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Utilizing a novel fecal sampling method to examine resistance of the honey bee (Apis mellifera) gut microbiome to a low dose of tetracycline

Disruption of host-associated microbial communities can have detrimental impacts on host health. However, the capacity of individual host-associated microbial communities to resist disturbance has not been well defined. Using a novel fecal sampling method for honey bees (Apis mellifera), we examined the resistance of the honey bee gut microbiome to disruption from a low dose of the antibiotic, tetracycline (4.5 μg). Prior to the experiment, bacterial communities from fecal samples were compared to communities from dissected whole guts of the same individuals to ensure fecal samples accurately represented the gut microbiome. Fecal samples were collected from lab-caged honey bees prior to, and five days after, tetracycline exposure to assess how antibiotic disturbance affected the communities of individuals. We used metrics of alpha and beta diversity calculated from 16S rRNA gene amplicon sequences to compare gut community structure. Low dose tetracycline exposure did not consistently change honey bee gut microbiome structure, but there was individual variation in response to exposure and specific taxa (one ASV assigned to Lactobacillus kunkeei and one ASV in the genus Bombella) were differentially abundant following tetracycline treatment. To assess whether individual variation could be influenced by the presence of tetracycline resistance genes, we quantified the abundance of tet(B) and tet(M) with qPCR. The abundance of tet(M) prior to tetracycline treatment was negatively correlated with change in community membership, assessed by difference in Jaccard dissimilarity over the five-day experiment. Our results suggest that the honey bee gut microbiome has some ability to resist or recover from antibiotic-induced change, specific taxa may vary in their susceptibility to tetracycline exposure, and antibiotic resistance genes may contribute to gut microbiome resistance.

CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization

Objective This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. Materials and methods The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer’s disease (AD) across 10 different observational data sources. Results By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease’s anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. Discussion We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study. Conclusion Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs.

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A comparison of disseminated intravascular coagulation scoring systems and their performance to predict mortality in sepsis patients: A systematic review and meta-analysis

Background Disseminated intravascular coagulation (DIC) is a common complication in sepsis patients which exacerbates patient outcomes. The prevalence and outcomes of DIC in sepsis is wide-ranging and highly depends on the severity of the disease and diagnostic approaches utilized. Varied diagnostic criteria of DIC have been developed and their performance in diagnosis and prognosis is not consistent. Therefore, this study aimed to determine the score positivity rate and performance of different DIC scoring systems in predicting mortality in sepsis patients. Methods Four databases, including Medline (through PubMed), Scopus, Embase, and Web of Science were searched for studies that determined DIC in sepsis patients using the three scoring systems namely: the International Society on Thrombosis and Hemostasis DIC (ISTH-DIC) criteria, the Japanese association for acute medicine DIC (JAAM-DIC) criteria, and the sepsis-induced coagulopathy (SIC) criteria. A random-effect meta-analysis was performed with a 95% confidence interval (CI). Subgroup analysis was conducted in view of geographic region and sepsis stages. the protocol was submitted to the Prospective Register for Systematic Reviews (PROSPERO) with an identifier (CRD42023409614). Results Twenty-one studies, published between 2009 and 2024, comprising 9319 sepsis patients were included. The pooled proportion of cases diagnosed as positive using ISTH-DIC criteria, JAAM-DIC criteria, and SIC were 28% (95% CI: 24–34%), 55% (95% CI:42–70%), and 57% (95% CI: 52–78%), respectively. The pooled mortality rates were 44% (95% CI:33–53%), 37% (95% CI: 29–46%), and 35% (95% CI: 29–41%), respectively. The pooled sensitivity and specificity of ISTH-DIC to predict mortality were 0.43 (95% CI: 0.34–0.52), and 0.81 (95% CI: 0.74–0.87), respectively, while for JAAM-DIC it was 0.73 (95% CI: 0.57–0.85) and 0.46 (95% CI: 0.28–0.65), respectively. Pooled sensitivity and specificity for SIC were 0.71 (95% CI: 0.57–0.82) and 0.49 (95% CI: 0.31–0.66), respectively. Conclusion The SIC and JAAM-DIC scores exhibited higher sensitivity to identify patients with coagulopathy and predict patient outcomes, and thus are valuable to identify patients for possible treatment at an early stage. The ISTH-DIC score perhaps identified patients at later stages and demonstrated better specificity to predict disease outcomes. Thus, early identification of patients using the SIC and JAAM-DIC scores and later confirmation using the ISTH-DIC score would be beneficial approach for improved management of patients with sepsis.

