Orbital malignant peripheral nerve sheath tumor: A case report and review.

Malignant peripheral nerve sheath tumor of the orbit is an exceedingly rare entity. These tumors exhibit locally aggressive behavior, recurrences, distant metastasis, and poor response to existing treatment protocols. Orbital nerve sheath tumors are often associated with neurofibromatosis 1, and malignant transformation of neurofibroma into malignant nerve sheath tumor has also been seen. The recommended treatment for localized disease is radical or wide surgical excision to achieve negative margins followed by chemoradiation. For extensive disease, chemotherapy and radiotherapy can be utilized to stabilize the disease. Due to poor response and outcomes with current regimens, the focus has been shifted to approaches utilizing molecular targets and immunological agents. Despite all the advancements, the outcomes still remain discouraging for moderate- to high-grade lesions and thus necessitate studies to design promising treatment modalities.

Plant dehydroascorbate reductase moonlights as membrane integrated ion channel.

Plant dehydroascorbate reductases (DHARs) are only known as soluble antioxidant enzymes of the ascorbate-glutathione pathway. They recycle ascorbate from dehydroascorbate, thereby protecting plants from oxidative stress and the resulting cellular damage. DHARs share structural GST fold with human chloride intracellular channels (HsCLICs) which are dimorphic proteins that exists in soluble enzymatic and membrane integrated ion channel forms. While the soluble form of DHAR has been extensively studied, the existence of a membrane integrated form remains unknown. We demonstrate for the first time using biochemistry, immunofluorescence confocal microscopy, and bilayer electrophysiology that Pennisetum glaucum DHAR (PgDHAR) is dimorphic and is localized to the plant plasma membrane. In addition, membrane translocation increases under induced oxidative stress. Similarly, HsCLIC1 translocates more into peripheral blood mononuclear cells (PBMCs) plasma membrane under induced oxidative stress conditions. Moreover, purified soluble PgDHAR spontaneously inserts and conducts ions in reconstituted lipid bilayers, and the addition of detergent facilitates insertion. In addition to the well-known soluble enzymatic form, our data provides conclusive evidence that plant DHAR also exists in a novel membrane-integrated form. Thus, the structure of DHAR ion channel form will help gain deeper insights into its function across various life forms.

Recurrent solitary fibrous tumor of eyelid: A rare entity.

Orbital and adnexal solitary fibrous tumors (SFT) are rare entities. The clinico-radiological and histologic features overlap with those of other spindle cell variants, and hence the use of immunohistochemical stains helps in making an accurate diagnosis. Furthermore, a thorough surgical resection is imperative to prevent tumor recurrences. We report a rare case of SFT arising primarily from the eyelid with multiple recurrences.

AtSWEET11 and AtSWEET12 transporters function in tandem to modulate sugar flux in plants.

The sugar will eventually be exported transporter (SWEET) members in Arabidopsis, AtSWEET11 and AtSWEET12 are the important sucrose efflux transporters that act synergistically to perform distinct physiological roles. These two transporters are involved in apoplasmic phloem loading, seed filling, and sugar level alteration at the site of pathogen infection. Here, we performed the structural analysis of the sucrose binding pocket of AtSWEET11 and AtSWEET12 using molecular docking followed by rigorous molecular dynamics (MD) simulations. We observed that the sucrose molecule binds inside the central cavity and in the middle of the transmembrane (TM) region of AtSWEET11 and AtSWEET12, that allows the alternate access to the sucrose molecule from either side of the membrane during transport. Both AtSWEET11 and AtSWEET12, shares the similar amino acid residues that interact with sucrose molecule. Further, to achieve more insights on the role of these two transporters in other plant species, we did the phylogenetic and the in-silico analyses of AtSWEET11 and AtSWEET12 orthologs from 39 economically important plants. We reported the extensive information on the gene structure, protein domain and cis-acting regulatory elements of AtSWEET11 and AtSWEET12 orthologs from different plants. The cis-elements analysis indicates the involvement of AtSWEET11 and AtSWEET12 orthologs in plant development and also during abiotic and biotic stresses. Both in silico and in planta expression analysis indicated AtSWEET11 and AtSWEET12 are well-expressed in the Arabidopsis leaf tissues. However, the orthologs of AtSWEET11 and AtSWEET12 showed the differential expression pattern with high or no transcript expression in the leaf tissues of different plants. Overall, these results offer the new insights into the functions and regulation of AtSWEET11 and AtSWEET12 orthologs from different plant species. This might be helpful in conducting the future studies to understand the role of these two crucial transporters in Arabidopsis and other crop plants.

A Case of Neuroretinitis following Inactivated Virion COVID-19 Vaccination.

PURPOSE: We report a rare presentation of neuroretinitis following vaccination with inactivated virion vaccine (COVAXIN). METHODS: Interventional Case Report. OBSERVATION: A 14-year-old female presented with sudden unilateral vision loss 3 days following COVID-19 vaccination. The clinical and radiological evaluation was consistent with classical neuroretinitis; the serological and immunological workup was negative. The patient responded well to the pulse steroid therapy and regained complete vision. CONCLUSION: The COVID-19 vaccine related adverse ocular events are beginning to emerge slowly and thus warrants close monitoring of all such cases. Also, ophthalmologists should be encouraged to seek vaccination status of patients presenting with inflammatory ocular conditions.

