Hasil untuk "Pharmacy and materia medica"

Debora Sabotinova, Petya Boycheva, Nadezhda Ivanova et al.

<b>Background</b>: <i>Geraniaceae</i> species are widely used in traditional medicine. <i>Pelargonium radula</i> and <i>Geranium macrorrhizum</i> are aromatic medicinal plants traditionally used in Bulgaria for their antimicrobial, anti-inflammatory, and wound-healing properties. Comparative phytochemical data on <i>Pelargonium radula</i> and <i>Geranium macrorrhizum</i> cultivated in Bulgaria, however, remain limited. The present work aimed to characterize and compare the chemical composition of essential oils and main phenols, in support of future pharmacological evaluation. <b>Methods</b>: Essential oils from aerial parts of both species were obtained by hydrodistillation and analyzed by GC-MS. Through HPLC-UV, ethanol extracts were evaluated to quantify the major phenolic acids and flavonoids. <b>Results</b>: The yield of essential oils was 0.10% for <i>P. radula</i> and 0.03% for <i>G. macrorrhizum</i>, dominated by oxidized monoterpenes, mainly citronellol and geraniol-type compounds. HPLC analysis revealed marked differences in their phenolic profiles. <i>P. radula</i> showed a composition with six phenolic acids—primary protocatechuic and ferulic acids, and very low levels of flavonoids, with rutin being the only quantifiable glycoside. In contrast, <i>G. macrorrhizum</i> contained nine phenolic acids and four flavonoids, with remarkably high levels of salicylic, rosmarinic, and <i>p</i>-coumaric acids, as well as catechins, absent in <i>P. radula</i>. <b>Conclusions</b>: The two species showed different phytochemical characteristics in both their volatile and non-volatile fractions. <i>P. radula</i> is characterized by a citronellol/geraniol-rich essential oil and a moderate phenolic profile, while <i>G. macrorrhizum</i> exhibits significantly higher phenolic diversity and abundance. These findings expand the current phytochemical knowledge of both taxa and provide a solid basis for future chemotaxonomic and pharmacological studies. The obtained results suggest that <i>Geranium macrorrhizum</i> may be more promising for antioxidant and anti-inflammatory applications, while <i>Pelargonium radula</i> may be preferentially explored for ant-microbial purposes.

Suyash Mishra, Qiang Li, Srikanth Patil et al.

Vision Language Models (VLMs) are poised to revolutionize the digital transformation of pharmacyceutical industry by enabling intelligent, scalable, and automated multi-modality content processing. Traditional manual annotation of heterogeneous data modalities (text, images, video, audio, and web links), is prone to inconsistencies, quality degradation, and inefficiencies in content utilization. The sheer volume of long video and audio data further exacerbates these challenges, (e.g. long clinical trial interviews and educational seminars). Here, we introduce a domain adapted Video to Video Clip Generation framework that integrates Audio Language Models (ALMs) and Vision Language Models (VLMs) to produce highlight clips. Our contributions are threefold: (i) a reproducible Cut & Merge algorithm with fade in/out and timestamp normalization, ensuring smooth transitions and audio/visual alignment; (ii) a personalization mechanism based on role definition and prompt injection for tailored outputs (marketing, training, regulatory); (iii) a cost efficient e2e pipeline strategy balancing ALM/VLM enhanced processing. Evaluations on Video MME benchmark (900) and our proprietary dataset of 16,159 pharmacy videos across 14 disease areas demonstrate 3 to 4 times speedup, 4 times cost reduction, and competitive clip quality. Beyond efficiency gains, we also report our methods improved clip coherence scores (0.348) and informativeness scores (0.721) over state of the art VLM baselines (e.g., Gemini 2.5 Pro), highlighting the potential of transparent, custom extractive, and compliance supporting video summarization for life sciences.

Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup et al.

