The adverse inpatient medication event and frailty (AIME-frail) risk prediction model.

BACKGROUND: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. AIM: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. METHODS: A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). RESULTS: A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. CONCLUSION: The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

An ultrawide field-of-view pinhole compound eye using hemispherical nanowire array for robot vision.

Garnering inspiration from biological compound eyes, artificial vision systems boasting a vivid range of diverse visual functional traits have come to the fore recently. However, most of these artificial systems rely on transformable electronics, which suffer from the complexity and constrained geometry of global deformation, as well as potential mismatches between optical and detector units. Here, we present a unique pinhole compound eye that combines a three-dimensionally printed honeycomb optical structure with a hemispherical, all-solid-state, high-density perovskite nanowire photodetector array. The lens-free pinhole structure can be designed and fabricated with an arbitrary layout to match the underlying image sensor. Optical simulations and imaging results matched well with each other and substantiated the key characteristics and capabilities of our system, which include an ultrawide field of view, accurate target positioning, and motion tracking function. We further demonstrate the potential of our unique compound eye for advanced robotic vision by successfully completing a moving target tracking mission.

Full-color fiber light-emitting diodes based on perovskite quantum wires.

Fiber light-emitting diodes (Fi-LEDs), which can be used for wearable lighting and display devices, are one of the key components for fiber/textile electronics. However, there exist a number of impediments to overcome on device fabrication with fiber-like substrates, as well as on device encapsulations. Here, we uniformly grew all-inorganic perovskite quantum wire arrays by filling high-density alumina nanopores on the surface of Al fibers with a dip-coating process. With a two-step evaporation method to coat a surrounding transporting layer and semitransparent electrode, we successfully fabricated full-color Fi-LEDs with emission peaks at 625 nanometers (red), 512 nanometers (green), and 490 nanometers (sky-blue), respectively. Intriguingly, additional polydimethylsiloxane packaging helps instill the mechanical bendability, stretchability, and waterproof feature of Fi-LEDs. The plasticity of Al fiber also allows the one-dimensional architecture Fi-LED to be shaped and constructed for two-dimensional or even three-dimensional architectures, opening up a new vista for advanced lighting with unconventional formfactors.

Advancing Breast Cancer Research Through Collaborative Computing: Harnessing Google Colab for Innovation.

This investigation explores the potential efficacy of machine learning algorithms (MLAs), particularly convolutional neural networks (CNNs), in distinguishing between benign and malignant breast cancer tissue through the analysis of 1000 breast cancer images gathered from Kaggle.com, a domain of publicly accessible data. The dataset was meticulously partitioned into training, validation, and testing sets to facilitate model development and evaluation. Our results reveal promising outcomes, with the developed model achieving notable precision (92%), recall (92%), accuracy (92%), sensitivity (89%), specificity (96%), an F1 score of 0.92, and an area under the curve (AUC) of 0.944. These metrics underscore the model's ability to accurately identify malignant breast cancer images. Because of limitations such as sample size and potential variations in image quality, further research, data collection, and integration of theoretical models in a real-world clinical setting are needed to expand the reliability and generalizability of these MLAs. Nonetheless, this study serves to highlight the potential use of artificial intelligence models as supporting tools for physicians to utilize in breast cancer detection.

Assertive community treatment as an alternative to incarceration for American pretrial detainees.

In the United States and elsewhere around the world, people with serious mental illness (SMI) are overrepresented in the criminal justice system. Clinical interventions to divert such individuals out of correctional settings, including Assertive Community Treatment (ACT), have been shown to reduce rates of criminal justice recidivism when modified to allow for the use of court sanctions to encourage treatment adherence. However, these interventions are noted to be underutilized as alternative to incarceration (ATI) programs. This paper summarizes the results of a retrospective cohort study conducted in a New York State forensic psychiatric hospital of 87 pretrial detainees admitted after being found incompetent to stand trial between January 2019 and January 2022. Of these, 49 patients were referred to an ACT team that served as an ATI program. The study outcomes noted that patients referred to this ACT team were 20% less likely to remain in pretrial detention than those that were not. Moreover, patients referred to the ACT program were also 34% more likely to be granted an ATI plea bargain in the community that did not involve serving a prison term. These results suggest that pretrial detainees with SMI are more likely to be granted an ATI program that offers more intensive treatment services such as ACT, due to the capability of such programs to also provide more intensive outreach and community supervision than traditional outpatient mental health service providers.

Nucleosome reorganisation in breast cancer tissues.

