Limited Availability Research Articles

United States Government-Supported Family Planning and Reproductive Health Outreach in the Democratic Republic of the Congo: Lessons Learned and Recommendations.

In response to limited contraception availability and a lack of knowledge about family planning (FP) in the Democratic Republic of the Congo (DRC), the United States Agency for International Development (USAID) Integrated Health Program (IHP) in the DRC has been providing FP services, including outreach programs in the DRC. Our study aims to assess the FP outreach program by understanding the participants' perception of the campaign, its impact on their behavior, and their feedback regarding the campaign. Additionally, we draw insights from lessons learned and provide recommendations. Between July and August 2022, we conducted 47 in-person participant interviews with women of reproductive age who used the outreach services provided by USAID IHP. Participants were randomly selected from Sud-Kivu, Kasai-Oriental, Haut-Katanga, and Tanganyika provinces. Consent and confidentiality were assured, and responses were recorded and transcribed in a Word document. We used Excel for data coding and analysis. The campaign reached 95.7% of women interviewed; however, some participants could not recall specific message details. Most respondents (89.3%) reported that the campaign motivated them to make FP decisions and change their behaviors. While 14.8% of women reported making FP decisions independently, 85.1% reported making the decision jointly with their partners. Our analysis resulted in three emerging themes: 1) Increased FP outreach and improved perception of FP, 2) Improved perceived behavioral changes due to FP outreach, and 3) The need for program improvement by including men and providing additional information about possible FP side effects. Our study provides insights into how women receive information and whether they find it useful and share it with other women in their community. In particular, women's feedback about the FP outreach program and our recommendations can inform future policies and interventions.

Enhancing the Management of Pediatric Sickle Cell Disease by Integrating Functional Evaluation to Mitigate the Burden of Vaso-Occlusive Crises

Sickle cell disease (SCD) imposes a significant health burden, particularly in low- and middle-income countries where healthcare professionals and resources are scarce. This opinion paper delves into the management strategies employed for vaso-occlusive crises (VOCs) in pediatric patients with SCD, advocating for the adoption of a transformative strategy. We explore the integration of functional assessment approaches into existing procedures, highlighting the potential of technology-assisted rehabilitation, including wearable sensors and digital biomarkers, to enhance the effectiveness of managing and preventing VOCs. Rehabilomics, as a comprehensive framework, merges rehabilitation-related data with biomarkers, providing a basis for personalized therapeutic interventions. Despite the promising advantages of these approaches, persistent obstacles such as the limited availability of rehabilitation programs, especially in resource-limited settings, pose challenges. This paper underscores the importance of a collaborative strategy to effectively address the unique obstacles faced by patients with SCD. This collaborative approach involves improving accessibility to rehabilitation services, incorporating technology-supported therapy, and fostering focused research endeavors. The primary objective of this comprehensive approach is to enhance the overall care of SCD patients, with a specific focus on preventing VOCs, as well as providing tailored (neuro)rehabilitation services in resource-limited settings. By examining the current state of SCD management and proposing transformative strategies, this opinion paper seeks to inspire collective action and collaboration to improve outcomes for pediatric SCD patients globally.

Systematic, active, contextual, technological, integrative (SAKTI) framework in the development of critical listening teaching materials

This research is motivated by the limited availability of digital module teaching materials, especially in critical listening learning. This study aims to produce critical listening learning modules that are structured on the principles of systematic, active, contextual, technological, integrative (SAKTI). This principle is formulated by researchers from various critical listening learning theories and the development of teaching materials that are relevant to current needs. The digital teaching materials are arranged to contain materials, listening materials, exercises, and evaluations in one teaching material. The research was conducted by applying the research procedure proposed by Tomlinson in 2012. The module was developed using the Flip Pdf Professional Application Version 2.4.9.41, Quizzes, Canva, Google Form, Ms. Word, Dolby On, Nitro Pdf. The module consists of 3 main components, namely the introductory, core, and closing sections. This study uses R&D methods with stages Analysis, Design, Development, Implementation, & Evaluations. Based on the results of product trials, it is known that the use of the designed product provides several advantages, among which it makes the learning process practical, can be used anywhere and at any time, helps guided or independent learning, helps online and offline learning, encourages students to actively complete each stage of learning and can be a means of obtaining new information for students.

