The high incidence of BB2 receptor (BB2r) expression in various cancers has prompted investigators to pursue the development of BB2r-targeted agents for diagnostic imaging, chemotherapy and radiotherapy. Development of BB2r-targeted agents, based on the bombesin (BBN) peptide, has largely involved the use of the bifunctional chelate approach in which the linking group serves several key roles including pharmacokinetic modification. Understanding the in vivo properties of the various pharmacokinetic modifying linking groups is crucial for developing BB2r-targeted agents with improved targeting and clearance characteristics. The goal of this study was to systematically evaluate the pharmacokinetic profile of aliphatic hydrocarbon, aromatic and polyethylene glycol (ether) functional groups in order to obtain a better understanding of the in vivo properties of these pharmacokinetic modifiers. Specifically, we synthesized six radioconjugates with the structure 111 In-DOTA-X-BBN(7-14)NH 2 , where X = 8-aminooctanoic acid (8-AOC), 5-amino-

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient’s optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool—risk groups developed by the National Cancer Center Network (NCCN)—our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization.

Over a span of 1 year, with millions infected, COVID-19 has spread to every part of the world and now poses a health threat to each and every one of us. The outbreak has consequently resulted in multiple health problems such as stress, anxiety, depressive symptoms, insomnia, panic, and denial globally. Several factors have contributed to this rising number of psychiatric consults all over the world. The primary objective of this study was to investigate the impact of COVID-19 pandemic on the mental health of Pakistani population during the second wave of the pandemic in this region. We conducted an online web-based cross-sectional survey comprising 500 participants. The questionnaire assessed the demographic information, attitude, and knowledge concerning COVID-19 outbreak in addition to generalized anxiety disorder (GAD) utilizing the GAD-7 scale and depressive symptoms using the Center for Epidemiology Scale for Depression (CES-D) scale. The response rate of the study was 90.9%. The results of the survey indicated a prevalence of 25.4% of GAD, and 18.8% of depressive symptoms. Furthermore, nearly 34.8% of participants feared contracting COVID-19, 62.8% obtained constant critical updates regarding COVID-19, while 17.6% did not understand the knowledge regarding COVID-19. In the multivariate regression models, GAD was significantly associated with gender, age, and checking constantly of critical updates regarding COVID-19. Similarly, participants under 30 years had a higher risk of developing depressive symptoms than those above (> 30 years). Lastly, participants with no formal education were also found to be more prone to developing depression. We identified a potential threat to mental health during the pandemic.