not-yet-known not-yet-known not-yet-known unknown Purpose: To determine the positive predictive values (PPVs) of ICD-9 and ICD-10-based diagnostic coding algorithms to identify periprosthetic joint infection (PJI) following total hip arthroplasty (THA) within the United States (US) Veterans Health Administration (VHA). Methods: We selected patients with: (1) any position hospital discharge ICD-9 or ICD-10 diagnosis of PJI, (2) ICD-9, ICD-10, or Current Procedural Terminology (CPT) procedure codes for THA any time prior to PJI diagnosis, (3) CPT code for hip X-ray within ±90 days of the PJI diagnosis, and (4) 1 or more CPT codes for arthrocentesis, arthrotomy, or revision arthroplasty all occurring within ±90 days of the PJI diagnosis date. We obtained separate samples of patients for ICD-9 and ICD-10-based PJI diagnoses. These samples were stratified by THA medical center volume. Infectious disease physicians adjudicated each identified PJI event. The PPV (95% confidence interval [CI]) for the ICD-9 and ICD-10 PJI algorithms were calculated. Results: Among the 90 sampled hip PJI events for the ICD-9 era, 79 were confirmed PJIs (PPV, 87.8%; 95% CI, 79.2%-93.7%). For the 90 sampled hip PJI events for the ICD-10 era, 72 were confirmed PJIs (PPV, 80.0%; 95% CI, 70.3%-87.7%). Conclusion: These algorithms yielded a PPV of 87.8% (ICD-9) and 80.0% (ICD-10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.

Objectives Long COVID, a condition marked by persistent symptoms following COVID-19 infection, poses significant challenges in clinical management. While emerging pharmacological treatments have demonstrated limited benefits in isolated studies, clinical experience and literature suggest that low-dose naltrexone may be a promising therapeutic option. Therefore, this systematic review aims to synthesise findings from available literature and evaluate the overall safety and efficacy of LDN as a potential treatment for long COVID. Methods A literature search was conducted using a combination of key terms ‘COVID,’ ‘COVID-19’, ‘SARS-COV-2’, and ‘Naltrexone’ through databases MEDLINE, Web of Science (Clavirate), Embase, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL) and Cumulative Index in Nursing and Allied Health Literature (CINAHL). The methodology is available on the PROSPERO database (CRD42025630362). Patient consent was not required as article was based on already published data from available literature Results Screening identified five eligible articles. Four studies were included, but only two provided comparable data suitable for meta-analysis, resulting in exclusion of two studies from the meta-analysis. Conclusion LDN may be effective in treating long COVID, particularly symptoms of fatigue, brain fog, headache and sleep disturbances. More robust studies such as randomised controlled trials are needed to confirm its safety and efficacy.

MicroRNAs are a class of endogenous noncoding small RNAs that have emerged as critical posttranscriptional regulators of the immune system. However, there have been few functional studies of the relationship between microRNA and immunosuppression. Here, the microRNA miR-125a-5p was identified as a key regulator of Tregs. We used miR-125a-5p knockout mice to explore the relationship between miR-125a-5p and Treg function and development in vivo and examine the underlying biochemical mechanisms. PCR and Western blotting were used to verify the regulatory relationship between miR-125a-5p and Foxp3 in mice in vivo, and flow cytometry was used to explore the proportions and numbers of Tregs in immune organs. The immunosuppressive effect of Tregs on other lymphocytes was analysed by measuring the proliferation of splenic lymphocytes after Con A induction. ELISA kits were used to detect inflammatory cytokines in mouse serum. In miR-125a-5p knockout mice, deletion of miR-125a-5p caused a decrease in Foxp3 expression, reduced Treg numbers, and an imbalance in inflammatory cytokines. Overall, our results indicated that miR-125a-5p contributes to Treg homeostasis and function, providing insights into potential miRNA-based therapies for autoimmune diseases.

The rise of social media has transformed tourism research, providing new ways to understand travelers’ perspectives. Picture analysis, in particular, offers valuable insights into tourist preferences, popular attractions, and emotions conveyed through images. This analysis can be performed manually or with artificial intelligence. However, a significant challenge arises from the presence of memes and advertisements related to informal markets, which complicate data usability. Manually filtering such content is labor-intensive and inefficient. To address this, we propose a robust analytical methodology that combines traditional and modern learning techniques. Our approach achieves over 89% accuracy in its classification task, streamlining data processing for tourism research. By automating image filtering, this method enhances dataset quality and improves the reliability of tourism analyses.

