Prospective Phase II Study of Radiotherapy Dose Escalation in Grade 4 Glioma Using 68Ga-Pentixafor PET Scan.

AIMS: Local failure remains the major concern in grade 4 glioma or glioblastoma (GBM). Pilot studies have shown a radiotherapy (RT) dose-response relationship in GBM. Here we present our preliminary data of RT dose escalation using 68Ga-Pentixafor PET scan. High 68Ga-pentixafor uptake in glioma cells helps in sharp demarcation between tumour and normal brain. MATERIALS AND METHODS: This phase II prospective study was conducted from 2018 to 2020. Thirty, biopsy-proven cases of grade 4 glioma were included. All patients underwent post-operative MRI of the brain and 68Ga-Pentixafor PET scan. RT was planned in 2-phases. Phase-1 GTV (GTV1) comprised of T2/flair abnormality, PET-avid disease and post-op cavity. A margin of 2cm was given to GTV-1 to create phase-1 CTV (CTV1), which was further expanded to 0.5cm to generate phase-1 PTV (PTV1). A radiation dose of 46Gy/23fr was prescribed to PTV-1. Phase-2 GTV (GTV2) consisted of CT/MRI contrast-enhancing lesion, PET avid disease and post-op cavity. A margin of 0.5 cm was given to GTV2 to create phase-2 CTV (CTV2) which was expanded to 0.5 cm to create phase-2 PTV (PTV2). RT dose of 14 Gy/7 fr was prescribed to PTV2. PET avid disease was delineated as GTV PET and a margin of 3mm was given to generate PTV-PET which received escalated RT dose of 21 Gy/7fr by simultaneous integrated boost (SIB) in phase 2 (Total dose to PTV PET = 67 Gy/30 fr). All patients received concurrent and adjuvant temozolomide. The data was prospectively maintained in Microsoft Excel sheet. SPSS v 23 was used for statistical analysis. The primary endpoints were estimation of the overall survival (OS) and progression-free survival (PFS), and secondary endpoint was to measure the incidence of radiation necrosis. Categorical variables were reported as frequency and percentage and quantitative variables were reported as median and range. RESULTS: Data from thirty patients were analysed. A median OS of 23 months was observed with estimated 1, 2 and 3 years OS of 90%, 40% and 17.8% respectively. A significant association of OS was seen with the extent of surgery (p = 0.04) and kernofsky performance status (p = 0.007). No patient developed significant radiation necrosis. CONCLUSIONS: The index study did not show any survival benefit from dose escalation RT. However, all of the patients tolerated the treatment well and none of them developed radiation necrosis. Considering the small sample size as a limitation of the index study, the role of 68Ga-pentixafor PET scan for radiation dose escalation should be further explored. CLINICAL TRIAL NUMBER: CTRI/2019/05/019146.

Pullulanase: unleashing the power of enzyme with a promising future in the food industry.

Pullulanases are the most important industrial group of enzymes in family 13 glycosyl hydrolases. They hydrolyze either α-1,6 and α-1,4 or both glycosidic bonds in pullulan as well as other carbohydrates to produce glucose, maltose, and maltotriose syrups, which have important uses in food and other related sectors. However, very less reports are available on pullulanase production from native strains because of low yield issues. In line with the increasing demands for pullulanase, it has become important to search for novel pullulanase-producing microorganisms with high yields. Moreover, high production costs and low yield are major limitations in the industrial production of pullulanase enzymes. The production cost of pullulanase by using the solid-state fermentation (SSF) process can be minimized by selecting agro-industrial waste. This review summarizes the types, sources, production strategies, and potential applications of pullulanase in different food and other related industries. Researchers should focus on fungal strains producing pullulanase for better yield and low production costs by using agro-waste. It will prove a better enzyme in different food processing industries and will surely reduce the cost of products.

Machine learning-based approach to predict ice meltdown in glaciers due to climate change and solutions.

