Integrative medicine during the intensive phase of chemotherapy in pediatric oncology in Germany: a randomized controlled trial with 5-year follow up

Background: Integrative medicine is used frequently alongside chemotherapy treatment in pediatric oncology, but little is known about the influence on toxicity. This German, multi-center, open-label, randomized controlled trial assessed the effects of complementary treatments on toxicity related to intensive-phase chemotherapy treatment in children aged 1-18 with the primary outcome of the toxicity sum score. Secondary outcomes were chemotherapy-related toxicity, overall and event-free survival after 5 years in study patients. Methods: Intervention and control were given standard chemotherapy according to malignancy & tumor type. The intervention arm was provided with anthroposophic supportive treatment (AST); given as anthroposophic base medication (AMP), as a base medication for all patients and additional on-demand treatment tailored to the intervention malignancy groups. The control was given no AMP. The toxicity sum score (TSS) was assessed using NCI-CTC scales. Results: Data of 288 patients could be analyzed. Analysis did not reveal any statistically significant differences between the AST and the control group for the primary endpoint or the toxicity measures (secondary endpoints). Furthermore, groups did not differ significantly in the five-year overall and event-free survival follow up. Discussion: In this trial findings showed that AST was able to be safely administered in a clinical setting, although no beneficial effects of AST between group toxicity scores, overall or event-free survival were shown

Inhibition of HERG1 K+ channel protein expression decreases cell proliferation of human small cell lung cancer cells

HERG (human ether-à-go-go-related gene) K+ currents fulfill important ionic functions in cardiac and other excitable cells. In addition, HERG channels influence cell growth and migration in various types of tumor cells. The mechanisms underlying these functions are still not resolved. Here, we investigated the role of HERG channels for cell growth in a cell line (SW2) derived from small cell lung cancer (SCLC), a malignant variant of lung cancer. The two HERG1 isoforms (HERG1a, HERG1b) as well as HERG2 and HERG3 are expressed in SW2 cells. Inhibition of HERG currents by acute or sustained application of E-4031, a specific ERG channel blocker, depolarized SW2 cells by 10–15 mV. This result indicated that HERG K+ conductance contributes considerably to the maintenance of the resting potential of about −45 mV. Blockage of HERG channels by E-4031 for up to 72 h did not affect cell proliferation. In contrast, siRNA-induced inhibition of HERG1 protein expression decreased cell proliferation by about 50%. Reduction of HERG1 protein expression was confirmed by Western blots. HERG current was almost absent in SW2 cells transfected with siRNA against HERG1. Qualitatively similar results were obtained in three other SCLC cell lines (OH1, OH3, H82), suggesting that the HERG1 channel protein is involved in SCLC cell growth, whereas the ion-conducting function of HERG1 seems not to be important for cell growth

Inductive Coding with ChatGPT - An Evaluation of Different GPT Models Clustering Qualitative Data into Categories

Qualitative data is invaluable, yet its analysis is very time-consuming. In times of online surveys, only one open-ended question can yield hundreds of responses. Handling such big volumes of qualitative data can be overwhelming, leading to neglecting rich insights. To prevent the loss of valuable information and to streamline the coding process, we introduce LLM-Assisted Inductive Categorization (LAIC), a novel method categorizing text responses using a Large Language Model (LLM). Our approach was tested in two studies with two GPT models (gpt-3.5-turbo-0125 and gpt-4o-2024-05-03) across three temperature settings (0, 0.5, 1), with 10 repetitions each, resulting in 120 runs. We evaluated the outputs based on established qualitative research criteria (credibility, dependability, confirmability, transferability, transparency). Two human coders also generated inductive categories and assigned text responses accordingly for comparison. Our findings reveal that both GPT models are highly effective for inductive category development, even outperforming human coders through higher agreement rates. Overall, gpt-4o-2024-05-03 outperformed gpt-3.5-turbo-0125 with better explanations and higher agreement. Therefore, we recommend using ChatGPT for inductive category development, ideally gpt-4o with a temperature setting of 0. Using ChatGPT not only saves considerable time and resources but also enhances quality. Nevertheless, researchers should familiarize themselves with the data beforehand and carefully review ChatGPT's outputs. Instructions and Python scripts for applying our new coding technique are available for free under a CC-BY 4.0 International license in our OSF project