Water electrolysis is considered one of the most promising methods for hydrogen production using renewable energy sources (RES). In megawatt (MW)-level hydrogen production systems, a MW-level hydrogen converter is essential for interfacing the medium-voltage DC bus (MVDCB) with the electrolyzer. This paper proposes a novel hydrogen converter that incorporates a combined Input Series Output Parallel (ISOP) configuration, which offers several advantages, including a simplified topology, high voltage ratio, high energy efficiency, low output current ripple, and enhanced reliability. The design of the proposed hydrogen converter emphasizes the optimization of the snubber circuit, which plays a critical role in ensuring safe operation. To guide the design process, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed. Furthermore, an accurate model of the proposed hydrogen converter is developed to facilitate controller design, enabling power balance among the modules of this combined topology. This step is crucial to maintaining the stable operation of the hydrogen converter. Finally, experimental results are presented to validate the effectiveness of the snubber circuit design and the control strategy for the proposed hydrogen converter.

A document by Sıdıka BAZİKİ ÇETİN. Click on the document to view its contents.

Faheem UddinAsian Institute of Fashion Design, Iqra University, Main Campus, Karachi. https://orcid.org/0000-0003-4024-2963 Email. faheem.badar@aifd.edu.pk , dfudfuca@yahoo.ca

Maternal effects are a prevalent source of early life phenotypic variation in offspring across diverse taxa and have been shown to provide offspring an adaptive advantage in response to maternal environmental stimuli. There are several well studied examples of adaptive maternally induced intergenerational effects in response to the parental environment mediated by mechanisms such as stress hormones and nutrients. DNA methylation is a fundamental cellular process that affects gene transcription and can respond rapidly to changing environments yet remains a largely unexplored mechanism for maternal effect signaling. We manipulated maternal environment in sexually maturing female Chinook salmon (Oncorhynchus tshawytscha) by reducing food availability or increasing day length. We used a factorial breeding design as well as gene-specific sequencing assays to analyze how maternal effects influenced DNA methylation in offspring at the eyed egg and alevin developmental stages We found significant maternal effects on gene-specific DNA methylation as well as heightened levels of maternal effects in response to an increase in maternal photoperiod. We report higher maternal effects in early life stages that decline through development in four gene functional categories: growth, immune response, metabolic function, and histone protein regulation. Despite a potential resetting of the methylome following fertilization, we provide evidence that maternal effects can modulate gene-specific DNA methylation, and that effect is sensitive to the environment the mothers experience. This pattern of epigenetically-mediated maternal effects responding to maternal environment is consistent with a fundamental process driving intergenerational phenotypic variation.

Characterizing the transit times or ‘ages’ of water through soil-plant systems is important for modeling ecohydrological processes and improving the accuracy of climate and terrestrial biosphere models (CTBMs). Soil-plant transit times, however, remain poorly characterized. Here, we revisit and leverage a unique isotope labeling dataset from a tropical mesocosm experiment to investigate soil-plant transit times over a 9-month period following a controlled drought. We propose a simple framework for modeling water movement through soil-plant systems, accessible to groups of researchers outside catchment hydrology. We employ two modeling approaches to estimating transit times – parametric (gamma/lognormal) and data-based (phenomenological). Our findings reveal that the parametric approach results in mean transit times (MTTs) that are generally longer than MTTs derived from the data-based approach, particularly in trees. Further, our results demonstrate significant preferential flows in the vadose zone and similar water flow patterns via trees at the scale of this ecosystem. Analogous to preferential flow in soils, we refer to this ecosystem-scale water flow via trees as ‘xylem bias’, whereby some trees have a stronger pull on water and/or larger pool of stored water than others. These results suggest a complex interplay of partitioning, storage, and release mechanisms that remain largely unaccounted for in soil-plant transit time literature. We suggest that our findings have significant implications for CTBMs, underlining the need for improved representations of root water uptake and internal water storage. Finally, our results point to the need for interdisciplinary approaches to unravel the complexities of water transport in soil-plant systems.

