Early Detection Of Problems Research Articles

Building An Android-Based Application For Teenage Women's Reproductive Health Education

Background: Improper nutritional intake during adolescence can cause health problems, such as: impaired physical growth, increased risk of iron deficiency (Iron Deficiency Anemia), decreased intellectual capacity, impaired bone health, impaired sexual organ function, impaired concentration and performance at school, eating disorders and obesity, as well as malnutrition, but teenagers' knowledge about nutrition is very limited. Teenagers easily adapt to digital literacy. Providing digital interventions can have a positive impact on various aspects of health so that efforts to empower teenagers through increasing health promotion and early detection of adolescent health problems digitally are growing rapidly. Aim: The aim of this research is to explore the needs of young women for Android-based nutritional and reproductive health educational applications. Method: The qualitative research method is in-depth interviews via the Zoom application, WhatsApp video call, or WhatsApp voice call. Result: The results of qualitative research show that young women prefer educational media that is easy and fast in providing health information so that educational efforts via smartphones, namely Android-based applications, are an effective solution in providing precise and accurate information about nutritional intake and reproductive health. Keywords: Reproductive Health, Young Women, Application

The M-POHL Health Literacy Population Survey 2024-2026 (HLS24)

Abstract Background Inspired by the first European Health Literacy Survey (HLS-EU) and its policy impact, the WHO Action Network on Measuring Population and Organizational Health Literacy (M-POHL) was established in 2018 under the auspices of WHO Europe to monitor and strengthen health literacy (HL) in Europe. The Health Literacy Survey 2019-2021 (HLS19) was the first project of M-POHL, focusing on both general and specific aspects of HL. The follow-up survey (HLS24) builds on this work and will provide even more comprehensive data on HL by including more countries and new aspects of HL. By also focusing on NCDs and prevention, HLS24 is a key partner in the European Joint Action PreventNCD. Methods The HLS24 is a cross-sectional study based on nationally representative samples of the adult population (18+) in more than 20 participating countries. The HLS24 core questionnaire includes items on general HL, sociodemographics, health behavior, health status, and health care utilization. Some countries also include questions on prevention and early detection of chronic diseases and health problems. Five optional packages (OPs) enable countries to focus on specific aspects of HL, namely digital, communicative, navigational, vaccination and mental HL. Results HLS24 uses a harmonized set of HL instruments that provide a solid database for health policy decisions to strengthen HL and health equity in Europe. In addition to the three OPs on digital, communicative and navigational HL discussed in the following presentations, an 11-item set on vaccination literacy and a 21-item set on mental HL are provided. Conclusions Covering NCDs, prevention, digital health, immunization, and mental health, the HLS24 provides important data on current health challenges and highlights the needs of the European population regarding health information and services upon which policy and action interventions can be based.

The maternity dashboard is an effective tool for the improvement of maternal and neonatal care outcomes: a prospective observational study from Iraq

Background: The implementation of maternal dashboards in hospitals is both feasible and effective in improving healthcare quality. The dashboards provide rapid feedback on local outcomes in an accessible format and enable early detection of potential problems. They serve as a simple computerized tool that graphically presents the health facility’s clinical data, utilizing a traffic-light coding system so as to alert front-line staff about changes in the frequency of clinical outcomes. Aim: The aim of this study was to assess the effectiveness of the maternity dashboard in improving maternal health, reducing Neonatal Intensive Care Unit (NICU) admissions, as well as enhancing maternal and neonatal outcomes in an Iraqi hospital. Methodology: Data were collected over one year, from January 1st to December 30th, 2023. A prospective observational study (hospital-based record review) was conducted at the Obstetrics and Gynaecology Teaching Hospital in Kerbala, Iraq. Data were obtained from the medical records of all patients admitted to the obstetrical ward for labor, delivery, and the postpartum period during 2023. The maternal dashboard, a traffic light program (green, amber, red), was used in order to monitor outcomes. Data were collected manually from the outpatient department, the labor ward, the NICU registers, and the electronic records by using Excel. Results: A total of 136,519 women (who were either pregnant, in labor, or postpartum for up to 42 days) were admitted to the Obstetrics and Gynaecology Teaching Hospital in Kerbala during the study period, with 18,791 of them delivering. The implementation of the maternity dashboard resulted in significant changes: the normal vaginal delivery rate increased from 66% to 68% (p=0.0024), the caesarean section rate decreased from 33% to 31% (p=0.0001), the stillbirth monthly cases declined from 16±5 to 12±3 (p<0.05), the NICU admission monthly instances decreased from 96±14 to 71±17 (p<0.05), and early neonatal death monthly cases were reduced from 12±5 to 9±3 (p<0.05). Conclusion: The study results show that maternity dashboards are vital instruments for monitoring and improving the quality of maternity care. They effectively enhance maternal and neonatal care quality indicators when comparing results before and after their implementation.

