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Proteasomes generate antigenic peptides that are presented on the tumor surface to cytotoxic T-lymphocytes (CTLs). Immunoproteasomes are highly-specialized proteasome variants that are expressed at higher levels in antigen-presenting cells and contain replacements of the three constitutive proteasome catalytic subunits to generate peptides with a hydrophobic C-terminus that fit within the groove of MHC class I (MHC-I) molecules. A hallmark of cancer is the ability to evade immunosurveillance by disrupting the antigen presentation machinery and downregulating MHC-I antigen presentation. High-throughput screening was performed to identify Compound A, a novel molecule that selectively increased immunoproteasome activity and expanded the number and diversity of MHC-I-bound peptides presented on multiple myeloma (MM) cells. Compound A increased the presentation of individual MHC-I-bound peptides >100-fold and unmasked tumor-specific neoantigens on myeloma cells. Global proteomic integral stability assays determined that Compound A binds the proteasome structural subunit PSMA1 and promotes association of the proteasome activator PA28α/β (PSME1/PSME2) with immunoproteasomes. CRISPR/Cas9 silencing of PSMA1, PSME1, or PSME2 as well as treatment with immunoproteasome-specific suicide inhibitors abolished the effects of Compound A on antigen presentation. Treatment of MM cell lines and patient bone marrow-derived CD138+ cells with Compound A increased the antimyeloma activity of allogenic and autologous T-cells. Compound A was well-tolerated in vivo and co-treatment with allogeneic T-cells reduced the growth of myeloma xenotransplants in NSG mice. Taken together, our results demonstrate the paradigm-shifting impact of immunoproteasome activators to diversify the antigenic landscape, expand the immunopeptidome, potentiate T-cell-directed therapy, and reveal actionable neoantigens for personalized T-cell immunotherapy.

Functional blockade of the transforming growth factor-beta (TGFβ) signalling pathway improves the efficacy of cytotoxic and immunotherapies. Here, we conducted a phase 1b study (ClinicalTrials.gov., NCT03143985) to determine the primary endpoints of safety, tolerability, and maximal tolerated dose (200 mg twice daily) for the orally-available TGFβ type I receptor kinase inhibitor vactosertib in combination with pomalidomide in relapsed and/or refractory multiple myeloma (RRMM) patients who had received ≥2 lines of chemoimmunotherapy. Secondary endpoints demonstrated sustained clinical responses, favorable pharmacokinetic parameters and a 6-month progression-free survival of 82%. Vactosertib combined with pomalidomide was well-tolerated at all dose levels and displayed a manageable adverse event profile. Exploratory analysis indicated that vactosertib co-treatment with pomalidomide also reduced TGFβ levels in patient bone marrow as well as the level of CD8 + T-cells that expressed the immunoinhibitory marker PD-1. In vitro experiments indicated that vactosertib+pomalidomide co-treatment decreased the viability of MM cell lines and patient tumor cells, and increased CD8 + T-cell cytotoxic activity. Vactosertib is a safe therapeutic that demonstrates tumor-intrinsic activity and can overcome immunosuppressive challenges within the tumor microenvironment to reinvigorate T-cell fitness. Vactosertib offers promise to improve immunotherapeutic responses in heavily-pretreated MM patients refractory to conventional agents., (© 2024. The Author(s).)

Kindlin-2 is a cytoskeletal adapter protein that is present in many different cell types. By virtue of its interaction with multiple binding partners, Kindlin-2 intercalates into numerous signaling pathways and cytoskeletal nodes. A specific interaction of Kindlin-2 that is of paramount importance in many cellular responses is its direct binding to the cytoplasmic tails of integrins, an interaction that controls many of the adhesive, migratory and signaling responses mediated by members of the integrin family of cell-surface heterodimers. Kindlin-2 is highly expressed in many cancers and is particularly prominent in prostate cancer cells. CRISPR/cas9 was used as a primary approach to knockout expression of Kindlin-2 in both androgen-independent and dependent prostate cancer cell lines, and the effects of Kindlin-2 suppression on oncogenic properties of these prostate cancer cell lines was examined. Adhesion to extracellular matrix proteins was markedly blunted, consistent with the control of integrin function by Kindlin-2. Migration across matrices was also affected. Anchorage independent growth was markedly suppressed. These observations indicate that Kindlin-2 regulates hallmark features of prostate cancer cells. In androgen expressing cells, testosterone-stimulated adhesion was Kindlin-2-dependent. Furthermore, tumor growth of a prostate cancer cell line lacking Kindlin-2 and implanted into the prostate gland of immunocompromised mice was markedly blunted and was associated with suppression of angiogenesis in the developing tumor. These results establish a key role of Kindlin-2 in prostate cancer progression and suggest that Kindlin-2 represents an interesting therapeutic target for treatment of prostate cancer., (© 2024. The Author(s).)

Carbon Quantum dot (CQDs) is one of the newest materials in carbon-based nanomaterials. It is pertinent to study the synthesis and the application of these carbon dots. Here we have studied the effect of precursor on the optical, morphological, and photocatalytic properties of CQDs. We have synthesized CQDs using pyrolysis method using the precursors citric acid, urea, polyethyleneimine. We have synthesized two samples: CQD-S1; synthesized using urea and polyethyleneimine, and CQD-S2; synthesized using citric acid and polyethyleneimine. In optical properties study two distinct peaks have been obtained at 243 nm and 345 nm for CQD-S1, and at 265 nm and 335 nm for CQD-S2. In fluorescence study, the maximum emission was found at excitation wavelength of 340 nm for CQD-S1 and at excitation wavelength of 350 nm for CQD-S2. In morphological studies, Transmission Electron Microscope (TEM) revealed particle size of sample CQD-S1 and CQD-S2 were 1.91 nm and 1.61 nm, respectively. EDX confirmed the elemental composition in both samples. The rhodamine B (RhB) dye degradation percentages in dark and under visible and UV light were found to 6, 13, and 98.4% respectively for CQD-S1. Similarly, dye degradation for CQD-S2 were 7, 11, and 99.63%, respectively. Effective degradation of photocatalysis performed under UV-light within 100 min using mineralization process., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Proteasomes degrade intracellular proteins to generate antigenic peptides that are recognized by the adaptive immune system and promote anticancer immunity. However, tumors subvert the antigen presentation machinery to escape immunosurveillance. We hypothesized that proteasome activation could concomitantly increase antigen abundance and diversity in multiple myeloma cells. High-throughput screens revealed that histone deacetylase 6 (HDAC6) inhibitors activated proteasomes to unmask neoantigens and amplify the tumor-specific antigenic landscape. Treatment of patient CD138+ cells with HDAC6 inhibitors significantly promoted the antimyeloma activity of autologous CD8+ T cells. Pharmacologic blockade and genetic ablation of the HDAC6 ubiquitin-binding domain released HR23B, which shuttles ubiquitinylated cargo to proteasomes, while silencing HDAC6 or HR23B in multiple myeloma cells abolished the effect of HDAC6 inhibitors on proteasomes, antigen presentation, and T-cell cytotoxicity. Taken together, our results demonstrate the paradigm-shifting translational impact of proteasome activators to expand the myeloma immunopeptidome and have revealed novel, actionable antigenic targets for T cell-directed immunotherapy., Significance: The elimination of therapy-resistant tumor cells remains a major challenge in the treatment of multiple myeloma. Our study identifies and functionally validates agents that amplify MHC class I-presented antigens and pave the way for the development of proteasome activators as immune adjuvants to enhance immunotherapeutic responses in patients with multiple myeloma., (© 2024 The Authors; Published by the American Association for Cancer Research.)