Elevated few-shot network intrusion detection via self-attention mechanisms and iterative refinement

The network intrusion detection system (NIDS) plays a critical role in maintaining network security. However, traditional NIDS relies on a large volume of samples for training, which exhibits insufficient adaptability in rapidly changing network environments and complex attack methods, especially when facing novel and rare attacks. As attack strategies evolve, there is often a lack of sufficient samples to train models, making it difficult for traditional methods to respond quickly and effectively to new threats. Although existing few-shot network intrusion detection systems have begun to address sample scarcity, these systems often fail to effectively capture long-range dependencies within the network environment due to limited observational scope. To overcome these challenges, this paper proposes a novel elevated few-shot network intrusion detection method based on self-attention mechanisms and iterative refinement. This approach leverages the advantages of self-attention to effectively extract key features from network traffic and capture long-range dependencies. Additionally, the introduction of positional encoding ensures the temporal sequence of traffic is preserved during processing, enhancing the model’s ability to capture temporal dynamics. By combining multiple update strategies in meta-learning, the model is initially trained on a general foundation during the training phase, followed by fine-tuning with few-shot data during the testing phase, significantly reducing sample dependency while improving the model’s adaptability and prediction accuracy. Experimental results indicate that this method achieved detection rates of 99.90% and 98.23% on the CICIDS2017 and CICIDS2018 datasets, respectively, using only 10 samples.

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Racism, homophobia, and the sexual health of young Black men who have sex with men in the United States: A systematic review

Black gay, bisexual, and other men who have sex with men (BMSM) experience the highest rates of HIV acquisition annually out of any population in the United States, and young BMSM (YBMSM) are heavily impacted by this inequity as they enter adulthood. Despite a high annual HIV incidence, extant literature has found BMSM to engage in fewer sexual risk behaviors than White and Hispanic/Latino men who have sex with men, resulting in a gap between risk behaviors and the inequity of HIV infection. Structural factors, such as racism and homophobia, are thus being examined in order to understand this disconnect between behavior and HIV incidence. The purpose of this systematic review was to examine the discrimination experiences of YBMSM due to racism and homophobia in the United States and to evaluate the effect of these experiences on their sexual health. Four databases (MEDLINE, CINAHL Complete, APA PsycINFO, and Sociology Source Ultimate) were searched to examine the available qualitative, quantitative, and mixed method studies relevant to the research question. Out of 17 included studies, the majority were qualitative in design and were conducted in urban settings. Racism and homophobia affected YBMSM’s sense of belonging, sexual identity, and sexual partnership choices. Often, masculinity would interact with these two constructs to impact how YBMSM engaged in sexual behavior, such as condomless sex, as well as their likelihood to seek sexual health care. Future research is needed to fully understand the relationships between discrimination and sexual health to develop effective structurally responsive interventions that will help decrease the inequities experienced by YBMSM.

Masking is good, but conforming is better: The consequences of masking non-conformity within the college classroom

In the years following the acute COVID-19 crisis, facemask mandates became increasingly rare, rendering masking a highly visible personal choice. Across three studies conducted in the U.S. in 2022 and 2023 (N = 2,973), the current work provided a novel exploration of the potential impacts of adhering to vs. deviating from group masking norms within college classrooms. Experiments 1 and 2 used causal methods to assess the impact of hypothetical target students’ masking behavior on participants’ beliefs about that student’s classroom fit (e.g., how well they fit in, how much their professor likes them, whether they are invited to study group). Maskers were expected to experience more classroom inclusion relative to non-maskers, but the largest effects were conformity effects: participants expected that students who deviated from a class’s dominant mask-wearing behavior would experience massively lower classroom fit. Study 3 used correlational and qualitative methods to establish the real-world impact of mask conformity in a diverse sample of college students. Students reported believing that masking–and mask conformity–impacted others’ perceptions of them, and reported avoiding deviating from masking norms. Students reported that their desire for mask-conformity impacted both their willingness to enroll in courses and their actual masking behavior, suggesting both academic and public health impacts. Across all three studies, we asked whether pressures to conform have disproportionate effects on particular groups, by exploring the effects of gender (Studies 1 and 3), immune-status (Studies 2 and 3) and race (Study 3). Our data raise important issues that should be considered when determining whether to e.g., enact mask mandates within college classrooms and beyond, and for understanding the cognitive and social consequences of mask wearing.

