Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression. METHODS: We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. RESULTS: Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; P<0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; P=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M1: 0.86-0.88). CONCLUSIONS: We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.

Presumably Corneal Graft Rejection after COVID-19 Vaccination.

We report two cases of corneal graft rejection following immunization with COVID-19-inactivated vaccine Sinopharm and describe the practical implications of this phenomenon in the clinical setting. A 36-year-old woman with a history of unilateral repeated PKP received the first dose of inactivated Sinopharm vaccine. Seven days later, she presented with symptoms and signs of unilateral corneal graft rejection on slit-lamp examination. A 54-year-old woman with a history of unilateral PKP secondary to herpes simplex keratitis (HSK) developed signs of unilateral corneal graft rejection just a week after injection of the first dose of the similar vaccine. Rejection in both patients was treated successfully with topical steroids. To the best of our knowledge, this is the first report of corneal graft rejection following the COVID-19-inactivated Sinopharm vaccine which occurred through the short period after the injection. We hypothesized that the rejection is the result of an allogenic reaction and the immunogenic role of aluminum hydroxide as an adjuvant of this type of vaccine. However, as the second case had a history of rejection following the HSK, a reactivation could not be definitely ruled out. Ophthalmologists should consider these types of adverse reactions after COVID-19 immunization and also monitor a close follow-up of graft health postimmunization. Patients should be informed about the signs and symptoms of rejection, urgent referral, and treatment.

Modified peroxamide-based reactive oxygen species (ROS)-responsive doxorubicin prodrugs.

Reactive oxygen species (ROS) plays a pivotal physiological role in intracellular signaling of any living organism. Due to the elevated levels of ROS in tumor microenvironment than normal tissues, an increasing number of ROS-responsive probes and prodrugs is being studied for the fight against cancer. This study describes the design and synthesis of a panel of novel modified peroxamide-based ROS-responsive prodrugs of doxorubicin, among which the OH-mOX-Dox prodrug showed very stable and highly specific ROS sensitivity. This novel Dox prodrug exerted potent anti-proliferation effects against the two breast cancer cell lines of MDA-MB-468 and MDA-MB-231 while it showed minimal toxicity toward the normal breast cell line, MCF-12A. The cytotoxicity of the OH-mOX-Dox prodrug was significantly enhanced at elevated ROS levels after co-incubation with l-buthionine sulfoximine (BSO). Our clonogenic assay data validated that enhanced intracellular ROS level upon X-ray irradiation resulted in an increase in the efficacy of the OH-mOX-Dox prodrug against the two breast cancer cell lines.

LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint.

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the same organ. This is due to the significant intensity variations of different image modalities. In this paper, we propose a novel end-to-end deep neural network to achieve multi-modality image segmentation, where image labels of only one modality (source domain) are available for model training and the image labels for the other modality (target domain) are not available. In our method, a multi-resolution locally normalized gradient magnitude approach is firstly applied to images of both domains for minimizing the intensity discrepancy. Subsequently, a dual task encoder-decoder network including image segmentation and reconstruction is utilized to effectively adapt a segmentation network to the unlabeled target domain. Additionally, a shape constraint is imposed by leveraging adversarial learning. Finally, images from the target domain are segmented, as the network learns a consistent latent feature representation with shape awareness from both domains. We implement both 2D and 3D versions of our method, in which we evaluate CT and MRI images for kidney and cardiac tissue segmentation. For kidney, a public CT dataset (KiTS19, MICCAI 2019) and a local MRI dataset were utilized. The cardiac dataset was from the Multi-Modality Whole Heart Segmentation (MMWHS) challenge 2017. Experimental results reveal that our proposed method achieves significantly higher performance with a much lower model complexity in comparison with other state-of-the-art methods. More importantly, our method is also capable of producing superior segmentation results than other methods for images of an unseen target domain without model retraining. The code is available at GitHub (https://github.com/MinaJf/LMISA) to encourage method comparison and further research.

