Endorsement of artificial intelligence guidelines across leading ophthalmology journals: A cross-sectional analysis

Background: Artificial intelligence (AI) is transforming ophthalmology by enhancing data analysis, facilitating systematic reviews, and improving clinical applications. However, its incorporation into research and publishing introduces challenges related to transparency, ethical considerations, and reproducibility. This study explores how top ophthalmology journals address these issues and leverage AI's potential through their author guidelines and editorial policies.Methods: A cross-sectional review of the top 100 peer-reviewed ophthalmology journals, ranked by the 2023 SCImago SJR indicator, was conducted. Data were extracted from the “Instructions for Authors” of each journal to assess AI-related policies, with a focus on authorship criteria, AI-specific reporting guidelines, and the use of AI in manuscript preparation and image generation. Correlational analyses were conducted to investigate potential links between AI policies and the distinctive characteristics of each journal.Results: Among the 100 journals reviewed, 79% addressed AI use in their author guidelines, with most prohibiting AI authorship but requiring disclosure of AI involvement in submissions. AI-generated content was permitted by 62% of journals, while 24% accepted AI-generated images. Journals with higher impact factors were more likely to implement detailed AI policies; however, significant gaps in standardization and guidance persist.Conclusion: Although many ophthalmology journals acknowledge AI’s growing role in research, few have adopted AI-specific reporting guidelines, limiting the consistency and transparency of AI integration. We advocate for the development and adoption of comprehensive guidelines to promote ethical, reproducible, and high-quality research in this evolving landscape of AI-driven innovation

Does experience in multiple contests affect cricket exploration behavior?

Previous experiences strongly influence animal behavior, particularly in response to social interactions. In this study, I investigated how sequences of positive and negative outcomes in aggressive contests influenced exploration and latency behaviors in male house crickets (Acheta domesticus). Crickets were divided into five experimental groups: two wins (WW), two losses (LL), win-loss (WL), loss-win (LW), and a control group (CG) without contests. Behavioral responses were recorded both before and after the contests. I documented observations on subsequent days to evaluate lasting impacts. I assessed exploration by recording movements between defined areas in a test arena, and I measured latency by timing how long crickets took to exit a shelter. Aggression contests were staged by pairing crickets with either larger or smaller opponents to produce specific outcomes. The staged contest design allowed me to examine how winning or losing, as well as the sequence of these outcomes, influenced key behavioral metrics. I hypothesized that the most recent contest experience strongly influenced exploration, latency, and time spent in the thigmotaxis area. I expected patterns to vary based on the sequence and proportion of wins and losses. Crickets experiencing predominantly negative outcomes likely displayed more pronounced and prolonged behavioral changes than those with positive experiences. This research highlighted the ecological importance of behavioral plasticity and demonstrated how social experiences dynamically shaped behaviors critical for survival, including dispersal, mating, and predator avoidance.Lew Wentz FoundationIntegrative Biolog

Effects of eastern redcedar encroachment on forest fire dynamics in upland oak forests

Before the 20th century, Native Americans maintained The Cross Timbers ecoregion, and frequent but low-intensity fires dominated it. During the 20th century, as land became increasingly occupied and sectioned off by settlers, fire exclusion and suppression became a common practice and due to this, a process called mesophication began to occur. Shade-tolerant tree species are able to outcompete the shade-intolerant and fire-adapted ones, eventually resulting in an altered, wetter soil composition, which will change the species present and completely alter or disrupt an ecosystem. Grassland researchers have recorded the impact of eastern redcedar on fire, but the effects on forested ecosystems are mostly unstudied. The rapid increase of eastern redcedar in the Cross Timbers has made it imperative to be wise to its functions and interactions on fire systems. Utilizing prescribed fire, our team studied how the increased frequency of eastern redcedar altered forest fire behavior. It was hypothesized a large factor at play would be fuel moisture. During the initial study, our team looked at the effect of eastern redcedar prominence and live crown ratio on fuel moisture. Data was collected in Cross Timbers forests on Oklahoma State University property adjacent to Lake Carl Blackwell. We used sixty-five 0.04-hectare plots, distributed throughout 8 hectares of total forested area. Data collected included fuel and duff depth; 1-, 10-, and 100-hour fuel loading; fuel moisture collected immediately before burning for live and dead 1-, 10-, and 100-hour fuels; tree species; DBH; height; crown height and distance measurements; and live crown ratio. Following preliminary data collection, plots were mechanically treated and subsequently burned in late August and early September. This analysis primarily points to relationships among tree diversity, fuel load, and fuel moisture. We neither found significant relationships between the percent eastern redcedar and tree species diversity, average eastern redcedar DBH and fuel moisture, nor total average DBH and fuel moisture. The team postulates the combined aspects of fuel loading, fuel architecture, and plot-scale limitations on how they may have been more impactful on fire behavior.Natural Resource Ecology and Managemen

