Virtual fit and design improvements of a filtering half-mask for sub-adult wearers.

The recent pandemic has shown that protecting the general population from hazardous substances or pathogens can be a challenging and urgent task. The key element to adequate protection is appropriately sized, well-fitted and sufficiently distributed personal protective equipment (PPE). While these conditions are followed for adult PPE wearers, they are less considered when it comes to protecting subadults. In this study, the assessment of the fit and design improvements of a 3D-printed facial half mask for subadult wearers (4-18 years) is designed. The target population was represented by 1137 subadults, aged 4.06-18.94 years, for whom 3D face models were acquired. The half mask tested, which was originally provided in one subadult size, did not fit appropriately the target population. This finding prompted the creation of four size categories using the age-dependent distribution of the centroid size calculated from 7 facial landmarks. For each size category, a modified half-mask virtual design was created, including resizing and reshaping, and fit was evaluated visually and numerically using averaged and random 3D face representatives.Practitioner summary: The reason for this study was to describe procedures which led to design improvement of an existing half-mask and provide respiratory protection for subadults. To address this, fit was assessed using an innovative metric approach. Four sizes were then created based on centroid size, resulting in improved fit and design.Abbreviations: CH: cheilion landmark; CS: centroid size; EX: exocanthion landmark; GN: gnathion landmark; N: nasion landmark; PPE: personal protective equipment; PR: pronasale landmark; RPE: respiratory protective equipment.

3D human face dataset was used for modifying and validating protective equipment for subadultsTo ensure optimal protection for subadults, four size categories were proposed based on 3D face landmarks and centroid sizeModified half-mask design fit was validated virtually using a visual and numerical approach.

PTML modeling for peptide discovery: in silico design of non-hemolytic peptides with antihypertensive activity.

Hypertension is a medical condition that affects millions of people worldwide. Despite the high efficacy of the current antihypertensive drugs, they are associated with serious side effects. Peptides constitute attractive options for chemical therapy against hypertension, and computational models can accelerate the design of antihypertensive peptides. Yet, to the best of our knowledge, all the in silico models predict only the antihypertensive activity of peptides while neglecting their inherent toxic potential to red blood cells. In this work, we report the first sequence-based model that combines perturbation theory and machine learning through multilayer perceptron networks (SB-PTML-MLP) to enable the simultaneous screening of antihypertensive activity and hemotoxicity of peptides. We have interpreted the molecular descriptors present in the model from a physicochemical and structural point of view. By strictly following such interpretations as guidelines, we performed two tasks. First, we selected amino acids with favorable contributions to both the increase of the antihypertensive activity and the diminution of hemotoxicity. Then, we assembled those suitable amino acids, virtually designing peptides that were predicted by the SB-PTML-MLP model as antihypertensive agents exhibiting low hemotoxicity. The potentiality of the SB-PTML-MLP model as a tool for designing potent and safe antihypertensive peptides was confirmed by predictions performed by online computational tools reported in the scientific literature. The methodology presented here can be extended to other pharmacological applications of peptides.

Effect of Split-File Digital Workflow on Crown Margin Adaptation.

PURPOSE: Computer-aided design/computer-aided manufacturing (CAD/CAM) is becoming increasingly integrated into dental practice workflow at a pace that exceeds scientific validation. The aim of this study is to evaluate a complete digital split-file protocol relative to segmental digital and analog techniques for restoring a single maxillary anterior edentulous space with custom abutment and crown. MATERIALS AND METHODS: Four treatment workflows were assessed: complete digital (CD), segmental digital (SD), milled wax (AM), and heat pressed and hand waxed (AH) and heat pressed. The CD workflow "split" an abutment and crown into separate files to fabricate a zirconia abutment and both zirconia/lithium disilicate crown restorations. The SD workflow scanned the existing abutment for design of segmental restorations in zirconia, lithium disilicate, and milled wax (AM). The AH specimens were conventionally hand waxed. Both the AM and AH specimens were heat pressed with lithium disilicate. All restorations were evaluated with standardized measurements using scanning electron microscopy (SEM) as manufactured without internal adjustments and after manual adjustment. The number of adjustments, adjustment time, and location of adjustments were recorded. One-way ANOVA with repeated measures was used to report geometric means with 95% confidence intervals. RESULTS: The mean marginal gap after adjustment of the CD group was 69 µm, with an upper bound (UB) of 79 µm and a lower bound (LB) of 60 µm. SD group mean was 26 µm with an UB of 31 µm and LB of 22 µm. The AM group mean was 32 µm, with an UB of 49 µm and a LB of 20 µm; AH group mean of 26 µm with an UB of 34 µm and a LB of 20 µm. The SD, AM, and AH workflows were statistically similar (p = 1.000), and the CD workflow was statistically greater than the other three (p < 0.001). CONCLUSIONS: The split-file (CD) protocol results in marginal gap size within clinical standards after adjustment; however, 52 of the 60 digitally produced restorations showed a horizontal marginal offset that required adjustment for proper contours.