BACKGROUND: Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. RESULTS: We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X. CONCLUSIONS: Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28 µA mm-1 cm-2 for glucose and 0.87 µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

Intravenous immunoglobulin resistance in Kawasaki disease patients: prediction using clinical data.

BACKGROUND: About 10-20% of Kawasaki disease (KD) patients are resistant to the initial infusion of intravenous immunoglobin (IVIG). The aim of this study was to assess whether IVIG resistance in KD patients could be predicted using standard clinical and laboratory features. METHODS: Data were from two cohorts: a Korean cohort of 7101 KD patients from 2015 to 2017 and a cohort of 649 KD patients from San Diego enrolled from 1998 to 2021. Features included laboratory values, the worst Z-score from the initial echocardiogram or during hospitalization, and the five clinical KD signs at presentation. RESULTS: Five machine learning models achieved a maximum median AUC of 0.711 [IQR: 0.706-0.72] in the Korean cohort and 0.696 [IQR: 0.609-0.722] in the San Diego cohort during stratified 10-fold cross-validation using significant laboratory features identified from univariate analysis. Adding the Z-score, KD clinical signs, or both did not considerably improve the median AUC in either cohort. CONCLUSIONS: Using commonly measured clinical laboratory data alone or in conjunction with echocardiographic findings and clinical features is not sufficient to predict IVIG resistance. Further attempts to predict IVIG resistance will need to incorporate additional data such as transcriptomics, proteomics, and genetics to achieve meaningful predictive utility. IMPACT: We demonstrated that laboratory, echocardiographic, and clinical findings cannot predict intravenous immunoglobin (IVIG) resistance to a clinically meaningful extent using machine learning in a homogenous Asian or ethnically diverse population of patients with Kawasaki disease (KD). Visualizing these features using uniform manifold approximation and projection (UMAP) is an important step to evaluate predictive utility in a qualitative manner. Further attempts to predict IVIG resistance in KD patients will need to incorporate novel biomarkers or other specialized features such as genetic differences or transcriptomics to be clinically useful.

The Septin Cytoskeleton is Required for Plasma Membrane Repair.

Mammalian cells are frequently exposed to mechanical and biochemical stressors resulting in plasma membrane injuries. Repair mechanisms reseal the plasma membrane to restore homeostasis and prevent cell death. In the present work, a silencing RNA screen was performed to uncover plasma membrane repair mechanisms of cells exposed to a pore-forming toxin (listeriolysin O). This screen identified molecules previously known to repair the injured plasma membrane such as annexin A2 (ANXA2) as well as novel plasma membrane repair candidate proteins. Of the novel candidates, we focused on septin 7 (SEPT7) because the septins are an important family of conserved eukaryotic cytoskeletal proteins. Using diverse experimental approaches, we established for the first time that SEPT7 plays a general role in plasma membrane repair of cells perforated by pore-forming toxins and mechanical wounding. Remarkably, upon cell injury, the septin cytoskeleton is extensively redistributed in a Ca 2+ -dependent fashion, a hallmark of plasma membrane repair machineries. The septins reorganize into subplasmalemmal domains arranged as knob and loop (or ring) structures containing F-actin, myosin II, and annexin A2 (ANXA2) and protrude from the cell surface. Importantly, the formation of these domains correlates with the plasma membrane repair efficiency. Super-resolution microscopy shows that septins and actin are arranged in intertwined filaments associated with ANXA2. Silencing SEPT7 expression prevented the formation of the F-actin/myosin II/ANXA2 domains, however, silencing expression of ANXA2 had no observable effect on their formation. These results highlight the key structural role of the septins in remodeling the plasma membrane and in the recruitment of the repair molecule ANXA2. Collectively, our data support a novel model in which the septin cytoskeleton acts as a scaffold to promote the formation of plasma membrane repair domains containing contractile F-actin and annexin A2.

Assembly of Luminescent Chiral Gold(I)-Sulfido Clusters via Chiral Self-Sorting.

Due to the ubiquity of chirality in nature, chiral self-assembly involving self-sorting behaviors has remained as one of the most important research topics of interests. Herein, starting from a racemic mixture of SEG-based (SEG=SEGPHOS) chlorogold(I) precursors, a unique chiral butterfly-shape hexadecanuclear gold(I) cluster (Au16 ) with different ratios of RSEG and SSEG ligands is obtained via homoleptic and heterochiral self-sorting. More interestingly, by employing different chlorogold(I) precursors of opposite chirality (such as RSEG -Au2 and SBIN -Au2 (BIN=BINAP)), an unprecedented heteroleptic and heterochiral self-sorting strategy has been developed to give a series of heteroleptic chiral decanuclear gold(I) clusters (Au10 ) with propellor-shape structures. Heterochiral and heteroleptic self-sorting have also been observed between enantiomers of homoleptic chiral Au10 clusters to result in the heteroleptic chiral Au10 clusters via cluster-to-cluster transformation. Incorporation of heteroleptic ligands is found to decrease the symmetry from S4 of homoleptic meso Au10 to C2 of heteroleptic chiral Au10 clusters. The chirality has been transferred from the axial chiral ligands and stored in the heteroleptic gold(I) clusters.