The fate of humankind and all other life forms on earth is threatened by a foe, known as climate change. All parts of the world are affected directly or indirectly by this phenomenon. The rivers are drying up in some places and in other places, it is flooding. The global temperature is rising every year and the heat waves are taking many souls. The cloud of "extinction" is upon the majority of flora and fauna; even humans are prone to various fatal and life-shortening diseases from pollution. This is all caused by us. The so-called "development" by deforestation, releasing toxic chemicals into air and water, burning of fossil fuels in the name of industrialisation, and many others have made an irreversible cut in the heart of the environment. However, it is not too late; all of this could be healed back with the help of technology and our efforts together. As per the international climate reports, the average global temperature has increased by a little more than 1 °C since 1880s. The research is primarily focused on the use of machine learning and its algorithm to train a model that predicts the ice meltdown of a glacier, given the features using the Multivariate Linear Regression. The research strongly encourages the use of features by manipulating them to determine the feature with a major impact on the cause. The burning of coal and fossil fuels is the main source of pollution as per the study. The research focuses on the challenges to gather data that would be faced by the researchers and the requirement of the system for the development of the model. The study is aimed to spread awareness in society about the destruction we have caused and urges everyone to come forward and save the planet.

Production, characterization, and application of novel fungal pullulanase for fruit juice processing.

The present study aimed to produce, characterize, and apply pullulanase from Aspergillus flavus (BHU-46) for fruit juice processing, assessing its enzymatic properties and impact on juice quality. Pullulanase was produced via solid-state fermentation using wheat bran as the substrate. Purification and characterization included specific activity, molecular weight, pH and temperature optima, and substrate specificity. The enzyme was immobilized in sodium alginate beads and used for clarifying mosambi, apple, and mango juices. Parameters such as yield, clarity, reducing sugar, total soluble solids (TSS), total phenol, and enzymatic browning were evaluated pre-and post-treatment. The purified pullulanase had a specific activity of 652.2 U/mg and a molecular weight of 135 kDa. Optimal pH values were 6.5 and 10, with maximum activity at 50 °C. Pullulanase showed a high affinity for pullulan and starch, indicating Pullulanase type II classification. Immobilized pullulanase improved yield, clarity, reducing sugar, TSS, and total phenol in fruit juices. The highest yield and clarity were observed in mosambi juice. Additionally, the enzyme reduced enzymatic browning, increasing the lightness of the juice. This study provides a significant contribution to the juice processing industry and represents the first report on the application of pullulanase for fruit juice processing.

Performance-based protocol for selection of economical portable sensor for air quality measurement.

An effective micro-level air quality management plan requires high-resolution monitoring of pollutants. India has already developed a vast network of air quality monitoring stations, both manual and real time, located primarily in urban areas, including megacities. The air quality monitoring network consists of conventional manual stations and real time Continuous Ambient Air Quality Monitoring Stations (CAAQMS) which comprise state-of-the-art analysers and instruments. India is currently in the early stages of developing and adopting economical portable sensor (EPS) in air quality monitoring systems. Protocols need to be established for field calibration and testing. The present research work is an attempt to develop a performance-based assessment framework for the selection of EPS for air quality monitoring. The two-stage selection protocol includes a review of the factory calibration data and a comparison of EPS data with a reference monitor, i.e. a portable calibrated monitor and a CAAQMS. Methods deployed include calculation of central tendency, dispersion around a central value, calculation of statistical parameters for data comparison, and plotting pollution rose and diurnal profile (peak and non-peak pollution measurement). Four commercially available EPS were tested blind, out of which, data from EPS 2 (S2) and EPS 3 (S3) were closer to reference stations at both locations. The selection was made by evaluating monitoring results, physical features, measurement range, and frequency along with examining capital cost. This proposed approach can be used to increase the usability of EPS in the development of micro-level air quality management strategies, other than regulatory compliance. For regulatory compliance, additional research is needed, including field calibration and evaluating EPS performance through additional variables. This proposed framework may be used as starting point, for such experiments, in order to develop confidence in the use of EPS.

Comparative efficacy of quadratus lumborum type-II and erector spinae plane block in patients undergoing caesarean section under spinal anaesthesia: a randomised controlled trial.