The rapid adoption of AI in Earth system science promises unprecedented speed and fidelity in the generation of climate information. However, this technological prowess rests on a fragile and unequal foundation: the current trajectory of AI development risks further automating and amplifying the North-South divide in the global climate information system. We outline the global asymmetry in High-Performance Computing and data infrastructure, demonstrating that the development of foundation models is almost exclusively concentrated in the Global North. Using three different domains, we show how this infrastructure inequality continues through models' inputs, processes and outputs. As an example, in weather and climate modelling, the reliance on historically biased data leads to systematic performance gaps that disproportionately affect the most vulnerable regions. In climate impact modelling, data sparsity and unrepresentative validation risk driving misleading interventions and maladaptation. Finally, in large language models, dependence on dominant textualised forms of climate knowledge risks reinforcing existing biases. We conclude that addressing these disparities demands revisiting the three phases, i.e. models Input, Process and Output. This involves (i) a perspective shift from model-centric to data-centric development, (ii) the establishment of a Climate Digital Public Infrastructure and human-centric evaluation metrics, and (iii) a move from producer-consumer dynamics toward knowledge co-production. This integration of diverse knowledge systems would truly democratise compute sovereignty and ensure that the AI revolution fosters genuine systemic resilience rather than exacerbating inequity.

Ana Carolina Pismel Lobo, Gabriela Cristina Santin, Elen de Souza Tolentino

Acute herpetic gingivostomatitis (AHGS) is the oral manifestation of HVS-1 primary infection. Despite being a self-limiting infection, AHGS can progress to severe complications. Dentists should be prepared to correctly diagnose and treat the disease. Therefore, the purpose of this study is to assess knowledge, attitude, and practice (KAP) of dentists regarding acute herpetic gingivostomatitis (AHGS) among children. A cross-sectional and descriptive study was carried out through a KAP Survey of 416 Brazilian dentists. Descriptive analyzes with absolute and relative frequencies were performed and possible associations between socio-demographic variables with the KAP questions were investigated using Chi-square and Fisher's exact tests (significance level 5%). Results revealed high knowledge scores among 68% of the dentists. The worst knowledge scores were found for AHGS complications. High scores were only associated with degree of education (p<0.005). For the treatment of AHGS, the responses were variable and signaled possible overtreatment in practice. Therapeutic possibilities beyond acyclovir are still lacking. This study highlights the importance of providing continuous education and integrating the practice of oral pathology into the practice of dentistry. It can help to increase knowledge, avoid overtreatment, and stimulate decision-making by the dentist in cases of complications.

Maoqin Duan, Luyun Guo, Zhen Long et al.

<b>Background/Objectives</b>: This study comprehensively characterized the <i>O</i>- and <i>N</i>-glycosylation profiles of bispecific antibodies (BsAbs) via advanced analytical techniques to evaluate their structural and functional implications. <b>Methods</b>: High-resolution MS revealed <i>O</i>-xylosylation at Ser468 within the (G4S)4 linker peptide, which was identified as xylose with a molecular weight of 132.042 Da. HILIC-HPLC analysis of <i>N</i>-glycosylation revealed glycan species engineered to eliminate Fc effector functions. <i>O</i>-glycosylation analysis via β-elimination followed by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) identified xylose as the predominant glycan. <b>Results</b>: <i>O</i>-xylosylation does not affect the binding of BsAbs to either antigen Programmed Death-1 (PD-1) or Vascular Endothelial Growth Factor (VEGF). Notably, <i>O</i>-xylosylation interactions with mannose receptor represent the first discovery highlighting potential immunomodulatory roles. <b>Conclusions</b>: This study highlights the critical importance of monitoring comprehensive glycosylation characterization during the development of BsAb with (G4S)n linkers to ensure optimal therapeutic efficacy, safety, and reduced immunogenic potential.

Pradeep K. Bhatia, Ghansham Biyani

Fabio Tarocco, Davide Materia, Leonardo Ratini et al.

