Field Of Health Care Research Articles

Factors influencing healthcare workers' performance before and after the coronavirus disease 2019 pandemic: A bibliometric analysis with supplementary comparative analysis.

The performance of healthcare workers directly impacts patient safety and treatment outcomes. This was particularly evident during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to analyze research trends on factors influencing work performance among healthcare workers through bibliometric analysis and conduct a comparative analysis from macro and micro perspectives before and after the COVID-19 pandemic to complement the existing research. This study involved a bibliometric analysis of 1408 articles related to work performance in the healthcare field published between 2010 and 2023, using the Web of Science, Scopus, and PubMed databases, and 37 articles were selected to determine the factors influencing work performance. By conducting a bibliometric analysis of the articles based on country, institution, journal, co-cited references, and keywords, this study identified a significant growth trend regarding the factors influencing work performance in the healthcare field, and research hotspots shifted from organizational factors like standard towards psychological factors such as burnout, anxiety, and depression following the outbreak of the COVID-19 pandemic. Subsequently, this study extracted 10 micro-level and 9 macro-level influencing factors from the selected articles for supplementary analysis. Furthermore, this study conducted a comparative analysis of the impact of these factors on work performance before and after the COVID-19 pandemic. This study addressed the limitations of previous studies regarding incomplete extraction of factors influencing work performance and unclear comparisons of parameters before and after the COVID-19 pandemic. The findings provide insights and guidance for improving the performance of healthcare workers.

Professionals’ ideas and observations on preschoolers’ experiences with physical symptoms: a qualitative interview study

BackgroundPreschoolers experience physical symptoms, like abdominal pain or minor injuries, almost every day. These experiences may shape how they deal with health issues later in life. To gain insight into these early life experiences, information from multiple perspectives is useful. This qualitative study aimed to explore important themes in preschoolers’ experience of physical symptoms, using adult professionals from various backgrounds as informants.Methods20 semi-structured interviews were performed with professionals from different fields in healthcare and education, to learn about their ideas and observations on preschoolers’ experiences with physical symptoms. The interviews were verbatim transcribed and coded in Atlas.ti by two independent coders, after which thematic content analysis was applied to define themes.ResultsThree themes emerged from the interviews: unawareness, seeking attention, and parental influence. Unawareness refers to the professionals’ idea that preschoolers have limited cognitions about causes and consequences of physical symptoms. Seeking attention was described as important for preschoolers with symptoms, both as comfort and in a social context. Professionals described diminished attention-seeking behavior in preschoolers with more severe symptoms. Parental influence was seen as highly relevant in preschoolers’ experiences with physical symptoms, with both supportive and disruptive aspects. Healthcare professionals differed from educational professionals in their observations and ideas, especially about underlying mechanisms influencing symptoms.ConclusionsProfessionals report attention-seeking and parental influence as important factors in preschoolers with physical symptoms, and they report limited cognitions about causality. Professionals in healthcare and education show different perceptions, suggesting the importance of incorporating both views into research.

From plate to waste: a cross-sectional study of food waste trends in Al Ahsa, Saudi Arabia

BackgroundFood waste is a global concern that needs special attention and urgent actions to reduce the amount of wasted food. This study aims to investigate the trend of food waste in Al Ahsa.MethodsThis is a cross-sectional study designed to analyze the factors associated with the generation of wasted food within Al Ahsa community.ResultsThe results showed that out of 257 participants, 82.9% were of Saudi nationality, 83.3% were females, 69.6% below 34 years and the family sizes mean was around 6. The majority of study participants were unemployed (62.3%), the employed ones were in healthcare field (56.4%). No significant gender differences was found in terms of other food waste-related variables. No significant association was found with age and occupation. Non-Saudi participants significantly report a lower frequency of wasting food and a higher rate of attitudes toward food waste sorting with p = 0.02 and p = 0.02, respectively. Working participants showed significant differences in terms of attitudes toward food waste sorting (p = 0.002), attitudes toward reducing food waste (p = 0.042), and the amount of food wasted per day (p = 0.010) when compared to those who are not working. Doctorate level of education exhibited significant (p = 0.002) attitudes toward food waste sorting and those who are in the commerce field of education had higher mean scores (M = 2.81, SD = 0.84) to reduce food waste. The cause of food waste was related to expiry date (61.1%). The common cause of wasting food when eating out or ordering online, was related to participants’ preference for trying a variety of dishes (40.1%). The participants adherent to home-cooked food during pandemic (mean 3.85, SD 1.40). Males reported a slightly higher impact of COVID-19 on their dietary habits when compared to females (p = 0.04). The majority of participants agreed that sharing food with neighbors decreases food waste (Mean = 3.96, SD = 1.32). Moreover, they do not throw food due to religious and cultural beliefs (Mean = 3.36, SD = 1.60).ConclusionCreating educational campaigns, promoting sustainable waste management and integrating technology to reduce food waste are recommended to achieve Saudi vision 2030.

