There are currently more than 38.9 million people over the age of 65 in the United States. Up to 3.6 million of these people are considered housebound and in need of home-based care. Although homebound status is not defined specifically, with a broad range of disability levels, it is evident that people who are homebound suffer from a multitude of medical and psychiatric illnesses. This review examines the current literature to identify the specific physical and psychiatric factors most responsible for the elderly becoming and remaining housebound. The homebound elderly suffer from metabolic, cardiovascular, cerebrovascular, and musculoskeletal diseases, as well as from cognitive impairment, dementia and depression, at higher rates than the general elderly population. The information in this review will explain the specific types of care the homebound population need, and discuss the care that could help ease their suffering and delay their entry into a nursing home or hospital.

Impulse-control disorders have received relatively little attention from the mental health community. An increasing awareness of the prevalence and impact of these disorders is emerging. Among impulse control disorders, problematic Internet use has been considered and examined. Prevalence estimate studies indicate that problematic Internet use is experienced across geographic locations by many individuals of diverse backgrounds. This review examines problematic Internet use from epidemiological and clinical perspectives. Clinicians should be familiar with the extent of problematic Internet use and the data regarding the efficacies and tolerabilities of available treatments.

Purpose Irinotecan (CPT-11) induced diarrhea occurs frequently in cancer patients and limits its usage. Bacteria β-glucuronidase (GUS) enzymes in intestines convert the non-toxic metabolite of CPT-11, SN-38G, to toxic SN-38, and finally lead to damage of intestinal epithelial cells and diarrhea. We previously reported amoxapine as potent GUS inhibitor in vitro. To further understand the molecular mechanism of amoxapine and its potential for treatment of CPT-11 induced diarrhea, we studied the binding modes of amoxapine and its metabolites by docking and molecular dynamics simulation, and tested the in vivo efficacy on mice in combination with CPT-11. Experimental Design The binding of amoxapine, its metabolites, 7-hydroxyamoxapine and 8-hydroxyamoxapine, and a control drug loxapine with GUS was explored by computational protocols. The in vitro potencies of metabolites were measured by E. Coli GUS enzyme and cell-based assay. Low dosage daily oral administration was designed to use along with CPT-11 to treat tumor-bearing mice. Results Computational modeling results indicated that amoxapine and its metabolites bound in the active site of GUS and satisfied critical pharmacophore features: aromatic features near bacterial loop residue F365’ and hydrogen bond toward E413. Amoxapine and its metabolites were demonstrated as potent in vitro. Administration of low dosages of amoxapine with CPT-11 in mice achieved significant suppression of diarrhea and reduced tumor growth. Conclusions Amoxapine has great clinical potential to be rapidly translated to human subjects for irinotecan induced diarrhea.

Abstract-Automated 3D modeling of building interiors is useful in applications such as virtual reality and entertainment. Using a human-operated backpack system equipped with 2D laser scanners and inertial measurement units (IMU), we develop scan matching based algorithms to localize the backpack in complex indoor environments such as a T-shaped corridor intersection, a staircase, and two indoor hallways from two separate floors connected by a staircase. When building 3D textured models, we find that the localization resulting from scan matching is not pixel accurate, resulting in misalignment between successive images used for texturing. To address this, we propose an image based pose estimation algorithm to refine the results from our scan matching based localization. Finally, we use the localization results within an image based renderer to enable virtual walkthroughs of indoor environments using imagery from cameras on the same backpack. Our renderer uses a three-step process to determine which image to display, and a RANSAC framework to determine homographies to mosaic neighboring images with common SIFT features. In addition, our renderer uses plane-fitted models of the 3D point cloud resulting from the laser scans to detect occlusions. We characterize the performance of our image based renderer on an unstructured set of 2709 images obtained during a five minute backpack data acquisition for a T-shaped corridor intersection.