Patient Enablement in Chronic Diseases in Primary Health Care, Riyadh City, KSA

Background: Chronic diseases continue to cause high morbidity and mortality in Saudi Arabia. Patients severing from diabetes mellitus, hypertension and associated complications have recently increased and most of these patients find it extremely difficult to understand or cope with their illness. The objective of this study is to determine the level of patients’ enablement in chronic disease and its predictors. Methods: A community based cross-sectional study was conducted between December 2014 and January 2015. Six hundred and four (604) Patients attending the Chronic Disease Clinic in Alwazarat Health center were randomly selected to participate in the study. Patients aged 18 years and above, who willingly agreed to participate, were included in the study. Self-reported questionnaire was used to determine patient level of enablement. Descriptive statistics such as mean and median were calculated and binary logistic regression was employed to determine the predictors of patient’s enablement to chronic disease. Results: Our results show that five hundred and sixty five (565) out of (604) patients participated in the study with 86.6% response rate. Type 2 diabetes mellitus affecting 40.65% while hypertension affecting 37.79% of the patients in Al wazarat health center. Patient’s enablement to chronic disease was very low and ranged between 2.41 and 1.53 out of 5.0. Binary logistic regression shows that age (male: OR;0.84, 95% CI, 0.72 - 1.04, female. OR;1.04, 95% CI 0.88 - 1.39), marital status (male: OR;0.72, 95% CI 0.54 - 1.11, female: OR 1.01;95% CI 0.82 - 1.29), patient educational level and number of problems discussed with physician and consultation length between male patients and their physician were statistically significant and correlated with patients enablement to chronic disease (P < 0.05). Conclusion: This study shows that patient’s enablement in chronic disease is very low but constitutes an important arm in patients care management. It should be considered as a measurable patient outcome from healthcare services. More prospective studies on this important topic are highly recommended.

Dendritic Deep Learning for Medical Segmentation

Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation.In this study,we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure,an upsampling index,and a deep supervision method.Furthermore,we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping,thereby further improving the effectiveness of our approach.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose tissues.OA presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial variants.Consequently,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches.Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues.This issue has led to standardization difficulties and hindered the success of clinical trials.Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease subtypes.Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular function.Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial data.This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates.Furthermore,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and tissues.Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes.This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis.It explores their applications,challenges,and potential opportunities in the field of OA.Additionally,this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Implantable probe with integrated reference electrode for in situ neural signal and calcium ion monitoring

Monitoring the electrophysiology activity of neurons and blood calcium signals can enable a better understanding of disease-related neural system circuits.However,currently,in situ calcium ion monitoring tools are scarce and exhibit low integration and limited sensitivity.In this letter,we propose an implantable probe with an integrated in situ Ag/AgCl reference electrode(ISA/ARE)that can monitor action potential(AP)and Ca^(2+) concentrations.

Proteins as nanomagnets and magnetoreceptors

It is an appealing notion that a protein molecule could act as a nanomagnet.A genetically encodable biomolecule with a permanent magnetic moment at room temperature could have a range of applications:a magnetogenetic actuator,a magnetic tag for purifying and immobilizing enzymes,a contrast agent for magnetic resonance imaging,and a basis for a biomimetic magnetic sensing device,to name just a few.A magnetic protein could perhaps also function as the sensor in the magnetic compass that enables small songbirds to navigate the huge distances between their breeding and wintering grounds.Attractive though such possibilities may be,how realistic are they?

Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal Control

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

Current-Induced Magnetization Switching Behavior in Perpendicular Magnetized L1_(0)-MnAl/B2-CoGa Bilayer

Rare-earth-free Mn-based binary alloy L1_(0)-MnAl with bulk perpendicular magnetic anisotropy(PMA) holds promise for high-performance magnetic random access memory(MRAM) devices driven by spin-orbit torque(SOT). However, the lattice-mismatch issue makes it challenging to place conventional spin current sources, such as heavy metals, between L1_(0)-MnAl layers and substrates. In this work, we propose a solution by using the B2-CoGa alloy as the spin current source. The lattice-matching enables high-quality epitaxial growth of 2-nm-thick L1_(0)-MnAl on B2-CoGa, and the L1_(0)-MnAl exhibits a large PMA constant of 1.04 × 10^(6)J/m^(3). Subsequently, the considerable spin Hall effect in B2-CoGa enables the achievement of SOT-induced deterministic magnetization switching. Moreover, we quantitatively determine the SOT efficiency in the bilayer. Furthermore, we design an L1_(0)-MnAl/B2-CoGa/Co_(2)MnGa structure to achieve field-free magnetic switching. Our results provide valuable insights for achieving high-performance SOT-MRAM devices based on L1_(0)-MnAl alloy.

