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Childbirth is a complex physiological process in which a foetal neuromusculoskeletal model is of great importance to develop realistic delivery simulations and associated complication analyses. However, the estimation of hip joint centre (HJC) in foetuses remains a challenging issue. Thus, this paper aims to propose and evaluate a new approach to locate the HJC in foetuses. Hip CT-scans from 25 children ( F  = 11, age = 5.5 ± 2.6 years, height = 117 ± 21 cm, mass = 26 kg ± 9.5 kg) were used to propose and evaluate the novel acetabulum sphere fitting process to locate the HJC. This new approach using the acetabulum surface was applied to a population of 57 post-mortem foetal CT scans to locate the HJC as well as to determine associated regression equations using multiple linear regression. As results, the average distance between the HJC located using acetabulum sphere fitting and femoral head sphere fitting in children was 1.5 ± 0.7 mm. The average prediction error using our developed foetal HJC regression equations was 3.0 ± 1.5 mm, even though the equation for the x coordinate had a poor value of R 2 (R 2 for the x coordinate = 0.488). The present study suggests that the use of the acetabulum sphere fitting approach is a valid and accurate method to locate the HJC in children, and then can be extrapolated to get an estimation of the HJC in foetuses with incomplete bone ossification. Therefore, the present paper can be used as a guideline for foetus specific neuromusculoskeletal modelling.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry., (© 2024. The Author(s).)

Finite element models ranging from single to multiscale models have been widely used to gain valuable insights into the physiological delivery process and associated complication scenarios. However, the fetus descent simulation with the active uterine contraction is still challenging for validation and uncertainty quantification issues. The present study performed a fetus descent simulation using the active uterine contraction. Then, simulation outcomes were evaluated using theoretical and in vivo MRI childbirth data. Moreover, parameter uncertainty and propagation were also performed. A maternal pelvis model was developed. The active uterine contraction was modeled using a transversely isotropic Mooney-Rivlin material. Displacement trajectories were compared between simulation, theoretical and in vivo MRI childbirth data. Monte Carlo (M.C) and Polynomial Chaos Expansion (PCE) methods were applied to quantify uncertain parameters and their propagations. Obtained results showed that fetal descent behavior is consistent with the MRI-based observation as well as the theoretical trajectory (curve of Carus). The head downward vertical displacement ranges from 0 to approximately 47 mm. A reduction of 50% in uterine size was observed during the simulation. Three high-sensitive parameters ( C 1 , C 2 , Ca 0 ) were also identified. Our study suggested that the use of the active uterine contraction is essential for simulating vaginal delivery but the global parameter sensitivity, parameter uncertainty, and outcome evaluation should be carefully performed. As a perspective, the developed approach could be extrapolated for patient-specific modeling and associated delivery complication simulations to identify risks and potential therapeutic solutions.

Immune cells transduce environmental stimuli into responses essential for host health via complex signaling cascades. T cells, in particular, leverage their unique T cell receptors (TCRs) to detect specific Human Leukocyte Antigen (HLA)-presented peptides. TCR activation is then relayed via linker for activation of T cells (LAT), a TCR-proximal disordered adapter protein, which organizes protein partners and mediates the propagation of signals down diverse pathways including NFAT and AP-1. Here, we studied how balanced downstream pathway activation is encoded in the amino acid sequence of LAT. To comprehensively profile the sequence-function relationship of LAT, we developed a pooled, single-cell, high-content screening approach in which a large series of mutants in the LAT protein were analyzed to characterize their effects on T cell activation. Measuring epigenetic, transcriptomic, and cell surface protein dynamics of single cells harboring distinct LAT mutants, we found functional regions spanning over 40% of the LAT amino acid sequence. Conserved sequence motifs for protein interactions along with charge distribution are critical sequence features, and contribute to interpretation of human genetic variation in LAT. While mutant defect severity spans from moderate to complete loss of function, nearly all defective mutants, irrespective of their position in LAT, confer balanced defects across all downstream pathways. To understand the molecular basis for this observation, we performed proximal protein labeling which demonstrated that disruption of LAT interaction with a single partner protein indirectly disrupts other partner interactions, likely through the dual roles of these proteins as effectors of downstream pathways and bridging factors between LAT molecules. Overall, we report widely distributed functional regions throughout a disordered adapter and a precise physical organization of LAT and interacting molecules which constrains signaling outputs. More broadly, we describe an approach for interrogating sequence-function relationships for proteins with complex activities across regulatory layers of the cell., Competing Interests: Competing Interests A.K.S. reports compensation for consulting and/or SAB membership from Honeycomb Biotechnologies, Cellarity, Bio-Rad Laboratories, Fog Pharma, Passkey Therapeutics, Ochre Bio, Relation Therapeutics, IntrECate biotherapeutics, and Dahlia Biosciences unrelated to this work. A.R. is employed by Genentech, Inc., South San Francisco, CA, USA, and is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas and, until 31 July 2020, was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov.

(1) Background: The detection of methylated SEPT9 (m SEPT9 ) in plasma is a promising approach to non-invasive colorectal cancer (CRC) screening. Traditional approaches have limitations in sensitivity and cost-effectiveness, particularly in resource-limited settings. (2) Methods: We developed a semi-nested realtime PCR assay utilizing extendable blocking probes (ExBP) to enhance the detection of low-level m SEPT9 based on DNA melting. This assay allows for the discrimination of m SEPT9 in the presence of high concentrations of non-methylated SEPT9 (up to 100,000 times higher). (3) Results: The assay demonstrated a sensitivity of 73.91% and specificity of 80%, showcasing its ability to detect very low levels of methylated DNA effectively. The innovative use of ExBP without costly modified probes simplifies the assay setup and reduces the overall costs, enhancing its applicability in diverse clinical settings. (4) Conclusions: This novel assay significantly improves the detection of m SEPT9 , offering a potential advance in CRC screening and monitoring. Its cost-efficiency and high sensitivity make it particularly suitable for the early detection and management of CRC, especially in settings with limited resources. Future studies are encouraged to validate this assay in larger populations to establish its clinical benefits and practical utility.

Uterine contractions in the myometrium occur at multiple scales, spanning both organ and cellular levels. This complex biological process plays an essential role in the fetus delivery during the second stage of labor. Several finite element models of active uterine contractions have already been developed to simulate the descent of the fetus through the birth canal. However, the developed models suffer severe reliability issues due to the uncertain parameters. In this context, the present study aimed to perform the uncertainty quantification (UQ) of the active uterine contraction simulation to advance our understanding of pregnancy mechanisms with more reliable indicators. A uterus model with and without fetus was developed integrating a transversely isotropic Mooney-Rivlin material with two distinct fiber orientation architectures. Different contraction patterns with complex boundary conditions were designed and applied. A global sensitivity study was performed to select the most valuable parameters for the uncertainty quantification (UQ) process using a copula-based Monte Carlo method. As results, four critical material parameters ( C 1 , C 2 , K , Ca 0 ) of the active uterine contraction model were identified and used for the UQ process. The stress distribution on the uterus during the fetus descent, considering first and second fiber orientation families, ranged from 0.144 to 1.234 MPa and 0.044 to 1.619 MPa, respectively. The simulation outcomes revealed also the segment-specific contraction pattern of the uterus tissue. The present study quantified, for the first time, the effect of uncertain parameters of the complex constitutive model of the active uterine contraction on the fetus descent process. As perspectives, a full maternal pelvis model will be coupled with reinforcement learning to automatically identify the delivery mechanism behind the cardinal movements of the fetus during the active expulsion process., (© 2024. International Federation for Medical and Biological Engineering.)

