Native starch derived from different botanical sources as an effective co-cushioning agent in MUPS tablets.

Compaction of sustained release coated pellets into multi-unit pellet system (MUPS) tablets has been associated with damage to the functional polymer layer, leading to a loss in desired sustained release function. Many filler materials and complex processes have been studied on their ability to mitigate compaction-induced pellet coat damage. Among these, native or unprocessed starches included in the filler material have not been explored well despite being a simple strategy.

Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.

Background: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of this study was to assess the consistency, quality, and accuracy of the responses generated by ChatGPT on healthcare-related inquiries.

Methods: A total of 18 open-ended questions including six questions in three defined clinical areas (2 each to address "what", "why", and "how", respectively) were submitted to ChatGPT v3.

Altered dynamic functional connectivity in antagonistic state in first-episode, drug-naïve patients with major depressive disorder.

Objective: Major depressive disorder (MDD) is known to be characterized by disrupted brain functional network connectivity (FNC) patterns, while the dynamic change mode of different functional networks is unclear. This study aimed to characterize specific dynamic alterations pattern on intrinsic FNC in MDD by combining static FNC (sFNC) and dynamic FNC (dFNC).

Methods: A total of 48 first-episode drug-naïve MDD and 48 matched healthy controls (HCs) were included in this study.

Geometry-Corrected Quadratic Optimization Algorithm for NDDO-Descendant Semiempirical Models.

The long-held assumption that the optimization of parameters for NDDO-descendant semiempirical methods may be performed without precise geometry optimization is assessed in detail; the relevant equations for the analytical evaluation of the geometry-corrected derivatives of molecular properties that account for changes in the optimum geometry are then presented. The first and second derivatives calculated from our implementation of MNDO are used for a limited reparameterization of 1,113 CHNO molecules taken from the PM7 training set, demonstrating an improvement over the PARAM program used in the optimization of parameters for the PMx methods.