Acculturation, or cultural change in individuals through continuous contact between distinct cultural groups, is an important construct to study in order to understand the mechanisms behind effects of cultural background, values, and environment on one’s thoughts and behaviors. This project focused on examining the impact of acculturation on cognitive strategy usage, demonstrating that change in acculturation is associated with change in self- and other-referencing strategies in memory among Chinese international students. The current study replicated in a new sample behavioral memory effects associated with acculturation. Importantly, it also extended research by examining the relationship between acculturation and ERP components (P300, LFP, LPP) that represent processes associated with cultural memory patterns (self-/other-referencing). In a sample of 56 Chinese students studying in the United States, we found that greater acculturation to the US was associated with a larger behavioral self-reference effect, contrasted by a smaller neural self-reference effect (LFP & LPP). Additionally, greater acculturation was also associated with both a smaller behavioral close other-reference effect and a smaller neural close other-reference effect (LFP & LPP). However, results provide mixed support for the predominantly proposed mechanism of level of self-relevance, and instead, we posit the potential for level of effortful processing as a mechanism for culture’s influence on self-related memory strategies. Future research should further explore these associations, including measuring change in EEG/ERP measures longitudinally over the course of the acculturation process.

In the present study, we test the hypothesis whether experiential emotion regulation works more in-depth after repeated regulation, while top-down cognitive emotion regulation would have an initial advantage, which will decrease over time. Participants (N = 58; all women) viewed negative, arousing pictures three times. One group was randomly assigned to apply experiential emotion regulation, the other group, cognitive reappraisal. A ‘watch’ control condition, in which participants were instructed to focus on the colours in the picture, served as a within-subject control condition for both groups. Outcome measures included self-reported negative emotional experience, facial expression (EMG) of the corrugator and zygomaticus muscles, skin conductance, and heart rate. In line with our expectations, relative to the control ‘watch’ condition, the first instance of experiential emotion regulation was associated with higher subjective negative emotional experience, whereas cognitive reappraisal was associated with lower subjective negative emotional experience and increased positive facial expressivity. Repeating the same emotion regulation strategy resulted in a steeper relative decrease of negative emotional experience for experiential emotion regulation relative to cognitive appraisal. At the third instance of emotion regulation there was no significant difference in negative emotional experience between experiential emotion regulation and cognitive reappraisal. The findings provide insight into the mechanisms underlying experiential emotion regulation versus cognitive reappraisal.

Quantum tunneling, the quantum mechanical phenomenon wherein particles traverse energy barriers despite possessing insufficient classical energy, plays a critical role in high-temperature catalytic mechanisms, especially in aerospace and defense applications. These systems often operate in extreme thermal environments where traditional modeling of reaction kinetics may fall short. The standard method of computing tunneling probabilities involves numerically solving the Schrödinger equation for a detailed potential energy curve (PEC), a process that is computationally demanding and sometimes infeasible in the early stages of catalyst design. This paper presents a new, analytically driven framework for estimating tunneling probabilities using physically grounded approximations, thus eliminating the need for full PEC simulations. Starting from the foundational Schrödinger equation, we systematically derive a simplified yet rigorous tunneling probability expression using the Wentzel-Kramers-Brillouin (WKB) approximation and further refine it using a square barrier simplification. We then introduce thermal effects using Boltzmann statistics to model energy distributions and account for temperature-induced barrier fluctuations via stochastic dynamics using the Ornstein-Uhlenbeck process. Finally, we present two consolidated expressions (named ABBE I and ABBE II) which encapsulate the quantum tunneling probability and its influence on reaction rate. This formalism aims to provide a physically interpretable, computationally lightweight method for incorporating quantum mechanical effects into high-energy catalytic design and analysis.

This article develops a variational formulation for modeling a protium hydrogen molecular ionization obtained through a high temperature scalar field and an appropriate electric one action. The results are based on standard tools of calculus of variations and optimization theory. Finally, we highlight the context here addressed is essentially an Euler-Bernoullian one and includes the establishment of a new approximate Bernoulli-interacting-gas type equation.