Comparative Evaluation of a Commercial Herbal Extract and 0.2% Chlorhexidine Mouthwash on Three Periodontal Facultative Anaerobes: An In Vitro Study.

Background: The application of herbal and/or chemical antimicrobial mouthwashes in addition to the mechanical methods of bacteria removal helps reduce the periopathogens and thus increase the periodontal tissues' health. The present study aimed to evaluate the antibacterial effect of Thymex (TMX) syrup on three periodontal facultative anaerobes in vitro and compare it with 0.2% chlorhexidine (CHX) mouthwash. Methods: In this in vitro experiment, the disc diffusion method was used to measure the inhibitory halo diameter (IhD) of Enterobacter cloacae, Actinomyces viscosus, and Eikenella corrodens. The paper discs containing TMX and CHX were placed on Mueller-Hinton agar media and cultured with the mentioned bacteria. Moreover, a blank disc containing distilled water was used as a control. From each of the three bacterial species, five samples were taken, and after 18 hours of storage in the incubator, the IhDs were measured in millimeters. A one-way ANOVA test and an independent sample t-test were used to compare the mean differences of IhDs between groups. The significance level was considered to be 0.05. Results: The IhDs ranged between 6.2-8.8 mm and 12.3-34 mm for TMX and CHX, respectively. CHX showed a more inhibitory effect on all three species of bacteria compared to TMX mouthwash (P < 0.001). Conclusions: Despite the inhibitory effect of TMX on bacterial growth, CHX showed significantly more antibacterial activity than TMX against three studied bacterial species.

Simvastatin attenuates spatial memory impairment via inhibiting microgliosis and apoptotic cell death against ethanol induced neurotoxicity in the developing rat hippocampus.

Ethanol is associated with oxidative stress. Exposure to ethanol during childhood may lead to neurological disorders. Congenital disorders induced by alcohol are mainly caused by an oxidative-inflammatory cascade due to extensive apoptotic neurodegeneration in the brain, particularly in the hippocampus. Simvastatin, which acts as an inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA), is widely used to manage cardiovascular diseases. Recently, the neuroprotective effects of simvastatin against nervous system disorders have been introduced. In this study, we examined the protective effects of simvastatin on ethanol-related neurotoxicity in the hippocampus of rat pups. Ethanol (5.27 g/kg) in a milk solution (27.8 mL/kg) was administered to male rat pups via intragastric intubation at 2-10 days after birth. Also, 10 and 20 mg/kg of simvastatin were injected to the animals. By using Morris water maze task, the hippocampus-dependent memory and spatial learning was evaluated 36 days after birth. An ELISA assay was performed to investigate the antioxidant and anti-inflammatory effects of simvastatin by measuring the levels of tumor necrosis factor-α (TNF-α), and antioxidant enzymes. To assess the expression levels of Iba1 immunohistochemical staining and caspase-3 immunofluorescence staining was performed. The current study demonstrated that administration of simvastatin significantly attenuates spatial memory impairment (P < 0.01) after ethanol neurotoxicity. Also simvastatin could considerably increase the total superoxide dismutaseand glutathione levels (P < 0.01). Moreover, it was associated with a greater reduction in malondialdehyde (P < 0.05) and TNF-α levels, compared to the ethanol group (P < 0.01). Furthermore, in the simvastatin group, the hippocampal level of caspase-3 and the level of Iba1-positive cells, reduced (P < 0.01). This study demonstrated that apoptotic signaling, mediated by the oxidative-inflammatory cascade, could be inhibited by simvastatin in rat pups with ethanol exposure in the postnatal period.

Fucoidan-Doxorubicin Nanoparticles Targeting P-Selectin for Effective Breast Cancer Therapy.

