Effect of COVID-19 pandemic on influenza; observation of a tertiary level virology laboratory.

The lockdown enforced amid the COVID-19 pandemic has affected the occurrence and trends of various respiratory virus infections, with a particular focus on influenza. Our study seeks to analyze the repercussions of the COVID-19 pandemic on the positivity of the influenza virus throughout a 4-year span, encompassing both the pre-COVID-19 era (2018 and 2019) and the COVID-19 period (2020 and 2021). Data collected from patients clinically diagnosed with Influenza-like Illness and Severe Acute Respiratory Illness (SARI) from January 2018 to December 2021 for influenza virus detection were acquired and analyzed through multiplex RT-qPCR. The statistical analysis was conducted using SPSS (Statistical Package for Social Sciences) Version 21.0 Software. A total of 4464 samples were tested over 4 years (2018-2021), with 3201 samples from the pre-COVID era and 1263 samples from the COVID era. Influenza A positivity dropped from 17.7 to 9.57% and Influenza B positivity decreased from 3.74 to 2.61%. Subtyping revealed changes in prevalence for both viruses. Seasonal variations showed more pronounced peaks in the pre-COVID-19 era with reduced activity during lockdown. Influenza A saw a resurgence in August 2021. Throughout the COVID-19 pandemic (2020-2021) SARI cases did not decrease. The positivity rate for Influenza A slightly rose to 7.79% from 4.23% in the COVID period (2020-2021). This increase correlates with heightened hospitalization rates during the pandemic, sparking concerns of potential coinfection with coronavirus and Influenza A. The notable drop in influenza cases in 2020-2021 is likely due to stringent precautions, lockdowns, drug repurposing, and prioritized testing, indicating no reduction in influenza transmission. Increased influenza positivity in SARI patients during COVID-19 highlights a heightened risk of coinfection. Emphasizing solely on COVID-19 may lead to underreporting of other respiratory pathogens, including influenza viruses.

Investigating the internal structure of multiple mini interviews-A perspective from Pakistan.

BACKGROUND: Healthcare professionals require many personal attributes in addition to cognitive abilities and psychomotor skills for competent practice. Multiple Mini- Interviews are being employed globally to assess personality attributes of candidates for selection in health professions education at all level of entry; these attributes are namely, communication skills, critical thinking, honesty, responsibility, health advocacy, empathy and sanctity of life. Considering the high stakes involved for students, faculty, institutions and the society, rigorous quality assurance mechanisms similar to those used for student assessment must be employed for student selection, throughout the continuum of medical education. It is a difficult undertaking as these psychological constructs are difficult to define and measure. Though considered to yield reliable and valid scores, studies providing multiple evidences of internal structure especially dimensionality of Multiple Mini-Interviews are sparse giving rise to questions if they are measuring a single or multiple constructs and even if they are measuring what they are purported to be measuring. OBJECTIVE: The main objective is to provide statistical support of the multi-dimensional nature of our Multiple Mini Interviews, hypothesized a-priori, through CFA. Another objective is to provide multiple evidences for the internal structure. Our study highlights the link between content and internal structure evidences of the constructs, thus establishing that our Multiple Mini Interviews measure what they were intended to measure. METHOD: After securing permission from the Institutional review board, an a-priori seven factor-model was hypothesized based on the attributes considered most essential for the graduating student of the institution. After operationally defining the attributes through extensive literature search, scenarios were constructed to assess them. A 5-point rating scale was used to rate each item on the station. A total 259 students participated in the multiple mini interviews over a period of three days. A training workshop had been arranged for the participating faculty. RESULTS: The reliability coefficient using Cronbach's alpha were calculated (range from 0.73 to 0.94), Standard Error of Measurement (ranged from 0.80 to1.64), and item to station-total correlation ranged from 0.43-0.50 to 0.75-0.83. Inter-station correlation was also determined. Confirmatory factor analysis endorsed the results of Exploratory factor analysis in the study revealing a seven model fit with multiple indices of Goodness-of-fit statistics such as Root mean square error of approximation (RMSEA) value 0.05, Standardized root mean square residual (SRMR) value with less than 0.08. All these indices showed that model fit is good. The Confirmatory factor analysis confirmed the multi-dimensional nature of our MMIs and also confirmed that our stations measured the attributes that they were supposed to measure. CONCLUSION: This study adds to the validity evidence of Multiple Mini-Interviews, in selection of candidates, with required personality traits for healthcare profession. It provides the evidence for the multi-dimensional structure of Multiple Mini interviews administered with multiple evidences for its internal structure and demonstrates the independence of different constructs being measured.

