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Inhibition of insulin amyloid fibrillogenesis using antioxidant copolymers with dopamine pendants.
No full textAmyloid aggregation, intricately related to various neurodegenerative and metabolic diseases, presents a significant growing health challenge. Dopamine, a potent antioxidant, plays a pivotal role in modulating protein misfolding by leveraging its potent anti-amyloidogenic and neuroprotective properties. However, its biological applications are limited by poor aqueous solubility and suboptimal biocompatibility. To address these challenges, water-soluble copolymers (DP1-DP3) featuring dopamine and glucose side-chain pendants are fabricated and investigated for their efficacy in inhibiting amyloid fibril formation from insulin and amyloid beta (Aβ42) peptide. The effects of DP1-DP3 copolymers on amyloid fibrillation are assessed using several biophysical techniques, which demonstrate excellent radical scavenging properties and the remarkable efficacy of DP3 copolymer in suppressing insulin amyloid fibrillation, achieving ≈97% inhibition. Isothermal titration calorimetry (ITC) and fluorescence binding experiments are carried out to quantify the insulin-DP3 complex formation. Molecular dynamics simulations validate the ability of DP3 to prevent amyloid fibrillogenesis of both insulin and Aβ42. These studies demonstrate beneficial interactions between DP3 and amyloidogenic protein/peptide, facilitating the stability of the resulting complexes. Overall, the present findings suggest that dopamine-based antioxidant polymers hold significant potential as advanced therapeutic agents for preventing amyloidogenic disorders
Facially amphiphilic cholate-conjugated polymers for regulating insulin fibrillation.
No full textTo understand the influence of facially amphiphilic polymers (FAPs) on insulin fibril (IF) inhibition, three different cholate-based FAPs [cationic (PFCAQA), anionic (PFCASF), and zwitterionic (PFCASB)] have been synthesized. Besides, two control polymers [cholate and sulfobetaine-pendant random copolymer PRCASB (without facial amphiphilicity) and sulfobetaine-tethered homopolymer PSBMA (without cholate pendants)] are also prepared. Several biophysical experiments such as spectroscopic techniques [thioflavin T (ThT), Nile red (NR), tyrosine (Tyr) fluorescence assay], turbidity assay by ultraviolet–visible (UV–vis) spectroscopy, dynamic light scattering (DLS), circular dichroism (CD) study, and microscopic investigation are performed to investigate the role of polymers as antiamyloidogenic agents during insulin fibrillation. Interestingly, the PFCASB zwitterionic polymer behaves as the most efficacious antiamyloidogenic agent. To clarify the interaction of PFCASB and native insulin (NI), an isothermal titration calorimetry (ITC) experiment is carried out. Tyr and the NR fluorescence investigation suggest the important role of hydrophobic interactions, whereas the ITC experiment confirms the significance of hydrophobic and electrostatic interactions in the IF inhibitory process. A hemolytic test is conducted to investigate the toxicity caused by IF and the efficacy of PFCASB in prohibiting erythrocyte disruption caused by IF. Overall, the present work reveals the impact of the facially amphiphilic cholic acid (CA)-based zwitterionic polymer in modulating the insulin aggregation process and gives a new perspective for investigations on different protein aggregations
The Utility and Impact of Telemedicine in Childhood Cancer Care: A Mixed Methods Study
No full textBackground
While teleconsultation has proven feasible for adult cancer patients, its utility in childhood cancer care in India is unknown. This study assesses caregiver satisfaction, feasibility, and the economic impact of teleconsultation for children with cancer.
Procedure
This mixed methods study was conducted in the pediatric cancer outpatient service at a tertiary care cancer center in India. Routine care teleconsultations were performed via telephone or email. Caregivers who received teleconsultations during the study period or 6 months prior were interviewed by phone. Data collected included demographic details, teleconsultation usage patterns, caregiver satisfaction, reasons for satisfaction or dissatisfaction, and cost savings. Logistic regression identified baseline characteristics associated with satisfaction. In-depth interviews were conducted in a separate cohort till thematic saturation.
Results
Caregivers of 465 children were included for telephonic and eight for in-depth interviews Among the respondents, 72.8% (n = 339) were satisfied with teleconsultation, and 85.4% (n = 397) of those planned to continue using it. Significant reasons for satisfaction were cost and time savings, while difficulty explaining issues caused dissatisfaction. Multivariable analysis showed parental education above high school [OR: 1.59, p = 0.035] increased satisfaction likelihood, while a residential distance of greater than 100 km from the hospital [OR: 0.62, p = 0.033] decreased it. Median cost savings per visit was 1–$846). Cost savings did not determine satisfaction magnitude. Frequently cited themes included cost and time savings and the need for literacy to benefit from teleconsultation.
