Open Research Europe

Microorganisms evolved within the geomagnetic field and can be affected by magnetic field exposure. However, the mechanisms underlying many magnetic phenomena in microbes remain to be elucidated. We develop a 3D-printed magnetic field exposure device to perform experiments on microbes. This device is designed in AutoCAD, modeled in COMSOL, and validated using a Gaussmeter. Using the magnetic field exposure device, we perform static magnetic field experiments on different strains of the budding yeast Saccharomyces cerevisiae. We find that static magnetic field exposure slows the spatially-structured expansion of yeast mats that expands in two dimensions, but not yeast mats that expand in three dimensions, across the surface of semi-solid media. We also find that magnetic fields do not affect the growth of yeast cells in well-mixed liquid media. This study provides a novel device for magnetic field exposure experiments on microorganisms and advances our understanding of the effects of magnetic fields on fungi. Why it mattersMicroorganisms have evolved to function, survive, and reproduce in Earths magnetic field. However, the mechanisms underlying magnetic phenomena in microorganisms are unknown. This is especially true for fungi, which are important microorganisms for microbiological research, industrial application, and infectious disease. To elucidate mechanisms driving magnetic phenomena, we need devices to perform controlled experiments in a variety of conditions. We develop a 3D-printed magnetic field exposure device using computer-aided design, physics modeling software, and a magnetometer. Using this novel magnetic field device, we discover that magnetic fields can slow the growth of yeast on agar plates, but that magnetic fields do not affect the growth of yeast in liquid media.

Major advances in Plasmodium sequencing approaches, bioinformatic pipelines, and data analysis tools have provided valuable insights into malaria epidemiology from parasite genomic data. However, translating genetic data into actionable information for decision-makers remains a challenge. Significant barriers limit the integration of these advances into a functional data analysis ecosystem that produces standardized, interpretable results for use by national malaria control programs. The Plasmodium Genomic Epidemiology (PlasmoGenEpi) network convened 18 subject matter experts across 15 institutions at the Reproducibility, Accessibility, Documentation, and Interoperability Standards Hackathon in 2023 (RADISH23) to identify available analysis tools, evaluate software standards, improve documentation, and outline workflows. Eight use cases for genomic data were identified, and a subset were developed into analysis workflows in terms of a series of connected functionalities. Software tools were then mapped against functionalities to outline a modular approach to data analysis for these use cases. In addition to outlining workflows, a set of objective criteria were developed for evaluating software standards. Forty Plasmodium genomic analysis tools were identified, of which 22 were prioritized for software standards evaluation. Additional tutorials were developed for 10 tools in the form of reproducible code applied to shared datasets. These resources are available on PGEforge (mrc- ide.github.io/PGEforge), a new community resource that serves as a central, open repository for current and future resources for malaria genomic data analysis.

Face and neck cooling has been found effective to improve thermal comfort during exercise in the heat despite the surface area of human face and neck regions accounts for only 5.5% of the entire body. Presently, very limited work in the literature has been reported on face and neck cooling to improve indoor thermal comfort. In this work, two energy-efficient wearable face and neck cooling fans were used to enhance occupants thermal comfort in two warm indoor conditions (30 & 32 {degrees}C). Local skin temperatures and perceptual responses while using those two wearable cooling fans were examined and compared. Results showed that both cooling fans could largely reduce local skin temperatures at the forehead, face and neck regions up to 2.1 {degrees}C. Local thermal sensation votes at the face and neck were decreased by 0.82-1.21 scale unit at two studied temperatures. Overall TSVs dropped by 1.03-1.14 and 1.34-1.66 scale unit at 30 and 32 {degrees}C temperatures, respectively. Both cooling fans could extend the acceptable HVAC temperature setpoint to 32.0 {degrees}C, resulting in an average energy saving of 45.7% as compared to the baseline HVAC setpoint of 24.5 {degrees}C. Further, the free-control cooling mode is recommended to occupants for further improving thermal comfort while using those two types of wearable cooling fans indoors. Lastly, it is concluded that those two wearable cooling fans could greatly improve thermal comfort and save HVAC energy despite some issues on dry eyes and dry lips associated with those wearable cooling fans were noted.