Virtual Design of 3D-Printed Bone Tissue Engineered Scaffold Shape Using Mechanobiological Modeling: Relationship of Scaffold Pore Architecture to Bone Tissue Formation.

Large bone defects are clinically challenging, with up to 15% of these requiring surgical intervention due to non-union. Bone grafts (autographs or allografts) can be used but they have many limitations, meaning that polymer-based bone tissue engineered scaffolds (tissue engineering) are a more promising solution. Clinical translation of scaffolds is still limited but this could be improved by exploring the whole design space using virtual tools such as mechanobiological modeling. In tissue engineering, a significant research effort has been expended on materials and manufacturing but relatively little has been focused on shape. Most scaffolds use regular pore architecture throughout, leaving custom or irregular pore architecture designs unexplored. The aim of this paper is to introduce a virtual design environment for scaffold development and to illustrate its potential by exploring the relationship of pore architecture to bone tissue formation. A virtual design framework has been created utilizing a mechanical stress finite element (FE) model coupled with a cell behavior agent-based model to investigate the mechanobiological relationships of scaffold shape and bone tissue formation. A case study showed that modifying pore architecture from regular to irregular enabled between 17 and 33% more bone formation within the 4-16-week time periods analyzed. This work shows that shape, specifically pore architecture, is as important as other design parameters such as material and manufacturing for improving the function of bone tissue scaffold implants. It is recommended that future research be conducted to both optimize irregular pore architectures and to explore the potential extension of the concept of shape modification beyond mechanical stress to look at other factors present in the body.

A blended model of interior design studio practice due to COVID-19.

Background: The coronavirus disease 2019 (COVID-19) pandemic upended the educational system around the globe. During this challenging period, universities and colleges looked for other effective alternative methods of learning, such as Virtual Learning Environments (VL). Besides, Ahlia University has implemented E-learning in response to COVID-19. There needs to be more attention given to the challenges associated with technology adoption facing interior design and architecture programs, where over 60% of courses are practical, especially design studios, which form the core of the curriculum. According to a review of the relevant literature, there needs to be more research on blended learning in interior design and architecture. In order to enhance the teaching and learning of interior design and architecture, further research is required to combine cutting-edge techniques and technology. The aim of this study was to review the classroom materials for Ahlia University's interior design studio. Methods: After completing the INTD 212, INTD 216, INTD 311, and INTD 404 studios in mid-March 2022, a short Qualtrics poll was done to assess the difficulties of e-learning and offer potential consequences. Results: Though students were conveniently attending courses online, there was not much discussion and interaction like in the face-to-face model. Blended teaching in design studio courses offered many benefits. The results showed that blended design studios achieved pedagogical results as students developed their knowledge. Conclusions: Based on the findings, this research concludes that teaching and learning should be shifted from face-to-face and online learning to the best practice of a blended format.

Clinical Application of Individualized 3D-Printed Templates in the Treatment of Condylar Osteochondroma.

