Measuring the Direct Medical Costs of Hospital-Onset Infections Using an Analogy Costing Framework.

BACKGROUND: The majority of recent estimates on the direct medical cost attributable to hospital-onset infections (HOIs) has focused on device- or procedure-associated HOIs. The attributable costs of HOIs that are not associated with device use or procedures have not been extensively studied. OBJECTIVE: We developed simulation models of attributable cost for 16 HOIs and estimated the total direct medical cost, including nondevice-related HOIs in the USA for 2011 and 2015. DATA AND METHODS: We used total discharge costs associated with HOI-related hospitalization from the National Inpatient Sample and applied an analogy costing methodology to develop simulation models of the costs attributable to HOIs. The mean attributable cost estimate from the simulation analysis was then multiplied by previously published estimates of the number of HOIs for 2011 and 2015 to generate national estimates of direct medical costs. RESULTS: After adjusting all estimates to 2017 US dollars, attributable cost estimates for select nondevice-related infections attributable cost estimates ranged from $7661 for ear, eye, nose, throat, and mouth (EENTM) infections to $27,709 for cardiovascular system infections in 2011; and from $8394 for EENTM to $26,445 for central nervous system infections in 2016 (based on 2015 incidence data). The national direct medical costs for all HOIs were $14.6 billion in 2011 and $12.1 billion in 2016. Nondevice- and nonprocedure-associated HOIs comprise approximately 26-28% of total HOI costs. CONCLUSION: Results suggest that nondevice- and nonprocedure-related HOIs result in considerable costs to the healthcare system.

Muscle-to-Bone Ratio in NCAA Division I Collegiate Football Players by Position.

ABSTRACT: Dengel, DR, Studee, HR, Juckett, WT, Bosch, TA, Carbuhn, AF, Stanforth, PR, and Evanoff, NG. Muscle-to-bone ratio in NCAA division I collegiate football players by position. J Strength Cond Res XX(X): 000-000, 2024-The purpose of this study was to compare the muscle-to-bone ratio (MBR) in National Collegiate Athletic Association Division I football players (collegiate football players [CFP]) to healthy, age-matched controls. In addition, we examined MBR in CFP by position. A total of 553 CFP and 261 controls had their total and regional lean mass (LM), fat mass (FM), and bone mineral content (BMC) determined by dual x-ray absorptiometry (DXA). College football players were categorized by positions defined as offensive linemen (OL), defensive linemen (DL), tight end, linebacker (LB), running back (RB), punter or kicker, quarterback (QB), defensive back (DB), and wide receiver (WR). There were significant differences between CFP and controls for total LM (80.1 ± 10.0 vs. 56.9 ± 7.8 kg), FM (22.2 ± 12.5 vs. 15.2 ± 7.1 kg), and BMC (4.3 ± 0.5 vs. 3.1 ± 0.5 kg). Although there were significant differences in body composition between CFP and controls, there was no significant differences in total MBR between CFP and controls (18.6 ± 1.4 vs. 18.8 ± 1.7). Regionally, CFP had significantly lower trunk MBR than controls (26.7 ± 2.7 vs. 28.7 ± 4.2), but no difference was seen in leg or arm MBR. Positional differences in CFP were noted as total MBR being significantly higher in DL (19.0 ± 1.4) than in DB (18.1 ± 1.3), WR (18.1 ± 1.3), and LB (18.2 ± 1.3). OL had a significantly higher total MBR (19.2 ± 1.3) than DB (18.1 ± 1.3), LB (18.2 ± 1.3), QB (18.1 ± 1.0), and WR (18.1 ± 1.3). In addition, RB had significantly higher total MBR (18.8 ± 1.3) than DB (18.1 ± 1.3) and WR (18.1 ± 1.3). This study may provide athletes and training staff with normative values when evaluating total and regional MBR with DXA.

Fly Me to the Micron: Microtechnologies for Drosophila Research.

