Cold plasma: a promising technology for improving the rheological characteristics of food.

At the beginning of the 21st century, many consumers show interest in purchasing safe, healthy, and nutritious foods. The intent requirement of end-users and many food product manufacturers are trying to feature a new processing technique for the healthy food supply. The non-thermal nature of cold plasma treatment is one of the leading breakthrough technologies for several food processing applications. The beneficial response of cold plasma processing on food quality characteristics is widely accepted as a substitution technique for new food manufacturing practices. This review aims to elaborate and offer crispy innovative ideas on cold plasma application in various food processing channels. It highlights the scientific approaches on the principle of generation and mechanism of cold plasma treatment on rheological properties of foods. It provides an overview of the behavior of cold plasma in terms of viscosity, crystallization, gelatinization, shear stress, and shear rate. Research reports highlighted that the cold plasma treated samples demonstrated a pseudoplastic behavior. The published literatures indicated that the cold plasma is a potential technology for modification of native starch to obtain desirable rheological properties. The adaptability and environmentally friendly nature of non-thermal cold plasma processing provide exclusive advantages compared to the traditional processing technique.

Reaction kinetics of physico-chemical attributes in coconut inflorescence sap during fermentation.

The study on fermentation kinetics of the coconut inflorescence sap is important to understand its shelf life at different storage conditions and to develop suitable value added products. The coconut inflorescence sap collected by using in-house developed coco-sap chiller device is called Kalparasa. The fermentation characteristics of Kalparasa were investigated at every 1-h interval under ambient (31 ± 2 °C) and refrigerated (5 ± 1 °C) storage conditions. The results reveal that pH of the sap and total sugar content decline rapidly under ambient conditions than under refrigerated conditions. Acidity, turbidity, and reducing sugar content significantly (p < 0.001) increases for the sap stored under ambient conditions. The reaction rate constant (k) of the vitamin C and total sugar degradation increases with the atmospheric fermentation. The degradation kinetics of vitamin C and total sugar in Kalparasa during natural fermentation (ambient condition) follow second-order equation whereas the reducing sugar follows the first-order equation.

Ultrasound curriculum taught by first-year medical students: A four-year experience in Tanzania.

BACKGROUND: Diagnostic imaging is an integral aspect of care that is often insufficient, if not altogether absent, in rural and remote regions of low to middle income countries (LMICs) such as Tanzania. The introduction of ultrasound can significantly impact treatment in these countries due to its portability, low cost, safety, and usefulness in various medical assessments. This study reviews the implementation of a four-week ultrasound course administered annually from 2013-2016 in a healthcare professional school in Mwanza, Tanzania by first-year allopathic US medical students. METHODS: Participants (n=582, over 4 years) were recruited from the Tandabui Institute of Health Sciences and Technology to take the ultrasound course. Subjects were predominantly clinical officer students, but other participants included other healthcare professional students, practicing healthcare professionals, and school employees. Data collected includes pre-course examination scores, post-course examination scores, course quiz scores, demographic surveys, and post-course feedback surveys. Data was analyzed using two-tailed t-tests and the single factor analysis of variance (ANOVA). RESULTS: For all participants who completed both the pre- and post-course examinations (n=229, 39.1% of the total recruited), there was a significant mean improvement in their ultrasound knowledge of 42.5%, P<0.01. CONCLUSION: Our data suggests that trained first-year medical students can effectively teach a point of care ultrasound course to healthcare professional students within four weeks in Tanzania. Future investigation into the level of long-term knowledge retention, impact of ultrasound training on knowledge of human anatomy and diagnostic capabilities, and how expansion of an ultrasound curriculum has impacted access to care in rural Tanzania is warranted.

Another Generation of Stigma? Assessing Healthcare Student Perceptions of HIV-Positive Patients in Mwanza, Tanzania.

HIV-related stigma remains a persistent global health concern among people living with HIV/AIDS (PLWA) in developing nations. The literature is lacking in studies about healthcare students' perceptions of PLWA. This study is the first effort to understand stigmatizing attitudes toward HIV-positive patients by healthcare students in Mwanza, Tanzania, not just those who will be directly treating patients but also those who will be indirectly involved through nonclinical roles, such as handling patient specimens and private health information. A total of 208 students were drawn from Clinical Medicine, Laboratory Sciences, Health Records and Information Management, and Community Health classes at the Tandabui Institute of Health Sciences and Technology for a voluntary survey that assessed stigmatizing beliefs toward PLWA. Students generally obtained high scores on the overall survey instrument, pointing to low stigmatizing beliefs toward PLWA and an overall willingness to treat PLWA with the same standard of care as other patients. However, there are gaps in knowledge that exist among students, such as a comprehensive understanding of all routes of HIV infection. The study also suggests that students who interact with patients as part of their training are less likely to exhibit stigmatizing beliefs toward PLWA. A comprehensive course in HIV infection, one that includes classroom sessions focused on the epidemiology and routes of transmission as well as clinical opportunities to directly interact with PLWA-perhaps through teaching sessions led by PLWA-may allow for significant reductions in stigma toward such patients and improve clinical outcomes for PLWA around the world.

Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.

The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between non-epileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<0.01). Using VC, the average accuracy was significantly lower (39.2%). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data.

Balancing Clinical and Pathologic Relevence in the Machine Learning Diagnosis of Epilepsy.

The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups are quite dissimilar. A few manuscripts, however, opt to mimic the clinical comparison of epilepsy to non-epileptic seizures, an approach we believe to be more clinically realistic. In this manuscript, we describe the relative merits of each control group. We demonstrate that in our clinical quality FDG-PET database the performance achieved was similar using each control group. Based on these results, we find that the choice of control group likely does not hinder the reported performance. We argue that clinically applicable computer-aided diagnostic tools for epilepsy must directly address the clinical challenge of distinguishing patients with epilepsy from those with non-epileptic seizures.