A Deep Learning Framework for Image-Based Screening of Kawasaki Disease.

Kawasaki disease (KD) is a leading cause of acquired heart disease in children and is characterized by the presence of a combination of five clinical signs assessed during the physical examination. Timely treatment of intravenous immunoglobin is needed to prevent coronary artery aneurysm formation, but KD is usually diagnosed when pediatric patients are evaluated by a clinician in the emergency department days after onset. One or more of the five clinical signs usually manifests in pediatric patients prior to ED admission, presenting an opportunity for earlier intervention if families receive guidance to seek medical care as soon as clinical signs are observed along with a fever for at least five days. We present a deep learning framework for a novel screening tool to calculate the relative risk of KD by analyzing images of the five clinical signs. The framework consists of convolutional neural networks to separately calculate the risk for each clinical sign, and a new algorithm to determine what clinical sign is in an image. We achieved a mean accuracy of 90% during 10-fold cross-validation and 88% during external validation for the new algorithm. These results demonstrate the algorithms in the proposed screening tool can be utilized by families to determine if their child should be evaluated by a clinician based on the number of clinical signs consistent with KD.Clinical Relevance- This screening framework has the potential for earlier clinical evaluation and detection of KD to reduce the risk of coronary artery complications.

Experiments with RouteNav, A Wayfinding App for Blind Travelers in a Transit Hub.

RouteNav is an iOS app designed to support wayfinding for blind travelers in an indoor/outdoor transit hub. It doesn't rely on external infrastructure (such as BLE beacons); instead, localization is obtained by fusing spatial information from inertial dead reckoning and GPS (when available) via particle filtering. Routes are expressed as sequences of "tiles", where each tile may contain relevant points of interest. Redundant modalities are used to guide users to switching goalposts within tiles. In this paper, we describe the different components of RouteNav, and report on a user study with seven blind participants, who traversed three challenging routes in a transit hub while receiving input from the app.

Surfacing the causal assumptions and active ingredients of healthcare quality improvement interventions: An application to primary care opioid prescribing.

Background: Efforts to maximize the impact of healthcare improvement interventions are hampered when intervention components are not well defined or described, precluding the ability to understand how and why interventions are expected to work. Method: We partnered with two organizations delivering province-wide quality improvement interventions to establish how they envisaged their interventions lead to change (their underlying causal assumptions) and to identify active ingredients (behavior change techniques [BCTs]). The interventions assessed were an audit and feedback report and an academic detailing program. Both focused on supporting safer opioid prescribing in primary care in Ontario, Canada. Data collection involved semi-structured interviews with intervention developers (n = 8) and a content analysis of intervention documents. Analyses unpacked and articulated how the interventions were intended to achieve change and how this was operationalized. Results: Developers anticipated that the feedback report would provide physicians with a clear understanding of their own prescribing patterns in comparison to others. In the feedback report, we found an emphasis on BCTs consistent with that assumption (feedback on behavior; social comparison). The detailing was designed to provide tailored support to enable physicians to overcome barriers to change and to gradually enact specific practice changes for patients based on improved communication. In the detailing materials, we found an emphasis on instructions on how to perform the behavior, for a range of behaviors (e.g., tapering opioids, treating opioid use disorder). The materials were supplemented by detailer-enacted BCTs (e.g., social support [practical]; goal setting [behavior]; review behavioral goal[s]). Conclusions: The interventions included a small range of BCTs addressing various clinical behaviors. This work provides a methodological example of how to apply a behavioral lens to surface the active ingredients, target clinical behaviors, and causal assumptions of existing large-scale improvement interventions that could be applied in other contexts to optimize effectiveness and facilitate scale and spread.