Multicellular model organisms, such as Drosophila melanogaster (fruit fly), are frequently used in a myriad of biological research studies due to their biological significance and global standardization. However, traditional tools used in these studies generally require manual handling, subjective phenotyping, and bulk treatment of the organisms, resulting in laborious experimental protocols with limited accuracy. Advancements in microtechnology over the course of the last two decades have allowed researchers to develop automated, high-throughput, and multifunctional experimental tools that enable novel experimental paradigms that would not be possible otherwise. We discuss recent advances in microtechnological systems developed for small model organisms using D. melanogaster as an example. We critically analyze the state of the field by comparing the systems produced for different applications. Additionally, we suggest design guidelines, operational tips, and new research directions based on the technical and knowledge gaps in the literature. This review aims to foster interdisciplinary work by helping engineers to familiarize themselves with model organisms while presenting the most recent advances in microengineering strategies to biologists.

Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods.

Objective: Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, and 2 rule-based and machine learning-based methods, namely, scispaCy and medspaCy. Materials and Methods: Phenotypes such as initial cancer stage, initial treatment, evidence of cancer recurrence, and affected organs during recurrence were identified from 13 646 clinical notes for 63 NSCLC patients from Washington University in St. Louis, Missouri. The performance of the GPT-4 model is evaluated against GPT-3.5-turbo, Flan-T5-xxl, Flan-T5-xl, Llama-3-8B, medspaCy, and scispaCy by comparing precision, recall, and micro-F1 scores. Results: GPT-4 achieved higher F1 score, precision, and recall compared to Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, medspaCy, and scispaCy's models. GPT-3.5-turbo performed similarly to that of GPT-4. GPT, Flan-T5, and Llama models were not constrained by explicit rule requirements for contextual pattern recognition. spaCy models relied on predefined patterns, leading to their suboptimal performance. Discussion and Conclusion: GPT-4 improves clinical phenotype identification due to its robust pre-training and remarkable pattern recognition capability on the embedded tokens. It demonstrates data-driven effectiveness even with limited context in the input. While rule-based models remain useful for some tasks, GPT models offer improved contextual understanding of the text, and robust clinical phenotype extraction.

Muskelin acts as a substrate receptor of the highly regulated Drosophila CTLH E3 ligase during the maternal-to-zygotic transition.

The maternal-to-zygotic transition (MZT) is a conserved developmental process where the maternally-derived protein and mRNA cache is replaced with newly made zygotic gene products. We have previously shown that in Drosophila the deposited RNA-binding proteins ME31B, Cup, and Trailer Hitch (TRAL) are ubiquitylated by the CTLH E3 ligase and cleared. However, the organization and regulation of the CTLH complex remain poorly understood in flies. In particular, Drosophila lacks an identifiable substrate adaptor, and the mechanisms restricting degradation of ME31B and its cofactors to the MZT are unknown. Here, we show that the developmental specificity of the CTLH complex is mediated by multi-pronged regulation, including transcriptional control by the transcription factor OVO and autoinhibition of the E3 ligase. One major regulatory target is the subunit Muskelin, which we demonstrate acts as a substrate adaptor for the Drosophila CTLH complex. Although conserved, Muskelin has structural roles in other species, suggesting a surprising functional plasticity. Finally, we find that Muskelin has few targets beyond the three known RNA binding proteins, showing exquisite target specificity. Thus, multiple levels of integrated regulation restrict the activity of the embryonic CTLH complex to early embryogenesis, seemingly with the goal of regulating three important RNA binding proteins.

Caspase-8 promotes scramblase-mediated phosphatidylserine exposure and fusion of osteoclast precursors.

Efficient cellular fusion of mononuclear precursors is the prerequisite for the generation of fully functional multinucleated bone-resorbing osteoclasts. However, the exact molecular factors and mechanisms controlling osteoclast fusion remain incompletely understood. Here we identify RANKL-mediated activation of caspase-8 as early key event during osteoclast fusion. Single cell RNA sequencing-based analyses suggested that activation of parts of the apoptotic machinery accompanied the differentiation of osteoclast precursors into mature multinucleated osteoclasts. A subsequent characterization of osteoclast precursors confirmed that RANKL-mediated activation of caspase-8 promoted the non-apoptotic cleavage and activation of downstream effector caspases that translocated to the plasma membrane where they triggered activation of the phospholipid scramblase Xkr8. Xkr8-mediated exposure of phosphatidylserine, in turn, aided cellular fusion of osteoclast precursors and thereby allowed generation of functional multinucleated osteoclast syncytia and initiation of bone resorption. Pharmacological blockage or genetic deletion of caspase-8 accordingly interfered with fusion of osteoclasts and bone resorption resulting in increased bone mass in mice carrying a conditional deletion of caspase-8 in mononuclear osteoclast precursors. These data identify a novel pathway controlling osteoclast biology and bone turnover with the potential to serve as target for therapeutic intervention during diseases characterized by pathologic osteoclast-mediated bone loss. Proposed model of osteoclast fusion regulated by caspase-8 activation and PS exposure. RANK/RANK-L interaction. Activation of procaspase-8 into caspase-8. Caspase-8 activates caspase-3. Active capase-3 cleaves Xkr8. Local PS exposure is induced. Exposed PS is recognized by the fusion partner. FUSION. PS is re-internalized.

