Whey Protein-Tannic Acid Conjugate Stabilized Emulsion-Type Pork Sausages: A Focus on Lipid Oxidation and Physicochemical Features

The purpose of this study was to investigate the oxidative stability and physicochemical properties of pork emulsion sausages with whey protein-tannic acid conjugate and native whey protein. Over the course of 21 days, the thiobarbituric acid reactive substances (TBARS) of sausages containing a whey protein-tannic acid conjugate were lower than those of sausages with regular whey protein (p < 0.05). Kinetically, sausage containing the whey protein-tannic acid conjugate (k = 0.0242 day^-1) appeared to last longer than sausage containing regular whey protein (k = 0.0667 day^-1). The addition of the whey protein-tannic acid conjugate had no effect on product texture because there was no difference in hardness, springiness, cohesiveness, or water-holding capacity between the control and treated samples at Day 0 (p > 0.05). Scanning electron microscopy revealed that, at Day 21, the control sausage exhibited emulsion coalescence, as evidenced by an increase in the number of oil droplets and large voids, but not the whey protein-tannic acid conjugate-added sausage. There was no variation in the L*, a*, and b* values of the sausages when the whey protein-tannic acid conjugate was added (p > 0.05). However, there was a little increase in ΔE value in the treated sample. Thus, the whey-protein-tannic acid conjugate appeared to stabilize the lipid and physicochemical properties of the sausages by lowering the rate of TBARS production, retaining texture, water-holding capacity, and color, as well as by minimizing lipid coalescence during refrigerated storage.

Effects of different cooking methods on the proximate composition and physical properties of Brown shrimp (Metapenaeus dobsonii) during cooking and freezing cycle

Present study aimed to evaluate the changes in proximate composition and physical attributes in brown shrimp (Metapenaeus dobsonii) exposed to different methods of cooking followed by freezing. For this, three different grades (100/200, 200/300, and 300/500 numbers per kg) of brown shrimp were cooked at 90°C till the core temperature of the product reaches 85°C using hot water, steam, and microwave (400W) techniques. The changes in yield, cooking loss, proximate composition, textural, and colour profile were assessed for cooked shrimps. The cooking loss was higher for larger grades of shrimp, whereas shrimp cooked using hot water exhibited the highest cooking loss. Lowest cooking loss was observed for microwave-cooked shrimp. Moisture content decreased after cooking whereas protein, fat, ash, and calorie content increased. After cooking, different grades of shrimp showed an increase in their lightness (L*), redness (a*), and yellowness (b*) values. The smaller grade shrimp exhibited lower value for cohesiveness, hardness, chewiness, and gumminess. Different cooking techniques yielded cooked shrimp of varying hardness values.

Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning

Background/Introduction

Mapping AF is challenging. Monophasic action potentials (MAPs) show that most of the recorded signals are not representing the atrial activity, and arise from far-field or other artifacts. Thus, reducing noise in electrophysiological signals is essential, yet it can be difficult for cross-talk from other chambers and pacing. Strategies to reduce noise include template matching, averaging, and smoothing, but all of them have major limitations. Furthermore, expert interpretation requires knowledge to discriminate signals from noise, but is subjective.

Purpose

We hypothesised a) that atrial and ventricular electrograms with varying artifact and noise can be denoised using autoencoder neural networks (NNs) without requiring manual annotation and in a reproducible manner, and b) we could train these NNs on a large available dataset ventricular signals, then apply transfer learning to the original smaller atrial dataset. We applied this approach to MAPs, which have interpretable shapes and would help identifying local from far-field signals helping in diagnosis, mapping and ablation.

Methods

We first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; Fig. 1A), with 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641 from 21 patients, 67±5y, 13 women; Fig. 2B, D, F). The autoencoder was used to eliminate pacing artifacts in ventricular MAPs (Fig. 1B, C) and denoise atrial MAPs (Fig. 2C, E, G) by reconstructing key learned features. The accuracy of the reconstruction was evaluated using Pearson Correlation Coefficient (PCC) and a novel similarity coefficient (SC). No manual annotation was required to identify noisy signals.

Results

The trained NN encoder learned key features of ventricular MAPs and reconstructed these clean signals with a SC=0.91±0.16 and PCC=0.99±0.01 (Fig. 1A). With this training, the NN was able to denoise ventricular MAPs with pacing artifact (Fig. 1B, C). After fine-tuning, the NN learned key signal features (upstroke, triangular descent, terminus) and thus reduced diverse noise without specific training or manual annotation. Namely, it was able to reconstruct atrial MAPs eliminating ventricular noise, high frequency noise and truncated signals (Fig. 2).

Conclusions

Machine learned encoder-decoders are powerful tools that can learn essential features of atrial and ventricular signals and hence isolate noise. Transfer learning is effective when large datasets are unavailable for training. This approach can separate atrial beats from far-field ventricular beats and other sources of noise. The ability to eliminate a diverse range of noise improves this approach over existing techniques and may have far-reaching applications in electrophysiology, such as mapping and ablation.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Novel electrogram featurization reveals a spectrum of response to ablation from atrial tachycardia to types of atrial fibrillation

Background

Although atrial tachycardia (AT) may interconvert with fibrillation (AF) in many patients, it is undefined if this represents a pathophysiological spectrum of organization, or whether it indicates that AF will respond better to ablation.

Objective

To test the hypothesis that the spatial area within which electrograms (EGMs) repeat in synchronized fashion over time indicates a spectrum from AT, in which areas span the entire atria, to AF, in which areas are limited. We further hypothesized that repetitive areas would be larger in AF patients with acute termination than in those with poor response to ablation.

Methods

We studied N=234 patients (47% women, 64±10Y), of whom (i) N=10 had AT, (ii) N=120 had AF that terminated with ablation (“Term”), (ii) N=104 had AF that did not terminate (“Non-term”). All patients had global left atrial mapping by 64 pole baskets (Abbott, IL). Spatial areas of repetitive activity (REACT) were calculated by correlating unipolar EGMs in 2x2 grids for 4 sec, repeated for the entire atria (Figure 1A, B). We quantified global organization by averaging the REACT map for each patient.