General ward nurses detection and response to clinical deterioration in three hospitals at the Kenyan coast: a convergent parallel mixed methods study

BackgroundIn low and middle-income countries like Kenya, critical care facilities are limited, meaning acutely ill patients are managed in the general wards. Nurses in these wards are expected to detect and respond to patient deterioration to prevent cardiac arrest or death. This study examined nurses' vital signs documentation practices during clinical deterioration and explored factors influencing their ability to detect and respond to deterioration.MethodsThis convergent parallel mixed methods study was conducted in the general medical and surgical wards of three hospitals in Kenya's coastal region. Quantitative data on the extent to which the nurses monitored and documented the vital signs 24 h before a cardiac arrest (death) occurred was retrieved from patients' medical records. In-depth, semi-structured interviews were conducted with twenty-four purposefully drawn registered nurses working in the three hospitals' adult medical and surgical wards.ResultsThis study reviewed 405 patient records and found most of the documentation of the vital signs was done in the nursing notes and not the vital signs observation chart. During the 24 h prior to death, respiratory rate was documented the least in only 1.2% of the records. Only a very small percentage of patients had any vital event documented for all six-time points, i.e. four hourly. Thematic analysis of the interview data identified five broad themes related to detecting and responding promptly to deterioration. These were insufficient monitoring of vital signs linked to limited availability of equipment and supplies, staffing conditions and workload, lack of training and guidelines, and communication and teamwork constraints among healthcare workers.ConclusionThe study showed that nurses did not consistently monitor and record vital signs in the general wards. They also worked in suboptimal ward environments that do not support their ability to promptly detect and respond to clinical deterioration. The findings illustrate the importance of implementation of standardised systems for patient assessment and alert mechanisms for deterioration response. Furthermore, creating a supportive work environment is imperative in empowering nurses to identify and respond to patient deterioration. Addressing these issues is not only beneficial for the nurses but, more importantly, for the well-being of the patients they serve.

A clustering multi-stage training transfer learning framework for cross simulation domain and experimental domain fault diagnosis

Deep learning methods have demonstrated remarkable achievements in the field of fault diagnosis for rotating machinery. However, their effectiveness heavily relies on high-quality labeled samples, which presents a significant challenge owing to the limited availability of such data in engineering applications. To address this realistic issue, we propose a novel simulation-driven transfer learning model called the clustering multi-stage training transfer learning framework (CMSTL) for fault diagnosis of rolling bearings. The fundamental concept of the proposed method is to utilize simulation data as a substitute for labeled actual device data and integrate the suggested clustering learning and multistage training strategies to extract domain-independent and fault-discriminative features from simulation and experimental domains. Specifically, the clustering learning strategy is embedded into the CMSTL model to encourage the feature extractor to acquire distinguishable features associated with different categories while eliminating domain-specific knowledge, which enables samples near the classification decision boundary to cluster towards their respective clustering centers. Additionally, the proposed multistage learning strategy leverages the model trained with a certain level of accuracy in the first stage to annotate actual device samples, thereby enhancing both the precision of the pseudo-label for real data and the overall training stability of the model. The effectiveness and superiority of the proposed method were validated using both artificially damaged and run-to-failure datasets. The comparative analysis results demonstrate that the CMSTL method exhibits a minimum 2.2% improvement in fault diagnosis accuracy and enhances the clustering capability compared to other advanced transfer learning fault diagnosis methods.

A genomic toolkit for winged bean Psophocarpus tetragonolobus

A sustainable supply of plant protein is critical for future generations and needs to be achieved while reducing green house gas emissions from agriculture and increasing agricultural resilience in the face of climate volatility. Agricultural diversification with more nutrient-rich and stress tolerant crops could provide the solution. However, this is often hampered by the limited availability of genomic resources and the lack of understanding of the genetic structure of breeding germplasm and the inheritance of important traits. One such crop with potential is winged bean (Psophocarpus tetragonolobus), a high seed protein tropical legume which has been termed ‘the soybean for the tropics’. Here, we present a chromosome level winged bean genome assembly, an investigation of the genetic diversity of 130 worldwide accessions, together with two linked genetic maps and a trait QTL analysis (and expression studies) for regions of the genome with desirable ideotype traits for breeding, namely architecture, protein content and phytonutrients.