Lung cancer is the leading cause of cancer deaths globally, necessitating effective early detection methods. Traditional diagnostics like low-dose computed tomography (LDCT) often yield high false positive rates. SHOX2 gene methylation has emerged as a promising biomarker. This study aimed to develop and validate a novel semi-nested real-time PCR assay enhancing sensitivity and specificity for detecting SHOX2 methylation using extendable blocking probes (ExBPs). The assay integrates a semi-nested PCR approach with ExBPs, enhancing the detection of low-abundance methylated SHOX2 DNA amidst unmethylated sequences. It was tested on spiked samples with varied methylation levels and on clinical samples from lung cancer patients and individuals with benign lung conditions. The assay detected methylated SHOX2 DNA down to 0.01%. Clinical evaluations confirmed its ability to effectively differentiate between lung cancer patients and those with benign conditions, demonstrating enhanced sensitivity and specificity. The use of ExBPs minimized non-target sequence amplification, crucial for reducing false positives. The novel semi-nested real-time PCR assay offers a cost-effective, highly sensitive, and specific method for detecting SHOX2 methylation, enhancing early lung cancer detection and monitoring, particularly valuable in resource-limited settings.

Background and Objective: The fetal representation as a 3D articulated body plays an essential role to describe a realistic vaginal delivery simulation. However, the current computational solutions have been oversimplified. The objective of the present work was to develop and evaluate a novel hybrid rigid-deformable modeling approach for the fetal body and then simulate its interaction with surrounding fetal soft tissues and with other maternal pelvis soft tissues during the second stage of labor., Methods: CT scan data was used for 3D fetal skeleton reconstruction. Then, a novel hybrid rigid-deformable model of the fetal body was developed. This model was integrated into a maternal 3D pelvis model to simulate the vaginal delivery. Soft tissue deformation was simulated using our novel HyperMSM formulation. Magnetic resonance imaging during the second stage of labor was used to impose the trajectory of the fetus during the delivery., Results: Our hybrid rigid-deformable fetal model showed a potential capacity for simulating the movements of the fetus along with the deformation of the fetal soft tissues during the vaginal delivery. The deformation energy density observed in the simulation for the fetal head fell within the strain range of 3 % to 5 %, which is in good agreement with the literature data., Conclusions: This study developed, for the first time, a hybrid rigid-deformation modeling of the fetal body and then performed a vaginal delivery simulation using MRI-driven kinematic data. This opens new avenues for describing more realistic behavior of the fetal body kinematics and deformation during the second stage of labor. As perspectives, the integration of the full skeleton body, especially the upper and lower limbs will be investigated. Then, the completed model will be integrated into our developed next-generation childbirth training simulator for vaginal delivery simulation and associated complication scenarios., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interest related to the present work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Childbirth simulations lack realism due to an oversimplification of the foetal model, particularly as most models do not allow joint motion. Foetus-specific neuromusculoskeletal (NMS) model with a detailed articulated skeleton is still not available in the literature. The present work aims at proposing the first-ever foetus-specific NMS model and then simulating the foetal descent during a vaginal delivery by using in vivo medical resonance imaging (MRI) childbirth data. Moreover, the developed model is provided open source for the community. Our foetus-specific NMS model was developed using the geometries reconstructed from a foetal computed tomography (CT) scan (Female, mass = 2.35 kg, length = 50 cm). The model contains 22 joints (64 degrees of freedom) and 65 muscles with a particular attention to the cervical spine level to enable the simulation of the cardinal movements. Then, the skull-to-cervical-spine (S/CP) and cervical-spine-to-torso (CP/T) deflection angles were extracted from in vivo MRI data for motion simulation. The S/CP and CP/T deflexion angles range from 12 degrees of flexion to 2 degrees of extension and from 7 degrees of flexion to 22 degrees of extension respectively. The developed model opens new avenues in more biofidelic childbirth simulations with a complete foetal NMS model. Obtained outcomes with the in vivo MRI data enabled to perform a first simulation of the foetal descent kinematics using real childbirth data. Future works will focus on developing a novel muscle formulation of the foetus and combining such a NMS model with a deformable model to simulate childbirth and associated complication scenarios.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The development of a comprehensive uterine model that seamlessly integrates the intricate interactions between the electrical and mechanical aspects of uterine activity could potentially facilitate the prediction and management of labor complications. Such a model has the potential to enhance our understanding of the initiation and synchronization mechanisms involved in uterine contractions, providing a more profound comprehension of the factors associated with labor complications, including preterm labor. Consequently, it has the capacity to assist in more effective preparation and intervention strategies for managing such complications. In this study, we present a computational model that effectively integrates the electrical and mechanical components of uterine contractions. By combining a state-of-the-art electrical model with the Hyperelastic Mass-Spring Model (HyperMSM), we adopt a multiphysics and multiscale approach to capture the electrical and mechanical activities within the uterus. The electrical model incorporates the generation and propagation of action potentials, while the HyperMSM simulates the mechanical behavior and deformations of the uterine tissue. Notably, our model takes into account the orientation of muscle fibers, ensuring that the simulated contractions align with their inherent directional characteristics. One noteworthy aspect of our contraction model is its novel approach to scaling the rest state of the mesh elements, as opposed to the conventional method of applying mechanical loads. By doing so, we eliminate artificial strain energy resulting from the resistance of soft tissues' elastic properties during contractions. We validated our proposed model through test simulations, demonstrating its feasibility and its ability to reproduce expected contraction patterns across different mesh resolutions and configurations. Moving forward, future research efforts should prioritize the validation of our model using robust clinical data. Additionally, it is crucial to refine the model by incorporating a more realistic uterus model derived from medical imaging. Furthermore, applying the model to simulate the entire childbirth process holds immense potential for gaining deeper insights into the intricate dynamics of labor., (© 2023. International Federation for Medical and Biological Engineering.)