Results

Figure 1C shows progressively decreasing areas of repetitive EGM from AT to AF Term to AF Non-term (p&lt;0.001, ANOVA). Figure 1D shows a case of AT in a 71 YO male and global REACT &gt;0.90, a case of AF REACT 0.45 in a 65 YO male with termination by ablation, and a case of AF with REACT 0.19 in an 85 YO male that did not terminate. Further, ROC analysis of REACT analysis in AF cases predicted termination with an AUC of 0.71.

Conclusion

Spatial areas of repeating electrogram shapes indicates a spectrum from AT to AF with good and AF with poor acute response to ablation. Future studies should investigate whether REACT areas can be identified non-invasively, such as by body surface ECG, to guide ablation or prognosis.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Spatiotemporal signatures of response to atrial fibrillation ablation

Background

Atrial fibrillation (AF) can have organized regions, in the form of consistent dominant frequency sites, focal or reentrant sites, but it is unclear how these overlap with or differ from focal atrial tachycardias (AT) or potential drivers. We set out to develop an intuitive method based on fundamental electrogram shape and timing to separate types of AF.

Objective

To test the hypothesis that spatial regions of electrogram (EGM) in AF that show similar shapes over time based on cross-correlation analysis may separate patients with differing response to ablation.

Methods

We recruited N=133 patients (63.8±12.1 Y, 32% women), (i) N=10 had AT, (ii) N=122 AF that was or was not terminated by ablation, and (iii) N=1 pacing. All patients had left atrial mapping by 64 pole baskets. We applied repetitive activity (REACT) mapping that correlates EGMs in contiguous 2x2 regions (Fig. 1A) over 4sec. To calibrate REACT, we introduced simulated variations in shape (gaussian noise) and timing (gaussian delay) to pacing EGMs and computed nomograph over 100 random trials (Fig. 1C).

Results

Fig. 1B shows that REACT in a 71-year-old man with AT is more organized than in a 65 YO man with AF (100% vs 40% mapped field). Overall, REACT was higher in AT than AF (0.63±0.15 vs 0.36±0.22, p&lt;0.001). There were 24 cases in which global REACT between AF and AT groups had the overlapping range of values, indicating organized “islands” in AF analogous to AT. From nomograph in Fig. 1C we identified that this overlap reflects 15 ms variation in cycle length and 20% variation in EGM shape (labelled “x” in Fig. 1C).

Conclusion

Basic electrogram properties in AF of similar shapes in spatial areas over time can separate response to ablation and may represent “islands” of AT. Future studies should investigate the mechanisms for such islands and whether they may be targeted for therapy.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Automatic left atrial segmentation from cardiac CT using computer graphics imaging and deep learning

Introduction

Segmenting left atrial (LA) substructures, including the LA body, appendage (LAA), and pulmonary veins (PVs), from computed tomography (CT) is central to electroanatomic mapping for ablation and functional studies in patients with atrial fibrillation (AF). However, this process requires manual outlining which needs special training, is subjective, and is difficult to scale. Computer graphics imaging (CGI) has been applied in media, film, and computer-aided design to reliably segment complex structures using their basic geometric representations.

Purpose

We hypothesized that LA substructures can be “virtually” dissected using CGI to separate geometric contours of the “convex ellipsoid” LA, “tubular” PVs, and “conical” LAA. We further hypothesized that the results of virtual dissection can be used to train a deep learning (DL) model to segment raw CT scans.

Methods

First, a mathematical method based on CGI techniques – erosion and dilation – was developed to “virtually dissect” the convex LA body from the original concave shell in publicly available digital atria with diverse simulated morphologies (Fig. 1A). The PVs and LAA were then automatically revealed and labeled by a 3D subtraction approach. Second, we refined precise LA/PV/LAA boundaries by tuning hyper-parameters from N=5 patient shells (Fig. 1B). Third, we used virtual dissection to train a DL model to segment CTs in N=20 patient atria (Fig. 1C). Finally, we applied this pipeline to segment raw CTs in a validation cohort of N=105 patients (23.8% women, 63.8±10.3Y; Fig. 1D).

Results

Virtual dissection accurately identified LA/PV/LAA boundaries in the training set (Dice coefficients 89–98%). In the independent test cohort (N=105), this automated pipeline accurately segmented raw CTs with Dice 81–95% (Fig. 1D) compared to a panel of experts (p&lt;0.001).

Conclusion

CGI of basic cardiac geometry combined with deep learning in small datasets can accurately segment raw CT scans in large populations. This computational pipeline may automate and simplify cardiac image processing and ablation procedures, and could be applied to the ventricle or other organ systems for diverse therapeutic strategies or to train machine learning.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institutes of Health

Artificial intelligence to reduce artifact in cardiac electrophysiological signals

Background/Introduction

Signals in Electrophysiology cases are often noisy despite laboratory shielding and filtering, and current noise-reduction methods are suboptimal. Template matching can identify a “nearest type” of electrogram, but libraries of signal shapes may be unavailable. Beat averaging can reduce noise but obscures beat-to-beat variations and is not optimal to analyze dynamically changing signals, such as when moving a catheter in the heart. Smoothing reduces noise yet blurs high frequency components.

Purpose

We set out to test if machine learned autoencoders could reduce noise in single beats without requiring massive training data or beat libraries. Specifically, we hypothesised that noisy electrograms in small datasets of atrial signals could be de-noised using an encoder-decoder neural network (NN) using transfer learning of machines trained to recognize key features in larger datasets of related signals.

Methods

We applied NN to monophasic action potentials (MAPs), because they have visually verifiable shapes. The NN was first trained to reconstruct 5706 left and right ventricular MAPs in 42 patients (67±13y; Fig. 1A). Transfer learning was then used to apply the NN to a much smaller dataset of 641 atrial MAPs in 21 patients (67±5y, 13 women; Fig. 1B, D, F).