The development of quantum algorithms and their application to quantum chemistry has introduced new opportunities for solving complex molecular problems that are computationally infeasible for classical methods. In quantum chemistry, the Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to estimate ground-state energies of molecular systems. Despite its promise, VQE faces challenges such as scalability issues, high circuit depths, and barren plateaus that make the optimization of the variational wavefunction. To mitigate these challenges, the Quantum Information Driven Ansatz (QIDA) leverages Quantum Mutual Information (QMI) to construct compact, correlation-driven circuits. In this work, we go back to the original field of application of QIDA, by applying the already defined Multi-Threshold Quantum Information Driven Ansatz (Multi-QIDA) methodology on Molecular Systems. to systematically construct shallow, layered quantum circuits starting from approximate QMI matrices obtained by Quantum Chemistry calculations. The Multi-QIDA approach combines efficient creation of the QMI map, reduction of the number of correlators required by exploiting Minimum/Maximum spanning tress, and an iterative layer-wise VQE optimization routine. These enhancements allow the method to recover missing correlations in molecular systems while maintaining computational efficiency. Additionally, the approach incorporates alternative gate constructions, such as SO(4) correlators, to enhance the circuit expressibility without significantly increasing the circuit complexity. We benchmark Multi-QIDA on systems ranging from small molecules like H2O, BeH2, and NH3 in Iterative Natural Orbitals (INOs) basis set, to active-space models such as H2O-6-31G-CAS(4,4) and N2-cc-pVTZ-CAS(6,6), comparing it to traditional hardware-efficient ansatze.

Lluís Palma, Alejandro Peraza, David Civantos et al.

Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or statistical techniques that fit past observations. GCMs require substantial computational resources, which limits their capacity. In contrast, statistical methods often lack robustness due to short historical records. Recent works propose machine learning methods trained on climate model output, leveraging larger sample sizes and simulated scenarios. Yet, many of these studies focus on prediction tasks that might be restricted in spatial extent or temporal coverage, opening a gap with existing operational predictions. Thus, the present study evaluates the effectiveness of a methodology that combines variational inference with transformer models to predict fields of seasonal anomalies. The predictions cover all four seasons and are initialised one month before the start of each season. The model was trained on climate model output from CMIP6 and tested using ERA5 reanalysis data. We analyse the method's performance in predicting interannual anomalies beyond the climate change-induced trend. We also test the proposed methodology in a regional context with a use case focused on Europe. While climate change trends dominate the skill of temperature predictions, the method presents additional skill over the climatological forecast in regions influenced by known teleconnections. We reach similar conclusions based on the validation of precipitation predictions. Despite underperforming SEAS5 in most tropics, our model offers added value in numerous extratropical inland regions. This work demonstrates the effectiveness of training generative models on climate model output for seasonal predictions, providing skilful predictions beyond the induced climate change trend at time scales and lead times relevant for user applications.

Tianai Huang, Jiayuan Chen, Lu Lu et al.

Large language models (LLMs) have demonstrated exceptional capabilities in general domains, yet their application in highly specialized and culturally-rich fields like Traditional Chinese Medicine (TCM) requires rigorous and nuanced evaluation. Building upon prior foundational work such as TCM-3CEval, which highlighted systemic knowledge gaps and the importance of cultural-contextual alignment, we introduce TCM-5CEval, a more granular and comprehensive benchmark. TCM-5CEval is designed to assess LLMs across five critical dimensions: (1) Core Knowledge (TCM-Exam), (2) Classical Literacy (TCM-LitQA), (3) Clinical Decision-making (TCM-MRCD), (4) Chinese Materia Medica (TCM-CMM), and (5) Clinical Non-pharmacological Therapy (TCM-ClinNPT). We conducted a thorough evaluation of fifteen prominent LLMs, revealing significant performance disparities and identifying top-performing models like deepseek\_r1 and gemini\_2\_5\_pro. Our findings show that while models exhibit proficiency in recalling foundational knowledge, they struggle with the interpretative complexities of classical texts. Critically, permutation-based consistency testing reveals widespread fragilities in model inference. All evaluated models, including the highest-scoring ones, displayed a substantial performance degradation when faced with varied question option ordering, indicating a pervasive sensitivity to positional bias and a lack of robust understanding. TCM-5CEval not only provides a more detailed diagnostic tool for LLM capabilities in TCM but aldso exposes fundamental weaknesses in their reasoning stability. To promote further research and standardized comparison, TCM-5CEval has been uploaded to the Medbench platform, joining its predecessor in the "In-depth Challenge for Comprehensive TCM Abilities" special track.

