As per national policy, all diagnosed tuberculosis patients in India are to be tested using Xpert® MTB/RIF assay at the district level to diagnose rifampicin resistance. Regardless of the result, ...samples are transported to the reference laboratories for further testing: first-line Line Probe Assay (FL-LPA) for rifampicin-sensitive samples and second-line LPA(SL-LPA) for rifampicin-resistant samples. Based on the results, samples undergo culture and phenotypic drug susceptibility testing. We assessed among patients diagnosed with tuberculosis at 13 selected Xpert laboratories of Karnataka state, India, i) the proportion whose samples reached the reference laboratories and among them, proportion who completed the diagnostic algorithm ii) factors associated with non-reaching and non-completion and iii) the delays involved.
This was a cohort study involving review of programme records. For each TB patient diagnosed between 1st July and 31st August 2018 at the Xpert laboratory, we tracked the laboratory register at the linked reference laboratory until 30th September (censor date) using Nikshay ID (a unique patient identifier), phone number, name, age and sex.
Of 1660 TB patients, 1208(73%) samples reached the reference laboratories and among those reached, 1124(93%) completed the algorithm. Of 1590 rifampicin-sensitive samples, 1170(74%) reached and 1104(94%) completed the algorithm. Of 64 rifampicin-resistant samples, only 35(55%) reached and 17(49%) completed the algorithm. Samples from rifampicin-resistant TB, extra-pulmonary TB and two districts were less likely to reach the reference laboratory. Non-completion was more likely among rifampicin-resistant TB and sputum-negative samples. The median time for conducting and reporting results of Xpert® MTB/RIF was one day, of FL-LPA 5 days and of SL-LPA16 days.
These findings are encouraging given the complexity of the algorithm. High non-reaching and non-completion rates in rifampicin-resistant patients is a major concern. Future research should focus on understanding the reasons for the gaps identified using qualitative research methods.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The security and privacy of healthcare data are crucial aspects within the healthcare industry, as accurate diagnoses rely on medical professionals accessing patient healthcare data. Similarly, ...patients often require access to their data. However, ensuring that sensitive health data is shared securely while prioritizing privacy is essential. This paper proposes an innovative solution called the quaternion based neural network, Advanced Data Security Architecture in Healthcare Environment (ADSAH), which combines Elliptical curve cryptography (ECC) with a blockchain mechanism and a Deep Fuzzy Based Neural Network (DFBNN) to safeguard cloud-stored health data. The proposed approach begins by encoding the input medical data using an encoder and then encrypting the encoded data using ECC techniques. The secret key for encrypting the data is securely stored within a blockchain framework. The key is divided into blocks to enhance security, and the SHA algorithm is employed to identify key events within these blocks. These key events are subsequently stored in a cloud storage system. A modified genetic algorithm is utilized to generate the encryption and decryption key. This algorithm is explicitly tailored to secure healthcare data. Authorized patients or physicians can access medical data using the secret key to decrypt and retrieve the necessary information. The performance of the proposed network is evaluated by considering factors such as time and cost and is compared against existing studies. The evaluation demonstrates notable improvements, including a reduction in the time required for the encryption and decryption process, as well as a decrease in transaction and execution costs when compared to previous research. By incorporating ECC with a blockchain mechanism and DNN, the ADSAH approach offers an advanced solution for ensuring the security and privacy of cloud-stored health data. It provides robust encryption and facilitates efficient and cost-effective access to authorized individuals while safeguarding sensitive health information.
The environment of startups in the field of information technology (IT) is strongly connected with the dynamics of finance and valuation. IT startups sometimes rely on a variety of funding sources, ...including Venture Capitalists (VCs), Private Equity funds, Government subsidies, and even Hedge funds. Overvaluation has emerged as a key hazard harming the IT startup ecosystem. This phenomenon occurs when the valuation of a startup exceeds its true worth, which is frequently driven by enthusiastic investor optimism, market trends, and competitive pressures. The case addresses factors that might lead to overvaluation during funding rounds of startups. The case also explores various conditions like global economic slowdown, regulatory measures, sustainability of the product or idea of a startup that mitigates downturn of valuation. The measures from all the stakeholders of startups viz., investors, founders, governments etc. is dealt with for tackling the overvaluation conundrum.
In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network ...(ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN to predict solar power generation based on historical data and using a GA to optimize the ANN’s architecture and activation function. The GA searches for the best combination of hidden layers and activation functions to minimize the error between the predicted and actual solar power generation. This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems.