Compassion fatigue and associated factors among nurses working in Jimma Zone public hospitals, southwest Ethiopia: A facility based cross-sectional study

Background Nurses are at risk of developing compassion fatigue, which has negative impacts on their well-being, quality care and leads to patient mortality and a financial burden on the healthcare system. However, data on compassion fatigue is scarce in Africa, particularly Ethiopia. Therefore, this study aimed to assess level compassion fatigue and associated factors among nurses in Jimma Zone public hospitals, Ethiopia. Method A facility-based cross-sectional study was employed from May 25 to June 25, 2023. A systematic sampling technique was employed to select among 422 respondents. Data were collected using pretested self-administered questionnaires. Professional Quality of Life Scale-5 was used for measuring compassion fatigues. Data were entered using Epi data version 4.6 and analyzed using SPSS version 25. Linear regression were done to identify factors associated with compassion fatigue. Statistically significant was declared at a p-value of ≤ 0.05 with 95% CI. Result From a total of 422 respondents, 412(97.6%) of them gave complete responses. 47% of respondents, had a moderate level of compassion fatigue. Total experience [β = -0.04; 95%CI (-0.06, -0.01); p = 0.005], perceived social support [β = -0.13; 95% CI (-0.17, -0.08); p<0.001], self-compassion [β = -0.09; 95% CI (-0.14, -0.03); p = 0.003], support seeking [β = -0.23; 95% CI (-0.42, -0.04 p = 0.017], emergency ward [β = 0.36; 95% CI (0.2, 0.51); p <0.001], ICU [β = 0.38; 95% CI (0.21, 0.54); p<0.001], pediatric ward [β = 0.23; 95% CI (0.10, 0.36); p < 0.001] and average sleep hours per day [β = 0.46; 95% CI (0.35, 0.57); p<0.001] were statistically signifantly factors. Conclusion and recommendation The study revealed that one in four nurses had high level of compassion fatigue. The factors associated were work experience, perceived social support, self-compassion, coping strategies, work unit, and sleep hours. Therefore, stakeholders including hospital managers should implement targeted strategies to prevent compassion fatigue including training on coping strategy and, self-compassion and creating culture of team work among nurses.

Philanthropic foundations must step in to shield science from Trump’s cuts

Association between age at first birth and risk of rheumatoid arthritis in women: Evidence from NHANES 2011–2020

Objective This study focused on investigating the relation of age at first birth (AFB) with rheumatoid arthritis (RA) risk in women based on the 2011–2020 NHANES (National Health and Nutrition Examination Survey) data. Methods Women were analyzed using National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2020 in the US. Both AFB and RA diagnoses were obtained through self-reported questionnaires. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined using logistic regression models. Results Among the 7,449 women included in this study, 552 (7%) were diagnosed with RA. In comparison with women who had an AFB of 30–32 years (reference group), those who had an AFB < 18, 18–20, 21–23, 24–26, and > 35 years had the fully adjusted ORs and 95% CIs of 4.00 (95% CI 1.70, 9.40), 2.90 (95% CI 1.25, 6.73), 3.00 (95% CI 1.32, 6.80), 3.18 (95% CI 1.36–7.42), and 3.36 (95% CI 1.04–10.7), respectively. Due to the limitations inherent in cross-sectional studies, we have not observed significant differences in the risk of RA between women aged 27–29 and 33–35 at the AFB. Further research is warranted to refine these findings. Conclusion Women with an AFB < 26, or > 35 years have a higher risk of developing RA later in life. Policymakers may consider focusing more on women in these AFB age ranges in screening RA and making preventive measures.

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Gaussian barebone mechanism and wormhole strategy enhanced moth flame optimization for global optimization and medical diagnostics

Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance. This improvement is achieved by incorporating a Gaussian barebone mechanism, a wormhole strategy, and an elimination strategy into the MFO. To assess the effectiveness of BWEMFO, a series of comparison experiments is conducted, comparing it against conventional metaheuristic algorithms, advanced metaheuristic algorithms, and various MFO variants. The experimental results reveal a significant enhancement in both the convergence speed and the capability to escape local optima with the implementation of BWEMFO. The scalability of the algorithm is confirmed through benchmark functions. Employing BWEMFO, we optimize the kernel parameters of the kernel-limit learning machine, thereby crafting the BWEMFO-KELM methodology for medical diagnosis and prediction. Subsequently, BWEMFO-KELM undergoes diagnostic and predictive experimentation on three distinct medical datasets: the breast cancer dataset, colorectal cancer datasets, and mammographic dataset. Through comparative analysis against five alternative machine learning methodologies across four evaluation metrics, our experimental findings evince the superior diagnostic accuracy and reliability of the proposed BWEMFO-KELM model.