Association between Breast Arterial Calcification on Mammography and Impaired Ocular Perfusion: A Novel Study Using Color Doppler Ultrasonography.

To investigate the relationship between the mammography-detected breast arterial calcification (BAC) and orbital color Doppler ultrasonography (CDUS) results. Our single-center study, included female patients who applied to our hospital between January and May 2022 and underwent mammography and orbital CDUS examinations. Two radiologists evaluated the mammograms, grouped the patients as BAC (+) and BAC (-), and performed orbital CDUS. Continuous variables obtained from CDUS were compared according to the presence of BAC. Also, receiver operating characteristics (ROC) analysis was used to determine the orbital CDUS threshold values for the presence of BAC. A total of 119 women were included with a median age of 62 years, 57 (47.90%) had BAC. Orbital CDUS examination was performed on both eyes of 119 patients (238 eyes in total). Peak systolic velocity (PSV) and end-diastolic velocity (EDV) values of the ophthalmic artery (OA) (P < .001) and EDV of the central retinal artery (CRA) (P < .001) were significantly lower in patients with BAC. Pulsatile index (PI) and resistive index (RI) values of OA (P < .001) and CRA (P < .001) were higher in patients with BAC. In ROC analysis, the cut-off values for the presence of BAC were calculated as OA PI ≥1.415 and OA RI ≥0.755 (P < .001); CRA PI ≥1.135 and CRA RI ≥0.655 (P < .001). Orbital perfusion disorders may be observed in patients with vascular calcification detected on routine mammography. Therefore, a more detailed evaluation of patients with BAC detected on mammography with orbital CDUS may enable early detection and treatment of ocular vascular problems.

A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method

One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions.

Echogenic bowel on the second-trimester ultrasound as an indicator of jejunal atresia: A case report

Introduction and importance: Fetal echogenic bowel (FEB) is a finding on prenatal ultrasound characterized by increased echogenicity of the fetal intestine. In this study we present a case of FEB with severe bowel dilatation identified on second-trimester ultrasound that was subsequently diagnosed with jejunal atresia postnatally. Case presentation: A 28-year-old primigravida woman was referred to our perinatology clinic at 18w/3d gestational age for fetal anomaly scan. The anomaly scan examination identified grade 3 fetal bowel hyperechogenicity. The results of fetal echocardiogram and maternal serum screening for TORCH infections were normal and the NIPT analysis showed low risk results for chromosomal aneuploidies. Clinical discussion: In our case the fetal bowel diameter measured 21 mm at 29 w/6d gestational age, which significantly exceeded the established clinical cut-off value for fetal bowel dilatation. This timing is crucial because if a specific problem is detected in the ultrasound that indicates an abortion of the fetus it can only be performed legally under 19th week of pregnancy, otherwise, termination of pregnancy is considered illegal according to the policies of this country. Conclusion: Nowadays ultrasonography as a non-invasive tool facilitates the early detection of many prenatal problems. In this case, with the early detection of FEB and further investigations, we were able to safely manage the pregnancy until the 39th week of pregnancy and plan the delivery in a tertiary referral center to ensure the availability of intensive care for the baby at birth.