Water pollutants of non-biodegradable toxic aromatic dye including Methylene blue (MB) and Rhodamine (RhB) are extremely carcinogenic thiazines used in various industries such as leather industry, paper industry, and the dyeing industry. The presence of dyes in wastewater causes severe threats to human health that are responsible for various harmful chronic or acute diseases and also shows an adverse impact on the environment as it reduces transparency and is harmful to water microorganisms. To overcome severe issues, many traditional techniques have been used to remove toxic pollutants, but these methods are insufficient to remove chemically stable dyes that remain in the treated wastewater. However, the photocatalytic degradation process is an efficient approach to degrade the dye up to the maximum extent with improved efficiency. Therefore, in this work, a new class of two-dimensional (2D) transition metal carbide of Titanium Carbide (Ti 3 C 2 Tx) MXene material was used for the organic dyes degradation such as MB and RhB using a photocatalytic process. A layered structure of hexagonal lattice symmetry of Ti 3 C 2 Tx MXene was successfully synthesized from the Titanium Aluminum Carbide of Ti 3 AlC 2 bulk phase using an exfoliation process. Further, the XRD spectrum confirms the transformation of bulk MAX phase having (002) plane at 9.2° to Ti 3 C 2 Tx MXene of (002) plane at 8.88° confirms the successful removal of Al layer from MAX phase. A smooth, transparent, thin sheet-like morphology of Ti 3 C 2 Tx nanosheet size were found to be in the range of 70 to 150 nm evaluated from TEM images. Also, no holes or damages in the thin sheets were found after the treatment with strong hydrofluoric acid confirms the formation Ti 3 C 2 Tx layered sheets. The synthesized Ti 3 C 2 Tx MXene possesses excellent photocatalytic activity for the degradation of dyes MB, RhB, and mixtures of MB and RhB dyes. MB dye degraded with a degradation percentage efficiency of 99.32% in 30 min, while RhB dye was degraded upto 98.9% in 30 min. Also, experiments were conducted for degradation of mixture of MB and RhB dyes by UV light, and the degradation percentage efficiency were found to be 98.9% and 99.75% for mixture of MB and RhB dye in 45 min, respectively. Moreover, reaction rate constant (k) was determined for each dye of MB, RhB, and mixtures of MB and RhB and was found to be 0.0215 min -1 and 0.0058 min -1 , and for mixtures, it was 0.0020 min -1 and 0.009 min -1 , respectively., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study presents the synthesis and characterization of composite material comprised of NiFe 2 O 4 and CuO. The preparation of this composites involves a facile and cost-effective co-precipitation method, followed by heat treatment. The aim of this study is to explore the potential of this composite material for various catalytic applications. The synthesized NiFe 2 O 4 /CuO composites were extensively characterized using various analytical techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), impedance analyzer, UV-Visible spectroscopy (UV-Vis.), Brunner-Emmett-Teller (BET), and X-ray photoelectron spectroscopy (XPS). These characterizations revealed the successful formation of a nanocomposite material with a well-defined structure and identified the oxygen vacancies/defects in the samples which might result in enhanced photocatalytic efficiency. Photocatalytic activity of 0.5NiFe 2 O 4 /0.5CuO composite showed degradation of methylene blue dye by 96.15% in 120 min. This work is not only to understand the photocatalytic mechanism but also to develop effective catalysts for the degradation of harmful organic pollutants., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The majority of T-cell responses involve proteasome-dependent protein degradation and the downstream presentation of oligopeptide products complexed with major histocompatibility complex (MHC) class I (MHC-I) molecules to peptide-restricted CD8 + T-cells. However, evasion of host immunity is a cancer hallmark that is achieved by disruption of host antigen processing and presentation machinery (APM). Consequently, mechanisms of immune evasion promote cancer growth and survival as well as de novo and acquired resistance to immunotherapy. A multitude of cell signaling pathways modulate the APM and MHC-I-dependent antigen presentation. Pharmacologics that specifically target and modulate proteasome structure and activity represent a novel emerging strategy to improve the treatment of cancers and other diseases characterized by aberrant protein accumulation. FDA-approved pharmacologics that selectively activate proteasomes and/or immunoproteasomes can be repositioned to overcome the current bottlenecks that hinder drug development to enhance antigen presentation, modulate the immunopeptidome, and enhance the cytotoxic activity of endogenous or engineered T-cells. Strategies to enhance antigen presentation may also improve the antitumor activity of T-cell immunotherapies, checkpoint inhibitors, and cancer vaccines. Proteasomes represent actionable therapeutic targets to treat difficult-to-treat infectious processes and neurodegenerative diseases that are characterized by the unwanted accrual of insoluble, deleterious, and potentially toxic proteins. Taken together, we highlight the breadth and magnitude of the proteasome and the immense potential to amplify and unmask the immunopeptidomic landscape to improve the treatment of a spectrum of human diseases.

In recent years, the increased population has led to an increase in the demand for various industrially processed edibles and other consumable products. These industries regularly alter the proteins found in raw materials to generate more commercially viable end-products in order to keep up with consumer demand. These modifications result in a substance that may cause allergic reactions in consumers, thereby creating a protein allergen. The detection of such proteins in various substances is essential for the prevention, diagnosis and treatment of allergic conditions. Bioinformatics and computational methods can be used to analyze the information contained in amino-acid sequences to detect possible allergens. The article presents a deep learning based ensemble approach to identify protein allergens using Extra Tree, Deep Belief Network (DBN), and CatBoost models. The proposed ensemble model achieves higher detection accuracy by combining the prediction results of the three models using majority voting. The evaluation of the proposed model was carried out on the benchmark protein allergen dataset, and the performance analysis revealed that the proposed model outperforms the other state-of-the-art literature techniques with a protein allergen detection accuracy of 89.16%., Competing Interests: The authors declare that they have no competing interests., (© 2023 Kumar and Rana.)

