En este emocionante proyecto STEM, los estudiantes explorarán las Leyes del Movimiento de Newton mediante una actividad práctica y divertida: ¡una carrera de carros impulsados por globos! Usando materiales simples como globos, hilo, pajillas y cinta adhesiva, los estudiantes construirán carros caseros que se deslizan por una cuerda. A través del diseño, prueba y modificación de sus carros, los estudiantes observarán directamente cómo actúan las fuerzas, el movimiento y la acción-reacción en la vida real. Imagen de referencia.

A document by Rebecca McKay. Click on the document to view its contents.

The multidrug-resistant (MDR) Escherichia coli, particularly β-lactamase producing strains, has become a global health challenge, where wastewater systems, specially from hospitals, serve as critical reservoirs for the dissemination of resistance genes. The objectives of the study were to investigate the whole genome sequence diversity and genetic variations focusing on their evolutionary relationships, genetic similarity, and single nucleotide polymorphisms (SNPs) of pathogenic β-lactamase producing E. coli strains. A total of four β-lactamase producing E. coli strains, from differently located tertiary care hospitals, were included in this study. A heatmap of genetic similarity revealed near-identical genetic makeup among the strains. A number of genes like, AcrAB-TolC, β-lactamase genes, and resistance genes of GlpT, gyrA, msbA, and tet (M) were found to be expressed in these strains. However, the presence of virulence genes of aerobactin synthesis gene (iucA, iutA) and type III secretion systems (espX1, espX4, espX5) in the strain ensured pathogenicity of the strains. These findings provide insights into the whole genome sequence diversity of E. coli in urban based tertiary care hospital wastewater, emphasizing the genetic variation and plasmid diversity in these E. coli strains, which may have implications in public health and microbial ecology of the environment.

Background: Significant racial and ethnic disparities continue to persist in clinical trial enrollment. This limits the generalizability of clinical trial data and consequently the application of novel therapeutics for diverse populations. We hypothesized that enrollment of underrepresented minorities (URM) is feasible, with improved access to clinical care and a tailored clinical trial portfolio addressing the unmet needs in URM. Methods: A retrospective cohort study was conducted at the University of California Irvine Chao Comprehensive Cancer Center (CFCCC), an NCI-Designated cancer center located in Orange County (OC), CA (a minority-majority county). Clinical trial enrollment data from 2015-2023 through the CFCCC clinical research database was included. Collected data on patient demographics, tumor types, and trial enrollment was compared to data provided by the NIH SEER reports in both OC and United States. Results: Between 2015-2023, 2317 subjects were enrolled. Demographics were as follows: White, Asian, Black, American Indian/Alaska Native, mixed/unknown race (66.4%/20.1%/2.4%/0.7%/0.7%/ 9.3%). Non-Hispanic vs Hispanic ethnicity (77.8%/20.5%). Female vs Male sex (47.6%/52.3%). Age <70 vs >70 years (73.3%/26.7%). Low Income/Health Professional Shortage Areas (HPSA) vs non-Low Income/HPSA (44.8%/55.1%). Study Phase: Phase I/II (35.6%), Phase II (25.4%), Phase II/III (2.8%), Phase III (26.6%), and Phase IV (0.3%). Study sponsor: Industry (61.1%), Institutional (23.6%), National (14.8%), and Externally Peer Reviewed (0.3%). Most notably, the Asian enrollment (20.1%) and Hispanic enrollment (20.5%) exceeded demographic representation of Asians (17%, RR 1.18) and Hispanics (19%, RR 1.07) at CFCCC. Asians enrolled at significantly higher rates for lung and liver cancer, Hispanics for breast cancer, and Blacks for prostate cancer. Conclusions: Our results demonstrate that URM clinical trial enrollment, particularly among Asian and Hispanic populations, is feasible at an NCI-designated cancer center. Further research is necessary to investigate factors that influence clinical trial participation in the pursuit of equitable cancer care.