What is already known about the topic?: The causal assumptions and key components of implementation interventions are often not well described, which limits the influence of implementation science on implementation practice. What does this paper add?: This work provides an approach for surfacing the causal assumptions from intervention developers (through interviews with eight participants) and active ingredients from intervention materials, focusing on two real-world interventions already delivered at scale and designed to promote safer opioid prescribing. The analysis provides a comprehensive intervention description and reveals the extent to which final interventions align with developers' intentions. What are the implications for practice, research, or policy?: The findings provide a foundation for future work which will describe the effectiveness of these interventions (alone and in combination) and explore whether they achieve change in the intended ways, thereby providing an example of a more fulsome intervention evaluation. More broadly, our methods can be used by implementation practitioners to review and reflect on their intervention development process and support comprehensive intervention descriptions.

Bilateral Relentless Placoid Chorioretinitis Following Pfizer-BioNTech COVID-19 Vaccination: Specific Antigenic Trigger or Nonspecific Immune Activation?

PURPOSE: To report bilateral relentless placoid chorioretinitis following Pfizer-BioNTech coronavirus disease 2019 (COVID-19) vaccine administration. CASE REPORT: A 55-year-old Caucasian New Zealand-born woman presented with progressive left eye vision loss and bilateral photopsias and floaters occurring 10 days after receiving the Pfizer-BioNTech COVID-19 vaccination. She had a similar self-limiting episode of photopsias and floaters without vision loss 1 year prior after receiving the influenza vaccine. Snellen visual acuity (VA) was 20/25 in the right eye, and count fingers at 2 m in the left eye. Bilateral, active, creamy, plaque-like lesions were present at the level of the retinal pigment epithelium and choroid, suggestive of relentless placoid chorioretinitis. Commencement of 100 mg oral prednisolone and 3 g mycophenolate daily resulted in recovery of the foveal ellipsoid layer with VA of 20/25 in each eye after 8 weeks. Subsequent activations occurred following COVID-19 infection and respiratory infection. CONCLUSION: This is the first reported case of relentless placoid chorioretinitis occurring as a potential side-effect of the Pfizer-BioNTech COVID-19 vaccine. Vaccination, and not infection, could be assumed to be the likely trigger. Subsequent flares following COVID-19 and a nonspecific respiratory infection during periods of inadequate immunosuppression suggest that a COVID-19 antigen or general immune activation could also be the trigger.

Toll-like receptor mediated inflammation directs B cells towards protective antiviral extrafollicular responses.

Extrafollicular plasmablast responses (EFRs) are considered to generate antibodies of low affinity that offer little protection from infections. Paradoxically, high avidity antigen-B cell receptor engagement is thought to be the main driver of B cell differentiation, whether in EFRs or slower-developing germinal centers (GCs). Here we show that influenza infection rapidly induces EFRs, generating protective antibodies via Toll-like receptor (TLR)-mediated mechanisms that are both B cell intrinsic and extrinsic. B cell-intrinsic TLR signals support antigen-stimulated B cell survival, clonal expansion, and the differentiation of B cells via induction of IRF4, the master regulator of B cell differentiation, through activation of NF-kB c-Rel. Provision of sustained TLR4 stimulation after immunization shifts the fate of virus-specific B cells towards EFRs instead of GCs, prompting rapid antibody production and improving their protective capacity over antigen/alum administration alone. Thus, inflammatory signals act as B cell fate-determinants for the rapid generation of protective antiviral extrafollicular responses.

Engagement is a necessary condition to test audit and feedback design features: results of a pragmatic, factorial, cluster-randomized trial with an embedded process evaluation.

BACKGROUND: While audit & feedback (A&F) is an effective implementation intervention, the design elements which maximize effectiveness are unclear. Partnering with a healthcare quality advisory organization already delivering feedback, we conducted a pragmatic, 2 × 2 factorial, cluster-randomized trial to test the impact of variations in two factors: (A) the benchmark used for comparison and (B) information framing. An embedded process evaluation explored hypothesized mechanisms of effect. METHODS: Eligible physicians worked in nursing homes in Ontario, Canada, and had voluntarily signed up to receive the report. Groups of nursing homes sharing physicians were randomized to (A) physicians' individual prescribing rates compared to top-performing peers (the top quartile) or the provincial median and (B) risk-framed information (reporting the number of patients prescribed high-risk medication) or benefit-framed information (reporting the number of patients not prescribed). We hypothesized that the top quartile comparator and risk-framing would lead to greater practice improvements. The primary outcome was the mean number of central nervous system-active medications per resident per month. Primary analyses compared the four arms at 6 months post-intervention. Factorial analyses were secondary. The process evaluation comprised a follow-up questionnaire and semi-structured interviews. RESULTS: Two hundred sixty-seven physicians (152 clusters) were randomized: 67 to arm 1 (median benchmark, benefit framing), 65 to arm 2 (top quartile benchmark, benefit framing), 75 to arm 3 (median benchmark, risk framing), and 60 to arm 4 (top quartile benchmark, risk framing). There were no significant differences in the primary outcome across arms or for each factor. However, engagement was low (27-31% of physicians across arms downloaded the report). The process evaluation indicated that both factors minimally impacted the proposed mechanisms. However, risk-framed feedback was perceived as more actionable and more compatible with current workflows, whilst a higher target might encourage behaviour change when physicians identified with the comparator. CONCLUSIONS: Risk framing and a top quartile comparator have the potential to achieve change. Further work to establish the strategies most likely to enhance A&F engagement, particularly with physicians who may be most likely to benefit from feedback, is required to support meaningfully addressing intricate research questions concerning the design of A&F. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02979964 . Registered 29 November 2016.

