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Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models’ capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.

Characterizing motor subtypes of Parkinson’s disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.

Tremor, defined as an “involuntary, rhythmic, oscillatory movement of a body part”, is a key feature of many neurological conditions including Parkinson’s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson’s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81–0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.

Polymer composites and blend systems are of increasing importance, due to the combination of unique and different material properties. Blending polybutylene terephthalate (PBT) with polycarbonate (PC) has been the focus of attention for some time in order to combine thermo-chemical with mechanical resistance. The right compounding of the two polymers is a particular challenge, since phase boundaries between PBT and PC lead to coalescence during melting, and thus to unwanted segregation within the composite material. Amorphization of the semi-crystalline PBT would significantly improve the blending of the two polymers, which is why specific miscibility aids are needed for this purpose. Recent research has focused on the functionalization of polymers with shape-anisotropic glass particles. The advantage of those results from their two-dimensional shape, which not only improves the mechanical properties but are also suspected to act as miscibility aids, as they could catalyze transesterification or act as crystallization modifier. This work presents a process route for the production of PBT-PC blends via co-comminution and an in-situ additivation of the polymer blend particles with anisotropic glass flakes to adjust the crystallinity and therefore enhance the miscibility of the polymers.

The effect of HIV pre-exposure prophylaxis (PrEP) depends on uptake, adherence, and sexual practices. We aimed to assess these factors in a cohort of HIV-negative people at risk of infection. In our cohort study, men and transgender women who have sex with men previously enrolled in PrEP trials (ATN 082, iPrEx, and US Safety Study) were enrolled in a 72 week open-label extension. We measured drug concentrations in plasma and dried blood spots in seroconverters and a random sample of seronegative participants. We assessed PrEP uptake, adherence, sexual practices, and HIV incidence. Statistical methods included Poisson models, comparison of proportions, and generalised estimating equations. We enrolled 1603 HIV-negative people, of whom 1225 (76%) received PrEP. Uptake was higher among those reporting condomless receptive anal intercourse (416/519 [81%] vs 809/1084 [75%], p=0·003) and having serological evidence of herpes (612/791 [77%] vs 613/812 [75%] p=0·03). Of those receiving PrEP, HIV incidence was 1·8 infections per 100 person-years, compared with 2·6 infections per 100 person-years in those who concurrently did not choose PrEP (HR 0·51, 95% CI 0·26–1·01, adjusted for sexual behaviours), and 3·9 infections per 100 person-years in the placebo group of the previous randomised phase (HR 0·49, 95% CI 0·31–0·77). Among those receiving PrEP, HIV incidence was 4·7 infections per 100 person-years if drug was not detected in dried blood spots, 2·3 infections per 100 person-years if drug concentrations suggested use of fewer than two tablets per week, 0·6 per 100 person-years for use of two to three tablets per week, and 0·0 per 100 person-years for use of four or more tablets per week (p<0·0001). PrEP drug concentrations were higher among people of older age, with more schooling, who reported non-condom receptive anal intercourse, who had more sexual partners, and who had a history of syphilis or herpes. PrEP uptake was high when made available free of charge by experienced providers. The effect of PrEP is increased by greater uptake and adherence during periods of higher risk. Drug concentrations in dried blood spots are strongly correlated with protective benefit. US National Institutes of Health.

This erratum corrects measurements of the prompt and secondary (from-b).

Polymer composites and blend systems are of increasing importance, due to the combination of unique and different material properties. Blending polybutylene terephthalate (PBT) with polycarbonate (PC) has been the focus of attention for some time in order to combine thermo-chemical with mechanical resistance. The right compounding of the two polymers is a particular challenge, since phase boundaries between PBT and PC lead to coalescence during melting, and thus to unwanted segregation within the composite material. Amorphization of the semi-crystalline PBT would significantly improve the blending of the two polymers, which is why specific miscibility aids are needed for this purpose. Recent research has focused on the functionalization of polymers with shape-anisotropic glass particles. The advantage of those results from their two-dimensional shape, which not only improves the mechanical properties but are also suspected to act as miscibility aids, as they could catalyze transesterification or act as crystallization modifier. This work presents a process route for the production of PBT-PC blends via co-comminution and an in-situ additivation of the polymer blend particles with anisotropic glass flakes to adjust the crystallinity and therefore enhance the miscibility of the polymers.