The study investigated the effect of positive emotion induced by olfactory stimuli (Aromatic) on the memory span under conditions of varying task difficulty. Eighty-two healthy college students performed the easy/difficult task while positive or neutral emotion was induced by aromatic/neutral olfactory stimulation in two separate experiments. Results showed that compared to neutral condition with air stimuli, a significant performance improvement in the presence of orange odor when the memory task was reading working memory span (the difficult task) but not the simple number span (the easy task). Our results provided the empirical demonstration that positive emotion induced by aromatic smell could facilitate memory performance, and also highlighted the potential modulatory role of task-difficulty. This result deepens the broaden-and-build theory of positive emotions.

【Abstract】Background: Small cell lung cancer (SCLC) is a common pathological type of lung cancer. Almost all patients with SCLC also relapse after achieving a complete response with first-line treatment. The role of second-line treatment strategies in SCLC has been a subject of extensive research and debate. Objective: To summarize the impact of regimens containing irinotecan in second-line or subsequent therapies on the overall survival (OS) of small cell lung cancer patients. Methods: We reviewed extensive stage SCLC patients at Wangjing Hospital of CACMS who had received irinotecan monotherapy or combination with others anti-tumor drugs from January 2016 and December 2023 as second-and subsequent-line treatment. Results: A total of 73 patients were included in the final analysis. The median overall survival (OS) time for irinotecan used as second-line therapy was 7.47 months. Hypertension and hepatic dysfunction were the most pronounced adverse events of combination therapy and two patients changed regimens due to severe fatigue and anorexia. Conclusions: The combination of bevacizumab and irinotecan effectively alleviate brain metastasis in small cell lung cancer and can prolong OS in second-line therapy.

Deduplicating and sorting large sets of integers is a significant requirement in the field of big data, posing particular challenges in single-threaded or resource-constrained environments. This paper proposes the SlowSort algorithm as an improvement and engineering optimization of the classic BitSort idea, which naturally includes a deduplication function. SlowSort supports negative integers through offset mapping; it significantly compresses space requirements by using the second smallest and second largest values to determine the bit vector range, excluding the influence of outliers; and it significantly improves single-threaded execution efficiency through optimizations such as bitwise operations. Comparative experiments implemented in Java and C++ show that: on high-density integer datasets within the 10 7 range, SlowSort’s average performance surpasses parallel sorting algorithms and is on par with radix sort; on integer sets within the 10 9 range, SlowSort’s performance is similar to parallel sorting and radix sort; on large-scale datasets of one billion elements or more, SlowSort’s performance is 50% lower than parallel sorting and radix sort, but its peak memory overhead is reduced by 50%; across all three scales mentioned above, SlowSort’s performance is more than 8 times faster than standard library sorting algorithms (Java Arrays.sort, C++ std::sort). Therefore, SlowSort provides a solution that balances performance and engineering practicality for large-scale integer deduplication and sorting tasks in specific scenarios.

Healthcare Patients are concerned about their information being hacked or misused in any way because AI systems require vast volumes of data, including private medical records. By increasing accuracy, efficiency, and accessibility, artificial intelligence (AI) technologies are being increasingly incorporated for early disease detection, leading to significant improvements in medical evaluation and therapy planning. The number of patients entering the majority of hospitals overwhelms them, resulting in lengthy wait times, less individualized treatment, and potentially subpar diagnostic testing for the detection of illness early. The purpose of this research is to investigate how artificial intelligence (AI) might improve early disease detection, with an emphasis on higher patient satisfaction by using a new P-algorithm. The study implements a novel P-algorithm to enhance accuracy and patient outcomes in medical services. The findings have a substantial and powerful influence on the contribution that might improve early disease detection, emphasizing higher patient satisfaction by using a new P-algorithm. The current study contributed that by overcoming these obstacles, AI systems that employ P-algorithms would be able to greatly enhance early disease detection and, as a result, further enhance the patient’s access to timely and personalized healthcare services while maintaining adherence to privacy, trust, and fairness principles.

