Category: Nevin Manimala

J Cell Biochem. 2022 Jun 13. doi: 10.1002/jcb.30297. Online ahead of print.

ABSTRACT

Epigenetics factors are critical for normal cell function and their regulation is sensitive to malignancy development. EHMT2/G9a and KDM2b are key epigenetics players in different cancer types. However, their expression profiles and related consequences in acute myeloid leukemia (AML) patients have not been known yet. In addition to routine lab work, expression levels of EHMT2/G9a and KDM2b were determined in 110 adult and pediatric patients with De Novo AML. Relations between their expression and patients’ clinical data were tested by statistical methods. EHMT2/G9a and KDM2b were highly expressed in AML patients against control cases and associated with the presence of adverse genomic alterations. In response to induction chemotherapy, EHMT2/G9a and KDM2b showed to be significantly high in resistant and relapsed patients in comparison to the complete remission group. KDM2b overexpression was associated with CD11c (integrin alpha X) downregulation. Kaplan-Meier analysis indicated that EHMT2/G9a and KDM2b overexpression was correlated with poor survival status in AML patients. We conclude that EHMT2/G9a and KDM2b expression levels could be used as independent prognostic factors for AML disease.

PMID:35696556 | DOI:10.1002/jcb.30297

J Cell Biochem. 2022 Jun 13. doi: 10.1002/jcb.30296. Online ahead of print.

ABSTRACT

The Hedgehog (Hh) pathway has emerged as a potential target for effectual hepatic repair based on convincing clinical and preclinical evidence that proves its significance in regulating hepatic damage. The purpose of this study is to probe the effect of quercetin on liver fibrosis through the modulation of the Hh pathway. Healthy male Wistar rats were divided into four groups (n = 10). The control group was treated with saline, rats in the remaining three groups received twice a week intoxication with intraperitoneal injections of thioacetamide (200 mg/kg) for the induction of hepatic fibrosis for 6 weeks. After 28 days of quercetin and silymarin treatment, histological changes, serum biochemical index, antioxidant enzyme activity, key mediators of Hh pathway and inflammation were analyzed. Serological analysis showed statistically improved cholesterol, H.D.L-Cholesterol, and L.D.L-Cholesterol in the treatment groups. Superoxide dismutase and glutathione levels were found to be increased after the treatment with quercetin and silymarin. mRNA expression of important mediators of the Hh signaling, and inflammation including Shh, Ihh, Ptch-1, Smo, Hhip, Gli-3, TNF-α, NFκ-β, and Socs-3 were significantly downregulated after the use of quercetin and silymarin. Quercetin also minimized the thioacetamide-induced histopathological changes, as confirmed by a lower degree of hepatic lobule degeneration, the intralobular occurrence of inflammatory cells, and a lower degree of hepatocytic necrosis. Sudan Black B staining showed remarked lipids improvements in the treatment groups. Taken together, these findings demonstrate that quercetin could ameliorate hepatic fibrosis by antagonizing the hedgehog pathway and also suggest the hedgehog pathway as a potential therapeutic target for the treatment of liver fibrosis.

PMID:35696520 | DOI:10.1002/jcb.30296

J Texture Stud. 2022 Jun 13. doi: 10.1111/jtxs.12703. Online ahead of print.

ABSTRACT

Children with Down syndrome (DS) display a disproportionate number of health challenges, including feeding and swallowing difficulties and food texture sensitivities. To start to address food texture challenges early in life, the aim of this research was to understand mealtime behaviours and identify preferred food textures of children with DS. Children (ages 11 to 58 months) with DS (n=111) and without (typically developing, TD; n=107) participated in a home use test evaluating snack products of varying textures. Parents recorded their children’s reactions to each food product; a panel of trained coders coded videos for mealtime behaviours. Children were also identified as food texture sensitive (TS) or non-texture sensitive (NTS). Results showed that age, TS, the presence of a DS diagnosis and the TS*DS interaction influenced the child’s behaviours to the food products. Children with DS were more likely to increase distance from the food, mouth/suck on the food, and less likely to chew/much on the food compared to children TD. Children with DS also ate significantly less than typically developing children. Similar mealtime behaviours were observed between the children with TD/<30 months of age and children with DS regardless of age (p<0.05). While children with DS/TS had a higher disposition for the dissolvable texture, along with products that were salty and cheesy, children with DS/NTS had a greater tolerance for more textures in general, including crunchy and grainy. Overall, this research identified mealtime behaviours in children with DS, determined overall disposition of children with DS to foods of differences sensory properties, and demonstrated the influence of TS on a child’s disposition to a food. This research will be extended to develop acceptable and innovative food products for children with DS/TS. This article is protected by copyright. All rights reserved.