Elise Potters, Behzad Mosalla Nezhad, Viktor Huiskes et al.

The significance of last-mile logistics in the healthcare supply chain is growing steadily, especially in pharmacies where the growing prevalence of medication delivery to patients' homes is remarkable. This paper proposes a novel mathematical model for the last-mile logistics of the pharmaceutical supply chain and optimizes a pharmacy's logistical financial outcome while considering medication synchronization, different delivery modes, and temperature requirements of medicines. We propose a mathematical formulation of the problem using Mixed Integer Linear Programming (MILP) evolved from the actual problem of an outpatient pharmacy of a Dutch hospital. We create a case study by gathering, preparing, processing, and analyzing the associated data. We find the optimal solution, using Python MIP package and the Gurobi solver, which indicates the number of order batches, the composition of these batches, and the number of staff related to the preparation of the order batches. Our results show that our optimal solution increases the pharmacy's logistical financial outcome by 34 percent. Moreover, we propose other model variations and perform extensive scenario analysis to provide managerial insights applicable to other pharmacies and distributors in the last step of cold supply chains. Based on our scenario analysis, we conclude that improving medication synchronization can significantly enhance the pharmacy's logistical financial outcome.

Bo Han, Xin-Yan Peng, Yan-Qing Gong et al.

Abstract Chidamide is the first oral subtype-selective histone deacetylase inhibitor approved in China for the treatment of relapsed and refractory peripheral T cell lymphoma. Due to the existence of isomers, many articles or patents have mistaken its structure. Herein we explored the synthesis of the key intermediate (E)-4-((3-(pyridin-3-yl)acrylamido)methyl)benzoic acid (A-3) and chidamide, using the condensing agent HBTU, instead of the unstable N,N'-carbonyldiimidazole. The single crystal of chidamide was determined by X-ray diffraction study. The optimized preparation process was easy to operate, and the purity of the final product can be up to 99.76%. Moreover, the structure of chidamide was established to be (E)-N-(2-amino-4-fluorophenyl)-4-((3-(pyridin-3-yl)acrylamido)methyl)benzamide.

Ayomide Ogundipe, Tin Fei Sim, Lynne Emmerton

Background: Health information communication technology (ICT) has rapidly evolved in contemporary pharmacy practice worldwide. The Australian healthcare system is experiencing a paradigm shift to real-time interconnectivity for practitioners and consumers and interoperable digital health. With these developments come a need to evaluate use of technologies specifically in pharmacy practice to optimize their clinical functionality. There are no published frameworks for evaluating ICT needs or implementation in pharmacy practice. Objective: This paper proposes a theoretical framework for evaluating health ICT in pharmacy. Methods: Development of the evaluation framework was informed by a systematic scoping review and health informatics literature. Specifically, the framework drew upon critical appraisal and concept mapping of the TAM, ISS and HOT-fit validated models, with respect to health ICT in contemporary pharmacy practice. Results: The proposed model was named the Technology Evaluation Key (TEK). The TEK comprises of 10 domains; healthcare system, organization, practitioner, user interface, ICT, use, operational outcomes, system outcomes, clinical outcomes and timely access to care. Conclusions: This is the first published proposed evaluation framework developed for health ICT specifically in contemporary pharmacy practice. TEK represents a pragmatic way to ensure the development, refinement and implementation of new and existing technologies in contemporary pharmacy practice to keep pace with the clinical and professional requirements of community pharmacists. Operational, clinical and system outcomes should be evaluated as coexisting factors that may impact implementation. Validation research utilizing Design Science Research Methodology will enhance utility for end users and ensure the relevance and application of the TEK to contemporary pharmacy practice.

