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Mortality, loss to follow-up and advanced HIV disease following virologic success in West African HIV-2 patients
Background People living with HIV-2 are mainly found in West Africa and their identification and treatment have been impaired by diagnostic challenges and availability of effective antiretroviral treatment (ART). With the roll out of first line dolutegravir (DTG)-based regimen, the situation may have improved, emphasizing the need for data on long-term treatment outcomes and advanced HIV disease among ART-experienced people living with HIV-2. Method A prospective cohort was initiated in 2012 in Côte d’Ivoire and Burkina Faso. All adult patients from Côte d’Ivoire with an undetectable viral load, were included and followed up. HIV-2 viral load and CD4 counts were done during the routine follow-up visits and a detailed clinical assessment was done during the last follow up visit of the year 2018 corresponding to the censor date of the cohort. Outcomes were described as follow: in care (known alive and present during the last ART follow up visit), loss to follow-up (absent for more than 90 days and not reported dead), and dead (reported dead with a date of event). Advanced HIV disease followed WHO definition and virologic failure was define as viral load > 50 copies/mm3. The Kaplan-Meier curve was used to estimate mortality and Loss to follow-up probability. Results Among the 108 HIV-2 patients in virologic success in 2012, 95 agreed to participate and were enrolled in the “success cohort”. Their median age was 53 [47–60] years and all of them were receiving boosted-lopinavir-based ART regimen. Of the 95 participants, 65 (68.4%) remained in care, 20 (21.1%) were loss to follow-up and 10 (10.5%) were reported dead. The survival analysis retrieved a decreasing probability of remaining alive and in care over the time, moving from 90% to 80.7% and to 73.0% after 24, 48 and 72 months respectively. Overall, 36 (37.9%) patients presented with advanced HIV disease at their last visit, higher among those dead/ loss to follow-up compared to those remaining in care (60.0% vs 27.7%; p-0.003). Conclusion High advanced HIV disease rate was found in HIV-2 patients, six years after an initial virologic success. This emphasizes the need to enable the one-stop-shop model that allow an early management of opportunistic infections while integrating non-communicable diseases services in HIV-2 care.
Seismic and geomechanical characterization of Asmari formation using well logs and simultaneous inversion in an Iranian oil field
Dissecting the Effects of Cage Structure in the Catalytic Activation of Imide Chlorenium-Ion Donors
Prevalence and regional disparities of undiagnosed diabetes mellitus in Bangladesh: Results from the Bangladesh Demographic and Health Survey data
Background While undiagnosed diabetes mellitus (DM) presents a substantial global concern, there is a dearth of research examining its prevalence and characteristics specifically within the regional context of Bangladesh. The study focused on assessing the prevalence of undiagnosed diabetes mellitus in Bangladesh and examining regional disparities. Methods The study analyzed data from the Bangladesh Demographic and Health Survey conducted between 2017 and 2018. The analysis focused on 11,911 participants aged 18 and above. Prevalence rates of both diagnosed and undiagnosed DM were calculated across various demographic and regional factors. To understand the impact of socio-demographic and regional variables on diagnosed and undiagnosed DM, the study employed multinomial regression analysis. Results The study encompassed 11,911 participants with an average age of 39, of whom 57% were females. Among them, 333 individuals (2.8%) were diagnosed with diabetes mellitus (DM), while 667 participants (5.6%) had undiagnosed DM. The prevalence of both diagnosed and undiagnosed DM was notably higher in elderly, hypertensive, overweight or obese, and rural residents. Regression analysis indicated that individuals aged 70 and above faced 2.14 times more likely of diagnosed diabetes compared to those aged 30-39 (RRR = 2.20; 95% CI = 1.35-3.58). Regarding residential regions, individuals from the city exhibited significantly higher prevalence rates for both diagnosed DM (RRR: 1.83; 95% CI = 1.31-2.57) and undiagnosed DM (RRR: 1.52; 95% CI = 1.18-1.95) compared to those from the rural of Bangladesh. Conclusion The high prevalence of undiagnosed DM in city areas suggests potential shortcomings in routine diabetes screening practices. Prioritizing screening, particularly for high-risk groups like older adults, individuals with elevated BMI, hypertension, and urban residents from the central region of the country, is crucial. These groups have elevated diabetes risk and face higher complications without timely detection and treatment. To address this issue, collaborative efforts among the Bangladeshi government, healthcare providers, and community organizations are imperative.