Urothelial cell carcinoma (UCC, bladder cancer, BC) remains a difficult-to-treat malignancy with a rising incidence worldwide. In the U.S., UCC is the sixth most incident neoplasm and ~90% of diagnoses are made in those >55 years of age; it is ~four times more commonly observed in men than women. The most important risk factor for developing BC is tobacco smoking, which accounts for ~50% of cases, followed by occupational exposure to aromatic amines and ionizing radiation. The standard of care for advanced UCC includes platinum-based chemotherapy and programmed cell death (PD-1) or programmed cell death ligand 1 (PD-L1) inhibitors, administered as frontline, second-line, or maintenance therapy. UCC remains generally incurable and is associated with intrinsic and acquired drug and immune resistance. UCC is lethal in the metastatic state and characterized by genomic instability, high PD-L1 expression, DNA damage-response mutations, and a high tumor mutational burden. Although immune checkpoint inhibitors (ICIs) achieve long-term durable responses in other cancers, their ability to achieve similar results with metastatic UCC (mUCC) is not as well-defined. Here, we discuss therapies to improve UCC management and how comprehensive tumor profiling can identify actionable biomarkers and eventually fulfill the promise of precision medicine for UCC patients.

Pathways that govern cellular bioenergetics are deregulated in tumor cells and represent a hallmark of cancer. Tumor cells have the capacity to reprogram pathways that control nutrient acquisition, anabolism and catabolism to enhance their growth and survival. Tumorigenesis requires the autonomous reprogramming of key metabolic pathways that obtain, generate and produce metabolites from a nutrient-deprived tumor microenvironment to meet the increased bioenergetic demands of cancer cells. Intra- and extracellular factors also have a profound effect on gene expression to drive metabolic pathway reprogramming in not only cancer cells but also surrounding cell types that contribute to anti-tumor immunity. Despite a vast amount of genetic and histologic heterogeneity within and between cancer types, a finite set of pathways are commonly deregulated to support anabolism, catabolism and redox balance. Multiple myeloma (MM) is the second most common hematologic malignancy in adults and remains incurable in the vast majority of patients. Genetic events and the hypoxic bone marrow milieu deregulate glycolysis, glutaminolysis and fatty acid synthesis in MM cells to promote their proliferation, survival, metastasis, drug resistance and evasion of immunosurveillance. Here, we discuss mechanisms that disrupt metabolic pathways in MM cells to support the development of therapeutic resistance and thwart the effects of anti-myeloma immunity. A better understanding of the events that reprogram metabolism in myeloma and immune cells may reveal unforeseen vulnerabilities and advance the rational design of drug cocktails that improve patient survival., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rana, Goparaju and Driscoll.)

In order to attain a solar energy-driven photocatalyst for wastewater remediation, cerium-doped WO 3 (W 1-x Ce x O 3 with x = 0, 0.02, 0.04, 0.06, and 0.08) nanoparticles have been synthesized via a chemical co-precipitation technique. X-ray diffraction (XRD) analysis confirmed that W 1-x Ce x O 3 nanoparticles retained their monoclinic structure even after doping. The presence of the vast number of defects produced in the WO 3 lattice was corroborated by Raman spectroscopy. Scanning electron microscopy confirmed the spherical shape of the nanoparticles with particle size range 50-76 nm. The optical band gap of W 1-x Ce x O 3 nanoparticles decreases from 3.07 to 2.36 eV with an increase in x, as confirmed by UV-Vis spectroscopy. Photoluminescence (PL) spectroscopy confirmed that the minimum rate of recombination was observed for W 1-x Ce x O 3 with x = 0.04. Degradation efficiency was explored for methyl violet (MV) and rhodamine-B (Rh-B) with 0.01 g of photocatalyst in a photoreactor chamber having a 200-W xenon lamp as a visible source of light. The results showed that the maximum photo-decolorization towards MV (94%) and rhodamine-B (79.4%) was observed in x = 0.04 sample in just 90 min because of its least recombination rate, highest adsorption capacity, and optimum band edge positions. Intriguingly, it has been observed that the modification with cerium in WO 3 nanoparticles enhances the photocatalytic activity by narrowing the band gap and by efficaciously lowering the recombination rate due to electron entrapment by defects produced in the lattice., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Metastatic breast cancer is responsible for the death of the majority of breast cancer patients. In fact, metastatic BC is the 2nd leading cause of cancer-related deaths in women in the USA and worldwide. Triple negative breast cancer (TNBC), which lacks expression of hormone receptors (ER-α and PR) and ErbB2/HER2, is especially lethal due to its highly metastatic behavior, propensity to recur rapidly, and for its resistance to standard of care therapies, through mechanisms that remain incompletely understood. WAVE3 has been established as a promoter of TNBC development and metastatic progression. In this study, we investigated the molecular mechanisms whereby WAVE3 promotes therapy-resistance and cancer stemness in TNBC, through the regulation of β-catenin stabilization., Methods: The Cancer Genome Atlas dataset was used to assess the expression of WAVE3 and β-catenin in breast cancer tumors. Kaplan-Meier Plotter analysis was used to correlate expression of WAVE3 and β-catenin with breast cancer patients' survival probability. MTT assay was used to quantify cell survival. CRISPR/Cas9-mediated gene editing, 2D and 3D tumorsphere growth and invasion assays, Immunofluorescence, Western blotting, Semi-quantitative and real-time quantitative PCR analyses were applied to study the WAVE3/β-catenin oncogenic signaling in TNBC. Tumor xenograft assays were used to study the role of WAVE3 in mediating chemotherapy resistance of TNBC tumors., Results: Genetic inactivation of WAVE3 in combination of chemotherapy resulted in inhibition of 2D growth and 3D tumorsphere formation and invasion of TNBC cells in vitro, as well as tumor growth and metastasis in vivo. In addition, while re-expression of phospho-active WAVE3 in the WAVE3-deficient TNBC cells restored the oncogenic activity of WAVE3, re-expression of phospho-mutant WAVE3 did not. Further studies revealed that dual blocking of WAVE3 expression or phosphorylation in combination with chemotherapy treatment inhibited the activity and expression and stabilization of β-catenin. Most importantly, the combination of WAVE3-deficiency or WAVE3-phospho-deficiency and chemotherapy suppressed the oncogenic behavior of chemoresistant TNBC cells, both in vitro and in vivo., Conclusion: We identified a novel WAVE3/β-catenin oncogenic signaling axis that modulates chemoresistance of TNBC. This study suggests that a targeted therapeutic strategy against WAVE3 could be effective for the treatment of chemoresistant TNBC tumors., (© 2023. The Author(s).)