Antibodies toward Na+,HCO3--cotransporter NBCn1/SLC4A7 block net acid extrusion and cause pH-dependent growth inhibition and apoptosis in breast cancer.

BACKGROUND: Na+,HCO3--cotransporter NBCn1/Slc4a7 accelerates murine breast carcinogenesis. Lack of specific pharmacological tools previously restricted therapeutic targeting of NBCn1 and identification of NBCn1-dependent functions in human breast cancer. METHODS: We develop extracellularly-targeted anti-NBCn1 antibodies, screen for functional activity on cells, and evaluate (a) mechanisms of intracellular pH regulation in human primary breast carcinomas, (b) proliferation, cell death, and tumor growth consequences of NBCn1 in triple-negative breast cancer, and (c) association of NBCn1-mediated Na+,HCO3--cotransport with human breast cancer metastasis. RESULTS: We identify high-affinity (KD ≈ 0.14 nM) anti-NBCn1 antibodies that block human NBCn1-mediated Na+,HCO3--cotransport in cells, without cross-reactivity towards human NBCe1 or murine NBCn1. These anti-NBCn1 antibodies abolish Na+,HCO3--cotransport activity in freshly isolated primary organoids from human breast carcinomas and lower net acid extrusion effectively in primary breast cancer tissue from patients with macrometastases in axillary lymph nodes. Inhibitory anti-NBCn1 antibodies decelerate tumor growth in vivo by ~50% in a patient-derived xenograft model of triple-negative breast cancer and pH-dependently reduce colony formation, cause G2/M-phase cell cycle accumulation, and increase apoptosis of metastatic triple-negative breast cancer cells in vitro. CONCLUSIONS: Inhibitory anti-NBCn1 antibodies block net acid extrusion in human breast cancer tissue, particularly from patients with disseminated disease, and pH-dependently limit triple-negative breast cancer growth.

Stalled translation by mitochondrial stress upregulates a CNOT4-ZNF598 ribosomal quality control pathway important for tissue homeostasis.

Translational control exerts immediate effect on the composition, abundance, and integrity of the proteome. Ribosome-associated quality control (RQC) handles ribosomes stalled at the elongation and termination steps of translation, with ZNF598 in mammals and Hel2 in yeast serving as key sensors of translation stalling and coordinators of downstream resolution of collided ribosomes, termination of stalled translation, and removal of faulty translation products. The physiological regulation of RQC in general and ZNF598 in particular in multicellular settings is underexplored. Here we show that ZNF598 undergoes regulatory K63-linked ubiquitination in a CNOT4-dependent manner and is upregulated upon mitochondrial stresses in mammalian cells and Drosophila. ZNF598 promotes resolution of stalled ribosomes and protects against mitochondrial stress in a ubiquitination-dependent fashion. In Drosophila models of neurodegenerative diseases and patient cells, ZNF598 overexpression aborts stalled translation of mitochondrial outer membrane-associated mRNAs, removes faulty translation products causal of disease, and improves mitochondrial and tissue health. These results shed lights on the regulation of ZNF598 and its functional role in mitochondrial and tissue homeostasis.

Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches.

