BACKGROUND: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our health care infrastructure.
OBJECTIVE: Screening for MDROs is an important mechanism for preventing spread but is resource intensive. The objective of this study was to develop automated tools that can predict colonization or infection risk using electronic health record (EHR) data, provide useful information to aid infection control, and guide empiric antibiotic coverage.
METHODS: We retrospectively developed a machine learning model to detect MRSA colonization and infection in undifferentiated patients at the time of sample collection from hospitalized patients at the University of Virginia Hospital. We used clinical and nonclinical features derived from on-admission and throughout-stay information from the patient\'s EHR data to build the model. In addition, we used a class of features derived from contact networks in EHR data; these network features can capture patients\' contacts with providers and other patients, improving model interpretability and accuracy for predicting the outcome of surveillance tests for MRSA. Finally, we explored heterogeneous models for different patient subpopulations, for example, those admitted to an intensive care unit or emergency department or those with specific testing histories, which perform better.
RESULTS: We found that the penalized logistic regression performs better than other methods, and this model\'s performance measured in terms of its receiver operating characteristics-area under the curve score improves by nearly 11% when we use polynomial (second-degree) transformation of the features. Some significant features in predicting MDRO risk include antibiotic use, surgery, use of devices, dialysis, patient\'s comorbidity conditions, and network features. Among these, network features add the most value and improve the model\'s performance by at least 15%. The penalized logistic regression model with the same transformation of features also performs better than other models for specific patient subpopulations.
CONCLUSIONS: Our study shows that MRSA risk prediction can be conducted quite effectively by machine learning methods using clinical and nonclinical features derived from EHR data. Network features are the most predictive and provide significant improvement over prior methods. Furthermore, heterogeneous prediction models for different patient subpopulations enhance the model\'s performance.

OBJECTIVE: This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder).
METHODS: The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance.
RESULTS: We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.
CONCLUSIONS: These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.

BACKGROUND: Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants.
METHODS: EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure.
RESULTS: Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures.
CONCLUSIONS: We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.

BACKGROUND: Accurate estimation of the specialized pediatric palliative care (PPC) burden and the definition of the extent and quality of PPC service in Italy represent urgent needs to enable the proper allocation of PPC resources and the definition of prevention and educational plans. The PalliPed project aimed to provide the first comprehensive assessment of the characteristics of Italian patients requiring PPC, the quality and extent of regional PPC networks/facilities, and the number of dedicated resources. In this paper, we present the results of the second part of the project, regarding the implementation and quality of PPC services in Italy.
METHODS: The PalliPed study had an observational cross-sectional design. All Italian specialized PPC centers/facilities were invited to participate in the project and complete a survey on the characteristics of PPC centers/facilities in different care settings, reporting data as of 24 October 2022. Data were collected online.
RESULTS: 19 PPC specialized centers/facilities from 12 Italian regions and two autonomous provinces responded to the survey. Among them, 11 are regional referral centers. Seven Italian regions out of 20 reported no PPC centers/facilities, mainly in central-southern Italy. Less than half (45%) of the regional referral centers cover the entire regional territory, and three offer 24/7 service. Ten centers have a dedicated team. Half of the eight non-referral centers offer 24/7 service and have a dedicated team. A total of 1,092 patients were reported by 18 centers as of 24 October 2022. Over the years, an increasing number of patients has been reported, rising from 1,202 (2019) to 1,544 (2021). The dedicated staff is inadequate, and most healthcare providers are not recognized at an institutional level. A shortage of \'young\' staff and a lack of specific training was reported, particularly among nurses (77% had no training in PPC).
CONCLUSIONS: The results obtained show how training, information, and research interventions are still necessary for the reorganization of the available resources and definition of proper strategies to respond dynamically to the new emerging needs of these populations. At the same time, our study represents a first step in defining a national registry of PPC models, useful for monitoring evolutions, and critical issues and planning any new or corrective strategy.

BACKGROUND: Before 2016, patients with isolated synchronous colorectal peritoneal metastases (PMCRC) diagnosed in expert centers had a higher odds of undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) and better overall survival (OS) than those diagnosed in referring centers. Nationwide efforts were initiated to increase awareness and improve referral networks.
METHODS: This nationwide study aimed to evaluate whether the between-center differences in odds of undergoing CRS-HIPEC and OS have reduced since these national efforts were initiated. All patients with isolated synchronous PMCRC diagnosed between 2009 and 2021 were identified from the Netherlands Cancer Registry. Associations between hospital of diagnosis and the odds of undergoing CRS-HIPEC, as well as OS, were assessed using multilevel multivariable regression analyses for two periods (2009-2015 and 2016-2021).
RESULTS: In total, 3948 patients were included. The percentage of patients undergoing CRS-HIPEC increased from 17.2% in 2009-2015 (25.4% in expert centers, 16.5% in referring centers), to 23.4% in 2016-2021 (30.2% in expert centers, 22.6% in referring centers). In 2009-2015, compared with diagnosis in a referring center, diagnosis in a HIPEC center showed a higher odds of undergoing CRS-HIPEC (odds ratio [OR] 1.64, 95% confidence interval [CI] 1.02-2.67) and better survival (hazard ratio [HR] 0.80, 95% CI 0.66-0.96). In 2016-2021, there were no differences in the odds of undergoing CRS-HIPEC between patients diagnosed in HIPEC centers versus referring centers (OR 1.27, 95% CI 0.76-2.13) and survival (HR 1.00, 95% CI 0.76-1.32).
CONCLUSIONS: Previously observed differences in odds of undergoing CRS-HIPEC were no longer present. Increased awareness and the harmonization of treatment for PMCRC may have contributed to equal access to care and a similar chance of survival at a national level.

