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Soil erodibility is challenging for agricultural soils, sustainable farming systems and water-food security in developing countries. Regional scale precise estimation of soil erosion is vital to save fertile soils. For this purpose, geospatial technology is the researchers’ top choice. The Revised Universal Soil Loss Equation (RUSLE) has the potential plausible estimates of soil erosion in the region. The study area comprises parts from two basins (Peshawar and Nizampur) having distinct land surface characteristics. This difference made the applicability of RUSLE model challenging. The influencing factors were combined using weighted sum in GIS. The study area was categorized into five soil erosion zones: very low (0–0.1 t ha−1 year−1), low (0.1–0.50 t ha−1 year−1), moderate (0.50–0.7 t ha−1 year−1) high (0.7–1 t ha−1 year−1), and very high (>1 t ha−1 year−1). The 1,748 km2 (174800 hectares) study region has an annual soil loss of 0.1 to > 1.0 t ha−1 year−1. The overall accuracy of the estimated soil erosion was found to be 90% with Kappa coefficient of 87.25% and receiver operating characteristics area under the curve (ROC-AUC) score of 0.819. This study’s will help to address the climate change-induced challenges of soil erosion in agricultural lands.

Microplastics (MPs) have become ubiquitous environmental pollutants, eliciting concerns about their negative impacts on terrestrial and agroecosystems. However, employing sustainable remediation approaches holds the potential to mitigate these negative impacts and bolster MPs tolerance in both the seeds and vegetative structures of plants. The current investigation explores the impact of arbuscular mycorrhizal fungi (AMF) and synthetic bacterial community (SynCom) as potential alleviators of MP-induced stress. Results exhibited that co-applied SynCom and AMF, resulted in the highest plant biomass, leaf area, pod per plant, and 100-seed weight under control (no MPs) and MPs stress. The combined application of SynCom and AMF enhanced colonization, association, and arbuscule abundance in the non-stressed plants (no MPs) as compared to stressed plants. The sole or co-application of SynCom and AMF enhanced the primary metabolites content and mitigated the MPs-induced reduction in soluble sugars, lipids, protein, and oil contents. Soybean inoculation stimulated the genes expression involved in lipid and protein biosynthesis, while a contradictory drift was noted for genes associated with lipid and protein degradation, underpinning the observed increment in protein and lipid content. Soybean inoculated with AMF exhibited the utmost α-amylase and β-amylase activities, demonstrating enhanced osmolyte (soluble sugar) production, mainly under MPs stress. Remarkably, sole or co-application further reinforces the positive effect of MPs stress on the osmoprotectants and antioxidant levels, for instance, phenol, flavonoid, glycine betaine contents, and glutathione-S-transferase (GST) activities. Subsequent to the stress release, a stress hormones known as abscisic acid (ABA) reduced in the seeds of inoculated soybean while gibberellin (GA), trans-zeatin riboside (ZR), and indole acetic acid (IAA) were increased. Therefore, it is concluded that the combined application of SynCom and AMF protected the soybean plants from MPs-induced oxidative damage by upregulating osmoprotectants and antioxidant levels. This study provides insights into the potential of sustainable remediation approaches in alleviating the complex impact of MPs on soybean, contributing to future strategies for environmental sustainability. Future studies could explore the optimization of SynCom and AMF formulations for different crops and environmental conditions, potentially leading to practical applications in sustainable agriculture and pollution management.

The conventional model of energy production and consumption has come under severe scrutiny. Concerns related to climate change, increased energy needs and issues surrounding conventional sources of energy generation have highlighted a need to adopt sustainable means of energy generation. Renewable energy sources are believed to have the potential to meet rising energy needs in a sustainable and environmentally friendly manner. Solar energy is particularly interesting in this respect as it has the potential to be used at commercial as well as household level; however, to this end, its contribution to global energy supply has remained limited. A number of studies have explored factors influencing the adoption of solar photovoltaics (PV) at the household level and proposed measures to foster its development. This paper aims to systematically review and analyse the state of solar PV adoption by exploring “What are the key factors influencing the adoption of solar PV at household level?”. From 146 studies published in peer-reviewed scientific journals, the study identifies 127 unique factors influencing adoption behaviour, and groups these into eight categories, namely economic, environmental, social, personal, demographic, technical, regulatory and market-related factors. Based on the findings, this paper proposes guidelines for practitioners, identifies knowledge gaps and suggests directions for future research to strengthen the scholarship in this domain.

Metabolomics has emerged as a valuable tool in drug discovery and development, providing new insights into the mechanisms of action and toxicity of potential therapeutic agents. Metabolomics focuses on the comprehensive analysis of primary as well as secondary metabolites, within biological systems. Metabolomics provides a comprehensive understanding of the metabolic changes that occur within microbial pathogens when exposed to therapeutic agents, thus allowing for the identification of unique metabolic targets that can be exploited for therapeutic intervention. This approach can also uncover key metabolic pathways essential for survival, which can serve as potential targets for novel antibiotics. By analyzing the metabolites produced by diverse microbial communities, metabolomics can guide the discovery of previously unexplored sources of antibiotics. This review explores some examples that enable medicinal chemists to optimize drug structure, enhancing efficacy and minimizing toxicity via metabolomic approaches.

Abstract Key message: A harmonized, comprehensive meteorological time series for 78 German intensive forest monitoring plots (Level II) has been made available from 1961 to 2019. The used hybrid spatial interpolation routine using simple linear regression and inverse distance weighting allows for gap filling of missing data and also for extrapolation outside measurement period to analyze long-term effects of climate on forest ecosystems. The dataset is available at https://www.openagrar.de/receive/openagrar_mods_00079174 . The associated metadata are available at: https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/433a028f-dfc8-4a7c-82af-b8d7efafd724 .

Nanostructures and quantum dots have substantial effects on enhancing photovoltaic energy conversion efficiency, as evidenced in this comprehensive study. Materials that are nanostructured and nanosized particles are commonly used to address the urgent issues related to energy conversion. The use of nanostructured substances to address issues with energy and natural resources has garnered a lot of interest lately. Directional nanostructures in particular show promise for the conversion, collection, and storage of energy. Due to their unique properties, such as electrical conductivity, mechanical energy, and photoluminescence, quantum dots made from carbon (CQDs) and graphene quantum dots (GQDs) have been integrated into hybrid photovoltaic-thermoelectric systems (PV-TE). It evaluates the effects of nanostructures on solar energy technologies, in particular how they can improve power conversion and light absorption in solar cells. Optical light detectors, which transform photonic energy into signals that are electrical, are among the many optoelectronic uses of CQDs that have drawn attention because they are essential components of contemporary imaging and communication systems, such as visible light cameras, machine vision, medical X-ray and near-infrared image processing, and visible light detection devices. Besides supercapacitors, the study investigates how nanostructures could play a crucial role in contributing to addressing the global energy crisis sustainably, by working as photocatalysts for hydrogen synthesis and supercapacitors.