A unified graph model based on molecular data binning for disease subtyping.

Molecular disease subtype discovery from omics data is an important research problem in precision medicine. The biggest challenges are the skewed distribution and data variability in the measurements of omics data. These challenges complicate the efficient identification of molecular disease subtypes defined by clinical differences, such as survival. Existing approaches adopt kernels to construct patient similarity graphs from each view through pairwise matching. However, the distance functions used in kernels are unable to utilize the potentially critical information of extreme values and data variability which leads to the lack of robustness. In this paper, a novel robust distance metric (ROMDEX) is proposed to construct similarity graphs for molecular disease subtypes from omics data, which is able to address the data variability and extreme values challenges. The proposed approach is validated on multiple TCGA cancer datasets, and the results are compared with multiple baseline disease subtyping methods. The evaluation of results is based on Kaplan-Meier survival time analysis, which is validated using statistical tests e.g, Cox-proportional hazard (Cox p-value). We reject the null hypothesis that the cohorts have the same hazard, for the P-values less than 0.05. The proposed approach achieved best P-values of 0.00181, 0.00171, and 0.00758 for Gene Expression, DNA Methylation, and MicroRNA data respectively, which shows significant difference in survival between the cohorts. In the results, the proposed approach outperformed the existing state-of-the-art (MRGC, PINS, SNF, Consensus Clustering and Icluster+) disease subtyping approaches on various individual disease views of multiple TCGA datasets.

Zinc2+ ion inhibits SARS-CoV-2 main protease and viral replication in vitro.

Zinc deficiency is linked to poor prognosis in COVID-19 patients while clinical trials with zinc demonstrate better clinical outcomes. The molecular targets and mechanistic details of the anti-coronaviral activity of zinc remain obscure. We show that zinc not only inhibits the SARS-CoV-2 main protease (Mpro) with nanomolar affinity, but also viral replication. We present the first crystal structure of the Mpro-Zn2+ complex at 1.9 Å and provide the structural basis of viral replication inhibition. We show that Zn2+ coordinates with the catalytic dyad at the enzyme active site along with two previously unknown water molecules in a tetrahedral geometry to form a stable inhibited Mpro-Zn2+ complex. Further, the natural ionophore quercetin increases the anti-viral potency of Zn2+. As the catalytic dyad is highly conserved across SARS-CoV, MERS-CoV and all variants of SARS-CoV-2, Zn2+ mediated inhibition of Mpro may have wider implications.

Zinc2+ ion inhibits SARS-CoV-2 main protease and viral replication in vitro.

Zinc deficiency is linked to poor prognosis in COVID-19 patients while clinical trials with Zinc demonstrate better clinical outcome. The molecular target and mechanistic details of anti-coronaviral activity of Zinc remain obscure. We show that ionic Zinc not only inhibits SARS-CoV-2 main protease (Mpro) with nanomolar affinity, but also viral replication. We present the first crystal structure of Mpro-Zn2+ complex at 1.9 [A] and provide the structural basis of viral replication inhibition. We show that Zn2+ coordinates with the catalytic dyad at the enzyme active site along with two previously unknown water molecules in a tetrahedral geometry to form a stable inhibited Mpro-Zn2+ complex. Further, natural ionophore quercetin increases the anti-viral potency of Zn2+. As the catalytic dyad is highly conserved across SARS-CoV, MERS-CoV and all variants of SARS-CoV-2, Zn2+ mediated inhibition of Mpro may have wider implications.

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors. OBJECTIVE: In this paper, an approach of deep bimodal learning is introduced to leverage the performance of DR risk progression identification. METHODS: In particular, we further involve valuable clinical information of fundus photography in addition to the aforementioned systemic attributes. Accordingly, a Trilogy of Skip-connection Deep Networks, namely Tri-SDN, is proposed to exhaustively exploit underlying relationships between the baseline and follow-up information of the fundus images and EMR-based attributes. Besides that, we adopt Skip-Connection Blocks as basis components of the Tri-SDN for making the end-to-end flow of signals more efficient during feedforward and backpropagation processes. RESULTS: Through a 10-fold cross validation strategy on a private dataset of 96 diabetic mellitus patients, the proposed method attains superior performance over the conventional EMR-modality learning approach in terms of Accuracy (90.6%), Sensitivity (96.5%), Precision (88.7%), Specificity (82.1%), and Area Under Receiver Operating Characteristics (88.8%). CONCLUSIONS: The experimental results show that the proposed Tri-SDN can combine features of different modalities (i.e., fundus images and EMR-based numerical risk factors) smoothly and effectively during training and testing processes, respectively. As a consequence, with impressive performance of DR risk progression recognition, the proposed approach is able to help the ophthalmologists properly decide follow-up schedule and subsequent treatment plans.

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation.

The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user's perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax.

OBJECTIVE: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. METHODS AND MATERIALS: A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. RESULTS: We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. CONCLUSION: Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

[Formula: see text]: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain.

