Community based multi-disease health screening as an opportunity for early detection of HIV cases and linking them to care.

BACKGROUND: The 95-95-95 UNAIDS global strategy was adapted to end the AIDS epidemic by 2030. The target is based on the premise that early detection of HIV-infected persons and linking them to treatment regardless of their CD4 counts will lead to sustained viral suppression. HIV testing strategies to increase uptake of testing in Western and Central Africa remain inadequate. Hence, a high proportion of people living with HIV in this region do not know their status. This report describes the implementation of a community based multi-disease health screening (also known as "Know Your Status" -KYS), as part of basic science research, in a way that contributed to achieving public health goals. METHODS: A community based multi-disease health screening was conducted in 7 communities within the Eastern region of Ghana between November 2017 and April 2018, to recruit and match HIV seronegative persons to HIV seropositive persons in a case-control HIV gut microbiota study. Health assessments included blood pressure, body mass index, blood sugar, Hepatitis B virus, syphilis, and HIV testing for those who consented. HIV seronegative participants who consented were consecutively enrolled in an ongoing HIV gut microbiota case-control study. Descriptive statistics (percentages) were used to analyze data. RESULTS: Out of 738 people screened during the exercise, 700 consented to HIV testing and 23 (3%) were HIV positive. Hepatitis B virus infection was detected in 4% (33/738) and Syphilis in 2% (17/738). Co-infection of HIV and HBV was detected in 4 persons. The HIV prevalence of 3% found in these communities is higher than both the national prevalence of 1.7% and the Eastern Regional prevalence of 2.7 in 2018. CONCLUSION: Community based multi-disease health screening, such as the one undertaken in our study could be critical for identifying HIV infected persons from the community and linking them to care. In the case of HIV, it will greatly contribute to achieving the first two 95s and working towards ending AIDS by 2030.

Molecular epidemiology and multidrug resistance of Mycobacterium tuberculosis complex from pulmonary tuberculosis patients in the Eastern region of Ghana.

BACKGROUND: Tuberculosis (TB) and drug-resistant TB (DR-TB) continue to persist as a serious public health challenges in Ghana. Although several research has evaluated the drug resistance of Mycobacterium tuberculosis complex (MTBc) strains across the country, there is a paucity of data on its magnitude as well as the various lineages circulating in the Eastern region of Ghana. OBJECTIVE: This study therefore evaluated the distribution of the various lineages of MTBc in the Eastern region of the country and the associated drug resistance. MATERIALS AND METHODS: One hundred and forty-three (143) patients with pulmonary TB attending the Eastern Regional Hospital, Koforidua/Ghana were included in the study. The BACTEC MGIT 960 tube media was used for both sputum culture and drug susceptibility of streptomycin (STR), isoniazid (INH), rifampicin (RIF) and Ethambutol (ETH). Isolates were initially typed using IS6110, followed by large sequence polymorphisms analysis and spoligotyping. RESULTS: The majority [108 (75.5%)] of the 143 patients were male gender and the 45-54 years [46 (32.2%)] age range had the highest frequency. Forty-one (28.7%) of the 143 isolates were IS6110 negative. Of the 102 spoligotyped isolates, the main sub-lineages included 45 (44.1%) Cameroon and 23 (22.5%) Ghana. SITs 61 and 53 represented the major cluster with 22/102 (21.6%) and 13/102 (12.7%) isolates respectively, while 59/65 (90.8%) isolates belonged to Lineage 4 with 27/65 (41.5%) LAM10_CAM. MDR-TB occurred in 26/79 (32.9%) isolates, and was not associated with neither gender [20/58 (34.5%) male vs 6/21 (28.6%) female, OR = 1.31; 95%CI, 0.44-3.92; p = 0.624)] nor age. No association was found between MDR-TB and the major sub-lineages [8/25 (32%) Cameroon (OR = 0.94; 95%CI, 0.34-2.59; p = 0.920) and 5/11 (45.5%) Ghana (OR = 1.87; 95%CI, 0.51-6.80; p = 0.489)], or previously treated [8/23 (34.8%), OR = 0.89; 95%CI, 0.32-2.48; p = 0.823)] patients. CONCLUSION: Despite the serious threat posed by MDR in the study area, no sub-lineage was shown to be associated with drug resistance. Nonetheless, a sustained surveillance of drug resistance pattern is advocated. A lower proportion of M. africanum was observed in the Eastern region of Ghana and will require further evaluation.

