are analyzed. It is observed that in more than 70% of the experiments the subjects synchronize their walking gaits; it is shown that these walking gaits can be associated to quadrupedal gaits. Moreover, in order to understand the extent by which the passive dynamics can explain this synchronization behaviour, a simple 2D model, made of two-coupled spring-loaded inverted pendulums, is developed, and a comparison between the experiments and simulations with this model is presented, showing that with this simple model we are able to reproduce some aspects of human walking behaviour when paired with another human.

Gait recovery after neurological disorders requires remastering the interplay between body mechanics and gravitational forces. Despite the importance of gravity-dependent gait interactions and active participation for promoting this learning, these essential components of gait rehabilitation have received comparatively little attention. To address these issues, we developed an adaptive algorithm that personalizes multidirectional forces applied to the trunk based on patient-specific motor deficits. Implementation of this algorithm in a robotic interface reestablished gait dynamics during highly participative locomotion within a large and safe environment. This multidirectional gravity-assist enabled natural walking in nonambulatory individuals with spinal cord injury or stroke and enhanced skilled locomotor control in the less-impaired subjects. A 1-hour training session with multidirectional gravity-assist improved locomotor performance tested without robotic assistance immediately after training, whereas walking the same distance on a treadmill did not ameliorate gait. These results highlight the importance of precise trunk support to deliver gait rehabilitation protocols and establish a practical framework to apply these concepts in clinical routine., (Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Caffeine is widely used, often consumed with food, and improves simple and complex/executive attention under fasting conditions. We investigated whether these cognitive effects are observed when personalized habitual doses of caffeine are ingested by caffeine consumers, whether they are influenced by nutriments and if various executive domains are susceptible to improvement. This was a double-blind, placebo-controlled study including 60 young, healthy, rested males randomly assigned to one of four treatments: placebo fasting, caffeine fasting, placebo meal and caffeine meal. Caffeine doses were individualized for each participant based on their self-reported caffeine consumption at the time of testing (morning). The test battery included measures of simple and sustained attention, executive domains (inhibiting, updating, shifting, dual tasking, planning and accessing long-term memory), control measures of subjective alterations, glucose and insulin levels, skin conductance, heart rate and pupil dilation. Regardless of meal intake, acute habitual doses of caffeine decreased fatigue, and improved simple and sustained attention and executive updating. This executive effect was not secondary to the habitual weekly dose consumed, changes in simple and sustained attention, mood, meal ingestion and increases in cognitive effort. We conclude that the morning caffeine "fix" has positive attentional effects and selectively improved executive updating whether or not caffeine is consumed with food., (Copyright © 2015 John Wiley & Sons, Ltd.)