TSCB-Net: transformer-enhanced semantic segmentation of surface damage of concrete bridges

Concrete bridges are susceptible to surface damage such as water-corrosion, spalling, and rebar-corrosion, which can pose serious safety risks, and to detect this is crucial to ensure their safety. Computer vision-based methods to detect surface damages in concrete bridges have seen great progress. However, mainstream methods can only locate surface damage but cannot obtain detailed information, such as the proportion of damaged pixels. To address these challenges, this paper propose TSCB-Net, a semantic segmentation model for surface damage detection in concrete bridges. TSCB-Net employs both Transformers and an encoder-decoder structure, offering a strong alternative to detect surface damage in concrete bridges. Its encoder uses improved baselines with pyramid vision transformer (PVTV2) to extract semantic information and spatial details of concrete bridge images, layer by layer, and to capture long-term dependence between features. The decoder incorporates a context information recovery (CIR) module in the final upsampling stage, using convolutional layers to realize a larger receptive field. This module effectively models context information for semantic segmentation and facilitates multi-scale surface damage detection of concrete bridges. In experiments, TSCB-Net achieves an overall mIoU of 75.84% and an mF1 of 85.54% when the number of parameters is 5.84 M, outperforming state-of-the-art models. TSCB-Net can quickly and accurately detect and identify surface bridge damage, which can help in the early detection of structural problems and the adoption of appropriate repair and protection measures.

Requirement Analysis of Different Variants of a Measurement and Training Station for Older Adults at Risk of Malnutrition and Reduced Mobility: Focus Group Study.

Demographic change is leading to an increasing proportion of older people in the German population and requires new approaches for prevention and rehabilitation to promote the independence and health of older people. Technical assistance systems can offer a promising solution for the early detection of nutritional and physical deficits and the initiation of appropriate interventions. Such a system should combine different components, such as devices for assessing physical and nutritional status, educational elements on these topics, and training and feedback options. The concept is that the whole system can be used independently by older adults (aged ≥70 years) for monitoring and early detection of problems in nutrition or physical function, as well as providing opportunities for intervention. This study aims to develop technical and digital elements for a measurement and training station (MuTs) with an associated app. Through focus group discussions, target group requirements, barriers, and favorable components for such a system were identified. Older adults (aged ≥70 years) were recruited from a community-based setting as well as from a geriatric rehabilitation center. Focus group interviews were conducted between August and November 2022. Following a semistructured interview guideline, attitudes, requirements, preferences, and barriers for the MuTs were discussed. Discussions were stimulated by videos, demonstrations of measuring devices, and participants' ratings of the content presented using rankings. After conducting 1 focus group in the rehabilitation center and 2 in the community, the interview guide was refined, making a more detailed discussion of identified elements and aspects possible. The interviews were recorded, transcribed verbatim, and analyzed using content analysis. A total of 21 older adults (female participants: n=11, 52%; mean age 78.5, SD 4.6 years) participated in 5 focus group discussions. There was a strong interest in the independent measurement of health parameters, such as pulse and hand grip strength, especially among people with health problems who would welcome feedback on their health development. Participants emphasized the importance of personal guidance and interaction before using the device, as well as the need for feedback mechanisms and personalized training for everyday use. Balance and coordination were mentioned as preferred training areas in a MuTs. New training options that motivate and invite people to participate could increase willingness to use the MuTs. The target group is generally open and interested in tracking and optimizing diet and physical activity. A general willingness to use a MuTs independently was identified, as well as a compelling need for guidance and feedback on measurement and training to be part of the station.

Dynamically crosslinked poly(vinyl alcohol)/borax strain sensors for organ motion monitoring

