De Sanctis-Cacchione Syndrome with Subdural Effusion: A Rare Case from India with Review of Literature.

De Sanctis-Cacchione syndrome (DCS) formerly known as xerodermic idiocy is characterised by cutaneous photosensitivity, microcephaly, mental retardation, short stature, hypogonadism, spasticity, peripheral neuropathy and sensorineural deafness. Here in, we present the case of a four and half years old male child with features of severe acute malnutrition (SAM) with a typical bird like facies and sunken eyes who had history of photosensitive pruritic pigmentary skin lesions on sun exposed areas from a very early age of six months. Gross developmental delay, ataxia, microcephaly, short stature, hypogonadism and cachectic wasting were identified on examination and hypertransaminasemia and hypothyroidism were recorded from biochemical profile. Subsequent visual evoked response and brainstem evoked response audiometry revealed anterior visual pathway dysfunction and bilateral profound sensorineural hearing loss. Magnetic resonance imaging of brain yielded subdural effusion with mass effect in addition to cerebro-cerebral atrophy and demyelination. Skin biopsy further detected dysplastic changes and early signs of squamous cell carcinoma (SCC). Although few cases are reported sporadically throughout the world, to our best of knowledge till date only 11 such cases have been reported completely in Indian medical literature which makes our case report the 12th one with distinctive novel association of subdural effusion.

CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19.

Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensive hyperparameter adjustment and are unsuitable for small datasets. By leveraging the data in a comparatively small dataset, few-shot learning algorithms aim to reduce the requirement of large datasets. This inspired us to develop a few-shot learning model for early detection of COVID-19 to reduce the post-effect of this dangerous disease. The proposed architecture combines few-shot learning with an ensemble of pre-trained convolutional neural networks to extract feature vectors from CT scan images for similarity learning. The proposed Triplet Siamese Network as the few-shot learning model classified CT scan images into Normal, COVID-19, and Community-Acquired Pneumonia. The suggested model achieved an overall accuracy of 98.719%, a specificity of 99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT scans per category for training data.

Phytochemical characterization of Styrax benzoin resin extract, molecular docking, ADME, and antibacterial activity study.

Styrax benzoin fumes have a spiritual aspect from ancient times, magical essence like a pleasant perfume, and are employed in religious ceremonies in India. This study aims to identify the volatile compounds in S. benzoin extract, their binding affinity to the bacterial target proteins, and study the antibacterial activity of the potential extract. The compounds obtained from GC-MS analysis of S. benzoin extract were subjected to molecular docking studies against DHFR of Staphylococcus aureus, tRNA synthetase of Escherichia coli, DHPS of Mycobacterium tuberculosis. Molecular docking studies revealed that seventeen compounds out of 20 compounds exhibited higher binding affinity than co-ligand (-7.00 kcal/mol) against the Staphylococcus aureus enzyme DHFR. Consequently, the crude extracts were evaluated for antibacterial activity against S. aureus, and the acetone extract showed promising findings. S. benzoin fumes might replace synthetic room fresheners, and promising compounds could be exploited in the cosmetics industry.

Bioengineered and functionalized silk proteins accelerate wound healing in rat and human dermal fibroblasts.

Wound healing is an intrinsic process directed towards the restoration of damaged or lost tissue. The development of a dressing material having the ability to control the multiple aspects of the wound environment would be an ideal strategy to improve wound healing. Though natural silk proteins, fibroin, and sericin have demonstrated tissue regenerative properties, the efficacy of bioengineered silk proteins on wound healing is seldom assessed. Furthermore, silk proteins sans contaminants, having low molecular masses, and combining with other bioactive factors can hasten the wound healing process. Herein, recombinant silk proteins, fibroin and sericin, and their fusions with cecropin B were evaluated for their wound-healing effects using in vivo rat model. The recombinant silk proteins demonstrated accelerated wound closure in comparison to untreated wounds and treatment with Povidone. Among all groups, the treatment with recombinant sericin-cecropin B (RSC) showed significantly faster healing, greater than 90% wound closure by Day 12 followed by recombinant fibroin-cecropin B (RFC) (88.86%). Furthermore, histological analysis and estimation of hydroxyproline showed complete epithelialization, neovascularization, and collagenisation in groups treated with recombinant silk proteins. The wound healing activity was further verified by in vitro scratch assay using HADF cells, where the recombinant silk proteins induced cell proliferation and cell migration to the wound area. Additionally, wound healing-related gene expression showed recombinant silk proteins stimulated the upregulation of EGF and VEGF and regulated the expression of TGF-ß1 and TGF-ß3. Our results demonstrated the enhanced healing effects of the recombinant silk fusion proteins in facilitating complete tissue regeneration with scar-free healing. Therefore, the recombinant silks and their fusion proteins have great potential to be developed as smart bandages for wound healing.

