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The advent of multidrug resistance among pathogenic bacteria is imperiling the worth of antibiotics, which have previously transformed medical sciences. The crisis of antimicrobial resistance has been ascribed to the misuse of these agents and due to unavailability of newer drugs attributable to exigent regulatory requirements and reduced financial inducements. Comprehensive efforts are needed to minimize the pace of resistance by studying emergent microorganisms, resistance mechanisms, and antimicrobial agents. Multidisciplinary approaches are required across health care settings as well as environment and agriculture sectors. Progressive alternate approaches including probiotics, antibodies, and vaccines have shown promising results in trials that suggest the role of these alternatives as preventive or adjunct therapies in future.

It is evident from previous reports that 5-aminolevulinic acid (ALA), like other known plant growth regulators, is effective in countering the injurious effects of heavy metal-stress in oilseed rape (Brassica napus L.). The present study was carried out to explore the capability of ALA to improve cadmium (Cd2+) tolerance in B. napus through physiological, molecular, and proteomic analytical approaches. Results showed that application of ALA helped the plants to adjust Cd2+-induced metabolic and photosynthetic fluorescence changes in the leaves of B. napus under Cd2+ stress. The data revealed that ALA treatment enhanced the gene expressions of antioxidant enzyme activities substantially and could increase the expression to a certain degree under Cd2+ stress conditions. In the present study, 34 protein spots were identified that differentially regulated due to Cd2+ and/or ALA treatments. Among them, 18 proteins were significantly regulated by ALA, including the proteins associated with stress related, carbohydrate metabolism, catalysis, dehydration of damaged protein, CO2 assimilation/photosynthesis and protein synthesis/regulation. From these 18 ALA-regulated proteins, 12 proteins were significantly down-regulated and 6 proteins were up-regulated. Interestingly, it was observed that ALA-induced the up-regulation of dihydrolipoyl dehydrogenase, light harvesting complex photo-system II subunit 6 and 30S ribosomal proteins in the presence of Cd2+ stress. In addition, it was also observed that ALA-induced the down-regulation in thioredoxin-like protein, 2, 3-bisphosphoglycerate, proteasome and thiamine thiazole synthase proteins under Cd2+ stress. Taken together, the present study sheds light on molecular mechanisms involved in ALA-induced Cd2+ tolerance in B. napus leaves and suggests a more active involvement of ALA in plant physiological processes than previously proposed.

Medical literature does not have clear consensus on inter-rater reliability of PS assessment by different oncology health care professionals (HCPs) although it plays an important role in treatment decision and prognosis for oncology patients. Eastern Cooperative Oncology Group (ECOG) and Karnofsky performance status (KPS) scores are commonly used for this purpose by oncology HCPs around the world. This study was conducted to find variability or similarities in assessment of PS among the different oncology HCPs. A survey based on four hypothetical clinical scenarios was devised and sent to 50 oncology HCPs to assess the PS using ECOG PS tool. No significant variations in PS assessment by oncology HCPs was noted in our study sample.

Floods are among the most devastating and recurring natural hazards and have caused extensive economic losses to human lives and infrastructures around the world. Swat valley in northern Pakistan is prone to frequent floods and was severely affected by the Flood2010 in the recent past. Flood hazard assessment is a non-structural strategy for flood mitigation in addition to the structure measure. In this study, 60 km long reach of the River Swat (Khwazakhela Bridge–Chakdara Bridge) was modeled using the HEC-RAS 2D model and high-resolution 12-m WorldDEM. The model was calibrated and validated for only historical maximum flood event, i.e., Flood2010 using Manning’s ‘n’ values, flood stage at the Chakdara Bridge and satellite imagery-based Flood2010-observed extent. In addition, flood model sensitivity to the DEM was carried out and simulated maximum depth was 12, 13, 14, and 25 m for the 12-m WorldDEM, 30-m SRTM, 30-m ALOS and 30-m ASTER DEMs, respectively. Designed hydrographs were prepared for 2, 5, 10, 25, 50, and 100-year return periods based on the Flood2010-observed hydrograph. Finally, the model was simulated for 2, 5, 10, 25, 50, and 100-year return periods with full momentum equation as the calculation method. Simulated extents based on the 12-m WorldDEM were used for the preparation of flood hazard maps. Landcover exposure to the designed flood events shows that agriculture including orchards is the major potential affected class with affected areas up to 55 Km2. The developed flood hazard maps will enable the policy makers to mainstream flood hazard assessment in the planning and development process for mitigating flood hazard in Swat Valley.

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure.

Dental fluorosis is a long-existing public health issue resulting from inequitable access to potable water. Socially disadvantaged rural communities in fluoride-endemic areas, where a conventional irrigation system is absent and groundwater containing natural fluoride is the predominant source of drinking water, face a significant oral public health threat. This study aimed to determine the association between water fluoride levels and dental fluorosis. A systematic review aligned with PRISMA principles was conducted using the SPIDER search methodology and relevant keywords on many search engines, such as Google Scholar, PubMed, Elsevier, Sage, Web of Science, Cochrane, and Scopus. This review sought to ascertain the PICO model’s application as a search strategy tool. The reviewers gathered and assessed 1164 papers from January 2010 to January 2023. In total, 24 research papers from diverse databases were included. Using the Newcastle–Ottawa Scale, grades resulting from several data screens were evaluated. According to a previous systematic review, there may be publication bias in studies examining the association between fluoride in drinking water and dental fluorosis. The findings of this systematic review indicate that subpar fluoride is detrimental to human health. The author outlines legislative tools and technological advancements that might reduce fluoride levels.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline’s acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time–frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.