Wearable electronic sensing devices can detect various physiological responses and subtle changes in the skin in real time for the early detection and treatment of potential health problems or diseases. Among them, flexible hydrogel-based sensors are attracting much attention because they can monitor human movements and physiological signals. However, developing hydrogel-based sensors with rapid self-healing, self-adhesion, excellent conductivity and sensitivity characteristics remains a challenge. In this study, a hydrogel with a dynamic reversible network structure was developed using poly(vinyl alcohol) (PVA), hyaluronic acid (HA), borax, and tannic acid (TA). Multiple hydrogen bonds resulting from abundant hydroxyl groups in the hydrogel composed of borax, PVA, and TA improved the self-healing ability of the hydrogel, and the conductivity of this system (∼0.62 S/m) was improved upon immersion in NaCl. The optimum hydrogel exhibited excellent elasticity (∼941 %), biocompatibility, and demonstrated adhesion to porcine skin (dry: 5.3 kPa; wet: 2 kPa) without external stimulation in air and underwater environments. In addition, the fabricated hydrogels exhibited excellent sensitivity (∼300 % strain, gauge factor (GF) = 2.86; 300–900 % strain, GF = 4.95), allowing them to detect and distinguish various large and small human movements. Hydrogels that exhibited stable electrical signals even after 500 cycles were obtained, and the PBHT-N hydrogels were implanted into rabbit hearts to verify their applicability as strain sensors for effective real-time heart rate monitoring during rapid body movement.

Simulation Training Is an Effective Tool for Implementing the Practice of a Screening Nurse in Viamedis Clinics

Screening is a vital part of practical healthcare in the Republic of Kazakhstan. Screening is a process of mass examination or testing of a group of people to identify potential diseases or conditions at an early stage, before obvious symptoms appear. The purpose of screening is early detection of health problems, which allows for treatment or prevention to begin at an earlier stage and improve the effectiveness of medical intervention.

Peningkatan Edukasi, Kesehatan, dan Ekonomi Masyarakat Desa Ciantra Melalui Lomba, Penyuluhan, Cek Kesehatan dan Bazar UMKM

This research aims to improve public health and economic education in Ciantra Village through stunting counseling programs, health checks, and MSME development. The methods used include counseling at Posyandu to increase awareness about stunting and the importance of routine health checks, as well as MSME bazaars accompanied by marketing training to strengthen the local economy. The results of the study showed an increase in public understanding of stunting prevention and early detection of health problems, as well as an increase in sales of MSME products after rebranding. This program is not only successful in overcoming existing health and economic problems, but also strengthens the status of Ciantra Village as an independent village. The implementation of this program can be a model for other villages in an effort to achieve community independence and welfare.

Technology for a Food-secure Future: A Review of Technology Advances in Sustainable Agriculture

There is no doubt that advanced technologies play a very important and crucial role in driving the agricultural sustainable practices in order to satisfy the increasing demand for food production as well as to minimize environmental impacts. This review article aims to provide comprehensive points of view regarding a current state as well as the future advances in smart farming techniques for developing an agricultural sustainability. The digital sensors are considered as key techniques for agricultural transformation which provide an accurate managing and monitoring the various agricultural activities such as crop production, soil moisture, micro climate data, and others. Valuable detailed data are acquired by these sensors to be delivered to the farmers in order to make suitable decisions, ensuring the sustainable and efficient utilization of the agricultural resources. Moreover, managing irrigation using the advanced technologies is benefited wireless monitoring remotely and controlling systems. These technologies and advanced tools help the farmers to reduce water consumption and increase their benefits as well as promotes sustainable management practices of the water resources. Additionally, the drones such as unmanned aerial vehicles (UAVs) can be attached with several kinds of sensors or cameras for capturing detailed information of crop health, soil status, required fertilizers, irrigation, and pesticides. Besides these benefits of drones, they provide a function of an early detection of agricultural problems which allow the decision makers for taking suitable actions. Furthermore, the biotechnology advances such as CRISPR gene editing, and transgenic animals’ developing are very beneficial for enhancing the crop yield, disease resistance, and nutrients availability, and agricultural sustainability. The precision agriculture (PA) integration with geographic information system (GIS) and remote sensing (RS) provides site-specific management and its decisions in order to optimize the inputs utilization, waste reduce, and sustainable practices promotion. Also, advanced technologies’ application (i.e. artificial intelligence ‘AI’, machine learning ‘ML’, and the Internet of Things ‘IoT’) has been found to be a promising for achieving an agricultural sustainability. Therefore, by using these powerful technologies, the farmers can enhance an agricultural production, decrease an environmental effect and achieve the food security.