Bronchoscopy and bronchoalveolar lavage (BAL) are common procedures in intensive care units; however, no contemporaneous safety and outcomes data have been reported, particularly for critically ill patients. This is a retrospective analysis of prospectively collected data from teaching hospital adult intensive care units. One hundred mechanically ventilated patients with severe sepsis, septic shock, acute lung injury (ALI), and/or acute respiratory distress syndrome underwent bronchoscopy with unilateral BAL. Data collected included demographics, presence of sepsis or ALI, Pao2 to Fio2 ratio, positive end-expiratory pressure, Acute Physiology and Chronic Health Evaluation score, Sequential Organ Failure Assessment score, and peri- or postprocedural complications. Men comprised 51% of the patients; 81% of the patients were black, and 15% were white. The mean age was 52 (SD, ±16) years. The mean Acute Physiology and Chronic Health Evaluation score was 22 (±7.5), whereas the median Sequential Organ Failure Assessment score was 9 (interquartile range, 5-12). Ten patients (10%) had complications during or immediately after the procedure. Hypoxemia during or immediately after the BAL was the most common complication. Ninety percent of the complications were related to transient hypoxemia, whereas bradycardia and hypotension each occurred in 1 patient. Age, female sex, and higher positive end-expiratory pressure were associated with complications. Bronchoscopy with BAL in critically ill patients with sepsis and ALI is well tolerated with low risk of complications, primarily related to manageable hypoxemia.

We determined the prevalence of risk factors for the development of acute respiratory distress syndrome (ARDS), outcomes of critical illness, and the impact of highly active antiretroviral therapy in HIV-1–infected patients. We hypothesized that in an urban county hospital, HIV-1–infected patients with ARDS would have a higher mortality than their HIV-1–uninfected counterparts. Subjects were enrolled between 2006 and 2012. Baseline patient demographics, comorbidities, illness severity, causes of ARDS, and clinical outcomes were obtained. The primary end point was hospital mortality. A total of 178 subjects with ARDS were enrolled in the study; 40 (22%) were infected with HIV-1. The median CD4 count was 75 (15.3-198.3), and 25% were on highly active antiretroviral therapy. HIV-1–infected subjects were significantly younger (44 vs 52 years; P < .01) and had higher rates of asthma, chronic obstructive pulmonary disease, pneumonia, history of hospital-acquired infections, and prior sepsis. HIV-1–infected subjects had greater illness severity by Acute Physiology and Chronic Health Evaluation II scores (29 [24-31] vs 24 [22-25]; P < .01). Hospital mortality was not higher among HIV-1–infected subjects compared with HIV-1–uninfected subjects (50.0% vs 38.4%; P = .19). In patients with ARDS, HIV-1 infection was associated with greater illness severity but was not associated with higher mortality in ARDS. Future studies need to be done to evaluate the factors that contribute to high morbidity and mortality in medically vulnerable populations who develop ARDS.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) and a common cause of injurious falls. Recent advances in wearable-based human activity recognition (HAR) enable FOG detection, but bias and fairness in these models remain understudied. Bias refers to systematic errors leading to unequal outcomes, while fairness refers to consistent performance across subject groups. Biased models could systematically underserve patients with specific FOG phenotypes or demographics, potentially widening care disparities. We systematically evaluated bias and fairness of state-of-the-art HAR models for FOG detection across phenotypes and demographics using multi-site datasets. We assessed four mitigation approaches: conventional methods (threshold optimization and adversarial debiasing) and transfer learning approaches (multi-site transfer and fine-tuning large pretrained models). Fairness was quantified using demographic parity ratio (DPR) and equalized odds ratio (EOR). HAR models exhibited substantial bias (DPR & EOR < 0.8) across age, sex, disease duration, and critically, FOG phenotype. Phenotype-specific bias is particularly concerning as tremulous and akinetic FOG require different clinical management. Conventional bias mitigation methods failed: threshold optimization (DPR=-0.126, EOR=+0.063) and adversarial debiasing (DPR=-0.008, EOR=-0.001) showed minimal improvement. In contrast, transfer learning from multi-site datasets significantly improved fairness (DPR=+0.037, p<0.01; EOR=+0.045, p<0.01) and performance (F1-score=+0.020, p<0.05). Transfer learning across diverse datasets is essential for developing equitable HAR models that reliably detect FOG across all patient phenotypes, ensuring wearable-based monitoring benefits all individuals with PD.