A neuromorphic bionic eye with filter-free color vision using hemispherical perovskite nanowire array retina.

Spherical geometry, adaptive optics, and highly dense network of neurons bridging the eye with the visual cortex, are the primary features of human eyes which enable wide field-of-view (FoV), low aberration, excellent adaptivity, and preprocessing of perceived visual information. Therefore, fabricating spherical artificial eyes has garnered enormous scientific interest. However, fusing color vision, in-device preprocessing and optical adaptivity into spherical artificial eyes has always been a tremendous challenge. Herein, we demonstrate a bionic eye comprising tunable liquid crystal optics, and a hemispherical neuromorphic retina with filter-free color vision, enabled by wavelength dependent bidirectional synaptic photo-response in a metal-oxide nanotube/perovskite nanowire hybrid structure. Moreover, by tuning the color selectivity with bias, the device can reconstruct full color images. This work demonstrates a unique approach to address the color vision and optical adaptivity issues associated with artificial eyes that can bring them to a new level approaching their biological counterparts.

Comparative assessment of treatment efficiency and patient experience between Dental Monitoring and conventional monitoring of clear aligner therapy: A single-center randomized controlled trial.

INTRODUCTION: This 2-arm randomized controlled clinical trial (RCT) aimed to assess the effect of Dental Monitoring (DM) on the efficiency of clear aligner therapy (CAT) and patient experience compared with conventional monitoring (CM) used for regularly scheduled clinical appointments. METHODS: Fifty-six patients with full permanent dentition treated with CAT participated in this RCT. Patients were recruited from a single private practice and treated by 1 experienced orthodontist. Randomization was performed with permutated blocks of 8 patients assigned to either a CM or DM group with allocations concealed in opaque, sealed envelopes. It was not feasible to blind subjects or investigators. The primary treatment efficiency outcome assessed was the number of appointments. Secondary outcomes included the time to reach the first refinement, the number of refinements, the total number of aligners, and treatment duration. The patient experience was assessed using a visual analog scale questionnaire administered at the end of CAT. RESULTS: No patients were lost to follow-up. There was no significant difference in the number of refinements (mean = 0.1; 95% confidence interval [CI], -0.2 to 0.5; P = 0.43) and the number of total aligners (median = 5; 95% CI, -1 to 13; P = 0.09). There was a significant difference in the number of appointments, with the DM group requiring 1.5 fewer visits (95% CI, -3.3, -0.7; p = 0.02) as well as overall treatment duration, with the DM group taking 1.9 months longer (95% CI, 0.0-3.6; P = 0.04). There was a difference between study groups regarding the importance of face-to-face appointments, with the DM group not perceiving face-to-face appointments as important (P = 0.03). CONCLUSIONS: DM with CAT resulted in 1.5 fewer clinical appointments and a longer treatment duration of 1.9 months. There were no significant intergroup differences in the number of refinements or total aligners. CM and DM groups had similarly high levels of satisfaction with CAT. REGISTRATION: The trial was registered at Australian New Zealand Clinical Trials Registry (ACTRN12620000475943). PROTOCOL: The protocol was published before trial commencement. FUNDING: This research did not receive any grant from funding agencies.

Interpreting vibrational circular dichroism spectra: the Cai•factor for absolute configuration with confidence.

Vibrational circular dichroism (VCD) spectroscopy can generate the data required for the assignment of absolute configuration, but the spectra are hard to interpret. We have recorded VCD data for thirty pairs of small organic compounds and we use this database to validate a method for the automated analysis of VCD spectra and the assignment of absolute configuration: the Cai•factor (Configuration: absolute information). The analysis of the data demonstrates that the procedure is a reliable and time-efficient method for determination of absolute configuration, which gives both the assignment and a measure of confidence in the outcome, even when the spectra are imperfect. The majority of molecules tested have a high confidence score and all of these have the correct assignment.