Human papillomavirus type 16 (HPV16) is a primary etiological agent of cervical cancer, underscoring the importance of early detection, especially of E7 mRNA expression, for effective clinical management. In this study, we developed a fluorescence-based nanobiosensor for the ultrasensitive and specific detection of HPV16 E7 mRNA. The catalytic hairpin assembly (CHA) was integrated with Fe 3O 4@Au core-shell nanoparticles (Fe 3O 4@Au NPs), enabling the combined advantages of magnetic enrichment and enzyme-free signal amplification. The nanobiosensor exhibited a linear fluorescence response over the ranges of 0.002 to 1 pM in PBS buffer and 0.1 to 1 pM in first-void urine (FVU) following magnetic separation. This approach demonstrated excellent reproducibility, high specificity with the ability to discriminate single-base mismatches, and stability for up to 45 days. The performance of the nanobiosensor was successfully validated in RNA extracted from HPV16 plasmid-transformed E. coli, CasKi cells, and clinical swab specimens. Validation of the developed nanobiosensor was conducted through the analysis of clinical samples, demonstrating complete concordance with results obtained from commercial diagnostic assays. These results highlight the potential of this nanobiosensor as a robust tool for early detection and monitoring of HPV16 infections.

A document by qian chen. Click on the document to view its contents.

The proliferation of mega-projects in the international engineering market has accelerated the widespread adoption of EPC (Engineering-Procurement-Construction) and its integrated delivery models. Given the inherent complexity of large-scale international EPC projects, numerous factors can impede objective attainment, and post-deviation corrective measures often incur substantial costs. This necessitates enhanced focus on proactive identification and mitigation of deviation-inducing factors, optimization of objective achievement environments, and improvement of Chinese contractors’ project objective governance capabilities. Through case studies and literature analysis of mega-projects, this research identifies 12 objective-influencing factors specific to international EPC projects. Through factor analysis, it was verified that the integrated management capability of the general contractor is the fundamental factor, including three dimensions: design leadership capability, procurement integration capability, and construction performance capability, and weight analysis was conducted on the indicator system. This study contributes to the theoretical enrichment of objective-influencing factor research in international EPC projects while providing practical insights to enhance contractors’ risk awareness and preventive capabilities, thereby facilitating superior project outcomes.

High-speed level-up shifters (HSLS) are key components in low-power, high-speed CMOS I/O links. Conventional HSLS designs rely on single-ended to differential converters, which add delay, power, and degrade output duty cycle accuracy. This work introduces a novel HSLS that eliminates the need for explicit conversion by using delay interpolation, improving differential duty cycle centering while reducing power and latency. Additionally, conventional HSLS architectures suffer from single ended duty-cycle distortion and P/N skew. To address this, an auxiliary source follower is proposed to correct these distortions systematically. The combined techniques result in a superior HSLS architecture, demonstrated through PVT and Monte Carlo simulations (1000 samples), with performance compared to a traditional design

Spatial variation is a major source of error in agricultural field experiments affecting genotype performance prediction. Implementing statistical models that account for spatial effects can improve the prediction of genotype performance. This study evaluated the impact of the B-spline spatial correction method on the estimation of genetic parameters and AIC values in two distinct crops – wheat and cassava – using four models: Block, Block + Spatial, Block + Marker, and Block + Marker + Spatial. Analyses were performed on data from 136 and 68 trials obtained from the T3/WheatCAP and Cassavabase databases, respectively. The results demonstrated that correcting for spatial variation, regardless of marker information, increased the heritability estimate of grain yield, test weight, plant height, powdery mildew, stripe rust, and bacterial streak disease in wheat. Similar increases were observed in cassava for dry matter content, dry yield, and plant height. However, no increase was observed for cassava mosaic disease or bacterial blight. Models incorporating spatial correction in both crops consistently provided the best fit based on AIC values across all traits in wheat and cassava. These results were consistent whether or not marker effects were fitted in the models. This showed the importance of spatial correction in field experiment analysis.

Objective: To assess global and regional trends in infertility burden among reproductive-aged women with polycystic ovary syndrome (PCOS) from 1990 to 2021. Design: Longitudinal observational study. Setting: Global data from the Global Burden of Disease (GBD) 2021 database. Population: Women aged 15-49 years diagnosed with PCOS in 204 countries and territories. Methods: We extracted data on PCOS prevalence and associated infertility from the GBD 2021 dataset. Infertility was categorized into primary and secondary types. Analyses were conducted across age groups, GBD regions, and socio-demographic index (SDI) levels. Trends were quantified using estimated annual percentage change (EAPC), and associations between infertility burden and SDI were evaluated. Main outcome measures: Age- and region-specific prevalence of PCOS and the proportion of associated infertility, including subtype distribution and temporal change. Results: From 1990 to 2021, global PCOS cases increased from 34.8 to 65.8 million. Infertility among these women rose from 18.22% to 19.00%, with the highest proportion (21.9%) in the 35-39 age group. Low-SDI regions had fewer cases but higher infertility proportions. Equatorial Guinea showed the largest increase in infertility cases (+772%), while the Central African Republic had the highest proportion in 2021 (21.84%). An inverse correlation was observed between SDI and infertility proportion. Conclusions: The global burden of PCOS-related infertility has grown over the past three decades, with significant disparities by age, region, and development level. Targeted strategies are needed to improve reproductive care access and reduce inequities, particularly in low-SDI settings.