Assessment of Erythrocyte Transketolase, Whole Blood Thiamine Diphosphate, and Human Milk Thiamine Concentrations to Identify Infants and Young Children Responding Favorably to Therapeutic Thiamine Administration: Findings from the Lao Thiamine Study, a Prospective Cohort Study.

Background: There is limited information on relationships among biomarkers of thiamine status (whole blood thiamine diphosphate [ThDP], erythrocyte transketolase activity coefficient [ETKac], and human milk thiamine [MTh]) and clinical manifestations of thiamine deficiency. Objectives: This study aimed to explore correlations among these biomarkers and thiamine responsive disorders (TRDs), a diagnosis based on favorable clinical response to thiamine. Methods: Hospitalized infants and young children (aged 21 d to <18 mo) with respiratory, cardiac, and/or neurological symptoms suggestive of thiamine deficiency were treated with parenteral thiamine (100 mg daily) for ≥3 d alongside other treatments and re-examined systematically. Clinical case reports were reviewed by 3 pediatricians, who determined TRD or non-TRD status. Children in a community comparison group were matched by age, sex, and residence. Venous whole blood ThDP and MTh were determined by high-performance liquid chromatography fluorescence detection and ETKac in washed erythrocytes by ultraviolet spectrophotometry. Associations between biomarkers were assessed using Spearman correlations, and biomarker cutoffs predictive of TRD and ETKac >1.25 were explored using area under the receiver operating characteristic curve framework. Results: Thiamine biomarkers were available for 287 hospitalized children and 228 community children (mean age 4.7 mo; 59.4% male). Median (interquartile range [IQR]) ThDP and ETKac were 66.9 nmol/L (IQR: 41.4, 96.9 nmol/L) and 1.25 nmol/L (IQR: 1.11, 1.48 nmol/L), respectively, among hospitalized children, and 64.1 nmol/L (IQR: 50.0, 85.3 nmol/L) and 1.22 nmol/L (IQR: 1.12, 1.37 nmol/L) among 228 community children (P > 0.05 for both). Forty-five percent of breastfeeding mothers of infants <6 mo had MTh <90 µg/L. ThDP and ETKac, but not MTh, were significantly different between 152 children with TRD and 122 without TRD, but overlapping distributions undermined prediction of individual responses to thiamine. Conclusions: Although ETKac, ThDP, and MTh are useful biomarkers of population thiamine status, none of the biomarkers reliably identified individual children with TRD. ThDP is more practical for population assessment because preparing washed erythrocytes is not required.This trial was registered at clinicaltrials.gov as NCT03626337.

Physics of microscale freeform 3D printing of ice.

Ice is emerging as a promising sacrificial material in the rapidly expanding area of advanced manufacturing for creating precise 3D internal geometries. Freeform 3D printing of ice (3D-ICE) can produce microscale ice structures with smooth walls, hierarchical transitions, and curved and overhang features. However, controlling 3D-ICE is challenging due to an incomplete understanding of its complex physics involving heat transfer, fluid dynamics, and phase changes. This work aims to advance our understanding of 3D-ICE physics by combining numerical modeling and experimentation. We developed a 2D thermo-fluidic model to analyze the transition from layered to continuous printing and a 3D thermo-fluidic model for the oblique deposition, which enables curved and overhang geometries. Experiments are conducted and compared with model simulations. We found that high droplet deposition rates enable the continuous deposition mode with a sustained liquid cap on top of the ice, facilitating smooth geometries. The diameter of ice structures is controlled by the droplet deposition frequency. Oblique deposition causes unidirectional spillover of the liquid cap and asymmetric heat transfer at the freeze front, rotating the freeze front. These results provide valuable insights for reproducible 3D-ICE printing that could be applied across various fields, including tissue engineering, microfluidics, and soft robotics.

Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework.

Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.

Delusions are distressing and disabling psychiatric symptoms. Cognitive behavioral therapy for psychosis (CBTp) is the leading psychotherapeutic approach for treating delusions. Predictive coding is a contemporary cognitive neuroscience framework that is increasingly being used to explain mechanisms of delusions. In this article, we attempt to integrate CBTp within the predictive coding framework, outlining how effective CBTp techniques impact aspects of the predictive coding model to contribute to cutting-edge treatment and cognitive neuroscience research on delusions and inform recommendations for treatment advancement.

Explainable El Niño predictability from climate mode interactions.

The El Niño-Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1-3, yet, quantifying the sources of skilful predictions is a long-standing challenge4-7. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible8-10, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16-18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO's seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO's long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes' contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework's holistic treatment of ENSO's global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.

Improved DiOlistic labelling technique for neurons in situ: Detailed visualisation of dendritic spines and concurrent histochemical-detection in fixed tissue.

DiOlistic labelling is a robust, unbiased ballistic method that utilises lipophilic dyes to morphologically label neurons. While its efficacy on freshly dissected tissue specimens is well-documented, applying DiOlistic labelling to stored, fixed brain tissue and its use in polychromatic multi-marker studies poses significant technical challenges. Here, we present an improved, step-by-step protocol for DiOlistic labelling of dendrites and dendritic spines in fixed mouse tissue. Our protocol encompasses the five key stages: Tissue Preparation, Dye Bullet Preparation, DiOlistic Labelling, Confocal Imaging, and Image Analysis. This method ensures reliable and consistent labelling of dendritic spines in fixed mouse tissue, combined with increased throughput of samples and multi-parameter staining and visualisation of tissue, thereby offering a valuable approach for neuroscientific research.

Classification of short and long term mild traumatic brain injury using computerized eye tracking.

Accurate, and objective diagnosis of brain injury remains challenging. This study evaluated useability and reliability of computerized eye-tracker assessments (CEAs) designed to assess oculomotor function, visual attention/processing, and selective attention in recent mild traumatic brain injury (mTBI), persistent post-concussion syndrome (PPCS), and controls. Tests included egocentric localisation, fixation-stability, smooth-pursuit, saccades, Stroop, and the vestibulo-ocular reflex (VOR). Thirty-five healthy adults performed the CEA battery twice to assess useability and test-retest reliability. In separate experiments, CEA data from 55 healthy, 20 mTBI, and 40 PPCS adults were used to train a machine learning model to categorize participants into control, mTBI, or PPCS classes. Intraclass correlation coefficients demonstrated moderate (ICC > .50) to excellent (ICC > .98) reliability (p < .05) and satisfactory CEA compliance. Machine learning modelling categorizing participants into groups of control, mTBI, and PPCS performed reasonably (balanced accuracy control: 0.83, mTBI: 0.66, and PPCS: 0.76, AUC-ROC: 0.82). Key outcomes were the VOR (gaze stability), fixation (vertical error), and pursuit (total error, vertical gain, and number of saccades). The CEA battery was reliable and able to differentiate healthy, mTBI, and PPCS patients reasonably well. While promising, the diagnostic model accuracy should be improved with a larger training dataset before use in clinical environments.

Renoprotective Effects of Metabolic Surgery Versus GLP1 Receptor Agonists on Progression of Kidney Impairment in Patients with Established Kidney Disease.

