The rapid integration of large language models into medical publishing offers considerable potential for improving drafting efficiency but simultaneously raises substantial concerns regarding research integrity, accountability, and the reliability of the scientific record. Recent incidents in which artificial intelligence (AI) systems were listed as coauthors have prompted urgent regulatory revisions. In this review, we identify a global consensus that strictly prohibits AI authorship, as algorithms lack both moral agency and legal accountability. Transparency has emerged as an essential requirement, and undisclosed AI use is increasingly regarded as a form of ethical misconduct. Key risks include “hallucinations” (notably citation fabrication), algorithmic bias, and potential violations of privacy regulations (e.g., the Health Insurance Portability and Accountability Act) when protected health information is processed through cloud-based platforms. The analysis indicates that rigid prohibitions are operationally unenforceable, supporting instead a “human stewardship” model in which AI functions as a drafting scaffold subjected to rigorous human verification. AI represents a lasting transformation in medical writing that necessitates a shift from simple prohibition to structured governance. To preserve epistemic validity, we propose a framework built on task segmentation, mandatory cross-referencing of claims, and data sovereignty. Ultimately, AI must remain a transparent assistive tool, with full responsibility for the manuscript residing exclusively with the human investigator.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

N-acetylcysteine (NAC) has been used as an antioxidant to prevent oxidative cell death.However, we found NAC itself to induce neuronal death in mouse cortical cultures.Therefore, the current study was performed to investigate the mechanism of neuronal death caused by NAC. Cell death was assessed by measuring lactate dehydrogenase efflux to bathing media after 24-48 h exposure to NAC. NAC (0.1-10 mM) induced neuronal death in a concentration- and exposure time-dependent manner. However, NAC did not injure astrocytes even at a concentration of 10 mM. Also, 10 mM NAC markedly attenuated oxidative astrocyte death induced by 0.5 mM diethyl maleate or 0.25 mM H2O2 . The NMDA receptor antagonist MK-801 (10 μM) markedly attenuated the neuronal death caused by 10 mM NAC, while NBQX did not affect the neuronal death. Cycloheximide (a protein synthesis inhibitor, 0.1 μg/mL) and z-VAD-FMK (a caspase inhibitor, 100 μM) also significantly attenuated neuronal death. Apoptotic features such as chromatin condensation, nuclear fragmentation, and caspase 3 activation were observed 1 h after the NAC treatment. The neuronal death induced by 1 or 10 mM NAC was significantly attenuated by the treatment with 100 μM Trolox or 1 mM ascorbic acid. NAC induced the generation of intracellular reactive oxygen species (ROS), as measured by the fluorescent dye 2’,7’-dichlorofluorescein diacetate. The ROS generation was almost completely abolished by treatment with Trolox or ascorbic acid. These findings demonstrate that NAC can cause oxidative, apoptotic, and excitotoxic neuronal death in mouse neuronal cultures.

We examined the effect of fluoxetine, a selective serotonin reuptake inhibitor antidepressant, on neuronal viability in mouse cortical near-pure neuronal cultures. Addition of fluoxetine to the media for 24 hours induced neuronal death in a concentration-dependent manner. To delineate the mechanisms of fluoxetine-induced neuronal death, we investigated the effects of trolox, cycloheximide (CHX), BDNF, z-VAD-FMK, and various metal-chelators on fluoxetine-induced neuronal death. Neuronal death was assessed by MTT assay. The addition of 20 µM fluoxetine to the media for 24 hours induced 60–70% neuronal death, which was associated with the hallmarks of apoptosis, chromatin condensation and DNA laddering. Fluoxetine-induced death was significantly attenuated by CHX, BDNF, or z-VAD-FMK. Treatment with antioxidants, trolox and ascorbate, also markedly attenuated fluoxetine-induced death. Interestingly, some divalent cation chelators (EGTA, Ca-EDTA, and Zn-EDTA) also markedly attenuated the neurotoxicity. Fluoxetine-induced reactive oxygen species (ROS) generation was measured using the fluorescent dye 2′,7′-dichlorofluorescin diacetate. Trolox and bathocuproine disulfonic acid (BCPS), a cell membrane impermeable copper ion chelator, markedly attenuated the ROS production and neuronal death. However, deferoxamine, an iron chelator, did not affect ROS generation or neurotoxicity. We examined the changes in intracellular copper concentration using a copper-selective fluorescent dye, Phen Green FL, which is quenched by free copper ions. Fluoxetine quenched the fluorescence in neuronal cells, and the quenching effect of fluoxetine was reversed by co-treatment with BCPS, however, not by deferoxamine. These findings demonstrate that fluoxetine could induce apoptotic and oxidative neuronal death associated with an influx of copper ions.