PMID:35696524 | DOI:10.1111/jtxs.12703

J Agric Food Chem. 2022 Jun 13. doi: 10.1021/acs.jafc.2c01447. Online ahead of print.

ABSTRACT

The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.

PMID:35696488 | DOI:10.1021/acs.jafc.2c01447

IEEE J Biomed Health Inform. 2022 Jun 13;PP. doi: 10.1109/JBHI.2022.3182869. Online ahead of print.

ABSTRACT

Real-time mental stress monitoring from surgeons and surgical staff in operating rooms may reduce surgical injuries, improve performance and quality of medical care, and accelerate implementation of stress-management strategies. Motivated by the increase in usage of objective and subjective metrics for cognitive monitoring and by the gap in reviews of experimental design setups and data analytics, a systematic review of 71 studies on mental stress and workload measurement in surgical settings, published in 2001-2020, is presented. Almost 61% of selected papers used both objective and subjective measures, followed by 25% that only administered subjective tools – mostly consisting of validated instruments and customized surveys. An overall increase in the total number of publications on intraoperative stress assessment was observed from mid-2010 s along with a momentum in the use of both subjective and real-time objective measures. Cardiac activity, including heart-rate variability metrics, stress hormones, and eye-tracking metrics were the most frequently and electroencephalography (EEG) was the least frequently used objective measures. Around 40% of selected papers collected at least two objective measures, 41% used wearable devices, 23% performed synchronization and annotation, and 76% conducted baseline or multi-point data acquisition. Furthermore, 93% used a variety of statistical techniques, 14% applied regression models, and only one study released a public, anonymized dataset. This review of data modalities, experimental setups, and analysis techniques for intraoperative stress monitoring highlights the initiatives of surgical data science and motivates research on computational techniques for mental and surgical skills assessment and cognition-guided surgery.

PMID:35696473 | DOI:10.1109/JBHI.2022.3182869

IEEE Trans Pattern Anal Mach Intell. 2022 Jun 13;PP. doi: 10.1109/TPAMI.2022.3181472. Online ahead of print.

ABSTRACT

Convolutional neural networks (CNNs) have achieved superior accuracy in many visual-related tasks. However, the inference process through a CNN’s intermediate layers is opaque, making it difficult to interpret such networks or develop trust in their operation. In this paper, we introduce SIGN method for modeling the network’s hidden layer activity using probabilistic models. The activity patterns in layers of interest are modeled as Gaussian mixture models, and transition probabilities between clusters in consecutive modeled layers are estimated to identify paths of inference. For fully connected networks, the entire layer activity is clustered, and the resulting model is a hidden Markov model. For convolutional layers, spatial columns of activity are clustered, and a maximum likelihood model is developed for mining an explanatory inference graph. The graph describes the hierarchy of activity clusters most relevant for network prediction. We show that such inference graphs are useful for understanding the general inference process of a class, as well as explaining the (correct or incorrect) decisions the network makes about specific images. In addition, SIGN provide interesting observations regarding hidden layer activity in general, including the concentration of memorization in a single middle layer in fully connected networks, and a highly local nature of column activities in the top CNN layers.

PMID:35696462 | DOI:10.1109/TPAMI.2022.3181472

PLoS Pathog. 2022 Jun 13;18(6):e1009946. doi: 10.1371/journal.ppat.1009946. Online ahead of print.