Fucoidan, a type of sulfated polysaccharide known for its anticoagulant, anti-tumor and anti-inflammatory effects, has been reported to have strong affinity towards P-selectin. P-selectin, which plays an important role in metastasis by enhancing the adhesion of cancer cells to endothelium and activated platelets in distant organs, is overexpressed on many cancer types. This study demonstrates the synthesis of a fucoidan-based drug delivery system for minimizing the side effects of doxorubicin (Dox) with the help of active targeting toward P-selectin. Fucoidan-doxorubicin nanoparticles (FU-Dox NPs), developed by direct conjugation of Dox to the fucoidan backbone, showed a well-controlled size distribution and sustained release. The active targeting capability of FU-Dox NPs toward P-selectin resulted in enhanced cellular uptake and cytotoxicity against the MDA-MB-231 cell line with high P-selectin expression compared to the MDA-MB-468 cell line with low P-selectin expression.

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management.

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model's architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system's prognostics and diagnostics without modifying the models' architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.

Relationship between heat stress exposure and some immunological parameters among foundry workers.

Occupational exposure to heat stress may lead to changes in blood cell count. The objective of this retrospective descriptive-analytical field study is to investigate the relationship between heat stress exposure and some immunological parameters among foundry workers. This study was carried out on 55 subjects of which, 35 were working in a foundry unit (exposed group) and 20 were working in a computer numerical control (CNC) machining unit (control group). The measurement method consisted of taking 10 cc of the subjects' blood between 10 am and 2 pm and then performing the automated blood cell counting and enzyme-linked immunosorbent assay (ELISA) on the sample. Environmental parameters such as noise levels, lighting, and the wet-bulb globe temperature (WBGT) index were measured at the subjects' workstations. All measurements were made in the first half of the month of July. The mean WBGT index was 22.5 ± 2.16 °C for the control group and 32.97 ± 3.22 °C for the exposed group. The laboratory test results show a significant decrease in white blood cell count and lymphocyte levels and a significant increase in neutrophil levels and neutrophil to lymphocyte ratio in exposed group compared with control ones (P < 0.05). IgM levels decrease under heat stress, but we do not observe significant differences between IgG and IgA levels between two groups. A significant negative correlation was observed between white blood cell count, lymphocyte levels, and IgM concentration with WBGT index and significant positive correlation was observed between neutrophil and neutrophil to lymphocyte ratio with WBGT index. No significant correlation was observed between sound pressure levels and light intensity with studied immunological parameters. In conclusion, under heat stress conditions, leukocytes levels and immunoglobulin concentration may reduce and it may weaken and suppress the human immune system.

Uncovering reaction sequences on surfaces through graphical methods.

ZStruct is a graph-based model that generates an ensemble of plausible reaction pathways starting from a given initial state, without requiring prior knowledge of reaction intermediates. In this article, a surface-reaction oriented implementation (S-ZStruct) is introduced for unimolecular and bimolecular reactions, including sampling of different binding sites and adsorption orientations. To test the unimolecular reaction generation ability and angle sampling feature of S-ZStruct, the propanoic acid to ethylene dissociation network is studied. Starting from multiple initial orientations of two key intermediates, multiple unique reaction pathways, each with different activation energies, were discovered. Atomic layer deposition of TiN on Si(100)-involving a challenging reaction mechanism-is also studied as an example of bimolecular reactions. In addition to locating a number of expected pathways, S-ZStruct found that the little-understood step of reduction of Ti(iv) to Ti(iii) likely occurs through ß-hydride transfer between diamido ligands on neighboring Ti centers. These results suggest that S-ZStruct is a powerful tool for exploring surface chemistry, which will permit discoveries of reaction mechanisms in a wide variety of environments.

Modulation of in vitro phagocytic uptake and immunogenicity potential of modified Herceptin®-conjugated PLGA-PEG nanoparticles for drug delivery.

