Controlled Ring Opening Metathesis Polymerization of a New Monomer: On Switching the Solvent-Water-Soluble Homopolymers to Degradable Copolymers.

A new heterocyclic monomer is developed via simple Diels-Alder reaction which is reluctant to polymerize in dichloromethane (DCM) whereas undergoes facile polymerization in tetrahydrofuran with excellent control over molecular weight (Mn ) and dispersities (D) using Grubbs' third generation catalyst (G3). The deprotection of the tert-butoxycarbonyl group from the polymeric backbone yielded a water-soluble ring opening metathesis polymerization (ROMP) polymer easily. Moreover, in DCM this new monomer copolymerizes with 2,3-dihydrofuran under catalytic living ROMP conditions to give backbone degradable polymers. All the synthesized polymers are characterized by size exclusion chromatography (SEC) and nuclear magnetic resonance (NMR) spectroscopy. It is believed that this new route to water soluble ROMP homopolymers as well as the cost-effective and environmentally friendly route to degradable copolymers and block-copolymers could find applications in biomedicine in the near future.

Catalytic Living Ring-Opening Metathesis Polymerization Using Vinyl Ethers as Effective Chain-Transfer Agents.

A catalytic living ring-opening metathesis copolymerization (ROMP) method is described that relies on a degenerative, reversible and regioselective exchange of propagating Fischer-carbenes. All characteristics of a living polymerization such as narrow dispersity, excellent molar mass control and the ability to form block copolymers are achieved by this method. The method allows the use of up to 200 times less ruthenium complex than traditional living ROMP. We demonstrate the synthesis of ROMP-ROMP diblock copolymers, ATRP from a ROMP macro-initiator and living ROMP from a PEG-based macro chain transfer agent. The cost-effective, sustainable and environmentally friendly synthesis of degradable polymers and block copolymers enabled by this strategy will find various applications in biomedicine, materials science, and technology.

A cross-sectional study to determine the psychological distress among pulmonary tuberculosis patients during COVID-19 pandemic.

COVID-19 pandemic had adversely affected the services of the National Tuberculosis (TB) Elimination Programme, resulting in psychological distress among pulmonary tuberculosis patients (PTB). This cross-sectional, hospital-based study included 361 PTB patients. Three pre-defined questionnaires were used for the analysis, a questionnaire to evaluate anxiety related to COVID-19, a patient health questionnaire (PHQ-9) for depression, and a fear of COVID-19 scale (FCV-19S) questionnaire. Among 361 PTB patients, 13% (n=47) had COVID-19 infection. Out of the total patients, 69% (n=250) were DR-TB (drug resistance-tuberculosis) cases. Proportion of anxiety, fear and depression due to COVID-19 was found in 49% (n=177), 23% (n=83), 67% (n=247) respectively. Delay in the initiation of anti-tubercular treatment was found in 58% (n=210) of the cases, among which the majority, i.e., 69% (n=172, p=0.011), were DR-TB. This pandemic has led to a sudden step-down of PTB. Trend analysis of the psychological distress showed a peak following the COVID-19 pandemic. Most DR-TB patients had delayed initiation of the anti-tubercular treatment during the pandemic. The preponderance of the younger age group was seen in the pulmonary tuberculosis patients, and a majority of them had DR-TB. Depression was the predominant psychological distress among the study subjects during the pandemic.

ROS-associated immune response and metabolism: a mechanistic approach with implication of various diseases.

The immune system plays a pivotal role in maintaining the defense mechanism against external agents and also internal danger signals. Metabolic programming of immune cells is required for functioning of different subsets of immune cells under different physiological conditions. The field of immunometabolism has gained ground because of its immense importance in coordination and balance of immune responses. Metabolism is very much related with production of energy and certain by-products. Reactive oxygen species (ROS) are generated as one of the by-products of various metabolic pathways. The amount, localization of ROS and redox status determine transcription of genes, and also influences the metabolism of immune cells. This review discusses ROS, metabolism of immune cells at different cellular conditions and sheds some light on how ROS might regulate immunometabolism.

Multiview Regularized Discriminant Canonical Correlation Analysis: Sequential Extraction of Relevant Features From Multiblock Data.

