Dexamethasone for the management of CRS Related to teclistamab in patients with relapsed/refractory multiple myeloma
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Tau is a receptor with low affinity for glucocorticoids and is required for glucocorticoid-induced bone loss
Glucocorticoids (GCs) are the most prescribed anti-inflammatory and immunosuppressive drugs. However, their use is often limited by substantial side effects, such as GC-induced osteoporosis (GIO) with the underlying mechanisms still not fully understood. In this study, we identify Tau as a low-affinity binding receptor for GCs that plays a crucial role in GIO. Tau deficiency largely abolished bone loss induced by high-dose dexamethasone, a synthetic GC, in both inflammatory arthritis and GIO models. Furthermore, TRx0237, a Tau inhibitor identified from an FDA-approved drug library, effectively prevented GIO. Notably, combinatorial administration of TRx0237 and dexamethasone completely overcame the osteoporosis adverse effect of dexamethasone in treating inflammatory arthritis. These findings present Tau as a previously unrecognized GC receptor with low affinity, and provide potential strategies to mitigate a spectrum of GC-related adverse effects, particularly osteoporosis.
Survival benefit of cytoreductive surgery in patients with primary stage IV endometrial cancer: a systematic review & meta-analysis
This systematic review and meta-analysis aimed to investigate the survival outcomes following cytoreductive surgery (CRS) in patients with primary stage IV endometrial cancer (EC).
Costunolide normalizes neuroinflammation and improves neurogenesis deficits in a mouse model of depression through inhibiting microglial Akt/mTOR/NF-κB pathway
Neuroinflammation is crucial for the pathogenesis of major depression. Preclinical studies have shown the potential of anti-inflammatory agents, specifically costunolide (COS), correlate with antidepressant effects. In this study, we investigated the molecular mechanisms underlying the antidepressant actions of COS. Chronic restraint stress (CRS) was induced in male mice. The mice were treated with either intra-DG injection of COS (5 μM, 1 μL per side) or COS (20 mg/kg, i.p.) for 1 week. We showed that administration of COS through the both routes significantly ameliorated the depressive-like behavior in CRS-exposed mice. Furthermore, administration of COS significantly improved chronic stress-induced adult hippocampal neurogenesis deficits in the mice through attenuating microglia-derived neuroinflammation. We demonstrated that COS (5 μM) exerted anti-neuroinflammatory effects in LPS-treated BV2 cells via inhibiting microglial Akt/mTOR/NF-κB pathway; inactivation of mTOR/NF-κB/IL-1β pathway was required for the pro-neurogenic action of COS in CRS-exposed mice. Our results reveal the antidepressant mechanism of COS that is normalizing neuroinflammation to improve neurogenesis deficits, supporting anti-inflammatory agents as a potential therapeutic strategy for depression.
The clinical journey of belantamab mafodotin in relapsed or refractory multiple myeloma: lessons in drug development
Patients with relapsed/refractory multiple myeloma (RRMM) have a poor prognosis and a need remains for novel effective therapies. Belantamab mafodotin, an anti–B-cell maturation antigen antibody-drug conjugate, was granted accelerated/conditional approval for patients with RRMM who have received at least 4 prior lines of therapy, based on response rates observed in DREAMM-1/DREAMM-2. Despite the 41% response rate and durable responses observed with belantamab mafodotin in the Phase III confirmatory DREAMM-3 trial, the marketing license for belantamab mafodotin was later withdrawn from US and European markets when the trial did not meet its primary endpoint of superiority for progression-free survival compared with pomalidomide and dexamethasone. This review reflects on key lessons arising from the clinical journey of belantamab mafodotin in RRMM. It considers how incorporating longer follow-up in DREAMM-3 may have better captured the clinical benefits of belantamab mafodotin, particularly given its multimodal, immune-related mechanism of action with responses deepening over time. A non-inferiority hypothesis may have been more appropriate rather than superiority in the context of a monotherapy versus an active doublet therapy. Further, anticipation of, and planning for, non-proportional hazards arising from response heterogeneity may have mitigated loss of statistical power. With the aim of improving the efficacy of belantamab mafodotin, other Phase III trials in the RRMM development program (DREAMM-7 and DREAMM-8) proceeded to evaluate the synergistic potential of combination regimens in earlier lines of treatment. The aim was to increase the proportion of patients responding to belantamab mafodotin (and thus the likelihood of seeing a clear separation of the progression-free survival curve versus comparator regimens). Protocol amendments reflecting DREAMM-3 learnings could also be implemented prospectively on the combinations trials to optimize the follow-up duration and mitigate risk. The wider implications of the lessons learned for clinical research in RRMM and in earlier treatment settings are discussed.
Machine learning empowered coherent Raman imaging and analysis for biomedical applications
In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.
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