BACKGROUND: Quadratus lumborum and erector spinae plane blocks have been used to provide analgesia in patients undergoing thoracic or abdominal surgeries. Our study compared the analgesic efficacy of the quadratus lumborum type-II block (QLB-II) and the erector spinae plane block (ESPB) in parturients who underwent caesarean section under spinal anaesthesia. METHODS: Fifty-two patients with comparable demographic profiles were randomised into two groups, QLB-II (n = 26) and ESPB (n = 26). After the surgery, patients received either ultrasound-guided QLB-II or ESPB using 0.25% bupivacaine 0.3 mL/kg. Comparison of analgesic efficacy was in terms of fentanyl consumption (primary outcome), pain scores, incidence of complications in the 24-h postoperative period, and quality of recovery (QoR-15) on postoperative days one and two, and day of discharge. RESULTS: There was no significant difference in cumulative number of fentanyl doses (W = 349.000, P = 0.840), numerical rating score at rest (P = 0.648) or with movement (P = 0.520), QoR-15 scores on postoperative day one (P = 0.549), day two (P = 0.927) or day of discharge (P = 0.676). CONCLUSION: We concluded that patients who underwent QLB-II or ESPB reported similar analgesic efficacy, complications, and quality of recovery in the postoperative period.

Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements.

The world is advancing to a new era where a new concept is emerging that deals with "wirelessness". As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN's scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations.

Utility of 3D printed models as adjunct in acetabular fracture teaching for Orthopaedic trainees.

OBJECTIVE: To evaluate the use of 3-D printed models as compared to didactic lectures in the teaching of acetabular fractures for Orthopaedic trainees. METHODS: This was a randomised prospective study conducted in a tertiary hospital setting which consisted of 16 Orthopaedic residents. Ten different cases of acetabular fracture patterns were identified and printed as 3-D models. The baseline knowledge of orthopaedic residents regarding acetabular fracture classification and surgical approach was determined by an x-ray based pre-test. Trainees were then randomly assigned into two groups. Group I received only lectures. Group II were additionally provided with 3-D printed models during the lecture. Participants were then assessed for comprehension and retention of teaching. RESULTS: Sixteen trainees participated in the trial. Both Group 1 and 2 improved post teaching with a mean score of 2.5 and 1.9 to 4.4 and 6 out of 10 respectively. The post test score for fracture classification and surgical approach were significantly higher for 3-D model group (p < 0.05). Trainees felt that the physical characteristics of the 3-D models were a good representation of acetabular fracture configuration, and should be used routinely for teaching and surgical planning. CONCLUSION: 3-D printed model of real clinical cases have significant educational impact compared to lecture-based learning towards improving young trainees' understanding of complex acetabular fractures.

Dark Web Data Classification Using Neural Network.

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.

Rapidly recurrent recalcitrant Rathke Cleft Cyst: Case report and review of the literature.

BACKGROUND: Rathke Cleft Cysts (RCC) are cystic sellar or suprasellar nonneoplastic lesions that are thought to originate from remnants of the rathke pouch. Postoperatively, RCCs have a tendency to reaccumulate, after which preoperative symptoms may recur. However, there exists very little evidence on which treatments are most effective in these patients. CASE-DESCRIPTION: In this report, we present a unique case of a symptomatic RCC that recurred four times after initial transsphenoidal surgery. Following each surgery, the patient had significant visual improvement with post-op imaging displaying decompression of the neural elements. However, RCC reaccumulated in strikingly rapid time intervals of 1.5 months, 0.5 months, 1.5 years, and 5 months after each respective prior surgery. Repeat interventions with transsphenoidal, pterional and supraorbital approaches were unsuccessful in providing a durable treatment response. The patient ultimately underwent radiotherapy after a final surgical marsupialization of the cyst and has since displayed stable imaging with improved vision. This patient represented a 'perfect storm' of factors that may contribute to cyst recurrence, including substantial visual field deficits, large cyst size, peripheral wall enhancements on MRI, an intraoperative CSF leak, use of a fat graft, subtotal resection of the portion of cyst wall that adhered to important suprasellar structures, squamous metaplasia noted in cyst wall, and suprasellar extension. CONCLUSIONS: We demonstrate that different surgical approaches through repeat surgeries may not assist in prevention of further recurrence; instead, we propose that radiotherapy should be offered early in the treatment course of recurrent cases that have additional risk factors for further reoccurrence.

Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP).

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.

Barrier enclosure in tracheostomy: a protective box for healthcare workers during the coronavirus disease 2019 pandemic.