Telephone-based telepsychiatry consultations: a qualitative exploration of psychiatrists’ experiences in Oman

ObjectivesThe utilisation of tele-mental health services has the potential to address challenges in mental health services within the Eastern Mediterranean Region. However, the adoption of tele-mental health in Oman remains...

Semi-Supervised Disease Classification Based on Limited Medical Image Data.

Inrecent years, significant progress has been made in the field of learning from positive and unlabeled examples (PU learning), particularly in the context of advancing image and text classification tasks. However, applying PU learning to semi-supervised disease classification remains a formidable challenge, primarily due to the limited availability of labeled medical images. In the realm of medical image-aided diagnosis algorithms, numerous theoretical and practical obstacles persist. The research on PU learning for medical image-assisted diagnosis holds substantial importance, as it aims to reduce the time spent by professional experts in classifying images. Unlike natural images, medical images are typically accompanied by a scarcity of annotated data, while an abundance of unlabeled cases exists. Addressing these challenges, this paper introduces a novel generative model inspired by Hölder divergence, specifically designed for semi-supervised disease classification using positive and unlabeled medical image data. In this paper, we present a comprehensive formulation of the problem and establish its theoretical feasibility through rigorous mathematical analysis. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments on five benchmark datasets commonly used in PU medical learning: BreastMNIST, PneumoniaMNIST, BloodMNIST, OCTMNIST, and AMD. The experimental results clearly demonstrate the superiority of our method over existing approaches based on KL divergence. Notably, our approach achieves state-of-the-art performance on all five disease classification benchmarks. By addressing the limitations imposed by limited labeled data and harnessing the untapped potential of unlabeled medical images, our novel generative model presents a promising direction for enhancing semi-supervised disease classification in the field of medical image analysis.

Reuse of Orthopaedic Equipment: Barriers and Opportunities.

» Reuse of orthopaedic equipment is one of many potential ways to minimize the negative impact of used equipment on the environment, rising healthcare costs and disparities in access to surgical care.» Barriers to widespread adoption of reuse include concerns for patient safety, exposure to unknown liability risks, negative public perceptions, and logistical barriers such as limited availability of infrastructure and quality control metrics.» Some low- and middle-income countries have existing models of equipment reuse that can be adapted through reverse innovation to high-income countries such as the United States.» Further research should be conducted to examine the safety and efficacy of reusing various orthopaedic equipment, so that standardized guidelines for reuse can be established.

Ecological and social factors influence interspecific pathogens occurrence among bees

The interspecific transmission of pathogens can occur frequently in the environment. Among wild bees, the main spillover cases are caused by pathogens associated with Apis mellifera, whose colonies can act as reservoirs. Due to the limited availability of data in Italy, it is challenging to accurately assess the impact and implications of this phenomenon on the wild bee populations. In this study, a total of 3372 bees were sampled from 11 Italian regions within the BeeNet project, evaluating the prevalence and the abundance of the major honey bee pathogens (DWV, BQCV, ABPV, CBPV, KBV, Nosema ceranae, Ascosphaera apis, Crithidia mellificae, Lotmaria passim, Crithidia bombi). The 68.4% of samples were positive for at least one pathogen. DWV, BQCV, N. ceranae and CBPV showed the highest prevalence and abundance values, confirming them as the most prevalent pathogens spread in the environment. For these pathogens, Andrena, Bombus, Eucera and Seladonia showed the highest mean prevalence and abundance values. Generally, time trends showed a prevalence and abundance decrease from April to July. In order to predict the risk of infection among wild bees, statistical models were developed. A low influence of apiary density on pathogen occurrence was observed, while meteorological conditions and agricultural management showed a greater impact on pathogen persistence in the environment. Social and biological traits of wild bees also contributed to defining a higher risk of infection for bivoltine, communal, mining and oligolectic bees. Out of all the samples tested, 40.5% were co-infected with two or more pathogens. In some cases, individuals were simultaneously infected with up to five different pathogens. It is essential to increase knowledge about the transmission of pathogens among wild bees to understand dynamics, impact and effects on pollinator populations. Implementing concrete plans for the conservation of wild bee species is important to ensure the health of wild and human-managed bees within a One-Health perspective.