The Internet has become a vital source of knowledge and communication in recent times. Continuous technological advancements have changed the way businesses operate, and everyone today lives in the digital world of engineering. Because of the Internet of Things (IoT) and its applications, people's impressions of the information revolution have improved. Malware detection and categorization are becoming more of a problem in the cybersecurity world. As a result, strong security on the Internet could protect billions of internet users from harmful behavior. In malware detection and classification techniques, several types of deep learning models are used; however, they still have limitations. This study will explore malware detection and classification elements using modern machine learning (ML) approaches, including K-Nearest Neighbors (KNN), Extra Tree (ET), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and neural network Multilayer Perceptron (nnMLP). The proposed study uses the publicly available dataset UNSWNB15. In our proposed work, we applied the feature encoding method to convert our dataset into purely numeric values. After that, we applied a feature selection method named Term Frequency-Inverse Document Frequency (TFIDF) based on entropy for the best feature selection. The dataset is then balanced and provided to the ML models for classification. The study concludes that Random Forest, out of all tested ML models, yielded the best accuracy of 97.68 %.

RbPbI3 Seed Embedding in PbI2 Substrate Tailors the Facet Orientation and Crystallization Kinetics of Perovskites.

High power conversion efficiencies (PCEs) in perovskite solar cells (PSCs) have always been awe-inspiring, but perovskite films scalability is an exacting precondition for PSCs commercial deployment, generally unachievable through the antisolvent technique. On the contrary, in the two-step sequential method, the perovskite's uncontrolled crystallization and unnecessary PbI2 residue impede the device's performance. These two issues motivated to empower the PbI2 substrate with orthorhombic RbPbI3 crystal seeds, which act as grown nuclei and develop orientated perovskites lattice stacks, improving the perovskite films morphologically and reducing the PbI2 content in eventual perovskite films. Thence, achieving a PCE of 24.17% with suppressed voltage losses and an impressive life span of 1140 h in the open air.

First-row transition metal carbide nanosheets as high-performance cathode materials for lithium-sulfur batteries.

Despite the prodigious potential of lithium-sulfur (Li-S) batteries as future rechargeable electrochemical systems, their commercial implementation is hindered by several vital issues, including the shuttle effect and sluggish migration of lithium-polysulfides leading to rapid capacity fading. Here, we systematically investigate the potential of first-row two-dimensional transition metal carbides (TMCs) as sulfur cathodes for Li-S batteries. The adsorption strength of lithium-polysulfides on TMCs is induced by the amount of charge transfer from the former to the latter and the proposed periodic relationship between sulfur in Li2S and 3d-transition metals. Our findings show that the VC nanosheet possesses immense anchoring potential and exhibits a comparatively low migration energy barrier for lithium-ion and Li2S molecules. Additionally, we report ab initio molecular dynamics simulations for lithiated polysulfide species anchored on a TMC-based model with a liquid-electrolyte medium. The microscopic reaction mechanism, revealed by the evolution of the reaction voltage during lithiation, demonstrates that the dissolution of high-order lithium-polysulfides in the electrolytes can be prevented due to their robust interaction with TMC-based cathode materials. These appealing features suggest that TMCs present colossal performance improvements for anchoring lithium-polysulfides, stimulating the active design of sulfur cathodes for practical Li-S batteries.

A Functional Biological Molecule Restores the PbI2 Residue-Induced Defects in Two-Step Fabricated Perovskites.

Coating the perovskite layer via a two-step method is an adaptable solution for industries compared to the anti-solvent process. But what about the impact of unreacted PbI2? Usually, it is generated during perovskite conversion in a two-step method and considered beneficial within the grain boundaries, while also being accused of enhancing the interface defects and nonradiative recombination. Several additives are mixed in PbI2 precursors for the purpose of improving the perovskite crystallinity and hindering the Pb2+ defects. Herein, in lieu of adding additives to the PbI2, the effects of the PbI2 residue via the electron transport layer/perovskite interface modification are explored. Consequently, by introducing artemisinin decorated with hydrophobic alkyl units and a ketone group, it reduces the residual PbI2 and improves the perovskites' crystallinity by coordinating with Pb2+. In addition, artemisinin-deposited perovskite enhances both the stability and efficiency of perovskite solar cells by suppressing nonradiative recombination.

Comparative Evaluation of Dental Caries Score Between Teledentistry Examination and Clinical Examination: A Systematic Review and Meta-Analysis.