The system of support for medical decision-making in the formation of a rehabilitation diagnosis in the categories of the International Classification of Functioning, Disability and Health

Introduction. Comprehensive rehabilitation of patients is a key task of the national healthcare system. To create an effective rehabilitation program, it is necessary to establish an accurate rehabilitation diagnosis. Designing an individual medical rehabilitation plan based on a biopsychosocial approach, the practical tool of which was the “International Classification of Functioning, Disability and Health” (ICF), has changed the global concept of rehabilitation. A priority in the field of digital healthcare is the introduction of medical decision support systems, one of the tasks of which is to help in diagnosis and minimize medical errors aimed at improving the quality of medical care. The purpose of the work: to create a software system for making medical decisions in the formation of a rehabilitation diagnosis in the ICF categories. Materials and methods. content analysis was used to study and analyze medical decision-making systems (foreign and domestic) and the ICF; scientific and methodological work was carried out on the development of software for medical decision-making systems. Results. based on the work carried out, software has been developed to automate, systematize, and optimize the process of establishing a rehabilitation diagnosis in patients with ischemic stroke, standardize approaches to individualizing diagnosis formulation taking into account the severity of functional disorders. Research limitations. During the development and description of the software, the capabilities of medical information systems applied in the healthcare of Moscow were used. Conclusion. The biopsychosocial approach is one of the main principles of modern rehabilitation. Digital healthcare facilitates the integration of decision support systems that improve the quality of medical care. The implementation of the developed program into digital platforms of the healthcare system will reduce time costs and unify the procedure for making a rehabilitation diagnosis and all related processes, consistently preserving the necessary information about the patient.

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

3D-Printed Polymeric Biomaterials for Health Applications.

3D printing, also known as additive manufacturing, holds immense potential for rapid prototyping and customized production of functional health-related devices. With advancements in polymer chemistry and biomedical engineering, polymeric biomaterials have become integral to 3D-printed biomedical applications. However, there still exists a bottleneck in the compatibility of polymeric biomaterials with different 3D printing methods, as well as intrinsic challenges such as limited printing resolution and rates. Therefore, this review aims to introduce the current state-of-the-art in 3D-printed functional polymeric health-related devices. It begins with an overview of the landscape of 3D printing techniques, followed by an examination of commonly used polymeric biomaterials. Subsequently, examples of 3D-printed biomedical devices are provided and classified into categories such as biosensors, bioactuators, soft robotics, energy storage systems, self-powered devices, and data science in bioplotting. The emphasis is on exploring the current capabilities of 3D printing in manufacturing polymeric biomaterials into desired geometries that facilitate device functionality and studying the reasons for material choice. Finally, an outlook with challenges and possible improvements in the near future is presented, projecting the contribution of general 3D printing and polymeric biomaterials in the field of healthcare.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Application of 3D printing in bacterial detection

Diseases caused by bacterial infections, especially pathogen variations and drug-resistant bacteria, are a serious threat to human health globally. Accurate and rapid identification of bacterial pathogens is essential for early diagnosis of infections and timely treatment of disease. At present, the routine diagnostic methods for identifying bacterial pathogens in clinic are bacterial culture, immunological detection, genetic analysis, etc. These methods occupy foundational position for bacterial detection that favors for clinical diagnosis of pathogen infection in daily life. Notably, 3D printing technology, together with 3D bioprinting technology, provides more options for bacterial detection due to its personalized design and manufacturing. Compared with traditional methods, 3D printing provides a more convenient, rapid and accurate bacterial detection platform, thus meeting the urgent needs of clinical and scientific research. Here, we have summarized the research progress and given a comprehensive review of 3D printing technology in bacterial detection, including bacterial detection sensors, microfluidic chips, bioprinting microarray technology, AI and spectroscopy technology, providing a scientific reference and filling in gaps in 3D printing-assistance bacterial detection. At the same time, technical advantages, challenges and future development trends will also be discussed in related healthcare fields.

