Beyond the microcirculation: sequestration of infected red blood cells and reduced flow in large draining veins in experimental cerebral malaria.

Sequestration of infected red blood cells (iRBCs) in the microcirculation is a hallmark of cerebral malaria (CM) in post-mortem human brains. It remains controversial how this might be linked to the different disease manifestations, in particular brain swelling leading to brain herniation and death. The main hypotheses focus on iRBC-triggered inflammation and mechanical obstruction of blood flow. Here, we test these hypotheses using murine models of experimental CM (ECM), SPECT-imaging of radiolabeled iRBCs and cerebral perfusion, MR-angiography, q-PCR, and immunohistochemistry. We show that iRBC accumulation and reduced flow precede inflammation. Unexpectedly, we find that iRBCs accumulate not only in the microcirculation but also in large draining veins and sinuses, particularly at the rostral confluence. We identify two parallel venous streams from the superior sagittal sinus that open into the rostral rhinal veins and are partially connected to infected skull bone marrow. The flow in these vessels is reduced early, and the spatial patterns of pathology correspond to venous drainage territories. Our data suggest that venous efflux reductions downstream of the microcirculation are causally linked to ECM pathology, and that the different spatiotemporal patterns of edema development in mice and humans could be related to anatomical differences in venous anatomy.

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

Multivariate Optimization for Determination of Favipiravir, a SARS-CoV-2 Molecule, by the Reverse-Phase Liquid Chromatographic Method Using a QbD Approach.

The object of the analytical work is to develop an analytical multivariate optimization for the determination of Favipiravir (FAV), a SARS-CoV-2 molecule, by the reverse-phase liquid chromatographic method using the analytical quality by design approach. FAV is used as an antiviral drug. Box-Behnken design is utilized for the optimization of the experiment and to identify the critical method parameters like the volume of acetonitrile, temperature and flow rate. Further, these factors are used to design the suitable mathematical models and illustrate their effect on various responses. This newly developed method utilized C18 column (5µm, 100 × 4.6 mm) and a temperature of 40°C with a flow rate of 0.5 mL/min. The mobile phase is composed of acetonitrile and ammonium acetate buffer (pH 4), in the ratio of 20:80v/v and the wavelength of HPLC UV-Detector was fixed to 323nm. This method is validated according to International Council for Harmonization Q2 (R1) guidelines. The System suitability is performed and the retention time of Favipiravir is 3.4min. The linearity range is obtained at 0.062 - 4 µg/mL with a correlation coefficient (r2 = 0.9979). The recovery is found to be in the range of 98.84-100%. Thus, the intended method is found to be simple and robust.

Safety and health risk assessment methodology of dermal and inhalation exposure to formulated products ingredients.

Consumers are commonly exposed to numerous chemical ingredients found in various formulated products especially household and personal care products. Therefore, identification of hazardous ingredients contained in those products should be performed at the early stages of product design to reduce the high cost of redesigning the products at the final stage. Thus, a systematic safety and health risk assessment methodology is required for the product formulation design. In this work, a two-step index-based methodology is presented to estimate the severity of the hazards and the magnitude of risks. In Tier 1 assessment, potential hazards of the ingredients were identified by following the Product Ingredient Safety Index (PISI). The basic toxicology information of ingredients was required for this assessment. In Tier 2 assessment, the extent of risks of the ingredients via dermal and inhalation exposure routes were evaluated. At this stage, the concentration of ingredients and the amount of exposure were considered. The value of Margin of Exposure (MOE) was used as an indicator in the development of Product Ingredient Exposure Index (PIEI). To demonstrate the proposed methodology, a case study on the evaluation of potential hazards and the risks from ingredients used in personal care product formulations were performed.

Design and performance optimisation of detergent product containing binary mixture of anionic-nonionic surfactants.

The manufacture of detergent products such as laundry detergents, household cleaners and fabric softeners are of increasing interest to the consumer oriented chemical industry. Surfactants are the most important ingredient in detergent formulations, as they are responsible for the bulk of the cleaning power. In this research, a methodology has been developed to design a detergent product using computational tools. Different surfactant systems, such as single anionic, single nonionic, and binary mixtures of anionic-nonionic surfactants are covered in this work. Important surfactant properties such as critical micelle concentration (CMC), cloud point (CP), hydrophilic-lipophilic balance (HLB) and molecular weight (MW) have been identified. A group contribution (GC) method with the aid of computer modelling was used to determine the CMC, CP, and MW of surfactant molecules. The design of a surfactant molecule can be formulated as a multi-objective optimization problem that tradeoffs between CMC, CP, HLB and MW. Consequently, a list of plausible nonionic surfactant structures has been developed with the selected surfactant being incorporated into a binary surfactant mixture. Additives such as antimicrobial agents, anti-redeposition agents, builders, enzymes, and fillers were also considered and incorporated into a hypothetical detergent formulation together with the binary surfactant mixture. The typical ingredients and their compositions in detergent formulations are presented in the final stage of the detergent product design.

Mitochondrial DNA analyses of Indian water buffalo support a distinct genetic origin of river and swamp buffalo.

Water buffalo (Bubalus bubalis) is broadly classified into river and swamp categories, but it remains disputed whether these two types were independently domesticated, or if they are the result of a single domestication event. In this study, we sequenced the mitochondrial D-loop region and cytochrome b gene of 217 and 80 buffalo respectively from eight breeds/locations in northern, north-western, central and southern India and compared our results with published Mediterranean and swamp buffalo sequences. Using these data, river and swamp buffalo were distinguished into two distinct clades. Based upon the existing knowledge of cytogenetic, ecological and phenotypic parameters, molecular data and present-day distribution of the river and swamp buffalo, we suggest that these two types were domesticated independently, and that classification of the river and swamp buffalo as two related subspecies is more appropriate.