In chronic hepatitis B (CHB) patients, quantification of HBV pgRNA in plasma has the potential to provide information on disease prognosis and liver injury or histopathology. However, current methods for detecting HBV pgRNA present technical difficulties due to the co-existence of HBV DNA in plasma samples. We have successfully established a novel one-step RT-PCR assay that allows selective quantification of HBV pgRNA. Two cohorts of participants were recruited for assay validation, including treatment-naïve patients with CHB and HBeAg-positive CHB patients who were treated with Tenofovir and monitored for 6 months to assess the predictive value of baseline HBV RNA for HBeAg seroclearance. Statistical analysis was performed using MedCalc version 20.019 software. The novel selective one-step RT-PCR assay for detecting HBV pgRNA was validated with a limit of detection of 100 copies/mL. The assay was able to selectively measure HBV pgRNA even in the presence of excess HBV rcDNA. In treatment-naïve CHB patients, HBV pgRNA levels were significantly lower than HBV DNA concentration. Serum HBV DNA levels and HBeAg status were positively associated with HBV pgRNA. Baseline serum HBV pgRNA levels were found to be strong predictors of HBeAg seroclearance after 6 months of Tenofovir treatment. The study presents a novel RT-PCR assay that allows accurate measurement of plasma HBV pgRNA in chronic hepatitis B patients, even in the presence of excess HBV DNA. The assay is highly selective and represents a significant advancement with potential for further breakthroughs in understanding the clinical significance of HBV pgRNA., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Endothelial cells (ECs) are capable of quickly responding in a coordinated manner to a wide array of stresses to maintain vascular homeostasis. Loss of EC cellular adaptation may be a potential marker for cardiovascular disease and a predictor of poor response to endovascular pharmacological interventions such as drug-eluting stents. Here, we report single-cell transcriptional profiling of ECs exposed to multiple stimulus classes to evaluate EC adaptation., Methods: Human aortic ECs were costimulated with both pathophysiological flows mimicking shear stress levels found in the human aorta (laminar and turbulent, ranging from 2.5 to 30 dynes/cm 2 ) and clinically relevant antiproliferative drugs, namely paclitaxel and rapamycin. EC state in response to these stimuli was defined using single-cell RNA sequencing., Results: We identified differentially expressed genes and inferred the TF (transcription factor) landscape modulated by flow shear stress using single-cell RNA sequencing. These flow-sensitive markers differentiated previously identified spatially distinct subpopulations of ECs in the murine aorta. Moreover, distinct transcriptional modules defined flow- and drug-responsive EC adaptation singly and in combination. Flow shear stress was the dominant driver of EC state, altering their response to pharmacological therapies., Conclusions: We showed that flow shear stress modulates the cellular capacity of ECs to respond to paclitaxel and rapamycin administration, suggesting that while responding to different flow patterns, ECs experience an impairment in their transcriptional adaptation to other stimuli., Competing Interests: Disclosures A.S. Kalluri reports compensation for consulting from nference, inc. A.K. Shalek reports compensation for consulting and scientific advisory board membership from Merck, Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Ochre Bio, and Dahlia Biosciences. The other authors report no conflicts.

Background: Noninvasive prenatal tests for monogenic diseases (NIPT-SGG) have recently been reported as helpful in early-stage antenatal screening. Our study describes the clinical and genetic features of cases identified by NIPT-SGG. Materials & methods: In a cohort pregnancy with abnormal sonograms, affected cases were confirmed by invasive diagnostic tests concurrently, with NIPT-SGG targeting 25 common dominant single-gene diseases. Results: A total of 13 single-gene fetuses were confirmed, including Noonan and Costello syndromes, thanatophoric dysplasia, achondroplasia, osteogenesis imperfecta and Apert syndrome. Two novel variants seen were tuberous sclerosis complex ( TSC2 c.4154G>A) and Alagille syndrome ( JAG1 c.3452del). Conclusion: NIPT-SGG and standard tests agree on the results for 13 fetuses with monogenic disorders. This panel method of screening can benefit high-risk Vietnamese pregnancies, but further research is encouraged to expand on the causative gene panel.

High-quality gynecologist and midwife training is particularly relevant to limit medical complications and reduce maternal and fetal morbimortalities. Physical and virtual training simulators have been developed. However, physical simulators offer a simplified model and limited visualization of the childbirth process, while virtual simulators still lack a realistic interactive system and are generally limited to imposed predefined gestures. Objective performance assessment based on the simulation numerical outcomes is still not at hand. In the present work, we developed a virtual childbirth simulator based on the Mixed-Reality (MR) technology coupled with HyperMSM (Hyperelastic Mass-Spring Model) formulation for real-time soft-tissue deformations, providing intuitive user interaction with the virtual physical model and a quantitative assessment to enhance the trainee's gestures. Microsoft HoloLens 2 was used and the MR simulator was developed including a complete holographic obstetric model. A maternal pelvis system model of a pregnant woman (including the pelvis bone, the pelvic floor muscles, the birth canal, the uterus, and the fetus) was generated, and HyperMSM formulation was applied to simulate the soft tissue deformations. To induce realistic reactions to free gestures, the virtual replicas of the user's detected hands were introduced into the physical simulation and were associated with a contact model between the hands and the HyperMSM models. The gesture of pulling any part of the virtual models with two hands was also implemented. Two labor scenarios were implemented within the MR childbirth simulator: physiological labor and forceps-assisted labor. A scoring system for the performance assessment was included based on real-time biofeedback. As results, our developed MR simulation application was developed in real-time with a refresh rate of 30-50 FPS on the HoloLens device. HyperMSM model was validated using FE outcomes: high correlation coefficients of [0.97-0.99] and weighted root mean square relative errors of 9.8% and 8.3% were obtained for the soft tissue displacement and energy density respectively. Experimental tests showed that the implemented free-user interaction system allows to apply the correct maneuvers (in particular the "Viennese" maneuvers) during the labor process, and is capable to induce a truthful reaction of the model. Obtained results confirm also the possibility of using our simulation's outcomes to objectively evaluate the trainee's performance with a reduction of 39% for the perineal strain energy density and 5.6 mm for the vertical vaginal diameter when the "Viennese" technique is applied. This present study provides, for the first time, an interactive childbirth simulator with an MR immersive experience with direct free-hand interaction, real-time soft-tissue deformation feedback, and an objective performance assessment based on numerical outcomes. This offers a new perspective for enhancing next-generation training-based obstetric teaching. The used models of the maternal pelvic system and the fetus will be enhanced, and more delivery scenarios (e.g. instrumental delivery, breech delivery, shoulder dystocia) will be designed and integrated. The third stage of labor will be also investigated to include the delivery of the placenta, and the clamping and cutting of the umbilical cord., (© 2023. International Federation for Medical and Biological Engineering.)

Backgrounds and Objective : Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods : 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results : The skull prediction's median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions : The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

Piezoelectric energy transducers offer great potential for converting the vibrations of pedestrian footsteps or cars moving on a bridge or road into electricity. However, existing piezoelectric energy-harvesting transducers are limited by their poor durability. In this paper, to enhance this durability, a piezoelectric energy transducer with a flexible piezoelectric sensor is fabricated in a tile protype with indirect touch points and a protective spring. The electrical output of the proposed transducer is examined as a function of pressure, frequency, displacement, and load resistance. The maximum output voltage and maximum output power obtained were 6.8 V and 4.5 mW, respectively, at a pressure of 70 kPa, a displacement of 2.5 mm, and a load resistance of 15 kΩ. The designed structure limits the risk of destroying the piezoelectric sensor during operation. The harvesting tile transducer can work properly even after 1000 cycles. Furthermore, to demonstrate its practical applications, the tile was placed on the floor of an overpass and a walking tunnel. Consequently, it was observed that the electrical energy harvested from the pedestrian footsteps could power an LED light fixture. The findings suggest that the proposed tile offers promise with respect to harvesting energy produced during transportation.

