Etiology, Mechanism and Treatment of Liver Cancer

Liver cancer or hepatocellular carcinoma (HCC) is a malignant tumor in liver tissue and worldwide it is fourth leading death cause among all cancers. The most common causes of liver cancer are hepatitis B or C virus infections, alcoholic liver disease (ALD), nonalcoholic fatty liver disease (NAFLD) to non-alcoholic steatohepatitis (NASH), smoking and obesity. The development and metastasis of liver cancer is a multistage and branched process of morphological and genetic traits. Various corresponding signaling pathways such as Yes-Associated Protein-Hippo Pathway (YAP-HIPPO), Wnt/β-catenin and inflammation by interleukin-6 (IL-6), tumor necrosis factor (TNF), nuclear factor-Κb (NF-κB), biological pathways including epithelial–mesenchymal transition (EMT), tumor microenvironment, tumor-stromal interactions and cancer stem cells and gut microbial dysbiosis are allied to both origination, progression and metastasis of liver cancer. Numerous therapeutic approaches are classified into different categories such as pharmacological therapy including sorafenib, lenvatinib and ramuciruma, surgery of HCC patients includes surgical resection, adjuvant therapy after surgical resection and liver transplantation. Loco-regional ablative therapy includes cryotherapy, ethanol injection and radiofrequency ablation, cytotoxic chemotherapy, natural compounds such as piperine, as curcumin and oleocanthal, oncolytic virus therapy, immunotherapies and nanotechnology

Computational materials discovery and development for Li and non-Li advanced battery chemistries

Since the discovery of batteries in the 1800s, their fascinating physical and chemical pro­perties have led to much research on their synthesis and manufacturing. Though lithium-ion batteries have been crucial for civilization, they can still not meet all the growing demands for energy storage because of the geographical distribution of lithium resources and the intrinsic limitations in the cell energy density, performance, and reliability issues. As a result, non-Li-ion batteries are becoming increasingly popular alternatives. Designing novel materials with desired properties is crucial for a quicker transition to the green energy ecosystem. Na, K, Mg, Zn, Al ion, etc. batteries are considered the most alluring and promising. This article covers all these Li, non-Li, and metal-air cell chemistries. Recently, com­putational screening has proven to be an effective tool to accelerate the discovery of active materials for all these cell types. First-principles methods such as density functional theory, molecular dynamics, and Monte Carlo simulations have become established techni­ques for the preliminary, theoretical analysis of battery systems. These computational methods generate a wealth of data that might be immensely useful in the training and vali­dating of artificial intelligence and machine learning techniques to reduce the time and capital expenditure needed for discovering advanced materials and final product develop­ment. This review aims to summarize the application of these techniques and the recent deve­lopments in computational methods to discover and develop advanced battery chemistries

Problem Management Plus (PM plus ) in the management of common mental disorders in a specialized mental healthcare facility in Pakistan; study protocol for a randomized controlled trial

Additional file 1. Appendix A

Evaluating effectiveness and cost-effectiveness of a group psychological intervention using cognitive behavioural strategies for women with common mental disorders in conflict-affected rural Pakistan: study protocol for a randomised controlled trial

The impact of humanitarian disasters upon mental health is well recognised. The evidence for psychological interventions for mental health is mounting, but few interventions have been rigorously tested in humanitarian settings. To be sustainable in humanitarian settings interventions need to be short, simple, deliverable by nonspecialists under supervision, and adopt a transdiagnostic approach where an array of mental health outcomes are addressed simultaneously. These elements have been incorporated into the newly developed WHO Problem Management Plus (PM+) Group intervention. The aim of this trial is to evaluate the locally adapted PM+ Group intervention for women in Swat, Pakistan. This PM+ Group trial is a two-arm, single-blind, cluster randomised controlled trial conducted in a community-based setting with women in rural Pakistan. PM+ is delivered in partnership with the Lady Health Worker (LHW) Programme which provides community-based health care to women in Pakistan. Thirty-four LHW clusters will be randomised in a 1:1 allocation ratio using a permuted-block randomisation method. Participants screened and found to meet the inclusion criteria will be allocated to either the PM+ intervention group (n = 306), or the control arm (n = 306). The manualised PM+ intervention involves five sessions, each lasting 3 h, and introduces four strategies applied by participants to problems that they are facing. It is delivered by local female facilitators with a minimum of 16 years of education who are provided with targeted training and supervision. The primary outcome is individual psychological distress, measured by levels of anxiety and depression on the Hospital Anxiety and Depression Scale at 20 weeks after baseline. Secondary outcomes include major depression, post-traumatic stress disorder, levels of social support, levels of functioning, and economic effectiveness. Intervention acceptability will be explored through an embedded qualitative study. The PM+ Group trial will provide important evidence on the effectiveness of an empirically supported psychological treatment delivered by nonspecialists in a humanitarian setting. If proven effective, the qualitative component will inform strategies for PM+ Group scale-up in health systems in other humanitarian settings. Australian New Zealand Clinical Trials Registry, identifier: ACTRN12616000037404. Registered on 19 January 2016; WHO Protocol ID RPC705, v.4, 2 November 2015

