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Water contaminants are prevalent in various sources and this poses high risks to the environment as well as human health thereby increasing the need for an efficient sustainable water management. Zeolites are a special type of crystalline aluminosilicate minerals that possess rich frameworks of interconnecting channels and cages. They have exceptionally high cation exchange capacities and has drawn much attention as suitable adsorbent material for the elimination of different types of pollutants in water systems. Besides the use in water purification, zeolites are also used environmentally as filters of toxicants and as sources of nutrition to crops. Zeolites have multiple functions in commercial and environmental processes because of their structural and chemical characteristics. They can act as requisite catalysts, adsorbents, and ion exchangers due to the broad applicability in commercial and environmental processes across industries. This review aims at examining the use of zeolites in different water purification processes with the view of explaining the nature/extent of adsorption properties and ion exchange of the material. The effectiveness of various kinds of zeolites, such as clinoptilolite, chabazite, etc is presented with special reference to the removal of heavy metals, ammonia, organic contaminants, and operation of desalination. Furthermore, the review provides an account of the limitations and comparison with other technologies.

Summary: Background: Ventilator-associated pneumonia (VAP) is a major cause of morbidity and mortality in patients receiving mechanical ventilation in India. Surveillance of VAP is essential to implement data-based preventive measures. Implementation of ventilator-associated events (VAE) criteria for surveillance has major constraints for low resource settings, which can lead to significant underreporting. Surveillance of VAP using common protocols in a large network of hospitals would give meaningful estimates of the burden of VAP in low resource settings. This study leverages a previously established healthcare-associated infections (HAI) surveillance network to develop and test a modified VAP definition adjusted for Indian settings. Methods: In this observational pilot study, thirteen hospitals from the existing HAI surveillance network were selected for developing and testing a modified VAP definition between February 2021 and April 2023. The criteria used for diagnosing VAP were adapted from the CDC’s Pediatric VAP definition and modified to cater to the needs of Indian hospitals. Designated nurses recorded each VAP event in a case report form (CRF) and also collected denominator data. The data was entered into an indigenously developed database for validation and analysis. At the time of data analysis, a questionnaire was sent to sites to get feedback on the performance of the modified VAP definitions. Findings: Out of 133,445 patient days and 40,533 ventilator days, 261 VAP events were recorded, with an overall VAP rate of 6.4 per 1000 ventilator days and a device utilization ratio (DUR) of 0.3. A total of 344 organisms were reported from the VAP events. Of these, Acinetobacter spp (29.6%, 102) was the most frequent, followed by Klebsiella spp (26.7%, 92). Isolates of Acinetobacter spp (98%) and Enterobacterales (85.5%) showed very high resistance against Carbapenem. Colistin resistance was observed in 6% of Enterobacterales and 3.2% of Acinetobacter spp. Interpretation: Data from this pilot study needs to validated in the larger Indian HAI surveillance network so that it can help in wider implementation of this protocol in order to assess its applicability p VAP across India. Funding: This work was supported by a grant received from the Indian Council of Medical Research (code I-1203).

ABSTRACT The availability of syndrome-based panels for various ailments has widened the scope of diagnostics in many clinical settings. These panels can detect a multitude of pathogens responsible for a particular condition, which can lead to a timely diagnosis and better treatment outcomes. In contrast to traditional identification methods based on pathogen growth on culture, syndrome-based panels offer a quicker diagnosis, which can be especially beneficial in situations requiring urgent care, such as intensive care units. One such panel is the Biofire Filmarray Pneumonia plus Panel (BFP), which we have compared against microbiological culture and identification. The lower respiratory samples from patients were tested with BFP, culture, and identification with culture considered the gold standard. The phenotypic antibiotic susceptibility results (Vitek 2) were compared with the antimicrobial resistance (AMR) genes detected in BFP. Statistical analysis was carried out using GraphPad 7.0 and MS Excel (Microsoft Inc.). The results showed a positive percent agreement of 100% and a negative percent agreement of 47.8% with an overall agreement of 76.72% compared to culture. BFP was better at identifying fastidious bacteria, and the agreement with culture was higher for high bacterial identification numbers (107 and 106). There was also a correlation between the number of pathogens detected and growth in culture. Carbapenemase genes were detected in around 80% of phenotypically resistant samples and correlated with in-house PCR 60% of the time. Hence, BFP results need to be interpreted with caution especially when multiple pathogens are detected. Similarly, the presence or absence of AMR genes should be used to guide the therapy while being watchful of unusual resistance or susceptibility. The cost constraints and low throughput call for patient selection criteria and prioritization in emergency or resource-limited conditions.IMPORTANCEApplication of syndrome-based panels in clinical microbiology is of huge support in infectious conditions requiring urgent interventions, such as pneumonia. Interpreting the results requires caution; hence, we have compared the results obtained from Biofire Filmarray Pneumonia plus Panel with standard microbiological methods.