Pollinators harbour diverse RNA viromes that play a vital role in their health. Yet, factors that shape viral communities are often unclear. The European honey bee (Apis mellifera) is experiencing a viral epidemic since the emergence of the parasitic mite Varroa destructor (varroa) introduced vector-borne transmission, which has also been linked to increased viral spillover into wild pollinator communities. Varroa-free island populations provide natural laboratories to study the effect of varroa, while also allowing us to ask how islands affect viral communities. Barriers that restrict the dispersal of wild pollinators and their pathogens to islands may be overcome by human-mediated transport in managed honey bees. In this study, we used islands with and without varroa and matched mainland populations of honey bees (A. mellifera) and bumble bees (Bombus terrestris) from 2015 and 2021 to explore how varroa presence and island location affect the virome of managed and wild bees. We find lower viral richness on islands in both species. Bumble bee viromes were not affected by varroa and showed a distinct community that was geographically structured. In honey bees, however, viral abundance was more evenly distributed on varroa-free islands but dominated by deformed wing virus (DWV) in varroa-present populations. Within the six years between the sampling events, DWV underwent a shift from DWV-B towards recombinant strains. Surprisingly, these shifts appeared independent of varroa. Viewing pollinator virome composition within an ecological framework provides valuable insights into the barriers to virus spread and could help to predict drivers of disease emergence.

Wetlands provide essential ecological services but face threats from anthropogenic activities and climate change. Floodplain wetlands, in particular, are vital for biodiversity and local livelihoods. Borsola Beel, covering approximately 84 hectares, is connected to the Brahmaputra River and supports diverse aquatic life. However, it is experiencing degradation due to pollution and hydrological changes.This study examines the trophic structure and energy dynamics of Borsola Beel, a floodplain wetland in Assam, India, using the Ecopath with Ecosim (EwE) modeling approach. The study was conducted over 20 months, from June 2017 to January 2019. The EwE model was employed to assess energy flow and trophic interactions.Results indicate that catfishes occupy the highest trophic level (3.76), serving as top predators. Ecotrophic efficiency (EE) values ranged from 0.001 to 0.99, with air-breathing fishes and catfishes exhibiting the highest EE, suggesting significant predation and fishing pressures. The total transfer efficiency was 12.97%, slightly above the global average, likely due to active fishing targeting lower trophic levels. Ecosystem maturity indicators suggest that Borsola Beel is in an early developmental stage, characterized by a simple food web structure and low nutrient recycling.These findings underscore the need for targeted conservation and sustainable management strategies to enhance the ecological stability of Borsola Beel. Implementing controlled fishing practices, habitat restoration, and community-led initiatives are recommended to promote ecosystem resilience and ensure the long-term productivity of this vital wetland.

This study investigates the relationship between soil organic carbon (SOC) and soil microbial communities under various vegetation types within alpine ecosystems. Soils from three tree and three shrub communities in the Maixiu National Forest Park, Qinghai, China, were analyzed for SOC content and microbial community composition. Tree soils exhibited higher SOC content, particularly in the surface layers, which is associated with deeper root systems and greater belowground biomass. In contrast, shrub soils had lower SOC content, indicating faster carbon cycling. Fungal communities, particularly Ascomycetes, were positively correlated with SOC, while bacterial communities, including Proteobacteria and Actinobacteria, were linked to rapid organic matter turnover. These findings highlight the role of vegetation type in regulating microbial communities and carbon dynamics in alpine soils.

The small yellow croaker (Larimichthys polyactis), a commercially and ecologically important fish species in East Asia, has experienced significant population declines due to overfishing, habitat degradation, and environmental changes. Understanding the spatio-temporal dynamics of this species is critical for its conservation and sustainable management. In this study, we investigated the distribution patterns of small yellow croaker in Hangzhou Bay, from 2017 to 2023, using a combination of fishery resource surveys and species distribution models. We evaluated the performance of eleven modeling algorithms to identify the most accurate model for predicting small yellow croaker distribution. Our results showed that the Random Forest algorithm outperformed other models, with high sensitivity (95.238) and specificity (99.49), demonstrating its ability to capture complex non-linear relationships between environmental factors and species distribution. Depth emerged as the most influential factor, accounting for 30% of the importance in the model, with small yellow croaker preferring deeper waters around 60m. Salinity was the second most important factor, with higher occurrence probabilities in areas where salinity exceeded 25. Other environmental factors, such as tempreture and dissolved oxygen, had relatively lower impacts on distribution. Spatially, small yellow croaker was predominantly distributed in offshore regions east of 122.5°E, where deeper waters and higher salinity levels provided suitable habitat conditions.This study underscore the need for targeted management measures, such as habitat restoration, to ensure the sustainable management of small yellow croaker populations.

