Objective Drug–drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients). Materials and Methods Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from >30 different resources, including >10 000 drugs and >1.7 million drug–gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities. Results Our approach achieves >0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records. Conclusions By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.

Autophagy, a physiologic mechanism that promotes energy recycling and orderly degradation through self-regulated disassembly of cellular components, helps maintain homeostasis. A series of evidences suggest that autophagy is activated as a response to ischemia and has been well-characterized as a therapeutic target. However, the role of autophagy after ischemia remains controversial. Activated-autophagy can remove necrotic substances against ischemic injury to promote cell survival. On the contrary, activation of autophagy may further aggravate ischemic injury, causing cell death. Therefore, the present review will examine the current understanding of the precise mechanism and role of autophagy in ischemia and recent neuroprotective therapies on autophagy, drug, and nondrug therapies, including electroacupuncture (EA).

The standard of care treatment consists of surgery, radiotherapy, and chemotherapy with temozolomide (TMZ). TMZ inhibits DNA replication and leads to cell death. Drug eruptions due to TMZ often present a diagnostic challenge due to delayed onset and concomitant medications. We present a patient with GBM who developed a TMZ-induced desquamative skin rash.A 58-year-old Caucasian female presented with progressive weakness in her left hand. Magnetic resonance imaging showed a right frontal cerebral brain lesion and biopsy revealed the diagnosis of GBM. The patient was started on dexamethasone for brain edema soon after her diagnosis. A month later she received a 6-week course of concurrent brain chemoradiation with oral TMZ, along with sulfamethoxazole-trimethoprim for PCP prophylaxis. The day after the patient finished the induction course and 12 days after she had discontinued dexamethasone, she developed erythema on the face that progressed to involve the rest of the body. The rash lasted for

No abstract

No abstract

Background Rare diseases collectively affect up to 10% of the population, but often lack effective treatment, and typically little is known about their pathophysiology. Major challenges include suboptimal phenotype mapping and limited statistical power. Population biobanks, such as the UK Biobank, recruit many individuals who can be affected by rare diseases; however, investigation into their utility for rare disease research remains limited. We hypothesized the UK Biobank can be used as a unique population assay for rare diseases in the general population. Methods We constructed a consensus mapping between ICD-10 codes and ORPHA codes for rare diseases, then identified individuals with each rare condition in the UK Biobank, and investigated their age at recruitment, sex bias, and comorbidity distributions. Using exome sequencing data from 167,246 individuals of European ancestry, we performed genetic association controlling for case/control imbalance (SAIGE) to identify potential rare pathogenic variants for each disease. Results Using our mapping approach, we identified and characterized 420 rare diseases affecting 23,575 individuals in the UK Biobank. Significant genetic associations included JAK2 V617F for immune thrombocytopenic purpura (p = 1.24 × 10−13) and a novel CALR loss of function variant for essential thrombocythemia (p = 1.59 × 10−13). We constructed an interactive resource highlighting demographic information (http://www-personal.umich.edu/~mattpat/rareDiseases.html) and demonstrate transferability by applying our mapping to a medical claims database. Conclusions Enhanced disease mapping and increased power from population biobanks can elucidate the demographics and genetic associations for rare diseases.

No abstract

No abstract