Background: The distinct epidemiological patterns and host immune responses between influenza A and B viruses during co-circulation periods remain insufficiently characterized, particularly in adult populations in East Asia. This study aimed to delineate subtype-specific differences and evaluate hematological biomarkers for clinical differentiation during the 2023–2024 influenza seasons in Shanghai. Methods: We conducted a retrospective analysis of 3,270 adult influenza-like illness (ILI) cases at a Shanghai tertiary hospital fever clinic (November 2023-February 2024). Influenza A/B were confirmed by rapid antigen testing (6.67% and 2.94% positivity, respectively), and compared with healthy controls. Hematological parameters including white blood cells (WBC), neutrophils (NEUT), lymphocytes (LY), monocytes (MONO), and C-reactive protein (CRP) were analyzed using receiver operating characteristic curve (ROC) analysis and Spearman correlation. Results: Influenza A demonstrated bimodal seasonal peaks with stronger inflammatory responses, including elevated WBC, NEUT, MONO, and CRP levels along with decreased LY counts (all P < 0.05 versus controls). In contrast, influenza B exhibited unimodal circulation with 2-month delayed peaks and attenuated hematological changes. Subtype comparisons revealed significantly higher WBC, NEUT and CRP in influenza A (all P < 0.05 versus influenza B). The combined biomarker panel (WBC+NEUT+LY+MONO+CRP) achieved near-perfect discrimination (AUC: 0.9988 [95% CI: 0.9968–1.000] for influenza A, 0.9952 [0.9871–1.000] for B), outperforming individual markers. Immune network analysis identified coordinated LY-MONO-CRP interactions in influenza A versus isolated LY-MONO correlations in influenza B. Conclusions: The study reveals distinct subtype-specific patterns, with the biomarker panel enabling rapid clinical differentiation to guide management during co-circulation.

Background: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. Flu has been responsible for a high disease burden in recent years, despite the confounding presence of SARS-CoV-2. However, the mechanisms behind seasonal variance in flu burden are not well understood. This study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological zone and provides different weather patterns. Methods: A Long short-term memory (LSTM)-based recurrent neural network was used to predict influenza-like-illness trends for three separate locations: Hawaii, Vermont, and Nevada. Flu data were gathered from the Center for Disease Control as weekly influenza-like-illness (ILI) percentages. Weather data were collected from Visual Crossing and included temperature, wind speed, UV index, solar radiation, precipitation, and humidity. Data were prepared and the model was trained as described previously. Results: All three regions showed strong seasonality of flu trends with Hawaii having the largest absolute ILI values. Temperature showed a moderate negative correlation with ILI in all three regions (Vermont = -54, Nevada = -0.56, Hawaii = -0.44). Humidity was moderately correlated in Nevada (0.47) and weakly correlated with ILI in Hawaii (0.22). Vermont ILI did not correlate with humidity. Precipitation and wind speed were weakly correlated in all three regions. Solar radiation and UV index showed moderate correlation in Vermont (-0.33, -0.36) and Nevada (-0.5263, -0.55), but only a weak correlation in Hawaii (-0.15, -0.18). When trained on the complete data sets, baseline model performances for all three datasets at +1 week were equivalent. Models trained on one region and used to predict cross-regional data performed uniformly and equivalent to baseline. Conclusions: Results indicate that climate variables were weak to moderate predictors in all regions. Initial modeling attempts revealed uniform performance in all regions. Despite strong climate differences, cross-regional LSTM models performed comparably, suggesting that seasonal patterns, rather than absolute climate variables, drive ILI trends. Additionally, this data suggests that absolute climate changes may not be influential as relative seasonal changes.

retired professor (USA) "By their deeds you will know who they are." The Gospel of Matthew 7:16

