Otolith microchemistry of freshwater indigenous minor carp (Bangana dero) as a biomonitoring tool to trace heavy metals in River Sutlej, Ropar Wetland (Ramsar site), Punjab, India.

The elemental composition of the fish otolith may represent a permanent record of the environmental condition the fish inhabited. Fish otolith grows incrementally from the core to a marginal region that acts as a repository of trace metal signatures. The present study explores the potential application of otolith microchemistry of the benthopelagic indigenous minor carp Bangana dero sampled from the Ropar wetland on River Sutlej, Punjab. The concentration of sixteen metals was evaluated in the otolith (n = 42) and water (n = 48) for the post-monsoon and pre-monsoon season from 2020 to 2022 using inductively coupled plasma mass spectrometry (ICP-MS) followed by element detection in the core and marginal region of whole otolith, using energy-dispersive mass spectroscopy (EDS). All the heavy metals exhibited an increase in metal concentrations in fish otolith than water during the post-monsoon season. By indices approach, the otolith was found to have a high bioaccumulation factor for Se in the post-monsoon and Hg in the pre-monsoon. Certain trace metals like As and Hg exhibited fluctuations in their core and marginal region. Thus, trace metal patterns in the otolith could act as a potential tool for monitoring the seasonal changes of metals in water bodies. The EFHg, EFSe and EFAs in the fish otolith predicted its anthropogenic source, while the remaining studied elements showed ambient water origin. Thus, using the otoliths of Bangana dero as a long-term monitoring tool in the future may be helpful for environmental assessments and the reconstruction of historical exposure for safeguarding of water bodies.

A novel allogeneic off-the-shelf dendritic cell vaccine for post-remission treatment of elderly patients with acute myeloid leukemia.

In elderly acute myeloid leukemia (AML) patients post-remission treatment options are associated with high comorbidity rates and poor survival. Dendritic cell (DC)-based immunotherapy is a promising alternative treatment strategy. A novel allogeneic DC vaccine, DCP-001, was developed from an AML-derived cell line that uniquely combines the positive features of allogeneic DC vaccines and expression of multi-leukemia-associated antigens. Here, we present data from a phase I study conducted with DCP-001 in 12 advanced-stage elderly AML patients. Patients enrolled were in complete remission (CR1/CR2) (n = 5) or had smoldering disease (n = 7). All patients were at high risk of relapse and ineligible for post-remission intensification therapies. A standard 3 + 3 dose escalation design with extension to six patients in the highest dose was performed. Patients received four biweekly intradermal DCP-001 injections at different dose levels (10, 25, and 50 million cells DCP-001) and were monitored for clinical and immunological responses. Primary objectives of the study (feasibility and safety) were achieved with 10/12 patients completing the vaccination program. Treatment was well tolerated. A clear-cut distinction between patients with and without detectable circulating leukemic blasts during the vaccination period was noted. Patients with no circulating blasts showed an unusually prolonged survival [median overall survival 36 months (range 7-63) from the start of vaccination] whereas patients with circulating blasts, died within 6 months. Long-term survival was correlated with maintained T cell levels and induction of multi-functional immune responses. It is concluded that DCP-001 in elderly AML patients is safe, feasible and generates both cellular and humoral immune responses.

Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

Real and synthetic Punjabi speech datasets for automatic speech recognition.

Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.

Acute Bilateral Pyelonephritis in the Setting of Newly Diagnosed Diabetes Mellitus: A Case Report.

Introduction: Acute pyelonephritis is a bacterial infection that starts in the bladder and ascends to the kidneys, causing inflammation of the renal parenchyma. Women are more likely to get infected compared to men, with diabetics being at higher risk. The pathophysiology of how diabetics are more prone to getting urinary tract infections/pyelonephritis has been studied, particularly the difference between bilateral pyelonephritis and unilateral pyelonephritis. Case Presentation: This case presentation follows a 51-year-old Spanish-speaking woman with a past medical history of prediabetes, bilateral tubal ligation, and perimenopause. She presented to the hospital for abdominal and back pain, fevers, and weakness that she had for a week. An intake of her history and a physical examination led to the initial diagnosis of cystitis, but the imaging drove the authors to the correct diagnosis of acute bilateral pyelonephritis with Escherichia coli growing in the urine. She was then treated with the appropriate antibiotics. During her hospital stay, she was also diagnosed with type 2 diabetes mellitus. Imaging is not usually used to diagnose pyelonephritis, but it is necessary in some cases and can help identify complications. There are multiple case reports about acute pyelonephritis, but there are few that touch on acute bilateral pyelonephritis. Conclusion: We are highlighting this case presentation since it shows how a patient with newly diagnosed diabetes is at more of a risk of developing acute bilateral pyelonephritis. This information is important not only to add to medical knowledge but also to allow physicians to emphasize diabetic control in order to minimize the chance of developing pyelonephritis.