One of the important issues associated with real-life high-dimensional data analysis is how to extract significant and relevant features from multiview data. The multiset canonical correlation analysis (MCCA) is a well-known statistical method for multiview data integration. It finds a linear subspace that maximizes the correlations among different views. However, the existing methods to find the multiset canonical variables are computationally very expensive, which restricts the application of the MCCA in real-life big data analysis. The covariance matrix of each high-dimensional view may also suffer from the singularity problem due to the limited number of samples. Moreover, the MCCA-based existing feature extraction algorithms are, in general, unsupervised in nature. In this regard, a new supervised feature extraction algorithm is proposed, which integrates multimodal multidimensional data sets by solving maximal correlation problem of the MCCA. A new block matrix representation is introduced to reduce the computational complexity for computing the canonical variables of the MCCA. The analytical formulation enables efficient computation of the multiset canonical variables under supervised ridge regression optimization technique. It deals with the "curse of dimensionality" problem associated with high-dimensional data and facilitates the sequential generation of relevant features with significantly lower computational cost. The effectiveness of the proposed multiblock data integration algorithm, along with a comparison with other existing methods, is demonstrated on several benchmark and real-life cancer data.

Predict2Protect: Machine Learning Web Application in Early Detection of Heart Disease.

Across the world, there are few universal scenarios, but the pain of losing a loved one to heart disease is an exception and a reality shared by millions every year. Heart disease is the greatest killer in society today, and one prevalent root of this issue is untimely diagnosis, often caused by unsustainable costs and lack of accessible healthcare for underserved populations. Recognizing these disparities, the goal of this project was to create an easily available application and interface for all that accurately indicates one's risk of heart disease. To address this, a machine learning model, Predict2Protect, was built in Python. An open-source dataset compiled of 1025 patients of diverse backgrounds was scaled, adjusted to include inquiries answerable by patients, and split into 75% for training, 15% for validation, and 25% for testing. Four models were tested with the hypothesis that if the RandomForestClassifier was used, it would have the highest validity. This was not supported, as the DecisionTree model had a 100% accuracy for training data and 95% for test data. Through the application software Streamlit, this program was processed into a web application that is now found in browser extensions. The application reports the risk of one having heart disease with a 95% accuracy and describes the risk percentage of developing heart disease within the next year. With a simple interface and high accuracy, Predict2Protect aims to provide a view into one's health with the goals of accessible heart disease prediction and early treatment for patients around the world.

Catalytic living ROMP: block copolymers from macro-chain transfer agents.

Vinyl ether based macro-chain transfer agents (m-CTAs) are used to produce different di or tri-block copolymers under catalytic living ROMP conditions. Polystyrene (PS) vinyl ether m-CTA and polycaprolactone (PCL) or polylactide vinyl ether (PLA) m-CTAs are synthesized straightforwardly via ATRP and ROP respectively. Regioselectivity as well as the high metathesis activity of these m-CTAs enabled us to synthesise a range of metathesis-based A-B diblock copolymers with controlled dispersities (D < 1.4). In this manner, PS-ROMP (here, ROMP refers to a poly(MNI-co-DHF) block), PCL-ROMP and PLA-ROMP were synthesized using substoichiometric amounts of ruthenium complex in a living fashion. Also, a more complex PEG-PCL-ROMP tri-block terpolymer was obtained catalytically. All block copolymers were characterized by SEC and DOSY NMR spectroscopy. We believe that this methodology of using macro-chain transfer agents to prepare degradable ROMP polymers under catalytic living ROMP conditions will find applications in biomedicine.

Catalytic Living ROMP: Synthesis of Degradable Star Polymers.

Star polymers have attracted considerable attention over the past few years due to their distinctive physical and chemical attributes that are different from conventional linear polymers. Here, we present a one-pot synthesis of narrowly dispersed and degradable homoarm and miktoarm star polymers exploiting the catalytic living ring-opening metathesis polymerization (ROMP) mechanism. Several complex polymeric architectures (such as A3-, A4-, A6-, A2B-, A3B-, and AB2-type star polymers) were synthesized quite straightforwardly by using appropriate vinyl ether chain transfer agents. SEC, 1H NMR, and DOSY NMR spectroscopy were employed to analyze and characterize all of the synthesized polymers. We believe that this sustainable and environmentally friendly synthesis of star polymers could become an important synthetic tool for polymer engineers working on supramolecular, industrial or biomedical applications.

