A document by Linda Thomas. Click on the document to view its contents.

Unexpected anti-depressant effects seen with full body pulsed electromagnetic treatment when given for chronic back pain in a patient with co-existent severe treatment resistant depression: A case report

A document by Mateus Dantas de Paula. Click on the document to view its contents.

Post-Radiotherapy Carotid Stenosis in a Patient with Head and Neck Cancer

Rainwater harvesting (RWH) is increasingly seen as a significant water source and a strategy for improving water supply in arid and semi-arid regions. However, RWH facilities are regularly threatened by various factors influencing its effectiveness and making them unsustainable. This paper aims to quantify the factors influencing RWH effectiveness in arid and semi-arid regions of Sudan. The factors categorized into socioeconomic variables indicated by household family size, household income, and household animals. Environmental variables include rainfall amount, soil type, and water availability at the home. The institutional variables are indicated by government support, nongovernmental (NGO) support, and community involvement. Five small dams of water harvesting located in different five states in Sudan were selected. Several 150 households, 30 households per dam were randomly assigned to respond to a questionnaire designed to collect data on factors influencing RWH facilities. Preliminarily, Pearson’s Chi-square test was used to determine the variables for the logistic model. Multiple logistic regression was used to identify the most influential factors on the effectiveness of water harvesting dams. The results show that 40% of the RWH facilities were effective. A significant (p-value < 0.05) relationship exists between socioeconomic, institutional and environmental factors and the effectiveness of the RWH facilities. The results revealed that household animals, government support, community participation, rainfall, and soil types significantly (p-value < 0.01) influence the RWH facilities to be effective. The results are of great importance for future effective and inclusive water harvesting initiatives in arid and semi-arid regions

Based on the advantages of higher output efficiency, higher utilization, higher production, lower cost, no requirement for advanced process, higher device density, improved electrical performance and enhanced thermal management, there are many foreign manufacturers and domestic manufacturers all have developed the FOPLP technology in the past ten years. The related technologies including die first & face down for FOPLP technology, die first & face up for FOPLP technology and RDL first for FOPLP technology have been developed. The challenges including die shift, panel warpage, RDL process capability, relayed equipment, and market share also need to be considered for a long time along with the developing of FOPLP technology. During the whole process of FOPLP packaging, organic materials including epoxy molding compound (EMC), dry film, photosensitive polyimide (PSPI) and photoresist (PR) were applied to guarantee the performance of packaged chips.

This paper presents the design and simulation of a solar Photovoltaic driven pumping system to irrigate a 10-hectare maize farm located at Ethiopia, Debre Markos, Wonka site. The system pumps 82 m3 of water from a nearby river to irrigate 10-hectare land. The proposed system is directly facing to solve the above problems and increase the profitability of the farmers. A life cycle analysis of the operating cost was performed to prove its economic feasibility compared to a diesel-based pumping system. System simulation and design were done using MATLAB/Simulink. The total power generated from solar PV was found to be 7.6 kW. The total cost of the PV driven pumping system for irrigation for a life span of 20 years was found to be $25700 and of the diesel system was $219791, which is about 9 times more than solar PV driven pumping system. By replacing the diesel pump system with the solar-driven one, an annual saving of emissions of 8 tonne of CO2 into the atmosphere is achieved while the use of diesel fuel of about 2856 L/year is avoided. The results of this study are expected to motivate and inspire the use of solar PV pumping systems in agriculture in remote areas of Ethiopia and avoid seasonal farming that is, extend the farming period and increase farmers’ income. Keywords: Solar photovoltaics; Water Pumping; System Simulation; System sizing; Economic feasibility; Environmental benefits

