Cervical cytology and HPV distribution in Cape Verde: A snapshot of a country taken during its first HPV nation-wide vaccination campaign.

Cervical cancer ranks as the third most common female cancer in Cape Verde and is the leading cause of cancer-related deaths among women in the country. While Human Papillomavirus (HPV) vaccination, which started in 2021, is anticipated to significantly reduce disease incidence, cervical screening remains crucial for non-vaccinated women. We retrospectively reviewed gynecologic cytology exams and HPV tests performed in Cape Verde between 2017 and April 2023 and processed at IMP Diagnostics. For this study, we considered 13035 women with cytology examinations performed and, 2013 of these, also with an HPV molecular test. Cytology diagnostics comprised 83 % NILM cases; 12 % ASC-US; 2.7 % LSIL; 1.2 % ASC-H; 0.5 % HSIL and 0.1 % SCC. In 505 (25.1 %) high-risk HPV infection was detected. Prevalence of HPV infection varied with age, peaking at young ages - ≤24 years old (55.5 %) and 25-35-year-old women (31.5 %) - and the lowest after 66 years old (9.7 %). Herein we present a comprehensive study regarding Cape Verde's cervical cytology and HPV distribution, aiming to provide a snapshot of the country's cervical cytology results and HPV distribution in recent years. Moreover, these data may contribute to establish a baseline to assess, in the future, the vaccination impact in the country.

An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.

Towards detection of structurally-diverse glycated epitopes in native proteins: Single-chain antibody directed to non-A1c epitope in human haemoglobin.

Over 500 million people worldwide are affected by diabetes mellitus, a chronic disease that leads to high blood glucose levels and causes severe side effects. The predominant biological marker for diagnosis of diabetes is glycated haemoglobin (GHb). In human blood the predominant reducing sugar, glucose, irreversibly conjugates onto accessible amine groups within Hb. Most methods for diagnosis and monitoring of diabetes selectively detect N-terminal glycation at Val-1 on the ß-globin chain, but not glycation at other sites. Detection of other glycated epitopes of GHb has the potential to provide new information on the extent, duration and timing of elevated glucose, facilitating personalised diagnosis and intelligent diabetic control. In this work, a new anti-GHb Fab antibody (Fab-1) specific for haemoglobin A1c (HbA1c) with nanomolar affinity was discovered via epitope-directed immunisation and phage display. A single chain variable fragment (scFv) antibody derived from Fab-1 retained affinity and specificity for HbA1c, and affinity was enhanced tenfold upon addition of an enhanced green fluorescent protein tag. Both the scFv and Fab-1 recognised an epitope within HbA1c that was distinct from ß-Val-1, and our data suggest that this epitope may include glycation at Lys-66 in the ß-globin chain. To our knowledge, this is the first report of an scFv/Fab anti-glycated epitope antibody that recognises a non-A1c epitope in GHb, and confirms that fructosamine attached to different, discrete glycation sites within the same protein can be resolved from one another by immunoassay.

Influence of Curing Temperature on the Strength of a Metakaolin-Based Geopolymer.

The present work focuses on the further development of a new family of geopolymers obtained by the alkaline activation of a binder. The aim is to find a viable alternative to concrete that can be used in civil construction. Regarding the influence of the curing temperature on this type of mixture, the recommendations in the existing literature are different for fly ash, ground granulated blast-furnace slag, and metakaolin-based geopolymers. While for fly ash and slag, increasing the curing temperature above 60 °C is reported to be advantageous, for metakaolin geopolymers, the opposite is reported. In this context, the objective of this work is to evaluate the mechanical strength of several metakaolin-based geopolymer specimens subjected to different curing temperatures (10, 15, 20, 30, 40 and 50 °C). Furthermore, several stress-strain diagrams are also shown. Based on the results, we recommend using curing temperatures below 30 °C in order to avoid reducing the strength of metakaolin-based geopolymers. Curing at 50 °C, relative to room temperature, results in a reduction of more than 35% in flexural strength and a reduction of more than 60% in compressive strength. Regarding the behavior of the geopolymers, it was found that the strain, at the ultimate stress, is about 2 to 2.5 times the strain of an equivalent cement mortar.

Forecasting distributional shifts of Patella spp. in the Northeast Atlantic Ocean, under climate change.