Pingsu He, Lingchuan Xu, Jun Jin et al.

The method for simultaneous determination of the two types of principal components, C21- steroids and acetophenones, in Cynanchum bungei, C. auriculatum and C. wilfordii by high-performance liquid chromatography-ultraviolet (HPLC-UV) was developed for the first time. Under the optimized conditions, good linearities (R2≥0.999) were obtained for all analytes, and relative standard deviations (RSDs) of HPLC-UV method validation ranged from 0.01-1.62%. The results of DPPH (1,1-diphenyl 2-picryl hydrazyl) and ABTS (2,2-azobis-3ethylbenzthiazoline-6-sulfonic acid) free radical scavenging assays indicated that the three species had a significant antioxidant activity, with the half maximal effective concentration (EC50) range of 64.56-593.38 μg/mL. Combined with bivariate analysis, the fingerprint-activity relationship of the offline antioxidant activity of the three species with their fingerprints peak was studied, and the results revealed a dose-effect relationship between acetophenones and antioxidant activity. This study provides a theoretical basis for the correct identification and application of the antioxidant activities of C. auriculatum, C. wilfordii and C. bungei.

Stefano Materia, Lluís Palma García, Chiem van Straaten et al.

Scientific and technological advances in numerical modelling have improved the quality of climate predictions over recent decades, but predictive skill remains limited in many aspects. Extreme events such as heat and cold waves, droughts, heavy rain and storms are particularly challenging to predict accurately due to their rarity and non-linear chaotic nature, and because of model limitations. However, recent studies have shown that predictive skill of extremes can be increased when using more sophisticated approaches, indicating that there might be systemic predictability that is not being leveraged. Recently, numerous studies have been devoted to the exploitation of Artificial Intelligence (AI) to study the predictability and make predictions of weather and climate. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large-scale and local drivers. Machine and deep learning, causal discovery, explainable AI, are only some of the approaches that have been tested to both improve our understanding of the processes underlying predictability and enhance prediction skill of extreme events. Results are promising, especially for hybrid predictions that combine the AI, which can reveal and exploit unknown spatio-temporal connections from data, and climate models, that provide the theoretical foundation and interpretability of the physical world. On the other hand, challenges are multiple in many aspects, from data curation to model uncertainty and generalizability, to the reproducibility of methods and workflows. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development.

Paul Primus and, Gerhard Widmer

Varying conditions between the data seen at training and at application time remain a major challenge for machine learning. We study this problem in the context of Acoustic Scene Classification (ASC) with mismatching recording devices. Previous works successfully employed frequency-wise normalization of inputs and hidden layer activations in convolutional neural networks to reduce the recording device discrepancy. The main objective of this work was to adopt frequency-wise normalization for Audio Spectrogram Transformers (ASTs), which have recently become the dominant model architecture in ASC. To this end, we first investigate how recording device characteristics are encoded in the hidden layer activations of ASTs. We find that recording device information is initially encoded in the frequency dimension; however, after the first self-attention block, it is largely transformed into the token dimension. Based on this observation, we conjecture that suppressing recording device characteristics in the input spectrogram is the most effective. We propose a frequency-centering operation for spectrograms that improves the ASC performance on unseen recording devices on average by up to 18.2 percentage points.

Jennifer Rogers and, Anamaria Crisan

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures AutoML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoMLTrace, a visual interactive sketch showing both the context and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage scenario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work.

Shuai Guo, Shuai Guo, Xuejiao Liao et al.