BackgroundPlasmodium vivax malaria research has yet to fully benefit from the advances in genomic surveillance that have revolutionized P. falciparum epidemiology. Closing this gap is critical because genomic tools are necessary to monitor the spread of drug resistance, classify infections as local or imported, and distinguish reinfection, recrudescence, and relapse. To achieve these objectives, microhaplotype marker panels that allow powerful genotyping of polyclonal infections are needed. MethodsWe designed a Globally-applicable Amplicon Panel for P. vivax (PvGAP), selecting targets based on both genetic diversity and genetic distance from each other to maximize discriminatory capability between geographic regions. We evaluated this panel with field samples from Ethiopia and in silico using whole genomes from the MalariaGEN Pv4 database. ResultsPvGAP has 80 high diversity targets suitable for population genomics and eight targets of specific epidemiological interest, such as putative markers of drug resistance. We demonstrate PvGAP achieves robust amplification with field data and that it provides competitive accuracy for relatedness inference in three disparate geographic regions. ConclusionsPvGAP joins existing P. vivax panels as a cost effective and practical option for genomic epidemiology of this neglected disease. It will support drug resistance surveillance, discrimination of local and imported cases, and it may aid in separating reinfection, recrudescence, and relapse in therapeutic efficacy studies, all critical needs of National Malaria Control Programs.

Two species of parasitic fungi from the phylum Chytridiomycota (chytrids) are annihilating global amphibian populations. These chytrid species--Batrachochytrium dendrobatidis and B. salamandrivorans--have high rates of mortality and transmission. Upon establishing infection in amphibians, chytrids rapidly multiply within the skin and disrupt their hosts vital homeostasis mechanisms. Current disease models suggest that chytrid fungi locate and infect their hosts during a motile, unicellular zoospore life stage. Moreover, other chytrid species parasitize organisms from across the tree of life, making future epidemics in new hosts a likely possibility. Efforts to mitigate the damage and spread of chytrid disease have been stymied by the lack of knowledge about basic chytrid biology and tools with which to test molecular hypotheses about disease mechanisms. To overcome this bottleneck, we have developed high-efficiency delivery of molecular payloads into chytrid zoospores using electroporation. Our electroporation protocols result in payload delivery to between 75-97% of living cells of three species: B. dendrobatidis, B. salamandrivorans, and a non-pathogenic relative, S. punctatus. This method lays the foundation for molecular genetic tools needed to establish ecological mitigation strategies and answer broader questions in evolutionary and cell biology.

BackgroundThe introduction of 5G technology as the latest standard in mobile telecommunications has raised concerns about its potential health effects. Prior studies of earlier generations of radiofrequency electromagnetic fields (RF-EMF) demonstrated narrowband spectral increases in the electroencephalographic (EEG) spindle frequency range (11-16 Hz) in non-rapid-eye-movement (NREM) sleep. However, the impact of 5G RF-EMF on sleep remains unexplored. Additionally, RF-EMF can activate L-type voltage-gated calcium channels (LTCC), which have been linked to sleep quality and EEG oscillatory activity. ObjectiveThis study investigates whether the allelic variant rs7304986 in the CACNA1C gene, encoding the 1C subunit of LTCC, modulates 5G RF-EMF effects on EEG spindle activity during NREM sleep. MethodsThirty-four healthy, matched participants, genotyped for rs7304986 (15 T/C and 19 T/T carriers), underwent a double-blind, sham-controlled study with standardized left-hemisphere exposure to two 5G RF-EMF signals (3.6 GHz and 700 MHz) for 30 min before sleep. Sleep spindle activity was analyzed using high-density EEG and the Fitting Oscillations & One Over f (FOOOF) algorithm. ResultsT/C carriers reported longer sleep latency compared to T/T carriers. A significant interaction between RF-EMF exposure and rs7304986 genotype was observed, with 3.6 GHz exposure in T/C carriers inducing a faster spindle center frequency in the central, parietal, and occipital cortex compared to sham. ConclusionThese findings suggest 3.6 GHz 5G RF-EMF modulates spindle center frequency during NREM sleep in a CACNA1C genotype-dependent manner, implicating LTCC in the physiological response to RF-EMF and underscoring the need for further research into 5G effects on brain health.