The Role of Cultural Factors in Attention Deficit Hyperactivity Disorder (ADHD) Diagnosis in Children in Nigeria

This paper explores the influence of cultural factors on the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) in Nigerian children. Examining traditional beliefs, societal norms, and the impact of stigma, the study addresses the challenges within the cultural context. Critiquing Western diagnostic criteria and exploring alternative explanatory models rooted in indigenous perspectives highlight the need for a culturally informed diagnostic approach. Additionally, the paper discusses the limited availability of mental health services, cultural barriers to seeking professional help, and the significance of cultural competence in ADHD diagnosis. Collaborative efforts with traditional healers and community leaders are emphasized. Ultimately, the study aims to contribute to a more inclusive and culturally sensitive approach to ADHD diagnosis in Nigeria.

Improving night driving behavior recognition with ResNet50

The issue of driving behavior at night poses significant challenges due to reduced visibility and increased risk of accidents. Recent works have leveraged deep learning techniques to enhance night-time driving safety. However, the limited availability of high-quality training data and the lack of robustness in existing models present significant problems. In this work, we propose a novel approach to improve driving behavior recognition at night using ResNet50 with contrast limited adapted histogram equalization (CLAHE). We collected a new dataset and developed a more effective and robust model that can accurately recognize driving behaviors under low-illumination conditions, thereby reducing the likelihood of collisions and improving overall road safety. The experimental results demonstrate significant improvements in the deep learning model’s performance compared to conventional methods. Notably, the ResNet50 model delivers the best performance with accuracy rates of 90.73% using NIGHT-VIS-CLAHE data, demonstrating a 16% improvement in accuracy. For benchmark purposes, the InceptionV3, GoogleNet, and MobileNetV2 models also show enhanced accuracy through CLAHE implementation. Furthermore, NIGHT-VIS-CLAHE implementation in ResNet50 achieved 90.29% accuracy, surpassing the best NIGHT-IR InceptionV3 at 89.27%, highlighting the advantage of ResNet50 with CLAHE in low-light conditions even against infra-red sensor.

A lightweight image-level segmentation method for steel surface defects based on cross-layer feature fusion

Steel is widely used in the aerospace, machinery and automotive industries. Surface defects not only have a negative impact on the appearance of steel but also significantly reduce its wear resistance, high temperature resistance, corrosion resistance and fatigue strength. Therefore, the detection of steel surface defects is very important to improve the quality of steel production. The limited availability of surface defect samples in the industrial sector poses significant challenges for the accurate detection of defects in high-quality materials. In addition, the existing defect detection model is highly complex and not easy to deploy. To solve this problem, a lightweight defect detection network suitable for steel defects is proposed. The cross-layer feature fusion (CFF) in the design enables effective utilisation of multi-layer semantic features, facilitating the detection of small defects in steel. Secondly, a new loss function is designed to make up for the problems of small data volume and uneven data distribution. The experimental results demonstrate that the steel surface defect detection method proposed in this paper achieves the highest detection performance on widely used public datasets such as RSDDS, NEUS and NRSD-CR(test), while maintaining the lowest model complexity.

Health system barriers to the first dose of measles immunization in Ethiopia: a qualitative study

BackgroundEthiopia has made considerable progress toward measles elimination. Despite ongoing efforts, the country remains among those with the highest number of children missing their initial dose of measles vaccine, and the disease continues to be a public health emergency. The barriers within the health system that hinder the first dose of measles immunization have not been thoroughly investigated. This study aims to identify these barriers within the Ethiopian context.MethodsQualitative research, using purposive expert sampling to select key informants from health organizations in Addis Ababa, Ethiopia was employed. We conducted in-depth face-to-face interviews using a semi-structured interview guide. A thematic analysis based on the World Health Organization’s health systems building blocks framework was conducted.ResultsThe study uncovered substantial health system barriers to the uptake of the first dose of the measles vaccine in Ethiopia. These barriers include; restricted availability of immunization services, vaccine stockouts, shortage of cold chain technologies, data inaccuracy resulting from deliberate data falsification or accidental manipulation of data, as well as data incompleteness.ConclusionOur research highlighted significant health system barriers to MCV1 immunization, contributing to unmet EPI targets in Ethiopia. Our results suggest that to accelerate the country towards measles elimination, there is an urgent need to improve the health systems components such as service delivery, information systems, as well as access to vaccine and cold chain technologies.