IntroductionPityriasis lichenoides et varioliformis acuta (PLEVA), also known as Mucha-Habermann disease, is a rare T-cell lymphoproliferative disorder that can occur at any age but most commonly affects children and young adults, with a slight male preponderance.1 The pathogenesis of PLEVA is thought to involve two mechanisms: a T-cell dyscrasia and an aberrant immune response to viral, bacterial, or protozoal infections.2PLEVA is marked by the sudden appearance of asymptomatic to multiple small erythematous papules that rapidly progress into polymorphic lesions with varying forms, including vesicles, pustules, hemorrhagic crusted papules, and shallow ulcers. These lesions typically resolve within weeks to months but can occasionally leave behind long-lasting complications such as hyperpigmented or hypopigmented, varioliform scars. The lesions primarily affect the anterior trunk, flexural areas, and proximal extremities, though they can also occur in other parts of the body, sparing the mucosa.1,3PLEVA typically has a self-limiting course, with lesions resolving spontaneously over time. However, in certain cases, the condition may persist and follow a more recalcitrant trajectory, characterized by recurring flare-ups and varying lengths of remission between episodes.3To our knowledge, this is the first documented case of PLEVA presenting with significant facial and mucosal involvement in a 15-year-old male from Nepal. This case highlights the atypical nature of the condition and underscores the rarity of such presentations.

Title: Multidisciplinary Management of Bilateral Ureteric Obstruction and Forniceal Rupture in Twin Pregnancy: a case report

INTRODUCTION.Exceptional and coexisting illnesses can exacerbate the complex character, making it especially perplexing for the researchers and doctors. This case report highlights the evaluation and management of a patient with polycystic kidney disease and stomach cancer, a rare co-occurrence of these two extreme illnesses. Adult polycystic kidney disease (APKD) and gastric adenocarcinoma are two discrete clinical conditions with unique pathophysiology. A commonly occurring gastrointestinal tract cancer, gastric adenocarcinoma is frequently linked to Helicobacter pylori (H. pylori) infection, genetic predisposition, and dietary variables. In contrast, APKD is a genetic illness that causes several kidney cysts to form, which impairs kidney function and causes other systemic problems. These illnesses’ concurrent occurrence highlights the importance of comprehensive assessment approaches and encourages investigation into the common underlying mechanisms that many of them share.A major contributing factor for carcinoma-related illness and death globally, gastric cancer is distinguished by it’s complicated and diverse presentations [1]. According to estimates, there were approximately 1.1 million newly identified cases and 770,000 deaths from stomach cancer in 2020 [2]. Simultaneously, multiple bilateral renal cysts are a hallmark of polycystic kidney disease, a hereditary illness [3]. It is exceedingly uncommon for malignant neoplasms and polycystic kidney disease to be associated in the literature; to our understanding, only nine instances have addressed the relationship between these two different conditions [4-11].This case study aims to provide significant insights into the unusual situation of a 60-year-old patient who was diagnosed with both adult polycystic kidney disease and gastric adenocarcinoma. It does this by highlighting the diagnostic methods, management techniques, and clinical difficulties that were encountered in this particular situation. Additionally, the literature is analyzed to determine whether there is a genetic connection among their association and potential new treatments.

Recurrent Takotsubo Cardiomyopathy with Variable Patterns and Psychiatric Comorbidities: A Case Report and Comprehensive Literature ReviewAuthors : Hamza AlKowatli¹, Oubada Alkowatli², Ahmad Karzoun¹, Bisher Sawaf3, Mhd Baraa Habib4, Nahush Bansal3, Muhammad Faiz¹, Robert Subbiondo¹

The activation of naive CD8+T cells by mature dendritic cells (DCs) through antigen presentation is essential for initiating effective anti-tumor immunity. In our previous study, Glycyrrhiza uralensis polysaccharides (GUPS) were prepared and found that they significantly promoted the maturation and cytokine secretion of human Mo-DC and murine BM-DC through TLR4 signaling pathways. In this study, HPV-DC vaccine prepared with GUPS (GUPS-DC+HPV) significantly suppressed the tumor growth and improved the survival of tumor mice, which was correlated with the induction of HPV-16-specific cellular response and the increased infiltration of CD8+T cells in tumors. Although HPV-DC vaccine can induce CTL response and inhibit tumor growth, the late treatment effect needs to be further improved. Therefore, the combination treatment of HPV-DC vaccine and anti-PD-1 Ab could further improve the therapeutic efficacy, improve the survival rate of tumor mice, generated memory immune response and suppressed lung metastasis of tumors. These findings suggest GUPS as a potent adjuvant that promotes DC maturation, and offering a combination strategy for the treatment of advanced tumors.