Conclusions
Teleconsultation is feasible and effective for routine childhood cancer care, even in resource-constrained settings
Ewing sarcoma of the hands and feet: Outcome and prognostic factors of a rare subsite in a low-middle income country
No full textBackground
The small bones of the hand and feet represent a rare site of Ewing sarcoma (ES) origin. This study presents a real-world dataset describing the clinical presentation, survival outcomes, and their determinants in this subsite.
Methods
This is a single-institutional retrospective study of patients with ES originating from the hands/feet (ES-HF), treated between 2003 and 2018. Clinical/demographic details and survival outcomes were retrieved from medical records. Descriptive statistics were used to summarize baseline characteristics. Prognostic factors for event-free survival (EFS) and overall survival (OS) were identified by Cox regression. The clinical features and outcomes were compared between ES-HF and ES-others (ES from other sites) in the cohort.
Results
Of 859 ES cases, 28 (3.2 %) patients had ES-HF including four ES-hands (0.5 %) and 24 ES-feet (2.8 %). The calcaneum was the most common site [11 of 28 cases; 39.3 %]. More than half of the patients (53.6 %) had metastatic disease at presentation. In comparison with ES-others, ES-HF had longer median symptom duration [12 versus 4 months; p < 0.001] and smaller tumor diameter [5.5 versus 9 cm; p < 0.001]. The median EFS and OS of the cohort were 30.5 and 39.13 months respectively. Only local therapy receipt was associated with improved EFS (multivariable HR:0.013; 95%CI:0.001–0.158; p < 0.001) and OS (multivariable HR:0.028; 95 % CI:0.003–0.272; p = 0.002). Patients receiving radiotherapy alone had inferior OS compared to those receiving surgery alone. (HR: 9.22; 95 % CI: 1.12–76.31; p = 0.039)
Conclusion
ES-HF is a rare ES subsite. Although indolent, metastases are common at presentation. Meticulous local control can improve survival in both localized and metastatic disease for this subsite
Inter-numerology interference in mixed numerology mimo-ofdm systems in spatially correlated, time-varying fading channels
No full text5G New Radio (NR) employs a mixed-numerology orthogonal frequency-division multiplexing (OFDM)-based physical layer and makes extensive use of multiple-input-multiple-output (MIMO) techniques. Unlike conventional single-numerology OFDM systems, subcarriers of different numerologies interfere with each other causing inter-numerology interference (INI). In such MIMO-OFDM systems, we derive novel expressions for the INI covariance matrix at each user. The novelty and strength of our results come from the comprehensive model our analysis is based on. Our model accounts for the wideband and spatial correlation aspects of the channel and time-variations within an OFDM symbol caused by Doppler spread as well as phase noise. The INI covariance turns out to be proportional to the receive spatial correlation matrix, with the proportionality constants depending on the transmit spatial correlation matrix, the temporal auto-correlation of the channel, phase noise auto-correlation, and transmit signal covariances of the users
Actor-critic driven deep reinforcement learning for optimising agri-food supply chain
No full textThe agri-food supply chain is a complex network enclosing various stakeholders, from farmers to consumers, with multifaceted interactions and dependencies. Traditional supply chain management approaches often need help adapting to dynamic environments and optimising decision-making processes. Deep reinforcement learning is employed by integrating value-based and policy-based models, enhanced by advanced learning techniques, to tackle these challenges. This paper explores applying Deep Reinforcement Learning (DRL) approaches, including Q-learning, Deep Q-Learning (DQL), and the Actor-Critic method, to optimise the efficiency of the agri-food supply chain. The actor-critic model significantly enhances decision-making processes across various supply chain stages by improving efficiency and increasing profit margins. A specific scenario of sugar processing and distribution is incorporated, considering real-world scenarios to validate our model. DRL methods optimise sugar production, storage and distribution, ensuring timely deliveries and enhancing profitability. The models address fluctuating demand and transportation logistics challenges, resulting in a more streamlined and responsive sugar distribution network. The findings reveal that Actor-Critic and DQL methods significantly outperform traditional Q-learning considering product profitability, offering unique advantages in handling complex state-action spaces
Contractive representations of odometer semigroup
No full textGiven a natural number, the odometer semigroup—also known as the adding machine or the Baumslag–Solitar monoid with two generators—is a well-known object in group theory. This paper examines the odometer semigroup in relation to representations of bounded linear operators. We focus on noncommutative operators and prove that contractive representations of it always admit nicer representations. We provide a complete description of representations on the Fock space and relate them to odometer lifting and subrepresentations. Along the way, we also classify Nica covariant representations