Advanced Technologies for Teaching Rachana Sharir: Implications for Academic Education

The study of Rachana Sharira is a cornerstone of educational programs in the biological and healthcare fields. It is crucial that the teaching methods and instructional strategies used in this subject effectively prepare students. Traditionally, Rachana Sharira has been taught through a combination of lectures and hands-on practical sessions involving cadaver dissections and pre-dissected specimens. However, with the advent of modern digital technologies, such as smartphones, QR codes, and virtual reality, along with advanced imaging techniques like radiography, ultrasonography, MRI, and CT scans, the landscape of Rachana Sharira education is evolving. This review aims to explore how these innovative tools are being integrated into academic settings to enhance the diversity and effectiveness of Rachana Sharira teaching, ultimately improving students' understanding of the subject and its clinical applications.

HARNESSING PRIVACY-PRESERVING FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE IoMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

A Blockchain based Secure Authentication Technique for Ensuring User Privacy in Edge Based Smart City Networks

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.

Updating the critical steps of the quality implementation framework: a protocol for an umbrella review of reviews

IntroductionImplementation science focuses on improving the dissemination, uptake and adoption of evidence into practice. Over the last decade, implementation science research has proliferated, particularly in healthcare and social science. The...

Synthesis, crystal structure and exploration of N,N'-(ethane-1,2-diyl)bis(4-acetylbenzenesulfonamide) (EDBABS) fabricated glassy carbon electrode as highly sensitive barium (Ba2+) sensor: An electrochemical approach

Environmental pollution spurred by dint of noxious chemicals and heavy metals has popped up as a pressing threat for present day community. In this study, we synthesized the N,N'-(ethane 1,2-diyl)bis(4-acetylbenzene-1-sulfonamide) (EDBABS) and N,N'-(cyclohexane-1,2-diyl)bis(4-acetylbenzenesulfonamide) (CDBABS) as bidentate chelating agents with the intention to detect the heavy metal cations in water systems. Conventional spectroscopic techniques such as UV-Vis spectroscopy, Fourier transform-infra red (FT-IR) spectroscopy, 1H-NMR and 13C-NMR in addition to single crystal X-ray diffraction studies were performed to expound structural characterization data of these newly synthesized non-reported bidentate chelating agents. After Characterization, a novel electrochemical current – potential (I-V) approach based on their modified GCEs was applied to detect the toxic cations in water systems. For this purpose, Keithley electrometer was employed as a constant voltage source. On the trail of fabricating an efficient as well as selective electrochemical sensor, for the detection of heavy metal cations in phosphate buffer system of pH = 7.0, EDBABS and CDBABS coupled with nafion as adhesive conducting binder were coated separately onto the Glassy Carbon Electrodes (GCEs). it was delineated that newly designed EDBABS/Nafion/GCE induced a high current response for Ba2+ cations in the presence of other interfering heavy metal cations (Y3+, Sn2+, Sb3+, Mn2+, Hg2+, Cr3+, As3+ and Ag+) than CDBABS/Nafion/GCE and found to be selective and sensitive towards Ba2+ cations, predominantly contributing towards environmental pollution. In order to optimize this newly designed EDBABS/Nafion/GCE as selective electrochemical Ba2+ cationic sensor, different analytical parameters such as sensitivity, limit of detection (LOD) at S/N of 3, limit of quantification (LOQ), and linear dynamic range (LDR) turned out to be satisfactory towards Ba2+ cations. The calibration curve emerged to be linear with a broad concentration range of Ba2+ cations from 0.1 nM to 0.01M and sensitivity was estimated as 3.481 × 10−3µAµM−1cm−2 in addition to (r2) as 0.9834, limit of detection (LOD) as 0.027nM (at S/N=3) and limit of quantification (LOQ) as 0.091nM. Within the realm of extensive environmental protection and public health care field, this work inducts a reliable and effective way for the fabrication of sensitive as well as selective cationic electrochemical sensors based on chelating agents which can be launched as an advanced approach for quick probing of heavy metal cations in water systems, reliably and effectively.