In vitro cultivation of cells in strictly controlled conditions of a biological incubator is a widely used experimental model in biological studies. The CO2-incubators allow control of temperature, atmospheric composition, and humidity; however, the recent reports point out at possible significant and incontrollable influence of incubators on magnetic background. We demonstrated that two types of CO2-incubators sufficiently alter the static magnetic field (SMF) in the inner chamber compared to natural geomagnetic field, and the effect depends on the incubator model. The values of SMF in the center of incubators chambers were lower than natural; nevertheless, the strongly expressed spatial inhomogeneity of SMF was registered, with both reduced, up to hypomagnetic, and increased SMF values. One of the incubators in operating mode generated oscillations in magnetic field with period of oscillations about several seconds and peak-to-peak amplitude depending on the measuring point within the chamber volume, up to 115% of the mean value. Since the magnetic background is considered to contribute in multiple biological effects, we emphasize the significant impact of CO2-incubators on magnetic background in cell culture experiments and assume that its spatial and temporal inhomogeneity may be a source for variability in cell study results. HighlightsO_LICO2-incubators sufficiently alter the static magnetic field in the inner chamber compared to natural geomagnetic field C_LIO_LISpatial inhomogeneity of the magnetic field depends on CO2-incubator type and can reach the gradient value of more than 60 T. C_LIO_LIIn one type of the incubator, the generated oscillations in magnetic field were registered with a period of several seconds and a peak-to-peak amplitude up to 115% of the mean value. C_LI

SignificanceFar infrared (IR) has a long history in thermometry and febrile screening. Concerns have been raised recently over the accuracy of non-contact body thermometry. Clinical testing with febrile individuals constitutes the standard performance assessment. This is challenging to replicate, which may have inadvertently allowed approval of IR systems that are unable to detect fevers. The ability to test performance without relying on febrile participants would have ramifications for public health, especially if this discovered undisclosed differences in accuracy in widely used devices. AimTo identify foundational issues in, demonstrate principles of, and develop test methods for non-contact body thermometry. ApproachWe review foundational literature and identify confounds impeding performance of IR thermography (IRT) and non-contact IR thermometry (NCIT) for febrile screening and demonstrate corrections for their effects, which would otherwise be unacceptable. Almost none of the devices we are aware of compensate for these confounds. We reverse-engineer surface-to-body temperature relations for several FDA-cleared NCITs. We note their similarity to recently reported bias-to-normal behavior in other devices and determine range of body temperatures for which the device would produce a "normal" (non-febrile) output. Finally, we generate predictable elevated face temperatures in healthy subjects and demonstrate this in several devices. ResultsThe surface-to-body relationships for two IRT and one NCIT were linear, while all others exhibited nonlinear bias-to-normal behavior that produce normal temperatures when presented with surface temperatures ranging from hypothermia to moderate-to-severe fever. The test method was used in healthy, non-febrile subjects to generate elevated temperatures corresponding to body temperatures from 97.35F to 102.45F. Three out of five systems had negligible sensitivity. ConclusionsThis demonstrates an alternative evaluation method without the limitations and risks of febrile patients. These results indicate many devices may be unusable for body thermometry and may be providing a false sense of security for public health surveillance.

Malaria caused by the P. vivax parasite can be difficult to diagnose due to dormant phases and genetic variants. Regions such as Sub-Saharan Africa, South America, and Southeast Asia experience diagnostic accessibility issues due to the gold standard diagnostic requiring expensive light microscopy of blood samples. This study focuses on expanding the potential of Loop-Mediated Isothermal Amplification (LAMP) as a remote-ready P. vivax malaria diagnostic. We detected as few as 103 copies of P. vivax 18S rRNA DNA sequence spiked into molecular water and as few as 106 spiked into saliva. These results were reproduced when reactions were incubated in a consumer-ready coffee thermos. This saliva-ready LAMP diagnostic for P. vivax shows promise for remote use and increasing diagnostic access to low-resource regions.

Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths increased between 2019 and 2020 [1]. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. Here, we describe an AI-based approach that boosts the performance of light (LM), atomic force (AFM) and fluorescence microscopy (FM)-based malaria diagnosis. As the main challenge, the stage-specific recognition of infected red blood cells (RBCs) usually requires large sets of microscopy images for training a neural network, which is difficult to obtain. Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition that works even with limited training sets due to a smart reduction of data dimension. Individual RBCs are extracted from an image, reduced to characteristic one-dimensional cross-sections, and classified. We show that our method is applicable to images recorded by various microscopy techniques. It is available as a software package at https://github.com/KatharinaPreissinger/Malaria_stage_classifier and can be used within a python environment. Technical support is provided by the corresponding author (katharina.preissinger@physik.uni-augsburg.de). Author summaryThe Malaria Stage Classifier is a software helping the user to detect and stage RBCs infected with malaria. Accurate recognition of malaria infected RBCs still imposes a challenge in endemic regions, as it is time-consuming and subjective. These deficiencies can be overcome by autonomous computer assisted recognition using neural networks (NNs). The Malaria Stage Classifier offers a user-friendly interface for the stage-specific classification of malaria infected RBCs into four categories--healthy ones and three classes of infected ones according to the parasite age. The use of data reduction, which forms the central element of the Malaria Stage Classifier, allows for a fast and accurate classification of RBCs. It is applicable for light, atomic force, and fluorescence microscopy images and allows for retraining the implemented NN with new images. Our simple concept further has the potential to be generalised for the classification of other cells or objects.

BackgroundMalaria caused by Plasmodium is a major global health burden. Rapid and accurate malaria detection is essential for effective disease control. However, most molecular diagnostics require DNA extraction, thermal cycling, and specialized infrastructure, limiting use in resource-limited settings. This study aimed to develop an extraction-free, one-pot CRISPR-based platform for rapid Plasmodium detection directly from whole blood under ambient conditions. MethodsCrude blood lysates were directly applied to a one-pot recombinase polymerase amplification (RPA)-CRISPR reaction coupled with lateral flow readout. Analytical sensitivity and specificity were evaluated using purified DNA and whole-blood lysates. Diagnostic performance was assessed using a clinical cohort comprising 116 malaria cases and 109 negative controls. ResultsThe extract-free RPA-CRISPR assay achieved an analytical limit of detection of 100 copies/{micro}L from crude blood lysates, corresponding approximately to 12-20 parasites/{micro}L. No cross-reactivity was observed across Plasmodium species. In clinical evaluation, the assay demonstrated strong concordance with reference PCR, achieving 93.1% sensitivity and 100% specificity. Sensitivity varied with parasite burden, reaching 97.8% for high-density infections, 95.1% for moderate density, and 82.8% for low-density infections. The whole workflow was completed within 40 minutes at room temperature without specialized instrumentation. ConclusionsAn extract-free, one-pot ambient-temperature RPA-CRISPR platform allows rapid detection of Plasmodium directly from whole blood with high specificity and sensitivity across parasite burdens, supporting its potential for decentralized malaria diagnostics. Larger multicenter field studies are needed to validate performance under real-world conditions.

BackgroundA key component of diagnostics in low- and middle-income (LMIC) nations is polymerase chain reaction (PCR). Despite its high sensitivity, PCR is vulnerable to laboratory contamination, particularly from aerosolized DNA. Glycerol, a common additive that enhances enzyme activity, may also promote aerosol generation--an issue not yet thoroughly investigated. ObjectiveTo determine whether PCR master mixes containing glycerol lead to increased aerosol-based contamination and false-positive results. MethodsUsing glycerol-free and 30% glycerol master mixes, SARS-CoV-2 N1,N2 and human RNaseP gene templates were pipetted 30 times in biosafety cabinets in two labs. Template-free tubes were exposed to ambient air for 1, 2, or 5 minutes and subjected to 40-cycle real-time PCR. Aerosol particle counts (<0.3 {micro}m) were measured at each time point. Positive PCR counts and Ct values were compared between circumstances. Analysis was also done on retrospective contamination data comparing 10% and 30% glycerol-mixes. FindingsNegative controls exposed to 30% glycerol containing master-mixes resulted in significantly earlier amplification (lower Ct values) and more frequent positivity compared to glycerol-free and 10% glycerol-mixes (P< 0.05 across all targets and time points). Aerosol particle counts were upto 3-fold higher in the 30% glycerol group than the 10% group (P< 0.001). Retrospective analysis confirmed significantly greater contamination in 30% glycerol incidents, with lower Ct values for all targets (P= 0.0001). ConclusionThis work is the first to show that aerosol-mediated contamination is increased when glycerol is added to PCR master mixes. These findings emphasize strengthening biosafety measures and improving diagnostic accuracy in molecular laboratories, particularly in LMICs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/25341814v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@6231edorg.highwire.dtl.DTLVardef@bbfa4corg.highwire.dtl.DTLVardef@12dc21dorg.highwire.dtl.DTLVardef@12334e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