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795,000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

Analysis of large galaxy surveys requires confidence in the robustness of numerical simulation methods. The simulations are used to construct mock galaxy catalogues to validate data analysis pipelines and identify potential systematics. We compare three N-body simulation codes, abacus, gadget-2, and swift, to investigate the regimes in which their results agree. We run N-body simulations at three different mass resolutions, 6.25 × 108, 2.11 × 109, and 5.00 × 109 h-1 M⊙, matching phases to reduce the noise within the comparisons. We find systematic errors in the halo clustering between different codes are smaller than the Dark Energy Spectroscopic Instrument (DESI) statistical error for s > 20 h-1 Mpc in the correlation function in redshift space. Through the resolution comparison we find that simulations run with a mass resolution of 2.1 × 109 h-1 M⊙ are sufficiently converged for systematic effects in the halo clustering to be smaller than the DESI statistical error at scales larger than 20 h-1 Mpc. These findings show that the simulations are robust for extracting cosmological information from large scales which is the key goal of the DESI survey. Comparing matter power spectra, we find the codes agree to within 1 per cent for k ≤ 10 h Mpc-1. We also run a comparison of three initial condition generation codes and find good agreement. In addition, we include a quasi-N-body code, FastPM, since we plan use it for certain DESI analyses. The impact of the halo definition and galaxy-halo relation will be presented in a follow-up study.

A postharvest experiment was conducted at the Department of Horticulture, AAU, Jorhat, to study the effect of different packaging technique(s) for the distant marketing of Foxtail orchid spikes. The seven treatments were as follows: T1: CFB box (control); T2: wrapping with 200 gauge polyethylene sheet + KMnO₄ sachet + CFB box; T3: wrapping with butter paper + KMnO₄ sachet + CFB box; T4: wrapping with Brown Paper + KMnO₄ sachet + CFB box; T₅: plastic vial containing 8-HQS (25 ppm) + wrapping with 200 gauge polyethylene sheet + CFB box; T6: plastic vial containing 8-HQS (25 ppm) + wrapping with butter paper + CFB box; and T7: plastic vial containing 8-HQS (25 ppm) + wrapping with Brown Paper + CFB box. Among these packaging treatments, T5 resulted in a significantly longer vase life (7.33 days after 3 days of storage under ambient conditions). Postharvest parameters such as the PLW (6.58%), TSS (10.08%), MSI (78.28%), and wilting of the first floret (3.41 days) were found to be greatest in T5. However, in terms of biochemical parameters, T5 was similar to T6 in terms of carbohydrate content (63.49 and 62.08 mg/100 ml, respectively), protein content (15.23 and 15.18%, respectively) and anthocyanin content (6.37 and 6.30 mg/L, respectively).

Abstract Background Breast cancer (BC) is the most common malignancy with very high incidence and relatively high mortality in women. The PIK3CA gene plays a pivotal role in the pathogenicity of breast cancer. Despite this, the mutational status of all exons except exons 9 and 20 still remains unknown. Methods This study uses the whole exome sequencing (WES) based approach to identify somatic PIK3CA mutations in Indian BC cohorts. The resultant hotspot mutations were validated by droplet digital PCR (ddPCR). Further, molecular dynamics (MD) simulation was applied to elucidate the conformational and functional effects of hotspot position on PIK3CA protein. Results In our cohort, PIK3CA showed a 44.4% somatic mutation rate and was among the top mutated genes. The mutations of PIK3CA were confined in Exons 5, 9, 11, 18, and 20, whereas the maximum number of mutations lies within exons 9 and 20. A total of 9 variants were found in our study, of which 2 were novel mutations observed on exons 9 (p.H554L) and 11 (p.S629P). However, H1047R was the hotspot mutation at exon 20 (20%). In tumor tissues, there was a considerable difference between copy number of wild-type and H1047R mutant was detected by ddPCR. Significant structural and conformational changes were observed during MD simulation, induced due to point mutation at H1047R/L position. Conclusions The current study provides a comprehensive view of novel as well as reported single nucleotide variants (SNVs) in PIK3CA gene associated with Indian breast cancer cases. The mutation status of H1047R/L could serve as a prognostic value in terms of selecting targeted therapy in BC.

Despite extensive research on particulate matter (PM) pollution in India’s Indo-Gangetic Plain (IGP), source apportionment remains challenging. This study investigates the effect of particulate matter (PM10)-associated water soluble inorganic ions (WSIIs) on ambient air concentration across the middle IGP from January to December 2018. Moreover, the seasonal fluctuation and chemical characterization of PM10 were assessed for the year 2018. The results revealed a high concentration of PM10 (156 µg/m3), exceeding the WHO and National Ambient Air Quality Standard (NAAQS) limits. The highest PM10 levels were observed during autumn, winter, summer, and the rainy season. The study identified SO42− and NH4+ as the most common WSIIs, constituting 46% and 23% of the total WSIIs. Source apportionment analysis indicated that street dust, biomass burning, and vehicle and industrial emissions together with secondary formation significantly contributed to IGP’s PM pollution. Additionally, the investigation of air mass back trajectory suggests that air quality in IGP is largely influenced by eastern and western Maritime air masses originated from the Arabian Sea, the Bay of Bengal, Gujarat, Afghanistan, Pakistan, and Bangladesh.