Aim: To test two null hypotheses: that I 2 testing with trial-adjusted SCT settings does not change the odds of identifying selection bias in clinical trials compared to I 2-testing with generic SCT settings, and that I 2 testing with trial-adjusted SCT settings does not change the odds of identifying selection bias in smaller trials (with sample size (n) = 100-199 per treatment group) compared to larger trials (n > 200 per group). Methods: Baseline data from 67 randomised controlled trials previously tested for selection bias using the I 2 test with generic SCT settings were extracted. The generic settings were: SCT sample size N SCT = 200 (100 per group A and B), minimum-maximum range of random values (R SCT) = 67 (Min = 18, Max = 85), number of generated SCTs used in all meta-analyses SCT N = 2. The trials were re-tested with trial-adjusted SCT settings. Additionally, the SCT sample sizes were further increased stepwise to N SCT = 400, 800 and 1200 and the resulting I 2 point estimates recorded. Positive test results (I 2 > 0%) were assigned a score 1, while negative test results (I 2 = 0%) were assigned a score of 0. From the resulting 0-and 1-scores of both types of SCT settings, odds ratios (OR) with 95% confidence interval (CI) and p-values were computed. The alpha level was set at 5%. Results: The original I 2 testing with generic SCT settings yielded 4 positive and 63 negative results. In contrast, testing with trial-adjusted SCT settings of the same trials revealed 13 positive and 54 negative results (OR 3.79; 95% CI: 1.17-12.32; p = 0.03). When the SCT sample size was increased with trial-adjusted SCT settings, the number of positive results rose from 13 to 16 (OR 1.30; 95% CI: 0.57-2.98; p = 0.53). Consequently, only the first null-hypothesis was rejected. Conclusion: I 2 testing with trial-adjusted SCT settings increased the odds of identifying selection bias in clinical trials and did not significantly alter the odds in smaller trials with fewer than 200 patients per intervention group.

Pupil size is a well-established marker of the level of cognitive effort, with greater efforts leading to larger pupils. This is particularly true for pupil size during task performance itself, whereas findings on anticipatory effort triggered by a cue stimulus are less consistent. For example, a recent report by Frömer et al. (2021) found that in a cued Stroop task, behavioural performance and electrophysiological markers of preparatory effort allocation were modulated by cued reward and ‘efficacy’ (the degree to which rewards depended on good performance), but pupil size did not show a comparable pattern. Here, we conceptually replicated this study in a pupillometry study. In line with previous findings, we found no modulation of absolute pupil size in the cue-to-target interval. Instead, we observed a significant difference in the rate of pupil dilation in anticipation of the target: Pupils dilated more rapidly for high-reward trials in which rewards depended on good performance. This was followed by a significant difference in absolute pupil size within the first hundreds of milliseconds following Stroop stimulus onset, likely reflecting a lagging effect of anticipatory effort allocation. Finally, the slope of pupil dilation was significantly correlated with behavioural response times, and this association was strongest for the high-reward, high-efficacy trials, further supporting that the rate of anticipatory pupil dilation reflects anticipatory effort. We conclude that pupil size is modulated by anticipatory effort, but in a highly temporally-specific manner, which is best reflected by the rate of dilation in the moments just prior to stimulus onset.

Angiogenesis is a complex process of new vessel formation on pre-existing blood vessels. It starts with functional or structural insult of the endothelium which triggers switch of quiescent endothelial cells to active angiogenic phenotype. Under physiological conditions, angiogenesis is limited in time and proliferative power of endothelial cells owing to the finely tuned balance between pro-angiogenic and anti-angiogenic programs. Stressful conditions as hypoxia rapidly disrupt this balanced state eliciting pro-angiogenic switch, and resulting in unrestricted endothelial proliferation and acceleration of angiogenesis which are crucial for tumorigenesis and development of other pathological conditions. Dipeptidyl peptidase-4 (DPP-4) is cell-surface glycoprotein which serves as serine ectopeptidase. DPP-4 plays a crucial role in the pathophysiological regulation of metabolism, immune and inflammatory responses, cancer development and cell adhesion via its catalytic and non-catalytic functions. PPP-4 inhibitors or gliptins are currently approved anti-hyperglycemic agents for type-2 diabetes mellitus. Pre-clinical studies and clinical experience have clearly acknowledged the organ-protective pleiotropic effects of DPP-4 inhibitors. Still, the literature shows conflicting results regarding the potential effects of these drugs on angiogenic processes. In this review, we will highlight the effects of DPP-4 inhibitors on different regulatory factors and conditions which control angiogenesis with specific emphasis on the most recent findings in this regard. Then, we will explore the underlying reasons and mechanisms behind the contradictory findings down to a conclusion of the ultimate effects of DPP-4 inhibitors in angiogenesis during physiological and pathological states.

