Telomere length is longer following diapause in two solitary bee species.

The mechanisms that underlie senescence are not well understood in insects. Telomeres are conserved repetitive sequences at chromosome ends that protect DNA during replication. In many vertebrates, telomeres shorten during cell division and in response to stress and are often used as a cellular marker of senescence. However, little is known about telomere dynamics across the lifespan in invertebrates. We measured telomere length in larvae, prepupae, pupae, and adults of two species of solitary bees, Osmia lignaria and Megachile rotundata. Contrary to our predictions, telomere length was longer in later developmental stages in both O. lignaria and M. rotundata. Longer telomeres occurred after emergence from diapause, which is a physiological state with increased tolerance to stress. In O. lignaria, telomeres were longer in adults when they emerged following diapause. In M. rotundata, telomeres were longer in the pupal stage and subsequent adult stage, which occurs after prepupal diapause. In both species, telomere length did not change during the 8 months of diapause. Telomere length did not differ by mass similarly across species or sex. We also did not see a difference in telomere length after adult O. lignaria were exposed to a nutritional stress, nor did length change during their adult lifespan. Taken together, these results suggest that telomere dynamics in solitary bees differ from what is commonly reported in vertebrates and suggest that insect diapause may influence telomere dynamics.

Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the American Board of Anesthesiology Examination.

BACKGROUND: Over the past decade, artificial intelligence (AI) has expanded significantly with increased adoption across various industries, including medicine. Recently, AI-based large language models such as Generative Pretrained Transformer-3 (GPT-3), Bard, and Generative Pretrained Transformer-3 (GPT-4) have demonstrated remarkable language capabilities. While previous studies have explored their potential in general medical knowledge tasks, here we assess their clinical knowledge and reasoning abilities in a specialized medical context. METHODS: We studied and compared the performance of all 3 models on both the written and oral portions of the comprehensive and challenging American Board of Anesthesiology (ABA) examination, which evaluates candidates' knowledge and competence in anesthesia practice. RESULTS: Our results reveal that only GPT-4 successfully passed the written examination, achieving an accuracy of 78% on the basic section and 80% on the advanced section. In comparison, the less recent or smaller GPT-3 and Bard models scored 58% and 47% on the basic examination, and 50% and 46% on the advanced examination, respectively. Consequently, only GPT-4 was evaluated in the oral examination, with examiners concluding that it had a reasonable possibility of passing the structured oral examination. Additionally, we observe that these models exhibit varying degrees of proficiency across distinct topics, which could serve as an indicator of the relative quality of information contained in the corresponding training datasets. This may also act as a predictor for determining which anesthesiology subspecialty is most likely to witness the earliest integration with AI. CONCLUSIONS: GPT-4 outperformed GPT-3 and Bard on both basic and advanced sections of the written ABA examination, and actual board examiners considered GPT-4 to have a reasonable possibility of passing the real oral examination; these models also exhibit varying degrees of proficiency across distinct topics.

Control of mean arterial pressure using a closed-loop system for norepinephrine infusion in severe brain injury patients: the COMAT randomized controlled trial.

Brain injury patients require precise blood pressure (BP) management to maintain cerebral perfusion pressure (CPP) and avoid intracranial hypertension. Nurses have many tasks and norepinephrine titration has been shown to be suboptimal. This can lead to limited BP control in patients that are in critical need of cerebral perfusion optimization. We have designed a closed-loop vasopressor (CLV) system capable of maintaining mean arterial pressure (MAP) in a narrow range and we aimed to assess its performance when treating severe brain injury patients. Within the first 48 h of intensive care unit (ICU) admission, 18 patients with a severe brain injury underwent either CLV or manual norepinephrine titration. In both groups, the objective was to maintain MAP in target (within ± 5 mmHg of a predefined target MAP) to achieve optimal CPP. Fluid administration was standardized in the two groups. The primary objective was the percentage of time patients were in target. Secondary outcomes included time spent over and under target. Over the four-hour study period, the mean percentage of time with MAP in target was greater in the CLV group than in the control group (95.8 ± 2.2% vs. 42.5 ± 27.0%, p < 0.001). Severe undershooting, defined as MAP < 10 mmHg of target value was lower in the CLV group (0.2 ± 0.3% vs. 7.4 ± 14.2%, p < 0.001) as was severe overshooting defined as MAP > 10 mmHg of target (0.0 ± 0.0% vs. 22.0 ± 29.0%, p < 0.001). The CLV system can maintain MAP in target better than nurses caring for severe brain injury patients.