Discontinuities of Banana Integrals in Dispersion Relation Representation

We derive the discontinuities of banana integrals using the dispersion relation iteratively,and find a series of identities between the parameterized discontinuities of banana integrals(p-DOBIs).Similar to elliptic integrals,these identities enable the reduction of various p-DOBIs to be a linear combination of some fundamental ones.We present a practical application of p-DOBIs for deriving the Picard–Fuchs operator.Then we establish the expression of generalized dispersion relation,which enables us to obtain the dispersion relation representation of arbitrary banana integrals.Moreover,we propose a hypothesis for generalized dispersion relation and p-DOBIs,which provides a simple way to calculate the discontinuities and transform dispersion relation representation to p-DOBIs.

Effective extraction of polyribosomes from astrocytes enables future discoveries on translation regulation

Translation regulation is an important layer of gene expression:Generation of genome-wide expression datasets at multi-omics levels in spatial,temporal,and cell-type resolution is essential for deciphering brain complexity.Regulation of gene expression is a highly dynamic process aiming at the production of precise levels of gene products to guarantee optimal cellular function,in response to physiological cues.Speedy advances in next-generation sequencing enabled the understanding of epigenomic and transcriptomic dynamic landscapes of different brain regions along development,aging,and disease progression.However,the correlation of the“transcriptome”with protein levels is poor because numerous mRNAs are subjected to manipulation of their translation efficiency,to warrant a favorable result under certain conditions.Hence,it is widely accepted that regulation at the translation level is a vital layer of gene expression.Quantification of actively translated mRNA populations(i.e.,“translatome”)is a more reliable predictor of the“proteome”(Wang et al.,2020).

A magic organic molecule assembled capping layer enables airprocessed α-FAPbI_(3) perovskite solar cell with state-of-the-art performances

The black-phase formamidine-lead iodide(α-FAPbI_(3)),boasting an optimal bandgap of 1.5 eV,stands out as a premier choice for narrow-bandgap perovskite solar cells(PSCs),achieving a certified power conversion efficiency(PCE)of 26.1%[1−5].This impressive performance hinges on the orderly and homogeneous crystallization ofα-phase pure FAPbI_(3),facilitated by coordinating solvents such as dimethyl sulfoxide(DMSO)to form intermediates like PbI_(2)-DMSO complex(D-complex).The D-complex plays a pivotal role in crystallization thermodynamics,enabling the direct formation of α-FAPbI_(3) without the photoinactiveδ-phase[6−9].However,DMSO,a commonly used coordinating solvent,is highly hygroscopic and prone to hydration upon moisture exposure.This tendency leads to incomplete perovskite crystallization and accelerates the transformation of α-FAPbI_(3) into itsδ-phase[2,10].Consequently,the best-performing α-FAPbI_(3)PSCs must be processed in an inert atmosphere with strictly controlled relative humidity(RH)and suffers from relatively poor reproducibility.Given the hard-to-control atmosphere at industrial scale,it is challenging yet imperative to eliminate the negative effects stemming from hygroscopic coordinating solvents[11−13].

How to manage the malposition of deep vein catheterization into the artery?

Central venous catheterization establishes temporary,efficient,and rapid use of deep venous access in patients,which provides high flow rate fluid perfusion,enables measurement of central venous pressure,and acts as an important reference for clinical decision-making.However,various complications such as pneumothorax,hemothorax,hematoma,and puncture failure can easily occur during the puncture and catheterization process.

Enabling Technologies for Autonomous Robotic Systems in Manufacturing

Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry.Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies,digital twins,artificial intelligence and novel communication techniques.These enable real-time monitoring of production processes,situation recognition and prediction,automated and adaptive(re)planning,teamwork and performance improvement by learning.This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems.Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly,welding,grinding,picking and placing,and machining.The various solutions have in common that they use a generic digital twin concept as their core.After making general recommendations for realizing autonomous robotic solutions in the industry,open issues for future research will be discussed.