Polygonum is a generalized genus of the Polygonaceae family that includes various herbaceous plants. In order to provide aid in understanding the evolutionary and phylogenetic relationship in Polygonum at the chloroplast (cp) genome-scale level, we sequenced and annotated the complete chloroplast genomes of four Polygonum species using next-generation sequencing technology and CpGAVAS. Then, repeat sequences, IR contractions, and expansion and transformation sites of chloroplast genomes of four Polygonum species were studied, and a phylogenetic tree was built using the chloroplast genomes of Polygonum. The results indicated that the chloroplast genome construction of Polygonum also displayed characteristic four types of results, comparable to the published chloroplast genome of recorded angiosperms. The chloroplast genomes of the four Polygonum plants are highly consistent in genome size (159,015 bp–163,461 bp), number of genes (112 genes, including 78 protein-coding genes, 30 tRNA genes, and 4 rRNA genes), gene types, gene order, codon usage, and repeat sequence distribution, which identifies the high preservation among the Polygonum chloroplast genomes. The Polygonum phylogenetic tree was recreated by a full sequence of the chloroplast genome, which illustrates that the P. bistorta, P. orientale, and P. perfoliatum are divided into the same branch, and P. aviculare belongs to Fallopia. The precise system site of lots base parts requires further verification, but the study would provide a basis for developing the available genetic resources and evolutionary relationships of Polygonum.

Shasha Yin, Yu’e Guo, Xinyue Wen et al.

Background: The expression levels of the programmed cell death ligand 1 (PD-L1), known as an immune-inhibitory molecule, are closely associated with cancer stem cell (CSCs) immune escape. Recently, PD-L1 has also been reported to be able to regulate the self-renewal of cancer stem cells. However, The expression and intrinsic role of PD-L1 in endometrial cancer stem-like cell (ECSC) maintenance and its underlying mechanism of action remain unclear. Methods: Using flow cytometry and western blot assays, we have demonstrated that PD-L1 expression is higher in ECSCs derived from endometrial cancer than in nonstem-like cancer cells. Using mouse xenograft assays for ECSC tumorigenicity. Using gene reporter assay for uncovering the regulation mechanism of PD-L1 in the hypoxia. Results: We revealed the high expression levels of PD-L1 in ECSCs and its correlation with self-renewal. We further found that PD-L1 knockdown reduced expression of several pluripotency-related genes (aldehyde dehydrogenase 1 (ALDH1), CD133, OCT4, SOX2, NANOG), impaired ECSC proliferation and undifferentiated colonies and decreased the number of CD133 positive ECSCs and the number of stem-like spheres. Furthermore, we found that PD-L1 knockdown inhibited ECSC tumorigenicity and the PD-L1 induced self-renewal capability of ECSCs was dependent upon hypoxia HIF-1α and HIF-2α activation. Conclusions: These data link ECSC maintenance to PD-L1 expression through hypoxia and suggest a promising target for PD1/PD-L1 immunotherapy.

Zeying Cao, Ningyun Sun, Hongyu Sun et al.

The consistency evaluation, which is largely unexplored is crucial in regulating the quality and efficacy of generic drugs. In this report, after generic ibuprofen (IBU) sustained-release pellets were developed and validated as bioequivalent to the reference list drug (RLD) in 48 healthy human volunteers (p < 0.001***), three-dimensional (3D) structures of generic IBU pellets from bioequivalence tests, along with RLD were investigated and compared for architecture using Micro-computed tomography (Micro-CT). The surface and internal architectures of the pellets, sphericity, pellet volume, core volume and gray value have been evaluated in both static and dynamic conditions. The material distribution and composition of IBU pellets were characterized using synchrotron radiation-FTIR mapping. Although the structures of RLD and the generic products were not similar dynamically, identical release profiles and bioequivalence were obtained. Overall, micro-CT could be very useful for characterization of internal structure, material design and development of new dosage form.