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches' future directions and existing drawbacks are discussed in detail., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Breast cancer is the most frequently diagnosed malignancy in women and the major cause of death because of its invasion, metastasis, and resistance to therapies capabilities. The most aggressive subtype of breast cancer is triple-negative breast cancer (TNBC) due to invasive and metastatic properties along with early age of diagnosis and poor prognosis. TNBC tumors do not express estrogen, progesterone, and HER2 receptors, which limits their treatment with targeted therapies. Cancer invasiveness and metastasis are known to be promoted by increased cell motility and upregulation of the WAVE proteins. While the contribution of WAVE2 to cancer progression is well documented, the WAVE2-mediated regulation of TNBC oncogenic properties is still under investigated, as does the molecular mechanisms by which WAVE2 regulates such oncogenic pathways. In this study, we show that WAVE2 plays a significant role in TNBC development, progression, and metastasis, through the regulation of miR-29 expression, which in turn targets Integrin-β1 ( ITGB1 ) and its downstream oncogenic activities. Conversely, we found WAVE2 expression to be regulated by miR-29 in a negative regulatory feedback loop. Reexpression of exogenous WAVE2 in the WAVE2-deficient TNBC cells resulted in reactivation of ITGB1 expression and activity, further confirming the specificity of WAVE2 in regulating Integrin-β1. Together, our data identify a novel WAVE2/miR-29/ ITGB1 signaling axis, which is essential for the regulation of the invasion-metastasis cascade in TNBC. Our findings offer new therapeutic strategies for the treatment of TNBC by targeting WAVE2 and/or its downstream effectors., Significance: Identification of a novel WAVE2/miR-29/ ITGB1 signaling axis may provide new insights on how WAVE2 regulates the invasion-metastasis cascade of TNBC tumors through the modulation of ITGB1 and miR-29., Competing Interests: No disclosures were reported., (© 2023 The Authors; Published by the American Association for Cancer Research.)

Multiple myeloma (MM) remains a lethal hematologic cancer characterized by the expansion of transformed plasma cells within the permissive bone marrow (BM) milieu. The emergence of relapsed and/or refractory MM (RRMM) is provoked through clonal evolution of malignant plasma cells that harbor genomic, metabolic and proteomic perturbations. For most patients, relapsed disease remains a major cause of overall mortality. Transforming growth factors (TGFs) have pleiotropic effects that regulate myelomagenesis as well as the emergence of drug resistance. Moreover, TGF-β modulates numerous cell types present with the tumor microenvironment, including many immune cell types. While numerous agents have been FDA-approved over the past 2 decades and significantly expanded the treatment options available for MM patients, the molecular mechanisms responsible for drug resistance remain elusive. Multiple myeloma is uniformly preceded by a premalignant state, monoclonal gammopathy of unknown significance, and both conditions are associated with progressive deregulation in host immunity characterized by reduced T cell, natural killer (NK) cell and antigen-presenting dendritic cell (DC) activity. TGF-β promotes myelomagenesis as well as intrinsic drug resistance by repressing anti-myeloma immunity to promote tolerance, drug resistance and disease progression. Hence, repression of TGF-β signaling is a prerequisite to enhance the efficacy of current and future immunotherapeutics. Novel strategies that incorporate T cells that have been modified to express chimeric antigen receptor (CARs), T cell receptors (TCRs) and bispecific T cell engagers (BiTEs) offer promise to block TGF-β signaling, overcome chemoresistance and enhance anti-myeloma immunity. Here, we describe the effects of TGF-β signaling on immune cell effectors in the bone marrow and emerging strategies to overcome TGF-β-mediated myeloma growth, drug resistance and survival., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rana, Soler, Kort and Driscoll.)

Androgen deprivation therapies aimed to target prostate cancer (PrCa) are only partially successful given the occurrence of neuroendocrine PrCa (NEPrCa), a highly aggressive and highly metastatic form of PrCa, for which there is no effective therapeutic approach. Our group has demonstrated that while absent in prostate adenocarcinoma, the αVβ3 integrin expression is increased during PrCa progression toward NEPrCa. Here, we show a novel pathway activated by αVβ3 that promotes NE differentiation (NED). This novel pathway requires the expression of a GPI-linked surface molecule, NgR2, also known as Nogo-66 receptor homolog 1. We show here that NgR2 is upregulated by αVβ3, to which it associates; we also show that it promotes NED and anchorage-independent growth, as well as a motile phenotype of PrCa cells. Given our observations that high levels of αVβ3 and, as shown here, of NgR2 are detected in human and mouse NEPrCa, our findings appear to be highly relevant to this aggressive and metastatic subtype of PrCa. This study is novel because NgR2 role has only minimally been investigated in cancer and has instead predominantly been analyzed in neurons. These data thus pave new avenues toward a comprehensive mechanistic understanding of integrin-directed signaling during PrCa progression toward a NE phenotype., (© 2022. The Author(s).)

The COVID-19 pandemic has strained surgical systems worldwide and placed healthcare providers at risk in their workplace. To protect surgical care providers caring for patients with COVID-19, in May 2020 we developed a COVID-19 Surgical Patient Checklist (C19 SPC), including online training materials, to accompany the World Health Organization Surgical Safety Checklist. In October 2020, an online survey was conducted via partner and social media networks to understand perioperative clinicians' intraoperative practice and perceptions of safety while caring for COVID-19 positive patients and gain feedback on the utility of C19 SPC. Descriptive statistics were used to characterise responses by World Bank income classification. Qualitative analysis was performed to describe respondents' perceptions of C19 SPC and recommended modifications. Respondents included 539 perioperative clinicians from 63 countries. One-third of respondents reported feeling unsafe in their workplace due to COVID-19 with significantly higher proportions in low (39.8%) and lower-middle (33.9%) than higher income countries (15.6%). The most cited concern was the risk of COVID-19 transmission to self, colleagues and family. A large proportion of respondents (65.3%) reported that they had not used C19 SPC, yet 83.8% of these respondents felt it would be useful. Of those who reported that they had used C19 SPC, 62.0% stated feeling safer in the workplace because of its use. Based on survey results, modifications were incorporated into a subsequent version. Our survey findings suggest that perioperative clinicians report feeling unsafe at work during the COVID-19 pandemic. In addition, adjunct tools such as the C19 SPC can help to improve perceived safety.

Arm Venous Segmentation plays a crucial role in smart venipuncture. The difficulties faced in locating veins for intravenous procedures can be diminished using computer vision for vein imaging. To facilitate this, a high-resolution dataset consisting of arm images was curated and has been presented in this study. Leveraging the ability of Near Infrared Imaging to easily detect veins, ambient lighting conditions were created inside a small enclosure to capture the images. The acquired images were annotated to create the corresponding masks for the dataset. To extend the scope and assert the usability of the dataset, the images, and corresponding masks were used to train an image segmentation model. In addition to using basic preprocessing and image augmentation based techniques, a U-Net based algorithmic architecture has been used to facilitate the task of segmentation. Subsequently, the results of performing image segmentation after applying the preprocessing methods have been compared using various evaluation metrics and have been visualised in the study. Furthermore, the possible applications of the presented dataset have been investigated in the study., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Avik Kuthiala et al.)