There is an increasing interest in engineered nanoparticle (NP) conjugates for targeted and controlled drug delivery. However, the practical applications of these NP delivery vehicles remain constrained because of their reactivity with the body's immune system defenses resulting in undesirable off-target effects. In this study, poly(D,L lactide-co-glycolide) (PLGA)-b-polyethylene glycol (PEG) NPs conjugated to different quantities of the commercial antibody Herceptin® meant to target HER2-positive breast cancer cells were studied for their immune cell uptake and immunogenic properties (using murine macrophages and human dendritic cells). We further modified the Herceptin®-NP conjugates with short PEG linkers with an aim to increase their biocompatibility. The 50% Herceptin®-NP conjugate group with short PEG modification to Herceptin® showed the best reduction in immune cell uptake by 82% along with the reduction by >50% for proinflammatory cytokine response (TNF-α and IL-6). In conclusion, optimum Herceptin® coverage with improved hydrophilic profile results in reduced phagocytic uptake and immunogenicity of engineered NP-antibody conjugates, potentially minimizing their undesirable off-target effects as a drug delivery vehicle.

Delayed Sequential Co-Delivery of Gefitinib and Doxorubicin for Targeted Combination Chemotherapy.

There are an increasing number of studies showing the order of drug presentation plays a critical role in achieving optimal combination therapy. Here, a nanoparticle design is presented using ion pairing and drug-polymer conjugate for the sequential delivery of gefitinib (Gi) and doxorubicin (Dox) targeting epidermal growth factor receptor (EGFR) signaling applicable for the treatment of triple negative breast cancers. To realize this nanoparticle design, Gi complexed with dioleoyl phosphatidic acid (DOPA) via ion paring was loaded onto the nanoparticle made of Dox-conjugated poly(l-lactide)-block-polyethylene glycol (PLA-b-PEG) and with an encapsulation efficiency of ∼90%. The nanoparticle system exhibited a desired sequential release of Gi followed by Dox, as verified through release and cellular uptake studies. The nanoparticle system demonstrated approximate 4-fold and 3-fold increases in anticancer efficacy compared to a control group of Dox-PLA-PEG conjugate against MDA-MB-468 and A549 cell lines in terms of half maximal inhibitory concentration (IC50), respectively. High tumor accumulation of the nanoparticle system was also substantiated for potential in vivo applicability by noninvasive fluorescent imaging.

Sequential delivery of erlotinib and doxorubicin for enhanced triple negative Breast cancer treatment using polymeric nanoparticle.

Recent studies of signaling networks point out that an order of drugs to be administrated to the cancerous cells can be critical for optimal therapeutic outcomes of recalcitrant metastatic and drug-resistant cell types. In this study, a development of a polymeric nanoparticle system for sequential delivery is reported. The nanoparticle system can co-encapsulate and co-deliver a combination of therapeutic agents with different physicochemical properties [i.e. epidermal growth factor receptor (EGFR) inhibitor, erlotinib (Ei), and doxorubicin (Dox)]. Dox is hydrophilic and was complexed with anionic lipid, 1,2-dioleoyl-sn-glycero-3-phosphate (DOPA), via ion pairing to form a hydrophobic entity. Then it was co-encapsulated with hydrophobic Ei in a poly(L-lactide)-b-polyethylene glycol (PLA-b-PEG) nanoparticle by nanoprecipitation. The complexation of Dox with DOPA greatly helps the encapsulation of Dox, and substantially reduces the release rate of Dox. This nanoparticle system was found to burst the release of Ei with a slow and sustained profile of Dox, which is an optimal course of administration for these two drugs as previously reported. The efficacy of this sequential delivery nanoparticle system was validated in vitro and its in vivo potential applicability was substantiated by fluorescent imaging of high tumor accumulation.

A hybrid framework for reverse engineering of robust Gene Regulatory Networks.

The inference of Gene Regulatory Networks (GRNs) using gene expression data in order to detect the basic cellular processes is a key issue in biological systems. Inferring GRN correctly requires inferring predictor set accurately. In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed. The purpose of the combination of various methods is to increase the accuracy of inferred GRN. The proposed framework offers a linear weighted combination of Pearson Correlation Coefficient (PCC) and two different feature selection approaches, namely: Information Gain (IG) and ReliefF. In order to set the appropriate weights, Genetic Algorithm (GA) is used. Similarity measure is considered as fitness function to guide GA. At the end, based on the obtained weights, the best predictor set of GRN using three aforementioned inference methods is selected and the network topology is formed. Due to the huge volume of gene expression data, GRN inference algorithms should infer GRN at a reasonable runtime. Hence, a novel criterion is provided to evaluate GRNs based on runtime and accuracy. The simulation results using biological data indicate that the proposed framework is fast and more reliable compared to other recent methods [1-7].

