Gold nanoparticle and mean inactivation dose of human intestinal colon cancer HT-29 cells

Background: Mean inactivation dose is a useful radiobiological parameter for the comparison of human cell survival curves. Objectives: Given the importance and accuracy of these parameters, in the present study, the radio sensitivity enhancement of colon cancer (HT-29) cells in the presence of gold nanoparticles (GNPs) were studied using the mean inactivation dose (MID). Materials and Methods: Naked-GNPs with 50 nm diameters were incubated with HT-29 cells. The cytotoxicity and uptake of these particles on HT-29 cells were assessed. After determining the optimum GNPs concentration, the cells were incubated with gold nanoparticle for 24 hours. The change in the MID value as well as the radio sensitization enhancement under irradiation with 9 MV X-ray beams in the presence of GNPs were evaluated by multiple (3-(4, 5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium)MTS assay. Results: Cell survival in the presence of GNPs was more than 90% and the maximum uptake of GNPs was observed at 60 μM of gold nanoparticles. In contrast, in the presence of GNPs combined with radiation, cell survival and MID value significantly decreased, so that the radio sensitization enhancement was 1.4. Conclusions: Due to the significant reduction in the mean inactivation dose of colon cancer cells in the presence of gold nanoparticles, it seems that GNPs are suitable options to achieve a new approach in order to improve radiotherapy efficiency without increasing the prescribed radiation dose

Conversational Health Agents: A Personalized LLM-Powered Agent Framework

Conversational Health Agents (CHAs) are interactive systems that provide healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically lacking multi-step problem-solving, personalized conversations, and multimodal data analysis. Our aim is to overcome these limitations. We propose openCHA, an open-source LLM-powered framework, to empower conversational agents to generate a personalized response for users' healthcare queries. This framework enables developers to integrate external sources including data sources, knowledge bases, and analysis models, into their LLM-based solutions. openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries. It facilitates knowledge acquisition, problem-solving capabilities, multilingual and multimodal conversations, and fosters interaction with various AI platforms. We illustrate the framework's proficiency in handling complex healthcare tasks via three demonstrations. Moreover, we release openCHA as open source available to the community via GitHub.Comment: 23 pages, 6 figures, 3 tables, journal pape

Therapeutic efficacy of Trifolium pratense L. on letrozole induced polycystic ovary syndrome in rats

Polycystic ovary syndrome (PCOS) is considered as one of the leading endocrine disorders during reproductive age in women. This study designed to determine the therapeutic effects of red clover (Trifolium pratense) on letrozole-induced PCOS in vivo. Forty female Sprague–Dawley rats were equally divided into five groups. Control group with a regular sexual cycle received normal saline (letrozole vehicle). Letrozole (1 mg/kg) was used to induce the PCOS to the rats in the treatment groups. After induction of PCOS, four treatment groups received the normal saline, or clomiphene citrate (1 mg/kg), or red clover extracts (500 or 750 mg/kg) for 30-days. After treatment, ovary and uterus were removed, weighed, and the ovaries were subjected to histopathological studies. Serum testosterone and estradiol levels, antioxidant activities, and lipid profiles were evaluated. Red clover extracts and clomiphene citrate decreased testosterone levels and showed a significant increase in estradiol levels in comparison to PCOS induced group (p<0.05). Red clover administration restored the GSH, SOD and CAT levels (p<0.05) and decreased the NO and MDA levels (p<0.05). Treatments caused no significant change in levels of TG, TC, and FBG factors when compared to PCOS induced group (P>0.05). However, red clover (750 mg/kg) significantly increased HDL and decreased LDL levels when compared to PCOS induced group (P<0.05). Treatment with red clover reduced ovarian weight, volumes of ovarian, medulla, cortex and number of cysts and increased number of oocytes compared to PCOS group. Both red clover and clomiphene citrate could treat the letrozole induced PCOS in rats; however, red clover indicated antioxidant activities more than clomiphene citrate. Red clover may be used for discovering anti-PCOS drugs with lower side effects

Computational approaches to predict protein functional families and functional sites.

Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features

Empathy Through Multimodality in Conversational Interfaces

Agents represent one of the most emerging applications of Large Language Models (LLMs) and Generative AI, with their effectiveness hinging on multimodal capabilities to navigate complex user environments. Conversational Health Agents (CHAs), a prime example of this, are redefining healthcare by offering nuanced support that transcends textual analysis to incorporate emotional intelligence. This paper introduces an LLM-based CHA engineered for rich, multimodal dialogue-especially in the realm of mental health support. It adeptly interprets and responds to users' emotional states by analyzing multimodal cues, thus delivering contextually aware and empathetically resonant verbal responses. Our implementation leverages the versatile openCHA framework, and our comprehensive evaluation involves neutral prompts expressed in diverse emotional tones: sadness, anger, and joy. We evaluate the consistency and repeatability of the planning capability of the proposed CHA. Furthermore, human evaluators critique the CHA's empathic delivery, with findings revealing a striking concordance between the CHA's outputs and evaluators' assessments. These results affirm the indispensable role of vocal (soon multimodal) emotion recognition in strengthening the empathetic connection built by CHAs, cementing their place at the forefront of interactive, compassionate digital health solutions.Comment: 7 pages, 2 figures, 2 tables, conference pape