Introduction:Systematic literature reviews (SLRs) are fundamental to evidence-based medicine, serving to synthesize existing research, inform clinical practice, and guide policy decisions. Most systematic reviews follow standardized guidelines, such as “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA), to maintain consistency and a holistic human review process (Liberati et al., 2009). However, the traditional process of conducting SLRs is notoriously time-consuming and resource-intensive, often spanning months or even years to complete and demanding significant human resources. This labor-intensive nature can delay the timely dissemination of critical findings and impede the swift translation of research into practice (Beller et al., 2013).In recent years, the emergence of artificial intelligence (AI), particularly large language models (LLMs), has presented enormous promise in alleviating the methodological burden of SLRs. When compared to the current human review process, many AI LLMs have shown comparable, and in some cases superior, performance in key steps such as literature screening and study selection. For instance, Matsui et al. found that GPT-4 achieved specificity between 85–99% and sensitivity around 88–96% across test cases in a mental health systematic analysis (Matsui et al., 2024). In this study GPT-4 missed none of the key studies using a “3-layer” prompting strategy that refined output iteratively, placing its performance within the 77–100% sensitivity range of human reviewers (Matsui et al., 2024). Similarly, Cao et al. demonstrated that with well-engineered prompts specifying review objectives and inclusion criteria, LLMs could reach even higher sensitivity than dual-human reviewers in some contexts; while maintaining similar accuracy and drastically reducing the time it takes to screen thousands of citations (Cao et al., 2024). Lai et al. further found that a Claude-based system could accurately extract information and perform risk-of-bias assessments in systematic review with ≥95% accuracy using automated data extraction from 107 randomized trials (Lai et al., 2025).Despite recent advancements, the use of AI for independent systematic review remains associated with a high risk of false negatives and variable results (Guo et al., 2024; Matsui et al., 2024; Khraisha et al., 2024; Li et al., 2024). Underwhelming sensitivities are particularly concerning since this indicates the presence of false negatives where crucial data to the research is being excluded. Therefore, retaining human involvement in the screening and review process is essential to ensure accuracy and methodological integrity (Sanghera et al., 2025). Several widely-used platforms, including ASReview, Abstrackr, RobotAnalyst, EPPI-Reviewer, DistillerSR, and Elicit employ machine learning algorithms to prioritize article screening and iteratively refine predictions based on human reviewer input. These tools exemplify the human-in-the-loop LLM model and have demonstrated effectiveness in systematic review (Khalil et al., 2022; van Dijk et al., 2023; Yao et al., 2024).Even with integrated human oversight, ensuring methodological transparency and reproducibility remains critical. The lack of a well-established, standardized protocol to approach LLM-assisted systematic review produces inconsistencies in LLM SLRS, making it difficult to compare results across studies or reliably implement LLM tools in practice. This manuscript introduces the Screening of Title and Abstracts, Re-evaluation, and full-text Review (STARR) protocol, a novel standardized approach to SLR screening using LLMs, designed to address these methodological deficiencies and enhance the reliability and efficiency of evidence synthesis.

A document by Kevin M. Kallmes. Click on the document to view its contents.

Introduction Systematic literature reviews (SLRs) are a vital aspect of evidence-based research, directing healthcare decisions and impacting policymaking-specific issues, however, the traditional process of conducting them could be lengthy, labor-intensive, and costly. This augments the need for more efficient strategies, such as automation using generative artificial intelligence (AI), which could help the researchers reduce their workload and streamline the SLR process. The current study investigates the relative efficiency of the generative AI models (Claude Sonnet 3.5, Gemini Flash 1.5, and GPT-4) in the title and abstract screening phase of SLRs. Methods Key biomedical databases, including Embase ®, Medline ®, and Cochrane, were searched to identify relevant randomised controlled trials in patients with schizophrenia. This study presented a hybrid approach for systematic reviews, where one reviewer is a human expert and the other leverages three large language models (LLM). A subject matter expert in conducting SLRs, optimized and fine-tuned the final prompt, delivered through a Python application programming interface, to identify evidence meeting key inclusion and exclusion criteria. The screening results obtained from one human reviewer and three AI models were reviewed by subject matter expert (SME). AI models’ performance was evaluated using metrics such as accuracy, sensitivity, specificity, and precision to assess their success in identifying publications included in the final SLR. Results All three AI models performed exceptionally well in screening based on titles and abstracts. While there were no significant differences in accuracy rates, Gemini Flash 1.5 exhibited the highest accuracy rate at 96.02%, followed by GPT-4 (95.00%) and Claude Sonnet 3.5 (94.69%). In terms of sensitivity, GPT-4 exhibited better results, attaining 95.97% of sensitivity, followed by 94.63% with Gemini Flash 1.5, and 88.59% with Claude Sonnet 3.5. Among the AI models evaluated, GPT demonstrated highest concordance with the human reviewer at 88.77%, followed closely by Gemini Flash at 86.63% and Claude Sonnet at 85.81%, indicating a consistently high level of agreement across all models.