An improved CNN model in image classification application on water turbidity
η<sup>6</sup>-Benzene Tetra-Anion Complexes of Early and Late Rare-Earth Metals
Effects of Ionizing Radiation on the Biophysical Properties of Type I Collagen Fibrils
Ionizing radiation is extensively employed in both diagnostic and therapeutic medical practices. The impact of this radiation on collagen, a primary structural protein in humans, remains underexplored, particularly at varying doses and hydration states. This study explores the impact of ionizing radiation on type I collagen fibrils at three radiation doses (diagnostic, therapeutic, and sterilization) and under two hydration conditions using an engineered acellular collagen membrane to reflect varying biological conditions. Techniques including atomic force microscopy (AFM), fluorescence lifetime imaging microscopy (FLIM), and Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) were utilized to assess changes in mechanical properties, biochemical stability, and molecular structure respectively. Our results demonstrate that ionizing radiation alters the mechanical properties of collagen fibrils, notably indentation modulus, which reflects changes in stiffness or elasticity. These modifications depended on the hydration state at the time of radiation exposure; hydrated fibrils typically exhibited increased stiffness, suggesting enhanced cross-linking, whereas dehydrated fibrils showed reduced stiffness, indicative of structural weakening, possibly due to bond breakdown. Morphological changes were minimal, suggesting that radiation primarily affects the internal structure rather than the overall appearance of the fibrils. Biochemically, variations in fluorescence lifetimes highlighted changes in the collagen’s biochemical environment, dependent on the dose and hydration state. Despite these biochemical and mechanical changes, FTIR analysis indicated that the primary structure of collagen was largely preserved post-irradiation for all examined dose levels. These findings imply that radiation can modify the mechanical properties of collagen, potentially affecting tissue integrity in clinical settings. This could influence the management of radiation-induced conditions like osteoradionecrosis, fibrosis and cancer metastasis. Overall, our study underscores the need for further research into the effects of radiation on structural proteins to better understand and mitigate radiation-induced tissue damage.
Quantitative investigation of moisture migration during microwave drying of coal slime dough through simulation and tracer analysis
Abstract Moisture migration during the microwave drying process of coal slime is critical for improving drying efficiency and reducing energy consumption. Nevertheless, current methods for quantifying these migration behaviors remain insufficient. In this research, we employed a comprehensive approach that combines experimental investigations with multi-physical field simulations to quantitatively characterize the distribution and state of moisture within coal slime dough at different locations during microwave drying. The entire drying process was divided into three distinct stages based on temperature distribution: preheating, constant temperature, and reheating. During the preheating stage, as temperature rose, more than 74.1% of the initial water content within the central region of the slime dough underwent vaporization. creating a pressure gradient between the interior and exterior of the coal slime dough. In the subsequent constant temperature stage, over 45.5% of the remaining water content within the slime dough was driven to the surface by the pressure gradient, where it vaporized and diffused into the surrounding atmosphere. During the reheating stage, moisture was initially vaporized as steam and subsequently diffused into the atmosphere through inter-particle voids, due to the inherent difficulty in forming a continuous liquid bridge between particles at this stage. Furthermore, we examined the influence of particle size and dough diameter of coal slime on moisture migration and diffusion during microwave drying, thereby substantiating the diffusion mechanism. By integrating experimental and simulation data, this study provides a detailed understanding of the moisture migration mechanisms within coal slime dough during microwave drying. These findings are valuable for the design of efficient microwave drying technologies, particularly for drying materials with high-water content and viscosity, such as typical coal slimes and sewage sludge.
Pore Space Partition Enabled by Lithium(I) Chelation of a Metal–Organic Framework for Benchmark C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub> Separation
Correction: Semiparametric modeling for the cardiometabolic risk index and individual risk factors in the older adult population: A novel proposal
Altered metabolic profiles in colon and rectal cancer
Flexible Organic Crystalline Fibers and Loops with Strong Second Harmonic Generation
Emergence to dominance: Estimating time to dominance of SARS-CoV-2 variants using nonlinear statistical models
Background/Objective : Relative proportion of cases in a multi-strain pandemic like the COVID-19 pandemic provides insight on how fast a newly emergent variant dominates the infected population. However, the behavior of relative proportion of emerging variants is an understudied field. We investigated the emerging behavior of dominant COVID-19 variants using nonlinear statistical methods and calculated the time to dominance of each variant. Method : We used a phenomenological approach to model national- and regional-level variant share data from the national genomic surveillance system provided by the Centers for Disease Control and Prevention to determine the best model to describe the emergence of two recent dominant variants of the SARS-CoV-2 virus: XBB.1.5 and JN.1. The proportions were modeled using logistic, Weibull, and generalized additive models. Model performance was evaluated using the Akaike Information Criteria (AIC) and the root mean square error (RMSE). Findings : The Weibull model performed the worst out of all three approaches. The generalized additive model approach slightly outperformed the logistic model based on fit statistics, but lacked in interpretability compared to the logistic model. These models were then used to estimate the time elapsed from emergence to dominance in the infected population, denoted by the time to dominance (TTD). All three models yielded similar TTD estimates. The XBB.1.5 variant was found to dominate the population faster compared to the JN.1 variant, especially in HHS Region 2 (New York) where the XBB.1.5 was believed to emerge. This research expounds on how emerging viral strains transition to dominance, informing public health interventions against future emergent COVID-19 variants and other infectious diseases.