Intelligent identification of road vehicles in a densely populated country like Bangladesh is challenging due to irregular traffic patterns, highly diverse vehicle types, and a cluttered environment. This study proposes a system that utilizes computer vision technology to identify road vehicles with greater speed and accuracy. Firstly, dataset was collected and organized in Roboflow to identify 21 classes of Bangladeshi native vehicle images, along with two additional classes for people and animals. Subsequently, YOLOv5 model underwent training on the dataset. This process produced bounding boxes, which were then refined using NMS technique. The loss function CIoU is employed to obtain the accurate regression bounding box of the vehicles. MS CO-CO dataset weights are included in the YOLOv5 deep learning network for transfer learning. Finally, Python TensorBoard was used to evaluate and visualize the model’s performance. The model was developed and validated on Google Colab platform. A set of experimental evaluations demonstrate that the proposed method is effective and efficient in recognizing Bangladeshi Vehicles. In all test road scenarios, the proposed computer vision system for road vehicle identification achieved 95.8% accuracy and 0.3ms processing time for 200 epochs. This research could lead to intelligent transportation systems and driverless vehicles in Bangladesh.

Obesity-related asthma (OA) is a severe asthma endotype that disproportionately affects children from minority ethnic groups. We previously identified downregulation of RPS27L (40S ribosomal protein S27-like) in CD4+ (T-helper) cells from children with OA compared to cells from healthy-weight asthma (HwA) that was associated with the C allele at rs6494395 locus. In keeping with elevated allele frequencies in populations with Latino and African ancestries, we found higher allele frequency of rs6494395 in Hispanic and African American children with OA. Therefore, we tested the hypothesis that rs6494395 is ancestry-specific. We performed global and local genomic ancestry analysis using the programs ADMIXTURE and RFMix, respectively, to characterize the genetic ancestry of the cohort and to identify the ancestry of the genomic region containing the variant rs6494395 in the same multi-ethnic pediatric cohort where the effect of the variant on obese-related asthma was discovered. Our results indicate that rs6494395 is of African genetic ancestry. Understanding the genetic underpinnings of pediatric OA in diverse populations can improve precision medicine approaches and address health disparities in asthma outcomes.

Florivory directly affects floral structures, especially petals and anthers. The physical damage to these whorls can alter the characteristics of the flowers, compromise their functions and, consequently, impact fertility and reduce the reproductive success of the species. We provide the floral anatomical description of Senna aversiflora (Herb.) H.S.Irwin & Barneby. We measured various anatomical traits of petals and quantified the levels of chemical compounds and the pollen produced by intact and damaged flowers in order to identify characters associated with the plant-florivore interaction. We found that the epidermis (adaxial and abaxial surfaces) and mesophyll of the petals of healthy flowers was thicker when compared to damaged flowers. We infer that the smaller thickness of traits associated with the absence of characters with deterrent effect on herbivores and greater production of attractive/nutritive chemical compounds in relation to defense compounds contribute to make the species highly susceptible to florivory. Pollen production in damaged flowers did not differ between the different stages of floral development. However, florivory has a negative effect on the amount of pollen produced. Damaged flowers had less pollen than healthy flowers. We conclude that florivory in S. aversiflora exerts significant pressure on petal anatomy and resource production by flowers.