Oral health-related quality of life in the Myanmar population: The first national oral health survey 2016 - 2017.

To describe the oral health-related quality of life (OHRQoL) and its potential influencing factors within the Myanmar population. Data were from the first national oral health survey, involving 3,513 participants aged 15-18 years, 35-44 years, and 60-74 years from 21 selected townships in Myanmar. Self-administered questionnaire-based surveys, conducted from December 2016 to January 2017, included socio-demographics, behavioral factors, self-reported oral conditions (number of teeth present, teeth and gingival conditions), and inquiries regarding OHRQoL (a set of 12 questions with 5 response options) using the recommended questions from WHO Oral Health Surveys. The most prevalent oral health issues were difficulties in chewing (32.2%) and biting foods (30.8%). In bivariate analysis, older individuals, rural residents, and participants with higher educational levels were associated with OHRQoL. In multiple regression analysis, self-reported number of teeth, teeth and gingival conditions were strong predictors of OHRQoL in all age groups. Self-rated oral health conditions predicted quality of life due to oral problems. The development of national oral health policies and strategies is imperative to facilitate early detection of oral health problems and promote the awareness of oral health importance.

Implementasi Manajemen Konseling Kesehatan Mental bagi Mahasiswa Institut Teknologi dan Kesehatan Muhammadiyah Kalimantan Barat

Undergraduate students are individuals who experience a transition period that focuses on searching for self-identity. During this transition period, students will experience changes in their behavior, emotions and cognition. This change will affect the mental health status of students. Mental health problems in students are a serious problem to be handled because students' mental health problems are invisible but can have bad consequences if not treated immediately. Negative stigma towards mental health has an impact on students' awareness and initiative to resolve their mental health problems. Students feel embarrassed and think that this mental health problem can heal by itself. In fact, mental health problems will get worse if they are not handled well. The importance of mental health counseling services for students as a medium for early detection of mental health problems. Mental health screening can detect several mental health problems so that existing problems can be prevented and students' mental health status can improve. The role of the academic community is very important to support the existence of mental health counseling services. Moreover, policies and service standards do not yet exist at ITEKES Muhammadiyah West Kalimantan. Thus, this underlies strategic planning activities for mental health services that can be beneficial for students and have an impact on student welfare in maintaining their mental status.

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.

Psychosocial Mental Health Analysis Of Post Earthquake Victims In Cianjur District

Disaster events are a serious threat to the continuity of human life and livelihood. Earthquakes are the most frequently occurring disaster and result in large losses such as human casualties, environmental damage, property loss, and psychosocial impacts felt by earthquake victims. The aim of this research is to determine psychosocial mental health problems among victims after the earthquake disaster in Cianjur Regency. This type of research uses quantitative. The population in this study were 100 earthquake victims in Cianjur Regency who were selected using purposive sampling. Frequency distribution analysis of the data was carried out with the results that 91 respondents (91%) experienced psychosocial problems and 9 respondents (9%) were mentally healthy. Apart from that, 74 respondents (74%) said they had PTSD symptoms after the earthquake disaster. Early detection of psychosocial problems is the first step in preventing mental disorders. Providing psychosocial mental health support is an appropriate step that can be used in the post-disaster stage.

Использование алгоритмов Process Mining для выявления паттернов поведения студентов

This work is devoted to studying the possibilities of using process analytics methods (Process Mining) to analyze student activity based on digital traces that students leave in learning management systems (LMS). This work examines the specifics of process mining algorithms that can be used to analyze educational processes, namely, heuristic and inductive algorithms are considered as the most effective for building models and suitable for use for the purpose of analyzing educational data. The work involved creating a way to use process analytics algorithms to identify clusters of students with similar behavior patterns. The development of a process analysis algorithm was carried out on the basis of the event log of the distance learning system of Kostroma State University. As a result of the work, models of student behavior were built and visualized, including the identification and clustering of students with similar behavior, the construction of heuristic networks, Petri nets, a direct sequence graph, a BPMN model and a decision tree. An analysis of the resulting models was carried out, which showed that the developed method makes it possible to study the behavioral patterns of students. The proposed method of using intellectual analysis of educational processes can be used to solve issues of increasing the productivity of the educational process, early detection of problems, especially in the context of changing student behavior in the system, as well as the development and optimization of educational programs. In addition, the limitations of this system have been identified, which may hinder its implementation and application in the educational environment of universities.