Leukemia cutis - A case of cutaneous manifestation of acute monoblastic leukemia.

Leukemia cutis is a comprehensive terminology for dermal manifestations of any type of leukemia either with accompanied or antecedent blood or bone marrow involvement. Although both myeloid and lymphoid neoplastic leukocytes can infiltrate the skin, the frequency is higher among children with congenital myeloid leukemia. However, the underlying pathogenesis of dermal tropism is not yet established. Clinical manifestation varies regarding appearance, site, and numbers. Skin biopsy is essential for the early establishment of the diagnosis and to guide for further testing and categorical management. We report the case of acute myeloid leukemia-cutis in a 22-year-old female where cutaneous manifestation preceded the hematological diagnosis of systemic leukemia.

Glycobiology of cellular expiry: Decrypting the role of glycan-lectin regulatory complex and therapeutic strategies focusing on cancer.

Often the outer leaflets of living cells bear a coat of glycosylated proteins, which primarily regulates cellular processes. Glycosylation of such proteins occurs as part of their post-translational modification. Within the endoplasmic reticulum, glycosylation enables the attachment of specific oligosaccharide moieties such as, 'glycan' to the transmembrane receptor proteins which confers precise biological information for governing the cell fate. The nature and degree of glycosylation of cell surface receptors are regulated by a bunch of glycosyl transferases and glycosidases which fine-tune attachment or detachment of glycan moieties. In classical death receptors, upregulation of glycosylation by glycosyl transferases is capable of inducing cell death in T cells, tumor cells, etc. Thus, any deregulated alternation at surface glycosylation of these death receptors can result in life-threatening disorder like cancer. In addition, transmembrane glycoproteins and lectin receptors can transduce intracellular signals for cell death execution. Exogenous interaction of lectins with glycan containing death receptors signals for cell death initiation by modulating downstream signalings. Subsequently, endogenous glycan-lectin interplay aids in the customization and implementation of the cell death program. Lastly, the glycan-lectin recognition system dictates the removal of apoptotic cells by sending accurate signals to the extracellular milieu. Since glycosylation has proven to be a biomarker of cellular death and disease progression; glycans serve as specific therapeutic targets of cancers. In this context, we are reviewing the molecular mechanisms of the glycan-lectin regulatory network as an integral part of cell death machinery in cancer to target them for successful therapeutic and clinical approaches.

Computer-Aided Diagnosis of Melanoma Subtypes Using Reflectance Confocal Images.

Lentigo maligna (LM) is an early form of pre-invasive melanoma that predominantly affects sun-exposed areas such as the face. LM is highly treatable when identified early but has an ill-defined clinical border and a high rate of recurrence. Atypical intraepidermal melanocytic proliferation (AIMP), also known as atypical melanocytic hyperplasia (AMH), is a histological description that indicates melanocytic proliferation with uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may, in some cases, progress to LM. The early diagnosis and distinction of LM from AIMP are important since LM requires a definitive treatment. Reflectance confocal microscopy (RCM) is an imaging technique often used to investigate these lesions non-invasively, without biopsy. However, RCM equipment is often not readily available, nor is the associated expertise for RCM image interpretation easy to find. Here, we implemented a machine learning classifier using popular convolutional neural network (CNN) architectures and demonstrated that it could correctly classify lesions between LM and AIMP on biopsy-confirmed RCM image stacks. We identified local z-projection (LZP) as a recent fast approach for projecting a 3D image into 2D while preserving information and achieved high-accuracy machine classification with minimal computational requirements.

Pulsed-Addition ROMP: Catalytic Syntheses of Heterotelechelic Polymers via Regioselective Chain Transfer Agents.