Mapping early-life stress-induced behavioral adaptations in adolescent rats

Introduction/ Objectives: Adverse childhood experiences (ACEs) are deviations from standard conditions requiring significant psychological and neurobehavioral adaptations and can be modeled in rats through limited bedding and nesting (LBN) for postpartum dams. LBN disrupts maternal care, leading to behavioral adaptations in adult offspring. Additionally, adolescence is a critical period for social development; however, few studies exist examining this time point. In this study, we investigated the impact of LBN on social preference in adolescent rat males and females.Methods: Female dams and their pups were exposed to either LBN conditions or normal bedding and nesting (naive) from postnatal day (PND) 2 to PND9. LBN conditions were normalized at PND10. Pups remained with their mothers until PND21, after which they were weaned and housed with same-sex littermates. At PND45, when rats reach adolescent age, social preference was assessed by measuring the amount of time an experimenter rat interacted with either a bystander rat or an inanimate object.Results: Our findings reveal that adolescent females exposed to LBN exhibited a significant preference for inanimate objects oversocial interaction with an unfamiliar bystander. In contrast, no significant differences were observed in social interactions with a familiar bystander, regardless of sex or stress condition.Conclusion: These results highlight sex-specific effects of early-life stress on social behavior and suggest adolescent females show preferential susceptibility to a disruption in social preference

Bridging the gap: Improving data sharing practices in surgical research: A cross-sectional analysis

Background: Data sharing is critical for advancing medical knowledge, improving patient outcomes, and ensuring research transparency and reproducibility. Despite the International Committee of Medical Journal Editors (ICMJE) guidelines mandating data-sharing statements (DSS) in clinical trials, compliance in surgical research remains inconsistent.Methods: Following PRISMA guidelines, a systematic review, and cross-sectional analysis were conducted on 1,094 articles from the top five h-5 indexed surgery journals published between January 1, 2020, and December 31, 2023. Factors influencing DSS inclusion were assessed using hierarchical logistic regression, and thematic analysis was performed on DSS content. A comprehensive search was conducted in MEDLINE (PubMed) using ISSNs of the selected journals, and articles were screened for eligibility based on predefined criteria.Results: Out of 1,094 articles, only 141 (12.89%) included DSS, with higher rates in clinical trials (18.05%) compared to cohort studies (5.20%). Government and industry-funded studies were more likely to include DSS. Open-access articles had higher DSS rates (18.95%) than non-open-access articles (4.72%). Hierarchical logistic regression indicated significant variability among journals, with higher impact factor journals more likely to include DSS. Thematic analysis revealed prevalent themes of gatekeeper roles, conditional data availability, and privacy concerns. Of the 96 corresponding authors contacted, 18 were willing to share data.Conclusion: Despite slight improvements, data sharing in top surgery journals remains low, particularly in journals with higher impact factors and funded studies. Implementing robust policies and promoting transparency in surgical research is essential for advancing medical knowledge and improving patient outcomes. Further efforts are required to enhance data-sharing practices within the surgical community

Evaluating artificial intelligence guidelines in the leading family medicine journals: A cross-sectional study