OBJECTIVE: To examine the renoprotective effects of metabolic surgery in patients with established chronic kidney disease (CKD). BACKGROUND: The impact of metabolic surgery compared with glucagon-like peptide-1 receptor agonists (GLP-1RA) in patients with established CKD has not been fully characterized. METHODS: Patients with obesity (BMI ≥30 kg/m2), type 2 diabetes (T2DM), and baseline estimated glomerular filtration rate (eGFR) 20-60 mL/min/1.73 m² who underwent metabolic bariatric surgery at a large U.S. health system (2010-2017) were compared with nonsurgical patients who continuously received GLP-1RA. The primary end point was CKD progression, defined as decline of eGFR by ≥50% or to <15 mL/min/1.73 m2, initiation of dialysis, or kidney transplant. The secondary end point was the incident kidney failure (eGFR <15 mL/min/1.73 m2, dialysis, or kidney transplant) or all-cause mortality. RESULTS: 425 patients, including 183 patients in the metabolic surgery group and 242 patients in the GLP-1RA group, with a median follow-up of 5.8 years (IQR, 4.4-7.6) were analyzed. The cumulative incidence of the primary end point at 8-years was 21.7% (95% CI, 12.2-30.6) in the surgical group and 45.1% (95% CI, 27.7-58.4) in the nonsurgical group, with an adjusted hazard ratio of 0.40 (95% CI, 0.21-0.76), P=0.006. The cumulative incidence of the secondary composite end point at 8-years was 24.0% (95% CI, 14.1-33.2) in the surgical group and 43.8% (95% CI, 28.1-56.1) in the nonsurgical group, with an adjusted HR of 0.56 (95% CI, 0.31-0.99), P=0.048. CONCLUSIONS: Among patients with T2DM, obesity, and established CKD, metabolic surgery, compared with GLP-1RA, was significantly associated with a 60% lower risk of progression of kidney impairment and a 44% lower risk of kidney failure or death. Metabolic surgery should be considered as a therapeutic option for patients with CKD and obesity.

Insurance payor status and risk of major adverse cardiovascular and cerebrovascular events after metabolic and bariatric surgery.

BACKGROUND: Patients with Medicare/Medicaid insurance receive metabolic and bariatric surgery (MBS) at lower rates than privately insured (PI) patients. Although studies on some surgical procedures report that Medicare/Medicaid insurance confers increased postoperative complication rates and a longer length of stay, less is known about these outcomes after MBS. Among often-feared postoperative complications are major adverse cardiovascular and cerebrovascular events (MACEs). Although these events are rare after MBS, they have a significant impact on morbidity and mortality. OBJECTIVES: This study aimed to examine the effect of insurance payor status on MACEs after MBS. SETTING: The Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS). METHODS: HCUP-NIS was queried for cases including sleeve gastrectomy or Roux-en-Y gastric bypass between 2012 and 2019. Bivariate associations between patient-level factors and MACEs were assessed via Rao-Scott χ2 tests. Adjusted and unadjusted risks of insurance payor status for MACEs were evaluated using logistic regression. RESULTS: Incidence of MACEs was higher in both Medicare (.75% versus .11%; P < .001) and Medicaid (.15% versus .11%; P < .001) groups than in the PI group. After adjustment for high-risk demographics, high-risk co-morbidities, socioeconomic variables, and hospital factors, insurance status of Medicare (odds ratio [OR]: 1.60, 95% confidence interval [CI]: 1.23, 2.07; P = .0026) or Medicaid (OR: 1.55, 95% CI: 1.12, 2.16; P = .0026) remained an independent risk factor for MACEs. CONCLUSIONS: Our findings underscore the significance of Medicaid/Medicare payor status as an independent predictor of postoperative MACEs in MBS. The results of this study can have a significant impact on deepening our understanding of socioeconomic and health system-related issues that can be targeted to improve outcomes in both MBS and other surgical specialties.

Changes in Emergency Department Utilization in Vulnerable Populations After COVID-19 Shelter-in-Place Orders.

OBJECTIVES: This study aims to compare emergency department (ED) utilization and admission rates for patients with a history of mental health (MH) disorders, substance use disorders (SUDs), and social determinants of health (SDOH) before and after implementing COVID-19 shelter-in-place (SIP) orders. METHODS: This was a retrospective, multicenter study leveraging electronic medical record (EMR) data from 20 EDs across a large Midwest integrated healthcare system from 5/2/2019 to 12/31/2019 (pre-SIP) and from 5/2/2020 to 12/31/2020 (post-SIP). Diagnoses were documented in the patient's medical records. Poisson and logistic regression models were used to evaluate ED utilization and admission rate changes. RESULTS: A total of 871,020 ED encounters from 487,028 unique patients were captured. Overall, 2,572 (0.53%) patients had a documented Z code for SDOH. Patients with previously diagnosed MH disorders or SUDs were more likely to seek ED care after the SIP orders were implemented (risk ratio (RR): 1.20, 95% confidence interval (CI): 1.18-1.22, p<0.001), as were patients with SDOH (RR: 2.37, 95% CI: 2.19-2.55, p<0.001). Patients with both previously diagnosed MH disorders or SUDs and a documented SDOH had even higher ED utilization (RR: 3.31, 95% CI: 2.83-3.88, p<0.001) than those with either condition alone. Patients with MH disorders and SUDs (OR: 0.89, 95% CI: 0.86-0.92, p<0.001) or SDOH (OR: 0.67, 95% CI: 0.54-0.83, p<0.001) were less likely to be admitted post-SIP orders, while patients with a history of diseases of physiologic systems were more likely to be admitted. CONCLUSION: Vulnerable populations with a history of MH disorders, SUDs, and SDOH experienced increased ED utilization but a lower rate of hospital admissions after the implementation of SIP orders. The findings highlight the importance of addressing these needs to mitigate the impact of public health crises on these populations.

