Hasil untuk "Therapeutics. Pharmacology"

Anqi Li, Chenxiao Wang, Yu Lu et al.

Client perceptions of the therapeutic alliance are critical for counseling effectiveness. Accurately capturing these perceptions remains challenging, as traditional post-session questionnaires are burdensome and often delayed, while existing computational approaches produce coarse scores, lack interpretable rationales, and fail to model holistic session context. We present CARE, an LLM-based framework to automatically predict multi-dimensional alliance scores and generate interpretable rationales from counseling transcripts. Built on the CounselingWAI dataset and enriched with 9,516 expert-curated rationales, CARE is fine-tuned using rationale-augmented supervision with the LLaMA-3.1-8B-Instruct backbone. Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance, achieving over 70% higher Pearson correlation with client ratings. Rationale-augmented supervision further improves predictive accuracy. CARE also produces high-quality, contextually grounded rationales, validated by both automatic and human evaluations. Applied to real-world Chinese online counseling sessions, CARE uncovers common alliance-building challenges, illustrates how interaction patterns shape alliance development, and provides actionable insights, demonstrating its potential as an AI-assisted tool for supporting mental health care.

Junwei Du, Leland C. Sudlow, Igor D. Luzhansky et al.

Oxaliplatin-triggered chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating side effect of cancer treatment that limits the efficacy of chemotherapy and negatively impacts patients’ quality of life dramatically. To better understand the mechanisms of CIPN and to screen for potential therapeutic targets, it is critical to have reliable in vitro assays that effectively mirror the neuropathy in vivo. In this study, we established a dorsal root ganglia (DRG) explant model. This model displayed dose-dependent inhibition of neurite outgrowth in response to oxaliplatin, while oxalic acid exhibited no significant impact on the regrowth of DRG. The robustness of this assay was further demonstrated by the inhibition of OCT2 transporter, which facilitates oxaliplatin accumulation in neurons, largely restoring the neurite regrowth capacity. Using this model, we revealed that oxaliplatin triggered a substantial increase of oxidative stress in DRG. Notably, inhibition of TXNIP with verapamil reduced oxidative stress levels. Our results demonstrated the use of DRG explants as an efficient model to study the mechanisms of CIPN and screen for potential treatments.

Xinchen Wan, Jinhua Liang, Huan Zhang

Existing digital mental wellness tools often overlook the nuanced emotional states underlying everyday challenges. For example, pre-sleep anxiety affects more than 1.5 billion people worldwide, yet current approaches remain largely static and "one-size-fits-all", failing to adapt to individual needs. In this work, we present EmoHeal, an end-to-end system that delivers personalized, three-stage supportive narratives. EmoHeal detects 27 fine-grained emotions from user text with a fine-tuned XLM-RoBERTa model, mapping them to musical parameters via a knowledge graph grounded in music therapy principles (GEMS, iso-principle). EmoHeal retrieves audiovisual content using the CLAMP3 model to guide users from their current state toward a calmer one ("match-guide-target"). A within-subjects study (N=40) demonstrated significant supportive effects, with participants reporting substantial mood improvement (M=4.12, p<0.001) and high perceived emotion recognition accuracy (M=4.05, p<0.001). A strong correlation between perceived accuracy and therapeutic outcome (r=0.72, p<0.001) validates our fine-grained approach. These findings establish the viability of theory-driven, emotion-aware digital wellness tools and provides a scalable AI blueprint for operationalizing music therapy principles.

Omer Faruk Durugol, Maximilian Rokuss, Yannick Kirchhoff et al.