A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abilities and impairs a person's capacity for independent functioning. It is a fatal neurodegenerative disease primarily affecting older adults. OBJECTIVES: The purpose of this literature review is to investigate various AD detection techniques, datasets, input modalities, algorithms, libraries, and performance evaluation metrics used to determine which model or strategy may provide superior performance. METHOD: The initial search yielded 807 papers, but only 100 research articles were chosen after applying the inclusion-exclusion criteria. This SLR analyzed research items published between January 2019 and December 2022. The ACM, Elsevier, IEEE Xplore Digital Library, PubMed, Springer and Taylor & Francis were systematically searched. The current study considers articles that used Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), APOe4 genotype, Diffusion Tensor Imaging (DTI) and Cerebrospinal Fluid (CSF) biomarkers. The study was performed following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. CONCLUSION: According to the literature survey, most studies (n = 76) used the DL strategy. The datasets used by studies were primarily derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The majority of studies (n = 73) used single-modality neuroimaging data, while the remaining used multi-modal input data. In a multi-modality approach, the combination of MRI and PET scans is commonly preferred. Also, Regarding the algorithm used, Convolution Neural Network (CNN) showed the highest accuracy, 100 %, in classifying AD vs. CN subjects whereas the SVM was the most common ML algorithm, with a maximum accuracy of 99.82 %.

Mass cytometric single cell immune profiles of peripheral blood from acute myeloid leukemia patients in complete remission with measurable residual disease.

Measurable residual disease (MRD) is detected in approximately a quarter of AML chemotherapy responders, serving as a predictor for relapse and shorter survival. Immunological control of residual disease is suggested to prevent relapse, but the mechanisms involved are not fully understood. We present a peripheral blood single cell immune profiling by mass cytometry using a 42-antibody panel with particular emphasis on markers of cellular immune response. Six healthy donors were compared with four AML patients with MRD (MRD+) in first complete remission (CR1MRD+). Three of four patients demonstrated a favorable genetic risk profile, while the fourth patient had an unfavorable risk profile (complex karyotype, TP53-mutation) and a high level of MRD. Unsupervised clustering using self-organizing maps and dimensional reduction analysis was performed for visualization and analysis of immune cell subsets. CD57+ natural killer (NK)-cell subsets were found to be less abundant in patients than in healthy donors. Both T and NK cells demonstrated elevated expression of activity and maturation markers (CD44, granzyme B, and phosho-STAT5 Y694) in patients. Although mass cytometry remains an expensive method with limited scalability, our data suggest the utility for employing a 42-plex profiling for cellular immune surveillance in whole blood, and possibly as a biomarker platform in future clinical trials. The findings encourage further investigations of single cell immune profiling in CR1MRD+ AML-patients.

The development of standard samples with a defined number of antigen-specific T cells to harmonize T cell assays: a proof-of-principle study.

The validation of assays that quantify antigen-specific T cell responses is critically dependent on cell samples that contain clearly defined measurable numbers of antigen-specific T cells. An important requirement is that such cell samples are handled and analyzed in a comparable fashion to peripheral blood mononuclear cells (PBMC). We performed a proof-of-principle study to show that retrovirally TCR-transduced T cells spiked at defined numbers in autologous PBMC can be used as standard samples for HLA/peptide multimer staining. NY-ESO-1157-165-specific, TCR-transduced CD8+ T cell batches were successfully generated from PBMC of several HLA-A*0201 healthy donors, purified by magnetic cell sorting on the basis of HLA tetramer (TM) staining and expanded with specific antigen in vitro. When subsequently spiked into autologous PBMC, the detection of these CD3+CD8+TM+ T cells was highly accurate with a mean accuracy of 91.6 %. The standard cells can be preserved for a substantial period of time in liquid nitrogen. Furthermore, TM staining of fresh and cryopreserved standard samples diluted at decreasing concentrations into autologous cryopreserved unspiked PBMC revealed that the spiked CD3+CD8+TM+ T cells could be accurately detected at all dilutions in a linear fashion with a goodness-of-fit of over 0.99 at a frequency of at least 0.02 % among the CD3+CD8+ T cell population. Notably, the CD3+CD8+TM+ cells of the standard samples were located exactly within the gates used to analyze patient samples and displayed a similar scatter pattern. The performance of the cryopreserved standard samples in the hands of 5 external investigators was good with an inter-laboratory variation of 32.9 % and the doubtless identification of one outlier.