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Determination of a vancomycin nephrotoxicity threshold and assessment of target attainment in hematology patients.

An area-under-the-curve (AUC24)-based approach is recommended to guide vancomycin therapeutic drug monitoring (TDM), yet trough concentrations are still commonly used despite associated risks. A definitive toxicity target is lacking, which is important for hematology patients who have a higher risk of nephrotoxicity. The aims were to (1) assess the impact of trough-based TDM on acute kidney injury (AKI) incidence, (2) establish a vancomycin nephrotoxicity threshold, and (3) evaluate the proportion of hematology patients achieving vancomycin therapeutic targets. Retrospective data was collected from 100 adult patients with a hematological malignancy or aplastic anemia who received vancomycin between April 2020 and January 2021. AKI occurrence was determined based on serum creatinine concentrations, and individual pharmacokinetic parameters were estimated using a Bayesian approach. Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of pharmacokinetic indices to predict AKI occurrence. The proportion of patients who achieved target vancomycin exposure was evaluated based on an AUC24/MIC ≥400 and the determined toxicity threshold. The incidence of AKI was 37%. ROC curve analysis indicated a maximum AUC24 of 644 mg.h/L over the treatment period was an important predictor of AKI. By Day 4 of treatment, 29% of treatment courses had supratherapeutic vancomycin exposure, with only 62% of courses achieving AUC24 targets. The identified toxicity threshold supports an AUC24 target range of 400-650 mg.h/L, assuming an MIC of 1 mg/L, to optimize vancomycin efficacy and minimize toxicity. This study highlights high rates of AKI in this population and emphasizes the importance of transitioning from trough-based TDM to an AUC-based approach to improve clinical outcomes.

Underserved groups could be better considered within population-based eye health surveys: A methodological study.

OBJECTIVE: In pursuit of health equity, the World Health Organization has recently called for more extensive monitoring of inequalities in eye health. Population-based eye health surveys can provide this information, but whether underserved groups are considered in the design, implementation, and reporting of surveys is unknown. We conducted a systematic methodological review of surveys published since 2000 to examine how many population-based eye health surveys have considered underserved groups in their design, reporting, or implementation. STUDY DESIGN AND SETTING: We identified all population-based cross-sectional surveys reporting the prevalence of objectively measured vision impairment or blindness. Using the PROGRESS+ framework to identify underserved groups, we assessed whether each study considered underserved groups within 15 items across the rationale, sampling or recruitment methods, or the reporting of participation and prevalence rates. RESULTS: 388 eye health surveys were included in this review. Few studies prospectively considered underserved groups during study planning or implementation, for example within their sample size calculations (n=5, ∼1%) or recruitment strategies (n=70, 18%). The most common way that studies considered underserved groups was in the reporting of prevalence estimates (n=374, 96%). We observed a modest increase in the number of distinct PROGRESS+ factors considered by a publication over the study period. Gender/sex was considered within at least one item by 95% (n=267) of studies. Forty-three percent (n=166) of included studies were conducted primarily on underserved population groups, particularly for sub-national studies of people living in rural areas, and we identified examples of robust population-based studies in socially excluded groups. CONCLUSION: More effort is needed to improve the design, implementation, and reporting of surveys to monitor inequality and promote equity in eye health. Ideally, national-level monitoring of vision impairment and service coverage would be supplemented with smaller-scale studies to understand the disparities experienced by the most underserved groups.

Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia.

Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n = 3) and compared performance with that of unoperated monkeys (n = 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.