ABSTRACT

Venezuelan equine encephalitis virus (VEEV) is a positively-stranded RNA arbovirus of the genus Alphavirus that causes encephalitis in humans. Cynomolgus macaques are a relevant model of the human disease caused by VEEV and are useful in exploring pathogenic mechanisms and the host response to VEEV infection. Macaques were exposed to small-particle aerosols containing virus derived from an infectious clone of VEEV strain INH-9813, a subtype IC strain isolated from a human infection. VEEV-exposed macaques developed a biphasic fever after infection similar to that seen in humans. Maximum temperature deviation correlated with the inhaled dose, but fever duration did not. Neurological signs, suggestive of virus penetration into the central nervous system (CNS), were predominantly seen in the second febrile period. Electroencephalography data indicated a statistically significant decrease in all power bands and circadian index during the second febrile period that returned to normal after fever resolved. Intracranial pressure increased late in the second febrile period. On day 6 post-infection macaques had high levels of MCP-1 and IP-10 chemokines in the CNS, as well as a marked increase of T lymphocytes and activated microglia. More than four weeks after infection, VEEV genomic RNA was found in the brain, cerebrospinal fluid and cervical lymph nodes. Pro-inflammatory cytokines & chemokines, infiltrating leukocytes and pathological changes were seen in the CNS tissues of macaques euthanized at these times. These data are consistent with persistence of virus replication and/or genomic RNA and potentially, inflammatory sequelae in the central nervous system after resolution of acute VEEV disease.

PMID:35696423 | DOI:10.1371/journal.ppat.1009946

PLoS Comput Biol. 2022 Jun 13;18(6):e1010129. doi: 10.1371/journal.pcbi.1010129. Online ahead of print.

ABSTRACT

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicating the matter further is the fact that not all zeros are created equal: some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros); others are indeed due to insufficient sequencing depth (sampling zeros or dropouts), especially for loci that interact infrequently. Differentiating between structural zeros and dropouts is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchical model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data have led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

PMID:35696429 | DOI:10.1371/journal.pcbi.1010129

PLoS Comput Biol. 2022 Jun 13;18(6):e1009598. doi: 10.1371/journal.pcbi.1009598. Online ahead of print.

ABSTRACT

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

PMID:35696417 | DOI:10.1371/journal.pcbi.1009598

PLoS One. 2022 Jun 13;17(6):e0269706. doi: 10.1371/journal.pone.0269706. eCollection 2022.

ABSTRACT

BACKGROUND: The incidence of childhood ALL in Indonesia is still largely unknown. The widely mentioned statistics from other countries turn out to be only estimated figures. Other data do not specify the types of leukemia and are not specifically focused on children. Therefore, this study aims to pool incidence and mortality statistics from available studies in Indonesia.

METHODS: We searched five different academic databases, including Pubmed, MEDLINE, Cochrane Library, Science Direct, and Google Scholar. Three Indonesian databases, such as the Indonesian Scientific Journal Database (ISJD), Neliti, and Indonesia One Search, were also utilized. Incidence was expressed as per 100,000 children. We used the Newcastle-Ottawa scale (NOS) to assess the quality of cohort studies. The inclusion criteria are cohort studies published in the languages of English or Indonesian. For this analysis, we define children as 0-18 years old.

FINDINGS: The incidence rate for childhood ALL was found to be 4.32 per 100,000 children (95% CI 2.65-5.99) with a prediction interval of 1.98 to 9.42 per 100,000 children. The incidence rate is higher in males, with 2.45 per 100,000 children (95% CI 1.98-2.91) and a prediction interval of 1.90 to 3.16 per 100,000 children. As for females, the incidence rate is 2.05 per 100,000 children (95% CI 1.52-2.77) with a prediction interval of 1.52 to 2.77 per 100,000 children. The mortality of childhood ALL ranges from 0.44 to 5.3 deaths per 100,000 children, while the CFR is 3.58% with varying true effect sizes of 2.84% to 4.52%.

INTERPRETATION: With 79.5 million children living in Indonesia in 2018, this means that there were roughly 3,434 new cases of childhood ALL. An organized effort between multiple sectors is needed to improve the registries of childhood ALL in Indonesia.

PMID:35696384 | DOI:10.1371/journal.pone.0269706