Results

NN reconstructed atrial MAPs with a Pearson correlation of 0.87±0.11. After fine-tuning, NN reconstruction accuracy improved dramatically (Pearson 0.99±0.01; p&lt;0.001). In Fig. 1B–G the NN learned key MAP features (upstroke, triangular descent, terminus) and thus could eliminate ventricular artifact and electrical circuit noise without specific training or manual annotation.

Conclusion

Machine learned autoencoders are a novel and powerful approach to de-noise electrophysiological signals in a dynamic, beat-to-beat fashion. The ability to learn fundamental signal features from models trained in large datasets, and apply them via transfer learning to small datasets in different heart chambers may have wide ranging applications for automated signal annotation, mapping and ablation.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Sleep apnea is associated with stroke in young patients with or without atrial fibrillation:A population study of &gt;2 million individuals

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH, R01 HL149134, R01HL83359

Background

Both Sleep Apnea (SA) and Atrial Fibrillation/flutter (AF) are known risk factors for stroke, and both are increasing in prevalence. They are both under-diagnosed in young adults &lt;60 Y. There is an urgent need to define stroke risk portended by SA and AF yet there a paucity of data in adults aged 20-60 years.

Purpose

To define the relationship between stroke, SA, and AF in a very large cohort of 2 million young-middle aged adults aged 20-60 Y in Northern California.

Methods

We probed the Stanford Research Repository of electronic health data from 01/01/2008 to 11/30/2021 for the diagnoses of stroke, transient ischemic attacks, AF and SA using relevant codes (stroke: 433.X, 434.X, 436.X, I63.X, I65.X, I66.X, G45.X, G46.X; AF: I48.X; SA: G47.X, 327.27).

Results

We identified 2267485 patients aged 20-60Y (55% F; 32% White, 12% Asian, 3% Black), of whom SA was diagnosed in 52730 (2.3%), AF in 10230 (0.4%) and incident stroke in 10385 (0.4%) (Figure 1A)

In patients with SA, 1.5% developed incident stroke. Stroke was more common in patients with SA than those without, regardless of co-existing diagnosis of AF; OR with AF: 1.5 [1.3-1.7, p&lt;0.001] and OR without AF: 3.0 [2.8-3.3 p&lt;0.0001]. Risk of stroke with SA than without was noted to be higher in the younger age subgroups (Figure 1B) regardless of AF.

Although AF was more common in patients with SA than without (odds ratio, OR: 10.1 [9.6-10.6, p&lt;0.0001]), the majority of SA patients (63% with CHADS2VASC ≥2) with stroke did not have a diagnosis of AF (75%), of whom 96% were not anticoagulated (Fig 1, left panel). Of the remaining patients with SA and incident stroke, who did have AF (25%), only 26% were taking OACs at the time of stroke despite median CHADS2 VASC score=3 (Fig 1A, left panel).

Finally, 7% of AF patients developed incident stroke. Of these, 73% had CHADS2VASC ≥2, yet 44% were not anticoagulated. Patients with SA comprised a third of all AF patients with stroke and, compared to AF patients with stroke and without SA, had higher CHADS2VASC (median 3 vs 2, p&lt;0.001) and a similarly low use of anticoagulation (56% vs 54% on OAC) (Fig 1A, right panel).

Conclusions

In &gt;2 million young individuals, we uncover a novel association between SA and incident stroke, regardless of the diagnosis of AF. Surprisingly, three quarters of patients with SA developed incident stroke in the absence of AF, and were not anticoagulated. These results underscore the need to screen for AF and sleep apnea in young adults.

Noise reduction in electrophysiological signals using transfer machine learning

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Background/Introduction

Reducing electrophysiological signal noise is essential for diagnosis, mapping and ablation, yet most approaches are suboptimal. Template matching requires libraries of known signal types, that are difficult to obtain. Beat averaging can reduce noise, yet cannot be applied to single beats and obscures beat-to-beat variations. Beat smoothing can lose critical and subtle signal features. We set out to use neural networks (NN) based on encoder-decoders, which are able to extract key signal features and hence reconstruct them without noise and artifact.

Purpose

We hypothesised that electrograms with varying sources of artifact can be denoised using autoencoder neural networks. We further hypothesised that this could be achieved in a small data set by developing the method in a larger dataset of related signals, then using transfer learning. We tested this approach for atrial monophasic action potentials (MAPs) that have verifiable shapes.

Methods

The NN was first trained with 5706 left and right ventricular MAPs from 42 patients with ischemic cardiomyopathy (age 65±13y; fig 1.A): 60% for training, 20% (validation) and 20% (testing). Transfer learning and parameter-tuning were then used to apply this NN to a smaller sample of atrial MAPs (N=641, 21 patients, 67±5y, 13 women; fig D,F,H).

Results

The autoencoder was able to learn key features of MAPs, and hence reconstruct them without artifacts. NN learned ventricular MAPs with similarity coefficient 0.91±0.16, Pearson correlation 0.99± 0.01 (fig A) and learned key features (upstroke, triangular descent, terminus) to reduce noise (fig B-C). Applying this trained NN to atrial MAPs, the approach automatically eliminated ventricular artifact (fig E), high frequency noise (fig G), truncation (fig I), saturation and other artifacts. After fine-tuning, the NN reconstructed atrial MAPs with Pearson correlation = 0.99±0.01 (p&lt;0.001).

Conclusions

Machine learned encoder-decoders are powerful tools that can automatically eliminate diverse types of noise in single beats by learning essential signal features. Transfer learning makes this possible without large datasets for training, even from signals in a different cardiac chamber. This approach may have far-reaching applications for mapping and ablation.

Defining refractoriness in single atrial beats using autoencoder neural networks

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Background

Mapping atrial fibrillation (AF) is complicated by signals which may be local or far-field, but which cannot currently be separated. This could be clarified by a knowledge of atrial refractory periods, yet these are difficult to define from monophasic action potentials (MAP) in patients. We hypothesized that transfer learning using an autoencoder neural network (ANN), first trained with less-noisy ventricular signals, can be applied to de-noise and classify atrial MAPs.