Bacillus subtilis is a critical host for producing recombinant proteins. However, the SDS-PAGE process, including the sample preparation steps, varies among B. subtilis -related studies, making it impossible to compare findings. Hence, this paper provides a simple guide to culture and collect B. subtilis cells through an OD 600 measurement and a protocol for SDS-PAGE. These techniques were applied to check the expression of a BgaB, a reporter protein and LukF-PV, a potential vaccine candidate against S. aureus , in the cytoplasm of B. subtilis under the control of a strong promoter, P grac212 . This protocol could be helpful for scientists in preparing samples and generating an SDS-PAGE experiment, as well as favoring the unification of research about protein expression in B. subtilis .

The SPOT-MAS assay "Screening for the Presence Of Tumor by Methylation And Size" detects the five most common cancers in Vietnam by evaluating circulating tumor DNA in the blood. Here, we validated its performance in a prospective multi-center clinical trial, K-DETEK. Our analysis of 2795 participants from 14 sites across Vietnam demonstrates its ability to detect cancers in asymptomatic individuals with a positive predictive value of 60%, with 83.3% accuracy in detecting tumor location. We present a case report to support further using SPOT-MAS as a complementary method to achieve early cancer detection and provide the opportunity for early treatment.

The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5±1.1 mm. The best error range is 1.9±1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.

Colloid-sized microplastics (MPs) are ubiquitous in aquatic environments and can share the same transport route together with various crystalline, poorly crystalline and freshly formed iron oxides. However, the colloidal interactions between these colloid constituents are not fully understood. This study was designed to investigate the colloidal properties of polystyrene microplastics (PSMPs) under the influence of haematite, goethite, ferrihydrite and freshly formed Fe oxide (FFFO). Dynamic light scattering was coupled with a test tube method to observe changes in the surface charge and colloidal dynamics of suspensions of PSMPs and Fe oxides. The overall effects on the aggregation of PSMPs are found to decrease in the following order: FFFO > ferrihydrite > goethite > haematite. The effects of these Fe oxides are found to strongly depend on pH. While the crystalline oxides play a dominant role in the acidic environment, poorly crystalline oxides show greater effects on PSMP aggregation in an alkaline environment. Heteroaggregation due to decreasing electrostatic interactions is the major mechanism that governs the colloidal dynamics of PSMPs and Fe oxides. It can be inferred that the copresence of Fe oxides and MPs can delay the transport of MPs or even change the destination for MPs., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Several inherited metabolic diseases are underreported in Vietnam, namely glucose-6-phosphate dehydrogenase deficiency (G6PDd), phenylketonuria (PKU) and galactosemia (GAL). Whilst massively parallel sequencing (MPS) allows researchers to screen several loci simultaneously for pathogenic variants, no screening programme uses MPS to uncover the variant spectra of these diseases in the Vietnamese population., Methods: Pregnant women (mean age of 32) from across Vietnam attending routine prenatal health checks agreed to participate and had their blood drawn. MPS was used to detect variants in their G6PD, PAH and GALT genes., Results: Of 3259 women screened across Vietnam, 450 (13.8%) carried disease-associated variants for G6PD, PAH and GALT. The prevalence of carriers was 8.9% (291 of 3259) in G6PD and 4.6% (152 of 3259) in PKU, whilst GAL was low at 0.2% (7 of 3259). Two GALT variants, c.593 T > C and c.1034C > A, have rarely been reported., Conclusion: This study highlights the need for routine carrier screening, where women give blood whilst receiving routine prenatal care, in Vietnam. The use of MPS is suitable for screening multiple variants, allowing for identifying rare pathogenic variants. The data from our study will inform policymakers in constructing cost-effective genetic metabolic carrier screening programmes., (© 2022 Gene Solutions. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

Rationale: Congenital bile acid synthesis defect (BASD) is a rare disease caused by mutations in the aldo-keto reductase 1D1 gene, which encodes the primary Δ4-3-oxosteroid 5β-reductase enzyme. Early disease diagnosis is critical for early treatment with bile acid replacement therapy, with an excellent chance for recovery. In contrast, protracted diagnosis and treatment may lead to poor outcomes, including decompensated hepatic cirrhosis, liver transplant, and even death., Patient Concerns: Three clinical congenital bile acid synthesis defect cases in the Vietnamese population are herein reported. These pediatric patients presented with symptoms of prolonged postpartum jaundice and abnormal loose stool (mucus, lipids, and white). The clinical examinations showed hepatosplenomegaly. Urinalysis showed a very low fraction of primary bile acids and atypical 3-oxo-Δ4- bile acids in all three patients., Diagnoses: The patients were diagnosed with primary Δ4-3-oxosteroid 5β-reductase deficiency. Next-generation gene sequencing revealed two homozygous mutations in the aldo-keto reductase family 1 member D1 gene. The first is a documented variant, c.797G>A (p.Arg266Gln), and the second is a novel mutation at c.155T>C (p.Ile52Thr)., Interventions: Immediately after diagnosis, patients were treated with oral chenodeoxycholate 5 mg/kg/d., Outcomes: The patients' symptoms, signs, and primary bile acids levels improved significantly., Lessons: Clinicians should consider genetic disorders related to cholestasis for effective and life-saving treatment. A prompt genetic analysis by next-generation gene sequencing enables patients to access bile acid replacement therapy earlier, significantly improving short- and long-term outcomes., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

Fine control of structural and morphological features in electrochromic materials is of paramount importance for realizing practical electrochromic devices (ECDs), which can dynamically adjust indoor light and temperature of buildings. To this end, herein we investigate impacts of two variants such as Ti-doping amount and the annealing temperature on physical and chemical properties of sol-gel derived electrochromic WO 3 films. We use a wide range of titanium coupling agents (TCAs) as Ti-dopants ranging from 0 wt% to 20 wt% and vary the annealing temperature between 200 °C and 400 °C with 50 °C interval. Both variants greatly influence the physical properties of the resulting WO 3 films, resulting in different crystallinities and morphologies. Through complementary analytical techniques, we find that the WO 3 film featuring an amorphous phase with nano-porous morphology enhances the electrochemical and electrochromic performances. The specific TCA used in this study helps stabilize the amorphous WO 3 structure and generate the nano-pores during the following thermal treatment via its thermal decomposition. As a result, the WO 3 film having an optimal 8 wt% TCA annealed at 300 °C shows a high optical density of 73.78% in visible light (400-780 nm), rapid switching speed ( t c = 5.12 s and t b = 4.74 s), and high coloration efficiency of 52.58 cm 2 C -1 along with a superior cyclic stability. Thus, understanding a structure-property relationship is of paramount importance in engineering the advanced electrochromic WO 3 for use in practical ECDs and other optoelectronic applications., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)