The Amyloid β peptide (Aβ) is a main component of senile plaques in Alzheimer's disease. Currently, NADPH oxidase (NOX) and mitochondria are considered as primary sources of ROS induced by Aβ. However, the contribution of NOX and mitochondria to Aβ-induced ROS generation has not been well defined. To delineate the relative involvement of NOX and mitochondria in Aβ-induced ROS generation and neuronal death in mouse cortical cultures, we examined the effect of NOX inhibitors, apocynin and AEBSF, and the mitochondria-targeted antioxidants (MTAs), mitotempol and mitoquinone, on Aβ-induced ROS generation and neuronal deaths. Cell death was assessed by measuring lactate dehydrogenase efflux in bathing media at 24 and 48 hrs after exposure to Aβ₁₋₄₂. Aβ₁₋₄₂ induced dose- and time-dependent neuronal deaths in cortical cultures. Treatment with 20 µM Aβ₁₋₄₂ markedly and continuously increased not only the DHE fluorescence (intracellular ROS signal), but also the DHR123 fluorescence (mitochondrial ROS signal) up to 8 hrs. Treatment with apocynin or AEBSF selectively suppressed the increase in DHE fluorescence, while treatment with mitotempol selectively suppressed the increase in DHR123 fluorescence. Each treatment with apocynin, AEBSF, mitotempol or mitoquinone significantly attenuated the Aβ₁₋₄₂-induced neuronal deaths. However, any combined treatment with apocynin/AEBSF and mitotempol/mitoquinone failed to show additive effects. These findings indicate that 20 µM Aβ₁₋₄₂ induces oxidative neuronal death via inducing mitochondrial ROS as well as NOX activation in mixed cortical cultures, but combined suppression of intracellular and mitochondrial ROS generation fail to show any additive neuroprotective effects against Aβ neurotoxicity.
Alzheimer Disease ; Amyloid beta-Peptides ; Amyloid* ; Animals ; Antioxidants* ; Baths ; Cell Death ; Fluorescence ; L-Lactate Dehydrogenase ; Mice* ; Mitochondria ; NADP* ; NADPH Oxidase* ; Neurons* ; Neuroprotective Agents ; Oxidative Stress ; Plaque, Amyloid

β-Amyloid peptide (Aβ) is the main component of senile plaques in patients with Alzheimer's disease, and is known to be a main pathogenic factor of the disease. Recent evidence indicates that activation of NADPH oxidase (NOX) in microglia or astrocytes may be a source of Aβ-induced reactive oxygen species (ROS). We investigated the role of neuronal NOX in Aβ-induced neuronal death in mouse mixed cortical cultures. Cell death was assessed by measuring lactate dehydrogenase efflux to bathing media 24 or 48 hr after exposure to Aβ₂₅₋₃₅, a fragment of Aβ with an equivalent neurotoxic effect. Aβ₂₅₋₃₅ induced neuronal death in concentration- and time- dependent manners with apoptotic features. Neuronal death was significantly attenuated, not only by anti-apoptotic drugs, such as z-VAD-fmk and cycloheximide, but also by antioxidants, such as trolox, ascorbic acid, and epigallocatethin gallate. We also demonstrated that treatment with 20 µM Aβ₂₅₋₃₅ increased fluorescent signals in mixed cortical cultures, but produced only weak signals in pure astrocyte cultures in the presence of 2',7'-dichlorofluorescin diacetate (DCF-DA), an indicator for intracellular ROS. Increased DCF-DA fluorescence was markedly inhibited, not only by trolox, but also by selective NOX inhibitors, such as apocynin and AEBSF. Western blot analyses revealed that Aβ₂₅₋₃₅ increased the expression of gp91phox, a main subunit of NOX in cells. The above antioxidants, apocynin, and AEBSF significantly attenuated neuronal death induced by Aβ₂₅₋₃₅. Furthermore, the gp91phox-specific siRNA-based knockdown of NOX significantly inhibited neuronal death. These results suggest that activation of neuronal NOX is involved in Aβ25-35-induced neuronal death.
Alzheimer Disease ; Amyloid beta-Peptides ; Animals ; Antioxidants ; Ascorbic Acid ; Astrocytes ; Baths ; Blotting, Western ; Cell Death ; Cycloheximide ; Fluorescence ; Humans ; L-Lactate Dehydrogenase ; Mice ; Microglia ; NADP ; NADPH Oxidase ; Neurons ; Plaque, Amyloid ; Reactive Oxygen Species