Automated segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) from MRI is critical for clinical workflows but is hindered by poor tumor-tissue contrast and a scarcity of annotated data. This paper details our submission to the PANTHER challenge, addressing both diagnostic T1-weighted (Task 1) and therapeutic T2-weighted (Task 2) segmentation. Our approach is built upon the nnU-Net framework and leverages a deep, multi-stage cascaded pre-training strategy, starting from a general anatomical foundation model and sequentially fine-tuning on CT pancreatic lesion datasets and the target MRI modalities. Through extensive five-fold cross-validation, we systematically evaluated data augmentation schemes and training schedules. Our analysis revealed a critical trade-off, where aggressive data augmentation produced the highest volumetric accuracy, while default augmentations yielded superior boundary precision (achieving a state-of-the-art MASD of 5.46 mm and HD95 of 17.33 mm for Task 1). For our final submission, we exploited this finding by constructing custom, heterogeneous ensembles of specialist models, essentially creating a mix of experts. This metric-aware ensembling strategy proved highly effective, achieving a top cross-validation Tumor Dice score of 0.661 for Task 1 and 0.523 for Task 2. Our work presents a robust methodology for developing specialized, high-performance models in the context of limited data and complex medical imaging tasks (Team MIC-DKFZ).

Sung-Hyun Kim, Bo-ram Choi

Background: Portable, small computers and smartphones are now considered essential tools in modern society and smartphone ownership and usage rates are rising every year. However, excessive smartphone use can have musculoskeletal and postural implications, leading to “smartphone addiction” and related dysfunctions. Objects: This study aimed to investigate the effects of smartphone addiction on scapular position and muscle activity during shoulder abduction in asymptomatic individuals. Methods: A total of 45 participants were classified into high-risk, middle-risk, and low-risk groups based on their smartphone addiction levels. Scapular position was measured using the scapular index, round shoulder posture (RSP), lateral scapular slide test, and scapulohumeral rhythm spine angle. Muscle activity was assessed using electromyography of the upper trapezius (UT), lower trapezius (LT), serratus anterior (SA), and anterior deltoid (AD) muscles during shoulder abduction. Results: Smartphone addiction was significantly associated with altered scapular position and muscle activity. The high-risk group exhibited greater forward head posture and more pronounced RSP. Additionally, the high-risk group had lower SA activation and higher UT, LT, and AD muscle activity, indicating compensatory mechanisms due to altered scapular positioning. Conclusion: These findings suggest that excessive smartphone use contributes to postural deviations and altered muscle activation patterns, which may lead to musculoskeletal dysfunction over time. Clinicians should consider smartphone use when assessing patients with scapular dysfunction, and future studies should explore interventions to mitigate these effects.

D.V. Dobrianskyi, I.P. Tarchenko, G.L. Gumeniuk et al.

ABSTRACT. The article describes the attacks of the Russian Federation on Ukrainian medical institutions. As of the end of 2024, it is known that 1938 medical facilities in Ukraine were damaged. It provides a retrospective review of war crimes against doctors in wars that the aggressor waged earlier – in Chechnya, Georgia, Syria. The actions of the Russians after the annexation of Crimea in 2014 and the invasion of Donbas are described. The international legal levers are listed through which war criminals can be brought to justice. It gives examples of aggressive actions of the invaders in different years of the war against medical institutions in different cities of Ukraine – Trostianets, Chernihiv, Mariupol, Kyiv. Arguments are presented about the intentionality of the actions by the occupiers, which exclude randomness, and therefore, they are undoubted war crimes. It highlights the difficult working conditions of doctors in Kherson during the occupation.

Roghayeh Karimirad, Baskaran Stephen Inbaraj, Bing-Huei Chen

Natural antioxidants isolated from fruits, vegetables, herbs and spices have drawn great attention owing to their numerous health-promoting effects. Cinnamaldehyde (CA), an abundant antioxidant in cinnamon spice, has been explored more intensely over the last decade as it has been demonstrated to be effective and safe in the treatment of various diseases. Structurally, a substituted aldehyde group with an unsaturated carbon–carbon double bond with two electrophilic sites for reaction with receptors and enzymes can exert diverse biological effects. Although cinnamon has been traditionally used as a spice and herbal remedy, many studies investigating the most dominant functional compound, CA, and its biological activities have been reported in recent years. This review article intends to present an overview of recent advances in analytical methods and the application of cinnamon extract/oil, CA and its derivatives, CA-polymer/biomolecule conjugates and CA micro/nanosystems in alleviating various chronic diseases including cancer, diabetes, obesity, cardiovascular disease, neurological disorders, osteoarthritis and osteoporosis. Both in vitro and in vivo studies have demonstrated the improved pharmacological efficiency of CA and its derivatives as well as their polymer/drug/biomolecule conjugates and micro/nanoencapsulated forms, suggesting a possible alternative natural therapy and adjuvant therapy with conventional drugs via a synergistic process.