ALCM: Autonomous LLM-Augmented Causal Discovery Framework

To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses a formidable challenge, recognized as an NP-hard problem. Recently, the advent of Large Language Models (LLMs) has ushered in a new era, indicating their emergent capabilities and widespread applicability in facilitating causal reasoning across diverse domains, such as medicine, finance, and science. The expansive knowledge base of LLMs holds the potential to elevate the field of causal reasoning by offering interpretability, making inferences, generalizability, and uncovering novel causal structures. In this paper, we introduce a new framework, named Autonomous LLM-Augmented Causal Discovery Framework (ALCM), to synergize data-driven causal discovery algorithms and LLMs, automating the generation of a more resilient, accurate, and explicable causal graph. The ALCM consists of three integral components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components autonomously collaborate within a dynamic environment to address causal discovery questions and deliver plausible causal graphs. We evaluate the ALCM framework by implementing two demonstrations on seven well-known datasets. Experimental results demonstrate that ALCM outperforms existing LLM methods and conventional data-driven causal reasoning mechanisms. This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs

МОДЕЛИРОВАНИЕ РАСПРЕДЕЛЕНИЯ МАГНИТНОЙ ИНДУКЦИИ КОАКСИАЛЬНО-ЛИНЕЙНОГО ДВИГАТЕЛЯ С АКСИАЛЬНЫМ И РАДИАЛЬНЫМ НАПРАВЛЕНИЕМ НАМАГНИЧИВАНИЯ ПОСТОЯННЫХ МАГНИТОВ

The paper presents results of computer simulation and experimental study of magnetic induction distribution in a coaxial linear motor air gap throughout the length of the runner active part at different heights of the air gap between the runner and the inductor magnetic core for motors with axial and radial direction of the permanent magnets magnetization.Представлены результаты компьютерного моделирования и экспериментального исследования распределения магнитной индукции в воздушном зазоре по всей длине активной части бегуна коаксиально-линейного двигателя и при разных высотах воздушного зазора между бегуном и магнитопроводом индуктора для двигателей с аксиальным и радиальным направлением намагничивания постоянных магнитов.Представлені результати комп'ютерного моделювання та експериментального дослідження розподілу магнітної індукції в повітряному зазорі по всій довжині активної частини бігуна коаксіально-лінійного двигуна та при різних висотах повітряного зазору між бігуном та магнітопроводом індуктора для двигунів з аксіальним та радіальним напрямком намагнічування постійних магнітів

МОДЕЛИРОВАНИЕ РАБОТЫ КОАКСИАЛЬНО-ЛИНЕЙНЫХ ДВИГАТЕЛЕЙ С АКСИАЛЬНЫМ И РАДИАЛЬНЫМ НАПРАВЛЕНИЯМИ НАМАГНИЧИВАНИЯ ПОСТОЯННЫХ МАГНИТОВ ПРИ ДИНАМИЧЕСКОМ РЕЖИМЕ

Theoretical and experimental investigations of the amplitude, phase and inertia-power frequency characteristics of two types of coaxial-linear electric motors of back-and-forth motion with permanent magnets, which magnetization vector is directed axially and radially relative to the axis of the runner are carried out. The comparative analysis of characteristics of these motors is presented.Проведены теоретические и экспериментальные исследования амплитудных, фазовых и инерционно-силовых частотных характеристик двух типов коаксиально-линейных электрических двигателей возвратно-поступательного движения с постоянными магнитами, вектор намагничивания которых направлен аксиально и радиально по отношению к оси бегуна, а также выполнен сравнительный анализ характеристик этих двигателей.Проведено теоретичні та експериментальні дослідження амплітудних, фазових та інерційно-силових частотних характеристик двох типів коаксіально-лінійних електричних двигунів зворотно-поступального руху з постійними магнітами, вектор намагнічування яких направлений аксіально та радіально по відношенню до вісі бігуна, а також здійснено порівняльний аналіз характеристик цих двигунів

Validation of Lexical Frequency Profiles As a Measure of Lexical Richness in Written Discourse

Technological developments and their utilities in various areas including education have offered great advantages for man. One of the greatest achievements in this trend has been the innovation in computer software like Lexical Frequency Profiles (LFP) and its pedagogical implications either in teaching or measurement. To take the maximum advantages, this study seeks to validate the LFP as a measure of lexical richness in written discourse of Iranian EFL Learners. 50 students majoring in English Translation participated in this study; each of them was encouraged to develop two compositions on general topics in order to establish VocabProfile indexes. To estimate the reliability of the LFP, the VocabProfile indexes of two writings were correlated, but for the validity purpose, first, a productive version of Vocabulary Levels Test (VLT) was administered and second, the students’ compositions were fed into P_Lex software to elicit P_Lex index. After that, VocabProfile indexes were correlated with VLT scores and P_Lex index separately. The findings of the study revealed that students’ VocabProfile indexes written on two different topics correlated significantly with each other. Because of such a significant correlation coefficients, and the LFP indexes are related to VLT active test and P_Lex index, it is conservatively safe to claim that VocabProfile indexes are to some extent reliable and valid measurement instruments but not strong enough to be used as a stand- alone measure for the assessment of lexical richness. Pedagogically speaking, the LPF is suggested as a relatively reliable and valid measure to be used along with other dependable devices in measuring lexical richness in discourses of various types