The neglected tropical disease schistosomiasis continues to be a global health concern, especially in low- and middle-income countries, with at least 250 million people infected worldwide and a further 779 million at risk of infection. Schistosoma japonicum, which is found in parts of South Asia, causes intestinal schistosomiasis in humans, as well as infecting up to forty other mammalian species. Therefore, novel diagnostics that can detect S. japonicum are desirable. In this study, we have further developed and refined the SNAILS DNA-based biosensor technology for the detection of schistosomes and have applied this to the specific detection of S. japonicum. Several new SNAILS probe pairs were developed, and our optimised design successfully detected and differentiated between genomic DNA isolated from laboratory-derived Schistosoma mansoni cercariae and S. japonicum cercariae isolated from sites in the Peoples Republic of China.

BackgroundIn 2017-2019, we conducted a large-scale, cluster-randomised trial (LLINEUP) to evaluate long-lasting insecticidal nets (LLINs) treated with a pyrethroid insecticide plus the synergist piperonyl butoxide (PBO LLINs), as compared to conventional, pyrethroid-only LLINs across 104 health sub-districts (HSDs) in Uganda. In LLINEUP, and similar trials in Tanzania, PBO LLINs were found to provide greater protection against malaria than conventional LLINs, reducing parasitaemia and vector density. In the LLINEUP trial, cross-sectional entomological surveys were carried out at baseline and then every 6 months for two years. In each survey, ten households per HSD were randomly selected for indoor household entomological collections. ResultsOverall, 5395 female Anopheles mosquitoes were collected from 5046 households. The proportion of mosquitoes infected with Plasmodium falciparum did not change significantly over time, while infection with non-falciparum malaria decreased in An. gambiae s.s, but not An. funestus. The frequency of genetic markers associated with pyrethroid resistance increased significantly over time, but the rate of change was not different between the two LLIN types. The knock-down resistance (kdr) mutation Vgsc-995S declined over time as Vgsc-995F, the alternative resistance mutation at this codon, increased. Vgsc-995F appears to be spreading into Uganda. ConclusionsDistribution of LLINs in Uganda was associated with reductions in parasite prevalence and vector density, but the proportion of infective mosquitoes remained stable, suggesting that the potential for transmission persisted. The increased frequency of markers of pyrethroid resistance indicates that LLIN distribution favoured the evolution of resistance within local vectors and highlights the potential benefits of resistance management strategies. Trial registration: This study is registered with ISRCTN, ISRCTN17516395. Registered 14 February 2017, http://www.isrctn.com/ISRCTN17516395

The queen bee is the central individual responsible for colony establishment, growth, and survival. Reliable confirmation of successful mating, continued queen presence, and normal reproductive performance is essential for effective colony management. We present a queen bee detection system based on an array of Hall-effect sensors and a miniature magnetic tag attached to the queen. The system is designed for continuous operation and real-time monitoring. A prototype was developed, constructed, and evaluated under both laboratory and field conditions. Field experiments conducted in an apiary demonstrated that the system can reliably detect queen bee passages through the hive entrance, enabling the identification of activities associated with mating flights. The results confirm the feasibility of Hall-effect sensing for automated, non-invasive queen bee monitoring and establish magnetic sensing as a promising new measurement modality for precision apiculture.