A deeper understanding of basic immunology principles and advances in bioengineering have accelerated the mass production of genetically-reprogrammed T-cells as living drugs to treat human diseases. Autologous and allogeneic cytotoxic T-cells have been weaponized to brandish MHC-independent chimeric antigen receptors (CAR) that specifically engage antigenic regions on tumor cells. Two distinct CAR-based therapeutics designed to target BCMA are now FDA-approved based upon robust, sustained responses in heavily-pretreated multiple myeloma (MM) patients enrolled on the KarMMa and CARTITUDE-1 studies. While promising, CAR T-cells present unique challenges such as antigen escape and T-cell exhaustion. Here, we review novel strategies to design CARs that overcome current limitations. Co-stimulatory signaling regions were added to second-generation CARs to promote IL-2 synthesis, activate T-cells and preclude apoptosis. Third-generation CARs are composed of multiple co-stimulatory signaling units, e.g., CD28, OX40, 4-1BB, to reduce exhaustion. Typically, CAR T-cells incorporate a potent constitutive promoter that maximizes long-term CAR expression but extended CAR activation may also promote T-cell exhaustion. Hypoxia-inducible elements can be incorporated to conditionally drive CAR expression and selectively target MM cells within bone marrow. CAR T-cell survival and activity is further realized by blocking intrinsic regulators of T-cell inactivation. T-Cells Redirected for Universal Cytokine Killing (TRUCKs) bind a specific tumor antigen and produce cytokines to recruit endogenous immune cells. Suicide genes have been engineered into CAR T-cells given the potential for long-term on-target, off-tumor effects. Universal allo-CAR T-cells represent an off-the-shelf source, while logic-gated CAR T-cells are designed to recognize tumor-specific features coupled with Boolean-generated binary gates that then dictate cell-fate decisions. Future generations of CARs should further revitalize immune responses, enhance tumor specificity and reimagine strategies to treat myeloma and other cancers., Competing Interests: The authors state that the manuscript was prepared without any commercial or financial relationships and without any conflict of interest., (Copyright © 2022 Rana, Murphy, Kort and Driscoll.)

Competing Interests: Dr Ambulkar, Dr Singh and Dr Moore have nothing to disclose. Dr Starr is a Senior fellow at Lifebox Foundation.

Breast cancer (BC) is one of the leading causes of cancer-related deaths due in part to its invasive and metastatic properties. Kindlin-2 (FERMT2) is associated with the pathogenesis of several cancers. Although the role of Kindlin-2 in regulating the invasion-metastasis cascade in BC is widely documented, its function in BC initiation and progression remains to be fully elucidated. Accordingly, we generated a floxed mouse strain by targeting the Fermt2 (K2 lox/lox ) locus, followed by tissue-specific deletion of Kindlin-2 in the myoepithelial compartment of the mammary glands by crossing the K2 lox/lox mice with K14-Cre mice. Loss of Kindlin-2 in mammary epithelial cells (MECs) showed no deleterious effects on mammary gland development, fertility, and lactation in mice bearing Kindlin-2-deletion. However, in a syngeneic mouse model of BC, mammary gland, specific knockout of Kindlin-2 inhibited the growth and metastasis of murine E0771 BC cells inoculated into the mammary fat pads. However, injecting the E0771 cells into the lateral tail vein of Kindlin-2-deleted mice had no effect on tumor colonization in the lungs, thereby establishing a critical role of MEC Kindlin-2 in supporting BC tumor growth and metastasis. Mechanistically, we found the MEC Kindlin-2-mediated inhibition of tumor growth and metastasis is accomplished through its regulation of the TGF-β/ERK MAP kinase signaling axis. Thus, Kindlin-2 within the mammary gland microenvironment facilitates the progression and metastasis of BC.

With the outbreak of the Coronavirus Disease in 2019, life seemed to be had come to a standstill. To combat the transmission of the virus, World Health Organization (WHO) announced wearing of face mask as an imperative way to limit the spread of the virus. However, manually ensuring whether people are wearing face masks or not in a public area is a cumbersome task. The exigency of monitoring people wearing face masks necessitated building an automatic system. Currently, distinct methods using machine learning and deep learning can be used effectively. In this paper, all the essential requirements for such a model have been reviewed. The need and the structural outline of the proposed model have been discussed extensively, followed by a comprehensive study of various available techniques and their respective comparative performance analysis. Further, the pros and cons of each method have been analyzed in depth. Subsequently, sources to multiple datasets are mentioned. The several software needed for the implementation are also discussed. And discussions have been organized on the various use cases, limitations, and observations for the system, and the conclusion of this paper with several directions for future research., Competing Interests: Conflict of interestNot applicable., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Objective: Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment., Methods: This study aims at examining the recent methodologies developed using automated learning and radiomics to automate the process of SP. Automated techniques use pre-operative raw magnetic resonance imaging (MRI) scans and clinical data related to GBM patients. All SP methods submitted for the multimodal brain tumour segmentation (BraTS) challenge are examined to extract the generic workflow for SP., Results: The maximum accuracies achieved by 21 state-of-the-art different SP techniques reviewed in this study are 65.5 and 61.7% using the validation and testing subsets of the BraTS dataset, respectively. The comparisons based on segmentation architectures, SP models, training parameters and hardware configurations have been made., Conclusion: The limited accuracies achieved in the literature led us to review the various automated methodologies and evaluation metrics to find out the research gaps and other findings related to the survival prognosis of GBM patients so that these accuracies can be improved in future. Finally, the paper provides the most promising future research directions to improve the performance of automated SP techniques and increase their clinical relevance., Competing Interests: Conflict of interestOn behalf of all the authors, the corresponding author declares no conflict of interest. This article does not contain any studies with human or animal subjects performed by any authors., (© The Author(s), under exclusive licence to Italian Association of Nuclear Medicine and Molecular Imaging 2022.)

