Notes

Approval regarding "Personal Protective clothing Preservation Strategies Tool" to Predict Usage of N95s, Facemasks, and also Attire Through Pandemic-Related Shortages.
The automatic segmentation of medical images is an important task in clinical applications. However, due to the complexity of the background of the organs, the unclear boundary, and the variable size of different organs, some of the features are lost during network learning, and the segmentation accuracy is low. To address these issues, This prompted us to study whether it is possible to better preserve the deep feature information of the image and solve the problem of low segmentation caused by unclear image boundaries.

In this study, we (1) build a reliable deep learning network framework, named BGRANet,to improve the segmentation performance for medical images; (2) propose a packet rotation convolutional fusion encoder network to extract features; (3) build a boundary enhanced guided packet rotation dual attention decoder network, which is used to enhance the boundary of the segmentation map and effectively fuse more prior information; and (4) propose a multi-resolution fusion module to generate high-resolution feature maps. We demonstrate the effffectiveness of the proposed method on two publicly available datasets.

BGRANet has been trained and tested on the prepared dataset and the experimental results show that our proposed model has better segmentation performance. For 4 class classifification (CHAOS dataset), the average dice similarity coeffiffifficient reached 91.73%. For 2 class classifification (Herlev dataset), the prediction, sensitivity, specifificity, accuracy, and Dice reached 93.75%, 94.30%, 98.19%, 97.43%, and 98.08% respectively. The experimental results show that BGRANet can improve the segmentation effffect for medical images.

We propose a boundary-enhanced guided packet rotation dual attention decoder network. It achieved high segmentation accuracy with a reduced parameter number.
We propose a boundary-enhanced guided packet rotation dual attention decoder network. It achieved high segmentation accuracy with a reduced parameter number.
Accurate segmentation of connective soft tissues in medical images is very challenging, hampering the generation of geometric models for bio-mechanical computations. Alternatively, one could predict ligament insertion sites and then approximate the shapes, based on anatomical knowledge and morphological studies.

In this work, we describe an integrated framework for automatic modelling of human musculoskeletal ligaments.

We combine statistical shape modelling with geometric algorithms to automatically identify insertion sites, based on which geometric surface/volume meshes are created. As clinical use case, the framework has been applied to generate models of the forearm interosseous membrane. Ligament insertion sites in the statistical model were defined according to anatomical predictions following a published approach.

For evaluation we compared the generated sites, as well as the ligament shapes, to data obtained from a cadaveric study, involving five forearms with 15 ligaments. Our framework permitted the creation of models approximating ligaments' shapes with good fidelity. However, we found that the statistical model trained with the state-of-the-art prediction of the insertion sites was not always reliable. Average mean square errors as well as Hausdorff distances of the meshes could increase by an order of magnitude, as compared to employing known insertion locations of the cadaveric study. Using those, an average mean square error of 0.59mm and an average Hausdorff distance of less than 7mm resulted, for all ligaments.

The presented approach for automatic generation of ligament shapes from insertion points appears to be feasible but the detection of the insertion sites with a SSM is too inaccurate, thus making a patient-specific approach necessary.
The presented approach for automatic generation of ligament shapes from insertion points appears to be feasible but the detection of the insertion sites with a SSM is too inaccurate, thus making a patient-specific approach necessary.Serious adverse events (serious AEs) following the therapeutic use of Botulinum Toxin Type A (BoNT-A) are infrequent. Children with pediatric spasticity often have comorbidities that can cloud causation around an adverse event (AE). If a serious AE occurs, clear documentation of information sharing and informed consent as well as the provider-patient relationship are critical to minimizing litigation risks. Reviewing the litigation that has occurred following BoNT-A for pediatric spasticity can offer insight into how providers' perspectives regarding this intervention may differ from those of the public who might serve as jurists. This article offers suggestions for content sharing during the consent process to optimize patient understanding about potential adverse events.This case report details the complex rehabilitation of an adolescent patient with congenital heart disease with anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) who presented with a sudden cardiac arrest. The International Classification of Functioning, Disability and Health for Children and Youth, World Health Organization (ICF-CY WHO) principles were used to guide the course of the patient's acute inpatient rehabilitation.Charcot-Marie-Tooth disease (CMT) is a progressive hereditary neuromuscular neuropathy with pathology in the myelin sheath or the axon. CMT caused by mutations in the Ganglioside-induced differentiation associated protein 1 (GDAP1) gene has been described by a spectrum of phenotypic presentations. GDAP1 is a mitochondrial protein responsible for protecting neuronal bodies from oxidative stress. It is associated with axonal and demyelinating pathophysiology with recessive and dominant modes of inheritance.We describe a case of a 9-year-old Puerto Rican female with clinical and electrodiagnostic results compatible with an axonal sensory-motor neuropathy where a genetic test describes a homozygous GDAP1 missense mutation at the c.692C>T (p.Pro231Leu), previously undetected in a pediatric Latino patient. Mutations in GDAP1 have been previously described in Tunisian, Old Order Amish, European and Japanese families with varying modes of inheritance. To our knowledge, this homozygous variant presentation of the GDAP1 gene is the first to be described in a pediatric Puerto Rican patient without a family history of hereditary sensory motor neuropathy.
With children who are unable to stand or walk independently in the community, therapists commonly use standing devices to assist lower-extremity weight-bearing which is important for bone and muscle health. In addition, positioning in hip abduction may improve hip stability and range of motion. This is the first study to explore the effect of angle of inclination, hip abduction, body orientation, and tone on weight-bearing in pediatric standing devices.