Objective. To evaluate the preclinical activity of Dato-DXd in vitro against CS cell lines with various TROP2 expression and in vivo against CS xenografts in mice. Design. Preclinical and Animal Studies Setting. Academic medical institution Population. Uterine and ovarian carcinosarcoma cell lines harvested from primary and metastatic patients harvested during staging procedures. Methods. TROP2 expression was determined using flow-cytometry. Cells were treated with Dato-DXd and a control ADC (CTL ADC) to evaluate IC 50 values. Double-strand DNA-breakage assay evaluated DNA damage while a 4-hour chromium-release assay evaluated antibody-dependent-cell-cytotoxicity (ADCC). Mice harboring CS xenografts were treated via retro-orbital Dato-DXd administration. Main Outcome Measures. TROP2 Expression, ADC-induced cell death, bystander effect, ADC-induced tumor inhibition, overall survival Results. TROP2 expressing CS lines were highly sensitive to killing induced by Dato-DXd . In contrast, low-expressing CS cell lines (SARARK1 and SARARK14) had no significant difference in cell cytotoxicity. Dato-DXd induced ADCC in the presence of peripheral blood lymphocytes. When TROP2 negative cells were admixed with TROP2 overexpressing cells, a significant bystander effect with Dato-DXd was appreciated. In vivo, mouse xenografts overexpressing TROP2 treated with Dato-DXd demonstrated tumor growth suppression and longer overall survival compared to CTL ADC treated xenografts. Conclusions. Dato-DXd is remarkably active against primary and metastatic uterine and ovarian CS overexpressing TROP2 in vitro and in vivo. Financial support: NIH U01 CA176067-01A1, the Deborah Bunn Alley Foundation, the Domenic Cicchetti Foundation, the Discovery to Cure Foundation, the Guido Berlucchi Foundation, NIH Research Grant CA-16359 from NCI and Standup-to-cancer (SU2C) convergence grant 2.0 to AS.

“‘latex Objective: This study investigates the efficacy of the novel Transvaginal Uterine Artery Clamp (TVUAC) as a second-line intervention for controlling post partum hemorrhage (PPH). Design: Descriptive, cross-sectional, observational study. Setting: Department of Obstetrics and Gynecology in a tertiary care center in Eastern India. Sample: 254 women with PPH (>500ml) following vaginal deliveries unresponsive to first-line uterotonics. Methods: Post Vaginal delivery, when uterotonics failed to control PPH, TVUAC were applied bilaterally thereby occluding the uterine arteries providing immediate hemostasis. Data collection and relevant statistical analysis was performed. Main Outcome Measures: TVUAC success rate, requirement of third-line surgical interventions, and factors associated with failure. Results: TVUAC successfully controlled PPH in 97% cases (247/254; 95% CI: 94.6%-98.6%), with only 3% (7/254; 95% CI: 1.4%-5.4%) requiring laparotomy. Clamp failure was significantly higher in referred cases (16.7%, p=0.034), instrumental deliveries (33.3%, p=0.02), and those with hypertensive disorders (6%). No maternal deaths or recurrent PPH occurred among successful cases. All patients received blood transfusions, with a moderate positive correlation between uncontrolled PPH and the need for ≥5 units of blood products. Conclusions: TVUAC, an effective, minimally invasive, and accessible second-line intervention, offers a scalable solution for settings with limited surgical capacity. Its potential to transform PPH management can save countless lives by either immediate control or limiting blood loss during transport to higher facility. Funding: None. Keywords: Postpartum hemorrhage, transvaginal uterine artery clamp, second-line intervention, maternal mortality, resource-limited settings.