Regioselective chain transfer agents are used to synthesize narrowly dispersed heterotelechelic polymers with a 15-fold decrease in catalyst consumption using the pulsed addition ROMP (PA-ROMP) technique. The commercially available Grubbs' third-generation catalyst (G3) is easily prefunctionalized with chain transfer agents in a short reaction time (30 min). After addition and consumption of a monomer, the excess chain transfer agent in the reaction medium end-functionalizes the polymer chain and regenerates the initiator very quickly (within 10 min) via a ring-opening-ring-closing sequence. This regenerated catalyst then initiates the polymerization of a subsequent batch of monomers, and the process is iterated for 15 times. Excellent control over molecular weight and dispersity from SEC analyses (over 15 pulses) confirmed the high efficacy of the chain transfer agents under this PA-ROMP method. The chain transfer agents are also extremely compatible with the synthesis of high molecular weight polymers (M/C = 150) with minimal catalyst decomposition. 1H NMR as well as MALDI-ToF mass spectrometry further confirmed the high degree of chain end functionalization of the synthesized polymers.

Sinonasal mass lesions: A clinicopathological study with p63 and p16 immunohistochemical expressions.

Introduction: The worldwide annual incidence of carcinomas of the sinonasal tract is 0.5 to 1.0 patients per 100,000 per year. P63 plays a role in epithelial development and is used as a marker for basal and myoepithelial cells. Expression of p16 occurs as a result of functional inactivation of the retinoblastoma protein (pRb) by the human papilloma virus (HPV) E7 protein. Aims: This study aims to study the histological spectrum of benign and malignant sinonasal mass lesions and to study the immunohistochemical expression of p63 in different type of sinonasal mass lesions. It also aims to ascertain the incidence of high-risk HPV in primary sinonasal mass lesions with p16 immunohistochemistry and delineate the histological spectrum of HPV-related sinonasal lesions. Materials and Methods: This cross-sectional study was conducted on 80 cases from June 2018 to June 2020 at a tertiary care hospital. Clinical history including demographic parameters were collected in the study proforma. The gross findings of the specimens noted and histopathological examination by H&E staining done. Immunohistochemistry staining for p63 and p16 expression was performed on all cases. Results: Most common age group affected was 41-60 years with male:female ratio of 1.67:1. Nonneoplastic lesions (38.7%) comprised majority of the cases followed by benign neoplastic lesions (31.3%) and malignant neoplastic lesions (30%). Among the malignant neoplastic lesions, p63 showed positive expression in 75% (p = 0.005) and p16 showed positive expression in 41.7% (p = 0.023). Among benign and nonneoplastic lesions, p63 showed positivity in 21.4% (p = 0.000) and p16 showed positivity in 44.6% (p = 0.040). Conclusion: We analyzed p63 and p16 expression in varied lineages like carcinomas, papillomas, and neuroectodermal differentiation arising from the sinonasal tract and also in relation to other clinicopathological parameters. This study revealed p63 expression was associated more with the squamous cell carcinomas and nasopharyngeal carcinomas. Sinonasal tract malignancies are also associated with HPV infections that are identifiable by p16 immunostaining and, thus, could provide new prospects in identifying any definite biological and clinical characteristics associated with HPV as well as advancement in the targeted therapies for this patient population.

Macrochain Transfer Agents for Catalytic Ring-Opening Metathesis Polymerization.

A monosubstituted 1,3-diene derivative attached to a polymer is demonstrated to act as a macrochain transfer agent in catalytic ring-opening metathesis polymerization. PEG- and PLA-based macrochain transfer agents were synthesized in a few steps and were characterized using NMR spectroscopy, size exclusion chromatography (SEC) and matrix-assisted laser desorption/ionization-time-of-flight (MALDI-ToF) mass spectrometry. Poly(l-lactide) based diblock copolymer, poly(ethylene glycol)-based diblock, and triblock (ABA type) copolymers of varied chain lengths were prepared catalytically in a one-pot approach via metathesis polymerization. Block copolymers were characterized by SEC and showed monomodal molecular weight distributions. Moreover, DOSY NMR spectroscopy further proved the block microstructures of the synthesized polymers.

Chain transfer agents for the catalytic ring opening metathesis polymerization of norbornenes.

