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Evaluation of fish nutritional content information could provide essential guidance for seafood consumption and human health protection. This study investigated the lipid contents, fatty acid compositions, and nutritional qualities of 22 commercially important marine fish species from the Pearl River Estuary (PRE), South China Sea. All the analyzed species had a low to moderate lipid content (0.51-7.35% fat), with no significant differences in fatty acid profiles among fishes from different lipid categories (p > 0.05). Compared with previous studies from other regions, the examined fish species exhibited higher proportions of saturated fatty acids (SFAs, 39.1 ± 4.00%) and lower contents of polyunsaturated fatty acids (PUFAs, 21.6 ± 5.44%), presumably due to the shifted diet influence from increased diatoms and decreased dinoflagellate over the past decades in the PRE. This study further revealed that there was a significantly negative correlation between the trophic levels and levels of PUFAs in the examined species (Pearson's r =-0.42, p = 0.04), likely associated with their differed dietary composition. Considering the health benefit of PUFAs, a few marine fish in PRE with low levels of PUFAs might have no significant contribution to the cardiovascular disease prevention, although fish with different fatty acid profiles most likely contribute differently towards human health. Additional studies are needed in order to comprehensively analyze the nutritional status of fish species in the PRE.

Prostate cancer is a common malignancy in men worldwide. Lysophosphatidic acid receptor 1 (LPAR1) is a critical gene and it mediates diverse biologic functions in tumor. However, the correlation between LPAR1 and prognosis in prostate cancer, as well as the potential mechanism, remains unclear. In the present study, LPAR1 expression analysis was based on The Cancer Genome Atlas (TCGA) and the Oncomine database. The correlation of LPAR1 on prognosis was also analyzed based on R studio. The association between LPAR1 and tumor-infiltrating immune cells were evaluated in the Tumor Immune Estimation Resource site, ssGSEA, and MCPcounter packages in R studio. Gene Set Enrichment Analysis and Gene Ontology analysis were used to analyze the function of LPAR1. TCGA datasets and the Oncomine database revealed that LPAR1 was significantly downregulated in prostate cancer. High LPAR1 expression was correlated with favorable overall survival. LPAR1 was involved in the activation, proliferation, differentiation, and migration of immune cells, and its expression was positively correlated with immune infiltrates, including CD4+ T cells, B cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, and natural killer cells. Moreover, LPAR1 expression was positively correlated with those chemokine/chemokine receptors, indicating that LPAR1 may regulate the migration of immune cells. In summary, LPAR1 is a potential prognostic biomarker and plays an important part in immune infiltrates in prostate cancer.

Background The COVID-19 pandemic has affected people’s daily lives and has caused economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased depression levels among the population. However, systematic studies of depression detection and monitoring during the pandemic are lacking. Objective This study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features’ importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates. Methods To study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. To examine the effect of depression on people’s Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model’s tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information, and investigated these features’ relations to depression signals. Finally, we demonstrated our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results Our fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states—New York, California, and Florida—shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19’s impact on people’s mental health. The noninvasi...

Enhanced aerobic glycolysis constitutes an additional source of energy for tumor proliferation and metastasis. Human papillomavirus (HPV) infection is the main cause of cervical cancer (CC); however, the associated molecular mechanisms remain poorly defined, as does the relationship between CC and aerobic glycolysis. To investigate whether HPV 16/18 E6/E7 can enhance aerobic glycolysis in CC, E6/E7 expression was knocked down in SiHa and HeLa cells using small interfering RNA (siRNA). Then, glucose uptake, lactate production, ATP levels, reactive oxygen species (ROS) content, extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were evaluated. RNA-seq was used to probe the molecular mechanism involved in E6/E7-driven aerobic glycolysis, and identified IGF2BP2 as a target of E6/E7. The regulatory effect of IGF2BP2 was confirmed by qRT-PCR, western blot, and RIP assay. The biological roles and mechanisms underlying how HPV E6/E7 and IGF2BP2 promote CC progression were confirmed in vitro and in vivo. Human CC tissue microarrays were used to analyze IGF2BP2 expression in CC. The knockdown of E6/E7 and IGF2BP2 attenuated the aerobic glycolytic capacity and growth of CC cells, while IGF2BP2 overexpression rescued this effect in vitro and in vivo. IGF2BP2 expression was higher in CC tissues than in adjacent tissues and was positively correlated with tumor stage. Mechanistically, E6/E7 proteins promoted aerobic glycolysis, proliferation, and metastasis in CC cells by regulating MYC mRNA m 6 A modifications through IGF2BP2. We found that E6/E7 promote CC by regulating MYC methylation sites via activating IGF2BP2 and established a link between E6/E7 and the promotion of aerobic glycolysis and CC progression. Blocking the HPV E6/E7-related metabolic pathway represents a potential strategy for the treatment of CC.

