This systematic review summarizes the evidence on the earliest patients with COVID-19-HIV co-infection. We searched PubMed, Scopus, Web of Science, Embase, preprint databases, and Google Scholar from December 01, 2019, to June 1, 2020. From an initial 547 publications and 75 reports, 25 studies provided specific information on COVID-19 patients living with HIV. Studies described 252 patients, 80.9% were male, the mean age was 52.7 years, and 98% were on antiretroviral treatment (ART). Co-morbidities in addition to HIV and COVID-19 (multimorbidity) included hypertension (39.3%), obesity or hyperlipidemia (19.3%), chronic obstructive pulmonary disease (18.0%), and diabetes (17.2%). Two-thirds (66.5%) had mild to moderate symptoms, the most common being fever (74.0%) and cough (58.3%). Among patients who died, the majority (90.5%) were over 50 years old, male (85.7%), and had multimorbidity (64.3%). Our findings highlight the importance of identifying co-infections, addressing co-morbidities, and ensuring a secure supply of ART for PLHIV during the COVID-19 pandemic.

This systematic review summarizes the evidence on the earliest patients with COVID-19-HIV co-infection. We searched PubMed, Scopus, Web of Science, Embase, preprint databases, and Google Scholar from December 01, 2019 to June 1, 2020. From an initial 547 publications and 75 reports, 25 studies provided specific information on COVID-19 patients living with HIV. Studies described 252 patients, 80.9% were male, mean age was 52.7 years, and 98% were on ART. Co-morbidities in addition to HIV and COVID-19 (multimorbidity) included hypertension (39.3%), obesity or hyperlipidemia (19.3%), chronic obstructive pulmonary disease (18.0%), and diabetes (17.2%). Two-thirds (66.5%) had mild to moderate symptoms, the most common being fever (74.0%) and cough (58.3%). Among patients who died, the majority (90.5%) were over 50 years old, male (85.7%), and had multimorbidity (64.3%). Our findings highlight the importance of identifying co-infections, addressing co-morbidities, and ensuring a secure supply of ART for PLHIV during the COVID-19 pandemic.

There might be an association between Internet addiction (IA) and loneliness; however, inconsistent evidence suggests that the severity of this association remains unclear. This study was conducted to assess the association between IA and loneliness. A systematic literature search was conducted in four online databases, including PubMed (MESH terms), Web of Science, Scopus, and Embase. Observational studies measuring the association between IA and loneliness were screened and included in this review. A meta-analysis was conducted using the Stata software. Twenty-six articles with a total sample size of 16496 subjects were included in the analysis. A moderate positive association (r = 0.15 (95% CI: 0.13, 0.16)) was found between IA and loneliness. The individuals with IA had significantly higher scores of loneliness. According to this meta-analysis, we need more attention to the early symptoms of loneliness in individuals with IA. Longitudinal studies are needed to determine the temporality of this association considering adjustment for time varying confounders.

Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliability of a model. The problem has gained significant attention due to its importance for safety deploying models in open-world settings. Detecting OOD samples is challenging due to the intractability of modeling all possible unknown distributions. To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection. Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently. Accordingly, these research avenues have not cross-pollinated, creating research barriers. While some surveys intend to provide an overview of these approaches, they seem to only focus on a specific domain without examining the relationship between different domains. This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas while identifying their commonalities. Researchers can benefit from the overview of research advances in different fields and develop future methodology synergistically. Furthermore, to the best of our knowledge, while there are surveys in anomaly detection or one-class learning, there is no comprehensive or up-to-date survey on out-of-distribution detection, which our survey covers extensively. Finally, having a unified cross-domain perspective, we discuss and shed light on future lines of research, intending to bring these fields closer together.

Colorectal cancer (CRC) is known as the third most common and fourth leading cancer associated death worldwide. The occurrence of metastasis has remained as a critical challenge in CRC, so that distant metastasis (mostly to the liver) has been manifested in about 20%-25% of patients. Several screening approaches have introduced for detecting CRC in different stages particularly in early stages. The standard treatments for CRC are surgery, chemotherapy and radiotherapy, in alone or combination. Immunotherapy is a set of novel approaches with the aim of remodeling the immune system battle with metastatic cancer cells, such as immunomodulatory monoclonal antibodies (immune checkpoint inhibitors), adoptive cell transfer (ACT) and cancer vaccine. Cancer vaccines are designed to trigger the intense response of immune system to tumor-specific antigens. In two last decades, introduction of new cancer vaccines and designing several clinical trials with vaccine therapy, have been taken into consideration in colon cancer patients. This review will describe the treatment approaches with the special attention to vaccines applied to treat colorectal cancer. K E Y W O R D S colorectal cancer, exosome, therapy, vaccine J Cell Biochem. 2019;120:8815-8828.wileyonlinelibrary.com/journal/jcb