Dental caries is a common dental health problem affecting all age groups across the globe. Accurate detection and assessment of dental caries are crucial for effective treatment and preventive measures. Teledentistry, which involves remote dental assessment using digital technologies, has shown promise as a potential tool for caries screening. This systematic review and meta-analysis aims to compare the dental caries scores obtained from clinical examinations and teledentistry assessments. Literature searches were conducted across databases such as PubMed, Embase, the Cochrane Library, Scopus, the Web of Science, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and PsycINFO by using predefined search terms and inclusion criteria. Two reviewers separately extracted the data. The study designs, evaluation techniques, dentition types, mean scores, and follow-up times of the included studies were examined. The New Castle-Ottawa Scale was used to assess the risk of bias. Review Manager (RevMan) (computer program) Version 5.4, The Cochrane Collaboration 2020, was used for the quantitative assessment of the data. Eight studies met the inclusion criteria and were included in the review. The findings revealed that teledentistry assessments (based on intraoral photographs captured using smartphones or intraoral cameras) demonstrated comparable accuracy to traditional clinical examinations in detecting and assessing dental caries. Among the four studies that were quantitatively analysed, no significant difference was noted at p = 0.09. A mean difference of 0.64 (95% confidence interval (CI): -0.10; 1.38) suggested that clinical examination and teledentistry-based checkup were on par with each other for the detection of dental caries. The New Castle-Ottawa scale (NOS) grading indicated that the studies were of good quality. Teledentistry may be an effective approach for identifying and evaluating dental caries. However, further research is required to substantiate the findings observed in the present review.

Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations.

The feature vector mapping used to represent chemical systems is a key factor governing the superior data efficiency of kernel based quantum machine learning (QML) models applicable throughout chemical compound space. Unfortunately, the most accurate representations require a high dimensional feature mapping, thereby imposing a considerable computational burden on model training and use. We introduce compact yet accurate, linear scaling QML representations based on atomic Gaussian many-body distribution functionals (MBDF) and their derivatives. Weighted density functions of MBDF values are used as global representations that are constant in size, i.e., invariant with respect to the number of atoms. We report predictive performance and training data efficiency that is competitive with state-of-the-art for two diverse datasets of organic molecules, QM9 and QMugs. Generalization capability has been investigated for atomization energies, highest occupied molecular orbital-lowest unoccupied molecular orbital eigenvalues and gap, internal energies at 0 K, zero point vibrational energies, dipole moment norm, static isotropic polarizability, and heat capacity as encoded in QM9. MBDF based QM9 performance lowers the optimal Pareto front spanned between sampling and training cost to compute node minutes, effectively sampling chemical compound space with chemical accuracy at a sampling rate of ∼48 molecules per core second.

Superior possibilities and upcoming horizons for nanoscience in COVID-19: noteworthy approach for effective diagnostics and management of SARS-CoV-2 outbreak.

The outbreak of COVID-19 has caused great havoc and affected many parts of the world. It has imposed a great challenge to the medical and health fraternity with its ability to continue mutating and increasing the transmission rate. Some challenges include the availability of current knowledge of active drugs against the virus, mode of delivery of the medicaments, its diagnosis, which are relatively limited and do not suffice for further prognosis. One recently developed drug delivery system called nanoparticles is currently being utilized in combating COVID-19. This article highlights the existing methods for diagnosis of COVID-19 such as computed tomography scan, reverse transcription-polymerase chain reaction, nucleic acid sequencing, immunoassay, point-of-care test, detection from breath, nanotechnology-based bio-sensors, viral antigen detection, microfluidic device, magnetic nanosensor, magnetic resonance platform and internet-of-things biosensors. The latest detection strategy based on nanotechnology, biosensor, is said to produce satisfactory results in recognizing SARS-CoV-2 virus. It also highlights the successes in the research and development of COVID-19 treatments and vaccines that are already in use. In addition, there are a number of nanovaccines and nanomedicines currently in clinical trials that have the potential to target COVID-19.

Mechanisms by which small molecules of diverse chemotypes arrest Sec14 lipid transfer activity.