Sensitivity study of heat transfer in a Jeffrey ferro-hybrid nanofluid bioconvective squeezing flow with inclined MHD and higher-order chemical reaction: entropy analysis

Abstract The present investigation concentrates on analyzing heat transfer and entropy formation in a time-reliant bioconvective flow of a blood-based Jeffrey hybrid nanofluid via a squeezing channel that is suctioned or injected at the lower plate. Cu nanoparticles and Fe3O4 ferro-nanoparticles are suspended in base-fluid blood. Adding ferro-nanoparticles to a flow process allows for better control of the external magnetic field and improved heat transmission. Noble integration of an aligned magnetic field, Joule's heating, thermal radiation, and higher-order chemical reactions is taken into account in the flow in a porous media. An appropriate choice of similarity variables leads to the non-dimensionalization of the governing equations, that are subsequently solved by the homotopy analysis method (HAM), yielding a semi-analytical solution. An innovative feature of this research is the optimization of heat transfer by the application of the response surface methodology (RSM) technique. Additionally, sensitivity analysis was carried out to identify the most influential parameter. The study's findings indicate that increased suction reduces both velocity and temperature distributions in both the nanofluid and hybrid nanofluid models. In terms of thermal performance, the Blood/Fe3O4 - Cu hybrid nanofluid surpasses the Blood/Fe3O4 nanofluid. The rate of thermal energy transfer is highly sensitive to variations in the Eckert number, while thermal radiation has a relatively lesser impact. Moreover, elevated levels of the magnetic parameter, Eckert number, and nanoparticle concentration lead to augmented entropy formation. This mathematical model is effective for analyzing drug transport mechanisms throughout the human body and presents extensive potential applications in the fields of biology and healthcare.

Perspectives of Regenerative Medicine in Dermatology and Cosmetology

Regenerative medicine has revolutionized the field of biological sciences, pharmacology and healthcare, bringing a new approach to treatments of various health conditions. Dermatology and aesthetic medicine are interested in these new scientific approaches as well. From this point of view, many areas of skin health and cosmetology may be involved, but several questions should be answered before these procedures become routine in offices and hospitals. Indeed scientific, ethical, and financial issues still have to be addressed in detail. Nevertheless, ongoing research and technological advancements are expected to further improve therapies. The integration of artificial intelligence and machine learning could enhance the precision of regenerative treatments, making them more effective and accessible in forthcoming years.

Challenges of Wireless Sensor Networks and their Solutions

Wireless Sensor Networks (WSNs) have emerged as a critical technology with uses in the fields of smart cities, industrial automation, healthcare, and environmental monitoring. This research paper provides a thorough review of the challenges inherent in WSNs, with the intention of providing light on the challenges that practitioners and researchers have while setting up and maintaining these networks. The challenges involve several aspects, comprising data management, security, scalability, energy efficiency, and dependability. Security concerns: given that these networks' communication is open and wireless, maintaining data integrity and secrecy presents substantial issues. Furthermore, it is essential to guarantee data transmission dependability in dynamic, frequently severe situations. This research analyzes these challenges, looks at the methods and solutions that are currently being offered in the literature, and also points out areas that need more research and development in the area of wireless sensor networks. Comprehending and resolving these obstacles is essential to realizing WSNs' complete potential and promoting their extensive integration in various fields.

Highly Conducting and Ultra-Stretchable Wearable Ionic Liquid-Free Transducer for Wireless Monitoring of Physical Motions.

Wearable strain transducers are poised to transform the field of healthcare owing to the promise of personalized devices capable of real-time collection of human physiological health indicators. For instance, monitoring patients' progress following injury and/or surgery during physiotherapy is crucial but rarely performed outside clinics. Herein, multifunctional liquid-free ionic elastomers are designed through the volume effect and the formation of dynamic hydrogen bond networks between polyvinyl alcohol (PVA) and weak acids (phosphoric acid, phytic acid, formic acid, citric acid). An ultra-stretchable (4600% strain), highly conducting (10 mScm-1), self-repairable (77% of initial strain), and adhesive ionic elastomer is obtained at high loadings of phytic acid (4:1 weight to PVA). Moreover, the elastomer displayed durable performances, with intact mechanical properties after a year of storage. The elastomer is used as a transducer to monitor human motions in a device comprising an ESP32-based development board. The device detected walking and/or running biomechanics and communicated motion-sensing data (i.e., amplitude, frequency) wirelessly. The reported technology can also be applied to other body parts to monitor recovery after injury and/or surgery and inform practitioners of motion biomechanics remotely and in real time to increase convalescence effectiveness, reduce clinic appointments, and prevent injuries.