Here, we present a detailed study of the metathesis activity of conjugated 1,3 diene derivatives in ring opening metathesis polymerization (ROMP) using Grubbs' 3rd generation catalyst (G3). A comprehensive screening of those derivatives revealed that monosubstituted 1,3 dienes show similar reactivities towards G3-alkylidenes as norbornene derivatives. Therefore, they represent perfect candidates for chain transfer agents in a kinetically controlled catalytic ROMP. This unprecedented reactivity allowed us to catalytically synthesize mono-end-functional poly(norborneneimide)s on the gram scale. Much more complex architectures such as star-shaped polymers could also be synthesized catalytically for the very first time via ROMP. This inexpensive and greener route to produce telechelic ROMP polymers was further utilized to synthesize ROMP block copolymers using bifunctional ROMP and ATRP/NCL initiators. Finally, the regioselective reaction of G3 with 1,3 diene derivatives was also exploited in the synthesis of a ROMP-PEG diblock copolymer initiated from a PEG macroinitiator.

One-Pot Heterotelechelic Metathesis Polymers via Regioselective Chain Transfer Agents.

Single chain transfer agents are used to synthesize narrowly distributed heterotelechelic ROMP polymers in one pot, exploiting a new mechanistic and synthetic approach. The chain transfer agents carrying different functional groups are synthesized in a few straightforward steps. Prefunctionalization of commercially available Grubbs' third-generation catalyst is realized in situ using regioselective chain transfer agents within a short reaction period. After monomer consumption, the excess chain transfer agent in the reaction medium automatically end-functionalizes the polymer chain, yielding a heterotelechelic polymer via a ring-opening-ring-closing sequence. 1H NMR, MALDI-ToF, and SEC analyses confirmed end-group functionalization as well as excellent control over molecular weight and dispersity. This strategy highlights a new way of synthesizing one-pot heterotelechelic ROMP polymers straightforwardly and efficiently.

The Wnt signaling pathway: a potential therapeutic target against cancer.

The role of the evolutionarily conserved Wnt signaling pathway is well documented in several cellular processes, such as cell proliferation, differentiation, cell motility, and maintenance of the stem cell niche. The very first indication that aberrant Wnt signaling can cause carcinogenesis came from a finding that the mutation of the adenomatous polyposis coli gene (APC) predisposes a person to colorectal carcinoma. Later, with progressing research it became clear that abnormal activation or mutation of the genes related to this pathway could drive tumorigenesis. Here, we review recent advances in research regarding Wnt signaling regulation and its role in several cancer subtypes. Additionally, the utility of Wnt pathway-targeted cancer therapy intervention is also highlighted, with an overview of current approaches to target the Wnt pathway in oncogenesis and the future scopes and challenges associated with them.

FaRoC: Fast and Robust Supervised Canonical Correlation Analysis for Multimodal Omics Data.

One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features. To generate canonical variables sequentially, an analytical formulation is introduced to establish the relation between regularization parameters and CCA. The formulation enables the proposed method to extract required number of correlated features sequentially with lesser computational cost as compared to existing methods. To compute both significance and relevance measures of a feature, the concept of hypercuboid equivalence partition matrix of rough hypercuboid approach is used. It also provides an efficient way to find optimum regularization parameters employed in CCA. The efficacy of the proposed FaRoC algorithm, along with a comparison with other existing methods, is extensively established on several real life data sets.

Multimodal Omics Data Integration Using Max Relevance--Max Significance Criterion.

OBJECTIVE: This paper presents a novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets. METHODS: The proposed method extracts a new set of features from two multidimensional datasets by maximizing the relevance of extracted features with respect to sample categories and significance among them. It integrates judiciously the merits of regularized canonical correlation analysis (RCCA) and rough hypercuboid approach. An analytical formulation, based on spectral decomposition, is introduced to establish the relation between canonical correlation analysis (CCA) and RCCA. The concept of hypercuboid equivalence partition matrix of rough hypercuboid is used to compute both relevance and significance of a feature. SIGNIFICANCE: The analytical formulation makes the computational complexity of the proposed algorithm significantly lower than existing methods. The equivalence partition matrix offers an efficient way to find optimum regularization parameters employed in CCA. RESULTS: The superiority of the proposed algorithm over other existing methods, in terms of computational complexity and classification accuracy, is established extensively on real life data.