Mapping species' geographical distribution is fundamental for understanding current patterns and forecasting future changes. Living on rocky shores along the intertidal zone, limpets are vulnerable to climate change, as their range limits are controlled by seawater temperature. Many works have been studying limpets' potential responses to climate change at local and regional scales. Focusing on four Patella species living on the rocky shores of the Portuguese continental coast, this study aims to predict climate change impacts on their global distribution, while exploring the role of the Portuguese intertidal as potential climate refugia. Ecological niche models combine occurrences and environmental data to identify the drivers of these species' distributions, define their current range, and project to future climate scenarios. The distribution of these limpets was mostly defined by low bathymetry (intertidal) and the seawater temperature. Independent of the climate scenario, all species will gain suitable conditions at the northern distribution edge while losing in the south, yet only the extent of occurrence of P. rustica is expected to contract. Apart from the southern coast, maintenance of suitable conditions for these limpets' occurrence was predicted for the western coast of Portugal. The predicted northward range shift follows the observed pattern observed for many intertidal species. Given the ecosystem role of this species, attention should be given to their southern range limits. Under the current upwelling effect, the Portuguese western coast might constitute thermal refugia for limpets in the future.

A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.

Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request.

Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers.

Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study's objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.

High-throughput proteomics and in vitro functional characterization of the 26 medically most important elapids and vipers from sub-Saharan Africa.

Venomous snakes are important parts of the ecosystem, and their behavior and evolution have been shaped by their surrounding environments over the eons. This is reflected in their venoms, which are typically highly adapted for their biological niche, including their diet and defense mechanisms for deterring predators. Sub-Saharan Africa is rich in venomous snake species, of which many are dangerous to humans due to the high toxicity of their venoms and their ability to effectively deliver large amounts of venom into their victims via their bite. In this study, the venoms of 26 of sub-Saharan Africa's medically most relevant elapid and viper species were subjected to parallelized toxicovenomics analysis. The analysis included venom proteomics and in vitro functional characterization of whole venom toxicities, enabling a robust comparison of venom profiles between species. The data presented here corroborate previous studies and provide biochemical details for the clinical manifestations observed in envenomings by the 26 snake species. Moreover, two new venom proteomes (Naja anchietae and Echis leucogaster) are presented here for the first time. Combined, the presented data can help shine light on snake venom evolutionary trends and possibly be used to further improve or develop novel antivenoms.

An Overview of the Impact of Pharmaceuticals on Aquatic Microbial Communities.

Pharmaceuticals are present as pollutants in several ecosystems worldwide. Despite the reduced concentrations at which they are detected, their negative impact on natural biota constitutes a global concern. The consequences of pharmaceuticals' presence in water sources and food have been evaluated with a higher detail for human health. However, although most of the pharmaceuticals detected in the environment had not been designed to act against microorganisms, it is of utmost importance to understand their impact on the environmental native microbiota. Microbial communities can suffer serious consequences from the presence of pharmaceuticals as pollutants in the environment, which may directly impact public health and ecosystem equilibrium. Among this class of pollutants, the ones that have been studied in more detail are antibiotics. This work aims to provide an overview of the impacts of different pharmaceuticals on environmental biofilms, more specifically in biofilms from aquatic ecosystems and engineered water systems. The alterations caused in the biofilm function and characteristics, as well as bacteria antimicrobial tolerance and consequently the associated risks for public health, are also reviewed. Despite the information already available on this topic, the need for additional data urges the assessment of emerging pollutants on microbial communities and the potential public health impacts.

Assessing data bias in visual surveys from a cetacean monitoring programme.

Long-term monitoring datasets are fundamental to understand physical and ecological responses to environmental changes, supporting management and conservation. The data should be reliable, with the sources of bias identified and quantified. CETUS Project is a cetacean monitoring programme in the Eastern North Atlantic, based on visual methods of data collection. This study aims to assess data quality and bias in the CETUS dataset, by 1) applying validation methods, through photographic confirmation of species identification; 2) creating data quality criteria to evaluate the observer's experience; and 3) assessing bias to the number of sightings collected and to the success in species identification. Through photographic validation, the species identification of 10 sightings was corrected and a new species was added to the CETUS dataset. The number of sightings collected was biased by external factors, mostly by sampling effort but also by weather conditions. Ultimately, results highlight the importance of identifying and quantifying data bias, while also yielding guidelines for data collection and processing, relevant for species monitoring programmes based on visual methods.

Impact of the Implementation of Laboratory Information System (WWDISA) on Timely Provision of HIV-1 Viral Load Results in a Rural Area, Inhambane, Mozambique.

WWDISA is an optional module of the DISA Laboratory Information system (LIS) that offers a web portal that allows access to test results over the internet for patient clinical management. This study aims to assess the applicability of using the WWDISA web application, and the lessons learned from its implementation in six health facilities in Mabote district, Inhambane province. Data from 2463 and 665 samples for HIV-viral load (HIVVL) tests, extracted from paper-based and WWDISA systems, respectively, were included, from January to December 2020. Data were simultaneously collected on a quarterly basis from both systems to allow comparison. The WWDISA turnaround time (TAT) from sample collection to results becoming available was found to be 10 (IQR: 8−12) days and significantly lower than the health unit manual logbook (p value < 0.001). Regarding the system efficiency, it was found that among 1978 search results, only 642 (32.5%) were found, and the main challenges according to the users were lack of connectivity (77%) and the website going down (62%). The WWDISA module has been shown to be effective in reducing the TAT, although a stable internet connection and accurate data entry are essential to make the system functional.

Limited Stress Response to Transplantation in the Mediterranean Macroalga Ericaria amentacea, a Key Species for Marine Forest Restoration.

In the Mediterranean Sea, brown macroalgae represent the dominant species in intertidal and subtidal habitats. Despite conservation efforts, these canopy-forming species showed a dramatic decline, highlighting the urge for active intervention to regenerate self-sustaining populations. For this reason, the restoration of macroalgae forests through transplantation has been recognized as a promising approach. However, the potential stress caused by the handling of thalli has never been assessed. Here, we used a manipulative approach to assess the transplant-induced stress in the Mediterranean Ericaria amentacea, through the analysis of biochemical proxies, i.e., phenolic compounds, lipids, and fatty acids in both transplanted and natural macroalgae over time. The results showed that seasonal environmental variability had an important effect on the biochemical composition of macroalgae, suggesting the occurrence of acclimation responses to summer increased temperature and light irradiance. Transplant-induced stress appears to have only amplified the biochemical response, probably due to increased sensitivity of the macroalgae already subjected to mechanical and osmotic stress (e.g., handling, wounding, desiccation). The ability of E. amentacea to cope with both environmental and transplant-induced stress highlights the high plasticity of the species studied, as well as the suitability of transplantation of adult thalli to restore E. amentacea beds.

Quality Control in Digital Pathology: Automatic Fragment Detection and Counting.

Manual assessment of fragments during the pro-cessing of pathology specimens is critical to ensure that the material available for slide analysis matches that captured during grossing without losing valuable material during this process. However, this step is still performed manually, resulting in lost time and delays in making the complete case available for evaluation by the pathologist. To overcome this limitation, we developed an autonomous system that can detect and count the number of fragments contained on each slide. We applied and compared two different methods: conventional machine learning methods and deep convolutional network methods. For conventional machine learning methods, we tested a two-stage approach with a supervised classifier followed by unsupervised hierarchical clustering. In addition, Fast R-CNN and YOLOv5, two state-of-the-art deep learning models for detection, were used and compared. All experiments were performed on a dataset comprising 1276 images of colorec-tal biopsy and polypectomy specimens manually labeled for fragment/set detection. The best results were obtained with the YOLOv5 architecture with a map@0.5 of 0.977 for fragment/set detection.

Prognostic Role of the Initial Grade of Dysplasia on Premalignant Vocal Fold Lesions.

PURPOSE: The main objective of this study was to analyze the prognostic role of the initial grade of dysplasia on the progression to SCC. STUDY DESIGN: Retrospective cohort. METHODS: This study was performed in the Otorhinolaryngology Department of a tertiary hospital center from January 2010 to December 2020. Every patient submitted to a microlaryngoscopy during this period with a histology of dysplasia on the first biopsy was included. RESULTS: A total of 112 patients were included and median follow-up was 24 months (range 1-120 months). Mean age at diagnosis was 59.71 (+/- 12.03) and 88 patients were male (78.6%). Initial grade of dysplasia was mild on 60 patients (53.6%), moderate on 24 (21.4%), severe on 18 (16.1%), and carcinoma in situ in 10 (8.9%). Overall, 25 patients (21.4%) developed invasive squamous cell carcinoma (SCC) and 15 (13.4%) died during follow-up. On an adjusted 5 year's progression free survival analysis, considering gender, age, dysplasia grade, tobacco and alcohol consumption, the initial grade of dysplasia was the only factor significantly associated with progression to carcinoma (P = .047). When compared to mild dysplasia, moderate dysplasia had a Hazard Ratio (HR) of 0.81 (95%CI 0.21-3.22); severe dysplasia had a HR of 1.76 (95%CI 0.59-5.30) and carcinoma in situ had a HR of 4.25 (95%CI 1.44-12.59). CONCLUSION: The initial dysplasia grade seems to be the most important prognostic factor regarding progression to SCC in patients with premalignant vocal fold disease.

Kelp carbon sink potential decreases with warming due to accelerating decomposition.

Cycling of organic carbon in the ocean has the potential to mitigate or exacerbate global climate change, but major questions remain about the environmental controls on organic carbon flux in the coastal zone. Here, we used a field experiment distributed across 28° of latitude, and the entire range of 2 dominant kelp species in the northern hemisphere, to measure decomposition rates of kelp detritus on the seafloor in relation to local environmental factors. Detritus decomposition in both species were strongly related to ocean temperature and initial carbon content, with higher rates of biomass loss at lower latitudes with warmer temperatures. Our experiment showed slow overall decomposition and turnover of kelp detritus and modeling of coastal residence times at our study sites revealed that a significant portion of this production can remain intact long enough to reach deep marine sinks. The results suggest that decomposition of these kelp species could accelerate with ocean warming and that low-latitude kelp forests could experience the greatest increase in remineralization with a 9% to 42% reduced potential for transport to long-term ocean sinks under short-term (RCP4.5) and long-term (RCP8.5) warming scenarios. However, slow decomposition at high latitudes, where kelp abundance is predicted to expand, indicates potential for increasing kelp-carbon sinks in cooler (northern) regions. Our findings reveal an important latitudinal gradient in coastal ecosystem function that provides an improved capacity to predict the implications of ocean warming on carbon cycling. Broad-scale patterns in organic carbon decomposition revealed here can be used to identify hotspots of carbon sequestration potential and resolve relationships between carbon cycling processes and ocean climate at a global scale.

iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images.

Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

Structural Equation Modelling for Predicting the Relative Contribution of Each Component in the Metabolic Syndrome Status Change.

Understanding the factor weighting in the development of metabolic syndrome (MetS) may help to predict the progression for cardiovascular and metabolic diseases. Thus, the aim of this study was to develop a confirmatory model to describe and explain the direct and indirect effect of each component in MetS status change. A total of 3581 individuals diagnosed with MetS, aged 18−102 years, were selected between January 2019 and December 2020 from a community-representative sample of Portuguese adults in a north-eastern Portuguese region to test the model's goodness of fit. A structural equation modelling (SEM) approach and a two-way ANOVA (age × body composition) were performed to compare the relative contribution of each MetS component using joint interim statement (JIS). Waist circumference (ß = 0.189−0.373, p < 0.001), fasting glucose (ß = 0.168−0.199, p < 0.001) and systolic blood pressure (ß = 0.140−0.162, p < 0.001) had the highest direct effect on the change in MetS status in the overall population and concerning both sexes. Moreover, diastolic blood pressure (DBP), triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c) had a low or non-significant effect. Additionally, an indirect effect was reported for age and body composition involving the change in MetS status. The findings may suggest that other components with higher specificity and sensitivity should be considered to empirically validate the harmonised definition of MetS. Current research provides the first multivariate model for predicting the relative contribution of each component in the MetS status change, specifically in Portuguese adults.

Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities.

Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists' validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.

Exhaustive reanalysis of barcode sequences from public repositories highlights ongoing misidentifications and impacts taxa diversity and distribution.

Accurate species identification often relies on public repositories to compare the barcode sequences of the investigated individual(s) with taxonomically assigned sequences. However, the accuracy of identifications in public repositories is often questionable, and the names originally given are rarely updated. For instance, species of the Sea Lettuce (Ulva spp.; Ulvophyceae, Ulvales, Ulvaceae) are frequently misidentified in public repositories, including herbaria and gene banks, making species identification based on traditional barcoding unreliable. We DNA barcoded 295 individual distromatic foliose strains of Ulva from the North-East Atlantic for three loci (rbcL, tufA, ITS1). Seven distinct species were found, and we compared our results with all worldwide Ulva spp. sequences present in the NCBI database for the three barcodes rbcL, tufA and the ITS1. Our results demonstrate a large degree of species misidentification, where we estimate that 24%-32% of the entries pertaining to foliose species are misannotated and provide an exhaustive list of NCBI sequences reannotations. An analysis of the global distribution of registered samples from foliose species also indicates possible geographical isolation for some species, and the absence of U. lactuca from Northern Europe. We extended our analytical framework to three other genera, Fucus, Porphyra and Pyropia and also identified erroneously labelled accessions and possibly new synonymies, albeit less than for Ulva spp. Altogether, exhaustive taxonomic clarification by aggregation of a library of barcode sequences highlights misannotations and delivers an improved representation of species diversity and distribution.