Dissolved arsenic (As) may appear at the tile line level through preferential flow (PF), leading to contamination of shallow water bodies. Limited work on the movement of As forms in field soils urged the need for more research. The PF of arsenate (As(V)) and arsenite (As(III)) compared to chloride (Cl) at constant flow under saturated (10 mm), slightly unsaturated (−10 mm), and unsaturated (−40 mm) pressure heads was evaluated in replicated large field columns varying in subsoil structure. A solute containing As(V), As(III), and Cl was pulsed until the Cl concentration ratio in the drainage samples reached maxima and flushed with solute-free irrigation. HYDRUS-1D software version 4.15 was utilized to fit the breakthroughs of As(V) and As(III) in the dual-porosity physical non-equilibrium model (DP-PNE). The Langmuir equation was used to fit the As(V) and As(III) sorption isotherms, and blue dye staining was used for the marking of flow paths. Dye leaching was observed up to 50 cm or deeper in the soils. Under saturated conditions (+10 mm), Kotli, Guliana, and Mansehra soils showed chemical non-equilibrium (CNE) for As(V) and As(III); however, the extent of CNE was less under unsaturated conditions (−40 mm). These results implied that these well-structured soils had enough large macropores, which cause PF, but at the same time, they were also small enough to retain water and leach solutes under unsaturated conditions (−40 mm). It is concluded that irrigation of contaminated water or dumping solid waste on well-aggregated soil may exhibit PF of dissolved As during and after rains, and additionally As(III), which is more toxic and mobile under reduced conditions, has equal or greater potential for movement.

Abstract We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of $${\mathcal {O}}(1000)$$ O ( 1000 ) particles in high-luminosity conditions with 200 pileup.

Abstract Background Attraction and oviposition preference of the green lacewing, Chrysoperla carnea (Steph.) (Neuroptera: Chrysopidae), in response to prey availability in an ovipositional site was studied. The study aimed to investigate whether an adult attraction of C. carnea to oviposition on the substrate was affected by aphid juice (AJ) of fresh brassica aphid, Brevicoryne brassica. Result In laboratory bioassay, the number of eggs laid by female was significantly higher on AJ-treated area of substrate than control, indicating that oviposition was influenced by the AJ application. Attraction period of AJ lasted for 4 days. In Y-maze olfactometer studies, video tracking software ANY-maze® indicated that C. carnea spent significant more time in the novel arm provided with AJ, showing an attraction. In the greenhouse study, AJ spray attracted a significantly higher number of male and female and considerably increased the number of eggs laid on brassica plant. Conclusion Fresh AJ may be a potential attractant for raising population of this predatory species at a particular location without being involved in rearing and augmentation process. This finding is of special interest and may be of benefit in biological control. As it is likely that the AJ spray could be used to enhance C. carnea population attraction to the desire field.

Objective: To evaluate the recurrence following open surgical excision and Seton placement in treating symptomatic wrist ganglions in Military Hospitals in a six-month follow-up study. Study Design: Quasi-experimental study Place and Duration of Study: Department of General Surgery, Combined Military Hospital, Bahawalpur Pakistan, Combined Military Hospital, Dera Nawab Sahib Pakistan and Pakistan Airforce Force Hospital, Shorkot Pakistan, from Jun 2015 to Dec 2020. Methodology: The patients were divided into two groups after informed consent. A consultant general surgeon carried out every procedure. The findings were recorded on a uniform proforma, and recurrence was recorded at ten days, six weeks and six months for both groups. Results: The mean age of the patients was 29.45±8.13 years in Group-A (Seton-Group) and 28.56±8.32 years in Group-B (OpenSurgery). There was no difference in the recurrence rates between the Seton-Group (n=7, 4.35%) and Open Surgery-Group (n=2, 4.65%, p=0.931). Conclusion: Seton insertion for treating Ganglions of the wrist is a simple, economical and cosmetic procedure which can be done in the outpatient department. It is a safe alternative to open resection for successful treatment of the wrist Ganglion. Keywords: Ganglion, Open surgery, Recurrence, Seton, Wrist.

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Antibiotic-resistant bacteria are becoming increasingly common, leading to a global health crisis. The effects of abusing antibiotics not only increase pathogenic resistance but also cause various diseases and syndromes. Gut microbiota contains many beneficial roles for health, while antibiotics kill both pathogens and gut microbiota which is considered one of the major side effects of antibiotics. In fact, new antibiotic compounds are needed in this urgent scenario; phytoremediation is the oldest but most effective method, and research on the antibacterial properties of several types of medicinal plants has already been conducted. Tea and agarwood plants are well known for their economic contribution in both beverage and cosmetic production, as well as for their medicinal value. In this study, tea and agarwood leaf extracts were analyzed for their antimicrobial activity against both pathogenic and beneficial bacteria. Fresh tea (Camellia sinensis) leaves were collected in three varieties, namely, BT-6 from Sylhet, BT-7 from Moulvibazar, and BT-8 from Habiganj; also, green tea (nonfermented tea), black tea (fully fermented tea), and agarwood (Aquilaria malaccensis) were collected from Sylhet region of Bangladesh. Unlike commercial antibiotics, which have side effects on probiotics (beneficiary bacteria), leaf extract activities were analyzed to check if they had positive effects on probiotics that can be found in the gastrointestinal tract as well as dairy products. Potential beneficiary bacteria, Lysinibacillus macroides strain SRU-001 (NCBI accession no. MW665108), and pathogenic bacteria, Aeromonas caviae strain YPLS-62 (NCBI accession no. MW666783), were isolated from the small intestine of poultry and curd, respectively. Tea and agarwood leaves (5 g powder/80 mL methanol) with solvents were kept for seven days at room temperature, and extracts were applied for antimicrobial assays by the disc diffusion assay against the isolated bacteria. 50 µL of each leaf extract was examined against 50 µL of each bacterial culture, where gentamicin was a control. After 24 hours of incubation, tea and agarwood leaf extracts showed an 11–15 mm zone of inhibition against pathogenic A. caviae, while only BT-8 showed 7 mm (disc diameter 6 mm) against probiotic L. macroides. However, compared to leaf extracts, gentamicin showed a 27 mm zone of inhibition against both L. macroides strain SRU-001 and A. caviae strain YPLS-62 bacteria. This research clearly indicates that gentamicin kills both pathogenic and beneficiary bacteria, while leaf extracts from tea and agarwood plants contain antimicrobial activity against only pathogenic A. caviae but no effects on probiotic L. macroides. This outcome indicates not only the potential therapeutic values of tea and agarwood leaves as antibiotics over commercial antibiotics but also the chance of having pathogens in curd and potential beneficial bacteria from the poultry small intestine.