Characterizing Chronic Pain and Adverse Childhood Experiences in the Lesbian, Gay, Bisexual, Transgender, or Queer Community.

BACKGROUND: Childhood adversity is associated with chronic pain in adulthood. Additionally, individuals identifying as lesbian, gay, bisexual, transgender, or queer (LGBTQ+) report a greater prevalence of chronic pain and increased adverse childhood experiences (ACEs). While the LGTBQ+ community has a disproportionately high chronic disease burden, limited research has been conducted on the associations between chronic pain conditions or intensity and childhood adversity in this population. METHODS: In this cross-sectional study, participants were 18 years or older, LGBTQ+ identifying, and reported chronic pain. Surveys were electronically distributed from August to November 2022 via LGBTQ+ organization email listservs and social media platforms. The survey included demographics and validated questionnaires measuring chronic pain (The Chronic Pain Questionnaire) and childhood adversity (ACE score). In analysis, ACE scores of 4 or more were defined as high. RESULTS: Responses from 136 individuals (average age of 29 ± 7.4 years) were analyzed. The mean for participants' average pain rating in the last 6 months was 5.9 of 10. Participants' worst pain was rated at least a 7 of 10 for 80% of respondents. Half (47%) had high ACE scores, and high ACE scores were significantly associated with higher average pain scores (6.27 ± 1.79, mean difference = -2.22, P = .028, 95% confidence interval [CI], -1.2 to -0.0), and higher perceived current pain ratings (4.53 ± 2.16, mean difference = -2.78, P = .007, 95% CI, -1.9 to -0.3). Transgender and gender diverse (TGD) participants (n = 75) had higher ACE scores (3.91 ± 1.78) and current pain scores compared to cisgender individuals (3.9 ± 1.8 vs 3.0 ± 1.9, P = .009, 95% CI, 0.0-0.3). History of any sexual trauma was prevalent in 36.7% and was associated with chronic pain located in the pelvic region (P = .016, effect size estimate 0.21). Specific histories of forced sexual and touch encounters were associated with a specific diagnosis of fibromyalgia (P = .008, effect size estimate 0.31 and P = .037, effect size estimate 0.31, respectively). CONCLUSIONS: Childhood adversity and chronic pain's dose-dependent relationship among our LGBTQ+ sample indicates a need to explore trauma's role in perceived pain. Given sexual trauma's association with pain location and diagnosis, type of trauma may also be crucial in understanding chronic pain development. Research into the relationships between childhood adversity, sexuality, gender identity, and chronic pain could improve chronic pain prevention and management for the LGBTQ+ community.

Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine.

A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. The ultimate goal is to use these advantages over manual care to improve patient outcome. For more than twenty years, clinical studies in anesthesia have demonstrated the superiority of closed-loop systems compared to manual control for stabilizing a single variable, reducing practitioner workload, and safely administering therapies. This research has focused on various closed-loops that coupled inputs and outputs such as the processed electroencephalogram with propofol, blood pressure with vasopressors, and dynamic predictors of fluid responsiveness with fluid therapy. Recently, multiple simultaneous independent closed-loop systems have been tested in practice and one study has demonstrated a clinical benefit on postoperative cognitive dysfunction. Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.

Perioperative Fluid and Vasopressor Therapy in 2050: From Experimental Medicine to Personalization Through Automation.

Intravenous (IV) fluids and vasopressor agents are key components of hemodynamic management. Since their introduction, their use in the perioperative setting has continued to evolve, and we are now on the brink of automated administration. IV fluid therapy was first described in Scotland during the 1832 cholera epidemic, when pioneers in medicine saved critically ill patients dying from hypovolemic shock. However, widespread use of IV fluids only began in the 20th century. Epinephrine was discovered and purified in the United States at the end of the 19th century, but its short half-life limited its implementation into patient care. Advances in venous access, including the introduction of the central venous catheter, and the ability to administer continuous infusions of fluids and vasopressors rather than just boluses, facilitated the use of fluids and adrenergic agents. With the advent of advanced hemodynamic monitoring, most notably the pulmonary artery catheter, the role of fluids and vasopressors in the maintenance of tissue oxygenation through adequate cardiac output and perfusion pressure became more clearly established, and hemodynamic goals could be established to better titrate fluid and vasopressor therapy. Less invasive hemodynamic monitoring techniques, using echography, pulse contour analysis, and heart-lung interactions, have facilitated hemodynamic monitoring at the bedside. Most recently, advances have been made in closed-loop fluid and vasopressor therapy, which apply computer assistance to interpret hemodynamic variables and therapy. Development and increased use of artificial intelligence will likely represent a major step toward fully automated hemodynamic management in the perioperative environment in the near future. In this narrative review, we discuss the key events in experimental medicine that have led to the current status of fluid and vasopressor therapies and describe the potential benefits that future automation has to offer.