Derivation, validation, and prediction of loading-induced mineral apposition rates at endocortical and periosteal bone surfaces based on fluid velocity and pore pressure.

The capacity of bone to optimize its structure in response to mechanical loads has been widely observed. The mechanical load acting on a bone at the macroscopic level influences the bone cells, particularly osteocytes within the lacunae canalicular network (LCN). Osteocytes are responsive to a range of physical signals, including strain, interstitial fluid flow, and pore pressure. However, physiological tissue strain is known to be typically smaller than that required to directly induce bone formation. On the other hand, as per evidence provided by this study from the literature, models based on fluid flow alone cannot simultaneously predict bone formation at both the periosteal and endocortical surfaces. This suggests that another component of the osteocyte's mechanical environment, such as pore pressure, may play an essential role in bone adaptation, either alone or in combination with other stimuli, such as tissue strain and/or interstitial fluid flow. In vitro experiments have also confirmed that osteocytes respond to cyclic pore pressure and, thus, have a mechanism to sense the pressure, possibly because of its viscoelasticity. In this work, dissipation energy density, being irreversible work done per unit volume, has been successfully used as a greater stimulus to incorporate all of the parameters of mechanical environments of the LCN, such as waveforms of both fluid velocity and pore pressure, number of loading cycles. Mineral Apposition Rate (MAR) has also been mathematically derived to be proportional to the square root of the dissipation energy density minus its reference value. A hypothesis is accordingly proposed and successfully tested/validated for both endocortical and periosteal surfaces with respect to an in-vivo study on mouse tibia available in the literature. The constant of proportionality and the reference/threshold value of the dissipation energy density are determined through a nonlinear curve fitting. The mathematical/computational method thus developed is then successfully used to predict MAR at both endocortical and periosteal surfaces induced by a different loading condition. Computational implementation of the mathematical model has been done through a poroelastic finite element analysis of bone, where bone is assumed to be porous and filled with fluid, with a boundary condition that the periosteum is impermeable to the fluid and the endosteal surface maintains a reference zero pressure. This work also provides evidence for these assumptions to be true based on the state-of-the-art literature on related experimental studies. The currently developed model shows that the bone uses these conditions (assumptions) to its advantage, as the greater stimulus, i.e., the dissipation energy due to both fluid flow and pore pressure, are of a similar order at both the surfaces, and hence osteogenesis of the same order at both the surfaces. As a bottom line, the resulting model is the first of its kind as it has been able to correctly predict MAR at both endocortical and periosteal surfaces. This study thus significantly advances the modeling of cortical bone adaptation to exogenous mechanical loading.

Pharmacophore derived 3D-QSAR, molecular docking, and simulation studies of quinoxaline derivatives as ALR2 inhibitors.

Aldose Reductase 2 (ALR2), a key enzyme of the polyol pathway, plays a crucial role in the pathogenesis of diabetic complications. Quinoxaline scaffold-based compounds have been identified as potential ALR2 inhibitors for the management of diabetic complications. In the present work, molecular dynamic simulation studies in conjugation with pharmacophore mapping and atom-based 3D-QSAR were performed on a dataset of 99 molecules in comparison with Epalrestat (reference) to mark the desirable structural features of quinoxaline analogs to generate a probable template for designing novel and effective ALR2 inhibitors. The most potent compound 81 was subjected to MD simulation studies and found to be stable, with better interactions with the binding pocket as compared to Epalrestat. The MM-GBSA and MM-PBSA calculations showed that compound 81 possessed binding free energies of -35.96 and -4.92 kcal/mol, respectively. Atom-based 3D-QSAR yielded various pharmacophoric features with excellent statistical measures, such as correlation coefficient (R2 value), F-value (Fischer ratio), Q2 value (cross-validated correlation coefficient), and Pearson's R-value for training and test sets. Furthermore, the pharmacophore mapping provided a five-point hypothesis (AADRR) and docking analysis revealed the active ligand-binding orientations on the active site's amino acid residues TYR 48, HIE 110, TRP 111, and TRP 219. The results of this study will help in designing potent inhibitors of ALR2 for the management of diabetic complications.Communicated by Ramaswamy H. Sarma.