Background:Steroid-induced osteonecrosis of the femoral head (ONFH) is the most common clinical nontraumatic ONFH. Once ONFH occurs, it seriously reduces patients’ quality of life. The matrix metalloproteinase/tissue inhibitor of metalloproteinase (MMP/TIMP) system was found to play a significant role in the development of ONFH. The aim of this study was to identify the associations between 7 genes selected from the MMP/TIMP system and steroid-induced ONFH.Methods:We genotyped 34 single-nucleotide polymorphisms (SNPs) of 7 genes selected from the MMP/TIMP system in a case–control study with 285 cases of steroid-induced ONFH and 308 healthy controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using the chi-squared test, genetic model analysis, haplotype analysis, and stratification analysis.Results:We found that the minor alleles of rs1940475 and rs11225395 in MMP8 were associated with a 1.32-fold increased risk of steroid-induced ONFH in the allelic model analysis (P = 0.021 and 0.022, respectively). In the genetic model analysis, we found that rs3740938, rs2012390, rs1940475, and rs11225395 were associated with an increased risk of steroid-induced ONFH. In further stratification analysis, rs3740938 and rs2012390 displayed a significantly increased risk of steroid-induced ONFH in females under the dominant (rs3740938, OR = 2.69, 95% CI: 1.50–4.83, P = 0.001; rs2012390, OR = 2.30, 95% CI: 1.31–4.03, P = 0.012) and additive (rs3740938, OR = 2.02, 95% CI: 1.24–3.29, P = 0.010; rs2012390, OR = 1.77, 95% CI: 1.12–2.80, P = 0.047) models. In addition, haplotype “AGTCA” of MMP8 was found to be associated with a 1.40-fold increased risk of steroid-induced ONFH (95% CI: 1.04–1.88, P = 0.025).Conclusion:Our results verify that genetic variants of MMP8 contribute to steroid-induced ONFH susceptibility in the population of northern China. In addition, we found that gender differences might interact with MMP8 polymorphisms to contribute to the overall susceptibility to steroid-induced ONFH.

The current development of vaccines for SARS-CoV-2 is unprecedented. Little is known, however, about the nuanced public opinions on the coming vaccines. We adopt a human-guided machine learning framework (using more than 40,000 rigorously selected tweets from more than 20,000 distinct Twitter users) to capture public opinions on the potential vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. We aggregate opinions at the state and country levels, and find that the major changes in the percentages of different opinion groups roughly correspond to the major pandemic-related events. Interestingly, the percentage of the pro-vaccine group is lower in the Southeast part of the United States. Using multinomial logistic regression, we compare demographics, social capital, income, religious status, political affiliations, geo-locations, sentiment of personal pandemic experience and non-pandemic experience, and county-level pandemic severity perception of these three groups to investigate the scope and causes of public opinions on vaccines. We find that socioeconomically disadvantaged groups are more likely to hold polarized opinions on potential COVID-19 vaccines. The anti-vaccine opinion is the strongest among the people who have the worst personal pandemic experience. Next, by conducting counterfactual analyses, we find that the U.S. public is most concerned about the safety, effectiveness, and political issues regarding potential vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. We believe this is the first large-scale social media-based study to analyze public opinions on potential COVID-19 vaccines that can inform more effective vaccine distribution policies and strategies.

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