Methods

We first developed an ANN to encode MAPs in 5706 ventricular MAPs from N=42 patients (age 65±13y) during pacing (fig1. A-B). This created a latent feature space. We now tuned the ANN to classify atrial MAPs in a different cohort of patients with AF. We used a statistical loss function based on mathematical optimization to evaluate the accuracy of final representations of the MAP and classify the different signals.

Results

The autoencoder ANN reconstructed ventricular MAPs with an average similarity of 0.85 (range 0-1) (an example is shown in fig 1.B). We tested on 3000 atrial MAPs in AF patients (N=21; 67±5y, 13 women). Atrial MAPs were accurately represented (fig 1.E-F) with similarity indices that were higher than those obtained by a panel of 3 experts. Fig. 1 shows the reconstruction of different signals: ventricular MAP (fig 1.A-B), ventricular MAP with pacing artifact (fig. 1.C-D), atrial MAP (transfer learning is assumed in here; fig 1.E-F) and noise or signals with morphologies of no interest (fig 1.G-H). Fig. 2 shows the classification of signals according to the similarity metric that allows distinguishing among the different types of signals without manual annotation (p&lt;0.05 between groups).

Conclusion

Atrial refractory periods can be defined in single beats in AF patients using autoencoder neural networks and transfer learning. This approach can separate atrial beats from far-field ventricular beats and other sources of noise. Future work can study if this approach can be used to improve AF mapping or define novel physiological phenotypes.

Machine-learned physiological signatures from the ECG predict sudden death in ischemic cardiomyopathy

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH)

Background

Low left ventricular ejection fraction (LVEF) is an imperfect predictor of sudden cardiac death (SCD) in patients with ischemic cardiomyopathy. Novel features from the ECG might provide a readily available tool to better predict risk.

Purpose

We hypothesized that machine learning (ML) of the ECG can be used to predict SCD, and the ML-learned ECG features could be referenced to interpretable intracardiac signals (monophasic action potentials: MAP) to provide mechanistic insights.

Methods

We studied 5603 ECG Lead V1 beats in 41 patients (64±10 Y) with coronary disease and LVEF≤40% in steady-state pacing. Patients were randomly allocated to independent training and test cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines were trained to predict mortality at 3Y from the top 20 features derived from these beats. Patient-level predictions were made by computing an ECG score that indicates the proportion of test set beats in that patient computed by the beat-level model to predict death. Explainability analysis was performed using the arithmetic mean of MAP and ECG beats that predicted SCD versus those that predicted survival.

Results

Fig 1A. shows ECG lead V1 and MAP in a 79 Y man with LVEF 29%. Fig 1B shows the dataflow in the study. Predictive accuracies of ML models were 78 and 70% and optimal with 20 features for both ECG and MAP models respectively (Fig. 1C). Beat-level predictions in the validation (n=1678 Lead I beats) cohorts yielded c-statistics of 0.78 with the ECG (95% CI, 0.62–0.91) and 0.75 with MAPs (95% CI, 0.75-0.76) (data not shown). In multivariable patient-level models, c-statistic was 0.87 with ECGs (95% CI, 0.76-0.98) (Fig 1D) and 0.82 with MAPs. On explainability analysis, ECG beats that predicted SCD (Fig 2; red) had lower amplitude and more notched T-waves in lead V1 than beats that predicted no SCD (Fig 2; blue). MAP that predicted SCD had higher repolarization current at the same time points. Both QT duration (ECG) and action potential duration (MAP) did not differ (Fig 2).

Conclusions

Machine learning of the ECG reveals novel predictors of SCD risk in patients with ischemic cardiomyopathy analogous to those identified in intracardiac signals. This approach can be used as a point-of-care ECG risk tool to improve risk stratification and allocation for ICD therapy beyond LVEF alone and may shed insights into the pathophysiology of ventricular arrhythmias.

Functional and structural characteristics of chocolate flavoured cake incorporated with surimi powder from Nemipterus species

Surimi is the concentrated myofibrillar protein extracted from fish. In this study, surimi prepared from Nemipterus species of fish was dried by two methods such as freeze drying and oven drying and was pulverized and sieved. The maida to bake chocolate flavoured cakes was replaced at 10% with freeze dried surimi powder (FDS) and oven dried surimi (ODS) powder separately. The aim of the study was to analyse the functional and structural characteristics of chocolate flavoured cake incorporated with surimi powder from Nemipterus Species. The functional characteristics of the ingredients and cakes were analysed by Fourier Transform Infrared Spectroscopy and assignments of the peaks were assigned. The presence of shifts of peak around 1550 cm^-1 in cakes as compared to the ingredients suggested a change in protein structure. FTIR spectra of ingredients and cake indicated the changes in protein structure in the cakes, probably due to the exposure of cakes to higher temperature during baking. Scanning electron microscopic images of control cake, cake incorporated with 10% FDS powder and cake incorporated with 10% ODS powder revealed the presence of large number of pores in all the cakes. FDS cakes had higher number of micro-pores than control and ODS cakes. Evenness in structure was higher in control cake than FDS and ODS cakes.

Extent of atrium with 1:1 electrogram activation predicts response to ablation of atrial fibrillation

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): US National Institutes of Health

Background

Mechanisms associated with successful termination of persistent atrial fibrillation (AF) are still under debate. We sought to study the association between spatial extent of atrium with organized conduction and AF ablation success. We hypothesized that patients with large areas of atrium having 1:1 electrogram activation akin to ‘atrial tachycardia’ may have a higher likelihood of AF termination by ablation.

Methods

In n = 40 AF patients, n = 20 had termination by ablation ("Term"), and the remaining did not have AF termination by ablation ("Non-term"). Basket catheters (64 poles) were used to record unipolar electrograms (EGMs) in one or both atrium. Ablation targeted localized rotational/focal regions, after which pulmonary vein isolation was performed. Unipolar EGMs of 4sec duration at each 2x2 electrode neighborhood within 8x8 catheter grid were processed using a statistical correlation technique to identify the duration of 1:1 activations. Any EGM activation cycle that had a correlation above 80% was considered to be 1:1. Duration of contiguous 1:1 cycles was determined as percentage of total duration (4 sec).