Metabolic and transcriptomic characterization of summer and winter dormancy in the solitary bee, Osmia lignaria.

The solitary bee Osmia lignaria is a native pollinator in North America with growing economic importance. The life cycle of O. lignaria provides a unique opportunity to compare the physiological and molecular mechanisms underlying two ecologically contrasting dormancies within the same species. O. lignaria prepupae become dormant during the summer to avoid high temperatures. Shortly after adult eclosion, they enter a second dormancy and overwinter as diapausing adults. To compare these two dormancies, we measured metabolic rates and gene expression across development as bees initiate, maintain, and terminate both prepupal (summer) and adult (overwintering) dormancies. We observed a moderate temperature-independent decrease in gas exchange during both the prepupal dormancy after cocoon spinning (45 %) and during adult diapause after eclosion (60 %). We sequenced and assembled a high-quality reference genome from a single haploid male bee with a contiguous n50 of 5.5 Mbp to facilitate our transcriptomic analysis. The transcriptomes of dormant prepupae and diapausing adults clustered into distinct groups more closely associated with life stage than dormancy status. Membrane transport, membrane-bound cellular components, oxidoreductase activity, glutathione metabolism, and transcription factor activity increased during adult diapause, relative to prepupal dormancy. Further, the transcriptomes of adults in diapause clustered into two groups, supporting multiple phases of diapause during winter. Late adult diapause was associated with gene expression profiles supporting increased insulin/IGF, juvenile hormone, and ecdysone signaling.

Closing the loop: automation in anesthesiology is coming.

Anesthesiology and intensive care medicine provide fertile ground for innovation in automation, but to date we have only achieved preliminary studies in closed-loop intravenous drug administration. Anesthesiologists have yet to implement these tools on a large scale despite clear evidence that they outperform manual titration. Closed-loops continuously assess a predefined variable as input into a controller and then attempt to establish equilibrium by administering a treatment as output. The aim is to decrease the error between the closed-loop controller's input and output. In this editorial we consider the available intravenous anesthesia closed-loop systems, try to clarify why they have not yet been implemented on a large scale, see what they offer, and propose the future steps towards automation in anesthesia.

Nesting cavity diameter has implications for management of the alfalfa leafcutting bee (Hymenoptera: Megachilidae).

Body size influences performance in many bee species and may be influenced by nesting cavity diameter in cavity-nesting bees. Megachile rotundata (Fabricius) (Hymenoptera: Megachilidae) is a commercially-managed, solitary cavity-nesting bee. In M. rotundata body size has low heritability and is strongly influenced by the size of the larval provision and the diameter of the nesting cavity. Commercial nesting boxes have cavities that are 7 mm in diameter. Our goal was to examine the effects that nesting cavity diameter has on M. rotundata body size and performance by manipulating the size of cavities that are available for nesting. We provided bees with nesting cavities that ranged in size from 4 to 9 millimeters in 1 mm increments. To assess body size we measured mass and intertegular span. To assess performance we measured wing area, wing loading, sex, overwintering survival, pollen ball occurrence, and diapause status in the offspring. We also examined the reproductive output from the different nest cavity diameters. We found that the 8 mm cavities reared bees with the largest mass, and 4 mm cavities reared bees with the smallest mass. We determined that the 7 mm nesting cavity is optimal for offspring yield, the 8 mm nesting cavity is optimal for performance, and the 5 mm nesting cavity may be optimal for conservation efforts of other cavity-nesting bees. Based on the desired outcome of the bee managers, nest sizes differing from the standard may provide an advantage.

Tight control of mean arterial pressure using a closed loop system for norepinephrine infusion after high-risk abdominal surgery: a randomized controlled trial.