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search ([Formula: see text]) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, [Formula: see text] ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables [Formula: see text] to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of [Formula: see text] is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations.

Aggregation-Induced Emission (AIE) Fluorophore Exhibits a Highly Ratiometric Fluorescent Response to Zn2+ in vitro and in Human Liver Cancer Cells.

Two novel organic fluorophores, containing bis-naphthylamide and quinoline motifs, have been designed and synthesized. One of the fluorophores contains an isobutylene unit and exhibits a significant aggregation-induced emission (AIE) and a remarkable highly selective ratiometric fluorescence response towards Zn2+ in solution as well as in human liver cancer cells. The AIE behavior of this fluorophore was fully verified by fluorescence and UV/Vis spectroscopy, quantum yield calculations, and single-crystal X-ray diffraction, which revealed an intricate crystal packing system. Conversely, a fluorophore that lacks the isobutylene moiety did not exhibit any significant fluorescent properties as a result of its more flexible molecular structure that presumably allows free intramolecular rotational processes to occur.

Tear film and ocular surface dysfunction in diabetes mellitus in an Indian population.

PURPOSE: Ophthalmic complications in diabetes such as retinopathy, cataract, and infections have been extensively studied. Recently, attention has been drawn toward ocular surface changes in diabetes mellitus (DM). This study has been carried out to investigate the tear film and ocular surface abnormalities in type II DM patients. MATERIALS AND METHODS: A total of 83 participants (130 eyes) were enrolled: 53 diabetics (80 eyes) and 30 healthy controls (50 eyes). Of the 53 diabetics, 24 patients (42 eyes) had some diabetic retinopathy. The tear film and ocular surface were evaluated using Schirmer test, tear film break-up time (TBUT), keratoepitheliopathy score (KES), Rose Bengal Staining (RBS) test, and conjunctival impression cytology. RESULTS: When compared with the healthy controls, diabetics showed significantly reduced Schirmer, TBUT measurements and the higher grades of KES and RBS test (P < 0.001). Impression cytology analysis showed goblet cell loss and conjunctival squamous metaplasia in diabetics. CONCLUSION: Tear film abnormality is a significant feature of diabetic ocular surface diseases. These abnormalities are likely on account of poor quality and function of tears, combined with the subnormal ocular surface. Therefore, all diabetic patients especially those with evidence of retinopathy changes should undergo routine early examination and follow-up of tear function and ocular surface parameters.

Supramolecular gel from self-assembly of a C3-symmetrical discotic molecular bearing pillar[5]arene.

A novel C3-symmetric benzene-1,3,5-tricarboxamide (BTAs) decorated with three identical pillar[5]arene tails was designed, synthesized and characterized. The compound can gelate acetonitrile at low concentration (0.2 wt%) upon sonication at room temperature, but a precipitate was obtained by a conventional heating-cooling process. Scanning electron microscopy revealed that the gel and precipitate were constructed by entangled, high-aspect-ratio flexible bundles of nanofibrils. UV-vis spectroscopy, circular dichroism, Fourier transform infrared microscopy and powder X-ray diffraction showed that the compound formed chiral, elongated, columnar aggregates with nanofiber morphology by a combination of intermolecular hydrogen bonding between the N-H and C[double bond, length as m-dash]O of amides, π-π stacking (H-aggregates) and hydrophobic interactions of peripheral groups.

Smart Extraction and Analysis System for Clinical Research.

BACKGROUND: With the increasing use of electronic health records (EHRs), there is a growing need to expand the utilization of EHR data to support clinical research. The key challenge in achieving this goal is the unavailability of smart systems and methods to overcome the issue of data preparation, structuring, and sharing for smooth clinical research. MATERIALS AND METHODS: We developed a robust analysis system called the smart extraction and analysis system (SEAS) that consists of two subsystems: (1) the information extraction system (IES), for extracting information from clinical documents, and (2) the survival analysis system (SAS), for a descriptive and predictive analysis to compile the survival statistics and predict the future chance of survivability. The IES subsystem is based on a novel permutation-based pattern recognition method that extracts information from unstructured clinical documents. Similarly, the SAS subsystem is based on a classification and regression tree (CART)-based prediction model for survival analysis. RESULTS: SEAS is evaluated and validated on a real-world case study of head and neck cancer. The overall information extraction accuracy of the system for semistructured text is recorded at 99%, while that for unstructured text is 97%. Furthermore, the automated, unstructured information extraction has reduced the average time spent on manual data entry by 75%, without compromising the accuracy of the system. Moreover, around 88% of patients are found in a terminal or dead state for the highest clinical stage of disease (level IV). Similarly, there is an ∼36% probability of a patient being alive if at least one of the lifestyle risk factors was positive. CONCLUSION: We presented our work on the development of SEAS to replace costly and time-consuming manual methods with smart automatic extraction of information and survival prediction methods. SEAS has reduced the time and energy of human resources spent unnecessarily on manual tasks.

Ontology-Based High-Level Context Inference for Human Behavior Identification.

Recent years have witnessed a huge progress in the automatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users.

On Curating Multimodal Sensory Data for Health and Wellness Platforms.

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user's lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user's sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user's lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.