This descriptive exploratory study used a convenience sample of 15 participants (2 with normal tone, 5 with generalized hypotonia, and 8 with hypertonia) (mean age of 5 years and 10 months, range of 3 years 4 months to 9 years 7 months); 13 of whom used standing devices at home, as well as 2 typically developing siblings (normal tone). Each child stood in 36 positions to measure the amount of weight-bearing through footplates.

Weight-bearing was highest with 60 degrees of abduction and no inclination (upright) in supine positioning for children with low and normal tone. Glesatinib Children with high muscle tone bore most weight through their feet with no abduction (feet together) and no inclination (upright) in prone positioning. Overall, supine positioning resulted in more weight-bearing in all positions for children with low and normal tone. Prone positioning resulted in slightly more weight-bearing in all positions for children with high tone.

Weight-bearing was affected by all three of the variables (inclination, abduction, and orientation) for participants with high, normal, and low tone. To determine optimal positioning, all standers should include a system to measure where and how much weight-bearing is occurring in the device.
Weight-bearing was affected by all three of the variables (inclination, abduction, and orientation) for participants with high, normal, and low tone. To determine optimal positioning, all standers should include a system to measure where and how much weight-bearing is occurring in the device.
Walking independently after a stroke can be difficult or impossible, and walking reeducation is vital. But the approach used is often arbitrary, relying on the devices available and subjective evaluations by the doctor/physiotherapist. Objective decision making tools could be useful.

To develop a decision making algorithm able to select for post-stroke patients, based on their motor skills, an appropriate mode of treadmill training (TT), including type of physiotherapist support/supervision required and safety conditions necessary.

We retrospectively analyzed data from 97 post-stroke inpatients admitted to a NeuroRehabilitation unit. Patients attended TT with body weight support (BWSTT group) or without support (FreeTT group), depending on clinical judgment. Patients' sociodemographic and clinical characteristics, including the Cumulative Illness Rating Scale (CIRS) plus measures of walking ability (Functional Ambulation Classification [FAC], total Functional Independence Measure [FIM] and Tinetti Performance-Oriented Mobility Assessment [Tinetti]) and fall risk profile (Morse and Stratify) were retrieved from institutional database.

No significant differences emerged between the two groups regarding sociodemographic and clinical characteristics. Regarding walking ability, FAC, total FIM and its Motor component and the Tinetti scale differed significantly between groups (for all, p <  0.001). FAC and Tinetti scores were used to elaborate a decision making algorithm classifying patients into 4 risk/safety (RS) classes. As expected, a strong association (Pearson chi-squared, p <  0.0001) was found between RS classes and the initial BWSTT/FreeTT classification.

This decision making algorithm provides an objective tool to direct post-stroke patients, on admission to the rehabilitation facility, to the most appropriate form of TT.
This decision making algorithm provides an objective tool to direct post-stroke patients, on admission to the rehabilitation facility, to the most appropriate form of TT.
Rowland Universal Dementia Assessment Scale (RUDAS) has demonstrated usefulness in cognitive assessment. Studies supporting the use of RUDAS as an evaluation tool in traumatic brain injury (TBI) patients remain limited. This study examined whether the Chinese version of RUDAS can be effectively applied to the cognitive assessment of TBI patients in China.

To compare the performance of Mini-Mental State Examination (MMSE) and the Chinese version of RUDAS in the cognitive assessment of Chinese patients with TBI so as to provide reference for clinical use.

86 inpatients with TBI in a hospital were selected from July 2019 to July 2020 and were enrolled as the TBI group, while another 40 healthy individuals matched with age, sex and education level were selected as the control group. All subjects were assessed by trained rehabilitation physicians with MMSE and RUDAS.

(1) Compared with the control group, the scores of MMSE and RUDAS in the TBI group decreased significantly; (2) The results of MMSE and RUDAS in the TBI group were positively correlated (r = 0.
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