PROJECT MANAGEMENT APPROACH IN HEALTHCARE SERVICES

Objective: This study investigates the terms project, project management, and project management cycle, focusing on the project management approach in healthcare services. It aims to emphasize the importance and necessity of project management in healthcare, contributing to the development of a project management culture within the healthcare sector. Theoretical Framework: Projects are generally a set of activities or efforts planned and implemented to achieve a specific goal or objective. Businesses often need to operate within a specific project or plan to reach their goals. The same applies to healthcare services. Today, it is crucial for healthcare institutions to achieve their strategic objectives by utilizing their limited resources most effectively. Therefore, healthcare institutions must continuously strive to manage their resources in alignment with their strategic goals. By adopting an effective resource management strategy, healthcare institutions can enhance the quality of patient care, patient satisfaction, operational efficiency, and cost optimization. Project management consists of specific phases: initiation, planning, execution, monitoring and control, and closure. Each phase is essential for the project's successful progress. Project management is a vital management system in healthcare institutions that reduces uncertainties and ensures the successful completion of projects. Planning and monitoring all phases of projects from start to finish allow for early detection and correction of errors and problems. Method: This study on project management in healthcare services uses document analysis as a qualitative data analysis method. It compiles and evaluates existing knowledge and data on project management in healthcare services. Research Implications: The results and recommendations of this study can increase awareness of project management in healthcare services and help manage practices in this field more effectively.

Electrochemically deposited Au nano-island on laser-scribed graphene substrates as EC-SERS biochips for uremic toxins detection

BackgroundChronic kidney disease (CKD) leads to uremic toxin buildup, requiring early detection for better management. Existing methods lack sensitivity, speed, or affordability for point-of-care diagnosis. This work addresses this by creating a novel sensor for rapid, sensitive uremic toxin detection. MethodsThe sensor leverages the combined effects of surface-enhanced Raman scattering (SERS) and electrochemistry (EC) for superior detection. The LIG substrate provides a stable platform for AuNPs, facilitating interaction with target molecules. Additionally, electrochemical deposition optimizes the sensor's sensitivity by amplifying the local electromagnetic field around AuNPs and offering specific binding sites for uremic toxins. Significant findingsThe developed sensor demonstrates exceptional performance in detecting uremic toxins. It achieves remarkably low detection limits (10-3 M for creatinine/uric acid, 10-4 M for urea) and offers distinct, concentration-dependent responses for different toxins in cyclic voltammetry (CV) measurements. Furthermore, characteristic oxidation peaks at specific potentials allow for direct identification and quantification of the toxins. These findings highlight the immense potential of this cost-effective and scalable sensor for point-of-care diagnostics and remote monitoring of kidney function. This advancement can significantly improve patient care by facilitating early detection of kidney problems and enabling timely intervention.

Tetralogy of Fallot in adults.

Following surgical repair in infancy, a high proportion of children born with tetralogy of Fallot are surviving into adulthood, and it is important that these patients are monitored by an adult congenital heart disease specialist for problems and complications that can occur in the adult patient. To provide an overview on the management of adult patients with repaired tetralogy of Fallot and emphasise the need for specialist follow-up. Following surgical repair of tetralogy of Fallot, it is important to have lifelong surveillance in adulthood for complications such as pulmonary valve dysfunction. Modern imaging modalities have helped with monitoring and the early detection of problems. Cardiac surgery has been refined over the years, leading to improved longevity. Over the past 23 years, percutaneous interventions have been developed to deal with valve complications and the results have been promising. Healthy behaviour, such as regular exercise, and behaviours to minimise the risk of endocarditis should be encouraged. Regular follow-up with an adult congenital heart disease specialist is important. Patients wishing to undertake a pregnancy should be supervised through a high-risk pregnancy clinic.