The role of macrocycles in supramolecular assembly with polymers.

Recently, supramolecular self-assembly has attracted the attention of researchers worldwide because it enables the creation of nanostructures with unique properties without additional costs. Spontaneous organization of molecules allows the design and development of new nanostructures that can interact with drugs and living cells and generate a response. Therefore, supramolecular structures have enormous potential and can be in demand in various fields of healthcare and ecology. One of the widely used building blocks of such supramolecular assemblies is polymers. This review examines the joint aggregation behavior of various macrocycles (cyclodextrins, calixarenes, cucurbiturils, porphyrins, and pillararenes) with polymers, the functional properties of these supramolecular systems and their potential applications.

Integrating Organ-on-Chip Models In Drug Discovery: A Comprehensive Review on Innovations and Implications

This review article examines the current developments in applying microfluidic technologies in cancer therapy and personalized medicine. This includes the fabrication of cancer cells onto the microfluidic chips, preclinical cancer model simulation development, biomarker detection, tumor heterogeneity detection, integration of microfluidics in robotic drug delivery systems, Artificial Intelligence (AI), and discuss the use of techniques such as Machine Learning (ML) to predict pharmacokinetics and pharmacodynamics of cancer cells. This review article also highlights how integrating cancer models with microfluidic devices helps to simulate disease progression more accurately, thereby improving treatment options. These devices also enable researchers to identify suitable doses for cancer treatment. Moreover, microfluidics chips facilitate cell transformation in many types of cancer, which is important for patient-specific therapy. Microfluidics technology in robotic drug delivery enables precise delivery of targeted drugs, thus reducing the potential side effects of the drugs. Integrating these fields into the medical and pharmaceutical fields helps researchers to develop the pharmaceutical product faster than the traditional method of drug discovery. Overall, this review article highlights the integration of interdisciplinary technologies in the healthcare field, which may decrease the timeline of drug discovery and provide efficient drugs to patients.

Evaluation of the Interdisciplinary Care Home-bAsed Reablement Program (I-CHARP) for People Living With Cognitive Impairment and Dementia in Care Homes.

Major knowledge and practice gaps exist in aged care home services to support independence of older people with dementia. This research evaluates an adaptation of a community-based rehabilitation model for care homes, namely Interdisciplinary Care Home-bAsed Reablement Program (I-CHARP), by examining whether (and, if so, how) I-CHARP produces its intended effects and how this programme can be practicably implemented, sustained and scaled up across care homes in Australia. I-CHARP is a 4-month bio-behavioural-environmental rehabilitation model of care, integrated in care home services, supported through the deployment of an implementation strategy, the Research Enabled Aged Care Homes (REACH) network. It consists of (1) 8-12 full individual sessions and additional eight brief follow-ups per resident, tailored to the resident's needs, delivered primarily by a team of an occupational therapist, registered nurse and other allied health staff; (2) environmental modifications/assistive devices up to the value of $400 per resident; and (3) engagement of intervention care home staff, managers and regular visitors. An overarching evaluation approach is participatory action research using a cluster quasi-experimental design and mixed methods. It involves testing of the implementation strategy (REACH network and other approaches) while observing/gathering information on the intervention (I-CHARP) and related outcomes in three cycles. Participants include residents (aged ≥ 60 years with early stages of dementia) and care staff from 16 care homes. Care quality indicators, health care costs, field notes and semi-structured interviews/focus groups with intervention site staff, regular visitors and managers will provide further insights into I-CHARP processes and implementation issues. In the final phase of the project, an Agile Implementation Playbook will be developed for the delivery of reablement care that can be used in routine practice across care homes in Australia. The study findings will also inform future policy development and strategic directions for dementia care in care homes. Australian New Zealand Clinical Trial Registry, ACTRN12623000885695 Registered 16 August 2023, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386088. 1.0 dated 20 July 2023.