Palash Moon, Pushpak Bhattacharyya

The detection of depression through non-verbal cues has gained significant attention. Previous research predominantly centred on identifying depression within the confines of controlled laboratory environments, often with the supervision of psychologists or counsellors. Unfortunately, datasets generated in such controlled settings may struggle to account for individual behaviours in real-life situations. In response to this limitation, we present the Extended D-vlog dataset, encompassing a collection of 1, 261 YouTube vlogs. Additionally, the emergence of large language models (LLMs) like GPT3.5, and GPT4 has sparked interest in their potential they can act like mental health professionals. Yet, the readiness of these LLM models to be used in real-life settings is still a concern as they can give wrong responses that can harm the users. We introduce a virtual agent serving as an initial contact for mental health patients, offering Cognitive Behavioral Therapy (CBT)-based responses. It comprises two core functions: 1. Identifying depression in individuals, and 2. Delivering CBT-based therapeutic responses. Our Mistral model achieved impressive scores of 70.1% and 30.9% for distortion assessment and classification, along with a Bert score of 88.7%. Moreover, utilizing the TVLT model on our Multimodal Extended D-vlog Dataset yielded outstanding results, with an impressive F1-score of 67.8%

Ljubisa Bojic, Joerg Matthes, Agariadne Dwinggo Samala et al.

The emergence of the metaverse - envisioned as a hyperreal virtual universe enabling boundless human interaction - has the potential to revolutionize our conception of media. This transformation could alter society as we know it. This paper identifies addictive features of social media, including immersion, interactivity, real-time access, and personalization. These features are examined within the context of virtual reality through a literature review and content analysis, aimed at exploring the potential consequences of metaverse development. From an initial pool of 193,218 documents, a refined selection of N = 44 relevant papers formed the basis of our qualitative analysis. About half of the analyzed papers indicate that these features contribute to VR addiction. Interestingly, the same features that contribute to addictive behaviors can also be harnessed for positive therapeutic interventions of VR, particularly in treating addictions and managing mental health conditions. This duality, observed in the other half of the papers, emphasizes the complex role of VR technologies, suggesting that they can serve as a substitute for other addictions. This phenomenon is placed into the historical context of evolving media technologies that increasingly mimic reality. The complex interplay of factors contributing to addiction necessitates the development of algorithmic solutions that actively curate diverse offerings, rather than promoting a closed loop of like-minded views. Traditional models of addiction should be adapted to address these unique challenges. Finally, the discussion turned to the implications of these findings for a society where the metaverse is widely accepted as a mainstream technology.

Giorgos Lysandrou, Roma English Owen, Vanja Popovic et al.

There exists an invisible barrier between healthcare professionals' perception of a patient's clinical experience and the reality. This barrier may be induced by the environment that hinders patients from sharing their experiences openly with healthcare professionals. As patients are observed to discuss and exchange knowledge more candidly on social media, valuable insights can be leveraged from these platforms. However, the abundance of non-patient posts on social media necessitates filtering out such irrelevant content to distinguish the genuine voices of patients, a task we refer to as patient voice classification. In this study, we analyse the importance of linguistic characteristics in accurately classifying patient voices. Our findings underscore the essential role of linguistic and statistical text similarity analysis in identifying common patterns among patient groups. These results allude to even starker differences in the way patients express themselves at a disease level and across various therapeutic domains. Additionally, we fine-tuned a pre-trained Language Model on the combined datasets with similar linguistic patterns, resulting in a highly accurate automatic patient voice classification. Being the pioneering study on the topic, our focus on extracting authentic patient experiences from social media stands as a crucial step towards advancing healthcare standards and fostering a patient-centric approach.