MPVNN: Mutated Pathway Visible Neural Network architecture for interpretable prediction of cancer-specific survival risk.

MOTIVATION: Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with a lack of interpretability. More interpretable visible neural network architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. RESULTS: We propose a novel Mutated Pathway Visible Neural Network (MPVNN) architecture, designed using prior signaling pathway knowledge and random replacement of known pathway edges using gene mutation data simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction of MPVNN over other similar-sized NN and standard survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that is important in risk prediction for particular cancer types, is reliable. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/gourabghoshroy/MPVNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transgenic Silkworms Overexpressing Relish and Expressing Drosomycin Confer Enhanced Immunity to Multiple Pathogens.

The sericulture industry faces substantial economic losses due to severe pathogenic infections caused by fungi, viruses, and bacteria. The development of transgenic silkworms against specific pathogens has been shown to enhance disease resistance against a particular infection. A single gene or its products that can confer protection against multiple pathogens is required. In an attempt to develop silkworms with enhanced immunity against multiple pathogens, we generated transgenic silkworm lines with an overexpressed NF-kB transcription factor, Relish 1, under two different promoters. Separately, a potent anti-fungal gene, Drosomycin, was also expressed in transgenic silkworms. Both Relish 1 and Drosomycin transgenic silkworms had single copy genomic integration, and their mRNA expression levels were highly increased after infection with silkworm pathogens. The overexpression of the Relish 1 in transgenic silkworms resulted in the upregulation of several defense-related genes, Cecropin B, Attacin, and Lebocin, and showed enhanced resistance to Nosema bombycis (microsporidian fungus), Nucleopolyhedrovirus (BmNPV), and bacteria. The Drosomycin expressing transgenic silkworms showed elevated resistance to N. bombycis and bacteria. These findings demonstrate the role of Relish 1 in long-lasting protection against multiple pathogens in silkworms. Further, the successful introduction of a foreign gene, Drosomycin, also led to improved disease resistance in silkworms.

Bow-tie architecture of gene regulatory networks in species of varying complexity.

The gene regulatory network (GRN) architecture plays a key role in explaining the biological differences between species. We aim to understand species differences in terms of some universally present dynamical properties of their gene regulatory systems. A network architectural feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of nodes, the in and the out layers. Though a bow-tie architecture has been observed in many networks, its existence has not been extensively investigated in GRNs of species of widely varying biological complexity. We analyse publicly available GRNs of several well-studied species from prokaryotes to unicellular eukaryotes to multicellular organisms. In their GRNs, we find the existence of a bow-tie architecture with a distinct largest strongly connected core layer. We show that the bow-tie architecture is a characteristic feature of GRNs. We observe an increasing trend in the relative core size with species complexity. Using studied relationships of the core size with dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged differently with biological complexity, based on the observed differences of the GRN bow-tie architectures.

PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data.

MOTIVATION: Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles. RESULTS: To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets. AVAILABILITY AND IMPLEMENTATION: Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MORFAN Syndrome: A Rarity but a Reality!

Acanthosis nigricans (AN) describes clinically hyperpigmented skin, which most commonly affects the flexural areas such as axilla, groin and neck. It is usually a benign condition associated with obesity, insulin resistance, and hyperinsulinemia; endocrinopathy; or malignancy, in particular, gastrointestinal adenocarcinoma. It can also occur in association with various genetic syndromes involving various organ systems. Few such known syndromes are Berardinelli-Seip syndrome, Alström syndrome, Leprechaunism, and Bardet-Biedl syndrome. MORFAN syndrome, which associates mild mental retardation, pre- and post-natal overgrowth, remarkable facies and diffuse and widespread AN, is a rare entity.

Dose-related effect of fly ash on edaphic properties in laterite cropland soil.

Short-term laboratory and field studies showed dose-based effect of coal fly ash on chemical and microbial properties of laterite cropland soil. Sandy loam soil mixed with farmyard manure (10% w/w) and amended with fly ash at 5%, 10%, 20%, 40% w/w (50-400 t ha(-1)) was incubated in the laboratory and added to field plots. The pH, EC, PO(4), Ca and Na of soil increased with dose and time, but OC, NO(3) and K decreased. There was temporary inhibition of bacteria, fungi and actinomycetes populations at 5% and 10% doses, but 20% and 40% were harmful. Activities of major soil enzymes declined at higher doses. Amylase, cellulase and invertase recovered in 5% and 10%, these doses did not affect dehydrogenase but benefited phosphatase and arylsulphatase activities. The above assessments showed that up to 100 t ha(-1) fly ash is apparently safe to microbial characteristics of tropical red laterite soil.