Building mm Wave on the Evolving C-V2X:mm Wave NR-V2X

Since the evolving standardization of cellular V2X(C-V2X)technologies is continuously developed by 3GPP,research on new radio(NR-V2X)has been on schedule by academic and industries.Though millimeter wave(mm Wave)frequency band is supposed to provide large transmission bandwidth,yet the development of mm Wave band in NR-V2X is still in preliminary stage.In this article,a comprehensive discussion of mm Wave NR-V2X is given covering trends,standardization landscape,and enabling technologies,aiming at tackling the challenge of channel modeling,directional channel access,beamforming and blockage management.The vision of mm Wave NR-V2X is to fully support the development of automated driving,holographic control display and improved in-car infotainment for the future.

Global AI Governance:Realistic Difficulties,Mode Selection,and Chinese Solutions

Today,global AI governance is still in the exploratory stage,and the complex nature and uncertainty of technology require global collaboration.THE rapid advancement of artificial intelligence(AI)technology has undoubtedly become the center of global attention in recent years,especially generative AI technology.This technology is rapidly shaping the trends of digital society,while its risks are also spreading,thus making it imperative to strengthen global AI governance so as to enable effective risk control.

CoRE:Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.

Spin-polarized electron beam generation in the colliding-pulse injection scheme

Employing colliding-pulse injection has been shown to enable the generation of high-quality electron beams from laser-plasma accelerators.Here,by using test particle simulations,Hamiltonian analysis,and multidimensional particle-in-cell simulations,we lay the theoretical framework for spin-polarized electron beam generation in the colliding-pulse injection scheme.Furthermore,we show that this scheme enables the production of quasi-monoenergetic electron beams in excess of 80%polarization and tens of pC charge with commercial 10-TW-class laser systems.

Special issue on machine learning-driven big data and blockchain techniques for communication

Guest Editorial Currently,the rapid development of storage technologies combined with some potential factors such as mobile networks,Internet of Things(IoT),cloud computing and the emergence of new technologies pose some problems for big data processing and blockchain security in the communication domain.Moreover,the complexity of network security and data processing has increased dramatically,making it more difficult and challenging to solve various problems in the communication domain.Therefore,Machine Learning(ML)algorithms have been proposed to process big data and enhance blockchain security and further enable intelligent analysis in the communication domain.

Health Phys.Abstracts,Volume 123,Number 5

Practical Tools for Patient-specific Characterization and Dosimetry of Radiopharmaceutical Extravasation Extravasation during radiopharmaceutical injection may occur with a frequency of more than 10%.In these cases,radioactivity remains within tissue and deposits unintended radiation dose.Characterization of extravasations is a necessary step in accurate dosimetry,but a lack of free and publicly available tools hampers routine standardized analysis.Our objective was to improve existing extravasation characterization and dosimetry methods and to create and validate tools to facilitate standardized practical dosimetric analysis in clinical settings.Using Monte Carlo simulations,we calculated dosimetric values for sixteen nuclear medicine isotopes:^(11 )C,^(64) Cu,^(18) F,^(67 )Ga,^(68) Ga,^(123) I,^(131) I,111 In,177 Lu,13 N,15 O,82 Rb,153 Sm,89 Sr,99m Tc,and 90 Y.We validated our simulation results against five logical alternative dose assessment methods.We then created three new characterization tools:a worksheet,a spreadsheet,and a web application.We assessed each tool by recalculating extravasation dosimetry results found in the literature and used each of the tools for patient cases to show clinical practicality.Average variation between our simulation results and alternative methods was 3.1%.Recalculation of published dosimetry results indicated an average error of 7.9%.Time required to use each characterization tool ranged from 1 to 5 min,and agreement between the three tools was favorable.We improved upon existing methods by creating new tools for characterization and dosimetry of radiopharmaceutical extravasation.These free and publicly available tools will enable standardized routine clinical analysis and benefit patient care,clinical follow-up,documentation,and event reporting.

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

Massimo D'Alema,Former Prime Minister of Italy&President of the Italianieuropei Foundation

What do we mean when we talk about development that places people and nations at its centre?First and foremost,we want economic growth to enable every woman,man,and child to have access to the basic,common goods for a dignified life.We want to reduce the intolerable social distances and inequalities that produce unjust and socially unsustainable societies.We want to ensure harmony between economic growth and the protection of the natural environment.For ten years,the Belt and Road Initiative has worked in this direction.