Effect of adding a psychological intervention to routine care of common mental disorders in a specialized mental healthcare facility in Pakistan: a randomized controlled trial

Abstract Background In many low resource settings, the provision of government mental health care services is limited to specialized psychiatry units in urban hospital care facilities, where the most common treatment for common mental disorders (CMDs) is pharmacotherapy, occasionally with adjunct nonspecific psychological support. We aimed to evaluate the effectiveness of adding a low intensity, psychological intervention, Problem Management Plus (PM+) for CMDs into routine care in a specialized mental health care facility in Pakistan. Methods A two arm, single-blind individual randomized controlled trial (RCT) was carried out with adults (N = 192), referred for psychological support by psychiatrists. The study participants were randomized (1:1) to PM + plus Treatment as Usual (TAU) (n = 96) or TAU only (n = 96). The primary outcomes were symptoms of anxiety and depression, measured by the Hospital Anxiety and Depression Scale (HADS) and functional impairment as measured by WHO Disability Assessment Schedule (WHODAS 2.0) at 20 weeks after baseline. Results The analysis was done on intention-to-treat principle. The linear mixed model analysis showed that at 20 weeks after baseline, there was a significant reduction in symptoms of anxiety and depression (mean [SD], 16.23 [8.81] vs 19.79 [7.77]; AMD, − 3.10; 95% CI, − 0.26 to − 5.76); p = 0.03 and improvement in functioning (mean [SD], 22.94 [9.37] vs 27.37 [8.36]; AMD, − 4.35; 95% CI, − 1.45 to − 7.24); p = 0.004 in PM + plus TAU versus TAU arm. The follow-up rate was 67% at primary end-point. Conclusions Specialized care facilities in LMICs may consider adding brief, evidence-based psychological treatments for CMDs to their routine care. Trial Registration Australian New Zealand Clinical Trials Registry, ACTRN12616000381482. Registered March 23, 2016. Retrospectively registered, https://www.anzctr.org.au/Default.aspx/ ACTRN12616000381482 </jats:sec

Effectiveness of a technology-assisted, family volunteers delivered, brief, multicomponent parents' skills training intervention for children with developmental disorders in rural Pakistan: a cluster randomized controlled trial.