Reliable and efficient reaction path and transition state finding for surface reactions with the growing string method.

The computational challenge of fast and reliable transition state and reaction path optimization requires new methodological strategies to maintain low cost, high accuracy, and systematic searching capabilities. The growing string method using internal coordinates has proven to be highly effective for the study of molecular, gas phase reactions, but difficulties in choosing a suitable coordinate system for periodic systems has prevented its use for surface chemistry. New developments are therefore needed, and presented herein, to handle surface reactions which include atoms with large coordination numbers that cannot be treated using standard internal coordinates. The double-ended and single-ended growing string methods are implemented using a hybrid coordinate system, then benchmarked for a test set of 43 elementary reactions occurring on surfaces. These results show that the growing string method is at least 45% faster than the widely used climbing image-nudged elastic band method, which also fails to converge in several of the test cases. Additionally, the surface growing string method has a unique single-ended search method which can move outward from an initial structure to find the intermediates, transition states, and reaction paths simultaneously. This powerful explorative feature of single ended-growing string method is demonstrated to uncover, for the first time, the mechanism for atomic layer deposition of TiN on Cu(111) surface. This reaction is found to proceed through multiple hydrogen-transfer and ligand-exchange events, while formation of H-bonds stabilizes intermediates of the reaction. Purging gaseous products out of the reaction environment is the driving force for these reactions. © 2017 Wiley Periodicals, Inc.

The effects of heat stress on a number of hematological parameters and levels of thyroid hormones in foundry workers.

The objective of this research was to determine the effects of heat stress on some hematological parameters and thyroid hormones among foundry workers. This study was performed on 25 heat-acclimated subjects while 10 office workers were selected as the control group. Wet bulb globe temperature (WBGT) was determined to estimate the heat stress. Blood sampling was conducted before and after the daily work shift. The mean value of the WBGT index was 35 °C. The levels of plasma osmolality (p = 0.04) and white blood cells (p = 0.03) in the case group (before exposure to heat) were significantly higher than those in control group. No significant differences were observed between the average levels of T3 (p = 0.79) and T4 (p = 0.17) hormones between two groups. A positive relationship was found between the variation of some hematological parameters and thyroid hormones with WBGT index and air temperature.

Effect of Ventilation on Occupational Exposure to Airborne Biological Contaminants in an Isolation Room.

BACKGROUND: Airborne pathogens play an important role in a hospital air quality. Respiratory infections are the most common occupational disease among the health care staff. The aim of this study was to determine the effect of ventilation system parameters and patient bed arrangements on concentration of airborne pathogens in indoor air of an isolation room. MATERIALS AND METHODS: A single-bed room was considered in which a patient diagnosed with tuberculosis had been admitted. Five different ventilation types, each at four different capacities were installed in the room while two different locations for the patient's bed were assessed. A direct-impact sampling method (blood agar plate) was used in order to determine the intensity of the bio-aerosols in indoor air of the isolation room. RESULTS: The results showed that when the air was supplied through a circular vent located on the northern wall and the vented air was exhausted via a linear vent located on the southern wall, the average concentration of the bio-aerosols in the air, (with 12 air changes per hour) was reduced to 25 colonies per cubic meter (cfu/m(3)) (in the range of 25-88 cfu/m(3) and a 95 percent confidence interval). In accordance with the analysis applied upon the two different locations of the bed, no significant difference was observed (P>0.05). CONCLUSION: Installation of ventilation systems as determined by the study is recommended for tuberculosis isolation rooms.