Search

The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni’s classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject’s gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR., Comment: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

BackgroundNightmares are a hallmark symptom of posttraumatic stress disorder (PTSD). This strong association may reflect a shared pathophysiology in the form of altered autonomic activity and increased reactivity. Using an acoustic startle paradigm, we investigated the interrelationships of psychophysiological measures during wakefulness and PTSD diagnosis, posttraumatic nightmares, and nontraumatic nightmares.MethodsA community sample of 122 trauma survivors were presented with a series of brief loud tones, while heart rate (HRR), skin conductance (SCR), and orbicularis oculi electromyogram (EMGR) responses were measured. Prior to the tone presentations, resting heart rate variability (HRV) was assessed. Nightmares were measured using nightmare logs. Three dichotomous groupings of participants were compared: (1) current PTSD diagnosis (n = 59), no PTSD diagnosis (n = 63), (2) those with (n = 26) or without (n = 96) frequent posttraumatic nightmares, and (3) those with (n = 22) or without (n = 100) frequent nontraumatic nightmares.ResultsPTSD diagnosis was associated with posttraumatic but not with nontraumatic nightmares. Both PTSD and posttraumatic nightmares were associated with a larger mean HRR to loud tones, whereas nontraumatic nightmare frequency was associated with a larger SCR. EMGR and resting HRV were not associated with PTSD diagnosis or nightmares.ConclusionsOur findings suggest a shared pathophysiology between PTSD and posttraumatic nightmares in the form of increased HR reactivity to startling tones, which might reflect reduced parasympathetic tone. This shared pathophysiology could explain why PTSD is more strongly related to posttraumatic than nontraumatic nightmares, which could have important clinical implications.

Interventions to prevent HIV-1 infection and alternative tools in HIV cure therapy remain pressing goals. Recently, numerous broadly neutralizing HIV-1 monoclonal antibodies (bNAbs) have been developed that possess the characteristics necessary for potential prophylactic or therapeutic approaches. However, formulation complexities, especially for multiantibody deliveries, long infusion times, and production issues could limit the use of these bNAbs when deployed, globally affecting their potential application. Here, we describe an approach utilizing synthetic DNA-encoded monoclonal antibodies (dmAbs) for direct in vivo production of prespecified neutralizing activity. We designed 16 different bNAbs as dmAb cassettes and studied their activity in small and large animals. Sera from animals administered dmAbs neutralized multiple HIV-1 isolates with activity similar to that of their parental recombinant mAbs. Delivery of multiple dmAbs to a single animal led to increased neutralization breadth. Two dmAbs, PGDM1400 and PGT121, were advanced into nonhuman primates for study. High peak-circulating levels (between 6 and 34 μg/ml) of these dmAbs were measured, and the sera of all animals displayed broad neutralizing activity. The dmAb approach provides an important local delivery platform for the in vivo generation of HIV-1 bNAbs and for other infectious disease antibodies.

Vaccination of immunocompromised patients is recommended in many national guidelines to protect against severe or complicated influenza infection. However, due to uncertainties over the evidence base, implementation is frequently patchy and dependent on individual clinical discretion. We conducted a systematic review and meta‐analysis to assess the evidence for influenza vaccination in this patient group. Healthcare databases and grey literature were searched and screened for eligibility. Data extraction and assessments of risk of bias were undertaken in duplicate, and results were synthesised narratively and using meta‐analysis where possible. Our data show that whilst the serological response following vaccination of immunocompromised patients is less vigorous than in healthy controls, clinical protection is still meaningful, with only mild variation in adverse events between aetiological groups. Although we encountered significant clinical and statistical heterogeneity in many of our meta‐analyses, we advocate that immunocompromised patients should be targeted for influenza vaccination.

BackgroundImmunocompromised patients are vulnerable to severe or complicated influenza infection. Vaccination is widely recommended for this group. This systematic review and meta-analysis assesses influenza vaccination for immunocompromised patients in terms of preventing influenza-like illness and laboratory confirmed influenza, serological response and adverse events.Methodology/principal findingsElectronic databases and grey literature were searched and records were screened against eligibility criteria. Data extraction and risk of bias assessments were performed in duplicate. Results were synthesised narratively and meta-analyses were conducted where feasible. Heterogeneity was assessed using I(2) and publication bias was assessed using Begg's funnel plot and Egger's regression test. Many of the 209 eligible studies included an unclear or high risk of bias. Meta-analyses showed a significant effect of preventing influenza-like illness (odds ratio [OR]=0.23; 95% confidence interval [CI]=0.16-0.34; pConclusions/significanceInfection prevention and control strategies should recommend vaccinating immunocompromised patients. Potential for bias and confounding and the presence of heterogeneity mean the evidence reviewed is generally weak, although the directions of effects are consistent. Areas for further research are identified.