Costing method plays a vital role in assuring the feasibility of any project. When we talk about energy systems, many factors influence the decision making process, which are directly or indirectly associated with socio-enviro-economic benefits. While talking about the associated benefits, appropriate costing approach can significantly absorb the concerned factors. In this context, economics is one of the relevant factors which comes to mind while choosing a right working fluid for thermodynamic cycles if consider this as a test case. Although a wide variety of relevant approaches is available. However, the literature points out the shortcomings, validity and limitations of available costing methods. Out of them, levelized cost of energy (LCOE) is considered a well-known costing method, which has received many criticisms in this regard. On the other hand, this work proposes the concept of employing benefit-cost ratio (BCR) analysis as an alternative costing tool to select suitable working fluids for thermodynamic cycles. This work compares and evaluates the effectiveness, openness and understanding of both methods. For in-depth analysis, a heat pump system as a case study is adopted to select the suitable working fluid. The study is further supported by considering multiple scenarios. Results of this study have shown very interesting outcomes, where one method lagging the other and vice versa. The results have shown the impact of thermophysical properties of different working fluids and their influence on overall economics of the system.

Background/Aim: Myths related to oral diseases and oral health-related practices are very common among the population. Inadequate knowledge along with the persisting myths regarding the etiology, course and outcome of oral diseases makes it difficult to initiate health behavioural changes. Present study scrutinizes various myths and misconceptions regarding oral health among the outpatients. The aim of this study was to assess myths and misconceptions about oral health and imparting education regarding the same.

The present research was carried out to examine the health and environmental impacts associated with coal mining activities in the Makarwal coal mining area in Pakistan. To achieve the objectives, 41 soil samples were collected from the coalmine-affected areas and analyzed for major and toxic trace elements (TTEs) using atomic absorption spectroscopy. Most of the soil samples have extremely high concentrations of toxic metals such as Ni, Cd, Cr, Cu, Zn, and Pb. The positive correlations and high concentration of trace elements in the Makarwal coalmine-affected region suggest an analogous origin of soil contamination. The factor analysis (FA) showed two components, i.e., F1 (53.4%) and F (74.21%), of total variability for soil. The F2 was loaded with Pb, Zn, Ni, and Cr, which was similar to cluster 2, while the F1 was loaded with Cu and Cr, having a similar pattern to cluster 1. This proves that the contamination in the surrounding area is mostly associated with geological ore strata existing in the Makarwal coalfield. Based on geoaccumulation (Igeo), the elemental concentration in the studied soil sample could be categorized as follows: (1) Pb, Ni, and Zn are moderate to severely polluted and (2) Cr and Cu are in the moderately polluted category. The ecological risk indices (ERIs) of the single trace element contamination index highlighted that Cd, Pb, Ni, and Cr pose a high risk to humans and the ecosystem. Based on different statistical tools performed for the source identification and distribution of metal contamination, it seems that the exposed sedimentary rocks, including limestone, dolomite, sandstone, and coal, are responsible for the toxic metal contamination in the study area.

Natural gas releases 45–50% less CO2 emissions as compared with coal and the liquefaction process is employed for storage and transport of liquefied natural gas that is an energy-intensive process. Numerous scientific efforts are being directed to reduce the energy consumption in the natural gas liquefaction process and to increase the process efficiency. Thus, this study proposes a uniquely modified and energetically enhanced small-scale natural gas liquefaction process by nitrogen single expander technology with CO2 precooling. The novelty of the designed liquefaction process is that the designed configuration does not only reduces the energy consumption of the liquefaction process but also improves the process efficiency. The proposed system is simulated and optimized using ASPEN HYSYS by different design key parameters, and energy consumption has been considered as a key function for the optimization of this cycle. Detailed characterization shows the energy consumption of 9.216 kWh·kmol−1 at a liquefaction rate of 0.77. Furthermore, the effect of pressure output of the valve on unit energy consumption and liquefaction rate is also observed and discussed. It has been observed that by decreasing the valve output pressure, the liquefaction rate decreases and energy consumption increases. Besides, the versatility of this cycle under various pressures, temperatures and composition of methane and other components in the feed gas are considered and effects of these parameters variation on system performance have been observed. The exergy destruction of each equipment is evaluated and investigated in detail for the optimization of results. Results are compared and it shows a 6.90% and 9.0% decrease of unit energy consumption and exergy as compared to referenced base cycle. The performance of numerous relationships is derived from the proposed study and compared with simulated referenced published data. By using energy consumption and liquefaction rate of compared LNG cycles, the maximum error is +0.93 and the minimum error is −0.12. Due to safe and simple operation, compact device, proposed liquefaction process shows a suitable application for development of small-scale, especially for offshore reserves.

The structure-properties relationships of novel cyclic olefinic copolymer (COC)/poly(L-lactic acid) (PLLA) polymer blends are investigated by varying the PLLA content from 5–20 wt % in COC matrix, forming an immiscible blends at all concentrations. Despite immiscibility, COC favors the formation of α- crystal form of PLLA in the blends, contrary to the α’- crystal form in neat PLLA due to non-covalent interactions of the chain moieties as evident from x-ray diffraction, Fourier transform infrared spectrometer and differential scanning calorimetry. The COC/PLLA blends revealed tunable superior mechanical and barrier properties as compared to the neat counterparts. The tensile modulus, strength and toughness of COC/PLLA blends at 15 wt% are significantly enhanced ∼ 89 %, 103 % and one order of magnitude, respectively. Such reinforcement and super toughening mechanisms are pertaining to the effective dissipation of loads across the interfaces, enabling them to delay the crack growth by avoiding stress concentration sites. Likewise, the water vapor permeation (WVP) of the blends is reduced ∼ 4 times than neat COC.