Climate Variability and Glacier Dynamics Linked to Fjord Productivity Changes Over the Last ca. 3300 Years in Nuup Kangerlua, Southwest Greenland

AbstractGreenlandic fjords, located between the ice sheet and the ocean, are dynamic systems that can sustain highly variable levels of primary productivity and are sensitive to climate change. In our current climate trajectory, meltwater discharge is expected to significantly increase but its long‐term effects on fjord productivity are still poorly constrained. Paleo‐archives can offer valuable insights into long‐term effects. Here, we present two marine sediment core records from Nuup Kangerlua, Southwest Greenland. Our goal is to better understand to what extent, and on what time‐scales, climate fluctuations and associated glacier dynamic changes have impacted fjord productivity over the past ca. 3300 years. Our multiproxy records include diatom fluxes and assemblage composition, sediment biogeochemistry, and grain‐size distribution. Our study reveals that fjord productivity is tightly linked to regional climate variability; relatively higher productivity levels coincided with mild climate periods whereas the climate cooling of the last millennium led to a decrease in productivity. The diatom records suggest that lower productivity is associated with shorter or less intense summer blooms, increased sea‐ice cover and/or a stratified water column. Diatom assemblages demonstrate cold sea‐surface conditions around 1600 CE that might be linked to local advance of glaciers. Cold conditions and decreasing productivity culminated at 1850 CE, when glaciers in the fjord retreated and high glacial meltwater discharge would have altered the fjord hydrography, likely leading to limited nutrient availability. Our long‐term records support the idea that changing climate and cryosphere conditions have a non‐linear impact on the productivity of Greenlandic fjords.

A review of the status, challenges, trends, and prospects of groundwater quality assessment in Nigeria: an evidence-based meta-analysis approach.

With the imminent industrial growth and population increase, Nigeria will continue to experience significant shifts in the quality of water, with a rise in emerging contaminants. This will increase the irregularity and complexity of the water quality information. Therefore, using the PRISMA meta-analysis approach, this review systematically identified the commonly used water quality assessment techniques in Nigeria, the drawback in the application of these techniques as well as the gaps in the area of water quality assessment and monitoring from 2003 to 2023. Recommendations were also made based on the evaluation of a new research direction; through the review of the effectiveness of advanced techniques for monitoring water quality in Nigeria. Sixty-eight published articles were chosen for the meta-analysis while the VOSviewer program was used to perform bibliographic coupling and visualization. The review revealed that the application of machine learning in water quality prediction has not been well explored in Nigeria. This is attributed to limited data availability and poor funding by the government. It was found that southwestern Nigeria has a greater amount of research on groundwater quality monitoring and evaluation than other regions. The variability was explained by variations in the underlying geology, aquifer features; variability in anthropogenic activities, and level of literacy among various geopolitical zones. Further studies should focus on the application of soft-computing and integrated biomonitoring techniques for effective prediction and monitoring of emerging contaminants for improved water quality. Effective collaboration between environmental stakeholders and government agencies is recommended for effective water resource sustainability.

Multi-level barriers to early detection of breast cancer among rural midlife women in Tanzania: A qualitative case study.

Breast cancer is the second most common cause of cancer mortality among women in Tanzania and thus, early detection and treatment methods are central to improving breast cancer outcomes. However, in low- and middle-income countries in Sub-Saharan Africa, the survival rates remains low due to late presentation. Hence, a significant number of deaths could be prevented if barriers and facilitators to early detection are known. This qualitative case descriptive study explored the possible barriers to awareness and early breast cancer diagnostic services among midlife women in rural Tanzania. Ten key informant interviews with health systems managers and community health workers and eight focus group discussions with women aged 40-65 years and their spouses were conducted to elicit the study data conducted from July to August 2021. The data revealed nine themes describing the barriers to early detection methods across five Socio-Ecological levels of influence, namely: 1) limited knowledge and 2) witchcraft beliefs (individual level); 3) limited male support (interpersonal level); 4) age and gender factors and 5) procrastination (community level) 6) limited availability of services 7) emphasis of curative over preventive care (institutional level); 8) poverty/inability to pay and 9) limitations of health insurance (societal/policy level). The study findings suggest a need to further the design, implementation and evaluation of evidence-based community breast health awareness and education interventions to promote early detection of breast cancer in Tanzania. Specifically, the study highlights the need to address multiple level determinants of influence in breast cancer control as part of the country's Community Health Strategy.