Background: Artificial intelligence (AI) is reshaping family medicine research by improving data analysis, systematic reviews, and clinical applications. However, its use also raises concerns about transparency, ethical practices, and reproducibility. This study explores how leading family medicine journals address these challenges and opportunities through their author instructions and policies.Methods: A cross-sectional review was conducted on 47 peer-reviewed family medicine journals, ranked by the 2023 SCImago SJR indicator. Data was collected from each journal’s “Instructions for Authors” to assess AI-related policies, such as reporting guidelines, authorship rules, and the use of AI in preparing manuscripts or generating images. Correlations between AI policies and journal characteristics were also analyzed.Results: Out of the 47 journals, 44.7% mentioned AI use in their author instructions. Of these, 40.4% prohibited AI authorship, and 42.6% required authors to disclose AI involvement in submissions. While 21.3% allowed AI-generated content, only 17% permitted AI-generated images. Journals with higher impact factors were more likely to have detailed AI policies, though inconsistencies and gaps in guidance were evident.Conclusion: Many family medicine journals recognize AI’s role in research, but few have adopted specific reporting guidelines. This lack of standardization limits the transparency and ethical use of AI. Adopting clear and comprehensive guidelines is essential to support ethical, reproducible, and high-quality research in the age of AI

Heartbeat evoked potential differences between anxiety disorders

This study examined differences in interoceptive processing between individuals with social anxiety and generalized anxiety, using the heartbeat-evoked potential (HEP) as a neural marker of attention to internal bodily signals. HEPs were recorded during a heartbeat counting task to assess the degree of interoceptive attention. Consistent with prior research linking elevated HEP amplitude to perceived internal threat, results showed that individuals with high social anxiety exhibited significantly greater HEP amplitudes compared to those with low anxiety. In contrast, participants with high generalized anxiety did not show increased HEP activity, suggesting distinct neural processing mechanisms across anxiety subtypes. These findings highlight the importance of differentiating between types of anxiety in interoception research and suggest that social anxiety may involve heightened sensitivity to internal cues, which may have implications for diagnosis and treatment.Lew Wentz FoundationPsycholog

Incidental diffuse gastric cancer in a patient with diabetic ketoacidosis

Background: Gastric cancer is the fifth most common and third most deadly cancer globally. It has three subtypes: intestinal, diffuse, and mixed, with intestinal being the most common. About 30% of gastric adenocarcinomas are diffuse (DGC). Although overall gastric cancer rates have declined due to H. pylori eradication and lifestyle changes, DGC incidence is rising. Risk factors include H. pylori infection, tobacco use, obesity, diet, and genetics. Around 1-3% of DGC cases are hereditary (HDGC), typically diagnosed before age 40, while non-hereditary DGC has a median diagnosis age of 70. DGC is most common in male Asians, Eastern Europeans, and South Americans. Over 40% of DGC patients present with metastatic disease, and the 5-year survival rate is 75% for localized disease, but only 7% for metastatic cases. DGC typically presents with abdominal pain, nausea, weight loss, and dysphagia. Histologically, it often features "signet ring" cells. The CDH1 mutation is most common, leading to reduced E-Cadherin expression, which promotes cancer spread.Case Presentation: A 77-year-old male with Type 2 diabetes, peripheral artery disease, heart failure, hyperlipidemia, benign prostate hypertrophy, and hypothyroidism presented with abdominal pain, nausea, and vomiting for 2 weeks, along with decreased mental status and missed insulin doses.Physical exam showed dry mucous membranes, tachycardia, mild abdominal tenderness, and lethargy. Lab results: WBC 22.2, Na 118, K 7.3, BUN 70, Cr 2.8, Glucose 738, AG 30, pH 7.09. Differential diagnoses included DKA, mesenteric ischemia, and others.CTA was negative for ischemia, but an abdominal CT revealed liver lesions, lymphadenopathy, and a past cholecystectomy. The patient was diagnosed with DKA and treated with fluids and insulin in the ICU. He developed Afib RVR, treated with amiodarone. An MRI showed pleural effusion, ascites, liver lesions, and gastroesophageal thickening. EGD biopsy confirmed diffuse gastric adenocarcinoma. After stabilization, he was transferred to a large medical institution for further oncological treatment.Discussion: Gastrectomy is the first-line treatment for localized diffuse gastric adenocarcinoma (DGC). As cancer increases in size, spreads to lymph nodes/adjacent organs, radiation and chemotherapy are often employed. Additionally, depending on the stage of cancer, palliative care is an option. Advanced age in conjunction with abdominal pain, nausea, and vomiting is a common presentation for gastric cancer. What made this case unique was the recent history of insulin noncompliance and the presence of diabetic ketoacidosis. Due to the considerable overlap in symptomatology between these two conditions, without appropriate imaging it is likely that his underlying malignancy would have been missed. Additionally, while diabetes and hyperglycemia are associated with worse outcomes in gastric cancer, DKA is not associated with diffuse gastric carcinoma

