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Background The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. Methods In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. Results We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. Conclusions We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Background As the numbers of HIV-positive diagnoses rise in South Africa, it is important to understand the determinants and consequences of HIV disclosure. Methods Cross-sectional survey from random community samples of men and women in urban and rural South Africa (n = 217 HIV-positive individuals, 89% female). Results Two thirds of all known HIV-infected adults in these communities had disclosed their status to sexual partner(s). On average, individuals who disclosed were 2 years older, higher in socioeconomic assets, and had known their HIV status 7 months longer than those who had not told their sexual partner(s). The “need for privacy” was the most cited reason (45%) for nondisclosure among those who had never disclosed. People who eventually disclosed their HIV status to sexual partner(s) were significantly more likely to report always or more frequently using condoms, reducing their number of sexual partners, and/or becoming monogamous. Among individuals who disclosed their HIV status, 77% reported increases in social support, with families providing the most support. Conclusions Disclosure is associated with reports of consequent safer sexual behavior and greater social support. Interventions might be informed by the costs and benefits of disclosure and differences in disclosure to sexual partner vs. to one’s social network.

This longitudinal study assessed the contributions of psychosocial factors to symptoms of posttraumatic stress disorder (PTSD) during pregnancy and at 7 and 13 months postpartum in a sample of 206 low-income Latinas receiving prenatal services. Bilingual interviewers administered semistructured interviews that assessed sociodemographic characteristics (income, age, marital status, acculturation) and psychosocial factors (intimate partner violence (IPV), other lifetime trauma, depressive symptoms, and social support). Hierarchical linear regression analyses were conducted at each of the 3 time points in pregnancy and postpartum to identify the best predictors of PTSD symptoms. Results revealed that low-income, depressive symptoms and a history of other lifetime trauma were associated with symptoms of PTSD during pregnancy. After controlling for PTSD symptoms at baseline, PTSD symptoms at 7 months postpartum were associated with depressive symptoms, low perceived social support, and IPV history, but not history of other trauma. After controlling for PTSD symptoms at 7 months postpartum, PTSD symptoms at 13 months were associated with depressive symptoms and IPV. Screening for depressive symptoms and noninterpersonal trauma history in early pregnancy and for depressive symptoms, IPV, and social support postpartum in low-income Latina women may aid in identifying those at heightened risk for mental distress.

Zoledronic acid had the highest probability of causing the greatest number of GI AE, possibly related to nausea. These results question the assumption that annual zoledronic acid will translate into better adherence. Little difference was found between alendronate and risedronate for serious AE. More research into real-world implications of the comparative safety of bisphosphonates is needed.

The intra-tumor microbiota has been increasingly implicated in cancer pathogenesis. In this study, we aimed to examine the microbiome in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and determine its compositional differences with relation to age and gender. After grouping 497 LUAD and 433 LUSC patients by age and gender and removing potential contaminants, we identified differentially abundant microbes in each patient cohort vs. adjacent normal samples. We then correlated dysregulated microbes with patient survival rates, immune infiltration, immune and cancer pathways, and genomic alterations. We found that most age and gender cohorts in both LUAD and LUSC contained unique, significantly dysregulated microbes. For example, LUAD-associated Escherichia coli str. K-12 substr. W3110 was dysregulated in older female and male patients and correlated with both patient survival and genomic alterations. For LUSC, the most prominent bacterial species that we identified was Pseudomonas putida str. KT2440, which was uniquely associated with young LUSC male patients and immune infiltration. In conclusion, we found differentially abundant microbes implicated with age and gender that are also associated with genomic alterations and immune dysregulations. Further investigation should be conducted to determine the relationship between gender and age-associated microbes and the pathogenesis of lung cancer.

The present study examined the relationship between different forms of childhood violence (emotional, physical, and sexual) and these same forms of violence in adulthood, using a cross-sectional baseline survey of 298 homeless and unstably housed women in San Francisco, California. We also examined other related factors, including mental illnesses diagnosis, sex exchange, jail time, HIV status, and sociodemographic information. Regression analysis indicated that while several of these factors were associated with experiences of violence as an adult, specific types of child violence (e.g. sexual violence) predicted instances of that same type of violence as an adult, but not necessarily other types. Thus, risk of adult violence among low-income women may be better predicted and addressed through histories of same-type childhood violence, despite competing current stressors.

Hepatocellular carcinoma (HCC) is one of the deadliest cancers in the world. Previous studies have identified the importance of alcohol and hepatitis B (HBV) infection on HCC carcinogenesis, indicating synergy in the methods by which these etiologies advance cancer. However, the specific molecular mechanism behind alcohol and HBV-mediated carcinogenesis remains unknown. Because the microbiome is emerging as a potentially important regulator of cancer development, this study aims to classify the effects of HBV and alcohol on the intratumoral liver microbiome. RNA-sequencing data from The Cancer Genome Atlas (TCGA) were used to infer microbial abundance. This abundance was then correlated to clinical variables and to cancer and immune-associated gene expression, in order to determine how microbial abundance may contribute to differing cancer progression between etiologies. We discovered that the liver microbiome is likely oncogenic after exposure to alcohol or HBV, although these etiological factors could decrease the abundance of a few oncogenic microbes, which would lead to a tumor suppressive effect. In HBV-induced tumors, this tumor suppressive effect was inferred based on the downregulation of microbes that induce cancer and stem cell pathways. Alcohol-induced tumors were observed to have distinct microbial profiles from HBV-induced tumors, and different microbes are clinically relevant in each cohort, suggesting that the effects of the liver microbiome may be different in response to different etiological factors. Collectively, our data suggest that HBV and alcohol operate within a normally oncogenic microbiome to promote tumor development, but are also able to downregulate certain oncogenic microbes. Insight into why these microbes are downregulated following exposure to HBV or alcohol, and why the majority of oncogenic microbes are not downregulated, may be critical for understanding whether a pro-tumor liver microbiome could be suppressed or reversed to limit cancer progression.