Photoactivation is a paradigm consisting in local molecular fluorescent activation by laser illumination in a chosen region (source) while measuring the concentration at a target region. Data-driven modeling is concerned with the following questions: how from the measurement in these two regions is it possible to infer the properties of molecular propagation? How is it possible to use such responses to infer motions occurring in networks such as the endoplasmic reticulum? In this book chapter, we shall review the data-driven analysis based on diffusion-transport models and numerical simulations to interpret the photoactivation dynamics and extract biophysical parameters. We will discuss modeling approaches to reconstruct local network properties from photoactivation transients.

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.

BACKGROUND: We have performed a detailed MM and DFT investigation of neutral water clusters (H2O)n (n = 3-12). Our results show the trend of interaction energies in these clusters as a function of the size of the cluster. They show that the H-bond strength increases with cluster size and that the model of water is better described if two different partial charges are used on the hydrogen, depending on whether hydrogen is H-bonded or not. The average binding enthalpy change due to the formation of H-bonds between water molecules is found to be - 25.9 kJ mol-1 at B3LYP/aug-cc-pVDZ level of theory. We observe the formation of cyclic H-bonded networks through the analysis of frontier orbitals and IR vibrational frequencies spectra. For the water cluster with n = 11, we observe an unusual reduction of the bandgap indicative of a cyclic H-bonded network.
METHODS: Calculations were performed with the MMFF94 force field and the B3LYP method using various large basis sets. Molecular orbital diagrams and population analysis were done using standard tools in Gaussian.

UNASSIGNED: The current road to developing treatments for rare diseases is often slow, expensive, and riddled with risk. Change is needed to improve the process, both in how we think about rare disease treatment development and the infrastructure we build to support ongoing science. The National Institutes of Health (NIH)-supported Rare Diseases Clinical Research Network (RDCRN) was established to advance the diagnosis, management, and treatment of rare diseases and to promote highly collaborative, multi-site, patient-centric, translational, and clinical research. The current iteration of the RDCRN intends to build upon and enhance successful approaches within the network while identifying innovative methods to fill gaps and address needs in the approach to the rare disease treatment development process through innovation, collaboration, and clinical trial readiness.
UNASSIGNED: The objective of this paper is to provide an overview of the productivity and influence of the RDCRN since it was first established 20 years ago.
UNASSIGNED: Using a suite of tools available to NIH staff that provides access to a comprehensive, curated, extensively linked data set of global grants, patents, publications, clinical trials, and FDA-approved drugs, a series of queries were executed that conducted bibliometric, co-author, and co-occurrence analysis.
UNASSIGNED: The results demonstrate that the entire RDCRN consortia and network has been highly productive since its inception. They have produced 2763 high-quality publications that have been cited more than 100,000 times, expanded international networks, and contributed scientifically to eight FDA-approved treatments for rare diseases.
UNASSIGNED: The RDCRN program has successfully addressed some significant challenges while developing treatments for rare diseases. However, looking to the future and being agile in facing new challenges that arise as science progresses is important.
A National Institute of Health-funded research network working toward better treatments for people with rare diseases The Rare Diseases Clinical Research Network (RDCRN) is a Federally directed research network that targets research to help investigators move closer to treatments for rare diseases. The network supports 20 different groups that study rare diseases. Each group focuses on three or more rare diseases and the research is conducted at multiple sites. Each group works closely with both the National Institutes of Health (NIH) and patient advocacy groups. The primary focus of the network is clinical trials readiness, which simply means knowing who to treat, when to treat, and how to treat, thus taking some of the risk out of clinical trials. This knowledge is gained through natural history studies. The network, supported by grants, holds a competition every five years to select groups to participate in the network. The RDCRN is supported by ten different institutes at the NIH. To date the RDCRN has published numerous manuscripts in topics ranging from findings from natural history studies and case reports to practice guidelines and clinical trials. To date the RDCRN has been involved in work that has led to eight treatments being approved by the Food and Drug Administration (FDA).

Management of the pediatric difficult airway can present unique clinical challenges. The Pediatric Difficult Intubation Collaborative (PeDI-C) is an international collaborative group engaging in quality improvement and research in children with difficult airways. The PeDI-C established a WhatsApp™ group to facilitate real-time discussions around the management of the difficult airway in pediatric patients. The goals of this study were to evaluate the patterns of use of the WhatsApp™ group, themes on messages posted on pediatric difficult airway management and to assess the perceived usefulness of the WhatsApp™ group by the PeDI-C members.
Following research ethics approval, we performed a database analysis on the archived discussion of the PeDI-C WhatsApp™ group from 2014 to 2019 and surveyed members to assess the perceived usefulness of the PeDI-C WhatsApp™ group.
5781 messages were reviewed with 350 (6.0%) original stems. The three most common original stem types were advice seeking 98 (28%), announcements 85 (24.2%), and clinical case-sharing 78 (22.2%). The median number of responses to original stems was 9 [2-21.3]. Post types associated with increased responses included those seeking advice on medication/equipment (regression coefficient 0.78, 95% CI [0.41-1.16]; p < .0001); seeking advice on patient care (regression coefficient 1.16, 95% CI [0.86-1.45]; p < .0001), sharing advice on medication/equipment availability (regression coefficient 0.87, 95% CI [0.33-1.40], p < .0016), and clinical case-sharing (regression coefficient 1.2547, 95% CI [0.9401-1.5693] p < .0001). 46/64 members of the group responded to the survey. Replies offering advice regarding patient management scenarios were found to be of most interest and 77% of surveyed members found the discussion translatable into their own clinical practice.
The PeDI-C WhatsApp™ group has facilitated timely knowledge exchange on pediatric difficult airway management across the world. Participants are satisfied with the role the Whatsapp™ group is playing.