Dual aromatase-steroid sulfatase inhibitors (DASI's) for the treatment of breast cancer: a structure guided ligand based designing approach.

Dual aromatase-steroid sulfatase inhibitors (DASIs) lead to significant deprivation of estrogen levels as compared to a single target inhibition and thereby exhibited an additive or synergistic effect in the treatment of hormone-dependent breast cancer (HDBC). Triazole-bearing DASI's having structural features of clinically available aromatase inhibitors are identified as lead structures for optimization as DASI's. To identify the spatial fingerprints of target-specific triazole as DASI's, we have performed molecular docking assisted Gaussian field-based comparative 3D-QSAR studies on a dataset with dual aromatase-STS inhibitory activities. Separate contours were generated for both aromatase and steroid sulphates showing respective pharmacophoric structural requirements for optimal activity. These developed 3D-QSAR models also showed good statistical measures with the excellent predictive ability with PLS-generated validation constraints. Comparative steric, electrostatic, hydrophobic, HBA, and HBD features were elucidated using respective contour maps for selective target-specific favourable activity. Furthermore, the molecular docking was used for elucidating the mode of binding as DASI's along with the MD simulation of 100 ns revealed that all the protease-ligand docked complexes are overall stable as compared to reference ligand (inhibitor ASD or Irosustat) complex. Further, the MM-GBSA study revealed that compound 24 binds to aromatase as well as STS active site with relatively lower binding energy than reference complex, respectively. A comparative study of these developed multitargeted QSAR models along with molecular docking and dynamics study can be employed for the optimization of drug candidates as DASI's.Communicated by Ramaswamy H. Sarma.

Molecular dynamics and 3D-QSAR studies on indazole derivatives as HIF-1α inhibitors.

Hypoxia-inducible factor (HIF) is a transcriptional factor which plays a crucial role in tumour metastasis thereby responsible for development of various forms of cancers. Indazole derivatives have been reported in the literature as potent HIF-1α inhibitor via interaction with key residues of the HIF-1α active site. Taking into consideration the role HIF-1α in cancer and potency of indazole derivative against HIF-1α; it was considered of interest to correlate structural features of known indazole derivatives with specified HIF-1α inhibitory activity to map pharmacophoric features through Three-dimensional quantitative structural activity relationship (3D-QSAR) and pharmacophore mapping. Field and Gaussian based 3D-QSAR studies were performed to realize the variables influencing the inhibitory potency of HIF-1α inhibitors. Field and Gaussian- based 3D-QSAR models were validated through various statistical measures generated by partial least square (PLS). The steric and electrostatic maps generated for both 3D-QSAR provide a structural framework for designing new inhibitors. Further; 3D-maps were also helpful in understanding variability in the activity of the compounds. Pharmacophore mapping also generates a common five-point pharmacophore hypothesis (A1D2R3R4R5_4) which can be employed in combination with 3D-contour maps to design potent HIF-1α inhibitors. Molecular docking and molecular dynamics (MD) simulation of the most potent compound 39 showed good binding efficiency and was found to be quite stable in the active site of the HIF-1α protein. The developed 3D-QSAR models; pharmacophore modelling; molecular docking studies along with the MD simulation analysis may be employed to design lead molecule as selective HIF-1α inhibitors for the treatment of Cancer.

Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches.

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.

Insight into the liver dysfunction in COVID-19 patients: Molecular mechanisms and possible therapeutic strategies.

