Resistance Profiles to Second-Line Anti-Tuberculosis Drugs and Their Treatment Outcomes: A Three-Year Retrospective Analysis from South India.

Background: Patients with first-line drug resistance (DR) to rifampicin (RIF) or isoniazid (INH) as a first-line (FL) line probe assay (LPA) were subjected to genotypic DST using second-line (SL) LPA to identify SL-DR (including pre-XDR) under the National TB Elimination Program (NTEP), India. SL-DR patients were initiated on different DR-TB treatment regimens and monitored for their outcomes. The objective of this retrospective analysis was to understand the mutation profile and treatment outcomes of SL-DR patients. Materials and Methods: A retrospective analysis of mutation profile, treatment regimen, and treatment outcome was performed for SL-DR patients who were tested at ICMR-NIRT, Supra-National Reference Laboratory, Chennai between the years 2018 and 2020. All information, including patient demographics and treatment outcomes, was extracted from the NTEP Ni-kshay database. Results: Between 2018 and 2020, 217 patients out of 2557 samples tested were identified with SL-DR by SL-LPA. Among them, 158/217 were FQ-resistant, 34/217 were SLID-resistant, and 25/217 were resistant to both. D94G (Mut3C) of gyrA and a1401g of rrs were the most predominant mutations in the FQ and SLID resistance types, respectively. Favorable (cured and treatment complete) and unfavorable outcomes (died, lost to follow up, treatment failed, and treatment regimen changed) were recorded in a total of 82/217 and 68/217 patients in the NTEP Ni-kshay database. Conclusions: As per the testing algorithm, SL- LPA is used for genotypic DST following identification of first-line resistance, for early detection of SL-DR in India. The fluoroquinolone resistance pattern seen in this study population corelates with the global trend. Early detection of fluoroquinolone resistance and monitoring of treatment outcome can help achieve better patient management.

Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation.

BACKGROUND: Among the 6-8 million animals that enter the rescue shelters every year, nearly 3-4 million (i.e., 50% of the incoming animals) are euthanized, and 10-25% of them are put to death specifically because of shelter overcrowding each year. The overall goal of this study is to increase the adoption rates at animal shelters. This involves predicting the length of stay of each animal at shelters considering key features such as animal type (dog, cat, etc.), age, gender, breed, animal size, and shelter location. RESULTS: Logistic regression, artificial neural network, gradient boosting, and the random forest algorithms were used to develop models to predict the length of stay. The performance of these models was determined using three performance metrics: precision, recall, and F1 score. The results demonstrated that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Upon further observation of the results, it was found that age for dogs (puppy, super senior), multicolor, and large and small size were important predictor variables. CONCLUSION: The findings from this study can be utilized to predict and minimize the animal length of stay in a shelter and euthanization. Future studies involve determining which shelter location will most likely lead to the adoption of that animal. The proposed two-phased tool can be used by rescue shelters to achieve the best compromise solution by making a tradeoff between the adoption speed and relocation cost.

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services.

The effects of traffic congestion are adverse, primarily including air pollution, commuter stress, an increase in vehicle operating costs, and accidents on road. In efforts to alleviate these problems in metropolitan cities, logistics companies plan to introduce a new Urban Air Mobility (UAM) service called air taxis. These are electric-powered vehicles that would be tested and operated in the forthcoming years by international transportation companies like Airbus, Uber, and Kitty Hawk. Since these flying taxis are an emerging mode of transportation, it is necessary to provide recommendations for initial design, implementation, and operation. This study proposes managerial insights for these upcoming UAM services by analyzing online customer reviews and conducting an internal assessment of helicopter operations. Helicopters are similar to air taxis in regards to their vertical takeoff and landing (VTOL) operations, and therefore, customer reviews pertaining to the former can enable us to obtain insights into the strengths and weaknesses of the short-distance aviation service, in general. A four-stage sequential approach is used in this research, wherein online reviews are mined in Stage 1, analyzed using bigram and trigram models in Stage 2, 7S internal assessment is conducted for helicopter services in Stage 3, and managerial recommendations for air taxis are proposed in Stage 4. The insights obtained in this paper could assist any air taxi company in providing better customer service when they venture into the market.

Stochastic Inventory Model for Minimizing Blood Shortage and Outdating in a Blood Supply Chain under Supply and Demand Uncertainty.