Intensive care unit (ICU) nurses frequently manually titrate norepinephrine to maintain a predefined mean arterial pressure (MAP) target after high-risk surgery. However, achieving this task is often suboptimal. We have developed a closed-loop vasopressor (CLV) controller to better maintain MAP within a narrow range. After ethical committee approval, fifty-three patients admitted to the ICU following high-risk abdominal surgery were randomized to CLV or manual norepinephrine titration. In both groups, the aim was to maintain MAP in the predefined target of 80-90 mmHg. Fluid administration was standardized in the two groups using an advanced hemodynamic monitoring device. The primary outcome of our study was the percentage of time patients were in the MAP target. Over the 2-hour study period, the percentage of time with MAP in target was greater in the CLV group than in the control group (median: IQR25-75: 80 [68-88]% vs. 42 [22-65]%), difference 37.2, 95% CI (23.0-49.2); p < 0.001). Percentage time with MAP under 80 mmHg (1 [0-5]% vs. 26 [16-75]%, p < 0.001) and MAP under 65 mmHg (0 [0-0]% vs. 0 [0-4]%, p = 0.017) were both lower in the CLV group than in the control group. The percentage of time with a MAP > 90 mmHg was not statistically different between groups. In patients admitted to the ICU after high-risk abdominal surgery, closed-loop control of norepinephrine infusion better maintained a MAP target of 80 to 90 mmHg and significantly decreased postoperative hypotensive when compared to manual norepinephrine titration.

Effects of temperature on metabolic rate during metamorphosis in the alfalfa leafcutting bee.

Spring conditions, especially in temperate regions, may fluctuate abruptly and drastically. Environmental variability can expose organisms to temperatures outside of their optimal thermal ranges. For ectotherms, sudden changes in temperature may cause short- and long-term physiological effects, including changes in respiration, morphology, and reproduction. Exposure to variable temperatures during active development, which is likely to occur for insects developing in spring, can cause detrimental effects. Using the alfalfa leafcutting bee, Megachile rotundata, we aimed to determine if oxygen consumption could be measured using a new system and to test the hypothesis that female and male M. rotundata have a thermal performance curve with a wide optimal range. Oxygen consumption of M. rotundata pupae was measured across a large range of temperatures (6-48°C) using an optical oxygen sensor in a closed respirometry system. Absolute and mass-specific metabolic rates were calculated and compared between bees that were extracted from their brood cells and those remaining in the brood cell to determine whether pupae could be accurately measured inside their brood cells. The metabolic response to temperature was non-linear, which is an assumption of a thermal performance curve; however, the predicted negative slope at higher temperatures was not observed. Despite sexual dimorphism in body mass, sex differences only occurred in mass-specific metabolic rates. Higher metabolic rates in males may be attributed to faster development times, which could explain why there were no differences in absolute metabolic rate measurements. Understanding the physiological and ecological effects of thermal environmental variability on M. rotundata will help to better predict their response to climate change.

Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.

Objectives: Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER). Materials and Methods: This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data. Results: MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers. Discussion: To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery. Conclusion: MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside.

BACKGROUND: Computational modeling of physiology has become a routine element in the development, evaluation, and safety testing of many types of medical devices. Members of the Food and Drug Administration have recently published a manuscript detailing the development, validation, and sensitivity testing of a computational model for blood volume, cardiac stroke volume, and blood pressure, noting that such a model might be useful in the development of closed-loop fluid administration systems. In the present study, we have expanded on this model to include the pharmacologic effect of sodium nitroprusside and calibrated the model against our previous experimental animal model data. METHODS: Beginning with the model elements in the original publication, we added six new parameters to control the effect of sodium nitroprusside: two for the onset time and clearance rates, two for the stroke volume effect (which includes venodilation as a "hidden" element), and two for the direct effect on arterial blood pressure. Using this new model, we then calibrated the predictive performance against previously collected animal study data using nitroprusside infusions to simulate shock with the primary emphasis on MAP. Root-mean-squared error (RMSE) was calculated, and the performance was compared to the performance of the model in the original study. RESULTS: RMSE of model-predicted MAP to actual MAP was lower than that reported in the original model, but higher for SV and CO. The individually fit models showed lower RMSE than using the population average values for parameters, suggesting the fitting process was effective in identifying improved parameters. Use of partially fit models after removal of the lowest variance population parameters showed a very minor decrement in improvement over the fully fit models. CONCLUSION: The new model added the clinical effects of SNP and was successfully calibrated against experimental data with an RMSE of <10% for mean arterial pressure. Model-predicted MAP showed an error similar to that seen in the original base model when using fluid shifts, heart rate, and drug dose as model inputs.

Clinical Outcome Comparison of Patients Requiring Extracorporeal Membrane Oxygenation With or Without COVID-19 Infection.