As of June 2022, more than 530 million people worldwide have become ill with coronavirus disease 2019 (COVID-19). Although COVID-19 is most commonly associated with respiratory distress (severe acute respiratory syndrome), meta-analysis have indicated that liver dysfunction also occurs in patients with severe symptoms. Current studies revealed distinctive patterning in the receptors on the hepatic cells that helps in viral invasion through the expression of angiotensin-converting enzyme receptors. It has also been reported that in some patients with COVID-19, therapeutic strategies, including repurposed drugs (mitifovir, lopinavir/ritonavir, tocilizumab, etc.) triggered liver injury and cholestatic toxicity. Several proven indicators support cytokine storm-induced hepatic damage. Because there are 1.5 billion patients with chronic liver disease worldwide, it becomes imperative to critically evaluate the molecular mechanisms concerning hepatotropism of COVID-19 and identify new potential therapeutics. This review also designated a comprehensive outlook of comorbidities and the impact of lifestyle and genetics in managing patients with COVID-19.

Towards the Objective Speech Assessment of Smoking Status based on Voice Features: A Review of the Literature.

BACKGROUND AND OBJECTIVE: In smoking cessation clinical research and practice, objective validation of self-reported smoking status is crucial for ensuring the reliability of the primary outcome, that is, smoking abstinence. Speech signals convey important information about a speaker, such as age, gender, body size, emotional state, and health state. We investigated (1) if smoking could measurably alter voice features, (2) if smoking cessation could lead to changes in voice, and therefore (3) if the voice-based smoking status assessment has the potential to be used as an objective smoking cessation validation method. METHODS: A systematic review of the scientific literature was conducted to compile studies on smoking status assessment based on voice features. We searched nine scientific databases for original studies involving the effects of smoking on voice features, the effects of smoking cessation on voice features. RESULTS: A total of 34 studies were identified for review. We found that fundamental frequency, jitter, shimmer, harmonics to noise ratio, and other voice features are affected by smoking and could be used to assess smoking status. CONCLUSION: Speech assessment of smoking status based on voice features has potential as a smoking status validation method, as it is simple, reliable, and less time-consuming. Furthermore, this study provides recommendations for future research on the objective speech assessment of smoking status based on voice features.

Design of neo-glycoconjugates that target the mannose receptor and enhance TLR-independent cross-presentation and Th1 polarization.

Cross-presentation is an important mechanism by which DCs present exogenous antigens on MHC-I molecules, and activate CD8(+) T cells, cells that are crucial for the elimination of tumors. We investigated the feasibility of exploiting the capacity of the mannose receptor (MR) to improve both cross-presentation of tumor antigens and Th polarization, processes that are pivotal for the anti-tumor potency of cytotoxic T cells. To this end, we selected two glycan ligands of the MR, 3-sulfo-Lewis(A) and tri-GlcNAc (N-acetylglucosamine), to conjugate to the model antigen OVA and assessed in vitro the effect on antigen presentation and Th differentiation. Our results demonstrate that conjugation of either 3-sulfo-Lewis(A) or tri-GlcNAc specifically directs antigen to the MR. Both neo-glycoconjugates showed, even at low doses, improved uptake as compared with native OVA, resulting in enhanced cross-presentation. Using MR(-/-) and MyD88-TRIFF(-/-) bone marrow-derived DCs (BMDCs), we show that the cross-presentation of the neo-glycoconjugates is dependent on MR and independent of TLR-mediated signaling. Whereas proliferation of antigen-specific CD4(+) T cells was unchanged, stimulation with neo-glycoconjugate-loaded DCs enhanced the generation of IFN-γ-producing T cells. We conclude that modification of antigen with either 3-sulfo-Lewis(A) or tri-GlcNAc enhances cross-presentation and permits Th1 skewing, through specific targeting of the MR, which may be beneficial for DC-based vaccination strategies to treat cancer.

The simultaneous ex vivo detection of low-frequency antigen-specific CD4+ and CD8+ T-cell responses using overlapping peptide pools.