Results

Spatial area of atrium (percentage of mapping field) and the corresponding 1:1 durations were assessed for patients in Term and Non-term groups. Fig A shows spatial 1:1 maps of a Term and a Non-term patient. Fig B shows examples of 1:1 and non-1:1 EGMs. Patients in Term group had higher average 1:1 atrial area than non-term group for any 1:1 duration (Fig C, 15 ± 22% vs 2 ± 4% with ≥70% 1:1 duration, p = 0.03). Positive and negative predictive values of duration≥70% for AF termination were 64.7%, and 75%, with specificity 60% and sensitivity 78.6%, exceeding clinical risk scores.

Conclusion

Persistent AF atrium shows areas of organized 1:1 conduction. Larger 1:1 atrial areas were identified in patients in whom AF terminated by ablation. Future studies should investigate mechanistic bases of organized conduction in AF. Abstract Figure.

Lipid Nanoparticles Improve the Uptake of α-Asarone Into the Brain Parenchyma: Formulation, Characterization, In Vivo Pharmacokinetics, and Brain Delivery

Treatment of brain-related diseases is one of the most strenuous challenges in drug delivery research due to numerous hurdles, including poor blood-brain barrier penetration, lack of specificity, and severe systemic toxicities. Our research primarily focuses on the delivery of natural therapeutic compound, α-asarone, for the treatment of brain-related diseases. However, α-asarone has poor aqueous solubility, bioavailability, and stability, all of which are critical issues that need to be addressed. This study aims at formulating a lipid nanoparticulate system of α-asarone (A-LNPs) that could be used as a brain drug delivery system. The physicochemical, solid-state properties, stability, and in vitro and in vivo studies of the A-LNPs were characterized. The release of α-asarone from the A-LNPs was prolonged and sustained. After intravenous administration of A-LNPs or free α-asarone, significantly higher levels of α-asarone from the A-LNPs were detected in murine plasma and brain parenchyma fractions, confirming the ability of A-LNPs to not only maintain a therapeutic concentration of α-asarone in the plasma, but also transport α-asarone across the blood-brain barrier. These findings confirm that lipid nanoparticulate systems enable penetration of natural therapeutic compound α-asarone through the blood-brain barrier and may be a candidate for the treatment of brain-related diseases.

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

Medicinal Profile, Phytochemistry, and Pharmacological Activities of Murraya koenigii and its Primary Bioactive Compounds

The discovery of several revitalizing molecules that can stop or reduce the pathology of a wide range of diseases will be considered a major breakthrough of the present time. Available synthetic compounds may provoke side effects and health issues, which heightens the need for molecules from plants and other natural resources under discovery as potential methods of replacing synthetic compounds. In traditional medicinal therapies, several plant extracts and phytochemicals have been reported to impart remedial effects as better alternatives. Murraya koenigii (M. koenigii) belongs to the Rutaceae family, which is commonly used as a medicinally important herb of Indian origin in the Ayurvedic system of medicine. Previous reports have demonstrated that the leaves, roots, and bark of this plant are rich sources of carbazole alkaloids, which produce potent biological activities and pharmacological effects. These include antioxidant, antidiabetic, anti-inflammatory, antitumor, and neuroprotective activities. The present review provides insight into the major components of M. koenigii and their pharmacological activities against different pathological conditions. The review also emphasizes the need for more research on the molecular basis of such activity in various cellular and animal models to validate the efficacy of M. koenigii and its derivatives as potent therapeutic agents.

Lipid-based nanodelivery approaches for dopamine-replacement therapies in Parkinson's disease: From preclinical to translational studies

The incidence of Parkinson's disease (PD), the second most common neurodegenerative disorder, has increased exponentially as the global population continues to age. Although the etiological factors contributing to PD remain uncertain, its average incidence rate is reported to be 1% of the global population older than 60 years. PD is primarily characterized by the progressive loss of dopaminergic (DAergic) neurons and/or associated neuronal networks and the subsequent depletion of dopamine (DA) levels in the brain. Thus, DA or levodopa (l-dopa), a precursor of DA, represent cardinal targets for both idiopathic and symptomatic PD therapeutics. While several therapeutic strategies have been investigated over the past decade for their abilities to curb the progression of PD, an effective cure for PD is currently unavailable. Even DA replacement therapy, an effective PD therapeutic strategy that provides an exogenous supply of DA or l-dopa, has been hindered by severe challenges, such as a poor capacity to bypass the blood-brain barrier and inadequate bioavailability. Nevertheless, with recent advances in nanotechnology, several drug delivery systems have been developed to bypass the barriers associated with central nervous system therapeutics. In here, we sought to describe the adapted lipid-based nanodrug delivery systems used in the field of PD therapeutics and their recent advances, with a particular focus placed on DA replacement therapies. This work initially explores the background of PD; offers descriptions of the most recent molecular targets; currently available clinical medications/limitations; an overview of several lipid-based PD nanotherapeutics, functionalized nanoparticles, and technical aspects in brain delivery; and, finally, presents future perspectives to enhance the use of nanotherapeutics in PD treatment.