Purpose: Blood, like fresh produce, is a perishable element, with platelets having a limited lifetime of five days and red blood cells lasting 42 days. To manage the blood supply chain more effectively under demand and supply uncertainty, it is of considerable importance to developing a practical blood supply chain model. This paper proposed an essential blood supply chain model under demand and supply uncertainty. Methods: This study focused on how to manage the blood supply chain under demand and supply uncertainty effectively. A stochastic mixed-integer linear programming (MILP) model for the blood supply chain is proposed. Furthermore, this study conducted a sensitivity analysis to examine the impacts of the coefficient of demand and supply variation and the cost parameters on the average total cost and the performance measures (units of shortage, outdated units, inventory holding units, and purchased units) for both the blood center and hospitals. Results: Based on the results, the hospitals and the blood center can choose the optimal ordering policy that works best for them. From the results, we observed that when the coefficient of demand and supply variation is increased, the expected supply chain cost increased with more outdating units, shortages units, and holding units due to the impacts of supply and demand fluctuation. Variation in the inventory holding and expiration costs has an insignificant effect on the total cost. Conclusions: The model developed in this paper can assist managers and pathologists at the blood donation centers and hospitals to determine the most efficient inventory policy with a minimum cost based on the uncertainty of blood supply and demand. The model also performs as a decision support system to help health care professionals manage and control blood inventory more effectively under blood supply and demand uncertainty, thus reducing shortage of blood and expired wastage of blood.

Insights on next-generation manufacturing of smart devices using text analytics.

With the mass expansion in technological user-friendly products, there is an increasing demand for smart devices, resulting in a highly competitive novel market. To ensure sustainable success, these products must remain robust and be perceived positively by customers. With the development of Web 2.0, individuals are able to make knowledgeable purchasing decisions, specifically with the availability of millions of online customer reviews. Companies manufacturing smart devices can utilize this unstructured data to analyze the customers' perceptions of their products and identify potential improvements. To the best of our knowledge, this paper is the first to propose next-generation manufacturing insights for companies producing smart devices by determining the current strengths, weaknesses, opportunities, and threats (SWOT) of these gadgets using text analytics. A three-stage methodology is utilized, consisting of bigram and trigram examination, topic identification, and SWOT analysis. After online review extraction, comments for each smart device are separated into positive, neutral, and negative categories, based on the customer ratings. Text analytic tools are then used to determine the most frequently occurring bigrams and trigrams to provide topics for conducting the SWOT analysis. Using the SWOT technique results, numerous next-generation smart device manufacturing recommendations are presented.

Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation's Blood Supply.

Purpose: The uncertainty in supply and the short shelf life of blood products have led to a substantial outdating of the collected donor blood. On the other hand, hospitals and blood centers experience severe blood shortage due to the very limited donor population. Therefore, the necessity to forecast the blood supply to minimize outdating as well as shortage is obvious. This study aims to efficiently forecast the supply of blood components at blood centers. Methods: Two different types of forecasting techniques, time series and machine learning algorithms, are developed and the best performing method for the given case study is determined. Under the time series, we consider the Autoregressive (AUTOREG), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, Seasonal Exponential Smoothing Method (ESM), and Holt-Winters models. Artificial neural network (ANN) and multiple regression are considered under the machine learning algorithms. Results: We leverage five years worth of historical blood supply data from the Taiwan Blood Services Foundation (TBSF) to conduct our study. On comparing the different techniques, we found that time series forecasting methods yield better results than machine learning algorithms. More specifically, the least value of the error measures is observed in seasonal ESM and ARIMA models. Conclusions: The models developed can act as a decision support system to administrators and pathologists at blood banks, blood donation centers, and hospitals to determine their inventory policy based on the estimated future blood supply. The forecasting models developed in this study can help healthcare managers to manage blood inventory control more efficiently, thus reducing blood shortage and blood wastage.

Self-reported depressive symptoms in adolescence increase the risk for obesity and high BP in adulthood.

BACKGROUND: Negative psychological symptoms may impact cardiovascular (CV) risk factors such as obesity and abnormal blood pressure (BP). Currently, a third of the US population is obese (BMI ≥ 30 kg/m2) and nearly half have high BP (BP > 120/80 mmHg). This research aims to investigate whether self-reported depressive symptoms in adolescence affect adulthood obesity and high BP. METHODS: We leveraged the data from a representative sample of US individuals collected by the National Longitudinal Study of Adolescent to Adult Health (Add Health). We identified the survey questions pertaining to self-reported depressive symptoms in over 14,000 adolescents. Based on their follow-up health test in adulthood, we evaluated the impact of adolescent depressive symptoms on adulthood obesity and high BP by adjusting for socio-demographic, socio-economic and adolescent health status. RESULTS: This study reveals a high prevalence of obesity (36%) and high BP (66%) among young US adults with an average age of only 28 years. Excessive moodiness in adolescence significantly impacted the development of obesity in early adulthood (p-value <0.001). 'Feeling sad' on most days in adolescence significantly increases the risk of obesity (p-value 0.01) and high BP (p-value <0.03) in early adulthood. CONCLUSIONS: This study demonstrates self-reported moodiness in adolescence to be a significant predictor of obesity in adulthood. Feeling sad on most days increase the subsequent risk for high BP. Early intervention may improve lifestyle and CV outcomes.