A combined severe heatwave and drought, starting in 2018 and lasting for several months, restarted the discussion on the resistance of European beech to climatic changes, with severe growth reductions, early leaf senescence, leaf browning, and diebacks reported across Central Europe. These responses may result in long-term impacts such as reduced vitality of beech, especially under potential future drought periods. While the 2003 drought caused severe crown damage and defoliation and a loss in vitality, resulting in insect and fungal infestations and subsequent dieback, the drought in 2018 was even more severe in terms of geographical scale, duration, and intensity with reports of complete diebacks and severe mortality across Central Europe. These impacts were exacerbated in some regions by the consecutive drought in 2019 and secondary attacks from pathogens, as well as a further loss in vitality. Such enhanced drought exposure of beech trees could push them beyond their hydraulic safety margins. Moreover, growth legacy effects due to past droughts may lead to lower recovery over time, potentially leading to subsequent tree death. In order to better predict the future of beech growth and vitality in Central Europe, both short- and long-term legacy effects of defoliation and their influence on post-drought growth should be explored, and adaptive forest management strategies evaluated. Moreover, synergistic or additive interactions of legacy effects with drought, as well as with biotic disturbances, require further investigation. Long-term forest monitoring data facilitates investigations of drought responses of beech.

The structural, electrical, and magneto-elastic properties of lanthanide base nitride (Ln = Dy-Lu) anti-perovskites were investigated using density functional theory (DFT). The reported structural outcomes are consistent with the experiment and decrease from Dy to Lu due to the decrease ofatomic radii of Ln atoms. According to the electronic band profile, the metallic characteristics of these compounds are due to the crossing over of Ln-f states at the Fermi level and are also supported by electrical resistivity. The resistivity of these compounds at room temperature demonstrates that they are good conductors. Their mechanical stability, anisotropic, load-bearing, and malleable nature are demonstrated by their elastic properties. Due to their metallic and load-bearing nature, in addition to their ductility, these materials are suitable as active biomaterials, especially when significant acting loads are anticipated, such as those experienced by such heavily loaded implants as hip and knee endo-prostheses, plates, screws, nails, dental implants, etc. In thesecases, appropriate bending fatigue strength is required in structural materials for skeletal reconstruction. Magnetic properties show that all compounds are G-type anti-ferromagnetic, with the Neel temperatures ranging from 24 to 48 K, except Lu3Nin, which is non-magnetic. Due to their anti-ferromagnetic structure, magnetic probes cannot read data contained in anti-ferromagnetic moments, therefore, data will be unchanged by disrupted magnetic field. As a result, these compounds can be the best candidates for magnetic cloaking devices.

The issue of climate change, greenhouse gas emissions, global warming, and their effect on nature and the ecosystem has raised serious concerns. The desire to sustain economic growth and development while keeping a check on the environmental footprints is one of the leading challenges the contemporary world is currently facing. To ensure sustained growth, there is a need for technologies and solutions that has the potential to meet industrial needs without compromising the environment. Cleantech offers a possibility to address these needs in a sustainable and environmentally friendly manner. Cleantech, being an umbrella term, is often confused and misunderstood, in terms of its definition and scope. This study seeks to explore what cleantech actually is, how this sector came into prominence, what are the driving factors behind its surge, and what kind of socio-economic, technical, and regulatory prerequisites are necessary for the advancement of this sector.

Abstract We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.

Recently, dual-mode imaging systems merging magnetic resonance imaging (MRI) and ultrasound (US) have been developed. Designing a dual-mode contrast agent is complex due to different mechanisms of enhancement. Herein, we describe novel phase change nanodroplets (PCNDs) with perfluoropentane encapsulated in a pre-polyglycerol sebacate (pre-PGS) shell loaded with polyethylene glycol (PEG)-coated iron oxide nanoparticles as having a dual-mode contrast agent effect. Iron oxide nanoparticles were prepared via the chemical co-precipitation method and PCNDs were prepared via the solvent displacement technique. PCNDs showed excellent enhancement in the in vitro US much more than Sonovue® microbubbles. Furthermore, they caused a susceptibility effect resulting in a reduction of signal intensity on MRI. An increase in the concentration of nanoparticles caused an increase in the MR contrast effect but a reduction in US intensity. The concentration of nanoparticles in a shell of PCNDs was optimized to obtain a dual-mode contrast effect. Biocompatibility, hemocompatibility, and immunogenicity assays showed that PCNDs were safe and non-immunogenic. Another finding was the dual-mode potential of unloaded PCNDs as T1 MR and US contrast agents. Results suggest the excellent potential of these PCNDs for use as dual-mode contrast agents for both MRI and US.

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

Virtual reality technologies, including head-mounted displays (HMD), can provide benefits to psychological research by combining high degrees of experimental control with improved ecological validity. This is due to the strong feeling of being in the displayed environment (presence) experienced by VR users. As of yet, it is not fully explored how using HMDs impacts basic perceptual tasks, such as object perception. In traditional display setups, the congruency between background environment and object category has been shown to impact response times in object perception tasks. In this study, we investigated whether this well-established effect is comparable when using desktop and HMD devices. In the study, 21 participants used both desktop and HMD setups to perform an object identification task and, subsequently, their subjective presence while experiencing two-distinct virtual environments (a beach and a home environment) was evaluated. Participants were quicker to identify objects in the HMD condition, independent of object-environment congruency, while congruency effects were not impacted. Furthermore, participants reported significantly higher presence in the HMD condition.

Abstract The Clustered Regularly Interspaced Short Palindromic Repeats–Cas-9 (CRISPR-Cas9) system has been a revolutionising tool in the field of molecular genetics, which provides a versatile range of editing potentials. Researchers can produce breaks or alter genomes with ease using the system. Cancer is one of the multi-gene diseases whose genes need to be studied in detail. The CRISPR-Cas9 technology may also provide a promising potential in the field of cancer genetics. The current narrative review comprised 50 research articles which were keenly analysed and the applications and outcomes of CRISPR-Cas9 system in cancer genetics were comprehensively and critically discussed. It was concluded that application of the system had great potential to help understand cancer biology of various types and could be used for its genetic modelling. However, much work is still needed to be done to apply the technology for understanding...Continuous...