The down regulation of PTP1B expression and attenuation of disturbed glucose and lipid metabolism using Borassus flabellifer (L) fruit methanol extract in high fat diet and streptozotocin induced diabetic rats

Borassus flabellifer L. is a tall palm traditionally used for its stimulating, diuretic and anti-inflammatory activities; it is rich in fibers and various pharmacologically important secondary metabolites. This study was undertaken to evaluate the antidiabetic effects of Borassus flabellifer fruit methanol extract (BF-M) on diabetic rats induced with High Fat Diet (HFD)/streptozotocin (STZ). When BF-M (100 or 200 mg/kg) was administered for 21 days orally it led to a sharp decline in triglycerides, total cholesterol, free unsaturated fat, glucose-6-phosphate, fasting blood glucose and fructose 1,6 bisphosphatase in contrast to diabetic control. BF-M also downregulated Protein Tyrosine Phosphatase 1B. In vitro study showed the IC₅₀ value to be 23.98 μg/mL. BF-M significantly increased serum insulin, glycogen content, and body weight. Western blot analysis exhibited significant inhibition of PTP1B in pancreatic tissue which was confirmed by histology and immunohistological studies. GC-MS analysis revelaled that the presence of major compounds such as 5-hydroxymethylfurfural (47.56%), Guanosine (21.01%) and n-hecxadeconoic acid (25.14%) in BF-M. In short, BF-M exerted antidiabetic property by down regulating PTP1B expression, and eventually enhancing glucose stimulated insulin release; it also exhibited favorable effects in diabetes and its secondary complications.

Therapeutic strategies and nano-drug delivery applications in management of ageing Alzheimer's disease

In recent years, the incidental rate of neurodegenerative disorders has increased proportionately with the aging population. Alzheimer's disease (AD) is one of the most commonly reported neurodegenerative disorders, and it is estimated to increase by roughly 30% among the aged population. In spite of screening numerous drug candidates against various molecular targets of AD, only a few candidates - such as acetylcholinesterase inhibitors are currently utilized as an effective clinical therapy. However, targeted drug delivery of these drugs to the central nervous system (CNS) exhibits several limitations including meager solubility, low bioavailability, and reduced efficiency due to the impediments of the blood-brain barrier (BBB). Current advances in nanotechnology present opportunities to overcome such limitations in delivering active drug candidates. Nanodrug delivery systems are promising in targeting several therapeutic moieties by easing the penetration of drug molecules across the CNS and improving their bioavailability. Recently, a wide range of nano-carriers, such as polymers, emulsions, lipo-carriers, solid lipid carriers, carbon nanotubes, metal based carriers etc., have been adapted to develop successful therapeutics with sustained release and improved efficacy. Here, we discuss few recently updated nano-drug delivery applications that have been adapted in the field of AD therapeutics, and future prospects on potential molecular targets for nano-drug delivery systems.

Peroxidase-Mimicking Nanoassembly Mitigates Lipopolysaccharide-Induced Endotoxemia and Cognitive Damage in the Brain by Impeding Inflammatory Signaling in Macrophages

Oxidative stress during sepsis pathogenesis remains the most-important factor creating imbalance and dysregulation in immune-cell function, usually observed following initial infection. Hydrogen peroxide (H₂O₂), a potentially toxic reactive oxygen species (ROS), is excessively produced by pro-inflammatory immune cells during the initial phases of sepsis and plays a dominant role in regulating the pathways associated with systemic inflammatory immune activation. In the present study, we constructed a peroxide scavenger mannosylated polymeric albumin manganese dioxide (mSPAM) nanoassembly to catalyze the decomposition of H₂O₂ responsible for the hyper-activation of pro-inflammatory immune cells. In a detailed manner, we investigated the role of mSPAM nanoassembly in modulating the expression and secretion of pro-inflammatory markers elevated in bacterial lipopolysaccharide (LPS)-mediated endotoxemia during sepsis. Through a facile one-step solution-phase approach, hydrophilic bovine serum albumin reduced manganese dioxide (BM) nanoparticles were synthesized and subsequently self-assembled with cationic mannosylated disulfide cross-linked polyethylenimine (mSP) to formulate mSPAM nanoassembly. In particular, we observed that the highly stable mSPAM nanoassembly suppressed HIF1α expression by scavenging H₂O₂ in LPS-induced macrophage cells. Initial investigation revealed that a significant reduction of free radicals by the treatment of mSPAM nanoassembly has reduced the infiltration of neutrophils and other leukocytes in a local endotoxemia animal model. Furthermore, therapeutic studies in a systemic endotoxemia model demonstrated that mSPAM treatment reduced TNF-α and IL-6 inflammatory cytokines in serum, in turn circumventing organ damage done by the inflammatory macrophages. Interestingly, we also observed that the reduction of these inflammatory cytokines by mSPAM nanoassembly further prevented IBA-1 immuno-positive microglial cell activation in the brain and consequently improved the cognitive function of the animals. Altogether, the administration of mSPAM nanoassembly scavenged H₂O₂ and suppressed HIF1α expression in LPS-stimulated macrophages and thereby inhibited the progression of local and systemic inflammation as well as neuroinflammation in an LPS-induced endotoxemia model. This mSPAM nanoassembly system could serve as a potent anti-inflammatory agent, and we further anticipate its successful application in treating various inflammation-related diseases.

Microfluidization trends in the development of nanodelivery systems and applications in chronic disease treatments

Plant bioactive compounds are known for their extensive health benefits and therefore have been used for generations in traditional and modern medicine to improve the health of humans. Processing and storage instabilities of the plant bioactive compounds, however, limit their bioavailability and bioaccessibility and thus lead researchers in search of novel encapsulation systems with enhanced stability, bioavailability, and bioaccessibility of encapsulated plant bioactive compounds. Recently many varieties of encapsulation methods have been used; among them, microfluidization has emerged as a novel method used for the development of delivery systems including solid lipid nanocarriers, nanoemulsions, liposomes, and so on with enhanced stability and bioavailability of encapsulated plant bioactive compounds. Therefore, the nanodelivery systems developed using microfluidization techniques have received much attention from the medical industry for their ability to facilitate controlled delivery with enhanced health benefits in the treatment of various chronic diseases. Many researchers have focused on plant bioactive compound-based delivery systems using microfluidization to enhance the bioavailability and bioaccessibility of encapsulated bioactive compounds in the treatment of various chronic diseases. This review focuses on various nanodelivery systems developed using microfluidization techniques and applications in various chronic disease treatments.