In severe COVID-19-related respiratory failure, extracorporeal membrane oxygenation (ECMO) is a useful modality that is used to provide effective oxygenation and ventilation to the patient. This descriptive study aimed to investigate and compare the outcomes between COVID-19-infected patients and patients who were not infected and required ECMO support. A retrospective study was undertaken on a cohort of 82 adult patients ([Formula: see text]18-year-old) who required venoarterial (VA-ECMO) and venovenous (VV-ECMO) ECMO between January 2019 and December 2022 in a single academic center. Patients who were cannulated for COVID-19-related respiratory failure (C-group) were compared to patients who were cannulated for non-COVID etiologies (non-group). Patients were excluded if data were missing regarding cannulation, decannulation, presenting diagnosis, and survival status. Categorical data were reported as counts and percentages, and continuous data were reported as means with 95% confidence intervals. Out of the 82 included ECMO patients, 33 (40.2%) were cannulated for COVID-related reasons, and 49 (59.8%) were cannulated for reasons other than COVID-19 infection. Compared to the non-group, the C-group had a higher in-hospital (75.8% vs. 55.1%) and overall mortality rate (78.8% vs. 61.2%). The C-group also had an average hospital length of stay (LOS) of 46.6 ± 13.2 days and an average intensive care unit (ICU) LOS of 44.1 ± 13.3 days. The non-group had an average hospital LOS of 24.8 ± 6.6 days and an average ICU LOS of 20.8 ± 5.9 days. Subgroup analysis of patients only treated with VV-ECMO yielded a greater in-hospital mortality rate for the C-group compared to the non-group (75.0% vs. 42.1%). COVID-19-infected patients may experience different morbidity and mortality rates as well as clinical presentations compared to non-COVID-infected patients when requiring ECMO support.

Environmental and Economic Impact of Using a Higher Efficiency Ventilator and Vaporizer During Surgery Under General Anesthesia: A Randomized Controlled Prospective Cohort.

Background Compared to traditional breathing circuits, low-volume anesthesia machines utilize a lower-volume breathing circuit paired with needle injection vaporizers that supply volatile agents into the circuit mainly during inspiration. We aimed to assess whether or not low-volume anesthesia machines, such as the Maquet Flow-i C20 anesthesia workstation (MQ), deliver volatile anesthetics more efficiently than traditional anesthesia machines, such as the GE Aisys CS2 anesthesia machine (GE), and, secondarily, whether this was in a meaningful economic or environmentally conscious way. Methodology Participants enrolled in the study (Institutional Review Board Identifier: 2014-1248) met the following inclusion criteria: 18-65 years old, scheduled for surgery requiring general anesthesia at the University of California Irvine Health, and expected to receive sevoflurane for the duration of the procedure. Exclusion criteria included age <18 years old, a history of chronic obstructive pulmonary disorder, cardiovascular disease, sevoflurane sensitivity, body mass index >30 kg/m2, American Society of Anesthesiologists >2, pregnancy, or surgery scheduled <120 minutes. We calculated the total amount of sevoflurane delivered and consumption rates during induction and maintenance periods and compared the groups using one-sided parametric testing (Student's t-test). There was no suspicion that the low-volume circuit could use more sevoflurane and that the outcome did not answer our research question. One-sided testing allowed for more power to be more certain of smaller differences in our results. Results In total, 103 subjects (MQ: n = 52, GE: n = 51) were analyzed. Seven subjects were lost to attrition of different types. Overall, the MQ group consumed significantly less sevoflurane (95.5 ± 49.3 g) compared to the GE group (118.3 ± 62.4 g) (p = 0.043), corresponding to an approximately 20% efficiency improvement in overall agent delivery. When accounting for the fresh gas flow setting, agent concentration, and length of induction, the MQ delivered the volatile agent at a significantly lower rate compared to the GE (7.4 ± 3.2 L/minute vs. 9.1 ± 4.1 L/minute; p = 0.017). Based on these results, we estimate that the MQ can save an estimated average of $239,440 over the expected 10-year machine lifespan. This 20% decrease in CO2 equivalent emissions corresponds to 201 metric tons less greenhouse gas emissions over a decade compared to the GE, which is equivalent to 491,760 miles driven by an average passenger vehicle or 219,881 pounds of coal burned. Conclusions Overall, our results from this study suggest that the MQ delivers statistically significantly less (~20%) volatile agent during routine elective surgery using a standardized anesthetic protocol and inclusion/exclusion criteria designed to minimize any patient or provider heterogeneity effects on the results. The results demonstrate an opportunity for economic and environmental benefits.

Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the ABA Exam.

Over the past decade, Artificial Intelligence (AI) has expanded significantly with increased adoption across various industries, including medicine. Recently, AI's large language models such as GPT-3, Bard, and GPT-4 have demonstrated remarkable language capabilities. While previous studies have explored their potential in general medical knowledge tasks, here we assess their clinical knowledge and reasoning abilities in a specialized medical context. We study and compare their performances on both the written and oral portions of the comprehensive and challenging American Board of Anesthesiology (ABA) exam, which evaluates candidates' knowledge and competence in anesthesia practice. In addition, we invited two board examiners to evaluate AI's answers without disclosing to them the origin of those responses. Our results reveal that only GPT-4 successfully passed the written exam, achieving an accuracy of 78% on the basic section and 80% on the advanced section. In comparison, the less recent or smaller GPT-3 and Bard models scored 58% and 47% on the basic exam, and 50% and 46% on the advanced exam, respectively. Consequently, only GPT-4 was evaluated in the oral exam, with examiners concluding that it had a high likelihood of passing the actual ABA exam. Additionally, we observe that these models exhibit varying degrees of proficiency across distinct topics, which could serve as an indicator of the relative quality of information contained in the corresponding training datasets. This may also act as a predictor for determining which anesthesiology subspecialty is most likely to witness the earliest integration with AI.

Thermal biology and overwintering behavior of the red sunflower seed weevil (Coleoptera: Curculionidae).

Most natural mortality of the red sunflower seed weevil, Smicronyx fulvus LeConte (Coleoptera: Curculionidae), occurs while larvae overwinter in the soil. To test the hypothesis that S. fulvus mortality is related to low temperatures, experiments were used to (i) evaluate the temperature at which larvae freeze (= supercooling point [SCP]), (ii) assess possible vertical movement between entry into the soil in fall and adult emergence in summer, and (iii) determine if realistic soil temperatures could explain patterns of overwintering mortality. Mean SCP for groups of S. fulvus larvae differed between years and months, but only ranged from -20.93 to -22.68 °C. Most overwintering larvae were found within 6 cm of the soil surface, but larvae appeared to move 1-2 cm deeper between pairs of successive sample dates (September to January, January to April). Significant larval mortality that occurred between January and April 2021 was tentatively attributed to a period in February where daily minimum soil temperatures ranged from -8 to -12 °C. When overwintering under control conditions (constant 4 °C) was interrupted with week-long exposure to -4, -8, or -12 °C in a cold bath, significant S. fulvus mortality was seen for temperatures at or below -8 °C. Combined results suggest that mortality of overwintering S. fulvus is likely caused by continuous exposure to low temperatures that may not be cold enough to freeze larvae. Additionally, the shallow overwintering by S. fulvus supports the idea that routine farm management, including tillage and herbicide incorporation, may help limit populations of this sunflower pest.

Effects of developmental state on low-temperature physiology of the alfalfa leafcutting bee, Megachile rotundata.

The success of agriculture relies on healthy bees to pollinate crops. Commercially managed pollinators are often kept under temperature-controlled conditions to better control development and optimize field performance. One such pollinator, the alfalfa leafcutting bee, Megachile rotundata, is the most widely used solitary bee in agriculture. Problematically, very little is known about the thermal physiology of M. rotundata or the consequences of artificial thermal regimes used in commercial management practices. Therefore, we took a broad look at the thermal performance of M. rotundata across development and the effects of commonly used commercial thermal regimes on adult bee physiology. After the termination of diapause, we hypothesized thermal sensitivity would vary across pupal metamorphosis. Our data show that bees in the post-diapause quiescent stage were more tolerant of low temperatures compared to bees in active development. We found that commercial practices applied during development decrease the likelihood of a bee recovering from another bout of thermal stress in adulthood, thereby decreasing their resilience. Lastly, commercial regimes applied during development affected the number of days to adult emergence, but the time of day that adults emerged was unaffected. Our data demonstrate the complex interactions between bee development and thermal regimes used in management. This knowledge can help improve the commercial management of these bees by optimizing the thermal regimes used and the timing of their application to alleviate negative downstream effects on adult performance.

MOVER: Medical Informatics Operating Room Vitals and Events Repository.

Artificial Intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository, which includes data from 58,799 unique patients and 83,468 surgeries collected from the UCI Medical Center over a period of seven years. MOVER is freely available to all researchers who sign a data usage agreement, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.