BACKGROUND: Tracheostomy, being a high aerosol-generating procedure, poses a great challenge to surgeons, especially during the coronavirus disease 2019 pandemic. It is important to preserve staff numbers as this fight may go on for a long time. Personal protective equipment plays a key role in the protection of healthcare workers. Barrier enclosure has been attempted in procedures such as intubation and tracheostomy. The use of boxes became popularised for intubation and they have been utilised in many centres. METHODS: This paper describes the box designed by our team and presents our surgical experience with the box. The box is made of transparent acrylic. It is sealed at all ends, with a negative-pressure environment. The hand ports were designed to allow maximum manoeuvrability for surgeons, without restricting hand movements. CONCLUSION: The proposed box will provide more protection to healthcare workers during tracheostomy. However, the box is yet to be validated.

Detection and molecular characterization of astro and bocaviruses in dogs in Minnesota.

Canine astrovirus (CAstV) and canine bocavirus (CBoV) are involved in cases of mild, and sometimes severe, gastroenteritis in dogs. Fecal samples from two dead dogs with gastroenteritis were received at the University of Minnesota Veterinary Diagnostic Laboratory to determine the cause of death. Small round viruses of 20-35 nm diameter were observed by negative contrast electron microscopy. The samples were subjected to Illumina MiSeq sequencing. Both samples were strongly positive for CAstV; all viral reads were related to CAstV. In addition, sample number 1 had a few reads of CBoV. Two complete sequences of CAstV were identified (6625 and 6627 nt in length) with 95% nt identity. RT-PCR and PCR were used to confirm CAstV and CBoV infections in successive samples of canine gastroenteritis. Sanger sequencing was done on nucleic acids from positive samples. Of a total of ten samples, CAstV and CBoV infections were confirmed in six and eight animals, respectively. Four animals had mixed infection with both viruses. All sequences of ORF1b gene of CAstVs showed closest clusters in phylogenetic tree with 96-100% nucleotide and amino acids identity. On the other hand, identity between VP2 gene of different CBoV strains in this study ranged from 93%- 100%. All strains were located close to each other except the divergent MT078234 strain, which was arranged in a separate branch and was closer to reference strain JN648103/USA/2010. This study highlights the importance of electron microscopy and next generation sequencing for early detection and characterization of viruses associated with dog gastroenteritis.

Performance evaluation of air pollution control device at traffic intersections in Delhi.

Urban air pollution and exposure-related health impacts are being noticed and discussed very intensely in India. On the other hand, source-specific control is the primary focus for policymakers; however, diverse and complex sources make it difficult to immediately see the action and consequent impacts on better air quality. Many cities across the world have witnessed high air pollution levels at traffic junctions, more so in all Indian cities. Site-specific air pollution reduction can be a promising solution for managing the pollution level at highly polluted locations. CSIR-National Environmental Engineering Research Institute, India, has designed and developed Wind Augmentation and purifYing Unit (WAYU) to remove particulate and gaseous pollutants from urban hot spots such as traffic locations. In the present study, the authors attempted to evaluate the performance of two different designs of WAYU for the removal of particulate matters from polluted air at different traffic locations in Delhi City, the national capital territory of India. The performance analyses show that the current design of WAYU removes PM10 and PM2.5 concentrations in the range of 34-49% and 19-25%, respectively from the inlet air. The total PM collected from all WAYU devices was 34.19 kg from 120,557 operating hours' at all the sampling sites. The PM removal rate depends on the size-segregated particulate matter pollution load in the ambient air. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13762-021-03641-3.

Pattern formation in Passiflora incarnata: An activator-inhibitor model.

Based on a careful examination of the onset of violet colored dots along the filaments in the developing floral bud stage and the formation of alternating bands of violet and white color in the matured flowers of Passiflora incarnata (Passion flower), it is concluded that the pattern arises from a competition between the production of violet colored anthocyanin and the colorless flavonols along the filaments. The activator-inhibitor model of Gierer and Meinhardt along with the reaction diffusion theory of Turing is used to explain the formation of concentric rings in the flower.

Identification of air pollution hotspots in urban areas - An innovative approach using monitored concentrations data.