BACKGROUND: Osteochondroma (OC) is one of the most common benign tumors of the long bones, but it rarely occurs in the maxillofacial skeleton. However, mandibular condylar OC often leads to severe facial deformity in affected patients, including facial asymmetry, deviation of the chin, and malocclusion. This study aimed to explore the clinical application of individualized 3D-printed templates to accurately and effectively treat condylar OC. METHODS: A total of 8 patients with mandibular condylar OC were treated from July 2015 to August 2021. The enrolled patients (5 women and 3 men) had a median age of 27 years (range: 21-32 years). All patients exhibited symptoms of facial asymmetry and occlusal disorders preoperatively. The digital software used to virtually design the process consisted of three-dimensional reconstruction, 3D-cephalometry analysis, virtual surgery, individualized templates, and postoperative facial soft-tissue prediction. A set of 3D-printed templates (DOS and DOT) were used in all cases to stabilize the occlusion and guide the osteotomy. Then, pre- and post-operative complications, mouth opening, clinical signs, and the accuracy of the CT imaging analysis were all evaluated. All the measurement data were presented as means ± SD; Bonferroni and Tamhane T2 multiple comparison tests were used to examine the differences between the groups. RESULTS: All patients healed uneventfully. None of the patients exhibited facial nerve injury at follow-up. In comparing the condylar segments with T0p and T1, the average deviation of the condylar segments was 0.5796 mm, indicating that the post-operative reconstructed condyles showed a high degree of similarity to the reconstruction results of the virtual surgical plan. CONCLUSIONS: Individualized 3D-printed templates simplified surgical procedures and improved surgical accuracy, proving to be an effective method for the treatment of patients with slight asymmetric deformities secondary to condylar OC.

PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors.

Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.

Digital Technologies for Restorative Dentistry.

Although the accuracy of direct digitization of oral structure has been improved, indirect digitization is still required in specific situations such as full-arch scanning. Once accurate images are imported, efficient designing can be achieved by CAD software. Although smile design using a 3-dimensional facial scan better predicts planned restorations, further improvement in virtual articulators is needed for complex cases. Computer-aided manufacturing can be offered in several formats such as chairside, laboratory, or centralized fabrications. The subtractive technique is mainly used for restorations, and many chairside CAM materials are available now, but the additive technique has the potential to save materials and an advantage in fabricating complex geometries. Limited evidence is available in applying CAD/CAM technologies in implant restorations. However, it is used to fabricate custom implant abutments and crowns from various materials such as titanium, zirconia, and PEEK and hybrid crowns using stock titanium base abutments.

QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites.

Parasitic diseases remain as unresolved health issues worldwide. While for some parasites the treatments involve drug combinations with serious side effects, for others, chemical therapies are inefficient due to the emergence of drug resistance. This urges the search for novel antiparasitic agents able to act through multiple mechanisms of action. Here, we report the first multi-target model based on quantitative structure-activity relationships and a multilayer perceptron neural network (mt-QSAR-MLP) to virtually design and predict versatile inhibitors of proteins involved in the survival and/or infectivity of different pathogenic parasites. The mt-QSAR-MLP model exhibited high accuracy (>80%) in both training and test sets for the classification/prediction of protein inhibitors. Several fragments were directly extracted from the physicochemical and structural interpretations of the molecular descriptors in the mt-QSAR-MLP model. Such interpretations enabled the generation of four molecules that were predicted as multi-target inhibitors against at least three of the five parasitic proteins reported here with two of the molecules being predicted to inhibit all the proteins. Docking calculations converged with the mt-QSAR-MLP model regarding the multi-target profile of the designed molecules. The designed molecules exhibited drug-like properties, complying with Lipinski's rule of five, as well as Ghose's filter and Veber's guidelines.

Digital healthcare technologies: Modern tools to transform prosthetic care.

INTRODUCTION: Digital healthcare technologies are transforming the face of prosthetic care. Millions of people with limb loss around the world do not have access to any form of rehabilitative healthcare. However, digital technologies provide a promising solution to augment the range and efficiency of prosthetists. AREAS COVERED: The goal of this review is to introduce the digital technologies that have the potential to change clinical methods in prosthetic healthcare. Our target audience are researchers who are unfamiliar with the field of prostheses in general, especially with the newest technological developments. This review addresses technologies for: scanning of amputated limbs, limb-to-socket rectification, additive manufacturing of prosthetic sockets, and quantifying patient response to wearing sockets. This review does not address biomechatronic prostheses or biomechanical design practices. EXPERT OPINION: Digital technologies will enable affordable prostheses to be built on a scale larger than with today's clinical practices. Large technological gaps need to be overcome to enable the mass production and distribution of prostheses digitally. However, recent advances in computational methods and CAD/CAM technologies are bridging this gap faster than ever before. We foresee that these technologies will return mobility and economic opportunity to amputees on a global scale in the near future.

Multi-target Drug Discovery via PTML Modeling: Applications to the Design of Virtual Dual Inhibitors of CDK4 and HER2.

