Critical Assessment of Novel Developments in HPGR Technology Using DEM.

Advances in high-pressure grinding roll (HGPR) technology since its first commercial application in the cement industry include new roll wear protection techniques and new confinement systems. The latter contribute to reductions in the edge effects in an attempt to reach a more homogenous product size along the rolls. Additional advances in this technology have been made in recent years, while modeling and simulation tools are also reaching maturity and can now be used to subject such novel developments to detailed scrutiny. This work applies a hybrid approach combining advanced simulations using the discrete element method, the particle replacement model and multibody dynamics to a phenomenological population balance model to critically assess two recent advances in HPGR technology: spring-loaded cheek plates and the offset roller press. Force and torque controllers, included in the EDEM 2022.1 software, were used to describe the responses of the geometries in contact with the granular material processed. Simulations showed that while the former successfully reduced the lateral bypass of the material by as much as 65% when cheek plates became severely worn, the latter demonstrated lower throughput and higher potential wear but an ability to generate a finer product than the traditional design.

National Burden and Trends for 29 Groups of Cancer in Mexico from 1990 to 2019: A Secondary Analysis of the Global Burden of Disease Study 2019.

The global burden of cancer is on the rise, with varying national patterns. To gain a better understanding and control of cancer, it is essential to provide national estimates. Therefore, we present a comparative description of cancer incidence and mortality rates in Mexico from 1990 to 2019, by age and sex for 29 different cancer groups. Based on public data from the Global Burden of Disease Study 2019, we evaluated the national burden of cancer by analyzing counts and crude and age-standardized rates per 100,000 people with 95% uncertainty intervals for 2019 and trends using the annual percentage change from 1990 to 2019. In 2019, cancer resulted in 222,060 incident cases and 105,591 deaths. In 2019, the highest incidence of cancer was observed in non-melanoma skin cancer, prostate cancer, and breast cancer. Additionally, 53% of deaths were attributed to six cancer groups (lung, colorectal, stomach, prostate, breast, and pancreatic). From 1990 to 2019, there was an increasing trend in incidence and mortality rates, which varied by 10-436% among cancer groups. Furthermore, there were cancer-specific sex differences in crude and age-standardized rates. The results show an increase in the national cancer burden with sex-specific patterns of change. These findings can guide national efforts to reduce health loss due to cancer.

COVID-19 and drivers of excess death rate in Peru: A longitudinal ecological study.

Background: Peru has experienced unprecedented mortality and economic toll due to the COVID-19 (Coronavirus disease 2019) pandemic in 2020. We aimed to assess the association between socioeconomic factors and excess death rate, and to explore the relative contribution of these factors to the differences in excess death rate during January-December 2020. Methods: Different national secondary data sources were used to describe excess death rates and different determinants, from distal to proximal. A confounding-adjusted multilevel mixed-effects linear regression was used to assess the association between these variables and excess death rates. Their relative contributions to the differences in excess death rate between the periods with the highest and lowest excess death rates were analyzed through regression-based Oaxaca-Blinder decomposition methods. Findings: The excess death rate showed an increasing trend in all regions, with different slopes. The confounding-adjusted multilevel analysis showed that higher healthcare access was associated with lower excess death rates (difference (95%CI) -0.004 (-0.005, -0.002)), whereas COVID-19 incidence was associated with higher excess death rates (difference (95%CI) 0.052 (0.042, 0.063)). The decomposition analysis showed COVID-19 incidence (41.9%), per capita income (19.4%) and unemployment rate (14.6%) as the main risk factors, while the main protective factors included per capita health expenditure (44.7%), healthcare access (33.2%) and health insurance (12.1%). Interpretation: Our study suggests that the excess death rate during the COVID-19 pandemic in Peru may have been influenced by other factors besides COVID-19 incidence, from distal to proximal drivers, including socioeconomic determinants, factors outside and within the health sector, and susceptibility factors. Further studies at individual level are needed to corroborate our findings.

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning.

BACKGROUND: The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors. PURPOSE: To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics. METHODS: In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2). RESULTS: We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design. CONCLUSIONS: By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

Phenotype Concept Set Construction from Concept Pair Likelihoods.

Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.

Clotrimazole Loaded Ufosomes for Topical Delivery: Formulation Development and In-Vitro Studies.

Global incidence of superficial fungal infections caused by dermatophytes is high and affects around 40 million people. It is the fourth most common cause of infection. Clotrimazole, a broad spectrum imidazole antifungal agent is widely used to treat fungal infections. Conventional topical formulations of clotrimazole are intended to treat infections by effective penetration of drugs into the stratum corneum. However, drawbacks such as poor dermal bioavailability, poor penetration, and variable drug levels limit the efficiency. The present study aims to load clotrimazole into ufosomes and evaluate its topical bioavailability. Clotrimazole loaded ufosomes were prepared using cholesterol and sodium oleate by thin film hydration technique and evaluated for size, polydispersity index, and entrapment efficiency to obtain optimized formulation. Optimized formulation was characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and differential scanning calorimetry (DSC). Skin diffusion studies and tape-stripping were performed using human skin to determine the amount of clotrimazole accumulated in different layers of the skin. Results showed that the optimized formulation had vesicle size <250 nm with ~84% entrapment efficiency. XRD and DSC confirmed the entrapment of clotrimazole into ufosomes. No permeation was observed through the skin up to 24 h following the permeation studies. Tape-stripping revealed that ufosomes led to accumulation of more clotrimazole in the skin compared to marketed formulation (Perrigo). Overall, results revealed the capability of ufosomes in improving the skin bioavailability of clotrimazole.

Phenotype Inference with Semi-Supervised Mixed Membership Models.

Disease phenotyping algorithms are designed to sift through clinical data stores to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of expert-labeled data, while unsupervised methods may learn spurious or non-disease phenotypes. To address these limitations, we propose the Semi-Supervised Mixed Membership Model (SS3M) - a probabilistic graphical model for learning disease phenotypes from partially labeled clinical data. We show SS3M can generate interpretable, disease-specific phenotypes which capture the clinical features of the disease concepts specified by the labels provided to the model. Furthermore, SS3M phenotypes demonstrate competitive predictive performance relative to commonly used baselines.

An experimental model of peripheral neuropathy induced in rats by Karwinskia humboldtiana (buckthorn) fruit.

Intoxication by Karwinskia humboldtiana (buckthorn) fruit presents a neurological picture similar to that of Guillain-Barré syndrome. In this report, we describe an experimental animal model of peripheral neuropathy induced by buckthorn fruit. Four groups of Wistar rats received one oral dose of 1.5 g/kg followed by oral doses of 0.5 g/kg at days 3, 7, 10, and 14 of dried and ground buckthorn fruit in aqueous suspension. Rats were sacrificed at 24, 48, 58, and 112 days after initial dose. Treated animals developed progressive paralysis through 58 days, then completely recovered by 112 days. Sciatic nerves showed segmental demyelination and cellular infiltrates until 58 days after exposure and then remyelinating changes at 112 days. This experimental model for peripheral neuropathy is reproducible and easy to handle. Its manipulation is relatively innocuous and allows us to study reversible peripheral nerve damage. This model can be developed in other animal species and may be useful to test new therapies for peripheral neuropathy.