Aqueous zinc ion batteries (ZIBs) are currently highly promising rechargeable batteries offering affordable prices and high reliability. However, the uncontrolled expansion of zinc dendrites, hydrogen evolution reaction, and adverse reaction greatly affect their potential sustainable application. In this work, we constructed Cu-Sn nanoparticle alloy modified carbon nanofibers (Cu/Sn-C) as a buffer interface region (BIR) onto the zinc anode. The high density of zincophilic sites of the Cu/Sn-C contributed a homogeneous Zn deposition behavior, ameliorated potential separator penetration of zinc dendrites, and weakened corrosions and adverse reactions. As a result, Cu/Sn-C exhibits a low zinc nucleation overpotential of 33.2 mV and dendrite-free zinc deposition. Besides, a symmetric cell with Cu/Sn-C@Zn anode exhibits outstanding cycling lifespan with over 3500 hours at 1 mA cm -2 for 1 mAh cm -2. Furthermore, Cu/Sn-C@Zn//Mn xV 2O 5 full battery also achieved stable cycling at 5 A g -1. This work provides a significant avenue for enhancing the reliability of the Zn anode, thereby facilitating the advancement of widespread applications for the next-generation AZIBs.

Aggregation of the protein tau is a key pathological hallmark of tauopathies such as Alzheimer’s Disease. Tau dissociates from microtubules and diffuses from within the axon into the soma-dendritic compartment, where it aggregates firstly into oligomers and ultimately into neurofibrillary tangles. There is gathering evidence that it is the soluble tau aggregates that are the major active species and that their effects on neuronal electrophysiological properties, synaptic transmission and plasticity could contribute to early cognitive decline. Here we have investigated the effects of incubating acute mouse hippocampal slices with tau aggregates. Incubation with the tau aggregates produced interictal events and an increase in excitability of CA3 pyramidal cells. Tau aggregates had little effect on basal synaptic transmission but antagonism of GABAA receptors revealed significant effects of tau aggregates on circuit excitability, enhancing the firing of population spikes and the occurrence of bursts following fEPSPs. Tau aggregates produced a concentration-dependent impairment of long-term potentiation (LTP), which could not be overcome by repeated LTP induction stimuli, showing the effects were not just through an elevation of LTP threshold. In contrast to the impairment of LTP, tau aggregates increased G1-mGluR-dependent LTD. Thus, tau aggregates increase hippocampal circuit excitability and shift synaptic plasticity towards depression.

One of the leading causes of dementia among older people is due to Alzheimer’s disease and the detection of cognitive impairment is essential for preventing the premature disease. Magnetic Resonance Imaging (MRI) plays a crucial role in studying the pathological condition of patients. Several models are developed for detecting the disease, but the traditional methods failed to prove their accurate of detection and the identification of congenital observations remained as a challenging concerm. In this research, a search and hunt optimization-based stacked deep convolutional neural network (SH-StNN) model is proposed for the effective prediction of the disease. The segmentation performance and the tuning of hyper-parameters are enhanced by the incorporation of the search and hunt optimization algorithm. The prediction model overcame the potential impacts caused by the loss of features, improper segmentation, and the prediction of minute abnormalities. The experimental analysis is done using the Alzheimer’s Neuro-imaging Initiative ADNI repository dataset and the modelattained a higher accuracyof 98%. The developed approach performs better than the conventional methods for classifying the diverse stages of the disease improving the therapeutic efficiency.

In the Prairie Pothole Region (PPR), potholes govern catchment hydrologic behavior through complex and dynamic fill-spill-connection mechanisms. This complexity—combined with predominantly ungauged catchments and limited fine-resolution pothole inventories—challenges the hydrologic models and purely data-driven deep learning approaches. To address this, we developed the δHBV-Pot model within differentiable modeling framework (δ). This physics-informed deep learning model integrates the conceptual HBV model with a probabilistic algorithm that emulates the aggregated effects of pothole fill-spill-connection processes. Applied to 98 PPR catchments, δHBV-Pot demonstrates stronger predictive accuracy and physical realism than purely data-driven Long Short-Term Memory (LSTM) model and two conceptual hydrology models. PPR-scale regional δHBV-Pot model successfully simulates the hydrologic behavior of the majority of pseudo-ungauged (or test) catchments withheld during model development, effectively regionalizing (1) high-flow magnitude and inter-annual variability, (2) intra-annual flashiness of high-flow and normal flow, and (3) inter-annual variability in pothole water storage dynamics. Moreover, the model identifies vulnerable catchments with large high-flow magnitude and variability—even where no streamflow data exist—and delineates catchments with varying temporal variability in pothole water storage without relying on detailed pothole inventories. Our analysis reveals a negative correlation between pothole storage extent and high-flow metrics, suggesting that greater pothole storage reduces both high-flow magnitude and variability. Our findings underscore the value of integrating conceptual hydrology with data-driven approaches in pothole-dominated regions. This combined strategy uncovers new patterns from big data while enabling the regionalization of high-flow and pothole storage characteristics to ungauged catchments—critical for vulnerability assessment and designing sustainable water/ecological strategies in these landscapes. 