Article type: Case reportTitle: Dowling Degos Disease, A rare genetic disorderMahesh Mathur1, Neha Thakur1, Nabita Bhattarai1, Supriya Paudel1, Sandhya Regmi1, Sambidha Karki11Department of Dermatology, College of Medical Sciences Teaching Hospital, Bharatpur, Nepal

Belowground competition is affected by the presence and identity of neighboring plants, as well as by environmental conditions. We examined the effects of the degree of relatedness (DOR) of neighboring Solanaceae relatives under salinity stress vs. control. Cherry tomato ( Solanum lycopersicum L.) (C) and bell pepper ( Capsicum annuum L.) (B) plants were grown individually or in pairs of high (H) DOR (CC and BB) and low (L) DOR (CB), under control and salinity conditions. In comparisons of plant responses to DOR and treatments, cherry tomato benefited from the presence of bell pepper, with increased CO 2 assimilation (A), stomatal conductance (gs), plant height (H), shoot and root growth, xylem area and root respiration, thus acclimating better to salinity with L-DOR pairing. In contrast, salinity-stressed bell pepper showed impairment in A, gs, H, biomass, root anatomy, and proliferation of fine roots with significantly increased root respiration, especially with L-DOR pairing. Expression of genes in the tricarboxylic acid cycle (TCA) was also affected by the neighbor’s presence, influencing respiration rate. Acclimation to salinity is, therefore, species-specific and depends on the neighbor’s presence and DOR, suggesting that cultivating major crops with different DORs under extreme environmental constraints could increase stress tolerance for sustainable agriculture.

Modern microscopy systems allow researchers to generate large volumes of image data with relative ease. However, the challenge of analyzing this data effectively is often hindered by a lack of computational skills. This bottleneck negatively impacts both research reproducibility and efficiency, as researchers frequently rely on manual or semi-automated analysis methods. Interactive image analysis workshops offer a valuable solution, equipping researchers with the skills and tools needed to automate image processing tasks. In this paper, we share our experiences and best practices from conducting such workshops, which emphasize the use of open-source software like ImageJ, FIJI, and Python-based tools such as JupyterLab and napari. We discuss key considerations for workshop design, logistics, and outcomes, while highlighting common pitfalls to avoid. Using two recent workshops as case studies, we also present strategies for optimizing participant engagement and learning. Our insights offer practical guidance for planning and conducting image analysis workshops and serve as a starting point for researchers looking to establish similar training initiatives and enrich their local imaging communities.

Africa grapples with a profound energy challenge affecting over 600 million people without electricity access. This scarcity impedes economic growth, restricts healthcare services, and limits educational opportunities. Traditional energy solutions prove to be costly, unreliable, and environmentally unsustainable. In response to this challenge, 3D printing technology emerges as a transformative force, potentially revolutionizing energy generation across the continent. This research explores the multifaceted applications of 3D printing in addressing Africa's energy needs. From decentralized renewable energy solutions like solar photovoltaic systems, wind turbines, and micro-hydro power to innovations in energy storage and transmission through lithium-ion batteries and smart grid infrastructure, 3D printing offers customizable and cost-effective alternatives. Energy-efficient devices, such as 3D-printed cooking stoves and water pumps, promise sustainability and reduced environmental impact. Ongoing research initiatives, including the Open-Source 3D Printed Micro-Hydro Power Project and the 3DPrinted Solar Kiosk Project, showcase the tangible effect of 3D printing on energy accessibility. However, challenges such as limited infrastructure, material availability, and the need for skill development must be addressed. The research underscores the importance of building skills and capacity through training programs, establishing FabLabs and maker spaces, and fostering collaboration with universities and research institutions. The socio-economic impacts of adopting 3D printing for energy in Africa are profound, ranging from increased access to energy and job creation to community empowerment and environmental benefits. In conclusion, strategic investments in infrastructure, capacity building, and innovation are crucial for realizing 3D printing's potential as a powerful tool in Africa's pursuit of a sustainable and equitable energy future.

Parkinson’s Disease (PD), which exhibits a rapidly developing pathology, is one of the most devastating neurodegenerative diseases. To understand its molecular and cellular mechanisms and to attain a truly effective treatment, it is essential to develop standard, rapid, and reliable in vitro testing platforms for diseases. Classical two-dimensional (2D) cell culture is the starting point for the study of PD diagnosis and treatment. However, 2D grown cells do not exhibit the physiological properties of native tissues and provide limited data for testing drugs in vitro and understanding the mechanisms of diseases. Therefore, realistic 3D models similar to human physiology are required. In this study, the 2D and 3D PD modeling potentials of two cell lines (SHSY5Y and PC12), which are frequently used in neural tissue engineering studies, were compared. PD models generated by SHSY5Y and PC12 cells were evaluated by lactate dehydrogenase (LDH), Live&Dead, immunofluorescence, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) analyses. It was determined that PC12 cells had weaker adherence properties than SHSY5Y cells, and therefore, SHSY5Y cells had higher microtissue formation potential. PC12 cells completely lost their dopaminergic properties in 3D conditions, whereas 6-OHDA applied SHSY5Y microtissues showed PD markers related to neurotoxicity. A practical and useful 3D disease model reflecting the characteristics of PD with SHSY5Y cells is presented.

