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Coccidioidomycosis is an endemic mycosis caused by inhalation of Coccidioides immitis or C. posadasii spores (1). Severe disease is infrequent, and extrapulmonary dissemination occurs in only 1% of diagnosed cases [...]

This study evaluated the training of Chinese American Community Health Workers (CHWs) to implement a small-group mammography video and discussion program as part of a randomized controlled trial that had the goal to increase adherence to mammography screening guidelines among Chinese American women. A total of 26 Chinese American CHWs in the metropolitan Washington DC area, Southern California, and New York City participated in a 4-h training workshop and completed surveys before and after the workshop to assess their knowledge regarding mammography screening guidelines and human subjects protection rules. The results showed significantly increased knowledge of mammography screening guidelines and human subjects protection rules (both p

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and environmental challenges, including chemotherapy, cause a cell state transition, which is accompanied by a continuous morphological alteration that is often extremely difficult to recognize even by direct microscopic inspection. To determine whether deep learning-based image analysis enables the detection of cell shape reflecting a crucial cell state alteration, we used the oral cancer cell line resistant to chemotherapy but having cell morphology nearly indiscernible from its non-resistant parental cells. We then implemented the automatic approach via deep learning methods based on EfficienNet-B3 models, along with over- and down-sampling techniques to determine whether image analysis of the Convolutional Neural Network (CNN) can accomplish three-class classification of non-cancer cells vs. cancer cells with and without chemoresistance. We also examine the capability of CNN-based image analysis to approximate the composition of chemoresistant cancer cells within a population. We show that the classification model achieves at least 98.33% accuracy by the CNN model trained with over- and down-sampling techniques. For heterogeneous populations, the best model can approximate the true proportions of non-chemoresistant and chemoresistant cancer cells with Root Mean Square Error (RMSE) reduced to 0.16 by Ensemble Learning (EL). In conclusion, our study demonstrates the potential of CNN models to identify altered cell shapes that are visually challenging to recognize, thus supporting future applications with this automatic approach to image analysis. [ABSTRACT FROM AUTHOR]

Background: Understanding the risk of chikungunya virus (CHIKV) infection and rheumatic sequelae across populations, including travelers and the military, is critical. We leveraged healthcare delivery data of over 9 million U.S. Military Health System (MHS) beneficiaries to identify cases, and sampled controls, to estimate the risk of post-CHIKV rheumatic sequelae. Methodology/principal findings: MHS beneficiary CHIKV infections diagnosed 2014–2018 were identified from the Disease Reporting System internet, TRICARE Encounter Data Non-Institutional, and Comprehensive Ambulatory/Professional Encounter Record systems. Non-CHIKV controls were matched (1:4) by age, gender, beneficiary status, and encounter date. The frequency of comorbidities and incident rheumatic diagnoses through December 2018 were derived from International Classification of Diseases codes and compared between cases and controls. Poisson regression models estimated the association of CHIKV infection with rheumatic sequelae. We further performed a nested case-control study to estimate risk factors for post-CHIKV sequelae in those with prior CHIKV. 195 CHIKV cases were diagnosed between July 2014 and December 2018. The median age was 42 years, and 43.6% were active duty. 63/195 (32.3%) of CHIKV cases had an incident rheumatic diagnosis, including arthralgia, polyarthritis, polymyalgia rheumatica, and/or rheumatoid arthritis, compared to 156/780 (20.0%) of controls (p < 0.001). CHIKV infection remained associated with rheumatic sequelae (aRR = 1.579, p = 0.008) after adjusting for prior rheumatic disease and demography. Those with rheumatic CHIKV sequelae had a median 7 healthcare encounters (IQR 3–15). Among CHIKV infections, we found no association between post-CHIKV rheumatic sequelae and demography, service characteristics, or comorbidities. Conclusions/significance: CHIKV infection is uncommon but associated with rheumatic sequelae among MHS beneficiaries, with substantial healthcare requirements in a proportion of cases with such sequelae. No demographic, clinical, or occupational variables were associated with post-CHIKV rheumatic sequelae, suggesting that prediction of these complications is challenging in MHS beneficiaries. These findings are important context for future CHIKV vaccine decision making in this and other populations. Author summary: We examined U.S. Military Health System (MHS) healthcare delivery data to identify the likelihood of rheumatic complications after chikungunya virus (CHIKV) infection. Overall, CHIKV infections were rare in the MHS, with 195 cases found in the records between 2014 and 2018 (a period which encompassed the peak of the CHIKV epidemic in the Americas). Of these, about 32% received a rheumatic diagnosis after infection, including arthralgia, polyarthritis, polymyalgia rheumatica, and rheumatoid arthritis. Patients who had a rheumatic diagnosis had on average 7 healthcare encounters for their post-CHIKV rheumatic complication, and a quarter had more than 15 healthcare encounters. We did not find any demographic, clinical, or occupational characteristics associated with developing rheumatic complications after CHIKV, suggesting that predicting rheumatic complications from CHIKV may be challenging in MHS beneficiaries. These findings may provide important context for decisions about implementing an approved chikungunya vaccine to military servicemembers and other MHS beneficiaries. [ABSTRACT FROM AUTHOR]

Background The consequences of low-level viremia in people with HIV are unclear. We used data from the US Military HIV Natural History Study to examine the association of low-level viremia (LLV) and serious non-AIDS events (SNAEs). Methods Included participants initiated antiretroviral therapy after 1996 and had ≥3 viral loads (VLs) measured, using an assay with a lower limit of detection of <50 copies/mL, ≥6 months after antiretroviral therapy initiation. VLs were categorized as lower levels of LLV (51–199 copies/mL), higher level of low-level viremia (HLLV; 200–999 copies/mL), and (VF; ≥200 copies/mL on 2 or more successive determinations or a single VL ≥1000 copies/mL), and virologic suppression (VS; ie, VL <50 copies/mL). Viral blips (ie, VLs between 50 and 999 copies/mL that are preceded and succeeded by VL <50 copies/mL) were analyzed in the VS category. Cox proportional hazards models were used to examine the association of LLV and SNAEs, adjusted hazard ratios and 95% confidence intervals are presented. Results A total of 439 (17.4%) SNAEs were recorded among the 2528 participants (93% male, 40% Caucasian, 43% African American) followed for a median of 11 years. In 8.5% and 4.6% of the participants, respectively, LLV and HLLV were the highest recorded viremia strata. Compared with VS, SNAEs were associated with LLV (1.3 [1.2–1.4]), HLLV (1.6 [1.5–1.7]), and virologic failure (1.7 [1.7–1.8]). Conclusions The results of this study suggest that LLV is associated with the occurrence of SNAEs and needs further study. [ABSTRACT FROM AUTHOR]

We conducted a retrospective cohort study that tested 2,000 US military personnel for Coccidioides antibodies in a disease-endemic region. The overall incidence of seroconversion was 0.5 cases/100 person-years; 12.5% of persons who seroconverted had illnesses requiring medical care. No significant association was found between demographic characteristics and seroconversion or disease. [ABSTRACT FROM AUTHOR]