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Lithium has a narrow therapeutic window. Frequent monitoring of both serum levels and clinical signs of toxicity is warranted because toxicity may be present even when concentrations are within the therapeutic range. We report the case of a man with lithium poisoning, with persistent neurologic signs and symptoms even after removal of lithium from circulation – a diagnosis of syndrome of irreversible lithium-effectuated neurotoxicity (SILENT) was made.

Cerebral salt wasting (CSW) is a rare cause of hypoosmolar hyponatremia usually associated with acute intracranial disease characterized by extracellular volume depletion due to inappropriate sodium wasting in the urine. We report a case of a 46-year-old male with recently diagnosed systemic lupus erythematosus (SLE) initially presenting with neurological involvement and an antiphospholipid syndrome (APS) who was admitted because of chronic asymptomatic hyponatremia previously assumed as secondary to syndrome of inappropriate antidiuretic hormone secretion (SIADH). Initial evaluation revealed a hypoosmolar hyponatremia with high urine osmolality and elevated urinary sodium concentration. Clinically, the patient's extracellular volume status was difficult to define accurately. After exclusion of other etiologies, a diagnosis of CSW was established and the patient started appropriate treatment with normalization of sodium levels. The challenge in diagnosing CSW relies on the differentiation from the SIADH, since it shares most of the laboratory features. The critical difference is the state of extracellular volume which can be difficult to access accurately in routine clinical practice. There is evidence that the fractional excretion of urate (FEUrate) can be of assistance. The relationship between SLE and hyponatremia is mostly limited to adverse pharmacological side effects, so to our knowledge, this is the first association between SLE with neurological involvement and CSW.

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Ex-vivo drug sensitivity screening (DSS) allows the prediction of cancer treatment effectiveness in a personalized fashion. However, it only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. To address this shortcoming, we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples with top-ranking accuracy compared to state-of- the-art deconvolution methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies and in-depth simulations. We then applied our deconvolution strategy to Acute Myeloid Leukemia (AML) context using the beatAML cohort dataset, currently the most extensive database of ex-vivo DSS. We generated a landscape of cell subtype-specific drug sensitivity and focused on four therapeutic compounds predicted to target leukemic stem cells: crenalotinib, AZD1480, bosutinib, and venetoclax. We defined their efficacy at the single-cell level and characterized a population of venetoclax-resistant cancer stem-like cells. Our work provides an attractive new computational tool for drug development and precision medicine.