Simultaneous determination of lesinurad and co-administered drugs used in management of gout comorbidities to uncover potential pharmacokinetic interaction in rat plasma
Abstract Gout is one of the most prevalent forms of arthritis that is usually accompanied by other comorbidities. For this reason, multiple drugs are routinely prescribed for gout patients, which may affect the clinical course outcomes, and increase the risk of drug-drug interactions. This work presents a novel, simple, and sensitive high performance liquid chromatography (HPLC) method for the simultaneous determination of lesinurad (LES) and other co-administered drugs that are subject to potential pharmacokinetic interactions such as etoricoxib (ETC), eplerenone (EPL), and amiodarone (AMD) in rat plasma. Moreover, a pharmacokinetic study was conducted by co-administration of LES and ETC to rats to assess any possible alteration in their pharmacokinetic profiles and the obtained samples were analyzed using the developed method. Chromatographic separation was achieved using a gradient elution of a mobile phase consisting of acetonitrile and potassium dihydrogen orthophosphate buffer, pH 4.2 on a Zorbax Eclipse Plus C18 (4.6 × 250 mm, 5 μm particle size) column. The developed method exhibits adequate sensitivity with a LLOQ of 100 ng/mL and was successfully validated as per the FDA bioanalytical guidelines and was found to be linear over the range from 100 to 50,000 ng/mL for all the selected drugs. The results of the pharmacokinetic study showed an increase in the area under the curve (AUC) of each of the two drugs (LES and ETC) following the repeated administration of the other. This raises concerns of the possible renal injurious effect of ETC when co-prescribed with LES. Moreover, this work uncovers the necessity for therapeutic dose adjustment or increased clinical vigilance for side effects and/or lack of efficacy upon concomitant administration of the selected drugs to gout patients.
Donor–Acceptor Nanohoops: Impact of the Ratio and Arrangement of the Fluorenone and Carbazole Moieties
Understanding patterns of loneliness in older long-term care users using natural language processing with free text case notes
Loneliness and social isolation are distressing for individuals and predictors of mortality, yet data on their impact on publicly funded long-term care is limited. Using recent advances in natural language processing (NLP), we analysed pseudonymised administrative records containing 1.1 million free-text case notes about 3,046 older adults recorded in a London council between 2008 and 2020. We applied three NLP methods—document-term matrices, pre-trained embeddings, and transformer-based models—to identify loneliness or social isolation. The best-performing model, a bidirectional transformer, achieved an F1 score of 0.92 on a test set of unseen sentences. Using this model, we generated predictions for the full dataset and assessed construct validity through comparison with survey data and the literature. Our measure is associated with expected characteristics, such as living alone and impaired memory, and is a strong predictor of social inclusion services. Approximately 43% of individuals had a sentence indicating loneliness or isolation in their case notes at their initial care assessment, comparable to survey-based estimates. Unlike surveys, our indicator is linked to other administrative data, enabling development of models of service use with loneliness or isolation as independent variables. An open-source version of the model is available in a GitHub repository.
An improved and advanced method for dehazing coal mine dust images
Ti-catalyzed 1,2-Diamination of Alkynes Using 1,1-Disubstituted Hydrazines
Integrating soil and crop metrics with precision agriculture: Pusa N Doctor app for sustainable nitrogen management in maize
Efficient nitrogen (N) management is critical for sustaining high maize yields while minimizing environmental impacts, as conventional practices often lead to N losses, greenhouse gas emissions, and reduced eco-efficiency. To address these challenges, the “Pusa N Doctor” app was developed using dark green colour index (DGCI) for precision N management in maize. The app was further validated in experiment conducted with three N rates- 0 kg/ha (N0PK), 50 kg/ha (N0PK), and 75 kg/ha (N75PK) as basal, along with two splits of N at 35 and 45 DAS as per app (N50PK+App and N75PK+App) and GSTM (N50PK + GSTM and N75PK+GSTM). The plant height, leaf area index, and plant N concentration was highest in N75PK+App. The highest crop growth rate between 0-30 DAS was observed in the N75PK+App treatment (9.97 g/m²/day). Conversely, the maximum relative growth rate between 30-60 DAS was in the N50PK+App, while the lowest was in N75PK+App. The highest harvest index of 35.13% was in N50PK+App. Except for N75PK+App and recommended dose of fertilizer (RDF), the partial N balance was close to 1, with a minimum value of 0.87 in N75PK+App. The lowest virtual N was in N50PK+App (0.45), while in N75PK+App it was 2.16 times higher than RDF. All N fertilized treatments except N50PK+App witnessed increased cost of cultivation over RDF. N50PK+App had 29.5% lower GHGI of N2O, with 11.6% and 13.3% higher energy and GHG-based eco-efficiency respectively than RDF. Thus, applying 50 kg N as basal along with its 2 splitting as per Pusa N Doctor, optimizes maize-growth, N use efficiency, eco-efficiency, and reduces GHG emissions.