Houssem Eddine Rezgui1,2, Mimoun Hamdi1,2, Fethi Tlili21 STD Lab, Military Research Center, Tunisia2 GRES’COM Lab, Higher School of Communications of Tunis (SUP’COM), TunisiaEmail address of corresponding author: my.hamdi@gmail.comAbstract. The growing demand for diverse services and applications in 5G networks requires an efficient resource management scheme to optimize the utilization of network resources. Network slicing has emerged as a promising solution to address this issue by enabling the creation of multiple virtual networks that can be customized to specific service requirements. However, the current approach of slice selection is often based on predefined policies or user input, which can lead to sub-optimal resource allocation and potential network congestion. In this paper, we propose a cooperative slicing mechanism for 5G networks based on machine learning. Our solution involves the deployment of a machine learning model in user equipment (UE) to recommend the most suitable slice based on historical network and service usage data. This model is trained on network data to identify patterns and predict future network usage, enabling the UE to make informed slice selection recommendations to the 5G core network. The cooperation between UE and the 5G core network ensures efficient resource allocation and optimal performance for different service requirements. Our proposal offers a promising solution to overcome the limitations of the current slice selection method and improve the performance of 5G networks.Keywords: 5G networks, mobility management, network slicing, machine learning

In the 1970s, the psychologist J.J. Gibson developed an “ecological approach to visual perception”, suggesting that humans perceive the environment exploiting environmental affordances - surrounding invariant features that define possible individuals-object interactions - without top-down mediation of cognitive processes. Shepard extended this approach, suggesting that common environmental features are internalized defining perceptual constraints, such as the circadian rhythm, three-dimensional space, and gravity. In this perspective review, we apply these concepts to neuroaesthetics and beauty perception, discussing how the internalization of in-variants may influence our perception of beauty. Within this framework, special emphasis was placed on symmetry and golden ratio, typically considered as two benchmarks for beauty, and two geometrical features that can be considered as environmental affordances. Moreover, we suggest that kinetic aspects play a role in beauty perception of these proportions, particularly by the internal model of gravity.

Background: Paediatric palliative care (PPC) is considered an essential component of the management of children and adolescents with cancer. The International Society of Paediatric Oncology Global Mapping Programme surveyed hospital-based paediatric oncology facilities across Africa from 2018-2020 to document PPC and provision of PPC services. Procedure: An electronic and paper survey were widely distributed to elicit the presence of components of PPC: PPC teams, bereavement counselling services, patient support groups, and spiritual and religious support. Results were correlated with the countries’ Gini coefficient, World Bank income status indicators and Human Development Index. Results: Hospital-based paediatric oncology facilities in 16/54 African countries reported having all four PPC services while those in 11 countries reported having none of the four PPC services. No clear correlations were found between provision of such services and selected economic factors. Conclusions: This study demonstrates that hospital-based paediatric oncology facilities with limited resources caring for children and adolescents can provide PPC. Adoption of the World Health Organisation’s conceptual framework for palliative care and knowledge transfer between African facilities on the integration of PPC into paediatric oncology care, would benefit the increasing numbers of children and adolescents with cancer across the continent.