Background During a preliminary study, effects of 0, 20, 40, and 60 mM NaCl salinity were assessed on germination rate in relation to electrolyte leakage (EL) in sweet pepper. Results explored significant rises in ethylene evolution from seeds having more EL. It was, therefore, hypothesized that excessive ethylene biosynthesis in plants due to salinity stress might be a root cause of low crop productivity. As salicylic acid is one of the potent ethylene inhibitors, thus SA was used to combat effects of ethylene produced under salinity stress of 60 mM NaCl on different physiological and morphological characteristics of sweet pepper. Methodology The effect of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 and 0.6 mM SA was evaluated on seed germination, growth and yield of sweet pepper cv. Yolo wonder at salinity stress on 60 mM NaCl. Seeds were primed with SA concentrations and incubated till 312 h in an incubator to study germination. Same SA concentrations were sprayed on foliage of plants grown in saline soil (60 mM NaCl). Results Seeds primed by 0.2 to 0.3 mM SA improved germination rate by 33% due to suppression of ethylene from 3.19 (control) to 2.23–2.70 mg plate−1. Electrolyte leakage reduced to 20.8–21.3% in seeds treated by 0.2–0.3 mM SA compared to 39.9% in untreated seeds. Results also explored that seed priming by 0.3 mM improved TSS, SOD and chlorophyll contents from 13.7 to 15.0 mg g−1 FW, 4.64 to 5.38 activity h−1 100 mg−1 and 89 to 102 ug g−1 compared to untreated seeds, respectively. Results also explore that SA up to 0.2 mM SA applied on plant foliage improved LAI (5–13%), photosynthesis (4–27%), WUE (11–57%), dry weight (5–20%), SOD activity (4–20%) and finally fruit yield (4–20%) compared to untreated plants by ameliorating effect of 60 mM NaCl. Foliar application of SA also caused significant increase in nutrient use efficiency due to significant variations in POD and SOD activities. Conclusion Salicylic acid suppressed ethylene evolution from germinating seeds up to 30% under stress of 60 mM NaCl due to elevated levels of TSS and SOD activity. Foliar application of SA upgraded SOD by lowering POD activity to improve NUE particularly K use efficiency at salinity stress of 60 mM NaCl. Application of 0.2 and 0.3 mM SA emerged as the most effective concentrations of SA for mitigating 60 mM NaCl stress on different physiological and morphological characteristics of sweet pepper.

Multidrug-resistant Vibrio parahaemolyticus has become a significant public health concern. The development of effective drugs and vaccines against Vibrio parahaemolyticus is the current research priority. Thus, we aimed to find out effective drug and vaccine targets using a comprehensive genome-based analysis. A total of 4822 proteins were screened from V. parahaemolyticus proteome. Among 16 novel cytoplasmic proteins, 'VIBPA Type II secretion system protein L' and 'VIBPA Putative fimbrial protein Z' were subjected to molecular docking with 350 human metabolites, which revealed that Eliglustat, Simvastatin and Hydroxocobalamin were the top drug molecules considering free binding energy. On the contrary, 'Sensor histidine protein kinase UhpB' and 'Flagellar hook-associated protein of 25 novel membrane proteins were subjected to T-cell and B-cell epitope prediction, antigenicity testing, transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis and molecular docking analysis to generate the most immunogenic epitopes. Three subunit vaccines were constructed by the combination of highly antigenic epitopes along with suitable adjuvant, PADRE sequence and linkers. The designed vaccine constructs (V1, V2, V3) were analyzed by their physiochemical properties and molecular docking with MHC molecules- results suggested that the V1 is superior. Besides, the binding affinity of human TLR-1/2 heterodimer and construct V1 could be biologically significant in the development of the vaccine repertoire. The vaccine-receptor complex exhibited deformability at a minimum level that also strengthened our prediction. The optimized codons of the designed construct was cloned into pET28a(+) vector of E. coli strain K12. However, the predicted drug molecules and vaccine constructs could be further studied using model animals to combat V. parahaemolyticus associated infections.

Sulfur deficiency arising due to intensive cultivation, use of sulfur free fertilizers and reduction in atmospheric sulfur depositions has become a major issue limiting crop production in many parts of the world. Elemental sulfur could be a good source of available S, but its slow oxidation is a problem for its efficient use as a sulfur fertilizer. Main objective of the study was to assess the effect of organic amendments (OA) and elemental sulfur (ES) on microbial activities, sulfur oxidation and availability in soil. A laboratory incubation experiment was carried out for a 56 days period using two sulfur deficient alkaline soils. Organic amendments (OA), i.e., farmyard manure (FYM), poultry litter (PL) and sugarcane filter cake (SF), were applied (1% w/w) with or without elemental sulfur (ES) at 50 mg kg−1. Application of ES alone or in combination with OA significantly increasedCO2-C evolution, microbial biomass, and enzyme activities in the soils, except dehydrogenase activity (DHA) which was not affected by ES application. Combined application of OA and ES had a more pronounced effect on microbial parameters compared to ES or OA applied alone. Ratios of dehydrogenase activity-to-microbial biomass C and arylsulfatase activity-to-microbial biomass C were high in ES+FYM and ES+SF treatments, respectively. Elemental sulfur got sufficiently oxidized resulting in significant improvement in plant available S. Relatively more ES was distributed into C-bonded-S than ester bonded-S. Increase in sulfur availability in ES+OA amended soils was the combined function of sulfur oxidation and mineralization processes through improved microbial activity.

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

The integration of commercial onshore large-scale wind farms into a national grid comes with several technical issues that predominately ensure power quality in accordance with respective grid codes. The resulting impacts are complemented with the absorption of larger amounts of reactive power by wind generators. In addition, seasonal variations and inter-farm wake effects further deteriorate the overall system performance and restrict the optimal use of available wind resources. This paper presented an assessment framework to address the power quality issues that have arisen after integrating large-scale wind farms into weak transmission grids, especially considering inter-farm wake effect, seasonal variations, reactive power depletion, and compensation with a variety of voltage-ampere reactive (Var) devices. Herein, we also proposed a recovery of significant active power deficits caused by the wake effect via increasing hub height of wind turbines. For large-scale wind energy penetration, a real case study was considered for three wind farms with a cumulative capacity of 154.4 MW integrated at a Nooriabad Grid in Pakistan to analyze their overall impacts. An actual test system was modeled in MATLAB Simulink for a composite analysis. Simulations were performed for various scenarios to consider wind intermittency, seasonal variations across four seasons, and wake effect. The capacitor banks and various flexible alternating current transmission systems (FACTS) devices were employed for a comparative analysis with and without considering the inter-farm wake effect. The power system parameters along with active and reactive power deficits were considered for comprehensive analysis. Unified power flow controller (UPFC) was found to be the best compensation device through comparative analysis, as it maintained voltage at nearly 1.002 pu, suppressed frequency transient in a range of 49.88–50.17 Hz, and avoided any resonance while maintaining power factors in an allowable range. Moreover, it also enhanced the power handling capability of the power system. The 20 m increase in hub height assisted the recovery of the active power deficit to 48%, which thus minimized the influence of the wake effect.