Hidden statistics: Examining the obesity epidemic across American Indians and Alaska Natives using self-reported identity compared to imputed racial categories

Introduction/Objectives: To identify obesity rates among high school students who identify solely as non-Hispanic, American Indian/Alaska Native (AI/AN) in comparison to a disaggregated approach that includes all youth identifying as AI/AN—alone or in combination with other ethnoracial groups using the 2021 Youth Risk Behavior Surveillance System (YRBSS).Design Methods: We conducted a cross-sectional analysis of the Youth Risk Behavior Surveillance System (YRBSS) to assess obesity rates among high school students in the United States, self-reporting as AI/AN alone or in combination, compared to the imputed raceeth variable in YRBSS.Results: According to the imputed raceeth variable, 119 high school students were classified as AI/AN only, with 30 being classified as obese (30; 29.43%). In contrast, 664 participants identified as AI/AN alone or in combination with other racial groups, with 149 students classified as obese (149; 22.11%). The self-report data yielded a total of 128 AI/AN-only high school students, with 31 students being classified as obese (31; 25.7%). Obesity rates varied among the other AI/AN subgroups: AI/AN and White/Caucasian (15.23%), AI/AN and Black (21.72%), AI/AN alone with Hispanic/Latino ethnicity (23.52%), or AI/AN in combination with 1 or more race (24.25%).Discussion/Conclusion: Disaggregation of ethnic groups into smaller subgroups by allowing individuals to self-report ethnoracial status limits bias and provides a more accurate dataset. Accurate data representation is crucial for adequately reporting obesity and other metabolic disorders in conjunction with race/ethnicity in medicine. Classifying AI/AN populations as non-Hispanic/Latino single-race limits the population size and hinders the amount of public resources that are allocated towards AI/AN health and well-being

Tailored preventative care recommendations using electronic health records

According to the U.S. Centers for Disease Control and Prevention (CDC), 90% of the nation’s $3.3 trillion annual healthcare expenditures are attributed to individuals with chronic and mental health conditions. Thus, preventing diseases is essential to improving public health and managing escalating healthcare costs. However, the preventive care clinical decision support (CDS) modules in most electronic health record (EHR) systems primarily rely on basic criteria such as age, gender, and screening intervals. This “one-size-fits-all” approach fails to provide personalized recommendations that consider patient-specific risk factors, such as family history, social history, ethnicity, and chronic conditions. This research develops an information system that analyze patient-specific data against the information extracted from preventive care guidelines to generate tailored recommendations along with justifications grounded in both the EHR data and preventive care guidelines. Our system empowers patients by providing personalized insights into their potential health risks before issues arise. By analyzing factors such as family history, social background, ethnicity, chronic conditions, age, gender, and past medical history, it delivers tailored recommendations based on established guidelines. With this information, individuals can take a proactive approach to their health, engaging in informed discussions with their physicians to explore preventive measures. By promoting early intervention and personalized care, this system has the potential to reduce the burden of preventable diseases and improve long-term health outcomes.Management Science and Information System