Environmental scientists often assess exposure to hazards using residential proximity (i.e., they consider an individual living near a hazard to be exposed). Such assessment requires large, fine-scale spatial datasets that describe locations of environmental hazards and residential populations. Manipulating such datasets is technically demanding, slow, memory-intensive, and difficult to optimize for speed and memory use. Currently, individual research teams each write their own algorithms for this task. This may lead to inconsistencies in assumptions, methods, and results. We developed an open-source Python package, popexposure, which quickly, efficiently, and consistently estimates the number of people living near environmental hazards. Given a set of distinct hazard geometries and corresponding buffer distances, popexposure can estimate the number of people living within the buffered area of each hazard using a gridded population dataset. popexposure can also estimate the number of people living within the buffer distance of each hazard by additional administrative geographies. For example, users can calculate the number of people exposed to hazards in each census tract or zip code tabulation area (ZCTA). popexposure addresses common issues encountered in this calculation: whether or not to double-count people exposed to more than one hazard, proper pixel apportionment, choosing appropriate map projections for data covering large areas, and optimizing speed and memory. In this paper, we describe popexposures functionality and provide an example use case, calculating the proportion of people exposed to any wildfire burn zone disaster in California in 2018 in each ZCTA. What this study addsEnvironmental epidemiologists often assess exposure to hazards using residential proximity (i.e., they consider an individual exposed if they live near a hazard). This computation presents technical difficulties, and different research teams each apply their own solution, since no software currently exists to do this task. We developed an open-source Python package, popexposure, which quickly, efficiently, and consistently estimates the number of people living near environmental hazards. Here, we describe the package and provide an example use case, applying popexposure to compute the proportion of people exposed to any wildfire burn zone disaster in California in 2018 in each ZCTA.

IntroductionIn accordance with AS5369:2023 and ISO 15883-1:2024 Standards, in-field validation testing of automated high-level disinfection (HLD) devices in clinical settings is necessary to qualify their performance. Responding to reports that some ultraviolet-C (UV-C) devices were failing to achieve sporicidal efficacy during routine in-field validation, we evaluated the sporicidal efficacy of these devices via a performance qualification test. MethodsSporicidal efficacy was assessed using commercially available stainless steel biological indicators (BIs) inoculated with 106 Geobacillus stearothermophilus spores (ATCC(R) 7953). BIs were clamped in top and bottom locations inside chambers of devices [UV-C light-emitting diode (LED), UV-C lamp and hydrogen peroxide (H2O2) mist]. BI test conditions included packaged, unwrapped and non-flamed and unwrapped and flame sterilised on the clamped coupon end. Results were evaluated on a pass (no growth) or fail (growth) basis. ResultsThe results showed that the UV-C LED device failed to inactivate spores in all tested positions and conditions (n=18). The UV-C lamp device passed 2/6 tests in the flamed condition but failed all other tests (n=12). The H2O2 mist system passed all tests, inactivating spores for all conditions and chamber positions (n=18). ConclusionOur findings show that despite claiming sporicidal efficacy, the UV-C devices both failed in-field validation tests using bacterial endospores. These results indicate that the UV-C devices were not sporicidal in these tests. The H2O2 mist device was the only system in this study that passed all tests, achieving sporicidal efficacy.

BackgroundLeishmaniasis and toxoplasmosis are major neglected zoonotic diseases affecting billions globally, with diverse clinical outcomes driven by complex host-pathogen immunogenomic interactions. While the One Health framework is theoretically essential for controlling these environmentally sensitive pathogens, the extent of its actual integration into high-throughput genomic research remains unquantified. A systematic mapping of this field is necessary to identify structural gaps and guide future interdisciplinary strategies. ObjectiveThis study aims to conduct a comprehensive bibliometric and visual analysis of global research trends in the immunogenomics of Leishmania spp. and Toxoplasma gondii, explicitly evaluating the integration of human, animal, and environmental health dimensions within a One Health context. MethodsA systematic analysis was performed on a curated, deduplicated dataset of 1,320 publications retrieved from Dimensions and Lens.org, covering the period from January 2000 to October 2025. Advanced scientometric techniques, including social network analysis, keyword co-occurrence mapping, and automated content classification, were executed using Python to quantify publication trajectories, collaboration networks, and thematic evolution. ResultsThe research domain exhibited robust exponential growth with a Compound Annual Growth Rate (CAGR) of 8.87% and a doubling time of 5.5 years. Brazil emerged as the leading contributor (142 publications), while the United States led in citation impact. Network analysis revealed a "small-world" collaboration structure (clustering coefficient 0.919) driven by strategic North-South partnerships, particularly between the USA and Brazil. Although 62.9% of publications were classified as integrating One Health concepts, a distinct "environmental blind spot" was identified, with only 5.9% of research addressing the human-environment interface. Thematically, the field has pivoted from observational cytokine profiling (dominated by IL-10 and IFN-{gamma}) to systems-level inquiry, with "molecular docking simulation" (Burst Score 72.7) emerging as a high-velocity frontier, signaling a rapid shift toward in silico therapeutic discovery. ConclusionsThe "genomic revolution" has successfully democratized research capacity, shifting the center of knowledge production toward endemic regions. However, the field faces a critical technological lag in Artificial Intelligence adoption and a lack of mechanistic environmental research. Future strategies must prioritize "eco-immunogenomics" and multi-omics integration to bridge the gap between genomic data generation and holistic One Health implementation. HighlightsO_LIResearch output grew exponentially (CAGR 8.87%) with a 5.5-year doubling time. C_LIO_LIBrazil leads global production, challenging traditional Global North dominance. C_LIO_LIStrategic North-South partnerships, notably USA-Brazil, drive global collaboration. C_LIO_LIOnly [~]6% of publications address the environmental component of One Health. C_LIO_LIIn silico docking is a rapid frontier, yet AI adoption remains a critical gap. C_LI