Frequency of drought years is expected to increase through climate warming. Mixed stands have often shown to be more productive than monospecific stands in terms of yield and of resistance against windthrows and bark beetle attacks. Mixture of beech and spruce is of particular interest. However, little is known about its growth reaction to drought. Therefore, we investigated the drought reaction of beech and spruce in mixed vs. monospecific stands along an ecological gradient. In particular, we sought evidence for mixture-related resilience on the individual tree level. Therefore, we quantified the response of tree ring width to drought. Moreover, we attempted to explain the relevance of individual tree response on the stand level by quantifying the stand level loss of volume growth after drought. At the individual tree level, beech was found to be more resilient and resistant in pure vs. mixed stands. Spruce, in contrast, was favored by mixture, and this was especially evident on drier sites. Along the gradient, growth losses at stand level increased in both mixed and pure stands in 2015, with growth gains on the drier sites observed in the same drought year, in accordance with the Stress Gradient Hypothesis. However, the stand level difference of growth loss between mixed and pure stands was not statistically significant. Mitigating mixture effects on the level of the individual tree thus did not become evident on the level of the whole stand.

This paper reports the rheological behavior of swelled and mineralized hydrogel prepared using polyvinylpyrrolidone (PVP) and carboxymethylcellulose (CMC) hydrogel as base polymer. Herein, the bio-mineral calcium carbonate (CaCO3) was incorporated into the hydrogel using simple liquid diffusion method. The morphology of the swelled and mineralized hydrogel was analyzed through scanning electron microscopy. Further, the normalized time of absorptivity was identified from the time dependent absorptivity behavior of calcite and water filled PVP-CMC hydrogel. The effect of the biomineral (CaCO3) and water on the dynamic viscoelastic properties, after penetrating inside the hydrogel matrix has been evaluated. The frequency sweep at 1 and 10 % strain and also strain sweep measurement were performed to determine the frequency and strain dependent viscoelastic moduli G’ and G” of both swelled and mineralized hydrogel. At higher strain the both moduli showed significant change over wide range of angular frequency region and the nature of mineralized polymer composites (MPC) turned from elastic to viscous. Based on the observed basic properties, MPC (calcite based polymer composites) can be recommended for the treatment of adyanamic bone disorder and water swelled hydrogel can be acclaimed as a scaffold for burned wound dressing.

Understanding of soil potassium (K) dynamics is essential for sustainable crop production. Bioavailability of K depends on forms and distribution within the soil profile. The objectives of this research were to determine which soil K forms control the maize (Zea mays) K contents and compare the extracting capability of sodium tetraphenylborate (NaTPB) with ammonium acetate (NH4OAc) method. Nine soils representing three different parent materials, i.e. loess, sandstone and shale were sampled at three surface genetic horizons. Within each parent material, three soils at varying level of development were selected. Besides basic soil parameters, K was fractioned into water soluble K, exchangeable K, non-exchangeable K, and NaTPB-extracted K. The maize was sown in pots having 2 kg soil from each genetic horizon. Crop was harvested at seven weeks and plant was analysed for K contents. Results show that NaTPB-extracted K gave best correlation as compared to NH4OAc method. This conveys that a non-exchangeable K portion that becomes available to plants can be better estimated by NaTPB method than NH4OAc extraction.

Global warming is an increase in average global temperature of the earth which lead to climate change. Heterogeneity in the earth-atmosphere system becomes difficult to capture at low resolution (1°x1°) by satellite. Such features may be captured by using high resolution model such as regional climate model (0.5°x 0.5°). This type of study is quite important for a monsoon dominated country like India where Indo-Gangetic Plains (IGP) faces highest heterogeneity due to its geographic location. Present study compares high resolution model features with satellite data over IGP for monsoon season during a normal rainfall year 2010 to understand the actual performance of model. Almost whole IGP simulates relative humidity (RH) with wide range (~50-100%), whereas satellite shows it with narrow range (~60-80%) during September, 2010. Thus model is able to pick the features which were missed by satellite. Hence further model simulation extends over India and adjoining oceanic regions which simulates data of southwest monsoon with high (~70-100%) RH, high (~0.4-0.7) cloud fraction (CF) and low (~80-200 W/m2) outgoing longwave radiation (OLR) over Arabian Sea during June, 2010. Such type of study can be useful to understand heterogeneity at regional scale with the help of high resolution model generated data.

Abstract Background Prostate cancer (PCa) cells preferentially metastasize to bone at least in part by acquiring osteomimetic properties. Runx2, an osteoblast master transcription factor, is aberrantly expressed in PCa cells, and promotes their metastatic phenotype. The transcriptional programs regulated by Runx2 have been extensively studied during osteoblastogenesis, where it activates or represses target genes in a context-dependent manner. However, little is known about the gene regulatory networks influenced by Runx2 in PCa cells. We therefore investigated genome wide mRNA expression changes in PCa cells in response to Runx2. Results We engineered a C4-2B PCa sub-line called C4-2B/Rx2dox, in which Doxycycline (Dox) treatment stimulates Runx2 expression from very low to levels observed in other PCa cells. Transcriptome profiling using whole genome expression array followed by in silico analysis indicated that Runx2 upregulated a multitude of genes with prominent cancer associated functions. They included secreted factors (CSF2, SDF-1), proteolytic enzymes (MMP9, CST7), cytoskeleton modulators (SDC2, Twinfilin, SH3PXD2A), intracellular signaling molecules (DUSP1, SPHK1, RASD1) and transcription factors (Sox9, SNAI2, SMAD3) functioning in epithelium to mesenchyme transition (EMT), tissue invasion, as well as homing and attachment to bone. Consistent with the gene expression data, induction of Runx2 in C4-2B cells enhanced their invasiveness. It also promoted cellular quiescence by blocking the G1/S phase transition during cell cycle progression. Furthermore, the cell cycle block was reversed as Runx2 levels declined after Dox withdrawal. Conclusions The effects of Runx2 in C4-2B/Rx2dox cells, as well as similar observations made by employing LNCaP, 22RV1 and PC3 cells, highlight multiple mechanisms by which Runx2 promotes the metastatic phenotype of PCa cells, including tissue invasion, homing to bone and induction of high bone turnover. Runx2 is therefore an attractive target for the development of novel diagnostic, prognostic and therapeutic approaches to PCa management. Targeting Runx2 may prove more effective than focusing on its individual downstream genes and pathways.