Monitoring marine phytoplankton is essential to understanding marine ecosystems functioning, especially in productive regions like the English Channel. This study applied high-throughput sequencing (HTS) and automated pulse shape-recording flow cytometry (PSR FCM) to investigate the spatial and seasonal variability of phytoplankton diversity in French waters of the English Channel during the ECOPEL cruises in April (spring) and July (summer) 2018. Our findings revealed significant seasonal shifts in size, structure, total red fluorescence (FLR, a biomass proxy) and community composition. PSR FCM provided high-resolution size class discrimination, revealing an increase in picoeukaryote abundance and lower FLR in summer compared to spring. HTS enabled detailed taxonomic insights: in spring, picoeukaryotes (e.g. Ostreococcus) dominated in the Western English Channel, except in Finistère/Celtic Seas, where microphytoplankton represented the majority of reads. Nanoeukaryotes ( Phaeocystis) dominated in the Eastern English Channel. In summer, diversity increased, with co-dominance of picoeukaryotes ( Micromonas, Bathycoccus, Ostreococcus), microphytoplankton ( Chaetoceros, Leptocylindrus, Guinardia) and nanoeucaryotes ( Teleaulax, Gephyrocapsa) in the Bay of Seine. Beyond a pronounced west-east disparity, the Bay of Seine exhibited remarkable taxonomic and functional diversity, with high local contribution to beta diversity (LCBD) values in both seasons. Diversity patterns were strongly influenced by temperature and nutrient concentrations (phosphate, nitrogen), with secondary influences from salinity and turbidity. PSR FCM further revealed sub-mesoscale variability in abundance and size structure, complementing the mesoscale patterns observed through HTS. This study highlights the importance of integrating both methods to capture fine-scale phytoplankton dynamics and high-resolution diversity, thereby enhancing ecosystem management, espcecially in nutrient-sensitive, productive marine regions.

Background Multisystem Inflammatory Syndrome in Children (MIS-C) is a severe systemic hyperinflammatory condition leading to multiorgan dysfunction in a small number of children with COVID-19. Immunomodulatory therapy, including Intravenous Immunoglobulin (IVIG) and corticosteroids, is the mainstay of treatment. This meta-analysis was conducted to evaluate the effectiveness of three treatment strategies (IVIG and steroids, IVIG-only, and steroids-only) in the management of MIS-C Methods A systematic search of databases and grey literature sources was conducted to identify relevant RCTs and observational studies. RevMan 5.4 was used to analyze all the outcomes with risk ratio (RR) as the effect measure. The Newcastle-Ottawa scale was used to assess the risk of bias in observational studies. Cochrane’s revised “Risk of Bias” tool (RoB 2.0) was used to assess bias in RCTs. Results We included six cohort studies and one RCT in our meta-analysis. Compared to the IVIG-only group, the combination of IVIG and steroids reduced the treatment failure/persistence of fever [RR 0.70; 95% CI: 0.57-0.87], cardiovascular dysfunction [RR 0.62; 95% CI: 0.40-0.96], and the need for adjunctive immunomodulatory therapy [RR 0.49; 95% CI: 0.40-0.59]. The combination therapy and IVIG-only therapy had similar rates of left ventricular dysfunction, the need for inotropes, and the need for ventilatory support. According to our analysis, the steroids-only therapy and IVIG-only therapy had similar clinical outcomes. Conclusion Our meta-analysis shows that combination therapy with IVIG and steroids is more effective than IVIG or steroids alone in reducing treatment failure, cardiovascular dysfunction, and the need for additional immunomodulatory drugs in MIS-C. However, these findings must be interpreted cautiously due to the predominance of observational data, heterogeneity among studies, and the limited number of studies included. More large-scale RCTs are needed to support or refute our findings.

The secretion of insulin and glucagon by pancreatic β and α cells, respectively, is critical for glucose homeostasis. While the insulin granule dynamics is well-characterized, the intracellular behavior of glucagon secretory granules (GSG) remains poorly understood. Here, we analyze the mobility of GSGs in α cells and insulin secretory granules (ISG) in β cells using spatiotemporal correlation spectroscopy and single-particle tracking (SPT), with a focus on the role of the cytoskeleton. Under basal conditions, SPT classification reveals that GSGs predominantly exhibit diffusive motion (57.6 ± 10%), with smaller fractions categorized as immobile (35.8 ± 10.6%) or drifted (6.6 ± 3%), resembling ISGs dynamics. By disrupting microtubules, we confirmed their role as active tracks for directed granule transport in both cell types. However, actin depolymerization, which enhances ISGs directed motion, has little effect on GSGs and instead increases their immobile fraction. This differential behavior persists under physiological secretory stimuli - high glucose for β cells and low glucose for α cells - indicating that, while both systems employ the cytoskeleton for secretion, α and β cells exhibit distinct recruitment and utilization patterns. These findings suggest that α and β cells may modulate granule dynamics differently to meet their specific secretory demands.