Background and Objective: Facial palsy patients or patients with facial transplantation have abnormal facial motion due to altered facial muscle functions and nerve damage. Computer-aided system and physics-based models have been developed to provide objective and quantitative information. However, the predictive capacity of these solutions is still limited to explore the facial motion patterns with emerging properties. The present study aims to couple the reinforcement learning and the finite element modeling for facial motion learning and prediction., Methods: A novel modeling workflow for learning facial motion was developed. A physically-based model of the face within the Artisynth modeling platform was used. Information exchange protocol was proposed to link reinforcement learning and rigid multi-bodies dynamics outcomes. Two reinforcement learning algorithms (deep deterministic policy gradient (DDPG) and Twin-delayed DDPG (TD3)) were used and implemented to drive the simulations of symmetry-oriented and smile movements. Numerical outcomes were compared to experimental observations (Bosphorus database) for evaluation and validation purposes., Results: As result, after more than 100 episodes of exploring the environment, the agent starts to learn from previous trials and can find the optimal policy after more than 300 episodes of training. Regarding the symmetry-oriented motion, the muscle excitations predicted by the trained agent help to increase the value of reward from R = -2.06 to R = -0.23, which counts for ∼89% improvement of the symmetry value of the face. For smile-oriented motion, two points at the edge of the mouth move up 0.35 cm, which is within the range of movements estimated from the Bosphorus database (0.4 ± 0.32 cm)., Conclusions: The present study explored the muscle excitation patterns by coupling reinforcement learning with a detailed finite element model of the face. We developed, for the first time, a novel coupling scheme to integrate the finite element simulation into the reinforcement learning process for facial motion learning. As perspectives, this present workflow will be applied for facial palsy and facial transplantation patients to guide and optimize the functional rehabilitation program., Competing Interests: Declaration of Competing Interest The authors declare that there are no conflicts of interest related to the present work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Reinforcement learning (RL) has been used to study human locomotion learning. One of the current challenges in healthcare is our understanding of and ability to slow the decline due to muscle ageing and its effect on human falls. The purpose of this study was to investigate reinforcement learning for human movement strategies when modifying muscle parameters to account for age-related changes. In particular, human falls with modified physiological factors were modelled and simulated to determine the effect of muscle descriptors for ageing on kinematic behaviour and muscle force control. A 3D musculoskeletal model (8 DoF and 22 muscles) of the human body was used. The deep deterministic policy gradient (DDPG) method was implemented. Different muscle descriptors for ageing were integrated, including changes in maximum isometric force, contraction velocity, the deactivation time constant and passive muscle strain. Additionally, the effects of isometric force reductions of 10, 20 and 30% were also considered independently. An environment for the simulation was developed using the opensim-rl package for Python with the training process completed on Google Compute Engine. The simulation outcomes for healthy young adult and elderly falls under modified muscle behaviours were compared to experimental observations for validation. The result of our elderly simulation for multiple ageing-related factors (M_all) produced a walking speed of 0.26 m/s for the two steps taken prior to the fall. The over activation of the hip extensors and inactivation of knee extensors led to a backward fall for this elderly simulation. The inactivated rectus femoris and right tibialis are main actors of the forward fall. Our simulation outcomes are consistent with experimental observations through the comparison of kinematic features and motion history evolution. We showed in the present study, for the first time, that RL can be used as a strategy to explore the effect of ageing muscle physiological factors on kinematics and muscle control during falls. Our findings show that the elderly fall model for the M_all condition more closely resembles experimental elderly fall data than our simulations which considered age-related reductions of force alone. As future perspectives, the behaviour preceding a fall will be studied to establish the strategies used to avoid falls or fall with minimal consequence, leading to the identification of patient-specific rehabilitation programmes for elderly people., (© 2022. International Federation for Medical and Biological Engineering.)

Background: Serum pepsinogen has been approved and used widely as an effective biomarker in diagnosis of atrophic gastritis and gastric cancer; however, its validity and appropriate cut-off values vary among different populations. This study aimed to initially assess the diagnostic value of the serum pepsinogen in diagnosis of moderate and severe atrophic gastritis for Vietnamese population., Materials and Methods: A cross-sectional study enrolled 273 participants from June 2008 to November 2019. All participants underwent a gastroscopy procedure and three tests including serum PG test, pathology test, and Hp-Igg Elisa test. The Kimura-Takemoto classification and OLGA system were used to classify the mild versus moderate-severe atrophic gastritis. Receiver Operating Characteristic curve was used to assess the value of PGI, PGII and PGR., Results: Based on Kimura-Takemoto classification, the AUC of PGI and PGR was 0.635 (p = 0.008, 95% CI 0.554-0.716) and 0.766 (p < 0.001, 95% CI 0.676-0.857) respectively. The best cut-off values were PGI ≤69.0 and PGR ≤4.6 (sensitivity: 73%, specificity: 83.9%, positive predictive value: 41.5%, negative predictive value: 95.2%, accuracy: 82.4%). According to the OLGA system, the AUC of PGI and PGR was 0.612 (p = 0.004, 95% CI 0.540-0.684) and 0.689 (p < 0.001, 95% CI 0.621-0.758) respectively. The best cut-off values were PGI ≤63.5 and PGR ≤5.2 (sensitivity: 49.4%, specificity: 82.1%, positive predictive value: 52.1%, negative predictive value: 80.5%, accuracy: 72.9%)., Conclusions: The serum pepsinogen II and pepsinogen I/II ratio had reliable diagnostic value for screening of moderate and severe atrophic gastritis among Vietnamese population. Further research was recommended to focus on larger scale to improve the diagnostic yield and to continue finding the cut-off values for diagnosis of gastric cancer among Vietnamese population., Competing Interests: The authors have no conflicts of interest to declare., (© 2022 The Authors.)