Randomized clinical trials are the gold standard for establishing the efficacy and safety of cardiovascular therapies. However, current pivotal trials are expensive, lengthy, and insufficiently diverse. Emerging artificial intelligence (AI) technologies can potentially automate and streamline clinical trial operations. This review describes opportunities to integrate AI throughout a trial's life cycle, including designing the trial, identifying eligible patients, obtaining informed consent, ascertaining physiological and clinical event outcomes, interpreting imaging, and analyzing or disseminating the results. Nevertheless, AI poses risks, including generating inaccurate results, amplifying biases against underrepresented groups, and violating patient privacy. Medical journals and regulators are developing new frameworks to evaluate AI research tools and the data they generate. Given the high-stakes role of randomized trials in medical decision making, AI must be integrated carefully and transparently to protect the validity of trial results., Competing Interests: Funding Support and Author Disclosures Dr Cunningham has received support from the KL2/Harvard Catalyst Medical Research Investigator Training program and the American Heart Association (23CDA1052151); and has received consulting fees from Roche Diagnosis, Edgewise Therapeutics, KCK, and Occlutech. Dr Abraham has served as a consultant to Boehringer Ingelheim, CVRx, Impulse Dynamics, Sensible Medical, Vectorious, V-Wave, and Zoll Respicardia. Dr Bhatt has received research grant support to his institution from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute (NHLBI) and National Institute on Aging, the American College of Cardiology Foundation, and the Centers for Disease Control and Prevention (CDC); and has received consulting fees from the Kinetix Group, Merck, Sanofi Pasteur, and Novo Nordisk. Dr Dunn has served as a scientific advisor to Veri. Dr Felker has received research grants from NIH, Bayer, Bristol Myers Squibb, Novartis, Daxor, Merck, Cytokinetics, and CSL-Behring; has acted as a consultant to Novartis, Bristol Myers Squibb, Cytokinetics, Innolife, Boehringer Ingelheim, Abbott, Sanofi, Regeneron, Myovant, Sequana, Windtree Therapeutics, and Whiteswell; and has served on clinical endpoint committees or data safety monitoring boards for Merck, Medtronic, EBR Systems, Rocket Pharma, V-Wave, and LivaNova. Dr Jain has received consulting fees from Bristol Myers Squibb, ARTIS Ventures, and Broadview Ventures outside of the submitted work. Dr Lindsell has received grants and contracts from the NIH, the U.S. Department of Defense, CDC, Biomeme, Novartis, bioMérieux, AstraZeneca, AbbVie, Entegrion, and Endpoint Health, all outside of the submitted work; has obtained patents for risk stratification in sepsis and septic shock issued to Cincinnati Children’s Hospital Medical Center; has served on data safety monitoring boards unrelated to the current work; has held stock options in Bioscape Digital unrelated to the current work; and has served as Editor-in-Chief of the Journal of Clinical and Translational Science. Mr Mace is an employee of Acorai AB; and has held stock interest in Abbott Laboratories. Dr Martyn has served as an advisor to or has received consulting fees from Fire1, Cleveland Clinic/American Well Joint Venture, Boehringer Ingelheim/Eli Lilly, NIRSense, Novo Nordisk, AstraZeneca, and Apricity Robotics; and has received grant support from Ionis Therapeutics, AstraZeneca, and the Heart Failure Society of America. Dr Shah is an employee of Meta, which had no role in this work or providing financial support. Dr Tison has received research grants from MyoKardia, a wholly owned subsidiary of Bristol Myers Squibb, and Janssen Pharmaceuticals; and is an advisor to Viz.ai and Prolaio. Dr Fakhouri is an employee of the Office of Medical Policy, Center for Drug Evaluation and Research, U.S. Food and Drug Administration; the views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Food and Drug Administration. Dr Krumholz has received options from Element Science and Identifeye; has received payments from F-Prime for advisory roles; has co-founded and held equity in Hugo Health, Refactor Health, and ENSIGHT-AI; and has been associated with research contracts through Yale University from Janssen, Kenvue, and Pfizer. Dr O’Connor has received consulting fees from Merck, Abiomed, and Zealcare. Dr Solomon has received research grants from Alexion, Alnylam, AstraZeneca, Bellerophon, Bayer, Bristol Myers Squibb, Boston Scientific, Cytokinetics, Edgewise, Eidos, Gossamer, GSK, Ionis, Lilly, MyoKardia, NIH NHLBI, Novartis, Novo Nordisk, Respicardia, Sanofi Pasteur, Theracos, and US2.AI; and has consulted for Abbott, Action, Akros, Alexion, Alnylam, Amgen, Arena, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardior, Cardurion, Corvia, Cytokinetics, Daiichi-Sankyo, GSK, Lilly, Merck, MyoKardia, Novartis, Roche, Theracos, Quantum Genomics, Janssen, Cardiac Dimensions, Tenaya, Sanofi-Pasteur, Dinaqor, Tremeau, CellProThera, Moderna, American Regent, Sarepta, Lexicon, AnaCardio, Akros, and Valo. Drs Psotka and Fiuzat have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

As COVID-19 cases begin to decrease in the USA, learning from the pandemic experience will provide insights regarding disparities of care delivery. We sought to determine if specific populations hospitalized with COVID-19 are equally likely to be enrolled in clinical trials. We examined patients hospitalized with COVID-19 at centers participating in the American Heart Association's COVID-19 CVD Registry. The primary outcome was odds of enrollment in a clinical trial, according to sex, race, and ethnicity. Among 14,397 adults hospitalized with COVID-19, 9.5% (n = 1,377) were enrolled in a clinical trial. The proportion of enrolled patients was the lowest for Black patients (8%); in multivariable analysis, female and Black patients were less likely to be enrolled in a clinical trial related to COVID-19 compared to men and other racial groups, respectively. Determination of specific reasons for the disparities in trial participation related to COVID-19 in these populations should be further investigated., (© 2022. W. Montague Cobb-NMA Health Institute.)