Phosphatidylinositol (PtdIns) transfer proteins (PITPs) enhance the activities of PtdIns 4-OH kinases that generate signaling pools of PtdIns-4-phosphate. In that capacity, PITPs serve as key regulators of lipid signaling in eukaryotic cells. Although the PITP phospholipid exchange cycle is the engine that stimulates PtdIns 4-OH kinase activities, the underlying mechanism is not understood. Herein, we apply an integrative structural biology approach to investigate interactions of the yeast PITP Sec14 with small-molecule inhibitors (SMIs) of its phospholipid exchange cycle. Using a combination of X-ray crystallography, solution NMR spectroscopy, and atomistic MD simulations, we dissect how SMIs compete with native Sec14 phospholipid ligands and arrest phospholipid exchange. Moreover, as Sec14 PITPs represent new targets for the development of next-generation antifungal drugs, the structures of Sec14 bound to SMIs of diverse chemotypes reported in this study will provide critical information required for future structure-based design of next-generation lead compounds directed against Sec14 PITPs of virulent fungi.

A novel method for efficient estimation of brain effective connectivity in EEG.

BACKGROUND AND OBJECTIVE: Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources. METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source. RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively. CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.

Nexuses Between the Chemical Design and Performance of Small Molecule Dopant-Free Hole Transporting Materials in Perovskite Solar Cells.

Perovskite solar cells (PSCs) have grabbed much attention of researchers owing to their quick rise in power conversion efficiency (PCE). However, long-term stability remains a hurdle in commercialization, partly due to the inclusion of necessary hygroscopic dopants in hole transporting materials, enhancing the complexity and total cost. Generally, the efforts in designing dopant-free hole transporting materials (HTMs) are devoted toward small molecule and polymeric HTMs, where small molecule based HTMs (SM-HTMs) are dominant due to their reproducibility, facile synthesis, and low cost. Still, the state-of-art dopant-free SM-HTM has not been achieved yet, mainly because of the knowledge gap between device engineering and molecular designs. From a molecular engineering perspective, this article reviews dopant-free SM-HTMs for PSCs, outlining analyses of chemical structures with promising properties toward achieving effective, low-cost, and scalable materials for devices with higher stability. Finally, an outlook of dopant-free SM-HTMs toward commercial application and insight into the development of long-term stability PSCs devices is provided.

Prognostic utility of microRNA-145 and CD 133 in oral squamous cell carcinoma: A pilot study from Northern India.

Introduction: Multiple factors alter the microRNAs (miRNAs) at cellular level and promote oncogenesis in oral mucosa. The present study was performed with an aim to find any expression of miRNA-145 in oral squamous cell carcinoma and correlate it with CD-133 immuno-expression, clinicopathological, and demographical variables in oral squamous cell carcinoma (OSCC) with respect to controls. Materials and methods: In a prospective observational study 50 samples from patients of OSCC and 20 from unremarkable oral mucosa were studied. After initial detailed histology, miRNA-145 profiling was performed using qRT-PCR, followed by CD-133 immunohistochemistry (IHC). Results: The mean age of patients with oral cancer was 47.5 ± 10.25 years. Mean miR-145 levels in OSCC were 0.4312 ± 0.32026 and mean in healthy controls was 0.99 ±0 .21771. There was significant downregulation of miRNA-145 in cases with respect to controls. Significant reduced levels of miRNA-145 with respect to higher clinical tumor size, pathological pT tumor, nodal status, and resultant clinical tumor stage was observed. As far as presence and absence of stem cells was concerned it was seen that tumors displaying presence of stem cells highlighted by CD-133 had lower levels of miRNA-145 as compared to tumors with absent CD-133 staining. Conclusions: miRNA-145 is significantly altered in OSCC in our patient population and its reduced values carry a poor prognosis. Its interaction with CSCs may not be significant but mean miRNA-145 levels are lower in tumors with CSCs. There should be further studies on the larger sample size for these two biomarkers, to know its value.

A Cephalometric Study of Sella Turcica: Correlation of Its Size with Different Skeletal Malocclusions.

