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Mitochondria-targeted antioxidants (MTAs) have been studied quite intensively in recent years as potential therapeutic agents and vectors for the delivery of other active substances to mitochondria and bacteria. Their most studied representatives are MitoQ and SkQ1, with its fluorescent rhodamine analog SkQR1, a decyl ester of rhodamine 19 carrying plastoquinone. In the present work, we observed a pronounced antibacterial action of SkQR1 against Gram-positive bacteria, but virtually no effect on Gram-negative bacteria. The MDR pump AcrAB-TolC, known to expel SkQ1, did not recognize and did not pump out SkQR1 and dodecyl ester of rhodamine 19 (C12R1). Rhodamine 19 butyl (C4R1) and ethyl (C2R1) esters more effectively suppressed the growth of ΔtolC Escherichia coli, but lost their potency with the wild-type E. coli pumping them out. The mechanism of the antibacterial action of SkQR1 may differ from that of SkQ1. The rhodamine derivatives also proved to be effective antibacterial agents against various Gram-positive species, including Staphylococcus aureus and Mycobacterium smegmatis. By using fluorescence correlation spectroscopy and fluorescence microscopy, SkQR1 was shown to accumulate in the bacterial membrane. Thus, the presentation of SkQR1 as a fluorescent analogue of SkQ1 and its use for visualization should be performed with caution.

Large language models are increasingly deployed as adaptive interviewers in qualitative research and human-computer interaction, yet systematic evaluation of their interviewing behavior remains limited. We introduce a modular LLM agent for conducting semi-structured psychological interviews and present a controlled, multi-faceted evaluation protocol to assess interviewer quality across six state-of-the-art models: Claude Sonnet 4, Gemini 2.5 Pro, GPT-5 Chat, Grok 4, Qwen3-235B A22B, and DeepSeek Chat V3.1. The agent conducts adaptive interviews over 54 main questions spanning biography, family, interests, challenges, values, work, and health, deciding for each response whether a follow-up is warranted and generating tailored follow-up questions. To enable fair comparison, we standardize interview context using transcripts from ten baseline human interviews, execute all models under identical orchestration and prompts, and use a single LLM interviewee to eliminate human response variability. Expert psycholinguists evaluate interviewer behavior on five binary criteria: benevolence (empathic tone), necessity, context-awareness, openness, and justified skip (when follow-ups are unnecessary), annotating over 2900 items with high inter-rater reliability (Fleiss$$\\kappa$$0.67–0.93). We complement human judgment with efficiency metrics (latency, questioning intensity) and linguistic profiling via morpho-syntactic and psycholinguistic features on the interview text. Results reveal systematic trade-offs: Gemini 2.5 Pro has the most empathic tone, GPT-5 Chat optimizes for speed and selective precision, Grok 4 achieves exhaustive coverage at the cost of latency and occasional over-contextualization, while Claude Sonnet 4 offers balanced versatility. Linguistic markers such as person pronouns, tense, intensifiers, or syntactic complexity align meaningfully with human judgments, suggesting that stylistic choices are aligned with perceived interview quality. DeepSeek’s format instability underscores the operational importance of schema compliance. Our reusable toolkit (prompts, orchestration code, annotation rubric) provides a foundation for principled deployment of LLM interviewers in psychological experiments, enabling researchers to match model capabilities to study goals and to audit agent behavior for empathy, appropriateness, and effectiveness.

Large language models are increasingly deployed as adaptive interviewers in qualitative research and human-computer interaction, yet systematic evaluation of their interviewing behavior remains limited. We introduce a modular LLM agent for conducting semi-structured psychological interviews and present a controlled, multi-faceted evaluation protocol to assess interviewer quality across six state-of-the-art models: Claude Sonnet 4, Gemini 2.5 Pro, GPT-5 Chat, Grok 4, Qwen3-235B A22B, and DeepSeek Chat V3.1. The agent conducts adaptive interviews over 54 main questions spanning biography, family, interests, challenges, values, work, and health, deciding for each response whether a follow-up is warranted and generating tailored follow-up questions. To enable fair comparison, we standardize interview context using transcripts from ten baseline human interviews, execute all models under identical orchestration and prompts, and use a single LLM interviewee to eliminate human response variability. Expert psycholinguists evaluate interviewer behavior on five binary criteria: benevolence (empathic tone), necessity, context-awareness, openness, and justified skip (when follow-ups are unnecessary), annotating over 2900 items with high inter-rater reliability (Fleiss 0.67–0.93). We complement human judgment with efficiency metrics (latency, questioning intensity) and linguistic profiling via morpho-syntactic and psycholinguistic features on the interview text. Results reveal systematic trade-offs: Gemini 2.5 Pro has the most empathic tone, GPT-5 Chat optimizes for speed and selective precision, Grok 4 achieves exhaustive coverage at the cost of latency and occasional over-contextualization, while Claude Sonnet 4 offers balanced versatility. Linguistic markers such as person pronouns, tense, intensifiers, or syntactic complexity align meaningfully with human judgments, suggesting that stylistic choices are aligned with perceived interview quality. DeepSeek’s format instability underscores the operational importance of schema compliance. Our reusable toolkit (prompts, orchestration code, annotation rubric) provides a foundation for principled deployment of LLM interviewers in psychological experiments, enabling researchers to match model capabilities to study goals and to audit agent behavior for empathy, appropriateness, and effectiveness.