Simulation-assisted Multimodal Deep Learning (Sim-MDL) fusion models for the evaluation of thermal barrier coatings using infrared thermography and terahertz imaging
No full textThermal Barrier Coatings (TBCs) are critical components in high-temperature applications, such as gas turbines and aerospace engines, where they protect the underlying substrate metals from extreme thermal stress and extend component life. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Popular non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth, and challenges in inspecting multi-layer coatings and detecting subsurface defects. To address these challenges, our study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that integrates the strengths of IRT and THz imaging for a comprehensive evaluation of TBCs. To generalize the study to a range of thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. The data from IRT and THz modalities are fused in the Sim-MDL models to enable characterization of the TBC topcoat layer. IRT and THz experimental data, together with simulations, form a large dataset that is used to train deep learning models. The framework is tested and optimized for multimodal data fusion using two DL architectures based on convolutional neural networks (CNN) and long short-term memory (LSTM), allowing the model to learn correlations and complex patterns across the IRT and THz modalities. The study is conducted on four newly coated samples ranging in thickness from 24 to 120 μm. An attention-based LSTM model trained on both simulation and experimental data shows high prediction accuracy with MAPE values ranging from 2.06–4.43% for thermal conductivity, 2.05–3.57% for heat capacity, 11.53–1.75% for topcoat thickness, and 0.27–1.05% for refractive index, respectively, for the topcoat layers of four samples. Our model outperformed the single-modality models and conventional parameter estimation methods in terms of accuracy and robustness, highlighting the potential of multimodal data for automated analysis of TBC in industrial settings
Earth observation for monitoring of shifting cultivation
No full textSystematic monitoring of shifting cultivation areas is important for assessing forest and cropland conversion in northeast India (NEI). Recent advances in satellite image processing techniques and computational facilities have improved the efficiency of forest cover change mapping. The current study aimed to map shifting cultivation areas by monitoring deforested and burned areas using a random forest (RF) machine learning algorithm-based on satellite data-derived spectral indices and topographic variables (elevation). This study was conducted in three states in NEI: Manipur, Meghalaya, and Nagaland. Cloud-free Landsat satellite images were acquired for the premonsoon season from 2016 to 2018 to map shifting cultivation. Spectral indices related to vegetation (normalized difference vegetation index [NDVI], enhanced vegetation index [EVI], and soil adjusted vegetation index [SAVI]), burned area (normalized burned ratio [NBR], normalized burned ratio-2 [NBR2]), and leaf water (normalized difference moisture index [NDMI]) were employed. The RF classification model was developed and validated with the training data points generated for images collected in 2017 and 2018. The model demonstrated an accuracy of >80% in burned area mapping. The developed model was then applied to the images of 2016 and 2017, and the classification accuracy was assessed with more than 11,000 points, which indicated a high classification accuracy (>90% overall accuracy). Moreover, the comparison with MODIS-burned area products showed good agreement with the identified shifting cultivation areas. A total of 690 km2 of forest was deforested and burned during 2016–17, which was reduced to 620 km2 during 2017–18. The indices NDMI, NBR, NBR2, and elevation were identified as the dominant contributors in burned area mapping. The current study demonstrates the utility of machine learning approaches for burned-area mapping using multitemporal satellite images. Specifically, the study demonstrates the utility of spatiotemporal deforestation and burned area mapping using machine learning approaches that are useful to forest resource managers, conservationists, and decision-makers
Sustaining floriculture and floral fragrance in a changing climate
No full textFlower scent is a composite character that is determined by a complex mixture of low-molecular-weight volatile molecules. The studies on floral fragrance focused on its chemical elucidation, coupled with chemical synthesis to produce the large quantities demanded by the perfume and food industries. This study investigates the concept that ecological niche models can identify potential areas where floral fragrance correlates with climatic conditions. By employing fragrance-based ecological niche models, the study maps the distribution of two fragrant flower species, viz., jasmine and tuberose, in India. The study also helped us in understanding how specific environmental factors may influence the distribution of fragrant flowers, thus indicating how climate change would impact the distribution of two floral crops with desired levels of fragrance. The findings have implications for expanding sustainable floriculture in India in the face of climate change, ecological conservation, and understanding plant-environment interactions