Critical assessment of spatio-temporal variations in pollution levels is a crucial step for identifying and prioritizing air pollution hotspots (APH) in urban areas. There is no universally accepted methodology for defining and delineating air pollution hotspot which can be source-specific, pollutant-specific and time-specific. The present research article is an attempt to develop a protocol for identifying APH for any pollutant within a city where-in three criteria-based innovative methodology has been derived. The three criteria are frequency of exceedance (% of days), scale of exceedance and consistency in exceedance (consecutive number of days) to the specified standards that need to be met continuously for at least three years. The suggested methodology has been applied on a three-year database (2018-2021) of 37 continuous ambient air quality stations to identify PM2.5 specific APH. The analysis indicates 11 APH in April, 9 in May, 2 in June and almost the entire city during the October-February months. Given prioritization of implementation of control actions, the identified APH during summer has been further physically examined to map source activity types and their suitability for ambient air quality monitoring stations as per the guidelines. The APH can be the priority areas for the implementation of control actions by urban local bodies. The management of air pollution at these priority areas would be more effective instead of city-scale management practice, which is difficult to implement and monitor.

Comparative growth kinetic study of Newcastle disease virus, infectious bursal disease virus and avian influenza virus in chicken embryo fibroblast and DF-1 cell lines.

Viral diseases have caused devastating effect on poultry industry leading to significant losses in economy of world. In the presented study, the ability of Newcastle disease virus (NDV), infectious bursal disease virus (IBDV) and avian influenza virus (AIV) to grow in two cell lines was evaluated. Both chicken embryo fibroblast (CEF) and DF-1 cells were used and cytopathic effects (CPE) produced by these viruses were observed. The titer of virus in terms of TCID was determined after 24h up to four days for each virus. The same type of CPE was observed for all virus- es used in the study in both DF-1 and CEF cells. IBDV showed CPE causing rounding of cells while NDV caused formation of multicellular large nuclei, cell fusion and rounding of cells. Giant cells with inclusions and aggregation of cells with intact monolayer was observed for AIV. In growth kinetic study, higher titer of IBDV and NDV was observed in CEF cells than DF-1 cells while for AIV, DF-1 cells showed higher titer than CEF cells. These results would be useful for furthers comparative studies on growth of different cell lines of various viruses to find a suitability for vaccine production.

Exploring the diversity of endophytic fungi and screening for their pullulanase-producing capabilities.

BACKGROUND: Pullulanases are the significant industrial group in the 13 glycosyl hydrolases category, known as the α-amylases family. There are very few reports on pullulanase from fungal sources. Based on the above research gap, the present study was undertaken to explore the endophytic fungi for their pullulanase-producing capabilities. RESULTS: A total of 126 endophytes were isolated from Tradescantia pallida, Zea mays, and Trifolium alexandrinum. Aspergillus, Penicillium, and Ganoderma species recovered highest from the stem of Tradescantia palida. Fusarium was dominant in the stem and leaf of Zea mays. Penicillium, Aspergillus, Ganoderma, Cladosporium, Fusarium, and Alternaria were recovered from the Trifolium alexandrium. The Shannon index in Tradescantia pallida was highest in leaves while in Zea mays and Trifolium alexandrinum, it is highest in the stem. The Simpson's index is highest in the case of Zea mays stem and root. Species richness was indicated by Menhinick's index, and it was found that this value was highest in the roots of Trifolium alexandrinum. As per our knowledge, no comparative data is available on the endophytic diversity of the above plants taken for the study. Out of 126 endophytes, only 2.38% produced pullulanase while 7.94% produced amylase. The recovery of pullulanase-producing endophytic fungi was very less. But the importance of pullulanase is high as compared to amylase because it has both α-1,6 and α-1,4 hydrolyzing ability. Therefore, the most promising isolates were identified by ITS sequence analysis. Based on spore chain morphology, isolates BHU-25 and BHU-30 were identified as Penicillium sp. and Aspergillus species, respectively. This is the first report of pullulanase from endophytic Aspergillus and Penicillium. CONCLUSION: Endophytes Aspergillus sp. and Penicillium sp. produce pullulanase enzyme. This is the first report of pullulanase from endophytic Aspergillus and Penicillium.