Introduction The COVID-19 pandemic highlighted the importance for accurate and timely forecasts to support public health preparedness. Automated surveillance systems fit with pre-built model offers efficiency, but identifying optimal model difficult due to variations in populations. This study assessed forecasting accuracy in regions with substantial differences in population-at-risk sizes. Materials and methods COVID-19 daily incidence was forecasted for Wellington-Dufferin-Guelph Public Health (WDGPH) and Toronto Public Health (TPH), Ontario, Canada with population sizes differing by a factor of approximately 20. Datasets were split into training data (18 January 2020, to 5 November 2021) and validation data (6 November 2021, to December 3, 2021). The models applied were General Linear Autoregressive Moving Average, Seasonal Autoregressive Integrated Moving Average, and Regression with ARIMA errors, Neural Network Autoregression and Random Forest. Ensembles combining several models were then generated to investigate improvement in predictive performance. Results and discussion Random Forest provided the highest 28-day forecast accuracy with Mean Absolute Scaled Prediction Error (MASE) of 0.68, Root Mean Squared Prediction Error (RMSE) of 13.66 for TPH and MASE of 0.71 and RMSE of 4.40 for WDGPH. Statistical models, although simpler to implement, did not perform as well while ensemble modeling provided no improvement in forecast accuracy.

To evaluate phytoplankton community structure and water quality post-construction of Changzhao Reservoir, hydroenvironmental characteristics and phytoplankton composition were analyzed via August 2018 sampling. Pearson correlation and redundancy analysis (RDA) identified key environmental drivers, while the comprehensive trophic state index (CTSI) and diversity indices (Shannon-Wiener H’, Margalef D) assessed water quality. Results showed phytoplankton comprised 47 genera from six phyla, dominated by Bacillariophyta (86.11%-98.29% relative abundance). Cell density (26.04×10⁴-60.01×10⁴ cells·L-1) and biomass (908.49-1701.70 μg·L-1) peaked at tributary-reservoir confluences, followed by reservoir tail and dam areas. CTSI indicated mesotrophic conditions at all sites, while H’ and D suggested light pollution. Integrated evaluation classified the reservoir as lightly polluted to moderately eutrophic. Nitrogen nutrients, chlorophyll-a, and CODMn were key factors influencing community dynamics.

“‘latex Recent advances in remotely operated vehicle technology and automated processing of visual data through deep-learning approaches have enabled us to track long-term ecological trends at marine rock walls. Here, we trained a deep-learning based object-detection model to classify prominent benthic invertebrate fauna on a slope/wall-section of the Koster fjord, part of the Swedish marine protected area Kosterhavet National Park. The model was applied to footage of the study site from 1997-2023, from which depth ranges and relative abundances of 17 invertebrate taxa were extracted, generating 72,369 occurrence records. The object-detection model was deemed reliable for its purpose with modeled depth distributions aligning with previously documented occurrences. Community structure was found to change along the study site’s depth gradient, with a higher taxon diversity at greater depths. Significant temporal increases in overall abundance across all depths were found in eight taxa and significant decreases in five taxa. The overall community structure shifted toward a higher abundance of small, heat-tolerant suspension-feeders. Temperature preference and size were found to be significant drivers behind taxon-specific abundance change. The documented loss over time of large, heat-sensitive taxa suggests that ongoing temperature increases are a likely cause for the altered community structure. However, a widespread trend of increasing abundance was noted throughout the remaining community, including species sensitive to trawling. This suggests that while species sensitive to climate change may disappear from the area, the remaining benthic community benefits from the protection measures in the national park. Our study demonstrates the application potential of video surveillance and deep learning technology, and we recommend the implementation of standardized video monitoring in adaptive management of marine ecosystems.