Background and Objective: Fast, accurate, and stable simulation of soft tissue deformation is a challenging task. Mass-Spring Model (MSM) is one of the popular methods used for this purpose for its simple implementation and potential to provide fast dynamic simulations. However, accurately simulating a non-linear material within the mass-spring framework is still challenging. The objective of the present study is to develop and evaluate a new efficient hyperelastic Mass-Spring Model formulation to simulate the Neo-Hookean deformable material, called HyperMSM., Methods: Our novel HyperMSM formulation is applicable for both tetrahedral and hexahedral mesh configurations and is compatible with the original projective dynamics solver. In particular, the proposed MSM variant includes springs with variable rest-lengths and a volume conservation constraint. Two applications (transtibial residual limb and the skeletal muscle) were conducted., Results: Compared to finite element simulations, obtained results show RMSE ranges of [2.8%-5.2%] and [0.46%-5.4%] for stress-strain and volumetric responses respectively for strains ranging from -50% to +100%. The displacement error range in our transtibial residual limb simulation is around [0.01mm-0.7 mm]. The RMSE range of relative nodal displacements for the skeletal psoas muscle model is [0.4%-1.7%]., Conclusions: Our novel HyperMSM formulation allows hyperelastic behavior of soft tissues to be described accurately and efficiently within the mass-spring framework. As perspectives, our formulation will be enhanced with electric behavior toward a multi-physical soft tissue mass-spring modeling framework. Then, the coupling with an augmented reality environment will be performed., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest related to the present work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Hyperelastic constitutive laws have been commonly used to model the passive behavior of the human skeletal muscle. Despite many efforts, the use of accurate finite element formulations of hyperelastic constitutive laws is still time-consuming for a real-time medical simulation system. The objective of the present study was to develop a deep learning model to predict the hyperelastic constitutive behaviors of the skeletal muscle toward a fast estimation of the muscle tissue stress.A finite element (FE) model of the right psoas muscle was developed. Neo-Hookean and Mooney-Rivlin laws were used. A tensile test was performed with an applied body force. A learning database was built from this model using an automatic probabilistic generation process. A long-short term memory (LSTM) neural network was implemented to predict the stress evolution of the skeletal muscle tissue. A hyperparameter tuning process was conducted. Root mean square error (RMSE) and associated relative error was quantified to evaluate the precision of the predictive capacity of the developed deep learning model. Pearson correlation coefficients (R) was also computed.The nodal displacements and the maximal stresses range from 70 to 227 mm and from 2.79 to 5.61 MPa for Neo-Hookean and Monney-Rivlin laws, respectively. Regarding the LSTM predictions, the RMSE ranges from 224.3 ± 3.9 Pa (8%) to 227.5 [Formula: see text] 5.7 Pa (4%) for Neo-Hookean and Monney-Rivlin laws, respectively. Pearson correlation coefficients (R) of 0.78 [Formula: see text] 0.02 and 0.77 [Formula: see text] 0.02 were obtained for Neo-Hookean and Monney-Rivlin laws, respectively.The present study showed that, for the first time, the use of a deep learning model can reproduce the time-series behaviors of the complex FE formulations for skeletal muscle modeling. In particular, the use of a LSTM neural network leads to a fast and accurate surrogate model for the in silico prediction of the hyperelastic constitutive behaviors of the skeletal muscle. As perspectives, the developed deep learning model will be integrated into a real-time medical simulation of the skeletal muscle for prosthetic socket design and childbirth simulator., (© 2022. International Federation for Medical and Biological Engineering.)

Three new polyketide-derived natural products, cladobotric acids G-I ( 1 - 3 ), and six known metabolites ( 4 , 5 , 8 - 11 ) were isolated from fermentation of the fungus Cladobotryum sp. grown on rice. Their structures were elucidated by extensive spectroscopic methods. Two metabolites, cladobotric acid A ( 4 ) and pyrenulic acid A ( 10 ), were converted to a series of new products ( 12 - 20 ) by semisynthesis. The antibacterial activities of all these compounds were investigated against the Gram-positive pathogen Staphylococcus aureus including methicillin-susceptible (MSSA), methicillin-resistant and vancomycin-intermediate (MRSA/VISA), and heterogeneous vancomycin-intermediate (hVISA) strains. Results of these antibacterial assays revealed structural features of the unsaturated decalins important for biological activity.

Objective: Although the application of transcranial Doppler (TCD) ultrasonography in clinical diagnosis of cerebral vasospasm is popular in clinical practice in Vietnam, available evidence of the predictive value of vasospasm on TCD in the literature was mostly reported from large institutions in developed countries. Hence, this study was conducted to evaluate the value of TCD ultrasonography in the diagnosis of vasospasm in patients with subarachnoid hemorrhage (SAH) in Vietnam., Patients and Methods: This is a prospective observational study of all aneurysmal SAH patients consecutively admitted to a single center between 2008 and December 2011. TCD and 64-slice computed tomographic angiography (CTA) were used to cerebral vasospasm in SAH patients., Results: 316 patients were analyzed (mean age = 52.97±12.27 years, 52.2% males). There were statistically significant difference rates of the cerebral vasospasm by Hunt and Hess Classification and Fisher classification (p <0.01). The proportion of the patients with cerebral vasospasm who were diagnosed exactly by TCD was 95.2%, while the proportion of the patients without cerebral vasospasm diagnosed exactly was 91.5%. TCD predictive diagnostic value was the highest, with the sensitivity of 0.95 (95% CI: 0.91-0.98), specificity of 0.91 (95% CI: 0.85-0.96), positive predictive value of 0.94 (5% CI: 0.90-0.97) and negative predictive value of 0.93 (95 CI: 0.87-0.97). Hemiplegia was the clinical symptom with the highest diagnostic value with the sensitivity of 0.34 (95% CI: 0.27-0.41), specificity of 0.92 (95% CI: 0.86-0.96), positive predictive value of 0.86 (95% CI: 0.76-0.93) and negative predictive value of 0.49 (95% CI: 0.41-0.54)., Conclusions: Evidence of vasospasm diagnosis on TCD ultrasonography was found with high accuracy. Current study enables to suggest the wide application of TCD in Vietnam health facilities from central to grassroots levels instead of the CTA use.

Skull prediction from the head is a challenging issue toward a cost-effective therapeutic solution for facial disorders. This issue was initially studied in our previous work using full head-to-skull relationship learning. However, the head-skull thickness topology is locally shaped, especially in the face region. Thus, the objective of the present study was to enhance our head-to-skull prediction problem by using local topological features for training and predicting. Head and skull feature points were sampled on 329 head and skull models from computed tomography (CT) images. These feature points were classified into the back and facial topologies. Head-to-skull relations were trained using the partial least square regression (PLSR) models separately in the two topologies. A hyperparameter tuning process was also conducted for selecting optimal parameters for each training model. Thus, a new skull could be generated so that its shape was statistically fitted with the target head. Mean errors of the predicted skulls using the topology-based learning method were better than those using the non-topology-based learning method. After tenfold cross-validation, the mean error was enhanced 36.96% for the skull shapes and 14.17% for the skull models. Mean error in the facial skull region was especially improved with 4.98%. The mean errors were also improved 11.71% and 25.74% in the muscle attachment regions and the back skull regions respectively. Moreover, using the enhanced learning strategy, the errors (mean ± SD) for the best and worst prediction cases are from 1.1994 ± 1.1225 mm (median: 0.9036, coefficient of multiple determination (R 2 ): 0.997274) to 3.6972 ± 2.4118 mm (median: 3.9089, R 2 : 0.999614) and from 2.0172 ± 2.0454 mm (median: 1.2999, R 2 : 0.995959) to 4.0227 ± 2.6098 mm (median: 3.9998, R 2 : 0.998577) for the predicted skull shapes and the predicted skull models respectively. This present study showed that more detailed information on the head-skull shape leads to a better accuracy level for the skull prediction from the head. In particular, local topological features on the back and face regions of interest should be considered toward a better learning strategy for the head-to-skull prediction problem. In perspective, this enhanced learning strategy was used to update our developed clinical decision support system for facial disorders. Furthermore, a new class of learning methods, called geometric deep learning will be studied., (© 2021. International Federation for Medical and Biological Engineering.)