Introduction: The present study was done to determine the size of the sella turcica in different skeletal type subjects and to evaluate if any significant difference exists between them, which could be the basis for early diagnosis. Materials and methods: A total of 60 lateral cephalograms of patients above 15 years of age were selected and distributed according to skeletal malocclusion into class I (n = 20), class II (n = 20), and 20 class III (n = 20). Syndromic patients (physically/mentally/both) or patients with major illnesses were not included. Two linear measurements of the sella turcica, that is, length and depth in mid-sagittal plane, were obtained in accordance with Silverman and Kisling method. Unpaired t-test and one-way analysis of variance (ANOVA) were performed. Results: The mean length of sella turcica in class I, class II, and class III subjects was 3.81, 3.37, and 3.9, respectively. Similarly, the mean depth of sella turcica in class I, class II, and class III subjects was 7.6, 6.83, and 9.075, respectively. Conclusion: No significant difference in length of the sella turcica could be found between different skeletal types. Maximum depth of sella turcica was found to be in subjects with class III and minimum with class II skeletal types. Clinical significance: The linear dimensions of sella turcica can be used to approximate the pituitary gland size. The pedodontist should be familiar with different morphologies of the sella turcica to differentiate normal from abnormal appearance so that treatment can be diagnosed and treated early. How to cite this article: Grover N, Khan DUZ, Bhagchandani J, et al. A Cephalometric Study of Sella Turcica: Correlation of Its Size with Different Skeletal Malocclusions. Int J Clin Pediatr Dent 2023;16(S-3):S229-S232.

Microscopy-Based Assessment of Fatty Acid Uptake and Lipid Accumulation in Cultured Cells.

The heart relies predominantly on the use of fatty acids to derive energy. Metabolic disorders such as obesity, insulin resistance, and diabetes pose a major risk factor for the development of heart failure. Dysregulation of lipid metabolism observed in these diseases manifests as cardiac lipotoxicity, and is associated with cardiac dysfunction. The alarming rise in the incidence of these metabolic disorders warrants the need for tools to investigate the underlying molecular mechanisms. In this article, we describe a confocal microscopy-based approach to monitor fatty acid uptake and lipid accumulation in vitro, in neonatal murine cardiomyocytes and H9c2 cells. The protocol for assessment of fatty acid uptake relies on the use of BODIPY FL C 12™ to study the kinetics of fatty acid uptake via real-time imaging of fatty acid uptake in live cells. Importantly, it circumvents the need for radioactive labeling of fatty acids to evaluate their uptake. Similarly, the protocol for assessment of lipid accumulation relies on the use of BODIPY™ 493/503 to stain the cytosolic neutral lipid population in fixed cells. We couple these confocal microscopy-based approaches with fluorescence intensity analysis using FIJI to quantify fatty acid uptake and lipid accumulation in vitro. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Assessment of fatty acid uptake Basic Protocol 2: Assessment of lipid accumulation.

The Relationship between Psychological Disability and Religious Practice and Coping Strategies in Caregivers of Children with Traumatic Brain Injury in Pakistani Population.

BACKGROUND: Traumatic brain injury (TBI) is a serious issue and a leading cause of death and disability worldwide. Caregivers of TBI patients experience psychological distress and a variety of social and financial issues. The present study aims to investigate the caregiver's burden and the factors that influence this burden. Furthermore, the present study will find out the association of religious practice, religious coping relations and psychological distress among caregivers of children affected with TBI. METHODS: A cross-sectional survey was conducted on 302 caregivers of children with TBI using Duke University Religion Index (DURL) for religious practice. General Health Questionaire-12 (GHQ-12) was used for anxiety and depression and Brief Religious Coping Scale (RCOPE) was used for coping strategies. The caregivers were conveniently chosen from different regions of Khyber Pakhtunkhwa province and data was collected from different tertiary care hospitals in Peshawar. RESULTS: Forty-nine (49) % of caregivers score ≥ 3 on GHQ suffer from psychological distress with a Mean of 20.957 ± 4.175). Positive coping methods were mostly used by caregivers than negative coping have a low level of distress with a Mean Positive Coping (P-COPE ) of 6.93 ± 0.41, Mean of Negative Coping (N-COPE) 0.486 ± 1.023. In religious practice, caregivers mostly participate in Organized Reliogious Activities (ORA) or some Non-Organized Reliogious Activities (NORA) with a Mean ORA of 4.20 ± 1.27, and NORA Mean of 4.17 ± 1.37 used by the caregivers. Coping methods were related to Caregiver psychological distress (GHQ-12 and P-COPE co-relation scores are (ρ -0.022, p b 0.05); GHQ-12 scores and N-COPE (ρ + 0.221=, p b 0.001). There is a negative correlation between GHQ 12 and PCOPE, while GHQ12 is positively correlated with NCOPE. CONCLUSION: According to this study, there is a significant association between religious coping methods, religious practice, and psychological distress among caregivers of children with traumatic brain injury.