Bi doped CdSe (CdSe:Bi) films were successfully deposited on glass and Si(111) substrates by radio frequency (RF) and direct current (DC) co-sputtering. The films were characterized by X-ray diffration (XRD), energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), ultraviolet-visible-near-infrared (UV-Vis-NIR) spectroscopy and the hall effect tester (HET). The diffraction peaks associated with elemental Bi are not detected in the XRD patterns. However, the intensity of the diffraction peaks of CdSe:Bi films significantly increases. The EDX analysis confirms the presence of Bi in all samples. As the Bi content increases, the transmittance of the CdSe:Bi films gradually decreases and the optical band gap first decreases then increases. The resistivity of CdSe:Bi films decreases by 1 to 2 orders of magnitude compared to undoped CdSe films, suggesting that Bi doping significantly enhance the electrical properties of CdSe.

The field of pediatric rare and diffuse lung diseases continues to advance, with ongoing research deepening our understanding of the diagnosis and treatment of conditions such as children’s interstitial and diffuse lung disease (chILD), non-cystic fibrosis (CF) bronchiectasis, and pulmonary complications of childhood cancer. Recent publications in Pediatric Pulmonology and other journals in 2024 have highlighted new insights into the pathophysiology, disease progression, and emerging diagnostic tools for these rare lung conditions, as well as innovative therapeutic approaches. This review features these important advancements within the context of current diagnostic practices and clinical care for pediatric patients with rare and diffuse lung diseases.

Objectives: To quantify and characterize systemic antibiotic prescribing among children with tracheostomies, and to identify predictors of higher prescribing. Study Design: This single-center retrospective cohort study included children with tracheostomies 2 months-18 years old cared for at a quaternary care pediatric institution between 2010-2018. Antibiotics prescribed for >1 day were recorded, classified by spectrum of activity, and stratified by setting and year. Presumptively prophylactic antibiotics were excluded. Child-level antibiotic prescribing was quantified using number of courses and days of therapy (DOT) per child per year. Group-level prescribing was summarized using total courses and DOT per 1000 person-days and analyzed by year over study course. Predictors of child-level prescribing were identified using backward elimination multivariable analyses within a generalized linear model. Results: The 548 children with tracheostomies were prescribed a median 4 antibiotic courses per child per year (IQR: 1.9-10.0) for a total median exposure of 33.8 DOT per child per year (IQR: 16.1-71.6). The group was prescribed 100.7 DOT per 1000 person-days. Most courses (72.7%) were broad-spectrum, with vancomycin, piperacillin-tazobactam, and ampicillin-sulbactam/ampicillin-clavulanate being the most frequently prescribed courses. Annual antibiotic prescribing decreased 30-33% between 2010 and 2018. Child ventilator use at baseline was associated with fewer DOT per child per year, while higher complexity and more ICU hospital days were risk factors for higher DOT. Conclusion: Children with tracheostomies have high systemic antibiotic use and are predominantly prescribed broad-spectrum antibiotics. Chronic ventilator use was associated with lower prescribing. This data may inform antimicrobial stewardship work.

In this letter, advances in flexible electronics technology are explored to build artificial neuron circuits using amorphous indium gallium zinc oxide (a-IGZO) for near-sensor signal processing used for applications such as dynamic tactile sensing and electronic skins (e-skin). Inspired by the efficiency of biological neural systems and spiking neural networks (SNNs), a neuron based on a simplified Axon-Hillock neuron model, operating in the subthreshold regime, was designed and optimized for low-power operation (400 nW) at biological time scale (hundreds of Hz). The results opens new possibilities for advanced near-sensor processing in biomedical and robotics applications.

Even when several crops depend on animals for their pollination, the effectiveness of each floral visitor remains largely unknown. Across five metrics and over two consecutive years, we compare for the first time the effectiveness of all the floral visitors of a self-incompatible mango cultivar (‘Ataulfo’) and evaluate whether the abundance of the exotic honeybee or wild pollinators influences the production of commercial (well-developed) and malformed fruits (known as nubbins). Even when honeybees were the most effective pollinators in both years due to their greater abundance, only the increase in wild pollinator abundance was positively related to the production of commercial fruits and negatively correlated with the incidence of nubbins, probably due to their greater mobility between trees. These findings highlight the importance of wild pollinators in the mango industry and the need to implement conservation practices to maintain these pollinators to ensure the growing global demand for this cultivar.

A document by Mohamad Fael. Click on the document to view its contents.

Mechanical thrombectomy in two children under the age of one year