Benzendicarboxylic acid (BDC)-based metal-organic frameworks (MOFs) have been widely utilized in various applications, including supercapacitor electrode materials. Manganese and copper have solid diamond frames formed with BDC linkers among transition metals chosen for MOF formation. They have shown the possibility to enlarge capacitance at different combinations of MOFs and polyaniline (PANI). Herein, reduced graphene oxide (rGO) was used as the matrix to fabricate electrochemical double-layer SCs. PANI and Mn/Cu-MOF's effect on the properties of electrode materials was investigated through electrochemical analysis. As a result, the highest specific capacitance of about 276 F/g at a current density of 0.5 A/g was obtained for rGO/Cu-MOF@PANI composite., (© 2022. The Author(s).)

Fungicide application for controlling fungal diseases can increase copper (Cu) accumulation in soil. More urgently, Cu released from fungicides can associate with soil clay and favour the mutual aggregation of Cu and soil clay, thereby potentially intensifying the accumulation of Cu. We investigated the effects of Cu salt and six common Cu-based fungicides on colloidal dynamics of a clay fraction from citrus cultivated soil. Batch experiments were carried out to provide the loading capacity of the clay fraction for Cu. The colloidal dynamic experiments were performed over a pH range from 3 to 8 following a test tube method, while surface charge, the key electrochemical factor of the solid-liquid interface, was quantified by a particle charge detector. It was found that all the studied fungicides, via releasing Cu 2+ , acted to effectively favour clay aggregation. The dissolved organic matter obtained from the dissolution of polymers in fungicides can theoretically stimulate clay dispersion. However, their effects were obscured due to the overwhelming effect of Cu 2+ . Therefore, Cu 2+ appears as the most active agent in the fungicides that intensifies clay aggregation. These findings imply that the intensive application of fungicides for plant protection purposes can inadvertently reduce clay mobility, favour the co-aggregation of clay and fungicides, and hence potentially exacerbate the contamination of the citrus soil., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The evolution of influenza viruses is fundamentally shaped by within-host processes. However, the within-host evolutionary dynamics of influenza viruses remain incompletely understood, in part because most studies have focused on infections in healthy adults based on single timepoint data. Here, we analyzed the within-host evolution of 82 longitudinally sampled individuals, mostly young children, infected with A/H1N1pdm09 or A/H3N2 viruses between 2007 and 2009. For A/H1N1pdm09 infections during the 2009 pandemic, nonsynonymous minority variants were more prevalent than synonymous ones. For A/H3N2 viruses in young children, early infection was dominated by purifying selection. As these infections progressed, nonsynonymous variants typically increased in frequency even when within-host virus titers decreased. Unlike the short-lived infections of adults where de novo within-host variants are rare, longer infections in young children allow for the maintenance of virus diversity via mutation-selection balance creating potentially important opportunities for within-host virus evolution., Competing Interests: AH, ZF, MW, RV, NT, TL, TP, DD, TT, MH, TN, QL, TL, TH, KC, PP, VN, MN, VN, TD, TT, HW, PH, AF, Hv, DE, Md, CR No competing interests declared, (© 2021, Han et al.)

Objective: Minimal prior research has examined the impact of inflammatory arthritis (IA) on men's perspectives on parenting. We aimed to describe fathering roles and experiences, the effect of IA on parenting activities, and strategies used by fathers with IA to fulfill this role., Methods: A grounded theory approach guided data gathering and analysis. Nine men with IA, parenting at least 1 child age <19 years, were recruited through rheumatology practices, therapy clinics, and social media. Each engaged in 1 in-depth personal interview. Transcripts were analyzed using inductive and iterative steps to identify key themes and a preliminary explanatory framework of fathering experiences of men with IA., Results: All men were married, ages 31-62 years, with 1 to 5 children ages 6 months to 28 years. "Being an involved father" describes participants' perspectives on fulfilling their role as hands-on parents, role models, and financial providers. "Taking ownership" explains how participants managed daily life, comprising 2 subthemes, "taking care of yourself," using strategies like exercise and communicating with loved ones, and "redefining yourself," a process of adapting to reframed identity and lifestyle adjustments. "Accessing support" indicates men who felt well-supported by social networks (most critically their wives), health care providers, and informational and educational resources., Conclusion: This small, grounded theory study offers an enriched understanding of fatherhood experiences of men with IA. When social, practical, and educational supports are in place, these men found parenting joyful and rewarding. Despite task limitations, their perspectives on being involved fathers was unrestricted by arthritis., (© 2020, American College of Rheumatology.)

Facial expression recognition plays an essential role in human conversation and human-computer interaction. Previous research studies have recognized facial expressions mainly based on 2D image processing requiring sensitive feature engineering and conventional machine learning approaches. The purpose of the present study was to recognize facial expressions by applying a new class of deep learning called geometric deep learning directly on 3D point cloud data. Two databases (Bosphorus and SIAT-3DFE) were used. The Bosphorus database includes sixty-five subjects with seven basic expressions (i.e., anger, disgust, fearness, happiness, sadness, surprise, and neutral). The SIAT-3DFE database has 150 subjects and 4 basic facial expressions (neutral, happiness, sadness, and surprise). First, preprocessing procedures such as face center cropping, data augmentation, and point cloud denoising were applied on 3D face scans. Then, a geometric deep learning model called PointNet++ was applied. A hyperparameter tuning process was performed to find the optimal model parameters. Finally, the developed model was evaluated using the recognition rate and confusion matrix. The facial expression recognition accuracy on the Bosphorus database was 69.01% for 7 expressions and could reach 85.85% when recognizing five specific expressions (anger, disgust, happiness, surprise, and neutral). The recognition rate was 78.70% with the SIAT-3DFE database. The present study suggested that 3D point cloud could be directly processed for facial expression recognition by using geometric deep learning approach. In perspectives, the developed model will be applied for facial palsy patients to guide and optimize the functional rehabilitation program.