BACKGROUND: Cyclin-dependent kinase 4 (CDK4) and the human epidermal growth factor receptor 2 (HER2) are two of the most promising targets in oncology research. Thus, a series of computational approaches have been applied to the search for more potent inhibitors of these cancerrelated proteins. However, current approaches have focused on chemical analogs while predicting the inhibitory activity against only one of these targets, but never against both. AIMS: We report the first perturbation model combined with machine learning (PTML) to enable the design and prediction of dual inhibitors of CDK4 and HER2. METHODS: Inhibition data for CDK4 and HER2 were extracted from ChEMBL. The PTML model relied on artificial neural networks to allow the classification/prediction of molecules as active or inactive against CDK4 and/or HER2. RESULTS: The PTML model displayed sensitivity and specificity higher than 80% in the training set. The same statistical metrics had values above 75% in the test set. We extracted several molecular fragments and estimated their quantitative contributions to the inhibitory activity against CDK4 and HER2. Guided by the physicochemical and structural interpretations of the molecular descriptors in the PTML model, we designed six molecules by assembling several fragments with positive contributions. Three of these molecules were predicted as potent dual inhibitors of CDK4 and HER2, while the other three were predicted as inhibitors of at least one of these proteins. All the molecules complied with Lipinski's rule of five and its variants. CONCLUSION: The present work represents an encouraging alternative for future anticancer chemotherapies.

Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines.

Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

PTML Modeling for Alzheimer's Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6.

BACKGROUND: Alzheimer's disease is characterized by a progressive pattern of cognitive and functional impairment, which ultimately leads to death. Computational approaches have played an important role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational models reported to date have been focused on only one protein associated with Alzheimer's, while relying on small datasets of structurally related molecules. OBJECTIVE: We introduce the first model combining perturbation theory and machine learning based on artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three Alzheimer's disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase 1 (HDAC1), and histone deacetylase 6 (HDAC6). METHODS: The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on a classification approach to predict chemicals as active or inactive. RESULTS: The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget inhibitory activity. Based on this information, we assembled ten molecules from several fragments with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of the designed molecules complied with Lipinski's rule of five and its variants. CONCLUSION: This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's therapies.

Optimal control simulation predicts effects of midsole materials on energy cost of running.

Testing sports equipment with athletes is costly, time-consuming, hazardous and sometimes impracticable. We propose a method for virtual testing of running shoes and predict how midsoles made of BOOSTTM affect energy cost of running. We contribute a visco-elastic contact model and identified model parameters based on load-displacement measurements. We propose a virtual study using optimal control simulation of musculoskeletal models. The predicted reduction in energy cost of ∼1% for BOOSTTM in comparison to conventional materials is consistent with experimental studies. This indicates that the proposed method is capable of replacing experimental studies in the future.

An innovative ski-boot: design, numerical simulations and testing.

The present work is concerned with the design of an innovative ski-boot. In order to optimize ergonomics and biomechanical behavior of the ski-boot it is important to take into account the orientation of the leg with respect to the ground. The SGS system (Stance Geometry System) developed in this work allows the skier to adjust for posture in the frontal plane by rotating the sole of the boot about the antero-posterior axis (ski-boot is then locked in the desired position before skiing). A simplified model of the effect of ski-boot deformation on skiing behavior is used to evaluate the minimal stiffness the system must have. An experimental analysis on the ski slopes was carried out to provide ski-boot deformations and loading data in different skiing conditions, to be used in numerical simulations. Finite Elements Method (FEM) simulations were performed for optimal design of the joint between ski-boot and sole. The active loads and local ski-boot deformations during small- and large-radius turns were experimentally determined and used to validate a FEM model of the ski-boot. The model was used to optimize the design for maximum stiffness and to demonstrate the efficacy of virtual design supported by proper experimental data. Mean loads up to 164% body weight were measured on the outer ski during turning. The new SGS design system allows the adjustment of lateral stance before using the ski-boot, optimizing the ski-boot stiffness through FEM analysis. Innovative aspects of this work included not only the stance geometry system ski-boot but also the setup of a virtual design environment that was validated by experimental evidence. An entire dataset describing loads during skiing has been obtained. The optimized SGS ski-boot increases intrinsic knee stability due to proper adjustment of lateral stance, guaranteeing appropriate stiffness of the ski-boot system. Key PointsLoad acting during different phases of active skiing have been investigated in both qualitative and quantitative ways.The effects of ski-boot - ski-boot sole stiffness during skiing has been investigated.A ski-boot stance geometry system and an innovative design environment have been developed to make skiing easier and safer.