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score., Competing Interests: Conflict of InterestsWe certify that there is no actual or potential conflict of interest in relation to this article., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Big data has been a topic of interest for many researchers and industries for the past few decades. Due to the exponential growth of technology, a tremendous amount of data is generated every minute. This article provides a strategic review of big data in the healthcare sector. In particular, this article highlights various applications and issues faced by the healthcare industry using big data by evaluating various journal articles between 2016 and 2021. Multiple issues related to data mining, storing, analyzing, and sharing of big data in healthcare, briefly summarizing deep-learning-based tools available for big data analytics, have been covered in this article. This article aims to benefit the research community by summarizing various research tools and processes available today to manage big data in healthcare., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Nonlinear dynamics is an exciting approach to describe the dynamical practices of COVID-19 disease. Mathematical modeling is a necessary method for investigating the dynamics of epidemic diseases. In the current article, an effort has been made to cultivate a novel COVID-19 compartment mathematical model by incorporating vaccinated populations. Primarily, the fundamental characteristics of the model, such as positivity and boundedness of solutions, are established. Thereafter, equilibrium analysis of steady states has been illustrated through vaccine reproduction number. Further, a nonlinear least square curve fitting technique has been employed to recognize the best fitted model parameters from the COVID-19 mortality data of five regions, namely Maharashtra, Delhi, Uttarakhand, Sikkim, and Russia. The numerical framework of the model has been added to interpret the consequence of various control schemes (pharmaceutical or non-pharmaceutical) on COVID-19 dynamics, and it has been ascertained that all the control protocols have a positive influence on curtailing the COVID-19 transference in the aforementioned regions. In addition, the essence of vaccine efficacy and vaccine-induced immunity are examined by considering different scenarios. Our analysis demonstrates that the disease will be wiped off from the Maharashtra, Delhi, Uttarakhand and Sikkim regions of India, while it shall persist in Russia for some more time. It is also found that, if a vaccine calamity arises, the government should majorly focus on permanent drug treatment of hospitalized individuals rather than vaccination., (© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022.)

Triple negative breast cancer (TNBC) is the most aggressive amongst all breast cancer (BC) subtypes. While TNBC tumors represent less than 20% of all BC subtypes, they are responsible for the most BC-related deaths. More significantly, when considering TNBC incidence across all racial/ethnic groups, TNBC accounts for less than 20% of all BCs. However, in non-Hispanic black women, the incidence rate of TNBC is more than 40%, which may be a contributing factor to the higher BC-related death rate in this population. These disparities remain strong even after accounting for differences in socioeconomic status, healthcare access, and lifestyle factors. Increased evidence now points to biological mechanisms that are intrinsic to the tumor that contribute to disparate TNBC disease burdens. Here, we show that YB1, a multifunction gene, plays a major role in the TNBC disparities between African American (AA) and Caucasian American (CA) women. We show in three independent TNBC tumors cohorts, that YB1 is significantly highly expressed in AA TNBC tumors when compared to CAs, and that increased levels of YB1 correlate with poor survival of AA patients with TNBC. We used a combination of genetic manipulation of YB1 and chemotherapy treatment, both in vitro and in animal models of TNBC to show that YB1 oncogenic activity is more enhanced in TNBC cell lines of AA origin, by increasing their tumorigenic and aggressive behaviors, trough the activation of cancer stem cell phenotype and resistance to chemotherapeutic treatments.

Competing Interests: Dr. Evans is a Global Governance Council member of Lifebox Foundation. Dr. Starr is the senior fellow at Lifebox Foundation. Dr. Mammo is the Global Clinical Director of Lifebox Foundation. Dr. Weiser is the Consulting Medical Officer for Lifebox Foundation. All the other authors report no conflicts of interest.

The Wiskott-Aldrich syndrome protein (WASP) and WASP family verprolin-homologous protein (WAVE)-WAVE1, WAVE2 and WAVE3 regulate rapid reorganization of cortical actin filaments and have been shown to form a key link between small GTPases and the actin cytoskeleton. Upon receiving upstream signals from Rho-family GTPases, the WASP and WAVE family proteins play a significant role in polymerization of actin cytoskeleton through activation of actin-related protein 2/3 complex (Arp2/3). The Arp2/3 complex, once activated, forms actin-based membrane protrusions essential for cell migration and cancer cell invasion. Thus, by activation of Arp2/3 complex, the WAVE and WASP family proteins, as part of the WAVE regulatory complex (WRC), have been shown to play a critical role in cancer cell invasion and metastasis, drawing significant research interest over recent years. Several studies have highlighted the potential for targeting the genes encoding either part of or a complete protein from the WASP/WAVE family as therapeutic strategies for preventing the invasion and metastasis of cancer cells. WAVE2 is well documented to be associated with the pathogenesis of several human cancers, including lung, liver, pancreatic, prostate, colorectal and breast cancer, as well as other hematologic malignancies. This review focuses mainly on the role of WAVE2 in the development, invasion and metastasis of different types of cancer. This review also summarizes the molecular mechanisms that regulate the activity of WAVE2, as well as those oncogenic pathways that are regulated by WAVE2 to promote the cancer phenotype. Finally, we discuss potential therapeutic strategies that target WAVE2 or the WAVE regulatory complex, aimed at preventing or inhibiting cancer invasion and metastasis.

The Y Box binding protein 1 (YB-1) is a member of the highly conserved Cold Shock Domain protein family with multifunctional properties both in the cytoplasm and inside the nucleus. YB-1 is also involved in various cellular functions, including regulation of transcription, mRNA stability, and splicing. Recent studies have associated YB-1 with the regulation of the malignant phenotypes in several tumor types. In this review article, we provide an in-depth and expansive review of the literature pertaining to the multiple physiological functions of YB-1. We will also review the role of YB-1 in cancer development, progression, metastasis, and drug resistance in various malignancies, with more weight on literature published in the last decade. The methodology included querying databases PubMed, Embase, and Google Scholar for Y box binding protein 1, YB-1, YBX1, and Y-box-1., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is most prominent in scourging over 20 million people around the world and still not much effort has been made in understanding the epidemiology and controlling the prevalence of this virus. This condition further worsens when most of the infected cases remain asymptomatic throughout their lifetime due to the limited diagnostic methods; that are most of the times unavailable for timely detection of infected individuals. Moreover, at present, there is no licensed vaccination for HTLV-1 infection. Therefore, there is a need to develop the faster and efficient diagnostic method for the detection of HTLV-1. Influenced from the outcomes of the machine learning techniques in the field of bio-informatics, this is the first study in which 64 hybrid machine learning techniques have been proposed for the prediction of different type of HTLVs (HTLV-1, HTLV-2, and HTLV-3). The hybrid techniques are built by permutation and combination of four classification methods, four feature weighting, and four feature selection techniques. The proposed hybrid models when evaluated on the basis of various model evaluation parameters are found to be capable of efficiently predicting the type of HTLVs. The best hybrid model has been identified by having accuracy, an AUROC value, and F1 score of 99.85 percent, 0.99, and 0.99, respectively. This kind of the system can assist the current diagnostic system for the detection of HTLV-1 as after the molecular diagnostics of HTLV by various screening tests like enzyme-linked immunoassay or particle agglutination assays there is always a need of confirmatory tests like western blotting, immuno-fluorescence assay, or radio-immuno-precipitation assay for distinguishing HTLV-1 from HTLV-2. These confirmatory tests are indeed very complex analytical techniques involving various steps. The proposed hybrid techniques can be used to support and verify the results of confirmatory test from the protein mixture. Furthermore, better insights about the virus can be obtained by exploring the physicochemical properties of the protein sequences of HTLVs.

