Background Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and understanding it to plan interventions to improve patients' experience. We demonstrate the use of natural language processing (NLP) to extract meaningful information from patient feedback obtained through Press Ganey surveys. Methods The first step was to standardize textual data programmatically using NLP libraries. This included correcting spelling mistakes, converting text to lowercase, and removing words that most likely did not carry useful information. Next, we converted numeric data pertaining to each category based on sentiment and care aspect into charts. We selected care aspect categories where there were more negative comments for more in-depth study. Using NLP, we made tables of most frequently appearing words, adjectives, and bigrams. Comments with frequent words/combinations underwent further study manually to understand factors contributing to negative patient feedback. We then used the positive and negative comments as the training dataset for a neural network to perform sentiment analysis on sentences obtained by splitting mixed reviews. Results We found that most of the comments were about doctors and nurses, confirming the important role patients ascribed to these two in patient care. “Room,” “discharge” and “tests and treatments” were the three categories that had more negative than positive comments. We then tabulated commonly appearing words, adjectives, and two-word combinations. We found that climate control, housekeeping and noise levels in the room, time delays in discharge paperwork, conflicting information about discharge plan, frequent blood draws, and needle sticks were major contributors to negative patient feedback. None of this information was available from numeric data alone. Conclusion NLP is an effective tool to gain insight from raw textual patient feedback to extract meaningful information, making it a powerful tool in processing large amounts of patient feedback efficiently.

This paper provides an overview of nondestructive imaging techniques for evaluating internal and external quality characteristics of fruits and vegetables and the future prospects of those technologies within the food industry. Low‐field nuclear magnetic resonance (LF‐NMR) and magnetic resonance imaging (MRI) are viable technologies in assessing water status, which can significantly impact the quality of fruits and vegetables' texture, tenderness, and microstructure. This review examined some of the most widely studied agricultural fruits and vegetables, described NMR/MRI techniques, and explained the benefits of their implementation in the assessment of internal quality attributes such as internal defects, water content, nutrition content, maturity, fruit firmness, and seed detection, as well as physicochemical and microbiological quality in both commercial and industrial applications. In spite of considerable developments in the quality measurement of fruits and vegetables and their products, the implementation of these techniques at an industrial level has been unsatisfactory. Practical applications This paper aimed to present a magnificent knowledge about fruit/vegetable processing and preservation techniques pertaining to quality evaluation. If the appropriate skills and the introduced tools are combined and utilized in an innovative and suitable way, a high quality of fruit/vegetable products with a high nutritional value can be achieved. This will be beneficial for both producers and customers.

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D airy development is considered an influential factor in eradicating poorness and creating prosperity in the developing world. Livestock comprises 30% of the agricultural Gross Domestic Product (GDP) within the developing world, while about 40% of the worldwide agricultural GDP, is one amongst the fastest growing subsectors in agriculture (FAO, 2009). Dairy farming is considered a key instrument for improving the financial conditions of farmers in several countries. It is not simply a helpful source of balanced diet and income but is a path for asset accumulation (Sharon, 2011). Besides Abstract | The adoption of dairy technology has a direct impact on the socio-economic development of rural milk producers as milk production has a significant contribution to sustainable rural livelihoods. For this purpose, the present research was conducted to determine the socio-economic factors that influence the rural milk producers in the adoption of dairy technology. A survey was carried out in district Muzaffarabad of Azad Jammu and Kashmir by selecting 8 villages purposively, based on the average number of livestock population i.e 2-3 heads per household, for dairy production. A structured and pretested interview schedule was used to collect the primary data from randomly selected 333 respondents. For analysis of data descriptive statistics and binary logistic model was used through the Statistical Package for Social Sciences (SPSS) and STATA. Information about dairy practices was studied and percentages recorded, about 70% of the farmers were aware of artificial insemination and vaccination while at least 51% was reported about livestock management. Improved feeding was very active as 70% of farmers were well informed followed by breed status recorded by 70% of the farmers. Demographic results showed that 63% of respondents were literate with an average age of 51 years and farming experience of upto 11 years. Results from the binary logistic model indicate that number of family members, farm knowledge, accessibility of extension services, gender, credit access, farm size, farming experience, and crossbreeds' availability had a positive association with dairy technology adoption, while age and market distance had a negative association. According to the study results, it is concluded that the adoption of technology depends on their socio-economic conditions. Hence, it is suggested that the administration should encourage a deliberate strategy in building farmers' awareness regarding dairy technology through trainings, demonstrations, field visits, and experience sharing at different levels. The government should provide credit facilities to the farmers in consideration of their socio-economic conditions.

Stem cells carry the remarkable ability to differentiate into different cell types while retaining the capability to self-replicate and maintain the characteristics of their parent cells, referred to as potency. Stem cells have been studied extensively to better understand human development and organogenesis. Because of advances in stem cell-based therapies, regenerative medicine has seen significant growth. Ophthalmic conditions, some of which are leading causes of blindness worldwide, are being treated with stem cell therapies. Great results have also been obtained in the treatment of oral and maxillofacial defects. Stem-cell-based therapies have great potential in the treatment of chronic medical conditions like diabetes and cardiomyopathy. The unique property of stem cells to migrate towards cancer cells makes them excellent vectors for the transportation of bioactive agents or for targeting cancer cells, both primary and metastatic. While these therapeutic strategies are extremely promising, they are not without limitations. Failure to completely eradicate the tumor and tumor relapse are some of those concerns. Stem cells share some characteristics with cancer stem cells, raising concerns for increasing the risk of cancer occurrence. Ethical concerns due to the fetal origin of stem cells and cost are other major obstacles in the large-scale implementation of such therapies.