IntroductionNovel automation or Artificial Intelligence (AI) applications for information retrieval (AI search tools) have the potential to expedite evidence synthesis tasks, such as study identification or search strategy development.1,2 These AI search tools, including generative AI Large Language Model (LLM) chatbots, require performance testing and validation to inform implementation recommendations for Canada’s Drug Agency (CDA-AMC) and other evidence synthesis producers.There is a long history of automation in the information sciences3 and we recognize AI as an umbrella term for technology tools that perform tasks that would ordinarily require biological brainpower to accomplish.4 Our definition of AI search tools is deliberately broad to include successive generations of technologies for information retrieval and to recognize the potential value of older, developing, emerging, or novel tools.In an earlier project phase, Research Information Services (RIS) at CDA-AMC evaluated 51 AI search tools using our evaluation instrument.5 In this successive phase, RIS evaluated the performance of 3 top-ranked tools:Lens.org (“The Lens”) https://www.lens.org,SpiderCite https://sr-accelerator.com/\#/spiderciteMicrosoft Copilot https://www.microsoft.com/en-ca/microsoft-copilotOur objective in testing these tools was to determine if they should replace or supplement current RIS information retrieval methods given the estimated contribution of eligible and unique studies, and the resources needed to conduct searches and screen the results. As these tools use different automation technologies to perform different search tasks, our investigation also aimed to compare their distinct strengths and weaknesses.

A document by Hyunggu Jung. Click on the document to view its contents.