All over the world, millions of devices are wirelessly connected and exchanging data as part of the Internet of Things (IoT). As more and more information is hoped to be monitored by means of a ...single platform, the importance of accuracy assessment in the pursuit of the perfect IoT platform has grown. To keep up with the ever-increasing data analysis needs for crucial, real-time decision making, IoT data collection is becoming increasingly crucial. In this study, we utilized the "IoT Sensor Data" dataset, consisting of sensor readings collected from various IoT devices. The proposed R-QCNN model, which combines a quantum neural network with a deep residual learning technique, was trained and evaluated on this dataset. Experimental results show that the R-QCNN achieved an accuracy of 95% in classifying the IoT sensor data, outperforming existing methods. Thus, our approach demonstrates promising results in optimizing the cost function routine for IoT platforms.
This study examines the impact of microfinance services on the socio‐economic welfare of urban households in Sabah, which is considered as the poorest state in Malaysia. The data were collected from ...recipients of microfinance services through self‐administered questionnaire and were analyzed using Smart Partial Least Squares (PLS). The results of this study show that microcredit, microinsurance, savings, training, and social intermediation service have significant effects on socio‐economic welfare of urban households in Sabah. The empirical findings can be used as a guideline for microfinance institutions to provide the best services that will reduce vulnerability of the households in Sabah. It can also assist the government to provide the best microfinance blueprints in the region.
Adverse effects of nanoparticles on aquatic environment and organisms have drawn much special attention to many researches. Aluminium oxide nanoparticles (Al2O3-NPs) have potential uses in varied ...fields and are seen entering into the ecosystem. Their potential toxicity to the freshwater fish is not much studied. Hence this study was framed to investigate the effect Al2O3 NPs on freshwater fish Oreochromis mossambicus in terms of sub lethal toxicity, histological changes and hepato somatic index (HSI) under laboratory conditions. Fishes were exposed to varying concentrations of Al2O3 NPs for 96hr. LC50 value was found to be in between 235 and 245ppm. The findings of the present work showed that the NPs were accumulated in the fish liver and caused major histological anomalies such as structural alterations in the portal vein, necrotic hepatocytes, vacuolation, aggregation of blood cells and melanomacrophages. Significant histological alterations were observed in the highest concentration. Our results evidenced that the Al2O3 NPs in the aquatic environment affects the health condition of the fishes.
The present study was carried out to investigate the impact of various concentrations of SiO2 nanoparticles (SiO2 NP) on the commonly available freshwater fish Oreochromis mossambicus. The 96 h ...median lethal concentration (LC50) of SiO2 NP was found to be between 270−280 ppm. This novel study has demonstrated histological alterations in the hepatic tissues and a dose-dependent depletion of tissue protein content and an elevated transaminases activity in the treated fish, which has facilitated understanding of the impact of SiO2 NP in O. mossambicus.
•Toxicological effect of Al2O3 nanoparticles were studied using the freshwater fish Oreochromis mossambicus.•Sub lethal concentrations (120, 150 and 180 ppm) of Al2O3 NPs were exposed to the fishes ...for a period of 96 h.•Histoarchitecture of selected organs (brain, gill, intestine, kidney and muscle) in the control and treated fishes were observed.•Histological anomalies were initiated in the fishes exposed to the lower concentrations of NPs.•The severity of lesions were more evident in fishes exposed to the highest concentration of nanoparticles.
In the present study, freshwater fish Oreochromis mossambicus were exposed to sub lethal concentrations (120, 150 and 180 ppm) of Aluminium oxide nanoparticles (Al2O3 NPs) for 96 h. Histological abnormalities were not observed in the organs of control fishes whereas severe damages and extensive architectural loss was found in the brain, gill, intestine, kidney and muscle tissues of treated fishes with more pronounced effects in 180 ppm. The results showed that the acute exposure to Al2O3NPs altered the histoarchitecture in various fish tissues.
An M/G/1 retrial queueing system with two phases of service of which the second phase is optional and the server operating under Bernoulli vacation schedule is investigated. Further, the customer is ...allowed to balk upon arrival if he finds the server unavailable to serve his request immediately. The joint generating functions of orbit size and server status are derived using supplementary variable technique. Some important performance measures like the orbit size, the system size, the server utilisation and the probability that the system is empty are found. Stochastic decomposition law is established when there is no balking permitted. Some existing results are derived as special cases of our model under study. Interestingly, these performance measures are compared for various vacation schedules namely exhaustive service, 1-limited service, Bernoulli vacation and modified Bernoulli vacation schedules. Extensive numerical analysis is carried out to exhibit the effect of the system parameters on the performance measures.
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