Data-Driven Insights on Behavioral Factors that Affect Diabetes Management.

The prevalence of personal health data from wearable devices enables new opportunities to understand the impact of behavioral factors on health. Unlike consumer devices that are often auxiliary, such as Fitbit and Garmin, wearable medical devices like continuous glucose monitoring (CGM) devices and insulin pumps are becoming critical in diabetes care to minimize the occurrence of adverse glycemic events. Joint analysis of CGM and insulin pump data can provide unparalleled insights on how to modify treatment regimen to improve diabetes management outcomes. In this paper, we employ a data-driven approach to study the relationship between key behavioral factors and proximal diabetic management indicators. Our dataset includes an average of 161 days of time-matched CGM and insulin pump data from 34 subjects with Type 1 Diabetes (T1D). By employing hypothesis testing and association mining, we observe that smaller meals and insulin doses are associated with better glycemic outcomes compared to larger meals and insulin doses. Meanwhile, the occurrence of interrupted sleep is associated with poorer glycemic outcomes. This paper introduces a method for inferring disrupted sleep from wearable diabetes-device data and provides a baseline for future research on sleep quality and diabetes. This work also provides insights for development of decision-support tools for improving short- and long-term outcomes in diabetes care.

Thermodynamic method for prediction of surfactant-modified oil droplet contact angle.

A model applying surfactant self-assembly theory and classical thermodynamics has been developed to aid in the prediction of solid surface cleaning by aqueous surfactant solutions. Information gained from a combination of surfactant self-assembly behavior and cleaning system parameters, such as oil species, surfactant type, temperature, alkalinity, and solid surface type has been shown to provide insight into surface cleaning. The model combines minimization of free energy, pertinent component distribution mechanisms, and surfactant self-assembly processes to provide a methodology for the predicting of oil droplet contact angles. Such predictive capabilities will allow for the development of beneficial environmental and economic changes to industrial and commercial surface cleaning and degreasing processes. Results from the model will be compared to experimental data to verify the capability of the theory to account for the effect of solutions parameters on oil droplet behavior. The model, while approximate in nature, has shown a remarkable quantitative predictive ability.

Direct analysis in real time mass spectrometry (DART-MS) of ionic liquids.

Direct analysis in real time mass spectrometry (DART-MS) was used to analyze ionic liquids (ILs) containing either imidazolium or phosphonium cations combined with different types of inorganic and organic anions. Ionic liquids were directly inserted into the ionization source using a glass probe without dissolution into organic solvents. Mass spectra of the ILs were collected in both positive and negative mode with a linear ion-trap instrument. The intact cation of the compound was typically the dominant peak in positive mass spectra and cluster ion formation was present. Some individual anions were not readily observed in the negative mass spectra (based on the type of anion); however, the mass difference of adjacent cluster ions equal the mass of a complete IL and the anion mass could be verified by subtracting the known cation mass. The degree and intensity of the cluster ion formations was found to be dependent on the nature of the specific ILs as well as the DART temperature gas stream.

Identification and thermochemical analysis of high-lignin feedstocks for biofuel and biochemical production.

BACKGROUND: Lignin is a highly abundant biopolymer synthesized by plants as a complex component of plant secondary cell walls. Efforts to utilize lignin-based bioproducts are needed. RESULTS: Herein we identify and characterize the composition and pyrolytic deconstruction characteristics of high-lignin feedstocks. Feedstocks displaying the highest levels of lignin were identified as drupe endocarp biomass arising as agricultural waste from horticultural crops. By performing pyrolysis coupled to gas chromatography-mass spectrometry, we characterized lignin-derived deconstruction products from endocarp biomass and compared these with switchgrass. By comparing individual pyrolytic products, we document higher amounts of acetic acid, 1-hydroxy-2-propanone, acetone and furfural in switchgrass compared to endocarp tissue, which is consistent with high holocellulose relative to lignin. By contrast, greater yields of lignin-based pyrolytic products such as phenol, 2-methoxyphenol, 2-methylphenol, 2-methoxy-4-methylphenol and 4-ethyl-2-methoxyphenol arising from drupe endocarp tissue are documented. CONCLUSIONS: Differences in product yield, thermal decomposition rates and molecular species distribution among the feedstocks illustrate the potential of high-lignin endocarp feedstocks to generate valuable chemicals by thermochemical deconstruction.