AbstractIntroductionGold nanoparticles are known for its antimicrobial and immune response eliciting properties. It has been using for centuries in prevention of infectious diseases by increasing the immunity of the host.ObjectiveThe role of antibacterial therapy is the gold standard in all the established guidelines for prevention, mitigation, treatment and control of the known bacterial species which are harmful to human health. In this study we determined the antibacterial effectiveness of high dose of amoxicillin/potassium clavulnate and Gold nanoparticles(AuNPs) sourced from different pharmaceutical companies.MethodologyAfter obtaining the informed written consent from the subject, culture swabs were obtained from the Fournier’s gangrene. The collected sample is made to grow on blood agar and MacConkey agar in ambient temperature of 370 Celsius over 24 hours. The growth is observed and confirmed as Klebsiella pneumonia a  Gram-negative, non-motile, encapsulated, lactose-fermenting, facultative anaerobic, rod-shaped bacterium. Appearing as a mucoid lactose fermenter on MacConkey agar.Klebsiella pneumonia is isolated and inoculated on Mueller-Hinton Agar. The agar well diffusion method is carried out with 4 well containing AuNPs I , AuNPs II , amoxicillin/potassium clavulnate and blank respectively. The results are observed after 24 hours and 48 hours of inoculation.ResultsThe Agar well diffusion method containing AuNPs I , AuNPs II , amoxicillin/potassium clavulnate and blank respectively after 24 hours showed higher Zone of Inhibition for amoxicillin/potassium clavulnate rather than AuNPs I & II and no Zone of inhibition around the blank well. The Zone of Inhibition observed after 48 hours of inoculation is 23 mm for amoxicillin/potassium clavulnate, 18 mm for AuNPs I and 15 mm for AuNPs II. No Zone of inhibition is observed around blank even after 48 hours.Discussion and ConclusionThe rate of diffusion of Gold nanoparticles in the particular agar is not known and due to heavy metal of gold and its insolubility property in water makes it difficult for liquid preparation. The zone of inhibition(ZOI), minimum inhibition concentration(MIC) of the gold nanoparticles are not well established. The accuracy of the agar well diffusion method for evaluation of antimicrobial property of gold nanoparticles is not yet standardized. Keeping all these limitations of this study we present our data about antimicrobial activity of gold nanoparticles against Klebsiella pneumonia is minimum as compared to amoxicillin/potassium clavulnate. The further studies can be carried with functionalized AuNPs in the prevention of antimicrobial resistance.KeywordsGold nanoparticles(AuNPs), Agar well diffusion method, Zone of inhibition(ZoI), amoxicillin/potassium clavulnate and Klebsiella pneumonia .1. INTRODUCTIONAntibiotic resistance is a major threat to global health. Resistant bacterial infections are currently resulting in over 1.2 million deaths every year1 and are projected to reach 10 million casualties annually by 2050. This crisis is due to the continuous emergence and spread of antibiotic-resistance genes across important human pathogens and the limited introduction of new, broad-acting, clinically useful antimicrobials since the 1970s2. Alongside genetic resistance, alternative bacterial lifestyles during infection, such as persistence3 and growth in biofilms4, also contribute to patient mortality due to ineffective antibiotic treatment. As such, it has become essential to not only boost our currently insufficient preclinical and clinical antimicrobial development pipelines5 but to also safeguard the longevity of our approved antibiotics. Both feats heavily rely on our ability to evaluate the efficacy of antimicrobial agents against bacteria, which underpins the identification of antibiotic-resistant strains in the clinic and enables testing of novel antibiotic or antibiotic-adjuvant candidates.A wide variety of medicinal products which originate from natural compounds and which are widely used to target and treat various appear diseases. The extraction of these complicated chemical molecules from plants, animals, microorganisms and minerals are common natural sources via several extraction processes, these compounds work as an initiator of future sinker molecules. Gold is inert and universally recognized as biocompatible. Until the recent past, it was only known as the metal. With the arrival of nanotechnology and the discovery of nanoparticles and the exploration of the physico-chemical properties of gold make it a supreme material for progress fields6,7. A nanoparticle is defined as a tiny particle with a size ranging between 1 and 100 nm.Agar well diffusion method is widely used to evaluate the antimicrobial activity of plants or microbial extracts 8,9. Similarly to the procedure used in disk-diffusion method, the agar plate surface is inoculated by spreading a volume of the microbial inoculum over the entire agar surface. Then, a hole with a diameter of 6 to 8 mm is punched aseptically.Zone of inhibition (ZOI), also known as a zone of clearing or a halo assay, refers to the clear zone surrounding an antimicrobial agent. These ZOIs result from a complete absence of bacteria on, or within a confluent bacterial lawn.The antimicrobial activity of the agent is screened against a test organism which is used to create a confluent lawn of bacterial growth on an agar plate. The ZOI is measured in mm after 24 to 48 hours of incubation.A primary function of the immune system is to protect the host from pathogenic microbial infections. Innate immune mechanisms dominate during the early phase of the antimicrobial immune response. As the innate response sometimes does not clear the infecting microbe, T lymphocytes and neutralizing antibodies may often be required for its complete elimination. The innate response is nonetheless indispensable for host survival since it prevents such microbes from spreading in an uncontrolled manner and thereby provides the host with the “grace period” of 3–4 d required for the activation of an efficient adaptive immune response.2. MATERIAL AND METHODSThe amoxicillin/potassium clavulanate is obtained from Karnataka antibiotic and pharmaceuticals limited plot no 14, II Phase, Peenya, Bengaluru 560058. AuNPs I is obtained from Shree Dhootapapeshwar Limited, 135, Nanubhai Desai Rd., Khetwadi, Mumbai – 400 004. AuNPs II is obtained from Nanoshel United kingdom through its subsaidiary located in Punjab, India.Composition of Blood AgarBlood agar, like most other nutritional media, has one or more protein sources, salt, and beef extract for vitamins and minerals. Besides these components, 5% defibrinated mammalian blood is also added to the medium. The blood agar base is commercially sold by various vendors, or it can also be prepared in the laboratory if the necessary ingredients are available. The exact composition of the blood agar base is given below:

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by abnormal aggregation of the tau protein. In this study, we designed a novel small molecule, Dalfina, through rational drug design and in silico docking simulations. Dalfina was optimized to selectively bind hyperphosphorylated tau isoforms and demonstrated favorable physicochemical properties for blood-brain barrier (BBB) permeability. Molecular docking was performed using Autodock Vina and revealed binding affinities ranging from-5.1 to-6.2 kcal/mol at key allosteric pockets (C1-C5) of tau. ADME profiling via SwissADME confirmed the compound's drug-likeness, CNS activity potential, and low toxicity risk. These findings support Dalfina as a promising lead compound for further experimental validation in tau-targeted Alzheimer's therapy.