Whilst in most northern European countries swimming shorts are the norm, swimming briefs, or Speedos, are compulsory in France and parts of Italy. Hygiene is the main justification for this harsh measure, but empirical data is lacking and consequences for psychological well-being remain unaddressed. This study aimed to empirically assess the hitherto theoretical risk to public pool hygiene posed by swimming shorts, as compared to form fitting swimming briefs (Speedos) that are required swimwear in several southern European countries. The study design was a cross-over study and photo elicitation, involving international adult male academics and technical staff, of varying age (20-54), BMI (17.8-25.7), and job title. Speedos or swimming shorts were worn for two hours during the normal working day to assess bodily contamination, or as swimwear in natural water bodies to assess environmental contamination. The primary outcome was the number of colony-forming units (CFU) of Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB) on agar plated with 100 {micro}L water collected from i) submersion of the swimming garments in clean tap-water (bodily contamination), or ii) residual water from natural sources (environmental contamination). The main secondary outcomes were self-perceived and externally assessed attractiveness of anonymized photos of the trunk (top of legs to neck) of study participants wearing both swimwear types. GPB and GNB growth was significantly higher after incubation with tap water rinsed from submersed shorts compared to Speedos (rank-sum, GPB p<0.0400, GNB p=0.0100). Swimming in natural water resulted in considerably higher levels of GNB on Speedos (median 600 CFU/litre, interquartile-range (IQR): 600 to 31400) and shorts (median 33800, IQR: 5200 to 91000), but sample sizes were too low for formal analysis. Participants were significantly less attractive to external judges in Speedos compared to shorts (p<0.0067), but not when judged by themselves (Wilcoxon paired sign rank test: p=0.3394). We conclude that both GPB and GNB contamination were more abundant in water exposed to swimming shorts, as compared to Speedos. The superior hygiene afforded by scant swimwear comes at the cost of markedly lower externally assessed attractiveness.

Artificial intelligence and automation technologies are displacing millions of workers across industries in developed countries, while many developing nations continue to grapple with chronically high unemployment. Meanwhile, healthcare laboratories--particularly in resource-limited settings (including rural and community sites within high-income countries)--face acute shortages of trained staff and the high cost of molecular diagnostics. Here, we propose a "train-and-assist" class of devices that aims to both (i) upskill--rather than replace--workers and (ii) expand diagnostic capacity in a cost-effective way. We describe a device that trains and assists laboratory-inexperienced personnel to perform sample-pooling procedures, which enable high-performance molecular testing at lower costs and higher throughput. A 48-participant user study demonstrated that the device enabled both skill acquisition and high-accuracy pooling. A device-validation study using clinical stool specimens demonstrated that device-assisted pooling agreed 100% with individual assays for soil-transmitted helminths, which affect more than 1.5 billion people worldwide. TeaserWe propose a new class of technologies that train and assist personnel to learn skills for complex laboratory tasks, with the goal of strengthening the diagnostics workforce and expanding healthcare access.