Microscopic Reservoir Characteristics of the Lacustrine Calcareous Shale: An Example from the Es4 s Shale of the Paleogene Shahejie Formation in Boxing Sag, Dongying Depression.

The Es4 s shale is taken as the main research object to understand and describe the reservoir characteristics in Boxing Sag, Dongying Depression. Through core observation, X-ray diffraction, thin section observation, field-emission scanning electron microscopy analysis, and low-pressure nitrogen gas adsorption experiment, the reservoir space of the Es4 s shale including pore types, pore size, and pore structure characteristics was elucidated. The study shows that the shale in the Boxing Sag has the following characteristics: (1) the reservoir space in the study area is diverse, with the development of inorganic pores, organic pores, and microfractures. The higher the content of calcite and organic matter, the more favorable the development of intergranular pores, dissolution pores, and organic matter pores and (2) complex pore structure. The average Barrett-Joyner-Halenda pore size of calcareous shale is 6.5-22.8 nm, and the Brunauer-Emmett-Teller cumulative specific surface area is 0.7588-4.744 m2/g. According to the morphological analysis of the adsorption and desorption curve, it is found that the shale samples in the target interval are mainly ink-bottle-shaped, cylindrical, and slit-shaped pores. The shale samples with relatively well-laminated intervals are mainly composed of ink-bottle-shaped and cylindrical pores, while the samples with relatively unlaminated intervals and high clay mineral content are mainly composed of slit-shaped pores. The contents of clay minerals and calcite are correlated with pore volume, specific surface area, and pore size, which further indicates the controlling effect of clay minerals and carbonate mineral components on pore structure parameters. This study not only helps us to understand the distribution of various micropores/nanopores in the lacustrine shale but also acts as a guiding note for the characterization of the shale oil reservoir, which ultimately offers a theoretical foundation for lacustrine shale oil exploration and development.

Sirtuin 6 inhibition protects against glucocorticoid-induced skeletal muscle atrophy by regulating IGF/PI3K/AKT signaling.

Chronic activation of stress hormones such as glucocorticoids leads to skeletal muscle wasting in mammals. However, the molecular events that mediate glucocorticoid-induced muscle wasting are not well understood. Here, we show that SIRT6, a chromatin-associated deacetylase indirectly regulates glucocorticoid-induced muscle wasting by modulating IGF/PI3K/AKT signaling. Our results show that SIRT6 levels are increased during glucocorticoid-induced reduction of myotube size and during skeletal muscle atrophy in mice. Notably, overexpression of SIRT6 spontaneously decreases the size of primary myotubes in a cell-autonomous manner. On the other hand, SIRT6 depletion increases the diameter of myotubes and protects them against glucocorticoid-induced reduction in myotube size, which is associated with enhanced protein synthesis and repression of atrogenes. In line with this, we find that muscle-specific SIRT6 deficient mice are resistant to glucocorticoid-induced muscle wasting. Mechanistically, we find that SIRT6 deficiency hyperactivates IGF/PI3K/AKT signaling through c-Jun transcription factor-mediated increase in IGF2 expression. The increased activation, in turn, leads to nuclear exclusion and transcriptional repression of the FoxO transcription factor, a key activator of muscle atrophy. Further, we find that pharmacological inhibition of SIRT6 protects against glucocorticoid-induced muscle wasting in mice by regulating IGF/PI3K/AKT signaling implicating the role of SIRT6 in glucocorticoid-induced muscle atrophy.