The catalytic application of the bis(amino)cyclopropenium ion has been investigated in conjugate addition reactions. The hydrogen atom, which is attached to the cyclopropene ring of bis(amino)cyclopropenium salts, is moderately acidic and can potentially serve as a hydrogen-bond donor catalyst in some organic transformations. This hypothesis has been successfully realized in the 1,6-conjugate addition reactions of p -quinone methides with various nucleophiles such as indole, 2-naphthol, thiols, phenols, and so forth. The spectroscopic studies (NMR and UV-vis) as well as the deuterium isotope labeling studies clearly revealed that the hydrogen atom (C-H) that is present in the cyclopropene ring of the catalyst is indeed solely responsible for catalyzing these transformations. In addition, these studies also strongly indicate that the C-H hydrogen of the cyclopropene ring activates the carbonyl group of the p -quinone methide through hydrogen bonding.

The escalating transmission intensity of COVID-19 pandemic is straining the healthcare systems worldwide. Due to the unavailability of effective pharmaceutical treatment and vaccines, monitoring social distancing is the only viable tool to strive against asymptomatic transmission. Pertaining to the need of monitoring the social distancing at populated areas, a novel bird eye view computer vision-based framework implementing deep learning and utilizing surveillance video is proposed. This proposed method employs YOLO v3 object detection model and uses key point regressor to detect the key feature points. Additionally, as the massive crowd is detected, the bounding boxes on objects are received, and red boxes are also visible if social distancing is violated. When empirically tested over real-time data, proposed method is established to be efficacious than the existing approaches in terms of inference time and frame rate., Competing Interests: Conflict of interestAuthors have no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.)

The standard theory of apoptotic volume decrease (AVD) posits activation of potassium and/or chloride channels, causing an efflux of ions and osmotic loss of water. However, in view of the multitude of possible channels that are known to support apoptosis, a model based on specific signaling to a channel presents certain problems. We propose another mechanism of apoptotic dehydration based on cytoskeletal compression. As is well known, cytoskeleton is not strong enough to expel a substantial amount of water against an osmotic gradient. It is possible, however, that an increase in intracellular pressure may cause an initial small efflux of water, and that will create a small concentration gradient of ions, favoring their exit. If the channels are open, some ions will exit the cell, relieving the osmotic gradient; in this way, the process will be able to continue. Calculations confirm the possibility of such a mechanism. An increase in membrane permeability for water or ions may also result in dehydration if accompanied even by a constant cytoskeletal pressure. We review the molecular processes that may lead to apoptotic dehydration in the context of this model., (Copyright © 2020 Rana and Model.)

Potassium loss and persistent shrinkage have both been implicated in apoptosis but their relationship and respective roles remain controversial. We approached this problem by clamping intracellular sodium and potassium in HeLa or MDCK cells using a combination of ionophores. Although ionophore treatment caused significant cell swelling, the initial volume could be restored and further reduced by application of sucrose. The swollen cells treated with ionophores remained viable for at least 8 h without any signs of apoptosis. Application of sucrose and the resulting shrinkage caused volume-dependent intrinsic apoptosis with all its classical features: inversion of phosphatidylserine, caspase activation and Bcl-2-dependent release of cytochrome c from mitochondria. In other experiments, apoptosis was induced by addition of the protein kinase inhibitor staurosporine at various degrees of swelling. Our results show that: (1) persistent shrinkage can cause apoptosis regardless of intracellular sodium or potassium composition or of the state of actin cytoskeleton; (2) strong potassium dependence of caspase activation is only observed in swollen cells with a reduced density of cytosolic proteins. We conclude that macromolecular crowding can be an important factor in determining the transition of cells to apoptosis., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

The major intent of peptide vaccine designs, immunodiagnosis and antibody productions is to accurately identify linear B-cell epitopes. The determination of epitopes through experimental analysis is highly expensive. Therefore, it is desirable to develop a reliable model with significant improvement in prediction models. In this study, a hybrid model has been designed by using stacked generalisation ensemble technique for prediction of linear B-cell epitopes. The goal of using stacked generalisation ensemble approach is to refine predictions of base classifiers and to get rid of the worse predictions. In this study, six machine learning models are fused to predict variable length epitopes (6-49 mers). The proposed ensemble model achieves 76.6% accuracy and average accuracy of repeated 10-fold cross-validation is 73.14%. The trained ensemble model has been tested on the benchmark dataset and compared with existing sequential B-cell epitope prediction techniques including APCpred, ABCpred, BCpred and [inline-formula removed].

Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.

In this study, efforts are created to develop a quantitative structure-activity relationship (QSAR)-based model, which are used for the prediction of toxicities to reduce testing in animals, time, and money in the early stages of drug development. An efficient machine learning model is developed to predict the toxicity of those drug molecules which binds to the androgen receptor (AR). Toxicity prediction is performed in terms of their activity, activity score, potency, and efficacy by using various physicochemical properties. A multilevel ensemble model is proposed, where its first level is performed ensemble-based classification of activity, and the second level is performed ensemble-based regression of activity score, potency, and efficacy of only those drug molecules which have been found active during the classification level. The AR dataset has 10,273 drug molecules where 461 are active, and 9812 are inactive, and each drug molecule has 1444 features. Therefore, our dataset is highly imbalanced having a very large number of features. Initially, we performed feature selection then the class imbalance problem is resolved. The k -fold cross-validation is accomplished to measure the consistency of the model. Finally, our proposed multilevel ensemble model has been validated and compared with some existing models.

In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers' disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2-antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi-level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi-level scheme. The validation of the proposed scheme is done using the K-fold cross-validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.

A series of Ni 1- x Zn x O ( x ═0.1 to 0.4) nanocomposites is synthesized by facile chemical coprecipitation method. The structural analysis is carried out using X-ray diffraction (XRD) graph which reveals both cubic (NiO) and hexagonal (ZnO) phases. Chemical bonding, morphology, elemental composition and optical properties of prepared nanocomposites are investigated by Fourier Transform Infrared (FTIR) Spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray (EDX), and UV-Vis spectroscopy respectively. FESEM micrographs show the formation of nanorods at higher concentration of zinc content. Introducing higher amount of Zn, optical band gap of composites is considerably reduced. Furthermore, solutions of Rose Bengal (RB) and Methylene Blue (MB) dyes are used to investigate the photocatalytic ability of synthesized samples in the presence of sunlight. It was found that the percentage degradation of dye concentration increases with irradiation time and also with the increase in concentration of zinc in prepared samples.