The increasing demand for high-quality synthetic data in various fields has driven research into more sophisticated generation techniques capable of producing data that is both realistic and diverse. The proposed method introduces a novel integration of Monte Carlo Tree Search (MCTS) with a Large Language Model (LLM), specifically tailored to guide the synthetic data generation process in a controlled and optimized manner. By leveraging MCTS to explore and navigate the vast search space of possible data sequences, the methodology ensures that the synthetic outputs maintain statistical fidelity to realworld datasets while achieving a balance between exploration and exploitation. The LLM is employed to synthesize contextually rich data, generating outputs that align with the defined parameters and reflect the complexity of the source data. Experimental results indicate that the synthetic data produced through this approach exhibits a high degree of similarity to real data in terms of statistical properties, diversity, and inter-feature correlations, outperforming traditional methods such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Furthermore, the method demonstrates computational efficiency and scalability, making it a viable solution for large-scale data generation tasks where maintaining data quality and fidelity is crucial.

Silver nanoparticles (AgNPs) have garnered significant attention due to their unique properties and wide range of applications. The green synthesis of AgNPs using plant extracts is an eco-friendly and cost-effective approach that has been extensively studied. This study presents the first-time synthesis and evaluation of silver nanoparticles (AgNPs) using black rosehip ( Rosa pimpinellifolia L.) seed aqueous extract as a potential pharmacological agent. The green synthesis approach yielded spherical Rp-AgNPs with dimensions between 5-200 nm and an average size of 51.93 nm, confirmed by various characterization techniques including UV-Vis spectroscopy, FTIR, TEM, SEM, XRD, DSC, Raman spectroscopy, and XPS analysis. The Rp-AgNPs demonstrated significant antioxidant, cytotoxic, and antimicrobial properties. Their antioxidant capacity surpassed that of the seed extract alone, with a lower IC 50 value in the DPPH assay. The nanoparticles exhibited cytotoxicity against H460 non-small cell lung cancer cells, with an IC 50 of 65.8 μg/mL. Antimicrobial activity was observed against both bacteria and fungi, with inhibition zones ranging from 10-14 mm for bacteria and 7-8 mm for Candida strains. Minimum Inhibitory Concentration values ranged from 50-100 μg/mL for bacteria and 100-200 μg/mL for Candida. This comprehensive study highlights the potential of black rosehip seed extract-mediated AgNPs for various pharmacological applications, especially antioxidant, anticancer and antimicrobial agents, paving the way for further exploration and development in the field of green nanotechnology for biomedical applications.

Critical Assessment of PRediction of Interactions (CAPRI) rounds 47 through 55 introduced 49 targets comprising multistage assemblies, antibody-antigen complexes, and flexible interfaces. For these rounds, we combined various Rosetta docking approaches (RosettaDock, ReplicaDock, and SymDock) with deep learning approaches (AlphaFold2, IgFold, and AlphaRED). Since prior CAPRI rounds, we have developed methods to better capture conformational changes, updated our scoring function, and integrated structure prediction tools such as AlphaFold2 in our docking routines. Here, we highlight several notable CAPRI targets and address the major challenges in the blind prediction of protein-protein interactions, including binding-induced conformational changes, large multimeric proteins, and antibody-antigen interactions. Although predictors have achieved modest improvements in accuracy of simpler targets post-AlphaFold2, performance for more flexible complexes remains limited. We employed RosettaDock 4.0, ReplicaDock 2.0, and AlphaRED to enhance backbone conformational sampling for flexible complexes. Our docking routines improved the DockQ score (0.77 vs. 0.62 for AF2-multimer) for a GP2 bacteriophage protein (T194), effectively capturing binding-induced conformational changes. Additionally, we introduce a fold-and-dock approach for predicting the assembly of a surface-layer SAP protein derived from Bacillus anthracis (T160), a large hetero-multimer comprising six distinct sub-units. For large symmetric complexes, we used Rosetta-based SymDock 2.0, successfully predicting a human DNA repair protein complex with A10 stoichiometry (T230) with high CAPRI-quality ranking. We also address the challenges in modeling antibody/nanobody-antigen interactions, particularly through the integration of deep learning tools and docking methods. Despite advances with tools like IgFold and AlphaFold2, accurately predicting CDR H3 loops and antibody-antigen binding interfaces remains challenging. Combining ReplicaDock 2.0 with deep learning highlights these difficulties and underscores the need for extensive sampling and CDR-focused strategies to improve prediction accuracy.