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Mucosal injuries from indwelling catheters: A scoping review

There is currently a lack of clarity concerning the types and frequency of mucosa injuries occurring in urine bladders among patients with indwelling urine catheters that are of modern design and material. The aim of the study was to identify and present the available information regarding mucosa injuries in urine bladders among adult patients with indwelling urine catheters. The research question was: What is known about mucosa injuries in urine bladders among patients with indwelling urine catheters? A scoping review applying the patient, exposure, and outcome framework. A preliminary search was made to identify the keywords, and the selection process followed the Preferred Reporting Items for Systematic Review and Meta-Analysis flow diagram. The final search across five databases retrieved a total of 8,883 records. Eight studies from three countries were included and the studies used two main methods for collecting data. Eleven concepts to describe the injuries were identified, with a range from one to five studies using the same concept. Mucosa injuries, of which polypoid cystitis was most frequently reported, occurred in all studies, and ranged from 41% to 100% per study. The size of injured area varied between 0.5 to 2.5 cm. The posterior wall of the bladder was the most common area where injuries were found. This scoping review sheds light on the limited understanding of mucosal injuries in urine bladders among adult patients with indwelling urinary catheters. Moving forward, concerted efforts are warranted to bridge existing knowledge gaps to enhance our understanding of mucosal injuries and improve clinical outcomes for adult patients with indwelling urinary catheters. The lack of a robust scientific base for the impact of indwelling urine catheters on the urine bladder mucosa warrants future studies.

Exploring the coupling coordination relationship and obstacle factors of rural revitalization, new-type urbanization, and digital economy in China

The digital economy injects new vitality into rural revitalization and new-type urbanization to achieve rural industrial transformation, while the countryside and the city provide the soil for the development of the digital economy. This research establishes the rural revitalization (RR), new-type urbanization (NU), and digital economy (DE) system and uses the coupled coordination degree (CCD) model and obstacle degree (OD) model to study the spatiotemporal evolution characteristics and obstacle factors of the composite system in China from 2011 to 2021. The result showed that: (1) the comprehensive development level of the composite systems is on an upward trend year by year, but still shows a low-quality state; (2) the CCD of China’s provinces shows a spatial evolution pattern of high in the east and low in the west; (3) The obstacle factors of the RR, NU, DE subsystem are mainly involved the number of rural doctors and health workers, local financial income per capita and science and technology expenditure, and the digital finance coverage breadth index. These results suggested that Strengthening the synergy between China’s urban-rural integrated development and digital construction in the future hinges upon providing valuable decision-making insights to facilitate the pursuit of regionally differentiated development and the achievement of sustainable development goals.

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Correction: Ex-ante impact of pest des petits ruminant control on micro and macro socioeconomic indicators in Senegal: A system dynamics modelling approach

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Light-dependent variations in fatty acid profiles and gene expression in Antarctic microalgal cultures

Photosynthetic eukaryotic microalgae are key primary producers in the Antarctic sea ice environment. Anticipated changes in sea ice thickness and snow load due to climate change may cause substantial shifts in available light to these ice-associated organisms. This study used a laboratory-based experiment to investigate how light levels, simulating different sea ice and snow thicknesses, affect fatty acid (FA) composition in two ice associated microalgae species, the pennate diatom Nitzschia cf. biundulata and the dinoflagellate Polarella glacialis. FA profiling and transcriptomic analyses were used to compare the impact of three light levels: High (baseline culturing conditions 90 ± 1 μmol photons m−2 s−1), mid (10 ± 1 μmol photons m−2 s−1); and low (1.5 ± 1 μmol photons m−2 s−1) on each isolate. Both microalgal isolates had altered growth rates and shifts in FA composition under different light conditions. Nitzschia cf. biundulata exhibited significant changes in specific saturated and monounsaturated FAs, with a notable increase in energy storage-related FAs under conditions emulating thinner ice or reduced snow cover. Polarella glacialis significantly increased production of polyunsaturated FAs (PUFAs) in mid light conditions, particularly octadecapentaenoic acid (C18:5N-3), indicating enhanced membrane fluidity and synthesis of longer-chain PUFAs. Notably, C18:5N-3 has been identified as an ichthyotoxic molecule, with fish mortalities associated with other high producing marine taxa. High light levels caused down regulation of photosynthetic genes in N. cf. biundulata isolates and up-regulation in P. glacialis isolates. This and the FA composition changes show the variability of acclimation strategies for different taxonomic groups, providing insights into the responses of microalgae to light stress. This variability could impact polar food webs under climate change, particularly through changes in macronutrient availability to higher trophic levels due to species specific acclimation responses. Further research on the broader microalgal community is needed to clarify the extent of these effects.