Multiple sclerosis (MS) is a neurodegenerative disease characterized by lesions in the central nervous system (CNS). Inflammation and demyelination are the leading causes of neuronal death and brain lesions formation. The immune reactivity is believed to be essential in the neuronal damage in MS. Cytokines play important role in differentiation of Th cells and recruitment of auto-reactive B and T lymphocytes that leads to neuron demyelination and death. Several cytokines have been found to be linked with MS pathogenesis. In the present study, serum level of eight cytokines (IL-1β, IL-2, IL-4, IL-8, IL-10, IL-13, IFN-γ, and TNF-α) was analyzed in USA and Russian MS to identify predictors for the disease. Further, the model was extended to classify MS into remitting and non-remitting by including age, gender, disease duration, Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) into the cytokines datasets in Russian cohorts. The individual serum cytokines data for the USA cohort was generated by Z score percentile method using R studio, while serum cytokines of the Russian cohort were analyzed using multiplex immunoassay. Datasets were divided into training (70%) and testing (30%). These datasets were used as an input into four machine learning models (support vector machine, decision tree, random forest, and neural networks) available in R programming language. Random forest model was identified as the best model for diagnosis of MS as it performed remarkable on all the considered criteria i.e., Gini, accuracy, specificity, AUC, and sensitivity. RF model also performed best in predicting remitting and non-remitting MS. The present study suggests that the concentration of serum cytokines could be used as prognostic markers for the prediction of MS.

The authors have proposed an efficient multilevel prediction model for better activity assessment to test whether certain chemical compounds can disrupt processes in the human body that may create negative health effects. Here, a computational method (in-silico) is proposed for the quality prediction of drugs in terms of their activity, activity score, potency, and efficacy for estrogen receptors (ERs) by using various physicochemical properties (molecular descriptors). PaDEL-Descriptor is used for features extraction. The ER dataset has 8481 drug molecules where 1084 are active, and 7397 are inactive, and each drug molecule has 1444 features. This dataset is highly imbalanced and has a substantial number of features. Initially, a class imbalance problem is resolved through synthetic minority oversampling technique algorithm, and feature selection is done using FSelector library of R. A machine learning based multilevel prediction model is developed where classification is performed on its first level and regression on its second level. By using all these strategies simultaneously, outperformed accuracy is achieved in comparison to many other computational approaches. The K -fold cross-validation is performed to measure the consistency of the model for all the target classes. Finally, the validity of the proposed method on some AIDS therapy's drug molecules is proved.

The response of fluorescent ion probes to ions is affected by intracellular environment. To properly calibrate them, intracellular and extracellular concentrations of the measured ion must be made equal. In the first, computational, part of this work, we show, using the example of potassium, that the two requirements for ion equilibration are complete dissipation of membrane potential and high membrane permeability for both potassium and sodium. In the second part, we tested the ability of various ionophores to achieve potassium equilibration in Jurkat and U937 cells and found a combination of valinomycin, nigericin, gramicidin and ouabain to be the most effective. In the third part, we applied this protocol to two potassium probes, APG-4 and APG-2. APG-4 shows good sensitivity to potassium but its fluorescence is sensitive to cell volume. Because ionophores cause cell swelling, calibration buffers had to be supplemented with 50 mM sucrose to keep cell volume constant. With these precautions taken, the average potassium concentrations in U937 and Jurkat cells were measured at 132 mM and 118 mM, respectively. The other tested probe, APG-2, is nonselective for cations; this is, however, a potentially useful property because the sum [K + ] + [Na + ] determines the amount of intracellular water., (Copyright © 2018. Published by Elsevier Inc.)

Prediction of drug synergy score is an ill-posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression-based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Apoptotic volume decrease (AVD) is a characteristic cell shrinkage observed during apoptosis. There are at least two known processes that may result in the AVD: exit of intracellular water and splitting of cells into smaller fragments. Although AVD has traditionally been attributed to water loss, direct evidence for that is often lacking. In this study, we quantified intracellular water in staurosporine-treated cells using a previously described optical microscopic technique that combines volume measurements with quantitative phase analysis. Water loss was observed in detached HeLa and in adherent MDCK but not in adherent HeLa cells. At the same time, adherent HeLa and adherent MDCK cells exhibited visually similar apoptotic morphology, including fragmentation and activation of caspase-3. Morphological changes and caspase activation were prevented by chloride channel blockers DIDS and NPPB in both adherent and suspended HeLa cells, while potassium channel blocker TEA was ineffective. We conclude that staurosporine-induced dehydration is not a universal cell response but depends on the cell type and substrate attachment and can only be judged by direct water measurements. The effects of potassium or chloride channel blockers do not always correlate with the AVD.

Intracellular protein concentration is an essential cell characteristic, which manifests itself through the refractive index. The latter can be measured from two or more mutually defocused brightfield images analyzed using the TIE (transport-of-intensity equation). In practice, however, TIE does not always achieve quantitatively accurate results on biological cells. Therefore, we have developed a calibration procedure that involves successive imaging of cells in solutions containing different amounts of added protein. This allows one to directly relate the output of TIE (T) to intracellular protein concentration C (g/L). The resultant relationship has a simple form: C ≈ 1.0(T/V), where V is the cell volume (μm 3 ) and 1.0 is an empirical coefficient. We used calibrated TIE imaging to characterize the regulatory volume increase (RVI) in adherent HeLa cells placed in a hyperosmotic solution. We found that while no RVI occurs over the first 30-60 min, the protein concentration fully recovers after 20 h. Because interpretation of such long experiments may depend on whether protein concentration varies significantly throughout the cell cycle, we measured this parameter in three cell lines: HeLa, MDCK and DU145. Our data indicate that protein concentration remains relatively stable in these cells. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

Necrotic cells are known to develop characteristic membrane blebs. We measured protein concentration within necrotic blebs and found that it can be reduced by as much as twenty-fold compared to the main cell body (CB). These results raise two questions: 1. Why do proteins vacate the bleb? 2. How can osmotic equilibrium be maintained between the bleb and CB? Our photobleaching and ultracentrifugation experiments indicate extensive protein aggregation. We hypothesize that protein aggregation within the CB shifts the chemical equilibrium and draws proteins out of the bleb; at the same time, aggregation reduces the effective molar concentration of protein in the CB, so that osmotic equilibrium between high-protein CB and low-protein necrotic blebs becomes possible., (Copyright © 2017 Elsevier Inc. All rights reserved.)