Neurotoxic Agent-Induced Injury in Neurodegenerative Disease Model: Focus on Involvement of Glutamate Receptors

Glutamate receptors play a crucial role in the central nervous system and are implicated in different brain disorders. They play a significant role in the pathogenesis of neurodegenerative diseases (NDDs) such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Although many studies on NDDs have been conducted, their exact pathophysiological characteristics are still not fully understood. In in vivo and in vitro models of neurotoxic-induced NDDs, neurotoxic agents are used to induce several neuronal injuries for the purpose of correlating them with the pathological characteristics of NDDs. Moreover, therapeutic drugs might be discovered based on the studies employing these models. In NDD models, different neurotoxic agents, namely, kainic acid, domoic acid, glutamate, β-N-Methylamino-L-alanine, amyloid beta, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, 1-methyl-4-phenylpyridinium, rotenone, 3-Nitropropionic acid and methamphetamine can potently impair both ionotropic and metabotropic glutamate receptors, leading to the progression of toxicity. Many other neurotoxic agents mainly affect the functions of ionotropic glutamate receptors. We discuss particular neurotoxic agents that can act upon glutamate receptors so as to effectively mimic NDDs. The correlation of neurotoxic agent-induced disease characteristics with glutamate receptors would aid the discovery and development of therapeutic drugs for NDDs.

Long-term outcome of diffuse large B-cell lymphoma: Impact of biosimilar rituximab and radiation

INTRODUCTION

Rituximab (R)-CHOP improves survival over CHOP in diffuse large B-cell lymphoma (DLBCL). The availability of biosimilar rituximab in India has increased access of this drug. We report on the impact of treatment on outcomes with special emphasis on the impact of biosimilar rituximab and radiation.

METHODS

Outcomes of adults (age 15-60 years) treated with CHOP+/- Rituximab radiation were analyzed retrospectively to look at baseline features, treatment, and event-free and overall survival (EFS and OS).

RESULTS

In the period 2000-2013, 444 patients (median age 47 years: 15-60; males: 288 [65%]; Stage III/IV: 224 [50%]; age-adjusted international prognostic index [aaIPI] Score 2 or 3 in 50%) received either CHOP (n = 325 [73%]) or RCHOP (n = 119 [27%]) therapy. Biosimilar rituximab and the original were used in 95 (80%) and 24 (20%) patients, respectively. Radiation was given in 134 (30%) patients (Stages I and II, 100/220 [45%] and Stages III and IV, 34/224 [15%]). After a median follow-up of 46 (0.2-126) months, the 5-year EFS and OS were 59% and 68%, respectively. The factors predicting inferior EFS and OS were age> 40 years, performance status 2-4, Stage III/IV, hemoglobin <12 g/dL, the aaIPI Score 2 or 3, and nonuse of rituximab and radiation. Radiation used in early stage disease benefitted all subgroups regardless of bulky disease, use of rituximab, or the number of cycles of chemotherapy. Addition of rituximab improved survival across all categories of aaIPI.

CONCLUSION

Availability of biosimilar rituximab has increased access and survival of patients with DLBCL in India. Radiotherapy improved outcomes in early stages.

Cellular phenotypes as inflammatory mediators in Parkinson's disease: Interventional targets and role of natural products

Pathogenesis of Parkinson's disease (PD) is undoubtedly a multifactorial phenomenon, with diverse etiological agents. Pro-inflammatory mediators act as a skew that directs disease progression during neurodegenerative diseases. Understanding the dynamics of inflammation and inflammatory mediators in preventing or reducing disease progression has recently gained much attention. Inflammatory neuro-degeneration is regulated via cytokines, chemokines, lipid mediators and immune cell subsets; however, individual cellular phenotypes in the Central Nervous System (CNS) acts in diverse ways whose persistent activation leads to unresolving inflammation often causing unfavorable outcomes in neurodegenerative disease like PD. Specifically, activation of cellular phenotypes like astrocytes, microglia, activation of peripheral immune cells requires different activation signals and agents like (cytokines, misfolded protein aggregates, infectious agents, pesticides like organophosphates, etc.,). However, what is unknown is how the different cellular phenotypes respond uniquely and the role of the factors they secrete alters the signal cascades in the complex neuron-microglial connections in the CNS. Hence, understanding the role of cellular phenotypes and the inflammatory mediators, the cross talk among the signals and their receptors can help us to identify the potential therapeutic target using natural products. In this review we have tried to put together the role of cellular phenotypes as a skew that favors PD progression and we have also discussed how the lack of experimental approaches and challenges that affects understanding the cellular targets that can be used against natural derivatives in alleviating PD pathophysiology. Together, this review will provide the better insights into the role of cellular phenotypes of neuroinflammation, inflammatory mediators and the orchestrating factors of inflammation and how they can be targeted in a more specific way that can be used in the clinical management of PD.

Ameliorative potential of desalted Salicornia europaea L. extract in multifaceted Alzheimer's-like scopolamine-induced amnesic mice model

The Salicornia europaea L. (SE) plant is a halophyte that has been widely consumed as a seasoned vegetable, and it has been recently reported to counteract chronic diseases related to oxidative and inflammatory stress. In this study, we performed an initial phytochemical analysis with in vitro biochemical tests and chromatographic profiling of desalted and enzyme-digested SE ethanol extract (SE-EE). Subsequently, we evaluated the anti-neuroinflammatory and ameliorative potential of SE-EE in LPS-inflicted BV-2 microglial cells and scopolamine-induced amnesic C57/BL6N mice, respectively. SE-EE possess considerable polyphenols and flavonoids that are supposedly responsible to improve its bio-efficacy. SE-EE dose-dependently attenuated LPS-induced inflammation in BV-2 cells, significantly repressed behavioural/cognitive impairment, dose-dependently regulated the cholinergic function, suppressed oxidative stress markers, regulated inflammatory cytokines/associated proteins expression and effectively ameliorated p-CREB/BDNF levels, neurogenesis (DCX stain), neuron proliferation (Ki67 stain) in scopolamine-administered mice. Thus, SE-EE extract shows promising multifactorial disease modifying activities and can be further developed as an effective functional food, drug candidate, or supplemental therapy to treat neuroinflammatory mediated disorders.