Dear Editor, I read the article titled “Development of an Innovation Pipeline With Fusion, Digital Planning, and three-Dimensional Printing to Improve Mitral Valve Interventional Care” by Man et al., published in your journal Echocardiography [(Man, 2025)](#ref-0002). This Manuscript presents a novel and technically innovative pipeline that integrates TEE and CT imaging to produce personalized, flexible 3D-printed mitral valve (MV) models for interventional planning. The integration of multimodal imaging with tactile simulation aligns well with current trends in structural heart intervention and surgical education. However, several methodological and design limitations may reduce the generalizability and clinical readiness of the proposed system. The study has a very small sample size, and the retrospective analysis of just three patients, chosen for different MR pathologies, limits statistical validity and generalizability. While small cohorts are acceptable in feasibility studies, the authors‘ conclusions regarding “technology readiness” are premature without broader validation. [(Pate, 2020)](#ref-0003). While the 3D models are promising, the authors do not evaluate any procedural or clinical endpoints, such as surgical decision-making, procedural time, or complications. Without outcome data, the utility of the pipeline in “interventional care” remains in doubt. [(Diment, 2017)](#ref-0001). The study suggests the use of five different software platforms, which would require a large team. In a busy clinical setting, this may hinder workflow efficiency. Therefore, the study should have focused on time, cost, and computational resources in a real-world context. Despite their critiques, the authors did well in presenting their insights and findings regarding innovative modalities for the treatment of MR. This study introduces a meaningful technical advancement but is not yet ready for clinical application. Hence, a major revision is warranted, and future research, ideally in the form of RCTs and cohort studies, should include an increased sample size, correlation with surgical outcomes, and insight into the real-world cost and workforce requirements for the integration of these procedures in the treatment of MR

Objective The purpose of this study is to assess patient perspectives in eating disorder treatment in an area where access to treatment has been prioritized. The current report provides descriptive data surrounding beliefs, expectations, desires, and experiences to identify patterns that may hold promise for increasing positive outcomes in eating disorders through better alignment of experiences and patient preference. Method Participants who reported experiencing treatment for an eating disorder in their lifetime provided responses in an online survey surrounding a wide range of patient perspectives. The current report provides descriptive data from questions from the survey including demographic characteristics, clinical information, treatment experience characteristics (such as: self-reported diagnoses, levels of care, and types of providers seen), and responses to questions assessing patient perspectives of knowledge, beliefs and perceptions, desires and expectations, and experiences in treatment. Results Analysis of responses show the broad range of characteristics, perspectives and experiences of individuals who

Biodiversity change in metacommunities, such as homogenization, is often measured using beta diversity metrics. However, other metrics can provide complementary information. Here we use spatial alpha (𝝰), beta (𝝱), gamma (𝛄) and zeta (𝛇) diversity to describe plant metacommunity development at successive time-periods over 12 millennia in a previously glaciated region, as applied to sedimentary ancient DNA data. We find that the metacommunity diversified (𝝱) and homogenized (𝛇) over millennia, concurrently with an increase in the number of taxa (𝝰 and 𝛄). The turnover of taxa between time-periods declined, with taxon appearance exceeding taxon disappearance in the communities. This suggests local co-existence of taxa increased. In contrast, the turnover of shared taxa (ζ) among communities was continuously high, suggesting the regional metacommunity homogenization was largely transient. That plant communities homogenized but remained distinctively different over millennia highlights how individual communities are essential to maintain metacommunity biodiversity.

The effects of environmental gradients on feather structure and function remain underexplored. We explored how temperature, rainfall, and body size influence feather morphology in 444 neotropical bird species from Colombian sites spanning 600 to 2500 meters in elevation and 732 to 7337 millimeters of rain. We found that temperature is the primary predictor of feather morphology variation, with a significant contribution from rainfall. Birds exposed to high rainfall have a downy region 4.4% larger in area than those under lower rainfall regimes. Birds inhabiting environments with low temperatures have 4% more downy area than those in hotter places. These changes in the area of the downy region of breast, back, and head feathers suggests differences in thermoregulatory demands across body regions. Our study shows that rainfall and temperature shape the general structure of contour feathers in “stable” climatic regions, providing insights into the evolutionary processes driving morphological diversity among birds.