With an estimated population of 7.8 billion, health professionals (HPs) are grossly inadequate globally and far below WHO’s benchmark of 1 physician, 4 nurses, and midwives per 1000 population. Despite their shortage, HPs are not equitably distributed, with low- and middle-income countries (LMICs) especially those in Sub-Saharan Africa having the greatest shortages. The Organization for Economic Co-operation and Development (OECD) member states exploit the socioeconomic vulnerability of LMICs to massively recruit HPs from these countries to fill the gaps in their systems. The prolonged hemorrhage of HPs from LMICs to OECD member states continues to complicate health equity in LMICs, which have some of the highest wealth gap among their populations; and has raised concerns about social justice and breach of ethics. The brain drain phenomenon in LMICs has been in existence for decades and several strategies focusing on push factors that drive HPs away from LMICs to OECD countries have been implemented without noticeable impact. This article proposes strategies to promote shared responsibilities, fairness, and social justice and circumvent potential ethical concerns in the migration of HPs from LMICs to OECD member countries. To achieve this, we propose a framework where OECD member states recruiting HPs from LMICs invest in the education and production of HPs from LMICs and offer in-service training for medical trainers and mentors in their countries to strengthen quality education in the LMICs’ medical schools. This initiative could potentially increase the population of HPs that can serve the needs of both the LMICs and the OECD beneficiaries. We further suggest a role for the World Health Organization, the International Labor Organization and other international public health institutions to collaborate in exploring innovative strategies like the OECD-LMICs partnership proposed in this article to ensure justice, fairness and equity in reversing the exodus of HPs from LMICs.

Online dissemination of conspiracy theories (CTs) during epidemics poses significant risks to public health. This paper addresses the problem of detecting CTs in social media posts with an emphasis on the resource-constrained scenarios characterized by the absence of labeled datasets and the high cost of expert annotation. To address these challenges, we investigate resource-efficient methods for CT detection across multiple epidemics. We construct a novel dataset of CT-labeled social media posts covering four major epidemics from the past decade: Ebola, Zika, COVID-19, and Monkeypox. We conduct extensive experiments addressing four research questions: (1) the performance of BERT-like models on individual epidemics, (2) the ability to transfer knowledge from past epidemics to new ones, (3) the efficacy of zero-shot classification using Large Language Models (LLMs), and (4) the feasibility of training BERT-like models on LLM-labeled datasets. Our findings indicate that BERT-like models exhibit highly variable performance across epidemics. Transfer learning from prior epidemics can be effective and their performance can be improved with the number of prior datasets. Zero-shot LLM classifiers, including ensemble methods, achieve performance that matches or surpasses that of fine-tuned BERT-like models. Finally, we demonstrate that BERT-like models trained on LLM-labeled datasets achieve results close to the models trained on expert-annotated data, offering a practical alternative when expert labeling is infeasible. While automated methods can be useful for data analysis, we caution against automatization of content filtering due to the inherent difficulty of CT detection and the potential biases of language models.

Joint entity and relation extraction aims to recognize entities and their relationships in text simultaneously.However, all existing relationship extraction methods suffer from the deficiency of relationship redundancy and overlapping, and can suffer from insufficient semantic information and noise due to insufficient contextual information. We propose a joint entity and relation extraction model—BiRLA, which integrates BiLSTM and multi-attention mechanisms to address the problems of incomplete information extraction and low extraction accuracy. The model uses position encoding to ensure that it can sense the positional information of the tokens in the sequences, solves the long-distance dependency problem using the bidirectional long short-term memory network (BiLSTM), and uses the multi-attention mechanism to further capture the long-distance dependency problem. BiLSTM solves the long-range dependency problem, and adopts the multi-head attention mechanism to further capture the complex and subtle correlations among different entities. The experimental results show that compared with the existing mainstream models, the F1 values of the proposed model on the three public datasets NYT*, WebNLG*, NYT and WebNLG are 93.3%, 94.5%, 93.2%, and 91.8%, respectively, which are improved compared with the baseline model, and the validity of the model is verified.