Eosinophilic pulmonary disease with ulcerative colitis: A case report.

RATIONALE: Eosinophilic pulmonary disease (EPD) is a general term for a large group of diseases with complex etiology. Ulcerative colitis is an inflammatory bowel disease (IBD). Patients with IBD may have pulmonary involvement. We herein present a case of ulcerative colitis complicated with EPD. PATIENT CONCERNS: A 34-year-old woman with ulcerative colitis presented with dry cough. She had peripheral eosinophilia and apical ground glass opacities on CT (computed tomography) of her chest. Antibiotic treatment was ineffective. DIAGNOSES: Lung biopsy revealed eosinophil infiltration in the alveolar space and interstitial space, so EPD was considered. INTERVENTIONS: After oral administration of prednisone, the lung shadow on CT disappeared when the cough symptoms resolved. However, the symptoms recurred after drug withdrawal, and the lung shadow reappeared on imaging. The cough symptoms and lung shadow disappeared after oral prednisone was given again. Prednisone was slowly discontinued after 6 months of treatment. OUTCOMES: The patient stopped prednisone for half a year. No recurrence or abnormal CT findings were detected during the half-year follow-up. LESSONS: The clinical manifestations of EPD are atypical, laboratory and imaging findings are not specific, and it is difficult to make a definite diagnosis before lung biopsy. The diagnosis depends on pathological examination. Glucocorticoid treatment is effective, but some patients may relapse after drug withdrawal. Active follow-up after glucocorticoid treatment is very important for identifying disease recurrence. Patients with IBD are relatively prone to developing EPD. The etiology of EPD is complex. In clinical practice, we need to make a diagnosis and differential diagnosis to clarify its etiology.

VSIG4 ameliorates intestinal inflammation through inhibiting macrophages NLRP3 inflammasome and pyroptosis.

Inflammatory bowel disease (IBD) is one of the most common diseases in the digestive system related to aberrant inflammation. V-set and immunoglobulin domain-containing 4 (VSIG4), a type I transmembrane receptor exclusively expressed in a subset of tissue-resident macrophages, has been reported to exert anti-inflammatory activity in immune-related diseases, which has been not explored in IBD yet. This study aims to explore the role and the potential mechanism of VSIG4 in IBD. Clinical samples were obtained from IBD patients and were examined by immunohistochemical staining. THP-1 cells were differentiated into macrophages, and then stimulated with IL-4 plus IL-13 or LPS to induce pro-inflammatory (M1) or anti-inflammatory (M2) phenotype. Cell transfection was conducted to overexpress VSIG4. Western blot and immunofluorescence assays were performed to assess NLRP3 inflammasome- and pyroptosis-related proteins. Cytokines were measured using ELISA. A cell co-culture model of Caco-2 cells and VSIG4-mediated macrophages were established. Cell viability and apoptosis was examined by CCK-8 and flow cytometry assays, respectively. VSIG4 was downregulated in IBD and was negatively correlated with NLRP3 inflammasome. M1 macrophages exhibited higher levels of NLRP3 inflammasome, pyroptosis and inflammatory response than M2 macrophages, while VSIG4 overexpression efficiently reversed these changes in M1 macrophages. In addition, VSIG4 overexpression partly abolished M1 macrophages-induced cell viability loss, inflammatory response, apoptosis and pyroptosis in Caco-2 cells. Collectively, VSIG4 might alleviate intestinal inflammation through regulating M1/M2 macrophages, providing novel insights for the treatment of human IBD.

Primary Central Nervous System Extranodal Natural Killer/T-Cell Lymphoma, Nasal Type Colliding with Meningioma.

BACKGROUND: Collision tumors are defined as coexistence of 2 histologically different neoplasms occurring in the same anatomic location. Such co-occurrence of tumors in the brain is uncommon. To our knowledge, meningioma colliding with extranodal natural killer/T-cell lymphoma has not been described previously. CASE DESCRIPTION: A 50-year-old man presented with a 1-year history of progressive memory decline and 2 weeks of drowsiness, bradykinesia, and aphasia. Magnetic resonance imaging revealed a heterogeneously enhanced mass beside the left frontal cerebral falx resulting in midline shift. The left frontal lobe mass was resected. Pathologic examination showed the tumor consisted of whorled spindle cells and diffuse medium-sized lymphoid cells. The spindle cells were positive for epithelial membrane antigen and negative for S-100. The lymphoid cells expressed CD3ε, CD56, TIA-1, and granzyme B. Epstein-Barr virus encoded small RNAs were detected by in situ hybridization. No monoclonal T-cell receptor gamma gene rearrangement was detected. Four weeks after surgery, the patient was treated with polychemotherapy and intrathecal methotrexate, but he died 2 months later. CONCLUSIONS: This is the first report of a unique brain collision tumor consisting of a meningioma and an extranodal natural killer/T-cell lymphoma. Diagnosis depends on histopathology. Awareness of this entity is important to distinguish it from other intracranial tumors.

Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

Adverse drug events: database construction and in silico prediction.

Adverse drug events (ADEs) are the harms associated with uses of given medications at normal dosages, which are crucial for a drug to be approved in clinical use or continue to stay on the market. Many ADEs are not identified in trials until the drug is approved for clinical use, which results in adverse morbidity and mortality. To date, millions of ADEs have been reported around the world. Methods to avoid or reduce ADEs are an important issue for drug discovery and development. Here, we reported a comprehensive database of adverse drug events (namely MetaADEDB), which included more than 520,000 drug-ADE associations among 3059 unique compounds (including 1330 drugs) and 13,200 ADE items by data integration and text mining. All compounds and ADEs were annotated with the most commonly used concepts defined in Medical Subject Headings (MeSH). Meanwhile, a computational method, namely the phenotypic network inference model (PNIM), was developed for prediction of potential ADEs based on the database. The area under the receive operating characteristic curve (AUC) is more than 0.9 by 10-fold cross validation, while the AUC value was 0.912 for an external validation set extracted from the US-FDA Adverse Events Reporting System, which indicated that the prediction capability of the method was reliable. MetaADEDB is accessible free of charge at http://www.lmmd.org/online_services/metaadedb/. The database and the method provide us a useful tool to search for known side effects or predict potential side effects for a given drug or compound.