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In Virginia, 56% of colorectal cancers (CRC) are diagnosed late, making it one of three enduring CRC mortality hotspots in the US. Cervical cancer (CCa) exhibits a similar pattern, with 48% late-stage diagnosis. Mortality for these cancers is worse for non-Latinx/e(nL)-Black people relative to nL-White people in Virginia, but preventable with equitable screening access and timely diagnostic follow-up. However, structural barriers, such as fractured referral systems and extended time between medical visits, remain. Because Federally Qualified Health Centers (FQHCs) care for a large proportion of racial and ethnic minorities, and underserved communities, regardless of ability to pay, they are ideal partners to tackle structural barriers to cancer screenings. We piloted a quality improvement initiative at five FQHCs in southcentral Virginia to identify and address structural, race-related barriers to CRC, as well as CCa screening and diagnostic follow-up using evidence-based approaches. Uniquely, FQHCs were paired with local community organizations in a didactic partnership, to elevate the community’s voice while together, increase support, acceptance, uptake, and intervention sustainability. We report on project development, and share preliminary data within the context of project goals, namely, to increase cancer screenings by 5–10%, improve knowledge and diagnostic follow-up processes, and build longitudinal partnerships.

<b><i>Introduction:</i></b> Diabetic kidney disease (DKD) remains the leading cause of chronic kidney disease. Dysregulation of circulating miRNAs has been reported, suggesting their pathological roles in DKD. This study aimed to investigate differentially expressed miRNAs in the sera of type 2 diabetes mellitus (T2DM) patients with and without albuminuria in a selected Malaysian population. <b><i>Method:</i></b> Forty-one T2DM patients on follow-up at a community clinic were divided into normo-(NA), micro-(MIC), and macroalbuminuria (MAC) groups. Differential levels of miRNAs in 12 samples were determined using the pathway-focused (human fibrosis) miScript miRNA qPCR array and was validated in 33 samples, using the miScript custom qPCR array (CMIHS02742) (Qiagen GmbH, Hilden, Germany). <b><i>Results:</i></b> Trends of upregulation of 3 miRNAs in the serum, namely, miR-874-3p, miR-101-3p, and miR-145-5p of T2DM patients with MAC compared to those with NA. Statistically significant upregulation of miR-874-3p (<i>p</i> = 0.04) and miR-101-3p (<i>p</i> = 0.01) was seen in validation cohort. Significant negative correlations between the estimated glomerular filtration rate (eGFR) and miR-874-3p (<i>p</i> = 0.05), miR-101-3p (<i>p</i> = 0.03), and miR-145-5p (<i>p</i> = 0.05) as well as positive correlation between miR-874-3p and age (<i>p</i> = 0.03) were shown by Pearson’s correlation coefficient analysis. <b><i>Conclusion:</i></b> Upregulation of previously known miRNA, namely, miR-145-5p, and possibly novel ones, namely, miR-874-3p and miR-101-3p in the serum of T2DM patients, was found in this study. There was a significant correlation between the eGFR and these miRNAs. The findings of this study have provided encouraging evidence to further investigate the putative roles of these differentially expressed miRNAs in DKD.

Abstract The study is one of the first to examine both the prevalence of life instability among older adults with HIV (OAWH) in a community clinic and its relationship to their mental health. OAWH (N=623) from a community medical clinic completed an interviewer-administered assessment (English/Spanish) which included an additive Life Instability Index (LII) composed of indicators at the individual (e.g. education, housing instability, employment status) and community (e.g. poverty, transportation) levels. Participants were a mean age of 60 years (SD = 5.90) with the majority identifying as Black-non-Hispanic (65.9%), cisgender male (60.8%), and heterosexual (80.6%). Participants reported an average of 6.08 destabilizing factors (SD = 1.44). In multiple linear regression analyses LII was significantly related to increased substance use among participants (b= 0.08, p &lt; 0.01), but not with anxiety or depression. An LII is an innovative approach to assess the relationship between OAWH’s mental health and social determinants of health.

Abstract Background Artificial intelligence can assist in interpreting chest X-ray radiography (CXR) data, but large datasets require efficient image annotation. The purpose of this study is to extract CXR labels from diagnostic reports based on natural language processing, train convolutional neural networks (CNNs), and evaluate the classification performance of CNN using CXR data from multiple centers Methods We collected the CXR images and corresponding radiology reports of 74,082 subjects as the training dataset. The linguistic entities and relationships from unstructured radiology reports were extracted by the bidirectional encoder representations from transformers (BERT) model, and a knowledge graph was constructed to represent the association between image labels of abnormal signs and the report text of CXR. Then, a 25-label classification system were built to train and test the CNN models with weakly supervised labeling. Results In three external test cohorts of 5,996 symptomatic patients, 2,130 screening examinees, and 1,804 community clinic patients, the mean AUC of identifying 25 abnormal signs by CNN reaches 0.866 ± 0.110, 0.891 ± 0.147, and 0.796 ± 0.157, respectively. In symptomatic patients, CNN shows no significant difference with local radiologists in identifying 21 signs (p > 0.05), but is poorer for 4 signs (p < 0.05). In screening examinees, CNN shows no significant difference for 17 signs (p > 0.05), but is poorer at classifying nodules (p = 0.013). In community clinic patients, CNN shows no significant difference for 12 signs (p > 0.05), but performs better for 6 signs (p < 0.001). Conclusion We construct and validate an effective CXR interpretation system based on natural language processing.

This study explored the contribution of viral respiratory infections (VRIs) in dengue-like illness (DLI) patients and their distinguishing clinicolaboratory parameters. Two hundred DLI patients were prospectively recruited (1 July–1 October 2019) from a community clinic in Southern Malaysia. Patients ≥18 years with acute fever and fulfilling the WHO criteria of probable dengue were recruited. They underwent blood testing: blood counts, rapid dengue tests (nonstructural antigen-1/IgM) and polymerase chain reaction (PCR) for dengue, Zika, chikungunya, and Leptospira. Nasopharyngeal swabs (NPSs) were collected for FilmArray®RP2plus testing. From the 200 NPSs, 58 respiratory viruses (RVs) were detected in 54 patients. Of the 96 dengue-confirmed cases, 86 had dengue mono-infection, and 10 were coinfected with RVs. Of the 104 nondengue, 44 were RV positive and 4 Leptospira positive. Zika and chikungunya virus were not detected. Overall, the etiological diagnosis was confirmed for 72% of patients. Clinicolaboratory parameters were compared between dengue mono-infection and VRI mono-infection. Patients with coinfections were excluded. Multiple logistic regression showed that recent household/neighborhood history of dengue (adjusted odds ratio [aOR]: 5.9, 95% CI = 1.7–20.7), leukopenia (aOR: 12.5, 95% CI = 2.6–61.4) and thrombocytopenia (aOR: 5.5, 95% CI = 1.3–23.0) predicted dengue. Inversely, rhinorrhoea (aOR: 0.1, 95% CI = 0.01–0.3) and cough (aOR: 0.3, 95% CI = 0.1–0.9) favored VRI. Thus, VRIs comprise many infections diagnosed initially as DLIs. Early clinicolaboratory parameters can guide physicians screen patients for further testing.