Expert Consensus on Improving Stroke Care Ecosystem in India

The stroke burden in India is alarmingly high compared to Western industrialised countries, necessitating immediate action to develop stroke systems of care. Tailored stroke-prevention strategies, adequate resource allocation for stroke management and research, and increased awareness of specialised care in acute Stroke are critical. The COVID-19 pandemic has underscored the need for improved healthcare delivery and infrastructure. To address the rising stroke burden in India, comprehensive strategies encompassing prevention, awareness, specialised care and governmental support are urgently required. While the concept of ‘stroke-ready centres’ has emerged, their limited availability hinders widespread benefits. This expert consensus examines the current state of stroke care in India, identifies challenges, and proposes strategies to enhance stroke care systems. It offers valuable insights into stroke management at different levels, empowering healthcare professionals and administrators to assess their existing systems and make necessary improvements. Special attention is given to the role of physicians, including the use of thrombolytic therapies like Tenecteplase, in reducing the stroke burden and improving outcomes across the country. Significant strides can be made in stroke management and care in India by addressing these critical areas.

Comprehensive Numerical Modeling of Prestressed Girder Bridges under Low-Velocity Impact

Accidental collisions involving over-height trucks that exceed vertical clearance limits and bridge superstructures frequently happen, resulting in compromised girders and potential threats to structural safety and performance. The numerical simulation of large-scale prestressed girder bridge collisions poses challenges due to the associated nonlinearities, as well as the limited availability of large-scale experimental testing data in the literature due to cost and complexity constraints. This study introduces a numerical modeling approach to efficiently capture the response of prestressed girder bridges under lateral impact loads. A finite element (FE) model was developed using LS-DYNA and meticulously validated against experimental data from the literature. The study explored four methods for applying prestressing forces and evaluated the performance of four concrete material constitutive models, including the Continuous Surface Cap Model (CSCM), Concrete Damage Plastic Model (CDPM), Karagozian & Case Concrete (KCC) model, and Winfrith concrete model, under impact loads. Furthermore, an impact study was conducted to investigate the influence of impact speed, impact mass, and prestressing force on the behavior of prestressed girder bridges. Utilizing the dynamic relaxation (DR) approach, the developed FE model precisely captured the response of prestressed girders under impact loads. The CSCM yielded the most accurate predictions of impact forces, with an error of less than 8%, and demonstrated a strong ability to predict damage patterns. Impact speed, mass, and the presence of prestressing force showed a significant influence on the resulting peak impact force experienced by the girder. Furthermore, the study underscores the composite nature of the bridge’s response and emphasizes the importance of analyzing the bridge as a whole rather than focusing solely on individual girders.

AI Assisting in Mental Health

Abstract: This research paper explores the transformative impact of artificial intelligence (AI) in the field of mental health counselling, aiming to enhance the effectiveness and accessibility of support services. As the demand for mental health care continues to rise globally, there is a growing need for innovative solutions to bridge the gap between the increasing demand for counselling and the limited availability of human counsellors. Our study focuses on the integration of AI technologies to assist mental health counsellors in various aspects of their practice. Through an extensive review of existing literature, we analyse the potential benefits and challenges associated with implementing AI in counselling settings. The paper highlights AI's ability to augment traditional counselling approaches by offering timely and personalised interventions, improving the overall efficiency of therapeutic processes. Drawing on case studies and pilot programs, this research presents empirical evidence supporting the positive outcomes and user acceptance of AI-assisted counselling interventions. Additionally, the paper discusses potential limitations, such as the need for continuous refinement of algorithms and the importance of addressing concerns related to data security and privacy.