Gerhard Hilgers, Miriam Schwarze, Hans Rabus

At the Heidelberg Ion-Beam Therapy Center, the track structure of carbon ions of therapeutic energy after penetrating layers of simulated tissue was investigated for the first time. Measurements were conducted with carbon ion beams of different energies and polymethyl methacrylate (PMMA) absorbers of different thicknesses to realize different depths in the phantom along the pristine Bragg peak. Ionization cluster size (ICS) distributions resulting from the mixed radiation field behind the PMMA absorbers were measured using an ion-counting nanodosimeter. Two different measurements were carried out: (i) variation of the PMMA absorber thickness with constant carbon ion beam energy and (ii) combined variation of PMMA absorber thickness and carbon ion beam energy such that the kinetic energy of the carbon ions in the target volume is constant. The data analysis revealed unexpectedly high mean ICS values compared to stopping power calculations and the data measured at lower energies in earlier work. This suggests that in the measurements the carbon ion kinetic energies behind the PMMA absorber may have deviated considerably from the expected values obtained by the calculations. In addition, the results indicate the presence of a marked contribution of nuclear fragments to the measured ICS distributions, especially if the carbon ion does not cross the target volume.

Zhu Tao, Zhu Tao, Zhu Tao et al.

BackgroundCyadox (CYA) is a derivative of quinoxaline 1,4-dioxide and a safe and effective synthetic antibacterial agent.ObjectiveThis study aimed to explore the drug transport in blood, distribution, depletion and hepatotoxicity of drugs in animals.MethodsThe transport of CYA in blood was studied using fluorescence, circular dichroism (CD) and molecular docking methods. Tissue distribution and depletion of CYA in rats were evaluated following oral administration of [3H]-CYA at different doses. Hepatotoxicity of drugs evaluated by transcriptomics.ResultsDuring transport in the bloodstream, the drug binds to bovine serum albumin (BSA) by hydrogen bonding and has only one binding site. Hydrogen bonds were formed between O (2) of CYA and ARG208, O (3) of CYA and LEU480, VAL481. The secondary protein conformation of BSA changed after binding with an increase in α-helix and a decrease in β-strand. After a single oral administration of [3H]-CYA, it was excreted rapidly within 7 days, with 34.81% from the urine and 60.25% from the feces. Higher and sustained levels of radioactivity were detected in the liver during the post-dose period, suggesting that the drug may concentrate in the liver. The transcriptomic data indicates that CYA exhibits low hepatotoxicity. However, there are indications that it may have an impact on steroid biosynthesis.ConclusionThis study could serve as a basis for conducting further studies on the use of CYA in food animals and improving the pharmacologic, pharmacokinetic, and toxicologic effects of CYA on food animals.

Shangen Zhang, Hongyan Cui, Yong Li et al.

This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across three distinct task modes (eyes open, eyes closed, and SSVEP stimulation) and two neuromodulation patterns (sham-tDCS and anodal-tDCS). Brain arousal and brain functional connectivity were measured by extracting features of fractal EEG and information flow gain, respectively. Anodal-tDCS led to diminished offsets and enhanced information flow gains, indicating improvements in both brain arousal and brain information transmission capacity. Additionally, anodal-tDCS markedly enhanced SSVEP-BCIs performance as evidenced by increased amplitudes and accuracies, whereas sham-tDCS exhibited lesser efficacy. This study proffers invaluable insights into the application of neuromodulation methods for bolstering BCI performance, and concurrently authenticates two potent electrophysiological markers for multifaceted characterization of brain states.

Anna Fang, Wenjie Yang, Raj Sanjay Shah et al.

Online mental health support communities have grown in recent years for providing accessible mental and emotional health support through volunteer counselors. Despite millions of people participating in chat support on these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Furthermore, although volunteers receive some training based on established therapeutic skills studied in face-to-face environments such as active listening and motivational interviewing, it remains understudied how the usage of these skills in this online context affects people's mental health status. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to measure how one-on-one support chats affect depression and anxiety symptoms. We measure how the techniques and characteristics of support providers, such as using affirmation, empathy, and past experience on the platform, affect support-seekers' mental health changes. We find that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that some behaviors (e.g. persuading) are actually harmful to depression and anxiety outcomes. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers.