Limited Availability Research Articles

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.

Cultural attitudes and human pressure towards vultures around the Comoé National Park, Côte d’Ivoire (West Africa)

BackgroundPopulations of vultures in Africa have experienced a rapid decline over recent decades, with some species suffering losses of more than 90%. Various forms of human pressures have been identified as the causes of this decline. However, very little is known about the complex interaction between cultural practises, traditional medicine and the vultures’ natural environment. The purpose of this study was to analyse human pressures on vultures in relation with cultural attitudes such as their demand for magico-traditional medicine in Côte d’Ivoire, around the Comoé National Park (CNP), one of the last major refuges of these organisms in West Africa.MethodsEleven surrounding villages were visited to carry out ethno-ornithology surveys. One hundred and ten people were interviewed, at a rate of ten people per village, using a semi-structured questionnaire and informal discussions.ResultsThe findings showed that vultures are still being seen both in and around the CNP. The most common species indicated to be observed and indicated by the interviewees were the Hooded Vulture (Necrosyrtes monachus) and the White-headed Vulture (Trigonoceps occipitalis). Nevertheless, 98.2% of the interviewees indicated having observed a decrease in the abundance of vultures over the last few years in the study area, as well as a decline in the number of sightings of these organisms. Interviewees attributed this scarcity of vultures to (1) the limited availability of food resources, (2) pesticide and tobacco poisoning, (3) hunting, (4) rapid population growth, (5) annual bushfires and (6) habitat loss. The uses of the vulture or parts of vultures by the populations surrounding the CNP (traditional medicine, mystic practises, consumption as food, cultural heritage) were also highlighted as real threats to these organisms. And the vulture parts commonly used in this area are: feathers, legs, head, heart, stomach, brain and excrement. The Hooded Vulture (Necrosyrtes monachus) appears to be the most sought-after species and the most widely used for these practises in the study area.ConclusionAppropriate conservation and communication initiatives are required to ensure the survival of these raptors, crucial for ecosystem well-being, while also ensuring a respect of cultural practises.

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences.

CD38-Specific Gallium-68 Labeled Peptide Radiotracer Enables Pharmacodynamic Monitoring in Multiple Myeloma with PET.

The limited availability of molecularly targeted low-molecular-weight imaging agents for monitoring multiple myeloma (MM)-targeted therapies has been a significant challenge in the field. In response, a first-in-class peptide-based radiotracer, [68Ga]Ga-AJ206, is developed that can be seamlessly integrated into the standard clinical workflow and is specifically designed to noninvasively quantify CD38 levels and pharmacodynamics by positron emission tomography (PET). A bicyclic peptide, AJ206, is synthesized and exhibits high affinity to CD38 (KD: 19.1 ± 0.99× 10-9 m) by surface plasmon resonance. Further, [68Ga]Ga-AJ206-PET shows high contrast within 60 min and suitable absorbed dose estimates for clinical use. Additionally, [68Ga]Ga-AJ206 detects CD38 expression in cell line-derived xenografts, patient-derived xenografts (PDXs), and disseminated disease models in a manner consistent with flow cytometry and immunohistochemistry findings. Moreover, [68Ga]Ga-AJ206-PET successfully quantifies CD38 pharmacodynamics in PDXs, revealing increased CD38 expression in the tumor following all-trans retinoic acid (ATRA) therapy. In conclusion, [68Ga]Ga-AJ206 exhibits the salient features required for clinical translation, providing CD38-specific high-contrast images in multiple models of MM. [68Ga]Ga-AJ206-PET could be useful for quantifying total CD38 levels and pharmacodynamics during therapy to evaluate approved and new therapies in MM and other diseases with CD38 involvement.

Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets

Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person’s emotional state in an image or a video. This process, called “Facial Expression Recognition (FER)”, has become one of the most popular research areas in computer vision. In recent times, deep FER systems have primarily concentrated on addressing two significant challenges: the problem of overfitting due to limited training data availability, and the presence of expression-unrelated variations, including illumination, head pose, image resolution, and identity bias. In this paper, a comprehensive survey is provided on deep FER, encompassing algorithms and datasets that offer insights into these intrinsic problems. Initially, this paper presents a detailed timeline showcasing the evolution of methods and datasets in deep facial expression recognition (FER). This timeline illustrates the progression and development of the techniques and data resources used in FER. Then, a comprehensive review of FER methods is introduced, including the basic principles of FER (components such as preprocessing, feature extraction and classification, and methods, etc.) from the pro-deep learning era (traditional methods using handcrafted features, i.e., SVM and HOG, etc.) to the deep learning era. Moreover, a brief introduction is provided related to the benchmark datasets (there are two categories: controlled environments (lab) and uncontrolled environments (in the wild)) used to evaluate different FER methods and a comparison of different FER models. Existing deep neural networks and related training strategies designed for FER, based on static images and dynamic image sequences, are discussed. The remaining challenges and corresponding opportunities in FER and the future directions for designing robust deep FER systems are also pinpointed.

Behavioural determinants shaping infection prevention and control behaviour among healthcare workers in Dutch general practices: a qualitative study reflecting on pre-, during and post-COVID-19 pandemic

BackgroundSince the Coronavirus Disease 2019 (COVID-19) pandemic, awareness of infection prevention and control (IPC) has increased in primary care settings. This study aimed to examine behavioural determinants shaping IPC behaviour pre-, during, and post-pandemic among healthcare workers (HCWs) in general practices, to inform optimised IPC in primary care.MethodsFor this qualitative study, semi-structured in-depth interviews were conducted during two study periods: (1) pre-COVID-19 pandemic: July 2019-February 2020, with 14 general practitioners (GPs) and medical assistants, and (2) during the COVID-19 pandemic: July 2022-February 2023, with 22 GPs and medical assistants. The design was informed by behaviour change theories. Data were analysed using thematic analysis.ResultsMain themes were: (1) risk perception and IPC awareness, (2) attitudes towards IPC and professional responsibility, (3) decision-making process and risk considerations for IPC adherence, (4) social norm and social influence in GP practice team, and (5) environmental context and resource availability in GP practice. During the pandemic, risk perception and awareness of the importance of IPC increased compared to the pre-pandemic period. A consistent belief emerged that IPC is part of professional responsibility, while needing to be balanced with other aspects of patient care. Decision-making is dependent on the individual GP and mainly influenced by risk assessments and sustainability considerations. The social context in the practice team can reinforce IPC behaviours. GP practice building and layout, and limited IPC resource and material availability were reported as main barriers.ConclusionsThe theory-informed insights of this study can be used for targeted interventions to optimise IPC behaviour in general practices. Adopting multifaceted strategies to target the various determinants is recommended to sustain IPC, by implementing continuous education using tailored communication, integrating IPC in work routines and organisational workflows, refining existing IPC protocols by incorporating decision-making tools for HCWs, fostering a culture of IPC through knowledge-sharing and teamwork, and addressing GP practice physical environment and IPC resource barriers.

Conventional in vitro fertilisation rather than intracytoplasmic sperm injection when only one oocyte is retrieved: Time to overcome irrational fears.

The use of intracytoplasmic sperm injection (ICSI) currently extends beyond male factor infertility, notably replacing conventional in vitro fertilisation (IVF) in scenarios like limited oocyte availability, where it is used as a precaution against complete fertilisation failure. While existing studies on the use of conventional IVF in such situations provide some reassurance, the available evidence is somewhat insufficient and ICSI is commonly used. To evaluate whether conventional IVF can be a feasible option when only one oocyte is retrieved. A retrospective study was performed to evaluate the fertilisation rate with conventional IVF in women retrieving only one oocyte and whose partner had normal semen. The study aimed at evaluating whether the fertilisation rate was aligned with the threshold indicated by recognized IVF laboratory performance indicators (Vienna Consensus). Clinical pregnancy and live birth rates were secondary outcomes. Out of 304 cycles with a single oocyte inseminated with conventional IVF, 209 achieved normal fertilisation and 82 did not. Thirteen had no mature oocytes. The fertilisation rate was 69% (95% CI: 63-74%) and increased to 72% (95% CI: 66-77%) when immature oocytes were excluded. The fertilisation rate surpassed the minimum competency threshold of the Vienna Consensus (60%), even if below the benchmark value (75%). Clinical pregnancy and live birth rates per oocyte retrieval were 10% and 8%, respectively. Univariate and multivariate analyses failed to identify any predictive factor of fertilisation. Conventional IVF with one oocyte met Vienna Consensus standards even if it fell short of higher benchmarks.