Background: Breast cancer susceptibility is greatly influenced by single nucleotide polymorphisms (SNPs) both in penetrance and non-penetrance genes. The Estrogen Receptor Alfa (ESR1- rs2234693 and rs2046210) have been reported as risk factor of breast cancer in different ethnic groups with inconsistent results. In this study the association of ESR1 (rs2234693 and rs2046210) with breast cancer risk was investigated in patients of Khyber Pakhtunkhwa., Methods: A total of 312 females including 162 breast cancer patients and 150 healthy controls were enrolled in this study. The polymorphism was confirmed using T-ARMS-PCR., Results: Our results revealed that ESR1-rs2234693 risk allele (C) (P = 0.21, OR = 1.27, CI = 0.87 to 1.87) and containing genotypes CC (P = 0.68, OR = 1.24, CI = 0.42 to 3.68) and TC (P = 0.23, OR = 1.32, CI = 0.83 to 2.13) were not associated with the risk of breast cancer. In case of rs2046210, the risk allele A (P < 0.0001, OR = 2.42, CI = 1.74 to 3.38) and corresponding genotypes GA (P = 0.0001, OR = 2.55, CI = 1.62 to 4.03) and AA (P = 0.02, OR = 2.20, CI = 1.12 to 4.34) were significantly associated with higher risk of breast cancer. Moreover, ESR1-rs2234693 was significantly (P < 0.05) associated with family history, stages, PR status, ER status and luminal B. The ESR1-rs2046210 showed significant (P ≤ 0.05) association with menstrual status, tumor grade and TNBC. Both the SNPs showed non-significant (P > 0.05) association with nulliparity, nodal status, HER2 status, metastasis, HER2 enriched subtype and luminal A., Conclusion: It is concluded that ESR1-rs2234693 is not associated with breast cancer, while rs2046210 is significantly associated with the risk of breast cancer in Khyber Pakhtunkhwa population. Further, to confirm the exact situation of ESR1 polymorphism, ESR1 existing and other SNPs need to be investigated in diverse data sets., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM 2.5 ), black carbon (BC), and nitrogen dioxide (NO 2 ) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM 2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO 2 . Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 μm 2 cm -3 ) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 μm 2 cm -3 ) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO 2 ) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM 2.5 .

Background: In 2018, United Network for Organ Sharing (UNOS) extended the radius for which a heart transplant candidate can match with a donor, and outcomes across population densities are unknown. We sought to determine whether the policy change was associated with differences in heart transplant waitlist time or death post-transplant for patients from rural, micropolitan, and metropolitan settings., Methods: Using the Scientific Registry of Transplant Recipients, we evaluated U.S. adult patients listed for heart transplant from Janurary 2017 to September 2019 with follow-up through March 2020. Patients were stratified by home zip-codes to either metropolitan, micropolitan, or rural settings. Fine and Gray and Cox models were respectively used to estimate Sub-distribution hazard ratios (SHR) of heart transplant with death or removal from transplant list as a competing event, and HR of death post-transplant within population densities after versus before the UNOS policy change date, October 18, 2018. Analyses were adjusted for demographics, comorbidities, and labs., Results: Among 8,747 patients listed for heart transplant, 84.7% were from metropolitan, 8.6% micropolitan, and 6.6% rural settings. The 2018 UNOS policy was associated with earlier receipt of heart transplant for metropolitan [SHR 1.56 (95% CI: 1.46-1.66)] and micropolitan [SHR 1.48 (95% CI: 1.21-1.82)] populations, but not significantly for rural [SHR 1.20 (95% CI: 0.93-1.54)]; however, the interaction between policy and densities was not significant (p = .14). Policy changes were not associated with risk of death post-transplant [metropolitan: HR 1.04 (95% CI: 0.80-1.34); micropolitan: HR 1.10 (95% CI: 0.55-2.23); rural: HR 1.04 (95% CI: 0.52-2.08); interaction p = .99]., Conclusions: The 2018 UNOS heart transplant policy was associated with earlier receipt of heart transplant and no difference in post-transplant survival within population densities. Additional follow-up is needed to determine whether improvements are sustained., (Copyright © 2022 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Patients with heart failure (HF) are heterogeneous with various intrapersonal and interpersonal characteristics contributing to clinical outcomes. Bias, structural racism, and social determinants of health have been implicated in unequal treatment of patients with HF. Through several methodologies, artificial intelligence (AI) can provide models in HF prediction, prognostication, and provision of care, which may help prevent unequal outcomes. This review highlights AI as a strategy to address racial inequalities in HF; discusses key AI definitions within a health equity context; describes the current uses of AI in HF, strengths and harms in using AI; and offers recommendations for future directions., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Burden of atrial fibrillation (AF), as a continuous measure, is an emerging alternative classification often assumed to increase linearly with progression of disease. Yet there are no descriptions of AF burden distributions across populations., Methods: We examined patterns of AF burden (% time in AF) across 3 different cohorts: outpatients with AF undergoing Holter monitoring in a national registry (ORBIT-AF II), routine outpatients undergoing Holter monitoring in a tertiary healthcare system (UHealth), and patients >= 65 years with cardiac implantable electronic devices (Merlin.net TM linked to Medicare)., Results: We included 2,058 ORBIT-AF II patients, 4,537 UHealth patients, and 39,710 from Merlin.net. Mean age ranged from 56 to 77 years, sex ranged from 40% to 61% male, and mean CHA 2 DS 2 -VASc scores ranged from 2.2 to 4.9. Across all cohorts, AF burden demonstrated skewed frequency towards the extremes, with the vast majority of patients having either very low or very high AF burden. This bimodal distribution was consistent across cohorts, across clinically-documented AF types (paroxysmal v persistent), patients with or without a known AF diagnosis, and among patients with different types of cardiac implantable electronic devices., Conclusions: Across 3 broad, diverse cohorts with continuous monitoring, distribution of AF burden was consistently skewed towards the extremes without an even, linear distribution or progression. As AF burden is increasingly recognized as a descriptor and potential risk-stratifier, these findings have important implications for future research and patient care., (Copyright © 2021 Elsevier Inc. All rights reserved.)