Recent developments in solid lipid nanoparticle and surface-modified solid lipid nanoparticle delivery systems for oral delivery of phyto-bioactive compounds in various chronic diseases

Solid lipid nanoparticle (SLN) delivery systems have a wide applicability in the delivery of phyto-bioactive compounds to treat various chronic diseases, including diabetes, cancer, obesity and neurodegenerative diseases. The multiple benefits of SLN delivery include improved stability, smaller particle size, leaching prevention and enhanced lymphatic uptake of the bioactive compounds through oral delivery. However, the burst release makes the SLN delivery systems inadequate for the oral delivery of various phyto-bioactive compounds that can treat such chronic diseases. Recently, the surface-modified SLN (SMSLN) was observed to overcome this limitation for oral delivery of phyto-bioactive compounds, and there is growing evidence of an enhanced uptake of curcumin delivered orally via SMSLNs in the brain. This review focuses on different SLN and SMSLN systems that are useful for oral delivery of phyto-bioactive compounds to treat various chronic diseases.

Emerging signals modulating potential of ginseng and its active compounds focusing on neurodegenerative diseases

Common features of neurodegenerative diseases (NDDs) include progressive dysfunctions and neuronal injuries leading to deterioration in normal brain functions. At present, ginseng is one of the most frequently used natural products. Its use has a long history as a cure for various diseases because its extracts and active compounds exhibit several pharmacological properties against several disorders. However, the pathophysiology of NDDs is not fully clear, but researchers have found that various ion channels and specific signaling pathways might have contributed to the disease pathogenesis. Apart from the different pharmacological potentials, ginseng and its active compounds modulate various ion channels and specific molecular signaling pathways related to the nervous system. Here, we discuss the signal modulating potential of ginseng and its active compounds mainly focusing on those relevant to NDDs.

Physicochemical, Microbial, and Sensory Properties of Queso Blanco Cheese Supplemented with Powdered Microcapsules of Tomato Extracts

The present study examined the physical, chemical, microbial, and sensory characteristics of Queso Blanco cheese supplemented with powdered microcapsules containing tomato extracts (0.5-2.0%) during storage at 7°C for 60 d. The lactic acid bacterial count and lycopene concentrations in Queso Blanco cheese supplemented with powdered microcapsules were significantly higher than those of the control. In a texture analysis, the gumminess, chewiness, and hardness values for Queso Blanco cheese were significantly higher with increasing concentrations of the powdered microcapsules containing tomato extracts. Total short-chain fatty acids in Queso Blanco cheese supplemented with powdered microcapsules containing tomato extracts were not significantly altered compared to the control. Sensory evaluation scores for the yellowness, tomato taste, and firmness of Queso Blanco cheese were significantly higher after supplementation with powdered microcapsules containing tomato extracts.

Phytobioactive compound-based nanodelivery systems for the treatment of type 2 diabetes mellitus - current status

Type 2 diabetes mellitus (T2DM) is a major chronic disease that is prevalent worldwide, and it is characterized by an increase in blood glucose, disturbances in the metabolism, and alteration in insulin secretion. Nowadays, food-based therapy has become an important treatment mode for type 2 diabetes, and phytobioactive compounds have gained an increasing amount of attention to this end because they have an effect on multiple biological functions, including the sustained secretion of insulin and regeneration of pancreatic islets cells. However, the poor solubility and lower permeability of these phyto products results in a loss of bioactivity during processing and oral delivery, leading to a significant reduction in the bioavailability of phytobioactive compounds to treat T2DM. Recently, nanotechnological systems have been developed for use as various types of carrier systems to improve the delivery of bioactive compounds and thus obtain a greater bioavailability. Furthermore, carrier systems in most nanodelivery systems are highly biocompatible, with nonimmunologic behavior, a high degree of biodegradability, and greater mucoadhesive strength. Therefore, this review focuses on the various types of nanodelivery systems that can be used for phytobioactive compounds in treating T2DM with greater antidiabetic effects. There is also additional focus on improving the effects of various phytobioactive compounds through nanotechnological delivery to ensure a highly efficient treatment of type 2 diabetes.

Factors predicting outcome in high risk febrile neutropenia in patients receiving intensive chemotherapy for acute sleukemia: A prospective, observational study from South India

BACKGROUND

Outcome of febrile neutropenia (FN) in acute leukemia patients undergoing intensive chemotherapy from India is scanty.

MATERIALS AND METHODS

A prospective, observational, single institutional study was conducted to evaluate the clinical features, microbiological aspects, risk factors influencing the outcome of high risk FN during intensive therapy in acute leukemia.

RESULTS

Among 115 febrile episodes, though 94 (81.7%) had indwelling central venous catheter (CVC) at the time of diagnosis of FN, infective foci clinically were identified in 70.4% of episodes, with lung as the major site (25.2%) followed by CVC (17.4%). Microbiological documentation was possible in 33% (n = 40) episodes. Gram-negative bacteria isolates were 58.3% and Gram-positive isolates were 41.7% of which Pseudomonas was the predominant Gram-negative and Staphylococcus aureus was the most common Gram-positive isolate. Piperacillin-tazobactam + amikacin were used as first line antibiotic in 93% episodes and second line antibiotics were necessary in 73% episodes. Granulocyte colony stimulating factor was used in 60.9% episodes of high risk FN mostly in acute myeloid leukemia consolidation patients. Eighteen episodes (15.7%) were assigned to have invasive fungal disease. Eleven (9.6%) out of 115 high risk FN had a fatal outcome. Presence of pulmonary infection predicted for fatal outcome (P = 0.02).

CONCLUSION

This study reports the outcome of high risk FN in patients with acute leukemia undergoing intensive chemotherapy. Gram-negative isolates are highly sensitive to piperacillin-tazobactum and hence in a cost restraint scenario, carbapenems needs to be judiciously used. Focus of Infection in lungs during FN predicted higher fatal outcomes.