BACKGROUND Globally, there is a large documented gap between needs of families and children with developmental disorders and available services. We adapted the World Health Organization's mental health Gap-Intervention Guidelines (mhGAP-IG) developmental disorders module into a tablet-based android application to train caregivers of children with developmental disorders. We aimed to evaluate the effectiveness of this technology-assisted, family volunteers delivered, parents' skills training intervention to improve functioning in children with developmental disorders in a rural community of Rawalpindi, Pakistan. METHODS In a single-blinded, cluster randomized controlled trial, 30 clusters were randomised (1:1 ratio) to intervention (n = 15) or enhanced treatment as usual (ETAU) arm (n = 15). After screening, 540 children (18 participants per cluster) aged 2-12 years, with developmental disorders and their primary caregivers were recruited into the trial. Primary outcome was child's functioning, measured by Childhood Disability Assessment Schedule for Developmental Disorders (DD-CDAS) at 6-months post-intervention. Secondary outcomes were parents' health related quality of life, caregiver-child joint engagement, socio-emotional well-being of children, family empowerment and stigmatizing experiences. Intention-to-treat analyses were done using mixed-models adjusted for covariates and clusters. RESULTS At 6-months post-intervention, no statistically significant mean difference was observed on DD-CDAS between intervention and ETAU (mean [SD], 47.65 [26.94] vs. 48.72 [28.37], Adjusted Mean Difference (AMD), - 2.63; 95% CI - 6.50 to 1.24). However, parents in the intervention arm, compared to ETAU reported improved health related quality of life (mean [SD] 65.56 [23.25] vs. 62.17 [22.63], AMD 5.28; 95% CI 0.44 to 10.11). The results were non-significant for other secondary outcomes. CONCLUSIONS In the relatively short intervention period of 6 months, no improvement in child functioning was observed; but, there were significant improvements in caregivers' health related quality of life. Further trials with a longer follow-up are recommended to evaluate the impact of intervention. Trial registration Clinicaltrials.gov, NCT02792894. Registered April 4, 2016, https://clinicaltrials.gov/ct2/show/NCT02792894

On MHD flow

In this paper, we apply Homotopy Perturbation Method (HPM) to find the analytical solutions of nonlinear MHD flow of an incompressible viscous fluid through convergent or divergent channels in presence of a high magnetic field. The flow of an incompressible electrically conducting viscous fluid in convergent or divergent channels under the influence of an externally applied homogeneous magnetic field is studied both analytically and numerically. The graphs are presented to reveal the physical characteristics of flow by changing angles of the channel, Hartmann and Reynolds numbers

Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System

Forests are an enduring component of the natural world and perform a vital role in protecting the environment. Forests are valuable resources to control global warming and provide oxygen for the survival of human life, including wood for households. Forest fires have recently emerged as a major threat to biological processes and the ecosystem. Unfortunately, almost every year, fire damages millions of hectares of forest land due to late and inefficient detection of fire. However, it is important to identify the forest fire at the initial level before it spreads to vast areas and destroys natural resources. In this paper, a formal model of the Internet of Things (IoT) and drone-based forest fire detection and counteraction system is presented. The proposed system comprises network maintenance. Sensor deployment is on trees, the ground, and animals in the form of subnets to transmit sensed data to the control room. All subnets are connected to the control room through gateway nodes. Alarms are being used to alert human beings and animals to save their lives, which will help to initially protect them from fire. The embedded sensors collect the information and transfer it to the gateways. Drones are being used for real-time visualization of fire-affected areas and to perform actions to control fires because they play a vital role in disasters. Graph theory is used to construct an efficient model and to show the connectivity of the network. To identify failures and develop recovery procedures, the algorithm is designed through the graph-based model. The model is developed by the Vienna Development Method-Specification Language (VDM-SL), and the correctness of the model is ensured using various VDM-SL toolbox facilities

Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System

Forests are an enduring component of the natural world and perform a vital role in protecting the environment. Forests are valuable resources to control global warming and provide oxygen for the survival of human life, including wood for households. Forest fires have recently emerged as a major threat to biological processes and the ecosystem. Unfortunately, almost every year, fire damages millions of hectares of forest land due to late and inefficient detection of fire. However, it is important to identify the forest fire at the initial level before it spreads to vast areas and destroys natural resources. In this paper, a formal model of the Internet of Things (IoT) and drone-based forest fire detection and counteraction system is presented. The proposed system comprises network maintenance. Sensor deployment is on trees, the ground, and animals in the form of subnets to transmit sensed data to the control room. All subnets are connected to the control room through gateway nodes. Alarms are being used to alert human beings and animals to save their lives, which will help to initially protect them from fire. The embedded sensors collect the information and transfer it to the gateways. Drones are being used for real-time visualization of fire-affected areas and to perform actions to control fires because they play a vital role in disasters. Graph theory is used to construct an efficient model and to show the connectivity of the network. To identify failures and develop recovery procedures, the algorithm is designed through the graph-based model. The model is developed by the Vienna Development Method-Specification Language (VDM-SL), and the correctness of the model is ensured using various VDM-SL toolbox facilities