The ability to measure antigen-specific T cells at the single-cell level by intracellular cytokine staining (ICS) is a promising immunomonitoring tool and is extensively applied in the evaluation of immunotherapy of cancer. The protocols used to detect antigen-specific CD8+ T-cell responses generally work for the detection of antigen-specific T cells in samples that have undergone at least one round of in vitro pre-stimulation. Application of a common protocol but now using long peptides as antigens was not suitable to simultaneously detect antigen-specific CD8+ and CD4+ T cells directly ex vivo in cryopreserved samples. CD8 T-cell reactivity to monocytes pulsed with long peptides as antigens ranged between 5 and 25 % of that observed against monocytes pulsed with a direct HLA class I fitting minimal CTL peptide epitope. Therefore, we adapted our ICS protocol and show that the use of tenfold higher concentration of long peptides to load APC, the use of IFN-α and poly(I:C) to promote antigen processing and improve T-cell stimulation, does allow for the ex vivo detection of low-frequency antigen-specific CD8+ and CD4+ T cells in an HLA-independent setting. While most of the improvements were related to increasing the ability to measure CD8+ T-cell reactivity following stimulation with long peptides to at least 50 % of the response detected when using a minimal peptide epitope, the final analysis of blood samples from vaccinated patients successfully showed that the adapted ICS protocol also increases the ability to ex vivo detect low-frequency p53-specific CD4+ T-cell responses in cryopreserved PBMC samples.

Preliminary Study of a Modular MR-Compatible Robot for Image-Guided Insertion of Multiple Needles.

Percutaneous needle-based interventions such as transperineal prostate brachytherapy require the accurate placement of multiple needles to treat cancerous lesions within the target organ. To guide needle placement, magnetic resonance imaging (MRI) offers excellent visualization of the target lesion without the need for ionizing radiation. To date, multi-needle insertion relies on a grid template, which limits the ability to steer individual needles. This work describes an MR-compatible robot designed for the sequential insertion of multiple non-parallel needles under MR guidance. The 6-DOF system is designed with an articulated arm to extend the reach of the robot. This strategy presents a novel approach enabling the robot to maneuver around existing needles while minimizing the footprint of the robot. Forward kinematics as well as optimization-based inverse kinematics are presented. The impact of the robot on image quality was tested for four sequences (T1w-TSE, T2w-TSE, THRIVE and EPI) on a 3T Philips Achieva system. Quantification of the signal-to-noise ratio showed a 46% signal loss in a gelatin phantom when the system was powered on but no further adverse effects when the robot was moving. Joint level testing showed a maximum error of 2.10 ± 0.72°s for revolute joints and 0.31 ± 0.60 mm for prismatic joints. The theoretical workspace spans the proposed clinical target surface of 10 x 10 cm. Lastly, the feasibility of multi-needle insertion was demonstrated with four needles inserted under real-time MR-guidance with no visible loss in image quality.

Targeting mitochondrial bioenergetics as a promising therapeutic strategy in metabolic and neurodegenerative diseases.

Mitochondria are the organelles that generate energy for the cells and act as biosynthetic and bioenergetic factories, vital for normal cell functioning and human health. Mitochondrial bioenergetics is considered an important measure to assess the pathogenesis of various diseases. Dysfunctional mitochondria affect or cause several conditions involving the most energy-intensive organs, including the brain, muscles, heart, and liver. This dysfunction may be attributed to an alteration in mitochondrial enzymes, increased oxidative stress, impairment of electron transport chain and oxidative phosphorylation, or mutations in mitochondrial DNA that leads to the pathophysiology of various pathological conditions, including neurological and metabolic disorders. The drugs or compounds targeting mitochondria are considered more effective and safer for treating these diseases. In this review, we make an effort to concise the available literature on mitochondrial bioenergetics in various conditions and the therapeutic potential of various drugs/compounds targeting mitochondrial bioenergetics in metabolic and neurodegenerative diseases.

Tumour-associated glycan modifications of antigen enhance MGL2 dependent uptake and MHC class I restricted CD8 T cell responses.

We recently showed that MGL2 specifically binds tumour-associated glycan N-acetylgalactosamine (GalNAc). We here demonstrate that modification of an antigen with tumour-associated glycan GalNAc, targets antigen specifically to the MGL2 on bone marrow derived (BM)-DCs and splenic DCs. Glycan-modification of antigen with GalNAc that mimics tumour-associated glycosylation, promoted antigen internalisation in DCs and presentation to CD4 T cells, as well as differentiation of IFN-γ producing CD4 T cells. Furthermore, GalNAc modified antigen enhanced cross-presentation of both BM-DCs and primary splenic DCs resulting in enhanced antigen specific CD8 T cell responses. Using MyD88-TRIFF(-/-) BM-DCs we demonstrate that the enhanced cross-presentation of the GalNAc modified antigen is TLR independent. Our data strongly suggest that tumour-associated GalNAc modification of antigen targets MGL on DCs and greatly enhances both MHC class II and class I presentation in a TLR independent manner.