Background and Objective: Facial palsy negatively affects both professional and personal life qualities of involved patients. Classical facial rehabilitation strategies can recover facial mimics into their normal and symmetrical movements and appearances. However, there is a lack of objective, quantitative, and in-vivo facial texture and muscle activation bio-feedbacks for personalizing rehabilitation programs and diagnosing recovering progresses. Consequently, this study proposed a novel patient-specific modelling method for generating a full patient specific head model from a visual sensor and then computing the facial texture and muscle activation in real-time for further clinical decision making., Methods: The modeling workflow includes (1) Kinect-to-head, (2) head-to-skull, and (3) muscle network definition & generation processes. In the Kinect-to-head process, subject-specific data acquired from a new user in neutral mimic were used for generating his/her geometrical head model with facial texture. In particular, a template head model was deformed to optimally fit with high-definition facial points acquired by the Kinect sensor. Moreover, the facial texture was also merged from his/her facial images in left, right, and center points of view. In the head-to-skull process, a generic skull model was deformed so that its shape was statistically fitted with his/her geometrical head model. In the muscle network definition & generation process, a muscle network was defined from the head and skull models for computing muscle strains during facial movements. Muscle insertion points and muscle attachment points were defined as vertex positions on the head model and the skull model respectively based on the standard facial anatomy. Three healthy subjects and two facial palsy patients were selected for validating the proposed method. In neutral positions, magnetic resonance imaging (MRI)-based head and skull models were compared with Kinect-based head and skull models. In mimic positions, infrared depth-based head models in smiling and [u]-pronouncing mimics were compared with appropriate animated Kinect-driven head models. The Hausdorff distance metric was used for these comparisons. Moreover, computed muscle lengths and strains in the tested facial mimics were validated with reported values in literature., Results: With the current hardware configuration, the patient-specific head model with skull and muscle network could be fast generated within 17.16±0.37s and animated in real-time with the framerate of 40 fps. In neutral positions, the best mean error was 1.91 mm for the head models and 3.21 mm for the skull models. On facial regions, the best mean errors were 1.53 mm and 2.82 mm for head and skull models respectively. On muscle insertion/attachment point regions, the best mean errors were 1.09 mm and 2.16 mm for head and skull models respectively. In mimic positions, these errors were 2.02 mm in smiling mimics and 2.00 mm in [u]-pronouncing mimics for the head models on facial regions. All above error values were computed on a one-time validation procedure. Facial muscles exhibited muscle shortening and muscle elongating for smiling and pronunciation of sound [u] respectively. Extracted muscle features (i.e. muscle length and strain) are in agreement with experimental and literature data., Conclusions: This study proposed a novel modeling method for fast generating and animating patient-specific biomechanical head model with facial texture and muscle activation bio-feedbacks. The Kinect-driven muscle strains could be applied for further real-time muscle-oriented facial paralysis grading and other facial analysis applications., (Copyright © 2020. Published by Elsevier B.V.)

The objective of this study was to describe the overall pattern of morbidity and mortality of children seen at the Thai Binh Paediatric Hospital in Vietnam, with a focus on infectious diseases. A retrospective review of hospitalisation records was conducted from 1 January 2015 to 31 December 2019. Data were obtained from a total of 113,999 records. The median age of patients was 18 months, with 84.0% of patients aged <5 years. Infectious diseases accounted for 61.0% of all cases. The most prevalent diseases were lower respiratory tract infections (32.8%), followed by gastrointestinal infections (13.3%) and confirmed influenza (5.4%). Most infections were not microbiologically documented. A total of 81.4% patients received at least one antibiotic. Most patients (97.0%) were hospitalised for less than 15 days. Regarding outcomes, 87.8% patients were discharged home with a favourable outcome. Twelve percent were transferred to the Vietnam National Children's Hospital because their condition had worsened and 0.1% died. In total, infectious diseases accounted for 40.4% of deaths, followed by neonatal disorders (34.6%). Our data serves a basis for the identification of needs for diagnostic tools and for future evaluation of the effect of the targeted implementation of such facilities. Point-of-care tests, including real-time polymerase chain reaction assays to identify common pathogens should be implemented for more accurate diagnosis and more appropriate antibiotic use., Competing Interests: The authors declare they have no conflicts of interest., (© 2020 The Authors. Published by Atlantis Press International B.V.)

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic., Competing Interests: No competing financial interests exist., (© Massimiliano Zanin et al., 2021; Published by Mary Ann Liebert, Inc.)

The use of filamentous fungi as cellular factories, where natural product pathways can be refactored and expressed in a host strain, continues to aid the field of natural product discovery. Much work has been done to develop host strains which are genetically tractable, and for which there are multiple selectable markers and controllable expression systems. To fully exploit these strains, it is beneficial to understand their natural metabolic capabilities, as such knowledge can rule out host metabolites from analysis of transgenic lines and highlight any potential interplay between endogenous and exogenous pathways. Additionally, once identified, the deletion of secondary metabolite pathways from host strains can simplify the detection and purification of heterologous compounds. To this end, secondary metabolite production in Aspergillus oryzae strain NSAR1 has been investigated via the deletion of the newly discovered negative regulator of secondary metabolism, mcrA (multicluster regulator A). In all ascomycetes previously studied mcrA deletion led to an increase in secondary metabolite production. Surprisingly, the only detectable phenotypic change in NSAR1 was a doubling in the yields of kojic acid, with no novel secondary metabolites produced. This supports the previous claim that secondary metabolite production has been repressed in A. oryzae and demonstrates that such repression is not McrA-mediated. Strain NSAR1 was then modified by employing CRISPR-Cas9 technology to disrupt the production of kojic acid, generating the novel strain NSARΔK, which combines the various beneficial traits of NSAR1 with a uniquely clean secondary metabolite background., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dao, de Mattos-Shipley, Prosser, Williams, Zacharova, Lazarus, Willis and Bailey.)

Recent learning strategies such as reinforcement learning (RL) have favored the transition from applied artificial intelligence to general artificial intelligence. One of the current challenges of RL in healthcare relates to the development of a controller to teach a musculoskeletal model to perform dynamic movements. Several solutions have been proposed. However, there is still a lack of investigations exploring the muscle control problem from a biomechanical point of view. Moreover, no studies using biological knowledge to develop plausible motor control models for pathophysiological conditions make use of reward reshaping. Consequently, the objective of the present work was to design and evaluate specific bioinspired reward function strategies for human locomotion learning within an RL framework. The deep deterministic policy gradient (DDPG) method for a single-agent RL problem was applied. A 3D musculoskeletal model (8 DoF and 22 muscles) of a healthy adult was used. A virtual interactive environment was developed and simulated using opensim-rl library. Three reward functions were defined for walking, forward, and side falls. The training process was performed with Google Cloud Compute Engine. The obtained outcomes were compared to the NIPS 2017 challenge outcomes, experimental observations, and literature data. Regarding learning to walk, simulated musculoskeletal models were able to walk from 18 to 20.5 m for the best solutions. A compensation strategy of muscle activations was revealed. Soleus, tibia anterior, and vastii muscles are main actors of the simple forward fall. A higher intensity of muscle activations was also noted after the fall. All kinematics and muscle patterns were consistent with experimental observations and literature data. Regarding the side fall, an intensive level of muscle activation on the expected fall side to unbalance the body was noted. The obtained outcomes suggest that computational and human resources as well as biomechanical knowledge are needed together to develop and evaluate an efficient and robust RL solution. As perspectives, current solutions will be extended to a larger parameter space in 3D. Furthermore, a stochastic reinforcement learning model will be investigated in the future in scope with the uncertainties of the musculoskeletal model and associated environment to provide a general artificial intelligence solution for human locomotion learning. Graphical abstract.